IMPACTS OF NUTRIENT LOADS ON THE INVASION POTENTIAL OF BUTOMUS UMBELLATUS L. ON OTTAWA NATIONAL WILDLIFE REFUGE DIKED WETLANDS

Erica L. Forstater

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

August 2020

Committee:

Helen Michaels, Advisor

Andrew Gregory

Kevin McCluney

Angélica Vázquez-Ortega © 2020

Erica Forstater

All Rights Reserved iii ABSTRACT

Helen Michaels, Advisor

Introduced to the Great Lakes Region from Europe before 1900, invasive Butomus

umbellatus (Flowering rush) forms monotypic stands that crowd native species and cover open

water systems across Great Lakes shorelines and reservoirs in the northern US. Factors

contributing to invasion persistence and impacts on ecosystem function by this species are poorly

understood. This study characterizes vegetation and environmental factors at the Ottawa National

Wildlife Refuge, which borders Lake Erie, to understand how sediment nutrient levels in

watersheds affect B. umbellatus invasion. We hypothesized that increased sediment nutrient

levels are important drivers of B. umbellatus invasion success. Sediment nutrient levels, matter,

water depth, and vegetation were sampled within 1m2 plots throughout the management units of

the marsh complex. Vegetation of B. umbellatus and 18 other species present were harvested or

canopy characteristics measured to estimate biomass.

B. umbellatus was the most abundant of all identified emergent invasive species found,

occurring at 55 % of the surveyed plots. B. umbellatus rhizome bud count averaged 509 per plot,

with a range of 0 – 2760 buds. While sediment nutrient analysis of nitrogen and phosphorus

showed heterogeneity within and across management units, nutrient levels did not predict B.

umbellatus abundance. However, B. umbellatus biomass decreased with increasing community

biomass. Vegetative propagule production via rhizome buds decreased with increased nutrients

and increased community biomass. B. umbellatus was found to have a wide range of nitrogen

and phosphorus in leaf tissue, and 2 – 4 times more average phosphorus than all analyzed native

species. This data will assist managers in identifying timing and approaches for controlling this

invasive species and restoring wetland biodiversity. iv ACKNOWLEDGMENTS

Thank you to my advisor, Dr. Helen Michaels, and my committee members, Dr. Kevin

McCluney, Dr. Andrew Gregory, and Dr. Angélica Vázquez-Ortega for their patience and commitment to guiding me through my degree. Thank you for encouraging me to continue my education and research. I would also like to thank my lab mates, Haley Meek, Rachel Wilson, and Meigan Day, for their assistance in my fieldwork and their company through our graduate careers. Thank you for all of your help at the refuge and in the lab, and for dealing with the mud everywhere. I would also like to thank Andie Fisher, Mary Jane Walther, and the General Botany undergraduates who assisted me in the field and in counting endless numbers of rhizome buds. I would likely still be counting buds if it were not for you.

Thank you as well to my family members, friends, and new coworkers for all of the support as I worked through the most difficult time of my college career. I would like to especially thank my partner, Tyler, for his unwavering support of my research and my career interests.

Finally, thank you to the Ottawa National Wildlife Refuge and Ron Huffman for allowing me to sample on refuge land. Without their investments in my project, I would not have been able to learn as much as I have in the last few years. This project was funded by through the

Ohio Lake Erie Commission (OLEC) and the Lake Erie Protection Fund (LEPF). The LEPF is supported by the citizens of Ohio through their purchase of the Lake Erie license plate. v

TABLE OF CONTENTS

Page

INTRODUCTION ...... 1

Wetlands ...... 1

Invasive Species in the Great Lakes ...... 1

Natural History of Butomus umbellatus ...... 3

Restoration Challenges ...... 4

Scientific Gaps – Butomus umbellatus ...... 6

Research Objective ...... 7

Questions ...... 7

METHODOLOGY ...... 8

Site Description ...... 8

Survey Design ...... 10

Vegetation Sampling ...... 11

Sediment Sampling ...... 13

Biomass Regression Models ...... 15

Statistical Analyses ...... 16

Question 1 ...... 16

Question 2 ...... 18

Question 3 ...... 18

RESULTS ...... 20

Site Description: Maps ...... 20

Question 1 ...... 21 vi

Community Composition and Environmental Correlates ...... 21

Effects of Landscape Variables on B. umbellatus Biomass and Vegetative Bud

Production ...... 23

Question 2 ...... 24

Spatial Autocorrelation of B. umbellatus and Landscape Variables ...... 25

Question 3 ...... 26

Aboveground Tissue Concentrations ...... 26

Effects of Landscape Variables on B. umbellatus Tissue Composition ... 27

DISCUSSION ...... 29

Effects of Landscape Variables on B.umbellatus Biomass and Bud Production .. 29

Effects of Landscape Variables on B.umbellatus Tissue Composition ...... 31

Aboveground Tissue Concentrations ...... 33

Implications for Management ...... 36

REFERENCES ...... 39

APPENDIX A: FIGURES ...... 49 APPENDIX B: TABLES ...... 72

APPENDIX C: ADDITIONAL MAPS OF SAMPLE LOCATIONS ...... 101

APPENDIX D: MODEL ANALYSES USING SEDIMENT N:P AS AN INDEPENDENT

VARIABLE ...... 109

APPENDIX E: COMPARISON OF CARBON CONCENTRATIONS IN B. UMBELLATUS

AND NATIVE PLANT SPECIES...... 127 1

INTRODUCTION

Wetlands

Since the beginning of the 20th century, 64 – 71 % of wetlands in the world have

disappeared (Davidson 2014). Today, wetlands make up approximately 6 % of the Earth's land

surface (Ramsar Convention Secretariat 2013). Despite their limited range, wetlands provide

almost 40 % of the world's renewable ecosystem services, including water filtration and nutrient

cycling (Costanza et al., 1997; Zedler and Kercher 2005). As technologies have improved and

our population continues to grow, a reduction in and degradation of wetlands has occurred,

through widespread habitat fragmentation for urban and agricultural development, alterations to

irrigation across the landscape, and the spread of invasive species (Bedford 1999; Zedler 2012;

Davidson 2014).

Invasive Species in the Great Lakes

Although an estimated $100 million is spent annually for invasive species management in

the Great Lakes region alone, invasive species are still problematic (Rosaen et al., 2012). Well-

studied invasive aquatic plant species, including Phalaris arundinacea L. (reed canary grass),

Phragmites australis (Cav.) Trin. Ex Steud. (common reed), Typha angustifolia L. (narrow-leaf

cattail), and Typha x glauca Godr. (pro sp.) (hybrid cattail) have been shown to have drastic and long-term impacts on wetland ecosystems (Green and Galatowitsch 2002; Angeloni et al., 2006;

Farrer and Goldberg 2009; Holdredge and Bertness 2011; Farrer and Goldberg 2014; Geddes et al., 2014). These impacts include alterations to community structure through the growth of invasive monocultures, and the changes in nutrients and chemical concentrations in sediment and water (Green and Galatowitsch 2002; Holdredge and Bertness 2011; Larkin et al., 2012). Such alterations result in ecosystem changes that impair the success of native species, which can then 2 lead to impacts in dependent trophic levels and the overall productivity of the ecosystem (Green and Galatowitsch 2002; Holdredge and Bertness 2011; Larkin et al., 2012). Ecosystem alterations caused by the above invasive species have been shown to lead to positive feedback loops that continue to benefit some invasive species, even after restoration is attempted

(Ehrenfeld 2003, Parkinson et al., 2010).

As nutrients, particularly nitrogen, become more available in wetlands due to agricultural runoff and other non-point source pollution, wetlands can become further imperiled by invasive species (Weiher and Keddy 1995; Zedler and Kercher 2004). Nitrogen uptake varies by species and by individual, but research has increasingly shown that invasive species can have greater nutrient uptake and subsequent increases in growth compared to native species when increased nutrients become available. This “luxury uptake” of nutrients has been demonstrated in several common Great Lakes invaders, including Phalaris arundinacea (reed canary grass), Phragmites australis (common reed), and Typha x glauca (hybrid cattail) (Green and Galatowitsch 2002;

Holdredge and Bertness 2011; Larkin et al., 2012).Lesser-studied invasive aquatic plant species such as Butomus umbellatus (Flowering rush), may invade similarly to these species, both in the

Great Lakes region and across the country. Butomus umbellatus L. (flowering rush) has been present in North America for approximately 100 years, but relatively little is known about its natural history or how to control invasions by it (Core 1941; Gunderson et al., 2016). The method by which B. umbellatus came to North America is unknown, but its presence was first recorded in the "St. Lawrence River somewhere near Montreal" in the early 1900s (Core 1941).

From there, it has spread across North America (Figure 1). No studies exist on how this invasive responds to variation in nutrient levels or that compare nutrient composition of B. umbellatus tissue and that of other invasive species or native species. 3

Natural History of Butomus umbellatus

B. umbellatus is an obligate emergent wetland species, typically found along the edges of

water bodies in depths of two meters or less, although depths of <0.6 meters appear to be optimal

(Marko et al., 2015; Gunderson et al., 2016). The highest densities of B. umbellatus biomass are

found in depths of up to 1 meter, with biomass sharply tapering off after that point (Marko et al.,

2015; Gunderson et al., 2016). It is typically no longer found as an emergent phenotype in depths

greater than around 1 meter and instead switches to a submergent phenotype in waters with low

turbidity (Madsen et al., 2012; Gunderson et al., 2016). B. umbellatus is considered a nuisance

for invasive species management when found at depths of 1 meter or less (Madsen et al., 2012).

Identified by its long green leaves with twisted tips and umbels of pink flowers, B.

umbellatus can reproduce in four ways: seeds, floral bulbils (not pictured), rhizome fragmentation, and rhizome buds (Figure 2; Hackett and Monfils 2014; Marko et al., 2015;

Gunderson et al., 2016). The rhizome buds allow rapid spread and establishment of B.

umbellatus by breaking off parental rhizomes and passively floating in the water column to new

locations (Marko et al., 2015; Gunderson et al., 2016). As the rhizome buds float, they sprout

roots and shoots, which likely facilitate rapid establishment upon hitting substrate (Marko et al.,

2015; Gunderson et al., 2016).

Once established, B. umbellatus forms monotypic stands that crowd natives and cover

previously open water systems, although it also performs well in multi-species stands (Parkinson

et al., 2010, Madsen et al., 2012). Thick stands of B. umbellatus may limit biomass of other

species (Gunderson et al., 2016). This ability to form monocultures, together with its ability as

an ecosystem engineer to colonize uninhabited areas with coarse substrates, enables it to alter

habitats through its high density and height in the water column, leading to the depletion of 4

suitable habitat for fish species that require open, unshaded water (Marko et al., 2015;

Gunderson et al., 2016). The thick stands created by B. umbellatus can also influence local

hydrology by slowing or blocking water movement (Parkinson et al., 2010; Jacobs et al., 2011).

B. umbellatus affects recreational and economical water uses, including irrigation, boating,

swimming, and fishing, leading to increases in maintenance costs (Jacobs et al., 2011). Like

other aquatic invasive plants, it can potentially decrease native plant species density or diversity

and may lead to changes in macroinvertebrate and fish diversity through altering available

habitat (Hackett and Monfils 2014). B. umbellatus also serves as a habitat for Lymnaea stagnalis

L. (great pond snail), which functions as a host during the life cycle of parasites that can cause

swimmer’s itch, posing a risk to recreational users of B. umbellatus-invaded areas (Hackett and

Monfils 2014).

Restoration Challenges

Restoration efforts typically focus on the removal of vast monocultures of invasive species, the strategy being that once removal occurs, invasive plants can no longer reproduce in that area and native species can then move in and recolonize. However, removal does not guarantee that native species will re-establish. Even after removal of invasive species occurs, human-caused alterations to the ecosystem, such as agricultural inputs, may make the ecosystem less optimal for native species. Compared to some invasive plants, some native species cannot quickly transform nutrients into new tissue growth, so they cannot spread as rapidly across the restored area, creating an opportunity for invasive species to return to the ecosystem (Ehrenfeld

2003; Chase and Knight 2006; Coetzee et al., 2007). Even biological control agents known to be effective at controlling invasive aquatic plants can be overwhelmed by the rapid growth response 5

of plant infestations to added nutrients, as reported for efforts to control water hyacinths in

eutrophic waters in South Africa and other areas (Coetzee and Hill 2011).

Decaying invasive plant tissue remaining in the ecosystem can also have adverse effects.

This can further inhibit the restoration of some native species, as seen in a field experiment

comparing the interspecific and intraspecific competition between Phragmites australis

(common reed) and Juncus gerardii (salt meadow rush) in the presence of P. australis litter

(Holdredge and Bertness 2011). The decaying aboveground tissues block light from reaching native seedlings, and as litter breaks down, lead to further increases in sediment nitrogen

(Holdredge and Bertness 2011, Larkin et al., 2012, Farrer and Goldberg 2014). Alterations to soil microbial communities can also occur due to the presence of invasive species. While invasive species removal and native species re-introduction can occur, microbial communities take much longer to recover, further impeding the long-term recovery of the ecosystem

(Ehrenfeld 2003).

Restoration can be further impaired in diked wetlands, which are prevalent around the

Great Lakes region (Monfils et al., 2014; Monfils et al., 2015). Diked wetlands are partially or entirely isolated from their natural hydrology, which affects the natural hydrologic water regime, as well as the movement of sediment and nutrients through the ecosystem (Monfils et al., 2015).

Increases in the nutrient concentration of diked wetlands appear in both vegetative biomass and the seed bank (Herrick and Wolf 2005). Diked wetlands have been found to have significantly higher concentrations of nutrients in sediments, which has been shown to increase the presence of some invasive plant species (Herrick and Wolf 2005). 6

Scientific Gaps – Butomus umbellatus

There is a knowledge gap in whether increased nutrient inputs can aid B. umbellatus invasions, and in how vital the production of vegetative nodules is for the invasion success of B. umbellatus. Prior soil nutrient analysis between sites dominated by a B. umbellatus monoculture and sites of moderate or no invasion has shown that monoculture sites have elevated phosphorus and nitrogen in the soil, indicating higher nutrient levels in these areas (Dietz 2015). This study also showed that the presence of rhizome buds reduces the growth of native seedlings, and reduces the rate of B. umbellatus litter decomposition, indicating the potential for B. umbellatus spread to impact the presence and growth of native species further if restoration practices fail to be implemented (Dietz 2015).

Recent invasive plant species research shows that increased nutrient inputs can aid the competitive success of some invasive species. Green and Galatowitsch (2002) demonstrated that

Phalaris arundinacea (reed canary grass) suppressed the growth of native species with increasing nutrient levels. Farrer and Goldberg (2009, 2014) and Larkin et al. (2012) showed similar results for Typha x glauca (hybrid cattail) in mesocosm and field experiments, with cattail performing better in increased nutrient environments. These studies also demonstrated that both live Typha x glauca and Typha x glauca litter increase inorganic sediment nitrogen and lower light levels (Farrer and Goldberg 2009; Larkin et al., 2012; Farrer and Goldberg 2014).

In sum, mesocosm and field experiments on the above invasive aquatic plant species show that as nutrient inputs to the environment increase, the biomass and nitrogen appropriation by these species increase as well, such that their ability to invade increases proportionately with nutrient increases in the environment (Ehrenfeld 2003). There are no comparable data on whether B. umbellatus performs similarly to these more well-studied invasive plant species; 7 however, the results from the above studies are potentially relevant to a range of wetland invasive aquatic plant species, including B. umbellatus (Farrer and Goldberg 2014).

Research Objective

The overarching objective of this project was to understand how altered nitrogen and phosphorus levels in the landscape affect the success of the invasive aquatic plant Butomus umbellatus. The central question explored here is whether sediment nitrogen and phosphorus levels are essential drivers of the invasion success of B. umbellatus, i.e., that nutrients are as vital in this system as they are in other invasive plant systems. If this proves true, we may observe an increase in the vegetation density and rhizome bud production of B. umbellatus following increases in nutrient levels in sediment, along with corresponding declines in native vegetation.

To answer this central question, we conducted an observational field study, through which we asked the following questions:

Questions 1. What are the relationships between environmental factors (water depth, sediment nutrient

levels), vegetation composition, and Butomus umbellatus biomass, density, and rhizome bud

production?

2. Are there sediment nutrient hotspots across the landscape? If so, is there a relationship

between Butomus umbellatus biomass and density and rhizome bud abundance hotspots and

sediment nutrient hotspots?

3. Does Butomus umbellatus have more nitrogen or phosphorus in its tissues compared to native

species or a higher N:P ratio compared to these same species, and do these nutrients or ratios

correlate with sediment nutrient content? 8

METHODOLOGY

Site Description

We conducted this study at the Ottawa National Wildlife Refuge (ONWR), located in

Oak Harbor, Ohio, along the southern edge of western Lake Erie. The Refuge manages and

protects approximately 6,500 acres of diked wetland, which includes restored wetlands, forests,

and grasslands (ONWR 2014). Initially, the forested wetlands and marshes constituting ONWR

were part of the Great Black Swamp but were drained and modified for agriculture, woodlots,

and hunting grounds (Mitsch 2017). In 1961, the Ottawa NWR was designated, with land

acquired and purchased from these prior land uses (Heitmeyer et al., 2016). Lake levels influence

the water levels in diked wetlands but are also monitored and manipulated by the Refuge staff for

waterfowl habitat, conservation, hunting, and fishing, particularly in the early spring and summer

during major bird migrations (ONWR 2014).

