MACKEY, MATTHEW M., M.S. May 2017 ECOLOGY & EVOLUTIONARY

BIOLOGY

ANALYSIS OF THE ICHTHYOFAUNAL COMMUNITY AT OLD WOMAN CREEK, A

LAKE ERIE COASTAL WETLAND (117 pp.)

Thesis Advisor: Mark W. Kershner

Great Lakes coastal wetlands are of significant ecological and economic importance. These wetlands collect runoff from surrounding land, mitigate natural disasters, and provide habitat for plants and animals at all life stages. There is a growing literature describing the factors effecting spatial and temporal patterns in fish diversity in brackish coastal wetlands that surround marine waters, but similar studies are lacking in freshwater estuaries and little is known about these patterns in larval fishes. Adult fishes were collected weekly between May and October 2015 using fyke nets set in monotypic stands of vegetation. Larval fishes were collected weekly between May and October 2015, and biweekly from April to August 2016. Water depth was measured at all fyke nets and all traps. A suite of abiotic variables was also measured during

2016. Patterns in fish distribution and diversity were assessed using univariate and multivariate statistics. Fish did not appear to choose specific types of vegetation, but preferred the presence of vegetation to open water areas. Patches of submerged aquatic vegetation and water lily/lotus had a significant effect on larval community structure, as did dissolved oxygen, pH, and secchi depth.

Larvae peaked in abundance in mid-July in 2015, and early June in 2016, but the 2016 peak had over 1000 more individuals than the 2015 peak. Further, abundance values from 2015 were

similar to those from the same dates in 2016. This suggests that spawning periods of Lake Erie fishes are predictable from year to year within the same estuary. These studies suggested that fish prefer macrophytic cover, and they improved understanding of the effects of water level on fish distributions. The studies also identified spawning patterns as constant from year-to-year. Future studies should examine whether larval fish actively choose habitats following spawning, as well as whether abiotic variables influence hatching and growth rates. Finally, the work on larval fish opens the door to studying the benefits of different spawning patterns (i.e. protracted and early spawning compared to single-burst spawning).

ANALYSIS OF THE ICHTHYOFAUNAL COMMUNITY AT OLD WOMAN CREEK, A LAKE ERIE COASTAL WETLAND

A thesis submitted To Kent State University in partial Fulfillment of the requirements for the Degree of Master of Sciences

by

Matthew M. Mackey

May, 2017

Thesis written by

Matthew M. Mackey

B.S., Saint Vincent College, 2014

M.S., Kent State University, 2017

Approved by

Mark W. Kershner______, Advisor

Laura G. Leff______, Chair, Department of Biological Sciences

James L. Blank______, Dean, College of Arts and Sciences

TABLE OF CONTENTS

TABLE OF CONTENTS ...... iii LIST OF FIGURES ...... iv LIST OF TABLES ...... vi ACKNOWLEDGEMENTS ...... vii CHAPTER 1 - INTRODUCTION ...... 1 General Benefits of Great Lakes Coastal Wetlands ...... 1 How Great Lakes Coastal Wetlands Affect Fishes ...... 3 How Fish Impact Great Lakes Coastal Wetlands...... 5 Fish Aid Understanding of the Benefits of Great Lakes Coastal Wetlands ...... 8 CHAPTER 2 - SPATIAL AND TEMPORAL VARIABILITY OF THE FISH ASSEMBLAGE IN A GREAT LAKES FRESHWATER ESTUARY ...... 2 Introduction ...... 2 Methods ...... 3 Results ...... 6 Discussion ...... 25 Conclusion ...... 33 CHAPTER 3 - LARVAL FISH COMMUNITY RESPONSE TO VEGETATION AND WATER QUALITY IN A COASTAL WETLAND ...... 36 Introduction ...... 36 Methods ...... 38 Results ...... 44 Discussion ...... 76 CHAPTER 4 - CONCLUSIONS ...... 84 General Importance of Spatial and Temporal Patterns in Fish Abundance, Diversity, and Spawning ...... 84 Key Findings from these Studies ...... 85 How these Findings Fit in the Bigger Picture ...... 87 REFERENCES ...... 90

iii

LIST OF FIGURES

Figure 1: Map of Old Woman Creek National Estuary Research Reserve...... 4

Figure 2: Average fish abundance and species richness during fyke net sampling...... 9

Figure 3: Average fish abundance in each microhabitat, by date...... 10

Figure 4: Fish abundance and species richness by OWC sampling site...... 12

Figure 5: Total Fish Abundance by water depth (cm)...... 14

Figure 6: Comparison of total and average fish abundance in OWC when the barrier beach at the estuary mouth was open or closed (24 June and 17 October 2015)...... 15

Figure 7: Effect of Vegetation Type on Total and Average Fish Abundance and Species

Richness...... 16

Figure 8: Fish Community RDA...... 17

Figure 9: Percent Tolerant Species in Each Cover Type...... 20

Figure 10: Species Growth During the Sampling Season ...... 22

Figure 11: Frequency Distribution of Fish Total Lengths During Sampling Season...... 24

Figure 12: Map of Old Woman Creek...... 40

Figure 13: 2015 Total Larval Abundance...... 46

Figure 14: 2015 Average Larval Fish Abundance over Time...... 48

Figure 15: Abundance of Three Most Abundant Fishes of 2015...... 49

Figure 16: Abundance of Common Carp, White Crappie, Goldfish, and White Bass...... 50

Figure 17: Total Larval Abundance and Species Richness by Cover Type ...... 51

Figure 18: Relative Abundance of Families in Each Cover Type in 2015 ...... 52

Figure 19: Proportion Abundance by Vegetation Type of

Three Most Abundant Species of 2015 ...... 53

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Figure 20: Lengths of Larval Fish by Vegetation Type over Time...... 55

Figure 21: 2015 Average Larval Fish Abundance Across Sampling Sites ...... 56

Figure 22: 2016 Average Larval Fish Abundance in Each Sampling Site...... 57

Figure 23: 2016 Total Abundance, Average Abundance, and Proportion of Catches with Zero-

Yield ...... 58

Figure 24: Total and Average Larval Fish Abundances from 2015 and 2016...... 59

Figure 25: Species-specific Abundance for the Four Most Common Species of 2016...... 61

Figure 26: 2016 Average Abiotic Measurements Over Time ...... 62

Figure 27: Change in Vegetative Coverage throughout the 2016 Sampling Season...... 64

Figure 28: Redundancy Analysis of all Measured Variables and Collected Species...... 65

Figure 29: Average Abundance of Larval Fishes by Percent Cover of all Vegetation Types...... 68

Figure 30: Average Common Carp Abundance by Vegetation Percent Cover...... 69

Figure 31: Average White Crappie Abundance by Vegetative Percent Cover ...... 70

Figure 32: Average Goldfish Abundance by Vegetation Percent Cover...... 71

Figure 33: Average White Bass Abundance by Vegetation Percent Cover ...... 72

Figure 34: Lengths of Four Most Abundant Species throughout the Sampling Period...... 74

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LIST OF TABLES

Table 1: Total fish captured during fyke net sampling (24 June - 17 October 2015)...... 7

Table 2: Species Codes...... 19

Table 3: List of Tolerant and Intolerant Species Captured During Fyke Net Sampling...... 21

Table 4: Linear Regression Results- Growth of 5 Most Abundant Species Over Time...... 23

Table 5: List of species in the OWC Watershed...... 26

Table 6: 2015 and 2016 Species Abundances...... 45

Table 7: Regression Results - 2016 Abiotic Variables...... 63

Table 8: Environmental Variables from Redundancy Analysis...... 67

Table 9: All Species Average Length and Abundance by Date...... 75

vi

ACKNOWLEDGEMENTS

Funding for this project was courtesy of the Friends of Old Woman Creek Travel Grant and the National Science Foundation’s Research Experience for Undergraduates program.

Special thanks are in order for all the hard work contributed by volunteer field technicians Dan

Navarro, Jorden McKenna, Matt Cooper, Nate Wehr, Colleen Cosgrove, Chris Boehler,

DeShawn Johnson, Megan Gottschalk, Sean Satterfield, Will Sova, and Matt Wuensch. Jorden and Matt were also instrumental in the identification process in the lab. Field equipment and lodging was made available to us by the OWC research staff and Dr. Lauren Kinsman-Costello.

Thank you to my committee members, Dr. Kristi Arend, Dr. Ferenc deSzalay. Most of all, thank you to my mentor and friend, Dr. Mark Kershner, for all his help, academic and otherwise, every step along the way. This undertaking could not possibly have been completed without all of you.

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CHAPTER 1 - INTRODUCTION

General Benefits of Great Lakes Coastal Wetlands

According to the Millennium Ecosystem Assessment (MA), ecosystem services are defined as those services provided by an ecosystem to the living things in and around the ecosystem. These services fall into 4 distinct categories: provisioning, regulating, habitat, and cultural (MA 2005, revised in TEEB 2013). The term provisioning services refers to tangible goods that can be harvested from a system, such as fruit or water. Regulating services act to maintain natural processes and protect against natural disasters (e.g. flood or erosion control).

Habitat services maintain ecosystem functions as well, but they act behind the scenes and their indirect benefits outnumber their direct benefits. Finally, cultural services provide more philosophical virtues (e.g. spiritual or aesthetic worth; Clarkson et al. 2013). Wetland ecosystems, including those in the Great Lakes, provide services that meet these definitions, however, the following text will only discuss the first three, as these are of particular ecological significance.

Provisioning Services of Coastal Wetlands

Coastal wetlands are considered nature’s water filtration systems, and they act to remove pollutants before water is discharged into the larger freshwater source, though the effectiveness of removal may be dictated by water level to a certain degree (Krieger 2003, Rodríguez and

1

Brisson 2015). In the wake of recent harmful algal and cyanobacterical blooms (HABs) in the

Great Lakes (e.g., the HAB Toledo, OH in 2011; Michalak et al. 2013) the power of wetlands to filter excess nutrients has been in the public eye more than ever before.

Wetlands are hotspots of biodiversity, which translates to food sources for the human population. In the Great Lakes alone, over 80 species of fish use coastal systems, and they are vital parts of Great Lakes fisheries (Jude and Pappas 1992), which presents various ecological, economic, and human values. Recent evaluations of the economic importance of fisheries in the

Great Lakes place a value of over $7 billion USD on this resource (Krantzberg and De Boer

2008). Likewise, waterfowl frequently use wetlands as foraging grounds (Hansson et al. 2005).

Although more of cultural service, humans will often hunt birds such as ducks in marshes, an industry that is valued at $1.7 million USD annually across all US marshes, many of which are part of the Great Lakes basin (Adusumilli 2015).

Regulating services of Coastal Wetlands

Wetlands help mitigate natural disasters. The most commonly cited example of wetlands’ regulating services is flood control (Stromberg et al. 1997, Zedler 2003). Coastal wetlands are in a unique class of wetland because many of them (but certainly not all) are dually fed by their respective lake and a stream (“drowned river mouths,” as defined in Keough 1999). Such wetlands operate by filling to accommodate excess stream flow following snowmelts or rain events, as well as large seiche events from the lake (Bedford 1992). Further, the ability of Great

Lakes coastal wetlands to cope with stochastic fluctuations in water level is expected to change with the progression of global climate change (Mortsch 1998) which could have important economic implications due to the high value of waterfront real estate. Such real estate is

1 estimated to be worth over $10,000/ha/year, while the ecosystem services altogether top

$200,000/ha/year (2007 USD, de Groot et al. 2012).

Habitat services of Coastal Wetlands

As briefly touched upon above, coastal marshes are hotspots of biodiversity, as hundreds of different taxa, including fish (Jude and Pappas 1992), macroinvertebrates such as dragonfly larvae and may fly larvae (Krieger 1992), and macrophytes (aquatic vascular plants; Carpenter et al. 1985). The biodiversity alone in coastal wetland systems is valued at nearly $70 billion USD

(Krantzberg and De Boer 2008). As such, coastal wetlands also serve to maintain the life cycles of these organisms. Perhaps the most notable taxonomic group is fish, many species of which have been shown to return to coastal wetlands annually to reproduce (Jude and Pappas 1992,

Uzarski et al. 2005, Schoen 2016). Beyond the use of wetlands for reproduction, fish also forage and seek shelter from predators in Great Lakes coastal wetlands. The various uses of wetlands by fishes are discussed in more detail below. Life cycles for other taxa are maintained in wetlands as well, including birds and macroinvertebrates. Many birds nest and reproduce in wetland- obligate vegetation (Riffell et al. 2001), as do a variety of macroinvertebrates (MacKenzie and

Kaster 2004).

Wetlands also preserve genetic diversity by supporting a large and diverse community of organisms. In maintaining many individual plants and animals, genetic material is preserved

(Denny 1994). If there was a decline in individuals of a given species, genetic material would decline, which could cause certain characteristics to go extinct, either locally or world-wide.

2

How Great Lakes Coastal Wetlands Affect Fishes

Through a combination of geology and vegetation, coastal wetlands provide shelter and food to their fish communities. In the Great Lakes alone, over 80 different species were shown to be related with coastal wetlands, over half of which are wetland residents or migrant species

(Jude and Pappas 1992).

Shelter

The role of vegetation in the life cycle of fishes is well documented. Adult fishes seem to partition plant cover, with smaller fishes preferring more densely packed stands of macrophytes, and larger species, intuitively, selecting sparser, more accessible habitat (Engel 1988). This is corroborated in another paper which found increased abundances of larval and juvenile fishes in high-density stands of vegetation (Conrow et al. 1990), with many larval fishes actively avoiding open water, which is thought to be anti-predator behavior (Lehtiniemi et al. 2015). Vegetation is used by both predatory and prey fishes in species-specific capacities (Savino and Stein 1989).

