Nutrient Cycling and the Role of Arbuscular Mycorrhizae in Created and Natural Wetlands of Central Ohio

Home , Acaena exigua, Aeluropus lagopoides, Agrostis magellanica, Almutaster, Ammannia robusta, Aponogeton distachyos

DISSERTATION

Presented in Partial Fulﬁllment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Katie Hossler, B.S., M.S.

Environmental Science Graduate Program

The Ohio State University

2010

Dissertation Committee:

Professor Virginie Bouchard, Co-Advisor Professor Robert J. Gates, Co-Advisor Professor Siobhan Fennessy Professor Dawn Gibas Ferris Professor Richard Moore c Copyright by

Katie Hossler

2010 ABSTRACT

This dissertation details a comprehensive study of the ecology and development of the soil and carbon, nitrogen and phosphorus cycles in freshwater marshes. Beyond broadening our understanding of wetland development and ecology, this work intents to enlighten evaluation of wetland mitigation policy, as well as facilitate the success of wetland creation projects. Ten created and five natural freshwater marshes of central Ohio are the focus throughout the study. One of the central chapters (Ch. 3) compares structure and function between created and natural wetlands. The most important finding is that there are significant differences between created and natural wetland soils; this leads to smaller nutrient stocks and slower nutrient cycles in the created wetlands. Time-to-equilibrium estimates for the development of soil and nutrient-related functions are explored in Ch. 4. It is determined that overcoming these differences will require more time than is acceptable for most mitigation policies.

The ﬁnal two chapters explore the habit and ecology of arbuscular mycorrhizae (AM) in created and natural marshes. The presence of AM was high in both created and natural wetlands and across created wetland age. Given their prevalence and known role in other ecosystems, including AM in wetland creation and restoration projects deserves more focus.

ii Dedicated to Mom, Dad and Josh,

and two gone too soon, somewhere over the rainbow

in memory of

Sabra June 21, 2007 to January 5, 2010 “my little companion”

in honor and memory of Elizabeth “Aunt Liz” Hossler May 12, 1954 to September 11, 2008 loving aunt, professor, and ovarian cancer survivor

iii ACKNOWLEDGMENTS

I would ﬁrst like to acknowledge and thank my advisor, Professor Virginie Bouchard—

I could not have had a better mentor, both professionally and personally. I am so

glad her tenure at Ohio State coincided with my graduate studies, and that, seven

years ago, she was willing to take a chance on a very shy former chemist.

I thank my committee members, Profs. Siobhan Fennessy, Dawn Ferris, Robert

Gates, and Richard Moore for valuable input during the initial and ﬁnal stages of the project. Thanks especially to Professor Gates for serving as my advisor of record over the past year, and to Professor Moore for joining my committee so late in the process. I also thank Professor Ralph Boerner, who served on my committee until his retirement in 2009.

Many long hard hours went into the data collection for this very large project.

Special thanks to those who assisted in the ﬁeld and in the lab: Evelyn Anemaet,

Erelyn Apolinar, Amy Barrett, Michael Billmire, Sarah Boley, Ryan Diederick, Gwen

Dubelko, Brie Elking, Erica Elliot, Becky Fauver, Michela Gentile, Dan Gillenwater,

Jenette Goodman, Ellen Herbert, Kyle Herrman, Melissa Knorr, Matt Lane, Nickla

Louisy, Lars Meyer, Diego Perez, Ryan Pulliam, Connie Rice, Abby Rokosch, Jesse

Rosenbluth, Erin Rothman, Eric Saas, Rachel Schultz, Lindsay Scott, Heather Sexton,

Leslie Smith, Michael Szuter, and Steve Wise.

iv Thanks to Columbus Metro Parks, the City of Columbus Recreation and Parks,

Groveport-Madison Highschool, the Ohio Department of Natural Resources and several private landowners for permission to use their properties.

And ﬁnally, for helping me to get here, thanks to the many teachers, co-workers and friends I have had over the years and to my family and Josh.

v VITA

June 23, 1974 ...... Born - Cincinnati, USA

1996 ...... B.S. Chemistry, University of Notre Dame 2005 ...... M.S.Environmental Science, The Ohio State University. 2005-present ...... Graduate Research and Teaching Asso- ciate, The Ohio State University.

PUBLICATIONS

K. Hossler, V. Bouchard “Soil development and establishment of carbon-based properties in created freshwater marshes”. Ecological Applications, 20:539–553, 2010.

K. Hossler, V. Bouchard “The joint estimation of soil trace gas ﬂuxes”. Soil Science Society of America Journal, 72:1382–1393, 2008.

E.R. Civitello, R.G. Leniek, K.A. Hossler, D.M. Stearns “Synthesis of Peptide- Oligonucleotide Conjugates for Chromium Coordination”. Bioconjugate, 12:459–463, 2001.

J.A. Riggs, K.A. Hossler, B.D. Smith, M.J. Karpa, G. Griﬃn, P.J. Duggan “Nu- cleotide carrier mixture with transport selectivity for ribonucleoside-5’-phosphates”. Tetrahedron Letters, 37:6303–6306, 1996.

FIELDS OF STUDY

Major Field: Environmental Science Graduate Program

vi TABLE OF CONTENTS

Page

Abstract...... ii

Dedication...... iii

Acknowledgments...... iv

Vita ...... vi

ListofTables...... x

ListofFigures ...... xiii

Chapters:

1. Introduction and brief history of wetlands in Ohio ...... 1

1.1 WetlandsinOhio...... 2

I Estimation and analysis of carbon ﬂux 6

2. The joint estimation of soil trace gas ﬂuxes ...... 7

2.1 Introduction ...... 8 2.2 MaterialsandMethods...... 18 2.3 Results ...... 28 2.4 Discussion...... 33 2.5 Conclusions...... 37

II Biogeochemical cycles in created and natural wetlands 44

vii 3. No-net-loss not met for nutrient function: recommendations for wetland mitigationpolicies ...... 45

3.1 SupplementaryInformation ...... 58

4. The importance of soil to development of plant- and microbial-function in createddepressionalwetlands ...... 87

4.1 Introduction ...... 87 4.2 MaterialsandMethods...... 92 4.3 Results ...... 101 4.4 Discussion...... 127 4.5 Conclusion ...... 134

III Arbuscular mycorrhizae 136

5. Arbuscular Mycorrhizae in Aquatic Systems: a Review ...... 137

5.1 Introduction ...... 137 5.2 Habit of mycorrhizae in aquatic systems ...... 141 5.3 Ecology of mycorrhizae in aquatic systems ...... 158 5.4 AMbeneﬁts...... 164 5.5 Conclusion ...... 166

6. Arbuscular Mycorrhizae in created and natural wetlands of Ohio . . . . . 204

6.1 Introduction ...... 204 6.2 MaterialsandMethods...... 207 6.3 Results ...... 218 6.4 Discussion...... 255 6.5 Conclusions...... 262

Appendices:

A. Sitedescriptionsandhistories ...... 264

A.1 Ballﬁeldmarsh ...... 265 A.2 BigIsland...... 270 A.3 Bluebird...... 276 A.4 Calamusswamp ...... 280 A.5 JMB...... 285 A.6 KilldeerPlains ...... 289

viii A.7 Mishne ...... 297 A.8 NewAlbanyCompany ...... 301 A.9 PickeringtonPonds...... 304 A.10Sacks ...... 310

B. Vegetation surveys and sampling station selection ...... 313

B.1 Clusteranalysis...... 313 B.2 Speciesdiversityindices ...... 316

C. Aerialmapsofwetlandsurveyareas ...... 323

D. Dendrograms of wetland vegetation survey data ...... 339

E. Guide to the Identiﬁcation of Arbuscular Mycorrhizae ...... 355

Bibliography ...... 373

ix LIST OF TABLES

Table Page

2.1 Description of seven diﬀerent models for estimating gas ﬂux ..... 27

2.2 Evaluation of model performance for real data ...... 30

2.3 Evaluation of model performance for simulated data ...... 40

2.4 Absolute and relative errors for gas ﬂux models ...... 41

3.1 Structural and functional properties and representative parameters . 50

3.2 C,NandPstocksandﬂuxes ...... 55

3.3 Studysitelocationsanddescriptions ...... 61

3.4 Abiotic structure typeandage ...... 74 ∼ 3.5 Biotic structure typeandage ...... 76 ∼ 3.6 Nutritional structure typeandage ...... 78 ∼ 3.7 Function typeandage...... 84 ∼ 3.8 PERMANOVA: function structure ...... 86 ∼ 4.1 Comparison of SEMs for C, N and P cycling ...... 109

4.2 Developmental trajectories for C, N and P stocks and ﬂuxes . . . . . 111

4.3 Developmental trajectories for shoot biomass and ρb by year and com- binedyears ...... 115

x 4.4 Developmental trajectories for ρb and water-stable aggregates . . . . . 118

4.5 Comparison of soil and root properties with depth, by type and age . 124

4.6 Developmental trajectories for ρb and root L:V ratios by depth . . . . 125

4.7 Comparison of SEMs for the eﬀect of time on combined C, N and P cycling...... 126

5.1 Literature surveys of mycorrhizae in aquatic systems ...... 142

5.2 Mycorrhizal status for 71 hydrophytes collected from 15 created and naturalmarshes...... 148

5.3 Mycorrhizal status by family and order for 952 hydrophytic species . 153

5.4 Annotated list of mycorrhizal status for 952 hydrophytes (compiled fromtheliterature) ...... 168

6.1 Qualitative and quantitative descriptors for the 15 study sites . . . . 209

6.2 Qualitative predictors and level deﬁnitions ...... 210

6.3 Quantitative predictor variables and eﬀects on arbuscular mycorrhizae 220

6.4 Quantitative response variables and eﬀects by arbuscular mycorrhizae 222

6.5 Mycorrhizal colonization and type: best main eﬀect model (ANOVA) 229

6.6 Mycorrhizal colonization and type: best interaction model (ANOVA) 241

6.7 Arbuscular colonization and type: best main eﬀect model (ANOVA) . 242

6.8 Arbuscular colonization and type: best interaction model (ANOVA) . 243

6.9 Mycorrhizal colonization and age: best main eﬀect model (ANOVA) . 243

6.10 Mycorrhizal colonization and age: best interaction model (ANOVA) . 244

6.11 Arbuscular colonization and age: best main eﬀects model (ANOVA) . 244

xi 6.12 Arbuscular colonization and age: best interaction model (ANOVA) . 245

6.13 Mycorrhizal colonization and predictors: best main eﬀects model (ANOVA)246

6.14 Mycorrhizal colonization and predictors: main eﬀects ...... 247

6.15 Mycorrhizal colonization and predictors: best interaction model (ANOVA)248

6.16 Arbuscular colonization and predictors: best main eﬀects model (ANOVA)249

6.17 Arbuscular colonization and predictors: main eﬀects ...... 250

6.18 Arbuscular colonization and predictors: best interaction model (ANOVA)251

B.1 SummaryofPDHCresults...... 315

B.2 Diversity metrics and weight factors for the wetland sampling stations 320

B.3 Summary of vegetation, biomass and diversity metrics ...... 322

xii LIST OF FIGURES

Figure Page

1.1 MapofOhiosoildrainge...... 5

2.1 “Stagnantboundary”gasﬂuxmodel ...... 13

2.2 Parameter eﬀects on gas ﬂux estimates in the exponential model . . . 15

2.3 Parameter eﬀects on gas ﬂux estimates in the NDFE model ...... 17

2.4 Cellmodelforgasﬂuxsimulation ...... 21

2.5 Pseudocodefortheﬂuxsimulation ...... 38

2.6 Example plots of real and simulated ﬂux data for CO2 and CH4 . . . 39

2.7 Boxplots of absolute error by model and turbulence ...... 42

2.8 Boxplots of relative error by model and turbulence ...... 43

3.1 Structureandfunction: PCAbiplots ...... 81

3.2 Functionvs.structure:RDAbiplot ...... 82

3.3 Structure:PCAbiplots...... 83

3.4 Bulk density as an indicator of development and function ...... 85

4.1 Biplots for RDA of function ˜soil ...... 102

4.2 Plant and litter N and P stocks versus soil ρb ,CandN...... 104

xiii 4.3 Microbial activity versus soil ρb ,CandN ...... 105

4.4 Soil properties versus ρb ...... 106

4.5 Path diagrams for wetland type eﬀect on C, N, and P cycling . . . . 108

4.6 Trajectories of development for select C, N and P stocks and ﬂuxes . 110

4.7 Developmental trajectories by sample year for shoot biomass . . . . . 113

4.8 Shoot biomass and ρb trajectoriespersite...... 114

4.9 Developmental trajectories of water-stable aggregates ...... 117

4.10 Plotsofvariousrootmetricsbydepth...... 119

4.11 Developmental trajectories for ρb androotL:Vbydepth ...... 120

4.12 Path diagram for time eﬀect on C, N and P cycling ...... 122

4.13 Estimated eﬀects of time based on natural wetland age ...... 123

6.1 Mycorrhizal and fungal colonization by site ...... 223

6.2 Mycorrhizal and fungal colonization by class and quality ...... 224

6.3 Mycorrhizal abundance versus wetland age (by genus) ...... 237

6.4 Arbuscular abundance versus wetland age (by genus) ...... 238

6.5 Mycorrhizal and fungal colonization by type and sample period . . . 239

6.6 Mycorrhizal and fungal colonization by type and station condition . . 240

6.7 Path diagram of hypothesized cause and eﬀect model ...... 254

6.8 Path diagrams of ﬁnal cause and eﬀect models ...... 256

A.1 Clay and Jackson Townships ...... 267

A.2 Ballﬁeld, 1960–2009 ...... 268

xiv A.3 Ballﬁeldsoildrainage...... 269

A.4 BigIslandTownship ...... 272

A.5 SanduskyPlains...... 273

A.6 Big Island, 1959–2009 ...... 274

A.7 BigIslandsoildrainage...... 275

A.8 GenoaTownship ...... 277

A.9 Bluebird,1955–2009 ...... 278

A.10Bluebirdsoildrainage...... 279

A.11PickawayPlains...... 281

A.12WayneTownship ...... 282

A.13 Calamus, 1960–2009 ...... 283

A.14Calamussoildrainage...... 284

A.15JMB,1954–2009 ...... 287

A.16JMBsoildrainage...... 288

A.17PittTownship...... 290

A.18 Killdeer Plains, 1959–2009 ...... 291

A.19 Killdeer Plains soil drainage ...... 292

A.20 Taylor Creek Township ...... 294

A.21 Lawrence Woods, 1959–2009 ...... 295

A.22LawrenceWoodssoildrainage ...... 296

xv A.23PrairieTownship ...... 298

A.24Mishne,1953–2009 ...... 299

A.25Mishnesoildrainage ...... 300

A.26 New Albany Company, 1965–2009 ...... 302

A.27 New Albany Company soil drainage ...... 303

A.28 Madison and Violet Townships ...... 306

A.29Pickeringtontopography ...... 307

A.30 Pickerington Ponds, 1953–2009 ...... 308

A.31 Pickerington Ponds soil drainage ...... 309

A.32Sacks,1960–2009 ...... 311

A.33Sackssoildrainge ...... 312

B.1 Species accumulation curves from vegetation surveys ...... 319

C.1 AerialmapofPPAandlocale ...... 324

C.2 AerialmapofPPBandlocale ...... 325

C.3 AerialmapofBBandlocale ...... 326

C.4 Aerial map of BIC and locale ...... 327

C.5 AerialmapofSAandlocale ...... 328

C.6 AerialmapofJMBandlocale ...... 329

C.7 Aerial map of BIA and locale ...... 330

C.8 AerialmapofNACandlocale ...... 331

C.9 Aerial map of BIB and locale ...... 332

xvi C.10AerialmapofKPandlocale ...... 333

C.11AerialmapofBFandlocale ...... 334

C.12AerialmapofCAandlocale...... 335

C.13AerialmapofPPNandlocale ...... 336

C.14AerialmapofLWandlocale...... 337

C.15AerialmapofMIandlocale ...... 338

D.1 ClusterdendrogramforPPA...... 340

D.2 ClusterdendrogramforPPB...... 341

D.3 ClusterdendrogramforBB ...... 342

D.4 ClusterdendrogramforBIC ...... 343

D.5 ClusterdendrogramforSA...... 344

D.6 ClusterdendrogramforJMB...... 345

D.7 ClusterdendrogramforBIA ...... 346

D.8 ClusterdendrogramforNAC ...... 347

D.9 ClusterdendrogramforBIB ...... 348

D.10ClusterdendrogramforKP ...... 349

D.11ClusterdendrogramforBF...... 350

D.12ClusterdendrogramforCA ...... 351

D.13ClusterdendrogramforPPN...... 352

D.14ClusterdendrogramforLW ...... 353

xvii D.15ClusterdendrogramforMI...... 354

E.1 Themagniﬁedroot-intersectionsmethod ...... 357

E.2 Arbuscules...... 358

E.3 Mycorrhizal structures for Bidens frondosus ...... 359

E.4 Mycorrhizal structures for Echinochloa spp.(BBS4)...... 360

E.5 Mycorrhizal structures for Eleocharis obtusa ...... 361

E.6 Mycorrhizal structures for Bidens cernua (BIBS1) ...... 362

E.7 Mycorrhizal structures for Typha spp...... 363

E.8 Mycorrhizal structures for Galium spp...... 364

E.9 Mycorrhizal structures for Bidens cernua (BIBS4) ...... 365

E.10 Mycorrhizal structures for Echinochloa spp.(JMBS2) ...... 366

E.11 Mycorrhizal structures for Bidens cernua (KPS2) ...... 367

E.12 Examples of nonmycorrhizal hyphae ...... 368

E.13Examplesofspores ...... 369

E.14 Miscellaneous arbuscule-like NM features ...... 370

E.15 Miscellaneous hyphal-like NM features ...... 371

E.16Unidentiﬁedfungalstructures ...... 372

xviii CHAPTER 1

INTRODUCTION AND BRIEF HISTORY OF WETLANDS IN OHIO

This dissertation details a comprehensive study of the ecology and development of the soil and carbon, nitrogen and phosphorus cycles in freshwater marshes. In Part I,

Ch. 2, I present a method for jointly estimating soil gas ﬂuxes. The method was originally developed to determine C emissions for 5 created and 3 natural wetlands in central Ohio. Part II focuses on the establishment of nutrient-related function and development of soil structure in created wetlands, and consists of two chapters. In

Ch. 3, I compare structure and function between 10 created and 5 natural wetlands of central Ohio, and evaluate current wetland mitigation policies in this context. In

Ch. 4, I explore the link between soil and nutrient function and estimate trajectories of development for newly created wetland systems. Part III continues to address establishment of nutrient cycles in created wetlands by comparing the abundance of arbuscular mycorrhizae (AM) in created and natural wetlands, and over time along a chronosequence of created wetlands. Ch. 5 introduces this section with a review of the literature regarding AM in aquatic systems. In this chapter, I also add results from this current study and compile an annotated list of mycorrhizal associations in

1 hydrophytes. Ch. 6 aims to speciﬁcally augment our understanding of AM ecology

and function in freshwater marshes.

The intent of this research is to enlighten the evaluation of wetland mitigation

policy through broadened understanding of wetland development—beyond just the

study locale of Ohio. Ohio, however, is particularly well-suited for a study of this

type, with one of the highest rates of wetland loss in the US, second only to California.

Within 200 y of European settlement, Ohio lost an estimated 90 % of its presettlement

wetland base—a wetland base which began at 19 % of the Ohio’s total land surface

and was reduced to less than 2 % (Dahl, 1990). I begin this dissertation ﬁrst with a

brief history of wetlands in Ohio.

1.1 Wetlands in Ohio

1.1.1 history

Carpenter and Arthur (1872, chap. 14) in their history of Ohio provide the fol-

lowing description of the land:

The country bordering on Lake Erie and in the interior, is generally level and in some places marshy.... The most fertile lands are situated in the interior, on both sides of the Scioto and of the Great and Little Miami Rivers. Vast prairies lie near the head-waters of the Scioto, the Musk- ingum, and the two Miami rivers, upon which there is no growth of timber. Some of these prairies are low and marshy, producing a great quantity of coarse grass from two to ﬁve feet high.... and, in his history of Wyandott County, Baughman (1913, p. 126) speculates on

conditions encountered by early settlers:

The ground was covered with water and decaying vegetable matter; the river and creeks were clogged with driftwood, and fallen timbers; beaver dams set the water back, thereby covering large tracts of lands, while wild cat swamps (as they were then called) were very numerous. There were

2 terrible thickets and jungles of brush bushes of various kinds growing on rich, boggy soil.

Narratives of early European settlers in Ohio abound with descriptions of its

“prairies” (Harris, 1805, p.97; Cutler, 1812, pp. 7–52; Thomas, 1819, pp. 97–99;

Atwater, 1831, pp. 211–217; Flint, 1833, p. 21; Drake, 1844, p. 231; Taylor, 1854, p.

112; Brown, 1883, pp. 341–342; Evans and Stivers, 1900, p. 37)—the generic term used to describe any treeless area, generally grassy, wet or dry (Sears, 1926). Using records of early land surveys from 1750–1850, Sears (1926) mapped the originally treeless areas, which included wet prairies, marshes, swamps and bogs.

Expanses of marshes were common, the most infamous being the “Black Swamp” of northwestern Ohio (e.g., see Everett, 1882, pp. 761–762). Other large tracts included the “Sandusky Plains” of north-central Ohio (Perrin, 1881; Sears, 1926;

Troutman, 1981), the “Scioto Marsh” also of north-central Ohio (Dobbins, 1937), and the “Darby Plains” of west-central Ohio (Sears, 1926).

Before long, however, it was discovered that these swamplands, once drained, made prime farmland...hence, initiating the emergence of Ohio as one of the premier agricultural states in the US. Legislation to promote the ditching and draining of swamplands in Ohio was enacted by the General Assembly of the State of Ohio in 1859

(Everett, 1882, pp. 205–208). This “Ditch Law” was originally proposed to improve both public health (e.g., drain land which harbored malaria-bearing mosquitoes) and public roadways; however, the added beneﬁt of converting former waste land into productive cropland gave the law momentum. By the end of the nineteenth-century, most of the newly begun drainage projects were complete.

3 1.1.2 physiography

The prominence of wetlands in Ohio, at least historically, is mainly because of glaciation. The most recent glacial episode, the Wisconsin glaciation, attained its maximum extent about 20,000 years ago (Goldthwait, 1959). The retreating glacier left behind a very ﬂat, thick layer of ﬁne-textured glacial till—transforming the well- drained pre-glacial bedrock into a poorly-drained landscape. Glacial drift also produced an occasionally pockmarked terrain with kettle-holes and post-glacial lakes

(Dobbins, 1937, p. 17; Andreas, 1985). The edge of the last glacial maximum provides a clear demarcation between the ﬂat, poorly drained region of central, west, and north Ohio (i.e., glaciated Ohio) and the well-drained, hilly region of east and south Ohio (i.e., unglaciated Ohio; Fig. 1.1).

Several scholars have made note of the connection between geology and vegetation (Dachnowski, 1912; Sears, 1926; Heﬀner, 1939; Thompson, 1939; Forsyth, 1970).

Vegetation established on much of the glacial landscape falls under the encompassing name of prairie—with up to 80 % of these prairies being wet or mesic (Forsyth, 1981).

In addition to these wet prairies, early Ohio settlers likely encountered a landscape

ﬁlled with coastal marshes, swamp forests, wet beech forests, fens, bogs and marshes

(Sears, 1926; Dobbins, 1937; Heﬀner, 1939). The work herein focuses on the depressional and wet prairie type, with 10 created or restored freshwater marshes and 5 natural freshwater marshes of central Ohio selected for study.

4 Figure 1.1: Map of Ohio shaded by soil drainage class: darkly shaded, very poorly drained; moderately shaded, poorly drained; lightly shaded, somewhat poorly drained; unshaded, moderately well to very well drained. Maximum extents of the two most recent glacial epochs: the boundary of the younger Wisconsin glaciation is indicated by the thin black line and the boundary of the older Illinoian glaciation is indicated by the thick black line. Also indicated are the approximate locations of the ten created wetlands (blue circles) and ﬁve natural wetlands (purple squares; darker shading indicates a higher-quality, or reference, wetland) of this study.

5 PART I

Estimation and analysis of carbon ﬂux

6 CHAPTER 2

THE JOINT ESTIMATION OF SOIL TRACE GAS FLUXES

As published in: Hossler, K. and Bouchard, V. (2008). The joint estimation of soil trace gas ﬂuxes. Soil Science Society of America Journal, 72, 1382–1393.

Abstract: Soil gas flux is commonly measured by monitoring the change in headspace gas concentration over time within a sealed compartment at the soil surface. Often, more than one trace gas is monitored at a time (e.g., CO2 and CH4), but the data fit separately. Flux estimates for CO2 and CH4 were obtained simultaneously by minimizing a weighted sum-of-squares error. The approximation of one model parameter for CH4, through theoretical relationship to the respective CO2 parameter, reduced the total parameter count by one and allowed for the joint estimation of one parameter using the combined CO2 and CH4 datasets. The method of joint optimization was compared to separate optimization for two nonlinear models, using both real and simulated data. The datasets were best fit with the jointly optimized models. Fur- thermore, the jointly optimized models more accurately estimated initial soil/air fluxes (simulated data only). The method of joint optimization is recommended as a means to apply better-fitting nonlinear models to typically small gas sample sets. This method is applicable to any number of trace gases monitored simultaneously.

Abbreviations: AIC, Akaike’s information criterion; Exp, exponential model; Jnt, jointly-fit optimization method; Lin, linear model; mn, squared-means weighting scheme; MOP, multi-objective optimization problem; NDFE and Ndfe, non-steady- state diffusive flux estimator; Sep, separately-fit optimization method; ss, sum-of- squares error weighting scheme; SSE, sum-of-squares error; SSEw, weighted sum- of-squares error. 7 2.1 Introduction

Soil gas ﬂux is commonly measured by monitoring the change in gas concentration

over time, within a sealed compartment placed at the soil surface (Hutchinson and

Livingston, 2002). Flux rates are then estimated by ﬁtting the data to linear or

nonlinear models. Often, more than one trace gas is monitored at a time (e.g., CO2

and CH4), but the data ﬁt separately.

Alternatively, the multiple gas ﬂuxes could be estimated simultaneously by linearly

combining the cost function for each gas ﬂux equation into one scalarized cost function

G ~ (g) ~ Jw = wg J(Cz,t ,~tg, φg) (2.1) g=1 X where Jw is the total scalarized, or weighted-sum, cost function; wg are the weights for G different gas fluxes to be estimated; and J is the cost function for a single gas. ~ (g) Associated with each gas g is a set of gas concentration measurements Cz,t , their associated sample times ~tg, and a set of flux model parameters φ~g to be estimated. In joint estimation, the optimization routine is performed on the single joint cost Jw for all parameter sets φ~ simultaneously, whereas in separate estimation, the parameter { g} set estimate of φ~g for each gas g is obtained separately by only considering the per- ~ (g) ~ gas cost J(Cz,t ,~tg, φg). The advantage of joint optimization is realized when one or more parameters is shared among the multiple sets of flux equation parameters φ~g, g = 1,...,G . In this circumstance, the combined G datasets are used to optimize { } one reduced, joint set of flux equation parameters as opposed to using each individual dataset to optimize one of the full, G sets of flux equation parameters. Many theory- based gas flux models include one parameter that, with some approximation, could

8 be estimated in common between the various monitored gases. Approximation of that parameter would reduce the total parameter count by one per additional gas monitored, further increasing the degrees of freedom and reducing the margin of estimate error.

In this paper we apply the general model of Equation (2.1) to the speciﬁc problem of estimating G = 2 gas ﬂuxes under sum-of-squares error cost criteria for each gas.

Our objective is to compare the estimation performance of joint ﬂux estimation with traditional separate per-gas ﬂux estimates. To this end, we consider estimations of

CO2 and CH4 fluxes for 355 real datasets and 480 simulated datasets. The simulated datasets enabled comparison of estimated fluxes to known true fluxes. The datasets were jointly and separately fit to two nonlinear theory-based models. Flux estimates were also obtained by simple linear regression for comparison.

2.1.1 Linear model

A linear model is one of the simplest models to describe the change in chamber concentration over time

Ch,t = Ch,0 + b1t (2.2)

3 where C is chamber gas concentration [M L− ] at height h [L] and time t [T]; h,t ·

Ch,0 is the initial gas concentration at height h and time t = 0; and b1 is the rate

3 1 of change in concentration per unit change in time [M L− T− ]. However, the · · linear model is only applicable under circumstances where the chamber concentration changes linearly over time (e.g., over short time intervals). The rate of change in chamber concentration typically decreases over time because of the increase in gas

9 concentration and subsequent decrease in ﬂux (see Eq. (2.3)). Change in chamber

concentration over time is therefore best described by nonlinear models.

2.1.2 Nonlinear theory-based models

Two nonlinear theory-based models were applied in this study. Gas flux theory suggests that the gas flux rate will be limited by molecular diffusion near the soil/air boundary (or water/air boundary for inundated soils), whereas transfer to, across, and from the boundary layer will occur relatively quickly (Schwarzenbach et al., 1993).

Accordingly, the change in gas concentration within a soil chamber over time can be described using Fick’s ﬁrst law for molecular diﬀusion

∂C F = D (one-dimension) (2.3) z − ∂z and Fick’s second law for molecular diﬀusion

∂C ∂2C = D (one-dimension) (2.4) ∂t ∂z2

2 1 where F is the ﬂux [M L− T− ] along length z [L]; D is the molecular diﬀusivity z · · 2 1 3 [L T− ]; C is the gas concentration [M L− ]; and t is time [T] (Schwarzenbach · · et al., 1993).

The simplest conception of gas transfer across the soil/air or water/air inter-

face is the “stagnant boundary theory” proposed by Whitman (1923) (as cited in

Schwarzenbach et al., 1993). Gas movement across the interface is controlled by the

rate of diﬀusion through a boundary layer on either side of the interface (Figure 2.1).

Beyond the boundary layers, gas movement occurs by advective or turbulent pro-

cesses (at least in the water and air mediums) and is relatively rapid. Diﬀusion of

10 the gas through the boundary layers follows Fick’s ﬁrst law (2.3), which in the soil

medium becomes

∂C ∂C F = D sa,t D sw,t , z z 0 soil − sa ∂z − sw ∂z − s ≤ ≤

2 1 where F is the gas flux [M L− soil T− ] through the soil boundary layer; D is soil · · sa 3 1 1 the soil-air gas diffusivity [L air L− soil T− ]; C is the soil-air gas concentration · · sa,t 3 3 1 [M L− air] at time t [T]; D is the soil-water gas diffusivity [L water L− soil · sw · 1 3 T− ]; C is the soil-water gas concentration [M L− water]; and z is the soil · sw,t · s boundary depth [L soil] (Sim˚unekˇ and Suarez, 1993). The soil diffusivities can be

measured, but are often approximated from models which account for soil porosity

and tortuosity (Moldrup et al., 2004). Often, the ﬂux component through the soil-

4 water is neglected, as Dsw is approximately 10 times slower than Dsa (Schwarzenbach

et al., 1993; Sim˚unekˇ and Suarez, 1993). However, near saturation the soil-water gas

ﬂux becomes signiﬁcant (Sim˚unekˇ and Suarez, 1993).

At the soil/air interface and under steady-state conditions, the ﬂux across the

soil/air interface is equal to the ﬂux on either side of the interface, or

∂C F = F = F = D air,t , 0 z z 0 soil air − a ∂z ≤ ≤ a

2 1 where F is the gas flux [M L− T− ] across the soil/air interface (z = 0); F 0 · · air 2 1 is the gas flux [M L− air T− ] through the air boundary layer; D is the gas · · a 3 1 3 diffusivity in free air [L air T− ]; C is the gas concentration [M L− air] in the · air,t · air boundary layer at time t [T]; and za is the air boundary height [L air]. As long as the system is at equilibrium (i.e., gas concentration within soil and air boundary layers does not change over time), the relationship will be true throughout the soil

11 and air boundary layers ( z z z ); otherwise, the relationship will hold only at − s ≤ ≤ a the soil/air interface, or z = 0.

Exponential model

Gas accumulation in a non-steady-state chamber can be modeled by looking only

at the gas movement from the soil surface into the chamber headspace. Assuming

steady-state conditions in the boundary layers, the gas concentration gradient across

the air boundary layer will determine the soil/air ﬂux (Eq. (2.3))

(Cza,t C0,t) F0 = Da − (2.5) − za

2 1 where D is the gas diﬀusivity in free air [L air T− ]; C is the gas concentration a · za,t 3 [M L− air] at the top of the air boundary layer (z = z ) and time t [T]; C is · a 0,t 3 the gas concentration [M L− air] at the bottom of the air boundary layer (z = 0); ·

and za is the thickness [L air] of the air boundary. Above the air boundary layer

(z > za), the chamber headspace is assumed to be well mixed and will accumulate

gas according to Fick’s second law (2.4),

∂C D C C h,t = a za,t − 0,t F (2.6) ∂t −h z z − h − a a 3 where C is the gas concentration [M L− air] at the top of the chamber (z = h) h,t ·

and at time t [T]; h is the chamber height [L air]; and Fh is the gas ﬂux at the top

2 1 of the chamber [M L− air T− ]. For the non-steady-state chamber, F = 0; and · · h

assuming the chamber air is perfectly mixed above the diﬀusive layer, Cza,t = Ch,t.

Equation (2.6) integrates to

12 depth concentration

turbulent air

za Cair,t

diffusive air layer Fair soil/air interface 0 C0,t

diffusive soil layer Fsa Fsw

-z s Csoil,t soil- water soil- air soil particle

Figure 2.1: Gas flux across the soil/air interface can be modeled following the Whitman (1923) “stagnant boundary theory” (as cited in Schwarzenbach et al., 1993). Molecular diffusion through the boundary layer on either side of the interface controls the rate of gas transport between the two mediums. In the soil, gas flux occurs through both air-filled (Fsa; no-shading) and water-filled (Fsw; light-shading) pores. The fluxes are driven by the change in gas concentration (Csoil,t C ,t) across − 0 the soil boundary layer ( zs z 0). Under steady-state conditions, the total flux through the soil − ≤ ≤ boundary layer (Fsa + Fsw, zs z 0) will equal the flux at the soil/air interface (F , z = 0) and − ≤ ≤ 0 the flux through the air boundary layer (Fair, 0 z za). Above the air boundary layer (z>za), gas transport is assumed to occur rapidly due to≤ turbulent≤ forces, and the system is no longer at steady-state.

13 kt C =(C C )e− + C (2.7) h,t h,0 − 0,t 0,t

3 where C is the initial chamber concentration [M L− air] of the mixed air layer h,0 · 3 (z z h) at t = 0; C is the gas concentration [M L− air] at the soil surface a ≤ ≤ 0,t · 1 (z = 0); and k is a rate constant [T− ]: k = D /(z (h z )), or assuming z << h, a a − a a

k = Da/(zah).The gas concentration at the soil surface (C0,t) is the maximum chamber

concentration that can be reached over time (Figure 2.2(a)); how quickly C0,t will be

attained is determined by the rate constant k (Figure 2.2(b)).

A model similar to (2.7) was obtained by Nakano et al. (2004), where the authors

equated the change in chamber concentration (assumed to be uniformly distributed)

2 1 to the gas ﬂux [M L− soil T− ] through a soil layer of thickness z [L soil]. · · d Non-steady-state diﬀusion model

Livingston et al. (2005) modeled the change in soil-air gas concentration as a

function of depth and time, using a form of Fick’s second law (2.4) speciﬁc for soil

gas ﬂux

∂C ∂2C Θ sa,t = D sa,t + λ(z) (2.8) sa ∂t sa ∂z2

3 3 where Θ is the soil air-ﬁlled porosity [L air L− soil]; C is the soil-air gas sa · sa,t 3 concentration [M L− air] at time t [T] and depth z [L soil]; D is the soil-air · sa 3 1 1 gas diﬀusivity [L air L− soil T− ]; and λ(z) is the depth-dependent zero-order · · 3 1 source strength [M L− soil T− ]. The authors assumed the chamber air was · · uniformly mixed throughout (i.e., za = 0 and Ch,t = C0,t), such that the chamber

14 50 C0,t=50 k=1 50 k=0.1

40 ) ) 3 3 − − 30 30

C0,t=25

20 20 Concentration (mg m Concentration (mg m k=0.01

10 10 C0,t=10 15

C0,t=5 k=0.001 0 0

0 20 40 60 80 100 0 20 40 60 80 100

Time (min) Time (min)

(a) (b)

Figure 2.2: Simulated data illustrating the eﬀect of (a) C0,t and (b) k on chamber concentration Ch,t for the exponential model (2.7). Data −3 −1 were generated with (a) Ch,0 = 0 and k = 0.1 and (b) Ch,0 = 0 and C0,t = 55. Units for Ch,0 and C0,t are mg m and the unit for k is min . height multiplied by the change in headspace gas concentration over time was equal to the soil gas ﬂux at the soil/air interface

∂C ∂C h z,t = D sa,t . (2.9) ∂t sa ∂z z=0 z=0

From these relationships, the authors developed the non-steady-state diﬀusive ﬂux estimator (NDFE) model

1 2 t/τ C = C + F τh− t/τ + e erfc( t/τ) 1 (2.10) h,t h,0 0 √π − h p p i 3 where C is the initial chamber gas concentration [M L− air] at t = 0; τ is an h,0 · 2 1 experimental time constant equivalent to h (ΘsaDsa)− and with units [T]; h is the height [L air] of the non-steady-state chamber; and erfc is the complementary error function.

The bracketed portion of (2.10) reduces to 2 t/τ 1 when time is large, with the √π − p result that Ch,t is unbounded and will approach inﬁnity as time approaches inﬁnity.

The shape of the concentration curve, or how quickly Ch,t increases, is determined by both F0 and τ (Figure 2.3). The parameter τ measures the responsiveness of the soil to chamber deployment. When τ is very large relative to t, gas transport is little aﬀected by chamber deployment and the concentration curve is approximately linear

(Figure 2.3(b)). Whereas when τ is very small, gas transport is greatly diminished with chamber deployment and the concentration curve is nonlinear (Livingston et al.,

2005, 2006). Inundated soils are expected to have large τ, due to their small soil air-ﬁlled porosities and the inverse relationship between τ and Θsa (Livingston et al.,

2006). Further explanation of the NDFE model and its derivation can be found in

Livingston et al. (2006).

16 τ=1000 500 F0=50 50 τ=100

400 40 ) ) 3 3 − −

300 30

F =25 0 τ=10 200

Concentration (mg m Concentration (mg m 20

100 F =10 10 0 τ=1 17

F0=5

0 0

0 20 40 60 80 100 0 20 40 60 80 100

Time (min) Time (min)

(a) (b)

Figure 2.3: Simulated data illustrating the effect of (a) F0 and (b) τ on the NDFE model (Eq. (2.10)). Monitored gas fluxes with small F0 and τ are strongly affected by chamber deployment and exhibit nonlinear behavior, while those with large F0 and τ are little affected by chamber deployment and appear linear. (Data were generated in (a) given Ch,0 = 0, h = 1 and τ = 10; and in (b) given Ch,0 = 0, h = 1 and F0 = 1. −3 −2 −1 The unit for Ch,0 is mg m , h is m, F0 is mg m min , and τ is min. 2.2 Materials and Methods

2.2.1 Real Data

Real CO2 and CH4 ﬂux data were measured using non-steady-state chambers constructed of 10.2 cm diameter PVC pipe. The chambers consisted of two sections: 1) a 20 cm base that was permanently installed at each sampling station and 2) a 70 cm section, equipped with cap and butyl septum, that was deployed only when sampling. Chambers were unvented, except during deployment; and chamber air was not mechanically mixed, other than pumping with a syringe prior to sample withdrawal.

Upon chamber deployment, 10 mL headspace samples were withdrawn every three to seven minutes for a total of six samples. The collected gas samples were stored at 4◦C until analysis by gas chromatography (Shimadzu GC-14A Gas Chromatograph). The gaseous compounds were separated along two Porapak Q columns (Alltech, 80/100 mesh, 1.8 m x 3 mm stainless steel tubing) arranged in series and maintained at

1 60◦C, with helium as the carrier gas (30 mL min− ). CO2 was measured on a thermal conductivity detector (TCD; 150◦C), and CH4 on a ﬂame ionization detector (FID;

200◦C). Soil CO2 and CH4 fluxes were monitored for five created and three natural freshwater depressional wetlands located in central Ohio, USA. Soil-water conditions ranged from dry to saturated to inundated. Effective chamber height equaled the total chamber height minus water depth. Flux measurements were obtained once per month between June 2004 and May 2005 (weather permitting), from 3 to 5 sampling stations per wetland, yielding 355 real CO2 and CH4 datasets.

18 2.2.2 Simulated Data

Simulated CO2 and CH4 ﬂux data were generated using a one-dimensional ﬂux

model. The simulation was a simpliﬁed approach based on the numeric diﬀusion

model developed by Ishii et al. (1989) and modiﬁed by Healy et al. (1996). In the

one-dimensional ﬂux simulation, the chamber system was modeled as a vector of m+n

cells (Figure 2.4). The lower m cells each underwent molecular diﬀusion across cell

height ∆z = za/m, where za was the total height [L] of the air boundary layer. Their

initial concentrations were C = C (i 1)(C C )/m, where i = 1,...,m ; i,0 0,0 − − 0,0 − za,0 { } 3 C was the gas concentration [M L− ] of the ith lowermost cell at t = 0; and i,0 · 3 C and C were the gas concentrations [M L− ] at the bottom (z = 0) and 0,0 za,0 ·

top (z = za) of the air boundary layer at t = 0, respectively. The upper n cells

each underwent turbulent diﬀusion across cell height ∆z = (h z )/n, where h was − a

the height [L] of the chamber. Their initial concentrations were Ci,0 = Cza,t, where

i = m + 1,...,m + n . Cell concentrations were incrementally recalculated over a { } series of time steps following Fick’s second law (2.4),

∆C ∆F i,t = ∆t − ∆z

3 where ∆C was the change in concentration [M L− ] of cell i over time step ∆t i,t · 2 1 [T]; and ∆F was the change in ﬂux [M L− T− ] across the cell. The change in · ·

flux equaled the difference between flux across the cell’s upper boundary (FU ) and

ﬂux across the cell’s lower boundary (F ): ∆F = F F , (Figure 2.4). The new L U − L concentration for cell i at time t + 1 was then calculated using the formula

19 Ci ,t+Ci− ,t 2Ci,t +1 1 − Ci,t + Da (∆z)2 ∆t, i = 1,...,m Ci,t+1 = Ci ,t+Ci− ,t 2Ci,t (2.11) +1 1 − ( Ci,t + Ez (∆z)2 ∆t, i = m + 1,...,m + n

3 1 where D was the molecular diﬀusivity in air [L air T− ]; and E was the turbulent a · z 3 1 diﬀusivity [L air T− ] (Schwarzenbach et al., 1993). · Two additional model conditions addressed the lowermost and uppermost cell

boundaries. For the lowermost boundary, the gas was modeled as having a source of

unknown concentration (C?,t) such that C0,t = C0,0, for all t (Eq. (2.11) and Figure

2.4). At the uppermost boundary, the ﬂux was assumed to be zero (an impermeable

boundary; Healy et al. (1996)); consequently, Cm+n+1,t = Cm+n,t, for all t (Eq. (2.11)

and Figure 2.4).

Finally, to better simulate real data, noise was periodically added to the system

by randomly perturbing each cell concentration. The perturbation was within a ﬁxed

range of the cell concentration

C∗ = C + αC , α U [ N ,N ] i,t i,t i,t ∼ − f f

where Ci,t∗ was the ‘noisy’ concentration and α was the fraction of noise added. The

random variable α was drawn uniformly between N and N . The periodic addition − f f of noise was intended to mimic experimental error resulting from random ﬂuctuations within the chamber-air system and measurement imprecision.

Figure 2.5 contains the pseudocode for the ﬂux simulation. The time step (∆t)

for each iteration was 0.01 min. The cell values were incremented over a total of 30

min (3000 steps). Every 5 min, noise was added to the system and the concentration

of the last cell recorded, yielding six ‘headspace samples’ for each simulation.

20 Ci+1,t h FU

Cm+n,t

Ci,t z Cm+6,t

FL Cm+5,t

Ci-1,t Cm+4,t turbulent

Cm+3,t

C Well-mixed m+2,t Air

Cm+1,t

za Cm,t

C3,t Diffusive

C2,t Air Layer diffusive

C1,t

Soil/Water SOURCE C?,t

Figure 2.4: The cell model utilized in the gas ﬂux simulation. The chamber system was modeled as a series of m lower cells with heights ∆z = za/m, where za was the total height of the air boundary layer [L]; and n upper cells with heights ∆z = (h za)/n, where h was the height of the chamber − [L]. The lower m cells had initial concentrations Ci,0 = C0,0 (i 1)(C0,0 Cza,0)/m, where Ci,0 −3 − − − was the gas concentration [M L ] of the ith lowermost cell at t = 0, and C0,0 and Cza,0 were the −3 · gas concentrations [M L ] at the bottom (z = 0) and top (z = za) of the air boundary layer at t = 0, respectively; and· underwent molecular diﬀusion. The n upper cells had initial concentrations

Cza,0, and underwent turbulent diffusion. At each time step ∆t, each cell concentration (Ci,t) was recalculated according to Fick’s second law (2.4), based on the flux across the cell’s upper boundary (FU ) and the flux across the cell’s lower boundary (FL).

21 Chamber height (h) was 0.7 m and divided into m = 20 lower cells and n = 20

upper cells. The fraction of noise added was Nf = 0.02. For CO2 ﬂux simulations,

3 4 2 1 C equaled 150 mg C m− and D equaled 9.4 10− m min− ; for CH ﬂux za,0 a × 4 3 3 2 1 simulations, C equaled 0.98 mg C m− and D equaled 1.3 10− m min− . za,0 a ×

The remaining parameters, za, Ez and C0,0, were varied over the simulations. The

height of the air boundary layer (za) was set at 0.005, 0.01, 0.02 or 0.05 m. Chamber

2 1 mixing was modeled as moderately turbulent (Ez = 0.01 m min− ), turbulent (Ez =

2 1 2 1 0.1 m min− ), or highly turbulent (Ez = 10 m min− ). Soil surface concentrations

3 (C0,0) were 500, 750, 1000, 2000 or 3000 mg C m− for CO2, and 5, 25, 50, 100, 500,

3 1000, 5000 or 10000 mg C m− for CH4. All combinations were modeled, yielding

480 simulated CO2 and CH4 datasets. The initial soil/air ﬂuxes (F0) were calculated across the air boundary layer following equation (2.5).

Model values for C0,0 and Cza,0 were selected based on CO2 and CH4 concentrations observed in the field, as well as standard air concentrations. The gas diffusivities in free air (Da) were calculated for CO2 and CH4 at 25◦C and 101 kPa according to Fuller et al. (1966) (as cited in Schwarzenbach et al., 1993). Turbulent diffusivities (Ez)

were within the typical atmospheric range (Schwarzenbach et al., 1993). The model

chamber height equaled the height of the chamber used during real data collection,

h = 0.7 m. Other parameters (e.g., m, n, za and Nf ) were arbitrarily selected to provide realistic simulated data.

2.2.3 Flux estimations Separate estimation

Flux estimates were obtained separately for each CO2 and CH4 dataset (real and simulated) using the linear model (2.2), the exponential model (2.7), and the

22 NDFE model (2.10). The linear model was ﬁt through simple least squares regression

and estimates obtained for parameters Ch,0 and b1. The exponential and NDFE

models were ﬁt iteratively through minimization of the sum-of-squares error (SSE).

The exponential model was minimized over parameters Ch,0, C0,t and k; and the

NDFE model was minimized over parameters Ch,0, F0 and τ.

Joint estimation

Following Eq. (2.1), the exponential and NDFE models were each jointly ﬁt to

the CO2 and CH4 datasets using a weighted sum-of-squares error cost criteria

N N 1 2 2 2 SSE = w C˜CO2 C (φ~ ) +w C˜CH4 C (φ~ ) (2.12) w 1 z,ti − z,ti CO2 2 z,ti − z,ti CH4 i=1 i=1 X X

SSECO2 SSECH4 | {z } | {z } ˜(g) where w1 was the weight for CO2; and w2 was the weight for CH4. In Eq. (2.12), Cz,ti ~ represents a measurement of gas g and Cz,ti (φg) was the model-predicted concentra- ~ ~ tion based on the parameters φg. The predicted concentration Cz,ti (φg) was derived

from either the exponential model (2.7), or the NDFE model (2.10). SSECO2 and

SSECH4 denote the sum-of-squares errors for CO2 and CH4, respectively.

Two weighting schemes were considered in assigning the relative weights wg (g =

1, 2). In the ﬁrst scheme, wg was calculated from the inverse of the initial sum-of- squares error 1 wg = (weighting scheme “ss”) SSEg,0

and in the second scheme, wg was calculated as the inverse of the squared-mean

concentration

2 Ng wg = 2 (weighting scheme “mn”), Ng (g) i=1 Cz,ti P 23 (g) where SSEg,0 was the initial sum-of-squared error for gas g; Cz,ti was the concentration of gas g at sample time t , i = 1,...,N ; and N was the number of samples for gas i { g} g g. Additionally, in the second weighting scheme (mn), weight factors were calculated

only once per optimization routine; whereas in the ﬁrst weighting scheme (ss), weight

factors were reﬁned throughout each optimization routine.

One further modiﬁcation was to approximate k (exponential model (2.7)) and τ

(NDFE model (2.10)) for CH4 from the respective parameters for CO2. The basis for this approximation was the theoretical relationship between diﬀusivity and molecular size

D(CO ) V (CH ) M(CH ) 1/2 2 = m 4 4 D(CH ) V (CO ) ≈ M(CO ) 4 m 2 2 2 1 3 1 where D is the gas diﬀusivity [L T− ]; V is the molar volume [L N− ]; and M is · m · 1 the molar mass [M N− ] (from Fuller et al., 1966, as cited in Schwarzenbach et al., · 1993). The exponential parameter k is directly proportional to diﬀusivity, and the

NDFE parameter τ is inversely proportional to diﬀusivity, hence

M(CO ) 1/2 k(CH ) k(CO ) 2 4 ≈ 2 M(CH ) 4 M(CH ) 1/2 τ(CH ) τ(CO ) 4 4 ≈ 2 M(CO ) 2

1 1 where M(CO2) = 44 g mol− ; and M(CH4) = 16 g mol− .

The simultaneous solution of CO2 and CH4 datasets, along with the approximation

of k and τ, permitted the ﬁtting of ﬁve parameters with twelve data points, as opposed

to ﬁtting three parameters with six data points twice.

24 Initial ﬂux

The ultimate goal of the model ﬁttings was the initial soil/air ﬂux estimate F0.

The initial ﬂux, or ﬂux at t = 0, was solved for explicitly in the NDFE model, and

was obtained through the following calculations in the linear and exponential models

F0 = b1h (linear) (2.13)

F = kh(C C ) (exponential) (2.14) 0 0,t − h,0 Optimization in R

All model ﬁtting was performed using R 2.4.0 (R Development Core Team, 2006).

The two nonlinear models were minimized using the ‘optim’ function with ‘L-BFGS-B’ method. Initial estimates for Ch,0, k, and F0 were obtained from the linear parameter estimates. The initial estimate for C0,t was obtained from the maximum measured concentration when b1 was positive and the minimum measured concentration when b1 was negative. The initial τ estimate was obtained by dividing the squared eﬀective chamber height (h) by the molecular diﬀusivity in free air (Da). Ch,0 was constrained

6 3 6 3 to be between 0 and 1 10 mg C m− ; C between 0 and 1 10 mg C m− ; k × 0,t × 1 6 2 1 between 0 and 1 min− ; F between 0.1 h min C and 1 10 mg C m− min− ; 0 − h,t × 1 13 and τ between 1.2 10− and 1 10 min. × × To ensure global minimum solutions, optimizations of the exponential and NDFE models were initiated from multiple starting points by varying the initial parameter estimates (i.e., k or τ). To ensure consistent and stable solutions, each model ﬁtting, or optimization routine, consisted of a series of optimizations, or steps. The solution

25 at the end of each step, provided the parameter starting values for the next step.

When using weighting scheme ss, the weight factors wg were recalculated at the beginning of each optimization step.

Outliers

For the real datasets, outlying data points were jointly (i.e., from both CO2 and

CH4 datasets) omitted during model ﬁtting. Data points were determined to be outlying by any of the following criteria: lab notes indicated gas sample was suspect; standardized residual about the mean or ﬁtted value was notably large (i.e., >2 or

<-2); data point appeared physically incongruent with other data points. Generally, no more than two data points per dataset were omitted; however, in four extreme cases, three data points were omitted per dataset.

2.2.4 Model comparisons Model ﬁt

Seven models were compared by SSECO2 , SSECH4 , SSEw and Akaike’s information criterion (AIC) using both real and simulated data (Table 2.1). The seven models were combinations of optimization method (i.e., separate or joint), model type (i.e., linear, exponential or NDFE) and weighting scheme (i.e., ss or mn). For separately

ﬁt models, a pseudo SSEw was calculated using the squared-mean weighting scheme

(mn). Signiﬁcant diﬀerences in median SSECO2 , SSECH4 , SSEw between methods

(exponential and NDFE, using mn only), model types (separate only), and weights

(joint only), for both real and simulated data, were determined by random permutation without replacement (n=1000).

26 One disadvantage of comparison by SSE is that the number of estimated param-

eters is not accounted for. AIC is a theory-based measure that accounts for both

model ﬁt and model parsimony (Burnham and Anderson, 1998, 2004). AIC values

were calculated with correction for small sample size

Model Model Optimization Method Weighting Scheme K Type 1 Sep Lin - 6 2 Sep Exp - 8 3 Sep Ndfe - 8 4 Jnt Exp ss 6 5 Jnt Exp mn 6 6 Jnt Ndfe ss 6 7 Jnt Ndfe mn 6 Table 2.1: Seven different models were fit to the real and simulated datasets. Each model was a combination of optimization method (Method): separate (Sep) or joint (Jnt); model type (Model): linear (Lin), exponential (Exp) or NDFE (Ndfe); and weighting scheme (Weight): sum-of-squares error (ss) or squared-means (mn). The total parameter count K used in calculating Akaike’s information criterion is indicated in the final column. Following the guidance of Burnham and Anderson (1998), K includes the error term(s).

SSE 2K(K + 1) AIC = N ln w,q,m + 2K + (2.15) q,m N N K 1 − − where q was the dataset (q = 1,..., 355 , for the real data; and q = 1,..., 480 , for { } { } the simulated data); m = 1,..., 7 is the model (e.g., jointly ﬁt exponential model { } using ss); N was the total number of data points (N = 12, 10, 8, or rarely 6, for the real data; and N = 12 for the simulated data); and K was the total number of estimated parameters. Following the instruction of Burnham and Anderson (1998), the total number of estimated parameters K included the error term: for example,

27 when using one of the jointly ﬁt exponential models, the total estimated parameters

included two C0,t, two Ch,0, one k and one error term, so K = 6 (Table 2.1).

For the model comparison, AIC values were placed on a relative scale by calculating the AIC diﬀerences (∆q,m)

∆q,m = AICq,m min AICq,m (2.16) − m

where minmAIC was the minimum AIC criterion across all seven models for dataset

q. The best model had the lowest AICq,m and lowest ∆q,m, indicating that the model

both ﬁt the dataset well and did not require superﬂuous parameters.

Estimate accuracy

The seven models were also evaluated by estimation accuracy using the simulated

data. For the simulated datasets, the true F0 and Ch,0 values were known and could be compared to the model estimates. Estimation accuracy was characterized by two measures of error

Absolute error = Yˆ Y − Yˆ Y Relative error = − Y

where Yˆ was the estimated value and Y was the true value.

2.3 Results

2.3.1 Real data

Among the seven tested models, for the 355 real datasets, the two best models were the jointly ﬁt exponential model using ss and the jointly ﬁt NDFE model using

28 ss. These two models had the lowest median SSEw and ∆q,m (Table 2.2). The jointly

ﬁt exponential model using mn also performed well and had a relatively low median

∆q,m. Furthermore, these three models resulted in the lowest ∆q,m in 293 (or 82%)

of the 355 datasets.

In general, the joint optimization method outperformed separate optimization.

2 Jointly ﬁt models had substantially lower median ∆q,m (factor of 10 ). Addition- ally, when comparing joint versus separate optimization between model types (i.e., exponential or NDFE), joint optimization always resulted in the lowest ∆q,m within a

given dataset. The lower ∆q,m obtained by joint optimization was due to the reduced

parameter set, as indicated by the similar SSECO2 , SSECH4 and SSEw provided by

the two methods.

Of the three model types, the exponential model provided the best ﬁt of the

real data, yielding signiﬁcantly lower median SSECO2 (p=0.006, vs. Lin) and SSEw

(p 0.001, vs. Lin; p=0.009, vs. Ndfe). Many of the real datasets were visibly non- ≤ linear and the curvature was best approximated with the exponential model (Figure

2.6(a)). In the example provided, the soil-water condition was dry at the time of gas

sample collection; however, the degree of nonlinearity within the datasets appeared to

be independent of soil-water conditions. Accordingly, estimates for τ (NDFE model

(2.10)) were also independent of soil-water conditions, in other words, large τ did not

correspond to saturated or inundated conditions as had been expected (Livingston

et al., 2006).

The disadvantage of the exponential model (and NDFE model) in comparison to

the linear model was the additional parameter requirement. The cost of the more

complex nonlinear models was reﬂected in the much higher median ∆q,m (separate ﬁt

29 only). Furthermore, AIC values for the two separately ﬁt nonlinear models could not be calculated for 71 of the 355 datasets due to an insuﬃcient number of data points

(i.e., N 8). However, this parameter constraint for the exponential and NDFE ≤ models was able to be circumvented by jointly optimizing the data.

The two weighting schemes performed similarly, with no signiﬁcant diﬀerence

between median SSECO2 , SSECH4 or SSEw. However, the use of ss provided slightly lower median ∆q,m than did the use of mn. This diﬀerence in median ∆q,m was especially pronounced for the NDFE models, for which use of ss provided the lowest

∆q,m in 229 (or 65%) of the 355 datasets.

Method Model Weight K SSECO2 SSECH4 SSEw ∆q,m pm Sep Lin 6 179.1a 0.163 8 673.8a 8.65 0.028 Exp 8 114.3b 0.041 98 414.6b 101. 0. Ndfe 8 148.6ab 0.088 37 589.a 109. 0. Jnt Exp ss 6 134.3 0.057 09 266. 1.1 0.24 mn 6 147.9 0.044 93 466.7 2.82 0.22 Ndfe ss 6 151.7 0.088 19 293.9 1.07 0.36 mn 6 157.1 0.088 19 602.3 5.64 0.14

Table 2.2: Median values of sum-of-squares error for CO2 (SSECO2 ) and CH4 (SSECH4 ), weighted sum-of-squares error (SSEw), and AIC differences (∆q,m) from flux estimations performed on 355 real datasets using seven different models. The models were combinations of optimization method: separate (Sep) or joint (Jnt); model type: linear (Lin) or exponential (Exp) or NDFE (Ndfe); and weighting scheme: sum-of-squares error (ss) or squared-mean concentration (mn). The total number of parameters (K) estimated for each model is listed (total includes error terms(s)). Superscripted stars denote a significant difference (p 0.05) between optimization method (i.e., Sep or Jnt, using mn only); letters denote significant differences≤ between model types (i.e., Lin or Exp or Ndfe); and daggers denote a significant difference between weighting scheme (i.e., ss or mn). Significance was determined by random permutation (n=1000). The final column indicates each model’s proportion (pm, where m = 1,..., 7 ) of minimum ∆q,m out of all 355 datasets. { }

30 2.3.2 Simulated data Model ﬁt

For the 480 simulated datasets, of the seven tested models, the three best models were the jointly ﬁt exponential model using either weighting scheme and the jointly

ﬁt NDFE model using ss (Table 2.3). The three models had the lowest median

∆q,m over all the datasets and within each turbulence category. The two exponential

2 1 models performed best under turbulent (Ez = 0.1 m min− ) and highly turbulent

2 1 (Ez =10m min− ) conditions, resulting in the lowest median ∆q,m and in the lowest

∆q,m for each of the 480 datasets, within the given turbulence category. The NDFE

2 1 model performed best under moderately turbulent (Ez = 0.01 m min− ) conditions, resulting in the lowest median ∆q,m, and in the lowest ∆q,m for 374 (or 78%) of the

480 datasets. When turbulence was moderate, the simulated concentrations varied linearly with time, favoring the NDFE model; whereas the exponential models were favored when turbulence was higher because of the nonlinear change in simulated concentration over time (Figure 2.6(b)).

Joint optimization was the better method, resulting in lower median ∆q,m. Fur-

thermore, when joint versus separate optimization was compared within model type

(i.e., exponential or NDFE), joint optimization always resulted in the lowest ∆q,m

within a given dataset. However, on the basis of best ﬁt only, separate optimization,

with the extra parameter, more closely ﬁt the data (median SSECO2 : p<0.001 for all

2 1 2 1 datasets, Ez = 0.1 m min− , and Ez = 10 m min− ; median SSEw: p<0.04 for all

2 1 datasets and p=0.05 for Ez = 0.1 m min− ).

Between the three model types, the exponential model most closely ﬁt the data

and yielded signiﬁcantly lower median SSECO2 and SSEw (except when Ez = 0.01

31 2 1 m min− ). As with the real data, the main beneﬁt of the exponential model was

in the approximation of curvilinear datasets: the exponential model ﬁt best under

turbulent and highly turbulent conditions, which generated simulated concentrations

that varied nonlinearly with time (Figure 2.6(b)). Some of the ﬁt advantage provided

by the exponential model was lost, however, when parameter cost was considered. For

separately ﬁt data, the linear model type resulted in slightly lower median ∆q,m and the lowest ∆q,m within the majority of the datasets. Again, however, the parameter cost was able to be circumvented by jointly ﬁtting the data.

The performance of the two weighting schemes was mixed. Although the use of ss provided signiﬁcantly lower median SSEw (p<0.001, all datasets and Ez = 0.01

2 1 m min− ), the median ∆q,m were generally similar between the two weights. The ss weighting scheme performed best when used with the exponential model under moderately turbulent conditions, or with the NDFE model under any condition, resulting in the lowest ∆q,m within the majority of the datasets; while the mn weighting scheme performed best when used with the exponential model under more turbulent conditions.

Estimate accuracy

There was no obvious best model in terms of estimate accuracy, although the two jointly ﬁt NDFE models generally had the smallest median errors for both F0 and

Ch,0 (Table 2.4). When comparing model estimate error within a dataset, the smallest errors were achieved using the linear model for F0 and the separately fit exponential model for Ch,0, in addition to the two jointly fit NDFE models. Initial fluxes (F0)

were typically underestimated, particularly under moderately turbulent conditions;

whereas initial concentrations (Ch,0) tended to be overestimated, particularly when

32 conditions were more turbulent (Figures 2.7 and 2.8). Underestimation of gas ﬂux

has also been observed in other studies (Nakano et al., 2004; Livingston et al., 2006).

Of the two optimization methods, joint optimization provided more accurate es-

timates of both F0 and Ch,0 for CO2 (p<0.001, both absolute and relative errors).

CO2 ﬂux estimates were particularly improved for the NDFE model when jointly optimized. For CH4, estimates of F0 were slightly improved with joint optimization, while both optimization methods estimated Ch,0 well.

The exponential model was most accurate in estimating both F0 and Ch,0 among the three model types (separate fit only). Median flux estimate errors were significantly reduced when using the exponential model; while median Ch,0 estimate errors were significantly reduced when using either nonlinear model. The NDFE model tended to overestimate F0, particularly under more turbulent conditions, while the linear and exponential models tended to underestimate F0. However, the linear model tended to overestimate Ch,0, particularly under more turbulent conditions, whereas the two nonlinear models tended to underestimate Ch,0.

There was no diﬀerence in estimation accuracy between the two weighting schemes.

2.4 Discussion

The joint optimization method improved model ﬁtting and ﬂux estimation for real and simulated data. For the real datasets, jointly optimized exponential and

NDFE models fit the data as well as the separately optimized models, but with fewer parameters. For the simulated datasets, the jointly optimized exponential and NDFE models did not fit the data as well as the separately optimized models, however, the reduction in SSE by the separately fit models did not justify the additional parameters

33 required by those models. This suggests that the additional parameters present in

the multiple separate models, allows those models to over-ﬁt the data. This is further

conﬁrmed by the observation that the joint optimization provided more accurate

estimates of both the initial soil/air ﬂux F0 and the initial chamber air concentration

Ch,0 for the simulated data, particularly for CO2. The caveat here is the credibility of the simulation. However, given the similarity in appearance between generated and real gas ﬂux data, the results from the simulations lend support to results observed from the real data.

There are two main advantages oﬀered by joint optimization. One advantage is the reduction of the total number of estimated parameters. Both real and simulated datasets ranged from linear to curvilinear and were ﬁt better by the two nonlinear models (particularly the exponential model) than by the simpler linear model.

The method of joint optimization allowed use of the more complex nonlinear models without increasing the total number of parameters. The reduction in the number of parameters is particularly important when analyzing datasets of small size, which is often the case for real gas ﬂux data, where time and cost of gas sample collection and analysis generally limit the dataset to a small number of samples. In this study, the reduced parameter set provided by joint optimization proved particularly valuable for datasets requiring omission of multiple outliers.

The other main advantage of joint optimization is the use of multiple datasets to optimize one joint set of parameters. The larger dataset will enable more accurate estimation of parameters. In this study, jointly optimized models fit the data well with fewer parameters, and provided more accurate estimates of initial flux and concentration. Further benefit to jointly optimizing data may be realized in instances

34 where data for one monitored gas is particularly noisy. In such cases, a combined dataset of noisy and non-noisy data would help distinguish the true pattern for the noisy data.

The method of joint optimization is applicable to any number of trace gases monitored simultaneously across a soil/air or water/air interface. In this paper we applied the method to only two gases, CO2 and CH4, however many examples can be found in the literature of monitoring combinations of two to several trace gas ﬂuxes. Mul- tiple gaseous compounds of carbon (e.g., CH4, CO2 and CO), nitrogen (e.g., NH3,

N2O, NO and NO2), and sulfur (e.g., H2S and SO2) are emitted from soil and water surfaces because of biogenic processes occurring within those mediums. Combined emissions of CO2, CH4 and N2O are often of interest because of the importance of these three compounds as greenhouse gases. Another common example of simultaneous monitoring involves the nitrogen-based compounds, NH3,N2O and NO, which represent the primary (gaseous) pathways of nitrogen loss from an ecosystem. For joint ﬂux estimation, the gases of interest will need to be measured during the same sampling session, but not necessarily at the same time instances (e.g., in theory, we could have jointly estimated ﬂuxes for CO2 samples withdrawn at 3, 8, 13, 18, 23 and

28 min, and for CH4 samples withdrawn at 5, 10, 15, 20, 25 and 30 min), because the fluxes are being evaluated from changes in gas concentrations over time, not at one particular time. The critical component required for the applicability of joint estimation is that each gas flux is being controlled by transport through a diffusive boundary where Fick’s laws (Eq. (2.3) and Eq. (2.4)) apply. This condition allows us to relate the rate constants of the different gases and link the parameters such that

35 measurements of one gas are informative about a portion of the parameters for the other gases.

Estimate accuracy may be further improved by joint optimization through application of other weighting schemes. Only two weighting schemes, ss and mn, were explored in this study. Model fit was slightly better when weighting by ss, particularly when using the NDFE model. However, estimate accuracy was similar between the two weighting schemes. Selection of weighting scheme is critical to the performance of joint optimization by weighted-sum. Weight factors which are refined throughout the optimization routine are more computationally complex, but generally are expected to perform better (Marler and Arora, 2004); although, the continually refined weight factor (ss) performed only slightly better than the constant initially-defined weight factor (mn) in this study. Additional weighting schemes should be explored: for example, in jointly modeling thermodynamic equations of state for multiple nonpolar

ﬂuids, Span et al. (1998) utilized a weighting scheme based on the sum-of-squares error for solutions to the separately optimized models.

Finally, joint optimization by weighted-sum is only one of several approaches to solving the general type of problem referred to as a multi-objective optimization problem (MOP; Marler and Arora 2004). Although typically an MOP involves some trade-off between criteria, joint flux estimation does fit the definition of having multiple objectives. The large body of literature devoted to solving MOPs may offer additional techniques to improve the accuracy of joint flux estimation and further increase the utility of joint optimization as a valuable tool in the estimation of soil trace gas fluxes.

36 2.5 Conclusions

As anticipated, the joint method of optimization provided more accurate estimates of CO2 and CH4 fluxes. One advantage provided by joint optimization was the simultaneous use of both datasets in the flux estimations. Additionally, approximation of the CH4 gas diffusivity by the gas diffusivity of CO2, resulted in the estimation of one less parameter. While the method of separate optimization allowed the model curves

to fit each CO2 and CH4 data set more closely (i.e., lower SSECO2 and SSECH4 ), the resultant flux estimates were less accurate than those provided within the constraints of joint optimization. In this study, the multi-objective flux optimization was limited to only two gases (CO2 and CH4) but would be expandable to any number of gases being monitored simultaneously. With each additional monitored gas, the cumulative dataset will increase proportionally, while the total number of flux parameters to be estimated will diminish by one.

Acknowledgements: We would like to thank three anonymous reviewers whose comments and suggestions helped to improve this paper. And, we are especially grateful to Dr. Joshua Ash for his assistance with the mathematical notation and his invaluable comments on the manuscript. This research was supported by the National Research Initiative of the USDA Cooperative State Research, Education and Extension Service (award #2005-35101-15593) and the Ohio Agricultural Research and Development Center.

37 (i−1)(C0,0−Cza,0) C0,0 m − .  .  INIT C , ,...,Ci, ,...,Cm n, { 1 0 0 + 0} ←    −C − −   za,0   .   .      FOR sample = 1 to 6

FOR time = 1 to 500

(C2,t−1+C0,0−2C1,t−1) C1,t−1 + ∆tD (∆z)2 .  .   (Ci+1,t−1+Ci−1,t−1−2Ci,t−1)  C1,t,...,Ci,t,...,Cm+n,t  Ci,t−1 + ∆tD (∆z)2  { } ←    .   .     (Cm+n−1,t−1−Cm+n,t−1)   Cm+n,t−1 + ∆tD 2   (∆z)   

IF Ci,t > Ci,t−1 THEN (Ci+1,t−1+Ci−1,t−1) Ci,t = MIN(Ci,t, 2

IF Ci,t < Ci,t−1 THEN (Ci+1,t−1+Ci−1,t−1) Ci,t = MAX(Ci,t, 2

ENDFOR

ADD NOISE

RECORD Cm+n,t

ENDFOR

Figure 2.5: Pseudocode for the flux simulation. An m + n-cell vector was initialized with m diffusively-mixed (D = Da) cells set to initial concentration Ci, = C , (i 1)(C , Cz , )/m, 0 0 0 − − 0 0 − a 0 where i was the cell index with range 1 to m and C0,0 and Cza,0 were the gas concentrations at the bottom (z = 0) and top (z = za) of the air boundary layer at t = 0, respectively; and n turbulently- mixed (D = Ez) cells set to Cza,0. The height (∆z) was za/m for the lower m cells, with za equal to 0.005, 0.01, 0.02 or 0.05 mm; and (0.7 za)/n for the upper n cells. At each time step (∆t), each − cell concentration (Ci,t) was recalculated so that the change in cell concentration equaled the change in flux across the cell. Each new cell concentration was constrained to increase to a concentration no greater than the mean concentration of its two neighboring cells ((Ci+1,t−1 + Ci−1,t−1)/2) and to decrease to a concentration no less than the mean concentration of its two neighboring cells. Time was incremented by ∆t = 0.01 min, with a total run time of 30 min (3000 time steps). Every 5 min (500∆t), noise was added to each cell and the concentration of the last cell (Cm+n,t) recorded. A total of 6 ‘headspace samples’ were recorded for each simulation.

38 450 260 ) ) 3 3 − 240 − 400

220 350 (mg C m (mg C m 2 200 2 300 CO CO 180 250

4.5 20 ) )

3 3 4.0 − − 15 3.5

3.0 (mg C m 10 (mg C m 4 4

CH CH 2.5 5 2.0

5 10 15 20 25 10 15 20 25 30 Time (min) Time (min)

(a) real data (b) simulated data

Figure 2.6: Example plots of (a) real and (b) simulated flux data for CO2 and CH4. The real flux data were collected under dry soil-water condition. The simulated flux data were generated with 2 −1 −2 −1 a turbulent diffusivity of 10 m min , and initial fluxes of 66 mg C m min for CO2 and 1.0 −2 −1 mg C m min for CH4. Linear model fits are indicated by dotted lines, exponential fits by solid lines, and NDFE fits by dashed lines. The exponential model provided the best fit when gas concentrations varied nonlinearly with time. (Solid horizontal lines indicate mean gas concentration for the dataset.)

39 Ez Method Model Weight K SSECO2 SSECH4 SSEw ∆i pm 0.01 Sep Lin 6 134.4 13.10a 526.3a 7.29 0.11 Exp 8 138.0 409.8b 8 475.b 74.2 0. Ndfe 8 134.3 407.4b 8 541.b 74.2 0. Jnt Exp ss 6 186.2 447.8 372.4† 2.27 0.056 mn 6 178.2 433.1 8 928.† 39.8 0. Ndfe ss 6 134.4 410.9 268.8† 0. 0.78 mn 6 144.9 407.2 8 544.† 39.0 0.062 0.1 Sep Lin 6 16 130.a 2 240.a 38 230.a 22.9 0. b b b Exp 8 278.3∗ 185.7 2 320.∗ 24.5 0. c ab c Ndfe 8 5 175.∗ 742.2 13 000.∗ 45.1 0. Jnt Exp ss 6 2 968. 357.4 5 880. 2.85 0.29 mn 6 4 674.∗ 197.3 6 671.∗ 0. 0.71 Ndfe ss 6 6 209. 759.4 12 420. 7.52 0. mn 6 6 267.∗ 756.2 14 280.∗ 10.7 0. 10 Sep Lin 6 14 670.a 2 047.a 37 380.a 22.8 0. b b b Exp 8 233.1∗ 157.7 2 307. 23.8 0. c ab c Ndfe 8 4 393.∗ 641.7 12 440. 44.9 0. Jnt Exp ss 6 2 983. 297.3 5 901. 3.57 0.26 mn 6 3 827.∗ 166.3 6 116. 0. 0.74 Ndfe ss 6 5 194. 657.8 10 390. 7.56 0. mn 6 5 321.∗ 646.2 13 640. 10.5 0. Sep Lin 6 6 656.a 166.4 18 810.a 20.9 0.035 b b Exp 8 213.0∗ 160.5 4 178.∗ 28.2 0. c c Ndfe 8 1 956.∗ 437.0 11 020.∗ 47.9 0. Jnt Exp ss 6 1 410. 326.8 2 821.† 2.89 0.20 mn 6 1 592.∗ 169.0 7 367.∗† 0.881 0.48 Ndfe ss 6 2 314. 438.3 4 627.† 5.71 0.26 mn 6 2 410.∗ 437.7 11 390.∗† 13.3 0.021

Table 2.3: Median values of sum-of-squares error for CO2 (SSECO2 ) and CH4 (SSECH4 ), weighted sum-of-squares error (SSEw), and AIC differences (∆q,m) from flux estimations performed on 480 simulated datasets using seven different models. The models were combinations of optimization method: separate (Sep) or joint (Jnt); model type: linear (Lin) or exponential (Exp) or NDFE (Ndfe); and weighting scheme: sum-of-squares error (ss) or squared-mean concentration (mn). The total number of parameters (K) estimated for each model is listed (total includes error terms(s)). 2 −1 Median values are provided within each turbulence category (Ez = 0.01, 0.1 or 10 m min ) or across all turbulence categories (bottom section). Superscripted stars denote a significant difference (p 0.05) between optimization method (i.e., Sep or Jnt, using mn only); letters denote significant differences≤ between model types (i.e., Lin or Exp or Ndfe); and daggers denote a significant difference between weighting scheme (i.e., ss or mn). Significance was determined by random permutation (n=1000). The final column indicates each model’s proportion (pm, where m = 1,..., 7 ) of { } minimum ∆q,m out of all 480 datasets.