The Ottawa NWR is composed of semi-autonomous management units with dikes between units to separate the water in each pool. Ditches line one or more sides of each unit, to store water for hydrological management activities. A pump system controls the water level in each management unit; however, water seepage can occur across the dikes and ditches in periods of high flow. Wildlife and plants can cross the dikes between units, so the units are not entirely isolated from each other. The units surveyed for this study (described in more detail below) became part of the Refuge between 1966 and 2003. Multiple renovations occurred over the years, including improvements to water control systems to allow water pumping and fluctuation with lake levels; repairs and building of dikes throughout the complex; and removal of drainage tiles (Heitmeyer et al., 2016). 9

The agricultural landscape surrounding Ottawa NWR may serve as local non-point sources of nitrogen and phosphorus from fertilizer use (Civil & Environmental Consultants, Inc.

Toledo, OH 2018). These anthropogenic nutrient sources have been shown to fuel the spread of other invasive species (Farrer and Goldberg 2009, 2014), and may play a similar role for B. umbellatus, which is widespread on the Refuge. Although Refuge managers have utilized various management and removal methods, including alterations to hydrology, aerial herbicide applications, and disking of the soil beneath mature stands, these efforts have had limited success in controlling the B. umbellatus invasion.

We created maps of the Refuge and its management units using Arc GIS 10.3.1 to understand the land cover and watershed hydrology of Ottawa NWR and the surrounding landscape (Environmental Systems Research Institute, Redlands, CA). Three sub-watersheds include the surveyed Refuge management units: Cedar Creek-Frontal Lake Erie sub-watershed,

Turtle Creek-Frontal Lake Erie sub-watershed, and Crane Creek-Frontal Lake Erie sub- watershed (U.S. Geological Survey 2019). We used the World Imagery base map for the area of

Ottawa NWR to illustrate the landscape (WV02 2015 and Lucas County 3-in Imagery 2017).

Using the Arc GIS 10.3.1 Hydrology toolbox and a Digital Elevation Model (DEM) downloaded from The National Map database the flow directions of the three sub-watersheds was added to the map (Environmental Systems Research Institute, Redlands, CA; U.S. Geological Survey

2017, https://nationalmap.gov/).

We used the 2011 National Land Cover Database (NLCD) to determine the total land area and percent land cover in each sub-watershed that contains a portion of the Ottawa NWR

(Homer et al., 2015, https://nationalmap.gov/). The landscape surrounding the Ottawa NWR is predominantly agricultural, with developed urban areas throughout the three sub-watersheds. 10

Closer to the Refuge and Lake Erie, the landscape shifts to primarily woody wetlands and emergent herbaceous wetlands (Figure 4). Using the Arc GIS 10.3.1 Reclassify toolset, we reclassified the land cover of each sub-watershed into the following cover types: agriculture, developed land, wetlands, open water, and unclassified (Environmental Systems Research

Institute, Redlands, CA; Figure 5). Reclassifying sorted the land cover classifications from the

National Land Cover database into broader categories, to determine the land cover percentage of each broader category, within each sub-watershed. For example, the Wetlands category includes both woody wetlands and emergent herbaceous wetlands, and the unclassified grouping lumps all land covers that did not fall within the previously listed categories, such as forested land or barrens. We summed the total number of cells found within each sub-watershed and then divided by the total land area found within each sub-watershed to produce the percentage of each land cover type within each sub-watershed.

Survey Design

We selected Entrance Pool (EP), Pool 9 East (9E), Pool 3 (P3), MS3, MS4, MS5, and

MS7 as part of the survey for this project in August-October 2017 based on prior survey work that indicated B. umbellatus presence in these locations (Figure 3, Dietz 2015). The selected units, distributed across the range of the Refuge, represent a range of positions on the landscape concerning proximity to Lake Erie, Crane Creek, and the surrounding agricultural uplands, and were permissible for access by ONWR. We sampled plots within Crane Creek as well, which were the only samples within the survey that are in un-diked locations.

We measured the maximum length and width of each management unit using Google

Earth and used with a random number generator within Excel to obtain approximately twenty pairs of numbers within those length and width ranges for each unit. Using Google Earth, each 11

pair from this list was located and pinned to determine the corresponding latitude and longitude

points for the survey. We created an excess number of sampling locations in the event that some

locations proved inappropriate or inaccessible for the survey, such as when any fell within non-

wetland areas or were in waters deeper than one meter. We excluded depths deeper than one

meter because emergent B. umbellatus populations in depths of approximately 1 meter or less are

most relevant for management and because they were too deep for access on foot (Madsen et al.,

2019). Eleven 1 m2 plots were sampled for each management unit, except for MS5 (N = 10) and

Crane Creek (N = 6), producing eighty-two 1 m2 sample points (Figure 3).

Vegetation Sampling

We recorded the water depth and vegetation composition within a 1-m2 quadrat at each sample point, and collected voucher specimens of each species to assist in later identifications using sources including Crow and Hellquist (2000) and GoBotany

(https://gobotany.newenglandwild.org/). The Integrated Taxonomic Information System (ITIS;

https://itis.gov/) was used to confirm scientific names were valid and appropriate. Because three

types of cattails, T. latifolia, T. angustifolia, and Typha x glauca (Broad-leaf, narrow-leaf, and

hybrid, respectively), occur on the Refuge and are difficult to distinguish in the field, we scored

all cattails as Typha spp. We estimated the abundance (aboveground biomass) of linear-leaved

species (B. umbellatus (flowering rush), Typha spp. (hybrid cattail), Sparganium eurycarpum

Engelm (giant bur-reed), and Sagittaria latifolia Willd. (common arrowhead)) using field

measurements of morphological traits determined to be good predictors of dry biomass based on

the above multiple regression models.

For species without species-specific or functional group dry biomass regression equations

(such as Persicaria amphibia (L.) Gray (water smartweed) or Persicaria pensylvanica (L.) M. 12

Gomez (Pennsylvania smartweed)), all aboveground biomass was harvested, dried, and weighed.

We recorded submerged or floating plants using a presence/absence scale. At all locations with

B. umbellatus present, a destructive harvest of B. umbellatus plants within a 50 cm x 25 cm subplot of each quadrat was used to collect rhizomes and count rhizome buds. Harvested samples were temporarily stored in a refrigerator, rinsed to remove remaining sediment, and then dried in paper bags at 29 – 32 C° until reaching a constant weight.

Samples were weighed to determine the dry mass and then until processed for nutrient content stored (~4 months after collection). After cycling them through the drying cabinets again to remove any water plants could potentially reabsorb while in storage, we prepared for tissue nutrient content analysis the five most commonly encountered harvested species: Butomus umbellatus (flowering rush), Persicaria amphibia (water smartweed), Persicaria pensylvanica

(Pennsylvania smartweed), Nelumbo lutea Willd. (American lotus), and Eleocharis palustris (L.)

Roem. & Schult (common spikerush). Eleocharis palustris is a species of interest for Refuge

management, because of its importance for habitat and food for wildlife. Refuge staff consider

smartweeds, particularly P. amphibia, to be nuisance species on the Refuge; while not invasive,

smartweeds can form monocultures if left unchecked. For these reasons, an understanding of the

tissue nutrient content in these species (n = 9 – 10) compared to B. umbellatus (n = 44) was

desired.

Aboveground and rhizome tissue nutrient content of all B. umbellatus samples for which

sufficient biomass was available were analyzed (44 of 45 (98 %) samples). Before nutrient

analysis, plant tissue samples were ground and passed through a 0.5 mm sieve after initial

grinding in a coffee grinder (Black & Decker CBG100S Stainless Steel Coffee Bean Grinder,

Mr. Coffee® Blade Coffee Grinder), followed by further grinding using a mortar and pestle. 13

Brookside Laboratories analyzed samples for total phosphorus, carbon, and nitrogen (test codes:

T121, T180). Carbon and nitrogen tissue analysis followed procedures in McGeehan and Naylor

(1988), and the phosphorus tissue analysis (Method P-4.30) followed the procedure in “Soil,

Plant, and Water Reference Methods for the Western Region” (Gavlak 1994).

Sediment Sampling

We sampled the top 5 – 10 centimeters of sediment from each 1-m2 quadrat using an auger, which we inserted into a bulb planter as needed to help retain unconsolidated samples. We collected five 10 cm length cores, one from each corner and one from the center, from each quadrat, then stored in a plastic bag and placed on ice in a cooler for transport and subsequent cold storage (Crosbie and Chow-Fraser 1999; Geddes et al., 2014). Sediment was stored in a refrigerator for up to 28 days before drying for later analysis (State of Utah, 2011; Kasich et al.,

2012). Twenty-eight of the 82 (34 %) sediment samples froze during storage due to an inaccurate setting on the refrigerator.

We homogenized the refrigerated samples by hand within the storage bag, before weighing out approximately 40 – 100 g of moist sediment to the nearest 0.1 gram to spread on a paper plate and dry (State of Utah 2011; Kasich et al., 2012; Mettler Toledo PL602-S). After air- drying between fans at room temperature for 36 – 48 hours, we scraped the samples off each plate with a spatula. We then ground the sample using a mortar and pestle and passed it through a 2 mm sieve, and weighed it to the nearest 0.1 g (State of Utah 2011). The equation used to determine percent water composition was as follows: [wet weight-dry weight] / dry weight * 100

= percent water composition.

We placed soil samples that appeared oversaturated with water on a filter paper-lined funnel for 30 – 45 minutes to allow draining of water before drying. We processed fifty-six of 14

eighty-two (68 %) soil samples this way (all samples from management units MS7, P3, MS3,

MS4, MS5, and two samples from Crane Creek). We did not drain the remaining twenty-six of eighty-two (32 %) soil samples using this step (all samples Entrance Pool, 9 East, and four from

Crane Creek). Before drying, we thawed the frozen samples by placement in a second plastic bag immersed in warm water for approximately 25 minutes, then homogenized, and processed as described above. Dry samples were stored in sealed bags until ground, sieved, weighed, and sent for nutrient analysis. Brookside Laboratories (New Bremen, OH) conducted an analysis of sediment total nitrogen (TN) through combustion using the Vario EL Cube (Brookside

Laboratories test code S112; McGeehan and Naylor, 1988; Nelson and Sommers, 1996, p. 961-

1010; Bremner et al., 1996). Analysis for sediment total phosphorus (TP) followed EPA Method

3051A (ascorbic acid digestion) using the Mars 6TM Microwave Digestion System (Brookside

Laboratories test code S230).

Comparisons of nutrient content between drained and not-drained soil samples and frozen

and not-frozen soil samples were conducted using Wilcoxon Rank Sum tests, which determined

that total sediment nitrogen (mg N / g) was not significantly influenced by draining or freezing

procedures (data not shown). However, the average sediment total phosphorus (mg P / g) was

found to be significantly higher in not-drained samples (0.67 mg P / g) compared to drained

samples (0.59 mg P / g) (p < 0.001), and significantly higher in not-frozen samples (0.64 mg P /

g) compared to frozen samples (0.57 mg P / g) (p < 0.01). The frozen samples may have lost

phosphorus during the thawing or draining process, indicating that sediment total phosphorus

estimates from these samples may be underestimated. 15

Biomass Regression Models

We created multiple linear regression models in JMP14 (SAS Institute Inc., Cary, NC) to determine sets of morphological traits that would best predict plant biomass for B. umbellatus

(flowering rush) Typha x glauca (hybrid cattail), S. eurycarpum (giant bur-reed) and Sagittaria

latifolia (common arrowhead) (McJannet et al., 1995; Angeloni et al., 2006; Larkin et al., 2012;

Numes and Camargo 2017). Morphological trait sets helped in understanding B. umbellatus

allocation patterns (which could potentially help in B. umbellatus management) and reduced the

time and labor invested in harvesting, transporting, drying, and weighing biomass. In summer

2017, 15-20 B. umbellatus, S. latifolia, and (in summer 2018) S. eurycarpum plants were harvested from the Entrance Pool management unit of Ottawa NWR to measure leaf length and width to the nearest 0.1 cm, stem length, number of leaves, and number of stems (Numes and

Camargo 2017). We recorded fresh rhizome length for B. umbellatus to the nearest 0.1 cm. We bagged the individual parts of each plant, dried each sample in a drying cabinet at 29-32 C° until reaching a constant weight, and then weighed each sample to the nearest 0.1 gram on a Mettler

Toledo PL602-S.

Stepwise procedures (direction = “forward,” stopping rule = minimum AICc) determined the most parsimonious model for predicting biomass based on the AICc score and adjusted R2

value for each species (Table 1). Assumptions of normality and equal variance were assessed by

examining plots of residuals and normal quantile plots, and data transformations were used when

necessary. Because B. umbellatus and Typha have similar growth forms and are in the same functional group (guild), we used the same model used to predict B. umbellatus to predict Typha

spp. biomass (Boutin and Keddy, 1993), but we excluded rhizome data from the Typha spp.

model as we did not collect or measure rhizomes for this species. Leaf count, rhizome length, 16

leaf length, and leaf area were the variables considered most explanatory across models, with

leaf count used in all models (Table 1). We created the model for predicting S. eurycarpum biomass in summer 2018 after completion of the field survey. At this time, the most parsimonious model created for this species used leaf width and number of leaves. We did not measure leaf width in the original survey for this species, so we used leaf length and number of leaves to create the final model used for S. eurycarpum (Table 1).

Because B. umbellatus plants had not yet developed rhizome buds at the time of

collection for model development, initial B. umbellatus models could not account for rhizome bud biomass. We estimated rhizome bud biomass through subsequent field harvests in which we

selected four rhizome buds at random from a randomized sample plot from each management

unit and Crane Creek, totaling 32 buds. Buds were dried 29 – 32 C° until reaching a constant

weight, weighed to the nearest 0.001 gram, and averaged to determine the mean biomass for a rhizome bud (Mettler AE 240). We then multiplied the rhizome bud count at each sample plot by the mean biomass of a single bud to determine the estimated rhizome bud biomass at each plot and added this to the total biomass estimates of B. umbellatus.

Statistical Analyses

Question 1. What are the relationships between environmental factors (water depth,

sediment nutrient levels), vegetation composition, and Butomus umbellatus biomass, density, and

rhizome bud production?

To understand the relationships between community composition and environmental

variables, non-metric multidimensional scaling (NMDS) using the “vegan” package in R version

3.4.3 (Oksanen et al., 2018), was used to graphically display the relationships between sediment

total nitrogen, total sediment phosphorus, water depth and the integrated biomass of each species 17

using the Bray-Curtis dissimilarity matrix. A type II permutational multivariate ANOVA

(Adonis.ii) test followed the NMDS to determine the relationship between the relative biomass

of each species and each independent variable. To further illustrate the relationships shown by

the PERMANOVA testing, we created figures plotting the average relative biomass of each

species against each independent environmental variable.

A two-sample Hotelling’s T2 test (R version 3.4.3) compared the means of water depth

(cm), total sediment nitrogen (mg N / g), sediment total phosphorus (mg P / g), and community biomass (g / m2) {the sum of all biomass present at each sampling location minus B. umbellatus

biomass} between plots where B. umbellatus was present vs. absent (n = 82). Assumptions of

normality and equal variance were assessed by examining plots of residuals and normal quantile

plots, and variables were square root transformed when necessary.

Preliminary data analysis explored correlations between independent variables using non-

parametric Spearman’s rho tests. A small correlation was found between sediment total

phosphorus and water depth (r = -0.28, p = 0.01, Figure 8), however given the weakness of the

relationship both variables were still included in subsequent analyses. Stepwise regression

models, including interaction terms, were created in JMP 14 (JMP®, Version 14. SAS Institute

Inc., Cary, NC, 1989-2007) (direction = “forward,” stopping rule = minimum AICc), identified

likely variables that best explained B. umbellatus biomass (g / m2) and rhizome bud production

of plots where B. umbellatus was present (n = 45). We examined assumptions of normality and

equal variance using normal quantile plots and residual versus estimate plots and transformed

dependent variables when appropriate (B. umbellatus biomass and rhizome bud count were cube

root transformed). We created two sets of models, with one set using sediment total nitrogen and

phosphorus, and the other set using the sediment N:P ratios as predictors instead. Following 18

inspection of residual plots, transformations of predictor variables were also used to improve fit

to model. Sediment total phosphorus (mg P / g) and community biomass (g / m2) were square

root transformed, while sediment N:P ratios were log-transformed. To examine the relationships among environmental factors and the dependent variables, we used mixed effects models, including Management Unit as a random variable (JMP®, Version 14. SAS Institute Inc., Cary,

NC, 1989-2007). We selected the final model for each dependent v ariable based on delta AICc scores, and then used adjusted R2 to determine the amount of variation explained by the best

model. Assumptions of normality and equal variance were assessed by examining plots of

residuals versus estimates and normal quantile plots, and data transformations were used when

necessary.

Question 2. Are there sediment nutrient hotspots across the landscape? If so, is there a

relationship between Butomus umbellatus biomass and density and rhizome bud abundance

hotspots and sediment nutrient hotspots?

Spatial autocorrelation analyses of sediment total nitrogen (mg N / g), phosphorus (mg P

/ g), water depth (cm), B. umbellatus biomass (g / m2), and rhizome bud count were conducted using Moran’s I in ArcMap 10.6.1 (Environmental Systems Research Institute, Redlands, CA),

to determine how data was distributed (random, clustered, or dispersed). This was followed by

hotspot analysis, where appropriate, using the Getis-Ord Gi * statistic in ArcMap 10.6.1, to test

for spatial patterns of sediment total nitrogen (mg N / g) and phosphorus (mg P / g), water depth

(cm), B. umbellatus biomass (g / m2), and rhizome bud count across the landscape.

