Historical Land Use Changes and Hydrochemical Gradients in Ohio's

Historical Land Use Changes and Hydrochemical Gradients

In Ohio’s Sphagnum-Dominated Peatlands

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science

in the Graduate School of The Ohio State University

Julie Mae Slater, B.S.

Graduate Program in Environment and Natural Resources

The Ohio State University

2018

Thesis Committee:

G. Matt Davies, Adviser

Lauren Pintor

Nicholas Basta

Julie Mae Slater

2018

Abstract

Peatlands are a type of wetland in which anoxic conditions cause low rates of decomposition, leading to the accumulation of partially decomposed plant matter – that is, peat.

Although peatlands occupy only ~3% of the earth’s terrestrial surface, peat contains around one- third of the earth’s total soil organic carbon, making peatlands an important contributor to the global carbon cycle. In North America, peatlands extend south into Ohio and the central

Appalachians. In my first chapter, I asses the usefulness of historical maps and literature in evaluating the current status of Ohio’s historical peat bogs. Historical sources provided useful and precise information about the location of historical sites. USGS maps from the early 1900s were helpful in estimating the extent of large sites since destroyed, but were not accurate enough to evaluate changes in area over time. The use of historical maps allowed analysis incorporating national databases such as the National Land Cover Database and the National Wetland

Inventory. In my second chapter, I examine four predictors of hydrology and hydrochemistry in

Ohio’s basin bogs. Vegetation zone was a good indicator of water level, water level range, and phosphorus concentrations, while water level was the best predictor of pH, electrical conductivity, and calcium concentrations. The relationship of water level to pH and alkalinity is thought to be related to minerotrophic conditions at the bog margin and the dilution of organic acids by lake water near the bog center. Ecospatial zones – lagg, wooded interior, and open mat – were moderately good indicators of hydrology and hydrochemistry, and can be used to compare ecosystem function in northern peatlands with glacial peatlands at their southernmost extent.

Variation in hydrology and hydrochemistry was largely explained by differences between sites, and future research should examine the drivers of between-site variation in temperate peatlands.

Acknowledgements

This research could not have happened without the generous support of many people. I am deeply grateful to my friends and colleagues for the professional and personal support that

I’ve received along the way. Our field crew experienced the beautiful and grueling terrain of

Ohio’s bogs; thanks goes to Dr. Roger Grau-Andres, whose expertise in the field and the lab was extremely helpful, to my labmate and fellow bog researcher Yushan Hao, and to Yuchen Liu and

Katie Gaffney. Several conservation organizations allowed access to their sites and shared their knowledge: Karen Seidel and Jaqueline Bilello of The Nature Conservancy, Gary Popotnik of

The Wilderness Center, J-me Braig of Buckeye Lake Historical Society, The Cleveland Museum of Natural History, Adam Wohlever of the Ohio Department of Natural Resources, and Dr.

Roger Laushman of Oberlin College. My collaborators on the PRO Peat project shared valuable input: Dr. Virginia Rich, Dr. Gil Bohrer, my advisor Dr. G. Matt Davies, Camilo Rey-Sanchez, and Yushan Hao. Alyssa Zearley, Dave Tomashefski, and Shane Whitacre of Dr. Nick Basta’s lab provided equipment and training for the water chemistry analysis. On a more personal note,

Alyssa deserves special thanks and for keeping me motivated and fed during the last semester of my master’s program. My counselor, Rebecca Gaines, helped manage the stresses and responsibilities of graduate school. My parents, Steve and Kit Slater, have been incredibly supportive through my winding career path. Their dedication to work they believe in inspires me.

My advisory committee members, Dr. Lauren Pintor and Dr. Nick Basta provided insightful advice and encouragement at key points in the research process. Finally, I would like to thank my advisor Dr. Matt Davies for everything. Matt is invested in all of his students, and it is obvious in his patience, involvement, and belief that we will succeed.

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Vita

2005...... George Wythe High School

2005...... Southwest Virginia Governor’s

School for Math, Science, and Technology

2010...... B.A. Studio Art, Eastern European and Russian

Studies, University of Virginia

2012–2014...... Environmental Education Specialist,

Virginia State Parks

2015...... B.S. Environmental Biology,

Christopher Newport University

2016–2018...... Graduate Fellow, School of Environment and

Natural Resources, The Ohio State University

2018–present ...... Graduate Teaching Associate, School of

Environment and Natural Resources, The Ohio

State University

Fields of Study

Major field: Environment and Natural Resources

Ecological Restoration

Table of Contents

Abstract ...... ii Acknowledgements ...... iii Vita ...... iv Table of Contents ...... v List of Tables ...... vii List of Figures ...... x Chapter 1: Historical land use changes in Ohio’s Sphagnum-dominated peatlands ..... 1 Introduction ...... 1 Peatland ecosystem services...... 1 Historical data on land use changes in wetlands and peatlands ...... 2 Wetland and peatland loss in Ohio ...... 4 Bogs and peatland classification ...... 5 Research Objectives ...... 7 Methods ...... 7 Identification and location of historical bog sites ...... 7 Classification of historical bog sites ...... 8 Utility of historical maps for estimation of changes in bog extent ...... 10 Historical extent and distribution of Ohio’s bogs ...... 11 Current land use within and surrounding Ohio’s historical bogs ...... 12 Results ...... 13 Location and size of historical bogs ...... 13 Conservation designation ...... 14 Utility of historical maps for estimation of changes in bog extent ...... 14 Current land use ...... 19 Surrounding land use ...... 24 Discussion ...... 25 Location, size, and conservation designation ...... 25 Utility of historical maps ...... 28 Current land use ...... 29 Conclusion ...... 30 Chapter 1 References ...... 32 Chapter 2: Hydrochemical gradients in Ohio’s Sphagnum-dominated peatlands ...... 35 Introduction ...... 35 Materials and Methods ...... 40 Site selection ...... 40 Study area ...... 41 Zonation schemes ...... 41

Well locations ...... 47 Water sampling ...... 47 Chemical analysis ...... 49 Data analysis ...... 50 Results ...... 54 Comparison to other bogs ...... 54 Objective 1. Inter-site variation in hydrology and hydrochemistry ...... 54 Objective 2. Comparison of indicators of hydrological and hydrochemical variation ...... 58 Objective 3. Disturbed zone of Flatiron Lake Bog ...... 64 Discussion ...... 64 Comparison to other bogs ...... 64 Objective 1. Inter-site variation in hydrology and hydrochemistry ...... 66 Objective 2. Comparison of indicators of hydrological and hydrochemical variation ...... 67 Objective 3. Disturbed zone of Flatiron Lake Bog ...... 70 Conclusions ...... 70 Chapter 2 References ...... 71 Complete References ...... 75 Appendix A: Historical data on Ohio’s peatlands ...... 81 Appendix B: Hydrological and hydrochemical measures of Ohio’s bog water ...... 88 Appendix C: Model selection tables ...... 93 Appendix D: Model summaries ...... 105

List of Tables

Table 2.1. Study site information. In 0.5 and 1 km dominant land use columns, N=Natural,

A=Agricultural, U=Suburban, and W=open water (lake)...... 43

Table 2.2. Reporting limits and percentage of samples found to be below the reporting limit for all water chemistry analyses...... 51

Table 2.3. Example of model comparisons with vegetation zone as an indicator and pH the hydrochemical variable...... 52

Table 2.4. Species scores from PCA of hydrological and hydrochemical measurements. Values greater than 0.90 are in bold...... 56

Third column (n) indicates number of wells representing each ecospatial zone at each site. Total number of samples included in calculation for each cell’s mean and standard deviation is equal to the product of the two n values...... 89

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number of samples included in calculation for each cell’s mean and standard deviation is equal to the product of the two n values...... 91

Table C.1. Model selection information for all indicators predicting water level at all sites...... 94

Table C.2. Model selection information for all indicators predicting water level range at all sites

...... 95

Table C.3. Model selection information for all indicators predicting pH at all sites...... 96

Table C.4. Model selection information for all indicators predicting electrical conductivity (EC) at all sites...... 97

Table C.5. Model selection information for all indicators predicting calcium concentrations at all sites...... 98

Table C.6. Model selection information for all indicators predicting phosphorus concentrations at all sites...... 99

Table C.7. Model selection information for all indicators predicting potassium concentrations at all sites...... 100

Table C.8. Model selection information for models of conditions at Flatiron Lake Bog, with vegetation zone as predictor...... 101

Table D.1. Summaries of models with water level as a dependent variable...... 106

Table D.2. Summaries of models with water level range as a dependent variable...... 107

Table D.3. Summaries of models with pH as a dependent variable...... 108

Table D.4. Summaries of models with electrical conductivity (EC) as a dependent variable. ... 109

Table D.5. Summaries of models with calcium concentration as a dependent variable...... 110

Table D.6. Summaries of models with phosphorus concentration as a dependent variable...... 111

Table D.7. Summaries of models with potassium concentration as a dependent variable...... 112

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Table D.8. Summaries of models with Flatiron Lake Bog water level as a dependent variable. 113

Table D. 9. Summaries of models with Flatiron Lake Bog water level range as a dependent variable...... 114

Table D.10. Summaries of models with Flatiron Lake Bog pH as a dependent variable...... 115

Table D.11. Summaries of models with Flatiron Lake Bog electrical conductivity (EC) as a dependent variable...... 116

Table D.12. Summaries of models with Flatiron Lake Bog calcium concentration as a dependent variable...... 117

Table D.13. Summaries of models with Flatiron Lake Bog phosphorus concentration as a dependent variable...... 118

Table D.14. Summaries of models with Flatiron Lake Bog potassium concentration as a dependent variable...... 119

Table D.15. Summaries of models with Flatiron Lake Bog ammonium concentration as a dependent variable...... 120

Table D.16. Summaries of models with Flatiron Lake Bog NO2+NO3 concentration as a dependent variable...... 121

List of Figures

Figure 1.1. Diagram of historical bog classification, location, and mapping. Number of sites at each step in the process is indicated as “n”...... 9

Figure 1.2. Histogram of estimated historical sizes of Ohio bogs...... 14

Figure 1.3. Map of located historical peatland sites in Ohio. Bog classification confidence – the confidence that a given site once supported bog vegetation – is represented by point color, and estimated historical size is represented by point size...... 15

Figure 1.4. Map of all located historical peatland sites in northeastern Ohio (inset of Fig. 1.1).

Bog classification confidence (the confidence that a given site once supported bog vegetation) is represented by point color, and estimated historical size is represented by point size...... 16

Figure 1. 5. Map of historical bog sites (peatland sites assigned a medium or high bog classification confidence) in Ohio. Conservation designation is represented by point color, and estimated historical size is represented by point size...... 17

Figure 1.6. Map of historical bog sites in Northeastern Ohio (inset of Fig. 1.5). Conservation designation is represented by point color, and estimated historical size is represented by point size...... 18

Figure 1.7. Change in mapped size of select Ohio bog sites as described by three historical USGS maps. Note that the y-axis varies, and graphs indicate patterns in relative (rather than absolute) area. Color indicates the presence or absence of open water at each site...... 19

Figure 1.8. Current land cover on the estimated locations of Ohio’s historical bogs, as classified by the NLCD...... 20

Figure 1.9. Current land cover on the estimated locations of Ohio’s historical bogs, as classified by the NWI...... 21

Figure 1.10. Map of current dominant land cover on historical bogs in Ohio, as classified by the

National Land Cover Database. Dominant land cover is represented by point color, and estimated historical size is represented by point size ...... 22

Figure 1.11. Map of current dominant land cover on historical bogs in northeast Ohio (Inset of

Fig. 1.9). Dominant land cover is represented by point color, and estimated historical size is represented by point size...... 23

Figure 1.12. Biplot of current land cover of Ohio’s historical bogs. Sites >100 ha in size are represented by large points, and protection status is represented by color...... 24

Figure 1.13. Map of current dominant land cover surrounding protected historical bogs in Ohio, as classified by the National Land Cover Database. Dominant land cover is represented by point color, and estimated historical size is represented by point size...... 26

Figure 1.14. Map of current dominant land cover surrounding protected historical bogs in Ohio

(inset of Fig. 1.13). Dominant land cover is represented by point color, and estimated historical size is represented by point size...... 27

Figure 2.1. Conceptual diagram of the processes affecting environmental indicators in bogs. Red boxes represent indicators, blue represents important ecological processes, and green hexagons represent hydrochemical measurements made in this study...... 37

Figure 2.2. Map of study sites...... 44

Figure 2.3. Map of vegetation zonation and water sampling scheme at Flatiron Lake Bog.