Given their low muscle mass and consequential slow swim speed, vegetative shelter is of particular importance to larval fish (Copp 1991, Houde 1969). Likewise, discharge and flow velocity have been shown to effect larval fish distribution (Mion et al. 1998). The result of these factors, among others, is increased abundance and species richness in larval fishes in patches of vegetative cover (Scheidegger and Bain 1995).

Numerous studies have attempted to assess the specifics of the macrophyte-fish relationship, but very few species-specific interactions have been highlighted and these are not replicated. The only real trend between fish and macrophytes is that more fish and higher diversity of fishes are typically found in denser patches of vegetation of any type, until

3 vegetation is too thick for fish to effectively move through their environment (Killgore et al.

1989, Brazner and Beals 1997).

Foraging

Several studies have noted the importance of macrophytic cover also shelters aquatic invertebrates, a major food source for fishes. Not unlike fishes (as described in Engel 1988), invertebrates seem to partition wetland habitats by vegetative cover, with the highest diversity and abundance found in mixed patches (Voigts 1976). Similar results were reported in

McLaughlin and Harris (1990), which also showed that invertebrate biomass was highest in more sparsely vegetated areas. In general, vegetated waters sustain macroinvertebrates in higher abundance and diversity (Gerrish and Bristow 1979, Keast 1984). These finding carry important implications for fish foraging success, and can aide in understanding of the spatial distribution of fishes. Fish also forage on macrophytes themselves (Carpenter et al. 1997), as well as other fish

(Savino and Stein 1989), so it appears that macrophytes are the key wetland factor for ichthyofaunal foraging. The final important dietary component for fish is algae, which is not necessarily related to macrophytic growth (Vanni and Layne 1997). Algae are the basis of the

Lake Erie food web and are commonly grazed upon by herbivorous fishes, such as gizzard shad

(Dorosoma cepedianum).

Reproduction

Many studies have noted the importance of wetland ecosystems as nursery habitats for a variety of freshwater fishes (Jude and Pappas 1992). Vegetation has been shown to be a key factor for successful nursery habitats, however, there is quite a disparity in knowledge of vegetative requirements between species with most studies focusing on northern pike (Esox

4 lucius) (Marean 1976, Holland and Huston 1984, Derksen and Gillies 1985). Northern pike and related Esocids (as well as bowfin and Lepidostids) are members of a group of fish that develop adhesive discs in their larval stage, with which they attach to macrophytic substrata, as opposed to swimming freely like other species (Auer et al. 1982). Therefore, vegetative cover may be of heightened importance to these fishes, rendering them ineffective as a representative species for all larval fish.

The geology of coastal wetlands and wave action in the Great Lakes also play roles fish reproduction, as mass-migrations in fishes are common and well-documented (Jude and Pappas

1992, Landsman et al. 2011, Schoen et al. 2016), and are influenced by physical barriers such as beaches (Hunn and Youngs 1980, Porto et al. 1999). These migrations are somewhat like the salmon runs that are witnessed annually in the Pacific Northwest, although the semelparous life histories of salmon differ from most Great Lakes fishes, which are typically iteroparous.

How Fish Impact Great Lakes Coastal Wetlands

Top-down food web regulation is a phenomenon in which top predators regulate population dynamics and ecosystem function (Hairston et al. 1960). Fish, which operate as both predator and prey have been shown to regulate wetlands in a number of direct and indirect ways.

As used here, direct effects refer to predation from fish on individuals from lower trophic levels, whereas indirect effects are those that come about through involuntary processes in fish (e.g., excrement).

5

Direct Effects: Foraging and Predator-Prey Dynamics

Studies of fish diets in the tropics have shown that fish consume prey from virtually every trophic level, including small proportions of terrestrial plants and animals (Winemiller

1990). Further, this study showed dietary preference to vary seasonally. Studies in northern lakes have illustrated patterns in trophic hierarchy among fish themselves, wherein larger species (e.g.

Sander vitreus and Esox lucius) prey on smaller, slower species (e.g. members of family

Centrarchidae; Vander Zanden et al. 1997). However, food web analysis in aquatic ecosystems such as these can be messy, due to the reality of omnivory. The problem arises because stable isotope analysis lumps species into trophic guilds based on isotopic ratio, as opposed to actual dietary preference (Vander Zanden and Rasmussen 1996).

Fish are the focal point of a common trophic cascade in aquatic food webs, in which larger, piscivorous fish directly influence primary production through their predation on smaller, herbivorous fish. This results in decreased herbivore biomass, which in turn results in increased producer biomass (Brett and Goldman 1996). Fish can also influence production by foraging on the producers themselves (Carpenter et al. 1985, Stephen et al. 1998). In an extreme case that was exacerbated by an invasive species, highly elevated levels of fish herbivory caused concern that the fish population may actually be too high to allow for sustainable ecosystem functioning

(Murray et al. 2010).

Indirect Effects: Rerouting Nutrients and Biomagnification

Fish have been shown to contribute to nutrient loading in wetlands through deposition of their fecal matter (Lamarra 1975, Nakashima and Leggett 1980, Braband et al. 1990, Schindler et al. 2001, Vanni 2002). However, there are reports detailing more conservative estimates of the

6 impact of fecal nutrient deposition at larger spatial scales (Karakassis et al. 2005). In the broader scope of wetland biota, nutrient loading via fish excretion has been shown to increase cyanobacterial blooms (Andersson et al. 1978). It also appears that fish feeding in different parts of the water column provide differing levels of available nutrients through excretion. For example, the contributions of fish feeding in the middle of the water column are less than smaller, more numerous planktonic species (Andersson et al. 1978, Kitchell et al. 1975,

Nakashima and Leggett 1980). However, this may vary by ecosystem (Braband et al. 1990).

Many of the studies investigating the importance of fish to nutrient cycling have focused primarily on phosphorous. This is because it was long viewed as the sole limiting nutrient for production in most aquatic ecosystems, and this perspective was carried over to wetlands

(Sawyer 1952, Sundareshwar et al. 2003). It was later found in studies on wetland production in the 1990s that nitrogen was of clear importance (Howarth 1988, NRC 1993, Justic et al. 1995,

Nixon 1995, Nixon et al. 1996, Vitousek et al. 1997). A prominent meta-analysis in 2007 found compromise in this N vs. P debate, showing evidence for co-limitation between the two (Elser et al. 2007). It has been suggested that fish age is a factor in nutrient cycling, with younger fish contributing more to the available nutrient pool through their excretion (Kraft 1992).

Bioturbation is the resuspension of sediments into the water column, and it is commonly done by benthos-feeding fishes, such as common carp (Cyprinus carpio). This has surprisingly wide-reaching implications in aquatic food webs, due mainly to its impact on phytoplankton

(Havens 1991). One study showed that bioturbation by common carp frees up cadmium that was initially bound to sediments in polluted ecosystems. This made cadmium available to zooplankton, such as daphnia, which comprise an important and relatively abundant food source for fish (Wall et al. 1996). The ecological concern here is that biomagnification of metals could

7 lead to contaminated fish resources. The effect of biomagnification sprawls into terrestrial ecosystems as well, due to predation on fishes by various species of birds (Pérez-Fuentetaja et al.

2015, Greaves et al. 2016). This is also a human health concern, as harmful compounds have been consumed by people who eat fish from contaminated waters (Cui et al. 2015, Turyk et al.

2015, Callahan et al. 2017).

It is suggested that one of the largest impacts fish, particularly common carp, have on nutrient availability in aquatic systems is through bioturbation. In one study, carp and other fishes accounted for over a 25% increase in bioavailable phosphate in the water (Jana and Sahu

1993). Most the literature on fish-induced bioturbation centers on the activity of adult fishes, however, larval and juvenile fishes have also been shown to effect nutrient availability in a similar manner. One study showed that the presence of just one individual carp fry can increase water phosphate concentration (Jana and Das 1992). This has been shown to alter growth and abundance of aquatic primary producers. For example, a study conducted in a Japanese lake, the presence of carp resulted in a significant decrease in macrophytic biomass. The same study noted an increase in zooplankton. In fact, the increased zooplankton at the top of the water column, coupled with the increase in suspended solids was thought to limit photosynthesis lower in the water column, leading to decreased macrophytic growth (Matsuzaki et al. 2007).

Fish Aid Understanding of the Benefits of Great Lakes Coastal Wetlands

Understanding spatial and temporal patterns in ichthyofaunal species richness and abundance has the potential to help humans to better understand the provisioning services of

Great Lakes coastal wetlands, which has large economic implications (Krantzberg and De Boer

8

2008, de Groot et al. 2012, Adusumilli 2015). The mass migrations that bookend spawning

(detailed in Jude and Pappas 1992, Landsman et al. 2011, Schoen et al. 2016), as well as reproductive efficiency and spatial and temporal patterns in spawning behavior can inform fishing efforts to help move towards sustainable fisheries. With this information and future research, it can become easier for fisheries managers to maximize provisioning ecosystem services to aid future generations. These patterns also help us understand the role of fish in nutrient cycling, which can help improve Great Lakes water quality and prevent future HABs, such as the catastrophic Lake Erie event in 2014. Fish have been shown to enable movement of nutrients, especially nitrogen through trophic levels, which can also help us understand the connectedness of terrestrial and aquatic food webs (Kwak and Zedler 1997).

Fish have also become vital components in indices of biotic integrity (IBIs), which are used to assess ecosystem health (Karr 1981, Minn et al. 1994, Holmlund and Hammer 1999,

Uzarski et al. 2005). These are important because they inform remediation projects, which can help promote biodiversity and maintain ecosystem function. Remediation efforts are of particular importance in the Great Lakes region because of the sheer size of the watershed and the massive human populations surrounding them.

9

CHAPTER 2 - SPATIAL AND TEMPORAL VARIABILITY OF THE FISH ASSEMBLAGE

IN A GREAT LAKES FRESHWATER ESTUARY

Introduction

The Great Lakes are lined with coastal marshes, wetland ecosystems that are connected to and significantly influenced by their respective Great Lake (Keough et al. 1999). Coastal wetlands in the Great Lakes basin are important transitional ecosystems connecting the surrounding watersheds and landscapes to the lakes themselves. They serve several important functions including acting as a filtration system to mediate both point and non-point source pollution (Krieger 2003), providing area for human recreation, mitigating against natural disasters such as floods (Stromberg et al. 1997), and providing habitat for wildlife of many different taxa (Zedler 2003, Hansson et al. 2005). Further, many sport fishes use these systems and Great Lakes sport fishing represents a significant economic resource to the region

(Krantzberg and De Boer 2008). All told, the economic value of the Great Lakes is estimated to be over $140 billion USD, $80 billion USD of which is directly attributable to Great Lakes coastal wetlands, when all ecosystem services are added together (Campbell et al. 2015).

The diverse Great Lakes fish assemblage represents a major component of coastal wetland food webs, and many species exist in all life stages in coastal wetlands. These fish can regulate primary production in wetland ecosystems (Carpenter et al. 1985, Stephen et al. 1998).

Fish also play a large role in the transport of contaminants to higher trophic levels through

2 bioaccumulation and biomagnification, which impact predators in aquatic and terrestrial food webs alike (Pérez-Fuentetaja et al. 2015, Greaves et al. 2016), and can influence human health

(Cui et al. 2015, Turyk et al. 2015, Callahan et al. 2017). Beyond predator-prey and top-down control interactions in wetland food webs, fishes have been shown to have profound effects on nutrient cycling in other ways, including via excretion (Schindler et al. 2001, Vanni 2002) and bioturbation (Roberts et al. 1995, Parkos III et al. 2003). Bioturbation is the resuspension of sediments by benthic foraging fish, such as common carp (Cyprinus carpio), and this can increase suspended solids in the water column by up to six-fold (Parkos III et al. 2003).

Coastal wetlands are vital habitats for Great Lakes fishes at all life stages. More than 80

Great Lakes species use coastal wetlands in one capacity or another, ranging from temporary nursery habitats to lifetime residence (Jude and Pappas 1992). Coastal wetlands are rich in food resources, including fish, invertebrates (Krieger 1992), algae (Vanni and Layne 1997), and macrophytes (i.e., aquatic vascular plants, Carpenter et al. 1985). The aquatic vegetation plays multiple roles as fish use it to avoid predation and to forage, often using specific vegetation types

(Savino and Stein 1989). Wetland plant communities are largely dependent on water levels because species-specific maximum heights and photosynthetic requirements limit areas in which plants can survive. (Chambers and Kaiff 1985). Wetlands also have substantive seed banks, which opportunistically grow when water levels drop (Keddy and Reznicek 1986). Thus, with varying Great Lakes water levels year to year, available fish habitat in the form of macrophytes can vary wildly, even in the same wetland in consecutive years (Wilcox et al. 2002). Much of the literature on the fish-macrophyte relationship focuses on the importance of cover (e.g., Brazner and Beals, 1997), but does not attempt to assess the relative importance of different macrophytes to fish abundance and diversity, despite structural differences in the cover they provide. For

1 example, water lily (Nymphaea spp.) provides thick cover high in the water column and sparse cover near the benthos, whereas submerged aquatic vegetation (SAV) offers the opposite.

Finally, cattail (Typha spp.) provides roughly even shelter throughout the water column. These species each have the potential to appeal to different fishes, depending on whether predator pressures coming from the water (e.g., other fishes, turtles) or outside of it (e.g., wading birds).

Objective

In this study, I provide insight into effects of vegetation on Great Lakes coastal wetland fish communities by assessing the influence of specific macrophyte types on adult fish abundance and species richness. Further, I investigated the impact of water depth on fish abundance, as water depths at which different macrophytes thrive can be quite variable. Finally, I characterized temporal patterns in fish abundance, species richness, and size distribution.