Question 3. Does Butomus umbellatus have more nitrogen or phosphorus in its tissues

compared to native species or a higher N:P ratio compared to these same species, and do these

nutrients or ratios correlate with sediment nutrient content 19

To compare the nitrogen and phosphorus tissue concentrations and N:P ratios between the aboveground biomass samples of B. umbellatus (N = 40), P. pensylvanica (N = 10), P. amphibia (N = 9), N. lutea (N = 10), and E. palustris (N = 9), a non-parametric

Wilcoxon/Kruskal Wallis Rank Sums test was used (JMP®, Version 14. SAS Institute Inc., Cary,

NC, 1989-2007). We conducted post hoc analysis using the Steel-Dwass All Pairs test to determine significant differences between pairs of species for each nutrient concentration and ratio.

Following the same procedure as the analyses for Question 1, we created stepwise regression models and mixed effects models to examine the relationships between the predictor environmental factors (sediment total phosphorus, sediment total nitrogen, community biomass, and water depth), interaction terms, and tissue nutrient concentrations (leaf tissue nitrogen, leaf tissue phosphorus, rhizome tissue nitrogen, and rhizome tissue phosphorus), with the management unit included as a random variable for each model. Sediment total phosphorus (mg

P / g) and community biomass (g / m2) were square root transformed. B. umbellatus leaf tissue nitrogen (mg N / g), leaf tissue phosphorus (mg P / g), rhizome tissue nitrogen (mg N / g) were square root transformed and rhizome tissue phosphorus (mg P / g) was log-transformed. We created two sets of models, with one set using sediment total nitrogen and phosphorus, and the other set using the sediment N:P ratios as a variable instead. The sediment N:P ratios were log- transformed. 20

RESULTS

Site Description: Maps

As stated in the site description above, the management units of Ottawa NWR are within three sub-watersheds: Cedar Creek-Frontal Lake Erie sub-watershed, Turtle Creek-Frontal Lake

Erie sub-watershed, and Crane Creek-Frontal Lake Erie sub-watershed (U.S. Geological Survey

2019). All three sub-watersheds ultimately drain into Lake Erie, and a portion of each sub- watershed passes through the Ottawa NWR system before reaching the lake, meaning all of the water that drains into Lake Erie from these sub-watersheds is potentially filtered by the wetlands

that predominantly make up Ottawa NWR (Figure 6).

The percent of land covers of each sub-watershed show that the landscape is

predominantly agricultural, with agricultural land cover ranging from 65 – 73 % (Table 2).

Developed land cover is the second most common type, at 10 – 28 % (Table 2). The sub-

watersheds contain relatively little open water, ranging from 0.5 – 3 %, which makes sense given

that the sub-watersheds are draining into Lake Erie, which is entirely open water (Table 2). The

percentage of wetlands in each sub-watershed varied, with the Cedar Creek-Frontal Lake Erie

sub-watershed having only 1.94 % wetland land cover, while the Crane Creek-Frontal Lake Erie

sub-watershed and Turtle Creek-Frontal Lake Erie sub-watershed had much more (5.77 % and

14.29 %, respectively, Table 2). The Turtle Creek-Frontal Lake Erie sub-watershed contains the

majority of the Ottawa NWR complex, which explains why it has a higher percentage of

wetlands when compared to the other sub-watersheds.

The management units themselves are largely emergent herbaceous wetlands, with

standing water across most of each unit during the growing season. Water depth varied across

each unit, with Entrance Pool (26.05 cm) among the lowest average depths and Pool 3 (63.50 21

cm) among the deepest (Table 3). A pattern in water depth emerged when sample points were

separated by B. umbellatus presence. Across all management units, sample plots with B.

umbellatus present were shallower compared to sample plots without B. umbellatus present

(Tables 4, 5). This difference in depth between plots with and without B. umbellatus varied

across management units and ranged from 4 – 26 cm. The average total sediment nitrogen varied

across management units, ranging from 238.18 mg N / g in MS4 to 331.82 mg N / g in MS3

(Table 3). The average total sediment phosphorus varied across management units as well, with a

range of 0.51 mg P / g in Pool 3 to 0.72 mg P / g in Pool 9 East (Table 3). We did not observe a

pattern in total sediment nitrogen or phosphorus in relation to B. umbellatus presence (Tables 4,

5).

Question 1

What are the relationships between environmental factors (water depth, sediment nutrient levels), vegetation composition, and Butomus umbellatus biomass, density, and rhizome bud production?

Community Composition and Environmental Correlates. The NMDS analysis of environmental variables and biomass community data (Figure 7) shows clear patterns of

association for B. umbellatus. Vector plots for sediment total phosphorus and water depth are

near opposite each other in direction, supporting earlier analyses showing an inverse correlation

between water depth and total sediment phosphorus (Figure 8). B. umbellatus (flowering rush)

biomass was most strongly associated with the sediment total phosphorus vector. Typha spp.

(cattail) abundance was also closely associated with the total sediment phosphorus vector,

although given its low occurrence in the survey (N = 5), interpretations for t his species are

limited. N. lutea (American lotus) did not fall directly along any of the vectors but was most 22

associated with sediment total phosphorus. S. eurycarpum (giant bur-reed) was also far away

from the vectors but similarly correlated with both sediment total phosphorus and nitrogen. Other natives were likewise influenced by more than one factor. S. latifolia (common arrowhead) biomass was correlated with both the total sediment phosphorus and water depth vectors, although it is plotted nearest to the depth vector, indicating a stronger influence. Biomass of E. palustris (common spikerush) and P. amphibia (water smartweed) are both associated with the water depth vector. P. amphibia was found in a wide range of depths but often found in deeper water than other species. N. odorata (water lily) is placed relatively evenly between the depth and sediment total nitrogen vectors, indicating an equal influence of both on the species.

Permutational analysis in R using the Adonis.ii test (Table 6) revealed that water depth (p

< 0.001), sediment total phosphorus (p < 0.05) and management unit (p = 0.002) best correlated with relative abundance of most species. Composition and abundance of species biomass varied with depth as some species, including B. umbellatus, were mostly found only in shallower water,

while other species, such as N. odorata (Water lily) and P. amphibia (Water smartweed), were

mostly found in deeper water depths (Figure 9).

Species relative abundance varied with sediment total phosphorus as well (Figure 10). P.

amphibia and P. pensylvanica (Pennsylvania smartweed), were only found when levels of

phosphorus were low - intermediate, between 0.4 – 0.7 mg P / g. Other species, such as N. lutea

(American Lotus) and B. umbellatus were distributed across nearly all levels of sediment total

phosphorus. Sediment total nitrogen was not significantly associated with relative species

abundance (p = 0.64, Table 6).

Average species biomass and species composition were highly variable across

management units (Figure 11). Entrance Pool had the largest number of species present (15 +), 23

while MS4 had the fewest (~ 7). Across the entire survey, B. umbellatus had an average biomass

of 570.61 g / m2, and produced an average of 508 rhizome buds, although this production was highly variable, ranging from zero to over 2,700 buds produced in one square meter (Table 7).

The abundance of B. umbellatus varied across management units, ranging from an average of

792.78 g / m2 in Entrance Pool to 211.27 g / m2 in Crane Creek (Table 8). Rhizome bud count

varied across management units as well, ranging from an average of 62 buds in Pool 3 to 924.8

buds in Entrance Pool (Table 8). Management units with more B. umbellatus biomass did not

always have large numbers of rhizome buds compared to units with lower B. umbellatus biomass

(Table 8).

The Hotelling’s T2 Test indicated that samples with and without B. umbellatus present differ for one or more of the observed independent variables (p < 0.0001). Inspections of means and standard deviations suggest that there may be some factors where differences between locations in which B. umbellatus is present and absent may influence its growth and reproduction

(Table 9). These relationships are explored in more detail in the following multiple linear regression models.

Effects of Landscape Variables on B. umbellatus Biomass and Vegetative Bud

Production. The selected model for B. umbellatus biomass indicated that sediment total phosphorus and sediment total nitrogen were not significant predictors of variation in biomass

(Table 10, Figure 12). Instead, community biomass best explained variation in B. umbellatus biomass in locations where it was present, accounting for 45 % of this variation in abundance (p

< 0.0002). B. umbellatus biomass decreased as community biomass increased (Figure 13). This decrease in B. umbellatus biomass as the abundance of other species increased was consistent across all management units. 24

In comparison, sediment total nitrogen and sediment total phosphorus were indicators of vegetative bud production of B. umbellatus (Table 11, Figure 14). The sediment nutrient levels, along with community biomass and the interaction terms between each nutrient and community biomass, explained 59 % of B. umbellatus’s vegetative bud production (Figure 13, Table 11).

Increases in almost every landscape variable was associated with reduced bud production, but the intensity of these increases varied. Sediment total phosphorus had the strongest association,

having approximately four times as much impact on bud count as total sediment nitrogen and ten

times as much cas ommunity biomass. Total sediment nitrogen was 2.5 times as influential as the community biomass. The interaction between sediment total phosphorus and the presence of

other vegetation had a negative effect, meaning that bud production was further reduced as sediment phosphorus increased when community biomass also increased. In comparison, the

interaction between sediment total nitrogen and the community biomass had a much smaller

positive effect on vegetative bud production, meaning that as nitrogen and community biomass

increased, bud production by B. umbellatus increased (Figure 13). However, the interaction

effect between community biomass and sediment total phosphorus was 16 times greater than that

of the interaction between community biomass and sediment nitrogen (Figure 13). Of all of the independent variables in the model, only community biomass was a significant m ain effect

predictor of bud count (p < 0.001).

Question 2

Are there sediment nutrient hotspots across the landscape? If so, is there a relationship

between Butomus umbellatus biomass and density and rhizome bud abundance hotspots and sediment nutrient hotspots? 25

Spatial Autocorrelation of B. umbellatus and Landscape Variables. Spatial autocorrelation (Moran’s I) tests demonstrated that B. umbellatus biomass has a clustered pattern across the landscape (Moran’s I = 0.30, p < 0.01). Vegetative bud count has a clustered pattern as well (Moran’s I = 0.68, p = 0.0). Of the landscape variables, only depth had a positive Moran’s I value, again with a clustered pattern (Moran’s I = 0.31, p < 0.001). Sediment nitrogen and phosphorus displayed a random distribution on the landscape.

The Hot Spot (Getis-Ord Gi*) analyses that followed the spatial autocorrelation tests showed similar patterns of biomass and bud count. There were large densities of B. umbellatus at points where it was present in Entrance Pool (hotspots, 99 % confidence), and lower B. umbellatus densities at points within Pool 3 and Management Unit MS5 (coldspots, 95 – 99 % confidence). This pattern indicates that there are comparatively high values of B. umbellatus biomass and bud count at Entrance Pool relative to all other points across the landscape, and comparatively low values in parts of Pool 3 and Management Unit 5 (Figures 15, 16). The Hot

Spot (Getis-Ord Gi*) for water depth showed the opposite (Figure 17). All points at Entrance

Pool were found to be depth coldspots, indicating shallower water depth compared to the rest of the landscape (95 – 99 % confidence). Parts of Management Units 4 and 5 had coldspots of low water depth, around locations where B. umbellatus was present. Water depth hotspots (indicating high water depth) were found in Pool 3 at all points in which B. umbellatus coldspots occurred, further illustrating this inverse relationship between B. umbellatus biomass and water depth (95 –

99 % confidence). 26

Question 3

Does Butomus umbellatus have more nitrogen or phosphorus in its tissues compared to native species or a higher N:P ratio compared to these same species, and do these nutrients or ratios correlate with sediment nutrient content

Aboveground Tissue Concentrations. Analysis of the percentage of nitrogen in B. umbellatus and native species using the Wilcoxon/Kruskal Wallis Rank Sums test found significant differences in nitrogen tissue concentration between species (Figure 18, Table 12, p =

0.0022). Post hoc analysis using the Steel-Dwass All Pairs test revealed that N. lutea (American lotus) had, on average, approximately twice as much nitrogen in its tissues compared to E. palustris (common spikerush) (Z = 3.14, p = 0.01) and P. pensylvanica (Pennsylvania smartweed) (Z = -2.84, p = 0.04). We found no other significant differences between species for percent nitrogen. The mean concentration of nitrogen was similar for E. palustris and both smartweed species, at approximately 1.4 %. Interestingly, B. umbellatus had the broadest range of percent nitrogen values, from 0.6 – 3.5 %, while all native species had narrower ranges

(Figure 18a).

Phosphorus concentrations in tissues of B. umbellatus and native species also proved to be significantly different (Wilcoxon/Kruskal Wallis Rank Sums test, ChiSquare = 16.75, p <

0.0001), with a higher average phosphorus concentration than all native species tested (Figure

18b, Table 12). B. umbellatus has an average phosphorus concentration triple the average of E. palustris (Z = -3.28, p = 0.009), and four times the averages of P. pensylvanica (Z = -4.23, p =

0.0002), P. amphibia (Z = -4.27, p = 0.0002), and N. lutea (Z = -4.40, p = 0.0001). Similar to nitrogen concentration, the range for phosphorus concentration for B. umbellatus was greater than for all other species tested (0.2 – 0.9 %). However, E. palustris and N. lutea often had 27 relatively low levels, as low as ~ 0.1 % (Figure 18b). None of the native species had observed phosphorus concentrations above the average concentration of B. umbellatus.

Nitrogen to phosphorus ratios of B. umbellatus and native species were similar in pattern to the individual nutrient concentrations (Figure 18c, Table 12, Wilcoxon/Kruskal Wallace Rank

Sums Test, p < 0.0001). B. umbellatus had a much lower N:P ratio than N. lutea (Z = 4.79, p <

0.0001), P. amphibia (Z = 3.91, p < 0.0001), and P. pensylvanica (Z = 3.58, p = 0.003), indicating that B. umbellatus has a higher amount of phosphorus in its tissues compared to nitrogen, which is consistent with the average concentrations of nitrogen and phosphorus in B. umbellatus tissues compared to the native species. While the range of nitrogen concentrations in

B. umbellatus included high percentages, average nitrogen levels were closer to the native species, compared to its average phosphorus concentration, which was much higher than the average concentration of the native species (Figure 18c). N. lutea had a higher N:P ratio than P. pensylvanica (Z = -3.21, p = 0.01), and P. amphibia (Z = -3.55, p = 0.004), as well as E. palustris (Z = 3.63, p = 0.002). N. lutea had an N:P ratio 2 – 3 times as great as these species, indicating a much higher level of nitrogen in its tissues compared to phosphorus. N:P ratios between B. umbellatus and E. palustris were not significantly different.

Effects of Landscape Variables on B. umbellatus Tissue Composition. The following landscape variables best-explained nitrogen concentration of B. umbellatus rhizome tissue: water depth, total sediment nitrogen (p = 0.005), total sediment phosphorus, and the interaction between water depth and total sediment phosphorus (p = 0.004) (Table 13, Figure 19). Rhizome tissue nitrogen concentrations were positively associated with each landscape variable, except total sediment phosphorus. The model explained 48 % of the variation in tissue nitrogen concentration (Figure 13). Sediment total phosphorus (p = 0.02) was found to have some 28 explanatory power for phosphorus concentrations in B. umbellatus rhizomes when management units were included in the model as fixed effects, rather than as random effects, with the overall model explaining 12 % of the variation of phosphorus in rhizome tissues (Table 14, Figures 13,

20).

Only total sediment phosphorus was an explanatory variable for both B. umbellatus leaf tissue phosphorus and leaf tissue nitrogen. Sediment total phosphorus has a strong positive effect on both tissue concentrations, although it has a slightly stronger relative effect size for the leaf nitrogen model (2.14 and 1.64, respectively). However, total sediment phosphorus was not a significant variable in the leaf nitrogen model, but it was for the leaf phosphorus model (p =

0.007). Sediment total phosphorus explained 49 % of the variation seen in the leaf tissue nitrogen model and 66% of the variation seen in the leaf tissue phosphorus model (Tables 15, 16; Figures

13, 21, 22). 29

DISCUSSION

Effects of Landscape Variables on B. umbellatus Biomass and Bud Production

The overarching goal of this study was to gain a better understanding of Butomus

umbellatus invasion ecology through observational surveys and analyses to explain the relationships between environmental factors and this aquatic invader. Prior studies have observed a link between B. umbellatus biomass and water depth, and this relationship was observed in our survey as well (Hroudova and Zakravsky 1993b; Madsen et al., 2016; Gunderson et al., 2016). In all instances where B. umbellatus occurred in our survey, water depth was 0.6 meters or less. The maximum water depth observed in this survey was just over one meter. At depths approaching and beyond one meter, B. umbellatus biomass declines until it is no longer an emergent plant, if it is present at all (Madsen et al., 2016). To receive adequate sunlight for growth, emergent plants have some or all of their biomass above water. B. umbellatus demonstrates this, as it continues to persist until it reaches a depth at which it does not break the water’s surface and begins to decline in biomass. This relationship may not hold in environments where B. umbellatus can be found as a submerged plant such as in the deep, clear waters of Flathead Lake,

Montana (Jacobs et al., 2011). B. umbellatus has not been found as a submergent plant at

OWNR, but it is likely not present given the turbidity of the water from sediments and algal growth. No relationship was found between water depth and the production of rhizome buds, contrary to Madsen et al., 2016, which reported reduced rhizome bud production with increased

water depth. Their survey occurred in lakes with deeper water than is found in the wetlands at

Ottawa NWR, so it is possible that this relationship between water depth and rhizome bud

production was undetectable here, or was not observed for some other unknown reason (Madsen

et al., 2016). 30

In comparison, lower biomass and rhizome bud density of B. umbellatus were associated

with increased community biomass, indicating that open water areas or areas more prone to

disturbances may be more vulnerable to invasion. As more vegetation establishes in a given area,

interspecific competition may increase and subsequently lead to a reduction in biomass per

species. B. umbellatus is an aggressive invader and can be found in patches of monocultures as

well as mixed in with other native species (Madsen et al., 2012; Gunderson et al., 2016). Well- established areas with one or more species seem to be more resilient to B. umbellatus invasion compared to open water areas. It is not clear if the number of species present influences the success of B. umbellatus invasion, or if the biomass of any number of species would do. Future research comparing the role of species diversity vs. amount of biomass present is needed to better understand these potential influences on invasion success of B. umbellatus. Manipulative field experiments comparing B. umbellatus invasion among various numbers and densities of species could be useful as well.