Vegetation zonation beyond wells was visually estimated using aerial imagery...... 45

Figure 2. 4. Map of ecospatial zonation and water sampling scheme at Flatiron Lake Bog.

Vegetation zonation beyond wells was visually estimated using aerial imagery...... 46

Figure 2.5. Diagram of microtopographic and water level measurements...... 48

Vitt and Bayley (1984) , [5] Bragazza et al. (2005), [6] Bragazza and Gerdol (2002), [7]

Verhoeven et al. 1996 . All studies report means values within or between multiple sites, except

Lynn and Karlin (1985) which reports min and max values at each of 7 sites...... 55

Figure 2.7. PCA of hydrological and hydrochemical variables colored by (2.7B) vegetation zone,

(2.7C) site, and (2.7D) ecospatial zone. Site name abbreviation key can be found in Table 2.1. 57

Figure 2.8. Results of partial RDA, showing percentage of variation in hydrological and hydrochemical conditions explained by vegetation zone, site, and interacting effects of vegetation zone and site...... 58

Figure 2.9. Marginal (R2m) and conditional (R2c) coefficients of determination for mixed models of hydrological and hydrochemical variables (for full model selection documentation, see

Appendix C). Dependent variables are shown as plot titles, and fixed effects are shown on the x axis. All models included site as a random effect, and models of dependent variables with multiple sampling dates (water level, pH, and EC) included ordinal day as a second fixed effect

...... 60

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and third quartile, and 95% confidence interval of median. Values sharing a letter are not significantly different (Tukey-adjusted lsmeans comparison, p<0.05)...... 62

Figure 2.11. Boxplot of hydrological and hydrochemical measurements within ecospatial zones across all sites. Boxes and whiskers represent median value, first and third quartile, and 95% confidence interval of median. Values sharing a letter are not significantly different (Tukey- adjusted lsmeans comparison, p<0.05)...... 63

Figure 2.12. Boxplot of hydrological and hydrochemical measurements within vegetation zones at Flatiron Lake Bog. Vegetation zones are arranged along the x-axis in an order reflecting their margin-to-center spatial pattern in the field. Boxes and whiskers represent median value, first and third quartile, and 95% confidence interval of median. Values sharing a letter are not significantly different (Tukey-adjusted lsmeans comparison, p<0.05) ...... 65

Figure 2.13. Conceptual diagram of the relationships between vegetation zone, nutrient concentration, and water level in Ohio’s bogs...... 69

Figure 2. 14. Conceptual diagram of the relationships between vegetation zone, nutrient concentration, and water level in Ohio’s bogs...... 69

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Chapter 1: Historical land use changes in Ohio’s

Sphagnum-dominated peatlands

Introduction

Peatland ecosystem services

In peatlands, as in many other ecosystems, historical changes in land use have allowed humans to maximize production of food and other resources but decreased the land’s capacity to sustain important ecosystem services (Foley et al. 2005). Peatlands are a type of wetland in which anoxic conditions cause low rates of decomposition, leading to the accumulation of partially decomposed plant matter – that is, peat. Global peatland distribution is concentrated in the boreal and temperate zones (Immirzi et al. 1992), though significant deposits are found in the artic and the tropics (Page et al. 2011). In North America, peatlands extend south into Ohio and the central Appalachians (Halsey 2000). Although peatlands occupy only ~3% of the earth’s terrestrial surface (Xu et al. 2018, Gorham 1991), peat contains around one-third of the earth’s total soil organic carbon (Limpens et al. 2008, Frolking et al. 2011, Page et al. 2011), making peatlands an important contributor to the global carbon cycle. Peatlands worldwide are used as agricultural land, harvested for timber production, and mined for peat to be used in horticulture or as fuel. These land use changes are often facilitated by peatland drainage, which exposes peat to aerobic conditions and increases the rate of decomposition and risk of fire (Turetsky et al.

2015). Approximately 11% of global peatlands are degrading due to drainage and will release an estimated 80.8 Gt carbon and 2.3 Gt nitrogen if not restored (Leifield and Manichetti 2018). In

addition, climate change threatens to transform northern peatlands from a carbon sink to a carbon source (Chaudhary et al. 2017), potentially creating a positive feedback loop whereby released greenhouse gases further increase rates of global climate change. Changes in land use can negatively impact a range of peatland ecosystem services such as water quality improvement

(Martin-Ortega et al. 2014), runoff storage (Shantz and Price 2006), and groundwater recharge

(Fraser et al., 2001; Dempster et al., 2006). Peatland land-use change can also cause loss in biodiversity (Minayeva et al. 2017). Although peatlands have limited species diversity, they contribute to regional biodiversity with their high occurrence of unique species and diversity of ecosystem types (Minayeva et al. 2017). In the case of isolated peatlands, these sites often provide habitat for locally or regionally rare species, such as spotted turtle (Clemmys guttata), prothonotary warbler (Prothonotaria citrea), and Sarracenia purpurea L. (Purple pitcher plant).

Historical data on land use changes in wetlands and peatlands

Detailed historical data on land use changes of peatlands in the North America are lacking; however, more general information on historical wetland extent is available. The U.S.

Fish and Wildlife Service, the U.S. Environmental Protection Agency, and the U.S. Department of Agriculture’s National Resources Conservation Service currently release periodic reports on the extent, status, and/or ecological condition of wetlands in the United States. These reports rely on aerial imagery and field-based surveys gathered no earlier than 1970. Estimates of historical nationwide wetland extent before the advent of aerial photography have relied on two sources of information: (1) maps of hydric soils and (2) acreage of agricultural land drainage combined with contemporaneous wetland extent (Roe and Ayres 1954, USDA, summarized in Dahl). Hydric soils maps may be used to isolate histosols and thus estimate historical peatland extent, but do

not distinguish between different types of peatlands.

Historical documents such as maps and written records have been used worldwide to investigate human impacts on the landscape, including wetlands. Researchers have, for example, relied on historical maps to estimate losses of New England salt marshes (Bromberg and

Bertness 2005), wetlands of the Swiss lowlands (Gimmi et al. 2011), tidal wetlands in the

Yellow Sea (Murray et al. 2014), and peat coverage in Danish cultivated soils (Greve et al.

2014). Historical maps can also be used to validate and improve field methods (Edvardsson et al.

2015) and land-cover change models (Petit and Lambin 2002, Yang et al. 2017, Fuchs et al.

2015). Combined with historical records, such as economic and ecological surveys, historical maps can produce high quality spatial data focused on individual ecological communities (Fuchs et al. 2015).

Comprehensive historical peatland maps are not available in the United States and historical inventories are rare (Soper and Osbon 1922). However, a 1912 survey of Ohio’s peat deposits (Dachnowski 1912) is a unique early document on the distribution of temperate peatlands throughout the State. The survey was conducted for the purpose of describing deposits

“favorable for commercial development” (Dachnowski 1912). It covers 46 of 88 Ohio counties and provides detailed descriptions of vegetation communities at many sites, including local reports of vegetation communities destroyed by the time of writing. The publication also includes 11 maps of large peatland complexes, likely encompassing a mosaic of vegetation communities.

Historical documents such as these can be used in conjunction with historical wetland maps to produce spatial data on Ohio’s peatland communities. Dachnowski’s (1912) report has previously been used as the basis for assessment of broad changes in the abundance of peat bogs

and fens in Ohio (Andreas and Knoop 1992). Andreas and Knoop (1992) examined the causes of peatland loss in Ohio at the site level through extensive ground surveys. However, no detailed assessment of changes in extent have been made and we have no understanding of how the abundance of peatlands has changed in the subsequent 26 years.

Wetland and peatland loss in Ohio

In 1992, 98% of surveyed historical peatlands in Ohio no longer supported characteristic peatland vegetation, primarily due to the impact of agricultural activity (Andreas and Knoop

1992). In order to convert Ohio’s wetlands into productive cropland, settlers installed thousands of miles of open ditches and subsurface tile drains. Today, approximately 50% of Ohio’s cropland is drained land (Keiffer 2006). The most extensive drainage has occurred in the northwestern quarter of the State, the site of the former Great Black Swamp. This near- impassable wetland complex inspired vivid descriptions by soldiers and travelers which illustrate and account for early settlers’ distaste for wetlands (Kaatz 1955). Laws passed in 1859 and 1871 granted county commissioners the authority and funding to install drainage systems (U.S. Census

1920). Combined with the introduction of machine-made clay tile drains in the 1850s, this resulted in the rapid conversion of much of Ohio’s wetland acreage, the majority of which was completed before the turn of the century (Keiffer 2006). By 1920, the first U.S. Agricultural

Census reported over 8 million acres of drained farmland. The drainage of Ohio’s wetlands has made Ohio the second-ranked state in the nation for wetland loss, a loss of 90% of their original extent (Dahl 1990).

Historical records also contain information about the destruction of Ohio peatlands specifically. Dachnowski (1912) describes the drainage of a number of large Ohio peatlands

before his survey. Drainage is likely to impact certain peatland types more than others: depression peatlands such as kettlehole bogs are impractical to drain and, as lower ground, may be impacted by receiving drainage inputs from surrounding fields (for example, Colwell 2009).

Ohio’s peatlands have also been harvested for horticultural peat. According to records kept since

1970 by the Ohio Department of Natural Resources, peat production peaked in 1990, when

45,000 tons were sold primarily for use as mulch and soil mix (ODNR 2000). There has been a lack of significant peat production in Ohio since the year 2000 due to federal and state restrictions on wetland development and competition from foreign markets (Mineral Commodity

Summaries 1999). Andreas and Knoop (1992) found peat mining to be a major cause of destruction in two Ohio peatlands, totaling 356 acres. Several large peatlands were destroyed by dams which left the sites underwater, and industrial or recreational development accounted for the majority of the remaining peatland loss (Andreas and Knoop 1992).

Government protection of Ohio’s peatlands began in 1942, when Cedar Bog (actually a calcareous fen) became Ohio’s first State Nature Preserve purchased with state money. By 1992,

32 peatlands totaling 824 ha were protected through public or private ownership (Andreas and

Knoop 1992). Following the passing of the Clean Water Act in 1972, wetland protection laws have reduced the destruction of further wetland areas. Ohio is one of six states that have passed legislation extending protection to isolated wetlands. Compensatory wetland regulation under the

Clean Water Act provides incentives for wetland restoration, and several extensive peatland restoration projects are ongoing throughout the State.

Bogs and peatland classification

Peatlands encompass a range of ecological and hydrological conditions and have been

classified using a variety of frameworks (Wheeler and Proctor 2000). The most commonly recognized ecological gradient in peatlands is the bog-fen gradient, often reflected in multiple ecohydrological variables. Broadly speaking, fens are wetlands that receive hydrological input from groundwater, which supplies basic cations such as calcium and magnesium. Fens generally have a pH above 6.0 and tend to be dominated by sedge species (Økland et al. 2001). Bogs, on the other hand, are ombrotrophic, receiving the majority of their water input from precipitation.

These sites have pH below 5.0 and tend to be dominated by Sphagnum moss and other acidophilic vegetation (Økland et al. 2001). Where hydrological, hydrochemical, and vegetation gradients do not covary in a way that allows easy classification, one must choose between classification systems. To add to the confusion, historical texts often uses terms such as swamp, bog, mire, and marsh interchangeably (Wheeler and Proctor 2000). Bogs have been defined on the basis of hydrology as ombrotrophic peatlands, that is, rainwater-fed peatlands isolated from the potential alkaline and nutrient inputs of groundwater (Rydin and Jeglum 2013). This study focuses on Ohio’s bogs, using a broad ecological definition of bogs “with no assumptions regarding hydrology, topography, ontogeny, nutrient availability, or the presence or absence of nondominant indicator plant species”, as recommended by Bridgham et al. (1996). Due to the absence of historical hydrochemical data, I rely on plant species assemblages to define bogs as sites dominated by acidophilic species such as Sphagnum moss and ericaceous shrubs, and potentially supporting populations of carnivorous plants such as Drosera rotundifolia L.