40 Absolute Error Relative Error

Method Model Weight CO2 CH4 CO2 CH4 a a a F0 Sep Lin -52.5 -11.6 -0.810 -0.858 a b b Exp -24.3∗ -8.27 -0.480∗ -0.674 b c b Ndfe 110.∗ -2.28 0.948∗ -0.352 Jnt Exp ss -32.9 -8.14 -0.645 -0.664 mn -35.0∗ -8.09 -0.648∗ -0.661 Ndfe ss -8.59 -1.79 -0.202 -0.266 mn -9.34∗ -1.85 -0.219∗ -0.291 a a a a Ch,0 Sep Lin 108. 6.61 0.721 6.74 b b b b Exp -99.7∗ -0.980 -0.665∗ -1.00 b b b b Ndfe -102.∗ -0.980 -0.679∗ -1.00 Jnt Exp ss 5.80 -0.980 0.0387 -1.00 mn 20.7∗ -0.980 0.138∗ -1.00 Ndfe ss -33.4 -0.980 -0.223 -1.00 mn -26.1∗ -0.980 -0.174∗ -1.00

Table 2.4: Absolute and relative errors for initial flux (F0) and concentration (Ch,0) estimates performed on 480 simulated datasets using seven different models. The models were combinations of optimization method: separate (Sep) or joint (Jnt); model type: linear (Lin) or exponential (Exp) or NDFE (Ndfe); and weighting scheme: sum-of-squares error (ss) or squared-mean concentration (mn). Model estimates for F0 and Ch,0 were compared to the true values for each dataset. Values reported are median values for the 480 datasets. Superscripted stars denote a significant difference (p 0.05) between optimization method (i.e., Sep or Jnt; mn only); letters denote significant differences≤ between model types (i.e., Lin or Exp or Ndfe); and daggers denote a significant difference between weighting scheme (i.e., ss or mn). Significance was determined by random permutation (n=1000).

41 CO2 CH4 CO2 CH4 0 0 * * * * * * * ******* 0 * ****** 0 −50 −50 −50 −50 −100 −100 −100 −150 −100 −150 =0.01 =0.01

z −150 z −200 −200 E E

Absolute Error −200 Absolute Error −150 −250 −250 −250 −300 −300 −200

400 600 1000 * * * * 100 * 800 * * * * 400 200 * 0 * 600 200 =0.1 * * * =0.1

z −100 z * * 400 0 * E E 0 * * 200 Absolute Error Absolute Error −200 −200 0 −200 ******

400 * * 100 600 * 1000 42 * * 800 * * * * * 400 200 0 * 600

=10 * * * =10 200 z z * * 400 0 −100

E * E 0 * * 200 Absolute Error Absolute Error −200 −200 0 −200 ****** lin exp ndf ejs ejm njs njm lin exp ndf ejs ejm njs njm lin exp ndf ejs ejm njs njm lin exp ndf ejs ejm njs njm

(a) F0 (b) Ch,0

2 −1 Figure 2.7: Boxplots of absolute error for the seven tested models under each turbulence category (Ez is the turbulent diffusivity in m min ). The models were combinations of optimization method (separate, unshaded; or joint, shaded), model type (linear, exponential or NDFE), and weighting scheme (sum-of-squares error, ss, dark gray; or squared-mean concentration, mn, light gray). Model estimates (a) initial flux F0 and (b) initial concentration Ch,0 were compared to true values for the 480 simulated datasets. The boxplot box indicates the median and lower and upper quartiles; boxplot ‘whiskers’ indicate the range of values within approximately three box lengths. Outliers were not plotted, but are indicated with starring. (Model abbreviations: lin, separately fit linear; exp, separately fit exponential; ndf, separately fit NDFE; ejs, jointly fit exponential using ss; ejm, jointly fit exponential using mn; njs, jointly fit NDFE using ss; and njm, jointly fit NDFE using mn.) CO2 CH4 CO2 CH4

−0.5 0.0 −0.4 * ****** 0 −0.2 * −0.6 −0.6 −0.4 −0.7 −100 −0.8 −0.6 −0.8 =0.01 =0.01 z z −1.0 −0.8 −200 E E −0.9 Relative Error Relative Error −1.0 −1.2 −1.0 −1.2 −300

* ** 4 * 1000 10 6 * 3 800 4 ** 2 600 5 =0.1 =0.1 z * * 2 z 1 * * 400 E * * E * Relative Error Relative Error 0 * 200 * 0 0 −1 ****** 0 −2 12 * ** 4 * 1000 43 10 6 3 800 8 * * * 6 4 2 600 =10 =10

z 4 z * 2 1 * * 400

E * E 2 * * Relative Error * Relative Error 0 200 0 0 −1 ****** 0 −2 −2 lin exp ndf ejs ejm njs njm lin exp ndf ejs ejm njs njm lin exp ndf ejs ejm njs njm lin exp ndf ejs ejm njs njm

(a) F0 (b) Ch,0

Figure 2.8: Boxplots of relative error for the linear, exponential, and NDFE models under each turbulence category (Ez is the turbulent diffusivity in m2 min−1). The models were combinations of optimization method (separate, unshaded; or joint, shaded), model type (linear, exponential or NDFE), and weighting scheme (sum-of-squares error, ss, dark gray; or squared-mean concentration, mn, light gray). Model estimates (a) initial flux F0 and (b) initial concentration Ch,0 were compared to true values for the 480 simulated datasets. The boxplot box indicates the median and lower and upper quartiles; boxplot ‘whiskers’ indicate the range of values within approximately three box lengths. Outliers were not plotted, but are indicated with starring. (Model abbreviations: lin, separately fit linear; exp, separately fit exponential; ndf, separately fit NDFE; ejs, jointly fit exponential using ss; ejm, jointly fit exponential using mn; njs, jointly fit NDFE using ss; and njm, jointly fit NDFE using mn.) PART II

Biogeochemical cycles in created and natural wetlands

44 CHAPTER 3

NO-NET-LOSS NOT MET FOR NUTRIENT FUNCTION: RECOMMENDATIONS FOR WETLAND MITIGATION POLICIES

Currently in review: Hossler, K., Bouchard, V., Fennessy, M.S., Frey, S., Anemaet, E. and Herbert, E. (In rev.). No-net-loss not met for nutrient function: recommendations for wetland mitigation policies. Nature.

Abstract: Wetlands provide many important services throughout the world, with an estimated economic value that, in comparison to other ecosystems, far exceeds their relatively small global extent (Costanza et al., 1997; Finlayson and Spiers, 1999; Millennium Ecosystem Assessment, 2005; Zedler and Kercher, 2005; Finlayson and D’Cruz, 2005). In recognition of their importance, both national and international regulations exist to protect the world’s remaining wetlands (Ramsar Convention, 1971; CWA, 1972; U.S. Army Corps of Engineers and U.S. Environmental Protection Agency, 1990; Government of Canada, 1991). Of growing interest is the “no-net-loss” policy which permits unavoidable destruction of wetlands if compensated by restoration of degraded wetlands or creation of new wetlands. This policy is currently in force in the US and Canada, and may soon become the model for nations elsewhere (U.S. Army Corps of Engineers and U.S. Environmental Protection Agency, 1990; Government of Canada, 1991; Ramsar Convention, 1999, 2008). The fundamental assumption of no-net- loss, however, is that we can create wetlands which function equiv- alently to natural wetlands. One integral function that wetlands perform is the global cycling of carbon, nitrogen and phosphorus. Here we demonstrate that loss of this nutrient-related function

45 is not being mitigated by creating or restoring wetlands. We compare plant- and microbial-mediated functions, as well as abiotic (e.g., soil character, hydrology) and biotic (e.g., plant community composition) structure, between 10 created or restored and 5 natural freshwater depressional wetlands in central Ohio, USA. Nu- trient stocks were generally smaller and transformations slower in created wetlands than in natural wetlands, with no apparent development over time. Of particular concern were the diﬀer- ences in C- and N-related processes. Created wetlands stored 90 % less C within litter and 80 % less C within soil and processed 60 % less N through denitriﬁcation, on average compared to natural wetlands. Our study suggests that subversion of natural wetlands into restored or created wetlands will have large-scale environmental consequences such as nitrate over-loading in surface waters (Mitsch et al., 2001) and reduced C sequestering capacity (Bridgham et al., 2007). We observe that one key factor to the development and function of wetland biogeochemical cycling is the soil and recommend, at minimum, preserving natural wetland soil.

Wetlands are among the world’s most unique and important ecosystems, providing valuable services such as water quality improvement, replenishment of water supply, carbon sequestration, ﬂood protection, biodiversity enhancement, and support for recreational activities (Millennium Ecosystem Assessment, 2005; Zedler and Kercher,

2005). These ecosystem services hold high economic value for humankind, belying the relatively small areal coverage of wetlands (Costanza et al., 1997; Zedler and Kercher,

2005; Finlayson and D’Cruz, 2005). Wetlands comprise less than 3 % of the earth’s surface (or about 9 % of the earth’s total landmass) (Finlayson and Spiers, 1999). An estimated 50 % of the world’s wetland habitat has been lost over the past century, along with their diverse products and services (Finlayson and Spiers, 1999). In spite of their esteemed value, as well as national and international regulations, wetland loss continues. In the US, for example, an estimated 35.6 kha of inland and coastal

46 vegetated wetlands are lost annually (about twice the area of the US capital district,

Washington DC; Dahl, 2006).

Much of the US wetland loss is recovered yearly through restoration programs, particularly the federal “no-net-loss” policy, which requires that wetland impacts be avoided or minimized, and if no other option is available, mitigated by restoration of other degraded wetlands or creation of new wetlands (CWA, 1972; U.S. Army

Corps of Engineers and U.S. Environmental Protection Agency, 1990). Implicit in the latter course is the assumption that the replacement wetlands are functionally equivalent to the subverted natural wetlands. A review by the US National Research

Council (NRC) Committee on Mitigating Wetland Losses concluded, however, that loss of natural wetland function was not being mitigated by creation of new wetlands, despite structural resemblance (NRC (National Research Council), 2001). The committee made several recommendations, including more studies of wetland creation outcomes, monitoring mitigation sites over longer time frames (e.g., mitigation projects are typically monitored only 5 years (y)), and assessing mitigation compliance with indicators of wetland function rather than just wetland structure (e.g., a structural assessment would look at the organization of components within the ecosystem, such as hydrology or plant community, whereas a functional assessment would focus on the processes performed by the ecosystem, such as nutrient cycling or

C sequestration; Table 3.1).

Although there have been many investigations into the ability of created wetlands to mitigate natural wetland loss (for a review see Appendix A in NRC (National Re- search Council), 2001), these studies either were very small in scope (i.e., monitored

47 only a few sites or functions) or used structure as a surrogate for function (e.g., measured plant cover to assess plant productivity). Physical resemblance may or may not equate to functional replacement; or in other words, establishment of wetland structure does not necessarily restore the ecosystem functions of a wetland. Furthermore, while these early studies provide some guidance to governmental and private agen- cies involved in wetland creation and restoration, much remains unknown about the mechanistic factors controlling development of ecosystem functions in these systems.

As a result, understanding whether and how wetlands can be created and restored to function tantamount to natural wetlands has emerged as one of the central questions in restoration ecology over the past decade (Mitsch et al., 1998; Zedler, 2000; Zedler et al., 2001).

Our objective was to evaluate the structural and functional development of created depressional wetlands, particularly plant- and microbial-mediated functions associated with carbon, nitrogen and phosphorus cycles. Natural inland wetlands (e.g., depressional wetlands) play an integral role in the global cycling of C, N and P, yet the ability of created wetlands to replace natural wetlands in this capacity has been largely ignored. These functions are crucial to ecosystem services such as water quality improvement and C sequestration. By quantifying a large number of structural and functional parameters across a representative range of sites, our comprehensive study allows critical evaluation of this significant issue. Here we present our findings and address two questions: 1) are there differences between created and natural wetlands? and 2) is there development of ecosystem function over time within created wetlands?

48 Between summer 2005 and summer 2008, we assessed abiotic and biotic structure,

nutrient limitation, and plant- and microbial-mediated functions for 10 created or

restored (sensu U.S. Army Corps of Engineers and U.S. Environmental Protection

Agency, 2008; hereafter referred to as created only) and 5 natural wetlands located in central Ohio, USA (Tables 3.1 and 3.3, and Supplementary Methods). The study sites were freshwater systems, either depressional or impounded, with primarily emergent vegetation. The created wetlands ranged in age from < 1 y to 39 y since construction at the onset of the study and provided a chronosequence of wetland development.

The natural wetlands represented a range in wetland quality with two of high-quality

(i.e., pristine), two of mid-quality, and one of low-quality (i.e., degraded).

We ﬁrst compared structural and functional components between the wetlands by either principal components analysis (PCA) or nonmetric multidimensional scaling

(NMDS), for broad, visual assessment. Effects of type (i.e., created wetland or natural wetland) and age (i.e., wetland development over time; created wetland data only) were formally tested in multi-variable space by permutational multivariate analysis of variance (PERMANOVA). Univariate permutation tests were used to determine specific differences by wetland type and age (see also Supplementary Methods and

Supplementary Discussion).

In our comparison of abiotic structure between created and natural wetlands, we observed similar water chemistry and hydrology, albeit substantial disparity in soil character (PERMANOVA: water chemistry, F1,12 = 1.27, p = 0.3; hydrology,

F1,13 = 0.24, p = 0.9; soil character, F1,13 = 2.72, p = 0.01; Figures 3.1a,b and ??a,

and Table 3.4). Ordination of the wetlands within a 14-dimensional soil-character

49 Property Parameters

STRUCTURE abiotic Soil character bulk density (ρb ), volumetric water content (θv ), total carbon (C ), − total nitrogen (N ), extractable nitrate (NO 3 ), extractable ammonium + (NH 4 ), Bray-1 phosphorus (Bray1 ), Bray-2 phosphorus (Bray2 ), ex- −3 tractable phosphate (PO 4 ), potassium (K ), magnesium (Mg), calcium (Ca), cation exchange capacity (CEC ) Water chemistry dissolved organic nutrients (DOC, DON and DOP); dissolved inorganic nutrients (DIC, DIN and DIP); total dissolved nutrients (TC, TN and TP) Hydrology water depth relative to ground level (mean, µ; maximum, max; minimum, min), proportion of time water level above a specified depth (e.g., -0.3, proportion of time water level above -0.3 m), water level fluctuation (standard deviation about mean water depth, σ; flashiness, δ) biotic Plant community proportional abundances for 27 obligate wetland species, ratio of shoot biomass to root biomass (shoot:root), species diversity, richness, evenness Microbial community proportional abundances for 15 phospholipid-fatty acid (PLFA) biomarkers (bacterial: 15:0, a15:0, i15:0, i16:0, a17:0, i17:0, cy17:0, cy19:0, 16:1ω7t, 16:1ω7c, 18:1ω7c; fungal: 18:1ω9c, 18:2ω6 ; actino- mycetes: 10me16:0 ), ratio of PLFA fungal biomass to PLFA bacterial biomass (fungal:bacterial) nutrient Plant-limiting plant tissue C, N and P concentrations; plant tissue nutrient ratios (e.g., plant tissue C:N, C:N ) Microbe-limiting C, N and P effects on basal respiration (BR), potential methane production (PMP), and denitrification (DEA); litter tissue nutrient ratios (e.g. litter tissue C:N, C:N )

FUNCTION Plant-mediated peak-standing plant biomass (Shoot), root biomass (Root), litter biomass (Litter); plant and litter C, N and P standing stocks (e.g., litter P stock, Litter P) Microbial-mediated BR, PMP, and DEA; aerobic C, N and P mineralization (e.g., aerobic C mineralization, Cmin); anaerobic C, N and P mineralization (e.g., AnCmin, anaerobic carbon mineralization); PLFA bacterial (Bacteria) and fungal (Fungi) biomass

Table 3.1: Structural and functional properties and representative parameters. Most comparative wetland studies focus on structural properties, particularly of the plant community, rather than functional properties. Parameter abbreviations are also indicated for reference throughout the paper.

50 space revealed distinct diﬀerences between higher-quality natural wetlands and lower-

quality natural and created wetlands, particularly with respect to C, N, volumetric

water content (θv ), and bulk density (ρb ). For example, created wetlands contained

less C and N and had higher ρb . Among all 14 soil characteristics, created wetlands

were not developing over time (PERMANOVA: F1,8 = 1.41, p = 0.20); however,

ρb exhibited a signiﬁcant trajectory of decreasing density with wetland age (univariate permutation: p = 0.02).

The dissimilarity in soil structure was somewhat expected since the soils were organic-based in the high-quality natural wetlands, but mineral-based in low-quality natural and created wetlands. Resolving this pedogenic opposition will likely require redesign of wetland construction, in addition to much longer developmental time- frames. In contrast, the similarity in wetland hydrology was both surprising and encouraging, given its recognition as a key forcing factor allowing the development of wetland ecosystems and processes (Mitsch and Gosselink, 2007, p. 108), as well as being reportedly diﬃcult to reproduce (Cole and Brooks, 2000). There was, however, a slight tendency for created wetlands to exist at the extremes of inundation (e.g., two created wetlands were most often ﬂooded above 0.3 m depth (wettest), while in two other created wetlands, water levels were most often at or below -0.3 m depth

(driest); Fig. 3.1b and Table 3.4).

Biotic structure, like water chemistry and hydrology, was similar between created and natural wetlands. We assessed plant community structure with proportional abundances of 27 obligate wetland species, as well as shoot:root, and did not observe any eﬀect of type (PERMANOVA: F1,13 = 0.932, p = 0.7; Fig. ??b). Likewise, metrics of species diversity, richness, and evenness were indistinguishable by type

51 when compared by univariate permutation (Table 3.5). Type appeared insigniﬁcant

to microbial community structure as well, which was characterized by analysis of

14 PLFA biomarkers, as well as fungal:bacterial (PERMANOVA: F1,13 = 0.53, p =

0.9). Microbial community instead appeared driven by gradients of θv and hydrology, with bacterial and anaerobe abundance increasing under wet conditions and fungal abundance increasing under dry conditions (Fig. ??c).

We next measured plant nutrient-limitation, using plant-tissue nutrient concentrations and ratios and root core in-growth under N and P amendment, as well as microbial nutrient-limitation, through response of microbial activity to C, N and P amendment. The availability of C, N and P controls plant productivity and microbial activity in many systems (Aerts and Berendse, 1989; Vitousek et al., 1997; Boyer and

Zedler, 1998; Smith et al., 1999); in our study, all sites appeared to be N limited, regardless of wetland type (PERMANOVA: plant-nutrition, F1,13 = 0.69, p = 0.5; microbe-nutrition, F1,13 = 1.05, p = 0.40; Fig. 3.1c,d and Table 3.6). Tissue ratios

suggested N limitation for all sites in the plant nutrient studies (i.e., N:P < 15; Ko-

erselman and Meuleman, 1996; Bedford et al., 1999) and root growth increased with

N addition; whereas additions of N or C tended to increase microbial processes such

as basal respiration (BR), methane production (PMP) and denitriﬁcation (DEA).

Despite similar hydrologic- and biotic-structure and nutrient limitation, plant- and

microbial-functions remained distinct between created and natural wetlands (Fig. 3.1e,f

and Table 3.7). Plant-mediated functions such as biomass production and C, N and

P stocks were lower in created wetlands than in natural wetlands, but increased with

age (PERMANOVA: type, F1,13 = 7.23, p = 0.008; age, F1,8 = 5.10, p = 0.004).

52 Microbial-mediated functions, such as BR, PMP, DEA and C, N and P mineraliza-

tion, were also signiﬁcantly lower in created wetlands than in natural wetlands, but

in contrast to plant-mediated functions, did not increase with age (PERMANOVA:

type, F1,13 = 5.48, p = 0.001; age, F1,8 = 1.65, p = 0.1).

Given the limited functionality of created wetlands compared to natural wetlands and the structural dissimilarity between their soils, we tested the link between wetland soil and function by PERMANOVA, Mantel test and redundancy analysis (see also Supplementary Methods and Supplementary Discussion). Using ﬁrst and second principal component (PC) scores from respective PCA or NMDS analyses, we compared plant and microbial functions to structural factors (i.e., soil, hydrology, plant-community, microbial-community, plant nutrition and microbial nutrition; Fig- ures 3.11 and ??). The relationship between plant- and microbial-mediated function to all 6 structural factors was signiﬁcant (PERMANOVA, F12,46 = 3.99, p = 0.001,

Mantel, r = 0.15, p = 0.04), with soil PC1 contributing most to plant- and microbial- function (Table 3.8). Within the 4-dimensional functional-PC space, constrained to be linearly-related to the 12-dimensional structural-PC space, the wetlands oriented along a primary functional gradient of increasing plant productivity and nutrient stock and increasing microbial biomass and activity, with created wetlands aligning toward the negative end and natural wetlands toward the positive end (Fig. 3.2). This primary functional gradient tied closely to a structural gradient of increasing soil C,

N, and CEC and decreasing ρb , as well as increasing N availability, decreasing soil base cations, and change in plant community composition.

To better assess the impact of natural to created wetland conversion on nutrient cycling, we estimated pools and annual ﬂuxes for C, N and P cycles. Created wetlands

53 diﬀered most from natural wetlands in C dynamics (Table 3.2). Compared to high quality natural wetlands, created wetlands stored 90 % less C within litter and 80 % less C within soil, on average. Plant C stocks were similar, however, with most of the

C stock recycled annually through decomposition. Other annual ﬂuxes were much smaller in created wetlands: for example, created wetlands cycled 70 % to 90 % less C through mineralization than high quality natural wetlands, depending on the anaerobic to aerobic ratio.

Functional differences between created and natural wetlands showed similar trends when comparing N pools and fluxes. Created wetlands stored 80 % less N within litter and 80 % less N within soil than high-quality natural wetlands. Annual N cycling through plant growth and decomposition was similar. Annual rates of mineralization were 110 % higher to 90 % lower, depending on the anaerobic to aerobic ratio; rates of denitrification were 60 % lower.

Phosphorus dynamics, however, were much more similar between created wetlands and natural wetlands. Created wetlands stored 80 % less P within litter than high- quality natural wetlands, but had comparable plant and soil stocks. Unlike the C and

N cycles, ﬂuxes mediated by the soil (e.g., mineralization) were also similar. Annual cycling through decomposition, however, was 40 % lower in the created wetlands.

Our detailed assessment of C-, N- and P-related processes conclusively demonstrates that functionally, created wetlands are not suitable replacements for natural wetlands, nor are they on a trajectory toward functional equivalence. Permitting sub- stitution of created wetlands for natural wetlands will lead to net loss of CNP-related functions and ecosystem services that accrue from them. One consequence may be increased nutrient export into already overwhelmed ecosystems such as the Gulf of

54 Created Natural Reference Type (natural) Type (reference) Carbon Plant stock 250. 470. 350. -370 to -70 -240 to +70 Litter stock 30. 160. 240. -190 to -60 -270 to -150 Decomposition release 220. 420. 340. -340 to -40 -250 to +10 Soil stock 30. 130. 190. -160 to -30 -240 to -90 Aerobic mineralization 2. 14. 23. -20 to -4 -28 to -15 Anaerobic mineralization 2. 7. 9. -6 to -2 -8 to -4 Basal respiration 1. 4. 5. -5 to -2 -6 to -3 Methane production 0.00 0.03 0.06 -0.04 to +0.00 -0.09 to -0.02 Nitrogen Plant stock 9. 16. 14. -12 to -2 -11 to +3 Litter stock 1. 6. 9. -7 to -2 -10 to -5 Decomposition release 8. 14. 13. -10 to -0 -11 to +3 Soil stock 3. 11. 17. -14 to -3 -22 to -8 Aerobic mineralization 0.1 0.7 1.2 -1.0 to -0.2 -1.6 to -0.7 Anaerobic mineralization 0.00 -0.11 -0.01 -0.04 to +0.23 -0.10 to +0.14 Denitrification 0.01 0.05 0.03 -0.06 to -0.02 -0.02 to -0.01 Phosphorus Plant stock 1.4 2.4 2.2 -1.6 to -0.3 -1.8 to +0.4 Litter stock 0.2 0.7 1.0 -0.8 to -0.0 -1.3 to -0.3 Decomposition release 1.3 2.2 2.2 -1.5 to -0.3 -1.8 to -0.3 Soil stock† 2.1 2.3 2.4 -1.3 to +0.9 -1.5 to +1.4 Aerobic mineralization† -0.07 -0.13 -0.06 -0.06 to +0.17 -0.17 to +0.14 Anaerobic mineralization† -0.06 -0.13 -0.10 -0.06 to +0.20 -0.15 to +0.26 † 10−2 × Table 3.2: Table of C, N and P stocks and fluxes: for each parameter, mean values for created, natural and reference wetlands (n = 10, 5 and 2, respectively) are listed. Also tested for each parameter were the effects of type using all natural wetlands and type using only reference natural wetlands (i.e., the two high-quality sites, CA and BF). Confidence intervals (95 %) for each effect appear in the final two columns, as the loss or gain in created wetland function relative to the natural or reference mean. Significant effects are in bold (i.e., confidence interval does not span 0). Decomposition data are from 2006; all other data are from 2005. [Units: plant and litter stocks, g m−2 ; soil stocks, g kg−1 ; decomposition, g m−2 y−1 ; all other fluxes, g kg−1 y−1 . Note: decomposition· data is missing· from one created site· (BIB)· and one natural site· (PPN).]·

55 Mexico, which suﬀers from seasonal hypoxia. Extensive conversion of wetlands into agricultural land within the Mississippi river basin has accelerated N loading to adjacent surface waters and ultimately the Gulf (Mitsch et al., 2001). The continued loss of natural wetlands and replacement with ineﬀective created wetlands will only exacerbate the situation. Another concern is the rise in greenhouse gas levels and con- sequent global climate change (IPCC (Intergovernmental Panel on Climate Change),

2001). One approach to oﬀset the greenhouse gas surplus is sequestration of C in terrestrial ecosystems—particularly wetlands, which are eﬀective C sinks because of their high plant productivity and slow litter decomposition (Bridgham et al., 2007).

As our study demonstrates, however, created wetlands are less eﬃcient in this capacity, with signiﬁcantly lower C storage (e.g., soil C, litter C stock) than natural wetlands.

In April 2008, the US Army Corps of Engineers and Environmental Protection

Agency revised compensatory mitigation guidelines in response to concerns raised by the NRC Committee on Mitigating Wetland Losses (NRC (National Research

Council), 2001; U.S. Army Corps of Engineers and U.S. Environmental Protection

Agency, 2008). Although this new regulation, if implemented in the field, will help achieve the goal of no-net-loss, a more drastic policy shift is needed to fulfill the ideal. We underscore the exhortation by former members of the NRC Committee in their most recent policy review that more emphasis be placed on avoidance of natural wetland impacts (Gardner et al., 2009). We further recommend that avoidance not be limited to traditionally difficult-to-replace wetlands such as bogs and forested swamps, but be the general rule for all wetland types unless functional mitigation can be demonstrated. Although depressional wetlands may be considered structurally simple

56 to replicate, especially compared to bogs or forest swamps, physical resemblance does not equate to functional replacement—and even physically we have failed to recreate natural depressional wetland soil structure.

When impact or destruction of a natural wetland is unavoidable, we suggest at minimum preserving the natural wetland topsoil. Poorly developed soil (e.g., high ρb , low C and N) limited CNP-related functional capacity among the created wetlands of this study. Amending created wetlands with salvaged natural wetland soil may improve functional capacity and mitigation success (Brown and Bedford, 1997; Stauﬀer and Brooks, 1997; Bruland and Richardson, 2004). Furthermore, considering the link between soil health and wetland function, a readily measurable metric such as ρb may be a practicable indicator for monitoring development of nutrient cycling within created wetlands (Fig. 3.4). The NRC committee, in both their original review (NRC

(National Research Council), 2001) and their more recent evaluation (Gardner et al.,

2009), observed that most performance standards for assessing mitigation projects are based on ﬂoristic metrics and inadequately measured the full range of wetland function.

Our concerns extend beyond the US, which has one of the better developed wetland inventory and regulatory systems, to other nations seeking to establish their own wetland programs. The global development and implementation of national wetland policies is sponsored by the Ramsar Convention, an intergovernmental treaty promoting the conservation and sustainable use of the world’s wetlands (Ramsar Convention,

1971). At the most recent Ramsar meeting, convened in 2008, member states set a high priority task to develop guidance for mitigation and compensation of wetland losses in upcoming years, with speciﬁc reference to US no-net-loss policy (Ramsar

57 Convention, 2008). We emphasize that the goal of compensatory wetland mitigation is no-net-loss of functions and services, not acreage (U.S. Army Corps of Engineers and U.S. Environmental Protection Agency, 1990; U.S. Army Corps of Engineers and

U.S. Environmental Protection Agency, 2008). Unless the science advances to enable full functional development in restored or newly created wetlands, impact avoidance and preservation should be the rule. Ultimately, even the science and possibility of compensatory mitigation will have limits for those structures and functions that simply require time to develop.

Acknowledgements: This research was supported by the Cooperative State Re- search, Education, and Extension Service, U.S. Department of Agriculture, under Award No. 2005-35101-15593; the Ohio Agricultural Research and Develop- ment Center; The Ohio State University; Kenyon College; and the University of New Hampshire. We thank the City of Columbus Recreation and Parks, Columbus Metro Parks, Groveport-Madison High School, the Ohio Depart- ment of Natural Resources, and several private landowners for permission to use their properties. For ﬁeld and lab assistance, we thank Erelyn Apolinar, Amy Barrett, Michael Billmire, Sarah Boley, Ryan Diederick, Gwen Dubelko, Brie Elking, Erica Elliot, Becky Fauver, Michela Gentile, Dan Gillenwater, Jenette Goodman, Kyle Herrman, Melissa Knorr, Matt Lane, Nickla Louisy, Lars Meyer, Diego Perez, Ryan Pulliam, Connie Rice, Abby Rokosch, Jesse Rosenbluth, Erin Rothman, Eric Saas, Rachel Schultz, Lindsay Scott, Heather Sexton, Leslie Smith, Michael Szuter, and Steve Wise. We gratefully acknowledge Carol Johnston, Ann Redmond and Charles Simenstad for their invaluable input on previous versions of this manuscript.

3.1 Supplementary Information

3.1.1 Supplementary Methods Site description and sampling-station selection

Ten created and ﬁve natural freshwater marsh wetlands located in central Ohio,

USA were selected for this study (Table 3.3). The wetlands were classiﬁed as palustrine emergent sensu Cowardin et al., 1979 and depressional sensu Brinson, 1993.

58 The created wetlands represented a chronosequence of wetland development, ranging in age from less than 1 y to almost 40 y since construction at the onset of the study. Three of the created wetlands were permittee-responsible mitigation projects from Section 401/404 permit compliance (BB, JMB and NAC), two were part of mitigation banks (BIC and BIA), three were constructed through the NRCS Wetland

Reserve Program (PPA, PPB and SA; ﬁrst two in conjunction with the Ohio EPA

Water Resource Restoration Sponsor Program), and two were constructed as waterfowl habitat by the Ohio Department of Natural Resources. Only three of the sites would strictly be considered established (i.e., created in formerly upland soil), whereas the remaining seven would more appropriately be termed restored (i.e., re-established in historically hydric soil); however, the “restored” sites had been drained and intensively farmed until recently. Furthermore, both the “established” and “restored” sites were excavated to some degree during construction. The natural wetlands were of unknown ages but provided a range in wetland quality, with one low quality, two mid quality, and two high quality sites (ORAM v5.0 (Mack, 2001)).

Vegetation surveys were conducted for each of the 15 wetlands in summer 2005.

Wetland perimeter was delineated by the presence of hydrophytic vegetation. Five parallel transects were then established following Magee et al., 1993. Along each transect, vegetation was sampled at regular intervals using a 0.84 m x 0.84 m quadrat frame; targeting 30 to 40 quadrats per wetland. For each quadrat, vegetation species and percent cover were recorded (percentages were recorded to within 10 % if greater than 30 %; within 5 % if between 5 % and 30 %; and as 1 % if present but less than

5 % cover). Quadrat data for each wetland was sorted and separated into vegetation communities. Within the dominant plant communities, 3 to 5 sampling locations

59 were then established by stratiﬁed random design (wetland size and heterogeneity determined the number of sampling stations).

Plant productivity

Aboveground biomass. Peak standing aboveground plant biomass was harvested from a 0.8 m x 0.8 m quadrat at each sampling station in late summer 2005, 2006 and

2007. The plant biomass was dried (55 ◦C ; also ashed at 450 ◦C if heavily contami- nated with sediment) and weighed. Plant tissue samples from the 2005 harvest were analyzed for total C and N (NC 2100 CHN-Analyzer, CE Instruments) and total P

(Midwest Laboratories).

Belowground biomass. Soil cores were collected from each sampling station in late summer 2005 (3 cores; 30 cm depth x 7.5 cm diameter). Soil cores were bulked by station and washed through a 500 µm sieve (Delta-T Root Washer). Roots were manually separated from the remaining organic material, dried (55 ◦C ), ashed (450

◦C ) and weighed.

Litter biomass. In late summer 2005, litter biomass was collected from a 0.8 m x 0.8 m quadrat at each sampling station. The litter was dried (55 ◦C ), ashed

(450 ◦C ) and weighed. Tissue samples were analyzed for total C and N (NC 2100

CHN-Analyzer, CE Instruments) and total P (Midwest Laboratories).

Root in-growth. One synthetic core (30 cm length x 10 cm diameter mesh tube

ﬁlled with calcined clay (Turface)) was installed at each sampling station in spring

2006 and retrieved after 90 d. Roots were separated from the calcined clay, scanned

(WinRHIZO), dried (55 ◦C ), ashed (450 ◦C ) and weighed.

60 Site Age ORAM Location Area n Hydrology Soil Series Dominant Vegetation Created PPA 0.5 39◦53′08′′N, 2.0 3 depression Kokomo (vp) Ammannia robusta, Echinochloa spp., Eleocharis obtusa, · 82◦47′46′′W Polygonum pensylvanicum PPB 3 39◦53′21′′N, 1.5 4 depression Kendallville (w) / Westland Echinochloa spp., Panicum dichotomiflorum, Typha spp. · 82◦47′51′′W (p/vp) BB 5 40◦11′37′′N, 1.2 5 depression Bennington (sp) / Centerburg Eleocharis obtusa, Juncus effusus, Leersia oryzoides, Typha · 82◦52′09′′W (mw) spp. BIC 6 40◦35′12′′N, 1.9 3 depression† Milford (p/vp) Eleocharis palustris, Ceratophyllum demersum, Leersia ory- · 83◦13′21′′W zoides, Potamogeton spp. SA 7 40◦20′59′′N, 2.5 4 depression† Lurray (vp) Bidens cernua, Ceratophyllum demersum, Juncus effusus, · 82◦19′32′′W Leersia oryzoides JMB 9 39◦52′34′′N, 0.4 4 depression Eldean (w) / Sloan (vp) Phalaris arundinacea, Xanthium strumarium · 82◦53′30′′W BIA 10 40◦34′17′′N, 4.7 5 depression† Latty (vp) Eleocharis palustris, Phalaris arundinacea, Polygonum hy- · 83◦17′11′′W dropiperoides, Typha spp. NAC 12 40◦07′22′′N, 2.2 3 depression† Pewamo (p/vp) Leersia oryzoides, Phalaris arundinacea, Polygonum pensyl- · 82◦50′19′′W vanicum BIB 32 40◦34′27′′N, 47.0 5 impounded Latty (vp) Butomus umbellatus, Juncus effusus, Phalaris arundinacea, · 83◦16′48′′W Typha spp. ◦ ′ ′′

61 KP 39 40 42 36 N, 3.3 4 impounded Paulding (vp) Phalaris arundinacea, Schoenoplectus tabernaemontani, · 83◦17′10′′W Scirpus fluviatilis, Typha spp. Natural MI 6 39◦54′29′′N, 0.2 3 depression Kokomo (vp) Leersia oryzoides, Polygonum amphibium, Typha spp. · 83◦10′12′′W LW 43 40◦33′26′′N, 0.3 3 depression Pewamo (p/vp) Agrostis capillaris, Lycopus uniflorus, Polygonum hy- · 83◦37′25′′W dropiperoides, Symphyotrichum spp. PPN 55 39◦52′56′′N, 7.4 4 depression Rockmill (vp) Leersia oryzoides, Phalaris arundinacea, Typha spp. · 82◦47′49′′W CA 78 39◦35′02′′N, 5.6 5 depression Montgomery (p/vp) / unclassi- Decodon verticillatus, Scirpus fluviatilis, Sparganium eu- · 82◦59′53′′W fied muck (vp) rycarpum, Typha spp. BF 82 40◦16′07′′N, 0.6 4 depression Linwood (vp) Leersia oryzoides, Onoclea sensibilis, Typha spp. · 82◦16′58′′W †Depression with one side impounded. Table 3.3: Site and age (years since creation at project onset in 2005; created wetlands only) or ORAM score (Ohio Rapid Assessment Method of wetland quality Mack (2001); natural wetlands only) are indicated for each of the fifteen wetlands included in this study. The wetlands are located within central Ohio and the coordinate location (WGS84) is provided, along with wetland area (calculated from GPS-mapped perimeter; 104 m2) and number of sampling stations (n) established at each wetland. The wetlands had similar hydrology (depressional or impounded). Soils were generally silty clay loam to silty clay and poorly to very-poorly drained (drainage indicated in parentheses: w=well drained, mw=moderately well drained, sp=somewhat poorly drained, p=poorly drained, vp=very poorly drained). Vegetation was mixed emergent and dominant plant species are indicated in the final column. Microbial activity

CNP mineralization. Three soil cores (10 cm depth x 7.5 cm diameter) were

collected from each sampling station and bulked, during mid to late summer 2005.

Field-moist soil subsamples (5 g oven-dried equivalent) were placed in specimen con-

tainers, sealed in wide-mouth canning jars (946.4 mL) and incubated at 25 ◦C for 30

days. Twenty mL of water were added to each jar to maintain humidity during the

incubation. Two sets of samples were prepared: one set for aerobic incubation and

one set for anaerobic incubation. For the anaerobic incubation, water was added to

the specimen container for 65 mL total volume (i.e., soil water volume + added water

volume = 65 mL). Headspace was sampled (2 mL) every 2–4 days and analyzed for

CO2 and CH4 (GC-14 Gas Chromatograph, Shimadzu Scientiﬁc Instruments). Pre-

and post-incubation subsamples were extracted with 2 M KCl and analyzed for NO3−

+ 3 and NH4 (QuikChem 8500 FIA System, Lachat Instruments) and PO4− (Spectronic

401, Spectronic Instruments). C mineralization rates were calculated from the cumulative CO2 and CH4 evolved over the 30 d incubation. N and P mineralization

+ 3 rates were calculated from the diﬀerence in pre- and post- NO3− , NH4 and PO4−

concentrations.

Potential methane production. Slurried subsamples (3 g oven-dried equivalent

in 30 mL total water) from the 10 cm cores collected in summer 2005 were incubated

(25 ◦C ) for 4 days under helium atmosphere. Headspace was sampled (3 mL) once

each day and analyzed for CO2 and CH4 (GC-14 Gas Chromatograph, Shimadzu

Scientiﬁc Instruments)—providing estimates of basal respiration (BR; CO2 ) and

potential methane production (PMP; CH4 ).

62 Denitriﬁcation. A second set of slurried subsamples (3 g oven-dried equivalent in

30 mL total water) amended with chloramphenicol (36 mg) was incubated (25 ◦C )

for 2 hours under 15 % (v/v) acetylene/helium atmosphere. Headspace was sampled

(3 mL) on the hour and half-hour and analyzed for N2O (GC-14 Gas Chromatograph,

Shimadzu Scientiﬁc Instruments)—providing estimates of denitriﬁcation enzyme ac-

tivity (DEA).

In-situ decomposition. Thirteen litter bags (1 mm mesh ﬁlled with 5 g dried

‘on-site’ litter) were deployed at each sampling station in summer 2006. Replicate

bags were retrieved from each station on days 4, 10, 21, 35 and 107. Following

retrieval, litter bags were rinsed, dried and weighed for mass loss determination.

Litter subsamples were analyzed for total N and P (Midwest Laboratories).

Nutrient limitation

Root in-growth. In addition to the unamended synthetic cores, additional syn-

thetic cores were amended with blood meal (N), bone phosphate (P), or both, for

a total of 3 nutrient treatments. Treatment cores were installed at each sampling

station (3 per station) along with the unamended cores in spring 2006 and retrieved

after 90 d. Roots were separated from the calcined clay, scanned (WinRHIZO), dried

(55 ◦C ) and weighed.

Microbial activity. Slurried subsamples from the 10 cm soil cores were amended

with glucose, KNO3, KH2PO4, or combinations thereof. One subset was incubated for

4 d and analyzed for CO2 and CH4 (GC-14 Gas Chromatograph, Shimadzu Scientiﬁc

63 Instruments); another subset was incubated for 2 h and analyzed for N2O (GC-14

Gas Chromatograph, Shimadzu Scientiﬁc Instruments).

Abiotic characterization

Soil physical and chemical properties. Bulk densities and volumetric water contents were calculated from the dry weights (105 ◦C ) and ﬁeld moist volumes of the 10 cm soil cores collected in summer 2005. Total soil C and N were measured on

ﬁnely ground subsamples (NC 2100 CHN-Analyzer, CE Instruments). Soil extracts

+ (5 g soil, 50 mL 2M KCl) were analyzed for NO3− and NH4 (QuikChem 8500 FIA

3 System, Lachat Instruments) and PO4− (Spectronic 401, Spectronic Instruments).

Air-dried and ground samples were analyzed by Midwest Laboratories for Bray-1 and

-2 P, potassium, magnesium, calcium, cation exchange capacity (CEC) and pH.

Water chemistry. Three water grab samples were collected at each wetland as conditions permitted, during summer and fall samplings in 2006–2008. Filtered (0.45

µm) and acidiﬁed (<2 pH; concentrated H2SO4) water samples were analyzed for NO3−

+ 3 + NO2−, NH4 , ortho PO4− , total P (persulfate digest) and total N (persulfate digest)

by ﬂow injection colorimetry (QuikChem 8500 FIA System, Lachat Instruments).

Filtered (0.45 µm) and non-acidiﬁed water samples were analyzed for total dissolved

carbon (TDC), dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC)

by high temperature catalytic combustion (DC-190 Total Organic Carbon Analyzer,

Rosemount-Dohrmann).

Hydrology and station elevation. One surface water level recorder (OTT Thal-

imedes 1100, Fondriest Environmental, Inc.) was installed near the deepest point at

64 each wetland in fall 2005 for hourly water level observations. In early spring 2007,

elevations of each sampling station relative to the water level recorder were measured

for each site. Elevation measurement was by level survey. Relative elevations were

used to determine water regime at each sampling station.

Biotic characterization

Plant community. For the 2005 peak biomass collection (see above), harvested

biomass was sorted by species: Echinochloa spp., Epilobium spp., Galium spp., Men-

tha spp., Najas spp., Potamogeton spp., Symphyotrichum spp., Typha spp. were identiﬁed only to genus level; all others were identiﬁed to species level or classed as unknown.

Microbial community. Soil from the 10 cm cores collected in summer 2005 were characterized for microbial community composition by phospholipid fatty acid (PLFA) analysis (White et al., 1979). Samples were analyzed for 12 bacterial biomarkers and

2 fungal biomarkers.

Calculations

Decomposition. Litter decomposition was modeled by simple exponential decay:

kt Mt = e− (Eq. 1), where Mt is the proportion of litter mass remaining at time t, and k is the ﬁrst-order rate constant. The model was ﬁt to the data by linear regression.

Immobilization. N and P immobilization rates (Rim) were calculated from the net change in nutrient mass over the course of the litter bag experiment: Rim =

(M C M C )/∆t, where M is the initial litter mass, C is the initial litter N or i i − f f i i

65 P concentration, Mf is the ﬁnal remaining litter mass, Cf is the ﬁnal litter N or P

concentration, and ∆t is the duration in days of the experiment.

Nutrient eﬀect. Response parameters (i.e., root-growth, BR, PMP, and DEA)

were compared between unenriched conditions and nutrient enriched conditions and

averaged. For example, to determine the eﬀect of N on root-growth, the diﬀerences

in root-growth between the N amended core compared to the unamended core, and

between the NP amended core compared to the P amended core, were averaged by

station. For the eﬀect of N on BR, PMP or DEA, diﬀerences between N-amended

versus unamended, CN-amended versus C-amended, and CNP-amended versus CP-

amended, were averaged.

Community composition. Diversity indices (H) were calculated from plant species S abundances by mass: H = p ln p , where p is the proportional abundance − i i i i=1 of species i, and S is the totalX number of species (or richness). Evenness (E)

was determined by dividing the diversity index by the maximum possible diver-

sity (Hmax = ln S): E = H/Hmax = H/ ln S. The plant community indices were computed for all identiﬁed species (i.e., excluding unknowns), only species with obligate (OBL) Wetland Indicator Status (WIS), and only species with Floristic Qual- ity Assessment Index (FQAI) > 0 (see Andreas et al., 2004), recorded during the

2005 vegetation surveys. Peak-standing aboveground plant biomass divided by the belowground root biomass (to 30 cm depth) provided the estimate for shoot:root biomass. This metric was not calculated for sampling stations dominated by ﬂoating plant species (e.g., Ceratophyllum demersum, Najas spp., etc.), because the low root biomass resulted in abnormally high shoot:root values.

66 2 CNP stocks. Aboveground plant biomass and litter biomass (g m− ) were mul- · 1 2 tiplied by tissue nutrient concentration (g g− ) to obtain the nutrient stock (g m− · · ). Soil C and N stocks were estimated from total C and N concentration (0–10 cm), while soil P stock was approximated by Bray2-P; Bray2-P extracts acid-soluble and adsorbed P (i.e., ‘available’ P (Bray and Kurtz, 1945)) and is an underestimate of

1 total soil P. Soil CNP stocks were left on a per mass basis (g kg− ). ·

1 CNP fluxes. Annual rates of CNP-mineralization, BR, PMP and DEA (g kg− · · 1 1 1 y− ) were estimated to be the observed rate (mg kg− d− ) multiplied by a · · 1 conversion factor of 152.1 d y− . The conversion factor was calculated using the · ((T T0)/10) modified van’t Hoff equation: R = R Q − , where R is the microbial T 0 × 10 0 rate at temperature T0, RT is the microbial rate at temperature T , and Q10 is the coefficient for change in activity per 10 ◦ (Celsius or Kelvin) increase in temperature

(Raich and Schlesinger, 1992; Howard and Howard, 1993; Lloyd and Taylor, 1994;

Davidson et al., 2006). Using an estimated Q10 of 2.4 (Raich and Schlesinger, 1992), daily average temperatures (1971–2000) measured at the Port Columbus International

Airport (NOWData, 2009) were compared to the laboratory incubation temperature 365 ((Ti 25)/10) 1 T = 25 ◦C and summed: 2.4 − = 152.1 d y− , where T is the average 0 · i i=1 temperature for the ith JulianX day.

2 1 Annual rates of CNP-release from decomposition of fresh plant litter (g m− y− · · ) were calculated from estimates of end-of-season plant decomposition over 365 d,

1 using Eq. 1 and the estimated ﬁrst-order rate constant k (d− ). We assumed the measured litter biomass represented a residual pool of well decomposed litter and approximated the annual fresh litter input as the aboveground peak-standing plant

67 2 biomass. Annual mass loss from fresh litter (M M ; g m− ) was then multiplied 0 − t · 1 by plant nutrient content (g g− ) for CNP-release from decomposition. ·

Weighted-averages. Data from the 2005 vegetation surveys were analyzed by polythetic divisive hierarchical clustering (PDHC; using the DIANA algorithm (Kauf- man and Rousseeuw, 1990) with euclidian distance metric) to determine weight factors for the sampling stations. With PDHC, all data (e.g., percent cover per species per quadrat) were considered in the clustering (polythetic); the data were initially assigned to a single large group, then progressively divided into smaller groups (divisive); groups were hierarchically ordered, with the between group relationships deﬁned

(hierarchical) (McGarigal et al., 2000). The PDHC returned a dendrogram of hierarchical clusters: ranging from one large cluster consisting of all quadrats, to multiple individual clusters consisting of only a single quadrat. Breakpoints were manually selected for each wetland dendrogram to yield one grouping of clusters per sampling station. If no suitable breakpoint existed between a pair of sampling stations, then the two stations were left in one larger grouping. The number of quadrats grouped with the sampling station relative to the total number of quadrats surveyed for the wetland provided the weight factor for that particular wetland sampling station. The sampling station weight factors were then used to combine multiple values into a single weight-averaged value per wetland.

Statistical analysis

Unconstrained ordination. Patterns of wetland distribution within structural

(i.e., soil character, water chemistry, hydrology, plant community, microbial community, plant nutrition, and microbial nutrition) and functional (i.e., plant-mediated

68 or microbially-mediated) multivariate space were visualized by either principal com-

ponents analysis (PCA) or nonmetric multidimensional scaling (NMDS). In PCA,

samples within a p-dimensional Euclidean space (where p is the number of parameters) were projected onto a lower 2-dimensional space, or plane, of maximum variance. PCA proceeded by eigenanalysis of the p p correlation matrix and provided a × unique solution. PCA also allowed determination of dominant factors and underlying

gradients of wetland distribution. All structural and functional data, except plant

community, were ordinated by PCA.

Plant community data were instead ordinated by NMDS of the n n Jaccardian × distance matrix (for the n samples in p-dimensional space). Non-euclidean distances

such as the Jaccard dissimilarity metric are generally preferred for species abundance

data so that double absences don’t contribute toward the distance determination

(Legendre and Legendre, 1998). In NMDS, the n n distance matrix was rank-ordered × and the solution was the ordination of the n sample points in 2-dimensional space,

or a plane, whose rank-ordered n n distance matrix best represented the original × rank-ordered matrix. The ‘best representation’ was determined by minimization of

the stress criterion S:

[θ(d ) d˜ ]2 ij − ij v i=j S = uX6 (3.1) u d˜2 u ij u i=j u X6 t where θ(dij) is the distance between points i and j in the new ordination and d˜ij is

the distance between the points in the original p-dimensional space. Unlike PCA,

NMDS was solved iteratively and had no unique solution. One advantage of NMDS

is that because the distances are rank-ordered, the variables need not be linearly

69 related (Minchin, 1987). One disadvantage is that because the solution is based on

minimizing stress of rank-ordered distances between samples, plotting the original

p-variables (‘species’) in the new 2-dimensional space is not very meaningful.

Constrained ordination. Scores from the ﬁrst and second principal axes of structural (i.e., soil character, water chemistry, hydrology, plant community, microbial community, plant nutrition, and microbial nutrition) and functional (i.e., plant- mediated or microbially-mediated) unconstrained ordinations (i.e, PCA or NMDS) were used to ordinate the wetlands by redundancy analysis (RDA) of function given structure. RDA is a method of constrained ordination where a high-dimensional set of observed response variables (e.g., function) is reduced to a lower-dimensional space with the constraint that the response variables are linear combinations of a set of explanatory variables (e.g., structure). Essentially, this method involves performing a PCA on ﬁtted values obtained from linearly regressing the observed responses on the explanatory variables.

Multivariate signiﬁcance tests. While ordination was useful for allowing visual inspection of multivariate relationships by wetland type (i.e., created or natural), age

(years since construction; created wetlands only) or between function and structure, relationships were formally tested (without distorting data into lower dimensions) by permutational multivariate analysis of variance (PERMANOVA (Anderson, 2001a)).

In PERMANOVA, between and within group variances were compared about group centroids in p-dimensional space. The test statistic was a pseudo F -ratio of mean between group sum-of-squares over mean within group sum-of-squares. Diﬀerences

70 by type or age were signiﬁcant if no more than 50 out of 1,000 F s calculated after

row-wise permutation were greater than the observed F , or α = 0.05.

Function to structure relationships were additionally tested by Mantel test, through determination of the Pearson correlation between functional and structural dissimilarity matrices (Euclidean distance). Rows and columns of one matrix were randomly permuted 999 times, with recalculation of the correlation coefficient. Comparison of the permutation-based coefficients to the unpermuted coefficient provided the probability of null effect.

Univariate signiﬁcance tests. Relationships with wetland type and age were also tested individually for each parameter. Approximate signiﬁcance was determined by random permutation of data and comparison of the sum of squared residual error

(SSE (Anderson, 2001b)). Diﬀerences by type or age were signiﬁcant if no more than

50 out of 1,000 SSEs were less than the observed SSE, or α = 0.05. The permutation test was preferred over traditional statistical tests (e.g., F -test in analysis of variance and t-test in linear regression) because it did not require that data be normally distributed, as some data could not be adequately transformed to meet normality assumptions.

In the comparison of CNP stocks and fluxes, approximate 95 % confidence intervals for the slope parameter (i.e., type effect for created versus natural or reference wetland) were calculated by random permutation and regression of y β x on x, − o where y is the response variable (e.g., biomass or bulk density), x is the explanatory variable (i.e., type) and βo is the observed slope for the unpermuted dataset (the adjustment of y by βox makes the null hypothesis β = βo rather than β = 0). The

71 slopes obtained for 10,000 permutations were rank ordered, with βo minus the 9750th value estimating the lower bound and βo plus the 250th value estimating the upper bound for βo (Manly, 1997, pp.41–42).

Additional notes. The full data set (i.e., n = 59, unless specific sites or station data were omitted) was used for all ordinations (i.e., PCA, NMDS, and RDA); while all significance tests were performed using a weight-averaged data set (i.e., n = 15, unless specific sites were omitted). Data were transformed as needed to meet univariate normality assumptions under PCA, NMDS, RDA, PERMANOVA and Mantel test; possible transformations included log, logit, negative-inverse, inverse-square and nth-root (2 to 7, with preservation of sign); however, some parameters could not be adequately transformed. Additionally, for PCA, RDA, PERMANOVA and Mantel, data were centered and scaled; for NMDS, data were scaled. All calculations and statistics were performed in R 2.9.1 (R Development Core Team, 2009), with use of the vegan package (Oksanen et al., 2009) for ordinations and multivariate significance tests, and the cluster package (Maechler et al., 2005) for PDHC.

Because we sampled only to 10 cm depth, we compared soil-based measures (e.g.,

C, N, mineralization rates) per mass rather than per volume. On a per volume or per area basis, measurements would have been biased against natural wetlands with low bulk densities but considerable depth of carbon-rich soil—soil which would also be nutrient rich and microbially active. There has been similar debate in comparing carbon stocks between no-till and conventional till because of diﬀerences in bulk density (i.e., the concern is that per volume calculations are biased toward no-till). For

72 comparison of systems with very diﬀerent bulk densities, the preference is becoming

ﬁxed mass rather than ﬁxed volume (VandenBygaart and Angers, 2006).

3.1.2 Supplementary Discussion Unconstrained ordination of wetland structure and function

Abiotic structure. The diﬀerentiation of soil by type (i.e., created or natural) was evident by PCA, with a primary axis (Fig. 3.1a; 33 % of variance) orienting natural wetlands toward the positive end (i.e., high C, N and volumetric water, low bulk density) and created wetlands toward the negative end (i.e., low C, N and volumetric water, high bulk density). In addition to C, N, volumetric water and bulk density,

+ 3 soils were characterized by NO3− , NH4 , Bray-1 and -2 P, PO4− , K, Mg, Ca, cation

exchange capacity, and pH. K, Mg, and Ca oriented the wetlands along a secondary

axis (18 % of variance), with higher base cation content at the positive end and lower

base cation content at the negative end.

Water chemistry was characterized by grab-sample analysis of total, organic and

inorganic forms of C, N and P, and in contrast to soil physics and chemistry, did

not orient the wetlands in any obvious patterns of type or age along gradients of

increasing total and organic C and total N (Fig. ??a; ﬁrst axis, 34 % of variance)

+ and increasing inorganic C and organic N and decreasing NH4 (second axis, 21 % of variance). Ordination by hydrology (monitored for 1 y with surface water level recorders) indiscriminantly placed wetlands along a strong primary gradient of increasing inundation (Fig. 3.1b; 61 % of variance), and a weak secondary gradient of decreasing variability in water depth (19 % of variance).

73 Type Age Created Natural p β p Soil character Bulk density (Mg m−3) 0.915(0.050) 0.47(0.13) 0.0020† -0.00875 0.020 Volumetric water content· (m3 m−3) 0.453(0.051) 0.53(0.10) 0.51 0.00769 0.068 · Soil C (g kg−1) 29.2(2.0) 129(51) 0.005† -0.00426 0.79 · Soil N (g kg−1) 2.53(0.16) 11.4(4.8) 0.0020† 0.000357 0.79 · Extractable NO− -N (mg kg−1 ) 10.1(2.7) 34(17) 0.095 -0.0773 0.045 3 · Extractable NH+ -N (mg kg−1 ) 7.2(2.0) 25(11) 0.11† 0.0119 0.16 4 · Total extractable N (mg kg−1 ) 17.3(3.5) 60(22) 0.0029† -0.0687 0.88 · Bray-1 P (mg kg−1 ) 10.3(1.4) 14.0(2.4) 0.14 -0.175 0.14 · Bray-2 P (mg kg−1 ) 20.6(3.7) 23.1(4.2) 0.64 -0.308 0.33 · Extractable PO−3 -P (mg kg−1 ) 0.370(0.064) 0.556(0.057) 0.084 0.00242 0.72 4 · K (mg kg−1 ) 148(11) 122(30) 0.34 0.649 0.49 · Mg (mg kg−1 ) 450(51) 473(58) 0.79 -2.14 0.68 · Ca (mg kg−1 ) 2100(200) 2340(290) 0.50 -13.7 0.47 CEC (meq· 100g−1) 18.1(1.4) 22.7(2.7) 0.11 -0.0430 0.75 pH · 5.83(0.15) 5.48(0.11) 0.17 -0.0104 0.46 Water chemistry Total C (µg L−1 ) 46.8(7.7) 70.1(16) 0.15 -0.808 0.20 · Inorganic C (µg L−1 ) 20.8(3.6) 44.5(13) 0.028 -0.335 0.29 · Organic C (µg L−1 ) 26(5.9) 25.6(6.9) 0.97 -0.473 0.36 · Total N (µg L−1 ) 840(100) 990(110) 0.40 -0.00671 0.43 · NO−-N (µg L−1 ) 4.1(1.7) 9.3(3.4) 0.17 -0.000151 0.27 3 · NH+-N (µg L−1 ) 220(61) 560(290) 0.095 -0.000495 0.94 4 · Organic N (µg L−1 ) 619(93) 420(260) 0.39 -0.00606 0.44 · Total P (µg L−1 ) 780(190) 860(200) 0.81 -0.0143 0.36 · PO3−-P (µg L−1 ) 194(86) 166(80) 0.86 -0.00356 0.80 4 · Organic P (µg L−1 ) 590(140) 700(160) 0.70 -0.0107 0.40 · Hydrology Mean water depth (m) 0.11(0.10) 0.102(0.071) 0.96 0.00783 0.40 Maximum depth (m) 0.377(0.085) 0.236(0.084) 0.31 0.00315 0.68 Minimum depth (m) -0.14(0.12) -0.199(0.084) 0.75 0.0151 0.13 Time above 0.3 m (h h−1) 0.27(0.13) 0.14(0.11) 0.50 0.00497 0.77 · Time above 0.0 m (h h−1) 0.65(0.13) 0.66(0.15) 0.97 0.00853 0.53 · Time above -0.3 m (h h−1) 0.878(0.082) 0.948(0.032) 0.71 0.00311 0.68 Depth variability (m)· 0.121(0.020) 0.118(0.032) 0.92 -0.00286 0.077 Flashiness (m d−1) 0.185(0.032) 0.150(0.039) 0.51 -0.00419 0.14 · †Significant difference (α = 0.05) between created wetlands and the two highest quality natural wetlands. Table 3.4: Table of abiotic characteristics: for each parameter, mean values for created and natural wetlands are listed with standard errors in parentheses; as well as the probability of no difference between wetland types by permutation (p). Also tested for each parameter was the linear effect of age among created wetlands: the slope value (β) is listed along with the probability of no effect by permutation (p). Soil bulk density is the average of four seasons (2005–2008); all other soil parameters are from 2005 only. Water chemical parameters are from grab samples collected in summer 2008. Hydrological parameters are from November 12, 2005 to November 14, 2006. Effects by type significant at α = 0.05 are in bold, as well as significant (α = 0.05) corresponding effects by age. [Note: water chemistry data is missing from one natural site (MI).]

74 Biotic structure. Proportional abundances of 27 obligate wetland species (accord-

ing to Wetland Indicator Status (Andreas et al., 2004) and present in at least two

sampling stations) and shoot:root biomass characterized plant community structure.

Ordination by NMDS indicated a slight distinction between one group of four natural

wetlands and one group of the ﬁfth natural wetland and created wetlands; however,

the projection of the 28-dimensional species space onto only two dimensions had a

moderately high stress level at 26 % (Fig. ??b). Soil microbial community was described by 12 bacterial and 2 fungal PLFA biomarkers, as well as fungal:bacterial biomass. Ordination by PCA followed gradients of volumetric water content and hydrology, with wetter sites tending toward the positive side of the ﬁrst axis and negative side of the second axis (Fig. ??c). Biomarkers associated with bacteria (e.g., i15:0 and a15:0 ) increased along the primary axis (27 % of variance), while fungal biomarkers (e.g., 18:1ω9c, 18:1ω7c, and 18:2ω6 ) decreased (Vestal and White, 1989; Olsson et al., 1995). The second axis (19% of variance) correlated with decreasing anaerobe abundance (e.g., 10me16:0, cy17:0 ) (Vestal and White, 1989; Bossio and Scow, 1998;

D’Angelo et al., 2005). By univariate permutation, fungal:bacterial biomass decreased slightly with age, but did not diﬀer by type; this age-eﬀect was likely driven by the very distinct microbial community of the oldest created site (Fig. ??c and Table 3.5).

Nutrient structure. Plant nutrient limitation was determined from absolute concentrations and relative ratios of C, N, P and K in plant tissue samples, as well as root core in-growth under N and P amendment. Only nutrient concentrations and ratios were included in the PCA; however, exclusion of the root in-growth data did not change the ordination and allowed inclusion of all 15 study sites. Wetland orientation

75 Type Age Created Natural p β p Plant community Diversity, all 1.57(0.11) 1.29(0.18) 0.16 -0.00293 0.79 Richness, all 11.80(0.85) 11.6(2.2) 0.93 0.00867 0.90 Evenness, all 0.646(0.043) 0.557(0.081) 0.32 -0.00161 0.72 Diversity, OBL 1.09(0.13) 1.01(0.16) 0.70 0.00559 0.62 Richness, OBL 6.6(1.1) 8.2(1.5) 0.45 0.179 0.058 Evenness, OBL 0.673(0.060) 0.508(0.077) 0.12 -0.00676 0.18 Diversity, FQAI>0 1.20(0.14) 1.10(0.17) 0.66 0.00191 0.88 Richness, FQAI>0 7.3(1.1) 9.8(1.8) 0.25† 0.131 0.12 Evenness, FQAI>0 0.691(0.061) 0.510(0.076) 0.11 -0.00667 0.22 Shoot:root (g g−1) 3.23(0.36) 3.05(0.75) 0.81 -0.0285 0.33 · Microbial community Fungal:bacterial (mol mol−1) 0.162(0.013) 0.136(0.017) 0.24 -0.00223 0.020 · †Significant difference (α = 0.05) between created wetlands and the two highest quality natural wetlands. Table 3.5: Table of biotic characteristics: for each parameter, mean values for created and natural wetlands are listed with standard errors in parentheses; as well as the probability of no difference between wetland types by permutation (p). Also tested for each parameter was the linear effect of age among created wetlands: the slope value (β) is listed along with the probability of no effect by permutation (p). All parameters are from 2005 sampling. The plant community indices were computed for all identified species (i.e., excluding unknowns), only species with obligate (OBL) Wetland Indicator Status (WIS), and only species with Floristic Quality Assessment Index (FQAI) > 0 (see Andreas et al., 2004). Shoot:root biomass excludes data from sampling stations dominated by floating plant species (e.g., Ceratophyllum demersum, Najas spp., etc.). Effects by type significant at α = 0.05 are in bold, as well as significant (α = 0.05) corresponding effects by age.

76 was indiscriminate of type and age along a primary axis (Fig. 3.1c; 59 % of variance)

corresponding to increasing N, P and K availability (less limiting) and a secondary

axis (25 % of variance) corresponding to increasing N (less limiting) and decreasing

P (more limiting) availability. Microbial nutrient limitation, assessed by quantifying

rates of BR, PMP and DEA under C, N and P amendment and litter ratios of C,

N, P and K, also lacked demonstrable eﬀect of type. Wetlands distributed along the

ﬁrst PCA axis (Fig. 3.1d; 22 % of variance) of increasing N availability (less limiting)

and decreasing P availability (more limiting); and along the second axis (22 %) of

decreasing N availability (less limiting).

Function. Plant-mediated function was measured by above- and below-ground plant biomass, litter biomass, and C, N and P plant and litter stocks. All metrics increased along the primary PCA axis (Fig. 3.1e; 63 % of variance), with natural wetlands orienting toward the positive side of the ﬁrst axis and created wetlands toward the negative side. In addition to the diﬀerentiation by type, there was a slight trajectory of increasing plant-function with wetland age. Living plant metrics decreased and dead plant metrics increased along the second axis (25 % of variance). This gradient seemed to represent only site-to-site variability.

To assess microbial-mediated functions, we quantiﬁed microbial biomass by PLFA;

BR, PMP, and DEA by short-term incubation of soil subsamples; C, N and P mineralization by long-term incubation of soil subsamples; and litter decomposition and N and P immobilization by in-situ litterbag mass-loss (however, data from the litterbag

study were excluded in the PCA, which did not change the ordination, but did allow

inclusion of all 15 study sites). A strong gradient of increasing microbial biomass and

77 Type Age Created Natural p β p Plant tissue nutrients and ratios Plant C ( mg g−1 ) 402(12) 432.4(2.8) 0.060 -0.191 0.85 Plant N ( mg · g−1 ) 18.1(2.3) 15.3(1.1) 0.45 -0.121 0.63 Plant P ( mg · g−1 ) 3.02(0.33) 2.45(0.39) 0.30 -0.0124 0.67 Plant K ( mg · g−1 ) 20.1(1.3) 16.6(2.5) 0.19 -0.134 0.18 Plant C:N (g · g−1 ) 30.0(3.5) 31.5(2.6) 0.79 0.231 0.48 Plant C:P (g · g−1 ) 202(33) 229(44) 0.62 3.61 0.18 Plant C:K (g · g−1 ) 39(13) 35.1(6.8) 0.90 1.94 0.087 Plant N:P (g · g−1 ) 7.05(0.66) 7.3(1.0) 0.85 -0.0156 0.79 · Litter nutrients and ratios Litter C ( mg g−1 ) 328(16) 414(14) 0.011† 1.80 0.19 Litter N ( mg · g−1 ) 14.32(0.90) 15.85(0.56) 0.29 -0.0204 0.76 Litter P ( mg · g−1 ) 1.90(0.25) 1.97(0.25) 0.88 0.00758 0.76 Litter K ( mg · g−1 ) 5.4(1.0) 4.7(1.3) 0.70 0.0604 0.46 Litter C:N (g · g−1 ) 25.3(3.4) 27.0(1.6) 0.76 0.359 0.19 Litter C:P (g · g−1 ) 212(33) 236(38) 0.68 3.03 0.30 Litter C:K (g · g−1 ) 81(13) 144(31) 0.044 1.68 0.089 Litter N:P (g · g−1 ) 8.22(0.53) 8.7(1.0) 0.64 0.000316 1.0 · Nutrient addition, N effect Root growth, mass (g m−2 90d−1 ) 9.6(5.5) -4.0(5.2) 0.18 -0.511 0.29 · · Basal respiration (mg kg−1 d−1 ) 3.3(1.3) 2.4(2.8) 0.74 0.244 0.0083 · · Methane production (mg kg−1 d−1 ) -1.04(0.44) 0.29(0.81) 0.16 -0.0562 0.14 · · Denitrification (mg kg−1 d−1 ) 1.85(0.59) 4.8(2.9) 0.18 0.0344 0.58 · · Nutrient addition, P effect Root growth, mass (g m−2 90d−1 ) 0.7(3.6) -5.9(8.1) 0.43 0.400 0.21 · · Basal respiration (mg kg−1 d−1 ) -0.9(1.1) -5.5(3.7) 0.14† 0.0384 0.64 · · Methane production (mg kg−1 d−1 ) 0.22(0.14) -1.8(1.8) 0.086† 0.0228 0.10 · · Denitrification (mg kg−1 d−1 ) 0.25(0.17) 0.98(0.52) 0.12 -0.0172 0.24 · · Nutrient addition, C effect Basal respiration (mg kg−1 d−1 ) 32.5(2.2) 28.6(5.8) 0.45† 0.0481 0.80 · · Methane production (mg kg−1 d−1 ) 1.15(0.47) 2.6(2.1) 0.46 0.0579 0.14 · · Denitrification (mg kg−1 d−1 ) 1.77(0.24) 4.5(1.5) 0.020 -0.0248 0.22 · · †Significant difference (α = 0.05) between created wetlands and the two highest quality natural wetlands. Table 3.6: Table of nutrient parameters: for each parameter, mean values for created and natural wetlands are listed with standard errors in parentheses; as well as the probability of no difference between wetland types by permutation (p). Also tested for each parameter was the linear effect of age among created wetlands: the slope value (β) is listed along with the probability of no effect by permutation (p). Plant and litter nutrients and ratios are from 2005, as well as nutrient effects on basal respiration, methane production, and denitrification. Nutrient effects on root growth are from 2006. Effects by type significant at α = 0.05 are in bold, as well as significant (α = 0.05) corresponding effects by age. [Note: root growth data is missing from one natural site (PPN).]

78 C- and N-related processes separated created wetlands from higher quality natural wetlands (Fig. 3.1f; ﬁrst axis, 44 %); unlike with the plant-mediated functions, the created wetlands failed to exhibit a trajectory of development with age. A weaker gradient of increasing methane production and decreasing denitriﬁcation rate formed the second axis (17 % of variance). This gradient also seemed to indicate site-to-site variability, similar to the PCA of plant-function.

Constrained ordination: function vs. structure

Given the dissimilarity between created and natural wetlands soil and similarities between created and natural wetland hydrology, biota and nutrition, soil was the most likely factor limiting created wetland function. The link between wetland function and soil was supported by RDA of ﬁrst and second principal component (PC) scores for plant and microbial functions (Fig. 3.1e,f) constrained by ﬁrst and second PC scores for 6 structural factors (i.e., soil, hydrology, plant-community, microbial-community, plant nutrition and microbial nutrition; Figures 3.1a–d and ??b,c); the structural factors accounted for 51 % (with 32 % by RDA1 and 9 % by RDA2) of the variation in function, with most of the functional variance explained by soil PC1 (Fig. 3.2 and Table 3.8). The relationships between function and factor along the primary

RDA axis suggested that higher plant productivity and nutrient stock (plant function

PC1, funcp1 ) and higher microbial biomass and activity (microbial function PC1, funcm1 ) were closely tied to increasing soil C, N and CEC, and decreasing bulk density (soil PC1, soil1 ), as well as decreasing base cations (soil PC2, soil2 ), increasing

N availability (microbial nutrition PC1, nutrm1 ), and plant community composition

(plant PC1, plant1 ).

79 The weaker secondary RDA axis correlated with increasing plant litter but decreasing plant biomass (plant function PC2, funcp2 ), in addition to decreasing P

mineralization (microbial function PC2, funcm2 ). Function to factor relationships indicated that plant biomass and litter were aﬀected most by plant- and microbial- community composition (plant1 and microb1 ), while P mineralization responded most to N availability (nutrm1 and nutrm2 ).

The gradient represented by RDA1 distinguished wetlands by type, orienting nat-

ural wetlands toward the positive end and created wetlands toward the negative end.

RDA2 reﬂected a more general site-to-site variability. There was no obvious trajectory

of development among the created wetlands in any direction.

80 Mg 0.8 K Ca 0.6 a pH b ρ 0.6 b CEC 0.4 0.4 0.2 −0.3 0.2 −0.1 θ 0.0 v min 0.0 +0.0 −0.2 C −0.2 +0.1 PCA 2 ( 18 %) N PCA 2 ( 19 %) +0.3 −0.4 NH+ 4 −0.4 max −0.6 δ σ −0.6 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 PCA 1 ( 33 %) PCA 1 ( 61 %) 0.8 c N:P d 0.6 N 1.0

0.4 PMP(C) C:P C:K 0.5 0.2 DEA(N)

0.0 PMP(P) K 0.0 −0.2 P

PCA 2 ( 25 %) PCA 2 ( 22 %) BR(C) C:N −0.4 BR(P) BR(N) N:P C:K C:N −0.5 C:P −0.6 PMP(N)

−1.5 −1.0 −0.5 0.0 0.5 −0.6 −0.2 0.2 0.4 0.6 PCA 1 ( 59 %) PCA 1 ( 22 %)

Pmin AnPmin 1.0 e f 0.4 AnNmin Litter P Litter N Litter C PMP Litter 0.2 Nmin 0.5 Cmin Bacteria 0.0 BRFungi AnCmin −0.2 DEA 0.0

PCA 2 ( 25 %) PCA 2 ( 17 %) −0.4 Shoot −0.5 Root Plant P −0.6 Plant N Plant C −0.5 0.0 0.5 1.0 1.5 −0.5 0.0 0.5 1.0 PCA 1 ( 63 %) PCA 1 ( 44 %)

Figure 3.1: Biplots from principal components analysis of structural and functional parameters − −3 describing (a) soil characteristics (not shown: Bray1, Bray2, NO3 , and P O4 ), (b) hydrology, (c) nutrient effects on plant-mediated functions, (d) nutrient effects on microbial-mediated functions (not shown: DEA(C)), (e) plant-mediated functions, and (f) microbial-mediated functions (not shown: PMP ). Variable loadings are indicated by the dashed arrows (significant variables only); weighted-average site scores are indicated by unshaded circles (created wetlands sized by increasing age) and shaded squares (natural wetlands sized by increasing quality); error bars indicate standard deviations about the weighted-average scores. Parameters were transformed as needed to meet normality assumptions. For parameter definitions see Table 3.1; for parameter units see Tables 3.4–3.7. 81 0.8 funcp2

0.6

0.4

0.2 soil1 0.0 funcm1 soil2 funcp1 RDA2 ( 8.8 %) RDA2 −0.2 plant1 nutrm1 funcm2 −0.4

−0.5 0.0 0.5 1.0 RDA1 ( 32 %)

Figure 3.2: Biplot from redundancy analysis of plant-mediated (funcp) and microbial-mediated (funcm) functions on soil (soil), hydrologic (hydro), plant-community (plant), microbial-community (microb), plant-nutrient (nutrp) and microbial-nutrient (nutrm) factors; using ﬁrst and second principal component scores from respective principal components analysis or nonmetric multidimensional scaling. Function loadings are indicated by dashed arrows (with italic labels) and factor loadings are indicated by solid arrows (with bold labels); weighted-average site scores are indicated by unshaded circles (created wetlands sized by increasing age) and shaded squares (natural wetlands sized by increasing quality); error bars indicate standard deviations about the weighted-average scores. Parameters were transformed as needed to meet normality assumptions.

82 0.6 DON 1.0 DIC stress=26% 0.4 TC 0.5 0.2 − NO3 TN 0.0 DOC 0.0

−0.2 TP NMDS2

−3

PCA 2 ( 21 %) PO −0.5 −0.4 4

−0.6 NH+ a b 4 −1.0 −0.5 0.0 0.5 1.0 −1.0 −0.5 0.0 0.5 1.0 1.5 PCA 1 ( 34 %) NMDS1

0.5 a15:0 i15:0 F:B 18:2ω6 18:1ω7c 18:1ω9c cy19:0 0.0 16:1ω7c

i17:0

PCA 2 ( 19 %) 10me16:0 16:1ω7t −0.5 cy17:0 i16:0 a17:0 c

−0.5 0.0 0.5 1.0 1.5 PCA 1 ( 27 %)

Figure 3.3: Biplots from ordination of structural parameters describing (a) water chemistry (principal components analysis (PCA); DOP not shown), (b) plant community proportional abundance (nonmetric multidimensional scaling), and (c) microbial community proportional abundance (PCA; biomarker 15 : 0 not shown). Variable loadings are indicated by the dashed arrows (signiﬁcant variables of PCA only); weighted-average site scores are indicated by unshaded circles (created wetlands sized by increasing age) and shaded squares (natural wetlands sized by increasing quality); error bars indicate standard deviations about the weighted-average scores. Parameters were transformed as needed to meet normality assumptions. For parameter deﬁnitions see Table 3.1; for parameter units see Tables 3.4–3.7. [Note: water chemistry data is missing from one natural site (MI).]