Variation in B. umbellatus vegetative biomass was not associated with variation in total sediment nitrogen and phosphorus levels. B. umbellatus may be like other invasive aquatic plant species that are able to incorporate increased nutrient levels into its tissues with increasing resource availability in the environment (Ehrenfeld 2003; Farrer and Goldberg 2014). This may mean that, while sediment total nitrogen and phosphorus levels do not predict the current biomass of B. umbellatus, they may influence other biological tissues and functions, such as allocation to seed production. Preliminary analyses of B. umbellatus allocation patterns found that allocation of biomass to flower and bud tissues increased as sediment phosphorus increased

(Michaels, pers. comm.). Further analyses of how nutrient concentrations are partitioned among 31

vegetative and reproductive tissues and sediment nutrient levels may continue to reveal such

relationships.

It is also possible that increased nitrogen and phosphorus in the environment could

positively influence community biomass, which could reduce B. umbellatus’ response to

competition as well as increase its rhizome bud production. As both sediment total phosphorus and community biomass increased, bud production decreased. In contrast, bud density increased

as sediment total nitrogen and community biomass increased, indicating that inw situations here

nitrogen is readily available and competition is high, this species prioritizes increased

reproduction over storing nutrients for further survival. In the alternative situation, perhaps rather than increase in bud production with increasingly available phosphorus, B. umbellatus changes

allocation patterns towards resource storage for the next season’s growth. Given that B.

umbellatus is a perennial species, this s trategy may ensure its survival to reproduce multiple

seasons, rather than immediately utilize available resources to produce more offspring (Childs et

al., 2010). A manipulative experiment over multiple seasons measuring nutrient accumulation in

tissues and relative allocation to rhizome bud production, seed production through flowers,

leaves, and rhizome storage would provide the data needed to examine this hypothesis.

Effects of Landscape Variables on B. umbellatus Tissue Composition

Rhizome nitrogen significantly increased with increasing sediment total nitrogen,

indicating that B. umbellatus may be able to store increasing levels of nutrients in its rhizomes with increasing nutrient levels in the environment. Similarly, rhizome phosphorus increased

significantly with total sediment phosphorus; however, the magnitude of this response was

double that of the relationship between rhizome nitrogen and total sediment nitrogen. This ability

to store more resources as t hey become available (sometimes called “luxury uptake”, Garbey et 32 al. 2004) may increase its survival during the non-growing season, through being able to grow earlier in the spring as has been demonstrated in other wetland invasive plants such as

Phragmites australis, or produce more or better quality rhizome buds (Granéli et al., 1992).

Total sediment phosphorus had a negative impact on rhizome nitrogen, which may be due to the increased rhizome growth in response to increased phosphorus. This could result in a depletion of stored rhizome nitrogen during the growing season, until the plant begins to store nutrients in rhizomes in preparation for winter. However, the interaction between water depth and total sediment phosphorus was also associated with a very strong increase in rhizome nitrogen, suggesting that in situations where increased sediment phosphorus is available, B. umbellatus is able to increase root growth, which enables it to forage further in its environment for more heterogeneously distributed nitrogen resources. This study also found a correlation between increased water depth and increased sediment total phosphorus. Perhaps more nitrogen is stored in rhizomes in plants growing in deeper water when phosphorus is readily available to support root growth to forage for nitrogen. A manipulative experiment growing B. umbellatus at varying water depths with differing phosphorus availability in the sediment may provide insight.

Total sediment phosphorus was the only landscape variable that influenced leaf nitrogen and phosphorus, and in both cases, the relationship was positive. The ability to store more phosphorus in leaf tissue with more availability may help increase its likelihood of survival by promoting earlier growth in the spring and establishment before native species can emerge from dormancy (Plank C.O. and Kissel D.E 1989). Alternatively, greater leaf production may be promoted with increasing phosphorus availability, as has been demonstrated in the wetland plant,

Littorella uniflora (Shoreweed; Christiansen et al., 1985). B. umbellatus may be able to increase rhizome and root growth with increased phosphorus in the sediment, which would increase 33

ability to forage for more limiting nutrients, which thereafter allows growth of more or larger

leaves. A manipulative experiment comparing the extent of root growth and leaf size as a

function of nutrient concentrations would be useful in exploring this idea further.

Aboveground Tissue Concentrations

B. umbellatus carbon, nitrogen, and phosphorus tissue concentrations observed in this

study were lower than those found in a prior B. umbellatus study in this lab (Dietz 2015).

However, this is likely attributable to the fact that this survey was conducted in late summer to

early fall, whereas the earlier study was conducted in late spring. As the season progresses, it is

likely that tissue concentrations decrease as tissues expand or as nutrients are reallocated towards

seed reproduction or later as storage in belowground biomass and that the late spring sampling

may have occurred before tissues had fully grown (Weber and Brandle 1994).

Invasive plant species have been shown to store more nutrients in their tissues compared

to native species, particularly nitrogen and phosphorus (Larkin et al., 2012). Phalaris

arundinacea, Phragmites australis, Typha x glauca, and Typha domingensis are well-

documented examples of invasive species capable of “luxury uptake” of nutrients (Davis, 1991;

Weiher and Keddy, 1995; Green and Galatowitsch, 2001; Lorenzen et al., 2001; Woo and

Zedler, 2002; Windham and Ehrenfeld, 2003; Kercher and Zedler, 2004; Larkin et al., 2012). B.

umbellatus occurs in the same areas of wetlands that are often also invaded by these species so

that it may also be an invader capable of such opportunistic uptake of resources.

B. umbellatus had a wide range of nitrogen and phosphorus concentrations in its leaf

tissues, wider than the ranges of all analyzed native species. However, B. umbellatus did not have a greater mean leaf tissue nitrogen than native species. N. lutea (American lotus) had the highest mean leaf tissue nitrogen of all analyzed species. The structure of N. lutea may explain 34 this. Lotuses have strong, thick petioles that support a large leaf floating on the water’s surface.

More nitrogen in its tissues may be needed to provide more support, particularly if lotuses have a high lignin content compared to other analyzed species. Lignin is slow to biodegrade, and it has been shown that increased nitrogen can increase lignin content in living tissue (Lui et al., 2016).

A comparison of lignin content across the analyzed species could support this idea.

Leaf tissue of B. umbellatus did have a much higher average phosphorus concentration than all analyzed native species, as well as the broadest range of phosphorus concentrations.

None of the native species had observed phosphorus concentrations above the average concentration of B. umbellatus. Being able to store higher amounts of phosphorus in leaf tissue compared to native plants could be key to B. umbellatus’ success as an invader (McJannet et al.,

1995; Leishman et al., 2007; Neves 2010). This theory could be tested in a manipulative field or lab experiment comparing B. umbellatus and native species.

B. umbellatus has a lower N:P ratio than analyzed native species, indicating that B. umbellatus has a much higher amount of phosphorus in its tissues compared to nitrogen.

This is consistent with the average concentrations of nitrogen and phosphorus in B. umbellatus tissues compared to the native species. The ability to intake phosphorus at a faster rate may influence invasion success, and B. umbellatus appears to fit this mold (Neves

2010; Wang et al., 2015). The agricultural environment surrounding the Ottawa NWR may provide phosphorus through runoff to the wetland, and B. umbellatus may be able to intake higher amounts of it through luxury uptake compared to native species (Garbey et al. 2004).

Effective invasion of B. umbellatus in areas of high phosphorus availability may also be explained by the likelihood that it is an N-limited species, not a P-limited species, as demonstrated by its low N:P ratio. Species with low N:P ratios are more likely to dominate in 35 environments where phosphorus is readily available and nitrogen is limited, which may be the case at Ottawa NWR (Tilman 1997, Güsewell 2004). Excess nutrient availability in the environment thus likely contributes to the invasion success of B. umbellatus, as has been demonstrated for Typha and several other aquatic invasive plant species (Green and Galatowitsch

2002; Holdredge and Bertness 2011; Larkin et al., 2012).

Furthermore, the low N:P ratio also indicates that B. umbellatus likely has a high relative growth rate compared to some native species, and is a successful invader because it is able to quickly grow “cheap” tissues that are less stress-tolerant (Güsewell 2004; Wang et al., 2015).

The low N:P also suggests an important distinction between B. umbellatus and other invasive species common at Ottawa NWR, such as Typha and Phragmites australis. These invaders are tall, structurally supported plants, who retain leaves after senescence and subsequently shade out surrounding natives. B. umbellatus leaf tissues senesce at the end of each growing season and decompose over the winter releasing any nutrients that were acquired and stored during the growing season. Therefore, B. umbellatus may be utilizing a different invasion strategy compared to Typha and P. australis. Rather than shade out competitors, B. umbellatus quickly invades and spreads over an area using its high relative growth rate, accumulating large stores of nutrients in belowground rhizomes, which allows it to release large numbers of clonal offspring

(rhizome buds) for the next growing season. Interestingly, the N:P ratio of N. lutea was the highest of all analyzed species, because of its higher average nitrogen content and lower average phosphorus content. It is possible that is related to differences in the growth forms of each species. The other analyzed species in this study did not have thick stems if they had stems at all.

Thus, N. lutea may require more nitrogen in its tissues to increase the amount of lignin available for structural support (Lui et al., 2016). 36

In conclusion, wetlands are commonly described as the “kidneys” of the landscape, given

their ability to filter the water of nutrients and pollution. As we continue to damage and remove

wetlands for urban and agricultural development, we also risk impairing the ability of wetlands

to perform their ecosystem services (Costanza et al., 1997; Zedler and Kercher 2005). This risk

is evident in the land cover maps created of the three sub-watersheds that the Ottawa National

Wildlife Refuge falls within. This section of Ohio used to be comprised of the Great Black

Swamp; now, only 2 - 15% of each sub-watershed is made of wetlands, while 65 - 73% of each sub-watershed is composed of agriculture. This increase in agriculture and decrease in wetland cover make it increasingly likely for non-point source pollution from agricultural production to occur. Non-point agricultural pollution has been demonstrated to fuel the establishment and spread of invasive plant species, such as P. arundinacea (Green and Galatowitsch 2002), and this study suggests it may do the same for B. umbellatus.

Implications for Management

This study will serve to begin filling the gaps in our knowledge of ecophysiology and invasive potential of Butomus umbellatus. Despite its presence in North America for the last 100 years, B. umbellatus is an understudied invasive species that is capable of changing environmental conditions and impact native species. Through its potential as an ecosystem engineer to colonize new habitats, crowd out native emergent aquatic species, and potentially alter nutrient cycles, B. umbellatus provides no end to reasons to study it further. This study adds to our knowledge of the responses of B. umbellatus, including its relationship with water depth and interactions with other species. Future studies that further explore the relationship between

B. umbellatus and sediment nutrient levels in controlled mesocosm experiments or in response to 37 management are essential for developing a more complete understanding of the data presented here.

At Ottawa NWR, various methods have been attempted to control B. umbellatus in

Management Unit 7 (MS7), including aerial herbicide, flooding, draining, and disking. This study suggests that one way of controlling B. umbellatus may be to find a way to reduce sediment phosphorus, to make the environment less advantageous for B. umbellatus. Planting and harvesting a species known to take in large amounts of phosphorus for a multi-year period may be successful, a method that has also been recommended by Lishawa et al. (2015) for wetlands invaded by Typha. This could be followed up with flooding management units to create water levels less preferable for emergent B. umbellatus. Water level increases are already occurring in Lake Erie, which may lead to increases in water levels in management units, and subsequently reduce available habitat for B. umbellatus (Johnston 2020).

B. umbellatus is challenging to remove because of its high invasion potential through rhizome fragments and buds. Knowing more about the conditions favoring development of these propagules and factors that increase its growth, resource acquisition, and nutrient storage provides further reasons for increased monitoring of this invader and preventive methods to discourage the establishment of this species. Removal of B. umbellatus may improve the economic value of wetlands, through the restoration of native species and nutrient cycling patterns. Through this study, we have documented the influence of landscape variables on rhizome bud density, and future management methods are needed that focus on the removal of these buds from the sediment and water column.

This study has clear environmental implications, in that it adds to our basic knowledge of an important invasive species in the Great Lakes region, and our understanding of increased 38 nutrient inputs on invasive species growth. Although additional research is needed to determine the best management methods for B. umbellatus based on this new information, this increase in knowledge will inform improvement of management plans, allowing for better control of B. umbellatus and better restoration practices as a whole. Improvements to management plans for wetland systems will provide economic benefits, as it will improve the effectiveness of the nutrient filtration ecosystem services of wetlands, and improve wildlife habitat and associated recreation activities, including hunting, fishing and boating. 39

REFERENCES

Angeloni, N.L., Jankowski, K.J., Tuchman, N.C., and Kelly, J.J. (2006). Effects of an invasive

cattail species (Typha x glauca) on sediment nitrogen and microbial community

composition in a freshwater wetland. FEMS Microbial Letters 263: 86-92

Bedford, B.L., Walbridge, M.R., and Aldous, A. (1999). Patterns in nutrient availability and

plant diversity of temperate North American wetlands. Ecology 80(7): 2151-2169

Bremner, J.M. (1996). Nitrogen-total. In: Methods of soil analysis: Part 3 Chemical

methods. (3rd Ed.) ASA and SSSA Book Series 5, Madison, WI.

Chase, J.M., and Knight, T.M. (2006). Effects of eutrophication and snails on Eurasian

watermilfoil (Myriophyllum spicatum) invasion. Biol Invasions 8: 1643

Childs, Dylan Z., Metcalf C.J.E., and Rees M. (2010). Evolutionary bet-hedging in the real

world: empirical evidence and challenges revealed by plants. Proceedings of The Royal

Society B 277: 3055-3064

Christiansen R., Friis N.J.S., and Sondergaard M. (1985). Leaf production and nitrogen and

phosphorus tissue content of Littorella uniflora (L.) Aschers. in relation to nitrogen and

phosphorus enrichment of the sediment in oligotrophic Lake Hampen, Denmark. Aquatic

Botany 23: 1-11

Coetzee, J.A., Byrne, M.J., and Hill, M.P. (2007). Impact of nutrients and herbivory by

Eccritotarsus catarinensis on the biological control of water hyacinth, Eichhornia

crassipes. Aquatic Botany 2: 179-186 40

Coetzee, J.A., and Hill, M.P. (2012). The role of eutrophication in the biological control of water

hyacinth, Eichhornia crassipes, in South Africa. Biocontrol 57: 247-261

Constanza, R., d’Arge, R., de Groot, R., Farber, S., Grasso, M., Hannon, B., Limburg, K.,

Naeem, S., O’Neill, R.V., Paruelo, J., Raskin, R.G., Sutton, P., and van den Belt, M.

(1997). The value of the world’s ecosystem services and natural capital. Nature 387: 253-

260

Core, E.L. (1941). Butomus umbellatus in America. Ohio Journal of Science 41(2):79-85

Crosbie, B., and Chow-Fraser, P. (1999). Percentage land use in the watershed determines the

water and sediment quality of 22 marshes in the Great Lakes basin. Aquatic Sciences 56:

1781-1791

Crow, G.E., and Hellquist, C.B. (2000). Aquatic and Wetland Plants of Northeastern North

America, Volume I: A Revised and Enlarged Edition of Norman C. Fassett's A Manual of

Aquatic Plants, Volume I: Pteridophytes, Gymnosperms, and Angiosperms: Dicotyledons

(Vol. 1). University of Wisconsin Press.

Crow, G.E., and Hellquist, C.B. (2000). Aquatic and Wetland Plants of Northeastern North

America, Volume II: A Revised and Enlarged Edition of Norman C. Fassett's A Manual

of Aquatic Plants, Volume II: Angiosperms: Monocotyledons (Vol. 2). University of

Wisconsin Press.

Davidson, N.C. (2014). How much wetland has the world lost? Long-term and recent trends in

global wetland area. Marine and Freshwater Research 65: 934-941 41

Dietz, A. (2015). Soil and litter legacy effects of invasive flowering rush (Butomus umbellatus)

on Lake Erie wetland restoration. Bowling Green State University Master’s Thesis

Ehrenfeld, J.G. (2003). Effects of exotic plant invasions on soil nutrient cycling processes.