(roundleaf sundew) and Sarracenia purpurea L. (purple pitcher plant).

Research Objectives

Overall, this chapter’s aim is to gather information about the history of human impact on

Ohio’s bogs using a combination of USGS wetland maps and an early 20th century survey of

Ohio’s peat deposits (Dachnowski 1912). My specific goals are to:

1) Compare estimates of the historical extent and distribution of Ohio’s bogs using USGS

maps and contemporaneous literature;

2) Evaluate land-use pressure on and surrounding Ohio’s historical bogs to assess the extent

of continuing threats.

Methods

Identification and location of historical bog sites

I attempted to locate all historical sites in Ohio that once supported characteristic bog vegetation as previously defined by searching historical sources for sites described as bogs or for sites described as containing bog vegetation. The initial long-list included all sites defined by

Andreas (1985) as bogs in a study of Ohio’s peatland distribution which contained many of the same sites as Andreas and Knoop (1992). In addition, I included sites not included in Andreas

(1985) but described in journal publications (e.g. Laushman 1991), theses and dissertations (e.g.

Hicks 1933), or inventories of ecologically significant places (e.g. Herrick 1974) as bogs, or with the word “bog” in their name. Our main source for historical references on Ohio’s bogs was

Andreas and Knoop’s (1992) article on changes in peatland extent in Ohio. As the article does not include site locations, I located sites using the authors’ primary sources, including

Dachnowski’s (1912) survey of peat deposits in Ohio and other historical peatland studies.

Andreas and Knoop (1992) also referred to herbarium records for their study. We searched all

collections within the Consortium of Midwest Herbaria online, using the criteria “United States” for country, “Ohio” for state, and the word “bog” within the locality description. Further searches were conducted within the Consortium of Midwest Herbaria using the criteria “United

States” for country, “Ohio” for state, but searching for known site names within locality descriptions and excluding the word “bog” (e.g. “Brown’s Lake” and “Browns Lake” for

Brown’s Lake Bog). Full locality descriptions from specimen records were used to relocate sites.

The Consortium of Midwest Herbaria encompasses 37 midwestern herbarium collections but only includes 2 of 4 collections used in Andreas and Knoop’s original study (1992).

Sites were located using the following information from historical sources: county, township and section, location relative to natural and manmade landmarks, landowner names

(which were searched in county deed records), and rarely, site coordinates. Protected bog sites with unchanged names were easy to locate by internet search.

Classification of historical bog sites

Andreas (1985) used the presence of indicator species to systematically classify Ohio’s peatlands into bogs and fens However, rather few true bog indicator species exist; bog vegetation is instead distinguished by a lack of calcareous fen indicator species (Wheeler and Proctor 2000).

With this in mind, historical site descriptions were revisited, and plant community descriptions used to divide these sites into high, medium, or low confidence in their classification as bogs.

Because historical site descriptions were often brief and not quantitative, species listed were considered to be dominant and representative of the entire site (even in the case of large peatlands). High confidence was assigned to sites where historical literature described open areas

Figure 1.1. Diagram of historical bog classification, location, and mapping. Number of sites at each step in the process is indicated as “n”.

dominated by Sphagnum moss or listed acidophilic species such as Sphagnum moss, ericaceous shrubs, and/or D. rotundifolia and S. purpurea (Figure 1.1). Medium confidence was assigned to sites whose classification by Andreas (1985) could be neither confirmed or denied due to lack of data, and low confidence was assigned to sites whose descriptions did not include acidophilic species and instead listed dominant characteristic fen or marsh vegetation such as Typha spp. and

Salix spp.

Utility of historical maps for estimation of changes in bog extent

Topographic maps from three different time periods were evaluated for their efficacy in estimating changes in historical bog extent.

1. Current National Wetland Inventory (NWI) polygon data based on 2007 surveys was used to

represent present-day extent. The NWI serves as the wetlands layer in all current USGS

maps, meaning the three resulting shapefiles represented three generations of USGS wetland

maps.

2. United States Geological Survey (USGS) maps from 1900-1925 were chosen to correspond

with Dachnowski’s 1910-1911 peat survey

3. USGS maps from 1960-1994 corresponded with Andreas and Knoop’s 1976-1991 field

inventories.

When multiple editions of USGS maps were available, the maps created closest to 1912 and 1991, respectively, were selected. Georeferenced historical USGS maps were accessed using topoView, the USGS’s historical map download portal.

For the USGS maps, shapefiles of wetland extent were created by digitizing mapped

wetland extent corresponding to the described bog location. The more recent NWI data often classified much larger areas at and around the bogs’ locations as wetlands. This made delineation of some bog sites difficult due to their connectivity with other wetland complexes. Therefore, in order examine mapping consistency of bogs in the three sets of maps, I selected 15 extant bog sites with clearly defined margins in the NWI. This criteria favored smaller kettle hole bogs and excluded large peatland complexes that were likely to have wide variation in hydrology and plant communities. NWI polygons were selected to represent each of these discrete sites. Areas of the

15 sites as shown in early 1990s, late 20th century, and 2007 maps were estimated in ArcGIS.

These values were compared under the assumption that actual peatland extent has not increased since 1912, meaning that a smaller area marked on an older map represents a mapping discrepancy.

Historical extent and distribution of Ohio’s bogs

In order to arrive at a best estimate of historical extent of Ohio’s bog sites circa 1912, I selected the most accurate estimate of bog extent for each site among the three map editions and

Dachnowski’s (1912) maps. In the case of sites destroyed after the early 1900s (typically larger wetland complexes), the earliest generation of maps was selected. Where available, Dachowski’s

(1912) maps were preferred, under the assumption that the author modified existing wetland maps with more detailed knowledge gained from field research and survey. Where sites did not appear or appeared distorted in the oldest maps, the late 20th century or 2007 maps were used.

Maps from 2007 were preferred due to the greater accuracy of NWI shapefiles compared to manually digitized USGS wetland maps, except in cases where the late 20th century extent was clearly larger or where the interconnectivity of wetland complexes in the 2007s maps made

discrete delineation difficult. Ohio’s historical bog sites were visually grouped based on their geographical location within the state.

Current land use within and surrounding Ohio’s historical bogs

County parcel maps were used to determine ownership and conservation designation of all located sites. The National Land Cover Database (NLCD) was used to characterize current land use within each bog’s best estimate of historical extent (described above). The area of each current land cover category at a site was calculated in ArcGIS at a resolution of 30 × 30 m, matching the spatial resolution of the NLCD. Dominant land use was determined as the land cover category with the greatest total area at a site. A 1 km buffer around each protected site was also evaluated using the method above in order to determine potential impacts from surrounding land-use pressure on protected bogs. Land cover data was also analyzed by principal component analysis. Areas of different land cover categories were converted to relative area (% of total site area). Land cover at each site was characterized by standardizing relative area to a mean of 0 and a standard deviation of 1, and conducting a PCA using the PCA function in the FactoMineR package in R (Le et al. 2008). Land cover “profiles” were defined based on clusters apparent in the PCA biplot and tested for correlation with bog size.

Although the NLCD and the National Wetland Inventory (NWI) both provide data on wetland extent, the two databases use different criteria. The NWI contained more detailed categorization of wetlands, and these were used to examine wetland types at historical bog sites in greater detail.

Results

Location and size of historical bogs

A total of 87 potential peat bogs were identified in this study, adding 10 new locations to

Andreas and Knoop’s (1992) list of peatlands (i.e. fens and bogs). Of these, 59 (68.8%) were relocated with a high degree of confidence (Fig. 1.3 and 1.4); coordinates are provided in Table

A.1. We were highly confident in the bog status of 29 relocated bog sites. Bog classification confidence was medium for 13 sites (14.9%), and low at 13 (14.9%). Ten of the 42 sites classified as bogs with high and medium confidence were not marked as wetlands in any historical maps. Current and/or historical USGS maps provided wetland extent for the remaining

32 sites, totaling 3,513 ha. All subsequent spatial analyses were performed on these bogs.

Bog locations were clustered in three geographic regions: northeastern, northwestern, and central Ohio (Fig. 1.3 and 1.4). The northeastern region is clustered in the kame and esker region of Summit, Portage, and Stark Counties. The northwestern region is clustered in the ground and ridge moraines of Williams, Defiance, and Fulton counties, north and west of the historical Great

Black Swamp. The central Ohio bogs are more dispersed and occur along the moraine system that bisects the state from north to south.

The estimated historical extent of Ohio’s bog sites displayed a right-skewed distribution with a median of 8.7 ha, and ranging from 0.45 to 379.9 ha. Bogs were separated into size classes between 0-50 ha, 50-100 ha, and five outliers between 100-380 ha (Fig. 1.2). All but one site over 100 ha were located in northeast Ohio.

Figure 1.2. Histogram of estimated historical sizes of Ohio bogs.

Conservation designation

Twenty-four of the 42 relocated bog sites (57%) are currently at least partially protected in public or private ownership (Fig. 1.5 and 1.6). The most important landowners are state government (the Ohio Department of Natural Resources), county and local government

(particularly Portage Park District), and non-governmental conservation organizations such as

The Nature Conservancy, the Western Reserve Land Conservancy, the Cleveland Museum of

Natural History, Ohio Appalachia Alliance, and The Wilderness Center. The remaining 18 bog sites are in private ownership, and it is not known whether any of these are protected through conservation easements.

Utility of historical maps for estimation of changes in bog extent

Eight of fifteen (53%) sites evaluated were not indicated as wetlands on early 1900s historical maps (Fig. 1.7). In three cases, bogs appear only on the most recent NWI maps:

Bonnett Bond Bog, Fox Lake, and Young’s Bog. Seven of the sites appeared in early 1990s

Figure 1.4. Map of all located historical peatland sites in northeastern Ohio (inset of Fig. 1.1). Bog classification confidence (the confidence that a given site once supported bog vegetation) is represented by point color, and estimated historical size is represented by point size.

Figure 1.6. Map of historical bog sites in Northeastern Ohio (inset of Fig. 1.5). Conservation designation is represented by point color, and estimated historical size is represented by point size.

maps, and in five of these cases, later maps showed increases in wetland area which was attributed to discrepancies in mapping methods. The 1990s and 2007 maps were more similar to one another in wetland extent than to the early 1900s maps. Two of the analyzed bogs show a substantial decrease in mapped area since the early 1900s: Cranberry Bog and Camden Lake

Bog.

Current land use

According to NLCD classifications, historical peatland extent is currently 56% non- cultivated vegetation (1,984 ha), and 44% (1,529 ha) developed, cultivated, or dammed and flooded – directly impacted by human activity (Fig. 1.8). According to the NWI, 43% (1,495 ha) of total estimated historical peatland extent is currently classified as wetlands, compared to 23%

(817 ha) according to the NLCD’s (Fig. 1.8 and 1.9). The majority of wetlands fell into the freshwater forested/shrub wetland category.

Figure 1.8. Current land cover on the estimated locations of Ohio’s historical bogs, as classified by the NLCD.

Unprotected sites in northwestern Ohio are mainly agricultural land today, while unprotected sites in northeastern Ohio are categorized as forested by the NLCD (Fig. 1.10 and

1.11). The impact of agriculture is seen most strongly in the central and northwestern regions of the state, while sites impacted by development are seen only in the northeast of the state. Several

large historical sites are dominated by open water, as a result of dam construction. However, one of these sites, Cranberry Bog in Licking County, retains a small remnant of bog vegetation on a floating island of peat.

Figure 1.9. Current land cover on the estimated locations of Ohio’s historical bogs, as classified by the NWI.

A PCA of current relative land cover revealed several clusters of current bog status (Fig.