Hypotheses

H1: Different vegetation types will facilitate different fish community compositions, due to a

combination of differences in plant structure and prey availability.

H2: Fish abundance will increase with water depth, because macrophyte species are linked to

water depth, altering habitat complexity.

H3: There will be temporal patterns in fish abundance, species richness, and total length, due to

differences in the use of this wetland, particularly with respect to reproduction in the Old

Woman Creek watershed.

2

Methods

Study site

The study took place at Old Woman Creek National Estuary Research Reserve (OWC), in Huron, Ohio (Fig. 1). The coastal wetland system is unique in that there is an intermittent barrier beach at the mouth that periodically separates the OWC wetland from Lake Erie, depending on upstream flow and water levels in the wetland, classifying it as a protected coastal wetland, per the definition in Keough et al. (1999). The wetland features several small embayments and a variety of cover types, including cattail (Typha spp.; TY), water lily and lotus

(LLY), submerged aquatic vegetation (SAV), and some areas lack vegetation completely, which we refer to as open water (OW). Land use upstream in the OWC watershed is highly agricultural, resulting in heavy nutrient input via non-point source pollution. Sampling took place in vegetated sites throughout OWC (Fig. 1). North Bay (NB), North Island (NI), Overlook (OL), and Eagle

Bay (EB) were all similar in their moderate levels of vegetation cover, water depth, and flow.

The backwater sites, South Island (SI) and South Tracks (ST), were more shallow, sparsely vegetated, and low in flow.

Adult and juvenile sampling methods

Adult and juvenile fish sampling took place between 27 May and 17 October 2015.

Fishes were collected using 10 sets of paired small mesh fyke nets (1m x 1.25m, lead length =

20.4m, wing length = 2.8m, mesh size = ¾”, 20 total nets). Nets were set for approximately 24 hours in one vegetation cover type of one site each night (Fig. 1). Sampling took place on a weekly basis until September, at point I switched to biweekly sampling. Water depth was measured at the mouth of each fyke net. Only monotypic patches of TY, LLY, SAV and areas of

3

Figure 1: Map of Old Woman Creek National Estuary Research Reserve. Approximate locations of sampling sites are represented by bold letters. Coordinates of estuary mouth: 41.383829°N, 82.514165°W. NB = North Bay; NI = North Island; SI = South Island; OLK = Overlook; EB = Eagle Bay; ST = South Tracks. Map from OWC NERR.

NB

NI

OL SI

EB

ST

4 open water were sampled and the leads of the fyke net were extended through the vegetation being sampled. This ensured that all fish captured came from that cover type. Upon retrieval of the nets, fish that were captured were carefully removed and placed in an oxygenated bucket, from which they were pulled, identified and measured for total length (TL) before being released. In the event that a large number of a particular species was captured in a single net, 25 individuals were randomly selected to be measured, and all remaining individuals were counted.

If a sample was unidentifiable in the field (due to small size or our own unfamiliarity), it was euthanized and preserved in ethanol for later identification in the lab. Voucher specimens were kept to a minimum. Further, fish were classified as either “tolerant,” or “intolerant,” per the Ohio

EPA IBI manual (1987). One of my nets was dismissed from statistical analyses because it had a larger mesh size, which resulted in zero fishes being captured, possibly because fish escaped through the mesh. Thus, only 19 nets were considered in my analysis.

Supplementary fish sampling data were also shared with by from the OWC research staff.

While the goal of their data collection differed from ours, their sampling protocol was similar, with 10 sets of paired fyke nets (20 total nets from OWC), and site-specific descriptions of vegetation and water depth. They sampled more sites, all of which were smaller than ours, resulting in some of their sites being nested within ours. The other complication in using the

OWC data was that their study did not focus on the effects of vegetation on fish community structure, and therefore, vegetation types were not sampled evenly during the collection of their data. This resulted in up to 10 more fyke nets set in LLY patches than each other cover types.

The two datasets were combined into one, with pairs nets set during the same week being treated as replicates of one sampling period. By combining the two data sets, the number of nets was

5 more than doubled (19 to 39). Thus, a much more complete view of spatial and temporal variability of the OWC fish assemblage was achieved.

Statistical analyses

Trends in fish abundance and species richness with respect to time of year and water depth were assessed using linear regression. Total abundance and water depth data were log- transformed to meet the assumption of linearity. There is a barrier beach that forms intermittently at the estuary mouth, which is of ecological relevance because it would prevent movement of fishes in and out of the ecosystem when closed. Differences in species richness and abundance with respect to beach openness were analyzed with a paired t-test, because samples were collected from the same sites under both open and closed conditions.

The effects of vegetation on community assembly were analyzed using redundancy analysis (R version 3.2.2, Vegan). A Hellinger transformation was used on the data prior to analysis. Data were then permuted 999 times and laid onto an ordination plot with vegetation centroids. For all analyses, we used an α level of 0.05.

Results

From 27 May through 17 October 2015, 36 fish species were collected for a total of 1719 individual fish (Table 1). The five most abundant species - Goldfish (459 individuals, 26.7% of total catch), white bass (181, 10.5%), gizzard shad (150, 8.7%), white sucker (103, 6.0%), and bluegill (96, 5.9%) - accounted for 57.5% of the total catch (989 individuals, 57.5%; Table 1).

6

Table 1: Total fish captured during fyke net sampling (24 June - 17 October 2015). Species are listed in order of decreasing abundance, and sites are arranged in order of increasing distance from the mouth of the estuary. NB = North Bay, NI = North Island, OL = Overlook, SI = South Island, EB = Eagle Bay, ST = South Tracks. Data represented here is aggregated from my sampling as well as OWC sampling.

Species Total % NB NI OL SI EB ST Goldfish (Carassius auratus) 459 26.7 238 12 16 32 82 79 White Bass (Morone chrysops) 181 10.5 56 73 39 7 6 Gizzard Shad (Dorosoma cepedianum) 150 8.7 16 59 5 24 38 8 White Sucker (Catostomus commersonii) 103 6.0 17 2 5 26 53 Bluegill (Lepomis macrochirus) 96 5.9 11 5 49 10 13 8 Green Sunfish (Lepomis cyanellus) 90 5.2 15 5 15 5 29 21 Common Carp (Cyprinus carpio) 84 4.9 2 17 3 16 2 44 Brown Bullhead (Ameiurus nebulosus) 74 4.3 9 17 30 2 14 2 Round Goby (Neogobius melanostomus) 59 3.4 10 1 4 12 32 Bigmouth Buffalo (Ictiobus cyprinellus) 58 3.4 2 2 1 1 2 50 Pumpkinseed (Lepomis gibbosus) 49 2.9 14 7 11 15 2 Bluntnose Minnow (Pimephales notatus) 47 2.7 8 3 9 11 16 Yellow Perch (Perca flavescens) 47 2.7 8 9 6 21 3 White Crappie (Pomoxis annularis) 41 2.4 10 6 12 13 Unidentified fish (genus unknown) 30 1.7 5 25 Spotfin Shiner (Cyprinella spiloptera) 26 1.5 8 9 9 Emerald Shiner (Notropis atherinoides) 19 1.1 4 7 8 Black Bullhead (Ameiurus melas) 18 1.0 2 2 1 13 Largemouth Bass (Micropterus salmoides) 14 1.0 1 5 1 6 1 Black Crappie (Pomoxis negromaculatus) 13 0.8 2 6 4 1 Orangespotted Sunfish (Lepomis humilis) 11 0.6 2 2 5 2 Spottail Shiner (Notropis hudsonius) 11 0.6 6 1 4 Logperch Darter (Percina caprodes) 8 0.5 3 3 2 Yellow Bullhead (Ameiurus natalis) 7 0.4 4 2 1 Bowfin (Amia calva) 4 0.2 1 2 1 River Chub (Nocomis micropogon) 4 0.2 1 1 1 1 Tadpole Madtom (Noturus gyrinus) 3 0.2 1 2 Johnny Darter (Etheostoma nigrum) 2 0.1 2 Quillback (Carpiodes cyprinus) 2 0.1 1 1 Redeared Sunfish (Lepomis microlophus) 2 0.1 2 Unidentified Sunfish (Lepomis spp.) 2 0.1 2 Brook Stickleback (Culaea inconstans) 1 0.1 1 Fathead Minnow (Pimephales promelas) 1 0.1 1 Mottled Sculpin (Cottus bairdii) 1 0.1 1 Spotted Sucker (Minytrema melanops) 1 0.1 1 Walleye (Sander vitreus) 1 0.1 1 36 Species 1719 Individuals

7

Average abundance caught per trap showed no distinct temporal pattern, ranging from 8 to 80 fish per fyke net throughout the sampling season (Fig. 2) Temporal trends in cumulative total abundance and species richness were not analyzed due to uneven sampling effort early in the season.

Along with the five most abundant species (listed above, and in Table 1), special attention was paid to common carp (Cyprinus carpio) and white crappie (Pomoxis annularis), due to their prevalence in our study on the larval fish community (Chapter 3). The abundance of these seven species fell into one of three temporal patterns (Fig. 3). Goldfish (Carassius auratus,

Fig. 3a) and white sucker (Catostomus commersonii, Fig. 3d) peaked in their abundance early in the season, before 8 July. Gizzard shad (Dorosoma cepedianum, Fig. 3c), bluegill (Lepomis macrochirus, Fig. 3e), common carp (Fig. 3f), and white crappie (Fig. 3g) all peaked later in the season, between 8 July and 4 August. The final abundant species, white bass (Morone chrysops,

Fig. 3b) displayed no distinct peak, its abundance remaining somewhat consistent throughout the summer. For many of these species, their peak abundance occurred in fyke nets set in water lily and lotus patches, with some exceptions (Fig. 3). White sucker abundance peaked in an SAV patch (Fig. 3d), and declined in abundance dramatically in the following weeks. Similarly, the mid-season bluegill peak occurred in an OW fyke net, but nets with high bluegill abundance throughout the rest of the season were mainly in LLY nets. White crappie (Fig. 3g) seemed to be found in each of the four cover types somewhat evenly.

Fish abundance was lowest in the middle of the estuary, with steady increase towards the estuary mouth and upstream portion of the estuary, with the South Tracks location (ST, Fig. 1) containing 513 individuals (29.8% total catch; Fig. 4a). Abundance was not distributed evenly in

OWC (χ2: P < 0.0001), and neither was species richness (χ2: P = 0.032). Interestingly, ST had a

8

Figure 2: Average fish abundance and species richness during fyke net sampling. (24 June - 17 October 2015). a) Average fish abundance (per fyke net) on each sampling date. b) Average species richness (per fyke net) on each sampling date. Error bars represent standard error of the mean. Data represented here is aggregated from my sampling as well as OWC sampling.

140 a 120

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Jun Jul Aug Sep Oct Nov 18 b 16

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9

Figure 3: Average fish abundance in each microhabitat, by date. Each bar represents a single pair of nets. Nets per date: 24 June: 1; 1 July: 4; 8 July: 3; 15 July 1; 21 July: 3; 4 August: 1; 18 August: 2; 12 September: 1; 26 September: 1; 17 October: 1. The only pair of nets that was replicated was Typha on July 1. Error bars represent standard error of the mean. a) Goldfish, N=459; b) White Bass, N=181; c) Gizzard Shad, N=150; d) White Sucker, N=103; e) Bluegill, N=84; f) Common Carp, N=84; g) White Crappie, N=41. Panels are spread out onto the following page.

250 40 a Goldfish b White Bass LLY OW 200 SAV 30 TY

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11

Figure 4: Fish abundance and species richness by OWC sampling site. a) Total and average fish abundance. b) Total and average species richness. Error bars represent standard error of the mean. Site designations: NB = North Bay, NI = North Island, OL = Overlook, SI = South Island, EB = Eagle Bay, ST = South Tracks.

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12 relatively low species richness. South Island (SI) had the lowest total and average abundance, as well as the lowest average and total species abundance (Fig. 4b). Eagle Bay (EB) and North Bay

(NB) were the most species rich sites, with 28 and 29 species throughout the season, respectively. The remaining sites, except for SI, ranged between 19 and 24 species (Fig. 4b).

The log-transformed water depth and fish abundance data showed an inverse relationship between total fish abundance and water depth (Fig. 5), however this trend was not significant

(Linear regression: log(abundance) = -1.1706 * log(water depth in cm) + 3.5917; R2 = 0.101, P =

0.056), so this relationship is weak at best. There was no clear relationship between individual sampling site and water depth. There was one major seiche event towards during sampling on 18

August, resulting in water depths greater than 90 cm, but there was a wide range of abundances in deeper water in the log-transformed data, so the effects of the seiche are unclear.

The number of fish captured using fyke nets was significantly higher when the barrier beach at the estuary mouth was open than closed (paired t-test; P = 0.002, Fig. 6a). However, average abundance was not significantly different (P > 0.1; Fig. 6b), so the difference in total abundance was likely because the beach was open for more sampling days than it was closed.

Water lily and lotus (LLY) patches were significantly higher in total fish abundance (χ2:

P < 0.0001) and they were also highest in species richness, although this result was not significant (χ2: P = 0.329; Fig. 7). However, this is likely due to highly uneven sampling in LLY patches compared to the other three cover types (LLY: N = 17, OW: N = 7, SAV: N = 8, TY: N

= 7). However, LLY also had the highest average abundance (Fig. 7a). Average species richness was very similar across cover types, ranged from 6.4 species/net in cattail patches (TY) to 8.8 species/net in LLY patches (Fig. 7b). The results of the redundancy analysis on the effects of vegetation on the fish community were insignificant (P = 0.29, Fig. 8). White bass (WHB)

13

Figure 5: Total Fish Abundance by water depth (cm). Points represent individual fyke nets collected in all sites over the course of sampling. Data are log-transformed to meet the assumption of linearity.