Ecosystems 6:503-523

Enriquez, S., Duarte, C.M., and Sand-Jensen, K. (1993). Patterns in decomposition rates among

photosynthetic organisms: the importance of detritus C:N:P content. Oecologia 94:457-

471

Evans, J.R. (1989). Photosynthesis and nitrogen relationships in leaves of C3 plants. Oecologia

78: 9-19

EPA Method 3051A: Microwave Assisted Acid Digestion of Sediments, Sludges, Soils,

and Oils, part of Test Methods for Evaluating Solid Waste, Physical/Chemical

Methods

Farrer, E.C., and Goldberg, D.E. (2009). Litter drives ecosystem and plant community changes

in cattail invasion. Ecological Applications (19) 2:398-412

Farrer, E.C., and Goldberg, D.E. (2014). Mechanisms and reversibility of the effects of hybrid

cattail on a Great Lakes marsh. Aquatic Botany 116: 35-43

Field, C. and Mooney, H.A. (1986). Photosynthesis-nitrogen relationship in wild plants.

Cambridge University Press 22-55

Garbey, C., Murphy K.J., Thiebaut, G., and Muller, S. (2004). Variation in P-content in aquatic

plant tissues offers an efficient tool for determining plant growth strategies along a

resource gradient. Freshwater Biology 49: 346-356 42

Geddes, P., Grancharova, T., Kelly, J.J., Treering, D., and Tuchman, N.C. (2014). Effects of

invasive Typha x glauca on wetland nutrient pools, denitrification, and bacterial

communities are influenced by time since invasion. Aquatic Ecology 48: 247-258

Green, E.K., and Galatowitsch, S.M. (2002). Effects of Phalaris arundinacea and nitrate-N

addition on the establishment of wetland plant communities. Applied Ecology 39:134-144

Graneli, W., Weisner S.E.B., and Sytsma M.D. (1992). Rhizome dynamics and storage in

Phragmites australis. Wetlands Ecology and Management 4: 239-247

Gunderson, M.D., Kapuscinski, K.L., Crane, D.P., and Farrell, J.M. (2016). Habitats colonized

by non-native flowering rush Butomus umbellatus (Linnaeus, 1753) in the Niagara River,

USA. Aquatic Invasions 11(4):369-380

Güsewell, S. (2004). N:P ratios in terrestrial plants: variation and functional significance. New

Phytologist 164: 243-266

Hacket, R.A., and Monfils, A.K. (2014). Status and strategy for flowering rush (Butomus

umbellatus L.) management. Michigan Department of Environmental Quality, Lansing,

Michigan

Heitmeyer, M.E., Aloia, C.M., Eash, J.D., and Gerlach, M.S. (2016). Hydrogeomorphic

evaluation of ecosystem restoration and management options for Ottawa National

Wildlife Refuge Complex. Prepared for U. S. Fish and Wildlife Service, Region 3. Report

No. 16-02. Blue Heron Conservation Design and Printing LLC, Bloomfield, MO.

Herrick, B.M., and Wolf, A.T. (2005). Invasive plant species in diked vs. undiked Great Lakes

wetlands. Great Lakes Restoration 31:277-287 43

Holdredge, C., and Bertness, M.D. (2011). Litter legacy increases the competitive advantage of

invasive Phragmites australis in New England wetlands. Biological Invasions 13:423-

433

Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D.,

Wickham, J.D., and Megown, K., 2015, Completion of the 2011 National Land Cover

Database for the conterminous United States-Representing a decade of land cover change

information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354

Hroudova, Z., and Zakravsky, P. (1993). Ecology of two cytotypes of Butomus umbellatus II.

Reproduction, growth, and biomass production. Folia Geobotanica et. Phytotaxonomica

28(4):413-424

Jacobs, J., Mangold, J., Parkinson, H., Dupuis, V., and Rice, P. (2011). Ecology and

management of flowering rush (Butomus umbellatus L.). Invasive Species 33:1-9

JMP®, Version 14. SAS Institute Inc., Cary, NC, 1989-2007

Johnston, L. (2020). “Lake Erie water level is already beating March record, set in 1986.”

Cleveland, Ohio. https://www.cleveland.com/news/2020/03/lake-erie-water-level-is-

already-beating-march-record-set-in-1986.html

Kao, J.T., Titus, J.E., and Zhu, W.X. (2003). Differential nitrogen and phosphorus retention by

five wetland plant species. Wetlands 23(4):979-987Kasich, J., Taylor, M., Nally, S.

(2012). Sediment sampling guide and methodologies. Ohio Environmental Protection

Agency Division of Surface Water 44

Kinsman-Costello L.E., Hamilton S.K., O’Brien J.M., and Lennon J.T. (2016). Phosphorus

release from the drying and reflooding of diverse shallow sediments. Biogeochemistry

130: 159-176

Larkin, D.J., Lishawa, S.C., and Tuchman, N.C. (2012). Appropriation of nitrogen by the

invasive cattail Typha x glauca. Aquatic Botany 100: 62-66

Leishman M.R., Haslehurst T., Ares A., and Baruch Z. (2007). Leaf trait relationships of native

and invasive plants: community- and global-scale comparisons. New Phytologist 176:

635-643

Lishawa, S.C., Lawrence, B.A., Albert, D.A., and Tuchman, N.C. (2015). Biomass harvest of

invasive Typha promotes diversity in a Great Lakes coastal wetland. Restoration Ecology

23(3): 228-237

Liu J., Wu N., Wang H., Sun J., Peng B., Jiang P., and Bai E. (2016). Nitrogen addition affects

chemical composition of plant tissues, litter and soil organic matter. Ecology 97(7): 1796-

1806

Madsen, J.D., Wersal, R.M., Marko, M.D., and Skogerboe, J.G. (2012). Ecology and

management of flowering rush (Butomus umbellatus) in the Detroit Lakes, Minnesota.

Geosystems Research Institute Report 5054

Madsen, J.D., Wersal, R.M., and Marko, M.D. (2016). Distribution and biomass allocation in

relation to depth of flowering rush (Butomus umbellatus) in the Detroit Lakes, Minnesota.

Invasive Plant Science and Management 9:161-170 45

Marko, M.D., Madsen, J.D., Smith, R.A., and Olson, C.L. (2015). Ecology and phenology of

flowering rush in the Detroit Lakes chain of Lakes, Minnesota. Aquatic Plant

Management 53: 54-63

McGeehan, S.L., and Naylor, D.V., 1988. Automated instrumental analysis of carbon and

nitrogen in plant and soil samples. Commun. Soil Sci. Plant Anal. 19:493-505.

McJannet, C.L., Keddy, P.A., and Pick, F.R. (1995). Nitrogen and phosphorus tissue

concentrations in 41 wetland plants: a comparison across habitats and functional groups.

Functional Ecology 9:231-238

Minerals (P, K, Ca, Mg, Na, Al, B, Zn, Mn, Cu, Mo, S, Fe): Nitric Acid and Hydrogen Peroxide

are used in a closed Teflon vessel and digested in a CEM Microwave and analyzed on a

Thermo 6500 Duo ICP. Method P-4.30 from the “Soil, Plant, and Water Reference

Methods for the Western Region”

Monfils, M.J., Brown, P.W., Hayes, D.B., Soulliere, G.J., and Kafcas, E.N. (2014). Breeding

bird use and wetland characteristics of diked and undiked coastal marshes in Michigan.

Wildlife Management 78(1): 79-92

Monfils, M.J., Brown, P.W., Hayes, D.B., and Soulliere, G.J. (2015). Post-breeding and early

migrant bird use and characteristics of diked and undiked coastal wetlands in Michigan,

USA. Waterbirds 38(4): 373-386

Nelson, D.W., and Sommers, L.E, 1996. Total carbon, organic carbon and organic matter.

p. 961-1010. 46

Neves, J.P., Simoes, M.P., Ferreira, L.F., Madeira, M., and Gazarini, L.C. (2010). Comparison of

biomass and nutrient dynamics between an invasive and native species in a

Mediterranean saltmarsh. Wetlands 30(4):817-826

Nunes, L.S.C., and Camargo, A.F.M. (2017). A simple non-destructive method for estimating

aboveground biomass of emergent aquatic macrophytes. Acra Limnologica Brasiliensia

29(e2)

Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R.,

O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., and Wagner, H.

(2018). Vegan: Community Ecology Package. R package version 2.5-2. https://CRAN.R-

project.org/package=vegan

Parkinson, H., Mangold, J., Dupuis, V., and Rice, P. (2010). Biology, ecology, and management

of flowering rush (Butomus umbellatus). Montana State University Extension 3-12

Penuelas, J., Poulter, B., Sardans, J., Ciais, P., van der Velde, M., Bopp, L., Boucher, O.,

Godderis, Y., Hinsinger, P., Llusia, J., Nardin, E., Vicca, S., Obersteiner, M., and

Janssens, I.A. (2013). Human-induced nitrogen-phosphorus imbalances alter natural and

managed ecosystems across the globe. Nature Communications 4:2934

Plank C.O., and Kissel D.E. (1989) Plant analysis handbook for Georgia. Cooperative Extension

Service, University of Georgia College of Agriculture

http://aesl.ces.uga.edu/publications/plant/default.asp

Ramsar Convention Secretariat, 2013. The Ramsar Convention Manual: a guide to the

Convention on Wetlands (Ramsar, Iran, 1971), 6th ed. Ramsar Convention Secretariat,

Gland, Switzerland. 47

Rosaen, A.L., Grover, E.A., and Spencer, C.W. (2012). The costs of aquatic invasive species to

the Great Lake states. Anderson Economic Group

RStudio Team, (2015). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA

URL http://www.rstudio.com/.

Schachtman D.P., Reid R.J., and Ayling S.M. (1998). Phosphorus uptake by plants: from soil to

cell. Plant Physiology 116: 447-453

State of Utah. (2011). Standard operating procedure for collection of sediment samples in

wetlands. GSL Impounded Wetland 2012 Monitoring Activities p1-21

Tilman, D. (1997). Mechanisms of plant competition. In: Crawley MJ, ed. Plant ecology, 2nd

edition. Oxford: Blackwell Science, 239-261

U.S. Geological Survey, 20171017, USGS NED 1/3 arc-second n42w084 1 x 1-degree ArcGrid

2017: U.S. Geological Survey.

U.S. Geological Survey (USGS), U.S. Department of Agriculture - Natural Resource

Conservation Service (NRCS), U.S. Environmental Protection Agency (EPA), and Other

Federal, State, and local partners (see dataset-specific metadata for details

ftp://rockyftp.cr.usgs.gov/ngtoc/hydro/outgoing/WBDArchivedMetadata), 20190102,

USGS Watershed Boundary Dataset (WBD) for 2-digit Hydrologic Unit - 04 (published

20190102)

Vaccaro, L.E., Bedford, B.L., and Johnston, C.A. (2009). Litter accumulation promotes

dominance of invasive species of cattails (Typha spp.) in Lake Ontario wetlands.

Wetlands 29(3):1036-1048. 48

Wang, W., Wang, C., Sardans, J., Zeng, C., Tong, C., and Penuelas, J. (2015). Plant invasive

success associated with higher N-use efficiency and stoichiometric shifts in the soil-plant

system in the Minjiang River tidal estuarine wetlands of China. Wetlands Ecological

Management 23:865-880

Weiher, E., and P.A. Keddy. (1995). The assembly of experimental wetland plant

communities. Oikos 73(3): 323–335.

Windham, L. (2001). Comparison of biomass production and decomposition between

Phragmites australis (common reed) and Spartina patens (salt hay grass) in brackish tidal

marshes of New Jersey, USA. Wetlands 21(2): 179-188

Zedler, J.B., and Kercher, S. (2004). Causes and consequences of invasive plants in wetlands:

opportunities, opportunists, and outcomes. Critical Reviews in Plant Sciences 23(5): 431-

452

Zedler, J.B., and Kercher, S. (2005). Wetlands resources: status, trends, ecosystem services, and

restorability. Annual Review of Environmental Resources 30: 39-74

Zedler, J.B., Doherty, J.M., and Miller, N.A. (2012). Shifting restoration policy to address

landscape change, novel ecosystems, and monitoring. Ecology and Society 17(4): 36 49

APPENDIX A: FIGURES

Figure 1: Flowering rush (Butomus umbellatus) distribution across continental North America as of 2018. Blue areas represent known areas of known invasion. Additional known populations are in Washington and Louisiana (Dietz 2015). Map available at https://plants.usda.gov/core/profile?symbol=BUUM. 50

Figure 2: (clockwise) Butomus umbellatus (flowering rush) monoculture (credit: Helen Michaels); the flower of B. umbellatus inflorescence (credit: Erica Forstater); B. umbellatus rhizome (credit: Helen Michaels); B. umbellatus rhizome buds (credit: Helen Michaels). 51

Figure 3: Map of Ottawa National Wildlife Refuge with sampled management units and sample points. 52

Figure 4: Map of land cover found within and surrounding the three sub-watersheds that contain Ottawa NWR. Blue outlines delineate sub-watersheds. Land uses are colored according to the National Land Cover Dataset color scheme. Data layers downloaded from The National Map database. 53

Figure 5: Map of reclassified land cover found within and surrounding the three sub-watersheds that contain Ottawa NWR. Black outlines delineate sub-watersheds. Land cover is reclassified into five categories, with colors corresponding to the National Land Cover Dataset color scheme. Data layers downloaded from The National Map database. 54

Figure 6: Map of the three sub-watersheds (blue) that contain Ottawa NWR. Surrounding sub- watersheds are displayed in gray. Arrows represent the direction of water flow based on a digital elevation dataset (DEM). Each arrow represents an area of 1500m. Data layers downloaded from The National Map database. 55

Figure 7: Non-metric multidimensional scaling ordination (PERMANOVA) of species biomass data from Ottawa NWR. Species names that are close together in ordination space are similar in response to environmental variables (sediment total phosphorus (mg P / g) = Sed_TP; sediment total nitrogen (mg N / g) = Sed_TN; water depth (cm) = Depth). Dissimilarity metric was based on Bray distances, and plot is composed of two dimensions. Fitted vector arrows represent each environmental variable, and their length is proportional to explanatory power. Data was not transformed or standardized. Stress = 0.11, stress type = 1, weak ties. B. umbellatus N = 45; S. latifolia N = 28; S. eurycarpum N = 10; Typha spp. N = 5; A. subcordatum N = 2; N. odorata N = 11; N. lutea N = 12; H. moscheutos N = 1; A. gramineum N = 4; P. cordata N = 2; P. arundinacea N = 3; E. palustris N = 9; P. pensylvanica N = 10; P. amphibia N = 9. 56

Figure 8: Correlation between sediment total phosphorus (mg P / g) and water depth (cm) (r = -0.28, p = 0.01). 57

Figure 9: Average relative proportion of biomass of each species with increasing depth with depth divided into equal bins of 10 cm to illustrate patterns better. 58

Figure 10: Average relative proportion of biomass of each species with increasing sediment total phosphorus, with phosphorus divided into equal bins of 0.1 mg P/g to illustrate patterns better. 59

Figure 11: Average relative proportion of biomass of each species per management unit. 60

Figure 12: Residual versus predicted plot and leverage plots for the B. umbellatus biomass (g/ m2) model. 61

Figure 13: Standardized regression coefficients of each independent variable’s effect on each dependent variable of B. umbellatus. Rhizome nitrogen (N) (mg / g) is modeled using water depth, total sediment nitrogen (Sediment N), sediment total phosphorus (Sediment P), and the interaction between water depth and sediment total phosphorus (Depth x Sed. P). Leaf tissue phosphorus (P) (mg / g) is modeled using water depth and total sediment phosphorus (Sediment P). Leaf tissue nitrogen (N) (mg / g) is modeled using water depth, total sediment nitrogen (Sediment N), community biomass (Other Sp. Biomass, g / m2), and the interaction between water 62 depth and community biomass (Depth x Other Sp., g / m2). Rhizome bud production is modeled using sediment total nitrogen (Sediment N), sediment total phosphorus (Sediment P), community biomass (Other Sp. Biomass, g / m2), and the interactions between sediment total nitrogen and community biomass (Sed. N x Other Sp., g / m2) and sediment total phosphorus and community biomass (Sed. P x Other Sp., g / m2). Biomass (mg / g) is modeled using water depth (cm) and community biomass (Other Sp. Biomass, g / m2). 63

Figure 14: Residual versus predicted plot and leverage plots for the B. umbellatus rhizome bud count model. 64

Figure 15: Map of Getis-Ord Gi* hot spot analysis of B. umbellatus biomass (g / m2) across Ottawa National Wildlife Refuge. Colors of points indicate whether the point is a cold spot, hot spot, or not significant, and to which confidence interval the point falls under. 65

Figure 16: Map of Getis-Ord Gi* hot spot analysis of B. umbellatus rhizome bud abundance per meter squared across Ottawa National Wildlife Refuge. Colors of points indicate whether the point is a cold spot, hot spot, or not significant, and to which confidence interval the point falls under. 66

Figure 17: Map of Getis-Ord Gi* hot spot analysis of water depth (cm) across Ottawa National Wildlife Refuge. Colors of points indicate whether the point is a cold spot, hot spot, or not significant, and to which confidence interval the point falls under. 67

a.

b.

c.