1.12). The majority of small (<100 ha) protected bog sites were associated with wetland and forested land. A distinct group of small unprotected sites were dominated by planted/cultivated land. Large sites (>100 ha) were less likely to be associated with wetland and forested land cover. Two large protected sites were associated with shrubland and herbaceous land cover, while the large unprotected sites were more associated with open water and developed land. PC1 accounted for 29.6% of variation in land cover, while PC2 accounted for 20.6%. PC1 was significantly correlated with bog area (R2=0.22, p=0.008), supporting the claim that larger peatlands are more susceptible to land use conversion than small depression bogs.

Figure 1.10. Map of current dominant land cover on historical bogs in Ohio, as classified by the National Land Cover Database. Dominant land cover is represented by point color, and estimated historical size is represented by point size. 22

Figure 1.11. Map of current dominant land cover on historical bogs in northeast Ohio (Inset of Fig. 1.9). Dominant land cover is represented by point color, and estimated historical size is represented by point size.

Shrubland 2 Herbaceous

Forest

)

6 Protected

. Developed

2 N

(

2 Y

D 0 Wetlands

Water

-1

Planted/Cultivated

-2

-2 0 2 Dim1 (29.6%)

Figure 1.12. Biplot of current land cover of Ohio’s historical bogs. Sites >100 ha in size are represented by large points, and protection status is represented by color.

Surrounding land use

State-wide patterns in land use of surrounding protected bogs mirrors patterns evident in land use within bogs (Fig. 1.13 and 1.14). Bogs in the central part of the State are found in an agricultural context, while bogs in northwestern part of the state are mainly surrounded by forest.

Two northeastern sites are found in predominantly developed settings. No bog sites are protected in the northwestern part of Ohio.

Discussion

Location, size, and conservation designation

With the use of Andreas and Knoop’s (1992) survey, Dachnowski’s (1912) survey, other historical and scientific literature, historical maps from online databases, online herbarium databases, online county property maps, conservation organization websites, and general web searches, I was able to relocate 69% the bog sites mentioned in historical literature and add 10 new sites to the list compiled by Andreas and Knoop (1992). Historical literature contained useful descriptions, and often referenced landmarks visible on current and historical maps. Soils maps were also referenced but, as noted by Andreas (1985), were less helpful in analysis of

Ohio’s peatlands due to low correspondence between mapped peatland areas and current histosol extent. Although precise locations were often apparent, both Dachnowski and Andreas contained estimates of peatland area within their text when these peatlands were not mapped. I was able to estimate the size of 30 bogs, covering 3,513 ha . Dachnowski (1912) field tested 206 peatlands

(including bogs and fens) that covered 13,030 ha but also estimated that total peatland extent in

Ohio was 29,947 ha. Dachnowski’s survey was conducted after the drainage of much of the

Great Black Swamp, and likely after the drainage of many peatlands – Dachnowski notes that original peatland extent was much larger than his estimate of 29,947 ha in 1912. Several sites classified by Andreas (1985) as bogs were designated as fens with my classification approach, and vice versa. Andreas’ (1985) study was the first to attempt a comprehensive classification of bogs and fens in Ohio, the study’s focus was on the relationships of glacial geology to Ohio’s peatland distribution, for which it remains an important source.

Figure 1.14. Map of current dominant land cover surrounding protected historical bogs in Ohio (inset of Fig. 1.13). Dominant land cover is represented by point color, and estimated historical size is represented by point size.

Utility of historical maps

It is highly unlikely that bogs have been created between the early 1900s and the 1980s, so the discrepancy between maps of different time periods can be attributed to differences in scale or criteria used to map wetlands. Early 1900s maps are of 1:62,500 scale, while more recent USGS topographic maps have a scale of 1:24,000 facilitating mapping of smaller landscape features. The criteria for wetlands to be marked on USGS maps in the early 1900s is unclear, but site inclusion seems to be inconsistent and these early maps appear to be less accurate at depicting wetland areas. In addition, Dachnowski’s survey maps (1912) overlay early

1900s USGS topographic maps, but their wetland demarcation differs slightly, often exceeding the area shown on the USGS maps. This is further evidence for the inaccuracy of the USGS’s early 1990s topographic map wetland delineation.

It is possible that smaller bogs were passed over in older maps, or that bogs with open water were less likely to be marked as wetlands where they were displayed on the map as ponds.

However, counterexamples exist. For instance, Young’s Bog is included in the NWI, but not in early 1900s or 1980s maps despite having no open water and being relatively large at 48 ha.

Of the two sites which decreased in size between the early 1900s and the 1980s, only one appears to be due to true loss of area. Cranberry Bog is a floating bog island in Buckeye Lake reservoir, where wave action from boating activity has decreased the size of the island. The second site is Camden Lake, which has experienced encroachment from agriculture and severe impacts from pumping of the lake water for agriculture. However, based on a visual assessment, the discrepancy in wetland area at Camden Lake appears to be due to differences in mapping scale rather than actual impact.

USGS wetland maps do not appear to be consistent enough for year-by-year comparisons of historical bog extent, but they are nevertheless useful for mapping the extent of sites that do not appear in later maps. The presence of roads and development in the early 1900s provide frequent reference points for map comparison. Because most of the assessed sites’ area was underestimated by early 1900s USGS maps, these appear to be conservative in their wetland estimates, and where data is available, can be used for conservative estimates of peatland loss.

Historical maps can also be used to collect data on certain types of human impact, such as construction of surface drainage, development, and peat mining, although mining is not indicated on USGS map editions before 1970.

Current land use

Comparison of my estimates with Andreas and Knoop’s (1992) description of the causes of loss of Ohio’s peatlands reflected (1) differences in methodology and (2) differences between

Ohio’s bog sites (mapped in my study) and peatlands in general (surveyed by Andreas and

Knoop). Use of historical map data allowed me to describe changes in peatland area rather than loss/impact of entire sites, and to connect peatland information with national databases on land use and wetland type. My results showed that 12 of 30 (40%) of historically mapped bog sites were still dominated by wetland vegetation, compared to Andreas and Knoop’s (1992) 54 of 125 peatland sites (43%). However, unlike Andreas and Knoop (1992) I could not confirm the presence of peatland-specific vegetation with my methods. Both studies showed a trend of larger sites being more likely to be altered by human impacts.

Differences in wetland extent described by the NLCD and NWI at Ohio’s bogs are due to different project missions. The NWI endeavors to map all areas with abiotic wetland conditions, including areas without wetland vegetation due to human impact (Cowardin et al. 1979), while

the NLCD aims to provide information about current land cover. The freshwater forested/shrub wetland category – found to dominate the extent of Ohio’s historical wetlands – describes Ohio’s peatlands well, although that category is not exclusive to peat bogs.

Conclusion

In this chapter, I assesed the usefulness of historical maps and literature in evaluating the current status of Ohio’s historical peat bogs. Historical sources provided useful and precise information about the location of historical sites. USGS maps form the early 1900s were helpful in estimating the extent of large sites since destroyed, but were not accurate enough to evaluate changes in area, at least for small basin bogs. The use of historical maps allowed the incorporation of national databases such as the National Land Cover Database and the National

Wetland Inventory.

Two main criteria for peatland conservation present themselves: the conservation of sites in underrepresented areas, the conservation of underrepresented ecosystem types, and the conservation of high-quality or easily restored sites. The northeast of the state is the most poorly represented in terms of protected historical bog locations. However, all of these are currently under agricultural production, and while abiotic conditions may still be amenable to restoration, there are likely no remnant bog species present.

A few sites provide an opportunity to restore large areas of bog habitat, but two of these are underwater, having been flooded by dam construction. Bloomfield Bog and Sugar Island are adjacent large sites under restoration by the Western Reserve Land Conservancy. There are also several potential high-quality sites still in private ownership, such as Norton Bog, which is

currently owned by a developer. Ground surveys are necessary to assess the current state of these sites.

Chapter 1 References

Andreas, B. K. 1985. The relationship between Ohio peatland distribution and buried river valleys. Ohio Journal of Science 85:116–125.

Andreas, B. K., and J. D. Knoop. 1992. 100 years of changes in Ohio peatlands. Ohio Journal of Science 92:130–138.

Bridgham, S. D., J. Pastor, J. A. Janssens, C. Chapin, and T. J. Malterer. 1996. Multiple limiting gradients in peatlands: A call for a new paradigm. Wetlands 16:45–65.

Bromberg, K. D., and M. D. Bertness. 2005. Reconstructing New England salt marsh losses using historical maps. Estuaries 28:823–832.

Chaudhary, N., P. A. Miller, and B. Smith. 2017. Modelling past, present and future peatland carbon accumulation across the pan-Arctic region. Biogeosciences 14:4023–4044.

Colwell, S. R. 2009. M.S. Thesis. Characterization of Upland/Wetland Community Types: Changes to Flatiron Lake Bog over a 24-Year Period. The Ohio State University.

Cowardin, L. M., U. S. Fish, V. Carter, and F. C. Golet. 1979. Classification Of Wetlands and Deepwater Habitats Of the United States.

Dachnowski, A. 1912. Peat Deposits of Ohio: Their Origin, Formation, and Uses. Geological Survey of Ohio, Columbus, Ohio, USA.

Dahl, T. E. 1990. Wetlands losses in the United States 1780’s to 1980’s. U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C.

Dempster, A., P. Ellis, B. Wright, M. Stone, and J. Price. 2006. Hydrogeological evaluation of a southern Ontario kettle-hole peatland and its linkage to a regional aquifer. Wetlands 26:49–56.

Edvardsson, J., R. Šimanauskienė, J. Taminskas, I. Baužienė, and M. Stoffel. 2015. Increased tree establishment in Lithuanian peat bogs — Insights from field and remotely sensed approaches. Science of The Total Environment 505:113–120.

Foley, J. A., R. DeFries, G. P. Asner, C. Barford, G. Bonan, S. R. Carpenter, F. S. Chapin, et al. 2005. Global Consequences of Land Use. Science 309:570–574.

Fraser, C. J. D., N. T. Roulet, and M. Lafleur. 2001. Groundwater flow patterns in a large peatland. Journal of Hydrology 246:142–154.

Frolking, S., J. Talbot, M. C. Jones, C. C. Treat, J. B. Kauffman, E.-S. Tuittila, and N. Roulet. 2011. Peatlands in the Earth’s 21st century climate system. Environmental Reviews 19:371–396.

Gimmi, U., T. Lachat, and M. Bürgi. 2011. Reconstructing the collapse of wetland networks in the Swiss lowlands 1850–2000. Landscape Ecology 26:1071.

Gorham, E. 1991. Northern Peatlands: Role in the Carbon Cycle and Probable Responses to Climatic Warming. Ecological Applications 1:182–195.

Greve, M. H., O. F. Christensen, M. B. Greve, and R. B. Kheir. 2014. Change in Peat Coverage in Danish Cultivated Soils During the Past 35 Years. Soil Science 179:250.

Halsey, L. A., D. H. Vitt, and L. D. Gignac. 2000. Sphagnum-dominated peatlands in North America since the last glacial maximum: their occurrence and extent. The Bryologist 103:334– 352.

Kaatz, M. R. 1955. The Black Swamp: A Study In Historical Geography. Annals of the Association of American Geographers 45:1–35.

Keiffer, A., ed. 2006. The Geography of Ohio: Revised and Updated Edition (2nd edition.). The Kent State University Press, Kent, Ohio.

Le, S., J. Josse, F. Husson. 2008. FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software, 25:1-18. 10.18637/jss.v025.i01

Leifeld, J., and L. Menichetti. 2018. The underappreciated potential of peatlands in global climate change mitigation strategies. Nature Communications 9:1071.

Limpens, J., F. Berendse, C. Blodau, J. G. Canadell, C. Freeman, J. Holden, N. Roulet, et al. 2008. Peatlands and the carbon cycle: from local processes to global implications – a synthesis. Biogeosciences 5:1475–1491.

Martin-Ortega, J., T. E. H. Allott, K. Glenk, and M. Schaafsma. 2014. Valuing water quality improvements from peatland restoration: Evidence and challenges. Ecosystem Services 9:34–43. Minayeva, T. Y., O. M. Bragg, and A. A. Sirin. 2017. Towards ecosystem-based restoration of peatland biodiversity. Mires and Peat 1–36.