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14

Figure 6: Comparison of total and average fish abundance in OWC when the barrier beach at the estuary mouth was open or closed (24 June and 17 October 2015). a) Total fish abundance. b) Average fish abundance. Error bars represent the standard error of the mean.

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15

Figure 7: Effect of Vegetation Type on Total and Average Fish Abundance and Species Richness. a) Total and average fish abundance in each vegetation type. b) Total and average species richness in each vegetation type. Error bars represent the standard error of the mean. LLY = Water Lily and Lotus; OW = Open Water; SAV = Submerged Aquatic Vegetation; TY = Cattail (Typha spp.).

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Figure 8: Fish Community RDA. Species are represented by small black text labels, while

vegetation types are represented by larger red text labels. All species codes are listed Table 3. 1.0

0.5 OW SAV

BLG UNK2BMB WHB BLBBNMWHCUNKWHSCAP STSYEBBONSUNWAEFHMBRSSPSBLCJDMSTPMQUBLMBGRSGOF 0.0 ORSRESFLOGEMSRIC YEPSPF GISROGPUS BRB

TY RDA2 (4%) RDA2

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17 separated from the main clump of species in center of the graph, between OW and LLY. Due to space constraints, three-letter species codes are used in Fig. 8, so an exhaustive list of all 36 species captured and their respective codes is included in Table 2.

Each cover type was dominated by tolerant species, with SAV patches containing zero intolerant species throughout the summer (Fig. 9). Tolerant and intolerant species are listed in

Table 3. Tolerant species comprised 48% - 70% on average of catch per fyke net in each cover type, and intolerant species made up less than 1% on average of catch per fyke net. The rest of fish captured were not classified as “tolerant,” or “intolerant,” in the Ohio EPA’s IBI manual

(1987). Further, tolerant species accounted for 60% of the total catch during sampling, whereas intolerant species made up less than 1%. There were 12 tolerant species captured during sampling, and only 2 intolerant species, which are listed in Table 3.

Four of the five most abundant species (Goldfish: P = 0.015, white bass: P = 0.0002, gizzard shad: P < 0.0001, and white sucker: P = 0.0005) increased significantly in total length

(mm) throughout the sampling period (Fig. 10a-d). Bluegill showed no such pattern of growth, with their largest sizes at the beginning and end of the sampling period and uniformly small sizes during the middle of the sampling season (Fig. 10e). A summary of all linear regression results with respect to fish growth, including P-values, R2 values, and regression equations is included in Table 4. White bass had a bimodal pattern in their total length frequency distribution, which suggests that I caught juveniles and adults during this study, with juveniles being more numerous

(Fig. 11b). Gizzard shad displayed a more normal size distribution (Fig. 11a). The other species size distributions were skewed left, as many were captured at relatively small sizes, with progressively fewer individuals being captured at longer total lengths (Fig. 11).

18

Table 2: Species Codes. These codes correspond to the small black labels in Figure 8.

Species Code Species BGB Bigmouth Buffalo Fish (Ictiobus cyprinellus) BLB Black Bullhead (Ameiurus melas) BLC Black Crappie (Pomoxis negromaculatus) BLG Blue Gill (Lepomis macrochirus) BNM Bluntnose Minnow (Pimephales notatus) BON Bowfin (Amia calva) BRB Brown Bullhead (Ameiurus nebulosus) BRS Brook Stickleback (Culaea inconstans) CAP Common Carp (Cyprinus carpio) EMS Emerald Shiner (Notropis atherinoides) FHM Fathead Minnow (Pimephales promelas) GIS Gizzard Shad (Dorosoma cepedianum) GOF Gold Fish (Carassius auratus) GRS Green Sunfish (Lepomis cyanellus) JOD Johnny Darter (Etheostoma nigrum) LMB Largemouth Bass (Micropterus salmoides) LOG Log Perch Darter (Percina caprodes) TPM Tadpole Madtom (Noturus gyrinus) MOS Mottled Sculpin (Cottus bairdii) ORS Orangespotted sunfish (Lepomis humilis) PUS Pumpkin Seed (Lepomis gibbosus) QUB Quillback (Carpiodes cyprinus) RES Redear Sunfish (Lepomis microlophus) RIC River Chub (Nocomis micropogon) ROG Round Goby (Neogobius melanostomus) SPF Spotfin Shiner (Cyprinella spiloptera) SPS Spotted Sucker (Minytrema melanops) STS Spottail Shiner (Notropis hudsonius) WAE Walleye (Sander vitreus) WHB White Bass (Morone chrysops) WHC White Crappie (Pomoxis annularis) WHS White Sucker (Catostomus commersonii) XXX Unknown Fish (2 distinct species) YEB Yellow Bullhead (Ameiurus natalis) YEP Yellow Perch (Perca flavescens)

19

Figure 9: Percent Tolerant Species in Each Cover Type. Tolerant species are comprised of 12 different species captured during sampling, whereas only 2 species were considered intolerant. Species were classified per Ohio EPA 1987. Due to low percentages of tolerant species in all cover types, scaling below the y-axis break is different than scaling above the break. Species included in this figure are listed in Table 3.

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20

Table 3: List of Tolerant and Intolerant Species Captured During Fyke Net Sampling.

Tolerant Species Intolerant Species White Sucker (Catostomus commersonii) Log Perch Darter (Percina caprodes) Green Sunfish (Lepomis cyanellus) River Chub (Nocomis micropogon) Blue Gill (Lepomis macrochirus) Pumpkin Seed (Lepomis gibbosus) Bluntnose Minnow (Pimephales notatus) Common Carp (Cyprinus carpio) Fathead Minnow (Pimephales promelas) Gold Fish (Carassius auratus) Spottail Shiner (Notropis hudsonius) Brown Bullhead (Ameiurus nebulosus) Yellow Bullhead (Ameiurus natalis) Black Bullhead (Ameiurus melas)

21

Figure 10: Species Growth During the Sampling Season. Species-specific lengths throughout the sampling period for a) Goldfish, b) White Bass, c) Gizzard Shad, d) White Sucker, and e) Bluegill. Individual lengths are represented by unfilled, small circles whereas species averages are shown as larger gray circles. Error bars represent the standard error of the mean. All individuals were collected between 24 June and 17 October 2015. Note that y-axis scales differ between panels.

350 100 a Goldfish b White Bass 300 80 250

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22

Table 4: Linear Regression Results- Growth of 5 Most Abundant Species Over Time. An * denotes a significant P-value at an α-level of 0.05.

Species P-value R2 Regression Equation Goldfish 0.015 * 0.66 y = 0.3715x - 15622 White Bass 0.0002 * 0.95 y = 0.6004x - 25298 Gizzard Shad 0.000004 * 0.95 y = 0.5869x - 24715 White Sucker 0.0005 * 0.92 y = 0.5987x - 25214 Bluegill 0.428 0.072 y = -0.2207x + 9379.4

23

Figure 11: Frequency Distribution of Fish Total Lengths During Sampling Season. Species- specific total length frequency distributions for a) Goldfish, b) White Bass, c) Gizzard Shad, d) White Sucker, and e) Bluegill. Due to wide ranges in cumulative abundance and total lengths between species, x- and y-axis ranges differ between panels.

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24

Discussion

Thirty-six species were captured during 2015, which allows for a comparison with previous sampling efforts within the Old Woman Creek watershed. Indeed, the fish community has changed quite a bit since the last major sampling effort. Brammell et al. (2009) collected and identified 34 species in 2002 and 2004, but the composition of species was quite different between these years (Table 5). Five species were added to the list of known species in the OWC watershed during 2015: bigmouth buffalo (Ictiobus cyprinellus), river chub (Nocomis micropogon), brook stickleback (Culaea inconstans), central mottled sculpin (Cottus bairdii), and redear sunfish (Lepomis microlophus). However, there were several species captured in 2002 and 2004 that were not present in 2015 sampling efforts: rock bass (Ambloplites rupestris), smallmouth bass (Micropterus dolomieu), silverjaw minnow (Notropis buccatus), striped shiner

(Luxilus chrysocephalus), golden shiner (Notemigonus crysoleucas), western blacknose dace

(Rhinichthys obstusus), creek chub (Semotilus atromaculatus), channel catfish (Ictalurus punctatus), longnose gar (Lepisosteus osseus), white perch (Morone americana), rainbow trout

(Oncorhyrncus mykiss), and freshwater drum (Aplodinotus grunniens). Further, sampling efforts dating back into the mid-1980s provided historical presence/absence data dating into the, in which 56 species were captured in four studies (Hoffman et al. 1985, Rotenbury et al. 1987,

Rotenbury et al. 1989, Herdendorf et al. 2001, as summarized in Brammel et al. 2009). Between these studies and Brammell et al. (2009), 24 species were lost from the watershed, while 27 were lost between the historical studies and this study. Interestingly, nine species that were present during historical studies, but not in Brammell et al. (2009), were sampled in 2015: including bowfin (Amia calva), spotted sucker (Minytrema melanops), white crappie, black crappie

25

Table 5: List of species in the OWC Watershed. Data from 2002 and 2004 are listed in Brammel et al. (2009), and historical data are from Hoffman (1985), Rotenbury et al. (1987 & 1989), and Herdendorf et al. (2001). This table is adapted from Brammell et al. (2009). This table is continued onto the next two pages.

Common 2002 & Family Scientific Name Name 2015 2004 Historical Amiidae Amia calva Bowfin X X Brook Atherinopsidae Labidesthes sicculus silverside X Catostomidae Carpiodes cyprinus Quillback X X X Catostomus commersonii White Sucker X X X Smallmouth Ictiobus bubalus Buffalo X Bigmouth Ictiobus cyprinellus Buffalo X Minytrema melanops Spotted Sucker X X Moxostoma Black duquesnei Redhorse X Moxostoma Golden erythrurum Redhorse X Moxostoma Shorthead macrolepidotum Redhorse X Ambloplites Centrarchidae rupestris Rock Bass X Lepomis cyanellus Green Sunfish X X X Lepomis gibbosus Pumpkinseed X X X Orangespotted Lepomis humilis Sunfish X X X Lepomis macrochirus Bluegill X X X Lepomis microlophus Redear Sunfish X Micropterus Smallmouth dolomieu Bass X X Micropterus Largemouth salmoides Bass X X X Pomoxis annularis White Crappie X X Pomoxis nigromaculatus Black Crappie X X Mottled Cottidae Cottus bairdii Sculpin X Alosa Clupeidae pseudoharengus Alewife X

26

Dorosoma cepedianum Gizzard Shad X X X Campostoma Central Cyprinidae anomalum Stoneroller X X Carassius auratus Goldfish X X X Cyprinella spiloptera Spotfin Shiner X X X Cyprinus carpio Common Carp X X X Silverjaw Ericymba buccata Minnow X X Luxilus chrysocephalus Striped Shiner X X Macrhybobsis storeriana Silver Chub X Nocomis micropogon River Chub X Notemigonus crysoleucas Golden Shiner X X Notropis Emerald atherinoides Shiner X X X Common Notropis cornutus Shiner X Notropis hudsonius Spottail Shiner X X X Rosyface Notropis rubellus Shiner X Notropis stramineus Sand Shiner X Bluntnose Pimephales notatus Minnow X X X Pimephales Fathead promelas Minnow X X X Rhinichthys Blacknose atratulus Dace X X Semotilus atromaculatus Creek Chub X X Esocidae Esox lucius Northern Pike X Brook Gasterosteidae Culaea inconstans Stickleback X Neogobius Gobiidae melanostomus Round Goby X X Yellow Ictaluridae Ameiurus natalis Bullhead X X X Brown Ameiurus nebulosus Bullhead X X X Ameiurus melas Black Bullhead X X Channel Ictalurus punctatus Catfish X X

27

Stonecat Noturus flavus Madtom X Tadpole Noturus gyrinus Madtom X X Lepisoteidae Lepisosteus osseus Longnose Gar X X Moronidae Morone americana White Perch X X Morone chrysops White Bass X X X Etheostoma Rainbow Percidae caeruleum Darter X X Etheostoma nigrum Johnny Darter X X Perca flavescens Yellow Perch X X X Percina caprodes Logperch X X Sander vitreus Walleye X X Salmonidae Oncorynchus kisutch Coho Salmon X Oncorynchus mykiss Rainbow Trout X X Osmerus mordax Rainbow Smelt X Aplodinotus Freshwater Sciaenidae grunniens Drum X X Central Umbridae Umbra limi Mudminnow X

28

(Pomoxis nigromaculatus), black bullhead (Ameiurus melas), tadpole madtom (Noturus gyrinus), johnny darter (Etheostoma nigrum), logperch (Percina caprodes), and walleye (Sander vitreus).

The changes in Old Woman Creek’s fish community structure over the past 100 years can largely be attributed to shifts in dominant vegetation and habitat degradation. There are two dominant invasive macrophytes in the OWC estuary (narrow leaf cattail [Typha angustifolia] and common reed [Phragmites australis]), both of which are unsuitable habitat for fish (Weinstein and Balletto 1999, Able and Hagan 2003, Aday 2007). For example, northern pike (Esox lucius), which were not captured in my study, have specific cover requirements for successful reproduction (McCarraher and Thomas 1972). Northern pike populations have consistently declined since the late 19th century (Brammell et al. 2007), coinciding with the invasion of

Phragmites (Saltonstall 2002). This decline increased following the 1950s (Brammell et al.