Figure 18: Box-and-whisker plots comparing the mean values of percent tissue nitrogen, percent tissue phosphorus, and N:P ratio of B. umbellatus (N = 40), E. palustris (N = 9), N. lutea (N = 10), P. amphibia (N = 9), and P. pensylvanica (N = 10). 68

Figure 19: Residual versus predicted plot and leverage plots for the rhizome nitrogen model. 69

Figure 20: Residual versus predicted plot and leverage plots for the rhizome phosphorus model. 70

Figure 21: Residual versus predicted plot and leverage plots for the leaf phosphorus model. 71

Figure 22: Residual versus predicted plot and leverage plots for the leaf nitrogen model. 72

APPENDIX B: TABLES Table 1: Candidate AICc models (∆i < 4) generated for aboveground, rhizome, and total Butomus umbellatus (N = 20); Typha x glauca (N = 20); Sparganium eurycarpum (N = 15); and Sagittaria latifolia (N = 15). H represents average stem or leaf height (cm); LC represents leaf count; LL represents average leaf length (cm); LW represents average leaf width (cm); SC - represents stem count; LA represents leaf area (cm2); and RL represents total rhizome length (cm). The final model for each species is in boldface text. The final model for B. umbellatus aboveground biomass is ((Rhizome length * 0.51) + (Leaf count * 1.31)) – 5.05. The final model for B. umbellatus rhizome biomass is ((Rhizome length * 0.52) + (Leaf count * 1.31)) – 5.05. The final model for B. umbellatus total biomass is ((Rhizome length * 2.52) + (Leaf count * 1.46)) – 11.91. The final model for Typha x glauca biomass is ((Leaf count * 1.47) + (Leaf length * 0.10)) – 14.12. The final model for S. eurycarpum is ((Leaf Count * 2.86) + (Leaf Length * 0.31)) – 41.06. The final model for S. latifolia biomass is ((Leaf count * 2.22) + (Leaf area * 0.05) + 1.66.

Model df Adjusted R2 AICc ∆AICc B. umbellatus H + LC + LL + LW + RL 5 0.77 148.11 0.00 H + LC +LL + RL 4 0.78 143.25 4.86 H + LC + RL 3 0.79 139.94 8.17 RL 1 0.72 140.81 7.30 LC + RL 2 0.77 139.02 9.10 Typha x glauca H + LC + LL + LW 4 0.57 156.66 0.00 LC + LL + LW 3 0.59 152.73 3.93 LC + LW 2 0.59 150.43 6.23 LC 1 0.57 149.44 7.22 S. eurycarpum LW + LL + LC 3 0.85 91.09 0 LW 1 0.62 99.40 -8.31 LL + LC 2 0.72 97.11 -6.02 LW + LC 2 0.84 88.5 2.59 S. latifolia H + LC + LL + LW + LA + SC 6 0.16 129.41 0.00 H + LC + LL + LW + LA 5 0.26 119.43 9.98 LC + LL + LW + LA 4 0.33 112.02 17.40 LC + LL + LA 3 0.38 106.29 23.12 LC 1 0.15 105.19 24.22 LC + LA 2 0.42 101.98 27.44 73

Table 2: Land cover reclassification for each sub-watershed. Wetlands = both woody and emergent herbaceous wetlands; developed land = all developed open space, low intensity, medium intensity, and high-intensity areas; agriculture = hay/pasture and cultivated crops areas; and open water. The unclassified category equals all land cover types that were not included in the other categories (evergreen, mixed, deciduous forest; barrens; shrub/scrub; herbaceous). Percentages were calculated by dividing the number of cells of each land cover reclassification by the total number of cells found in the sub-watershed. Reclassification categories are based on the National Land cover Database (NLCD) 2011.

Land cover Cedar Creek-Frontal Turtle Creek-Frontal Crane Creek-Frontal Lake Erie Lake Erie Lake Erie Unclassified 4.76% 4.92% 4.17% Wetlands 1.94% 14.29% 5.77% Developed Land 27.83% 10.39% 14.09% Agriculture 64.92% 68.72% 73.24% Open Water 0.55% 1.68% 2.73% 74

Table 3: Average water depth (cm), total sediment nitrogen (mg N / g), and total sediment phosphorus (mg P / g) across sampled management units (N = 82). Averages include standard error. Management Mean Water Depth Mean Sediment Nitrogen Mean Sediment Phosphorus Unit (cm) ± SE (mg N / g) ± SE (mg P / g) ± SE Entrance 26.05 ± 6.27 277.27 ± 24.24 0.64 ± 0.03 Pool Pool 9 East 41.09 ± 4.79 239.09 ± 20.91 0.72 ± 0.05 MS7 37.27 ± 4.30 265.45 ± 15.10 0.65 ± 0.07 Pool 3 63.50 ± 5.76 300.91 ± 21 0.51 ± 0.01 MS3 36.68 ± 2.78 331.82 ± 89.32 0.62 ± 0.04 MS4 44.23 ± 3.70 238.18 ± 8.72 0.60 ± 0.02 MS5 24.45 ± 5.80 269 ± 33.01 0.53 ± 0.04 Crane Creek 24.58 ± 6.44 315 ± 37.31 0.63 ± 0.03 75

Table 4: Average water depth (cm), total sediment nitrogen (mg N / g), and total sediment phosphorus (mg P / g) at sample points with B. umbellatus present across sampled management units (N = 45). Averages include standard error.

Management Mean Water Mean Sediment Nitrogen Mean Sediment Phosphorus Unit Depth (cm) ± SE (mg N / g) ± SE (mg P / g) ± SE Entrance Pool 24.3 ± 6.65 285 ± 25.4 0.66 ± 0.03 Pool 9 East 36.39 ± 4.12 246.67 ± 23.92 0.74 ± 0.05 MS7 30.88 ± 3.64 282.5 ± 14.11 0.74 ± 0.06 Pool 3 47 ± 10 295 ± 55 0.54 ± 0.03 MS3 33.67 ± 3.18 260 ± 32.15 0.59 ± 0.03 MS4 39.88 ± 3.98 228.75 ± 10.08 0.62 ± 0.03 MS5 19.17 ± 4.05 213.33 ± 80.9 0.45 ± 0.08 Crane Creek 16 ± 6 350 ± 90 0.65 ± 0.02 76

Table 5: Average water depth (cm), total sediment nitrogen (mg N / g), and total sediment phosphorus (mg P / g) at sample points without B. umbellatus present across sampled management units (N = 37). Averages include standard error.

Management Mean Water Mean Sediment Nitrogen Mean Sediment Phosphorus Unit Depth (cm) ± SE (mg N / g) ± SE (mg P / g) ± SE Entrance Pool 43.5 ± 0 200 ± 0 0.51 ± 0 Pool 9 East 62.25 ± 10.25 205 ± 45 0.60 ± 0.02 MS7 49.88 ± 5.25 220 ± 30 0.44 ± 0.19 Pool 3 67.17 ± 6.22 302.22 ± 24.25 0.51 ± 0.01 MS3 37.81 ± 3.67 358.75 ± 123.21 0.63 ± 0.06 MS4 55.83 ± 3.17 263.33 ± 3.33 0.54 ± 0.03 MS5 26.72 ± 8.19 292.86 ± 32.93 0.56 ± 0.04 Crane Creek 28.88 ± 8.91 297.5 ± 42.7 0.62 ± 0.04 77

Table 6: Permutational analysis of water depth (cm), total sediment nitrogen (mg N / g), total sediment phosphorus (mg P / g), and management unit using Adonis.ii test following non-metric multidimensional scaling ordination (PERMANOVA) of species biomass data from Ottawa NWR.

Sum of Squares Mean of Squares Df F Pr(>F) Management Unit 3.78 0.54 7 1.73 0.0034 Sediment Total Nitrogen 0.23 0.23 1 0.73 0.66 Sediment Total Phosphorus 0.68 0.68 1 2.18 0.03 Depth (cm) 1.58 1.58 1 5.07 0.0002 Residuals 21.48 0.31 69 Total 29.17 79 78

Table 7: Average and range of B. umbellatus biomass (g / m2) and rhizome bud count across all sample plots (N = 82). Averages include standard error.

Mean Biomass Range of Biomass Mean Rhizome Bud Range of Bud (g / m2) ± SE (g / m2) Count / m2 ± SE Count 570.61 ± 72.93 4.42 – 1902.20 508.09 ± 96.29 0 - 2760 79

Table 8: Average Butomus umbellatus biomass (g / m2), rhizome bud count, leaf tissue phosphorus (mg P / g), leaf tissue nitrogen (mg N / g), rhizome tissue phosphorus (mg P / g), rhizome tissue nitrogen (mg N / g) across sampled management units. Averages include standard error.

Management Mean Biomass Mean Rhizome Leaf Tissue P Leaf Tissue N Rhizome Tissue P Rhizome Tissue N Unit (g/m2) Bud Count / m2 (mg P / g) ± (mg N / g) (mg P / g) (mg N / g) Entrance 792.78 ± 154.26 924.8 ± 288.13 3.94 ± 0.52 11.48 ± 1.16 27.70 ± 2.51 1.04 ± 0.16 Pool Pool 9 East 575.41 ± 180.74 654.22 ± 285.32 3.71 ± 0.51 17.64 ± 2.43 33.00 ± 4.57 1.68 ± 0.44 MS7 581.37 ± 171.27 199.5 ± 58.44 5.22 ± 0.38 23.71 ± 2.12 32.97 ± 4.17 2.13 ± 0.22 Pool 3 392.84 ± 275.67 62.0 ± 46.0 5.30 ± 0.53 19.30 ± 0.50 31.10 ± 1.70 1.60 ± 0.01 MS3 813.75 ± 544.72 477.33 ± 285.01 8.09 ± 0.17 29.83 ± 1.78 41.83 ± 6.42 2.40 ± 0.66 MS4 333.45 ± 130.62 374.5 ± 114.59 3.78 ± 0.16 22.63 ± 2.53 30.65 ± 3.54 1.55 ± 0.27 MS5 534.33 ± 159.55 386.67 ± 151.55 6.06 ± 0.97 25.90 ± 2.65 42.80 ± 7.41 1.99 ± 1.05 Crane Creek 211.27 ± 4.20 210 ± 78 6.23 ± 0.09 20.50 ± 0.80 27.90 ± 5.40 1.69 ± 0.40 80

Table 9: Means and standard deviations of each independent variable when B. umbellatus is present or absent. Sediment total nitrogen (mg N / g), sediment total phosphorus (mg P / g), and community biomass (g / m2) were square root transformed. The table describes the means and standard deviations of each independent variable that were compared using the Hotelling’s T2 test, which indicated that there is a significant difference between independent variables when B. umbellatus is present or absent. B. umbellatus B. umbellatus T2 Df1 Df2 p-value Present Absent Independent Mean Standard Mean Standard 17.59 1 80 < 0.0001 Variable Deviation Deviation Depth (cm) 31.58 15.07 46.16 21.76 Sediment total N 1.61 0.23 1.62 0.21 (mg N / g) Sediment total P 0.81 0.09 0.75 0.07 (mg P / g) Community biomass 6.34 5.22 9.05 3.81 (g / m2) 81

Table 10: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus biomass (g / m2) and the Fixed effects (water depth (cm), sediment total nitrogen (mg N /g), sediment total phosphorus (mg P / g), community biomass (g / m2), and interaction terms) and Random effects (Management Unit). Asterisks indicate statistical significance at α = 0.05 level. The most parsimonious model was determined by the lowest AICc value and the highest adjusted R2 and is identified by the bolded dependent variable. Models within two AICc units were considered equivalent.

Dependent Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc Variable R2 Water depth (cm) -0.03 0.2 Water depth (cm) * Sediment Total -0.03 0.56 Nitrogen (mg N / g) Water depth (cm) * Sediment Total 0.1 0.86 Phosphorus (mg P / g) Water depth (cm) * Community biomass (g 0.002 0.7 / m2) Sediment Total Nitrogen -0.27 0.65 (mg N / g) Sediment Total Nitrogen (mg N / g) * Biomass 4.11 0.43 Sediment Total Phosphorus (mg P / g) 0.52 0.37 244.04 0 (g / m2) Sediment Total Nitrogen (mg N / g) * 0.13 0.27 Community biomass (g / m2) Sediment Total Phosphorus (mg P / g) 1.34 0.84 Sediment Total Phosphorus (mg P / g) * -0.41 0.75 Community biomass (g / m2) Community biomass -0.31 0.002 * (g / m2) % of Total Wald p- Random Variance value Management Unit 35.87 0.26 Adjusted Biomass Fixed Effect Estimate p-value R2 AICc ΔAICc R2 (g / m2) Water depth (cm) -0.03 0.19 0.51 0.39 240.52 3.52 82

Water depth (cm) * Sediment Total -0.02 0.53 Nitrogen (mg N / g) Water depth (cm) * Community biomass (g 0.002 0.71 / m2) Sediment Total Nitrogen -0.24 0.67 (mg N / g) Sediment Total Nitrogen (mg N / g) * Sediment Total Phosphorus 4.51 0.34 (mg P / g) Sediment Total Nitrogen 0.12 0.25 (mg N / g) * Community biomass (g / m2) Sediment Total Phosphorus (mg P / g) 1.99 0.72 Sediment Total Phosphorus (mg P / g) * -0.37 0.77 Community biomass (g / m2) Community biomass -0.31 0.002 * (g / m2) % of Total Wald p- Random Variance value Management Unit 34.55 0.26 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) -0.04 0.2 Water depth (cm) * Sediment Total -0.02 0.56 Nitrogen (mg N / g) Water depth (cm) * Community biomass (g 0.002 0.76 Biomass / m2) (g / m2) Sediment Total Nitrogen -0.22 0.69 0.51 0.4 239.04 5 (mg N / g) Sediment Total Nitrogen (mg N / g) * Sediment Total Phosphorus 4.56 0.33 (mg P / g) Sediment Total Nitrogen 0.11 0.26 (mg N / g) * Community biomass (g / m2) 83

Sediment Total Phosphorus (mg P / g) 1.7 0.76 Community biomass -0.32 0.001 * (g / m2) % of Total Wald p- Random Variance value Management Unit 35.18 0.25 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) -0.03 0.24 Water depth (cm) * Sediment Total -0.01 0.74 Nitrogen (mg N / g) Water depth (cm) * Community biomass (g 0.0005 0.93 / m2) Biomass Sediment Total Nitrogen -0.24 0.62 (g / m2) (mg N / g) 0.51 0.43 243.16 0.88 Sediment Total Nitrogen 0.09 0.34 (mg N / g) * Community biomass (g / m2) Community biomass -0.33 < 0.0001 * (g / m2) % of Total Wald p- Random Variance value Management Unit 38.54 0.22 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) -0.03 0.21 Water depth (cm) * Sediment Total -0.01 0.72 Nitrogen (mg N / g) Biomass Sediment Total Nitrogen (g / m2) -0.25 0.6 (mg N / g) 0.51 0.44 231.11 12.93 Sediment Total Nitrogen 0.09 0.34 (mg N / g) * Community biomass (g / m2) Community biomass -0.33 < 0.0001* (g / m2) 84

% of Total Wald p- Random Variance value Management Unit 38.45 0.2 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) -0.04 0.13 Sediment Total Nitrogen -0.23 0.62 (mg N / g) Biomass Sediment Total Nitrogen 0.08 0.36 (g / m2) (mg N / g) * Community biomass (g / m2) 0.5 0.45 223.08 20.96 Community biomass -0.32 < 0.0001 * (g / m2) % of Total Wald p- Random Variance value Management Unit 37.92 0.2 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) -0.04 0.11 Biomass (g / Community biomass -0.31 < 0.0001 * m2) (g / m2) 0.5 0.47 215.73 28.31 % of Total Wald p- Random Variance value Management Unit 40.52 0.2 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Community biomass Biomass -0.32 < 0.0002 * (g / m2) (g / m2) % of Total Wald p- 0.46 0.45 210.09 33.95 Random Variance value Management Unit 41.02 0.18 85

Table 11: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus rhizome vegetative bud production (g / m2) and the Fixed effects (water depth (cm), sediment total nitrogen (mg N /g), sediment total phosphorus (mg P / g), community biomass (g / m2), and interaction terms) and Random effects (Management Unit). Asterisks indicate statistical significance at α = 0.05 level. The most parsimonious model was determined by the lowest AICc value and the highest adjusted R2 and is identified by the bolded dependent variable. Models within two AICc units were considered equivalent.