Murray, N. J., R. S. Clemens, S. R. Phinn, H. P. Possingham, and R. A. Fuller. 2014. Tracking the rapid loss of tidal wetlands in the Yellow Sea. Frontiers in Ecology and the Environment 12:267–272.

Økland, R. H., T. Økland, and K. Rydgren. 2001. A Scandinavian perspective on ecological gradients in north-west European mires: reply to Wheeler and Proctor. Journal of Ecology 89:481–486.

Page, S. E., J. O. Rieley, and C. J. Banks. 2011. Global and regional importance of the tropical peatland carbon pool. Global Change Biology 17:798–818.

Petit, C. C., and E. F. Lambin. 2002. Long-term land-cover changes in the Belgian Ardennes (1775–1929): model-based reconstruction vs. historical maps. Global Change Biology 8:616– 630.

Shantz, M. A., and J. S. Price. 2006. Characterization of surface storage and runoff patterns following peatland restoration, Quebec, Canada. Hydrological Processes 20:3799–3814.

Soper, E. K., and C. C. Osbon. 1922. The occurrence and uses of peat in the United States. 234.

Speck, S. W., and T. M. Berg. 2000. 2000 Report on Ohio Mineral Industries.

Turetsky, M. R., B. Benscoter, S. Page, G. Rein, G. R. van der Werf, and A. Watts. 2015. Global vulnerability of peatlands to fire and carbon loss. Nature Geoscience 8:11–14.

Wheeler, B. D., and M. C. F. Proctor. 2000. Ecological gradients, subdivisions and terminology of north-west European mires. Journal of Ecology 88:187–203.

Xu, J., P. J. Morris, J. Liu, and J. Holden. 2018. PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. CATENA 160:134–140.

Yang, Y., S. Zhang, Y. Liu, X. Xing, and A. de Sherbinin. 2017. Analyzing historical land use changes using a Historical Land Use Reconstruction Model: a case study in Zhenlai County, northeastern China. Scientific Reports 7:41275.

Chapter 2: Hydrochemical gradients in Ohio’s

Sphagnum-dominated peatlands

Introduction

Hydrology and hydrochemistry are of key impotance to peatland ecosystem function, in part due to their effects on plant communities. The importance of these environmental factors has resulted in an extensive literature on peatland ecohydrology. However, much of this research has occurred in northern latitudes, where peatlands make up a significant proportion of the landscape. Environmental conditions are likely to differ in temperate peatlands, where frequent summer droughts influence peatland succession (Swinehart and Parker 2000) and increase dependence on groundwater input (Glaser et al. 1997). Due to their relatively small size and agricultural landscape context, temperate Midwestern peatlands experience disproportionate impacts from human land use (e.g. Andreas and Knoop 1992). Reference data on the hydrology and hydrochemistry of North America’s southernmost glacial peatlands is needed as new projects undertake their restoration.

Variation in hydrochemistry between bogs can be driven by differences in climate, bedrock geochemistry, topography, and surrounding land use. Cation concentrations in bog waters can vary along a coastal-inland gradient, and mean annual precipitation can influence pH and nutrient cycling (Verhoeven et al. 1996, Howie and van Meerveld 2013). However, in northwestern Minnesota, hydrochemical differences between bogs did not appear to be related to climatic gradients (Glaser et al. 1997). In bogs separated by large distances, hydrochemistry can vary due to differences in bedrock composition (Bragazza et al. 2003) and atmospheric nitrogen

deposition (Verhoeven et al. 1996, Bragazza et al. 2003). Within a smaller region, the chemistry of surface runoff (Waddington et al. 2005) and catchment topography may have a strong effect on hydrochemistry. Understanding the range of natural between-site variation in bog hydrochemistry is important for informing restoration goals specific to a given region.

Studies of within-site hydrochemical gradients are common in northern raised bogs, where peat accumulates in glacial depressions, filling in lakes and creating a domed profile (e.g.

Bragazza et al. 2005, Howie and van Meerveld 2013). These sites frequently feature bog margin- bog expanse gradients, where diminishing influence of groundwater entering from bogs margins is reflected in changes in plant community composition and/or obvious changes in vegetation structure. Although species composition varies in raised bogs globally, zones such as lagg, rand forest, and bog expanse have been used to broadly identify areas dominated by different ecological processes. These ecospatial zones can be thought of as being derived from vegetation structure/dominant plant species, distance from bog margin, and water levels (Figure 2.1). The lagg is defined as “a transition zone at the margin of a (usually raised) bog receiving water from both the bog and surrounding mineral ground” (Howie and Meerveld 2011) and is typically distinguished by minerotrophic conditions: increased water levels, pH, alkalinity, ph-corrected electrical conductivity, and calcium concentrations (Howie and van Meerveld 2013). The rand forest slopes upwards as it approaches the raised bog center, and due to lower water levels is dominated by woody vegetation. The bog expanse, or open bog, is dominated by Sphagnum mosses and, due to the peat dome’s separation from the groundwater level, has ombrotrophic conditions: low pH and low concentrations of basic cations like calcium. The relative importance of different ecological gradients in peatlands is still a matter of significant debate. In

Scandinavia, foundational peatland ecology studies have long recognized a single complex poor-

rich gradient, which is reflected in changes in pH, alkalinity, and nutrients (Økland et al. 2001).

In central and southern Europe and North America, however, peatlands have been seen as exhibiting two separate hydrochemical gradients: a pH-alkalinity gradient and a nutrient gradient

(Bridgham et al. 1996, Wheeler and Proctor 2000). These differences may be attributed to regional differences in climate and geology; particularly notable is the strong influence of atmospheric nutrient deposition in European peatlands, which may significantly alter nutrient budgets. Data representative of the southern edge of glacial peatland extent is not currently available.

Basin bogs surrounding small lakes are common in the southern great lakes region, but

research in these temperate bogs has focused largely on hydrology (e.g. Mouser et al. 2004,

Dempster et al. 2006) and on development and paleoecology (e.g. Ireland and Booth 2011,

Lamentowicz et al. 2007, Guadig and Joosten 2006, Warner 1993, Wilcox and Simonin 1988).

The following exceptions highlight these sites’ differences from raised bogs lacking central lakes. An early study of peatland ecohydrology examines the physical gradients controlling vegetation communities in open Sphagnum mats surrounding glacial lakes in northern Michigan

(Vitt and Slack 1975). Vitt and Slack (1975) found that lake pH and cation concentrations strongly influenced communities along lake edges, but had less influence on the consistently acidophilous communities located further from both acidic and alkaline lake edges. In basin bogs in northwestern Ontario, areas associated with lake edges and inflow streams had lower pH values than areas without open water (Vitt and Bayley 1984). Otherwise, concentrations of nitrate, sulfate, and basic cations such as calcium and magnesium were higher in the bog margin than the bog interior (Vitt and Bayley 1984). Certain sites did not show a relationship between pH and calcium concentrations. As for bogs at the southernmost edge of glacial peatland extent, hydrochemical data is presented in vegetation studies from a small number sites in Ohio

(Andreas and Bryan 1990) and the New Jersey-New York border (Lynn and Karlin 1985), but limited water sampling does not capture within-site hydrochemical gradients.

This study focuses on Ohio’s Sphagnum-dominated peatlands, North American glacial basin peatlands at the southern extent of their range. I refer to these sites as “bogs” based on their moderate acidity (ph<5.0) and plant communities, but make no assumptions about their hydrological connectivity and trophic status, as recommended by Damman et al. (1995),

Bridgham et al. (1996), and Wheeler and Proctor (2000). In Ohio, 98% of surveyed peatlands have been degraded or destroyed by human activity (Andreas and Knoop 1992). The majority of

intact bogs occur in small basins associated with glacial landforms (Andreas 1985, Andreas and

Knoop 1992) and can be classified as basin bogs according to the Canadian Wetland

Classification System (National Wetlands Working Group 1997) due to their relatively flat profiles. A large proportion of Ohio’s bogs encircle a central lake, and their well-defined concentric vegetation zones can be coarsely divided into ecospatial zones which correspond to those found in northern bogs: lagg, wooded interior, and floating Sphagnum mat.

Different indicators may reflect different dominant processes within and between sites

(Fig. 2.1), and understanding their predictive power can elucidate underlying processes in Ohio’s understudied basin bogs. Further, research on the environmental processes governing these temperate sites may provide insights into the effects of climate change on northern peatlands.

Research objectives

The overarching aim of this study is to understand variation in hydrology and hydrochemistry between and within Ohio’s basin bogs. Specific research objectives and hypotheses are listed below.

Objective 1: Quantify the variation in hydrology and hydrochemistry explained by between-site differences, and by differences between vegetation zones.

H1: Variation in hydrology and hydrochemistry are greatest between vegetation zones.

H2: Variation in hydrology and hydrochemistry are greatest between sites.

Objective 2. Compare multiple explanatory variables – vegetation zone, ecospatial zone, water level, and distance from bog margin – as indicators of hydrological and hydrochemical conditions (Fig. 2.1).

Objective 3. Determine how the disturbed zone of Flatiron Lake Bog compares to its undisturbed

vegetation zones and advise the best course for restoration.

H1: The primary impact in the disturbed zone is an alteration in water level.

H2: The disturbed zone is outside the range of hydrochemical characteristics occurring in the undisturbed areas of the bog.

Materials and Methods

Site selection

The site identification process is described in detail in Chapter 1. Using historical literature, 70 potential bog sites were identified across Ohio. Of these, 51 could be located with confidence based on site descriptions, historical maps, and aerial photos. Due to inconsistencies in peatland classification conventions through history, past and current site descriptions were used to identify 26 sites which once supported bog vegetation: areas of Sphagnum moss- dominated vegetation with a cover of acidophilic ericaceous shrubs such as Vaccinium corymbosum L. (highbush blueberry), Chamaedaphne calyculata L. (leatherleaf), Gaylussacia baccata (Wangenh.) K. Koch (black huckleberry), and Vaccinium macrocarpon Aiton

(cranberry).

From these 26 sites, nine were selected to provide a representative sample of bogs and their surrounding land cover context across Ohio (Fig. 2.2; Table 2.1). Surrounding land-use was classified at two spatial scales: buffers were drawn around the perimeter of each site at distances of 0.5 km and 1 km. Using aerial imagery, the dominant land use within each buffer was visually assigned to one of four categories: forested, agricultural, suburban, or open water (Table 2.1).

Study area

The majority of sites were located in northwest Ohio, except for Cranberry Bog, which is in central Ohio (Figure 2.2). While most of the sites were relatively undisturbed, three had unique histories of human disturbance. Cranberry Bog is a floating bog within Buckeye Lake, which was created in 1830 by impoundment of a large peatland. When the impoundment occurred, a 50-acre section of peat rose with the lake’s surface. After some reduction in size due to wave action, Cranberry Bog remains in the form of a 13-acre island with bog vegetation,

Camden Lake Bog experienced severe hydrological impacts when water from the pond was pumped out for use in irrigation. The surrounding floating peat mat became attached to the lake bottom, and when water levels were restored, only a portion floated to the top. The lake also receives tile drainage directly from adjacent farmland. Finally, the area adjacent to Flatiron Lake

Bog was once exploited for sand and gravel mining (Colwell 2009). A small southern section of the bog was drained prior to the cessation of mining activities in 2001 and 2003. The water level has since been raised by the installation of the weir, which killed the Acer rubrum L. (red maple) which had become established. Sphagnum mosses and V. corymbosum were transplanted to the area in an attempt to restore bog conditions, but V. corymbosum has not successfully established.

This disturbed area is currently dominated by a mosaic of trees and swamp shrubs such as Ilex verticillata (L.) A. Gray (common winterberry), C. occidentalis , A. rubrum, and invasive

Frangula alnus Mill. (glossy buckthorn).

Zonation schemes

Vegetation communities in basin bogs are frequently distributed in concentric zones.