83 Type Age Created Natural p β p Plant-mediated functions Shoot biomass (g m−2 ) 515(46) 930(140) 0.0050† 5.96 0.12 Root biomass (g ·m−2 ) 320(80) 530(100) 0.11 17.0 0.0010 · Litter biomass (g m−2 ) 100(20) 370(110) 0.0060† 4.09 0.024 · Root growth, mass (g m−2 90d−1 ) 35(11) 47(13) 0.58 -0.867 0.38 · · Plant C stock (g m−2 ) 248(36) 471(89) 0.015 6.91 0.0020 Plant N stock (g · m−2 ) 9.1(1.4) 16.1(2.7) 0.023 0.134 0.27 Plant P stock (g · m−2 ) 1.44(0.21) 2.39(0.30) 0.021 0.0212 0.26 · Litter C stock (g m−2 ) 32.6(8.4) 160(50) 0.0040† 1.67 0.0090 · Litter N stock (g m−2 ) 1.43(0.43) 6.1(2.0) 0.0060† 0.0703 0.051 · Litter P stock (g m−2 ) 0.213(0.092) 0.70(0.25) 0.029† 0.0137 0.12 · Microbial-mediated functions Microbial biomass (nmol g−1 ) 64.6(7.9) 151(27) 0.0010† -0.270 0.70 · Bacterial biomass (nmol g−1 ) 55.8(7.1) 133(24) 0.0020† -0.141 0.80 Fungal biomass (nmol g·−1 ) 8.8(1.0) 18.0(4.1) 0.0040 -0.128 0.14 · C mineralization, ae (mg kg−1 d−1 ) 13.1(2.2) 91(39) 0.014† 0.303 0.10 · · N mineralization, ae (mg kg−1 d−1 ) 0.53(0.14) 4.3(2.2) 0.015† 0.00212 0.88 · · P mineralization, ae (µg kg−1 d−1 ) -4.7(2.5) -3.4(6.8) 0.83 -0.471 0.0045 · · C mineralization, an (mg kg−1 d−1 ) 16.0(2.1) 45.8(9.4) 0.0027† -0.205 0.26 · · N mineralization, an (mg kg−1 d−1 ) 0.01(0.17) -0.71(0.67) 0.18 0.00211 0.89 · · P mineralization, an (µg kg−1 d−1 ) -3.9(2.8) -8.3(3.1) 0.35 -0.316 0.19 · · Basal respiration (mg kg−1 d−1 ) 6.83(0.92) 27.8(8.1) 0.00020† -0.00168 0.99 · · Methane production (mg kg−1 d−1 ) 0.0112(0.0050) 0.17(0.15) 0.065† 0.000368 0.43 · · Denitrification (mg kg−1 d−1 ) 0.127(0.023) 0.67(0.24) 0.0019† 0.000111 0.95 · · Decomposition rate constant (d−1) -0.0122(0.0011) -0.0122(0.0011) 0.98 -0.0000410 0.73 Net N immobilization (µg g−1 d−1 ) -82(16) -47.9(1.2) 0.20 -3.50 0.026 · · Net P immobilization (µg g−1 d−1 ) -7.9(1.5) -3.5(1.6) 0.10 0.0619 0.61 · · †Significant difference (α = 0.05) between created wetlands and the two highest quality natural wetlands. Table 3.7: Table of functional parameters: for each parameter, mean values for created and natural wetlands are listed with standard errors in parentheses; as well as the probability of no difference between wetland types by permutation (p). Also tested for each parameter was the linear effect of age among created wetlands: the slope value (β) is listed along with the probability of no effect by permutation (p). Shoot biomass is the average of three seasons (2005–2007), while the root biomass is from 2005 only. Root growth and decomposition data are from 2006; all other plant and microbial parameters are from 2005. Effects by type significant at α = 0.05 are in bold, as well as significant (α = 0.05) corresponding effects by age. [Note: root growth data is missing from one natural site (PPN); decomposition data is missing from one created site (BIB) and one natural site (PPN). Abbreviations: ae = aerobic, an = anaerobic.]

84 4.0 R2=0.73 2 R =0.50 0.8 3.5 0.7 3.0 0.6 2.5 0.5 2.0 0.4 1.5 0.3 Soil Carbon (log)

1.0 Water Volumetric a b 0.2 0.5

R2=0.62 2 R =0.65 500 1500 400

1000 300

200 500

Shoot Biomass 100 Microbial Biomass c d 0

2 40 R =0.74 R2=0.25 300 30 200 20 100 10

0 Denitrification C Mineralization e f 0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Bulk Density Bulk Density

Figure 3.4: The readily quantiﬁable bulk density (Mg m−3) correlates well with many soil properties and functions: (a) log-transformed soil C (g kg−1),· (b) volumetric water content (m3 m−3), (c) aboveground plant biomass (g m−2 ), (d) microbial· biomass (nmol g−1 ), (e) C mineralization· (g kg−1 y−1 ), and (f) denitriﬁcation· (mg kg−1 y−1 ). · · · · ·

85 funcm1 funcp1 funcp2 funcm2 F p( F ) ≥ soil1 0.75 0.48 0.17 0.04 14.69 0.00 nutrm1 0.21 0.35 -0.11 0.33 4.47 0.00 soil2 -0.38 -0.37 -0.06 0.03 4.36 0.00 plant1 -0.24 -0.39 -0.22 0.06 4.01 0.00 nutrp1 0.03 -0.34 0.24 0.06 2.61 0.06 nutrm2 0.22 0.06 0.09 0.28 2.04 0.10 hydro2 0.26 0.16 -0.18 -0.10 2.02 0.10 microb2 -0.27 -0.17 -0.01 -0.10 1.64 0.17 plant2 0.03 0.05 0.28 0.02 1.15 0.32 microb1 -0.06 0.09 0.26 -0.02 1.14 0.35 hydro1 0.00 -0.02 0.15 0.04 0.33 0.86 nutrp2 0.04 -0.00 -0.10 -0.09 0.27 0.90 F 17.95 6.43 2.00 1.26 p( F ) 0.00 0.00 0.05 0.27 ≥ Table 3.8: Output from permutational multivariate analysis of variance (PERMANOVA) of unaveraged plant- and microbial-functions with probable factors. Analyses were conducted on first and second principal component (PC) scores from respective principal component analysis or nonmetric multidimensional scaling of plant-mediated functions (funcp), microbial-mediated functions (funcm), soil chemistry and physics (soil), hydrology (hydro), plant community composition (plant), microbial-community composition (microb), plant nutrition (nutrp) and microbial nutrition (nutrm). A PERMANOVA was run separately for the effect of each factor PC on the four function PCs; coefficients are listed in columns 2–5 and the F -statistic and probability of no effect for each factor PC are listed in columns 6 and 7, respectively. F -statistics and probabilities of no effect from PERMANOVAs of each function PC on all factor PCs are also indicated. Factors and functions are listed in order of decreasing significance; the two highest loading factors per function PC are in bold.

86 CHAPTER 4

THE IMPORTANCE OF SOIL TO DEVELOPMENT OF PLANT- AND MICROBIAL-FUNCTION IN CREATED DEPRESSIONAL WETLANDS

4.1 Introduction

Bradshaw (1983), in his presidential address to the British Ecological Society, propositioned that ecosystem reconstruction is the “acid test” of our ecological understanding. His statement was speciﬁcally in reference to the rehabilitation of derelict

(i.e., severely degraded) land resulting from mining activities, but with application to the correction of any human activity causing denudation of the soil. Throughout his discourse, as well as later works, Bradshaw (1983, 1996, 1997, 2003) underscored the importance of soil development to ecosystem restoration. Soil provides not only a rooting-medium for vegetation, but essential macro- and micro-nutrients, refugia for microorganisms, and porosity for movement of water and gases.

The soil component is often neglected in wetland creation and restoration, where the focus tends to be on hydrology and vegetation. In Ch. 3, we compared wetland structure (i.e., plant and microbial community-composition, nutrition, hydrology, water chemistry, and soil) between created and natural freshwater marshes and found them to be similar in all aspects except the soil. We then compared plant- and

87 microbial-mediated functions between the wetlands and observed smaller nutrient

stocks and slower nutrient transformations in the created wetlands compared to nat-

ural wetlands. Given the biotic and hydrologic structural similarities, but edaphic

dissimilarity, it seemed likely that soil development, or lack of, was the reason for

the functional disparity between created and natural wetlands. And indeed, further

analysis suggested a strong link between soil properties such as carbon (C), nitrogen

(N), and bulk density (ρb ), and wetland functions such as plant productivity and microbial activity. Here, we explore the development of wetland soil and function in newly created wetlands and speciﬁcally address the question: how are created wetlands developing over time?

4.1.1 Problems of derelict land: structure, nutrition, and toxicity

Bradshaw (1983) identiﬁed three soil attributes that hinder ecosystem development: 1) physical factors, 2) nutrition, and 3) toxicity (see also Bradshaw, 1996,

1997, 2003). In our systems, it is highly unlikely that soil toxicity hindered wetland development (e.g., most systems that Bradshaw (1983) addressed were former mine sites, where extreme acidity and heavy metals were common problems), which leaves physical factors and nutrition. physical factors

Soil physical factors include ρb , compaction, aggregation, porosity, and texture.

Higher ρb and either ﬁner or coarser textures have been observed for many created wetlands relative to natural counterparts (e.g., Bishel-Machung et al., 1996; Galatow- itsch and van der Valk, 1996; Campbell et al., 2002; Craft et al., 2002). Likewise, for

88 our study sites and other similar wetlands in Ohio, soils of created systems had higher

ρb , ﬁner texture, and fewer macroaggregates than soils of natural systems (Fennessy et al., 2004; Hossler and Bouchard, 2010; see also Ch. 3). These diﬀerences can be attributed to lower C content in created systems and to construction processes that disturb and compact the soil (see Hossler and Bouchard, 2010). nutrition

Nitrogen, the mineral nutrient most required by plants (Bradshaw, 1983), is typically the limiting resource early in ecosystem development: while most essential nutrients (e.g., phosphorus (P), potassium (K), sulfur) derive from the parent material, the main input for N is fixation from the atmosphere (Anderson, 1988). Over time, N accumulates in the system: as biotic productivity increases, C fixation also increases, supplying the requisite energy for N fixation. Additional sources of N include atmospheric deposition (Lawrence et al., 2000; Galloway et al., 2004) and for depressional wetlands in particular, inflow from adjacent uplands (Craft and Casey,

2000). Eventually, N will be in ample supply and some other resource will become most limiting: for example, light (Tilman, 1985) or P (Walker and Syers, 1976).

We expected a similar process in developing wetland ecosystems based upon our own and other observations of lower C and N content of created wetland soils relative to natural wetland soils and of similar P content between the created and natural wetlands (e.g., Lindau and Hossner, 1981; Langis et al., 1991; Bishel-Machung et al.,

1996; Galatowitsch and van der Valk, 1996; Zedler and Callaway, 1999; Campbell et al., 2002; Craft et al., 2002; Ballantine and Schneider, 2009; see also Ch. 3). Created and restored wetlands tend to have C and N deﬁcient soils because typically they are established in upland or degraded-wetland soils, with typically lower C and N

89 densities than natural wetland soils; and the construction process, which generally

involves excavation into C- and N-poor subsoil or at minimum, some removal of the

topsoil (Hossler and Bouchard, 2010).

Several pedogenic studies have demonstrated a gradual transformation of available

P (derived from the parent material) to unavailable occluded and stable-organic P

(Walker and Syers, 1976; McGill and Cole, 1981; Smeck, 1985; Cross and Schlesinger,

1995), suggesting that P becomes less available while N becomes more available as wetland soils develop. For depressional wetlands, there will be some replenishment of available P through atmospheric depostion (Mahowald et al., 2008) and inﬂow from adjacent uplands (Smeck, 1985; Rubio et al., 1995b; Craft and Casey, 2000; Graham et al., 2005; Wang et al., 2006). A shift from N limitation to P and C limitation was further supported by developmental studies of salt marshes (Craft et al., 2002) and fens (Verhoeven and Schmitz, 1991) and comparisons of mineral to organic wetland soil (Bedford et al., 1999) and upland to wetland soil (Craft and Chiang, 2002).

We hypothesized that created wetlands would be N limited, whereas natural wetlands would be co-limited by P and labile C, and assessed plant nutrient limitation by plant tissue N:P ratio (Koerselman and Meuleman, 1996; Bedford et al., 1999; Olde

Venterink et al., 2003) and N and P amendment of root in-growth cores. We assessed microbial nutrient limitation by litter tissue N:P ratio and C, N and P amendment of basal respiration (BR), methane production (PMP) and denitriﬁcation (DEA) assays. Contrary to our hypothesis, both created and natural wetlands appeared to be

N limited (Ch. 3).

90 4.1.2 Trajectory or subclass?

While the created and natural wetlands did not differ in limiting nutrients, this does not preclude a difference in overall nutrient availability. In other words, created wetlands may need to overcome lower fertility to become functionally equivalent to natural wetlands. Fertility tends to be self-promoting, most often through effect on litter quality and in turn, the rate of decomposition and nutrient release (Aerts and

Berendse, 1988; Anderson, 1988; Aerts et al., 1989; Aerts and Berendse, 1989; van

Oorschot et al., 1997; Berendse, 1998; Aerts, 1999; Bridgham and Richardson, 2003).

Few systems, if any however, are completely closed, and there is some inﬂow and outﬂow of nutrients that drive development and overcome nutrient feedback loops

(Aerts and Berendse, 1988). In particular, created freshwater marshes, as depressions in the landscape, are expected to import nutrients, eventually achieving the fertility and functionality of natural wetlands. Initially, imports are expected to equal exports because of sparse vegetation for nutrient uptake and storage (Odum, 1969; Vitousek and Reiners, 1975). As vegetation establishes, nutrients will gradually accumulate within the plant and soil. Eventually, the created marshes should become more structurally and functionally similar to natural freshwater marshes. Whether this transition does occur, or created wetlands persist as a separate subclass distinct from natural wetlands, remains uncertain (Zedler and Callaway, 1999; Choi, 2004; Fennessy et al., 2004; Hoeltje and Cole, 2007).

To help answer this question, as well as progress fundamental understanding of wetland function and development, we evaluated soil properties and CNP-related functions for 10 created freshwater depressional wetlands, which represented a forty- year chronosequence of development. For comparison, 5 natural wetlands, ranging

91 from highly impacted to pristine, were also included in the study. All wetlands were located in central Ohio, USA, and had similar hydrology (freshwater; depressional or impounded) and vegetation (mixed emergent or cattail). Our central hypothesis was that plant-mediated functions (e.g., primary productivity) would develop in created wetlands over the short-term ( 10 years), while it would take longer (>20 years) for ∼ microbial-mediated functions (e.g., denitriﬁcation, methanogenesis) to attain levels occurring in natural wetlands.

4.2 Materials and Methods

For a description of sites and data collection see Section 3.1.1. All calculations and statistics were performed in R 2.9.1 R Development Core Team (2009), with use of the vegan package (Oksanen et al., 2009) for the ordinations and multivariate signiﬁcance tests and the sem package (Fox, 2009) for the structural equation models.

4.2.1 Function vs. factor soil factors

Plant and microbial functions were compared to soil factors only by RDA, followed by PERMANOVA and Mantel test. Data were transformed as needed to meet univariate normality assumptions; possible transformations included log, logit, negative-inverse, and nth-root (however, some parameters could not be adequately transformed). Analyses were performed with inclusion of decomposition but exclusion of sites BIB and PPN (i.e., factor-inclusive) and with inclusion of all sites but exclusion of decomposition data (i.e., site-inclusive).

92 signiﬁcance tests

Type diﬀerences between C, N and P stocks and ﬂuxes were compared by PER-

MANOVA and permutation test using weighted site averages (with weights determined from vegetation surveys conducted in 2005; see Section ?? and Appendix B).

In the PERMANOVA, stocks and ﬂuxes were separated by nutrient (i.e., C, N or

P) for multivariate analysis of nutrient cycle diﬀerences between created and natural

wetlands. The PERMANOVA was performed 1) with inclusion of decomposition but

exclusion of sites BIB and PPN (i.e., factor-inclusive) and 2) with inclusion of all sites

but exclusion of decomposition data (i.e., site-inclusive). Diﬀerences were signiﬁcant

if no more than 50 out of 1,000 F s calculated after row-wise permutation were greater

than the observed F , or α = 0.05.

Untransformed parameters were compared individually by type in the permutation

test. Approximate signiﬁcance was determined by random permutation of data and

comparison of the sum of squared residual error (SSE; Anderson, 2001b). Diﬀerences

by type were signiﬁcant if no more than 500 out of 10,000 SSEs were less than the

observed SSE, or α = 0.05. The permutation test was preferred over traditional

statistical tests (e.g., F -test in analysis of variance and t-test in linear regression)

because it did not require that data be normally distributed, as some data could

not be adequately transformed to meet normality assumptions. Approximate 95%

conﬁdence intervals for the slope parameter (i.e., type eﬀect for created versus natural

or reference wetland) were calculated by random permutation and regression of y −

βox on x, where y is the response variable (e.g., biomass or bulk density), x is the

explanatory variable (i.e., type) and βo is the observed slope for the unpermuted

dataset (the adjustment of y by βox makes the null hypothesis β = βo rather than

93 β = 0). The slopes obtained for 10,000 permutations were rank ordered, with βo minus the 9750th value estimating the lower bound and βo plus the 250th value estimating the upper bound for βo (see the “second percentile method” in Manly,

1997, pp.41–42). structural equation models

The effect of wetland type on nutrient dynamics was tested by Structural Equa- tion Modeling (SEM; Malaeb et al., 2000; Shipley, 2000). SEM is a multivariate statistical method for testing direct and indirect causal relationships. A model is first proposed, specifying the hypothesized relationships between variables (i.e., a path diagram). The model-implied variance-covariance structure can then be constructed from the hypothesized relationships (i.e., the path coefficients) using basic properties of covariance. The set of path coefficients best fitting the observed covariance structure of the associated dataset are estimated iteratively, then used to calculate the model-predicted variance-covariance (Σ). The null hypothesis in SEM is that the specified model is true; this hypothesis is then tested by comparing Σ to the observed variance-covariance (S): if Σ differs greatly from S, it is unlikely that the proposed model is correct, the p-value will be very low, and the null hypothesis will be rejected; if Σ is similar to S, the proposed model is plausible, the p-value will be high, and the null hypothesis will not be rejected.

SEM evaluation. Wetland type was considered a fixed effect and latent variables were assumed to have a variance of one. SEMs were fit using numeric derivatives as opposed to analytic first derivatives (Fox, 2006). The general SEM assumes multivariate normality: data were transformed as needed to meet univariate normality

94 assumptions; possible transformations included log, logit, negative-inverse, and nth- root (however, some parameters could not be adequately transformed).

While calculation of sample variance-covariance is straightforward for quantitative data, several methods exist for estimating the variance-covariance with qualitative data. We used the phi coeﬃcient for covariance between dichotomous data and the point-biserial coeﬃcient for covariance between dichotomous and quantitative data

(e.g., see Rayward-Smith, 2007). Model ﬁt was evaluated primarily by the Bayesian

Information Criterion (BIC) and additionally by the Non-normed Fit Index (NNFI)

(Fox, 2002). feedback loops. The total eﬀect of one variable on another in an SEM is the summation of all direct and indirect paths leading from the ﬁrst variable to the second

(for calculation of total, direct and indirect effects see Bollen, 1987). When feedback loops appear within the SEM, total effect calculation becomes more complicated and a finite solution is possible only when the feedback loop is convergent. A feedback loop can be represented as a geometric series which has the property of convergence when the common ratio is less than one. For example, if a feedback loop exists between two variables y1 and y2, with coefficient β12 as the effect of y1 on y2, and coefficient

β21 as the eﬀect of y2 on y1, then the feedback loop for y1 on itself through y2 can be written as

95 2 3 4 y1 ↽⇀ y2 = β12β21 +(β12β21) +(β12β21) +(β12β21) + . . .

2 3 = β12β21 1+(β12β21)+(β12β21) +(β12β21) + . . .

∞ k = β12β21 (β12β21) Xk=0 which converges to

β β 12 21 (4.1) 1 β β − 12 21 for

β β < 1 (4.2) | 12 21| 4.2.2 Developmental trajectories time-to-equivalence

The development of various structural and functional parameters across the chronosequence of created and natural wetlands was evaluated by simple exponential model and logistic (or sigmoidal) growth model (see also Hossler and Bouchard, 2010). The simple exponential model has been applied in estimates of soil carbon accumulation

(Jenny, 1980; Jenkinson, 1990; Jastrow, 1996) and soil aggregate formation (Kay et al., 1988; Perfect et al., 1990; Jastrow, 1996). The model assumes zero-order input and ﬁrst-order loss

dC = I kC (4.3) dt −

96 dC where dt is the change in the property of interest (C ) with time (t); I is the rate of input; and k is the constant for rate of development. The equation integrates to

kt C = C (1 λe− ) (4.4) t e −

where Ct is the property value at time t; Ce is the property value at equilibrium;

and λ is a constant related to the initial and equilibrium values (1 C0 ). Data were − Ce ﬁt to the model using non-linear regression to solve for parameters k and λ; k was constrained to be greater than zero and λ was constrained to be less than one. Ce was approximated as the mean of either the natural or reference wetlands, while the youngest created wetland (PPA) provided estimates for C0.

The logistic (or sigmoidal) growth model (Verhulst, 1838) is commonly applied in population ecology and allows for a lag time before any change in structure or function becomes evident in newly created systems

dC C = hC(1 ) kC (4.5) dt − Ce −

dC where dt is the change in property of interest (C ) with time (t); h is the constant for rate of input; Ce is the property value at equilibrium; and k is the constant for rate of development. In this model, the rate of input (hC(1 C )) is directly proportional − Ce (h) to the property value, so it increases with time, but slows in proportion to 1 C , − Ce as the system approaches equilibrium. A solution to the equation is

Ce Ct = k∗t (4.6) (1 + λe− )

97 where Ct is property value at time t, λ is a constant related to the initial and ﬁnal

values, and k∗ is a new constant specifying rate of development, derived from the

original model constants k and h: k∗ = h k, where h < k. − Models were ﬁt using weighted-averages (i.e., one value per site). Because the ages

of the natural systems were unknown, when possible, only created wetland data were

ﬁt to the model (and the natural wetland data were used only to estimate Ce). If the

optimization failed to converge, however, the model was ﬁt to a dataset consisting

of the created wetlands and the two lowest quality natural wetlands (i.e., MI and

LW), with approximated age at 100 years. The time required for a newly created

system to achieve structural or functional equivalence with a natural wetland (teq)

was calculated for 99% equilibrium, or Ct = 0.99Ce, using ﬁtted model parameters.

4.2.3 Linking soil to function Pearson correlation

Measures of structure and function were compared by Pearson correlation using all complete pairs of observations. Unaveraged and untransformed data were correlated among all sites (N = 59), created sites only (N = 40), natural sites only (N = 19), and sites with organic-based soils only (N = 13; sites PPN, CA and BF). exponential regression

Various structural and functional properties were regressed on soil bulk density,

C and N by simple exponential model

y = bxm (4.7)

98 where y is the dependent variable (i.e., structure or function); x is the independent

variable (i.e., soil bulk density (ρb ), C or N); and b and m are model parameters

to be solved for. Unaveraged data (N = 59) were ﬁt to the model by least squares

optimization. Independent variables were transformed (for even distribution across

the range of values) and shifted (for non-negativity) as needed: ρb , untransformed;

soil C, log-transformed; and soil N, log-transformed then shifted by 2.

4.2.4 Soil development water-stable aggregates

Subsamples from soil cores (7.5 cm diameter, 20 cm length) collected in 2005, at

sites NAC, BIB and KP, were analyzed for percent water-stable aggregates (WSA; 2

to 8 mm, 1 to 2 mm, 0.25 to 1 mm, and 53 to 250 µm) at 0–5 cm depth and 5–20 cm depth. Twenty-ﬁve g air-dried soil, passed through an 8 mm sieve and retained on a 3.35 mm sieve, were placed on a stack of nested sieves (2000, 1000, and 250

µm), capillary wetted for 30 min, and wet sieved in tap water for 30 min (Kemper and Chepil, 1965) using a Yoder (1936) wet-sieving machine. Reservoir waters were poured through a 53 µm sieve to collect the 53 to 250 µm aggregates. The fractions were oven-dried (40◦C, 48 h) and weighed. Subsamples from each fraction were dried further at 105◦C for 24 h for moisture correction. The remainder of each fraction was then dispersed and sieved in 0.2% (w/w) sodium hexametaphosphate (HMP); collected coarse particles were oven-dried and the mass subtracted from the mass of the same-size aggregate fraction (Nimmo and Perkins, 2002).

Weight-averaged values were combined with data collected in 2003 (Hossler and

Bouchard, 2010) and ﬁt to the exponential (Eq. 4.4) and logistic (Eq. 4.6) models.

99 root metrics

Soil cores collected in late summer 2005 (3 cores per sampling station; 30 cm depth x 7.5 cm diameter) were separated into 0–5, 5–10, 10–20, and 20–30 cm sections, bulked by station and washed through a 500 µm sieve (Delta-T Root Washer). Roots were manually separated from the remaining organic material and scanned using

WinRHIZO software (settings: automatic threshold, pale root detection, ﬁlter less than 0.005 cm2 area; very small samples required image editing, such as whiting out scratches, debris, or shadows, and manual analysis). After scanning, roots were dried

(55◦C), ashed (450◦C) and weighed.

The WinRHIZO software estimated root length and volume. From these estimates and measures of soil core volume and dry weight and root dry weight, root density

(RD; root dry weight / soil volume), root length density (RLD; root length / soil volume), length-to-volume ratio (L:V; root length / root volume) and speciﬁc root length (SRL; root length / root dry weight) were calculated.

Weight-averaged ρb , volumetric water content (θv), RD, RLD, L:V and SRL at each depth increment (0–5, 5–10, 10–20 and 20–30 cm) were compared by type and age by permutation test. Parameters differing significantly by type were fit to exponential

(Eq. 4.4) and logistic (Eq. 4.6) models for estimates of teq. time eﬀects

The eﬀect of time on nutrient cycling was tested by SEM (single-group). Because the age of the natural wetlands was unknown, the SEM was evaluated using a range of estimated ages for the natural wetlands. Wetland age, or time, was considered a

ﬁxed eﬀect and was log-transformed. Additional data were transformed as needed to

100 meet univariate normality assumptions; possible transformations included log, logit, negative-inverse, and nth-root (however, some parameters could not be adequately transformed). Pearson correlations were used instead of variance-covariance; otherwise, SEM ﬁtting and interpretation are as previously described.

4.3 Results

4.3.1 Linking soil to function function vs. factor (RDA)

In Ch. 3 the link between soil and function was tested using first and second principal component scores to represent various structural factors (e.g., soil, hydrology) and functional responses (e.g., plant-mediated functions); the first principal component for soil contributed most to explanation of function (Fig. 3.2 and Table 3.8). Here, the relationship between soil and function was examined more closely by comparing plant and microbial functions to specific soil factors (Fig. 4.1). The relationship between plant- and microbial-function to 14 soil factors was significant (factor-inclusive:

PERMANOVA, F14,35 = 3.36, p = 0.001, Mantel, r = 0.46, p = 0.001; site-inclusive:

PERMANOVA, F14,44 = 4.37, p = 0.001, Mantel, r = 0.54, p = 0.001), with soil factors accounting for 57 % (with 32 % by RDA1 and 7 % by RDA2) and 58 % (with

34 % by RDA1 and 8 % by RDA2) of functional variance in the factor-inclusive and site-inclusive ordinations, respectively.

RDA1 correlated most with increasing litter stocks and microbial activity, while

RDA2 correlated most with increasing P mineralization (Fig. 4.1). Wetlands segre- gated by type along RDA1, with natural wetlands (except site LW) orienting toward

101 AnPmin AnNmin AeP Roots 0.6 min 0.6 AnPmin PlantC 0.4 PMP 0.4 LitterP PlantN Litter LitterC LitterN Shoots 0.2 AeNmin 0.2 PlantP PlantP AeC ρ Fungi min N b 0.0 ρ 0.0 LitterLitterC b PlantC LitterP PlantN BR C LitterN Shoots Bacteria −0.2 Bacteria NH4 PMP AnCmin RDA2 ( 6.9 %) RDA2 ( 7.7 %) RDA2 Fungi −0.2 BR N AeCmin C AeNmin AnCmin −0.4 −0.4 NH4 DEA −0.6

−0.6 PO4 PO4 NO3 −0.5 0.0 0.5 1.0 1.5 −0.5 0.0 0.5 1.0 1.5 RDA1 ( 32 %) RDA1 ( 34 %) (a) factor-inclusive (b) site-inclusive

Figure 4.1: Biplots from redundancy analysis of plant- and microbial-mediated functions on soil factors: (A) includes decomposition data, but excludes sites BIB and PPN and (B) includes all sites, but excludes decomposition data. Function loadings are indicated by dashed arrows (with italic labels) and factor loadings are indicated by solid arrows (with bold labels); weighted-average site scores are indicated by unshaded circles (created wetlands sized by increasing age) and shaded squares (natural wetlands sized by increasing quality); error bars indicate standard deviations about the weight-averaged scores.

the positive end and created wetlands toward the negative end. The most signiﬁ- cant functional diﬀerences occurred with BR, C and N mineralization and bacterial biomass. Key soil factors were ρb and soil N and C.

soil ρb , C and N (regression)

To further explore the link between ρb and soil C and N with indicators of wetland

function, we employed exponential regression: ﬁtting the data to an exponential

model (Eq. 4.7), provided 30 % to 40 % predictability of plant and litter NP stocks

and 50 % to 80 % predictability of microbial CN ﬂuxes by the soil properties (Figures

4.2 and 4.3). For the microbial ﬂuxes, ρb oﬀered slightly better predictability than C

or N.

102 Soil bulk density also provided good predictability for several soil properties, in-

cluding soil C and N (Eq. 4.7). Among all sites, ρb provided 90 % prediction for soil

+ C and N, 50 % for extractable NH4 , and 40 % for θv (Fig. 4.4). The relationships

were particularly evident in the organic-based soils.

nutrient cycling vs. type (SEM)

To test whether diﬀerences in soil by wetland type were leading to diﬀerences

in nutrient cycling, three sets of structural equation models were ﬁt to the data

(Fig. 4.5): the models included a direct eﬀect of type on plant stock, soil stock, or

microbial activity, or combinations thereof. While there were a few convergence issues

for C cycling, most models ﬁt the data reasonably well with large probabilities and

NNFIs, and low BICs (Table 4.1). The best model for C cycling included type eﬀects

on soil C and microbial activity (mod2sm), with a large negative total eﬀect of type on

microbial activity (i.e., lower in created wetlands) and a slightly less negative total

eﬀect of type on soil C (primarily mediated through microbial activity; the direct

eﬀect of type on soil C was positive). Plant C stock was also negatively aﬀected

(indirectly) by type.

For N cycling, only two models actually converged: of those two, the best model

included only the eﬀect of type on microbial activity (mod1m). Type had a large

direct negative eﬀect on microbial activity which resulted in large indirect negative

eﬀects on soil N and plant N.

For the P cycle, most models ﬁt the data well: the best model included only type

eﬀect on soil P (mod1s); however, type eﬀects could not be determined because of

unstable feedback loops. The next best model included only type eﬀect on plant P

103 iue42 ln n itrNadPsok (g stocks P and N litter and Plant 4.2: Figure faldata. all of ae ol drl hddsurs ..PN AadB) aawr tt nex an to fit were Data BF). and wet CA natural PPN, and LW), form: i.e. the and squares; of MI shaded natural i.e., (darkly circles), (unshaded soils squares; wetlands based shaded created for (lightly indicated soils are Values mineral-based %). scale; (log N n aua rai-ae ny otdln.I oeta n iei niae,the indicated, is line one than more If line. dotted only, organic-based natural and R (i.e., 2 0 = ρ b . r niae:aldt,sldln;cetdol,dse ie aua ny long-da only, natural line; dashed only, created line; solid data, all indicated: are 3 rN,and N), or C , Litter P Stock Litter N Stock Plant P Stock Plant N Stock 0.0 0.5 1.0 1.5 2.0 2.5 10 15 20 10 15 20 25 30 y 0 5 0 1 2 3 4 0 5 = ...... 1.4 1.2 1.0 0.8 0.6 0.4 0.2 bx R 2 =0.37 m Bulk Density where , b and y m stedpnetvral ie,stock), (i.e., variable dependent the is r nnw oe aaees nyrgeso t to above or at fits regression Only parameters. model unknown are ...... 3.5 3.0 2.5 2.0 1.5 1.0 0.5 R · 2 =0.30 m − 104 Soil C(log) 2 versus ) R 2 =0.40 ρ b (g · cm ...... 3.0 2.5 2.0 1.5 1.0 0.5 0.0 − 3 ,si lgsae )adsoil and %) scale; (log C soil ), R R x 2 2 =0.31 =0.35 steidpnetvariable independent the is Soil N(log) ad ihorganic- with lands R oeta model ponential R 2 =0.37 elnswith wetlands 2 sfrtefit the for is hdline; shed R2=0.54 R2=0.57 R2=0.55 400

300

200

100 Cmin (aerobic) 0

2 2 2 20 R =0.59 R =0.57 R =0.55

5 Nmin (aerobic) 0

150 R2=0.68 R2=0.60 R2=0.57

100

50 Cmin (anaerobic) 0

3000 R2=0.81 R2=0.79 R2=0.77 2500 2000

BR 1500 1000 500 0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0.5 1.0 1.5 2.0 2.5 3.0 3.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Bulk Density Soil C (log) Soil N (log)

−3 Figure 4.3: Indicators of microbial activity versus ρb (g cm ), soil C (log scale; %) and soil N (log scale; %). Rates of C and N mineralization (e.g., aerobic· Cmin) have units of mg kg−1 d−1 ; the unit for basal respiration (BR) is µg kg−1 h−1. Values are indicated for created· wetlands· (unshaded circles), natural wetlands with mineral-based· · soils (lightly shaded squares; i.e., MI and LW), and natural wetlands with organic-based soils (darkly shaded squares; i.e. PPN, CA and BF). Data were fit to an exponential model of the form: y = bxm, where y is the dependent variable (i.e., microbial activity), x is the independent variable (i.e., ρb , C or N), and b and m are unknown model parameters. Only regression fits at or above R2 = 0.3 are indicated: all data, solid line; created only, dashed line; natural only, long-dashed line; and natural organic-based only, dotted line. If more than one line is indicated, the R2 is for the fit of all data.

105 40 0.9 2 100 2 2 R =0.93 R =0.51 0.8 R =0.39 80 30 0.7

+ 4 60 0.6 20

NH 0.5

Soil C 40 0.4 10 20 0.3 Water Content Water 0 0.2

35 3.0 R2=0.90 1200 2.5 1000 30

3 800

2.0 − 4 25 1.5 600 CEC PO

Soil N 20 1.0 400 15 0.5 200 0 10 0.2 0.6 1.0 1.4 0.2 0.6 1.0 1.4 0.2 0.6 1.0 1.4 Bulk Density Bulk Density Bulk Density

−3 Figure 4.4: Soil properties versus ρb (g cm ). For the soil properties, units are as follows: soil C + −·1 −3 −1 and N, %; extractable NH4 , mg kg ; extractable PO4 , µg kg ; volumetric water content, cm3 cm−3 ; cation exchange capacity· (CEC), meq g−1. Values are· indicated for created wetlands (unshaded· circles), natural wetlands with mineral-based· soils (lightly shaded squares; i.e., MI and LW), and natural wetlands with organic-based soils (darkly shaded squares; i.e. PPN, CA and BF). Data were fit to an exponential model of the form: y = bxm, where y is the dependent variable (i.e., soil property other than ρb ), x is the independent variable (i.e., ρb ), and b and m are unknown model parameters. Only regression fits at or above R2 = 0.3 are indicated: all data, solid line; created only, dashed line; natural only, long-dashed line; and natural organic-based only, dotted line. If more than one line is indicated, the R2 is for the fit of all data.

106 (mod1p). In this model, the direct and total eﬀects of type on plant P were negative,

as were the indirect eﬀects of type on soil P and microbial activity.

4.3.2 Developmental trajectories C, N and P pools and ﬂuxes

In Ch. 3, we estimated C, N and P pools and ﬂuxes in the created and natural

wetlands and found smaller nutrient stocks and slower nutrient transformations in the

created sites (Table 3.2). To estimate developmental trajectories for the pools and

ﬂuxes, data were ﬁt to exponential and logistic models (Fig. 4.6 and Table 4.2). Times

to equilibrium for plant C, N and P stocks ranged from 20 y to 70 y, depending on the

model, stock and estimated equilibrium value—this “short” recovery time accounts

for the similarities in plant stocks between wetland types. Litter stocks, however,

were signiﬁcantly lower in created wetlands; accordingly, teq estimates ranged from

100 y to 500 y, with the exception of the logistic trajectory for litter P stock (Ce based

on all natural sites) which estimated only 30 y. In contrast to the plant and litter

stocks, where all three nutrients exhibited similar trajectories, soil P stock changed

little with type or age, whereas soil C and N stocks were signiﬁcantly lower in created

wetlands, with only very slight developmental trajectories. For soil C and N stocks,

teq was estimated to be 1,000 y to 6,000 y. C, N and P ﬂuxes generally mirrored

soil development. Like soil P stock, rates of aerobic and anaerobic P mineralization

were similar across wetland types and ages. In contrast, most C and N ﬂuxes were

distinctly lower in created wetlands, with lengthy times to equilibrium; exceptions

were PMP and anaerobic N mineralization which were consistent across type and

age.

107 Plant C Litter C Plant N Litter N

? ?

? ? Type Soil C Type Soil N

? ?

Microbial Activity Microbial Activity

AeCmin AnCmin BR PMP AeNmin AnNmin DEA

(a) Carbon (b) Nitrogen

Plant P Litter P

? Type Soil P

Microbial Activity

AePmin AnPmin

Figure 4.5: Path diagrams for the eﬀect of wetland type (i.e., created or natural) on (a) carbon cycling, (b) nitrogen cycling, and (c) phosphorus cycling. Unidirectional arrows indicate direct effects between variables; bidirectional arrows indicate either covariance between variables or variance within a single variable. Following path diagram convention, observable variables are represented by rectangles or squares and unobservable (latent) variables are represented by ellipses or circles.

108 mod0 mod1s mod1p mod1m mod2sp mod2sm mod2pm mod3

Carbon Cycling χ2 23.0 11.3 10.2 10.2 df 11 10 10 9 p 0.02 0.34 0.42 0.33 NNFI 0.89 0.99 1.00 0.99 BIC -21.9 -29.5 -30.6 -26.5 type effects soil -1.58 -1.58 plant -0.88 -0.61 microb -2.60 -3.24 Nitrogen Cycling χ2 6.49 6.07 df 8 7 p 0.59 0.53 NNFI 1.00 1.00 BIC -26.1 -22.5 type effects soil -1.62 -1.62 plant -1.39 -0.98 microb -1.53 -1.68 Phosphorus Cycling χ2 1.82 5.27 1.88 1.77 4.91 1.77 df 5 5 4 4 43 p 0.87 0.38 0.76 0.78 0.30 0.62 NNFI 1.00 0.99 1.00 1.00 0.94 1.00 BIC -18.6 -15.1 -14.4 -14.5 -11.4 -10.5 type effects soil -0.40 -0.56 plant -0.79 -0.70 microb -0.06 -0.01 Table 4.1: Comparison between competing SEMs for C, N and P cycling. Models were evaluated primarily by the Bayesian Information Criterion (BIC), but other indicators of model fit included probability of the sample covariance given the specified model (p; calculated from χ2 and degrees of freedom (df)) and the Non-normed Fit Index (NNFI). Also indicated are the model-estimates for total direct and indirect effects of type on soil and plant stocks, and microbial activity (microb). Best models are in bold; models with missing values had convergence or stability issues. [Models: mod0, no type effect; mod1s, type effect on soil only; mod1p, type effect on plant only; mod1m, type effect on microbe only; mod2sp, type effect on soil and plant; mod2sm, type effect on soil and microbe; mod2pm, type effect on plant and microbe; mod3, type effect on soil, plant and microbe].

109 2 4 2 exp: R =0.7, t =70 ± 20 exp: R =0.27, t =40 ± 20 1000 a eq a eq 2 2 ) R =0.63, t =40 ± 20 ) 3 R =0.22, t =30 ± 10 2 log: a eq 2 log: a eq − 800 − 600 2 400 1 200 Plant P (g m Plant C (g m 0 0

1 5 10 20 40 natural 1 5 10 20 40 natural Age (y) Age (y) (a) (b)

2 ± 2.5 2 ± 400 exp: R =0.66, teq=300 80 exp: R =0.38, teq=100 70 2 2 ) ± ) ±

2 R =0.62, t =100 30 R =0.45, t =30 20 log: eq 2 2.0 log: eq − 300 − 1.5 200 1.0 100 Litter P (g m Litter C (g m 0.5

0 0.0 1 5 10 20 40 natural 1 5 10 20 40 natural Age (y) Age (y) (c) (d) 0.08 2 ± 400 exp: Ra=0.13, teq=4000 2000 2 ) log: R =0.14, teq=1000 ± 600 ) 1 a 1 0.06 − 300 − 0.04 200 Soil P (g kg Soil C (g kg 100 0.02

0 1 5 10 20 40 natural 1 5 10 20 40 natural Age (y) Age (y) (e) (f)

) ) 3

1 2 1 2

− ± − ± 50 exp: Ra=0.21, teq=1000 600 exp: Ra=−0.12, teq=8000 40000 y y

1 2 1 2 − 40 ± − ± log: Ra=0.2, teq=300 100 2 log: Ra=−0.12, teq=2000 9000 30 20 1 10 0 0 −10 Aerobic Cmin (g kg Aerobic Nmin (g kg −1 1 5 10 20 40 natural 1 5 10 20 40 natural Age (y) Age (y) (g) (h)

Figure 4.6: Trajectories of development for select C, N and P stocks and ﬂuxes: (a) plant C stock, (b) plant P stock, (c) litter C stock, (d) litter P stock, (e) soil C stock, (f) soil P stock, (g) aerobic C mineralization, and (h) aerobic N mineralization. Weighted-averages are plotted against age (square-root scale) for created (unshaded circles; sized by increasing age) and natural (shaded squares; sized by increasing quality) wetlands. Developmental trajectories based on the exponential (dashed line) and logistic (dotted line) models and with means of all natural wetlands for the estimated equilibriums are indicated. All measurements are from the 2005 sampling season. [Error bars indicate standard deviations about the weighted-average scores.]

110 exponential model logistic model ∗ ∗ 2 ∗ ∗ 2 Ce λ k R teq λ k R teq × All natural sites Plant C stock 471 10 0.81 0.061 0.73 7.0(2.0) 3.5 0.16 0.67 4.0(2.0) Plant N stock 16.1 10 0.96 0.12 0.37 4.0(1.0) 6.4 0.30 0.38 2.0(0.6) Plant P stock 2.39 10 0.82 0.10 0.35 4.0(2.0) 2.9 0.20 0.31 3.0(1.0) Litter C stock 160 102 0.94 0.015 0.66 3.0(0.8) 8.2 0.053 0.62 1.0(0.3) Litter N stock 6.10 102 0.94 0.018 0.46 3.0(1.0) 6.9 0.055 0.42 1.0(0.4) Litter P stock 0.695 102 1.0 0.037 0.38 1.0(0.7) 46 0.27 0.45 0.3(0.2) Soil C stock† 129 103 0.78 0.0012 0.21 4.0(2.0) 3.6 0.0047 0.22 1.0(0.6) Soil N stock† 11.4 103 0.79 0.0014 0.28 3.0(2.0) 3.7 0.0054 0.28 1.0(0.5) C mineralization (ae) 13.8 103 0.9 0.004 0.30 1.0(0.6) 8.1 0.024 0.29 0.3(0.1) N mineralization (ae) 0.655 104 0.88 0.00058 0.00 0.8(4.0) 7.4 0.0038 0.00 0.2(0.9) C mineralization (an)† 6.96 103 0.66 0.0025 0.13 2.0(1.0) 1.9 0.0067 0.14 0.8(0.5) Basal respiration† 4.23 103 0.78 0.0029 0.33 1.0(0.7) 3.4 0.0094 0.34 0.6(0.2) Denitrification† 0.0508 102 0.91 0.013 0.38 3.0(2.0) 7.8 0.039 0.44 2.0(0.7) Reference sites only Plant C stock 349 10 0.84 0.13 0.72 3.0(0.9) 3.5 0.27 0.75 2.0(0.6) Plant N stock† 13.5 10 1 0.18 0.54 3.0(0.8) 6.3 0.39 0.55 2.0(0.5) Plant P stock 2.24 10 0.84 0.13 0.41 3.0(1.0) 2.9 0.24 0.38 2.0(1.0) Litter C stock 244 102 0.96 0.0086 0.65 5.0(1.0) 12 0.045 0.60 2.0(0.3) Litter N stock 9.19 102 0.95 0.010 0.45 5.0(2.0) 10 0.044 0.40 2.0(0.5) Litter P stock 1.04 102 1.0 0.021 0.38 2.0(1.0) 9.5 0.066 0.33 1.0(0.4) Soil C stock† 191 103 0.85 0.00073 0.21 6.0(3.0) 5.8 0.0043 0.22 1.0(0.8) Soil N stock† 17.3 103 0.86 0.00082 0.28 5.0(3.0) 6.2 0.0048 0.28 1.0(0.6) C mineralization (ae) 23.4 103 0.94 0.0022 0.30 2.0(1.0) 14 0.022 0.29 0.3(0.1) N mineralization (ae) 1.24 104 0.94 0.00028 0.00 2.0(8.0) 15 0.0035 0.00 0.2(1.0) C mineralization (an)† 8.94 103 0.73 0.0017 0.13 2.0(2.0) 2.8 0.0059 0.15 1.0(0.6) Basal respiration† 5.38 103 0.82 0.0021 0.33 2.0(0.9) 4.6 0.0086 0.34 0.7(0.3) Denitrification† 0.0266 102 0.79 0.03 0.24 1.0(1.0) 3 0.048 0.26 1.0(0.8) Table 4.2: Developmental trajectories for C, N and P stocks and fluxes under the exponential and logistic models using weight-averaged data. Ce is the equilibrium value that each trajectory approaches and was approximated as either the mean natural wetland value (all natural sites) or the mean reference wetland value (reference sites only; i.e., sites CA and BF); its units are the units of the property being modeled. The third column ( ) provides the multiple for teq estimates and errors. The parameters λ∗ and k∗ were solved for: under× the exponential model, λ∗ = λ is a unitless constant with theoretical value λ = 1 C0 and k∗ = k is the rate constant for the trajectory with unit − Ce years−1; under the logistic model, λ∗ = λ′ is a unitless constant with theoretical value λ′ = Ce 1, C0 ∗ ′ −1 − and k = k is the rate constant for the trajectory with unit years ; C0 is the property value at t = 0 (i.e., property value for a newly created wetland) and was approximated as the value for the youngest created wetland (i.e., site PPA). The time to 99% equilibrium, teq, was calculated for each ∗ ∗ property from the λ and k estimates and has unit of year (y). Error for teq (in parentheses) was estimated from the square-root of the inverted hessian matrix. Models were fit using only created wetland data when possible; when the optimization failed to converge, the model was fit to a dataset consisting of the created wetlands and the two lowest quality natural wetlands (i.e., MI and LW), with approximated age at 100 years (†). [Units: plant and litter stocks, g m−2 ; soil stocks, g kg−1 ; all rates (e.g., C mineralization), g kg−1 y−1 (also, for the mineralization· rates, ‘ae’ indicates· aerobic condition and ‘an’ indicates anaerobic· · condition)].

111 multi-year trajectories

Shoot biomass and ρb (0 cm to 10 cm depth) were both measured over multiple years (Figures 4.7 and 4.8 and Table 4.3). Comparison of developmental trajectories by year provided some indication of reliability for the estimates of teq . Shoot

biomass, in particular, tracked development of C, N and P plant stocks (compare 2005

shoot biomass estimates in Table 4.3 to plant stock estimates in Table 4.2). Yearly

teq estimates for shoot biomass, however, were quite variable and ranged from 20 y to

1,000 y. Large inter-annual (and intra-site) variability in biomass was also apparent

from plotting per station data by year (Fig. 4.8a). Consequently, the single-year estimates for plant stock development are probably not reliable; a better estimate would be based on the multi-year shoot biomass teq of 90 y to 200 y.

Yearly teq estimates for ρbwere much more consistent, ranging from 200 y to 3,000 y.

The combined data estimated 200 y to 1,000 y for development of ρb . Plots of station

ρb by year also demonstrated less inter-annual and intra-site variability compared to shoot biomass (Fig. 4.8b). Trajectories of development over the three years within a wetland were also apparent for many of the sites. These comparisons suggest that single-year estimates for development of ρb are fairly reliable and consequently, teq estimates for soil C and N and most microbial-mediated functions; which as will be shown, correlate well with ρb . soil aggregates

Water-stable aggregate measurements for the three oldest created sites (NAC,

BIB, and KP) were combined with data from an earlier study (see Hossler and

Bouchard, 2010) to provide estimates of soil structure development at 0 cm to 5 cm

112 2 1.5 ) 2500 R =0.71, t =70 ± 20 ) 2 exp: a eq 3 − 2 ± − 2000 log: Ra=0.64, teq=40 20 1.0 1500 1000 0.5 2 ± 500 exp: Ra=−0.026, teq=600 600 2 ± 0 Bulk Density (g cm log: Ra=−0.022, teq=900 1000 Shoot Biomass (g m 0.0 1 5 10 20 40 natural 1 5 10 20 40 natural Age (y) Age (y) (a) 2005 (e) 2005

2 1.5 ) 2500 R =0.32, t =600 ± 800 ) 2 exp: a eq 3 − 2 ± − 2000 log: Ra=0.32, teq=500 800 1.0 1500 1000 0.5 2 ± 500 exp: Ra=0.095, teq=300 200 2 ± 0 Bulk Density (g cm log: Ra=0.1, teq=500 400 Shoot Biomass (g m 0.0 1 5 10 20 40 natural 1 5 10 20 40 natural Age (y) Age (y) (b) 2006 (f) 2006

2 1.5 ) 2500 R =−0.1, t =1000 ± 2000 ) 2 exp: a eq 3 − 2 ± − 2000 log: Ra=−0.11, teq=700 2000 1.0 1500 1000 0.5 2 ± 500 exp: Ra=0.64, teq=200 50 2 ± 0 Bulk Density (g cm log: Ra=0.66, teq=300 80 Shoot Biomass (g m 0.0 1 5 10 20 40 natural 1 5 10 20 40 natural Age (y) Age (y) (c) 2007 (g) 2007

2 1.5 ) 2500 R =0.14, t =200 ± 100 ) 2 exp: a eq 3 − 2 ± − 2000 log: Ra=0.11, teq=200 80 1.0 1500 1000 0.5 2 ± 500 exp: Ra=0.23, teq=200 80 2 ± 0 Bulk Density (g cm log: Ra=0.24, teq=300 100 Shoot Biomass (g m 0.0 1 5 10 20 40 natural 1 5 10 20 40 natural Age (y) Age (y) (d) 2005–2007 (h) 2005–2007

Figure 4.7: Developmental trajectories by sample year for shoot biomass: (a) 2005, (b) 2006, (c) 2007 and (d) 2005–2007; and soil bulk density: (e) 2005, (f) 2006, (g) 2007 and (h) 2005–2007. Weighted-averages are plotted against age (square-root scale) for created (unshaded circles; sized by increasing age) and natural (shaded squares; sized by increasing quality) wetlands (in the multi-year trajectories, (d) and (h), values are also shaded by sample year). Developmental trajectories based on the exponential (dashed line) and logistic (dotted line) models and with means of all natural wetlands for the estimated equilibriums are indicated. [Error bars indicate standard deviations about the weighted-average scores.]

113 2000 ) 2 − 1500

1000

500 Shoot Biomass (g m 0 PPA PPB BB BIC SA JMB BIA NAC BIB KP MI LW PPN CA BF (a)

1.4 ) 114 3 − 1.2 1.0 0.8 0.6 0.4

Bulk Density (g cm 0.2

PPA PPB BB BIC SA JMB BIA NAC BIB KP MI LW PPN CA BF (b)

Figure 4.8: Shoot biomass and soil bulk density trajectories per site for years 2005–2007. Marker shape indicates sampling station (3–5 per site) and marker color indicates soil volumetric water content (unshaded, 0.25 cm3 cm−3 ; lightly shaded, > 0.25 and 0.4 cm3 cm−3 ; shaded, > 0.4 and 0.6 cm3 cm−3 ; darkly shaded, > 0.6 cm3 cm−3 ≤). Within-site· three-year trajectories (simple linear≤ regression)· are indicated by dashed≤ lines. [Note:· soil bulk densities for most of the· super-saturated created wetland samples appear to be exceptionally low, which may be an artifact of accidental inclusion of the loose, ﬂocculent material overlying the consolidated soil when sampling, in addition to the normal swelling associated with wetting.] exponential model logistic model ∗ ∗ 2 ∗ ∗ 2 Ce λ k R teq λ k R teq × shoot biomass All natural sites 2005 1080 101 0.8 0.062 0.75 7.0(2.0) 3.2 0.15 0.68 4.0(2.0) 2006 848 102 0.45 0.0067 0.37 6.0(8.0) 0.78 0.0084 0.37 5.0(8.0) 2007 875 103 0.51 0.0039 0.02 1.0(2.0) 1 0.0063 0.02 0.7(2.0) 2005–2007 935 102 0.57 0.02 0.17 2.0(1.0) 1.2 0.027 0.14 2.0(0.8) Reference sites only 2005 804 101 0.84 0.14 0.75 3.0(0.7) 3.2 0.27 0.77 2.0(0.5) 2006 748 103 0.35 0.0046 0.01 0.8(3.0) 0.54 0.0053 0.00 0.7(3.0) 2007 1020 103 0.58 0.0027 0.02 1.0(4.0) 1.3 0.0052 0.02 0.9(2.0) 2005–2007 858 102 0.63 0.046 0.39 0.9(0.5) 1.2 0.049 0.38 1.0(0.4) soil bulk density All natural sites 2005 0.521 103 -1.1 0.0082 0.09 0.6(0.6) -0.53 0.0044 0.09 0.9(1.0) 2006 0.482 102 -1.2 0.015 0.20 3.0(2.0) -0.54 0.0081 0.20 5.0(4.0) 2007 0.447 102 -1.5 0.026 0.68 2.0(0.5) -0.61 0.014 0.70 3.0(0.8) 2005–2007 0.564 102 -0.93 0.02 0.26 2.0(0.8) -0.49 0.013 0.27 3.0(1.0) Reference sites only 2005 0.301 103 -2.6 0.0056 0.09 1.0(1.0) -0.73 0.0017 0.09 3.0(3.0) 2006 0.32 103 -2.2 0.01 0.19 0.5(0.4) -0.69 0.0038 0.20 1.0(0.8) 2007 0.234 102 -3.6 0.016 0.66 4.0(0.9) -0.79 0.0047 0.69 9.0(3.0) 2005–2007 0.285 102 -2.8 0.011 0.25 0.5(0.2) -0.74 0.0035 0.25 1.0(0.5)

Table 4.3: Developmental trajectories for shoot biomass (g m−2 ) and soil bulk density (g cm−3 ) · · by year and combined years under the exponential and logistic models. Ce is the equilibrium value that each trajectory approaches and was approximated as either the mean natural wetland value or the mean reference wetland value (i.e., sites CA and BF; reference values for each parameter are indicated in parentheses); its units are the units of the property being modeled. The third column ∗ ∗ ( ) provides the multiple for teq estimates and errors. The parameters λ and k were solved × C for: under the exponential model, λ∗ = λ is a unitless constant with theoretical value λ = 1 0 − Ce and k∗ = k is the rate constant for the trajectory with unit years−1; under the logistic model, λ∗ = λ′ is a unitless constant with theoretical value λ′ = Ce 1, and k∗ = k′ is the rate constant C0 −1 − for the trajectory with unit years ; C0 is the property value at t = 0 (i.e., property value for a newly created wetland) and was approximated as the value for the youngest created wetland (i.e., ∗ ∗ site PPA). The time to 99% equilibrium, teq, was calculated for each property from the λ and k estimates and has unit of years. Error for teq (in parentheses) was estimated from the square-root of the inverted hessian matrix. Models were ﬁt using only created wetland data when possible; when the optimization failed to converge, the model was ﬁt to a dataset consisting of the created wetlands and the two lowest quality natural wetlands (i.e., MI and LW), with approximated age at 100 years (†).

115 and 5 cm to 20 cm depth. For comparison, trajectories for ρb were also determined for the combined sites at both depths (Fig. 4.9 and Table 4.4). Of particular interest was the development of the 2 mm to 8 mm fraction: time to equilibrium was 20 y to

300 y for the top 0 cm to 5 cm and 500 y to 1,000 y for the bottom 5 cm to 20 cm.

The teq for ρb was similar in magnitude at the 5 cm to 20 cm depth, but slightly longer at the 0 cm to 5 cm depth. soil development and root dynamics by depth

Soil and root development were assessed by depth increments of 0 cm to 5 cm,

5 cm to 10 cm, 10 cm to 20 cm, and 20 cm to 30 cm (Fig. 4.10 and Table 4.5).

Bulk density increased with depth, particularly in the created wetlands, resulting in greater differences by type in the deeper soil. Volumetric water content also increased with depth, but did not differ significantly by type. Compared to reference wetlands only, however, created wetlands were significantly drier at 0 cm to 20 cm depth. Root density generally decreased with depth and was significantly lower in created wetlands than in natural wetlands at 5 cm to 10 cm and 20 cm to 30 cm depth; however, not in comparison to the reference wetlands. Root length density also decreased with depth, but was similar between created and natural wetlands throughout the 30 cm soil profile. Length:volume ratio and SRL were both indicators for root diameter, with finer roots having higher L:V and SRL and coarser roots having lower L:V and

SRL. Roots tended to be finer in the created wetlands, with created wetlands having a significantly higher L:V at all depths, and a higher SRL at all depths, but significant only at 20 cm to 30 cm depth.

Developmental trajectories were estimated for ρb and root L:V by depth (Fig. 4.11 and Table 4.6). For ρb , teq estimates increased with depth: from 100 y to 600 y in the

116 1.2

) ) 1.5

3 1.0 3 − −

0.8 1.0 0.6

0.4 2 ± 2 ± exp: R =0.35, teq=200 100 0.5 exp: R =0.41, teq=400 200 2 2 Density (g cm 0.2 log: R =0.35, teq=300 ± 200 Density (g cm log: R =0.4, teq=700 ± 300

2 2 80 exp: R =0.28, teq=30 ± 20 exp: R =0.076, teq=700 ± 900 80 2 2 log: R =0.37, teq=20 ± 5 log: R =0.08, teq=500 ± 600 60 60

40 40

2−8 mm WSA (%) 2−8 mm WSA (%) 20

25 2 ± 30 exp: R =0.036, teq=1000 2000 2 ± 20 25 log: R =0.035, teq=1000 2000

15 20 15 10 10

5 5 1−2 mm WSA (%) 1−2 mm WSA (%) 0

30 2 30 exp: R =0.31, teq=40 ± 30 25 2 25 log: R =0.3, teq=70 ± 60 20 20

15 15

10 10 2 exp: R =0.13, teq=800 ± 800 5 5 2 log: R =0.13, teq=1000 ± 1000

0.25−1 mm WSA (%) 0.25−1 mm WSA (%) 0

2 30 exp: R =0.86, teq=20 ± 4 25 25 2 ± log: R =0.8, teq=40 20 20 20 15

m WSA (%) 15 m WSA (%)

µ µ 10 10

5 5

53−250 0 53−250 0 1 5 10 20 40 natural 1 5 10 20 40 natural Age (y) Age (y) (a) 0 cm to 5 cm depth (b) 5 cm to 20 cm depth

Figure 4.9: Developmental trajectories of water-stable aggregates (WSA) at ((a) 0 cm to 5 cm depth and (b) 5 cm to 20 cm depth, for aggregate size fractions: 2 mm to 8 mm, 1 mm to 2 mm, 0.25 mm to 1 mm, and 53 µm to 250 µm. Weighted-averages are plotted against age (square-root scale) for created (circles; sized by increasing age) and natural (squares; sized by increasing quality) wetlands. Most data are from sampling in 2003 (lightly shaded; see Hossler and Bouchard, 2010), with some data from 2005 (unshaded). Developmental trajectories based on the exponential (dashed line) and logistic (dotted line) models and with means of all natural wetlands for the estimated equilibriums are indicated. [Error bars indicate standard deviations about the weighted-average scores.]

117 exponential model logistic model ∗ ∗ 2 ∗ ∗ 2 Ce λ k R teq λ k R teq 0–5 cm depth All natural sites ρb 0.413 -1.3 0.023 0.35 200(100) -0.58 0.012 0.35 300(200) 2–8 mm 55.2 1 0.15 0.28 30(20) 12 0.44 0.37 20(5) 0.25–1 mm 10.9 -1.8 0.13 0.31 40(30) -0.64 0.059 0.3 70(60) 53-250 µm 7.15 -11 0.45 0.86 20(4) -0.99 0.11 0.8 40(20) Reference sites only ρb 0.254 -2.8 0.016 0.35 400(200) -0.74 0.0051 0.35 800(500) 2–8 mm 66.8 0.56 0.014 0.082 300(400) 1.2 0.019 0.066 200(300) 0.25–1 mm 5.72 -2.9 0.028 0.22 200(200) -0.77 0.012 0.25 300(300) 53-250 µm 2.33 -22 0.24 0.72 30(9) -0.99 0.028 0.78 200(60) 5–20 cm depth All natural sites ρb 0.681 -1 0.011 0.41 400(200) -0.51 0.0058 0.4 700(300) 2–8 mm† 60.2 0.38 0.0052 0.08 700(900) 0.61 0.0078 0.08 500(600) 1–2 mm† 9.63 -0.46 0.0039 0.04 1000(2000) -0.32 0.0028 0.04 1000(2000) 0.25–1 mm† 12.0 -0.78 0.0053 0.13 800(800) -0.44 0.0031 0.13 1000(1000) Reference sites only ρb 0.461 -2 0.0078 0.41 700(300) -0.67 0.0028 0.41 1000(700) 2–8 mm† 70.5 0.47 0.0034 0.08 1000(1000) 0.89 0.006 0.08 700(800) 1–2 mm† 6.85 -1.1 0.0022 0.04 2000(4000) -0.51 0.0011 0.04 3000(6000) 0.25–1 mm† 8.08 -1.6 0.0037 0.14 1000(1000) -0.62 0.0015 0.13 3000(3000) Table 4.4: Developmental trajectories for soil bulk density (g cm−3 ) and water-stable aggregates · (%) at 0–5 and 5–20 cm depth under the exponential and logistic models. Ce is the equilibrium value that each trajectory approaches and was approximated as either the mean natural wetland value or the mean reference wetland value (i.e., sites CA and BF; reference values for each parameter are indicated in parentheses); its units are the units of the property being modeled. The parameters λ∗ and k∗ were solved for: under the exponential model, λ∗ = λ is a unitless constant with theoretical value λ = 1 C0 and k∗ = k is the rate constant for the trajectory with unit years−1; under the − Ce logistic model, λ∗ = λ′ is a unitless constant with theoretical value λ′ = Ce 1, and k∗ = k′ is the C0 −1 − rate constant for the trajectory with unit years ; C0 is the property value at t = 0 (i.e., property value for a newly created wetland) and was approximated as the value for the youngest created wetland (i.e., site PPA). The time to 99% equilibrium, teq, was calculated for each property from ∗ ∗ the λ and k estimates and has unit of years. Error for teq (in parentheses) was estimated from the square-root of the inverted hessian matrix. Models were ﬁt using only created wetland data when possible; when the optimization failed to converge, the model was ﬁt to a partial dataset which excluded the reference sites and approximated the natural wetland age at 100 years (†).

118 0

20 Soil Depth (cm)

30 0.5 1.0 1.5 2.0 0.4 0.5 0.6 0.7 0.8 0.9 0 1 2 3 4 5 − − − Bulk Density (g cm 3 ) Moisture (cm3 cm 3 ) Root Density (mg cm 3 ) 0

20 Soil Depth (cm)

30 20 40 60 80 100 120 400 600 800 1000 120020 40 60 80 100 120 − − − RLD (mm cm 3 ) Length:Volume (cm cm 3 ) SRL (m g 1 ) Figure 4.10: Plots of various root metrics by depth: 0 cm to 5 cm, 5 cm to 10 cm, 10 cm to 20 cm, and 20 cm to 30 cm. Means and standard errors are indicated for created (unshaded circles) and natural (shaded squares) wetlands. Asterisks indicate mean values for reference wetlands (CA and BF).

top 0 cm to 5 cm, to 2,000 y to 5,000 y in the bottom 20 cm to 30 cm. Estimate errors tended to be large in the lower depths (2,000 y to 20,000 y) because of the lack of detectable trajectory over the forty year time span of created wetland development.

Similarly, trajectories were undetectable for root L:V, resulting in teqestimates of 700 y to 10,000 y (depending on depth, equilibrium estimate, and model) with estimate errors up to 300,000 y.

119 2 ± 2500 2 ± 1.5 exp: R =0.21, teq=100 100 exp: R =0.00013, teq=7000 2 2 log: R =0.28, teq=100 ± 100 2000 log: R =0.00014, teq=10000 ±

1.0 1500

1000 0 to 5 cm 0.5 0 to 5 cm

500 0.0

2000 1.5 1500

1.0 1000

500 2 2 5 to 10 cm 0.5 exp: R =0.22, teq=200 ± 100 5 to 10 cm exp: R =0.018, teq=1000 ± 3000 2 0 2 log: R =0.22, teq=200 ± 200 log: R =0.019, teq=2000 ± 4000 −500 2000

1.5 1500

1.0 1000

0.5 R2=0.027, t =4000 ± 6000 500 10 to 20 cm exp: eq 10 to 20 cm R2=0.027, t =5000 ± 9000 log: eq 0 3.0 2 2500 exp: R =0.012, teq=700 ± 2000 2.5 2 2000 log: R =0.014, teq=1000 ± 3000 2.0 1500 1.5 1000

1.0 500 R2=0.066, t =2000 ± 2000 20 to 30 cm exp: eq 20 to 30 cm 0 0.5 2 ± log: R =0.065, teq=3000 3000 −500 0.0 1 5 10 20 40 natural 1 5 10 20 40 natural Age (y) Age (y) (a) Bulk Density (b) Length:Volume

Figure 4.11: Developmental trajectories for (a) soil bulk density (g cm−3 ) and (b) length:volume ratio (cm cm−3 ) by depth: 0 cm to 5 cm, 5 cm to 10 cm, 10 cm· to 20 cm, and 20 cm to 30 cm. Weighted-averages· are plotted against age (square-root scale) for created (unshaded circles; sized by increasing age) and natural (shaded squares; sized by increasing quality) wetlands. Developmental trajectories based on the exponential (dashed line) and logistic (dotted line) models and with means of all natural wetlands for the estimated equilibriums are indicated. All measurements are from the 2005 sampling season. [Error bars indicate standard deviations about the weighted-average scores.]

120 nutrient cycling vs. time (SEM)

A structural equation model was proposed to test the eﬀect of time on soil C

and microbial activity (Fig. 4.12). The best models placed the direct eﬀect of time

on microbial activity rather than soil C (Table 4.7); furthermore, the direct eﬀect

of time on soil C was not significant by t-test (p = 0.90). Model fit improved as the estimated natural wetland age increased. Unfortunately, feedback loops between microbial activity and soil C and N tended to be amplifying based on model-predicted path coefficients—this instability precluded calculation of most time effects. A stable model was obtained, however, by estimating natural wetland age to be 10200 y; which, although unrealistic, at least allowed relative comparison of the effect of time on model parameters (Fig. 4.13). The steady-state model predicted strong positive effects of time on soil C, microbial activity, soil N, soil P, aerobic N mineralization, aerobic

C mineralization, and anaerobic C mineralization (in order of decreasing strength); moderately positive effects on litter stocks; slight positive effects on plant N and P stocks and anaerobic P mineralization; slight negative effects on plant C stock and anaerobic N mineralization; and no effect on aerobic P mineralization.

121 Plant C Plant N Plant P

AeCmin

Litter C Microbial Activity

AeNmin

Litter N

AePmin

Litter P

AnCmin

AnNmin Time

AnPmin

Soil C Soil N Soil P

Figure 4.12: Path diagram for the eﬀect of time on combined carbon, nitrogen, and phosphorus cycling. Unidirectional arrows indicate direct eﬀects between variables; bidirectional arrows indicate either covariance between variables or variance within a single variable. Following path diagram convention, observable variables are represented by rectangles or squares and unobservable (latent) variables are represented by ellipses or circles.

122 1.0 1.0

0.5 0.5

0.0 Time −> Soil 0.0 Time −> Microbes

105 1015 1025 1035 105 1015 1025 1035

Natural Wetland Age (log10) Natural Wetland Age (log10) (a) microbial activity (b) soil stocks

1.0 1.0

0.5 0.5 Time −> Litter Time −> Plants 0.0 0.0

105 1015 1025 1035 105 1015 1025 1035

Natural Wetland Age (log10) Natural Wetland Age (log10) (c) plant stocks (d) litter stocks

1.0 1.0

0.5 0.5

0.0 0.0 Time −> Aerobes Time −> Anaerobes

105 1015 1025 1035 105 1015 1025 1035

Natural Wetland Age (log10) Natural Wetland Age (log10) (e) aerobic mineralization (f) anaerobic mineralization

Figure 4.13: Estimated eﬀect of time, given natural wetland age (log10 scale), on (a) microbial activity, (b) soil CNP stocks, (c) plant CNP stocks, (d) litter CNP stocks, (e) aerobic CNP mineralization, and (f) anaerobic CNP mineralization. Steady-state estimates (based on natural wetland age of 10200 y) are indicated by dotted lines for C (black), N (dark gray), and P (light gray). Solid lines follow the same color scheme and indicate trends in model-based estimates as natural wetland age is increased. Incomplete lines are due to amplifying feedback loops which prevented steady-state estimates. Steady-state estimates for the eﬀect of time using a natural wetland age of 10200 y are as follows, in order of most positive to most negative: soil C, 0.95; microbial activity, 0.76; soil N, 0.67; soil P, 0.59; aerobic N mineralization, 0.52; aerobic C mineralization, 0.40; anaerobic C mineralization, 0.36; litter N, 0.25; litter P, 0.24; litter C, 0.23; anaerobic P mineralization, 0.11; plant P, 0.10; plant N, 0.06; aerobic P mineralization, 0.00; anaerobic N mineralization, -0.05; plant C, -0.16

123 Type Age Created Natural p β p Bulk Density (g cm−3 ) 0–5 cm 0.806(0.087)· 0.36(0.12) 0.012† -0.00861 0.24 5–10 cm 1.091(0.074) 0.68(0.22) 0.046† -0.00862 0.17 10–20 cm 1.324(0.042) 0.82(0.21) 0.0048† -0.0000828 0.98 20–30 cm 1.917(0.060) 1.22(0.31) 0.0067† -0.000812 0.88 Volumetric Water ( cm3 cm−3 ) 0–5 cm 0.425(0.052)· 0.45(0.10) 0.83† 0.00796 0.052 5–10 cm 0.388(0.039) 0.55(0.11) 0.12† 0.00733 0.0098 10–20 cm 0.401(0.041) 0.54(0.10) 0.14† 0.00649 0.042 20–30 cm 0.612(0.067) 0.74(0.12) 0.33 0.0111 0.039 Root Density (mg cm−3 ) 0–5 cm 2.61(0.73)· 2.24(0.39) 0.74 0.133 0.026 5–10 cm 1.49(0.36) 4.1(1.3) 0.019 0.0453 0.13 10–20 cm 0.86(0.37) 1.34(0.27) 0.46 0.0733 0.0089 20–30 cm 0.273(0.071) 0.82(0.22) 0.011 0.00776 0.19 Root Length Density (mm cm−3 ) 0–5 cm 89(16)· 102(22) 0.64 2.80 0.032 5–10 cm 71(19) 77(23) 0.80 1.75 0.29 10–20 cm 29.6(6.7) 29.9(7.6) 0.98 0.795 0.16 20–30 cm 18.0(3.5) 26.1(8.8) 0.31 0.622 0.023 Length:Volume (cm cm−3 ) 0–5 cm 1109(74)· 706(36) 0.0043 -0.195 0.98 5–10 cm 1099(51) 600(110) 0.00060† -1.64 0.72 10–20 cm 847(69) 510(140) 0.033† 3.14 0.63 20–30 cm 1018(83) 560(140) 0.014† -1.96 0.78 Speciﬁc Root Length (cm g−1 ) 0–5 cm 104(21)· 54.0(6.8) 0.11 0.398 0.84 5–10 cm 106(23) 43(15) 0.064 -1.08 0.53 10–20 cm 78.3(8.3) 44(17) 0.062† -0.227 0.80 20–30 cm 114(14) 49(12) 0.0085† -0.584 0.64

†Significant difference (α = 0.05) between created wetlands and reference wetlands. Table 4.5: Table of functional parameters: for each parameter, mean values for created and natural wetlands are listed with standard errors in parentheses; as well as the probability of no difference between wetland types by permutation (p). Also tested for each parameter was the linear effect of age among created wetlands: the slope value (β) is listed along with the probability of no effect by permutation (p). All data are from soil cores collected in 2005. Effects by type significant at α = 0.05 are in bold, as well as significant (α = 0.05) corresponding effects by age.