Dependent Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc Variable R2 Water depth (cm) -0.007 0.85 0.64 0.53 253.42 0 Water depth (cm) * Sediment Total Nitrogen (mg -0.04 0.46 N / g) Water depth (cm) * Sediment Total Phosphorus 0.36 0.56 (mg P / g) Water depth (cm) * Community biomass (g / m2) < 0.0001 0.99 Sediment Total Nitrogen (mg N / g) -1.14 0.09 Sediment Total Nitrogen (mg N / g) * Sediment 0.23 0.97 Total Phosphorus (mg P / g) Rhizome Sediment Total Nitrogen (mg N / g) * Community Bud Count 0.16 0.22 biomass (g / m2) Sediment Total Phosphorus (mg P / g) -6.27 0.41 Sediment Total Phosphorus (mg P / g) * -1.99 0.16 Community biomass (g / m2) Community biomass -0.36 0.001 * (g / m2) % of Total Wald p- Random Variance value Management Unit 56.55 0.14 Adjusted Rhizome Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Bud Count Water depth (cm) -0.006 0.83 0.64 0.55 240.99 12.43 Water depth (cm) * Sediment Total Nitrogen (mg -0.04 0.45 N / g) 86

Water depth (cm) * Sediment Total Phosphorus 0.36 0.55 (mg P / g) Sediment Total Nitrogen (mg N / g) -1.14 0.09 Sediment Total Nitrogen (mg N / g) * Sediment 0.2 0.97 Total Phosphorus (mg P / g) Sediment Total Nitrogen (mg N / g) * Community 0.16 0.2 biomass (g / m2) Sediment Total Phosphorus (mg P / g) -6.35 0.39 Sediment Total Phosphorus (mg P / g) * -1.99 0.14 Community biomass (g / m2) Community biomass -0.36 0.001 * (g / m2) % of Total Wald p- Random Variance value Management Unit 57.14 0.14 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) -0.006 0.83 0.64 0.56 242.39 11.03 Water depth (cm) * Sediment Total Nitrogen (mg -0.04 0.44 N / g) Water depth (cm) * Sediment Total Phosphorus 0.37 0.51 (mg P / g) Sediment Total Nitrogen (mg N / g) -1.14 0.07 Rhizome Sediment Total Nitrogen (mg N / g) * Community 0.16 0.2 Bud Count biomass (g / m2) Sediment Total Phosphorus (mg P / g) -6.49 0.36 Sediment Total Phosphorus (mg P / g) * -1.98 0.13 Community biomass (g / m2) Community biomass -0.36 < 0.001 * (g / m2) % of Total Wald p- Random Variance value Management Unit 58.1 0.13 87

Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Sediment Total Nitrogen (mg N / g) -0.95 0.1 0.64 0.59 223.39 30.03 Sediment Total Nitrogen (mg N / g) * Community 0.11 0.27 biomass (g / m2) Sediment Total Phosphorus (mg P / g) -4.98 0.38 Rhizome Sediment Total Phosphorus (mg P / g) * Bud Count -1.63 0.16 Community biomass (g / m2) Community biomass -0.37 < 0.001 * (g / m2) % of Total Wald p- Random Variance value Management Unit 60.3 0.12 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Sediment Total Nitrogen (mg N / g) -1.13 0.03 * 0.6 0.57 228.04 25.38 Sediment Total Nitrogen (mg N / g) * Community 0.05 0.61 Rhizome biomass (g / m2) Bud Count Community biomass -0.37 0.0001 (g / m2) % of Total Wald p- Random Variance value Management Unit 57.08 0.12 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Sediment Total Nitrogen (mg N / g) -1.1 0.04 * 0.59 0.57 222.72 30.7 Rhizome Community biomass -0.36 0.0001 * Bud Count (g / m2) % of Total Wald p- Random Variance value Management Unit 58.22 0.12 88

Table 12: Comparison of leaf tissue nitrogen, leaf tissue phosphorus, and leaf tissue N:P of B. umbellatus (N = 40), E. palustris (N = 9), N. lutea (N = 10), P. amphibia (N = 9), and P. pensylvanica (N = 10) using the non-parametric Wilcoxon/Kruskal Wallace Rank Sums test.

Species Score Expected Score (Mean-Mean0) ChiSquare DF Prob>ChiSq Sum Score Mean / Std0 Nitrogen B. umbellatus 1778.50 1580.00 44.46 1.98 16.75 4 0.0022 E. palustris 210.00 355.50 23.33 -2.27 N. lutea 559.50 395.00 55.95 2.45 P. amphibia 234.50 355.50 26.06 -1.89 P. pensylvanica 298.50 395.00 29.85 -1.44 Phosphorus B. umbellatus 2230.50 1580.00 55.76 6.50 44.04 4 <0.0001 E. palustris 263.50 355.50 29.28 -1.43 N. lutea 203.50 395.00 20.35 -2.86 P. amphibia 141.50 355.50 15.72 -3.34 P. pensylvanica 242.00 395.00 24.20 -2.28 N:P B. umbellatus 1015.00 1580.00 25.38 -5.64 44.56 4 <0.0001 E. palustris 326.00 355.50 36.22 -0.45 N. lutea 725.00 395.00 72.50 4.93 P. amphibia 514.00 355.50 57.11 2.47 P. pensylvanica 501.00 395.00 50.10 1.58 89

Table 13: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus rhizome nitrogen (mg N / g) and the Fixed effects (water depth (cm), sediment total nitrogen (mg N /g), sediment total phosphorus (mg P / g), community biomass (g / m2), and interaction terms) and Random effects (Management Unit). Asterisks indicate statistical significance at α = 0.05 level. The most parsimonious model was determined by the lowest AICc value and the highest adjusted R2 and is identified by the bolded dependent variable. Models within two AICc units were considered equivalent.

Dependent Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc Variable R2 Water depth (cm) 0.005 0.27 Water depth (cm) * Sediment Total 0.002 0.8 Nitrogen (mg N / g) Water depth (cm) * Sediment Total 0.13 0.09 Phosphorus (mg P / g) Water depth (cm) * Community biomass (g -0.0003 0.71 / m2) Sediment Total Nitrogen (mg N / g) 0.21 0.02 * Sediment Total Nitrogen (mg N / g) * Rhizome -0.85 0.23 Sediment Total Phosphorus (mg P / g) Nitrogen 0.56 0.43 101.44 0 Sediment Total Nitrogen (mg N / g) * (mg N / g) -0.004 0.79 Community biomass (g / m2) Sediment Total Phosphorus (mg P / g) 0.98 0.26 Sediment Total Phosphorus (mg P / g) * -0.18 0.32 Community biomass (g / m2) Community biomass -0.02 0.17 (g / m2) % of Total Wald p- Random Variance value Management Unit 3.59 0.86 Water depth (cm) 0.006 0.14 Rhizome Sediment Total Nitrogen (mg N / g) 0.2 0.05 * Nitrogen 0.19 0.11 57.02 44.42 Sediment Total Phosphorus (mg P / g) 0.06 0.95 (mg N / g) Community biomass -0.04 0.13 90

(g / m2) % of Total Wald p- Random Variance value Management Unit 0 0.07 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.005 0.2 Water depth (cm) * Sediment Total 0.14 0.01 * Phosphorus (mg P / g) Water depth (cm) * Community biomass (g -0.0003 0.68 / m2) Sediment Total Nitrogen (mg N / g) 0.21 0.01* Sediment Total Nitrogen (mg N / g) * Rhizome -0.87 0.21 Sediment Total Phosphorus (mg P / g) Nitrogen Sediment Total Nitrogen (mg N / g) * (mg N / g) -0.003 0.85 0.57 0.46 88.99 12.45 Community biomass (g / m2) Sediment Total Phosphorus (mg P / g) -1 0.22 Sediment Total Phosphorus (mg P / g) * -0.19 0.27 Community biomass (g / m2) Community biomass -0.02 0.15 (g / m2) % of Total Wald p- Random Variance value Management Unit 5.43 0.78 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.005 0.19 Rhizome Water depth (cm) * Sediment Total 0.14 0.009 * Nitrogen Phosphorus (mg P / g) (mg N / g) Water depth (cm) * Community biomass (g 0.57 0.47 78.36 23.08 -0.0003 0.71 / m2) Sediment Total Nitrogen 0.21 0.009* (mg N / g) 91

Sediment Total Nitrogen (mg N / g) * Sediment Total Phosphorus -0.87 0.21 (mg P / g) Sediment Total Phosphorus (mg P / g) -1.02 0.21 Sediment Total Phosphorus (mg P / g) * -0.2 0.2 Community biomass (g / m2) Community biomass -0.02 0.14 (g / m2) % of Total Wald p- Random Variance value Management Unit 5.33 0.79 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.004 0.2 Water depth (cm) * Sediment Total 0.14 0.01 * Phosphorus (mg P / g) Sediment Total Nitrogen 0.21 0.008 * (mg N / g) Sediment Total Nitrogen Rhizome (mg N / g) * Sediment Total Phosphorus -0.82 0.23 Nitrogen (mg P / g) (mg N / g) 0.55 0.46 61.89 39.55 Sediment Total Phosphorus (mg P / g) -0.96 0.2 Sediment Total Phosphorus (mg P / g) * -0.2 0.2 Community biomass (g / m2) Community biomass -0.02 0.11 (g / m2) % of Total Wald p- Random Variance value Management Unit 1.69 0.92 Rhizome Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc Nitrogen R2 (mg N / g) Water depth (cm) 0.004 0.27 0.49 0.41 61.07 40.37 92

Water depth (cm) * Sediment Total 0.11 0.03 * Phosphorus (mg P / g) Sediment Total Nitrogen 0.23 0.007 * (mg N / g) Sediment Total Phosphorus (mg P / g) -0.69 0.37 Sediment Total Phosphorus (mg P / g) * -0.15 0.38 Community biomass (g / m2) Community biomass -0.03 0.19 (g / m2) % of Total Wald p- Random Variance value Management Unit 0 0.85 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.005 0.17 Water depth (cm) * Sediment Total 0.13 0.004 * Rhizome Phosphorus (mg P / g) Nitrogen Sediment Total Nitrogen 0.22 0.005 * (mg N / g) (mg N / g) 0.53 0.48 51.52 49.92 Sediment Total Phosphorus (mg P / g) -0.59 0.43 % of Total Wald p- Random Variance value Management Unit 19.09 0.3 93

Table 14: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus rhizome phosphorus (mg P / g) and the Fixed effects sediment total phosphorus (mg P / g) and Management Units. Asterisks indicate statistical significance at α = 0.05 level. Dependent p- Adjusted Model Prob > Fixed Effect Estimate R2 AICc Variable value R2 F Management Unit Pool 9 East -0.41 0.22 0.28 0.12 0.13 130.25 Management Unit Crane Creek -0.47 0.41 Management Unit Entrance Pool -0.58 0.06 Rhizome Management Unit MS3 0.82 0.09 Phosphorus Management Unit MS4 -0.20 0.53 (mg P / g) Management Unit MS5 1.18 0.02* Management Unit MS7 -0.45 0.24 Sediment Total Phosphorus (mg P / 2.02 0.02* g) 94

Table 15: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus leaf nitrogen (mg N / g) and the Fixed effects (water depth (cm), sediment total nitrogen (mg N /g), sediment total phosphorus (mg P / g), community biomass (g / m2), and interaction terms) and Random effects (Management Unit). Asterisks indicate statistical significance at α = 0.05 level. The most parsimonious model was determined by the lowest AICc value and the highest adjusted R2 and is identified by the bolded dependent variable. Models within two AICc units were considered equivalent. Dependent Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc Variable R2 Water depth (cm) 0.02 0.1 Water depth (cm) * Sediment Total 0.002 0.89 Nitrogen (mg N / g) Water depth (cm) * Sediment Total 0.21 0.28 Phosphorus (mg P / g) Water depth (cm) * Community biomass -0.002 0.25 (g / m2) Sediment Total Nitrogen 0.34 0.52 (mg N / g) Sediment Total Nitrogen Leaf Nitrogen (mg (mg N / g) * Sediment Total Phosphorus -1.61 0.4 0.68 0.56 152.07 0 N / g) (mg P / g) Sediment Total Nitrogen (mg N / g) * 0.03 0.43 Community biomass (g / m2) Sediment Total Phosphorus (mg P / g) -1.27 0.58 Sediment Total Phosphorus (mg P / g) * -0.4 0.33 Community biomass (g / m2) Community biomass -0.02 0.51 (g / m2) % of Total Wald p- Random Variance value Management Unit 51.89 0.22 Adjusted Leaf Nitrogen (mg Fixed Effect Estimate p-value R2 AICc ΔAICc R2 N / g) Water depth (cm) 0.01 0.1 0.57 0.52 113.93 38.14 95

Sediment Total Nitrogen 0.29 0.12 (mg N / g) Sediment Total Phosphorus (mg P / g) 0.51 0.76 Community biomass -0.03 0.21 (g / m2) % of Total Wald p- Random Variance value Management Unit 45.69 0.25 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.02 0.08 Water depth (cm) * Sediment Total 0.22 0.2 Phosphorus (mg P / g) Water depth (cm) * Community biomass -0.002 0.24 (g / m2) Sediment Total Nitrogen 0.13 0.52 (mg N / g) Sediment Total Nitrogen Leaf Nitrogen (mg (mg N / g) * Sediment Total Phosphorus -1.57 0.4 N / g) (mg P / g) 0.68 0.58 140.98 11.09 Sediment Total Nitrogen (mg N / g) * 0.03 0.33 Community biomass (g / m2) Sediment Total Phosphorus (mg P / g) -1.24 0.58 Sediment Total Phosphorus (mg P / g) * -0.42 0.27 Community biomass (g / m2) Community biomass -0.02 0.52 (g / m2) % of Total Wald p- Random Variance value Management Unit 53.15 0.21 Adjusted Leaf Nitrogen (mg Fixed Effect Estimate p-value R2 AICc ΔAICc R2 N / g) Water depth (cm) 0.02 0.02 * 0.64 0.59 132.55 19.52 96

Water depth (cm) * Community biomass -0.002 0.13 (g / m2) Sediment Total Nitrogen (mg N / g) 0.28 0.1 Sediment Total Nitrogen (mg N / g) * 0.02 0.48 Community biomass (g / m2) Community biomass -0.03 0.22 (g / m2) % of Total Wald p- Random Variance value Management Unit 55.08 0.18 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.02 0.02 * Water depth (cm) * Community biomass -0.002 0.08 (g / m2) Sediment Total Nitrogen (mg N / g) 0.26 0.11 Leaf Nitrogen (mg Community biomass 0.63 0.59 124.81 27.26 -0.03 0.26 N / g) (g / m2) % of Total Wald p- Random Variance value Management Unit 53.86 0.18 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.02 0.03 * Water depth (cm) * Community biomass -0.002 0.05 Leaf Nitrogen (mg (g / m2) N / g) Community biomass -0.03 0.23 0.58 0.54 122.65 29.42 (g / m2) % of Total Wald p- Random Variance value Management Unit 46.71 0.22 Leaf Nitrogen (mg Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc N / g) R2 97

Water depth (cm) 0.01 0.13 % of Total Wald p- Random 0.49 0.47 106.74 45.33 Variance value Management Unit 43.43 0.16 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Leaf Nitrogen Sediment Total Phosphorus (mg P / g) 2.17 0.15 0.5 0.49 96.85 55.22 (mg N / g) % of Total Wald p- Random Variance value Management Unit 50.06 0.15 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.01 0.1 Leaf Nitrogen (mg Sediment Total Nitrogen (mg N / g) 0.28 0.12 N / g) Sediment Total Phosphorus (mg P / g) 1.3 0.39 0.59 0.56 106.83 45.24 % of Total Wald p- Random Variance value Management Unit 57.06 0.13 98

Table 16: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus leaf phosphorus (mg P / g) and the Fixed effects (water depth (cm), sediment total nitrogen (mg N /g), sediment total phosphorus (mg P / g), community biomass (g / m2), and interaction terms) and Random effects (Management Unit). Asterisks indicate statistical significance at α = 0.05 level. The most parsimonious model was determined by the lowest AICc value and the highest adjusted R2 and is identified by the bolded dependent variable. Models within two AICc units were considered equivalent. Dependent Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc Variable R2 Water depth (cm) 0.006 0.18 Water depth (cm) * Sediment Total -0.0002 0.98 Nitrogen (mg N / g) Water depth (cm) * Sediment Total 0.02 0.81 Phosphorus (mg P / g) Water depth (cm) * Community biomass 0.0002 0.77 (g / m2) Sediment Total Nitrogen (mg N / g) 0.06 0.56 Sediment Total Nitrogen (mg N / g) * -0.38 0.64 Leaf Phosphorus Sediment Total Phosphorus (mg P / g) 0.73 0.64 106.58 0 (mg P / g) Sediment Total Nitrogen (mg N / g) * 0.02 0.32 Community biomass (g / m2) Sediment Total Phosphorus (mg P / g) 1.14 0.26 Sediment Total Phosphorus (mg P / g) * -0.04 0.8 Community biomass (g / m2) Community biomass -0.004 0.76 (g / m2) % of Total Wald p- Random Variance value Management Unit 69.94 0.14 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Leaf Phosphorus Water depth (cm) 0.006 0.09 (mg P / g) Sediment Total Nitrogen (mg N / g) 0.6 0.37 0.71 0.68 53.31 53.27 Sediment Total Phosphorus (mg P / g) 1.39 0.04 * 99

Community biomass -0.004 0.97 (g / m2) % of Total Wald p- Random Variance value Management Unit 72.23 0.11 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.006 0.17 Water depth (cm) * Sediment Total 0.02 0.8 Phosphorus (mg P / g) Water depth (cm) * Community biomass 0.0002 0.77 (g / m2) Sediment Total Nitrogen (mg N / g) 0.06 0.52 Sediment Total Nitrogen (mg N / g) * -0.25 0.63 Leaf Phosphorus Sediment Total Phosphorus (mg P / g) (mg P / g) Sediment Total Nitrogen (mg N / g) * 0.02 0.27 0.73 0.65 93.8 12.78 Community biomass (g / m2) Sediment Total Phosphorus (mg P / g) 1.17 0.24 Sediment Total Phosphorus (mg P / g) * -0.04 0.78 Community biomass (g / m2) Community biomass -0.003 0.77 (g / m2) % of Total Wald p- Random Variance value Management Unit Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.006 0.16 Leaf Phosphorus Water depth (cm) * Community biomass 0.0002 0.74 (mg P / g) (g / m2) 0.74 0.67 86.2 20.38 Sediment Total Nitrogen (mg N / g) 0.06 0.43 Sediment Total Nitrogen (mg N / g) * -0.25 0.67 Sediment Total Phosphorus (mg P / g) 100