Ecological zones were classified using two schemes with differing taxonomic and/or geospatial resolution. The first scheme defined six vegetation zones according dominant plant functional

types. No site included every vegetation zone, but within and between sites, vegetation zones occurred in a more or less predictable sequence from bog margin to bog center: marginal emergent vegetation, swamp shrubs, bog shrubs, hardwood forest, coniferous forest, Sphagnum mat, and a central zone of emergent vegetation (Fig. 2.3). At all sites where present, the emergent vegetation zone was typically dominated by D. verticillatus. The swamp shrub zone was dominated by C. occidentalis and/or I. verticillata. The bog shrub zone was dominated by

V. corymbosum and G. bacchata. The hardwood forest was dominated by A. rubrum and Betula alleghaniensis Britton (yellow birch). The coniferous forest was dominated by Larix laricina

(Du Roi) K. Koch (tamarack). The Sphagnum mat was dominated by Sphagnum moss and low ericaceous shrubs – C. calyculata and/or V. macrocarpon.

The second scheme described three ecospatial zones defined by location, water levels, and vegetation structure: lagg, interior, and open mat (Fig. 2.4). Lagg was defined as the area surrounding each bog that experienced standing water during spring thaw period. Interior was defined as the area dominated by tall woody vegetation (including mature trees and shrubs over

1.5 m tall) without frequent flooding. Mat was defined as the open, central area of each bog with predominantly dwarf-shrub and herbaceous vegetation often growing on a floating layer of peat.

The ecospatial zonation scheme can be thought of as a simplification of the vegetation zonation scheme which reflects spatial patterns, dominant vegetation, and water level gradients. The lagg is closest to the bog margin, and the open mat is typically found in the bog’s center (Fig. 2.4).

The lagg typically contains emergent vegetation and/or swamp shrubs, the interior is typically composed of bog shrubs, hardwood forest, and/or coniferous forest, and the open mat is composed of Sphagnum mat and/or emergent vegetation.

Table 2.1. Study site information. In 0.5 and 1 km dominant land use columns, N=Natural, A=Agricultural, U=Suburban, and W=open water (lake).

0.5 km 1 km Open Site ID County Latitude Longitude Landowner dominant dominant water land use land use

Bonnett Pond Ohio Department of BP Holmes 40.663116 -82.138806 Y A A Bog Natural Resources

Brown's Lake The Nature BL Wayne 40.682147 -82.062734 Y N N Bog Conservancy

Camden Lake CL Lorain 41.242805 -82.335142 Y Oberlin College A A Bog

Ohio Department of Cranberry Bog CB Licking 39.93143 -82.46866 W W Natural Resources

Cleveland Museum Fern Lake and of Natural History FL Geauga 41.444401 -81.17503 Y N N Lake Kelso and Geauga Park District

Flatiron Lake The Nature FB Portage 41.04476 -81.366542 Y A A Bog Conservancy

The Wilderness Lash's Bog LB Stark 40.701147 -81.614349 A A Center

Cleveland Museum Singer Lake Bog SL Summit 40.916745 -81.486234 U N of Natural History

Triangle Lake Ohio Department of TB Portage 41.118102 -81.261695 Y N N Bog Natural Resources

Figure 2.2. Map of study sites.

4544

Figure 2.3. Map of vegetation zonation and water sampling scheme at Flatiron Lake Bog. Vegetation zonation beyond wells was visually estimated using aerial imagery.

Figure 2. 4. Map of ecospatial zonation and water sampling scheme at Flatiron Lake Bog. Vegetation zonation beyond wells was visually estimated using aerial imagery.

Well locations

To capture hydrological and hydrochemical gradients from bog margin to center, monitoring dip wells were installed at each site along 2-4 margin-to-center transects which intersected major vegetation zones. All but two sites contained three transects: Bonnet Pond contained two transects due to its small size, and Flatiron Lake Bog contained four transects in order to allow detailed characterization of its disturbed zone. Monitoring wells were established at the center of each intersected vegetation zone (Fig. 2.2 and 2.3). The inclusion of multiple transects per site ensured both within-site and between-site replication of vegetation communities.

Water sampling

Dip wells were constructed from 1.5-inch diameter PVC pipe perforated with holes 1 cm in diameter in 5 cm intervals along the lower 30 cm of the well. The perforated zones of the wells were wrapped with geotextile material to prevent sedimentation and root penetrations.

Wells were installed with their base at a depth of 90 m below ground level to ensure that water samples would be collected from a standard depth of 90-60 cm below ground level.

Water level was measured within each well over the course of five sampling events roughly monthly between 19th June and 28th November 2017. Water level was calculated by first measuring the distance from the top of each well to the surface of the water level within or outside of the well. This measurement was then subtracted from the average distance from the top of the well to the ground surface (well “stick-up”). A negative water level value indicates water level below the ground surface, and a positive value indicates inundation. Peatlands often feature hummocks and hollows, and this microtopographic variation can influence water level

measurements due to small-scale elevation of the ground surface above the water table. Wells were therefore installed at an elevation visually estimated to be halfway between the height of surrounding hummocks and hollows: a microtopographic “middle ground”. To further account for microtopographic variation, 12 measurements were made of surrounding microtopographic relief relative to the top of each dipwell; using a spirit level to mark the height of the top of the well, its height above the ground surface was measured at 12 surrounding points: six points at a distance of 1 m from the well, and six at a distance of 1.5 m (Fig. 2.5) These microtopography measurements were averaged to create a composite “stick-up” value to be used in water level calculations.

Water sampling was conducted in four sampling events, roughly monthly between June

29th and October 17th 2017. There was a difference of no more than two weeks between the first and last well sampled during each sampling event.

Figure 2.5. Diagram of microtopographic and water level measurements.

Before collecting water samples, dip wells were emptied completely using a vacuum syringe and allowed to refill. When it was impossible to completely empty a well due to rapid recharge, a volume equivalent to the volume of the well was removed before collecting samples. On rare occasions, wells did not refill rapidly enough to provide a sample at the end of the day, resulting in missing data.

Chemical analysis

To characterize hydrochemistry in Ohio’s bogs, I measured pH, electrical conductivity

(EC), and concentrations of Al, Ca, Fe, K, Mg, Mn, Na, P, S, and Zn at all sites, as well as concentrations of NH4 nitrogen and NO2+NO3 nitrogen as Flatiron Lake Bog. In cases of sufficient well recharge, EC and pH were measured in the field using a YSI Pro1030 pH, conductivity and salinity instrument. Electrical conductivity was not corrected for hydrogen ion concentrations. When well recharge after emptying did not provide a large enough water depth for EC and pH measurements in the field, these measurements were made within 48 hours in the lab using a YSI EcoSense EC30A conductivity and TDS pen tester and a YSI EcoSense EH10A pH/temperature pen tester. Further analysis was performed on all water samples at all sites collected during the second sampling campaign from 24 July to 5 August 2017, and on all water samples collected at Flatiron Lake Bog between 27 June and 3 October 2017. Samples were filtered using Whatman binder-free glass microfiber 0.7μm filters that had been combusted at

500ºC to remove organic contamination. Water samples were stored in HDPE coated bottles and frozen at -22ºC for 10 months prior to analysis. Inductively coupled plasma -optical emission spectrometry (ICP-OES) (USEPA 6010D) was carried out using a Varian Vista-MPX to measure concentration of Al, Ca, Fe, K, Mg, Mn, Na, P, S, and Zn. On samples from Flatiron Lake Bog

only, concentrations of NO3+NO2 nitrogen (measured as a combined value) and NH4 nitrogen. were determined by colorimetry using a Lachat's QuikChem® 8500 Series 2 Flow

Injection Analysis System (USEPA 350.1, USEPA 353.2).

Quality assurance and quality control protocols were followed for both the ICP-OES and flow injection anlayses. Recoveries of matrix spikes and serial dilutions were at least 75% and

90%, respectively. The reporting limit (RL) for each batch of samples was the lowest concentration in the calibration curve. The RL for NH4-N was 0.1 mg/L and the RL for all other analytes was 0.01-0.05 mg/L (Table 2.2). Where concentrations were below the reporting limit, the measured concentration was substituted with one-half the reporting limit. Check standards and blanks were analyzed every 10 samples. Check standard recoveries did not exceed +/-10% error and blanks did not exceed reporting limits. No blanks were allowed to exceed the reporting limits. Accuracy of pH and EC measurements was measure through regular calibration of equipment.

Data analysis

All data analyses were performed in R (R Core Team 2018).

Objective 1

To identify the primary gradients of environmental variation and visualize differences between sites, vegetation zones, and ecospatial zones, a PCA was applied to all environmental variables measured in the second campaign, between 24 July and 5 August 2017, (water level, pH, EC, Al, Ca, Fe, K, Mg, Mn, Na, P, S, and Zn), as well as water level range from June 19th to

November 28th 2017. Three axes were selected, based on visual assessment a scree plot.

Table 2.2. Reporting limits and percentage of samples found to be below the reporting limit for all water chemistry analyses. Below Reporting Percent Variable reporting limit of data limit? 87.2 Al 0.01 Below RL 12.8 Ca 0.05 100.0 0.01 44.2 49.6 Fe 0.05 Below RL 0.3 0.1 6.0 0.01 58.2 K 0.05 41.8 Mg 0.01 100.0 99.1 Mn 0.01 Below RL 0.9 0.01 21.2 Na 0.05 76.1 0.1 2.7 93.7 P 0.01 Below RL 6.3 0.01 36.1 S 0.05 63.9 92.2 Zn 0.01 Below RL 7.8 0.1 97.9 NH4-N 0.5 2.1 87.5 0.01 NO2+NO3 Below RL 2.1 N 1.0 0.05 Below RL 9.4

To determine the percentage of variation in hydrology and hydrochemistry explained by site and vegetation zone, a partial redundancy analysis (RDA) was applied to the same data as the PCA, using site, vegetation zone, and the combined effect of the two as constraining

variables. PCA and RDA were both conducted using the “rda” function in the “vegan” package

(Oksanen et al. 2018).

Objective 2

Linear mixed-effects models were used to examine variation in ecologically important hydrological and hydrochemical variables: water level, water level range, pH, electrical conductivity, and the concentrations of Ca, K, and P. Models were fitted using the “lme” function in the “nlme” package (Pinheiro et al. 2018). Four explanatory variables – vegetation zone, ecospatial zone, distance from bog margin, and water level – were compared for their ability to predict each hydrological and hydrochemical variable. (To investigate bog ecosystem processes, water level was evaluated as a predictor of bog hydrochemistry. However, it was also used as a dependent variable in models with other indicators, being itself an important hydrochemical variable.) First, a model including each explanatory variable was compared to a null model using AIC and p-values. An example of model comparisons is below (Table 2.3).

(See Appendix C for all model comparisons.)

Table 2.3. Example of model comparisons with vegetation zone as an indicator and pH the hydrochemical variable. Name Fixed effects Random effect Variance structure Final pH ~ vegetation zone + ordinal day site varIdent Full pH ~ vegetation zone + ordinal day site Null pH ~ ordinal day site

All models included site as a random effect, and models of dependent variables measured on multiple dates (water level, pH, and EC) included ordinal day (day of the year, ranging from 1 to

365) as a fixed effect.

If the full model outperformed the null model, it was validated through visual assessment of the normality of the residuals and heterogeneity of variance (Zuur et al. 2009). Where

heterogeneity of variance was apparent, a version of the model was run using the “varIdent” option (function “weights”’ within the “nlme” package; Pinheiro et al. 2018) to allow different variances between groups. This model was assessed quantitatively for reduction in AIC and visually for reduction in heterogeneity of variance, as recommended by Zuur et al. (2009).

Where “varIdent” improved model performance, it was included in the final model.

The performance of the final selected models (including models that did not outperform the null model) were compared using pseudo-R2 for generalized mixed-effect models calculated in the “MuMIn” package in R (Bartoń 2018). Both marginal R2 and conditional R2 were calculated. Marginal R2 denotes the proportion of variation explained by the models’ fixed effects alone: in this case, vegetation zone and date (when applicable). Conditional R2 is the proportion of variation explained by the fixed and random effects: vegetation zone, date (when applicable), and site.