124 exponential model logistic model ∗ ∗ 2 ∗ ∗ 2 Ce λ k R teq λ k R teq Bulk Density (g cm−3 ) All natural sites · 0–5 cm 0.36 -1.8 0.04 0.21 100(100) -0.69 0.03 0.28 100(100) 5–10 cm 0.679 -0.8 0.025 0.22 200(100) -0.45 0.016 0.22 200(200) 10–20 cm† 0.818 -0.63 0.0011 0.027 4,000(6,000) -0.38 0.00071 0.027 5,000(9,000) 20–30 cm† 1.22 -0.58 0.002 0.066 2,000(2,000) -0.37 0.0013 0.065 3,000(3,000) Reference sites only 0–5 cm 0.192 -4.0 0.02 0.19 300(200) -0.82 0.0077 0.23 600(500) 5–10 cm 0.338 -2.6 0.013 0.22 400(300) -0.72 0.0041 0.22 1000(700) 10–20 cm† 0.556 -1.4 0.00074 0.027 7,000(10,000) -0.58 0.00031 0.027 10,000(20,000) 20–30 cm† 0.962 -1 0.0014 0.066 3,000(4,000) -0.5 0.00072 0.066 5,000(6,000) Length:Volume (cm cm−3 ) All natural sites · 0–5 cm 706 -0.57 0.00056 0.00013 7,000(200,000) -0.36 0.00038 0.00014 10,000(300,000) 5–10 cm 596 -0.88 0.0037 0.018 1,000(3,000) -0.47 0.0022 0.019 2,000(4,000) 20–30 cm 564 -0.86 0.0059 0.012 700(2,000) -0.47 0.004 0.014 1,000(3,000) Reference sites only 0–5 cm 756 -0.47 0.00065 0.00013 6,000(200,000) -0.32 0.00047 0.00014 7,000(200,000) 125 5–10 cm 539 -1.1 0.0033 0.018 1,000(3,000) -0.52 0.0017 0.019 2,000(5,000) 20–30 cm 350 -2 0.0036 0.011 1,000(5,000) -0.67 0.0014 0.012 3,000(9,000)

Table 4.6: Developmental trajectories for soil bulk density and root length:volume ratios by depth under the exponential and logistic models. Ce is the equilibrium value that each trajectory approaches and was approximated as either the mean natural wetland value or the mean reference wetland value (i.e., sites CA and BF; reference values for each parameter are indicated in parentheses); its units are the units of the property being modeled. The parameters λ∗ and k∗ were solved for: under the exponential model, λ∗ = λ is a unitless constant with theoretical value λ = 1 C0 and k∗ = k is the rate constant for the trajectory with unit years−1; under the logistic model, λ∗ = λ′ is a unitless constant with − Ce theoretical value λ′ = Ce 1, and k∗ = k′ is the rate constant for the trajectory with unit years−1; C is the property value at t = 0 (i.e., C0 0 property value for a newly− created wetland) and was approximated as the value for the youngest created wetland (i.e., site PPA). The time ∗ ∗ to 99% equilibrium, teq, was calculated for each property from the λ and k estimates and has unit of years. Error for teq (in parentheses) was estimated from the square-root of the inverted hessian matrix. Models were fit using only created wetland data when possible; when the optimization failed to converge, the model was fit to a dataset consisting of the created wetlands and the two lowest quality natural wetlands (i.e., MI and LW), with approximated age at 100 years (†). Microbial Activity Soil Carbon 100 250 500 1 103 5 103 1 104 5 104 100 250 500 1 103 5 103 1 104 5 104 × × × × × × × × χ2 183 180 178 177 175 174 173 191 189 187 186 183 183 181 df 76 76 76 76 76 76 76 76 76 76 76 76 76 76 p 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NNFI 0.84 0.84 0.84 0.85 0.85 0.85 0.85 0.82 0.83 0.83 0.83 0.84 0.84 0.84 BIC -127 -130 -132 -133 -135 -136 -137 -119 -121 -123 -124 -127 -127 -129 time effects (unpropagated) Microbes 0.96 1.08 1.15 1.20 1.28 1.30 1.33 4.08 6.57 9.99 10.80 12.13 12.29 12.94 Soil C 2.24 2.45 2.26 2.74 2.06 1.94 1.81 0.86 1.36 2.03 2.26 2.63 2.72 2.91 Soil N 2.40 2.57 2.37 2.85 2.12 2.00 1.86 2.19 3.38 4.82 5.15 5.80 5.87 6.18 Soil P 1.15 1.30 1.18 1.40 1.05 0.98 0.92 -0.13 -0.24 -0.35 -0.39 -0.47 -0.48 -0.52 Plant C -0.21 -0.26 -0.24 -0.30 -0.22 -0.21 -0.20 -0.12 -0.17 -0.26 -0.29 -0.34 -0.36 -0.39 Plant N 0.16 0.15 0.13 0.16 0.12 0.11 0.10 0.06 0.14 0.21 0.23 0.25 0.24 0.24 Plant P 0.20 0.22 0.20 0.23 0.17 0.16 0.15 -0.02 -0.03 -0.04 -0.05 -0.06 -0.06 -0.07 time effects (propagated) Microbes Soil C 0.92 Soil N

126 Soil P 2.56 1.66 1.19 0.25 0.43 0.65 0.71 0.81 0.83 -0.52 Plant C -0.21 -0.26 -0.24 -0.29 -0.22 -0.21 -0.20 -0.12 -0.17 -0.24 -0.26 -0.31 -0.33 -0.37 Plant N 0.16 0.15 0.13 0.16 0.12 0.11 0.10 0.06 0.13 0.20 0.21 0.23 0.23 0.23 Plant P 0.15 0.17 0.17 0.19 0.16 0.15 0.14 0.03 0.06 0.09 0.10 0.12 0.12 0.13 Table 4.7: Comparison between competing SEMs for the effect of time on combined carbon, nitrogen and phosphorus cycling. Models were evaluated primarily by the Bayesian Information Criterion (BIC), but other indicators of model fit included probability of the sample covariance given the specified model (p; calculated from χ2 and degrees of freedom (df)) and the Non-normed fit index (NNFI). Models with missing values had convergence issues. The column heading indicates the estimated natural wetland age. Also indicated are the model-estimates for total direct and indirect effects of time on microbial activity and soil and plant stocks, both without propagation through feedback loops and with propagation through feedback loops. Missing values for propagated effects indicate an unstable effect which became amplified with each pass through the cycle. Unpropagated effects are included primarily for comparison. 4.4 Discussion

4.4.1 Linking soil to function

Soil bulk density is relatively easy to measure and may be a practicable surrogate

metric for the development of microbially-mediated functions in created wetlands.

Meyer et al. (2008) made a similar suggestion in their study of wet prairie restoration.

Soil bulk density demonstrated a faster trajectory of development compared to soil C,

which would be advantageous in monitoring wetland restoration by making it easier to

detect change in soil structure over time. The relationship between ρb and carbon was asymptotic, with ρbapproaching a minimum value equivalent to the density of mineral- free organic matter as soil C continued to accumulate. Ruehlmann and K¨orschens

(2009) proposed a similar model relating soil bulk density to soil C in their meta-

1 1 3 analysis of published soil data spanning 3g kg− to 574g kg− SOC and 0.03g cm− · · · 3 to 2.0g cm− ρ . Given the asymptotic relationship between ρ and soil C, as well as · b b most microbially-mediated functions, one criterion for success in the restoration and creation of freshwater depressional wetlands might be a minimum threshold ρb in the

3 3 range of 0.3g cm− to 0.4g cm− , with modiﬁcation as needed based on soil texture · · and water content (Heuscher et al., 2005; Ruehlmann and K¨orschens, 2009).

4.4.2 Developmental trajectories

Developmental models supported our initial hypothesis that plant-mediated functions would develop over the short-term, while microbial-mediated processes would require longer to develop. This pattern of development has been supported in studies of other depressional wetland systems (Ballantine and Schneider, 2009), as well as salt marshes (Craft et al., 1988, 1999, 2002; Morgan and Short, 2002; Edwards and

127 Proﬃtt, 2003; but see also Gibson et al., 1994; Zedler and Callaway, 1999) and wet

prairies (Meyer et al., 2008). Our estimates of 20 y to 70 y for plant C, N and P

stocks were much lower than an earlier estimate of 500 y for developing aboveground

plant productivity (Hossler and Bouchard, 2010), but similar to other estimates from

the literature (Seneca et al., 1985; Broome et al., 1986; Craft et al., 1999, 2002;

Morgan and Short, 2002; Edwards and Proﬃtt, 2003; Balcombe et al., 2005; Meyer

et al., 2008; Ballantine and Schneider, 2009). One probable cause of the discrepancy

in teq estimates is the inter-annual variability in plant biomass production. Over a three year period, yearly teq estimates for shoot biomass ranged from 20 y to 1,000 y.

Whigham et al. (2002) observed similar variability for 12 restored depressional wetlands over a three-year period, mainly because of year-to-year changes in hydrology.

Annual ﬂuctuation in plant biomass was also documented for 2 long-running salt marsh studies (Craft et al., 1999; Zedler and Callaway, 1999; Craft et al., 2002).

Soil property measurements, however, appeared much more consistent year to year (based on the three-year trends for ρb ). Our teq estimates for soil C and N stocks ranged from 1,000 y to 6,000 y. The soil C estimate was much longer than the 100 y to 300 y we estimated for soil C (0 cm to 5 cm) in a previous study (see

Hossler and Bouchard, 2010); however, one caveat from our earlier study was the lack of data from older created wetlands, which was needed to ﬁll in critical parts of the trajectories. Even with 40 y of development, however, the trajectory for soil C based only on created systems was almost imperceptible. Ballantine and Schneider (2009) sampled depressional wetlands that had been developing for 55 y, but found SOM to be still less than half that of natural wetlands. Developmental studies of salt marshes typically estimate less than 100 y for SOC equivalence with natural systems (Craft

128 et al., 2002; Morgan and Short, 2002; Edwards and Proﬃtt, 2003; but see also Zedler and Callaway, 1999). Estimates for tallgrass prairies have ranged from 50 y (Baer et al., 2002) to 400 y (Jastrow, 1996), depending on the predicted trajectory.

While the question of whether restoration follows a trajectory of development is under debate (Zedler and Callaway, 1999; Choi, 2004), within-site increases in SOM over three-years provided some support. Zedler and Callaway (1999) observed year- to-year increases in SOM in a constructed marsh; however, the increase was mirrored by annual SOM changes in a nearby natural marsh. We observed some intra-site and inter-annual variability in ρb (which correlates with SOM), but in addition to a deﬁnite decrease in ρb with created wetland age. The directional variation indicates a clear increase in soil C, while the non-directional changes in ρb are most likely attributed to ﬂuctuating water regime (Heuscher et al., 2005).

Most microbially-mediated processes appeared to track the development of soil C and N stocks. Several studies have demonstrated reduced microbial activity in created systems (Langis et al., 1991; Atkinson and Cairns, 2001; Duncan and Groﬀman, 1994;

Hunter and Faulkner, 2001; Fennessy et al., 2008); however, very few studies have monitored development of microbial processes. In a 12 y chronosequence of restored grasslands, Baer et al. (2002) observed faster recovery for active C pools compared to total soil C, and accordingly, faster development of microbial-mediated functions such as C mineralization. Likewise, Meyer et al. (2008) found C and N mineralization rates to develop more quickly than total soil C and N in a 7 y chronosequence of restored wet prairies. In general, however, microbial activity can be linked to soil C, with

129 lower activity levels in low-C or mineral-based soils and higher activity levels in high-

C or organic-based soils (Berendse, 1990; Groﬀman et al., 1996; D’Angelo and Reddy,

1999; Van Hoewyk et al., 2000).

4.4.3 Soil development

To better understand the development of soil and function in created wetlands, we examined soil aggregate formation and root dynamics. Soil aggregation is an important component of healthy soil structure with strong correlation to other physical soil properties such as ρb , penetrability, and porosity; soil aggregation also plays a critical role in the protection and stabilization of soil C. Soil macroaggregates (> 250 µm), in particular, help stabilize and protect the organic materials, as roots and hyphae bind together the carbon materials with primary particles and smaller microaggre- gates (< 250 µm) (Tisdall and Oades, 1980, 1982; Oades, 1984). We reﬁned projected trajectories for soil aggregation at 0 cm to 5 cm and 5 cm to 20 cm soil depth from an earlier study (Hossler and Bouchard, 2010) using WSA values determined for the three oldest created wetlands of this study (i.e., NAC, BIB and KP). The teq estimates for aggregate formation increased only slightly from the earlier predictions at 0 cm to 5 cm, but lengthened considerably at 5 cm to 20 cm. The same pattern was true for teq estimates of ρb using the same data set—the ρb teq s were in turn comparable to the multi-year ρb teq estimates (0 cm to 10 cm) of this study. As concluded in our earlier study (Hossler and Bouchard, 2010), soil aggregation, at least in the surﬁcial soil, appears to develop rather quickly (< 50 y) in these systems, with additional time required for development of ρ ( 500 y), and even more time for recovery of soil C b ∼ (> 1000 y). Jastrow (1996) observed a similar pattern of development in restored

130 tallgrass prairies, with 40-times faster rate of macroaggregate formation compared to

C accumulation.

Estimates of teq increased with soil depth, which was also evident in our exami-

nation of ρb along the 0 cm to 30 cm soil proﬁle. At 0 cm to 5 cm and 5 cm to 10 cm, a decrease in ρb with created wetland age was apparent and teq estimates were 100 y and 200 y, respectively; however, at 10 cm to 20 cm and 20 cm to 30 cm, change in ρb with age was almost imperceptible, resulting in teq estimates of 700 y to 800 y and 3,000 y to 5,000 y, respectively. The slower rate of development in deeper soil probably reﬂects lower microbial and root activity with depth.

Another probable cause of slower development with depth may be greater compaction of deeper soil in created wetlands resulting from the use of heavy machinery during wetland construction. Nair et al. (2001) measured soil penetrability in five young created and three natural depressional wetlands of central Florida and found the one-year-old created wetlands to be extremely compacted at 20 cm depth—soil compaction and penetrability can affect other properties such as root density and diameter and water permeability. Fennessy et al. (2004) monitored surface- and groundwater levels for several created and natural wetlands in Ohio (including many of our study sites) and observed a disconnect between surface water and ground water in some of the created sites. A hydrologic disconnect has implications for water-quality function, as well as primary productivity—particularly in dry seasons or droughts where access to groundwater is critical for plant survival. The greater difference in soil moisture between created and natural wetlands at 5 cm to 10 cm and 10 cm to 20 cm (although significant only compared to reference wetlands) supports the disconnect hypothesis proposed by Fennessy et al. (2004).

131 Soil penetrability and related properties are also important for root growth, which in turn is important to the development of the soil structure, in addition to the accumulation of soil C (Oades, 1988; Jobb´agy and Jackson, 2000). Contrary to expectation, RD was signiﬁcantly lower for created wetlands only at 5 cm to 10 cm and

20 cm to 30 cm depth, but not in comparison to reference wetlands. In general, root biomass appeared to be developing at a rate similar to the aboveground biomass.

Similar above- and below-ground development has also been observed in salt marshes

(Craft et al., 1999, 2002), wet heathlands (Berendse, 1990), and wet prairies (Meyer et al., 2008). As mentioned, root growth is critical to soil development as a source of soil C (Oades, 1988; Jobb´agy and Jackson, 2000), stimulus for microbial activity

(Nannipieri et al., 2007), and formative agent for soil aggregation (Tisdall and Oades,

1980, 1982; Oades, 1984).

Other root metrics, however, differed substantially between created and natural wetlands: significantly higher L:V and SRL in created wetlands, particularly in comparison to reference wetlands, suggested a much finer root morphology in the created systems. Root morphology, as well as biomass allocation, can be impacted by environmental conditions such as nutrient availability (Powell, 1974; Boot and Mensink,

1990; Aerts et al., 1991; Elberse and Berendse, 1993; Rubio et al., 1995a; Aerts, 1999;

Aerts and Chapin, 2000; Schippers and Olff, 2000; Cronin and Lodge, 2003; Güsewell et al., 2003; Güsewell, 2005a,b) and hydrology (Naidoo and Naidoo, 1992; Rubio et al.,

1995a, 1997; Mendoza et al., 2005; Smith and Brock, 2007). We hypothesized that the ratio of belowground-to-aboveground plant biomass would be higher in created wetlands primarily because of lower soil fertility. Although soil C, N and P stocks were signiﬁcantly lower in created wetlands, however, root-to-shoot ratios did not

132 diﬀer signiﬁcantly from natural wetlands (Table 3.5). An increase in R:S in response

to decrease in nutrient supply (particularly N) has been reported in several studies

(Boot and Mensink, 1990; Aerts et al., 1991; Rubio et al., 1995a; Schippers and Olﬀ,

2000; Cronin and Lodge, 2003; G¨usewell et al., 2003; G¨usewell, 2005a,b).

Other fertility studies have suggested instead an increase in SRL under low nutrient conditions (Powell, 1974; Elberse and Berendse, 1993). What is clear, is that plant adaptations to nutrient supply, whether morphological (e.g., R:S, SRL) or phys- iological (e.g., uptake kinetics), vary by species and genotype, in addition to other environmental factors (Aerts, 1999; Aerts and Chapin, 2000; Forde and Lorenzo, 2001;

Pierret et al., 2007). While R:S was similar between created and natural wetlands, the higher SRL and L:V observed in the created wetlands may be indicative of an overall lower nutrient supply. Fine roots are particularly suited to soil exploration and nutrient acquisition; however, other characteristics which aid in nutrient acquisition were not measured (e.g., uptake kinetics, mycorrhizae, root hair length and density), and in fact, there were no diﬀerences in RLD between created and natural wetlands.

The similarity in RLD suggests comparable soil exploitation by plants in created and natural wetlands; however, as noted for other soil-based measures, because of the extreme diﬀerences in ρb , per volume comparisons may misrepresent the low ρb natural systems. nutrient cycling vs. time (SEM)

The effect of time on wetland nutrient cycling was tested by SEM. Similar to the type-effect SEMs, the best models indicated that the effect of time was mediated through microbial activity. All models suggested a strong positive effect of time on microbial activity and soil C and N. Feedback loops between these three parameters

133 tended to be amplifying, with increasing microbial activity leading to increasing soil

C and N leading to increasing microbial activity, and so forth. Steady-state eﬀects

could only be estimated for all model parameters when the natural wetland age was

set at 10200 y or older.

4.5 Conclusion

While nutrient cycling is signiﬁcantly lower in created depressional wetlands, our

study of created wetland development over a 40 y chronosequence supports improve-

ment of CNP-related functions in newly constructed systems with time. The biggest

limitation to functional equivalence for mitigation wetlands is the lengthy time scale

required for pedogenesis. Developmental trajectories suggest gradual accumulation of

soil C and formation of soil aggregates, leading to a slow decrease in ρb and increase in microbially-mediated CNP processes in the initial stages of development. Eventually the soil structure reaches maximal conﬁguration (i.e., well-aggregated with low ρb ); however, organic matter continues to accumulate in the soil, with the concomitant increase in energy and nutrient supply continuing to drive microbial activity.

Mitigation success depends on what is deemed an acceptable “lag-time” for recovery of lost function. Bradshaw (1997) recommended no more than the length of a childhood (i.e., 10 y) in order to “not inﬂict dereliction on the next generation.”

Few could argue that one-thousand-plus years (i.e., minimum average teq for 99% recovery of soil-based C and N stocks and ﬂuxes) would be an acceptable time frame for replacement of lost CNP-related natural wetland functions. Current wetland mitigation policy which permits destruction of natural wetlands with replacement by

134 constructed or restored systems is unacceptable. Although wetland science, engineer-

ing and technology continue to improve, the time required for pedogensis presents an

insurmountable constraint. Amending constructed systems with organic material or

salvaged natural wetland topsoil may hasten the pedogenic process (Brown and Bed-

ford, 1997; Stauﬀer and Brooks, 1997; Bruland and Richardson, 2004); however, the

impact will likely be surficial. Based on our profile study of ρb and root dynamics, teq estimates increase substantially with depth along the soil profile. Wetland construction and restoration still has value in terms of regaining already lost wetland acreage; and some functions do achieve equivalence with natural wetlands in the short-term.

Plant biomass, for example, can develop fairly quickly (at least non-woody biomass) and provide important habitat for local fauna. Whenever possible, however, it is our recommendation that these important and irreplaceable ecosystems be preserved.

135 PART III

Arbuscular mycorrhizae

136 CHAPTER 5

ARBUSCULAR MYCORRHIZAE IN AQUATIC SYSTEMS: A REVIEW

5.1 Introduction

Until just about 30 years ago, mycorrhizal fungi—as obligate aerobes—were commonly believed to be absent from wetland and other aquatic environments. Since that time, however, numerous studies have documented the presence of mycorrhizae in these periodically- to permanently-wet habitats. What remains to be determined is how prevalent are mycorrhizal associations within these ecosystems? And, what is their function?

5.1.1 Mycorrhizae in terrestrial systems

In contrast to aquatic environments, it is well established that mycorrhizae are ubiquitous throughout terrestrial systems, and their ecology in these systems is fairly well understood (Powell and Bagyaraj, 1984; Smith and Read, 1997). The most common types of these plant root-fungal symbioses are the arbuscular mycorrhizae

(AM). AM are characterized by the formation of highly branched hyphae, or arbuscules, within the cortical cells of plant roots (Bonfante-Fasolo, 1984). The arbuscules

137 are the primary sites for nutrient exchanges between the plant and fungal symbionts

(Bonfante-Fasolo, 1984). controls

AM form primarily under conditions of low soil fertility (Daft and Nicolson, 1969;

Mosse, 1973; Hetrick, 1984; Smith and Read, 1997; Blanke et al., 2005). High concentrations of both phosphorus and nitrogen reduce root colonization (Daft and Nicolson,

1969; Mosse, 1973; Hetrick, 1984; Smith and Read, 1997; Blanke et al., 2005). The extent of root colonization is most likely mediated by plant nutrient concentrations rather than soil nutrient concentrations (Menge et al., 1978; Ratnayake et al., 1978;

Cooper, 1984; Hetrick, 1984; Smith and Read, 1997). One mechanism of control may be through the plant root membrane permeability. When plant phosphorus concentrations are low, there is a decrease in membrane phospholipid levels which results in an increase in membrane permeability and an increase in plant root colonization

(Ratnayake et al., 1978; Cooper, 1984). Contrarily, mycorrhizal associations become inhibited when plant phosphorus concentrations are high. Under these conditions, membrane phospholipid levels increase and root membrane permeability decreases

(Ratnayake et al., 1978; Cooper, 1984). Temperature, soil moisture and light are other environmental factors that can aﬀect the establishment of AM (Hetrick, 1984;

Smith and Read, 1997). beneﬁts

Increased nutrient uptake is the primary plant beneﬁt of AM (Abbott and Robson,

1984; Cooper, 1984; Bolan, 1991; Smith and Read, 1997). Phosphorus is the principle plant nutrient that increases with AM, but other diﬀusion-limited nutrients such

138 as zinc, copper and nitrogen (ammonium form) can also be enhanced (Abbott and

Robson, 1984; Cooper, 1984; Smith and Read, 1997). Additionally, AM can access organic forms of nitrogen that may otherwise be unavailable to the plant (Ames et al.,

1983; Michelsen et al., 1998; Hawkins et al., 2000; M¨ader et al., 2000; Hodge et al.,

2001; Govindarajulu et al., 2005). There are several mechanisms through which AM enhance nutrient uptake.

First, the fungal hyphae exploit a larger volume of soil. Fungal hyphae extend beyond the nutrient depletion zone and utilize nutrient sources much farther away from the root surface (Rhodes and Gerdemann, 1975; Abbott and Robson, 1984;

Cooper, 1984; Bolan, 1991; Li et al., 1991). The smaller diameter hyphae can also access pore spaces inaccessible to thicker plant roots (Bolan, 1991). The smaller diameter also increases the surface area available for nutrient absorption (Bolan,

1991).

Another mechanism may be a faster rate of nutrient transfer into the plant root.

Sanders and Tinker (1973) observed a P-inﬂow rate into mycorrhizal roots six times faster than for non-mycorrhizal roots. The faster transfer could be due to a higher aﬃnity for nutrient absorption (Bolan, 1991). Fungal hyphae may also have a lower threshold concentration for nutrient absorption, so that mycorrhizal plants can absorb nutrients at lower soil solution concentrations than non-mycorrhizal plants (Bolan,

1991).

Increased P uptake may also occur through utilization of insoluble or non-labile

P sources (Abbott and Robson, 1984; Cooper, 1984; Bolan, 1991; Smith and Read,

1997). Bolan et al. (1987) found that mycorrhizal plants beneﬁted more from application of non-soluble iron phosphate than did non-mycorrhizal plants. AM fungi have

139 been suggested to increase availability of non-labile P sources through exudation of organic acids (solubilize insoluble inorganic P) or phosphatases (hydrolyze non-labile organic P); however, the ability of AM to exploit less available P sources remains to be convincingly demonstrated (Cooper, 1984; Bolan, 1991; Smith and Read, 1997).

Other AM benefits include increased resistance to root pathogens (Borowicz, 2001) and drought tolerance (Safir et al., 1971; Cooper, 1984; Smith and Read, 1997; Augé,

2001) for the plant host, and improved soil structure through soil particle aggregation

(Tisdall, 1991; Beare et al., 1997; Wright and Upadhyaya, 1998)

There is still much to understand about mycorrhizae in the aquatic ecosystem.

Three literature reviews have been published, most recently in 2004, each of which summarized the current status of the science regarding mycorrhizae in aquatic ecosystems (Khan and Belik, 1995; Mukerji and Mandeep, 1998; Khan, 2004). In this chapter, we expand on these reviews, first presenting a comprehensive list of studies documenting mycorrhizae in aquatic habitats; and then a list by hydrophytic species for recorded mycorrhizal status. Added to the latter list will be results from an independent study of mycorrhizal presence in 10 created and 5 natural wetlands of central Ohio. Finally, we review the recent literature on the ecology of mycorrhizae in wetlands and other aquatic ecosystems. We focus particularly on the arbuscular mycorrhizae (AM), but with some attention to ecto-, arbutoid-, ericoid-, and orchid- mycorrhizal associations (EM, AB, ER, and OR, respectively). We define “aquatic ecosystem” to encompass habitats which are flooded or waterlogged, either periodically (e.g., floodplain, tidal marsh, vernal pool) or permanently (e.g., lake, stream, bog).

140 5.2 Habit of mycorrhizae in aquatic systems

The AM fungi (AMF) are obligate aerobes and consequently, plants growing in anoxic aquatic habitats (i.e., hydrophytes) have been assumed to be nonmycorrhizal

(Harley, 1969; Khan, 1974; Powell, 1975). As early as 1927, however, the presence of

AM was reported in a peat bog in Alberta, Canada (Rayner, 1927) and not long after in a salt marsh in Wales (Mason, 1928). Over the past few decades, the literature on mycorrhizal hydrophytes has grown and occurrence of AM in aquatic ecosystems is becoming well documented (Table 5.1; see also Khan and Belik, 1995). AM now have been observed in almost every type of aquatic ecosystem: fens (e.g., Turner et al.,

2000; Weishampel and Bedford, 2006), wet prairies (e.g., K¨uhn, 1991; Turner et al.,

2000), freshwater marshes (e.g., Mejstrik, 1972; Fontenla et al., 2001; Bauer et al.,

2003), salt marshes (e.g., Rozema et al., 1986; Hildebrandt et al., 2001), freshwater swamps (e.g., Cooke and Lefor, 1998; Stevens et al., 2010), mangrove swamps (e.g.,

Sengupta and Chaudhuri, 2002; Kumar and Ghose, 2008), bogs (e.g., Malloch and

Malloch, 1982; Thormann et al., 1999), lakes (e.g., Clayton and Bagyaraj, 1984;

Ragupathy et al., 1990), and streams (e.g., Beck-Nielsen and Madsen, 2001; de Marins et al., 2009). In fact, among 97 studies conducted in aquatic habitats, only 5 failed to detect mycorrhizae (Khan, 1974; V¨are et al., 1992; Nielsen et al., 1999; Agwa and

Al-Sodany, 2003; Gouraud et al., 2008; Table 5.1).

141 Reference Locale Habitat AM SE O NM Total Season Description

1Agwa and Al-Sodany, 2003 west coast, Egypt coastal saline de- 0 0 3 3 spring survey of 26 spp in Mediterranean coastal pression plain and inland plateau (only 3 spp from wetland habitat) 2Anderson et al., 1984 Illinois, USA mesic prairie to 3 0 8 11 summer study of AM along nutrient-moisture gradient emergent aquatic 3Aziz et al., 1995 Florida, USA glade 42 0 1 43 May,Nov,Decsurvey of AM in restored everglade (began 1 y after full and partial soil removals) and one mature site 4Bagyaraj et al., 1979 Bangalore, India eutrophic lake 3 0 9 12 Oct–Nov survey of 12 lake spp 5Bajwa et al., 2001 Pakistan aquatic and sub- 8 0 0 8 all year seasonal study of AM in 8 spp aquatic 6Bauer et al., 2003 Indiana, USA marsh 21 0 0 21 Jun,Aug survey of AM along hydrologic gradient in 2 restored (5 y) and 1 natural marsh 7Beck-Nielsen and Madsen, 2001 Mid-Jutland, Den- lake and stream 9 0 36 45 summer survey of lake and stream spp mark 8Béreau and Garbaye, 1994 Sinnamary, French coastal flat 1 0 0 1 all year survey of tropical rain forest (only report spp Guyana from salt flat) 9Bledsoe et al., 1990 Devon and wet meadow, wet 2 10 1 20 23 Jun–Jul summer survey of spp in arctic lowlands (only Ellesmere Islands, tundra and salt report wetland habitat); no arbuscules or vesicles observed Canada marsh 10 142 Bohrer et al., 2004 Ohio, USA fen and marsh 17 0 0 17 Mar–Sep survey of dominant vegetation in 2 fens and 2 marshes, along hydrologic gradient 11Brown and Bledsoe, 1996 California, USA tidal salt marsh 1 0 0 1 all year study of spatial and temporal dynamics for AM of Jaumea carnosa 12Burni et al., 2007 Kohat District, marsh 1 0 0 1 ? survey of AM in Typha elephantina Pakistan 13Burni et al., 2008 Kohat District, marsh 6 0 1 7 ? survey of angiosperms in waterlogged habitat Pakistan 14Carvaca et al., 2005a southeast Spain salt marsh 8 0 0 8 Dec study of 8 halophytes rhizosphere effect 15Carvaca et al., 2005b southeast Spain salt marsh 1 0 0 1 ? study of Inula crithmoides along a salinity gradient 16Carvalho et al., 2001 Portugal estuary 3 0 4 7 all year 17Chaubal et al., 1982 Shillong, India lake, stream and 16 0 15 31 ? survey of AM in aquatic subtropical plant marsh communities 18Clayton and Bagyaraj, 1984 New Zealand lake 22 0 9 31 ? survey of 31 submerged aquatic spp

Continued on Next Page. . .

Table 5.1: Literature surveys of root endophytes in aquatic systems: number of surveyed plant species with arbuscular mycorrhizae (AM ); (dark) sepatate endophytes (SE ); ectomycorrhizae or ericaceous mycorrhizae (O); no mycorrhizae (NM ). Also indicated is the locale, habitat and season in which the sampling took place; the total number of plant species sampled and a brief description of the general study. Reference superscripts are for citation referral with Table 5.4. Table 5.1 – Continued

Reference Locale Habitat AM SE O NM Total Season Description

19Confer and Niering, 1992 Connecticut, USA created and natural 3 0 0 3 Jun–Jul sampled Typha spp. and Phragmites spp. in marsh created (3 y to 4 y) and natural wetlands 20Cooke and Lefor, 1990 Connecticut, USA salt marsh 5 0 2 7 Jun–Oct survey of AM in restored (8 y) and natural salt marsh 21Cooke and Lefor, 1998 Connecticut, USA herbaceous, shrub, 95 0 7 102 May–Nov surveyed several wetlands, some disturbed and forested wet- (i.e., drainage or retention basins) land 22Corkidi and Rincón, 1997 Veracruz, Mexico wet dune slack 10 0 0 10 Apr–Oct survey of dune succession (10 spp from wetland habitat) 23Cornwell et al., 2001 New York, USA fen 11 0 7 18 Jul survey of AM in nutrient-poor fen 24de Marins et al., 2009 Upper Parana river floodplain 9 18 0 15 24 Jan survey of 24 spp during summer at peak River, Brazil flooding 25Dhillion, 1993 Norway and USA mire, marsh, lake 9 0 0 9 Jun–Jul survey of 10 Equisetum spp., including one and river with 3 subspecies (note: 2 of the species are now considered the same) 26Dhillion, 1994 Hedmark province, fen 5 550 5 Jul survey of alpine and boreal Salix spp. (only Norway report wetland species) 27Dowding, 1959 Alberta, Canada swamp 4 0 0 4 ? study of AMF (Endogone spp.) in swamp

143 28Farmer, 1985 Scotland loch 3 0 3 6 ? survey of 6 isoetides 29Fernández et al., 2008 Patagonia, Ar- peat bog 1 1 0 0 1 fall sampled Lycopodium paniculatum in a peat gentina bog and temperate forest 30Fontenla et al., 2001 Patagonia, Ar- marsh 40 0 19 59 all year survey of patagonian steppe and marsh spp gentina 31Fuchs and Haselwandter, 2004 Salzburg, Austria peat bog and fen 6 6 0 0 6 spring, survey of 4 red list spp (plus 2 additional spp meadow fall for comparison) 32Garc´ıa and Mendoza, 2008 pampas, Argentina wet prairie 3 0 0 3 all year survey of Lotus tenuis, Paspalum vaginatum, and Stenotaphrum secundatum in lowland and upland of the Pampas 33Grigera and Oesterheld, 2004 pampas, Argentina wet prairie 1 0 0 1 Jul–Jan study of topography and grazing effects on AM in Paspalum dilatatum 34Gouraud et al., 2008 Camargue, France marsh 0 0 1 1 Sep survey of Schoenoplectus maritimus 35Hashimoto and Higuchi, 2003 Hokkaido Prefec- river floodplain 2 2 0 2 Jun–Sep survey of Chosenia arbutifolia and Salix sacha- ture, Japan linensis under 5 different soil conditions (e.g., muddy, sandy, dry) 36Hildebrandt et al., 2001 northern Nether- coastal and inland 20 0 0 20 Apr–Nov survey of halophytes near the North and lands and Germany salt marsh Baltic Seas 37Hodson et al., 2009 western and arctic tundra (pond mar- 4 4 2 0 4 summer survey of Equiseteum spp Canada gin, floodplain)

Continued on Next Page. . . Table 5.1 – Continued

Reference Locale Habitat AM SE O NM Total Season Description

38Hoefnagels et al., 1993 North Carolina, salt marsh 5 0 1 6 Aug USA 39Hussain et al., 1995 Islamabad and stream, lake and 14 0 0 14 ? survey of 14 hydrophytes Rawlapindi, Pak- wetland istan 40Ingham and Wilson, 1999 Oregon, USA prairie wetland 6 0 0 6 May,Aug study of 6 native spp in 5 sites with diﬀerent disturbance histories 41Johnson-Green et al., 1995 west-central Mani- salt ﬂat 15 0 1 16 May–Oct study of distribution and phenology along a toba, Canada salinity gradient 42Kai and Zhiwei, 2006 southwest China lakes and streams 7 4 0 26 33 summer 43Khan, 1974 Pakistan marsh, swamp, 0 0 18 18 Jul–Aug survey of 18 hydrophytes (also 52 xerophytes pond and wet ditch and 21 halophytes) 44Khan, 1993 New South Wales, riverine and swamp 3 3 0 3 all year survey of 3 aquatic trees Australia 45Khan, 2004 Sydney, Australia 6 0 7 13 (citing Belik and Khan 1992, 1993) 46Kohn and Stasovski, 1990 Alexandra Fiord, wet sedge arctic 0 1 2 1 3 Jul–Aug survey of 24 spp from xerix, mesic and wet Canada meadow arctic sites (only 3 from wet habitat) 47

144 Koske et al., 1992 Kauai, Hawaii bog 2 0 1 3 ? survey of 147 Hawaiian angiosperms, but only 3 spp in wetland (bog) habitat 48K¨uhn, 1991 Marburg, Germany wet meadow 29 16 0 13 42 ? survey of plants of moist meadow used in agriculture (author noted Rhizoctonia spp., so considered that SE) 49Kumar and Ghose, 2008 Sundarbans, India mangrove swamp 15 0 1 16 Sep–Feb survey of mangrove spp and associates 50Landwehr et al., 2002 Hungarian Steppe, salt marsh 9 0 1 10 Apr–Oct survey of spp in saline, sodic and gypsum soils Hungary 51Laursen et al., 1997 Macquarie Island, mire, bog, grass- 18 21 1 21 40 ? survey of subantarctic spp Australia land 52Likar et al., 2009 Slovenia degraded peat bog 5 3 0 0 5 May–Oct seasonal study of AM and SE in 3 spp; but also noted AM in 2 other spp 53Malloch and Malloch, 1981 Ontario, Canada black spruce swamp1 40 5 Jul survey of 29 boreal spp; only 5 were likely from black spruce swamp habitat (based on sample location and WIS) 54Malloch and Malloch, 1982 Ontario, Canada bog and wet forest 4 2 1 7 Aug survey of 31 boreal spp (only 7 from wetland habitat) 55Maremmani et al., 2003 Tuscany, Italy backdune depres- 13 1 6 20 ? survey of 82 spp from coastal Mediterranean sion plant communities (only some from wetland habitat) 56Mason, 1928 Borth and Tal- salt marsh 8 3 0 5 13 Jul–Aug sanau 57McDougall and Glasgow, 1929 Illinois, USA river ﬂoodplain 5 0 1 6 fall survey of 33 Compositae spp (only 6 from wetland habitat) 58Mejstrik, 1972 Bohemia, Czech marsh 46 0 9 55 Apr–Sep survey of molinietum coeruleae association Republic

Continued on Next Page. . . Table 5.1 – Continued

Reference Locale Habitat AM SE O NM Total Season Description

59Miller, 2000 South Carolina, Carolina bay 2 0 0 2 Aug survey of Panicum hemitomon and Leersia USA hexandra along hydrologic gradient and seasonal gradient 60Miller et al., 1999 Illinois, USA prairie, savanna, 16 3 0 6 23 Jul survey of Carex spp. and wetland 61Nielsen et al., 2004 southern Sweden softwater lake 4 0 1 5 ? survey of Littorella uniflora and Lobelia dortmanna 62Nielsen et al., 1999 Roskilde Fjord, fjord and lagoon 0 0 2 2 May,Jun,Sep survey of AM in Zostera marina and Thalassia Denmark; Chin- testudinum coteague Bay, USA; Laguna de Terminos, Mexico 63Pietikäinen et al., 2005 Kilpisjarvi, Finland alpine meadow 4 40 0 4 Jul study of AM and seedling establishment in alpine meadow 64Poole and Sylvia, 1990 Florida, USA swamp and bog 1 1 0 1 Apr–Jan study of AM in Myrica cerifera 65Powell, 1975 New Zealand sea coast, forest, 36 0 61 97 ? survey of Juncaceae and Cyperaceae spp grassland, fell field, and herbfield

145 66Radhika and Rodrigues, 2007 Goa, India marsh 15 0 5 20 Jul–Sep 67Ragupathy et al., 1990 Tamil Nadu, India eutrophic lake and 33 0 37 70 ? survey of 70 tropical hydrophytes wetland 68Ray and Inouye, 2006 Idaho, USA constructed marsh 1 0 0 1 Jul–Sep survey of Typha latifolia over ﬂooding/drying cycles 69Read et al., 1976 England eutrophic marsh 3 0 3 6 growing survey of major vegetation types of England season 70Reddell et al., 1997 north Queensland, stream 1 0 0 1 Dec–May study of AM, EM and cluster roots in Casua- Australia rina cunninghamiana during the wet season 71Rickerl et al., 1994 South Dakota, semipermanent 6 0 2 8 Jul study of AM of wetland spp in 2 dry (water USA wetland below soil surface) and 2 wet (+ 10 cm water) zones of 2 wetlands 72Rozema et al., 1986 The Netherlands salt marsh 10 0 8 18 May 73Saint-Etienne et al., 2006 Guadeloupe, coastal swamp 1 0 0 1 wet/dry survey of AM in Pterocarpus oﬃcinalis along France a salinity gradient 74Sengupta and Chaudhuri, 1989 West Bengal, India mangrove swamp 1 00 1? survey of AM in Suaeda maritima 75Sengupta and Chaudhuri, 1990 West Bengal, India salt marsh 4 0 0 4 Nov–Feb 76Sengupta and Chaudhuri, 2002 Sundarbans, India mangrove swamp 51 0 2 53 Feb–Apr survey of mangrove spp and associates 77Søndergaard and Laegaard, Jutland, Denmark oligotrophic lake 5 0 2 7 Jan–Mar survey of 7 aquatic/wetland spp 1977 78Sraj-Krˇziˇcetˇ al., 2006 SW Slovenia intermittent stream 8 8 0 0 8 wet/dry study of 8 amphibious spp: sampled all sub- and lake mergent form, then 4 also in emergent form (1˜ mo dry conditions)

Continued on Next Page. . . Table 5.1 – Continued

Reference Locale Habitat AM SE O NM Total Season Description

79Sraj-Krˇziˇcetˇ al., 2009 SW Slovenia intermittent 7 0 0 7 summer survey of submergent and emergent forms of aquatic habitat 7 amphibious spp 80Stenlund and Charvat, 1994 Minnesota, USA floating wetland 3 0 0 3 May–Nov survey of AM in Typha spp. mat 81Stevens et al., 2010 Texas, USA bottomland forest 35 30 0 2 37 Oct survey of AM and SE in colonizing species following a 100 y flood 82Tawaraya et al., 2003 central Kaliman- peat swamp forest 17 0 5 22 Sep–Nov tan, Indonesia 83Thoen, 1987 Cap-Vert, Senegal brackish pond and 7 0 13 20 Mar–Apr survey of 69 spp in the Lake Retba region of marsh Senegal during the dry season (only 20 spp from wetland habitat) 84Thormann et al., 1999 Alberta, Canada bog, fen and marsh 3 19 11 14 25 Jul survey of dominant vascular plants along bog- fen-marsh nutrient gradient 85Torti et al., 1997 Darien, Panama swamp 1 0 0 1 Jun survey of AM and EM in Mora excelsa and Prioria copaifera (only P. copaifera wetland spp) 86Treu et al., 1996 Alaska, USA alpine riparian and 2 11 12 23 37 Aug survey of alpine spp in interior Alaska (may meadow not all be wetland habitat) 87Turner et al., 2000 Ohio, USA prairie fen and wet 18 0 2 20 May–Jun survey of AM in groundwater fed, mineral- 146 meadow rich, nutrient-poor fens (including one 20 y restored site) during peak growing season 88van der Heijden and Vosatka, Wadden Isles, wet dune slack 1 1 0 1 Apr–Oct study of AM and EM in Salix repens occurring 1999 Netherlands in coastal dunes ranging from dry to wet and calcareous to acidic 89van Duin et al., 1989 The Netherlands salt marsh 13 0 2 15 all year 90Van Hoewyk et al., 2001 New York, USA calcareous fen 2 0 1 3 Aug sampled 3 native spp, but studied primarily Dasiphora fruticosa 91Väre et al., 1992 Spitsbergen, Nor- water-filled depres- 0 3 0 8 8 Jun–Aug survey of mycorrhizas for 76 native species way sion and wet habi- (only 8 from wetland habitat) tat 92Wang et al., 2004 Shangdong river delta 5 0 0 5 May survey of wild plants in saline-alkali soils of Province, China the Yellow River Delta 93Weishampel and Bedford, 2006 New York, USA calcareous fen 56 54 5 7 67 Jul–Aug 94Wetzel and van der Valk, 1996 Iowa and North prairie pothole 22 0 0 22 Jun,Aug comparison of AM species in potholes of IA Dakota, USA and ND 95Whitbeck, 2001 La Selva, Costa tropical wet forest 1 0 0 1 Jul–Aug study of light effects on AM in Inga leioca- Rica lycina 96Wigand et al., 1998 Jutland, Denmark oligotrophic lake 2 0 0 2 ? study of AM in Littorella uniflora and Isoetes lacustris along hydrologic gradient 97Wigand and Stevenson, 1994 Maryland, USA flat 1 0 0 1 Jul study of Vallisneria americana In our own study of AM in 10 created and 5 natural marshes of central Ohio,

USA (for details see Ch. 6), we observed at least one incidence of AM for 50 out

of 71 wetland plant species (Table 5.2). Most species were sampled multiple times

(e.g., early and late summer over two years) at multiple locations. Of the 50 species

observed to be mycorrhizal, 19 are ﬁrst records for AM—but only 12 are of merit: 2

species (i.e., Juncus acuminatus and Potamogeton foliosus) had infection rates less

than 0.01 when AM were observed; and 4 species accounts are based on only one

sample (i.e., Bidens aristosa, Cirsium muticum, Lycopus virginicus and Polygonum punctatum). For the single specimen of Polygonum punctatum, we observed an AM infection rate of 0.1, but no arbuscules or hyphal coils; there were two literature records for this species (Cooke and Lefor, 1998; de Marins et al., 2009), neither of which observed any AM. The last record in question is for Najas minor, which for 1 of

6 samples analyzed had AM-like structures covering 10 % of the root; this species had also been previously recorded as non-mycorrhizal (Radhika and Rodrigues, 2007).

147 Species AM A HC SE EM n Alisma subcordatum† 0–0.6 0–0.1 + 22 Ammannia robusta 0–0.1 0 0 0–0.1 0 3 Asclepias incarnata 0–0.1 0 0 0–0.1 0 3 Bidens aristosa†∗ 0.7 0 - 1 Bidens cernua†∗∗ 0–0.6 0–0.3 + 19 Bidens discoidea†∗∗ 0–0.3 0–0.2 + 4 Bidens frondosa† 0.1–0.7 0–0.3 + 12 Bidens tripartita† 0–0.7 0–0.2 + 4 Bidens vulgata†∗∗ 0.1–0.4 0–0.1 + 2 Butomus umbellatus 0 0 0 004 Carex comosa 0 0 0 002 Carex frankii 0 0 0 002 Carex lupuliformis 0–0.5 0 0 0–0.5 0 2 Carex scoparia 0 0 0 0–0.1 0 2 Carex tribuloides 0 0 0 001 Carex vulpinoidea 0 0 0 0–0.1 0 3 Cephanlanthus occidentalis 0–0.3 0–0.1 0–0.1 0 0 3 Cicuta bulbifera∗∗ 0–0.1 0–0.1 0 0–0.7 0 3 Cirsium muticum∗ 0.7–1 0–0.1 0.1–0.3 0 0 1 Cyperus erythrorhizos 0 0 0 0–0.7 0 2 Cyperus esculentus 0 0 0 0–0.5 0 5 Cyperus odoratus 0 0 0 0–0.1 0 2 Decodon verticillatus 0 0 0 0.3–0.5 0 3 Echinochloa spp.† 0–0.7 0–0.4 + 26 Eleocharis obtusa†∗∗ 0–0.7 0–0.1 + 36 Eleocharis palustris† 0–0.6 0 + 26 Epilobium ciliatum 0.1–0.3 0.1–0.3 0–0.1 0 0 2 Eragrostis hypnoides∗∗ 0.7–1 0–0.3 0.3–0.5 0.1–0.5 0 2 Fraxinus spp. 0.5–0.7 0.1–0.3 0.3–0.5 0 0 1 Galium spp.† 0–0.6 0–0.2 + 20 Juncus acuminatus†∗ 00 + 4 Juncus eﬀusus† 0–0.4 0–0.1 + 23 Juncus torreyi † 0–0.4 0 + 2 Leersia oryzoides† 0–0.6 0–0.2 + 52 Lindernia dubia 0.1–0.7 0–0.7 0–0.7 0 0 5 Ludwigia palustris† 0–0.6 0–0.3 + 16 Ludwigia polycarpa 0 0 0 002

Continued on Next Page. . .

Table 5.2: New results from a survey of root endophytes in 15 created and natural freshwater marshes of central Ohio, USA: AM, total arbuscular mycorrhizae; A, arubscules; HC, hyphal coils; SE, (dark) sepatate endophytes; EM, ectomycorrhizae. Numbers provided indicate range of proportional root length colonized. Also provided is the total number of specimens examined (n). Stained roots were examined at 400 magniﬁcation: species denoted with †were scored by root-intersection and are presented in more× detail in Ch. 6; remaining species were scored by visual estimation of AM infection category (i.e., 0, 0.00–0.05, 0.06–0.25, 0.26–0.50, 0.51–0.75, or 0.76–1). [∗new record; ∗∗new record of merit] 148 Table 5.2 – Continued

Species AM A HC SE EM n Lycopus americanus 0.7–1 0.5–0.7 0.5–0.7 0.3–0.5 0 3 Lycopus uniflorus 0–1 0–0.5 0–0.5 0.1–0.5 0 4 Lycopus virginicus∗ 0.1–0.3 0–0.1 0–0.1 0 0 1 Lysimachia nummularia 0.3–0.5 0.3–0.5 0.3–0.5 0 0 2 Lythrum alatum 0 0 0 003 Mimulus ringens 0 0 0 003 Najas minor †∗ 0–0.2 0 + 6 Nuphar lutea 0 0 0 003 Onoclea sensibilis 0–0.5 0–0.5 0–0.5 0 0 2 Panicum dichotomiflorum∗∗ 0.3–0.7 0–0.7 0.1–0.7 0.1–0.5 0 4 Penthorum sedoides 0 0 0 0.1–0.3 0 5 Phalaris arundinacea† 0–0.4 0–0.2 + 40 Phyla lanceolata∗∗ 0.7–1 0.5–0.7 0.1–0.3 0 0 2 Pilea fontana∗∗ 0.3–0.7 0–0.3 0–0.3 0 0 2 Polygonum amphibium† 0–0.6 0 + 28 Polygonum hydropiper † 0.1 0 - 1 Polygonum hydropiperoides† 0–0.7 0 + 28 Polygonum lapathifolium† 0–0.4 0 + 4 Polygonum pensylvanicum†∗∗ 0–0.6 0–0.1 + 16 Polygonum persicaria† 0–0.1 0 + 4 Polygonum punctatum†∗ 0.1 0 + 1 Polygonum sagittatum†∗∗ 0.1–0.4 0 - 4 Populus deltoides 0.1–1 0–0.3 0–0.5 0 0 3 Potamogeton foliosus†∗ 00 - 7 Potamogeton nodosus† 0 0 + 11 Ranunculus sceleratus 0–0.1 0 0 0–0.3 0 2 Rotala ramosior 1 Rumex verticillatus 0 0 0 0.3–0.5 0 2 Sagittaria latifolia† 0–0.2 0 + 6 Salix exigua 1 Salix nigra 0–0.7 0–0.3 0 0–0.1 0.7–1 3 Schoenoplectus tabernaemontani 0–0.1 0–0.1 0 0–1 0 7 Scirpus cyperinus 0 0 0 0–0.1 0 3 Scirpus fluviatilis 0–0.1 0 0 0–0.5 0 4 Scirpus georgianus 1 Scutellaria lateriflora∗∗ 0–0.1 0–0.1 0 0–0.1 0 2 Sparganium eurycarpum 0 0 0 003 Symphyotrichum puniceum 0 0 0 0.3–0.5 0 2 Symphyotrichum racemosum 2 Symplocarpus foetidus 0 0 0 0–0.1 0 2 Typha angustifolia† 0–0.4 0 + 9 Typha latifolia† 0–0.3 0 + 54 Veronica scutellata 0 0 0 002

149 The remaining observations are in general agreement with the existing literature. Species such as Bidens frondosa, B. tripartita, Populus deltoides seem to be consistently mycorrhizal with high rates of infection. Other species appear to be frequently mycorrhizal with low to moderate infection (e.g., Alisma subcordatum, Jun- cus eﬀusus, Leersia oryzoides, Ludwigia palustris, Phalaris arundinacea, Polygonum amphibium, P. hydropiperoides); occasionally mycorrhizal with low infection (e.g.,

Eleocharis palustris, Typha latifolia) or never mycorrhizal (e.g., Butomus umbellatus,

Cyperus esculentus, Nuphar lutea).

5.2.1 Status by species and genus

Mycorrhizal status for 952 hydrophytes is compiled in Table 5.4 based on data from the literature (Table 5.1) and this study. We attempted to use the most current naming convention for species; generally as noted in USDA, ARS, National Genetic

Resources Program, but referring to other reputable databases or literature sources as needed and when available. Alternate names used in the literature are also noted in the table. Mycorrhizal status according to four earlier compilations is provided when available: the earliest comes from Harley and Harley (1987a,b), who consolidated mycorrhizal status for British ﬂora; and the most recent, from Wang and Qiu

(2006) who expanded the list by Harley and Harley to encompass 3,617 land plants.

The collation by Khan and Belik (1995) is speciﬁc for aquatic plants and that by

Muthukumar et al. (2004) for sedges.

Of the 952 hydrophytic species, 717 had at least on record of mycorrhizal infection— or, 75 %. At the level of genus, 84 % (367 out of 439) had at least one record of mycorrhizae. Additionally, the list spans 112 families, 90 % having at least on incidence of

150 mycorrhizae, and 42 orders, with 93 % having at least one occurrence of mycorrhizae.

Table 5.3 summarizes mycorrhizal presence by family and order. Naming conventions

for angiosperm families and orders were based on the Angiosperm Phylogeny Group

III system through an on-line database (Stevens); this database was also referenced

for gymnosperm and pteridophyte nomenclature.

5.2.2 Status by family and order

Mycorrhizal distribution by family is in general agreement with a list amassed by Newman and Reddell (1987) for vascular plant families. Families with commonly mycorrhizal species include Apiaceae, Asteraceae, Betulaceae, Dipterocarpaceae, Er- icaceae, Euphorbiaceae, Fabaceae, Lamiaceae, Myrtaceae, Poaceae, Polygonaceae,

Ranunculaceae, Rosaceae, Rubiaceae, Salicaceae, and Scrophulariaceae . About half of the recorded species in families Amaranthaceae, Caryophyllaceae and Juncaceae were found to be mycorrhizal. One inconsistency between these data and Newman and Reddell (1987) was for Cyperaceae, which we found to be 63 % mycorrhizal out of 133 species, while Newman and Reddell (1987) reported 26 % mycorrhizal for 123 species. Brundrett (2002) also reports this family as being predominantly non-mycorrhizal (NM), along with Amaranthaceae, Brassicaceae, Caryophyllaceae,

Commelinaceae, Juncaceae and Polygonaceae. Predominance of NM for Brassicaceae is in agreement with Newman and Reddell (1987) and this compilation; however, the other reportedly NM families were found to be equally or more often mycorrhizal in

Newman and Reddell (1987) and this compilation (Commelinaceae is the exception, which had too few records to evaluate). Additionally, our compiled list adds 69 families, with at least one report of AM species, to the list published by Newman and

151 Reddell (1987). The more noteworthy additions include Hydrocharitaceae (67 % of

18), Onagraceae (71 % of 17), and Potamogetonaceae (41 % of 17); other new family

distributions were based on 14 or fewer species.

Brundrett (2002) went one taxonomic level higher, specifying mycorrhizal status by order, in his treatise on the coevolution of land plants and mycorrhizal fungi.

Orders observed to be predominantly mycorrhizal (e.g., incidence 80 %; Brun- ≥ drett, 2002 and this report) include Apiales, Asterales, Ericales, Fabales, Gentianales,

Malpighiales, and Rosales. Frequently mycorrhizal orders (e.g., incidence 60 % ≥ and < 80 %) include Brassicales, Lamiales, Myrtales, Ranunculales and Saxifragales;

while orders such as Alismatales appear to be occassionally mycorrhizal (e.g., inci-

dence 40 % and < 60 %). Discrepancies between this report and Brundrett (2002) ≥ include Caryophyllales and Poales. Brundrett (2002) listed Caryophyllales as ‘many

NM,’ however, the report observed an incidence rate of 71 % for 78 species (i.e., fre-

quently mycorrhizal). Its subordinate families Amaranthaceae, Caryophyllaceae and

Polygonaceae had also been noted earlier as having greater mycorrhizal prevalance

than previously speciﬁed (Newman and Reddell, 1987; Brundrett, 2002). Brundrett

(2002) also listed Poales as ‘many NM,’ but this report ﬁnds it to be frequently

mycorrhizal (72 % of 281); particularly in its subordinate families Cyperaceae and

Juncaceae, which we also found to have higher incidence of mycorrhizae than in earlier

reports (Newman and Reddell, 1987; Brundrett, 2002).

152 Mycorrhizae Classiﬁcation AM EM AB ER OR NM %M Ref Dicots Apiales 18 0 0 0 0 3 86(21) Most AM Apiaceae 18 0 0 0 0 2 90(20) 75(24) Araliaceae 0 0 0 0 0 1 0(1) Aquifoliales 1 0 0 0 0 0 100(1) Most AM Aquifoliaceae 1 0 0 0 0 0 100(1) Asterales 72 0 0 0 0 7 91(79) Most AM Asteraceae (Compositae) 63 0 0 0 0 5 93(68) 93(204) Calyceraceae 1 0 0 0 0 0 100(1) Campanulaceae (Lobeliaceae) 7 0 0 0 0 0 100(7) Menyanthaceae 1 0 0 0 0 2 33(3) Brassicales 7 0 0 0 0 10 41(17) Some NM Brassicaceae (Cruciferae) 7 0 0 0 0 10 41(17) 13(63) Caryophyllales 55 2 0 0 0 23 71(78) Many NM Aizoaceae 1 0 0 0 0 0 100(1) Amaranthaceae 14 0 0 0 0 12 54(26) 39(46) Caryophyllaceae 5 0 0 0 0 4 56(9) 50(28) Droseraceae 4 0 0 0 0 1 80(5) Frankeniaceae 1 0 0 0 0 0 100(1) Molluginaceae 1 0 0 0 0 0 100(1) Plumbaginaceae 6 0 0 0 0 0 100(6) Polygonaceae 21 2 0 0 0 5 81(26) 63(30) Portulacaceae 0 0 0 0 0 1 0(1) Tamaricaceae 2 0 0 0 0 0 100(2) Ceratophyllales 0 0 0 0 0 1 0(1) Many NM Ceratophyllaceae 0 0 0 0 0 1 0(1) Cornales 2 0 0 0 0 0 100(2) Most AM Cornaceae 2 0 0 0 0 0 100(2) Cucurbitales 2 0 0 0 0 0 100(2) Most AM Cucurbitaceae 2 0 0 0 0 0 100(2)

Continued on Next Page. . .

Table 5.3: Mycorrhizal observations in hydrophytes by order and family: AM = arbuscular mycorrhizae, EM = ectomycorrhizae, AB = arbutoid mycorrhizae, ER = ericoid mycorrhizae, OR = orchid mycorrhizae; a species was counted in each category with at least one record of occurrence. Species without any record of mycorrhizae were counted as nonmycorrhizal (NM). Percent mycorrhizal species is also provided, with the total number of species evaluated in parentheses (M%). Statistics per order are in italic font, and statistics per family are in normal font. Families are listed alphabetically by order and by division. For reference, mycorrhizal status by order as appearing in Brundrett (2002) is provided for most orders; and for several families, percent mycorrhizal species as appearing in Newman and Reddell (1987) is provided.

153 Table 5.3 – Continued Mycorrhizae Classiﬁcation AM EM AB ER OR NM %M Ref Dipsacales 3 0 0 0 0 1 75(4) Most AM Caprifoliaceae 3 0 0 0 0 1 75(4) Ericales 14 0 1 12 0 1 96(27) AM,EM,ER Balsaminaceae 4 0 0 0 0 0 100(4) Ericaceae 0 0 1 12 0 1 92(13) 100(81) Primulaceae 8 0 0 0 0 0 100(8) Sapotaceae 1 0 0 0 0 0 100(1) Tetrameristaceae 1 0 0 0 0 0 100(1) Fabales 20 1 0 0 0 6 77(26) Some EM Fabaceae 19 1 0 0 0 5 79(24) 96(202) Polygalaceae 1 0 0 0 0 1 50(2) Fagales 6 5 0 0 0 0 100(7) Many EM Betulaceae 4 4 0 0 0 0 100(5) 100(27) Casuarinaceae 1 1 0 0 0 0 100(1) Myricaceae 1 0 0 0 0 0 100(1) Gentianales 16 0 0 0 0 3 84(19) Most AM Apocynaceae 5 0 0 0 0 1 83(6) Gentianaceae 2 0 0 0 0 1 67(3) Loganiaceae 1 0 0 0 0 0 100(1) Rubiaceae 8 0 0 0 0 1 89(9) 90(39) Geraniales 1 0 0 0 0 0 100(1) Most AM Geraniaceae 1 0 0 0 0 0 100(1) Lamiales 58 0 0 0 0 16 78(74) Some NM Acanthaceae 3 0 0 0 0 2 60(5) Lamiaceae (Labiatae) 17 0 0 0 0 1 94(18) 94(30) Lentibulariaceae 1 0 0 0 0 1 50(2) Oleaceae 2 0 0 0 0 1 67(3) Plantaginaceae 8 0 0 0 0 2 80(10) Scrophulariaceae 19 0 0 0 0 9 68(28) 79(32) Verbenaceae 8 0 0 0 0 0 100(8) Laurales 1 0 0 0 0 0 100(1) Most AM Lauraceae 1 0 0 0 0 0 100(1)

Continued on Next Page. . .

154 Table 5.3 – Continued Mycorrhizae Classiﬁcation AM EM AB ER OR NM %M Ref Malpighiales 39 11 0 0 0 7 86(49) Some EM Clusiaceae 5 0 0 0 0 0 100(5) Elatinaceae 2 0 0 0 0 2 50(4) Euphorbiaceae 4 0 0 0 0 0 100(4) 90(49) Hypericaceae 1 0 0 0 0 0 100(1) Linaceae 1 0 0 0 0 0 100(1) Rhizophoraceae 7 0 0 0 0 1 88(8) Salicaceae 16 11 0 0 0 4 83(23) 100(47) Violaceae 3 0 0 0 0 0 100(3) Malvales 8 0 0 0 0 3 73(11) Some EM Cistaceae 0 0 0 0 0 1 0(1) Dipterocarpaceae 3 0 0 0 0 1 75(4) 100(51) Malvaceae 4 0 0 0 0 0 100(4) Thymelaeaceae 1 0 0 0 0 1 50(2) Myrtales 24 1 0 0 0 10 71(34) Some EM Lythraceae 10 0 0 0 0 4 71(14) Myrtaceae 2 1 0 0 0 1 67(3) 100(175) Onagraceae 12 0 0 0 0 5 71(17) Nymphaeales 1 0 0 0 0 6 14(7) Most AM Cabombaceae 0 0 0 0 0 1 0(1) Nymphaeaceae 1 0 0 0 0 5 17(6) Proteales 0 0 0 0 0 1 0(1) Many NM Nelumbonaceae 0 0 0 0 0 1 0(1) Ranunculales 16 0 0 0 0 7 70(23) Some NM Ranunculaceae 16 0 0 0 0 7 70(23) 89(35) Rosales 18 1 0 0 0 1 95(19) Some EM Moraceae 1 0 0 0 0 0 100(1) Rosaceae 15 1 0 0 0 1 94(16) 88(84) Urticaceae 2 0 0 0 0 0 100(2) Santalales 0 0 0 0 0 1 0(1) Many NM Santalaceae 0 0 0 0 0 1 0(1) Sapindales 8 0 0 0 0 0 100(8) Some EM Anacardiaceae 4 0 0 0 0 0 100(4) Meliaceae 3 0 0 0 0 0 100(3) Sapindaceae 1 0 0 0 0 0 100(1)

Continued on Next Page. . .

155 Table 5.3 – Continued Mycorrhizae Classiﬁcation AM EM AB ER OR NM %M Ref Saxifragales 10 0 0 0 0 6 62(16) Most AM Crassulaceae 1 0 0 0 0 2 33(3) Grossulariaceae 1 0 0 0 0 0 100(1) Haloragaceae 6 0 0 0 0 1 86(7) Saxifragaceae 2 0 0 0 0 3 40(5) Solanales 7 0 0 0 0 0 100(7) Most AM Convolvulaceae 2 0 0 0 0 0 100(2) Solanaceae 4 0 0 0 0 0 100(4) Sphenocleaceae 1 0 0 0 0 0 100(1) unplaced families Boraginaceae 4 0 0 0 0 1 80(5) Monocots Acorales 1 0 0 0 0 0 100(1) Most AM Acoraceae 1 0 0 0 0 0 100(1) Alismatales 37 0 0 0 0 28 57(65) Many NM Alismataceae 9 0 0 0 0 2 82(11) Aponogetonaceae 1 0 0 0 0 1 50(2) Araceae 6 0 0 0 0 4 60(10) Butomaceae 0 0 0 0 0 1 0(1) Hydrocharitaceae 12 0 0 0 0 6 67(18) Juncaginaceae 1 0 0 0 0 2 33(3) Potamogetonaceae 7 0 0 0 0 10 41(17) Ruppiaceae 1 0 0 0 0 1 50(2) Zosteraceae 0 0 0 0 0 1 0(1) Arecales 2 0 0 0 0 0 100(2) Most AM Arecaceae 2 0 0 0 0 0 100(2) Asparagales 3 0 0 0 3 1 86(7) AM,OR Iridaceae 3 0 0 0 0 1 75(4) Orchidaceae 0 0 0 0 3 0 100(3) Commelinales 8 0 0 0 0 1 89(9) Many NM Commelinaceae 4 0 0 0 0 0 100(4) Pontederiaceae 4 0 0 0 0 1 80(5) Liliales 4 0 0 0 0 1 80(5) Most AM Liliaceae 4 0 0 0 0 1 80(5) 82(23)

Continued on Next Page. . .

156 Table 5.3 – Continued Mycorrhizae Classiﬁcation AM EM AB ER OR NM %M Ref Poales 202 1 0 0 0 79 72(281) Many NM Cyperaceae 84 0 0 0 0 49 63(133) 26(123) Eriocaulaceae 2 0 0 0 0 0 100(2) Juncaceae 16 0 0 0 0 8 67(24) 43(39) Poaceae (Gramineae) 90 1 0 0 0 19 83(109) 91(202) Typhaceae 10 0 0 0 0 3 77(13) Gymnosperms Pinales 0 2 0 0 0 0 100(2) Pinaceae 0 2 0 0 0 0 100(2) 100(100) Ferns Osmundales 1 0 0 0 0 0 100(1) Osmundaceae 1 0 0 0 0 0 100(1) Polypodiales 4 0 0 0 0 4 50(8) Blechnaceae 0 0 0 0 0 1 0(1) Dryopteridaceae 1 0 0 0 0 1 50(2) Polypodiaceae 0 0 0 0 0 1 0(1) Pteridaceae 1 0 0 0 0 1 50(2) Thelypteridaceae 2 0 0 0 0 0 100(2) Salviniales 6 0 0 0 0 6 50(12) Marsileaceae 3 0 0 0 0 2 60(5) Salvinaceae 3 0 0 0 0 4 43(7) Horsetails Equisetales 10 0 0 0 0 0 100(10) Equisetaceae 10 0 0 0 0 0 100(10) Lycopods Isoetales 3 0 0 0 0 1 75(4) Isoetaceae 3 0 0 0 0 1 75(4) Lycopodiales 4 0 0 0 0 0 100(4) Lycopodiaceae 4 0 0 0 0 0 100(4)

157 5.3 Ecology of mycorrhizae in aquatic systems

With the prevalence of mycorrhizae in aquatic systems now well established, we can begin to focus on their ecology and importance within these ecosystems. As already noted, the extent of mycorrhizal colonization varies by species (as well as genus, family, and order), some are consistently mycorrhizal (i.e., obligately mycorrhizal), some occasionally mycorrhizal (i.e., facultatively mycorrhizal), and some never mycorrhizal (i.e., nonmycorrhizal; see Brundrett, 2004 for more detail on terminology).

This species dependence has also been observed in several other studies (Hoefnagels et al., 1993; Wetzel and van der Valk, 1996; Miller et al., 1999). Formation of mycorrhizae can also vary seasonally (Rabatin, 1979; van Duin et al., 1989; Wetzel and van der Valk, 1996; Turner and Friese, 1998; Miller, 2000; Titus and Lepˇs,2000; Car- valho et al., 2001; Bauer et al., 2003; Bohrer et al., 2004; Fuchs and Haselwandter,

2004; Escudero and Mendoza, 2005). The driving factor appears to be plant phenology, with colonization highest during periods of maximum plant growth or ﬂowering

(Rabatin, 1979; Wetzel and van der Valk, 1996; Carvalho et al., 2001; Bohrer et al.,

2004).

Another controlling aspect, often dictated by species, is root morphology. We categorized species by group (e.g., dicot, monocot) and growth cycle (e.g., annual, perennial), both of which provide some indication of root type. Occurrence of mycorrhizae, however, was similar between monocots and dicots: our compiled list contained

368 monocots, 259 of which had at least one record of mycorrhizae (i.e., 70 %); and

543 dicots, 427 of which had at least on record of mycorrhizae (i.e., 79 %; Table 5.4).

Likewise, by growth cycle, 85 % (83/98) of the species determined to be annuals were mycorrhizal and 80 % (383/480) of perennial species were mycorrhizal.

158 5.3.1 Environmental factors controlling AM fertility

Edaphic factors can also regulate AM formation. As in terrestrial systems, soil fertility is probably the leading edaphic factor in AM formation. Field studies of AM along nutrient gradients, generally show a decrease in AM as available P increases

(Rabatin, 1979; Anderson et al., 1984; Wetzel and van der Valk, 1996; Wigand et al.,

1998; Miller, 2000; Titus and Lepˇs,2000; Ipsilantis and Sylvia, 2007b). Where positive or no correlations were observed, typically the gradient was very small or the available P was very low (Clayton and Bagyaraj, 1984; Miller et al., 1999; Van Hoewyk et al., 2001; Bohrer et al., 2004; Fuchs and Haselwandter, 2004). AMF colonization decreased under high P addition in laboratory P manipulations (Tanner and Clayton,

1985; White and Charvat, 1999; Cornwell et al., 2001; Sasaki et al., 2001; Tang et al.,

2001; Stevens et al., 2002; Ipsilantis and Sylvia, 2007a). Similar to ﬁeld observations, under low P conditions, AM increased with P addition; however at high P conditions,

AM were inhibited or decreased with P addition (White and Charvat, 1999; Tang et al., 2001). In one ﬁeld P manipulation, Cornwell et al. (2001) found the application of phosphorus decreased AMF colonization in the mycorrhizal species, Solidago patula. Soil P availability will also determine whether the mycorrhizal symbiosis is mutualistic or parasitic (Johnson et al., 1997).

The actual mechanisms by which AMF colonization becomes inhibited at high

P concentrations have not been investigated in aquatic systems, but likely involve mechanisms similar to those of terrestrial systems (Menge et al., 1978; Ratnayake et al., 1978; Cooper, 1984; Hetrick, 1984; Smith and Read, 1997). While not directly investigated in aquatic systems, in one laboratory P manipulation, AMF colonization

159 was inhibited at very high P additions; measurement of plant tissue P revealed sub-

stantially higher P concentrations under the P treatments where AM was inhibited

(White and Charvat, 1999).

hydrology

Hydrology of the system will obviously be an important factor in aquatic systems.

Hydrology aﬀects the availability of oxygen, which AMF, as obligate aerobes, require

to function (Saif, 1981). Several studies observed a negative correlation between

AMF colonization and either soil moisture, water column depth or ﬂooding duration

(Rabatin, 1979; Anderson et al., 1984; Clayton and Bagyaraj, 1984; Lodge, 1989;

Dhillion, 1993; Rickerl et al., 1994; Stevens and Peterson, 1996; Wetzel and van der

Valk, 1996; Wigand et al., 1998; Miller et al., 1999; Miller, 2000; Miller and Sharitz,

2000; Jayachandran and Shetty, 2003; Escudero and Mendoza, 2005; Ipsilantis and

Sylvia, 2007a). A few studies, however, found hydrology to have no eﬀect on mycor-

rhizal colonization (Farmer, 1985; Turner and Friese, 1998; Van Hoewyk et al., 2001;

Bauer et al., 2003; Bohrer et al., 2004), including one meta-analysis (Muthukumar

et al., 2004). The most compelling studies measured AMF colonization within a sin-

gle species along a wide hydrologic gradient. Stevens and Peterson (1996) observed

higher levels of AM for Lythrum salicaria in dry and intermediate locations than in wet locations. Wigand et al. (1998) monitored AM in two submerged isoetides

(Littorella uniﬂora and Iosetes lacustris) along a gradient of water column depth and measured highest AMF colonization at the shallow end of the gradient. Along a mesic prairie to emergent aquatic gradient, three border species (Eleocharis smallii,

160 Prosperpinaca palustris, and Polygonum coccineum) were mycorrhizal under dry con-

ditions and nonmycorrhizal under wet conditions (Anderson et al., 1984). AMF col-

onization increased with a decrease in water column depth for Panicum hemitomon

and Leersia hexandra along a hydrologic gradient (Miller, 2000). Furthermore, AMF colonization increased with seasonal drying in ﬂooded plots (Miller, 2000). Micro- topography within a wetland (e.g., hummocks and hollows) can create aerobic and anaerobic microsites. Cantelmo and Ehrenfeld (1999) observed AM frequency for

Chamaecyparis thyoides to be higher on the tops and sides of hummocks than at the bottoms. In contrast, Ray and Inouye (2006) monitored AM in Typha latifolia over a series of flooding events and observed high colonization during both flooded and unflooded periods with lowest colonization immediately following drawdowns. The decreases in colonization may have been due to phenological responses of T. latifolia to the drawdowns or shifts between flood tolerant and intolerant AMF species.

The eﬀect of hydrology is mediated through the redoximorphic potential. Redox potential has been positively correlated with AMF colonization and the eﬀect may be due to either changes in O2 or P availability (Wigand et al., 1998; Miller, 2000;

Beck-Nielsen and Madsen, 2001). Soil P availability generally increases with ﬂooding

(Rubio et al., 1997; Mendoza et al., 2005). One mechanism may be reduction of ferric phosphates to the more soluble ferrous form (Rubio et al., 1997). Flooding might also increase P availability through solubilization of organic P fractions or by an increase in the diffusion coefficient of P (Rubio et al., 1997). As discussed earlier, higher P availability will inhibit AM formation. Several authors, however, have observed the opposite effect of flooding on P availability, with P becoming less available in the rhizosphere when flooded (Wium-Andersen and Andersen, 1972; Sand-Jensen et al.,

161 1982; Wigand and Stevenson, 1994, 1997). While ﬂooding induces reduction of ferric

iron, releasing P, within the rhizosphere, conditions remain aerobic causing oxidation

of ferrous iron and co-precipitation of P.

Oxygen becomes depleted and redox potential decreases in the sediment under

prolonged ﬂooding. Other factors can inﬂuence O2 concentration and redox potential.

Wigand et al. (1998) noted that high organic matter content (OM) can lower redox status by consumption of O2 due to microbial decomposition. They found AMF colonization to be the highest in areas of low OM and high redox potential (Wigand et al., 1998). Another contributing factor is the degree of soil rhizosphere aeration by the plant host. Many hydrophytes develop air spaces (lacunae) within their tissue in response to ﬂooding. These lacunae transport oxygen from the emersed portions of the plant to the submersed portions. Typically an oxidized rhizosphere develops around the roots of hydrophytes in anoxic soil due to this eﬀective O2 transport system

(Mitsch and Gosselink, 2000). Plant host species that are good root aerators can raise the redox potential of the rhizosphere and create a more suitable habitat for AMF.

Wigand et al. (1998) observed that the sediment redox potential corresponded with plant density. They studied AMF colonization of isoetids at various water depths; isoetids have a well developed root and lacunal system and are eﬀective rhizosphere aerators. Beck-Nielsen and Madsen (2001) measured redox potential in sediments of mycorrhizal and non-mycorrhizal populations. The redox potential for mycorrhizal populations ranged from 250 mV to 530 mV while for non-mycorrhizal populations in ranged from 54 mV to 280 mV (Beck-Nielsen and Madsen, 2001). Furthermore, in a transplant study, specimens of poor-aerating and non-mycorrhizal Myriophyllum

alterniﬂorum that were transplanted into a stand of good-aerating and mycorrhizal

162 Littorell uniﬂora soon became infected with AMF. Cooke et al. (1993) did not measure redox potential, but did measure oxygen concentrations in well water drawn at various rooting depths. They detected no oxygen but still observed AM at depths up to 42 cm, suggesting that the plant hosts (Spartina patens and Distichlis spicata) were transporting enough oxygen to their roots to sustain the mycorrhizae. AMF have also been observed to colonize the air-ﬁlled lacunae (Brown and Bledsoe, 1996).

Khan (2004) cautioned that low incidence of AM in hydrophytes may be mis- takenly attributed to ﬂooding, when the real factor is landscape position. Wetlands commonly occur at low points in the landscape, positions which favor water collection and retention, as well as nutrient deposition. Consequently, it may be the increased fertility limiting AM formation, not the hydrology.

Whatever the actual mechanism, hydrology appears to have a slight negative eﬀect on formation of mycorrhizae—reducing the plant-fungi dependency, but not eliminat- ing it. From the compiled dataset, we were able to determine wetland indicator status for 721 out of the 952 species (Table 5.4; see also U.S. Fish and Wildlife Service, 1997):

72 % (278 out of 388) of those considered to be obligate wetland species (OBL) were mycorrhizal, which was slightly less than the 84 % (172/205) for facultative wetland species (FACW) and 85 % (109/128) for facultative species (FAC). When compared by growth habit (e.g., submergent, emergent), formation of mycorrhizae was reduced in the more emersed species: more emersed species included those with typical growth habits of submerged, ﬂoating, or ﬂoating-leaved with mycorrhizae observed in 65 %

(55/85), 46 % (13/28) and 50 % (17/34) of the species, respectively (for deﬁnition of these terms see Cronk and Fennessy, 2001, pp. 7–15). In contrast, mycorrhizae were observed in 84 % (415/495) and 84 % (75/89) of the species whose growth habits

163 included emergent and terrestrial, respectively (we considered emergent plants to be

those rooted in non-aerated soils (i.e., waterlogged or ﬂooded) and terrestrial plants

to be those rooted in aerated soils).

5.4 AM beneﬁts

5.4.1 plant nutrition

Several studies have shown that as in terrestrial systems, AM increase the phosphorus content of the host plant (Tanner and Clayton, 1985; Rickerl et al., 1994;

Wigand and Stevenson, 1997; Miller and Sharitz, 2000; Dunham et al., 2003; Jay- achandran and Shetty, 2003; Ipsilantis and Sylvia, 2007a). One study directly measured phosphate uptake in Vallisneria americana and found that the rate of uptake nearly doubled when AM were present (Wigand and Stevenson, 1997). Plant nitrogen content also has been found to be higher in mycorrhizal plants (Miller and Sharitz,

2000; Dunham et al., 2003). However, White and Charvat (1999) observed no nutritional beneﬁts to AMF colonization when they grew Lythrum salicaria under AM and NM conditions.