2007), which was the onset of the cattail invasion (Tuchman et al. 2009). The effects of narrow leaf cattail are discussed in deeper detail below. Since the historical studies (Hoffman et al. 1985,

Rotenbury et al. 1987, Rotenbury et al. 1989, Herdendorf et al. 2001, Brammel et al. 2009) OWC fish community has shifted from being more diverse to being dominated by tolerant species, such as common carp and goldfish. This may be a sign that changes in environmental variables have been limiting survival of young-of-the-year cohorts from many species to the point that only tolerant species can survive their critical growth periods.

The past few decades have seen an increase in studies using fish in indices of biotic integrity (IBIs). Many such studies have used water quality parameters, human development, and vegetation in union with fish community data to assess wetland degradation and health (Karr

1981, Minns et al. 1994, Uzarski et al. 2005). Applied efforts such as these serve a dual purpose

29 in the field of wetland ecology, as they simultaneously aim to improve wetland habitat quality and gain further understanding of the relationship between fish and their environments.

Round goby have persisted in the watershed since at least the 2002-2004 study, which is of interest because of this species’ reputation as an invasive species throughout the Great Lakes

(Jude et al. 1992). At the onset of their invasion, scientists projected that round gobies would have measurable impact on other benthos-dwelling fishes (Jude et al. 1992), which was later realized when they were found to be responsible for the decline of central mottled sculpin in parts of Lake Michigan, followed by at-risk species hotspots throughout the Great Lakes.

(Janssen and Jude 2001, Poos et al. 2010). Their main impact on the Great Lakes basin has been a reduction in fish diversity, but there are also concerns about how round goby may change how contaminants bioaccumulate in food webs, which could expand the impact of this invasion to terrestrial ecosystems (Charlebois et al. 2001, Cristol et al. 2008).

There was a marked decrease in abundance and species richness throughout the 2015 sampling period. These patterns are likely attributable to different species-specific uses of Great

Lakes coastal wetlands in relation to reproductive biology. Great Lakes fishes fall into two over- arching categories: fishes that live exclusively in one or more of the Great Lakes, and fishes that use coastal wetlands to varying degrees. Of the fishes that use Great Lakes coastal wetlands, some migrate before and after spawning, while others stay in wetlands for the majority of their life cycles (Jude and Pappas, 1992). A variety of migration patterns for many of the fishes in our study have been documented in the literature (Landsman et al. 2011, Schoen et al. 2016). These migrations are likely the cause of the decrease in abundance, as many fishes would have exited the estuary for open Lake Erie. Likewise, as a number of species exit the estuary and others remain, one would expect to see a dip in species richness. Larval fish abundance and species

30 richness (Chapter 2) shows that larval fish abundance and diversity typically peak in late June and early July and then slows. Shortly after this peak in larval abundance is when we saw the decrease in adult fish abundance and species richness.

The barrier beach had minimal impact on the movement of fishes over the course of our study, as it was open for much of the season. Barriers such as beaches can prevent the migration of fishes between bodies of water, although there is little literature documenting the magnitude of this effect on fish communities. While literature on the impacts of natural barriers on fish movement was unavailable, man-made physical barriers have been effective in controlling the spread of select species such as the invasive sea lamprey through the Great Lakes (Hunn and

Youngs 1980, Porto et al. 1999).

Fish movement patterns associated with reproduction may also explain some of the variability in spatial distribution in fish abundance, where they were most abundant at upstream and downstream ends of the estuary. Fishes that use the wetland for spawning purposes may enter from either the upstream watershed or from Lake Erie via the estuary opening.

Fish abundance had an inverse relationship with water depth, which is supported in lakes

(Jeppesen et al. 1997), despite lower survival due to increased predation risk in shallow waters

(Harvey and Stewart 1990). Studies in coastal ecosystems note the positive influence of rising water levels on fish species diversity, though many individuals of these species are often large and abundance is not often addressed (Gelwick et al. 2001, Thomas and Connolly 2001).

There were no apparent patterns in abundance over time within sites, even as a result of the beach opening late in the season. This, coupled with the lack of any real pattern in species richness among sites suggests either that environmental variables other than vegetation type are

31 playing an important role in the distribution of fishes in the estuary, or fish distribution is random. Future studies aimed at assessing this pattern should pay close attention to abiotic variables, especially those that factor into macroinvertebrate and macrophyte ecology (e.g. turbidity, as in Brazner and Beals 1997). Further, dissolved oxygen can also play a significant role in determining patterns of fish distribution, given the potential for daily dissolved oxygen fluctuations.

The fish-macrophyte relationship has been relatively well-described as a positive correlation where increasing macrophyte cover and species richness in a coastal wetland allows for higher fish abundance and species richness (Brazner and Beals 1997), up to a point at which vegetation is too thick for fish to be able to move effectively (Killgore et al. 1989). Increased abundance and diversity may be due to increased availability of macroinvertebrate prey in vegetated waters (Gerrish and Bristow 1979, Keast, 1984), and some studies highlight certain cover types as more important for macroinvertebrates than others (Olson et al. 1995, Batzer

2013, but see Cooper et al. 2006).

While total fish abundance and species richness were highest in LLY patches in my study, this is probably an artifact of uneven sampling efforts among different vegetation cover types. This is borne out in by the fact that average abundance and species richness in LLY were very similar to the other habitat types. Further, average abundance per fyke net was not significantly lower in open water patches, which does not support previous claims that fish prefer vegetative cover to open habitats.

In this study, I found no significant vegetation cover type relationships with either individual species or community structure, and thus, found no support for hypothesis one. This is not surprising given that much of the literature on fish-macrophyte relationships stresses the

32 importance of vegetation in determining fish abundance, but does not highlight relationships between any specific fish species and specific species of macrophytes or vegetation cover types

(Brazner and Beals 1997). This also provides context for the impacts of invasive macrophytes on fish communities. For example, the invasion of Phragmites australis, has concerned wetland biologists for quite some time, as it has negative impacts on macrophytes (Odum et al. 1984) wetland birds (Benoit and Askins 1999), and fish (Weinstein and Balletto 1999, Able and Hagan

2003). While Phragmites australis was present in OWC, I examined fish captured in patches of narrow leaf cattail (Typha angustifolia), which is also an invasive species that may negatively affect fish communities by limiting usable habitat (Aday 2007).

In general, individual fish species showed increasing growth over the sampling season, which is not surprising as individual cohorts increased in size during the growing season. The seasonality of the bluegill size of distribution is probably due to an initial mixed group of adults and second-year juveniles and development of the young of the year cohort during their protracted spawning season. Bluegill also experience ontogenetic shifts in habitat use and prey preference, which alter growth rates in individuals in different size classes (Mittelbach 1984,

Werner and Hall 1988, Osenberg et al. 1988).

Conclusion

Hypothesis one, which stated that there would be significant relationships between specific macrophytes and different species of fish was not supported, suggesting that the vegetation type does not alter fish community composition. The second hypothesis, that increasing water depth would lead to increased fish abundance was also not supported, as there

33 was a trend of decreasing abundance with increasing water depth, although this trend was not significant. Clear temporal patterns fish abundance and species richness were observed as they decreased over the course of the sampling period, likely due to movements of various species surrounding spawning, supporting hypothesis three. Further, most fish species showed significant growth patterns throughout the season, with the exception of bluegill. Bluegill have been shown in other studies to have complicated growth patterns that can be highly influenced by environmental variables and ontogenetic shifts in prey preference, which likely led to the lack of any discernable temporal pattern in growth in this species.

With my findings, I propose several management implications to promote fish community health in Old Woman Creek. As there was no significant impact of specific vegetation cover types on fish community composition, managers should promote vegetative cover of any kind, while making sure not to compromise vegetation diversity with invasive plant growth. Increased water depth limited fish abundance, but it is difficult to control water levels at a local scale, as it is influenced by regional forces. I suggest that channelization in the upstream watershed be limited, as channelized areas tend to increase discharge and flow velocity. These increases in discharge and flow may deliver water at a rate that is faster than the retention time in the OWC estuary, which would increase water levels. At a regional and global scale, policy makers must be sensitive to the reality of climate change, which is projected to increase the intensity and frequency of storms in the Great Lakes region. Finally, my research highlighted temporal patterns in fish abundance and species richness, which are likely due to fish movement associated with reproduction. I suggest that OWC managers work to maintain the linkage between estuary and Lake Erie in order to promote fish reproduction. These management

34 suggestions are meant to promote and sustain the health of the fish community and the health of the OWC estuary as a whole.

35

CHAPTER 3 - LARVAL FISH COMMUNITY RESPONSE TO VEGETATION AND WATER

QUALITY IN A COASTAL WETLAND

Introduction

Larval Fishes in Great Lakes Estuaries

Healthy and abundant larval fish populations are imperative for sustaining adult fish populations in surrounding waters (Stephens et al 1986). While this is a clear issue for ecosystem health, it also has economic implications as the combined revenue from commercial and recreational fishing in the Great Lakes alone tops $5 billion annually (NOAA n.d.). The two main targeted species for sport fishing in Ohio’s portion of Lake Erie – Walleye (Sander vitreus) and Yellow Perch (Perca flavescens) – account for over $100 USD/day, and this Fig. only considers private-boat fishing (Hushak et al. 1988). These species utilize the fringing marshes adjacent to Lake Erie as spawning and nursery habitats (Auer 1982), making such ecosystems important for maintaining this economic asset.

In fact, a variety of fishes use coastal wetlands and estuaries in a variety of ways. Some species use wetlands strictly as spawning habitat, and their larvae then use the wetland as a nursery. These fishes grow to a species-specific size or age and then leave the wetland for the open lake. Other fishes live exclusively in coastal wetlands throughout all life stages (Jude and

Pappas 1992).

36

Factors Affecting Larval Fish Distribution

Despite their importance for healthy fish communities, there are few larval community studies from freshwater estuary/coastal wetland ecosystems. Estuarine larval fish communities are well-studied however, they point to salinity as the main driving force behind community assembly (Bulger et al. 1993, Barletta et al. 2005). Water temperature can also strongly affect brackish estuarine larval fishes (Harris et al. 1999).

However, salinity is a non-factor in freshwater ecosystems such as Great Lakes estuaries and coastal wetlands. This opens the door for other abiotic variables, such as turbidity and water temperature, to dictate community assembly in freshwater estuaries, although another study failed to find evidence supporting the impact of abiotic variables on larval fish community structure (Dewey and Jennings, 1992). For example, larval northern pike (Esox lucius), seek vegetative cover more frequently which is thought to be anti-predator behavior (Lehtiniemi et al.

2005).

Vegetation cover has also been identified as a factor of importance in larval fish distribution in stream habitats. The presence of cover, regardless of type, can influence the abundance of larval fishes (Dewey and Jennings 1992, Scheidegger and Bain 1995). Further, different types of vegetation may be more widely used by larval fish. For example, submerged aquatic vegetation had a higher abundance of larval fish than other cover types in one study

(Holland and Huston 1984), although different species of fish may prefer different densities of cover (Petering and Johnson 1991). Larval fish may also undergo ontogenetic shifts in habitat preference as they grow (Scott and Nielson 1989).

37

Flow rate can also influence larval fish distribution, with loss of suitable habitat due to strong flow and because fish larvae lack the muscle mass to combat high-velocity conditions

(Copp 1991, Houde 1969). Discharge has also been shown to impact larval fish survival due to increased suspended sediments in the water column, which can damage and kill larval fish (Mion et al. 1998). These studies examine the effects of individual factors, but ultimately ignore the possibility for any synergistic effects between multiple variables. Further, most studies examining the effects of these variables on larval fish communities have been in marine or stream habitats.

Questions

In this study, I aimed to document the diversity and spawning dates of the larval fish community, as well as any relationships between larval abundance and vegetative cover and abiotic variables. The following hypotheses drove study design and analysis.

Hypotheses

H1: Larval fish community structure is driven primarily by water depth and plant

cover type.

H2: Larval abundance and diversity vary over time with species-specific

differences in spawning period and duration

Methods

Study Site

Old Woman Creek National Estuarine Research Reserve (OWC) is in Huron, Ohio

(41.383829°N, 82.514165°W, Fig. 12). OWC is managed by the Ohio Department of Natural

Resources and the National Oceanic and Atmospheric Administration (NOAA). OWC is one of

38 two fresh water estuaries in NOAA’s National Estuarine Research Reserve System. The estuary is fed by the Old Woman Creek watershed and drains into Lake Erie. The mouth of the estuary is periodically obstructed by a barrier beach on an annual basis, which prevents the passage of water and fishes between the estuary and the lake when closed.

Fish Research at OWC

While I am not aware of larval fish studies at OWC, the adult fish community has been studied several times during the past three decades (Hoffman 1985, Rotenberry et al. 1987,

Rotenberry et al. 1989, Herdendorf et al. 2001, Brammell et al. 2009). These studies offer snapshots of the adult fish community, but they did not sample larval fishes. They catalogue a variety of fishes that potentially spawn in the wetland, including yellow perch and walleye, among others.

Light Trap Construction

Light traps were constructed using 750 mL Nalgene© bottles. Circles were cut out of the bottom of the bottle and the lid. Funnels were then fixed into the circles on either end using galvanized metal screws. Traps were sealed with waterproof caulk and outfitted with galvanized metal collars. Two submersible LED tea lights were then placed in each trap when they were deployed.

Traps were periodically attached to 8’wooden stakes with 16-gauge metal wires. The stakes were permanently placed in the estuary for the duration of the sampling period.

Larval Fish Collection

Larval fish sampling/collection was accomplished using light traps. Light traps were constructed with 750 mL Nalgene© bottles using a design by Ferenc deSzalay (Kent State

University). Circles were cut out of the bottom of the bottle and the lid. Funnels were then fixed

39

Figure 12: Map of Old Woman Creek. Approximate locations of sampling sites are represented by bold letters. Coordinates of estuary mouth: 41.383829°N, 82.514165°W. NB = North Bay; NI = North Island; SI = South Island; OLK = Overlook; NEB = North Eagle Bay; SEB = South Eagle Bay; ST = South Tracks. Map from OWC NERR.