For the vegetation zone and ecospatial zone models (categorical variables), pairwise comparisons between zones were made using a pairwise Tukey comparison through the

“lsmeans” package in R (Lenth 2016). Vegetation zones and ecospatial zone were arranged along the x-axis in an order reflecting their margin-to-center pattern in the field (described above) in order to facilitate visual interpretation of spatial trends.

Objective 3

The disturbed portion of Flatiron Lake Bog was compared to the rest of vegetation zones at the site using mixed effects models with vegetation zone and ecospatial zone as an explanatory variable. The model selection process was identical to Objective 2, but did not use site as a random effect. Due to the lack of a random effect, the “gls” function was used instead. Pseudo-

R2 was used to compare models, using the ‘“rsquared” function from the “piecewiseSEM”

packages in R (Lefcheck 2016). Differences between hydrological and hydrochemical data in disturbed and natural vegetation areas were assessed visually and using lsmeans comparisons using the “lsmeans” package (Lenth 2016).

Results

Comparison to other bogs

The environmental conditions observed in Ohio’s bogs appear to be within the range of basin bogs worldwide (Figure 2.6), although more studies report the environmental conditions of open Sphagnum-dominated zones than of lagg or zones dominated by woody vegetation. Slightly higher water levels were measured in Ohio’s bogs than are reported in raised bogs in British

Columbia, with lagg zones being especially highly inundated in Ohio (Howie and van Meerveld

2013). Two studies examined Sphagnum-dominated peatlands relatively close to Ohio: Vitt and

Slack (1975) described four sites in Northern Michigan, and Lynn and Karlin (1985) described seven sites in New Jersey and New York. Open mats of Ohio’s bogs tended to have higher pH and calcium and potassium concentrations than reported in most studies used for comparison; only sites in Northern Michigan tended to report higher values (Vitt and Slack 1975).

Objective 1. Inter-site variation in hydrology and hydrochemistry

The PCA of hydrological and hydrochemical measurements produced three interpretable axes which together explained 65.1% of the variance in the data (Table 2.3). The first primary

Figure 2.6. Comparison of selected environmental measures in Ohio’s bogs with conditions in sphagnum-dominated peatlands described in other studies. Boxplots and black points show values measured in this study; boxes and whiskers represent median value, first and third quartile, and 95% confidence interval of median. Colored points show values reported in other sources: [1] Lynn and Karlin (1985), [2] Vitt and Slack (1975), [3] Howie and van Meerveld, [4] Vitt and Bayley (1984) , [5] Bragazza et al. (2005), [6] Bragazza and Gerdol (2002), [7] Verhoeven et al. 1996 . All studies report means values within or between multiple sites, except Lynn and Karlin (1985) which reports min and max values at each of 7 sites.

component explained 35.1% of the variance in the data and described a complex water level-pH- alkalinity-phosphorus gradient. The second primary component explained 18.1% of the variance and described a gradient in water level range, and sulfur, aluminum, and iron concentrations. The third primary component explained 11.9% of the variance and described an EC, and iron, and potassium concentration gradient.

Table 2.4. Species scores from PCA of hydrological and hydrochemical measurements. Values greater than 0.90 are in bold. PC1 PC2 PC3 Water level 0.97 -0.19 -0.24 Water level 0.47 1.12 -0.12 range pH 1.13 -0.65 0.09 EC 0.99 0.26 0.93 Ca 1.45 0.20 -0.24 Mg 1.46 0.17 -0.19 P -0.96 0.34 0.21 K -0.29 0.51 1.20 Al -0.90 1.17 -0.19 Fe 0.00 0.93 -1.02 Mn 1.20 -0.04 -0.36 Na 1.04 0.53 0.67 S 0.50 1.29 0.08 Zn -0.36 0.11 0.07

A plot of the ordination revealed trends in the hydrological and hydrochemical data, but there was no clustering by vegetation zone or site (Fig. 2.7). Ecospatial zonation, however, showed a clear difference between the lagg zone and the mat and interior zones, where lagg scored higher along the water level-pH-alkalinity-phosphorus gradient and the water level range- sulfur-aluminum-iron gradients (Fig. 2.7D). Notably for Objective 3, Flatiron Lake Bog scored higher along the water level range-sulfur-aluminum-iron gradient than any other site.

The partial RDA showed that inter-site differences explained more variation in hydrology and hydrochemistry differences in vegetation zone (Fig. 2.8). Redundancy analysis showed that

Figure 2.7. PCA of hydrological and hydrochemical variables colored by (2.7B) vegetation zone, (2.7C) site, and (2.7D) ecospatial zone. Site name abbreviation key can be found in Table 2.1.

the combination of site and vegetation zone explained 44.5% of the variation in measured hydrological and hydrochemical data. Partial RDA revealed that site alone explained 27.2% of the variation, more than twice as much as the 11.6% explained by vegetation zone alone. The interacting effect of vegetation zone and site explained the remaining 18%.

Objective 2. Comparison of indicators of hydrological and hydrochemical variation

Comparison of model performance

The best explanatory variables overall were vegetation zone and water level, with

ecospatial zone generally having intermediate predictive ability. Distance from margin was a poor predictor of all hydrological and hydrochemical variables. Vegetation zone and ecospatial zone both outperformed null models in predicting all hydrochemical variables except potassium concentration (Appendix C). Water level outperformed the null model as a predictor of pH, EC, and calcium and phosphorus concentrations (Appendix C). Distance from margin outperformed the null model as a predictor of water level and EC (Appendix C). Marginal R2 of models with vegetation zone as an explanatory variable ranged from 0.09 to 0.47, and conditional R2 ranged from 0.31 to 0.82. Marginal R2 of ecospatial zone models ranged from 0.08 to 0.36, and conditional R2 ranged from 0.29 to 0.64 (Fig. 2.9). Vegetation zone was the best predictor of water level, water level range, and phosphorus concentrations. Water level was the best predictor of pH (R2m=0.20 and R2c=0.49), EC (R2m = 0.1, R2c = 0.4), and calcium concentrations

(R2m=0.36, R2c=0.65). R-squared values of the ecospatial zone models generally fell between that of vegetation zone and water level models, except in the case of pH, where it performed worse than the other two. Distance from margin models performed the worst in predicting all hydrological and hydrochemical variables.

Variation over time

Water level and pH significantly decreased between June and November, while EC did not show a significant relationship with ordinal day (Table D.1, Table D.4).

Vegetation zone models

Vegetation zones showed parallel trends in hydrological and hydrochemical variables which are supported by significant differences in pairwise comparisons between extremes (Fig.

2.7). Emergent vegetation had significantly higher water levels than all other vegetation zones.

Figure 2.9. Marginal (R2m) and conditional (R2c) coefficients of determination for mixed models of hydrological and hydrochemical variables (for full model selection documentation, see Appendix C). Dependent variables are shown as plot titles, and fixed effects are shown on the x axis. All models included site as a random effect, and models of dependent variables with multiple sampling dates (water level, pH, and EC) included ordinal day as a second fixed effect.

Swamp shrubs had the second-highest water level and also significantly differed from all other vegetation zones. Emergent vegetation, swamp shrub, and bog shrub zones had significantly higher water level ranges than the coniferous forest zone. Emergent vegetation had a

significantly higher pH than any other vegetation zone whilst swamp shrub pH was significantly higher than coniferous forest and bog shrub zones. Interestingly there was a significant difference in pH between the two inner-most zones with the coniferous forest having a lower pH than the Sphagnum mat. Emergent vegetation, swamp shrubs, and hardwood forest had significantly higher EC than the bog shrub zone and Sphagnum mat. Coniferous forest had significantly lower calcium concentrations than emergent vegetation. Hardwood forest and bog shrub zones had significantly higher phosphorus concentrations than the Sphagnum mat and emergent vegetation. No significant differences were found in potassium concentrations between vegetation zones (Fig. 2.10).

Ecospatial zone models

Water level and EC differed significantly between all ecospatial zones. The lagg had a significantly higher water level than the open mat and bog interior, and the open mat had a significantly higher water level than the bog interior. EC was also significantly higher in the lagg than the interior or open mat, but EC was lower in the open mat than in the interior. Lagg had significantly higher water level range and calcium concentration than either the interior or the mat. The bog interior had a significantly higher phosphorus concentration and lower pH than either lagg or mat.

Water level models

EC, pH, and calcium concentration increased with higher water level. Phosphorus concentration decreased with water level. K was not significantly related to water level (Table

D.6).

Figure 2.10. Boxplot of hydrological and hydrochemical measurements within vegetation zones across all sites. Vegetation zones are arranged along the x-axis in an order reflecting their margin-to-center spatial pattern in the field. Boxes and whiskers represent median value, first and third quartile, and 95% confidence interval of median. Values sharing a letter are not significantly different (Tukey-adjusted lsmeans comparison, p<0.05).

Distance from edge models

Water level and EC decreased with distance from margin. No other hydrological or hydrochemical variables were significantly related to water level (Table D.1, Table D.4).

Objective 3. Disturbed zone of Flatiron Lake Bog

Due to the strong variation in environmental conditions between sites, the disturbed area of Flatiron Lake Bog is best compared with undisturbed vegetation zones within the same site.

The disturbed zone has a higher water level range than any other vegetation zone, significantly so when compared all but the swamp shrub zone (Figure 2.12). The extreme change in water level between highly inundated conditions in June and dry conditions in November was apparent in the field.

Discussion

Comparison to other bogs

Overall, environmental conditions of Ohio’s bogs were within the general range of conditions reported in basin bogs worldwide (Fig. 2.6). However, open mats of Ohio’s bogs tended to have slightly higher pH and calcium and potassium concentrations than reported in most basin bogs. Deductions about degree of groundwater influence (minerotrophy) in Ohio’s bogs cannot be made based on water chemistry measurements alone, as these values also depend on bedrock geochemistry, which can vary from region to region. It is also possible that this difference is due in part to my sampling at a depth of 90 cm rather than at the top of the water

table, as groundwater influence can increase with depth in arid regions (Glaser et al 1997). This sampling decision may also have lead to a conservative estimate of hydrochemical variation in this study. Wheeler and Proctor (2000) propose a bimodal distribution in pH in peatland water chemistry, with modes falling at pH<5.0 and pH>6.0. In Ohio bogs, wooded interior appears to fall within the more acidophilic category with a mean pH of 4.5, but open mat and lagg zones have means pH of 4.9 and 5.0 respectively, and do not conform to the bimodal distribution observed in certain other regions. It is possible that degree of minerotrophy in Ohio’s bogs varies over time. In northern Minnesota bogs, hydraulic flow reversals have been observed where groundwater input increases during times of drought (Glaser et al. 1997). Ohio’s bogs, in contrast, showed water levels and pH decreasing in tandem over the course of the growing season, suggesting that drier conditions did not result in more groundwater influence. This could be due to a greater overall groundwater influence in Ohio, and a “concentrating” effect on organic acids due to evapotranspiration during drier conditions. Further research is necessary to fully understand the hydrology of these sites. Such research could be valuable for predicting the hydrological responses of northern peatlands to climate change.

Objective 1. Inter-site variation in hydrology and hydrochemistry

My results supported the hypothesis that variation in hydrology and hydrochemistry are primarily explained by site differences between Ohio’s bogs. Previous multi-site studies of temperate basin bogs also describe noticeable site-level differences (Vitt and Bayley 1984).

Despite being dominated by Sphagnum moss and maintaining relatively ombrotrophic conditions, sites within both northern Michigan (Vitt and Slack 1975) and British Columbia

(Howie and van Meerveld 2013) are separated into clusters with different environmental conditions. Vitt and Slack (1975) observed different concentric sequences of plant communities

in open mats surrounding acidic lakes (pH 4.7-6.5) and alkaline lakes (pH >7.4). Raised bogs in

British Columbia showed consistently ombrotrophic (low pH and high calcium concentration) conditions in plots close to bog centers, but diverged in more marginal plots and lagg zones, with some sites exhibiting low pH (<5.0) and calcium concentrations in margins and laggs, while others had high pH (>5.0) and calcium concentrations in margins and laggs. Since site-level differences are so important, future studies of Ohio’s bogs should determine the drivers of variation in environmental conditions between sites, such as groundwater chemistry, catchment topography, and surrounding land use. Better models of bog hydrology and hydrochemistry can help understand and mitigate the additional influence of human activity on peatlands.