5.4.2 plant growth

Beneﬁts conferred by the enhanced nutrition are still unclear. A few studies have observed an increase in growth or biomass for mycorrhizal plants (Keeley, 1980;

Wigand and Stevenson, 1994; Miller and Sharitz, 2000; Jayachandran and Shetty,

2003; Fraser and Feinstein, 2005). Mycorrhizal specimens of Vallisneria americana were collected from the ﬁeld and grown under conditions of no treatment (control), treatment with a fungicide and treatment with a fungicide and phosphorus (Wigand and Stevenson, 1994). Shoot elongation was signiﬁcantly lower for specimens grown

164 under the fungicide treatment than for either the control or fungicide+phosphorus

specimens (Wigand and Stevenson, 1994). Jayachandran and Shetty (2003) grew

Cladium jamaicense with and without AMF inoculation under saturated conditions and found that presence of AM increased plant growth by 14%, shoot biomass by

52% and root biomass by 66%. However, several studies have found either no or

negative eﬀect of AM on plant growth or biomass (Tanner and Clayton, 1985; White

and Charvat, 1999; Stevens et al., 2002; Dunham et al., 2003). The studies conducted

over shorter time frames tended to show the non or negative responses to mycorrhizal

colonization and several authors noted that longer studies may be necessary to observe

the beneﬁt of plant growth to mycorrhizal colonization (Tanner and Clayton, 1985;

White and Charvat, 1999; Dunham et al., 2003). An intial drain on plant carbon by

mycorrhizae that is compensated for later in the growing season has been noted in

non-wetland studies (Bethlenfalvay et al., 1982; Koide, 1985; Johnson et al., 1997).

5.4.3 plant diversity

Plant species richness is an additional beneﬁt that may be conferred by AM in wetland and aquatic systems. Cornwell et al. (2001) suggested that AM may increase species richness in fens by promoting the survival of subdominant dicots in monocot- dominated communities. In their ﬁeld survey of a phosphorus poor fen, they found nine of ten dicots to be mycorrhizal while the six monocot species examined were not or only weakly mycorrhizal. Likewise, higher AMF colonization was observed for dicots than monocots among 67 plant species sampled from three calcareous fens

(Weishampel and Bedford, 2006). In a study of AM in restored everglades, Aziz et al. (1995) found species richness to increase with AM, but it was unclear which

165 was the cause and which was the eﬀect. Cooke et al. (1993) suggested that the

presence of AMF may be responsible for the increase in species diversity sometimes

observed during seasonal drawdowns in wetlands. Studies in terrestrial systems have

also found species richness to increase when mycorrhizae are present (Grime et al.,

1987; Newman and Reddell, 1988; Gange et al., 1993).

5.5 Conclusion

It is apparent that AM are a signiﬁcant presence in wetland and aquatic systems.

While obligate aerobes, AMF can exist in these typically anoxic systems due to morphological adaptations of the plant hosts (i.e., lacunae) which provide an aerobic environment for the symbiosis. Microtopography and water ﬂuctuations within these systems also create conditions suitable for AMF survival. As in terrestrial systems,

AM appear to increase the uptake of plant nutrients, particularly phosphorus, and may confer beneﬁts to plant growth as well. AM may also increase species richness by allowing less adapted or less competitive species to survive. The role that AM play in aquatic systems is far from understood and should continue to be investigated.

Creation and restoration of wetlands is one area in particular that might beneﬁt from a better understanding of AM in aquatic systems. This idea has been promoted by Khan (2004) and will be the focus of the next chapter (Ch. 6). Agriculture of wetland-based crops (e.g. rice paddies) might also beneﬁt from this knowledge

(Khan and Belik, 1995; Khan, 2004). In fact, several studies have already begun to explore the potential use of AM in rice cultivation (Solaiman and Hirata, 1995,

1996; see also references in Khan and Belik (1995)). Given the importance of AM to terrestrial ecosystem development, the spatial and temporal variability of wetlands,

166 the numerous studies demonstrating the presence of AM in aquatic systems and the few studies comparing AM between created and natural wetlands, it is likely that

AM are also important to wetland ecosystem development.

167 Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Dicots Acanthaceae Acanthus ilicifolius OBL E S P NM + 49,76 Dicliptera brachiata FACW∗ E F A,P 0.36 81 Hygrophila auriculata H. Schulli FACW E F P NM 0 67† Hygrophila balsamica FACW E F A NM 0 67† Hygrophila costata FACW E F P 0.45 24

Aizoaceae Sesuvium portulacastrum FACW∗ E F P AM AM 0–0.6,+ 22,75†,76,83†

Amaranthaceae Alternanthera paronychioides FAC E F P + 76 Alternanthera philoxeroides OBL∗ EFP NM 017† Amaranthus sp. 0.008 81 Arthrocnemum macrostachyum 0.1 14 Atriplex patula FACW∗ E AM AM 0.7 41 168 Atriplexprostrata A.hastata OBL E F A NM NM 0–0.3 72,89 Blutaparon vermiculare FACW∗ EFP NM 083† Camphorosma annua 0–0.04 50 Halimione portulacoides Atriplex p. OBL E S P AM AM 0–0.3 14,16,72,89 Nitrophila australis - 30†

Continued on Next Page. . .

Table 5.4: Documentation of arbuscular mycorrhizae for hydrophytic species. Mycorrhizal status (AM=arbuscular-mycorrhizal, NM=nonmycorrhizal) from four compilations are listed seperately: HH (Harley and Harley, 1987a,b); WQ (Wang and Qiu, 2006); KB (Khan and Belik, 1995); and M (Muthukumar et al., 2004). Mycorrhizal status based on studies featured in the review is summarized in column AM : a proportional range is provided when available; however, some studies reported only ‘+’ or ‘-’, so these indicators are also included. References for AM are listed in the final column [†study was also cited in one of the four compilations]. The table also provides characteristic information: wetland indicator status (WIS: OBL=obligate, FACW=facultative wetland, FAC=facultative) [∗designated status from U.S. Fish and Wildlife Service, 1997]; growth habit (S=submerged, FL=floating-leaved, F=floating, E=emergent, T=terrestrial, P=epiphytic); growth form (F=forb, G=grass, S=shrub, T=tree, C=climber); and growth cycle (A=annual, B=biennial, P=perennial). Species are ordered by family then genus within the following groups: dicots, monocots, gymnosperms, ferns, horsetails, and lycopsids. [∗∗this species also includes our own data.] Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Salicornia brachiata NM Salicornia depressa S. virginica OBL∗ NM Salicornia dolichostachya OBL E F A 0.01–0.3 72 Salicornia europaea S. brachystachya OBL∗ E F A AM AM 0–0.64,- 36,50,56,72 Salicornia rubra OBL∗ T 0.009 41 Salicornia senegalensis NM 0 83† Salicornia spp. 0 89 Sarcocornia fruticosa Arthrocnemum fru- OBL E S P 0 16 ticosum Sarcocornia perennis Arthrocnemum OBL∗ E F,S P 0 16 perenne Suaeda calceoliformis S. depressa FACW∗ T 0 41 Suaeda glauca AM 0.008–0.01 92 Suaeda maritima OBL∗ E F A,P AM AM AM 0–0.5,+ 50,72,74,75†,76,83†,89 Suaeda monoica NM Suaeda nudiﬂora NM Suaeda vera AM AM 0.05 14 169 Tecticornia indica Arthrocnemum in- OBL E F P AM 0.6,+ 75†,76 dicum

Anacardiaceae Campnosperma auriculatum OBL E AM 0.28 82 Mangifera sp. 0.06 82 Schinus terebinthifolius FAC∗ E,T S,T P AM 0.16–0.25 3† Toxicodendron vernix OBL∗ E S,T P 0.32 23†

Apiaceae Angelica sylvestris FACW E AM AM 0.58 48 Apium nodiﬂorum OBL∗ E F P NMNMNM 0 69† Azorella trifurcata FAC E,T S P + 30† Berula erecta OBL∗ E F P NM AM 0–0.11 7 Centella asiatica FAC∗ E,T F P + 66 Cicuta bulbifera∗∗ OBL∗ E F P 0–0.05

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Hydrocotyle americana OBL∗ E F P 0.29–0.81 23†,93 Hydrocotyle bonariensis FACW∗ E F P AM 0.76 22 Hydrocotyle novae-zeelandiae H. novae-zelandiae FAC E AM + 51† Hydrocotyle sibthorpioides FAC∗ EFP NM 017† Hydrocotyle umbellata OBL∗ S,E F P 0.29 3† Hydrocotyle vulgaris OBL E AM AM + 55,61 Lilaeopsis novae-zelandiae L. lacustris OBL S F P AM 0–0.82 18† Lilaeopsis macloviana OBL S,E F P + 30† Oenanthe ﬁstulosa OBL E 0.05–0.59 78 Oenanthe javanica OBL E F P 0–0.8 5,17†,42 Oenanthe lachenalii OBL E 0.91–0.93 36 Ptilimnium capillaceum OBL∗ E F A 0.3 3† Sium latifolium OBL E NM NM 0–0.5 78,79 Zizia aurea FAC∗ T F P 0.92 93

Apocynaceae 170 Apocynum cannabinum FAC∗ E,T F P AM 0.29 71† Asclepias incarnata∗∗ OBL∗ E F P AM 0–0.8 6,21†,93 Calotropis gigantea FAC E,T S P AM + 76 Finlaysonia obovata OBL E C + 76 Nerium oleander N. odorum FACW E,T S P AM + 76 Periploca graeca NM - 55

Aquifoliaceae Ilex verticillata FACW∗ E S,T P AM 0.1–0.9 21†

Araliaceae Stilbocarpa polaris OBL∗ NM - 51†

Asteraceae (Compositae) Achillea ptarmica FAC F P NM AM 0.12 48 Almutaster pauciﬂorus Aster p. FACW∗ T 0.51 41 Ambrosia maritima AM

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Artemisia maritima FACW E F P AM AM 0.1–0.64 36,50,89 Artemisia rupestris 0.36 36 Artemisia sp. - 86† Aster sp. 0.8 21† Asteriscus maritimus 0.25 14 Baccharis halimifolia FAC∗ E,T S,T P 0.2–0.36 3† Bellis perennis FAC E F PAMAM + 58† Bidens aristosa∗∗ FACW∗ E F A,B 0–0.75 Bidens cernua∗∗ OBL∗ E F A 0–0.75 Bidens discoidea∗∗ FACW∗ E F A 0–0.75 Bidens frondosa∗∗ FACW∗ E F A AM 0–0.75,+ 21†,55,81 Bidens pilosa FAC∗ AM 0.36 22 Bidens tripartita∗∗ FACW∗ E F A AM AM 0–0.75 21† Bidens vulgata∗∗ OBL∗ E F A 0–0.75 Cirsium canum FACW E F P + 58† ∗∗ ∗ 171 Cirsium muticum OBL E F B 0.75–1 Cirsium oleraceum OBL E F PAMAM + 58† Cirsium palustre FACW E F B,P AM AM AM 0.65,+ 48,58† Cirsium sp. 0.38–0.81 6 Conoclinium coelestinum Eupatorium c. FAC∗ E,T F P AM 0.13–0.66 3†,81 Conzyanthus sp. 1 39 Cotula plumosa NM - 51† Crassocephalum picridifolium 0 83† Doellingeria umbellata FACW∗ 0.58 93 Ecliptaprostrata E.alba FACW∗ E F A,P AM NM 0–0.54 67†,81 Eupatorium perfoliatum FACW∗ E F P AM 0.28–1 21†,93 Eupatorium pupureum FAC∗ + 57 Eupatorium serotinum FAC∗ E,T F P AM 0.14–0.36 87 Euthamia graminifolia FAC∗ E,T F P AM 0.39–1 21†,93 Eutrochium maculatum Eupatorium m., FACW∗ E F P AM 0.077–1 21†,23†,93 Eupatoriadelphus maculatus

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Flaveria linearis FACW∗ E F P 0.22–0.25 3† Gnaphalium psilophyllum + 30† Grangea maderaspatana AM + 83† Inula crithmoides FACW E F P 0.06–0.67 15,16 Iva frutescens FACW∗ ESP AM +20† Jaumea carnosa OBL∗ AM AM 0.1–0.43 11 Mikania scandens FACW∗ E F,C P 0.04–0.22 3† Omalotheca norvegica Gnaphalium AM AM 0.4–0.65 63 norvegicum Packera aurea FACW∗ E F P 0.56–0.78 23†,93 Parthenium hysterophorus FAC∗ E,TF A AM + 76 Perezia linearis F P + 30† Perezia recurvata + 30†

172 Petasites frigidus FACW∗ NM - 84†,86† Pluchea odorata FACW∗ E F A,P 0.1–0.66 3†,81 Rudbeckia fulgida FAC∗ + 57 Senecio bracteolatus AM + 30† Senecio neaei + 30† Serratula tinctoria FAC T F P AM AM 0.35–0.55 31 Solidago gigantea FACW∗ E F P AM 0.8–0.9 21† Solidago patula OBL∗ E F P 0.35–0.84 23†,93 Solidago rugosa FAC∗ 0.56 93 Solidago sp. 0.62–0.7 6 Solidago uliginosa OBL∗ 0.73 93 Solidago virgaurea AM AM 0.3–0.6 63 Sonchus sp. AM 0.52 17† Symphyotrichum boreale OBL∗ T 0.4 93 Symphyotrichum cordifolium Aster sagittifolius + 57 Symphyotrichum praealtum Aster praealtus FACW∗ 0.44 81 Symphyotrichum puniceum∗∗ OBL∗ 0–0.46 93

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Symphyotrichum spp. Symphyotrichum subulatum Aster subulatus FACW∗ T AM AM 0.62 45 Taraxacum palustre FACW E F P AM + 58† Tripolium pannonicum Aster tripolium FAC E,T F P AM AM 0.01–0.96,+ 16,36,50,56,72,89 Vernonia noveboracensis FAC∗ E,T F P AM 0.6–1 21† Xanthium strumarium FAC∗ T 0.14–0.64 13,81

Balsaminaceae Impatiens aquatilis 0.24 42 Impatiens capensis FACW∗ E F A AM 0.1–1 10,21†,93 Impatiens chinensis AM 1 17† Impatiens pallida FACW∗ AM

Betulaceae Alnus glutinosa FACW∗ AM,EM AM,EM + 55 Alnus incana FACW∗ E S,T P AM,EM AM,EM 0–0.03 23†,93

173 Alnus serrulata FACW∗ E S,T P 0.1 21† Betula nana OBL∗ EM EM EM 86† Betula pumila OBL∗ E S P +,EM 54,84†

Boraginaceae Heliotropium indicum FAC∗ E,T F A 0.11,+ 76,81 Hydrolea zeylanica FACW E F A AM 0.33 67† Mertensia maritima FAC∗ - 91† Myosotis scorpioides M. palustris OBL∗ E F P AM AM 0–0.32 7,48,78 Plagiobothrys ﬁguratus OBL∗ E F A AM 0.3–1 40

Brassicaceae (Cruciferae) Alliariapetiolata A.oﬃcinalis FAC∗ E,T F A,B NM NM 0.09 10 Brassica juncea AM 0.12 17† Braya glabella B. purpurascens FAC∗ - 9† Cardamine corymbosa NM - 51† Cardamine macrophylla NM 0 17† Cardamine multijuga 0.02 42

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Cardamine pratensis OBL∗ E F P NM NM 0,- 48,58†,91† Cochlearia anglica OBL E F B,P AM AM 0 72 Cochlearia officinalis FACW∗ E F A,B,P AM AM 9†,56 ± Nasturtium indicum NM 0 17† Nasturtium microphyllum Rorippa micro- OBL∗ E F P NM (check 10) 0 69† phylla Nasturtium officinale OBL∗ S,E F P NMNM NM - 43† Nasturtium sp. S 0 7 Rorippa amphibia FACW∗ S,EFP NM 07 Rorippa islandica OBL E F A,B AM 0.9 21† Rorippa sessiliflora OBL∗ 0.003 81 Subularia aquatica OBL∗ S,EFA NM 028†

Cabombaceae Cabomba furcata OBL S F P 0 24

Calyceraceae 174 Boopis australis OBL + 30†

Campanulaceae (Lobeliaceae) Downingia elegans OBL∗ E F A AM 0.55–1 40 Lobelia cardinalis OBL∗ T F P AM 0.1 21† Lobelia dortmanna OBL∗ S F P AM AM AM 0.2–0.9 7,28†,61,77† Lobelia senegalensis AM 0.1–0.5 83† Lobelia siphilitica OBL∗ 0.74 93 Lobeliaperpusilla Pratiap. OBL SE F P AM 0.51–0.8 18† Pratia repens F A,P + 30†

Caprifoliaceae Valeriana dioica FACW∗ NMNM 048 Viburnum dentatum FAC∗ 0.65 93 Viburnum lentago FAC∗ E,T S,T P AM 0.1 21† Viburnum recognitum V. dentatum (var. FACW∗ E S,T P 0.1 21† lucidum)

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Caryophyllaceae Cerastium arcticum NMNM - 9† Colobanthus apetalus AM + 51† Colobanthus muscoides NM - 51† Drymaria cordata FAC∗ AM 0.33 17† Sagina maritima 0.43 36 Spergularia media S. maritima, S. FACW∗ E,T F A,P AM AM 0–0,- 56,72,89 marginata Spergularia salina OBL∗ 0–0.19 36,50 Stellaria humifusa OBL∗ - 9† Stellaria parviﬂora NM - 51†

Casuarinaceae Casuarina cunninghamiana FAC T T P AM,EM 0–0,+ 44,70

Ceratophyllaceae 175 Ceratophyllum demersum OBL∗ S,F F P NM 0–0 42,67†

Cistaceae Helianthemum lippii OBL 0 1

Clusiaceae Calophyllum sclerophyllum OBL AM 0.18 82 Calophyllum soulattri OBL AM 0–0.6 82 Calophyllum sp. OBL 0.04 82 Cratoxylum arborescens OBL AM 0.69 82 Triadenum virginicum OBL∗ 0.64 93

Convolvulaceae Ipomoea aquatica OBL∗ F F,C P AM 0.55 67† Ipomoea pes-caprae FAC∗ E,T F,C P AM 0.39,+ 22,76

Cornaceae Cornus amomum FACW∗ E S P 0.1 21† Cornus sericea C. stolonifera FACW∗ E S,T P 0.51–0.8,+ 54,93

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Crassulaceae Crassula moschata NM - 51† Crassula sinclairii Tillaea s. OBL S F P AM 0.64 18† Penthorum sedoides∗∗ OBL∗ E F P 0–0

Cucurbitaceae Melothria pendula FAC∗ 0.035 81 Sicyos angulatus FAC∗ 0.07 81

Dipterocarpaceae Hopea mengarawan FAC∗ NM 0 82 Shorea balangeran FAC∗ AM 0–0.14 82 Shorea teysmanniana FAC∗ AM 0.09–0.1 82 Shorea uliginosa FAC∗ AM 0.17 82

Droseraceae ∗ †

176 Drosera anglica OBL E F PNMNM 0 47 Drosera indica + 66 Drosera intermedia OBL∗ E F P NM AM 0.01–0.07 31 Drosera rotundifolia OBL∗ E F P AM AM 0–0.5,- 21†,84†,93 Drosera sp. AM 0.64 17†

Elatinaceae Bergia capensis OBLEFA NM 067† Elatine ambigua OBL∗ 0 42 Elatine gratioloides OBL S,E F A AM 0–0.8 18†,45 Elatine hexandra OBL S,E F A,P AM 0.19–0.25 7

Ericaceae Andromeda polifolia OBL∗ E S PERER ER 84† Arctostaphylos alpina FAC∗ ER ER,AB AB 86† Calluna vulgaris FAC∗ ER ER Chamaedaphne calyculata FACW∗ E S P ER 54,84† Empetrum nigrum FAC∗ ER ER ER 86†

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Gaultheria hispidula FACW∗ ER 53 Ledum groenlandicum OBL∗ E S P ER ER 53,84† Ledum palustre FACW∗ ER ER ER 86† Loiseleuria procumbens ER ER ER 86† Lyonia ligustrina FACW∗ E S P 0 21† Vaccinium oxycoccos Oxycoccus OBL∗ E S PERER ER 84† quadripetalus Vaccinium uliginosum FAC∗ T ER ER86† Vaccinium vitis-idaea FAC∗ E,T S P ER ER ER 84†

Euphorbiaceae Chamaesyce serpens FAC∗ NONE 0.15 81 Croton punctatus AM NONE 0.24 22 Excoecaria agallocha FACW∗ E T NONE + 49,76 Hevea brasiliensis AM NONE 0.47–0.72 82 177

Fabaceae Acacia auriculiformis FAC E,T T P AM + 76 Acacia mangium FAC P AM,EM 0.65 82 Acacia nilotica FAC E,T T P AM 0.16,+ 13,76 Adesmia lanata - 30† Aeschynomene aspera FACW FL?,E S P AM 0.35 67† Aeschynomene indica FACW∗ FL?,E F A,P AM AM 0.2 67† Astragalus alpinus FAC∗ T NM - 86† Canavalia rosea FAC∗ AM 0.46 22 Crotalaria quinquefolia FAC E F AM 0.43 67† Derris scandens FAC E,T C,S + 76 Derris trifoliata FAC∗ E,T C,S + 76 Inga leiocalycina AM 0.25–0.3 95 Koompassia malaccensis NM 0 82 Lotus uliginosus AM AM 0.45 48

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Lupinus arcticus NM - 86† Medicago littoralis AM + 55 Millettiapinnata Derrisindica FAC E,T T P + 76 Neptunia prostrata N. oleracea OBLF,E S P NM 0 67† Oxytropis scammaniana AM - 86† Prioria copaifera AM 0.26–0.73 85 Pterocarpus oﬃcinalis OBL∗ 0.14–0.83 73 Sesbania herbacea S. exaltata FAC∗ E,T F A,P 0.07–0.33 3† Vicia cracca AM AM 0.68 48 Vigna luteola FACW∗ E F,C P AM 0.07–0.44 3†

Frankeniaceae Frankenia corymbosa FACW∗ 0.1 14

178 Gentianaceae Blackstonia perfoliata AMAM + 55 Gentiana algida FAC∗ T NM - 86† Sabatia grandiﬂora FACW∗ E F A 0.12–0.32 3†

Geraniaceae Geranium robertanum FACW∗ AM AM AM

Grossulariaceae Ribes hirtellum FAC∗ 0.58 93

Haloragaceae Myriophyllum alterniﬂorum OBL∗ S F P NM NM NM 0–0 7,77† Myriophyllum aquaticum OBL∗ S F P NM 0–0.05 18†,24 Myriophyllum pedunculatum OBL S,E F P AM 0–0.58 18† Myriophyllum propinquum OBL S,E F P AM 0–0.78 18† Myriophyllum spicatum OBL∗ S F P 0–0.98 7,39,42 Myriophyllum triphyllum OBL S F P AM 0–0.28 18† Proserpinaca palustris OBL∗ S,E F P AM NM 0.8,+ 2†,21†

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Hypericaceae Hypericum perforatum FAC∗ AM AM 0.35 48

Lamiaceae (Labiatae) Ajuga reptans AM AM 0.58 48 Collinsonia canadensis FAC∗ E,T F P AM 0.1 21† Lycopus americanus∗∗ OBL∗ E F P 0.64–1 93 Lycopus europaeus OBL∗ E F P AM AM AM(check9) Lycopus uniﬂorus∗∗ OBL∗ E F P 0–1 23†,93 Lycopus virginicus∗∗ OBL∗ E F P 0.051–0.25 Mentha aquatica OBL∗ AM AM AM 0.05–0.59,+ 55,78 Mentha arvensis FACW∗ T AM AM 0.38 48 Mentha piperita FACW∗ E F P AM AM 0–0.84,+ 30†,52,93 Menthaspicata M.longifolia FACW∗ E 1 39 Prunella vulgaris FAC∗ E,T F P AM AM 0.83,- 58†,93 Pycnanthemum tenuifolium FAC∗ E,T F P AM 0.1–0.5 21†,87 179 Pycnanthemum virginianum FAC∗ E,T F P AM 0.8–0.9 21† Salvia lanigera 0 1 Scutellaria galericulata OBL∗ E AM AM 0.51 48 Scutellaria lateriﬂora∗∗ FACW∗ E F P 0–0.05 Teucrium canadense FACW∗ E 0.49 81 Teucrium scordium AM AM 0–0.7 78,79

Lauraceae Lindera benzoin FACW∗ E S P AM 0–0.1 21†

Lentibulariaceae Utricularia reticulata FAC∗ - 66 Utricularia sp. FAC∗ AM 0.73 17†

Linaceae Linum catharticum FAC E,T F A AM AM + 58†

Loganiaceae Mitreola petiolata FACW∗ 0.53 81

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Lythraceae Ammannia auriculata OBL∗ 0.49 81 Ammannia robusta∗∗ A. coccinea OBL∗ E F A 0.001–0.58 81 Ammannia baccifera Ammania b. OBL∗ E F A,P AM 0.15 67† Decodon verticillatus∗∗ OBL∗ E F P 0–0 Lythrum alatum∗∗ OBL∗ E F P AM 0–0.6 3†,87 Lythrum salicaria OBL∗ E F P AM AM AM 0.2–1,- 21†,58† Nesaea radicans NM 0 83† Rotala densiﬂora + 66 Rotala malampuzhensis + 66 Rotala rotundifolia AM 0–0.35 17†,42 Sonneratia apetala OBL E T P + 76 Sonneratia caseolaris OBL E T P + 76 Trapa natans T. bispinosa OBL∗ FLFP NM - 43† Trapa quadrispinosa 0 42 180 Malvaceae Heritiera fomes OBL E T P + 49,76 Hibiscus laevis OBL∗ 0.58 81 Talipariti tiliaceum Hibiscus tortuosus FACW∗ E T P + 76 Thespesia populnea FAC∗ E,TT P AM + 76

Meliaceae Xylocarpus granatum OBL∗ E T P + 49 Xylocarpus mekongensis OBL E T P + 49 Xylocarpus sp. T P + 76

Menyanthaceae Menyanthes trifoliata OBL∗ E F PNMNM - 84† Nymphoides cristata N. hydrophylla OBL FL F A AM 0.3 67† Nymphoides peltata OBL∗ NMNM 0 42

Molluginaceae Glinus oppositifolius OBL∗ AM 0.05–0.1 83†

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Moraceae Ficus sp. OBL∗ 0.15 82

Myricaceae Morella cerifera Myrica c. FAC∗ E S,T P AM 0–0.75 3†,64

Myrtaceae Melaleuca quinquenervia FAC∗ AM,EM + 44 Syzygium sp. 0.07–0.2 82 Tristaniopsis whiteana NM 0 82

Nelumbonaceae Nelumbo nucifera Nelumbium specio- OBL∗ FL F P NM 0–0,- 42,43†,67† sum

Nymphaeaceae Nuphar lutea∗∗ OBL∗ S,FL F P NM NM 0–0 7 181 Nymphaea alba NM NM AM 0.12 17† Nymphaea amazonum OBL∗ FL F P 0 24 Nymphaea lotus OBL∗ FLFP NM - 43† Nymphaea nouchali N. stellata OBL FL F P NM 0–0,- 4†,66,67† Nymphaea tetragona OBL∗ 0 42

Oleaceae Fraxinus nigra FACW∗ E T P + 54 Fraxinus oxycarpa TP AM +55 Fraxinus sp. T P

Onagraceae Chamerion angustifolium Epilobium a. FAC∗ E,T F PAMAM + 54 Epilobium barbeyanum OBL S,E F P + 30† Epilobium brunnescens OBLEFP NM - 51† Epilobium ciliatum∗∗ E. adenocaulon FAC∗ E,T F P AM AM 0.051–0.35,+ 30†,48 Epilobium coloratum OBL∗ E F P AM 0.16–1 21†,93 Epilobium hirsutum FACW∗ S,E F P AM AM 0–0.12 7 Epilobium pedunculare FP AM +51†

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Ludwigia adscendens OBL E,FL,F F P AM 0–0.33 4†,67† Ludwigia decurrens OBL∗ 0.54 81 Ludwigia hyssopifolia FACW∗ E FA,P NM 0 67† Ludwigia microcarpa OBL∗ E F P 0.1–0.48 3† Ludwigia octovalvis OBL∗ E F P 0.15–0.27 3† Ludwigia palustris∗∗ OBL∗ S,E F P AM 0–1 21† Ludwigia parviﬂora + 66 Ludwigia peploides OBL∗ NM 0 45 Ludwigia perennis OBLE FA NM 0 67† Ludwigia polycarpa∗∗ OBL∗ E F P 0–0

Plantaginaceae Callitriche antarctica + 51† Callitriche cophocarpa OBL S,F,E F P 0 7 Callitriche hamulata C. intermedia OBL S,F,E F A,P AM AM AM 0–0.54 7,77† Callitriche stagnalis OBL∗ S,F,E F A,P 0 7 Littorella uniﬂora OBL∗ S,E F P AM AM AM 0.13–0.96,+ 7,28†,61,77†,96

182 Plantago barbata OBL E F P + 30† Plantago coronopus FAC∗ E,T F A,B,P AM AM 0.44–0.46,+ 36,56 Plantago lanceolata FAC∗ T AM AM 0.82 48 Plantago major FAC∗ T AM AM AM 0.19–0.22,+ 13,17†,55 Plantago maritima FACW∗ E F P AM AM 0.02–0.7,+ 36,50,56,72,89

Plumbaginaceae Aegialitis rotundifolia OBL E S P + 49,76 Armeria maritima FAC∗ E,T F P AM AM 0–0.64,+ 36,56,72 Limonium caesium 0.15 14 Limonium carolinianum OBL∗ EFP AM +20† Limonium cossonianum 0.15 14 Limonium vulgare OBL E F P AM AM 0–0.1 36,72,89

Polygalaceae Polygala salasiana F P + 30† Polygala serpyllacea

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Polygonaceae Polygonum acuminatum OBL∗ E F P 0.4 24 Polygonum amphibium∗∗ P. coccineum OBL∗ S,FL,E F P NM AM NM 0–0.75,+ 2†,7,21†,71†,94† Polygonum barbatum 0.1–0.9 5 Polygonum bistorta FAC∗ T AMAM -86† Polygonum capitatum AM,EM NM 0.51 17† Polygonum decipiens 0 45 Polygonum ferrugineum OBL E F 0.55 24 Polygonum glabrum OBL∗ E F A,P NM 0–0 4†,67† Polygonum hydropiper ∗∗ OBL∗ E F A NM NM AM 0–0.75 17†,42 Polygonum hydropiperoides∗∗ OBL∗ E F P AM 0–0.75,- 2†,81 Polygonum lapathifolium∗∗ FACW∗ E F A NM NM 0–0.75 42 Polygonum pensylvanicum∗∗ FACW∗ E F A 0–0.75 Polygonum persicaria∗∗ FACW∗ E F A,P AM AM 0–0.75 94† Polygonum pulchrum OBLEFP NM 067† Polygonum punctatum∗∗ Persicaria punc- OBL∗ E F A,P 0–0.75 21†,24 tata Polygonum sagittatum∗∗ OBL∗ E C,F A,P 0–0.75 183 Polygonum sp. 0.07–0.4 6 Polygonum spp. 0–0.3 21† Polygonum stelligerum F 0.7 24 Polygonum viviparum FAC∗ T AM,EMAM,EM +,EM 9†,86† Rumex acetosella FAC∗ T NM NM 0.05 48 Rumex altissimus FACW∗ E F P AM 0.1–0.2 21† Rumex crispus FAC∗ T AM AM 0.04 81 Rumex hydrolapathum OBL E F P NM 0 7 Rumex nepalensis AM 0.28 17† Rumex verticillatus∗∗ OBL∗ E F P 0–0

Portulacaceae Montia fontana OBL∗ E NM NONE - 51†

Primulaceae Aegiceras corniculatum A. majus, Rhi- OBL E S P + 49,76 zophora cornicu- lata Glaux maritima OBL∗ E F P AM AM 0–0.42,+ 36,56,72,89

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Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Lysimachia ciliata FACW∗ T 0.65 93 Lysimachia nummularia∗∗ FACW∗ E F P AM AM 0.25–0.5 Lysimachia terrestris OBL∗ E F P AM AM 0.1 21† Lysimachia thyrsiﬂora OBL∗ E F PAMAM 0 7 Lysimachia vulgaris FACW∗ E F P AM AM AM 0.52,+ 48,55,58† Samolus valerandi OBL∗ AMAM + 55

Ranunculaceae Aconitum delphiniifolium FAC∗ AM + 86† Anemone nemorosa AM AM 0.37 48 Anemone rivularis NM 0 17† Batrachium peltatum Ranunculus pelta- OBL FL FA,P NM 0 7 tus Caltha palustris OBL∗ E F P AM AM AM 0–0.28,+ 7,10,21†,48,58† Clematis virginiana FAC∗ E,T C P 0.56–0.86 23†,93 ∗ 184 Coptis trifolia FACW + 53 Ranunculus aquatilis OBL∗ SFP NM - 43† Ranunculus crassipes AM + 51† Ranunculus cymbalaria OBL∗ E F P + 30† Ranunculus ﬂammula R. reptans FACW∗ E F P AM AM 0.06–0.36,+ 7,48,61 Ranunculus hyperboreus OBL∗ T - 91† Ranunculus limosella OBL S,E F P AM 0.71–0.86 18† Ranunculus lingua AM AM 0–0.5 79 Ranunculus circinatus Batrachium circi- OBL∗ S,F F A,P NM 0 7 natum Ranunculus repens FAC∗ E,T F P AM AM 0.35,+ 48,58† Ranunculus amphitrichus R. rivularis OBL S,E F P AM 0.36–0.84 18† Ranunculus sceleratus∗∗ OBL∗ E F A,P AM AM 0–0.56 81 Ranunculus sp. S AM 0–0.84 18† Ranunculus spitsbergensis - 91† Ranunculus sulphureus FACW∗ - 9† Thalictrum pubescens FACW∗ 0.98 93 Trollius europeus AM AM 0.1–0.2 63

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Rhizophoraceae Bruguiera parviﬂora OBL E T P D + 49 Bruguiera sexangula B. gymnorrhiza OBL∗ E T P + 49,76 Bruguiera sp. T P + 76 Ceriops decandra OBL E T P + 49,76 Ceriops tagal OBL∗ E S,T P + 49,76 Combretocapus rotundatus NM 0 82 Rhizophora apiculata R. candelaria OBL∗ E ST + 76 Rhizophora mucronata OBL∗ E T + 76

Rosaceae Acaena magellanica FACW AM + 51† Acaena platyacantha FAC + 30† Dasiphora fruticosa D. ﬂoribunda, Po- FACW∗ T S P AM AM 0.67–0.92 10,90,93 tentilla f. Dryas octopetala AM,EM AM,EM EM 86† 185 Filipendula ulmaria FACW E F P AM AM NM 0,+ 58†,69† Geum rivale FACW∗ T AM AM 0.61 93 Geum virginianum FAC∗ E,T F P AM 0.9 21† Photinia pyrifolia FACW∗ E S P 0.1 21† Pleurophyllum hookeri AM + 51† Potentilla anserina Argentina a. FACW∗ E F P AM AM 0.17–0.18,+ 58†,94† Rosa sp. 0.1 21† Rubus chamaemorus FACW∗ E F,S P NM NM - 84† Rubus pubescens FACW∗ T F P 0.28–0.85 23†,93 Sanguisorba oﬃcinalis FACW∗ E F P AM AM 0.25,+ 48,58† Spiraeaalba S.latifolia FACW∗ E S P AM 0.1 21† Spiraea tomentosa FACW∗ E S P AM 0.1 21†

Rubiaceae Cephalanthus occidentalis OBL∗ E S P AM 0.1–0.6 21† Coprosma perpusilla NM - 51†

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Cruciata laevipes FAC E F P + 52 Galium labrodoricum OBL∗ 0.48 93 Galium palustre OBL∗ E F P AM AM 0.06,+ 48,58† Galium rotundifolium AM 0.16 17† Galium sp. C 0.1 21† Galium uliginosum NM NM 0.06 48 Spermacoce ﬂoridana FACW E F A 0.19–0.3 3†

Ruppiaceae Ruppia maritima OBL∗ SFPD ? 083† Ruppia polycarpa OBL S F A,P AM 0–0.96 18†,45

Salicaceae Chosenia arbutifolia 0–0.008 35 Populus balsamifera FACW∗ ETP EM +54 Populus canadensis P. euroamericana E AM,EM AM,EM ∗∗ ∗ †

186 Populus deltoides FAC T T P AM 0.051–1 21 Salix arctica FAC∗ T EM EM 9†,86† Salix babylonica FAC∗ T AM,EMAM,EM + 44 Salix bebbiana FACW∗ T 0 93 Salixcalcicola S.lanata FACW∗ 0.07 26 Salix candida OBL∗ E S P EM 84† Salix caroliniana OBL∗ E T P 0 3† Salix discolor FACW∗ 0 93 Salix glauca FAC∗ T 0.19 26 Salix lucida FACW∗ T 0 93 Salix nigra∗∗ FACW∗ T T P AM 0–0.75,EM 21† Salix nigricans S. myrsinifolia EM,NM EM,NM 0.2 26 Salix pedicellaris OBL∗ E S P + 84† Salix planifolia OBL∗ E S,T P + 84† Salix polaris FACW∗ P EM EM86† Salixpulchra S.phylicifolia P EM EM 0.34 26 Salix repens FACW E S P AM,EM AM,EM 0.003–0.11,+ 58†,88 Salix reticulata FAC∗ T P EM EM 0.2–0.26,EM 26,86† Salix sericea OBL∗ E S,T P 0–0.03 23†,93 Salix udensis S. sachalinensis P 0–0.01 35

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Santalaceae Arjona pusilla OBL∗ - 30†

Sapindaceae Acer rubrum FAC∗ T T P AM 0.1–0.8 21†,93

Sapotaceae Palaquium gutta OBL∗ AM 0–0.17 82

Saxifragaceae Parnassia glauca OBL∗ + 90 Parnassia palustris OBL∗ E F P AMAM 27†,58† ± Saxifraga hieraciifolia FAC∗ NM - 86† Saxifraga hirculus OBL∗ T NM -9†,86† Saxifraga rivularis FACW∗ T - 9†

Scrophulariaceae ∗ † 187 Agalinis linifolia FACW E F P 0.21 3 Bacopa monnieri OBL∗ E F P NM 0–0.12 3†,4†,67† Calceolaria biflora F P + 30† Centranthera cochinchinensis C. hispida FAC E F A NM 0 67† Chelone glabra OBL∗ 0.35–0.39 93 Dopatrium nudicaule OBL E F NM 0 67† Euphrasia rostkoviana E. officinalis FACW E F A - 58† Glossostigma elatinoides OBL S,E F P AM 0–0.88 18† Glossostigma submersum OBL S,E F AM 0–0.98 18† Gratiola officinalis 0.002–0.33 78,79 Leucospora multifida OBL∗ 0.05 81 Limnophila indica OBL∗ S,E F P NM 0,+ 66,67† Limosella australis OBL∗ S,E F A + 30† Limosella lineata OBL S,E F AM 0–0.8 18†

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Lindernia ciliata + 66 Lindernia dubia∗∗ OBL∗ E F A,B 0.051–0.75 81 Mimulus alatus OBL∗ 0.39 81 Mimulus glabratus OBL∗ E F P + 30† Mimulus guttatus OBL∗ E FA,P AM 0 7 Mimulus ringens∗∗ OBL∗ E F P AM 0–1 21† Pedicularis lanata P. kanei FAC∗ NM - 86† Pedicularis langsdorﬃi FACW∗ NM - 86† Pedicularis sudetica FACW∗ E - 9† Pedicularis verticillata FAC∗ NM - 86† Peplidium maritimum FACW F,E F A NM 0 67† Veronica anagallis-aquatica OBL∗ E F B,P AM AM 0–0.4,+ 7,30†,79 Veronica beccabunga OBL∗ E F A,P NM NM 0–1 7,39 Veronica scutellata∗∗ OBL∗ E F P 0–0

Solanaceae

188 Physalis longifolia 0.28 81 Physalis turbinata 0.16 81 Solanum dulcamara FAC∗ T S,C PAMAM 0 7 Solanum ptycanthum 0.11 81

Sphenocleaceae Sphenoclea zeylanica OBL∗ E F A AM 0.23 67†

Tamaricaceae Tamarix chinensis FACW∗ AM 0.025–0.031 92 Tamarix gallica FACW∗ ESP AM +76

Tetrameristaceae Tetramerista glabra OBL∗ AM 0.15 82

Thymelaeaceae Gonystylus bancanus FACW∗ AM 0–0.58 82 Thymelaea hirsuta FACW∗ 0 1

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Urticaceae Pilea fontana∗∗ FACW∗ E F A 0.25–0.75 Pilea pumila FACW∗ 0.32 93

Verbenaceae Avicennia alba OBL E T P 49,76 Avicennia marina OBL∗ E S,T P +± 49,76 Avicennia oﬃcinalis OBL E T P + 49,76 Clerodendrum inerme FACW∗ E S P + 76 Phyla lanceolata∗∗ OBL∗ E F P 0.75–1 Phylanodiﬂora Lippian. FACW∗ E F P AM AM 0.17–0.72 3†,22,67†,81 Verbena hastata FACW∗ E F B,P AM 0.8–1 21† Verbena scabra FACW∗ E F A,B,P 0.31–0.43 3†

Violaceae Viola cucullata FACW∗ 0.58–0.76 93 Viola nephrophylla FACW∗ + 27† 189 Viola sp. 0.1 21†

MONOCOTS Acoraceae Acorus calamus OBL∗ E F P NM AM 0.19 10

Alismataceae Alisma plantago-aquatica OBL∗ E F P NM AM 0–0.37 6,7,42,78 Alisma subcordatum∗∗ OBL∗ E F P AM 0–0.75 94† Alisma triviale OBL E F P AM 0–0.5 21† Baldellia ranunculoides OBLEF P AM +55 Sagittaria graminea OBL∗ E F P AM 0.21 45 Sagittaria guayanensis OBL∗ FLF P NM - 43† Sagittaria latifolia∗∗ OBL∗ E F P AM NM 0–0.75,- 2†,6,94† Sagittaria montevidensis OBL∗ E F P 0 24 Sagittaria sagittifolia OBL∗ E 0.22 42 Sagittaria spp. OBL 0–0.2 21† Sagittaria trifolia OBL E 0–0.03 42

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Aponogetonaceae Aponogeton distachyos OBLFLF P NM 0 18† Aponogeton natans OBL FL F P AM 0.3 67†

Araceae Colocasia esculenta FACW∗ E F P AMNM 0 67† Lasia spinosa NM 0 17† Lemna gibba OBL∗ F F P NM 0–0.2 39,67† Lemna minor OBL∗ F 0 42 Lemna perpusilla OBL∗ F* NM 0 83† Peltandra virginica OBL∗ E F P 0.1 21† Pistia stratiotes OBL∗ F F P NM NM 0–0,+ 24,66,67† Spirodela polyrrhiza Lemna p., S. OBL∗ F F P AM 0.1,- 43†,67† polyrhiza Steudnera colocasioides NM 0 17† 190 Symplocarpus foetidus∗∗ OBL∗ E F P 0–0.1 21†,93

Arecaceae Nypa fruticans OBL∗ E T + 76 Phoenix paludosa OBL E S + 49,76

Butomaceae Butomus umbellatus∗∗ OBL∗ E F P NMNMNONE 0–0

Commelinaceae Commelina diﬀusa FACW∗ E F A,P 0.03–0.46 3† Commelina sp. E AM 0 67† Cyanotis cristata Commelina c. F A AM 0.81 4† Murdannia semeteres + 66

Cyperaceae Bolboschoenus robustus Scirpus r. OBL∗ E G P AM AM 0.7 21† Bolboschoenus ﬂuviatilis Scirpus f. OBL∗ E G P AM NM AM 0–0.3,- 2†,21†,71†

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Carex amphibola FACW∗ G NM NM 0 60† Carex annectens FACW∗ G AM AM 0.05 60† Carex aquatilis OBL∗ E G P NM NM - 9†,84† Carex atherodes OBL∗ E G P AM AM 0–0.56 60†,71†,94† Carex bicknellii FAC∗ G AM AM 0.26 60† Carex bigelowii FAC∗ TG NM NM - 86† Carex binervis G Carex blanda FAC∗ G AM AM 0.05 60† Carex brevior FAC∗ E? G AM AM 0.27 60† Carex buxbaumii OBL∗ E G NMAM AM 0.2 60† Carex cephalophora FAC∗ G AM NM 0.08 60† Carex comosa∗∗ OBL∗ E G P 0–0.15 10 Carex crawei FACW∗ T G AM AM 0.05 60† Carex cristatella FACW∗ E G P AM AM 0.09–0.44 6,60† Carex davalliana OBL E G PAMAM + 58† Carex ﬂacca FACW E G P AMAM NM - 58† Carex ﬂava OBL∗ E G P AMAM NM 0–1, 21†,23†,58†,90,93 Carex frankii∗∗ OBL∗ E G P 0–0± 191 Carex granularis FACW∗ E G P AM AM 0.16–0.72 60†,87 Carex gravida G AM AM 0.2 60† Carex hystericina OBL∗ E G P NM NM 0–0.36 10,23†,93 Carex interior FACW∗ TG NM NM 0 60† Carex lacustris OBL∗ EGP NM -2† Carex lasiocarpa OBL∗ E G P NM AM AM 0–0.34,- 6,23†,84†,93,94† Carex leporina FAC∗ T G Carex leptalea OBL∗ T G 0 93 Carex lupulina OBL∗ E G P Carex lurida OBL∗ E G P AM AM 0.19–0.8 6,21† Carex lyngbyei OBL∗ G NM Carex maritima FAC∗ E,T G P NM NM - 30† Carex membranacea FACW∗ G NM NM+9† Carex nigra C. acuta FACW∗ E G P AM AM AM AM 0–0.1 7,21†,48 Carex panicea OBL E G P AMAMAM + 58† Carex pellita OBL∗ E G P NM NM 0–0 60†,87 Carex pendula G NMNM - 55 Carex pensylvanica G AM AM 0.04 60† Carex podocarpa FAC∗ TG NM NM - 86† Carex prairea FACW∗ G 0 93

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Carex rosea G AM AM 0.16 60† Carex rostrata OBL∗ E G P NM NM NM 0,- 48,84† Carex scoparia∗∗ FACW∗ E G P AM AM 0–0.1 21†,60† Carex sp. G AM 0–0.04 31 Carex spp. G 0.1–0.7 21† Carex sprengelii FAC∗ TG NM NM 0 60† Carex sterilis OBL∗ E G P NM NM 0–0.55 10,23†,93 Carex stipata OBL∗ E G P AM AM 0.32–0.6 21†,60† Carex stricta OBL∗ E G P AM NM AM 0–0.33,- 2†,10,21†,60†,94† Carex subantarctica G NM - 30† Carex tenera FAC∗ G NM NM 0 60† Carex tetanica FACW∗ G AM 0.05 60† Carex tribuloides∗∗ FACW∗ E G P AM AM 0–0.5 6,21† Carex trichocarpa OBL∗ E G P AM 0.44–0.48 87 Carex trifida G NM NM - 51† Carex trisperma OBL∗ E G P - 54 Carex ursina FACW∗ G NM NM - 9† Carex utriculata OBL∗ E G P NM NM - 84† 192 Carex vesicaria OBL∗ E G AM AM AM 0–0.13 48,94† Carex vulpinoidea∗∗ OBL∗ E G P AM AM 0–0.8 6,21†,60† Cladium mariscus C. jamaicense OBL∗ E G P AM AM AM AM 0.03–0.23,- 3†,55 Cyperus articulatus OBL∗ E G P AM AM AM 0.08–0.27 3†,22,67† Cyperus bulbosus G NMNMNM 0 83† Cyperus difformis OBL∗ E G A AMNMAM - 43† Cyperus distans OBL∗ G AMNMAM 0 17† Cyperus eleusinoides OBL E G P NM 0,- 4†,43† Cyperus erythrorhizos∗∗ OBL∗ E G A,P 0–0.043 81 Cyperus esculentus∗∗ FACW∗ E G P NM NM 0–0 Cyperus exaltatus FACW E G P NMNMNM 0 67† Cyperus flavescens OBL∗ E G A AM 0.14–0.34 87 Cyperus giganteus OBL∗ E G P 0 24 Cyperus haspan OBL∗ E G P AM NM 0.05–0.13 3† Cyperus javanicus FACW∗ E G P NMNMNM 0 67† Cyperus laevigatus Juncellus l. FACW∗ G AMNMAM 0 83† Cyperus ligularis FACW∗ E G P AM AM 0.08–0.44 3† Cyperus odoratus∗∗ FACW∗ G AM AM 0–0 Cyperus polystachyos Pycreus p. FACW∗ E G A,P AMNMAM 0 67†

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Cyperus spp. G AM AM 0.7–0.8 21† Cyperus strigosus FACW∗ E G P AM NM 0.14–0.26 87 Cyperus surinamensis FACW∗ E G P AM AM 0.24–0.38 3† Cyperus tenuispica FACW E G P NMNMNM 0 67† Dulichium arundinaceum OBL∗ E G P AM 0.7 21† Eleocharis acutangula FACW E G P AM AM AM 0.1 67† Eleocharis atropurpurea FACW∗ T G A 0.23 3† Eleocharis congesta OBL∗ G NM 0 17† Eleocharis elliptica G 0 93 Eleocharis erythropoda OBL∗ EGP NM 087 Eleocharis geniculata FACW∗ G AMNMAM 0 83† Eleocharis obtusa∗∗ OBL∗ E G A,P 0–0.75 Eleocharis ovata OBL∗ E G A AM AM 0–0.9 21† Eleocharis palustris∗∗ OBL∗ S,E G P AM AM AM 0–0.75,+ 2†,7,10,77† Eleocharis pusilla OBL S,E G A AM 0–0.56 18† Eleocharis quinqueﬂora E. pauciﬂora FACW∗ T G NMNMAM + 27† Eleocharis sp. G AM - 30† 193 Eleocharis spp. G 0–0.7 21† Eleocharis tenuis FACW∗ E G P NM NM 0 23† Eriophorum angustifolium E. triste OBL∗ TG AMAM -9† Eriophorum scheuchzeri OBL∗ T G - 9†,46,91† Eriophorum vaginatum OBL∗ EGP NM -84† Eriophorum viridcarinatum OBL∗ G 0.11 93 Fimbristylis cymosa FAC∗ G NM AM NM 0.05–0.1 83† Fimbristylis ferruginea OBL∗ G AMNM + 83† Fimbristylis hispidula G NM 0 83† Fimbristylis miliacea OBL∗ E G A AMNMAM 0 67† Fimbristylis vahlii FACW∗ 0.047 81 Fuirena breviseta OBL∗ E G P 0.08–0.1 3† Isolepis aucklandica G NM NM - 51† Oreobolus furcatus FACW∗ E G P AM AM 0–0.25 47† Oxycaryum cubense Oxycarium c. OBL∗ F,P G P 0 24 Rhynchospora microcarpa FACW∗ E G P 0.05–0.14 3†

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Schoenoplectus acutus OBL∗ E G P AM AM 0–0.43 6,93,94† Schoenoplectus juncoides Scirpus j. OBL∗ G NM NM 0 17† Schoenoplectus maritimus OBL∗ E G P NM AM AM 0.096–0.49,- 34,39,94† Schoenoplectus pungens Scirpus p. OBL∗ E G P AM 0.18–0.42 87 Schoenoplectus sp. EG NMNM 0 67† Schoenoplectus tabernaemontani∗∗ Scirpus validus OBL∗ E G P AM AM 0–0.85,- 2†,6,45,87 Schoenus ferrugineus OBL E G PAMAM + 58† Schoenus nigricans OBL∗ E G PAMAM + 58† Schoenus sp. G NM - 30† Scirpoides holoschoenus G NM - 55 Scirpus articulatus FACW∗ EGP NM 0 17† Scirpus atrovirens OBL∗ E G P AM AM 0–0.9 6,21†,87,93,94† Scirpus cyperinus∗∗ OBL∗ E G P AM AM 0–0.8 6,21† Scirpus lateriﬂorus G - 66 Scirpus pendulus OBL∗ E G P AM 0.14–0.48 87 Scirpus perpusillus G NM NM - 30† †

194 Scirpus pterolepis G NM 0 83 Scirpus sp. G AM 0.2–1,- 21†,39,76 Scirpus sylvaticus G NMNM 0 48 Trichophorum cespitosum Scirpus cespitosus OBL∗ E G P AMAM NM - 84† Trichophorum pumilum Scirpus pumilus FACW∗ TG AM +27† Uncinia divaricata G NM + 51† Uncinia hookeri G NM + 51†

Eriocaulaceae Eriocaulon aquaticum E. septangulare OBL∗ S,E F P AM 0.13–0.14 28† Eriocaulon cinereum OBL∗ E F A,P AM 0.3,+ 66,67†

Hydrocharitaceae Blyxa echinosperma + 66 Blyxa octandra OBL∗ S F AM 0.39 67† Egeria densa OBL∗ S,F F P NMNM 0 18† Egeria najas OBL S,F F P 0 24 Elodea canadensis OBL∗ S F P NM NM AM 0–0.32 7,18†

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Hydrilla verticillata OBL∗ S F P NM NM AM 0–0.95,- 17†,18†,39,42,43†,67† Hydrocharis dubia OBL∗ 0 42 Lagarosiphon major OBL S F P AM 0–0.36 18† Limnobium variegatum 0.015 24 Najas graminea OBL∗ S,F F A AM 0.15,- 43†,67† Najas minor ∗∗ OBL∗ S,F F A 0–0.75,- 66 Nechamandra alternifolia OBL S F AM 0.4 67† Ottelia alismoides OBL∗ S,FL F P AM 0.45 67† Ottelia ovalifolia OBL S,FL F P NM 0–0 18†,45 Thalassia testudinum OBL∗ - 62 Vallisneria americana V. spiralis OBL∗ S F P NM NM 0.8–1,- 39,43†,97 Vallisneria gigantea OBL∗ SFP NM 018† Vallisneria natans OBL S F A AM 0–0.24 42,67†

195 Iridaceae Gladiolus gandavensis NONE 0 42 Iris pseudacorus OBL∗ E F P AMAMNONE + 52,55 Iris versicolor OBL∗ NONE 0.41 93 Sisyrinchium arenarium F P NONE + 30†

Juncaceae Juncus acuminatus∗∗ OBL∗ E G P 0–0.75 Juncus arcticus OBL∗ E G P - 30† Juncus articulatus OBL∗ E G AMAM 0 48 Juncus biglumis OBL∗ EG NMNM -9† Juncus brachycephaulus OBL∗ E G 0–0.11 93 Juncus bufonius FACW∗ E G A AMAMNM - 43† Juncus bulbosus OBL∗ S,FL,E G P NMNM 0 7 Juncus canadensis OBL∗ E G P 0.5 21† Juncus dudleyi FACW∗ E G P AM 0.08–0.56 87 Juncus eﬀusus∗∗ FACW∗ E G P AM AM 0–1,- 7,21†,48,58† Juncus gerardii FACW∗ E G P AM AM AM 0–0.06, 20†,21†,36,56,72 Juncus maritimus OBL E G P NM NM 0,-± 56,72 Juncus megacephalus OBL∗ E G P 0.19–0.31 3† Juncus nodosus OBL∗ E G P AM 0–0.4 21†,87

Continued on Next Page. . . Table 5.4 – Continued Juncus roemerianus OBL∗ EGP AM +38 Juncus scheuchzerioides G AM + 51† Juncus spp. G 0.1–1 21† Juncus squarrosus G AM AM Juncus stipulatus G - 30† Juncus tenuis FAC∗ E,T G P 0–0.05 21†,71†,93 Juncus torreyi∗∗ FACW∗ E G P AM 0–0.78 87 Luzulaarctica L.nivalis FAC∗ G - 9† Luzula confusa FAC∗ G - 91† Luzula crinita G NM - 51†

Juncaginaceae Triglochin maritima T. concinna OBL∗ E G P AM AM 0–0.05,- 30†,36,41,56,72,89 Triglochin palustris OBL∗ EGPNM NM - 30† Triglochin procera OBL G NM 0 45

Liliaceae Colchicum autumnale FAC E,T F P AM AM + 58† ∗ † 196 Maianthemum trifolium Smilacina trifolia OBL E F P - 84 Narthecium ossifragum AM AM Streptopus lanceolatus S. roseus FAC∗ E,T F P + 54 Veratrum viride FACW∗ E F P AM 0.1 21†

Orchidaceae Corybas dienemus OR OR 51† Epipactis palustris OBL E F P OR,NM OR,NM - 58† Spiranthes spiralis OR OR OR 55

Poaceae (Gramineae) Aeluropus lagopoides A. repens FAC E G P AM NM 0–0.035 67†,92 Aeluropus littoralis FAC E,T G P Agrostis gigantea A. alba FAC E G P AM AM 0.1–1,+ 21†,56 Agrostis glabra G P + 30† Agrostis magellanica G AM - 51† Agrostis sp. G 0.41,- 30†,39 Agrostis stolonifera FACW∗ T G AM AM 0.08–0.45 48,93

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Alopecurus alpinus FACW∗ T G - 9† Andropogon glomeratus FACW∗ E G P 0.13–0.25 3† Brachiaria eruciformis G 1 13 Bromus ciliatus FAC∗ G 0.57–0.78 93 Calamagrostis canadensis FACW∗ E G P 0.28–0.41, 54,84†,93,94† Calamagrostis stricta C. inexpansa FACW∗ E G AM AM 0.45± 41 Cinna arundinacea FACW∗ E G P AM 0.8–0.9 21† Cortaderia araucana G + 30† Crypsis vaginiﬂora C. aculeata OBL∗ G 0.08 50 Deschampsia cespitosa FACW∗ E G P AM AM AM 0.32–0.61, 40,48,51†,58† ± Deschampsia chapmanii G AM + 51† Deschampsia ﬂexuosa G AM AM 0.45–0.5 63 Dichanthelium clandestinum Panicum c. FAC∗ E,T G P AM 0.41–0.62 6 †

197 Distichlis scoparia FACW E G P + 30 Distichlisspicata D.stricta FACW∗ E G P AM AM 0–0.9,+ 20†,38,41,94† Dupontia ﬁsheri FACW∗ G - 9† Dupontia pelligera G - 91† Echinochloa colona FACW∗ E G A AM 0–0,+ 4†,67†,76 Echinochloa crus-galli FACW∗ E G A AM AM 0–1 21† Echinochloa picta FACW E G P NM 0 67† Echinochloa spp.∗∗ G 0–0.75 Elymus pycnanthus E. pycnantha, FACW∗ E G P AM AM 0.2–0.75 89 Thinopyrum pyc- nanthum Eragrostis hypnoides∗∗ OBL∗ E G A 0.75–1 Eragrostis prolifera E. domingensis FACW E G P 0.1–0.22 3† Festuca contracta G NM - 51† Festuca pallescens GP AM +30† Festuca pseudovina G 0.82 50 Festuca rubra FAC∗ T G P AM,EM AM,EM 0–0.85,+ 36,58†,72,89 Festuca sp. G 0.7 21†

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Glyceria fluitans OBL∗ G NM NM 0–0.16 78,79 Glyceria maxima OBL∗ S,E G PNMNM 0 7 Glycerianotata G.plicata OBL E G P AMAMAM 0.15 69† Glyceria striata OBL∗ E G 0.37–0.47 93 Hierochloe odorata FACW∗ E G P AM AM 0.24–0.46 87 Hierochloe pauciflora FACW∗ G - 9† Holcus lanatus FAC∗ T G P AM AM 0.08,+ 30†,48,58† Hordeum brachyantherum FACW∗ E G P AM 0.71–0.96 40 Hordeum jubatum FAC∗ T G AM 0.2–0.9 41,94† Hordeum pubiflorum G P + 30† Leersia hexandra OBL∗ E G P AM 0–0.55,+ 59,76 Leersia oryzoides∗∗ OBL∗ E G P NM AM 0–1 6,21†,93 Leptochloa panicea L. filiformis FACW∗ E G A,P 0.05–0.58,+ 3†,76 Lygeum spartum G AM 0.3 14 Molinia caerulea OBL E G P AM AM AM 0.06–0.14,+ 31,58† 198 Nardus stricta G AMAM Oryza coarctata Porteresia c. OBL E G P AM 0.54,+ 49,75†,76 Oryza sativa OBL∗ E G A AM AM 0.5 67† Panicum brevifolium G AM 0.46 17† Panicum dichotomiflorum∗∗ FACW∗ T G A 0.25–0.75 Panicum hemitomon OBL∗ G AM 0–0.9 59 Panicum psilopodium FAC E,T G A AM 0–0.1 4†,67† Panicum sp. G AM 0.25 67† Panicum virgatum FAC∗ T G AM 0.022–0.093 94† Paspalidium geminatum OBL∗ E,FL G P AM 0–0 4†,67† Paspalum dilatatum FAC∗ G AM AM 0.15–0.6 17†,33 Paspalum distichum P. paspaloides FACW∗ E G AM 0.3–0.5 5,45 Paspalum fluitans P. repens OBL∗ S,F,E G A 0.02 24 Paspalum scrobiculatum FAC∗ E,FL,T G A,P AM 0.4 67† Paspalum vaginatum FACW∗ T G AM 0–0.6 32,83† Phalaris arundinacea∗∗ FACW∗ E G P AM AM AM 0–0.9 6,7,10,21†,48,69†,71† Phippsia algida OBL∗ T G - 9†

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Phragmites australis P. communis FACW∗ E G P AMAMAM 0–1, 7,13,19,20†,21†,42,45, 55,58†,77†,92 ± Phragmites karka OBL∗ E G P NM 0.5, 39,43†,76 ± Poa annua FAC∗ TG AMAM - 51† Poa arctica FAC∗ TG NM -86† Poa cookii G AM + 51† Poa foliosa G NM - 51† Poa holciformis G - 30† Poa literosa G NM - 51† Poa palustris FAC∗ E,T G P AM AM 0.26 10 Poa trivialis FACW∗ E G P AM AM AM 0.06–0.08 48,69† Pseudoraphis spinescens OBL E,FL G P NM 0–0 45,67† Puccinellia distans FACW∗ T G AM AM 0–0.49 36 Puccinellia limosa G 0–0.4 50 Puccinellia macquariensis G AM + 51† Puccinellia maritima Glyceria m. OBL∗ E G P AM AM 0–0.35,+ 16,36,56,72,89 Puccinellia nuttalliana FACW∗ G AM 0.015–0.18 41,94† ∗ † 199 Puccinellia phryganodes OBL G - 9 Puccinellia vahliana Colpodium G - 9† vahlianum Rytidosperma sorianoi G + 30† Saccharum giganteum FACW∗ E G P 0.13 3† Saccharum spontaneum FAC∗ G AM AM 0.2–0.8 5 Sacciolepis interrupta OBL E,F G P AM 0.43 67† Sesleria uliginosa S. caerulea FACW E G P + 58† Setaria parviﬂora FAC∗ T G P 0.1–0.27 3† Setaria sp. G 1 39

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Spartina alterniﬂora OBL∗ E G P AM NM 0,- 20†,38 Spartina anglica OBL E G P NM NM 0–0.05 36,72,89 Spartina cynosuroides OBL∗ E G P AM 0.21–0.22 38 Spartina gracilis FACW∗ T G AM 0.65–0.85 41 Spartina maritima OBL S,E G P 0 16 Spartina patens FACW∗ E G P AM AM 0.08–0.52,+ 20†,38 Spartina pectinata FACW∗ T G AM 0.4–0.7 94† Sphenopholis obtusata FAC∗ E,T G A,P AM 0.34–0.42 87 Sporobolus virginicus FACW∗ G AM 0.48 22 Stenotaphrum secundatum FAC∗ G 0.7–0.8 32 Stipa chrysophylla G P - 30† Thinopyrum junceiforme Agropyron G 0.08–0.17 36 junceum Urochloa mutica FACW∗ E G P 0.11–0.31 3† Vetiveria zizanioides FAC∗ G 0.1–0.5 5 200 Zizania aquatica OBL∗ E G A 0.1 21† Zizanialatifolia Z.caduciﬂora OBL∗ G 0 42

Pontederiaceae Eichhornia azurea OBL∗ E F P 0 24 Eichhornia crassipes OBL∗ F,FL F P AM 0–0.92,- 4†,24,39,42,43†,67† Monochoria hastata M. hastaefolia OBL E,FL F P AM 0–0.25 17†,67† Monochoria vaginalis OBL∗ E,FL F A,P AM 0.39,+ 66,67† Pontederia cordata OBL∗ E F P 0.03–0.1 21†,24

Potamogetonaceae Lepilaena bilocularis OBL S FA,P NM 0 18†

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Potamogeton cheesemanii OBL S,FL F P AM 0–0.24 18† Potamogeton crispus OBL∗ S F A,B NM NM AM 0–0.3,- 7,18†,43† Potamogeton foliosus∗∗ OBL∗ S F P 0–0.75 Potamogeton gramineus OBL∗ S,FLFP NM 07 Potamogeton illinoensis OBL∗ S,FLF P NM 0 42 Potamogeton lucens OBL∗ S,FL F P NM 0–0 7,42 Potamogeton malaianus 0 42 Potamogeton natans OBL∗ S,FL F P NM AM 0–0.84 6,7 Potamogeton nodosus∗∗ P. indicus, P. OBL∗ S,FL F P AM 0–1,- 39,43†,67† malaianus Potamogeton ochreatus OBLSFP NM 018† Potamogeton oxyphyllus OBL S F P 0 42 Potamogeton perfoliatus OBL∗ S F P NM 0–0 7,42 Potamogeton praelongus OBL∗ SFP NM 07 Potamogeton tepperi OBL FL F P 0 42 Stuckenia pectinata Potamogeton pecti- OBL∗ S F P NM 0–0.09 18†,42 natus

201 Zannichellia palustris OBL∗ S F P AM 0.05 18†

Typhaceae Sparganium angustifolium OBL∗ S,FLFP NM 07 Sparganium emersum OBL∗ S,FL,E F P AM 0–0.18 6,7,79 Sparganium erectum S. chlorocarpum OBL∗ S,FL,E F P AM AM 0 7 Sparganium eurycarpum∗∗ OBL∗ E F P AM (weak) NM 0–0.21,- 2†,10,94† Sparganium ramosum NM 0 17† Sparganium sp. 0.1 21† Typha angustifolia∗∗ OBL∗ E F P AM AM AM 0–0.75, 7,19,55,80,93 ± Typha domingensis T. angustata OBL∗ E F P AM 0–0.21,- 3†,4†,43†,67†,83† Typha elephantina 0.28 12 Typha glauca OBL∗ E F P AM (weak) 0.01–0.09 71†,80 Typha latifolia∗∗ OBL∗ E F P AM NM 0–1, 2†,7,10,19,21†,23†, 68,80,84†,87,93 Typha orientalis NM 0–0± 42,45 Typha spp. E F P 0.04–0.54 6

Zosteraceae Zostera marina OBL∗ NONE - 62

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references GYMNOSPERMS Pinaceae Larix laricina FACW∗ E T P EM 53,84† Picea mariana FACW∗ E T P EM EM 53,84†

FERNS Azollaceae Azolla ﬁliculoides OBL∗ F F P NMNM NM Azolla pinnata OBLFFA AM 067†

Blechnaceae Blechnum penna-marina NM NONE - 51†

Ceratopteridaceae Ceratopteris thalictroides OBL∗ NM - 66

202 Dryopteridaceae Onoclea sensibilis∗∗ FACW∗ E F P AM 0–0.7 21†,93 Polystichum vestitum NM - 51†

Marsileaceae Marsilea crenata OBL NONE 0 42 Marsilea minuta NONE 0.3–0.7 5 Marsilea quadrifolia OBL∗ NONE 0,+ 42,66 Pilularia novae-hollandiae OBL S,E F P NONE 0 18† Pilularia globulifera OBL S,E F P AMAMNONE 0 28†

Osmundaceae Osmunda cinnamomea FACW∗ E F P AM 0.1 21†

Polypodiaceae Grammitis poeppigiana FACW∗ NM - 51†

Pteridaceae Adiantum sp. 0.6–0.8 5

Continued on Next Page. . . Table 5.4 – Continued

Species Synonyms WIS Habit Form Cycle HH WQ KB M AM references Salvinaceae Salvinia auriculata OBL∗ F F A,P 0 24 Salvinia biloba OBL F F 0 24 Salvinia cucullata OBL F F A,P? AM 0–0.21 4†,67† Salvinia minima OBL∗ F F A,P 0 24 Salvinia natans + 66

Thelypteridaceae Thelypteris dentata FAC∗ 0.1–0.8 5 Thelypteris palustris Dryopteris the- FACW∗ E F P AM 0–0.75 21†,23†,93 lypteris

HORSETAILS Equisetaceae Equisetum arvense FAC∗ E,T F P AM AM NONE 0–0.7, 23†,25,37,52,86†,87,93 ± Equisetum ﬂuviatile OBL∗ E F P NM NM NONE 0–0.05,- 25,48,84† Equisetum hyemale FACW∗ E F P NM AM NONE 0.05–0.4 25,37 ∗

203 Equisetum laevigatum E. kansanum FACW E F P NONE 0.03–0.2 25 Equisetum palustre FACW∗ E F P AM NM NONE 0.25–0.33 25 Equisetum pratense FACW∗ E F P NMNMNONE 0.3 25 Equisetum ramosissimum FACW E F P NMAMNONE - 55 Equisetum scirpoides FAC∗ E F P NONE 0.18–0.23 25,37 Equisetum sylvaticum FACW∗ E F P AM AM NONE 0.39 25 Equisetum variegatum FACW∗ E F P AM AM NONE 0–0.5,- 25,37,86†

LYCOPODS Isoetaceae Isoetes coromandelina FACW E AM 0.4,+ 66,67† Isoetes japonica Isoetes kirkii OBL S G P AM 0–0.28 18† Isoetes lacustris OBL∗ S G P NM NM NM 0–0,+ 28†,61,77†,96

Lycopodiaceae Huperzia australiana AM + 51† Lycopodiella inundata OBL∗ E F P AM AM 0–0.17 31 Lycopodium clavatum FAC∗ AMAM - 86† Lycopodium paniculatum 0–0.23 29 CHAPTER 6

ARBUSCULAR MYCORRHIZAE IN CREATED AND NATURAL WETLANDS OF OHIO

6.1 Introduction

Wetlands are among the world’s most unique and important ecosystems, providing valuable services such as improvement of water quality, ﬂood protection, biodiversity enhancement, and support for recreational activities. Yet over 50 % of the world’s wetlands have already been lost, and more continue to be lost annually (Finlayson and

Spiers, 1999; Dahl, 2006). In the US, programs and legislation, such as the 1985 Food

Security Act, the Wetlands Reserve Program, and Section 404 of the Clean Water

Act, have been enacted to curtail wetland loss and preserve or restore the nation’s wetland base. As demonstrated in Chapters 3 and 4, however, created wetlands diﬀer considerably from natural wetlands in soil structure and nutrient-related function.

Arbuscular mycorrhizae (AM) may be one key to establishing nutrient function in newly created systems. In more terrestrial systems, plant succession typically pro- gresses from domination by non-mycorrhizal ruderals to facultatively mycotrophic species to obligate mycotrophs (Allen and Allen, 1980; Janos, 1980; Allen et al.,

204 1987; Cook et al., 1988; Barni and Siniscalco, 2000; C¸akan and Karata¸s,2006). Con-

sequently, many terrestrial restoration projects—particularly for prairie ecosystems—

have begun inoculating with arbuscular mycorrhizal fungi (AMF) to ensure the suc-

cess of the restoration (Smith et al., 1998; Requena et al., 2001; Bever et al., 2003). It

may be that as in prairie ecosystems, mycorrhizae will be crucial to the establishment

of functional created wetlands.

Mycorrhizal fungi are obligate aerobes and the common assumption has been that they would not be found in anaerobic environments such as wetlands (Harley, 1969;

Khan, 1974; Powell, 1975); in Chapter 5, however, we demonstrated that they are prevalent in such environments, and likely perform similar functions as in terrestrial ecosystems. Only a few studies thus far have considered the role of AM in wetland creation projects. Cooke and Lefor (1990) monitored the restoration of a salt marsh that was revegetated with both purchased plants and plants taken from an adjacent undisturbed marsh. Ten years after replanting, AM were observed in the areas replanted with native plants and in the adjacent undisturbed marsh; the areas replanted with purchased stock, however, lacked AM. Furthermore, in the stock-planted areas, large patches remained unvegetated. The authors suggested that AMF infection might be the key to successful salt marsh restoration. Other studies have observed equal or greater levels of AM in created and restored wetlands compared to natural wetlands

(Confer and Niering, 1992; Aziz et al., 1995; Bauer et al., 2003). The construction and restoration techniques ranged from excavation and soil removal (Confer and Niering,

1992; Aziz et al., 1995) to tile drainage removal (Bauer et al., 2003); however, none

205 were seeded or planted, but instead allowed to revegetate naturally. When AM lev-

els where higher in created wetlands, it was attributed to lower nutrient availability

(Confer and Niering, 1992; Aziz et al., 1995).

Given the importance of AM to terrestrial ecosystem development and the numerous studies demonstrating the presence of AM in aquatic systems (Ch. 5), it is likely that AM are also important to wetland ecosystem development. For 10 created (ages < 1 to 40 years since creation) marshes of central of Ohio, we observed

smaller nutrient stocks and slower nutrient cycles when compared to 5 similar natural

marshes (Ch. 3); diﬀerences which were later determined to be primarily because of

the soil (Ch. 4). Here we measure the abundance of AM with these same systems,

with the expectation that AM will be least abundant in young created wetlands and

most abundant in old created and natural wetlands. Our central hypothesis is that

arbuscular mycorrhizae are critical to the establishment of carbon and nutrient cy-

cling within created wetlands. We further aim to enhance our understanding of AM

ecology and function within the wetland ecosystem, by correlating AM abundance

with various environmental factors.

Four questions relevant to the central hypothesis and research aim will be explored

in this report:

Q1: Does the abundance of arbuscular mycorrhizae diﬀer

between created and natural wetlands?

Q2: Does the abundance of arbuscular mycorrhizae increase

with created wetland age?

206 Q3: What additional environmental factors aﬀect the abun-

dance of arbuscular mycorrhizae?

Q4: Does the presence of arbuscular mycorrhizae improve

wetland function?

6.2 Materials and Methods

6.2.1 Site description and quality assessment

Ten created and ﬁve natural freshwater marsh wetlands located in central Ohio,

USA were selected for this study. The wetlands were classiﬁed as palustrine emergent sensu Cowardin et al. (1979) and depressional sensu Brinson (1993). The created wetlands represented a chronosequence of wetland development, ranging in age from less than 1 y to almost 40 y since construction at the onset of the study. See 3.1.1 for more detailed description of site history and sampling design.

The wetlands were additionally assigned a score representing overall quality ( ), Q following the Ohio Rapid Assessment Method (ORAM; Mack, 2001). The ORAM objectively rates wetland quality by accounting for factors such as habitat intact- ness and heterogeneity, surrounding land-use, presence of rare or endangered species, presence of alien or invasive species, etc. (Mack, 2001). Although the ORAM scor- ing system was designed for use with natural wetlands, we also scored the created wetlands for general indication of quality relative to natural wetlands (Table 6.1).

In addition to the quantitative quality score, a qualitative rating (“quality”) was assigned to each wetland: very low (vlo), < 20; low (lo), 20 < 35; medium (med), Q ≤Q 35 < 55; high (hi), 55 < 75; or very high (vhi), 75 . A second qual- ≤ Q ≤ Q ≤ Q itative rating (“class”) was also assigned to each wetland, combining wetland type,

207 age and quality: young created (Y ), y 7; middle-aged created (M ), 7 < y 20; ≤ ≤ old created (O), 20 < y; impacted natural (N ), < 75; or reference natural (R), Q 75 . ≤Q 6.2.2 Arbuscular Mycorrhizal colonization and root metrics

In fall 2006, summer and fall 2007, and summer and fall 2008, root specimens were collected from dominant plant species (> 5 % cover) at each sampling station.