40 into the circles on the bottle’s bottom and lid using galvanized metal screws. The ends were further sealed using waterproof caulk and outfitted with galvanized metal collars for mounting on stakes.

For sampling. Two submersible LED tea lights were placed in each trap they were deployed. During each sampling period, traps were attached to 2.4 m wooden stakes with 16- gauge metal wires so that they sat 5-10 cm above the wetland bottom.

Larval fishes were collected during 2015 and 2016, using different sampling designs.

During 2015, ten sampling dates took place from 26 June 2015 to 18 October 2015, and all samples were taken from monodominant stands of vegetation in eight different zones within

OWC (Fig. 12). Four different classes of vegetation were sampled in the wetland: cattail (TY), lotus/water lily (LLY), submerged aquatic vegetation (SAV), and open water (OW), which is the lack of any vegetation. Vegetation was sampled based on availability in a site, meaning that not all cover types were sampled in all sites. However, each cover type was sampled six times each date. Three samples were taken from each zone on each sampling date for a total of 207 samples.

Water depth was the only abiotic variable measured at each stake during 2015.

In 2016, sampling took place biweekly from 15 April 2016 to 8 August 2016, for a total of 9 sample dates. For this year, sampling was not done in the North Road zone because it was relatively small in comparison to other zones, and most heavily influenced by Lake Erie, with extreme fluctuations in water level and a lack of suitable vegetation in 2015. Thus, for 2015, data from North Road were added to North Bay for resulting figures and analyses. Further, in 2015

Eagle Bay represented a single zone. However, it was split into two sites (North Eagle Bay and

South Eagle Bay) for 2016, due to its large size in relation to other sites. Three light traps were set in each zone (for a total of 21 traps each week, 189 for all of 2016). Locations of the traps

41 were randomly selected prior to the growth of any vegetation. Traps in a zone were set 10-20 m away from one another, and zones were greater than 50 m away from one another.

Stakes were not set in monodominant vegetation stands during 2016, rather they were set in more representative locations with a mixture of vegetation types. Thus, each vegetation type within a 2-m radius of each stake was identified and its percent cover was estimated on each sample date. This survey provided a dynamic vegetation profile for each site across the entire sampling season.

Larval fish samples were collected using the same light traps as used in 2015. Traps were set at 21 permanent locations/stakes throughout the estuary (Fig. 1). Each trap was set and left overnight (for approximately 18 hours total). Upon collection, the contents of each trap were filtered and rinsed into Whirl-Paks (Nasco) with 100% ethanol. Samples were then examined under a magnifying glass and larval fish specimens were removed for later identification. Larval fishes were identified to species and catalogued. Due to the inherent difficulty of distinguishing between different cohorts of the same species within a sample, larvae were also measured for total length to plot species-specific size distributions over time. These same processing methods were also used for the 2015 samples.

Ecological Parameters

During the 2016 sampling period, abiotic variables were recorded at each site/stake using a YSI Professional Plus water quality monitor. Data were collected for water temperature, pH, conductivity, specific conductivity, and dissolved oxygen concentration and percent saturation.

Water depth and secchi depth (as a surrogate for turbidity) were also measured at each site/stake.

Statistical Analysis

42

Chi-squared (χ2) analysis was used to test the effects of vegetation on larval abundance and diversity throughout the entirety of the 2015 sampling season. This statistical test assumed even distributions of fishes and species in each of the vegetative cover types (LLY, SAV, TY, and OW).

The effects of vegetation on community assembly in 2016 were analyzed using redundancy analysis (R version 3.2.2, Vegan). Species data were Hellinger-transformed and permuted 999 times and laid onto an ordination plot with vegetation centroids. For all analyses, we used an α-level of 0.05. Redundancy analysis (RDA) was selected because of its ability to test hypotheses, which is a unique property that is uncommon for many multivariate statistical techniques. Statistical methodology for assessment of vegetation differed between years due to changes in sampling protocol. In 2015, locations for light trap stakes were chosen after vegetation was already well-developed, making it much easier to identify and choose mono- dominant stands of vegetation. In 2016, permanent stake locations were selected prior to the growth of vegetation. The result of this change was that traps were set in patches with highly mixed vegetation, which became apparent after the first few dates as vegetation began to emerge.

Linear regression was used to assess trends in abiotic variables across 2016 sampling dates. Variables included in regression analysis were water depth (cm), water temperature (°C), dissolved oxygen (mg/L), secchi disk depth (cm), specific conductivity (μS/cm), and pH. All readings for these variables were treated as continuous data, as was date.

To detect differences in abiotic variables across sampling sites for 2016, one-way analysis of variance was applied to data collected during the main peak in larval abundance (24

May – 21 June 2016). I only included data from these dates because they would be most relatable to the larval fish community based upon proximity in time. These dates featured very high

43 abundance of fish larvae, as well as a high rate of successful catches in nearly every trap deployed. Further, 21 of the 22 species captured in 2016 were collected on at least one of these three dates, and the only species that did not appear (greenside darter, Etheostoma blennioides) was represented by only one individual for the entire season.

Results

During the 2016 sampling season nearly seven times as many larval fishes were captured as in 2015 (2042 fish larvae in 2016 compared to 297 larvae in 2015; Table 6), likely due to earlier sampling dates in 2016. More than half of the 2016 larvae were captured on a single date

(6 June 2016), which falls earlier than when traps were initially set in 2015 (2 July 2015).

Further, more species captured in 2016 than in 2015 (Table 6). Over the course of the two sampling seasons, 26 species were collected, 12 of which were collected in both seasons. During

2015, 16 species were collected, compared to 22 in 2016. Species that were only caught in 2015 were channel catfish (Ictalurus punctatus), common shiner (Luxilus cornutus), creek chub

(Semotilus atromaculatus), and redear sunfish (Lepomis microlophus). Species that were only captured in 2016 were black bullhead (Ameiurus melas), spotfin shiner (Cyprinella spiloptera), rosyface shiner (Notropis rubellus), western blacknose dace (Rhinichthys obstusus), gizzard shad

(Dorosoma cepedianum), spotted sucker (Minytrema melanops), green sunfish (Lepomis cyanellus), largemouth bass (Micropterus salmoides), green side darter (Etheostoma blennioides), and yellow perch (Table 6). During larval fish were caught on all sample dates from 26 June 2015 until 24 August 2015 (Fig. 13). Larval fish abundance peaked early in the

2015 season, with >50% of all larvae collected on 8 July (Fig. 13). Based upon 2016 sampling, it is clear that the 2015 sampling period did not begin early enough to be confident that the samples

44

Table 6: 2015 and 2016 Species Abundances. All species captured through light trapping in both sampling seasons (26 June – 18 October 2015; 15 April – 8 August 2016). There were 297 individuals and 16 species in 2015, compared to 2042 individuals and 22 species in 2016. Species codes in the left-hand column correspond to labels in Fig. 28.

Species Abundance Code Species 2015 2016 BLB Black Bullhead (Ameiurus melas) 7 CHC Channel Catfish (Ictalurus punctatus) 3 GOF Goldfish (Carassius auratus) 57 394 SPF Spotfin Shiner (Cyprinella spiloptera) 9 CAP Common Carp (Cyprinus carpio) 78 702 CNS Common Shiner (Luxilus cornutus) 1 EMS Emerald Shiner (Notropis atherinoides) 1 1 RFS Rosyface Shiner (Notropis rubellus) 3 BNM Bluntnose Minnow (Pimephales notatus) 59 2 FHM Fathead Minnow (Pimephales promelas) 20 3 BND Black Nose Dace (Rhinichthys atratulus) 1 CRC Creek Chub (Semotilus atromaculatus) 1 GIS Gizzard Shad (Dorosoma cepedianum) 4 BGB Big Mouth Buffalo (Ictiobus cyprinellus) 21 27 SPS Spotted Sucker (Minytrema melanops) 11 MOS Mottled Sculpin (Cottus bairdii) 16 12 ROG Round Goby (Neogobius melanostomus) 14 5 WHB White Bass (Morone chrysops) 12 254 GSF Green Sunfish (Lepomis cyanellus) 16 PUS Pumpkin Seed (Lepomis gibbosus) 1 8 RES Redear Sunfish (Lepomis microlophus) 1 BLG Blue Gill (Lepomis macrochirus) 1 65 LMB Large Mouth Bass (Micropterus salmoides) 19 WHC White Crappie (Pomoxis annularis) 9 490 GSD Greenside Darter (Etheostoma blennioides) 1 YEP Yellow Perch (Perca flavescens) 7 26 TOTAL SPECIES 16 22

45

Figure 13: 2015 Total Larval Abundance. There were 297 total individuals collected in 24 traps set weekly between 26 June – 18 October 2015. No fish larvae were captured after 19 August 2015.

180

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46 collected on the first date were actually the first larval fishes of the year. Average larval abundance followed the same pattern as total abundance, peaking on 8 July 2015 (Fig. 14a).

Species richness varied slightly across sampling dates during 2015, with no clear pattern with respect to date, ranging from 2 species (27 July 2015) to 10 species (13 July 2015). Average species richness per trap varied little over the course of the 2015 sampling season (ranging from

0.27 to 1.19). Light traps set in LLY patches often had more larvae per trap than other cover types, but all cover types approached zero larvae per trap near the end of the season (Fig. 14b).

The three most abundant species in 2015 were common carp (Cyprinus carpio, 78 individuals, 26% of total catch), bluntnose minnow (Pimephales notatus, 59, 20%), and goldfish

(Carassius auratus, 57, 19%), which made up 65% of all individuals in 2015. Common carp, bluntnose minnow, and goldfish abundance peaked early in the season, trailing off through the remainder of the spawning period (Fig. 15). The most abundant species in 2016 were common carp, goldfish, white crappie (Pomoxis annularis), and white bass (Morone chrysops), the latter two of which only accounted for a combined 7% of total catch in 2015 (Fig. 16).

Vegetation cover had a significant effect on total abundance in 2015 (χ2, P < 0.001), but not species richness (χ2, P = 0.763; Fig. 17). Most fishes captured in submerged aquatic vegetation (SAV) and cattail (TY) patches were cyprinids, while family composition in LLY and open water (OW) was mixed (Fig. 18). Ictalurids were only collected in OW traps.

In 2015, the three most abundant species showed varying degrees of habitat association.

Common carp was a habitat generalist, appearing frequently in all cover types except for OW during its peak abundance (8 July - 22 July; Fig. 19a). Bluntnose minnows were found almost exclusively in TY patches (Fig 19b), while goldfish shifted in habitat association from TY to

47

Figure 14: 2015 Average Larval Fish Abundance over Time. a) Average abundance over time. b) Average abundance over time in each vegetation type. Error bars represent standard error of the mean. LLY = Water Lily and Lotus (N = 45); OW = Open Water (N = 44); TY = Cattail (Typha spp., N = 43); SAV = Submerged Aquatic Vegetation (N = 40).

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Figure 15: Abundance of Three Most Abundant Fishes of 2015. All individuals were collected between 26 June – 18 October 2015. These three species account for 194 (65%) of the 295 fishes collected.

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Figure 16: Abundance of Common Carp, White Crappie, Goldfish, and White Bass. These species were selected for this Fig. because of their prevalence throughout the 2016 sampling season. These four species accounted for 90% of 2016 catch, compared to 54% in 2015. For 2016 abundances, see Fig. 23.

60 Common Carp White Crappie 50 Goldfish White Bass

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Figure 17: Total Larval Abundance and Species Richness by Cover Type. Total larval abundance was highest in TY patches, while species richness was highest in LLY and OW patches.

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Figure 18: Relative Abundance of Families in Each Cover Type in 2015. Total larval fish abundance in each cover type was: LLY, 45; SAV, 60; TY, 146; OW, 23. Each bar represents proportions of total catch in those cover types, not total catch in the estuary. The “Other” classification is comprised of white bass, round goby, and mottled sculpin. Fishes in the families Percidae and Clupeidae (gizzard shad) were omitted, as 0 individuals from both families were collected in 2015.

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Figure 19: Proportion Abundance by Vegetation Type of Three Most Abundant Species of 2015. For clarity, only fishes captured on three dates (8 July, 15 July, and 22 July 2015) are included here, as these three dates make up 90% of all fish captured in 2015 a) Common Carp, b) Bluntnose Minnow, and c) Goldfish. Each panel has a different y-axis scale. LLY = Water Lily and Lotus; OW = Open Water; SAV = Submerged Aquatic Vegetation; TY = Cattail (Typha spp.).

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53

SAV between 15 July and 22 July, although they were present in the three other cover types as well (Fig 19c). Vegetation did not affect growth of these three species, except for goldfish, which were often smaller in TY patches (Fig 20c). Changes in total length for these species appeared to be more correlated with sampling date than vegetation (Fig. 20). Goldfish showed a unique growth pattern, appearing to “restart” with smaller individuals in August, which may have been a second cohort that spawned after the initial peak in July (Fig. 20c).

The Overlook site (OL), which was roughly in the center of the estuary had the highest average catch per trap in 2015, and average catch gradually decreased moving away from the center in either direction (Fig 21). This trend was remarkably similar in 2016, although the key difference between the two sampling seasons was that 2016 had much more abundant traps (Fig.

22). Another key difference was that Eagle Bay (EB) was divided into South Eagle Bay (SEB) and North Eagle Bay (NEB) after 2015.

Larval fishes were not present in the first sample taken in 2016. The first sample containing fish was collected on 29 April 2016, and fishes were consistently collected on each sampling date until 7 July 2016, peaking in early June (1552 individuals, 76% of the total, Fig.