Objective 2. Comparison of indicators of hydrological and hydrochemical variation

The conceptual diagram of bog ecological processes presented earlier in this chapter provides a framework for understanding the relationships between the four indicators and bog hydrology and hydrochemistry (Fig. 2.1). Although the indicators – vegetation zone, ecospatial zone, water level, and distance from edge – were used as explanatory variables predicting environmental conditions in mixed-effects models, my results do not necessarily imply a one- way causative relationship of environment on community assembly. Peatlands are ecosystems where vegetation exerts strong control over environmental conditions, the most prominent example being Sphagnum moss’s ability to influence acidity, moisture, and accumulate peat

(Clymo 1963).

Vegetation zone was the best indicator of water level, water level range, and phosphorus

(Fig. 2.9). The relationship of vegetation zone and water level is well-documented in peatlands.

Phosphorus may act as a limiting nutrient, particularly in areas with high aerial nitrogen

deposition (Wheeler and Proctor 2000). Water level models performed best in predicting pH, calcium concentrations, and EC. The conceptual diagram below (Fig. 2.13) describes a potential mechanism for this relationship. Two processes appear to be at play: the inflow of mineral-rich groundwater and surface runoff causing high water levels in the bog margin, and either groundwater influence or a dilution effect from the central lake or unconsolidated peat underlying the open mat.

Ecospatial zone was a moderately good indicator of hydrological and hydrochemical conditions across the board. The ecospatial zones described in this study – lagg, wooded interior, and open mat – may be compared to the lagg, forest rand, and open mat of raised bogs. Studies of raised bogs consistently find higher pH and cation concentrations in the lagg than the bog expanse (Howie and van Meerveld 2013, Bragazza et al. 2005). This can be explained by mineral water influence at the bog margin. However, these studies find describe clear margin-to-center gradients in water chemistry, with pH and calcium concentrations decreasing from bog margin to bog center (Howie and van Meerveld 2013). In Ohio’s bogs, in contrast, the most acidic conditions with lowest concentrations of basic cations occurs in the wooded interior rather than the central open mat. Few studies describe hydrochemical gradients in bogs with central lakes, but the studies that exist report conditions similar to Ohio’s. In northern Michigan, bogs with open water have high pH and calcium concentrations at the bog margin and by the lake edge, with the most acidic conditions occurring in the open Sphagnum mat occurring some (Vitt and

Slack 1975). However, the site described in northern Michigan featured a large open mat where acidic conditions prevailed further from the lake edge. Comparisons of basin bogs at the southernmost edge of historical glacial extent with sites in the upper Midwest may provide insight onto the influence of climate on bog function.

Figure 2.13. Conceptual diagram of the relationships between vegetation zone, nutrient concentration, and water level in Ohio’s bogs.

Figure 2. 14. Conceptual diagram of the relationships between vegetation zone, nutrient concentration, and water level in Ohio’s bogs.

Objective 3. Disturbed zone of Flatiron Lake Bog

The hydrological and hydrochemical data from Flatiron Lake Bog reveal that the greatest difference between the disturbed and undisturbed vegetation zones is water level range. In all other measures, the disturbed zone was not significantly different from any zone except the

Sphagnum mat. However, in phosphorus concentration and mean water level the disturbed zone is more comparable to hardwood forest, potentially explaining the presence of red maple in that area. The first step towards better management of the disturbed zone should be installation of a water control system that would reduce the extreme inundation of the early growing season while maintaining more saturated conditions during the rest of the summer.

Conclusions

Much of the variation in the hydrology and hydrochemistry of Ohio’s bogs is explained by site-level differences. Ecospatial zones – lagg, wooded interior, and open bog – were moderately good predictors of hydrology and hydrochemistry. Vegetation zone was found to be a strong predictor of water level, water level range, and phosphorus concentrations, while water level was most strongly related to EC, pH, and calcium concentrations. The relationship of water level to pH and alkalinity is thought to be related to strong minerotrophic water input at the bog margin, and the dilution of organic acids by lake water and unconsolidated peat in the central open mat. Future studies should investigate the drivers of site-level variability in Ohio’s bog water chemistry.

Chapter 2 References

Amon, J. P., C. A. Thompson, Q. J. Carpenter, and J. Miner. 2002. Temperate zone fens of the glaciated Midwestern USA. Wetlands 22:301–317.

Andreas, B. K., and G. R. Bryan. 1990. The vegetation of three Sphagnum-dominated basin-type bogs in northeastern Ohio. Ohio Journal of Science 90:54–66.

Andreas, B. K., and J. D. Knoop. 1992. 100 years of changes in Ohio peatlands. Ohio Journal of Science 92:130–138.

Bragazza, L., and R. Gerdol. 2002. Are nutrient availability and acidity-alkalinity gradients related in Sphagnum-dominated peatlands? Journal of Vegetation Science 13:473–482.

Bragazza, L., H. Rydin, and R. Gerdol. 2005. Multiple gradients in mire vegetation: a comparison of a Swedish and an Italian bog. Plant Ecology 177:223–236.

Bridgham, S. D., J. Pastor, J. A. Janssens, C. Chapin, and T. J. Malterer. 1996. Multiple limiting gradients in peatlands: A call for a new paradigm. Wetlands 16:45–65.

Campbell, D. R., H. C. Duthie, and B. G. Warner. 1997. Post-glacial development of a kettlehole peatland in southern Ontario. Écoscience 4:404–418.

Colwell, S. R. 2009. M.S. Thesis. Characterization of Upland/Wetland Community Types: Changes to Flatiron Lake Bog over a 24-Year Period. The Ohio State University.

Clymo, R. S. 1963. Ion Exchange in Sphagnum and its Relation to Bog Ecology. Annals of Botany 27:309–324.

Clymo, R. S. 1984. The limits to peat bog growth. Philosophical Transactions of the Royal Society of London B: Biological Sciences 303:605–654.

Everard, K. Irvine, R. J. McInnes, B. A. Middleton, A. A. van Dam, and N. C. Davidson, eds. The Wetland Book. Springer Netherlands, Dordrecht.

Glaser, P. H., D. I. Siegel, E. A. Romanowicz, and Y. P. Shen. 1997. Regional Linkages Between Raised Bogs and the Climate, Groundwater, and Landscape of North-Western Minnesota. Journal of Ecology 85:3–16.

Glover, K. C., T. V. Lowell, G. C. Wiles, D. Pair, P. Applegate, and I. Hajdas. 2011. Deglaciation, basin formation and post-glacial climate change from a regional network of sediment core sites in Ohio and eastern Indiana. Quaternary Research 76:401–410.

Guadig, G., and H. Joosten. 2006. Peat accumulation in kettle holes: bottom up or top down? Mires and Peat 1.

Howie, S. A., and I. T. Meerveld. 2011. The Essential Role of the Lagg in Raised Bog Function and Restoration: A Review. Wetlands 31:613–622.

Ireland, A. W., and R. K. Booth. 2011. Hydroclimatic variability drives episodic expansion of a floating peat mat in a North American kettlehole basin. Ecology 92:11–18.

Bartoń, K. 2018. MuMIn: Multi-Model Inference. R package version 1.42.1. URL https://CRAN.R-project.org/package=MuMIn

Kratz, T. K. 1988. A new method for estimating horizontal growth of the peat mat in basin- filling peatlands. Canadian Journal of Botany 66:826–828.

Lamentowicz, M., K. Tobolski, and E. A. D. Mitchell. 2007. Palaeoecological evidence for anthropogenic acidification of a kettlehole peatland in northern Poland. Holocene 17:1185–1196.

Lefcheck, J. S. 2016. piecewiseSEM: Piecewise structural equation modeling in R for ecology, evolution, and systematics. Methods in Ecology and Evolution. 7(5):573-579. DOI: 10.1111/2041-210X.12512

Lenth, R. V. 2016. Least-Squares Means: The R Package lsmeans. Journal of Statistical Software, 69(1), 1-33. doi:10.18637/jss.v069.i01

Lynn, L. M., and E. F. Karlin. 1985. The Vegetation of the Low-Shrub Bogs of Northern New Jersey and Adjacent New York: Ecosystems at their Southern Limit. Bulletin of the Torrey Botanical Club 112:436–444.

Mouser, P. J., W. C. Hession, D. M. Rizzo, and N. J. Gotelli. 2005. Hydrology and Geostatistics of a Vermont, USA Kettlehole Peatland. Journal of Hydrology 301:250–266.

Økland, R. H., T. Økland, and K. Rydgren. 2001. A Scandinavian perspective on ecological gradients in north-west European mires: reply to Wheeler and Proctor. Journal of Ecology 89:481–486.

Oksanen, J., F. G. Blanchet, M. Friendly, R. Kindt, P. Legendre, D. McGlinn, P. R. Minchin, R. B. O'Hara, G. L. Simpson, P. Solymos, M. Henry, H. Stevens, E. Szoecs and H. Wagner. 2018. vegan: Community Ecology Package. R package version 2.5-2. URL https://CRAN.R- project.org/package=vegan

Pinheiro, J., D. Bates, S. DebRoy, D. Sarkar, R Core Team (2018). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-137. URL https://CRAN.R- project.org/package=nlme>.

R Core Team. 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Damman, A. W. H. 1995. Major mire vegetation units in relation to the concepts of ombrotrophy and minerotrophy: a world-wide perspective. Regional variation and conservation of mire ecosystems. Summary of papers. Presented at the 6th field symposium of the International Mire Conservation Group, University of Trondheim, Trondheim.

Rubec, C. 1997. The Canadian Wetland Classification System. Pages 1–5 in C. M. Finlayson, M.

Swinehart, A. L., and G. R. Parker. 2000. Palaeoecology and Development of Peatlands in Indiana. The American Midland Naturalist 143:267–297.

U.S. Environmental Protection Agency. 1993. Method 353.2: Determination of nitrate-nitrite nitrogen by automated colorimetry. Cincinatti, Ohio.

U.S. Environmental Protection Agency. 1993. Method 350.1: Determination of ammonia nitrogen by semi-automated colorimetry. Cincinatti, Ohio.

U.S. Environmental Protection Agency. 2018. Method 6010d: Inductively coupled plasma— optical emission spectrometry. Cincinatti, Ohio.

Verhoeven, J. T. A., A. Keuter, R. Van Logtestijn, M. B. van Kerkhoven, and M. Wassen. 1996. Control of Local Nutrient Dynamics in Mires by Regional and Climatic Factors: A Comparison of Dutch and Polish Sites. Journal of Ecology 84:647–656.

Vitt, D. H., and S. Bayley. 1984. The vegetation and water chemistry of four oligotrophic basin mires in northwestern Ontario. Canadian Journal of Botany 62:1485–1500.

Vitt, D. H., and N. G. Slack. 1975. An analysis of the vegetation of Sphagnum-dominated kettlehole bogs in relation to environmental gradients. Canadian Journal of Botany 53:332–359.

Warner, B. G. 1993. Palaeoecology of floating bogs and landscape change in the Great Lakes drainage basin of North America. Pages 237–245 in Climate Change and Human Impact on the Landscape. Springer, Dordrecht.

Warner, B. G., H. J. Kubiw, and P. F. Karrow. 1991. Origin of a postglacial kettle-fill sequence near Georgetown, Ontario. Canadian Journal of Earth Sciences 28:1965–1974.

Wheeler, B. D., and M. C. F. Proctor. 2000. Ecological gradients, subdivisions and terminology of north-west European mires. Journal of Ecology 88:187–203.

Wilcox, D. A., and H. A. Simonin. 1988. The Stratigraphy and Development of a Floating Peatland, Pinhook Bog, Indiana. Wetlands 8.

Zuur, A. F., ed. 2009. Mixed effects models and extensions in ecology with R. Statistics for biology and health. Springer, New York, NY.

Complete References