Collected roots were washed and samples (< 2 mm diameter) cleared in 10 % KOH and stained with a 5 % ink-vinegar solution; samples which could not be processed quickly were preserved in 70 % ethanol until time permitted (Vierheilig et al., 1998, 2005).

The percentage of AM colonization was quantiﬁed using the gridline intersect method

(Giovannetti and Mosse, 1980) at 40 magniﬁcation on 2–4 sample rearrangments for × 100–200 total intersections. Root segment intersections were recorded as mycorrhizal

(M) or non-mycorrhizal (NM).

The ratio of mycorrhizal intersections to total intersections was originally used to quantify the proportion of root length colonized by mycorrhizal fungi; however, later examination of the roots at higher magniﬁcation revealed that this method provided a better measure of total root-fungi abundance, not just mycorrhizal fungi.

The presence of root hairs (RH) was also recorded for each root-gridline intersection.

Proportional root length colonized by fungi (pF ) or covered with root hairs (pRH ) was calculated as

M pF = (6.1) M + NM RH pRH = (6.2) M + NM

208 Site Type Age Quality Class Soil Saturation Condition n Plant species Q PPA created 0.5 26 lo Y mineral r–fc Dry–Deep 24 ALISUB, ECHSPP, ELEOBT, LUDPAL, POLPER, TY- PANG, TYPLAT PPB created 3 33 lo Y mineral r–s Dry–Deep 32 ALISUB, BIDFRO, ECHSPP, ELEOBT, LEEORY, LUD- PAL, POLPEN, POTFOL, POTNOD, TYPANG, TY- PLAT BB created 5 43 med Y mineral r,s Dry–Deep 51 ALISUB, BIDCER,BIDFRO, ECHSPP, ELEOBT, ELEPAL, GALSPP, JUNEFF, LEEORY, LUDPAL, NAJMIN, POLHYD, POLPEN, POTFOL, SAGLAT, TYPANG, TYPLAT BIC created 6 51 med Y mineral fc–s Dry–Moist,Deep 30 ALISUB, ECHSPP, ELEPAL, LEEORY, PHAARU, PO- LAMP, POLLAP, POTFOL, POTNOD, TYPLAT SA created 7 35 med Y mineral r–wp,s Dry–Deep 41 BIDCER, ECHSPP, ELEOBT, GALSPP, JUNEFF, LEE- ORY, LUDPAL, NAJMIN, PHAARU, POLSAG, POT- FOL, TYPLAT JMB created 9 13 vlo M mineral r–fc Dry,Deep 19 ALISUB, ECHSPP, LUDPAL, PHAARU, POLAMP, POLPEN BIA created 10 44 med M mineral r–fc Dry–Deep 32 ALISUB, ECHSPP, ELEPAL, GALSPP, JUNEFF, LEE- ORY, PHAARU, POLHYD NAC created 12 24 lo M mineral r–wp Dry,Shallow 25 BIDTRI, ECHSPP, ELEOBT, LEEORY, PHAARU, PO-

209 LAMP, POLLAP, POLPEN BIB created 32 67 hi O mineral–organic r,fc–s Dry–Deep 35 BIDCER, ECHSPP, ELEPAL, GALSPP, JUNEFF, LEE- ORY, PHAARU, POLHYD, POLLAP, POTNOD, TY- PLAT KP created 39 61 hi O mineral–organic wp–s Dry–Deep 35 BIDCER, ELEPAL, GALSPP, LEEORY, NAJMIN, PHAARU, POLHYD, POLPEN, POTNOD, SAGLAT, TYPLAT

MI natural 6 vlo N mineral r,wp Dry,Moist 35 ALISUB, BIDFRO, ELEOBT, JUNTOR, LEEORY, PO- LAMP, POLPEN, POLPER, POLPIP, TYPANG, TY- PLAT LW natural 43 med N mineral r–wp Dry–Deep 26 ALISUB, BIDFRO, BIDTRI, BIDVUL, ELEOBT, JU- NACU, JUNEFF, LEEORY, POLHYD PPN natural 55 hi N mineral–organic fc–s Moist–Shallow 33 ALISUB, BIDCER, ELEOBT, LEEORY, LUDPAL, PHAARU, POLPEN, TYPLAT CA natural 78 vhi R mineral–organic fc–s Moist–Deep 31 BIDDIS, GALSPP, PHAARU, POLAMP, SAGLAT, TY- PLAT BF natural 82 vhi R mineral–organic wp–s Moist–Deep 39 BIDARI, ECHSPP, ELEPAL, GALSPP, LEEORY, LUD- PAL, POLAMP, POLHYD, POLPUN, POLSAG, TY- PANG, TYPLAT Table 6.1: Select qualitative and quantitative site descriptors for the 15 wetlands included in this study. See Table 6.2 for explanation of terms and symbols. The number of samples per wetland included in the analyses is provided in the second to last column (n). Property ℓ Levels Wetland type 2 created, natural Quality 5 very low (vlo; < 20), low (lo; 20 < 35), medium (med; 35 Q ≤ Q ≤ < 55), high (hi ; 55 < 75), very high (vhi; 75 ) Q ≤ Q ≤ Q Wetland class 5 young created (Y ; y 7), middle-aged created (M ; 7 < y 20), old ≤ ≤ created (O; 20

Soil material 2 mineral (0.286 < ρb ), organic (ρb 0.286) ≤ Saturation 4 residual (r; θv 0.25), wilting point (wp; 0.25 < θv 0.4); ﬁeld ≤ ≤ capacity (fc; 0.4 <θv 0.6), saturated (s; 0.6 <θv ) ≤ Condition 4 dry, moist (soil appeared saturated), shallow (standing water, 5 cm depth), deep (standing water, > 5 cm depth) ≤

Table 6.2: Qualitative predictor variables for formation of arbuscular mycorrhizae. The number of levels (column ‘ℓ’) per variable is indicated with levels and deﬁnitions itemized in the ﬁnal column. Parameter abbreviations are provided for reference throughout the paper.

210 Root samples were next scanned using WinRHIZO software (settings: automatic threshold, pale root detection, ﬁlter areas < 0.005 cm2; images were edited as needed

to white out scratches, debris, or shadows, prior to analysis). The software estimated

root length, volume and diameter. Root length-to-volume ratio (L:V ) and mean

diameter (D) provided quantitative measure of root structure. Root structure was

also assessed qualitatively by “size class”: very fine (vfine), D < 0.5; fine (fine),

0.5 D< 0.75; coarse (coarse), 0.75 D < 1; or very coarse (vcoarse), D 1. ≤ ≤ ≥ Root subsamples were mounted semipermanently on microscope slides (Koske

and Tessier, 1983) for quantification of mycorrhizal structures at 400 magnification. × Following the magnified intersections method (McGonigle et al., 1990), root-crosshair

intersections were recorded as arbuscular/hyphal (A), vesicular/hyphal (V), arbuscu-

lar/vesicular/hyphal (AV), hyphal (H), or non-mycorrhizal (N), for a total of 100–200

total intersections. Proportional root length colonized by total arbuscular mycor-

rhizae (pAM ) or arbuscular-mycorrhizal structures (i.e., pArb and pVes) was calcu-

lated as

A + V + AV + H pAM = (6.3) A + V + AV + H + N A + AV pArb = (6.4) A + V + AV + H + N V + AV pV es = (6.5) A + V + AV + H + N

Because of the very large number of slides and prohibitively long time requirement for analysis at higher magniﬁcation, mycorrhizal structures were quantiﬁed for only a subset of the total samples. The subset included samples from roots of 31 plant

211 species, representing 14 genera, that were selected to maximize representation of sam-

pling stations and contiguous representation between sites with a minimum number of

samples. For example, ideally there would have been a single plant species present in

every site (and sampling station): the most represented species was Leersia oryzoides, which was present in 12 of the 15 sites; however, at the level of genus, Polygonum

was most common, with at least one species occurring at each site.

6.2.3 Environment characterization

Sampling station water depth was recorded as dry (water below soil surface),

moist (water just at soil surface), shallow (standing water 5 cm depth), or deep ≤ (standing water > 5 cm depth), at each ﬁeld collection. Additionally, during each

of the fall ﬁeld collections, three soil cores (7.5 cm diameter 10 cm depth) were ×

collected per sampling station to quantify soil bulk density (ρb : soil dry weight (105

◦C) divided by ﬁeld-moist soil volume) and volumetric water content (θv : water loss

upon drying (105 ◦C) divided by ﬁeld-moist soil volume). The quantitative variables

ρb and θv were also represented qualitatively as “soil material” (2 levels: mineral,

ρ > 0.286; organic, ρ 0.286) and “saturation” (4 levels: residual (r), θ 0.25; b b ≤ v ≤ wilting point (wp), 0.25 < θ 0.4; ﬁeld capacity (fc), 0.4 < θ 0.6; saturated (s), v ≤ v ≤

θv > 0.6).

Sample environment was also characterized by soil chemistry, hydrology, and nutri-

ent limitation (Table 6.1). Soil chemistry was based on analysis of soil cores collected

in summer 2005. Hydrology was determined by installation of surface water-level

recorders, which monitored water level at each site over one year, from November

212 12, 2005 to November 14, 2006. Limiting nutrients were assessed by plant and lit-

ter nutrient ratios and nutrient-addition assays: plant and litter tissue samples were

collected and analyzed in 2005; microbial nutrient assays were performed using the

soil samples collected in summer 2005. For more detail on data collection and sample

analysis see 3.1.1.

6.2.4 Statistical Analysis

All calculations and statistics were performed in R 2.9.1 (R Development Core

Team, 2009), with use of the vegan package (Oksanen et al., 2009) for the ordinations

and multivariate signiﬁcance tests and the sem package (Fox, 2009) for the structural equation models.

Univariate signiﬁcance tests

Relationships between metrics of AM abundance (i.e., pAM, pArb and pVes) and various qualitative factors were tested by permutation. Approximate 95 % conﬁdence intervals (CI) for the slope parameter (e.g., type eﬀect) were calculated by random permutation and regression of y β x on x, where y is the response variable (e.g., − o pAM ), x is the explanatory variable (e.g., type) and βo is the observed slope for the unpermuted dataset (the adjustment of y by βox makes the null hypothesis β = βo

rather than β = 0). The slopes obtained for 10,000 permutations were rank ordered,

with βo minus the 9750th value estimating the lower bound and βo plus the 250th

value estimating the upper bound for βo (Manly, 1997, pp.41–42). If the 95 % CIs

did not include zero, the qualitative factor was considered to have a signiﬁcant eﬀect.

For factors with more than two levels, the process was repeated for each level by level

213 comparison using a data subset which contained only observations from the two levels

being compared.

Generalized linear models

In a generalized linear model (GLM), the dependent variable is assumed to be a random variable, with observed values that are independently, but not necessarily normally, distributed. A GLM also permits non-linear relationships between the dependent variable and its covariates (i.e., the predictor variables); for example, there might be a logistic or exponential relationship between the dependent variable and covariates (McCullagh and Nelder, 1999). Here, we assumed that the number of mycorrhizal root intersections (plus 1 to prevent 0 counts) were observed values for an independent binomial random variable: Y B(m ,p ), where m is the total x ∼ x x x number of root intersections observed and px is the proportion having AM arbuscules

(i.e., pAM or pArb) for covariate combination x. The expected value of Yx/mx, or

µ /m = p , was logit transformed (logit(p) = log(p/(1 p))), then ﬁt by binomial x x x − GLM to possible covariates (i.e., explanatory variables) using maximum-likelihood estimation.

The variance for a binomially-distributed random variable is expected to be var(Yx)= var(µ )= m p (1 p ); however, our models typically had greater than expected vari- x x x − x ance, or overdispersion (i.e., var(Y )= φˆvar(µ )= φmˆ p (1 p ), where φ>ˆ 1; see x x x x − x McCullagh and Nelder, 1999). Accounting for φˆ, the signiﬁcance of each sequential covariate addition was determined by analysis of deviance (ANOVA): for example, to test the signiﬁcance adding covariate q to the model, with ℓ levels in q (and with ℓ = 1

varˆ (Yx ) varˆ (Yx ) when q is quantitative), the statistic +q − −q was assumed to be approxi- ℓφˆ mately distributed as Fℓ,n k; where ˆvar(Yx) is the model variance (or deviance) with − 214 or without covariate q, respectively; and n k is the degrees of freedom for the ﬁnal − model with n observations, k total parameters, and dispersion φˆ. To estimate model coeﬃcient errors (ˆσ(x)), we assumed that the mean-variance relationship depended

2 on x and estimated the empirically-based error: var(Yx) = varx(µx) =σ ˆ(x) (µx)

(e.g., see Breslow, 1996). The signiﬁcance of each coeﬃcient β in x was then tested

βˆ ˆ by assuming the statistic ˆ was distributed approximately as tn k; where β is the σˆ(β) − estimated model coefficient β;σ ˆ(βˆ) is the empirically-based estimate of coefficient error; and n k is the degrees of freedom for the final model with n observations and − k total parameters.

To address the first three research questions (Q1–Q3), we selected a best main effects model and a best model with interactions from a pool of possible covariates, depending on which question was being addressed: for Q1, possible covariates included all terms in Tables 6.2 and 6.3 except Age; for Q2, possible covariates included all terms in Tables 6.2 and 6.3 except Type; and for Q3, possible covariates included all “non-specific” terms (i.e., excluding Type, Age, , Quality, Class, Site, Q Sample, Year, Genus, and Species). Ideally, the best models would have been selected by comparison of all possible combinations of potential covariates; however, as a more computationally-feasible approach, we selected the best models by forward stepwise selection. Forward stepwise selection (or ‘greedy’ algorithm) begins with a null model

(i.e., intercept-only model), then proceeds to sequentially add best-fitting parameters from a pool of possible covariates. The best-fitting parameter at each step was determined by comparison of a modified Akaike Information Criterion (QAICc), adjusted for overdispersion and small sample size (Anderson et al., 1994):

215 2log (βˆ) − L 2(k + 1)(k + 2) QAICc = + 2k + (6.6) φˆ n k 2 − − where (βˆ) is the likelihood of the observations given the model; φˆ is the estimated L overdispersion in the most complex model; k is the number of parameters in the model; and n is the number of observations. The parameter resulting in the lowest QAICc is then added to the model, assuming it is also lower than the previous model QAICc. When parameter addition fails to improve upon the QAICc, the model selection process stops. Using QAICc for parameter selection is one method of weigh- ing the gain in explained variance against the loss of freedom with each parameter addition.

Selection of the best interaction model actually involved a three-step process— also to make the problem more computationally feasible. Beginning with the best main eﬀect model, the included covariates were interacted with all possible covariates to construct a pool of two-way interactions. From this pool, the best two-way interaction model was then selected by forward stepwise selection. The included two-way interactions were then interacted with all possible covariates to create a pool of three- way interactions. The ﬁnal interaction model was then selected by forward stepwise selection using all possible covariates, the pool of two-way interactions, and the pool of three-way interactions. (Note, not all interactions were permitted: covariates with redundant information (e.g., and quality) were not interacted; as well as squared Q or cubed terms.)

Interpretation of model coeﬃcients was based on the odds-ratio, or wins over

p losses: odds = 1 p . Because the models were fit using the logit transformed pAM − and pArb, the exponential of a coefficient provided the covariate effect on odds of

216 pAM or pArb. For example, to calculate the eﬀect of covariate x with coeﬃcient βx on the odds for response variable Y : odds(Y x)= eβxodds(Y ). | Structural equation models

One limitation of GLM, as with other linear models, is its inability to handle indirect effects. When indirect effects are present, models which rely on total effects

(e.g., GLM), might incorrectly exclude significant covariates (e.g., a covariate with a positive direct effect, but negative indirect effect might appear to have no effect in a

GLM) or include insignificant covariates (e.g., a covariate with a common dependency as the response variable might appear to have an effect in a GLM). Indirect effects are often present and highly important in complex systems (e.g., ecosystems); to interpret these complicated interactions, one suitable method is Structural Equation Modeling

(SEM; Malaeb et al., 2000; Shipley, 2000).

SEM is a multivariate statistical method for testing direct and indirect causal relationships. A model is first proposed, specifying the hypothesized relationships between variables (i.e., a path diagram). The model-implied variance-covariance structure can then be constructed from the hypothesized relationships (i.e., the path coefficients) using basic properties of covariance. The set of path coefficients best

ﬁtting the observed covariance structure of the associated dataset are estimated iteratively, then used to calculate the model-predicted variance-covariance (Σ). The null hypothesis in SEM is that the speciﬁed model is true; this hypothesis is then tested by comparing Σ to the observed variance-covariance (S). For additional details on

SEM see 4.2. One caveat for SEM is that it cannot determine if a hypothesized model is correct; it can only determine whether the model is likely given the observations.

217 We employed SEM to elucidate the role of AM in the 15 wetlands (Q4). From the AM covariates determined by GLM and select indicators of wetland function, we developed a plausible model of AM causes and eﬀects within the wetland ecosystem.

Specified relationships were selectively removed (generally relationships which were insignificant by t-test), or in some cases added, until a final model was obtained which adequately fit the data. Model fit was evaluated primarily by the Bayesian

Information Criterion (BIC), as well as the Normed Fit Index (NFI) and Root Mean

Square Error of Approximation (RMSEA). The BIC was mainly used to compare the revised model to the previous model, with lower being better. Similar to AIC,

BIC weighs the gain in explained variance against the cost of added complexity. The

NFI and RMSEA provided more absolute measure of model ﬁt: an NFI value above

0.95 indicates a good model, while an NFI between 0.90 and 0.95 is acceptable; an

RMSEA value below 0.05 indicates a good model, with less than 0.10 being acceptable.

Because of the large sample size (n = 488), the basic chi-square test was not an appropriate statistic.

6.3 Results

6.3.1 Visual assessment and permutation tests Q1: created vs. natural

Diﬀerences by type were signiﬁcant only for vesicular proportion, with 0.00 to

0.01 higher pVes in created wetlands compared to natural wetlands (95 % CI; Figure

6.1). While class as a factor was also significant only for pVes, there were significant differences between certain levels for pAM, pF and pVes. Mycorrhizal proportions tended to be highest in Y and M class wetlands, while fungal proportions tended to

218 be lowest. For Y compared to R class wetlands: pAM was 0.02 to 0.11 higher (also for N compared to R); pVes was 0.00 to 0.02 higher; and pF was 0.01 to 0.11 lower

(95 % CI). For M compared to R class wetlands, pVes was 0.00 to 0.01 lower and pF

was 0.01 to 0.13 higher (95 % CI). Additionally, pVes was signiﬁcantly higher in O

compared to R class wetlands (0.00 to 0.01, 95 % CI; Figure 6.2a–d).

Quality was actually a better indicator of mycorrhizal and fungal abundances than

type or class (or age): as a factor, quality was signiﬁcant for pAM, pArb and pVes, with all abundance indicators (including pF ) tending to decrease as wetland quality increased (Figure 6.2e–h). Level-wise comparison indicated signiﬁcantly lower pAM in vhi quality wetlands compared to med, lo and vlo quality wetlands (0.01 to 0.10,

0.01 to 0.11, and 0.06 to 0.16, respectively), and hi compared to vlo wetlands (0.02

to 0.13, 95 % CI). Both pArb and pVes were 0.00 to 0.03 and 0.00 to 0.02 lower in vhi compared to vlo, respectively; while pF was 0.02 to 0.16 lower. Additionally, hi compared to vlo had 0.00 to 0.03 lower pArb and vhi compared to lo had 0.01 to

0.02 lower pVes. The related quantitative predictor also had a signiﬁcant eﬀects Q mycorrhizal and fungal abundances, with pAM, pArb, pVes and pF all decreasing in response to increasing (Table 6.3). Q

219 Parameter unit range pAM pArb pVes pF × − ORAM score ( ) 6–82 10 3 -2.1 to -0.7 -0.49 to -0.07 -0.28 to -0.06 -1.7 to -0.1 Q − Wetland age years (y) 2–42 10 3 -2.3 to 0.8 -8.93 to 0.05 -4.83 to 0.05 -9.1 to 2.3 Root morphology − Mean diameter (D) mm 0.2–2.0 10 1 -1.4 to 0.1 -0.3 to 0.1 -0.227 to 0.002 -0.5 to 1.2 − − Length-to-volume ratio (L:V ) cm cm 3 30–3240 10 5 -1.2 to 4.3 -0.82 to 0.82 -0.28 to 0.56 -3.3 to 3.0 · − Root hair coverage (pRH ) 0.0–0.9 10 1 -0.9 to 0.8 -0.09 to 0.41 -0.1 to 0.1 -0.4 to 1.5 Soil chemistry − Total carbon (C) % 2–40 10 3 -5.7 to -1.7 -1.491 to -0.009 -0.73 to -0.12 -2.0 to 2.6 − Total nitrogen (N) % 0.2–3.3 10 2 -6.4 to -2.0 -1.382 to -0.005 -0.84 to -0.15 -2.1 to 3.1 − + −1 −4 Extractable N (NO3 + NH4 ) mg kg 2–191 10 -7.1 to 0.5 -1.6 to 0.7 -1.11 to 0.03 -5.5 to 3.2 · −1 − Bray-1phosphorus(P) mg kg 5–27 10 3 0.6 to 6.1 -0.06 to 1.53 -0.34 to 0.50 2.0 to 8.3 · −1 − Bray-2 P mg kg 6–86 10 3 -0.3 to 1.8 -0.20 to 0.42 -0.22 to 0.10 0.1 to 2.6 −3 · −1 −5 Extractable P (PO4 ) µg kg 0–2390 10 -2.9 to 3.8 -1.6 to 0.4 -0.61 to 0.43 0.8 to 8.5 · −1 − Potassium (K) mg kg 50–250 10 4 -5.7 to 0.6 -1.4 to 0.4 -0.62 to 0.35 4.4 to 2.7 · −1 − Magnesium (Mg) mg kg 200–880 10 4 -2.2 to -0.3 -0.48 to 0.09 -0.16 to 0.13 -1.8 to 0.4 · −1 − Calcium (Ca) mg kg 1000–3600 10 5 -4.7 to -0.4 -0.85 to 0.43 -0.26 to 0.40 -5.7 to -0.8 · −1 − Cation exchange capacity (CEC) meq kg 110–350 10 3 -7.1 to -1.9 -1.3 to 0.3 -0.74 to 0.05 -5.7 to 0.2 · pH 4.8–7.2 -0.015 0.070 to -0.005 0.011 to 0.003 0.012 to -0.049 0.014 to Soil physics

220 −3 −1 Bulk density (ρb ) g cm 0.1–1.3 10 0.4 to 1.3 0.07 to 0.35 0.07 to 0.21 -0.29 to 0.79 ·3 −3 −1 Water content (θv ) cm cm 0.1–1.0 10 -3.1 to -1.8 -0.59 to -0.21 -0.32 to -0.12 -2.4 to -0.9 · Hydrologic range −1 Mean depth (µw ) m -0.5–0.8 10 -1.8 to -0.6 -0.43 to -0.08 -0.21 to -0.03 -1.2 to 0.2 − Maximum depth (max) m -0.2–1.1 10 1 -1.7 to -0.5 -0.38 to -0.03 -0.176 to 0.008 -1.2 to 0.1 − Minimum depth (min) m -0.9–0.6 10 1 -1.5 to -0.5 -0.40 to -0.10 -0.21 to -0.05 -1.1 to 0.1 Hydrologic duration − Depth > 0.3m(+0.3 ) 0.0–1.0 10 1 -1.3 to -0.5 -0.28 to -0.02 -0.15 to -0.02 -0.92 to 0.08 − Depth > 0.1m(+0.1 ) 0.0–1.0 10 1 -1.1 to -0.4 -0.23 to -0.02 -0.11 to 0.01 -0.95 to -0.08 − Depth > 0.0m(+0.0 ) 0.0–1.0 10 2 -9.5 to -0.5 -2.60 to 0.04 -1.2 to 0.2 -4.9 to 5.6 − Depth > 0.1m(-0.1 ) 0.0–1.0 10 1 -1.2 to -0.1 -0.37 to -0.05 -0.18 to -0.01 -0.74 to 0.54 − − Depth > 0.3m(-0.3 ) 0.1–1.0 10 1 -1.1 to 0.4 -0.39 to 0.05 -0.17 to 0.06 -0.7 to 1.0 − Hydrologic periodicity −1 Standard deviation (σw ) m 0.02–0.23 10 0.3 to 5.1 0.2 to 1.6 0.15 to 0.88 0.4 to 5.9 −1 −1 Flashiness (δw ) m d 0.01–0.32 10 -1.6 to 1.7 -0.21 to 0.78 -0.10 to 0.40 -1.7 to 2.2 · Continued on Next Page. . .

Table 6.3: Quantitative predictor variables for formation of arbuscular mycorrhizae (pAM, total colonization; pArb, arbuscular proportion; pVes, vesicular proportion) and other fungi (pF). The ﬁnal four columns indicate 95 % CI for predictor eﬀect on each response variable (note scaling factor ‘ ’). Unit and range are also indicated for each predictor. Parameter abbreviations are provided for reference throughout the paper. × Table 6.3 – Continued

Parameter unit range pAM pArb pVes pF × Nitrogen limiting Plant C:N g g−1 8–76 10−3 0.8 to 2.8 -0.08 to 0.55 0.04 to 0.36 0.1 to 2.5 Litter C:N g · g−1 10–70 10−3 -1.9 to 1.0 -0.54 to 0.32 -0.25 to 0.19 -2.7 to 0.6 · BR(N) µg kg−1 h−1 -540–610 10−4 0.007 to 1.406 -0.22 to 0.20 -0.13 to 0.08 0.3 to 1.9 · · PMP(N) µg kg−1 h−1 -450–150 10−4 1.2 to 7.5 -0.04 to 1.90 -0.15 to 0.85 0.6 to 8.0 · · DEA(N) µg kg−1 h−1 -250–830 10−4 -2.4 to -1.0 -0.59 to -0.14 -0.28 to -0.05 -1.2 to 0.6 · · Phosphorus limiting Plant C:P g g−1 40–940 10−4 0.02 to 2.15 -0.29 to 0.33 0.001 to 0.335 -1.2 to 1.2 Litter C:P g · g−1 40–580 10−4 -1.9 to 0.9 -0.57 to 0.28 -0.23 to 0.20 -2.7 to 0.5 · BR(P) µg kg−1 h−1 -710–240 10−4 0.2 to 1.6 -0.10 to 0.32 -0.07 to 0.15 -0.02 to 1.60 · · PMP(P) µg kg−1 h−1 -380–200 10−4 -3.5 to 3.8 -1.2 to 1.2 -0.53 to 0.65 -7.3 to 0.8 · · DEA(P) µg kg−1 h−1 -110–340 10−4 -0.4 to 3.7 -0.31 to 0.86 -0.26 to 0.35 0.7 to 5.2 · · N or P limiting Plant N:P g g−1 2–16 10−2 -0.63 to 0.46 -0.24 to 0.10 -0.078 to 0.089 -1.18 to 0.08 Litter N:P g · g−1 4–13 10−2 -1.0 to 0.4 -0.27 to 0.14 -0.10 to 0.11 -1.3 to 0.2 · Potassium limiting Plant C:K g g−1 10–390 10−4 0.02 to 5.10 -0.53 to 0.97 0.03 to 0.81 -2.7 to 3.3 Litter C:K g · g−1 20–270 10−4 -3.1 to 1.5 -0.75 to 0.61 -0.53 to 0.19 -4.2 to 1.1 · 221 Parameter unit range pAM pArb pVes pF × Plant productivity Aboveground plant biomass (Shoot) g m−2 84–1590 103 -0.14 to 0.19 -0.84 to 0.28 -1.7 to 0.4 0.008 to 0.296 · Belowground plant biomass (Root) g m−2 0–2040 103 -0.22 to 0.22 -1.1 to 0.4 -1.8 to 1.2 -0.20 to 0.20 · Shoot to root biomass ratio (S:R) g g−1 0–587 10 -0.08 to 0.39 -0.5 to 1.0 -0.7 to 2.3 -0.22 to 0.19 · Plant nutrition Plant tissue C mg g−1 20–46 10 0.15 to 0.61 -0.03 to 1.54 0.2 to 3.2 0.11 to 0.51 Plant tissue N mg · g−1 0.6–4.3 -0.99 to -0.18 -2.4 to 0.3 -6.7 to -1.3 -0.66 to 0.07 Plant tissue P mg · g−1 0.07–0.73 10−1 -1.4 to -0.1 -2.7 to 1.6 -8.50 to -0.08 -0.83 to 0.29 Plant tissue K mg · g−1 0.3–5.2 -0.85 to -0.01 -2.4 to 0.4 -5.3 to 0.1 -0.61 to 0.14 · Table 6.4: Quantitative response variables to formation of arbuscular mycorrhizae (pAM, total colonization; pArb, arbuscular proportion; pVes, vesicular proportion) and other fungi (pF). The ﬁnal four columns indicate 95 % CI for predictor eﬀect on each response variable (note scaling factor ‘ ’). Unit and range are also indicated for each response. Parameter abbreviations are provided for reference throughout the paper. × 222 40 a

Arbuscular Mycorrhizae (%) Arbuscular 0 15 b

5 Arbuscules (%) Arbuscules

0 10 c 8

4 Vesicles (%) Vesicles

0 50 d 40

20 Fungi (%)

0 PPAPPB BB BIC SA JMB BIA NAC BIB KP MI LW PPN CA BF

Figure 6.1: Percent root colonization with (a) arbuscular mycorrhizae (400 ), (b) arbuscules (400 ), (c) vesicles (400 ), and (d) fungi (40 ), averaged by site. × × × ×

223 35 a 40 e 30

30 25

20 20 15

10 10

Arbuscular Mycorrhizae (%) Arbuscular 0 Mycorrhizae (%) Arbuscular 0 10 10 b f 8 8

6 6

4 4 Arbuscules (%) Arbuscules (%) Arbuscules 2 2

0 0 5 c 5 g 4 4

3 3

2 2 Vesicles (%) Vesicles (%) Vesicles

1 1

0 0 40 d 40 h

30 30

20 20 Fungi (%) Fungi (%)

10 10

0 0 YMONR vlo lo med hi vhi

Figure 6.2: Percent root colonization with (a,e) arbuscular mycorrhizae (400 ), (b,f) arbuscules (400 ), (c,g) vesicles (400 ), and (d,h) fungi (40 ), averaged by either wetland× class (a–d) or wetland× quality (a–d; see Table× 6.2 for parameter deﬁnitions).× Bars are shaded by wetland type (unshaded = created; medium shading = natural) and with dark shading for reference natural. In the plots by quality, the dominant shade present in each category is used (e.g., vlo, med and hi each contain one N class wetland. 224 Q2: created wetland age

There were very slight decreases in pAM, pArb and pVes, and a very slight increase in pF, with age; however, none of these eﬀects were signiﬁcant (Table 6.3; see also

Figure 6.1). Both pAM and pArb were plotted against age with separation by plant genus (Figures 6.3 and 6.4); however, there were no visible trends in AM abundance with wetland age.

Q3: AM predictors

Plant phenology inﬂuenced mycorrhizal and fungal abundance as indicated by eﬀects of predictor variables such as genus, species and season. Proportions of AM were highest in genera such as Bidens and Galium and lowest in genera such as

Najas, Potamogeton and Sagittaria (see also Figures 6.3 and 6.4). Species such as

BIDARI and BIDTRI also typically had higher AM colonization than species such as JUNACU, POTNOD and POTFOL. By season, pAM, pArb and pF were 0.02 to

0.09, 0.00 to 0.02, and 0.03 to 0.10 higher in fall than in summer, respectively (95 %

CI). Summer 2008, in particular, had the lowest abundances (typically lower by 0.05 to 0.16 for pAM, 0.01 to 0.03 for pArb, 0.00 to 0.01 for pVes, and 0.05 to 0.19 for pF ).

Root colonization appeared to have a nonlinear relationship with root size, with highest AM colonization in fine roots and lowest colonization in coarse and vcoarse roots. Fungal abundance, however, was not significantly affected by root size as a factor or level-wise. For root hair presence/absence, roots with hairs had 0.00 to

0.06 higher pAM and 0.00 to 0.02 higher pArb. In contrast, none of the quantitative variables describing root morphology were signiﬁcant as mycorrhizal or fungal predictors—probably because the relationship was nonlinear (Table 6.3).

225 Abundances of AM and fungi were strongly inﬂuenced by soil chemistry and

physics. All AM metrics were lower in organic-based soils than in mineral-based

soils by 0.03 to 0.11, 0.00 to 0.03, and 0.00 to 0.02, for pAM, pArb and pVes, re-

spectively (95 % CI). These metrics likewise decreased signiﬁcantly under increasing

concentrations of C and N (Table 6.3). P-based indicators, however, seemed to only

aﬀect pF, which increased as concentrations of Bray1-P, Bray2-P and extractable

3 PO4− increased (although, pAM also increased as Bray1-P increased). The predictor

ρb , which is closely linked with soil C (e.g., see Ch. 4), also had a signiﬁcant positive

eﬀect on all three AM metrics. Both θv and the qualitative parameter saturation

combine aspects of soil and hydrology, and caused signiﬁcant decreases in all AM and

fungal metrics. The eﬀect of saturation, for example, was to decrease pAM, pArb,

pVes and pF by 0.05 to 0.17, 0.00 to 0.03, 0.00 to 0.02, and 0.00 to 0.16, respectively, for soils rated as fc or s (and with the actual 95 % CI depending on whether the comparison was with respect to r or wp).

As with θv and saturation, AM abundance decreased under increasing inundation.

The hydrology-based predictors µw , min, +0.3 and -0.1 all resulted in lower pAM,

pArb and pVes (Table 6.3). Additionally, pAM and pArb, as well as pF, decreased

with +0.1. All four metrics were also positively aﬀected by one of the two indicators

of hydrologic variability, σw . The eﬀect of hydrology appeared to be a little more

complicated when comparing AM and fungal metrics by station condition. While

the AM metrics generally decreased linearly across the four condition levels, pF was

lowest under shallow condition and highest under dry and deep conditions (Fig. 6.6).

Colonization by AM and fungi appeared to be more aﬀected by N availability

than by availability of P or K (Table reftab6:pred). As N became more limiting (i.e.,

226 increasing plant C:N, BR(N) and PMP(N)), both pAM and pF increased. (Although in contrast to this generalization, pAM, pArb and pVes all decreased as DEA(N) increased, which suggests the opposite: a decrease in abundance under N limitation).

Increasing plant C:N also positively aﬀected pVes. AM and fungal metrics were much less aﬀected by indicators of P and K availability. Both pAM and pVes increased as plant C:P and C:K increased. Mycorrhizal abundance also responded positively to

BR(P), while pF responded positively to DEA(P).

Q4: AM eﬀects

For metrics of plant productivity and nutrition, AM colonization seemed to have

1 the biggest eﬀect on plant C (Table 6.4). Plant tissue C increased by 1.5 mg g− to · 1 1 1 1 1 6.1mg g− , 2mg g− to 32mg g− , and 1.1mg g− to 5.1mg g− , as pAM, pVes and pF · · · · · increased, respectively (95 % CI). Plant tissue N and P, however, decreased as pAM

1 1 1 1 (0.2 mg g− to 1.0 mg g− and 0.01 mg g− to 0.14 mg g− decrease, respectively) and · · · · 1 1 1 1 pVes (1.3mg g− to 6.7mg g− and 0.01mg g− to 0.85mg g− decrease, respectively) · · · · decreased.

6.3.2 GLM

From the univariate permutation tests, it was apparent that several different factors (e.g., season, ρb , condition) might be affecting AM abundance. To determine the best explanatory factors when in combination, we used GLM to construct main effect and interaction models for pAM and pArb. The metric pAM provided the most general indication of mycorrhizal colonization; while, arbuscule presence is often used to establish the plant-fungi relationship as truly symbiotic. There are some concerns for relying solely on arbuscule counts,however: first, arbuscules are somewhat ephemeral

227 and may disappear during period of dormancy and reform during periods of activity

(while hyphae and storage structures such as vesicles remain throughout); second, some mycorrhizas rarely form typical arbuscules, relying instead on hyphal coils or abusculate coils to facilitate C and nutrient exchange between the plant and fungus

(Smith and Smith, 1997; Dickson, 2004).

Q1: created vs. natural

Total AM. The best main eﬀects model explained 50 % of the total variance and included 6 parameters, but did not include type or class (Table 6.5). Sample station and plant species accounted for the majority of the explained variance (29 % and 16

%, respectively), with the remainder explained by parameters related to hydrology and root morphology; although, the latter were marginally insigniﬁcant. Aside from station and plant species, the model suggested a decrease in AM under wetter conditions: shallow and deep inundation were predicted to decrease AM odds by factors of 0.22 to 0.77 and 0.20 to 0.87, respectively (not shown). The odds of AM were also predicted to decrease under saturation level fc by 0.27 to 0.89 times, relative to level r; but not however, under saturation level s.

The best model with interactions accounted for 71 % of the total variance and included 21 parameters (Table 6.6); neither type nor class appeared in the selected model. Most of the explained variance (42 %) was due to the interaction of plant species and site condition, while interactions of saturation season year, and root × × hair presence quality plant N:P explained a small portion of the variance (8 % × × and 6 %, respectively). Two main eﬀect parameters, BR(P) and plant C:N, were included in the model, but accounted for less than 2 % of the variance combined.

228 ℓ,ν F p (> F ) DDR | | NULL 13,395 . Station 57,430 3,942 9,452 3.81 < 0.001 Species 30,400 2,100 7,352 3.85 < 0.001 Condition 3,397 409 6,943 7.51 < 0.001 Saturation 3,394 155 6,788 2.85 0.037 Root hairs 1,393 52 6,736 2.87 0.091 L:V 1,392 31 6,705 1.72 0.19 φ = 18.2 with p 7119.7 > χ2 < 0.001 | 392| QAIC : 707.2 c 0 parameters were aliased Table 6.5: Analysis of Deviance for best main eﬀects model addressing Q1 for total AM colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model D D dispersion estimate (φ) and signiﬁcance, quasi-AIC (QAICc), and number of aliased parameters are also provided.

The remaining 13 % of explained variance was due to a combination of parameters primarily describing hydrology, root morphology, and nutritient availability.

Certain terms appeared multiple times: for example, root hairs was a factor in 7 of the parameters, typically in interaction with plant N:P; however, the total effect of root hairs on AM appeared quite variable and nonsignificant. The ratio L:V appeared in 5 terms, but was also nonsignificant.

Arbuscules. For pArb, the best main eﬀect model explained 65 % of the variance, with 33 % and 21 % due to plant species and station, respectively (Table 6.7). Year accounted for an additional 4 %, while the remaining 7 % of explained variance involved parameters related to root structure, hydrology and soil structure. Neither type nor class appeared in the model.

229 Four of the included terms, however, were marginally nonsigniﬁcant: year, satura-

tion, pRH and soil material. The ratio L:V, while significant, had only a very slight effect on AM odds. Soil bulk density had the largest predicted effect on AM odds,

3 with a factor of 2 to 23 times per 1g cm− increase. Root hair presence was predicted · to increase AM odds by 1.1 to 2.1 times, and shallow inundation was predicted to

decrease AM odds by a factor of 0.26 to 0.94.

The interaction of species site ρ explained 67 % of the variance in the best × × b model with interactions (Table 6.8); another 16 % was explained by the interaction of year season genus. Class pRH DEA(P) explained an additional 2 % of the × × × × variance and the remaining 5 % of explained variance was due to various parameters describing root morphology, nutrient availability, hydrology and time.

Increasing D in combination with increasing pH tended to decrease pArb. The ratio L:V had only a very slight positive eﬀect on pArb. Odds of pArb were predicted

1 to increase by a factor of 1.0 to 1.1 per 1 mg kg− increase in exN, depending on ·

ρb and L:V (higher values of both tended toward upper bound). The eﬀect of class depended on pRH and particularly DEA(P): when DEA(P) was high (i.e., P more limiting), pArb tended to be lower in classes Y, M, N and R, relative to class O, particularly as pRH increased; the eﬀect of class was most pronounced in M.

Q2: created wetland age

Total AM. The best main eﬀects model explained 52 % of the total variance, with station accounting for 28 % and species for 16 % (Table 6.9). Saturation and condition, combined, explained an additional 6 %, with the remaining 1 % due to root hair presence. Age, however, was not included in the model.

230 Among the plant species, the odds of pAM were signiﬁcantly higher in BIDTRI (6 to 80 times) and lower in SAGLAT (0.04 to 0.5 times). Aside from eﬀects of species and station, pAM was higher when soil saturation was wp (1.2 to 3.3 times) and root hairs were present (1.0 to 2.2 times), and lower when shallowly inundated (0.25 to

0.83 times).

The best model with interactions accounted for 72 % of the total variance and included 10 parameters with 223 degrees of freedom (Table 6.10); age, however, was among the 10 selected parameters. Most of the explained variance (38 %) was due to the interaction of root hairs year genus, while interactions of station +0.3 soil × × × × material and year saturation season, explained an additional 15 % and 11 %, × × respectively. Four main eﬀect parameters (i.e., soil material, plant N:P, -0.1 and

θv ) were included in the model, accounting for 3 % of the variance combined. The remaining 5 % of explained variance was due to a combination of parameters primarily describing hydrology, root morphology, and nutritient availability.

Depending on year and root hair absence or presence, a few plant genera were more or less likely to have AM: Galium, Juncus and Leersia had higher pAM odds compared to other genera by factors of 1 to 90, 1 to 20, and 1 to 14, respectively; whereas, Sagittaria had lower pAM odds by a factor of 0.01 to 1. Two parameters appeared in the model as main eﬀects only: -0.1 and θv . Odds of pAM were predicted

4 to increase by a factor of 1.1 to 3.0 per 1 m in -0.1 and decrease by a factor of 2 10− × 1 3 3 to 9 10− per 1 cm cm− increase in θ ; suggesting, that AM colonization was less × · v likely at hydrologic extremes (i.e., very wet or very dry).

The model predicted an increase in pAM odds of 2 to 49 times for organic-based soils when root hairs were present; when root hairs were absent, pAM odds were

231 higher in organic soils only when L:V was low (i.e., coarser roots). The eﬀect of plant N:P depended on litter C:K and root hairs: when root hairs were absent, odds of pAM decreased by a factor of 0.29 to 0.93 per unit increase in plant N:P (i.e.,

P more limiting); however, when root hairs were present, pAM odds decreased with increasing plant N:P, only with low litter C:K.

Arbuscules. For pArb, the best main effect model explained 66 % of the variance, with 4 % due to age (Table 6.11); however, the marginal contribution of age to the model was insignificant, with an estimated coefficent of -0.33, but 95 % confidence interval of -0.78 to 0.11 (not shown). Plant species and station accounted for most of the explained variance, 31 % and 20 % respectively. Saturation accounted for an additional 5 %, while the remaining 6 % of explained variance involved parameters related to hydrology, root structure and soil structure. Age appeared in the final model, but was not marginally significant by t-test, nor were pRH and ρb .

Odds of pArb were higher for plant species BIDCER, BIDFRO, BIDTRI, ECHSPP and LUDPAL by a factor of 1 to 30 (depending on the species), and lower for plant

species POLHYD by a factor of 0.1 to 0.8. Aside from plant species and sample

station, the odds of pArb were higher under saturation level wp and root hair presence

by factors of 1.7 to 6.3 and 1.1 to 2.4, respectively; and lower under shallow inundation

by a factor of 0.16 to 0.71. Odds of pArb also increased by a factor of 1.04 to 1.15

3 per 100cm cm− increase in L:V. · In the best model with interactions, 68 % of the variance was explained by the

interaction of species site ρ (Table 6.12); another 8 % was explained by the in- × × b teraction of condition L:V exN. While age, did not appear in the ﬁnal model, the × ×

232 interaction of quality with condition accounted for 6 % of the variance. Additional

parameters describing root morphology, nutrient availability, hydrology and time ex-

plained another 4 % of the variance, for a total of 86 % variance explained by the

selected model.

Odds of pArb increased for species ALISUB, BIDCER, BIDFRO, BIDTRI, ECH-

SPP, ELEOBT, ELEPAL, GALSPP, JUNEFF, LEEORY, LUDPAL, NAJMIN, PO-

LAMP, POLLAP, POTFOL, POTNOD, and SAGLAT by a factor of 1 to 2 109 × 3 per 1g cm− increase in ρ , but depending on the site; whereas, pArb decreased by · b 3 a factor 0.02 to 0.95, per 1g cm− increase in ρ , for POLHYD, also depending on · b the site. Under conditions of dry to shallowly inundated, pArb tended to be higher,

particularly for higher L:V and exN. Condition also mediated the eﬀect of quality:

4 low quality sites had 6 10− to 1 lower pArb odds and medium quality sites had × 4 1 10− to 0.98 lower odds, depending on station condition. × As min and +0.1 increased, odds of pArb decreased, particularly with higher

pRH and plant C:K. Higher litter C:P and DEA(P) resulted in reduction of pArb

odds. The only main eﬀect parameter included in the model, D, was statistically

nonsigniﬁcant based on the empirically-derived errors.

Q3: AM prediction

Total AM. The best main eﬀects model explained only 29 % of the total variance,

mainly due to condition (14 %) and saturation (4 %; Table 6.13). The remaining 11 %

of explained variance was due to minor contributions from 19 additional parameters,

primarily describing root structure, nutrition, and hydrology. The model indicated

signiﬁcant decreases in AM colonization under wetter conditions: for example, the

odds of AM colonization were predicted to decrease by a factor of 0.23 to 0.74 (95 %

233 CI) under shallow inundation, and by factors of 0.25 to 0.40 and 0.25 to 0.78 under

moist to saturated soil, respectively (Table 6.14). Root size seemed to have a nonlinear

eﬀect on AM colonization, with a slight decrease in odds (0.02 to 0.52) for each 1

mm increase in root diameter, but highest odds for the ﬁne root size class (factor

increase of 1.2 to 3.3 over the other size classes). Other marginally signiﬁcant factors

included min, σw , ρb , CEC and plant C:N, which tended to increase AM colonization

2 6 3 by factors of 6 to 7 10 per 1m, 2to1 10 per 1 m, 1.5 to 5.5 per 1g cm− , 1.2 × × · 1 1 to 2.4 per 1 µeq g− and 1.00 to 1.03 per 1g g− , respectively; as well as Bray2-P · · 1 and K, which decreased pAM odds by 0.970 to 0.997 per 1mg kg− and 0.990 to 0.999 · 1 per 1mg kg− . · The best model with interactions accounted for 77 % of the total variance and included 42 parameters with 362 degrees of freedom (Table 6.15). The interaction of condition saturation season accounted for 28 % of the explained variance, × × while the remainder of explained variance was due to a combination of parameters, primarily describing root morphology, soil chemistry and physics, hydrology, nutrient availability, and season.

Specific parameter effects on AM colonization were complicated by the many interactions pAM ; however, specific effects could be discerned for parameters occurring in only a few terms. Root size classes vfine and fine, for example, increased pAM

1 odds by factors of 1.03 to 1.05 and 1.03 to 1.08, respectively, per 1g g− increase in · litter C:K, and depending on the presence of root hairs. Eﬀects of the other root size classes were more diﬃcult to determine because of interactions with plant C:K and min.

234 Odds of pAM tended to decrease with increasing -0.3 and plant N:P, particularly

3 1 + under high PO− availability. Per 1mg kg− increase in NO− + NH , pAM odds were 4 · 3 4 predicted to increase by a factor of 1.00 to 1.02, depending on season and presence of root hairs. Colonization by AM decreased with increasing CEC and litter C:P, depending on L:V ; with increasing max, depending on Bray2-P; and with increasing

+0.3 depending on ρb . Odds of pAM were higher with increases in Ca and δw ; depending on pRH and +0.1, and D and Bray2-P, respectively.

Arbuscules. For pArb, the best main eﬀect model explained only 31 % of the variance, with 10 % due to condition (Table 6.16). Bulk density and root size accounted for an additional 3 % each and DEA(N) and root hairs another 2 % each. The remaining model parameters each accounted for only 1 % or less of the variance.

Predicted parameter eﬀects using the empirically-based error estimates, however, suggested that most of the included parameters were either negligible or nonsigniﬁcant

(Table 6.17). Exceptions included ρb , CEC and plant C:K, which were predicted to

3 increase pArb odds by 2 to 14 times per 1g cm− increase, 1.1 to 1.2 times per · 1 1 1 meq g− increase, and 1.01 to 1.03 times per 1g g− increase, respectively; and · · plant C:P and litter C:N, with predicted eﬀects of decreasing pArb odds by 0.99 to

1 1.00 times and 0.88 to 0.99 times, per 1g g− increase, respectively. Similar to the · main eﬀect model for pAM, arbuscules appeared to decrease under wetter conditions:

for example, shallow inundation was predicted to decrease AM odds by 0.23 to 0.97

times over dry conditions; soil saturation, however, was not signiﬁcant in this model.

Also similar to the pAM main eﬀects model, pArb odds were predicted to be 1.1 to

3.8 times higher in the ﬁne root size class.

235 The best model with interactions explained 89 % of the variance, mainly due to

the interaction of condition saturation season, which accounted for 21 % of the × × variance (Table 6.18). Another 51 % was attributed to 14 interaction terms, each contributing 1 % to 8 %, and describing root morphology, hydrology, and nutrient availability, primarily.

Many parameters were insigniﬁcant when considering the model as a whole: for example, pRH was present in 20 terms (with 33 levels) and L:V was present 13 terms

(with 30 levels), however, both an overall insigniﬁcant eﬀect on arbuscules, although this would change as quantitative interactions deviated from unity.

Odds of pArb generally increased with increasing K, although there was some interaction with soil condition, root hairs, and plant C:N and C:K. An increase in +0.1 decreased the odds of pArb, with a factor dependent also on pRH and Bray2-P. Odds of pArb increased slightly with plant C:N, litter C:P, and CEC, and max; depending on K, δw , and plant N:P and Bray1-P, and plant N:P and Bray1-P, respectively; but

3 decreased with soil C and -0.1 ; depending on pRH and PO4− , and D and root hair presence, respectively.

236 80 80 60 a60 b 40 40

Alisma 20 20 Leersia 0 0

80 80 60 c60 d 40 40

Bidens 20 20 Ludwigia 0 0

80 80 60 e60 f 40 40 20 20 Phalaris

Echinochloa 0 0

80 80 60 g60 h 40 40 20 20 Eleocharis 0 Polygonum 0

80 80 60 i60 j 40 40

Galium 20 20 Sagittaria 0 0

80 80 60 k60 l 40 40 Typha

Juncus 20 20 0 0 1 3 610 15 30 50 75 100 1 3 610 15 30 50 75 100 Age (y) Age (y)

Figure 6.3: Plots of proportional root colonization by arbuscular mycorrhizae against wetland age (square-root scale), for genuses: (a) Alisma, (b) Leersia, (c) Bidens, (d) Ludwigia, (e) Echinochloa, (f) Phalaris, (g) Eleocharis, (h) Polygonum, (i) Galium, (j) Sagittaria, (k) Juncus, and (l) Typha. Created wetlands are unshaded circles sized by age and natural wetlands are shaded squares sized by quality. [Note: for graphical purposes, natural wetland age was estimated at 65 y to 105 y depending on quality.]

237 40 40 30 a30 b 20 20 10 10 Alisma Leersia 0 0

40 40 30 c30 d 20 20 10 10 Bidens Ludwigia 0 0

40 40 30 e30 f 20 20 10 10 Phalaris

Echinochloa 0 0

40 40 30 g30 h 20 20 10 10 Eleocharis 0 Polygonum 0

40 40 30 i30 j 20 20 10 10 Galium 0 Sagittaria 0

40 40 30 k30 l 20 20

10 Typha 10 Juncus 0 0 1 3 610 15 30 50 75 100 1 3 610 15 30 50 75 100 Age (y) Age (y)

Figure 6.4: Plots of percent root colonization by arbuscules against wetland age (square-root scale), for genuses: (a) Alisma, (b) Leersia, (c) Bidens, (d) Ludwigia, (e) Echinochloa, (f) Phalaris, (g) Eleocharis, (h) Polygonum, (i) Galium, (j) Sagittaria, (k) Juncus, and (l) Typha. Created wetlands are unshaded circles sized by age and natural wetlands are shaded squares sized by quality. [Note: for graphical purposes, natural wetland age was estimated at 65 y to 105 y depending on quality.]

238 20 a

Arbuscular Mycorrhizae (%) Arbuscular 0 4 b

Arbuscules (%) Arbuscules 1

0 2.0 c

1.5

1.0 Vesicles (%) Vesicles 0.5

0.0 25 d

10 Fungi (%)

0 SU07 FA07 SU08 FA08

Figure 6.5: Percent root colonization with (a) arbuscular mycorrhizae (400 ), (b) arbuscules (400 ), (c) vesicles (400 ), and (d) fungi (40 ), averaged by wetland type× and sample period. Sample× period is as labeled× along the horizontal× axis: summer or fall of 2007 or 2008. Bars are shaded by wetland type (unshaded = created; medium shading = natural) and with dark shading for reference natural.

239 20 a

Arbuscular Mycorrhizae (%) Arbuscular 0 3.5 b 3.0

2.5

2.0

1.5

1.0 Arbuscules (%) Arbuscules

0.5

0.0 2.0 c

1.5

1.0 Vesicles (%) Vesicles 0.5

0.0 30 d 25

Fungi (%) 10

0 Dry Moist Shallow Deep

Figure 6.6: Percent root colonization with (a) arbuscular mycorrhizae (400 ), (b) arbuscules (400 ), (c) vesicles (400 ), and (d) fungi (40 ), averaged by wetland type and× station condition. Station× condition (i.e., extent× of inundation at× time of sampling) is as labeled along the horizontal axis: dry, moist, shallow or deep. Bars are shaded by wetland type (unshaded = created; medium shading = natural) and with dark shading for reference natural.

240 ℓ,ν F p (> F ) DDR | | NULL 13,395 . Species Condition 83,404 5621 7,774 5.2 < 0.001 × Saturation Season Year 15,389 999 6,775 5.1 < 0.001 × × Root hairs Quality Plant N:P 10,379 857 5,918 6.6 < 0.001 × × Saturation min 4,375 282 5,635 5.4 < 0.001 × L:V BR(P) 1,374 141 5,495 10.7 0.0012 × BR(P) 1,373 116 5,379 8.9 0.0031 Root hairs Plant C:K 2,371 132 5,247 5.0 0.0070 × Year L:V BR(N) 2,369 102 5,145 3.9 0.021 × × Year Root hairs 1,368 91 5,054 7.0 0.0087 × 3 Root hairs Plant N:P PO− 2,366 102 4,952 3.9 0.022 × × 4 Root hairs Plant N:P +0.1 2,364 109 4,843 4.2 0.016 × 3 × Root hairs PO− +0.3 2,362 111 4,732 4.2 0.015 × 4 × Condition θ 4,358 188 4,543 3.6 0.0068 × v Condition Litter N:P 4,354 209 4,335 4.0 0.0035 × Plant C:N 1,353 96 4,239 7.3 0.0072 Year L:V 1,352 58 4,182 4.4 0.037 × Root hairs Plant N:P θv 2,350 70 4,111 2.7 0.069 × +× Saturation NO− + NH 4,346 104 4,007 2.0 0.097 × 3 4 L:V δ pRH 1,345 40 3,968 3.0 0.082 × × Year min L:V 2,343 77 3,891 2.9 0.054 × × φ = 13.1 with p 4485.1 > χ2 < 0.001 | 343| QAIC : 848.1 c 45 parameters were aliased Table 6.6: Analysis of Deviance for best interaction model addressing Q1 for total AM colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model disper- D D sion estimate (φ) and signiﬁcance, quasi-AIC (QAICc), and number of aliased parameters are also provided.

241 ℓ,ν F p (> F ) DDR | | NULL 3,232 . Species 30,457 1,065 2,167 10.2 < 0.001 Station 57,400 678 1,490 3.4 < 0.001 Year 1,399 134 1,355 38.7 < 0.001 L:V 1,398 50 1,305 14.5 < 0.001 Root hairs 1,397 45 1,260 12.9 < 0.001 Saturation 3,394 40 1,220 3.9 0.0093 ρb 1,393 32 1,188 9.2 0.0025 Condition 3,390 33 1,155 3.1 0.026 pRH 1,389 14 1,141 3.9 0.050 Soil material 1,388 5 1,136 1.4 0.23 φ = 3.5 with p 1348.5 > χ2 < 0.001 | 388| QAIC : 918.1 c 0 parameters were aliased Table 6.7: Analysis of Deviance for best interaction model addressing Q1 for arbuscule colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model disper- D D sion estimate (φ) and signiﬁcance, quasi-AIC (QAICc), and number of aliased parameters are also provided.

242 ℓ,ν F p (> F ) DDR | | NULL 3,232 . Species Site ρ 147,340 2,176 1,056 11.5 < 0.001 × × b Year Season Genus 50,290 508 548 7.9 < 0.001 × × pRH DEA(P) Class 5,285 76 472 11.9 < 0.001 × × ρ exN L:V 1,284 44 427 34.5 < 0.001 b × × DEA(P) Saturation min 4,280 43 384 8.4 < 0.001 × × Season pRH DEA(P) 1,279 14 370 10.7 0.0012. × × min Root hairs 1,278 20 350 15.7 < 0.001 × Year pH D 2,276 17 333 6.7 0.0014. × × Year Root hairs 1,275 9 324 7.0 0.0085. × Root hairs 1,274 10 314 7.8 0.0056. φ = 1.3 with p 352.4 > χ2 < 0.001 | 274| QAIC : 1924.8 c 325 parameters were aliased Table 6.8: Analysis of Deviance for best interaction model addressing Q1 for arbuscule colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model disper- D D sion estimate (φ) and signiﬁcance, quasi-AIC (QAICc), and number of aliased parameters are also provided.

ℓ,ν F p (> F ) DDR | | NULL 9,306 . Station 38,285 2,645 6,661 3.92 < 0.001 Species 23,262 1,500 5,160 3.68 < 0.001 Saturation 3,259 350 4,810 6.58 < 0.001 Condition 3,256 272 4,538 5.12 0.0019 Root hairs 1,255 94 4,444 5.32 0.022 φ = 17.7 with p 4524.3 > χ2 < 0.001 | 255| QAIC : 493.7 c 0 parameters were aliased Table 6.9: Analysis of Deviance for best main eﬀects model addressing Q2 for total AM colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model D D dispersion estimate (φ) and signiﬁcance, quasi-AIC (QAICc), and number of aliased parameters are also provided.

243 ℓ,ν F p (> F ) DDR | | NULL 9,306 . Root hairs Year Genus 50,273 3,511 5,795 5.81 < 0.001 × × Station +0.3 Soil material 24,249 1,438 4,358 4.96 < 0.001 × × Year Saturation Season 14,235 980 3,377 5.79 < 0.001 × × Root hairs Year L:V 4,231 227 3,150 4.69 0.0012 × × Root hairs Soil material L:V 2,229 130 3,020 5.40 0.0051 × × Soil material 1,228 106 2,914 8.75 0.0034 Plant N:P 1,227 67 2,847 5.52 0.020 Root hairs Plant N:P Litter C:K 2,225 91 2,757 3.75 0.025 × × -0.1 1,224 63 2,694 5.18 0.024 θv 1,223 48 2,646 3.93 0.049 φ = 12.1 with p 2695.6 > χ2 < 0.001 | 223| QAIC : 612.4 c 62 parameters were aliased Table 6.10: Analysis of Deviance for best interaction model addressing Q2 for total AM colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model dispersion D D estimate (φ) and signiﬁcance, quasi-AIC (QAICc), and number of aliased parameters are also provided.

ℓ,ν F p (> F ) DDR | | NULL 2,351 . Species 23,300 738 1,614 8.0 < 0.001 Station 38,262 463 1,151 3.0 < 0.001 Saturation 3,259 121 1,030 10.1 < 0.001 Age 1,258 94 936 23.6 < 0.001 Condition 3,255 49 886 4.1 0.0071 L:V 1,254 38 849 9.4 0.0024 Root hairs 1,253 26 823 6.4 0.012 pRH 1,252 7 816 1.9 0.17 ρb 1,251 6 810 1.5 0.22 φ = 4.0 with p 1003.7 > χ2 < 0.001 | 251| QAIC : 588.7 c 0 parameters were aliased Table 6.11: Analysis of Deviance for best main eﬀects model addressing Q2 for arbuscule colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model D D dispersion estimate (φ) and signiﬁcance, quasi-AIC (QAICc), and number of aliased parameters are also provided.

244 ℓ,ν F p (> F ) DDR | | NULL 2,351 . Species Site ρ 101,222 1,608 744 9.2 < 0.001 × × b Condition L:V exN 4,218 188 556 27.4 < 0.001 × × Condition Quality 12,206 136 419 6.6 < 0.001 × pRH min Plant C:K 1,205 38 381 22.3 < 0.001 × × L:V Year 1,204 23 358 13.3 < 0.001 × pRH Litter C:P DEA(P) 1,203 20 338 11.8 < 0.001 × × pRH +0.1 1,202 9 329 5.1 0.025 × D 1,201 0 329 0.1 0.74 φ = 1.7 with p 345.8 > χ2 < 0.001 | 201| QAIC : 1047.7 c 139 parameters were aliased Table 6.12: Analysis of Deviance for best interaction model addressing Q2 for arbuscule colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model dispersion D D estimate (φ) and signiﬁcance, quasi-AIC (QAICc), and number of aliased parameters are also provided.

245 ℓ,ν F p (> F ) DDR | | NULL 13,395 . Condition 3,484 1893 11,501 28.7 < 0.001 Saturation 3,481 517 10,985 7.8 < 0.001 Root size 3,478 149 10,836 2.3 0.081 D 1,477 138 10,698 6.3 0.013 BR(P) 1,476 81 10,617 3.7 0.055 DEA(N) 1,475 96 10,521 4.4 0.037 Bray2 1,474 64 10,457 2.9 0.088 3 PO4− 1,473 69 10,387 3.2 0.076 Season 1,472 55 10,333 2.5 0.12 Bray1 1,471 58 10,275 2.6 0.11 min 1,470 47 10,228 2.1 0.14 max 1,469 79 10,149 3.6 0.058 ρb 1,468 84 10,064 3.8 0.051 Plant C:N 1,467 103 9,961 4.7 0.031 K 1,466 59 9,902 2.7 0.10 CEC 1,465 97 9,805 4.4 0.036 Root hairs 1,464 46 9,759 2.1 0.15 0.1 1,463 46 9,713 2.1 0.15 σw 1,462 62 9,651 2.8 0.094 µw 1,461 43 9,608 2.0 0.16 Plant C:P 1,460 34 9,574 1.6 0.21 φ = 22.0 with p 10109.7 > χ2 < 0.001 | 460| QAIC : 576.5 c 0 parameters were aliased Table 6.13: Analysis of Deviance for best main eﬀects model addressing Q3 for total AM colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model D D dispersion estimate (φ) and signiﬁcance, quasi-AIC (QAICc), and number of aliased parameters are also provided.

246 βˆ σˆ βˆ lower upper (Intercept) -1.50 0.61 -2.70 -0.29 Condition (relative to Dry) Shallow -0.89 0.30 -1.48 -0.30 Saturation (relative to r) fc -0.91 0.24 -1.38 -0.44 s -0.82 0.29 -1.40 -0.24 Root size (relative to vfine) fine 0.68 0.26 0.17 1.19 D -2.38 0.88 -4.10 -0.66 Bray2 -0.0168 0.0070 -0.0305 -0.0031 min 4.2 1.2 1.7 6.6 ρb 1.06 0.33 0.42 1.71 Plant C:N 0.0155 0.0068 0.0021 0.0289 K -0.0056 0.0022 -0.0098 -0.0013 CEC 0.055 0.017 0.021 0.089 σw 7.3 3.4 0.7 14.0 Table 6.14: Summary output for best main effects model addressing Q3 for total AM colonization: indicating, coefficient estimates and standard errors (βˆ andσ ˆ βˆ , respectively; and 95 % confidence intervals for βˆ (lower and upper). Only coefficients with 95 % confidence interval excluding zero are indicated.

247 ℓ,ν R F p (> F ) D D | | NULL 13,395 . Condition Saturation Season 28,459 3,444 9,951 9.0 < 0.001 × −3 × Saturation PO4 L:V 4,455 609 9,342 11.1 < 0.001 Condition ×Root hairs× Plant C:K 8,447 646 8,695 5.9 < 0.001 Plant C:P × pRH PMP(N)× 1,446 372 8,324 27.2 < 0.001 × × Saturation Season ρb 8,438 414 7,910 3.8 < 0.001 Saturation × Season × PMP(P) 8,430 423 7,487 3.9 < 0.001 Root hairs × Root size× Litter C:K 8,422 504 6,983 4.6 < 0.001 × × PO−3 -0.3 Plant N:P 1,421 201 6,782 14.7 < 0.001 4 × × Condition Season θv 8,413 372 6,410 3.4 < 0.001 L:V pRH× Bray1× 1,412 175 6,235 12.8 < 0.001 Plant× C:P ×pRH +0.0 1,411 156 6,080 11.4 < 0.001 Saturation× L:V × min 4,407 211 5,869 3.9 0.0044 Root hairs × Litter× C:K D 2,405 147 5,722 5.4 0.0049 Condition ×Litter C:K× 3,402 192 5,529 4.7 0.0032 Season Root× hairs exN 4,398 228 5,301 4.2 0.0026 L:V CEC× Litter× C:P 1,397 82 5,220 6.0 0.015 Season× Litter× C:N Mg 2,395 123 5,096 4.5 0.012 L:V Plant× C:P Bray1× 1,394 89 5,007 6.5 0.011 Bray2× max × 1,393 137 4,870 10.0 0.0017 Saturation× Bray1 DEA(N) 4,389 184 4,686 3.4 0.010 Litter C:K × D Litter× N:P 1,388 91 4,595 6.7 0.010 Season Plant× C:K× 1,387 95 4,500 6.9 0.0088 × ρb Litter C:K Litter N:P 1,386 90 4,410 6.6 0.011 Plant× C:K pRH× min 1,385 98 4,311 7.2 0.0076 Root hairs× Bray1× Litter C:N 2,383 108 4,203 4.0 0.020 Root hairs × Bray1 × D 2,381 155 4,048 5.7 0.0038 Root hairs × Plant C:K× PMP(N) 2,379 107 3,941 3.9 0.021 pRH +0.1× Ca× 1,378 71 3,870 5.2 0.024 × × Plant C:P ρb DEA(N) 1,377 57 3,813 4.2 0.041 D Bray2× δ × 1,376 46 3,767 3.4 0.067 × ×−3 Season PO4 L:V 1,375 53 3,714 3.9 0.050 pRH ×K DEA(P)× 1,374 45 3,669 3.3 0.072 Plant× C:K× Root size min 3,371 112 3,558 2.7 0.044 Plant C:P ×pRH +0.1× 1,370 56 3,502 4.1 0.044 −3 × × PO4 L:V PMP(N) 1,369 51 3,451 3.8 0.053 pRH ×Bray1 × -0.1 1,368 34 3,416 2.5 0.11 −3× × PO θv K 1,367 35 3,381 2.6 0.11 4 × × Plant C:K ρb -0.1 1,366 39 3,342 2.9 0.092 × × ρb +0.3 1,365 35 3,307 2.6 0.11 Soil× material 1,364 52 3,255 3.8 0.053 Bray1 D BR(P) 1,363 72 3,183 5.3 0.022 × × ρb Litter C:K Bray1 1,362 40 3,143 2.9 0.088 × × φ = 13.7 with p 4947.6 > χ2 < 0.001 | 362| QAICc: 701.4 4 parameters were aliased Table 6.15: Analysis of Deviance for best interaction model addressing Q3 for total AM colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model dispersion D D estimate (φ) and significance, quasi-AIC (QAICc), and number of aliased parameters are also provided. 248 ℓ,ν F p (> F ) DDR | | NULL 3,232 . Condition 3,484 318 2,914 15.7 < 0.001 ρb 1,483 84 2,831 12.4 < 0.001 Root size 3,480 90 2,740 4.4 0.0044 DEA(N) 1,479 48 2,693 7.0 0.0083 Root hairs 1,478 50 2,643 7.3 0.0071 BR(P) 1,477 24 2,619 3.6 0.059 σw 1,476 24 2,595 3.5 0.062 K 1,475 20 2,576 2.9 0.089 Plant C:K 1,474 24 2,551 3.6 0.058 Plant C:P 1,473 35 2,516 5.2 0.023 Saturation 3,470 34 2,482 1.7 0.17 CEC 1,469 26 2,457 3.8 0.053 Ca 1,468 39 2,417 5.8 0.016 Plant N:P 1,467 22 2,395 3.3 0.069 L:V 1,466 22 2,372 3.3 0.069 -0.3 1,465 13 2,360 1.9 0.17 min 1,464 16 2,343 2.4 0.12 +0.0 1,463 22 2,321 3.3 0.069 Litter C:N 1,462 17 2,304 2.5 0.11 µw 1,461 16 2,288 2.4 0.12 Litter C:P 1,460 21 2,267 3.1 0.078 pRH 1,459 9 2,258 1.3 0.26 δw 1,458 10 2,248 1.4 0.23 Soil material 1,457 5 2,243 0.7 0.39 Plant C:N 1,456 3 2,240 0.5 0.48 Season 1,455 3 2,237 0.4 0.51 exN 1,454 3 2,234 0.4 0.51 max 1,453 3 2,231 0.4 0.53 φ = 6.8 with p 3067.2 > χ2 < 0.001 | 453| QAIC c: 579.1 c 0 parameters were aliased Table 6.16: Analysis of Deviance for best main effects model addressing Q3 for arbuscular colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model D D dispersion estimate (φ) and significance, quasi-AIC (QAICc), and number of aliased parameters are also provided.

249 βˆ σˆ βˆ lower upper (Intercept) -4.8 1.0 -6.7 -2.8 Condition (relative to Dry) Shallow -0.76 0.37 -1.48 -0.03 ρb 1.55 0.57 0.43 2.66 Root size (relative to vfine) fine 0.70 0.32 0.08 1.33 DEA(N) -0.00185 0.00057 -0.00297 -0.00073 Plant C:K 0.0187 0.0045 0.0098 0.0276 Plant C:P -0.0054 0.0027 -0.0108 -0.0001 CEC 0.152 0.037 0.080 0.225 Litter C:N -0.066 0.030 -0.126 -0.007 Table 6.17: Summary output for best main effects model addressing Q3 for arbuscular colonization: indicating, coefficient estimates and standard errors (βˆ andσ ˆ βˆ , respectively; Student’s t statistic (t) and marginal significance (p(> t )); and the 95 % confidence intervals for βˆ (lower and upper). Only coefficients with 95 % confidence| | interval excluding zero are indicated.

250 ℓ,ν R F p (> F ) D D | | NULL 3232 . Condition Saturation Season 28,459 690 2542 18.2 < 0.001 Root size ×Root hairs ×σ 8,451 240 2302 22.2 < 0.001 Root size × pRH Plant× C:K 4,447 270 2032 50.0 < 0.001 Condition× Root× hairs K 8,439 180 1852 16.7 < 0.001 Condition × Root hairs × Plant N:P 8,431 163 1689 15.0 < 0.001 Saturation× BR(P) L:V× 4,427 100 1589 18.4 < 0.001 Saturation × Root size× Bray1 16,411 131 1459 6.04 < 0.001 pRH Plant× N:P +0.1× 1,410 138 1321 102 < 0.001 Condition× Saturation× L:V 12,398 102 1219 6.29 < 0.001 × × L:V Bray1 ρb 1,397 70 1148 51.8 < 0.001 pRH× Plant× C:K D 1,396 71 1078 52.5 < 0.001 × × −3 Saturation pRH PO4 4,392 66 1012 12.2 < 0.001 Root size ×pRH ×DEA(N) 4,388 42 970 7.76 < 0.001 Saturation× D ×-0.3 4,384 43 927 7.99 < 0.001 Root size ×Litter× C:N PMP(N) 4,380 44 883 8.10 < 0.001 Season Plant× C:K Bray1× 2,378 30 852 11.2 < 0.001 pRH ×Plant C:K ×K 1,377 23 829 17.2 < 0.001 Condition× Root hairs× L:V 4,373 32 797 5.88 < 0.001 Season K× Plant C:N× 2,371 22 776 8.09 < 0.001 × × sd L:V ρb 1,370 15 760 11.2 < 0.001 × × Saturation ρb D 4,366 32 728 5.90 < 0.001 × × ρb D -0.3 1,365 17 712 12.3 < 0.001 -0.3× Bray2× 1,364 18 694 13.3 < 0.001 L:V × Litter C:K BR(N) 1,363 14 680 10.6 0.0013 Plant× C:K L:V ×Bray1 1,362 21 659 15.3 < 0.001 D δ +0.3× × 1,361 13 645 9.90 0.0018 pRH× ×D Soil material 1,360 14 632 10.1 0.0017 pRH × Bray1× DEA(N) 1,359 10 622 7.40 0.0069 sd Plant× C:K× L:V 1,358 9 613 6.41 0.012 K ×Plant N:P× 1,357 10 603 7.37 0.007 Saturation× pRH µ 4,353 22 582 3.97 0.0037 pRH Bray2× µ × 1,352 8 573 6.16 0.014 Season× BR(P)× PMP(N) 2,350 11 562 4.22 0.016 Plant N:P× Bray1× CEC 1,349 7 555 4.90 0.028 × ×−3 Season pRH PO4 1,348 6 549 4.44 0.036 × × −3 pRH L:V PO4 1,347 7 542 5.26 0.022 L:V ×Bray1× δ 1,346 7 536 5.01 0.026 × × ρb θv +0.0 1,345 7 528 5.46 0.02 Root× size× Root hairs Plant N:P 5,340 25 503 3.67 0.0031 Root hairs× Plant N:P× D 2,338 19 484 7.11 < 0.001 × × Continued on Next Page. . .

Table 6.18: Analysis of Deviance for best interaction model addressing Q3 for arbuscule colonization; indicating, for each sequentially added parameter, the parameter levels (ℓ) and residual degrees of freedom (ν), the explained deviance ( ), and the residual deviance ( R). The model dispersion D D estimate (φ) and signiﬁcance, quasi-AIC (QAICc), and number of aliased parameters are also provided.

251 Table 6.18 – Continued

ℓ,ν R F p (> F ) D D | | δ Litter C:P 1,337 8 476 5.89 0.016 × L:V Bray2 θv 1,336 7 469 5.36 0.021 pRH× µ DEA(P)× 1,335 7 462 4.98 0.026 pRH × DEA(N)× Soil N 1,334 7 455 5.46 0.020 × × pRH PO−3 Soil C 1,333 7 448 4.93 0.027 × 4 × -0.3 θv 1,332 6 442 4.46 0.036 Root× hairs D -0.1 2,330 10 432 3.71 0.026 −×3 × L:V PO4 Bray2 1,329 6 426 4.49 0.035 pRH× Bray1 × µ 1,328 8 418 6.05 0.014 Root× size pRH× µ 2,326 11 407 4.13 0.017 pRH 0.1× Bray2× 1,325 6 400 4.71 0.031 PMP(N)× Ca× 1,324 7 393 5.15 0.024 Plant N:P× Bray1 max 1,323 7 386 5.52 0.019 × × pRH θv +0.0 1,322 8 380 4.28 0.039 × × ρb Bray2 θv 1,321 6 374 4.29 0.039 Root× hairs ×BR(P) L:V 1,320 5 369 3.97 0.047 × × pRH Plant C:K δw 1,319 5 364 3.87 0.050 × × Root size Plant C:K δw 2,317 10 354 3.75 0.025 × × φ = 1.4 with p 428.7 > χ2 < 0.001 | 317| QAICc: 1660.6 17 parameters were aliased

252 6.3.3 SEM and AM eﬀect (Q4)

Based on the GLM results, we devised a cause and eﬀect model for AM in wetlands,

which incorporated signiﬁcant covariates of pAM and pArb, as well as indicators of wetland function (Figure 6.7). Our initial model provided a very poor ﬁt of the data,

2 for both pAM (χ145 = 8018.7, BIC = 7121.1, NFI = 0.10, RMSEA = 0.33) and pArb

2 (χ145 = 8064.0, BIC = 7166.4, NFI = 0.08, RMSEA = 0.33).