23a). Unsurprisingly, average larval abundance per trap coincided with the only sampling date on which 100% of our traps caught at least one fish (Fig. 23b & c). In 2016 2041 individual fishes were captured in light traps. Light trap sampling ceased following two sampling weeks without the capture of any fishes (21 July and 8 August 2016; Fig. 23c). When plotted on the same axes, 2015 and 2016 total and average abundance fit together well, which suggests that I did indeed miss peak larval fish abundance in 2015 (Fig. 24).

54

Figure 20: Lengths of Larval Fish by Vegetation Type over Time. These three species were the three most abundant during 2015: a) Common carp, b) Bluntnose minnow, and c) Goldfish. Due to low variability in bluntnose minnow lengths, text annotations with the number of individuals of particular lengths are included for individuals from Cattail patches. TY = Cattail (Typha spp.); LLY = Water Lily and Lotus; OW = Open Water; SAV = Submerged Aquatic Vegetation.

14 a Common Carp 12 TY LLY OW 10 SAV

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55

Figure 21: 2015 Average Larval Fish Abundance Across Sampling Sites. Each bar represents the average of 24 traps set throughout the sampling period (2 July through 18 October 2015). Error bars represent the standard error of the mean. The x-axis is arranged in increasing order of distance from the estuary mouth, with NB closest to Lake Erie. NB = North Bay; NI = North Island; OL = Overlook; SI = South Island; SEB = South Eagle Bay; NEB = North Eagle Bay; ST = South Tracks. Consult Fig. 12 for approximate site locations.

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Figure 22: 2016 Average Larval Fish Abundance in Each Sampling Site. Each bar represents the average of 27 traps set throughout the sampling period (15 April through 9 August 2016). Error bars represent the standard error of the mean. The x-axis is arranged in increasing order of distance from the estuary mouth, with NB closest to Lake Erie. NB = North Bay; NI = North Island; OL = Overlook; SI = South Island; SEB = South Eagle Bay; NEB = North Eagle Bay; ST = South Tracks. Consult Fig. 12 for approximate site locations.

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Figure 23: 2016 Total Abundance, Average Abundance, and Proportion of Catches with Zero- Yield. a) Total larval abundance throughout the sampling period (15 April – 8 August 2016; N = 189 traps, 2041 total larval fish). b) Average larval abundance per trap throughout the sampling period. c) Percentage of traps with zero fish per sampling date (left y-axis; gray bars) relative to average larval abundance per trap (right y-axis; solid line). All error bars represent the standard error of the mean.

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Figure 24: Total and Average Larval Fish Abundances from 2015 and 20162. a) 015 and 2016 total larval fish abundance plotted using same axes. b) 2015 and 2016 average larval fish abundance plotted using same axes. Error bars represent standard error of the mean. The 2015 sampling spanned 26 June – 18 October 2015, while 2016 sampling spanned 15 April – 8 August 2016.

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In 2016, species richness ranged from 2 species (29 April 2016) to a peak species richness 15 species (6 June 2016) which coincided with the peak abundance. Common carp, white crappie, goldfish, and white bass were the most abundant larval fish in 2016, making up

90% of total catch (common carp: 702 individuals, 34%; white crappie: 490, 24%; goldfish: 394,

19%; white bass: 254, 12%). Goldfish also appear to have a more protracted spawning period than the other species (Figs. 16 & 25), similar to 2015, where they persisted beyond the other species (Fig. 25). The other three common species peaked on 6 June (Fig. 25). The only other noteworthy spawning pattern was exhibited in blue-gill (L. macrochirus), which were caught in low numbers for most of the 2016 sampling season (only 17 individuals from 15 April 2016 to

21 June 2016), but saw a late spike on July 7 (48 individuals). Lastly, only 2 bluntnose minnows were collected during 2016, despite it being the second most abundant species in 2015.

Early on during the 2016 measurement period, abiotic variables showed large fluctuations, likely due to spring conditions and large precipitation events. As the season progressed into summer, variability was greatly reduced. More specifically, water temperature increased throughout the summer, particularly as water levels in OWC declined (Figs. 26a & b).

Similar declines were seen in dissolved oxygen, secchi depth, and pH (Figs. 26c, d & f). Linear regression analysis showed significant temporal trends for both water temperature and dissolved oxygen (P = 0.008 and 0.020, respectively). Remaining variables did not display statistically significant trends (Table 7). Further, there were no significant differences among sites for any abiotic variables measured in 2016 (all P > 0.10).

Average percent cover of water lily and SAV increased across the summer, while open water and cattail decreased over time (Fig. 28). Decreased open water is intuitive because vegetative cover is expected to increase through the growing season. Cattail likely decreased

60

Figure 25: Species-specific Abundance for the Four Most Common Species of 2016. These four species accounted for 90% of all fishes captured between 15 April – 8 August 2016 (N = 1840, total N = 2041).

600 Common Carp White Crappie 500 Goldfish White Bass

400

300

200

100

Total Larval Larval Abundance Total

0 Apr May Jun Jul Aug Sep

61

Figure 26: 2016 Average Abiotic Measurements Over Time. a) Average depth (m), b) average temperature (°C), c) average dissolved oxygen (mg/L), d) average secchi depth (cm), e) average specific conductivity (μS/cm), and f) average pH. All 6 panels are compressed to fit onto 1 page, as there are no discernable important patterns in the data, except for the increase in temperature over time.

110 30 a b 100 28

90 26 24 80 22 70 20 60 18

Average Average Temp (°C) Average Average Depth (cm) 50 16 40 14 30 12

16 50 c d 14 45

12 40

10 35

8 30

6 25

Average Average DO (mgL) 4 20

Average Average Secchi (cm)

2 15

0 10

1000 8.6 e f 8.4 900 8.2 800 8.0

700 7.8

7.6

Average Average pH

Average Average SPC 600 7.4 500 7.2

400 7.0

Jul Jul Apr May Jun Aug Sep Apr May Jun Aug Sep

62

Table 7: Regression Results - 2016 Abiotic Variables. This table contains results from linear regressions assessing correlation between averages of these variables and date. An * denotes a significant p-value. Note that regressions on SPC and pH were nearly significant.

Variable R2 P-value Regression Equation Depth (cm) 0.338 0.100 y = -0.273x + 11665 Temperature (°C) 0.653 0.008* y = 0.1186x - 5021.9 DO (mg/L) 0.565 0.020* y = -0.082x + 3495 Secchi (cm) <0.001 0.953 y = -0.0052x + 249.17 SPC 0.548 0.054 y = -1.2106x + 52172 pH 0.428 0.056 y = -0.0069x + 301.02

63

Figure 27: Change in Vegetative Coverage throughout the 2016 Sampling Season. Percent cover of each of the four cover types in our sampling protocol, sampled between 15 April and 8 August 2016: a) Open water, b) Water lily and lotus, c) Typha spp. d) Submerged Aquatic Vegetation (SAV). All error bars represent the standard error of the mean.

Average % Cover Typha Average % Cover Open Water 100 120 10 20 30 40 50 20 40 60 80

Apr 0 0 c a

May

Jun

Jul

Aug

Sep

Apr Average % Cover SAV Average % Cover Lily d b

May

Jun

Jul

Aug

Sep

64

Figure 28: Redundancy Analysis of all Measured Variables and Collected Species. Species are represented by small, black text labels, while independent variables are represented by larger red labels and vectors. Only abiotic data collected between 24 May and 21 June 2016 were included in this analysis, corresponding to peak larval fish abundances. Data underwent permutation testing (number of permutations = 999). Easily read species codes are listed here, whereas an exhaustive list of codes can be found in Table 1. GOF = Goldfish, SPS = Spotted Sucker, CAP = Common Carp, BLG = Blue Gill, WHC = White Crappie, WHB = White Bass, GSF = Green Sunfish. Variable codes: DO = Dissolved oxygen; SAV = Submerged aquatic vegetation; LLY =

Water lily and lotus. In list of P-values, * denotes statistical significance. 1.0

GOF 0.5 Secchi

pHDO SPSGSF EMSRFSSPFBNDBNMGSDMOSBLBYEPROGLMBPUS 0.0 BGBGISFHM WHB

RDA2 (6%) RDA2 CAP BLG

SAV WHCLLY

-0.5 -1.0 -1.0 -0.5 0.0 0.5 1.0 RDA1 (13%)

65 because standing dead biomass was included in estimates early on and then either floated away or became overgrown with other new vegetative cover.

Redundancy analysis revealed four abiotic variables (Secchi depth - P = 0.017, dissolved oxygen - P = 0.001, pH - P = 0.001, water temperature - P = 0.033) and two cover types (SAV -

P = 0.025, LLY - P = 0.008) that were of significant importance to larval community structure.

(Fig. 28, Table 8). Further common carp were associated with SAV patches, while white crappie were highly associated with LLY patches. Thick cattail patches were not commonly inhabited by larvae (Fig. 29c), but other cover types featured approximately equal larval abundances at all densities (Fig. 29). Species richness was highest in OW and LLY in 2015, with 11 species in each over the course of the sampling year. These cover types shared eight species in common

(White crappie, goldfish, bigmouth buffalo, white bass [Morone chrysops], round goby

Neogobius melanostomus, common carp, mottled sculpin [Cottus bairdii], fathead minnow

[Pimephales promelas]).

The highest average abundances of common carp were found in low density patches of vegetation (0-20% cover TY, SAV, LLY; Fig. 30). White crappie were found in their highest numbers in higher density LLY, and in moderate numbers (20-60 fish per trap) in a wide range of open water coverage (Fig. 31). Goldfish were found in high abundance across all densities of all cover types (Fig. 33), except for TY, which was relatively low in percent cover throughout

2016 (Fig. 16c). White bass were primarily captured fairly evenly throughout all cover densities, with a slight peak between 20-60% OW (Fig. 33).

66

Table 8: Environmental Variables from Redundancy Analysis. All environmental variables were included in redundancy analysis meant to identify factors important to larval fish community structure, along with their respective P-values. Environmental variables matching cover vegetation classes were included using percent cover measurements. Variables marked with an * were significant (α = 0.05), and appear in the ordination plot (Fig. 28).

Environmental Variables P-value Depth (cm) 0.169 Secchi Depth (cm) 0.017 * Temperature (°C) 0.033 * Dissolved Oxygen (mg/L) 0.001 * SPC (μS/mg) 0.080 pH 0.001 * TY 0.089 SAV 0.025 * LLY 0.008 * OW 0.115

67

Figure 29: Average Abundance of Larval Fishes by Percent Cover of all Vegetation Types. In the interest of clarity, only larval fishes captured between 24 May and 21 June 2016 are included in this figure. These dates featured the highest abundances of larval fishes, as illustrated in Fig. 9a. Each panel represents all 63 traps from these 3 dates (21 per date) with a total of 1957 fishes (96% of the total). a) OW = Open Water, b) LLY = Water lily and lotus, c) TY = Cattail (Typha spp.), and d) SAV = Submerged Aquatic Vegetation. Error bars represent the standard error of the mean.

Average Larval Abundance (Per Trap) 100 150 200 250 300 100 120 50 20 40 60 80 0 0 c a 0 20 % Cover Typha % Cover OW 40 60 80 June 21 June 6 May 24 100 100 150 200 250 100 150 200 250 50 50 0 0 d b 0 20 % Cover SAV % Cover LLY 40 60 80 100

68

Figure 30: Average Common Carp Abundance by Vegetation Percent Cover. Only individuals collected during the 2016 peak abundance are included here (24 May, 6 June, 21 June 2016). a) Average common carp abundance by % cover TY; b) Average common carp abundance by % cover SAV; c) Average common carp abundance by % cover LLY; d) Average common carp abundance by % cover OW. TY = Cattail; SAV = Submerged aquatic vegetation; LLY = Water lily and lotus; OW = Open water.

Average Larval Common Carp Abundance (Per Trap) 100 150 200 250 300 100 120 50 20 40 60 80 0 0 b a 0 0 20 20 % Cover % LLY % Cover % TY 40 40 60 60 80 80 June 21 June 6 June 24 May 100 100 100 120 100 150 200 250 300 20 40 60 80 50 0 0 d c 0 0 20 20 % Cover % SAV % Cover % OW 40 40 60 60 80 80 100 100

69

Figure 31: Average White Crappie Abundance by Vegetative Percent Cover. Only individuals collected during 2016 peak abundance are included here (24 May, 6 June, 21 June 2016). a) Average white crappie abundance by % cover TY; b) Average white crappie abundance by % cover SAV; c) Average white crappie abundance by % cover LLY; d) Average white crappie abundance by % cover OW. TY = Cattail (Typha spp.); SAV = Submerged aquatic vegetation; LLY = Water lily and lotus; OW = Open water. Average Larval White Crappie Abundance (Per Trap) 100 120 140 160 20 40 60 80 10 20 30 40 50 0 0 a c 0 0 20 20 % Cover LLY % Cover TY 40 40 60 60 80 80 June 21 June 6 June 24 May 100 100 100 120 140 160 20 40 60 80 10 20 30 40 50 0 0 d b 0 0 20 20 % Cover SAV % Cover OW 40 40 60 60 80 80 100 100

70

Figure 32: Average Goldfish Abundance by Vegetation Percent Cover. Only individuals collected during 2016 peak abundance are included here (24 May, 6 June, 21 June 2016). a) Average goldfish abundance by % cover TY; b) Average goldfish abundance by % cover SAV; c) Average goldfish abundance by % cover LLY; d) Average goldfish abundance by % cover OW. TY = Cattail (Typha spp.); SAV = Submerged aquatic vegetation; LLY = Water lily and lotus; OW = Open water.