Sediment Total Nitrogen (mg N / g) * 0.02 0.27 Community biomass (g / m2) Sediment Total Phosphorus (mg P / g) 1.34 0.07 Sediment Total Phosphorus (mg P / g) * -0.04 0.76 Community biomass (g / m2) Community biomass -0.003 0.76 (g / m2) % of Total Wald p- Random Variance value Management Unit 72.11 0.13 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.006 0.06 Sediment Total Nitrogen (mg N / g) 0.05 0.45 Leaf Phosphorus Sediment Total Nitrogen (mg N / g) * -0.41 0.42 (mg P / g) Sediment Total Phosphorus (mg P / g) 0.72 0.69 44.76 61.83 Sediment Total Phosphorus (mg P / g) 1.42 0.02 * % of Total Wald p- Random Variance value Management Unit 73.92 0.1 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Water depth (cm) 0.006 0.09 Leaf Phosphorus Sediment Total Phosphorus (mg P / g) 1.6 0.006 * (mg P / g) % of Total Wald p- 0.71 0.69 37.41 69.17 Random Variance value Management Unit 73.89 0.09 Adjusted Fixed Effect Estimate p-value R2 AICc ΔAICc R2 Leaf Phosphorus Sediment Total Phosphorus (mg P / g) 1.64 0.007 * (mg P / g) % of Total Wald p- Random 0.67 0.66 28.06 78.52 Variance value Management Unit 70.04 0.1 101

APPENDIX C: ADDITIONAL MAPS OF SAMPLE LOCATIONS

Figure A.1: Map of a portion of Crane Creek and surrounding floodplain. Basemap sourced from ArcMap 10.3. Sample points displayed with yellow circles are points where Butomus umbellatus was not present. Sample points displayed with blue stars are points with Butomus umbellatus present. 102

Figure A.2: Map of Entrance Pool (EP). Basemap sourced from ArcMap 10.3. Sample points displayed with yellow circles are points where Butomus umbellatus was absent, while those where it was found are displayed with blue stars. 103

Figure A.3: Map of Management Unit 3 (MS3). Basemap sourced from ArcMap 10.3. Sample points displayed with yellow circles are points where Butomus umbellatus was not present. Sample points displayed with blue stars are points with Butomus umbellatus present. 104

Figure A.4: Map of Management Unit 4 (MS4). Basemap sourced from ArcMap 10.3. Sample points displayed with yellow circles are points where Butomus umbellatus was not present. Sample points displayed with blue stars are points with Butomus umbellatus present. 105

Figure A.5: Map Management Unit 5 (MS5). Basemap sourced from ArcMap 10.3. Sample points displayed with yellow circles are points where Butomus umbellatus was not present. Sample points displayed with blue stars are points with Butomus umbellatus present. 106

Figure A.6: Map of Management Unit 7 (MS7). Basemap sourced from ArcMap 10.3. Sample points displayed with yellow circles are points where Butomus umbellatus was not present. Sample points displayed with blue stars are points with Butomus umbellatus present. 107

Figure A.7: Map of Pool 9 East (9E). Basemap sourced from ArcMap 10.3. Sample points displayed with yellow circles are points where Butomus umbellatus was not present. Sample points displayed with blue stars are points with Butomus umbellatus present. 108

Figure A.8: Map of Pool 3 (P3). Basemap sourced from ArcMap 10.3. Sample points displayed with yellow circles are points where Butomus umbellatus was not present. Sample points displayed with blue stars are points with Butomus umbellatus present. 109

APPENDIX D: MODEL ANALYSES USING SEDIMENT N:P AS AN INDEPENDENT VARIABLE Table B.1: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus rhizome bud count and the Fixed effects (water depth (cm), sediment nitrogen to phosphorus ratio (N:P), community biomass (g / m2), and interaction terms) and Random effects (Management Unit). Asterisks indicate statistical significance at α = 0.05 level. The most parsimonious model was determined by the lowest AICc value and highest adjusted R2, and is identified by the bolded dependent variable. Models within two AICc units were considered equivalent.

Dependent Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Variable Water depth (cm) - 0.01 0.81 Water depth (cm) * 0.01 0.91 Sediment N:P Water depth (cm) * Community biomass (g / 0.90 0.001 m2) Bud Count Sediment N:P - 2.04 0.21 0.55 0.48 251.78 0 Sediment N:P * Community 0.21 0.53 biomass (g / m2) Community biomass (g / - 0.32 0.002 m2) Random % of Total Variance Wald p-value Management Unit 49.3 0.17 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Water depth (cm) - 0.01 0.83 Water depth (cm) * Community biomass (g / 0.0008 0.88 Bud Count m2) 0.55 0.49 246.35 5.43 Sediment N:P - 2.04 0.20 Sediment N:P * Community 0.23 0.44 biomass (g / m2) 110

Community biomass (g / - 0.32 0.002 * m2) Random % of Total Variance Wald p-value Management Unit 49.72 0.16 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Water depth (cm) - 0.01 0.86 Sediment N:P - 2.04 0.20 Sediment N:P * Community 0.22 0.44 Bud Count biomass (g / m2) Community biomass (g / 0.55 0.50 234.85 16.93 - 0.32 0.001 * m2) Random % of Total Variance Wald p-value Management Unit 49.65 0.15 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Sediment N:P - 1.98 0.20 Sediment N:P * Community 0.22 0.43 biomass (g / m2) Bud Count Community biomass (g / 0.55 0.52 226.65 25.13 - 0.32 0.001 * m2) Random % of Total Variance Wald p-value Management Unit 50.74 0.15 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Water depth (cm) - 0.01 0.85 Sediment N:P - 1.87 0.23 Bud Count Community biomass (g / - 0.32 0.001 * 0.55 0.51 231.88 19.9 m2) Random % of Total Variance Wald p-value Management Unit 51.77 0.15 Bud Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Count Sediment N:P - 1.81 0.23 0.55 0.53 223.81 27.97 111

Community biomass (g / - 0.33 0.001 * m2) Random % of Total Variance Wald p-value Management Unit 52.84 0.14 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Community biomass (g / - 0.35 0.0003 * Bud Count m2) 0.54 0.53 225.35 26.43 Random % of Total Variance Wald p-value Management Unit 56.82 0.13 112

Table B.2: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus biomass (g / m2) and the Fixed effects (water depth (cm), sediment nitrogen to phosphorus ratio (N:P), community biomass (g / m2), and interaction terms) and Random effects (Management Unit). Asterisks indicate statistical significance at α = 0.05 level. The most parsimonious model was determined by the lowest AICc value and highest adjusted R2, and is identified by the bolded dependent variable. Models within two AICc units were considered equivalent. Dependent Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Variable Water depth (cm) - 0.04 0.18 Water depth (cm) * Sediment N:P - 0.08 0.48 Sediment N:P - 0.96 0.48 Sediment N:P * Community 0.25 0.39 Biomass biomass (g / m2) 0.51 0.43 236.36 0 (g / m2) Community biomass (g / m2) - 0.30 0.001 * Depth (cm) * Community 0.002 0.75 biomass (g / m2) % of Total Variance Wald p-value Random 38.26 0.23 Water depth (cm) - 0.033 0.18 Water depth (cm) * Sediment N:P - 0.07 0.5 Sediment N:P - 0.96 0.47 Biomass Sediment N:P * Community 0.21 0.42 0.50 0.44 224.45 12.11 (g / m2) biomass (g / m2) Community biomass (g / m2) - 0.30 0.08 Random % of Total Variance Wald p-value Management Unit 36.81 0.21 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Water depth (cm) - 0.04 0.10 Community biomass (g / m2) - 0.30 0.001 * Biomass Sediment N:P - 0.93 0.48 (g / m2) 0.49 0.44 219.27 17.09 Sediment N:P * Community 0.14 0.56 biomass (g / m2) Random % of Total Variance Wald p-value 113

Management Unit 36.2 0.22 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Water depth (cm) - 0.04 0.10 Biomass Community biomass (g / m2) - 0.30 0.0004 * (g / m2) Sediment N:P - 0.84 0.52 0.49 0.46 215.68 20.68 Random % of Total Variance Wald p-value Management Unit 38.12 0.21 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Water depth (cm) - 0.04 0.11 Biomass Community biomass (g / m2) - 0.31 0.0002 * (g / m2) 0.5 0.47 215.73 20.63 Random % of Total Variance Wald p-value Management Unit 40.52 0.20 Biomass Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc (g / m2) Community biomass (g / m2) - 0.32 0.0002 * 0.46 0.45 210.09 26.27 114

Table B.3: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus leaf tissue phosphorus (mg P / g) and the Fixed effects (water depth (cm), sediment nitrogen to phosphorus ratio (N:P), community biomass (g / m2), and interaction terms) and Random effects (Management Unit). Asterisks indicate statistical significance at α = 0.05 level. The most parsimonious model was determined by the lowest AICc value and highest adjusted R2, and is identified by the bolded dependent variable. Models within two AICc units were considered equivalent.

Dependent Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Variable Depth 0.01 0.10 Community biomass - 0.01 0.45 Sediment N:P - 0.03 0.87 Leaf Tissue Depth x Community biomass - 0.00002 0.98 Phosphorus Depth x Sediment N:P - 0.02 0.37 0.65 0.58 87.57 0 (mg P / g) Community biomass x Sediment 0.06 0.20 N:P Random % of Total Variance Wald p-value Management Unit 61.91 0.15 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Depth 0.01 0.08 Community biomass - 0.01 0.44 Leaf Tissue Sediment N:P - 0.04 0.86 Phosphorus Depth x Sediment N:P - 0.02 0.35 0.65 0.58 71.52 16.05 (mg P / g) Community biomass x Sediment 0.06 0.15 N:P Random % of Total Variance Wald p-value Management Unit 62.76 0.14 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Depth 0.01 0.12 Leaf Tissue Community biomass - 0.01 0.38 Phosphorus Sediment N:P - 0.001 0.99 0.63 0.58 62.78 24.79 (mg P / g) Community biomass x Sediment 0.04 0.28 N:P 115

Random % of Total Variance Wald p-value Management Unit 59.20 0.15 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Depth 0.01 0.14 Leaf Tissue Community biomass - 0.01 0.36 Phosphorus Sediment N:P 0.04 0.87 0.60 0.56 56.12 31.45 (mg P / g) Random % of Total Variance Wald p-value Management Unit 54.26 0.16 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Leaf Tissue Depth 0.01 0.13 Phosphorus Community biomass - 0.01 0.36 0.60 0.58 51.87 35.70 (mg P / g) Random % of Total Variance Wald p-value Management Unit 55.75 0.14 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Leaf Tissue Depth 0.01 0.12 Phosphorus Random % of Total Variance Wald p-value 0.60 0.59 42.78 44.79 (mg P / g) Management Unit 60.06 0.12 116

Table B.4: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus leaf nitrogen (mg N / g) and the Fixed effects (water depth (cm), sediment nitrogen to phosphorus ratio (N:P), community biomass (g / m2), and interaction terms) and Random effects (Management Unit). Asterisks indicate statistical significance at α = 0.05 level. The most parsimonious model was determined by the lowest AICc value and highest adjusted R2, and is identified by the bolded dependent variable. Models within two AICc units were considered equivalent.

Dependent Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Variable Depth 0.02 0.03 * Community biomass - 0.04 0.15 Sediment N:P 0.45 0.30 Leaf Depth x Community biomass - 0.002 0.18 Tissue Depth x Sediment N:P - 0.01 0.69 0.64 0.57 136.34 0 Nitrogen Community biomass x Sediment (mg N / g) 0.13 0.16 N:P Random % of Total Variance Wald p-value Management Unit 50.81 0.21 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Depth 0.02 0.03 * Community biomass - 0.04 0.14 Leaf Sediment N:P 0.48 0.27 Tissue Depth x Community biomass - 0.002 0.15 Nitrogen 0.63 0.58 128.23 8.11 Community biomass x Sediment (mg N / g) 0.12 0.16 N:P Random % of Total Variance Wald p-value Management Unit 50.49 0.21 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Leaf Depth 0.02 0.03 * Tissue Community biomass - 0.04 0.15 Nitrogen Sediment N:P 0.48 0.23 0.59 0.55 123.90 12.44 (mg N / g) Depth x Community biomass - 0.002 0.06 Random % of Total Variance Wald p-value 117

Management Unit 45.22 0.23 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Leaf Depth 0.02 0.03 * Tissue Community biomass - 0.03 0.15 Nitrogen Depth x Community biomass - 0.003 0.06 0.58 0.54 122.65 13.69 (mg N / g) Random % of Total Variance Wald p-value Management Unit 46.71 0.22 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Leaf Depth 0.01 0.12 Tissue Community biomass - 0.05 0.05 * Nitrogen Sediment N:P 0.56 0.24 0.48 0.44 113.14 23.20 (mg N / g) Random % of Total Variance Wald p-value Management Unit 26.14 0.38 118

Table B.5: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus rhizome nitrogen (mg N / g) and the Fixed effects (water depth (cm), sediment nitrogen to phosphorus ratio (N:P), community biomass (g / m2), and interaction terms) and Random effects (Management Unit). Asterisks indicate statistical significance at α = 0.05 level. The most parsimonious model was determined by the lowest AICc value and highest adjusted R2, and is identified by the bolded dependent variable. Models within two AICc units were considered equivalent.

Dependent Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Variable Depth 0.01 0.08 Community biomass - 0.03 0.03 * Sediment N:P 0.53 0.02 * Rhizome Depth x Community biomass - 0.001 0.49 Tissue Depth x Sediment N:P - 0.01 0.51 0.35 0.24 89.08 0 Nitrogen Community biomass x Sediment (mg N / g) 0.01 0.80 N:P Random % of Total Variance Wald p-value Management Unit 0.68 0.96 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Depth 0.01 0.08 Rhizome Community biomass - 0.03 0.03 * Tissue Sediment N:P 0.53 0.02 * Nitrogen Depth x Community biomass - 0.001 0.40 0.35 0.26 81.53 7.55 (mg N / g) Depth x Sediment N:P - 0.01 0.53 Random % of Total Variance Wald p-value Management Unit 0.64 0.97 Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Depth 0.01 0.09 Rhizome Community biomass - 0.03 0.03 * Tissue Sediment N:P 0.53 0.02 * Nitrogen 0.33 0.26 72.46 16.62 Depth x Community biomass - 0.001 0.43 (mg N / g) Random % of Total Variance Wald p-value Management Unit 0.04 0.99 119

Fixed Effect Estimate p-value R2 Adjusted R2 AICc ΔAICc Rhizome Depth 0.01 0.13 Tissue Community biomass - 0.03 0.02 * Nitrogen Sediment N:P 0.49 0.03 * 0.26 0.21 57.25 31.83 (mg N / g) Random % of Total Variance Wald p-value Management Unit 0.00 0.57 120

Table B.5: Least Squares Fit Regression Model analyses showing the relationship between B. umbellatus rhizome phosphorus (mg P / g) and the Fixed effects (water depth (cm), Management Unit). Asterisks indicate statistical significance at α = 0.05 level. The most parsimonious model was determined by the lowest AICc value and highest adjusted R2, and is identified by the bolded dependent variable. Models within two AICc units were considered equivalent.

Fixed Effect Estimate p-value R2 Adjusted R2 Model Prob > F AICc Sediment N:P 0.42 0.63 Management Unit Pool 9 East 0.01 0.97 Rhizome Management Unit Crane Creek - 0.56 0.38 Tissue Management Unit Entrance Pool - 0.50 0.12 Phosphorus 0.18 - 0.01 0.49 136.24 Management Unit MS3 0.70 0.18 (mg P / g) Management Unit MS4 - 0.20 0.58 Management Unit MS5 0.77 0.14 Management Unit MS7 0.03 0.94 121

Figure B.1: Residual versus predicted plot and leverage plots for the B. umbellatus leaf nitrogen (mg / g) model using sediment N:P as an independent variable. 122

Figure B.2: Residual versus predicted plot and leverage plots for the B. umbellatus leaf phosphorus (mg / g) model using sediment N:P as an independent variable. 123

Figure B.3: Residual versus predicted plot and leverage plots for the B. umbellatus rhizome nitrogen (mg / g) model using sediment N:P as an independent variable. 124

Figure B.4: Residual versus predicted plot and leverage plots for the B. umbellatus biomass (g / m2) model using sediment N:P as an independent variable. 125

Figure B.5: Residual versus predicted plot and leverage plots for the B. umbellatus rhizome bud count model using sediment N:P as an independent variable. 126

Figure B.6: Residual versus predicted plot and leverage plots for the B. umbellatus rhizome phosphorus (mg / g) model using sediment N:P as an independent variable. 127

APPENDIX E: COMPARISON OF CARBON CONCENTRATIONS IN B. UMBELLATUS AND NATIVE PLANT SPECIES

Figure C.1: Box-and-whisker plot comparing the mean values of percent tissue carbon of B. umbellatus (N = 40), E. palustris (N = 9), N. lutea (N = 10), P. amphibia (N = 9), and P. pensylvanica (N = 10). 128

Figure C.2: Box-and-whisker plot comparing the mean values of percent tissue carbon-to- nitrogen ratio of B. umbellatus (N = 40), E. palustris (N = 9), N. lutea (N = 10), P. amphibia (N = 9), and P. pensylvanica (N = 10). 129

Figure C.3: Box-and-whisker plot comparing the mean values of percent tissue carbon-to- phosphorus ratio of B. umbellatus (N = 40), E. palustris (N = 9), N. lutea (N = 10), P. amphibia (N = 9), and P. pensylvanica (N = 10).