Total AM. Although indicators of K availability (e.g., soil K, plant C:N) were

included in several of the GLM models, removal of the K-cycle from the SEM model

substantially improved model ﬁt. Model ﬁt also improved upon removal of all plant

nutrient ratios, and surprisingly, all elements of the P-cycle. A few added relationships

+ helped to improve the model: condition eﬀects on ρb , NO3− + NH4and shoot, and

ρb eﬀect on exN. Other parameters dropped from the model included L:V, root hairs,

saturation and σ, yielding a ﬁnal model which provided reasonable ﬁt to the data

2 (χ16 = 64.0, BIC = -35.0, NFI = 0.89, RMSEA = 0.08; Figure 6.8a).

Based on the final model, AM abundance had a small direct positive effect on biomass (0.14) and a small direct negative effect on plant N; however, both AM effects were small in comparison to the effects of ρbon biomass (direct = -0.59, indirect = -0.3, total = -0.62) and exN on plant N (direct = 0.27). Condition also had a comparable net positive effect on biomass (direct = -0.10, indirect = 0.25, total = 0.15). Factors affecting AM abundance included condition, size and season (-0.28, 0.11 and 0.18, respectively).

Arbuscules. As with the pAM SEM model, removal of the K-cycle and all plant nutrient ratios from the pArb SEM model substantially improved model ﬁt. Unlike

253 ρ b exN Bray1 K L:V Size Hairs

C:N Sat C:P Mycorrhizae Cond N:P σ

C:K

Season

Shoot Plant N Plant P Plant K

Figure 6.7: Path diagram illustrating the hypothesized cause and eﬀect model for AM in wetlands. Unidirectional arrows indicate direct eﬀects between variables; bidirectional arrows indicate covariance between variables. Variance is not indicated, but was assumed to be present for each variable.

254 the pAM SEM model, however, portions of the P-cycle remained in the ﬁnal pArb

SEM model. Some added relationships also helped to improve the model: condition

effects on ρb , exN and shoot, ρb effect on exN, as well as, exN effect on root hairs. The

parameter shoot was also dropped from the pArb SEM model to yield a ﬁnal model,

2 providing reasonable ﬁt to the data (χ28 = 75.3, BIC = -98.0, NFI = 0.86, RMSEA

= 0.06; Figure 6.8a).

Based on the ﬁnal model, arbuscular abundance had only a very small negative

eﬀect on plant N (direct = -0.07); plant N was more aﬀected by exN (direct =

0.27, indirect = -0.01, total = 0.27). Additional factors aﬀecting plant N included

ρb (indirect = -0.07) and condition (indirect = 0.04), as well as minor contributions

from Bray1-P, season, L:V and root hairs (all indirect eﬀects less than 0.01).

Factors most aﬀecting pArb were condition (direct = -0.10, indirect = -0.04, total

= -0.14), ρb (direct = 0.12, indirect = 0.00, total = 0.12), exN (direct = 0.09, indirect

= 0.01, total = 0.11), and root hair presence (direct = 0.12, indirect = 0.00, total

= 0.12). Arbuscule abundance was also aﬀected to some extent by season (direct =

-0.07, indirect = -0.01, total = -0.08), Bray1-P (direct = -0.06) and L:V (indirect =

-0.01).

6.4 Discussion

6.4.1 AM predictors (Q1–Q3)

Descriptive parameters, such as type, age and quality, were poor predictors of

AM colonization. Type, age and failed to appear as signiﬁcant terms in any of Q the models; class appeared only in one model and accounted for only 2 % of the variance. Among created wetlands, quality had some predictive ability for arbuscule

255 ρ −0.21 b exN Size a

0.12

−0.46 0.11

−0.59 0.16

−0.28 0.27 Mycorrhizae Cond

0.18 −0.099

0.14 −0.17 Season

Shoot Plant N

−0.21 ρ exN b Bray1 b

−0.46 −0.087 0.12 L:V 0.095 0.12 0.19 0.12 −0.11 −0.063

0.12 −0.1 Hairs Arbuscules Cond

0.27 −0.068 0.093 Size −0.068

Season

Plant N

Figure 6.8: Path diagrams of final cause and effect models for AM in wetlands, using (a) pAM and (b) pArb. Unidirectional arrows indicate direct effects between variables; bidirectional arrows indicate covariance between variables. Variance is not indicated, but was present for each variable.

256 colonization (explaining 5.8 % of variance), which tended to be lower in lo to med quality wetlands, depending on station condition. The general absence of type, age and class in the models suggest that, in answer to Q1 and Q2, AM abundance does

not diﬀer between created and natural wetlands and does not increase with age.

Non-general parameters (i.e., speciﬁc to this study), such as site, station and year,

when permitted in the models, typically explained much of the observed variance in

AM abundance. Site, for example, appeared in two models in combination with

species and ρb , and accounted for 67 % to 68 % of variance in arbuscule abundance.

Station was included as a main eﬀect in four of the models, accounting for 28 % to

29 % and 20 % to 21 % variance in pAM and pArb, respectively. Up to 51 % of the

variance was explained by year in combination with other parameters, particularly

season, genus, and saturation, suggesting considerable year-to-year variability in AM

abundance.

Phenology

Season, genus and species were all related to plant phenology and typically large

contributors to explained variance. Season appeared as an interaction term in 5 of the

models. In combination with saturation and condition, it was particularly important

in the models addressing Q3, and accounted for 23 % to 39 % of the variance in the

Q3 models. Season was also included in both ﬁnal SEM models, with a positive eﬀect

on pAM (i.e., higher in fall) and a negative eﬀect on pArb (i.e., higher in summer).

Increased arbuscular presence in the summer would support other seasonal studies, which observed an increase in AM during periods of rapid growth and ﬂowering

(Ch. 5).

257 Species appeared in 7 of the models, and seemed to be a better indicator of AM

abundance than genus. As a main eﬀect, species accounted for 16 % of the variance in

pAM and 31 % to 33 % of the variance in pArb; it also appeared in combination with condition, explaining 42 % of pAM variance, and with site and ρb , explaining 67 % to

68 % of pArb variance. Arbuscular mycorrhizae were particularly prevelant in several

of the Bidens species (e.g., BIDARI, BIDCER, BIDDIS, BIDFRO, BIDTRI ), as well

as ECHSPP and GALSPP.

Root morphology

A common assumption is that plants with coarser roots or lacking root hairs will

be more reliant on AM formation (Ch. 5); however, as main eﬀects, aspects of root

structure accounted for only a small portion of variance. Two quantitative and one

qualitative parameters were included to characterize root ﬁneness. The ratio L:V

provided better predictability than D and explained 0.7 % to 1.6 % as a main eﬀect

and 1.4 % to 12.3 % as an interaction term. The qualititative root size class seemed

to best capture the eﬀect of root ﬁneness on AM abundance, with 1.1 % to 2.8 %

as a main eﬀect and 4.6 % to 24 % as an interaction. The presence of root hairs

likewise seemed best represented qualitatively: the simple absence/presence indicator

appeared as a signiﬁcant term in 8 of the models, accounting for 0.34 % to 1.5 % of

the variance as a main eﬀect, and 1.1 % to 43 % in combination.

Aspects of root structure were also capture by the genus and species parameters.

Consequently, when these two parameters were excluded (i.e., the Q3 models), L:V,

root size class, and root hairs became more important to prediction of AM abundance.

Root size class, for example, only appeared in the Q3 models. Both L:V and root

hairs appeared also in the Q1 and Q2 models, but either had decreased predictability

258 or appeared in conjunction with genus rather than species. In general, AM increased in prevalence as roots became more fine (i.e., increasing L:V ) and were most prevalent in roots with 0.5 D < 0.75 (i.e., fine size class). Contrary to expectation, however, ≤ AM were also more abundant when root hairs were present. Indicators of root morphology also appeared in both final SEM models: root size class had a positive effect on pAM, while root hair presence had a positive effect on pArb.

Soil chemistry and fertility

Parameters related to soil chemistry and nutrient availability were only weakly predictive of AM. Formation of AM is most associated with improved P uptake and several studies have demonstrated a decrease in AM as P availability increases (i.e., plants become less reliant on AM for P acquisition; Ch. 5). In this study, soil P metrics had little direct eﬀect on AM and were typically only included in the GLM models as interaction terms with variable eﬀect. Bray1-P, however, remained in the

ﬁnal SEM model for pArb, but had only a slight negative eﬀect. For indicators of

P limitation, only plant C:P appeared as a signiﬁcant factor in the GLM models; however, it was not included in either ﬁnal SEM model. One probable reason for the lack of correlation between P and AM in this study is the general availability of P in these systems, which instead appear to be more N limited (Ch. 4).

Accordingly, soil N metrics and indicators of N limitation tended to be more strongly correlated to AM abundance, although typically not with the expected ef-

+ fect. Extractable NO3− + NH4appeared as a signiﬁcant interaction term in 4 models, accounting for up to 8 % of model variance; however, its eﬀect was generally to in-

+ crease AM abundance. Extractable NO3− + NH4was also included in both ﬁnal SEM

259 models, but only as a positive factor for pArb; its main eﬀect in both models was on

plant N, with which it had a strong positive relationship.

Although soil K was the only soil chemical to appear as a main eﬀect in the GLM

models, it accounted for less than 1 % of the variance. Furthermore, soil K, along

with plant C:K and plant K, failed to remain in the ﬁnal SEM models.

Soil physics and hydrology

Some of the soil chemistry parameters (e.g. soil N) may have appeared less inﬂu-

ential to AM abundance because of inclusion of ρb . Soil bulk density is known to be a reliable indicator of overall soil health: in Chapters 3 and 4, we demonstrated strong correlation between ρb and various other soil metrics (particularly soil C and N), as well as indicators of microbial activity; making it a reliable indicator of wetland and wetland soil development. From this study, ρb appeared to also be a good predictor of AM abundance. As a sole predictor, it had a significant positive effect on all AM metrics; and appeared as a main effect in 3 GLM models, accounting for up to 3 % of model variance. As an interaction term in the models, ρb explained 5 % to 69 % of the variance. Additionally, ρb appeared in both final SEM models, with a strong positive effect on pArb, but only as a factor for biomass in the pAM model. In general, AM abundance increased as ρb increased.

The parameter saturation incorporated aspects of both soil physics and hydrology and was one of the two most important predictors of AM in the GLM models; condition being the other parameter. Both saturation and condition appeared in all

6 main eﬀect models (although saturation was signiﬁcant for only 4), with variance accountability of up to 5 % and 14 %, respectively. The two terms also formed strong interactions, with saturation accounting for up to 40 % of model variance, particularly

260 in combination with year season and year condition; and condition accounting × × for up to 45 % of variance, in combination with species and saturation season. × Condition was also included in the ﬁnal SEM models exhibiting a strong negative eﬀect on both pAM and pArb. In particular, shallow inundation seemed to reduce

AM abundance.

Additional hydrologic parameters appeared in several models, but were much less important than either saturation or condition—possibly indicative of a nonlinear relationship between AM and hydrology. Minimum water depth and 0.1, in combination with other parameters, each accounted for up to 4 % of AM variance, but with mixed eﬀects. Up to 8 % of variance was explained by σw , particularly in combination with root size class and root hairs; however, the parameter was included in only 2 models.

The eﬀects of both σw and δw suggested an increase in AM with increasing hydrologic variability, but were infrequent in the GLM models. Furthermore, while σw was included in the initial SEM model, it did not appear in either ﬁnal SEM model.

6.4.2 AM eﬀects (Q4)

In the univariate permutation tests, AM presence appeared to increase plant C but decrease plant N and P, and have no effect on shoot or root biomass, or S:R ratio. Only plant N appeared in both final SEM models, with a negative response to pAM and pArb. In both models, however, plant N was most affected by extractable

+ NO3− + NH4. Shoot biomass also appeared in the model for pAM ; while pAM had a positive eﬀect on shoot biomass, biomass was most aﬀected by ρb (negatively) and condition (positively).

261 6.5 Conclusions

Environmental controls on AM abundance tended to agree with other studies, with important factors including season and hydrology. The effect of hydrology, however, appeared to be more than just a simple linear relationship, with lowest AM abundance under shallow inundation and higher AM abundance under both deep inundation and dry conditions. As discussed in Ch. 5, some of the hydrologic effect may be indirect, through changes in nutrient solubilities. Our models, however, were not designed to detect these types of interactions. Another important determinant for AM was ρb . As previously noted, ρb is closely linked to many other soil properties, as well as several nutrient-related functions. Soils with high ρb tend to be more nutrient deficient, more impervious to plant roots, and less well aerated—all properties that are expected to increase AM importance. In contrast, soils with low ρb tend to be more nutrient rich, easily penetrated by plant roots, and well aerated—properties likely to decrease AM reliance.

While this study did not detect any effects of type or age on AM, it did demonstrate that AM are a significant presence in both created and natural wetlands, and further suggest that AM play a role in C and nutrient cycling. Although the effect of AM on plant N in the final SEM models was negative, because the effects of AM on biomass (SEM) and plant C (univariate test) were both positive, one possibility is that AM presence allows the plant to invest more energy in shoot growth which might then result in increased plant C but decrease plant N and P. Or possibly, the apparent effects of AM on biomass and plant C and N are only consequences of species-dependent colonization. For example, BIDARI, which generally had high AM colonization, typically had higher plant C and lower plant N than the less colonized

262 POTNOD; however, species such as JUNACU or TYPLAT were seldom to moderately colonized and had high plant C and low plant N. Either way, the role of AM in wetland development is a subject worthy of pursuit, with promise for improvement of wetland restoration and creation projects.

Acknowledgements: I particularly wish to thank Amy Barrett and Michael Szuter for the countless hours they diligently spent in front of the microscope. Jenette Goodman assisted with most of the ﬁeld collection and sample prepa- ration. Additional assistance was provided by Erelyn Apolinar, Sarah Boley and Brie Elking. Professor Peter Craigmile provided instruction on use of generalized linear models. This research was supported by the Cooperative State Research, Education, and Extension Service, U.S. Department of Agri- culture, under Award No. 2005-35101-15593; the Ohio Agricultural Research and Development Center; The Ohio State University; Kenyon College; and the University of New Hampshire. We thank the City of Columbus Recreation and Parks, Columbus Metro Parks, Groveport-Madison High School, the Ohio De- partment of Natural Resources, and several private landowners for permission to use their properties.

263 APPENDIX A

SITE DESCRIPTIONS AND HISTORIES

264 A.1 Ballﬁeld marsh

Ballﬁeld Marsh is located in Jackson Township (section 16) of Knox County, just south of Bladensburg. Property atlases from 1871 and 1896 list the landowners as

Davis Parks and John Taylor, respectively (Fig. A.1; Caldwell and Starr, 1871; Cald- well, 1896). The marsh is proximate ( 200 m) to Wakatomika Creek. Its current ∼ owners are Vaughn Ullman and Nancy Rose.

The topography of Jackson Township is described as broken with numerous springs.

The following description comes from Hill (1881, p. 487):

Every part of Jackson was once densely wooded, oak, sugar and chestnut being the principal varieties. Although much of this timber has been cleared away, yet the hills along the Wakatomica and elsewhere in the township exhibit a large growth of oak and chestnut. In the early history of the country, the hills Jackson township abounded with wild animals and venomous reptiles. The early settlers killed large numbers of deer, bear and other animals. Though multitudes reptiles have been killed in former years, still at this later period they are to be found.

European settlement of the township began during 1810 in section 7 (Hill, 1881, p. 488) and by 1830 there were 626 settlers residing in the township (Norton, 1862, p.

373). The village of Bladensburg was established in 1833 (Hill, 1881, p. 490). Many of the early settlers were farmers, with tobacco being their principle crop (Hill, 1881, p. 489). The lands surrounding Ballﬁeld marsh remain heavily farmed, although there seems to have been some reforestation of the area in recent decades (Fig. A.2).

The entire county is covered with Illinoian glacial till which forms the parent material for most of the soils (White, 1937; Redmond and Graham, 1986). The soil of Ballﬁeld marsh is classiﬁed as Linwood muck (Ln), an organic-based soil typically forming in till plain depressions, former lake beds and outwash terraces (Fig. A.3;

265 Redmond and Graham, 1986). Linwood mucks are typically characterized by 0.5 m to 1.3 m of black, friable muck, underlain by 1.5 m of silty to sandy loam.

266 (a) 1871

(b) 1896

Figure A.1: Portions of Clay and Jackson townships from Knox County land ownership atlases of 1871 and 1896 (Caldwell and Starr, 1871; Caldwell, 1896)

267 2009 1994

1988 1970

1960

Figure A.2: Aerial photos of Ballﬁeld Marsh locale. (source: U.S. Geological Survey’s Earth Re- sources Observation and Science Center).

268 Figure A.3: Soil map of Ballﬁeld marsh locale, colored by drainage class: blue, very poorly drained; cyan, somewhat poorly drained; green, moderately well drained; yellow, well drained (Soil Survey Staﬀ).

269 A.2 Big Island

The three created wetlands of Big Island Wildlife Area are located in Big Island

Township (sections 23, 29 and 30) of Marion County. The two older sites lie about 2 km east of New Bloomington (formerly Agosta); the youngest site is approximately equidistant between New Bloomington and Marion. A property atlas from 1878 indicates the present-day sites spanned properties belonging to W. Baker, Lucy A.

White, G. F. Carpenter, Joshua Mitten, and S. S. Bennett (Fig. A.4; Howland, 1878).

The two older sites lie just north of the Scioto River.

The topography of Big Island Township is described as level with black prairie soil and some hardpan projections supporting forest (Winchell, 1873a). The township was

ﬁrst settled by Europeans around 1819 and was formally organized in 1824 (Leggett and Company, 1883, pp. 650–651). By 1860 there were 911 settlers residing in the township (Leggett and Company, 1883, p. 377). This area would have formed the southern extent of the “Sandusky Plains” (Fig. A.5). The following description is provided by Randall (1912, pp. 342–343):

The plains was the level section bounded, in general, on the north by the (Little) Sandusky, on the east by the Olentangy, on the south by the Scioto, and on the west by the Tymochtee. These “plains” lying between the headwaters of the Sandusky and Tymochtee, that flowed north and those of the Olentangy and Scioto that flowed south, were in those Indian days overgrown with high coarse grass, with here and there slight surface elevations called “islands” which were covered with timber. Over these “Sandusky Plains” some forty miles in extent, east and west, and perhaps reaching twenty miles, north and south, says Butterfield, “birds of a strange plumage” flew and “prairie hens sailed away, slowly dropping into the grass, while sand-hill cranes blew their shrill pipes;” “prairie owls, on cumbrous wings, fluttered away in the distance and the noisy bittern was heard along the streamlets; wild geese were frightened from their nests, and occasionally a bald or gray eagle, soared far above them; many fox squirrels were seen and rattlesnakes were also found to be

270 very numerous.” Deer, turkeys and pheasants there were in abundance. Little wonder that “these plains were always a favorite hunting ground for the Indians.”

The properties are currently owned by the Ohio Department of Natural Resources and are most likely sites of former wet prairies. The oldest study site was constructed by the agency in 1973 for waterfowl habitat. This site is a full impoundment with managed water level. The two younger sites belong to two mitigation banks created for Section 401/404 permit compliance: the older was constructed in 1995 as part of the Big Island Mitigation Bank and the younger in 1999 as part of the Little

Scioto Mitigation Bank. Both younger wetlands are partial impoundments, also with managed water levels. All three sites had been artiﬁcially-drained and intensively farmed through much of the twentieth-century (Fig. A.6). Restoring the sites required removal of the tile drainage system.

The entire county is covered with Wisconsin glacial till which forms the parent material for most of the soils, other Marion County soils formed in glacial outwash, post-glacial lacustrine deposits, or alluvium (Miller and Martin, 1989). The soil underlying the two older wetlands is classiﬁed as Latty (La), a very poorly drained silty clay (Fig. A.7; Miller and Martin, 1989). The far western section of the youngest site is also underlain by Latty soil, while the remainder is Milford (Mf) silty clay loam and also very poorly drained (Miller and Martin, 1989). Both Latty and Milford soils tend to develop in slight depressions, which are often remnant post-glacial lake plains

(Miller and Martin, 1989).

271 Figure A.4: Portions of Big Island township from the Marion County land ownership atlas of 1878 (Howland, 1878)

272 Figure A.5: Excerpt from a map of Ohio published in 1819 indicating the location of the “Sandusky Plains” (Tanner, 1819)

273 2009 1995

1988 1973

1959

Figure A.6: Aerial photos of Big Island locale. (source: U.S. Geological Survey’s Earth Resources Observation and Science Center).

274 (a) older two sites

(b) youngest site

Figure A.7: Soil maps of Big Island locale, colored by drainage class: blue, very poorly drained; cyan, somewhat poorly drained; yellow, well drained (Soil Survey Staﬀ).

275 A.3 Bluebird

Bluebird is located in Genoa Township of Delaware County, approximately 2 km south of Galena and 1 km east of Hoover Reservoir. Property atlases from 1866 and 1875 list the landowner as W. Cox (Fig. A.8; Beers, 1866; L. H. Everts and

Company, 1875). The eastern part of Delaware County, where Genoa Township resides, is described as rolling, with a generally ﬂat surface disected by numerous streams (Perrin, 1880, p. 166). Much of the county was originally forested with oak, hickory, walnut, ash, birch, and sugar-maple, interspersed with scattered small fens and marshes (Winchell, 1874a; Perrin, 1880; Jenny and Glanville, 2006).

By 1830, Genoa Township had become established and supported a population of 658 (Perrin, 1880, p. 211). The ﬁrst European settler established on a tract of land in 1805, probably just north of what is now the site of Bluebird (Perrin, 1880, p. 605). Two years later, William Cox, the landowner listed in the 1866 and 1875 atlases, erected a cabin near the “Ox Bow” of Big Walnut Creek, probably in the vicinity of the present-day Bluebird site (Perrin, 1880, p. 602).

Bluebird is currently owned by the city of Columbus and forms part of the Hoover

Meadow Nature Preserve. The wetland was constructed in 2000 for Section 401/404 permit compliance. Construction involved extensive excavation into the soil and was adjacent to an existing small pond/wetland complex. The site was also planted. Much of the surrounding area is intensively farmed and a few subdivisions have sprung up in recent decades (Fig. A.9). The large reservoir to the west (Hoover Reservoir) was created in 1955 by impoundment of Big Walnut Creek.

276 The entire county is covered with Wisconsin glacial till, with ground moraines developing into ﬂat poorly drained tracts and end moraines developing into well- drained areas of varied relief (Winchell, 1874a; Jenny and Glanville, 2006). Bluebird was constructed primarily in Centerburg (CeB) silt loam, which is a moderately well drained soil with 2 % to 6 % slopes (Fig. A.10; Jenny and Glanville, 2006). Bennington silt loam (BeA), with 0 % to 2 % slopes and somewhat poorly drained, comprises the remaining soil substrate for the created marsh (Jenny and Glanville, 2006).

Figure A.8: Portion of Genoa Township from the Delaware County land ownership atlas of 1875 (L. H. Everts and Company, 1875)

277 2009 1995

1988 1973

1966 1955

Figure A.9: Aerial photos of Bluebird locale. (source: U.S. Geological Survey’s Earth Resources Observation and Science Center).

278 Figure A.10: Soil map of Bluebird locale, colored by drainage class: blue, very poorly drained; cyan, somewhat poorly drained; green, moderately well drained; yellow, well drained (Soil Survey Staﬀ).

279 A.4 Calamus swamp

Calamus swamp is located in Wayne township of Pickaway County, about 3 mi west of Circleville. The ﬁrst European settled in Wayne township around 1798, and the township was formally erected soon after, just prior to the organization of Pick- away County in 1801 (Van Cleaf, 1906, p. 191).

The land on either side of the Scioto, just south of Circleville was noted for its levelness, rich black soil, abundance of natural grasses, and lack of trees; on the east were the “Pickaway Plains” and on the west the “Darby Plains” (Fig. A.11). Possibly,

Calamus was part of the latter Darby Plains. The earliest property record dates from

1871, with Calamus spanning two land parcels, one belonging to S.H. Ruggles and the other to J.H. Anderson (Fig. A.12; Lake, 1871). Calamus Swamp is currently owned by the Columbus Audubon Society, to whom it was donated in 1971 by previous owner Ada May Burke (Sweitzer, 1971).

Although there is some speculation the swamp originated as a borrow pit for the

Pennsylvania Railroad bed which borders it to the south (Sweitzer, 1971), it is likely an intermorainal depression (or ‘kettle-hole’) left by the Wisconsin glacier (Schuster,

1952). The swamp next appears in a topographical map of the county in 1914, and is mentioned as one of two large marshes in Pickaway County in a thesis of the original

Pickaway County vegetation (Shupe, 1930).

The entire county is covered with glacial till which forms the parent material for

most of the soils (Kerr and Christman, 1980). The borders of Calamus are comprised

of Montgomery (Mt) soils, which typically develop in lacustrine deposits and are

poorly drained (Fig. A.14). The center is listed only as water, but is likely Carlisle

muck (Cf), an organic-based soil typical of the county. Carlisle muck is found in

280 frequently ﬂooded depressions and is characterized by about 20 cm of black, friable muck, underlain by 1.2 m of dark brown friable muck. The presence of Montgomery and Carlisle soils suggest that Calamus formed in a clay-lined depression, a remnant of a post-glacial lake.

Figure A.11: Map of the ancient Shawnee towns, on the Pickaway Plains. (source: Howe, 1850, p. 402).

281 Figure A.12: Portion of Wayne township from the Pickaway County land ownership atlas of 1871 (Lake, 1871)

282 2009 1994

1988 1974

1960

Figure A.13: Aerial photos of Calamus Swamp locale. (source: U.S. Geological Survey’s Earth Resources Observation and Science Center).

283 Figure A.14: Soil map of Calamus locale, colored by drainage class: blue, very poorly drained; cyan, somewhat poorly drained; yellow, well drained (Soil Survey Staﬀ).

284 A.5 JMB

The JMB wetland is located in Franklin County, Madison township, Section 16, approximately 3 km north of Groveport and 500 m west of Blacklick Creek. Section

16 was appropriated by the government for school land and is still retained as such

(Taylor, 1909, p. 207). Landowner records indicate occupancy by Augustus Sallee in

1855 and J. W. Cromwell in 1872, respectively (Caldwell and Gould, 1872; Bareis,

1902). Madison Township was organized in 1809 (Taylor, 1909, p. 206) and by 1820 supported a population of 1,097 residents (Historical Publishing Company, 1901, p.

21). The ﬁrst European settlers began arriving around 1802 (Taylor, 1909, p. 206).

Bareis (1902, p. 205) provides the following description of the early township

The township is well watered by several good sized streams as shown on the map Big Walnut (also called Big Belly and Gahanna), Blacklick, Little Walnut, and Alum Creek; there are also numerous smaller streams, among them George creek, Spring run and Big run—which with the network of tile drains carry off the surplus water. The adjoining lowlands along the larger streams are often flooded when a rich fertilizing sediment is left. Large portions of the township were originally covered with large ponds and swamps, often spoken of as prairies; especially was this true of the eastern section, giving it the name of “the flats.” Scarcely a trace of these remain, the larger ditches only reminding one of them....Originally the township was densely timbered with giant oak, ash, walnut, hickory, elm, maple, beech, linden, cottonwood and other trees. Along the streams grew buckeye, pawpaw, willow and immense sycamores.

JMB was constructed in 1996 for Section 401/404 permit compliance. Con- struction involved excavation into the soil and planting. The site is owned by the

Groveport-Madison school district and lies just south of the Three Creeks Metro

Park. The surrounding land has a history of farming, but is becoming a more urban development (Fig. A.15).

285 The entire county is covered with Wisconsin glacial till, which has become tran- sected by a series of north-south running streams (Orton, 1878; Jenny and Glanville,

2006). The JMB wetland was constructed primarily in Sloan (So) silt loam, subject to frequent ﬂooding and very poorly drained (Fig. A.16; McLoda and Parkinson, 1980).

The western border, however, appears to lie in well drained Eldean (ElC2) silt loam, with 6 % to 12 % slopes.

286 2009 1994

1988 1974

1963 1954

Figure A.15: Aerial photos of JMB locale. (source: U.S. Geological Survey’s Earth Resources Observation and Science Center).

287 Figure A.16: Soil maps of JMB wetland locale, colored by drainage class: blue, very poorly drained; cyan, somewhat poorly drained; green, moderately well drained; yellow, well drained (Soil Survey Staﬀ).

288 A.6 Killdeer Plains

The Killdeer Plains study site is located in Pitt Township (sections 6 and 7) of

Wyandot County, approximately 6 km southwest of Harpster (formerly known as

Fowler) and 3 km west of the Little Sandusky. A property atlas from 1879 lists the landowners as P. A. Anderson and David Harpster (Fig. A.17; Hare, 1879). The western portion of Wyandot County is described as ﬂat to gently undulating with extensive prairie-like tracts; Pitt Township occurs along the eastern extent this topographic region (Winchell, 1873b).

Pitt township was organized along with the formation of the county in 1845 Baugh- man (1913, p. 358). European settlers probably ﬁrst arrived in 1820, settling mainly along the Little Sandusky (Baughman, 1913, p. 359). By 1860 there were 911 settlers residing in the township Leggett and Company (1883, p. 377).

The study site is part of a large wetland complex in the Killdeer Plains Wildlife

Area, which is managed by the Ohio Department of Natural Resources. The 320 ha marsh was constructed in 1966 primarily for waterfowl habitat. Construction required removal of tile drainage from the former farmland, followed by impoundment of the area (Fig. A.18). Historically, this area was probably part of an expansive prairie known as the “Sandusky Plains” (see Fig. A.5).

The entire county is covered with Wisconsin glacial till which forms the parent material for most of the soils, other Marion County soils formed in glacial outwash, post-glacial lacustrine deposits, or alluvium (Steiger and Hendershot, 1982). The wetland is underlain by very poorly drained Paulding (Pd) clay (Fig. A.19; Steiger and Hendershot, 1982). Paulding soils typically develop on former lake plains and are very similar to Latty soils (Steiger and Hendershot, 1982).

289 Figure A.17: Portion of Pitt township from the Wyandot County land ownership atlas of 1879 (Hare, 1879)

290 2009 1995

1988 1973

1959

Figure A.18: Aerial photos of Killdeer Plains locale. (source: U.S. Geological Survey’s Earth Re- sources Observation and Science Center).

291 Figure A.19: Soil map of Killdeer Plains locale, colored by drainage class: blue, very poorly drained; cyan, somewhat poorly drained (Soil Survey Staﬀ).

292 Lawrence Woods

The lower marsh of Lawrence Woods in located in the Taylor Creek Township of

Hardin County, approximately 10 km south of Kenton and 1 km east of Taylor Creek.

A property atlas from 1879 lists the landowner as S. Walker (Fig. A.20; Howland,

1879). The township is fairly diverse in terrain, ranging from broken and hilly in the northeast, to generally ﬂat in the northwest and west, to gently undulating in the remaining portions (Warner, Beers and Company, 1883, p. 578). Much of the land was originally forested with oak, ash and beech (Warner, Beers and Company, 1883, p. 579).

Taylor Creek Township was organized following the formation of Hardin County in 1820 (Warner, Beers and Company, 1883, p. 578). The ﬁrst European settlers arrived in 1827 (Baughman, 1913, p. 579).

The small natural marsh lies at the south end of the Lawrence Woods State

Nature Preserve, which is managed by the Ohio Department of Natural Resources.

The marsh was reportedly part of a cow pasture prior to its purchase by the state.

Some of the surrounding lands continue to be farmed (Fig. A.21).

The entire county is covered with unstratiﬁed Wisconsin glacial till except for a few knolls and ridges of assorted gravel (Winchell, 1874b). The wetland is underlain by very poorly drained Pewamo (Pm) silty clay loam (Fig. A.22; Miller and Robbins,

1994). Pewamo soils develop on broad ﬂats or depressions in glacial till and are characterized by a dark gray silty clay loam topsoil ( 28 cm) and a grayish brown ∼ silty clay subsoil ( 138 cm) (Miller and Robbins, 1994). ∼

293 Figure A.20: Portion of Taylor Creek township from the Hardin County land ownership atlas of 1879 (Howland, 1879)

294 2009 1994

1988 1973

1959

Figure A.21: Aerial photos of Lawrence Woods locale. (source: U.S. Geological Survey’s Earth Resources Observation and Science Center).

295 Figure A.22: Soil map of Lawrence Woods locale, colored by drainage class: blue, very poorly drained; cyan, somewhat poorly drained; green, moderately well drained (Soil Survey Staﬀ).

296 A.7 Mishne

Mishne wetland is located in Franklin County, Prairie township, approximately

1 km southwest of Galloway and about 500 m south of Hellbranch Run. Property

atlases from 1842 and 1872 list the landowners as J. Davis and J. L. Belt, respec-

tively (Fig. A.23; Wheeler, 1842; Caldwell and Gould, 1872). Prairie Township was

organized in 1819 (Taylor, 1909, p. 436) and by 1820 supported a population of 322

residents (Historical Publishing Company, 1901, p. 21). The ﬁrst European settlers

began arriving around 1813 (Taylor, 1909, p. 437).

The small natural marsh lies at the edge of a corn/soybean ﬁeld, currently owned

by Paul and Jimilea Gutheil. The surrounding land is also intensively farmed (Fig. A.24).

The entire county is covered with Wisconsin glacial till, which has become tran- sected by a series of north-south running streams (Orton, 1878; Jenny and Glanville,

2006). Mishne wetland is underlain with Kokomo (Ko) soil, a very poorly drained silty clay loam (Fig. A.25; McLoda and Parkinson, 1980).

297 Figure A.23: Portion of Prairie Township from the Franklin County land ownership atlas of 1872 (Caldwell and Gould, 1872)

298 2009 1994

1988 1973

1965 1953

Figure A.24: Aerial photos of Mishne locale. (source: U.S. Geological Survey’s Earth Resources Observation and Science Center).

299 Figure A.25: Soil map of Mishne locale, colored by drainage class: blue, very poorly drained; cyan, somewhat poorly drained; green, moderately well drained; yellow, well drained (Soil Survey Staﬀ).

300 A.8 New Albany Company

The New Albany Company wetland is located in Plain Township of Franklin

County, approximately 6 km northwest of New Albany and 500 m west of Rocky Fork creek. A property atlases from 1872 and 1883 list the landowners as L. Needles and

Abraham Irwin, respectively (Caldwell and Gould, 1872; Boehm Stamp and Printing

Company, 1883). Plain Township was organized in 1810 (Taylor, 1909, p. 419) and by 1820 supported a population of 373 residents (Historical Publishing Company,

1901, p. 21). The ﬁrst European settler to the township arrived in 1802 (Taylor,

1909, p. 421).

The New Albany Company wetland was constructed in 1993 for Section 401/404 permit compliance. The site is the largest in a small wetland complex which was constructed by a combination of excavation and diking. The property was recently purchased by the Columbus and Franklin County Metro Park District for future development as metro park at the headwaters of the Rocky Fork watershed.

The entire county is covered with Wisconsin glacial till, which has become tran- sected by a series of north-south running streams (Orton, 1878; Jenny and Glanville,

2006). The New Albany wetland was constructed in very poorly drained Pewamo

(Pm) silty clay loam (Fig. A.27; McLoda and Parkinson, 1980).

301 2009 1995

1988 1973

1965

Figure A.26: Aerial photos of NAC locale. (source: U.S. Geological Survey’s Earth Resources Observation and Science Center).

302 Figure A.27: Soil maps of New Albany Company wetland locale, colored by drainage class: blue, very poorly drained; light blue, poorly drained; cyan, somewhat poorly drained; green, moderately well drained; yellow, well drained (Soil Survey Staﬀ).

303 A.9 Pickerington Ponds

The natural wetland at Pickerington Ponds is located primarily in Violet Town- ship (Sections 8 and 17) of Fairﬁeld County, with some portion in Madison Township

(Sections 7 and 18, formerly part of Violet Township, Fairﬁeld County) of Franklin

County. The two constructed wetlands at Pickerington Ponds are located in Madi- son Township of Franklin county, just north of the natural site. All three sites are approximately 2 km west of the town of Pickerington. A property map from 1866 lists R. Bowers, T. Bowen, J. Ebright, and W. Miller as partial owners of the land

(Fig. A.28; Hannum, 1866; Caldwell and Gould, 1872). A tributary to Georges Creek

ﬂows west and north of the wetland, and a map from 1900 indicates a smaller branch

ﬂowing across the northern portion of the wetland (Fig. A.29). Of the township, the following description is given by Graham (1883, p. 254):

The township was set off and incorporated in 1808, and from the va- riety and abundance of its wild flowers it took the name of Violet. Its surface is slightly undulating, slopes southward, and is drained by Black Lick, Sycamore and Walnut Creeks. There are many swamps on the low lands, and the valley of Sycamore Creek frequently suffers from inundation.

The geology of the area is glacial till and the following description appears in

Stauﬀer et al. (1911, p.263–4):

Two and one-half to three miles due north of Canal Winchester, is a group of gravelly hills of rather weak relief resting partly on the sandstone and partly on drift. In places the gravel is well assorted but in other parts it is not. Two large kettles in the group contain water, and in the northern part of the area was once a lake of two or three hundred acres. It is now drained and furnishes ﬁne rich soil, as such places usually do. About a mile north of Winchester is another minor lake bed as is shown by its black, humic soil and sandy margin.

304 A description in Dachnowski (1912, p.58):

The northern part of the county has a relatively ﬂat surface with occa- sional saucer-shaped depressions a few acres in extent, which hold water only long enough to form shallow accumulations of vegetable remains. Neither the quantity nor the quality of the material would warrant com- mercial development, for it is usually a black mucky peat with considerable mineral matter.

The three marshes are part of the Pickerington Ponds Metro Park and managed

by the Columbus and Franklin County Metro Park District. The two created sites

were established jointly through the NRCS Wetlands Reserve Program and the Ohio

EPA Water Resource Restoration Sponsor Program in 2002 and 2005, respectively.

Construction of both sites required excavation of the soil and planting.

The natural marsh is underlain primarily with Rockmill (Rp) silty clay loam, with an eastern border underlain by Patton silty clay loam (Pb). The Rockmill soil is very poorly drained and occasionally flooded; the Patton soil is listed as poorly drained and rarely flooded (Fig. A.31; Hamilton and Deaton, 2005). This classification represents a change from that published in an early survey (Meeker et al., 1960) which identified most of the underlying as Willette muck. In fact, this latter classification seems more likely give the high organic matter in the soil. Willette soils are very poorly drained and typically develop on former lake beds (Meeker et al., 1960).

The younger created site was constructed in Kokomo (Ko) silty clay loam and frequently ﬂooded Sloan (So) silt loam; both very poorly drained soils. The older created site was constructed in a mixture of Kendallville silt loam with 0 % to 2 % slopes (KeA) and 2 % to 6 % slopes (KeB), and Westland (Wt) silty clay loam; the

Westland soil is listed as very poorly drained, while the two Kendallville soils are well

305 drained. Kokomo soils tend to develop in ﬂats and depressions of Wisconsin glacial till.

Figure A.28: Portions of Madison (west) and Violet (east) townships from Franklin and Fairﬁeld County land ownership atlases published in 1872 and 1866, respectively (Hannum, 1866; Caldwell and Gould, 1872).

306 Figure A.29: Topographical map of Pickerington locale in 1900.

307 2009 2000

1991 1973

1963 1953

Figure A.30: Aerial photos of Pickerington Ponds locale. (source: U.S. Geological Survey’s Earth Resources Observation and Science Center).

308 Figure A.31: Soil maps of Pickerington Ponds locale, colored by drainage class: blue, very poorly drained; light blue, poorly drained; cyan, somewhat poorly drained; green, moderately well drained; yellow, well drained (Soil Survey Staﬀ).

309 A.10 Sacks

Sacks is located in Harrison Township (section 13) of Knox County, about 4 km southeast of Kenyon College. A property atlas from 1871 lists the landowner as John

Dudgeon (Caldwell and Starr, 1871); an atlas from 1896 list the owner as Belle Elliott

(Caldwell, 1896). The created marsh lies near a small tributary of Indianﬁeld Run.

The topography of Harrison Township is described as level to moderately rolling with numerous springs and streams (Hill, 1881). Pre-settlement, the township was densely wooded, primarily with oak, sugar maple and beech (Hill, 1881, p. 470).

The township was ﬁrst settled by Europeans around 1808 in section 16 and was formally organized in 1825 (Hill, 1881, p. 470). By 1830 there were 726 settlers residing in the township (Norton, 1862, p. 379). Kenyon College was established in

1838 (Norton, 1862, p. 390).

The wetland is owned by Howard and Judy Sacks and was constructed in 1998 as part of the NRCS Wetlands Reserve Program.

The entire county is covered with Illinoian glacial till which forms the parent material for most of the soils (White, 1937; Redmond and Graham, 1986). The soil of Sacks is classiﬁed as Lurray (Ly), a silty clay loam typically forming in post-glacial shallow lake beds (Fig. A.33; Redmond and Graham, 1986). The areas where Lurray soils develop are characterized as being level and poorly drained, with seasonally high water table, and vegetated by swamp forest (Redmond and Graham, 1986, p. 130).

310 2009 1994

1988 1974

1960

Figure A.32: Aerial photos of Sacks locale. (source: U.S. Geological Survey’s Earth Resources Observation and Science Center).

311 Figure A.33: Soil map of Sacks locale, colored by drainage class: blue, very poorly drained; light blue, poorly drained; cyan, somewhat poorly drained; green, moderately well drained; yellow, well drained (Soil Survey Staﬀ).

312 APPENDIX B

VEGETATION SURVEYS AND SAMPLING STATION SELECTION

B.1 Cluster analysis

B.1.1 methods

Vegetation survey data were analyzed by polythetic divisive hierarchical clustering

(PDHC) to determine weight factors for the sampling stations and to conﬁrm the

earlier plant groupings used in establishing the sampling stations. With PDHC, all

data (e.g., percent cover per species per quadrat) were considered in the clustering

(polythetic); the data were initially assigned to a single large group, then progressively divided into smaller groups (divisive); groups were hierarchically ordered, with the

between group relationships deﬁned (hierarchical) (McGarigal et al., 2000). PDHC

was performed in R (R Development Core Team, 2006) using the DIANA algorithm

(Kaufman and Rousseeuw, 1990) with euclidian distance metric.

Percent cover data for each wetland was ﬁrst organized into a quadrat-by-species

matrix (entered as proportions rather than percentages). Certain species were down-

weighted to improve clustering. A weighting factor of 0.01 was used for small ﬂoating

313 plants: Lemna spp., Riccia spp., Spirodela polyrrhiza, and Wolﬃa spp.. Ceratophyl- lum demersum was weighted by 0.1 and Nuphar lutea was weighted by 0.5. Large trees (e.g., Fraxinus pennsylvanica, Quercus bicolor, Quercus rubra) were weighted by

0.1. Vegetation that could not be conﬁdently identiﬁed to species was listed by genus

(Cirsium, Echinochloa, Eleocharis, Lacuta, Lemna (excluding L. trisulca), Mentha,

Najas, Potamogeton, Riccia, Rumex, Salix, Taraxacum, Trifolium, and Wolﬃa) or denoted as unknown and numbered. Water was also included as a ‘species’, with value ranging from 0 to 1 depending on the extent of inundation; saturated quadrats were rated 0.01. Vegetation data from the sampling stations were also included in the quadrat-by-species matrix to aid in interpretation of the clusters. Ideally, each sampling station clustered with the quadrat it was intended to represent.

The PDHC returned a dendrogram of hierarchical clusters (see Appendix D): ranging from one large cluster consisting of all quadrats, to multiple individual clusters consisting of only a single quadrat. Breakpoints were manually selected for each wetland dendrogram to yield one grouping of clusters per sampling station. If no suitable breakpoint existed between a pair of sampling stations, then the two stations were left in one larger grouping. A weight factor for each sampling station within a wetland was calculated according to the number of quadrats grouped with the sampling station relative to the total number of quadrats surveyed for the wetland.

B.1.2 results

Table B.1 lists the clusters identiﬁed for each wetland, along with the associated sampling station(s), hydrologic condition, and dominant vegetation. Dendrograms from the PDHC are provided in Appendix D. The number of quadrats within a

314 cluster were used to calculate the sampling station weight factors (Table B.2). If two stations were associated with the same cluster, each station was assigned half of the cluster weight.

Site Cluster Station(s) Hydrology Vegetation community PPA 1 2 F Lindernia 2 1, 3 D Cyperus / Polygonum PPB 1 1,3 D Echinochloa / Setaria / Panicum 2 2 F Potamogeton 3 4 F Typha / Potamogeton BB 1 4 D Juncus / Typha / Leersia 2 2 F Najas / Potamogeton 3 1 D Leersia / Eleocharis / Juncus 4 3 D Eleocharis / Alisma / Cyperus 5 5 D Leersia / Cephalanthus / Polygonum BIC 1 3 S Leersia / Echinochloa / Typha 2 2 F Ceratophyllum / Potamogeton 3 1 S Leersia / Eleocharis / Alisma SA 1 4 S Typha / Leersia / Bidens 2 2, 3 F Najas / Ceratophyllum 3 1 F Barren JMB 1 1, 3 D Phalaris 2 2, 4 D Xanthium / Cyperus BIA 1 1 D Populus / Cyperus 2 4 D Eleocharis / Leersia 3 3 D Phalaris / Juncus 4 2 D Polygonum 5 5 D Typha / Leersia / Carex NAC 1 2 D Phalaris / Leersia 2 1 D Polygonum / Echinochloa 3 3 S Barren

Continued on Next Page. . . Table B.1: Summary of PDHC results. Cluster groupings are indicated by number for each wetland. The sampling station(s) contained within each cluster is listed in the adjacent column. Hydrologic status (F, ﬂooded; S, saturated; D, dry) at the time of sampling and the dominant plant genuses are listed for each cluster.

315 Table B.1 – Continued

Site Cluster Station(s) Hydrology Vegetation community BIB 1 2 F Typha / Polygonum / Butomus 2 1 D Juncus / Eleocharis / Phalaris 3 4 F Butomus/Juncus/Leersia 4 3 D Phalaris 5 5 F Butomus KP 1 1 F Schoenoplectus / Eleocharis 2 3 D Phalaris / Typha 3 2 S Phalaris / Scirpus 4 4 F Najas / Ceratophyllum

MI 1 1 D Leersia / Typha / Polygonum 2 2 D Typha / Polygonum 3 3 D Echinochloa / Eleocharis / Bidens / Polygonum LW 1 1 D Lycopis / Calamagrostis 2 2 D Lycopis / Phyla 3 3 D Polygonum / Penthorum PPN 1 2 S Typha / Phalaris 2 1 S Leersia / Eleocharis 3 3, 4 S Phalaris CA 1 4, 5 S Sparganium / Scirpus / Sagittaria / Cephanlanthus 2 1, 2 F Sparganium / Typha / Nupha 3 3 F Typha / Decodon / Polygonum BF 1 4 S Typha / Pilea / Polygonum 2 1, 2 F Leersia / Eleocharis / Polygonum 3 3 S Typha / Lysimachia / Onoclea / Eupatorium

B.2 Species diversity indices

B.2.1 methods

Diversity, richness and evenness metrics were determined using the following equations:

H = p ln p [Shannon Diversity] − i i S = n [Richness] P J = H/ ln S [Evenness] where pi was the proportional coverage of species i and n was the total number of plant species. The diversity indices were calculated for each wetland, and for each

316 sampling station within each wetland. For the wetland calculations, the metrics were

determined from the 2005 vegetation survey data using mean species coverages over

all the quadrats. For the sampling stations calculations, the metrics were determined

from species coverage data recorded during the 2005 and 2006 biomass samplings.

The same plant ID scheme was used as with the PDHC (i.e., some species were listed only to genus and some species were labeled as unknowns), but without the downweighting.

The extrapolated species richness was also estimated for each wetland from the

vegetation survey data by ﬁrst- and second-order Jackknife

(n 1) − Sp = So + a1 n [ﬁrst-order] (2n 3) (n 2)2 − − Sp = So + a1 n a2 n(n 1) [second-order] − −

where So was the observed species richness, a1 and a2 were the numbers of species

occurring only in one or only in two quadrats, and n was the number of quadrats.

Species accumulation curves were also constructed for each wetland by random sub-

sampling (n=100) without replacement. All species diversity metrics were obtained

using the vegan package in R (R Development Core Team, 2006).

B.2.2 results

Shannon’s diversity, richness and evenness values determined from the wetland

vegetation surveys are provided in Table B.3. The table also lists the number of

quadrats sampled in each wetland, the approximate area (hectares), and the per-

centage of area sampled. Each diversity index was also calculated for the sampling

stations within each wetland (Table B.2).

317 The species accumulation curves for each wetland are plotted in Figure B.1. First and second-order jackknife estimates of species richness are listed in Table B.3.

318 50 PPA PPB BB BIC SA 40

20 Species Young

0 50 JMB BIA NAC 40

Mid 20 Species

0 50 BIB KP 40

Old 20 Species

0 50 BF CA PPN LW MI 40

20 Species Natural 10

0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80

Quadrats Quadrats Quadrats Quadrats Quadrats

Figure B.1: Species accumulation curves for each wetland from the 2005 vegetation survey data. The curves were generated by random subsampling of the quadrat data (n=100). Shaded regions indicate 1 standard deviation. ±

319 2005 2006 Site Station Cluster n Weight H S J H S J Young PPA 1 2 14 0.32 1.6 8 0.76 1.4 8 0.69 2 1 8 0.36 0.80 3 0.72 0.75 3 0.69 3 2 14 0.32 1.5 6 0.83 1.2 7 0.62 PPB 1 1 25 0.34 0.95 7 0.49 1.2 5 0.77 2 2 7 0.19 0.0 1 0.0 0.60 3 0.55 3 1 25 0.34 1.6 7 0.80 2.0 11 0.82 4 3 5 0.14 0.79 3 0.72 1.0 3 0.95 BB 1 3 13 0.19 1.4 6 0.76 1.7 8 0.81 2 2 13 0.19 0.69 2 1.0 0.0 1 0.0 3 4 15 0.22 0.99 5 0.62 1.0 8 0.49 4 1 11 0.16 1.5 9 0.68 1.6 11 0.65 5 5 15 0.22 0.47 3 0.43 0.82 4 0.59 BIC 1 3 6 0.23 1.1 6 0.63 1.4 7 0.73 2 2 17 0.65 0.0 1 0.0 0.76 3 0.69 3 1 3 0.11 1.1 5 0.69 0.93 3 0.85 SA 1 3 17 0.31 1.1 3 1.0 1.2 6 0.65 2 2 18 0.16 1.2 4 0.87 0.30 2 0.44 3 2 18 0.16 1.0 4 0.74 1.4 5 0.88 4 1 20 0.36 0.99 4 0.71 1.3 5 0.82 Mid JMB 1 1 23 0.31 0.0 1 0.0 0.0 1 0.0 2 2 14 0.19 0.94 3 0.86 1.1 4 0.82 3 1 23 0.31 0.0 1 0.0 0.0 1 0.0 4 2 14 0.19 1.7 8 0.81 1.7 10 0.74 BIA 1 1 4 0.11 1.3 4 0.96 1.2 9 0.53 2 4 8 0.23 0.0 1 0.0 1.1 4 0.81 3 3 11 0.31 0.64 2 0.92 0.70 3 0.64 4 2 5 0.14 0.66 2 0.95 0.68 2 0.98 5 5 7 0.20 1.5 5 0.91 0.76 3 0.69 NAC 1 2 17 0.45 0.37 3 0.33 1.1 7 0.54 2 1 14 0.37 1.0 3 0.94 1.1 6 0.62 3 3 7 0.18 0.80 3 0.72 0.0 1 0.0

Continued on Next Page. . . Table B.2: Diversity metrics and weight factors for the wetland sampling stations. Diversity (H), richness (S) and evenness (J) were determined from vegetation percent coverages recorded during the biomass collections in late summer 2005 and 2006. Weight factors were determined from PDHC of vegetation survey data collected early summer 2005, along with the station vegetation data recorded at biomass collection. The cluster number associated with each sampling station is listed, follwed by the number of quadrats belonging to the cluster (n), and the corresponding weight factor (if two stations belonged to the same cluster, each station was assigned half of the quadrats and weight associated with the cluster).

320 Table B.2 – Continued

2005 2006 Site Station Cluster n Weight H S J H S J Old BIB 1 2 7 0.16 0.76 5 0.47 0.83 3 0.76 2 1 3 0.067 1.7 6 0.95 1.4 4 1. 3 4 2 0.044 0.056 2 0.080 0.37 3 0.33 4 3 19 0.42 1.4 6 0.81 1.7 6 0.93 5 5 14 0.31 0.63 3 0.58 0.58 4 0.42 KP 1 1 12 0.29 1.4 5 0.85 1.7 6 0.93 2 3 14 0.33 0.45 2 0.65 1.4 6 0.78 3 2 4 0.095 0.64 2 0.92 0.72 3 0.66 4 4 12 0.29 0.0 1 0.0 0.45 2 0.65 Natural BF 1 2 8 0.083 1.2 5 0.76 1.3 4 0.95 2 2 8 0.083 0.58 3 0.53 1.3 4 0.96 3 3 12 0.25 1.8 8 0.85 2.1 12 0.83 4 1 28 0.58 1.3 7 0.68 1.5 7 0.76 CA 1 2 100 0.32 1.5 6 0.84 1.5 6 0.86 2 2 100 0.32 1.9 7 0.95 1.1 5 0.70 3 3 16 0.20 1.5 7 0.77 1.2 4 0.84 4 1 14 0.085 1.7 6 0.96 1.4 7 0.73 5 1 14 0.085 1.2 6 0.68 1.3 4 0.94 PPN 1 2 7 0.17 0.91 3 0.82 0.92 4 0.66 2 1 25 0.61 0.69 2 1.0 0.69 2 1.0 3 3 9 0.11 0.43 3 0.39 0.47 3 0.43 4 3 9 0.11 0.0 1 0.0 0.54 2 0.78 LW 1 1 24 0.51 1.3 4 0.94 1.2 6 0.66 2 2 11 0.23 1.3 5 0.78 1.2 4 0.85 3 3 12 0.26 1.9 9 0.85 1.3 4 0.94 MI 1 1 11 0.23 0.90 3 0.82 1.2 6 0.67 2 2 35 0.73 0.69 2 0.99 0.64 2 0.92 3 3 2 0.042 1.4 7 0.74 1.7 8 0.79

321 Jacknife Biomass Site n Area n/A H S J 1st 2nd 2005 2006 Young PPA 22 2.0 0.08 2.0 18 0.68 24 28 189.2 206.3 PPB 37 1.5 0.2 2.5 33 0.71 38 35 401.3 456.1 BB 67 1.2 0.4 2.7 33 0.77 38 40 423.7 406.1 BIC 26 1.9 0.1 2.0 14 0.74 19 23 616.8 696.7 SA 55 2.5 0.2 2.7 32 0.79 44 54 500.1 591.6 Mid JMB 37 0.36 0.7 1.4 22 0.45 29 30 551.8 456.5 BIA 35 4.7 0.05 2.4 27 0.74 34 35 916.9 591.4 NAC 38 2.2 0.1 1.8 14 0.68 18 21 607.0 762.3 Old BIB 45 47. 0.007 2.4 30 0.71 43 52 1170. 512.6 KP 42 3.3 0.09 2.3 29 0.69 41 49 889.0 412.1 Natural BF 48 0.63 0.5 2.7 49 0.68 61 67 673.5 512.2 CA 82 5.6 0.1 2.3 21 0.76 26 30 999.5 1100. PPN 41 7.9 0.04 1.7 24 0.54 33 35 1859. 1284. LW 50 0.23 2. 2.9 42 0.78 56 64 690.0 457.9 MI 58 0.28 1. 1.9 28 0.56 41 49 1183. 1004.

Table B.3: Summary of vegetation sampling, biomass and diversity metrics by wetland. The number of quadrats (n) sampled, approximate wetland area (hectares), and percent of area surveyed (n/A) are listed, along with diversity (H), richness (S) and evenness (J) indices. Two extrapolated species estimates are also provided (1st and 2nd order Jackknife) and the weighted sums of biomass for 2005 and 2006.

322 APPENDIX C

AERIAL MAPS OF WETLAND SURVEY AREAS

323 Figure C.1: Aerial map of PPA and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

324 Figure C.2: Aerial map of PPB and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

325 Figure C.3: Aerial map of BB and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

326 Figure C.4: Aerial map of BIC and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

327 Figure C.5: Aerial map of SA and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

328 Figure C.6: Aerial map of JMB and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

329 Figure C.7: Aerial map of BIA and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

330 Figure C.8: Aerial map of NAC and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

331 Figure C.9: Aerial map of BIB and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

332 Figure C.10: Aerial map of KP and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

333 Figure C.11: Aerial map of BF and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

334 Figure C.12: Aerial map of CA and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

335 Figure C.13: Aerial map of PPN and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

336 Figure C.14: Aerial map of LW and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

337 Figure C.15: Aerial map of MI and locale. The wetland border is outlined in pink, with green lines indicating the approximate locations of the survey transects. Sampling stations are marked and labeled in blue and red (blue is 2005 location, and red is 2006 location if relocated). The position of the water level recorder is marked and labeled in yellow.

338 APPENDIX D

DENDROGRAMS OF WETLAND VEGETATION SURVEY DATA

339 PPA Cluster Dendrogram S1 340 Average Distance 205 S3 504 302 501 0.0 0.2 0.4 0.6 0.8 1.0 1.2 503 303 204 103 S2 203 401 404 304 502 202 307 201 402 403 102 306 305 405

Figure D.1: The cluster dendrogram for PPA resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. PPB Cluster Dendrogram 102 101 341 S2 S4 Average Distance 407 S3 301 401 502 303 205 403 104 302 0.0 0.5 1.0 1.5 2.0 307 505 206 503 S1 501 103 110 201 306 506 408 204 507 405 406 402 305 504 109 202 304 404 105 108 106 107

Figure D.2: The cluster dendrogram for PPB resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. BB Cluster Dendrogram S2 401 342 117 303 304 Average Distance 510 212 S1 404 508 114 317 108 112 116 309 414 501 211 410 210 505 415 502 305 206 306 507 201 0.0 0.5 1.0 1.5 2.0 315 408 308 111 504 311 511 411 310 313 101 S4 503 509 314 301 409 207 208 312 413 402 107 S3 115 209 S5 403 407 213 202 405 406 102 104 103 105 106 203 204 205 302 316

Figure D.3: The cluster dendrogram for BB resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. BIC Cluster Dendrogram 503 104 343 Average Distance S3 407 102 103 S1 0.0 0.5 1.0 1.5 2.0 101 204 203 502 205 504 406 501 306 201 S2 401 202 303 302 305 405 301 402 403 404 304

Figure D.4: The cluster dendrogram for BIC resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. SA Cluster Dendrogram 901 502 101 311 344 111 501 712 Average Distance 701 711 512 902 908 301 302 110 513 0.0 0.5 1.0 1.5 2.0 S4 305 904 303 702 708 S2 709 306 307 707 710 903 S1 S3 907 703 905 102 109 103 304 510 704 705 706 107 108 308 309 505 506 507 508 509 104 105 106 310 511 906 503 504

Figure D.5: The cluster dendrogram for SA resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. JMB Cluster Dendrogram 345 Average Distance 303 501 104 202 S2 S4 507 308 0.0 0.5 1.0 1.5 102 504 502 307 203 206 304 404 405 406 103 505 403 408 204 309 506 205 407 101 302 503 305 306 S1 S3 301 401 508 509 201 207 402

Figure D.6: The cluster dendrogram for JMB resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. BIA Cluster Dendrogram 503 S5 504 109 505 346 404 201 Average Distance 101 105 407 506 203 301 305 401 405 S3 306 304 S4 501 502 0.0 0.5 1.0 1.5 S1 402 104 206 204 403 102 103 207 302 205 S2 106 107 108 202 303 406

Figure D.7: The cluster dendrogram for BIA resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. NAC Cluster Dendrogram 408 347 401 306 Average Distance 406 307 502 402 405 S2 203 103 101 210 501 0.0 0.5 1.0 1.5 308 107 104 106 305 301 S1 102 105 304 202 309 108 109 S3 407 204 205 302 303 403 404 206 207 208 201 209

Figure D.8: The cluster dendrogram for NAC resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. BIB Cluster Dendrogram S4 S2 109 508 106 348 402 102 Average Distance 107 405 206 306 407 307 101 204 302 209 303 506 207 108 406 403 409 0.0 0.5 1.0 1.5 2.0 309 408 S1 507 103 304 501 105 308 205 510 202 310 201 S3 S5 509 203 301 410 401 502 503 504 404 505

Figure D.9: The cluster dendrogram for BIB resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. KP Cluster Dendrogram 405 401 206 411 349 207 209 506 Average Distance 308 S1 404 101 403 208 301 306 307 S3 0.0 0.5 1.0 1.5 2.0 503 412 311 402 S4 103 102 302 305 406 201 205 S2 409 410 507 501 202 309 310 505 303 304 502 504 407 408 203 204

Figure D.10: The cluster dendrogram for KP resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. BF Cluster Dendrogram 350 412 401 503 309 Average Distance 405 506 105 204 408 414 413 S3 317 410 311 403 501 S4 402 502 312 504 505 S1 S2 101 102 201 301 406 407 308 310 302 313 203 0.0 0.5 1.0 1.5 2.0 2.5 103 104 306 404 411 202 409 305 307 205 507 316 314 315 303 304

Figure D.11: The cluster dendrogram for BF resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. CA Cluster Dendrogram 501 415 351 108 201 S5 311 Average Distance 408 206 301 518 S3 207 513 413 S4 310 505 402 507 419 0.0 0.5 1.0 1.5 2.0 110 218 417 106 412 416 302 109 409 312 418 420 407 216 101 506 410 503 306 217 308 309 103 104 105 504 411 213 307 209 202 401 214 215 107 515 404 210 205 510 208 305 S1 102 211 314 512 S2 304 517 516 212 414 421 514 303 203 502 315 519 406 313 508 511 403 204 405

Figure D.12: The cluster dendrogram for CA resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. PPN Cluster Dendrogram 311 352 503 Average Distance 310 301 403 502 304 406 S1 0.0 0.5 1.0 1.5 2.0 206 309 101 107 205 S2 305 210 106 409 307 308 S3 209 408 207 208 105 405 407 501 103 S4 504 204 302 102 104 404 201 401 303 402 202 306 203

Figure D.13: The cluster dendrogram for PPN resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. LW Cluster Dendrogram 105 703 304 310 S2 353 702 901 912 503 102 910 S3 Average Distance 904 906 508 911 312 313 106 S1 701 705 306 505 311 712 903 909 309 303 305 504 902 711 913 706 707 104 507 0.0 0.5 1.0 1.5 905 907 708 908 308 506 101 103 704 710 709

Figure D.14: The cluster dendrogram for LW resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. MI Cluster Dendrogram S3 115 202 354 210 211 117 118 103 408 Average Distance 509 302 402 116 309 0.0 0.5 1.0 1.5 2.0 303 104 508 S1 105 208 307 403 407 306 203 503 505 112 106 207 305 113 209 304 504 506 109 406 S2 308 507 205 405 206 404 107 204 110 114 108 111

Figure D.15: The cluster dendrogram for MI resulting from PDHC of vegetation data. Vegetation was surveyed in summer 2005. Percent coverages were recorded for plant species within a 0.84 m x 0.84 m quadrat. Quadrats were placed at regular intervals along each of ﬁve transects. APPENDIX E

GUIDE TO THE IDENTIFICATION OF ARBUSCULAR MYCORRHIZAE

355 The Ohio State University Aquatic Systems Ecology Lab

Guide to the Identiﬁcation of Arbuscular Mycorrhizae

This guide was created to assist in the determination of arbuscular mycorrhizal features under the magnified-intersections method. Determination of arbuscular mycorrhizae is difficult because other non-mycorrhizal fungi also stain. This guide illustrates some of the key features of arbuscular mycorrhizae and additionally provides examples of non-mycorrhizal features. There are 16 figures with explanatory captions: Fig. 1 explains the magnified-intersections method; Fig. 2 provides detailed images of arbuscules—the key feature of arbuscular mycorrhizae; Figs. 3–11 are labeled images of arbuscular mycorrhizae from our samples; Figs. 12–15 are images of non-mycorrhizal features from our samples; and Fig. 16 contains images of uncertain identification.

356 Figure E.1: Illustration of the magnified root-intersections method (source: http://mycorrhizas.info/method.html ). Try to count at least 75 intersections, skip- ping any that are uncertain. Count each reticle/root crossing as one intersection (although very broad roots can be counted as more than one intersection). Each intersection is then classified as one of five mutually exclusive categories: A = arbuscules (& hyphae); V = vesicles (& hyphae); A+V = arbuscules & vesicles (& hyphae); H = hyphae; NM = root only (i.e., non-mycorrhizal). For the fungal features (A, V, A+V, and H ), we are interested in those that are intraradical (i.e., occurring within the plant root), so try to avoid counting extraradical features as anything other than NM (although sometimes it is hard to tell; arbuscules will always be intraradical). We’ll be counting intersections at 400x magnification; although when making identifications it is sometimes helpful to switch down to 100x for a zoomed out view. Also, the eyepiece reticle, or crosshair, is graduated: each unit (i.e., 1, 2, 3, etc.) is approximately 25 µm and divided into 10 approximately 2.5 µm subunits—to give you some idea of the scale.

357 Figure E.2: The main fungal structure we are interested in is the arbuscule—this is the distinguishing feature of the arbuscular mycorrhizae (AM); probably the site were most carbon/nutrient exchange between the plant and fungus occurs. The arbuscules, as well as the vesicles and hyphae, can be quite variable in appearance, depending on the fungal species, the plant species, and the condition (e.g., active versus scenescent); the fungal structures will also vary in staining intensity and coloration. This figure shows several examples of arbuscules, most at a slightly higher magnification than what we’ll be using (sources: first column, http://mycorrhizas.info/method.html , scale bars = 10 µm; second column, http://invam.caf.wvu.edu/fungi/taxonomy/speciesID.htm ).

358 A

Figure E.3: Some examples of hyphae, vesicles (V) and arbuscules (A) from BBS4, Bidens frondosus. [bar = 20 µm]

359 HC

Figure E.4: Some examples of hyphae, hyphal coils (HC), and arbuscules (A) from BBS4, Echinochloa spp.. [bar = 20 µm]

360 H V

Figure E.5: Some examples of hyphae (H) and vesicles (V) from BBS4, Eleocharis obtusa. [bar = 20 µm]

361 A

Figure E.6: Some examples of hyphae (H) and arbuscules (A) from BIBS1, Bidens cernua. [bar = 20 µm]

362 H

Figure E.7: Some examples of hyphae (H) and vesicles (V) from BIBS2, Typha spp.. [bar = 20 µm]

363 A

Figure E.8: Some examples of hyphae (H) and arbuscules (A) from BIBS3, Galium spp.. [bar = 20 µm]

364 H

Figure E.9: Some examples of hyphae (H) from BIBS4, Bidens cernua. [bar = 20 µm]

365 H

V A

Figure E.10: Some examples of mycorrhizal hyphae (H),vesicles (V), arbuscules (A) and septate hyphae (SH) and conidia (C) from JMBS2, Echinochloa spp.. [bar = 20 µm]

366 V

A V

Figure E.11: Some examples of hyphae (H),vesicles (V), and arbuscules (A) from KPS2, Bidens cernua. [bar = 20 µm]

367

COC C

Figure E.12: Examples of nonmycorrhizal hyphae. These hyphae tend to be thinner, darker and septate (i.e., having divisions at regular intervals; red arrows), with small clusters of spores (conidia; yellow arrow) compared to the mycorrhizal hyphae. These are probably fungal molds belong to the class Hyphomycetes.

368 Figure E.13: Examples of spores. The top two images are probably of resting spores from Polymyxa graminis—a common plant pathogen (and actually a protist). The bottom four images are possibly fungal spores, but would as we can’t be certain they are from arbuscular mycorrhizae, they would be considered NM. [bar = 20 µm]

369 @ @ @ @R

Figure E.14: Miscellaneous examples of arbuscule-like NM features. Plant cells making up the central part of the root (i.e., stele; see arrow) are often darker in color and could be mistaken for arbuscules. Some roots are poorly cleared and appear blotchy. Some roots have a particular “knobby” pattern (e.g., Eleocharis and Juncus spp.; see bottom two images). Some plant root cells retain the ink stain to varying degrees which can also make it diﬃcult to discern the AM features. [bar = 20 µm]

370 H

Figure E.15: Miscellaneous examples of hyphal-like NM features. Some root cell walls are very thick and may refract the light, appearing hyphal-like (see top left image). Root hairs can also appear hyphal-like, particularly when they are very long and/or retain more ink stain than surrounding cells. The image on the bottom right has some light-blue stained hyphae (H), as well as unstained root hairs and some pink/purple stained cell structures (‘knobs’). [bar = 20 µm]

371 PPPq COC ¡ A A £ C¡ AU - £ @ £ @ @R ££

¡ ¡ PiPP¡

Figure E.16: Some hyphae I am uncertain of—these stain purple and resemble the AM-hyphae, but are septate. Although, AM-hyphae can have septa (red arrows), just not at regular intervals as in septate hyphae. So possibly most of these hyphae are from arbuscular mycorrhizae, with the exception of the very wide hyphae in the second image of the ﬁrst column (yellow arrow)— these hyphae are about 25 µm wide, too wide for AM fungi, possibly a saprophytic fungus of genus Rhizopus. The second image of the second column is also unusual, but some AM fungi have ‘knobby’ hyphae and some have irregularly lobed vesicles, so possibly this is AM as well. Remember: skip feature you are uncertain of and take notes! With the slides, we can always go back. [bar = 20 µm]

372 BIBLIOGRAPHY

Clean Water Act. 33 U.S.C. 1251–1387, 1972. §§ L. K. Abbott and A. D. Robson. VA Mycorrhiza, chapter The eﬀect of VA mycorrhizae on plant growth, pages 113–130. CRC Press, Inc., Boca Raton, Florida, 1984.

R. Aerts. Interspeciﬁc competition in natural plant communities: mechanisms, trade- oﬀs and plant-soil feedbacks. Journal of Experimental Botany, 50:29–37, 1999.

R. Aerts and F. Berendse. The eﬀect of increased nutrient availability on vegetation dynamics in wet heathlands. Vegetatio, 76:63–69, 1988.

R. Aerts and F. Berendse. Above-ground nutrient turnover and net primary production of an evergreen and a deciduous species in a heathland ecosystem. Journal of Ecology, 77:343–356, 1989.

R. Aerts and F. S. Chapin, III. The mineral nutrition of wild plants revisited: a re-evaluation of processes and patterns. Advances in Ecological Research, 30:1–67, 2000.

R. Aerts, F. Berendse, N. M. Klerk, and C. Bakker. Root production and root turnover in two dominant species of wet heathlands. Oecologia, 81:374–378, 1989.

R. Aerts, R. G. A. Boot, and P. J. M. van der Aart. The relation between above- and belowground biomass allocation patterns and competitive ability. Oecologia, 87:551–559, 1991.

H. E. Agwa and Y. M. Al-Sodany. Arbuscular-mycorrhizal fungi (Glomalea) in Egypt. III: Distribution and ecology in some plants in El-Omayed Biosphere Reserve. Egyp- tian Journal of Biology, 5:19–26, 2003.

E. B. Allen and M. F. Allen. Natural re-establishment of vesicular-arbuscular mycorrhizae following stripmine reclamation in Wyoming. Journal of Applied Ecology, 17:139–147, 1980.

E. B. Allen, J. C. Chambers, K. F. Connor, M. F. Allen, and R. W. Brown. Natural reestablishment of mycorrhizae in disturbed Alpine ecosystems. Arctic and Alpine Research, 19:11–20, 1987.

373 R. N. Ames, C. P. P. Reid, L. K. Porter, and C. Cambardella. Hyphal uptake and transport of nitrogen from two 15N-labelled sources by Glomus mosseae, a vesicular- arbuscular mycorrhizal fungus. New Phytologist, 95:381–396, 1983.

D. R. Anderson, K. P. Burnham, and G. C. White. AIC model selection in overdis- persed capture-recapture data. Ecology, 75:1780–1793, 1994.

D. W. Anderson. The eﬀect of parent material and soil development on nutrient cycling in temperate ecosystems. Biogeochemistry, 5:71–97, 1988.

M. J. Anderson. A new method for non-parametric multivariate analysis of variance. Austral Ecology, 26:32–46, 2001a.

M. J. Anderson. Permutation tests for univariate or multivariate analysis of variance and regression. Canadian Journal of Fisheries and Aquatic Sciences, 58:626–639, 2001b.

R. C. Anderson, A. E. Liberta, and L. A. Dickman. Interaction of vascular plants and vesicular-arbuscular mycorrhizal fungi across a soil moisture-nutrient gradient. Oecologia, 64:111–117, 1984.

B. K. Andreas. The relationship between Ohio peatland distribution and buried river valleys. Ohio Journal of Science, 85:116–125, 1985.

B. K. Andreas, J. J. Mack, and J. S. McCormac. Floristic Quality Assessment Index (FQAI) for vascular plants and mosses for the State of Ohio. Technical report, Ohio Environmental Protection Agency, Division of Surface Water, Wetland Ecology Group, Columbus, OH, 2004.

R. B. Atkinson and J. Cairns, Jr. Plant decomposition and litter accumulation in depressional wetlands: functional performance of two wetland age classes that were created via excavation. Wetlands, 21:354–362, 2001.

C. Atwater. Remarks made on a tour to Prairie du Chien; thence to Washington City, in 1829. Isaac N. Whiting, Columbus, OH, 1831.

R. M. Aug´e. Water relations, drought and vesicular-arbuscular mycorrhizal symbiosis. Mycorrhiza, 11:3–42, 2001.

T. Aziz, D. M. Sylvia, and R. F. Doren. Activity and species composition of arbuscular mycorrhizal fungi following soil removal. Ecological Applications, 5:776–784, 1995.

S. G. Baer, D. J. Kitchen, J. M. Blair, and C. W. Rice. Changes in ecosystem structure and function along a chronosequence of restored grasslands. Ecological Applications, 12:1688–1701, 2002.

374 D. J. Bagyaraj, A. Manjunath, and R. B. Patil. Occurence of vesicular-arbuscular mycorrhizas in some tropical aquatic plants. Transactions of the British Mycological Society, 72:164–167, 1979.

R. Bajwa, A. Yaqoob, and A. Javaid. Seasonal variation in VAM in wetland plants. Pakistan Journal of Biological Sciences, 4:464–470, 2001.

C. K. Balcombe, J. T. Anderson, R. H. Fortney, J. S. Rentch, W. N. Grafton, and W. S. Kordek. A comparison of plant communities in mitigation and reference wetlands in the mid-Appalachians. Wetlands, 25:130–142, 2005.

K. Ballantine and R. Schneider. Fifty-ﬁve years of soil development in restored freshwater depressional wetlands. Ecological Applications, 19(6):1467–1480, 2009.

G. F. Bareis. History of Madison Township, including Groveport and Canal Winch- ester, Franklin County, Ohio. Geo. F. Bareis, Canal Winchester, OH, 1902.

E. Barni and C. Siniscalco. Vegetation dynamics and arbuscular mycorrhiza in old- ﬁeld successions of the western Italian Alps. Mycorrhiza, 10:63–72, 2000.

C. R. Bauer, C. H. Kellogg, S. D. Bridgham, and G. A. Lamerti. Mycorrhizal colonization across hydrologic gradients in restored and reference freshwater wetlands. Wetlands, 23:961–968, 2003.

A. J. Baughman, editor. Past and present of Wyandot County Ohio: a record of settlement, organization, progress and achievement, volume I. The S. J. Clarke Publishing Company, Chicago, IL, 1913.

M. H. Beare, S. Hu, D. C. Coleman, and P. F. Hendrix. Inﬂuences of mycelial fungi on soil aggregation and organic matter storage in conventional and no-tillage soils. Applied Soil Ecology, 5:211–219, 1997.

D. Beck-Nielsen and T. V. Madsen. Occurrence of vesicular-arbuscular mycorrhiza in aquatic macrophytes from lakes and streams. Aquatic Botany, 71:141–148, 2001.

B. L. Bedford, M. R. Walbridge, and A. Aldous. Patterns in nutrient availability and plant diversity of temperate North American wetlands. Ecology, 80:2151–2169, 1999.

F. W. Beers. Atlas of Delaware County Ohio. Beers, Ellis and Soule, 1866. assisted by A. Leavenworth and G. E. Warner.

M. B´ereau and J. Garbaye. First observations on the root morphology and symbioses of 21 major tree species in the primary tropical rain forest of French Guyana. Annals of Forest Science, 51:407–416, 1994.

375 F. Berendse. Organic matter accumulation and nitrogen mineralization during secondary succession in heathland ecosystems. The Journal of Ecology, 78:413–427, 1990.

F. Berendse. Eﬀects of dominant plant species on soils during succession in nutrient- poor ecosystems. Biogeochemistry, 42:73–88, 1998.

G. J. Bethlenfalvay, R. S. Pacovsky, and M. S. Brown. Parasitic and mutualistic associations between a mycorrhizal fungus and soybean: development of the host plant. Phytopathology, 72:889–893, 1982.

J. D. Bever, P. A. Schultz, R. M. Miller, L. Gades, and J. D. Jastrow. Inoculation with prairie mycorrhizal fungi may improve restoration of native prairie plant diversity. Ecological Restoration, 21:311–312, 2003.

L. Bishel-Machung, R. P. Brooks, S. S. Yates, and K. L. Hoover. Soil properties of reference wetlands and wetland creation projects in Pennsylvania. Wetlands, 16: 532–541, 1996.

V. Blanke, C. Renker, M. Wagner, K. Füllner, M. Held, A. J. Kuhn, and F. Buscot. Nitrogen supply affects arbuscular mycorrhizal colonization of Artemisia vulgaris in a phosphate-polluted field site. New Phytologist, 166:981–992, 2005.

C. Bledsoe, P. Klein, and L. C. Bliss. A survey of mycorrhizal plants on Truelove Lowland, Devon Island, N.W.T., Canada. Canadian Journal of Botany, 68:1848– 1856, 1990.

Boehm Stamp and Printing Company. Map of Franklin County Ohio. Boehm Stamp and Printing Company, 1883.

K. E. Bohrer, C. F. Friese, and J. P. Amon. Seasonal dynamics of arbuscular mycorrhizal fungi in diﬀering wetland habitats. Mycorrhiza, 14:329–337, 2004.

N. S. Bolan. A critical review on the role of mycorrhizal fungi in the uptake of phosphorus by plants. Plant and Soil, 134:189–207, 1991.

N. S. Bolan, A. D. Robson, and N. J. Barrow. Eﬀects of vesicular-arbuscular mycorrhiza on the availability of iron phosphates to plants. Plant and Soil, 99:401–410, 1987.

K. A. Bollen. Total, direct, and indirect eﬀects in structural equation models. Soci- ological Methodology, 17:37–69, 1987.

P. Bonfante-Fasolo. VA Mycorrhiza, chapter Anatomy and morphology of VA mycorrhizae, pages 5–33. CRC Press, Inc., Boca Raton, Florida, USA, 1984.

376 R. G. A. Boot and M. Mensink. Size and morphology of root systems of perennial grasses from contrasting habitats as affected by nitrogen supply. Plant and Soil, 129:291–299, 1990. V. A. Borowicz. Do arbuscular mycorrhizal fungi alter plant-pathogen relations? Ecology, 82:3057–3068, 2001. D. A. Bossio and K. M. Scow. Impacts of carbon and flooding on soil microbial communities: phospholipid fatty acid profiles and substrate utilization patterns. Microbial Ecology, 35:265–278, 1998. K. E. Boyer and J. B. Zedler. Effects of nitrogen additions on the vertical structure of a constructed cordgrass marsh. Ecological Applications, 8:692–705, 1998. A. D. Bradshaw. The reconstruction of ecosystems: presidential address to the British Ecological Society, December 1982. Journal of Applied Ecology, 20:1–17, 1983. A. D. Bradshaw. Underlying principles of restoration. Canadian Journal of Fisheries and Aquatic Sciences, 53(S1):3–9, 1996. A. D. Bradshaw. Restoration ecology and sustainable development, chapter The importance of soil ecology in restoration science, pages 33–64. Cambridge University Press, 1997. A. D. Bradshaw. The restoration and management of derelict land: modern approaches, chapter The importance of soil, pages 31–37. World Scientific, 2003. R. H. Bray and L. T. Kurtz. Determination of total, organic, and available forms of phosphorus in soils. Soil Science, 59:39–45, 1945. N. E. Breslow. Generalized linear models: checking assumptions and strengthening conclusions. Staistica Applicata, 8:23–41, 1996. S. D. Bridgham and C. J. Richardson. Endogenous versus exogenous nutrient control over decomposition and mineralization in north carolina peatlands. Biogeochem- istry, 65:151–178, 2003. S. D. Bridgham, J. P. Megonigal, J. K. Keller, N. B. Bliss, and C. Trettin. The first State of the Carbon Cycle Report (SOCCR): the North American carbon budget and implications for the global carbon cycle, chapter Wetlands, pages 137–148. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. National Oceanic and Atmo- spheric Administration, National Climatic Data Center, Asheville, NC, USA, 2007. http://www.climatescience.gov/Library/sap/sap2-2/final-report/default.htm. M. M. Brinson. A hydrogeomorphic classification for wetlands. Technical Report WRP-DE-4, U.S. Army Engineer Waterways Experiment Station, Vicksburg, MS, 1993.

377 S. W. Broome, E. D. Seneca, and W. W. Woodhouse, Jr. Long-term growth and development of transplants of the salt-marsh grass Spartina alterniﬂora. Estuaries, 9:63–74, 1986.

A. M. Brown and C. Bledsoe. Spatial and temporal dynamics of mycorrhizas in Jaumea carnosa, a tidal saltmarsh halophyte. Journal of Ecology, 84:703–715, 1996.

R. C. Brown. The history of Madison County, Ohio. W.H. Beers & Co., Chicago, IL, 1883.

S. C. Brown and B. L. Bedford. Restoration of wetland vegetation with transplanted wetland soil: and experimental study. Wetlands, 17:424–437, 1997.

G. L. Bruland and C. J. Richardson. Hydrologic gradients and topsoil additions aﬀect soil properties of Virginia created wetlands. Soil Science Society of America Journal, 68:2069–2077, 2004.

M. Brundrett. Diversity and classiﬁcation of mycorrhizal associations. Biological Reviews, 79:473–495, 2004.

M. C. Brundrett. Coevolution of roots and mycorrhizas of land plants. New Phytol- ogist, 154:275–304, 2002. Tansley review no. 134 .

K. P. Burnham and D. R. Anderson. Model selection and inference: a practical information-theoretic approach. Springer-Verlag, New York, NY, 1998.

K. P. Burnham and D. R. Anderson. Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods and Research, 33(2):261–304, 2004.

T. Burni, S. Paracha, and A. Liaqat. Occurrence and characterization of VAM in typha elephantina roxb district Kohat. Pakistan Journal of Plant Science, 13:13– 19, 2007.

T. Burni, A. Liaqat, S. Paracha, and Sabiha. VAM studies in plants of wet and waterlogged habitat of District Kohat, Pakistan. Pakistan Journal of Plant Sciences, 14:21–27, 2008.

H. C¸akan and C. Karata¸s.Interactions between mycorrhizal colonization and plant life forms along the successional gradient of coastal sand dunes in the eastern Mediter- ranean, Turkey. Ecological Restoration, 21:301–310, 2006.

J. A. Caldwell. Atlas of Knox county Ohio. E.R. Caldwell, 1896.

J. A. Caldwell and H. T. Gould. Caldwell’s atlas of Franklin County and the city of Columbus Ohio. J. A. Caldwell and H. T. Gould, 1872. assisted by H. Cring, J. N. Headington and J. Kinnear.

378 J. A. Caldwell and J. W. Starr. Atlas of Knox county Ohio. J.A. Caldwell and J.W. Starr, 1871. D. A. Campbell, C. A. Cole, and R. P. Brooks. A comparison of created and natural wetlands in Pennsylvania, USA. Wetlands Ecology and Management, 10:41–49, 2002. A. J. Cantelmo, Jr and J. G. Ehrenfeld. Effects of microtopography on mycorrhizal infection in Atlantic white cedar (Chamaecyparis thyoides (L.) Mills.). Mycorrhiza, 8:175–180, 1999. W. H. Carpenter and T. S. Arthur, editors. The History of Ohio, from its earliest settlement to the present time. Claxton, Remsen & Haffelfinger, Philadelphia, PA, 1872. F. Carvaca, M. M. Alguacil, P. Torres, and A. Rold/’an. Plant type mediates rhizo- spheric microbial activities and soil aggregation in a semiarid Mediterranean salt marsh. Geoderma, 124:375–382, 2005a. F. Carvaca, M. M. Alguacil, P. Torres, and A. Rold/’an. Microbial activities and arbuscular mycorrhizal fungi colonization in the rhizosphere of the salt marsh plant inula crithmoides l. along a spatial salinity gradient. Wetlands, 25:350–355, 2005b. L. M. Carvalho, I. Ca¸cador, and Martins-Lou¸cão. Temporal and spatial variation of arbuscular mycorrhizas in salt marsh plants of the Tagus estuary (Portugal). Mycorrhiza, 11:303–309, 2001. R. Chaubal, G. D. Sharma, and R. R. Mishra. Vesicular arbuscular mycorrhiza in subtropical aquatic and marshy plant communities. Proceedings of the Indian Academy of Sciences, Section B, 91:69–77, 1982. Y. D. Choi. Theories for ecological restoration in changing environment: Toward ‘futuristic’ restoration. Ecological Research, 19:75–81, 2004. J. S. Clayton and Bagyaraj. Vesicular-arbuscular mycorrhizas in submerged aquatic plants of New Zealand. Aquatic Botany, 19:251–262, 1984. C. A. Cole and R. P. Brooks. A comparison of the hydrologic characteristics of natural and created mainstem floodplain wetlands in Pennsylvania. Ecological Engineering, 14:221–231, 2000. S. R. Confer and W. A. Niering. Comparison of created and natural freshwater emergent wetlands in Connecticut (USA). Wetlands Ecology and Management, 2 (3):143–156, 1992. B. D. Cook, J. D. Jastrow, and R. M. Miller. Root and mycorrhizal endophyte development in a chronosequence of restored tallgrass prairie. New Phytologist, 110:355–362, 1988.

379 J. C. Cooke and M. W. Lefor. Comparison of vesicular-arbuscular mycorrhizae in plants from disturbed and adjacent undisturbed regions of a coastal salt marsh in Clinton, Connecticut, USA. Environmental Management, 14:131–137, 1990.

J. C. Cooke and M. W. Lefor. The mycorrhizal status of selected plant species from Connecticut wetlands and transition zones. Restoration Ecology, 6:214–222, 1998.

J. C. Cooke, R. H. Butler, and G. Madole. Some observations on the vertical distribution of vesicular arbuscular mycorrhizae in roots of salt marsh grasses growing in saturated soils. Mycologia, 85:547–550, 1993.

K. M. Cooper. VA Mycorrhiza, chapter Physiology of VA mycorrhizal associations, pages 155–186. CRC Press, Inc., Boca Raton, Florida, 1984.

L. Corkidi and E. Rinc´on. Arbuscular mycorrhizae in a tropical sand dune ecosystem on the Gulf of Mexico: I. Mycorrhizal status and inoculum potential along a successional gradient. Mycorrhiza, 7:9–15, 1997.

W. K. Cornwell, B. L. Bedford, and C. T. Chapin. Occurence of arbuscular mycorrhizal fungi in a phosphorus-poor wetland and mycorrhizal response to phosphorus fertilization. American Journal of Botany, 88:1824–1829, 2001.

R. Costanza, R. d’Arge, R. de Groot, S. Farber, M. Grasson, B. Hannon, K. Limburg, S. Naeem, R. O’Neill, J. Paruelo, R. Raskin, P. Sutton, and M. van den Belt. The value of the world’s ecosystem services and natural capital. Nature, 387:253–260, 1997.

L. M. Cowardin, V. Carter, F. C. Golet, and E. T. LaRoe. Classiﬁcation of Wetlands and Deepwater Habitats of the United States. Number FWS/OBS-79/31. U.S. Fish and Wildlife Service, Washington, D.C., 1979.

C. Craft, J. Reader, J. N. Sacco, and S. W. Broome. Twenty-ﬁve years of ecosystem development of constructed Spartina alterniﬂora (loisel) marshes. Ecological Applications, 9:1405–1419, 1999.

C. Craft, S. Broome, and C. Campbell. Fifteen years of vegetation and soil development after brackish-water marsh creation. Restoration Ecology, 10:248–258, 2002.

C. B. Craft and W. P. Casey. Sediment and nutrient accumulation in ﬂoodplain and depressional freshwater wetlands of Georgia, USA. Wetlands, 20:323–332, 2000.

C. B. Craft and C. Chiang. Forms and amounts of soil nitrogen and phosphorus across a longleaf pine-depressional wetland landscape. Soil Science Society of America Journal, 66:1713–1721, 2002.

C. B. Craft, S. W. Broome, and E. D. Seneca. Nitrogen, phosphorus and organic carbon pools in natural and transplanted marsh soils. Estuaries, 11:272–280, 1988.

380 G. Cronin and D. M. Lodge. Effects of light and nutrient availability on the growth, allocation, carbon/nitrogen balance, phenolic chemistry, and resistance to herbivory of two freshwater macrophytes. Oecologia, 137:32–41, 2003. J. K. Cronk and M. S. Fennessy. Wetland plants: biology and ecology. CRC Press LLC, Boca Raton, Florida, USA, 2001. A. F. Cross and W. H. Schlesinger. A literature review and evaluation of the hed- ley fractionation: Applications to the biogeochemical cycle of soil phosphorus in natural ecosystems. Geoderma, 64:197–214, 1995. J. Cutler. A topographical description of the state of Ohio, Indiana territory, and Louisiana. Charles Williams, Boston, MA, 1812. A. Dachnowski. Peat deposits of Ohio: their origin, formation and uses. In J. A. Bownocker, editor, Geological Survey of Ohio, Fourth Series, Bulletin 16. Springfield Publishing Company, Springfield, Ohio, 1912. M. J. Daft and T. H. Nicolson. Effect of endogone mycorrhiza on plant growth. New Phytologist, 68:945–952, 1969. T. E. Dahl. Wetland losses in the United States 1780’s to 1980’s. U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C., 1990. T. E. Dahl. Status and trends of wetlands in the conterminous United States 1998 to 2004. U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C., 2006. E. M. D’Angelo and K. R. Reddy. Regulators of heterotrophic microbial potentials in wetland soils. Soil Biology and Biochemistry, 31:815–830, 1999. E. M. D’Angelo, A. D. Karathanasis, E. J. Sparks, S. A. Ritchey, and S. A. Wehr- McChesney. Soil carbon and microbial communities at mitigated and late successional bottomland forest wetlands. Wetlands, 25:162–175, 2005. E. A. Davidson, I. A. Janssens, and Y. Luo. On the variability of respiration in terrestrial ecosystems: moving beyond Q10. Global Change Biology, 12:154–164, 2006. J. F. de Marins, R. Carrenho, and S. M. Thomaz. Occurrence and coexistence of arbuscular mycorrhizal fungi and dark septate fungi in aquatic macrophytes in a tropical river-floodplain system. Aquatic Botany, 91:13–19, 2009. S. S. Dhillion. Vesicular-arbuscular mycorrhizas of Equisetum species in Norway and the U.S.A.: occurrence and mycotrophy. Mycological Research, 97:656–660, 1993. S. S. Dhillion. Ectomycorrhizae, arbuscular mycorrhizae, and rhizoctonia sp. of Alpine and Boreal salix spp. in Norway. Arctic and Alpine Research, 26:304–307, 1994.

381 S. Dickson. The arum–paris continuum of mycorrhizal symbioses. New Phytologist, 163:187–200, 2004.

R. A. Dobbins. Vegetation of the northern “Virginia Military Lands” of Ohio. PhD thesis, The Ohio State University, 1937.

E. S. Dowding. Ecology of Endogone. Transactions of the British Mycological Society, 42:449–457, 1959.

S. G. Drake. Tragedies of the wilderness. Antiquarian bookstore and institute, Boston, MA, 1844.

C. P. Duncan and P. M. Groﬀman. Comparing microbial parameters in natural and constructed wetlands. Journal of Environmental Quality, 23:298–305, 1994.

R. M. Dunham, A. M. Ray, and R. S. Inouye. Growth, physiology, and chemistry of mycorrhizal and nonmycorrhizal Typha latifolia seedlings. Wetlands, 23:890–896, 2003.

K. R. Edwards and C. E. Proﬃtt. Comparison of wetland structural characteristics between created and natural salt marshes in southwest Louisiana, USA. Wetlands, 23:344–356, 2003.

W. T. Elberse and F. Berendse. A comparative study of the growth and morphology of eight grass species from habitats with diﬀerent nutrient availabilities. Functional Ecology, 7:223–229, 1993.

V. Escudero and R. Mendoza. Seasonal variation of arbuscular mycorrhizal fungi in temperate grasslands along a wide hydrologic gradient. Mycorrhiza, 15:291–299, 2005.

N. W. Evans and E. B. Stivers. A history of Adams county, Ohio: From its earliest settlement to the present time. E. B. Stivers, West Union, OH, 1900.

H. Everett. History of Sandusky County Ohio, with portraits and biographies of prominent citizens and pioneers. H. Z. Williams & Bro., Cleveland, Ohio, 1882.

A. M. Farmer. The occurrence of vesicular-arbuscular mycorrhiza in isoetid-type submerged aquatic macrophytes under naturally varying conditions. Aquatic Botany, 21:245–249, 1985.

M. S. Fennessy, J. J. Mack, A. Rokosch, M. Knapp, and M. Micacchion. Integrated wetland assessment program. part 5: Biogeochemical and hydrological investigations of natural and mitigation wetlands. Ohio EPA Technical Report WET/2004- 5, Ohio Environmental Protection Agency, Wetland Ecology Group, Division of Surface Water, Columbus, Ohio, 2004.

382 M. S. Fennessy, A. Rokosch, and J. J. Mack. Patterns of plant decomposition and nutrient cycling in natural and created wetlands. Wetlands, 28:300–310, 2008.

N. Fern´andez, M. I. Messuti, and S. Fontenla. Arbuscular mycorrhizas and dark septate fungi in lycopodium paniculatum (lycopodiaceae) and equisetum bogotense (equisetaceae) in a Valdivian temperate forest of Patagonia, Argentina. American Fern Journal, 98:117–127, 2008.

C. M. Finlayson and R. D’Cruz. Ecosystems and Human Well-being: Current State and Trends, Volume 1, chapter Inland Water Systems, pages 551–583. Millen- nium Ecosystem Assessment; Condition and Trends Working Group. Island Press, Washington, DC, USA, 2005. Chapter 20.

C. M. Finlayson and A. G. Spiers, editors. Global review of wetland resources and priorities for wetland inventory, Supervising Scientist Report 144; Wetlands Inter- national Publication 53, Canberra, Australia, 1999. Supervising Scientist.

T. Flint. The history and geography of the Mississippi Valley. To which is appended a condensed physical geography of the Atlantic United States, and the whole American continent. E. H. Flint, Cincinnati, OH, 3rd edition, 1833.

S. Fontenla, J. Puntieri, and J. A. Ocampo. Mycorrhizal associations in the Patago- nian steppe, Argentina. Plant and Soil, 233:13–29, 2001.

B. Forde and H. Lorenzo. The nutritional control of root development. Plant and Soil, 232:51–68, 2001.

J. L. Forsyth. A geologist looks at the natural vegetation map of Ohio. The Ohio Journal of Science, 70:180–191, 1970.

J. L. Forsyth. Geological setting of Ohio prairies. Ohio Biological Survey Biology notes, 15:54–56, 1981.

J. Fox. Structural equation models: Appendix to an r and s-plus companion to applied regression. web appendix, January 2002. URL http://socserv.mcmaster.ca/jfox/Books/Companion/appendix-sems.pdf .

J. Fox. Structural equation modeling with the sem package in R. Structural Equation Modeling, 13:465–486, 2006.

J. Fox. sem: Structural Equation Models, 2009. URL http://CRAN.R-project.org/package=sem . R package version 0.9-19.

L. H. Fraser and L. M. Feinstein. Eﬀects of mycorrhizal inoculant, N:P supply ratio, and water depth on the growth and biomass allocation of three wetland species. Canadian Journal of Botany, 83:1117–1125, 2005.

383 B. Fuchs and K. Haselwandter. Red list plants: colonization by arbuscular mycorrhizal fungi and dark septate endophytes. Mycorrhiza, 14:277–281, 2004. E. N. Fuller, P. D. Schettler, and J. C. Giddings. A new method for prediction of gas-phase diffusion coefficient. Industrial and Engineering Chemistry Research, 58: 19–27, 1966. S. M. Galatowitsch and A. G. van der Valk. Vegetation and environmental conditions in recently restored wetlands in the prairie pothole region of the USA. Vegetatio, 126:89–99, 1996. J. N. Galloway, F. J. Dentener, D. G. Capone, E. W. Boyer, R. W. Howarth, S. P. Seitzinger, G. P. Asner, C. C. Cleveland, P. A. Green, E. A. Holland, D. M. Karl, A. F. Michaels, J. H. Porter, A. R. Townsend, and C. J. Vörösmarty. Nitrogen cycles: past, present, and future. Biogeochemistry, 70:153–226, 2004. A. C. Gange, V. K. Brown, and G. S. Sinclair. Vesicular-arbuscular mycorrhizal fungi: a determinant of plant community structure in early succession. Functional Ecology, 7:616–622, 1993. I. V. Garc´ıa and R. E. Mendoza. Relationships among soil properties, plant nutrition and arbuscular mycorrhizal fungi-plant symbioses in a temperate grassland along hydrologic, saline and sodic gradients. FEMS Microbiology Ecology, 63:359–371, 2008. R. C. Gardner, J. Zedler, A. Redmond, R. E. Turner, C. A. Johnston, V. R. Alvarez, C. A. Simenstad, K. L. Prestegaard, and W. J. Mitsch. Compensating for wetland losses under the clean water act (redux): evaluating the federal compensatory mitigation regulation. Stetson Law Review, 38:213–249, 2009. K. D. Gibson, J. B. Zedler, and R. Langis. Limited response of cordgrass (Spartina foliosa) to soil amendments in a constructed marsh. Ecological Applications, 4: 757–767, 1994. M. Giovannetti and B. Mosse. An evaluation of techniques for measuring vesicular arbuscular mycorrhizal infection in roots. New Phytologist, 84:489–500, 1980. R. P. Goldthwait. Scenes in Ohio during the last ice age. The Ohio Journal of Science, 59:193–216, 1959. C. Gouraud, J. Giroux, F. Mesléard, S. Gutjahr, and L. Desnouhes. Pas de mycorhize sur le Scirpe maritime (schoenoplectus maritimus) en Camargue (No mycorrhizae on Schoenoplectus maritimus in the Camargue). Revue d’Ecologie (Terre et Vie), 63:297–282, 2008. in French with English abstract. Government of Canada. The Federal Policy on Wetland Conservation. Environment Canada, Ottawa, Ontario, Canada, 1991.

384 M. Govindarajulu, P. E. Pfeﬀer, H. Jin, J. Abubaker, D. D. Douds, J. W. Allen, H. B¨ucking, P. J. Lammers, and Y. Shachar-Hill. Nitrogen transfer in the arbuscular mycorrhizal symbiosis. Nature, 435:819–823, 2005.

A. A. Graham. History of Fairﬁeld and Perry Counties, Ohio: their past and present. W. H. Beers & Co., Chicago, IL, 1883.

S. A. Graham, C. B. Craft, P. V. McCormick, and A. Aldous. Forms and accumulation of soil P in natural and recently restored peatlands—Upper Klamath Lake, Oregon, USA. Wetlands, 25:594–606, 2005.

G. Grigera and M. Oesterheld. Mycorrhizal colonization patterns under contrasting grazing and topographic conditions in the Flooding Pampa (Argentina). Journal of Range Management, 57:601–605, 2004.

J. Grime, M. Mackey, S. Hillier, and D. Read. Floristic diversity in a model system using experimental microcosms. Nature, 328:420–422, 1987.

P. M. Groﬀman, G. C. Hanson, E. Kiviat, and G. Stevens. Variation in microbial biomass and activity in four diﬀerent wetland types. Soil Science Society of America Journal, 60:622–629, 1996.

S. G¨usewell. High nitrogen:phosphorus ratios reduce nutrient retention and second- year growth of wetland sedges. New Phytologist, 166:537–550, 2005a.

S. G¨usewell. Responses of wetland graminoids to the relative supply of nitrogen and phosphorus. Plant Ecology, 176:35–55, 2005b.

S. Güsewell, U. Bollens, P. Ryser, and Klötzli. Contrasting effects of nitrogen, phosphorus and water regime on first- and second-year growth of 16 wetland plant species. Functional Ecology, 17:754–765, 2003.

S. J. Hamilton and M. H. Deaton. Soil survey of Fairﬁeld County, Ohio. U.S. Department of Agriculture, 2005.

E. S. Hannum. Hannum’s atlas of Fairﬁeld County Ohio. E. S. Hannum, 1866.

A. J. Hare. Atlas of Wyandot County Ohio. Harrison and Hare, 1879. illustrations by William Engel.

J. L. Harley. The Biology of Mycorrhiza. Leonard Hill, London, 1969.

J. L. Harley and E. L. Harley. A check-list of mycorrhiza in the British ﬂora. New Phytologist, 105:1–102, 1987a.

J. L. Harley and E. L. Harley. A check-list of mycorrhiza in the British ﬂora—addenda, errata and index. New Phytologist, 107:741–749, 1987b.

385 T. M. Harris. The journal of a tour into the territory northwest of the Alleghany Mountains; made in the spring of the year 1803. Manning & Loring, Boston, MA, 1805.

Y. Hashimoto and R. Higuchi. Ectomycorrhizal and arbuscular mycorrhizal colonization of two species of ﬂoodplain willows. Mycoscience, 44:339–343, 2003.

H.-J. Hawkins, A. Johansen, and E. George. Uptake and transport of organic and inorganic nitrogen by arbuscular mycorrhizal fungi. Plant and Soil, 226:275–285, 2000.

R. W. Healy, R. G. Striegl, T. F. Russell, G. L. Hutchinson, and G. P. Livingston. Numerical evaluation of static-chamber measurements of soil-atmosphere gas exchange: Identiﬁcation of physical processes. Soil Science Society of America Jour- nal, 60:740–747, 1996.

G. A. Heﬀner. Vegetation survey of an area in central Ohio at the edge of the Alleghany Plateau. Master’s thesis, The Ohio State University, 1939.

B. A. D. Hetrick. VA Mycorrhiza, chapter Ecology of VA mycorrhizal fungi, pages 35–55. CRC Press, Inc., Boca Raton, Florida, 1984.

S. A. Heuscher, C. C. Brandt, and P. M. Jardine. Using soil physical and chemical properties to estimate bulk density. Soil Science Society of America Journal, 69: 51–56, 2005.

U. Hildebrandt, K. Janetta, F. Ouziad, B. Renne, K. Nawrath, and H. Bothe. Ar- buscular mycorrhizal colonization of halophytes in Central European salt marshes. Mycorrhiza, 10:175–183, 2001.

N. N. Hill. History of Knox County, Ohio: its past and present. A. A. Graham & Co., Mt. Vernon, OH, 1881.

Historical Publishing Company, editor. Franklin County at the beginning of the twentieth century. Historical Publishing Company, Columbus, OH, 1901.

A. Hodge, C. D. Campbell, and A. H. Fitter. An arbuscular mycorrhizal fungus accelerates decomposition and acquires nitrogen directly from organic material. Nature, 413:297–299, 2001.

E. Hodson, F. Shahid, J. Basinger, and S. Kaminskyj. Fungal endorhizal associates of equisetum species from Western and Arctic Canada. Mycological Progress, 8: 19–27, 2009.

M. H. Hoefnagels, S. W. Broome, and S. R. Shafer. Vesicular-arbuscular mycorrhizae in salt marshes in North Carolina. Estuaries, 16:851–858, 1993.

386 S. Hoeltje and C. Cole. Losing function through wetland mitigation in central Penn- sylvania, USA. Environmental Management, 39:385–402, 2007.

K. Hossler and V. Bouchard. Soil development and establishment of carbon-based properties in created freshwater marshes. Ecological Applications, 20(2):539–553, 2010.

D. M. Howard and P. J. A. Howard. Relationships between CO2 evolution, moisture content and temperature for a range of soil types. Soil Biology and Biochemistry, 25:1537–1546, 1993.

H. Howe. Historical collections of Ohio: containing a collection of the most interesting facts, traditions, biographical sketches, anecdotes, etc., relating to its general and local history; with descriptions of its counties, principal towns and villages. Bradley and Anthony, Cincinnati, OH, 1850. illustrated by 177 engravings.

H. G. Howland. Atlas of Hardin County Ohio. R. Sutton and Company, 1879. illustrations by H. G. Howland, C. Gasche, and W. Engel.

H. G. Howland. Atlas of Marion County Ohio. Harrison, Sutton and Hare, 1878.

R. G. Hunter and S. P. Faulkner. Denitriﬁcation potentials in restored and natural bottomland hardwood wetlands. Soil Science Society of America Journal, 65:1865– 1872, 2001.

R. Hussain, N. Ayub, J. Gul, A. Chaudhary, and A. G. Khan. Incidence of vesicular-arbuscular mycorrhizal (VAM) fungi in aquaphytes growing in and around Rawalpindi/Islamabad. Pakistan Journal of Phytopathology, 7:98–103, 1995.

G. L. Hutchinson and G. P. Livingston. Soil-atmosphere gas exchange. In J. H. Dane and G. C. Topp, editors, Methods of soil analysis. Part 4., pages 1159–1182. Soil Science Society of America, Madison, WI, 2002.

E. R. Ingham and M. V. Wilson. The mycorrhizal colonization of six wetland plant species at sites diﬀering in land use history. Mycorrhiza, 9:233–235, 1999.

IPCC (Intergovernmental Panel on Climate Change). Climate Change 2001: The Scientiﬁc Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, 2001. Editors: J T Houghton and Y Ding and D J Griggs and M Noguer and P J van der Linden and X Dai and K Maskell and C A Johnson.

I. Ipsilantis and D. M. Sylvia. Interactions of assemblages of mycorrhizal fungi with two Florida wetland plants. Applied Soil Ecology, 35:261–271, 2007a.

I. Ipsilantis and D. M. Sylvia. Abundance of fungi and bacteria in a nutrient-impacted Florida wetland. Applied Soil Ecology, 35:272–280, 2007b.

387 A. L. Ishii, R. W. Healy, and R. G. Striegl. A numerical solution for the diﬀusion equation in hydrogeologic systems. Water-Resources Investigations Report 89-4027, U.S. Geology Survey, U.S. Department of the Interior, Urbana, IL, 1989.

D. P. Janos. Mycorrhizae inﬂuence tropical succession. Biotropica, 12(2):56–64, 1980. Supplement: Tropical Succession.

J. D. Jastrow. Soil aggregate formation and the accrual of particulate and mineral- associated organic matter. Soil Biology and Biochemistry, 28:665–676, 1996.

K. Jayachandran and K. G. Shetty. Growth response and phosphorus uptake by arbuscular mycorrhizae of wet prairie sawgrass. Aquatic Botany, 76:281–290, 2003.

D. S. Jenkinson. The turnover of organic carbon and nitrogen in soil. Phil. Trans. R. Soc. Lond. B, 329:361–368, 1990.

H. Jenny. The Soil Resource: origin and behavior. Springer-Verlag, New York, NY, 1980.

P. C. Jenny and J. A. Glanville. Soil survey of Delaware County, Ohio. U.S. Depart- ment of Agriculture, 2006.

E. G. Jobb´agy and R. B. Jackson. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecological Applications, 10:423–436, 2000.

N. C. Johnson, J. H. Graham, and F. A. Smith. Functioning of mycorrhizal associations along the mutualism-parasitism continuum. New Phytologist, 135:575–585, 1997.

P. C. Johnson-Green, N. C. Kenkel, and T. Booth. The distribution and phenology of arbuscular mycorrhizae along an inland salinity gradient. Canadian Journal of Botany, 73:1318–1327, 1995.

W. Kai and Z. Zhiwei. Occurrence of arbuscular mycorrhizas and dark septate endophytes in hydrophytes from lakes and streams in southwest China. International Review of Hydrobiology, 91:29–37, 2006.

L. Kaufman and P. J. Rousseeuw. Finding groups in data: an introduction to cluster analysis. Wiley, New York, NY, 1990.

B. D. Kay, D. A. Angers, P. H. Groenevelt, and J. A. Baldock. Quantifying the inﬂuence of cropping history on soil structure. Canadian Journal of Soil Science, 68:359–368, 1988.

J. E. Keeley. Endomycorrhizae inﬂuence growth of blackgum seedlings in ﬂooded soils. American Journal of Botany, 67:6–9, 1980.

388 W. D. Kemper and W. S. Chepil. Size distribution of aggregates. In C. Black, editor, Methods of soil analysis, part 1, pages 499–510. American Society of Agronomy, Inc., Madison, WI, 1965.

J. W. Kerr and R. L. Christman. Soil Survey of Pickaway County. U.S. Department of Agriculture, Washington, DC, 1980.

A. G. Khan. The occurrence of mycorrhizas in halophytes, hydrophytes and xerophytes, and of Endogone spores in adjacent soils. Journal of General Microbiology, 81:7–14, 1974.

A. G. Khan. Occurrence and importance of mycorrhizae in aquatic trees of New South Wales, Australia. Mycorrhiza, 3:31–38, 1993.

A. G. Khan. Wetlands ecosystems in Asia: function and management, volume I of Developments in Ecosystems, chapter Mycotrophy and its signiﬁcance in wetland ecology and wetland management, pages 95–115. Elsevier, Amsterdam, The Netherlands, 2004.

A. G. Khan and M. Belik. Mycorrhiza: Structure, Function, Molecular Biology and Biotechnology, chapter Occurence and ecological signiﬁcance of mycorrhizal symbiosis in aquatic plants, page 747. Springer-Verlag, 1st edition, 1995.

W. Koerselman and A. F. M. Meuleman. The vegetation N:P ratio: a new tool to detect the nature of nutrient limitation. Journal of Applied Ecology, 33:1441–1450, 1996.

L. M. Kohn and E. Stasovski. The mycorrhizal status of plants at Alexandra Fiord, Ellesmere Island, Canada, a high arctic site. Mycologia, 82:23–35, 1990.

R. Koide. The nature of growth depression in sunﬂower caused by vesicular-arbuscular mycorrhizal infection. New Phytologist, 99:449–462, 1985.

R. E. Koske and B. Tessier. A convenient permanent slide mounting medium. Myco- logical Society of America Newsletter, 34:59–64, 1983.

R. E. Koske, J. N. Gemma, and T. Flynn. Mycorrhizae in Hawaiian angiosperms: a survey with implications for the origion of the native ﬂora. American Journal of Botany, 79:853–862, 1992.

K. D. K¨uhn. Distribution of vesicular-arbuscular mycorrhizal fungi on a fallow agricultural site. II. Wet habitat. Angewandte Botanik, 65:187–203, 1991.

T. Kumar and M. Ghose. Status of arbuscular mycorrhizal fungi (AMF) in the Sundarbans of India in relation to tidal inundation and chemical properties of soil. Wetlands Ecology and Management, 16:471–483, 2008.

389 L. H. Everts and Company. Illustrated historical atlas of Delaware County Ohio. L. H. Everts and Company, 1875. D. J. Lake. Atlas of Pickaway County Ohio. C.O. Titus, 1871. M. Landwehr, U. Hildebrandt, P. Wilde, K. Nawrath, T. Tóth, B. Biró, and H. Bothe. The arbuscular mycorrhizal fungus glomus geosporum in European saline, sodic and gypsum soils. Mycorrhiza, 12:199–211, 2002. R. Langis, M. Zalejko, and J. B. Zedler. Nitrogen assessments in a constructed and a natural salt marsh of San Diego Bay. Ecological Applications, 1:40–51, 1991. G. A. Laursen, R. Treu, R. D. Seppelt, and S. L. Stephenson. Mycorrhizal assessment of vascular plants from subantarctic Macquarie Island. Arctic and Alpine Research, 29:483–491, 1997. G. B. Lawrence, D. A. Goolsby, W. A. Battaglin, and G. J. Stensland. Atmospheric nitrogen in the Mississippi river basin : emissions, deposition and transport. Science of the Total Environment, 248:87–99, 2000. P. Legendre and L. Legendre. Numerical Ecology. Number 20 in Developments in Environmental Modelling. Elsevier, Amersterdam, The Netherlands, 2nd english edition, 1998. C. Leggett and Company. The History of Marion County, Ohio: containing a history of the county; its townships, towns, churches, schools, etc.; general and local statistics; military record; portraits of early settlers and prominent men; history of the northwest territory; history of Ohio; miscellaneous matters, etc., etc. Leggett, Conaway and Company, Chicago, IL, 1883. X.-L. Li, E. George, and H. Marschner. Extension of the phosphorus depletion zone in VA-mycorrhizal white clover in a calcareous soil. Plant and Soil, 136:41–48, 1991. M. Likar, M. Regvar, I. Mandic-Mulec, B. Stres, and H. Bothe. Diversity and seasonal variations of mycorrhiza and rhizosphere bacteria in three common plant species at the Slovenian Ljubljana Marsh. Biology and Fertility of Soils, 45:573–583, 2009. C. W. Lindau and L. R. Hossner. Substrate characterization of an experimental marsh and three natural marshes. Soil Science Society of America Journal, 45:1171–1176, 1981. G. P. Livingston, G. L. Hutchinson, and K. Spartalian. Diffusion theory improves chamber-based measurements of trace gas emissions. Geophysical Research Letters, 32:L24817, 2005. G. P. Livingston, G. L. Hutchinson, and K. Spartalian. Trace gas emission in chambers: a non-steady-state diffusion model. Soil Science Society of America Journal, 70:1459–1469, 2006.

390 J. Lloyd and J. A. Taylor. On the temperature dependence of soil respiration. Func- tional Ecology, 8:315–323, 1994.

D. J. Lodge. The inﬂuence of soil moisture and ﬂooding on formation of VA-endo and ectomycorrhizae in Populus and Salix. Plant and Soil, 117:243–253, 1989.

J. J. Mack. Ohio Rapid Assessment Method for Wetlands, Manual for Using Version 5.0. Ohio Environmental Protection Agency, Division of Surface Water, 401 Wet- land Ecology Unit, Columbus, OH, ohio epa technical bulletin wetland/2001-1-1 edition, 2001.

P. M¨ader, H. Vierheilig, R. Streitwolf-Engel, T. Boller, B. Frey, P. Christie, and A. Wiemken. Transport of 15N from a soil compartment separated by a polyte- traﬂuoroethylene membrane to plant roots via the hyphae of arbuscular mycorrhizal fungi. New Phytologist, 146:155–161, 2000.

M. Maechler, P. Rousseeuw, A. Struyf, and M. Hubert. Cluster analysis basics and extensions. Rousseeuw et al provided the S original which has been ported to R by Kurt Hornik and has since been enhanced by Martin Maechler: speed improve- ments, silhouette() functionality, bug ﬁxes, etc. See the ’Changelog’ ﬁle (in the package source), 2005.

T. K. Magee, S. E. Gwin, R. G. Gibson, C. C. Holland, J. Honea, P. W. Shaﬀer, J. C. Sifneos, and M. E. Kentula. Research plan and methods manual for the Oregon wetlands study. Number EPA/600/R-93/072. U.S. EPA, Environmental Research Laboratory, Corvallis, OR, 1993.

N. Mahowald, T. D. Jickells, A. R. Baker, P. Artaxo, C. R. Benitez-Nelson, G. Berga- metti, T. C. Bond, Y. Chen, D. D. Cohen, B. Herut, N. Kubilay, R. Losno, C. Luo, W. Maenhaut, K. A. McGee, G. S. Okin, R. L. Siefert, and S. Tsukuda. Global distribution of atmospheric phosphorus sources, concentrations and deposition rates, and anthropogenic impacts. Global Biogeochemical Cycles, 22:GB4026, 2008.

Z. A. Malaeb, J. K. Summers, and B. H. Pugesek. Using structural equation modeling to investigate relationships among ecological variables. Environmental and Ecological Statistics, 7:93–111, 2000.

D. Malloch and B. Malloch. The mycorrhizal status of boreal plants: species from northeastern Ontario. Canadian Journal of Botany, 59:2167–2172, 1981.

D. Malloch and B. Malloch. The mycorrhizal status of boreal plants: additional species from northeastern Ontario. Canadian Journal of Botany, 60:1035–1040, 1982.

B. F. J. Manly. Randomization, bootstrap and Monte Carlo methods in biology. CRC Press, 1997.

391 A. Maremmani, S. Bedini, I. Matoˇsevic, P. E. Tomei, and M. Giovannetti. Type of mycorrhizal associations in two coastal nature reserves of the Mediterranean basin. Mycorrhiza, 13:33–40, 2003.

R. T. Marler and J. S. Arora. Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26:369–395, 2004.

E. Mason. Note on the presence of mycorrhiza in the roots of salt marsh plants. New Phytologist, 27:193–195, 1928.

P. McCullagh and J. A. Nelder. Generalized Linear Models. Chapman and Hall/CRC Press, London; New York, 2nd edition, 1999. reprint of 1989 Chapman and Hall book.

W. B. McDougall and O. E. Glasgow. Mycorhizas of the Compositae. American Journal of Botany, 16:225–228, 1929.

K. McGarigal, S. Cushman, and S. Staﬀord. Multivariate statistics for wildlife and ecology research. Springer-Verlag, New York, NY, 2000.

W. B. McGill and C. V. Cole. Comparative aspects of cycling of organic C, N, S and P through soil organic matter. Geoderma, 26:267–286, 1981.

T. P. McGonigle, M. H. Miller, D. G. Evans, G. L. Fairchild, and J. A. Swan. A new method which gives an objective measure of colonization of roots by vesicular- arbuscular mycorrhizal fungi. New Phytologist, 115(3):495–501, 1990.

N. A. McLoda and R. J. Parkinson. Soil Survey of Franklin County, Ohio. U.S. Department of Agriculture, Washington, DC, 1980.

R. L. Meeker, J. H. Petro, and S. W. Bone. Soil Survey of Fairﬁeld County Ohio. U.S. Department of Agriculture, 1960.

V. K. Mejstrik. Vesicular-arbuscular mycorrhizas of the species of a molinietum coeruleae L.I. association: the ecology. New Phytologist, 71:883–890, 1972.

R. Mendoza, V. Escudero, and I. Garc´ıa. Plant growth, nutrient acquisition and mycorrhizal symbioses of a waterlogging tolerant legume (Lotus glaber Mill.) in a saline-sodic soil. Plant and Soil, 275:305–315, 2005.

J. A. Menge, D. Steirle, D. J. Bagyaraj, E. L. V. Johnson, and R. T. Leonard. Phos- phorus concentrations in plants responsible for inhibition of mycorrhizal infection. New Phytologist, 80:575–578, 1978.

C. K. Meyer, S. G. Baer, and M. R. Whiles. Ecosystem recovery across a chronosequence of restored wetlands in the Platte River Valley. Ecosystems, 11:193–208, 2008.

392 A. Michelsen, C. Quarmby, D. Sleep, and S. Jonasson. Vascular plant 15N natural abundance in heath and forest tundra ecosystems is closely correlated with presence and type of mycorrhizal fungi in roots. Oecologia, 115:406–418, 1998.

Millennium Ecosystem Assessment. Ecosystem services and human well-being: Wet- lands and water synthesis. Technical report, World Resources Institute, Washing- ton, DC, USA, 2005. 68 pp.

K. E. Miller and N. H. Martin. Soil Survey of Marion County, Ohio. U.S. Department of Agriculture, Washington, DC, 1989.

K. E. Miller and R. A. Robbins. Soil survey of Hardin County, Ohio. U.S. Department of Agriculture, 1994.

R. M. Miller, C. I. Smith, J. D. Jastrow, and J. D. Bever. Mycorrhizal status of the genus Carex (cyperaceae). American Journal of Botany, 86:547–553, 1999.

S. P. Miller. Arbuscular mycorrhizal colonization of semi-aquatic grasses along a wide hydrologic gradient. New Phytologist, 145:145–155, 2000.

S. P. Miller and R. R. Sharitz. Manipulation of ﬂooding and arbuscular mycorrhiza formation inﬂuences growth and nutrition of ftwo semiaquatic grass species. Func- tional Ecology, 14:738–748, 2000.

P. R. Minchin. An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio, 69:89–107, 1987.

W. J. Mitsch and J. G. Gosselink. Wetlands. John Wiley and Sons, Inc., New York, NY, third edition, 2000.

W. J. Mitsch and J. G. Gosselink. Wetlands. John Wiley and Sons, Inc., 4th edition, 2007.

W. J. Mitsch, X. Wu, R. Nairn, P. Weihe, N. Wang, R. Deal, and C. E. Boucher. Creating and restoring wetlands: a whole-ecosystem experience in self-design. Bio- science, 48:1019–1030, 1998.

W. J. Mitsch, J. W. Day, J. W. Gilliam, P. M. Groﬀman, D. L. Hey, G. W. Randall, and N. Wang. Reducing nitrogen loading to the Gulf of Mexico from the Mississippi River Basin: strategies to counter a persistent ecological problem. Bioscience, 51: 273–288, 2001.

P. Moldrup, T. Olesen, S. Yoshikawa, T. Komatsu, and D. E. Rolston. Three-porosity model for predicting the gas diﬀusion coeﬃcient in undisturbed soil. Soil Science Society of America Journal, 68:750–759, 2004.

393 P. A. Morgan and F. T. Short. Using functional trajectories to track constructed salt marsh development in the Great Bay Estuary, Maine/New Hampshire, U.S.A. Restoration Ecology, 10:461–473, 2002. B. Mosse. Plant growth responses to vesicular-arbuscular mycorrhiza. New Phytolo- gist, 72:127–136, 1973. K. G. Mukerji and Mandeep. Mycorrhizal relationships of wetlands and rivers associated plants. In S. Majumdar, E. Miller, and F. J. Brenner, editors, Ecology of Wetlands and Associated Systems, pages 240–257. The Pennsylvania Academy of Science, 1998. T. Muthukumar, K. Udaiyan, and P. Shanmughavel. Mycorrhiza in sedges - an overview. Mycorrhiza, 14:65–77, 2004. G. Naidoo and S. Naidoo. Waterlogging responses of Sporobolus virginicus (L.) Kunth. Oecologia, 90:445–450, 1992. V. D. Nair, D. A. Graetz, K. R. Reddy, and O. G. Olila. Soil development in phosphate-mined created wetlands of Florida, USA. Wetlands, 21:232–239, 2001. T. Nakano, T. Sawamoto, T. Morishita, G. Inoue, and R. Hatano. A comparison of regression methods for estimating soil-atmosphere diffusion gas fluxes by a closed- chamber techinque. Soil Biology and Biochemistry, 36:107–113, 2004. P. Nannipieri, J. Ascher, M. T. Ceccherini, L. Landi, G. Pietramellara, G. Renella, and F. Valori. Microbial diversity and microbial activity in the rhizosphere. Ciencia del suelo, 25:89–97, 2007. E. Newman and P. Reddell. Relationship between mycorrhizal infection and diversity in vegetation: evidence from the great smoky mountains. Functional Ecology, 2: 259–262, 1988. E. I. Newman and P. Reddell. The distribution of mycorrhizas among families of vascular plants. New Phytologist, 106:745–751, 1987. K. B. Nielsen, R. Kjoller, P. A. Olsson, P. F. Schweiger, F. O. Andersen, and S. Rosendahl. Colonisation and molecular diversity of arbuscular mycorrhizal fungi in the aquatic plants littorella uniflora and lobelia dortmanna in southern Sweden. Mycological Research, 108:616–625, 2004. S. L. Nielsen, I. Thingstrup, and C. Wigand. Apparent lack of vesicular-arbuscular mycorrhiza (VAM) in the seagrasses zostera marina l. and thalassia testudinum banks ex König. Aquatic Botany, 63:261–266, 1999. J. Nimmo and K. Perkins. Aggregate stability and size distribution. In J. Dane and G. Topp, editors, Methods of soil analysis, part 4: physical methods, pages 317–328. Soil Science Society of American, Inc., Madison, WI, 2002.

394 A. B. Norton. A history of Knox County, Ohio, from 1779 to 1862 inclusive. Richard Nevins, Columbus, OH, 1862.

NOWData. NOAA Online Weather Data. 2009. URL http://www.weather.gov/climate/xmacis.php?wfo=iln .

NRC (National Research Council). Compensating for Wetland Losses under the Clean Water Act. National Academy Press, Washington, D.C., 2001.

J. M. Oades. Soil organic matter and structural stability: mechanisms and implications for management. Plant and Soil, 76:319–337, 1984.

J. M. Oades. The retention of organic matter in soils. Biogeochemistry, 5:35–70, 1988.

E. P. Odum. The strategy of ecosystem development. Science, 164:262–270, 1969.

J. Oksanen, R. Kindt, P. Legendre, B. O’Hara, G. L. Simpson, P. Solymos, M. H. H. Stevens, and H. Wagner. vegan: Community Ecology Package, 2009. URL http://CRAN.R-project.org/package=vegan . R package version 1.15-3.

H. Olde Venterink, M. J. Wassen, A. W. M. Verkroost, and P. C. de Ruiter. Species richness-productivity patterns diﬀer between N-, P- and K-limited wetlands. Ecol- ogy, 84:2191–2199, 2003.

P. A. Olsson, E. B˚a˚ath, I. Jakobsen, and B. S¨oderstr¨om. The use of phospholipid and netural lipid fatty acids to estimate biomass of arbuscular mycorrhizal fungi in soil. Mycological Research, 99:623–629, 1995.

E. Orton. Report of the Geological Survey of Ohio, volume III. Geology and Palaeon- tology, chapter Geology of Franklin County, pages 596–646. Nevins and Meyers, Columbus, OH, 1878.

E. Perfect, B. Kay, W. Van Loon, R. Sheard, and T. Pojasok. Rates of change in soil structural stability under forages and corn. Soil Science Society of America Journal, 54:179–186, 1990.

W. H. Perrin, editor. History of Delaware County and Ohio. O. L. Baskin and Company, Chicago, IL, 1880.

W. H. Perrin. History of Crawford county and Ohio: Containing a history of the state of Ohio, from its earliest settlement to the present time. Baskin and Battey, 1881.

A. Pierret, C. Doussan, Y. Capowiez, F. Bastardie, and L. Pag`es. Root functional architecture; a framework for modeling the interplay between roots and soil. Vadose Zone Journal, 6:269–281, 2007.

395 A. Pietikäinen, M. M. Kytöviita, and U. Vuoti. Mycorrhiza and seedling establishment in a subarctic meadow: Effects of fertilization and defoliation. Journal of Vegetation Science, 16:175–182, 2005. B. C. Poole and D. M. Sylvia. Companion plants affect colonization of myrica cerifera by vesicular-arbuscular mycorrhizal fungi. Canadian Journal of Botany, 68:2703– 2707, 1990. C. L. Powell. Effect of P fertilizer on root morphology and P uptake of carex coriacea. Plant and Soil, 41:661–667, 1974. C. L. Powell. Rushes and sedges are non-mycotrophic. Plant and Soil, 42:481–484, 1975. C. L. Powell and D. J. Bagyaraj. VA Mycorrhiza, chapter VA mycorrhizae: why all the interest?, pages 1–3. CRC Press, Inc., Boca Raton, Florida, 1984. R Development Core Team. R: A Language and Environment for Statistical Com- puting. R Foundation for Statistical Computing, Vienna, Austria, 2006. URL http://www.R-project.org . ISBN 3-900051-07-0. R Development Core Team. R: A Language and Environment for Statistical Com- puting. R Foundation for Statistical Computing, Vienna, Austria, 2009. URL http://www.R-project.org . ISBN 3-900051-07-0. S. C. Rabatin. Seasonal and edaphic variation in vesicular-arbuscular mycorrhizal infection of grasses by Glomus tenuis. New Phytologist, 83:95–102, 1979. K. P. Radhika and B. F. Rodrigues. Arbuscular mycorrhizae in association with aquatic and marshy plant species in Goa, India. Aquatic Botany, 86:291–294, 2007. S. Ragupathy, V. Mohankumar, and A. Mahadevan. Occurrence of vesicular- arbuscular mycorrhizae in tropical hydrophytes. Aquatic Botany, 36:287–291, 1990. J. W. Raich and W. H. Schlesinger. The global carbon dioxide flux in soil respiration and its relationship to vegetation and climate. Tellus, 44B:81–99, 1992. Ramsar Convention. Convention on wetlands of international importance especially as waterfowl habitat. Technical Report UN Treaty Series No. 14583, Ramsar (Iran), 2 February 1971, 1971. URL http://www.ramsar.org . As amended by the Paris Protocol, 3 December 1982, and Regina Amendments, 28 May 1987. Ramsar Convention. Creating a global approach to avoiding, minimizing and offset- ting wetland loss. In 10th Meeting of the Conference of the Parties to the Convention on Wetlands (Ramsar, Iran, 1971), volume COP10, Changwon, Republic of Korea, 28 October–4 November 2008. URL http://www.ramsar.org . Proceedings from a workshop held during the IUCN World Conservation Congress Forum (Barcelona, Spain, October 2008).

396 Ramsar Convention. Compensation for lost wetland habitats and other functions. In 7th Meeting of the Conference of the Parties to the Convention on Wetlands (Ramsar, Iran, 1971), volume COP7, San Jos´e, Costa Rica, 1999 1999. URL http://www.ramsar.org .

E. O. Randall. History of Ohio: the rise and progress of an American state, volume II. The Century History Company, New York, NY, 1912.

M. Ratnayake, R. Leonard, and J. Menge. Root exudation in relation to supply of phosphorus and its possible relevance to mycorrhizal formation. New Phytologist, 81:543–552, 1978.

A. M. Ray and R. S. Inouye. Eﬀects of water-level ﬂuctuations on the arbuscular mycorrhizal colonization of Typha latifolia L. Aquatic Botany, 84:210–216, 2006.

M. C. Rayner. Mycorrhiza. New Phytologist, 26:85–114, 1927.

V. J. Rayward-Smith. Statistics to measure correlation for data mining applications. Computational Statistics and Data Analysis, 51:3968–3982, 2007.

D. J. Read, H. K. Koucheki, and J. Hodgson. Vesicular-arbuscular mycorrhiza in natural vegetation systems. New Phytologist, 77:641–653, 1976.

P. Reddell, Y. Yun, and W. A. Shipton. Cluster roots and mycorrhizae in casuarina cunninghamiana: their occurrence and formation in relation to phosphorous supply. Australian Journal of Botany, 45:41–51, 1997.

C. E. Redmond and T. E. Graham. Soil Survey of Knox county. U.S. Department of Agriculture, Washington, DC, 1986.

N. Requena, E. Perez-Solis, C. Azcón-Aguilar, P. Jeffries, and J. Barea. Management of indigenous plant-microbe symbioses aids restoration of desertified ecosystems. Applied and Environmental Microbiology, 67:495–498, 2001.

L. H. Rhodes and J. W. Gerdemann. Phosphate uptake zones of mycorrhizal and non-mycorrhizal onions. New Phytologist, 75:555–561, 1975.

D. Rickerl, F. Sancho, and S. Ananth. Vesicular-arbuscular endomycorrhizal colonization of wetland plants. Journal of Environmental Quality, 23:913–916, 1994.

J. Rozema, W. Arp, J. Van Diggelen, M. Van Esbroek, R. Broekman, and H. Punte. Occurrence and ecological signiﬁcance of vesicular arbuscular mycorrhiza in the salt marsh environment. Acta Botanica Neerlandica, 35:457–467, 1986.

G. Rubio, G. Casasola, and R. S. Lavado. Adaptations and biomass production of two grasses in response to waterlogging and soil nutrient enrichment. Oecologia, 102:102–105, 1995a.

397 G. Rubio, L. R. S., A. Rendina, and M. Bargiela. Waterlogging eﬀects on organic phosphorus fractions in a toposequence of soils. Wetlands, 15:386–391, 1995b.

G. Rubio, M. Oesterheld, C. R. Alvarez, and R. S. Lavado. Mechanisms for the increase in phosphorus uptake of waterlogged plants: soil phosphorus availability, root morphology and uptake kinetics. Oecologia, 112:150–155, 1997.

J. Ruehlmann and M. K¨orschens. Calculating the eﬀect of soil organic matter concentration on soil bulk density. Soil Science Society of America Journal, 73:876–885, 2009.

G. R. Saﬁr, J. S. Boyer, and J. W. Gerdemann. Mycorrhizal enhancement of water transport in soybean. Science, 172:581–583, 1971.

S. R. Saif. The influence of soil aeration on the efficiency of vesicular-arbuscular mycorrhizae I. Effect of soil oxygen on the growth and mineral uptake of Eupatorium odoratum L. inoculated with Glomus macrocarpus. New Phytologist, 88:649–659, 1981.

L. Saint-Etienne, S. Paul, D. Imbert, M. Dulormne, F. Muller, A. Toribio, C. Plenchette, and A. M. Bˆa. Arbuscular mycorrhizal soil infectivity in a stand of the wetland tree pterocarpus oﬃcinalis along a salinity gradient. FEMS Microbi- ology Ecology, 232:86–89, 2006.

K. Sand-Jensen, C. Prahl, and H. Stokholm. Oxygen release from roots of submerged aquatic macrophytes. Oikos, 38:349–354, 1982.

F. E. Sanders and B. P. Tinker. Phosphate ﬂow into mycorrhizal roots. Pesticide Science, 4:385–395, 1973.

A. Sasaki, M. Fujiyoshi, S. Shidara, and T. Nakatsubo. Eﬀects of nutrients and arbuscular mycorrhizal colonization on the growth of Salix gracilistyla seedlings in a nutrient-poor ﬂuvial bar. Ecological Research, 16:165–172, 2001.

P. Schippers and H. Olﬀ. Biomass partitioning, architecture and turnover of six herbaceous species from habitats with diﬀerent nutrient supply. Plant Ecology, 149:219–231, 2000.

R. L. Schuster. The glacial geology of Pickaway County, Ohio. Master’s thesis, The Ohio State University, 1952.

R. P. Schwarzenbach, P. M. Gschwend, and D. M. Imboden. Environmental Organic Chemistry. Wiley-Interscience, 1st edition, 1993.

P. B. Sears. The natural vegetation of Ohio: II. the prairies. Ohio Journal of Science, 26:128–146, 1926.

398 E. D. Seneca, S. W. Broome, and W. W. Woodhouse, Jr. The inﬂuence of duration-of- inundation on development of a man-initiated Spartina alterniﬂora Loisel. marsh in North Carolina. Journal of Experimental Marine Biology and Ecology, 94:259–268, 1985.

A. Sengupta and S. Chaudhuri. Occurrence of vesicular-arbuscular mycorrhiza in suaeda maritima (l.) dumort—a pioneer mangrove of the Chenopodiaceae. Current Science, 58:1372, 1989.

A. Sengupta and S. Chaudhuri. Vesicular arbuscular mychorrhiza (VAM) in pioneer salt marsh plants of the Ganges river delta in West Bengal (India). Plant and Soil, 122:111–113, 1990.

A. Sengupta and S. Chaudhuri. Arbuscular mycorrhizal relations of mangrove plant community at the Ganges river estuary in India. Mycorrhiza, 12:169–174, 2002.

B. Shipley. Cause and correlation in biology: A user’s guide to path analysis, structural equations and causal inference. Cambridge University Press, Cambridge, UK, 2000.

L. M. Shupe. The original vegetation of Pickaway County, Ohio. Master’s thesis, The Ohio State University, 1930.

J. Sim˚unekˇ and D. L. Suarez. Modeling of carbon dioxide transport and production in soil 1. model development. Water Resources Research, 29:487–497, 1993.

N. E. Smeck. Phosphorus dynamics in soils and landscapes. Geoderma, 36:185–199, 1985.

F. A. Smith and S. E. Smith. Structural diversity in (vesicular)-arbuscular mycorrhizal symbioses. New Phytologist, 137:373–388, 1997. Tansley Review No. 96.

M. R. Smith, I. Charvat, and R. Jacobson. Arbuscular mycorrhizae promote establishment of prairie species in a tallgrass prairie restoration. Canadian Journal of Botany, 76:1947–1954, 1998.

R. G. B. Smith and M. A. Brock. The ups and downs of life on the edge: the inﬂuence of water level ﬂuctuations on biomass allocation in two contrasting aquatic plants. Plant Ecology, 188:103–116, 2007.

S. E. Smith and D. J. Read. Mycorrhizal Symbiosis. Academic Press, San Diego, California, USA, 1997.

V. H. Smith, G. D. Tilman, and J. C. Nekola. Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems. Environmental Pollution, 100:179–196, 1999.

399 Soil Survey Staﬀ. Web soil survey. Natural Resources Conservation Service, United States Department of Agriculture. URL http://websoilsurvey.nrcs.usda.gov/ .

M. Z. Solaiman and H. Hirata. Effects of indigenous arbuscular mycorrhizal fungi in paddy fields on rice growth and N, P, K nutrition under different water regimes. Japanese Society of Soil Science and Plant Nutrition, 41:505–514, 1995.

M. Z. Solaiman and H. Hirata. Eﬀectiveness of arbuscular mycorrhizal colonization at nursery-stage on growth and nutrition in wetland rice (oryza satival.) after trans- planting under diﬀerent soil fertility and water regimes. Japanese Society of Soil Science and Plant Nutrition, 42:561–571, 1996.

M. Søndergaard and S. Laegaard. Vesicular-arbuscular mycorrhiza in some aquatic vascular plants. Nature, 268:232–233, 1977.

R. Span, H. J. Collmann, and W. Wagner. Simultaneous optimization as a method to establish generalized functional forms for empirical equations of state. International Journal of Thermophysics, 19:491–500, 1998.

N. Sraj-Krˇziˇc,ˇ P. Pongrac, M. Klemenc, A. Kladnik, M. Regvar, and A. Gaberˇsˇcik. Mycorrhizal colonization in plants from intermittent aquatic habitats. Aquatic Botany, 85:331–336, 2006.

N. Sraj-Krˇziˇc,ˇ P. Pongrac, M. Regvar, and A. Gaberˇsˇcik. Photon-harvesting eﬃciency and arbuscular mycorrhiza in amphibious plants. Photosynthetica, 47:61–67, 2009.

A. L. Stauﬀer and R. P. Brooks. Plant and soil responses to salvaged marsh surface and organic matter amendments at a created wetland in central Pennsylvania. Wetlands, 17:90–105, 1997.

C. R. Stauffer, G. D. Hubbard, and J. A. Bownocker. Geology of the Columbus Quadrangle. In J. A. Bownocker, editor, Geological Survey of Ohio, Fourth Series, Bulletin 14. Springfield Publishing Company, Springfield, Ohio, 1911.

J. R. Steiger and R. L. Hendershot. Soil Survey of Wyandot County, Ohio. U.S. Department of Agriculture, 1982.

D. L. Stenlund and I. D. Charvat. Vesicular arbuscular mycorrhizae in ﬂoating wetland mat communities dominated by Typha. Mycorrhiza, 4:131–137, 1994.

K. Stevens and R. Peterson. The eﬀect of a water gradient on the vesicular-arbuscular mycorrhizal status of Lythrum salicaria L. (purple loosestrife). Mycorrhiza, 6:99– 104, 1996.

K. Stevens, S. Spender, and R. Peterson. Phopshorus, arbuscular mycorrhizal fungi and performance of the wetland plant Lythrum salicaria L. under inundated conditions. Mycorrhiza, 12:277–283, 2002.

400 K. J. Stevens, M. R. Wellner, and M. F. Acevedo. Dark septate endophyte and arbuscular mycorrhizal status of vegetation colonizing a bottomland hardwood forest after a 100 year ﬂood. Aquatic Botany, 92:105–111, 2010.

P. F. Stevens. Angiosperm phylogeny website. Version 9, June 2008 onwards. URL http://www.mobot.org/MOBOT/research/APweb/ .

Sweitzer. Master’s thesis, The Ohio State University, 1971.

F. Tang, J. A. White, and I. Charvat. The eﬀect of phosphorus availability on arbuscular mycorrhizal colonization of Typha angustifolia. Mycologia, 93:1042–1047, 2001.

C. Tanner and J. Clayton. Eﬀects of vesicular-arbuscular mycorrhizas on growth and nutrition of a submerged aquatic plant. Aquatic Botany, 22:377–386, 1985.

H. S. Tanner. Map of Ohio and Indiana. Tanner, Vallance, Kearny and Company, 1819.

K. Tawaraya, Y. Takaya, M. Turjaman, S. J. Tuah, S. H. Limin, Y. Tamai, J. Y. Cha, T. Wagatsuma, and M. Osaki. Arbsucular mycorrhizal colonization of tree species grown in peat swamp forests of Central Kalimantan, Indonesia. FEMS Microbiology Ecology, 182:381–386, 2003.

J. W. Taylor. History of the state of Ohio: First period, 1650–1787. H.W. Derby & Co., Cincinnati, OH, 1854.

W. A. Taylor. Centennial history of Columbus and Franklin County, Ohio, volume I. The S. J. Clarke Publishing Company, Columbus, OH, 1909.

D. Thoen. First observations on the occurrence of vesicular-arbuscular mycorrhizae (VAM) in hydrophytes, hygrophytes, halophytes and xerophytes in the region of Lake Retba (Cap-Vert, Senegal) during the dry season. Mémoires de la Société Royale de Botanique de Belgique, 9:60–66, 1987.

D. Thomas. Travels through the western country in the summer of 1816. David Rumsey, Auburn, NY, 1819.

I. Thompson. Geographical aﬃnities of the ﬂora of Ohio. American Midland Natu- ralist, 21:730–751, 1939.

M. N. Thormann, R. S. Currah, and S. E. Bayley. The mycorrhizal status of the dominant vegetation along a peatland gradient in southern boreal Alberta, Canada. Wetlands, 19:438–450, 1999.

D. Tilman. The resource-ratio hypothesis of plant succession. The American Natu- ralist, 125:827–852, 1985.

401 J. Tisdall and J. Oades. Organic matter and water-stable aggregates in soils. Journal of Soil Science, 33:141–163, 1982. J. M. Tisdall. Fungal hyphae and structural stability of soil. Australian Journal of Soil Research, 29:729–743, 1991. J. M. Tisdall and J. M. Oades. The eﬀect of crop rotation on aggregation in a red-brown earth. Australian Journal of Soil Research, 18:423–433, 1980. J. H. Titus and J. Lepˇs. The response of arbuscular mycorrhizae to fertilization, mowing, and removal of dominant species in a diverse oligotrophic wet meadow. 87:392–401, 2000. S. D. Torti, P. D. Coley, and D. P. Janos. Vesicular-arbuscular mycorrhizae in two tropical monodominant trees. Journal of Tropical Ecology, 13:623–629, 1997. R. Treu, G. A. Laursen, S. L. Stephenson, J. C. Landolt, and R. Densmore. My- corrhizae from Denali National Park and Preserve, Alaska. Mycorrhiza, 6:21–29, 1996. K. R. Troutman. Prairie remnants of Marion, Crawford, and Wyandot counties in north-central Ohio. Ohio Biological Survey Biology Notes, 15:97–107, 1981. S. D. Turner and C. F. Friese. Plant-mycorrhizal community dynamics associated with a moisture gradient within a rehabilitated prairie fen. Restoration Ecology, 6: 44–51, 1998. S. D. Turner, J. P. Amon, R. M. Schneble, and C. F. Friese. Mycorrhizal fungi associated with plants in ground-water fed wetlands. Wetlands, 20:200–204, 2000. U.S. Army Corps of Engineers and U.S. Environmental Protection Agency. Compen- satory mitigation for losses of aquatic resources. In Federal Register, volume 73, pages 19594–19705, April 2008. U.S. Army Corps of Engineers and U.S. Environmental Protection Agency. Memo- randum of agreement between the Department of the Army and the Environmental Protection Agency: The determination of mitigation under the Clean Water Act Section 404(b)(1) guidelines. Technical report, Washington, DC, USA, 1990. Signed 6 February 1990. U.S. Fish and Wildlife Service. National List of Vascular Plant Species that Oc- cur in Wetlands: 1996 National Summary. Washington, DC, USA, 1997. URL http://library.fws.gov/Pubs9/wetlands plantlist96.pdf . 209 pp. USDA, ARS, National Genetic Resources Program. Germplasm Re- sources Information Network - (GRIN) [online database]. Na- tional Germplasm Resources Laboratory, Beltsville, Maryland. URL http://www.ars-grin.gov/cgi-bin/npgs/html/taxon.pl?100678 .

402 A. R. Van Cleaf. History of Pickaway County, Ohio and representative citizens. Biographical Publishing Company, Circleville, OH, 1906.

E. W. van der Heijden and M. Vosatka. Mycorrhizal associations of salix repens l. communities in succession of dune ecosystems. II. Mycorrhizal dynamics and interactions of ectomycorrhizal and arbuscular mycorrhizal fungi. Canadian Journal of Botany, 77:1833–1841, 1999.

W. E. van Duin, J. Rozema, and W. H. O. Ernst. Seasonal and spatial variation in the occurrence of vesicular-arbuscular (VA) mycorrhiza in salt marsh plants. Agriculture, Ecosystems and Environment, 29:107–110, 1989.

D. Van Hoewyk, P. M. Groﬀman, E. Kiviat, G. Mihocko, and G. Stevens. Soil nitrogen dynamics in organic and mineral soil calcareous wetlands in eastern New York. Soil Science Society of America Journal, 64:2168–2173, 2000.

D. Van Hoewyk, C. Wigand, and P. M. Groﬀman. Endomycorrhizal colonization of dasiphora ﬂoribunda, a native plant species of calcareous wetlands in eastern New York state, USA. Wetlands, 21:431–436, 2001.

M. van Oorschot, E. Robbemont, M. Boerstal, I. van Strien, and M. van Kerkhoven- Schmitz. Eﬀects of enhanced nutrient availability on plant and soil nutrient dynamics in two english riverine ecosystems. Journal of Ecology, 85:167–179, 1997.

A. J. VandenBygaart and D. A. Angers. Towards accurate measurements of soil organic carbon stock change in agroecosystems. Canadian Journal of Soil Science, 86:465–471, 2006.

H. V¨are, M. Vestberg, and S. Eurola. Mycorrhiza and root-associated fungi in Spits- bergen. Mycorrhiza, 1:93–104, 1992.

J. T. A. Verhoeven and M. B. Schmitz. Control of plant growth by nitrogen and phosphorus in mesotrophic fens. Biogeochemistry, 12:135–148, 1991.

P. F. Verhulst. Notice sur la loi que la population suit dans son accroissement. Correspondence Math´ematique et Physique, 10:113–121, 1838.

J. R. Vestal and D. C. White. Lipid analysis in microbial ecology. BioScience, 39: 535–541, 1989.

H. Vierheilig, A. P. Coughlan, U. Wyss, and Y. Pich´e. Ink and vinegar, a simple staining technique for arbuscular-mycorrhizal fungi. Applied and Environmental Microbiology, 64:5004–5007, 1998.

H. Vierheilig, P. Schweiger, and M. Brundrett. An overview of methods for the detection and observation of arbuscular mycorrhizal fungi in roots. Physiologia Plantarum, 125:393–404, 2005.

403 P. M. Vitousek and W. A. Reiners. Ecosystem succession and nutrient retention: a hypothesis. BioScience, 25:376–381, 1975.

P. M. Vitousek, J. D. Aber, R. W. Howarth, G. E. Likens, P. A. Matson, D. W. Schindler, W. H. Schlesinger, and D. G. Tilman. Human alteration of the global nitrogen cycle: sources and consequences. Ecological Applications, 7(3):737–750, 1997.

T. W. Walker and J. K. Syers. The fate of phosphorus during pedogenesis. Geoderma, 15:1–19, 1976.

B. Wang and Y. L. Qiu. Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza, 16:299–363, 2006.

F. Wang, R. Liu, X. Lin, and J. Zhou. Arbuscular mycorrhizal status of wild plants in saline-alkaline soils of the Yellow River Delta. Mycorrhiza, 14:133–137, 2004.

G.-P. Wang, J.-S. Liu, J.-D. Wang, and J.-B. Yu. Soil phosphorus forms and their variations in depressional and riparian freshwater wetlands (Sanjiang Plain, North- east China). Geoderma, 132:59–74, 2006.

Warner, Beers and Company, editor. The history of Hardin County Ohio: containing a history of the county; its townships, towns, churches, schools, etc. Warner, Beers and Company, Chicago, IL, 1883.

P. A. Weishampel and B. L. Bedford. Wetland dicots and monocots diﬀer in colonization by arbuscular mycorrhizal fungi and dark septate endophytes. Mycorrhiza, 16: 495–502, 2006.

P. R. Wetzel and A. G. van der Valk. Vesicular-arbuscular mycorrhizae in prairie pothole wetland vegetation in Iowa and North Dakota. Canadian Journal of Botany, 74:883–890, 1996.

H. F. Wheeler. Map of Franklin County. H.F. Wheeler, 1842.

D. Whigham, M. Pittek, K. H. Hofmockel, T. Jordan, and A. L. Pepin. Biomass and nutrient dynamics in restored wetlands on the outer coastal plain of Maryland, USA. Wetlands, 22:562–574, 2002.

J. L. Whitbeck. Eﬀects of light environment on vesicular-arbuscular mycorrhiza development in inga leiocalycina, a tropical wet forest tree. Biotropica, 33:303–311, 2001.

D. C. White, W. M. Davis, J. S. Nickels, J. S. King, and R. J. Bobbie. Determination of the sedimentary microbial biomass by extractable lipid phosphate. Oecologia, 40:51–62, 1979.

404 G. W. White. Illinoian drift region of northeast central Ohio. The Ohio Journal of Science, 37:1–19, 1937. J. A. White and I. Charvat. The mycorrhizal status of an emergent aquatic, Lythrum salicaria L., at different levels of phosphorus availability. Mycorrhiza, 9:191–197, 1999. W. G. Whitman. The two-film theory of gas absorption. Chemical & Metallurgical Engineering, 29:146–148, 1923. C. Wigand and C. J. Stevenson. The presence and possible ecological significance of mycorrhizae of the submersed macrophyte, Vallisneria americana. Estuaries, 17: 206–215, 1994. C. Wigand and J. C. Stevenson. Facilitation of phosphate assimilation by aquatic mycorrhizae of Vallisneria americana Michx. Hydrobiologia, 342/343:35–41, 1997. C. Wigand, F. Ø. Andersen, K. K. Christensen, M. Holmer, and H. S. Jensen. En- domycorrhizae of isoetids along a biogeochemical gradient. Limnology and Oceanog- raphy, 43:508–515, 1998. N. H. Winchell. Report of the Geological Survey of Ohio, volume I. Geology and paleontology, chapter Geology of Marion County, pages 640–645. Nevins and Myers, 1873a. N. H. Winchell. Report of the Geological Survey of Ohio, volume I. Geology and paleontology, chapter Geology of Wyandot County, pages 625–639. Nevins and Myers, 1873b. N. H. Winchell. Report of the Geological Survey of Ohio, volume II. Geology and paleontology, chapter Geology of Delaware County, pages 272–213. Nevins and Meyers, Columbus, OH, 1874a. N. H. Winchell. Report of the Geological Survey of Ohio, volume II. Geology and paleontology, chapter Geology of Hardin County, and general conclusions, pages 325–357. Nevins and Meyers, Columbus, OH, 1874b. S. Wium-Andersen and J. M. Andersen. The influence of vegetation on the redox profile of the sediment of Grange Langso, a Danish lobelia Lake. Limnology and Oceanography, 17:948–952, 1972. S. F. Wright and A. Upadhyaya. A survey of soils for aggregate stability and glomalin, a glycoprotein produced by hyphae of arbuscular mycorrhizal fungi. Plant and Soil, 198:97–107, 1998. R. E. Yoder. A direct method of aggregate analysis of soils and a study of the physical nature of soil erosion losses. Journal of the American Society of Agronomy, 28:337– 351, 1936.

405 J. B. Zedler. Progress in wetland restoration ecology. TREE, 15:402–407, 2000.

J. B. Zedler and J. C. Callaway. Tracking wetland restoration: do mitigation sites follow desired trajectories? Restoration Ecology, 7:69–73, 1999.

J. B. Zedler and S. Kercher. Wetland resources: Status, trends, ecosystem services, and restorability. Annual Review of Environment and Resources, 30:39–74, 2005.

J. B. Zedler, J. C. Callaway, and G. Sullivan. Declining biodiversity: why some species matter and how their functions might be restored in California tidal marshes. Bio- sciences, 51:1005–1017, 2001.

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