Average Larval Goldfish Abundance (Per Trap) 10 20 30 40 50 10 20 30 40 50 0 0 c a 0 0 20 20 % Cover LLY % Cover TY 40 40 60 60 80 80 June 21 June 6 June 24 May 100 100 10 20 30 40 50 10 20 30 40 50 0 0 b d 0 0 20 20 % Cover SAV % Cover OW 40 40 60 60 80 80 100 100

71

Figure 33: Average White Bass Abundance by Vegetation Percent Cover. Only individuals collected during 2016 peak abundance are included here. (24 May, 6 June, 21 June 2016). a) Average white bass abundance by % cover TY; b) Average white bass abundance by % cover SAV; c) Average white bass abundance by % cover LLY; d) Average white bass abundance by % cover OW. TY = Cattail (Typha spp.); SAV = Submerged aquatic vegetation; LLY = Water lily and lotus; OW = Open water.

Average Larval White Bass Abundance (Per Trap) 20 40 60 80 10 20 30 40 50 60 0 0 c a 0 0 20 20 % Cover LLY % Cover TY 40 40 60 60 80 80 June 21 June 6 June 24 May 100 100 20 40 60 80 10 20 30 40 50 60 0 0 d b 0 0 20 20 % Cover SAV % Cover OW 40 40 60 60 80 80 100 100

72

Common carp, white crappie, goldfish, and white bass each had periods of increasing and decreasing average total length (mm), which was likely due to multiple cohorts spawning at various times throughout the year (Fig. 34). The average total lengths for each species on each date are listed in Table 9.

73

Figure 34: Lengths of Four Most Abundant Species throughout the Sampling Period. Total of individuals of the four most common species (common carp, white crappie, goldfish, white bass) collected in 2016. Smaller, unfilled points represent individual measurements, whereas connected, larger, filled points show the average total length (mm) for each species on each date.

80 Common Carp White Crappie Goldfish White Bass 60

40

20

Total Length (mm) Length Total

0

5/2/16 8/8/16 4/18/16 5/16/16 5/30/16 6/13/16 6/27/16 7/11/16 7/25/16

74

Table 9: All Species Average Length and Abundance by Date. Each species captured on each sampling date is listed below, along with species-specific average total length and number of each species captured for each date. Only dates on which fish were captured are included here.

4/29/2016 5/13/2016 5/24/2016 6/6/2016 6/21/2016 7/7/2016 Species and Length (mm) ± SE (N) 11.79 ± Carassius 6.67 ± Dorosoma 16.67 ± Cyprinus 5.86 ± 5.59 ± 5.33 ± C. carpio C. auratus M. chrysops 0.27 auratus 0.17 (9) cepedianum 0.33 (3) carpio 0.08 (116) 0.06 (571) 0.07 (119) (66) 4.35 ± Pomoxis 4.00 ± Ictiobus 9.83 ± 5.63 ± 5.42 ± 7.03 ± L. C. auratus P. annularis P. annularis 0.07 annularis 0.00 (1) cyprinellus 0.17 (3) 0.07 (86) 0.17 (463) 0.74 (16) macrochirus (49) Etheostoma 5.00 ± Minytrema 6.50 ± Morone 8.93 ± 9.83 ± 5.70 ± C. carpio P. annularis blennioides 0.00 (1) melanops 0.19 (11) chrysops 0.41 (240) 1.22 (12) 0.44 (5) Cyprinella 6.21 ± 6.01 ± L. 5.57 ± 20.50 ± C. auratus I. cyprinellus spiloptera 0.10 (7) 0.08 (183) macrochirus 0.34 (7) 1.50 (2) 5.67 ± Micropterus 5.95 ± I. 5.36 ± 5.75 ± P. annularis C. auratus 0.17 (3) salmoides 0.10 (19) cyprinellus 0.26 (7) 0.75 (2) Notropis 5.33 ± 17.68 ± 9.50 ± N. 18.00 ± I. cyprinellus M. chrysops rubellus 0.33 (3) 1.11 (17) 2.63 (4) melanostomus 0.00 (1) Rhinichthys 14.00 ± Lepomis 7.00 ± 13.75 ± 11.50 ± L. gibbosus A. melas atratulus 0.00 (1) cyanellus 0.41 (14) 8.75 (2) 0.00 (1) Notropis 6.50 ± 11.64 ± 7.50 ± 6.50 ± Cottus bairdii L. cyanellus C. carpio atherinoides 0.00 (1) 1.06 (11) 1.50 (2) 0.00 (1) Lepomis 5.40 ± 7.00 ± P. promelas macrochirus 0.29 (10) 0.00 (2) 6.64 ± Pimephales 5.00 ± Perca flavescens 0.48 (7) notatus 0.00 (2) Lepomis 6.42 ± 8.00 ± C. bairdii gibbosus 0.20 (6) 0.00 (1) Neogobius 8.38 ± melanostomus 0.24 (4) 14.83 ± Ameiurus melas 1.42 (3) 5.50 ± C. spiloptera 0.50 (2) 7.50 ± D. cepedianum 0.00 (1) Pimephales 5.50 ± promelas 0.00 (1)

75

Discussion

The most abundant larval fish captured in both years (goldfish and common carp) were also captured in high numbers in our adult fish study (see Chapter 2), possibly indicating that these species are year-round residents of this system. The high abundance of these species at the larval, juvenile, and adult stages implies that OWC is suitable habitat for these two species to reproduce, grow, and survive within the estuary.

Seventeen species captured in sampling the adult fish community (see Chapter 2) did not appear as larvae in either of the sampling years. Two of these species (bowfin [Amia calva] and walleye) have a unique morphological characteristic in the larval phase that prevented us from capturing them in light traps. They develop adhesive discs on their heads that they use to stick to vegetation, and are therefore not phototactic (Auer 1982). It is highly likely that we did not catch any bowfin or walleye larvae due to this bias in our sampling gear. Logperch, another species caught as an adult, but not as larvae, spawn more frequently in riverine habitats than estuarine or pelagic habitats (Trautman 1981, Auer 1982), so it is not surprising that I caught adults, but no larvae.

During both sampling years, the highest abundance of larval fish in both sampling years was in our overlook site (Fig. 1), which appears to sit at the confluence of two ecosystems: the riverine upstream system, and the more pelagic estuary ecosystem, which is more strongly influenced by Lake Erie (often through seiche events). There is a growing body of literature on the effects of ecotones on species richness and abundance (Odum 1953, Risser 1995). The term

“ecotone,” was typically used to refer to changes in types of vegetation, but in recent years has broadened to refer to transitional zones between different ecosystems (Risser 1995). While there was no clear effect on species richness, this increased abundance may be attributable to increased

76 abundance of species such as common carp and white crappie. While this study did not attempt to assess the effects of ecotones, this change in abundance of some species (which can be positive or negative) is consistent with patterns of species abundances at ecotones in between different ecosystems, including wetland ecotones (Landhausser and Wein 1994, Chapman et al.

2000, Sunderland and Samu 2000).

Interestingly, four species were captured during larval sampling efforts, but not during adult efforts (western blacknose dace, channel catfish, common shiner, and greenside darter).

These species, pooled together, were represented by a total of six total fish, three of which were channel catfish. It is also evident that the dominant adult fish species in OWC are tolerant species (e.g., common carp and goldfish) indicating that the estuary may not be of high quality for spawning for many species (see Chapter 2). This could mean that recruitment from the larval stage to the adult stage may be limited in many species by unfavorable environmental conditions.

Spawning patterns fell into two main categories: explosive spawning and protracted spawning. Explosive spawning species (e.g. white bass) tend to peak on one or two specific dates over a very short time span, with nearly 100% of all representative individuals being collected on one or two sampling dates. Such species peaked on various dates throughout the summer, some in May, some in mid-June, some in August. Protracted spawning species (e.g. goldfish) began spawning earlier than most species, and were collected in lower abundance over a longer range of sampling dates. Patterns in total length of larvae collected throughout the season indicated that several species spawned multiple cohorts, as average lengths had periods of growth and decline from May – August 2016. Finally, it appears that species-specific spawning dates are somewhat predictable from year to year.

77

There were no distinct patterns in larval fish growth during sampling, with many species experiencing periods of increasing and decreasing average total length. This is likely due to the spawning of multiple cohorts in many species, which would result in fluctuating species-specific average total length.

Species within the family Ictaluridae (in this study: black bullhead, brown bullhead, yellow bullhead, channel catfish, tadpole madtom) are known to grow quickly (Auer 1982). In fact, only black bullhead and channel catfish were captured in the larval sampling, so it is likely that many ictalurids grew past the larval stage and into the juvenile stage prior to sampling.

Several ictalurids that were deemed too large and well developed to be considered larvae were also captured in light traps. Typically, the accepted cutoff between larval and juvenile fishes is the complete development of fin rays and absorption of the finfold (Auer 1982, but see Hoar and

Randall 1988 for a detailed discussion of ichthyofaunal ontogeny), so all individuals collected from light traps meeting these criteria, many of which were ictalurids, were excluded from larval analysis. Since they were captured in light traps and not in fyke nets (as in our adult and juvenile fish study), they were excluded from our analysis of the adult fish community as well. Many of the remaining species that only appeared in the adult sampling efforts were captured in very low numbers (see Chapter 2).

Larvae are thought to avoid open water, as they are likely easily seen and preyed upon in these microhabitats (Lehtiniemi et al. 2005). However, it is more likely that larvae actively avoid clear water, rather than open water, which is an important distinction to make. Open water, as seen in OWC, is simply water devoid of vegetation, whereas clear water is low in turbidity. The water at OWC has low clarity levels as the average secchi depth for the 2016 sampling season was28.8 cm, while the average site depth was twice as deep (55.5 cm), suggesting that light

78 penetration is greatly reduced. Reduced water clarity could be the combined effects of organismal activity (i.e. phytoplankton, zooplankton, and ichthyoplankton) and suspended particles coming from erosion resulting from surrounding land use (Havens 1991, Olmanson et al. 2008). Due to relatively low clarity throughout the estuary as a whole, larval fish in open water areas are not necessarily more susceptible to predation than in vegetative cover. Further, in this study, secchi depth had a significant effect on larval fish community structure, and it showed limited temporal variability in this study, which speaks to the importance of select abiotic variables to larval community structure.

Other abiotic variables besides secchi depth also significantly affected larval fish community structure. For example, dissolved oxygen declined significantly during the sampling season, and significantly affected community structure. This may be the result of its inverse relationship with increasing water temperature, where higher water temperatures as the season progressed resulted in low dissolved oxygen, which is a documented stressor for fish, inhibiting metabolic processes and causing fish to move habitats (Kramer 1987).

Site-specific pH levels also significantly altered fish community structure as it appears that all species actively avoided areas with relatively high pH values. In marine systems, increases in pH (up to 7.8, a value that was not uncommon during our sampling) have been shown to weaken olfactory abilities in larvae, to the point that they are unable to detect predatory cues (Dixon et al. 2009, Munday et al. 2009). Adult fish have also been shown to prefer specific conditions in terms of pH for metabolic purposes, although ranges are species-specific (Moss and

Scott 1961). The absence of larvae in high-pH sites could be because adults avoided laying and fertilizing eggs in these sites, or because larval processes were impaired, leading to predatory success.

79

Water temperature also appears to be a factor in determining community structure, although this may be related to seasonal changes in temperature. Extensive data exists documenting spawning temperatures for Great Lakes fishes. Further, favorable spawning habitats for fishes with spawning temperature preferences are projected to change with the onset of global climate change (Magnuson et al. 1990). These changes are projected to be positive and negative, depending on the species, and will likely vary from lake to lake. Further, temperature has been shown to negatively impact the swimming ability of larvae by altering the muscles’ ability to contract, as well as the hydrodynamics of the water through which they attempt to swim (von

Herbing 2002).

More generally, larval fish abundance and spatial distribution did not appear to be driven by abiotic variables measured in this study, similar to a previous study (Petering and Johnson

1991). This could be due to low spatial variability in abiotic measurements throughout the wetland, which suggests that the larvae are experiencing similar conditions. Spawning periods by individual species may also take advantage of abiotic conditions amenable to successful growth and survival.

Typha samples in 2015 had the highest average abundance per trap, where OW samples had the lowest. Species richness did not differ significantly in cover types in 2015. In 2016, I found patches of SAV and LLY to be significant drivers of fish community structure. Common carp were associated with SAV patches and goldfish were associated with LLY patches in 2016.

In other studies, cover type had a stronger effect on larval fish abundance and distribution than abiotic variables. They found that larval diversity was highest in microhabitats with emergent vegetation, while cyprinids and gizzard shad were the most abundant species in open water areas and sunfish (Lepomis spp.) were most abundant in vegetated microhabitats (Dewey and Jennings

80

1992). My findings contradict these results, as no significant differences were detected for total or species-specific larval fish abundance. The 2015 season was designed to assess community composition in monodominant stands of vegetation, and no gizzard shad was collected regardless of cover type. Further, while cyprinids accounted for about half of the fishes captured during

2015, they showed no clear habitat associations. The centrarchids also showed no clear associations with any of the cover types. More generally, species richness was similar in emergent vegetation and open water stands. Given that Dewey and Jennings (1992) sampled larval communities in river systems, it is not surprising that their results differed from this study’s findings, as open water areas in rivers are typically hazardous areas for larvae, relative to marginal, vegetated zones (Mion et al. 1998, Lehtiniemi et al. 2005).

The low abundance of larval fish in open water may be a result of the hazardous nature of this habitat. Open water areas can represent three major hazards for larval fishes: high-velocity flow, high discharge, and high predatory activity. Larval fish can only sustain slow swimming speeds, and cannot combat flows faster than a few cm/second (Copp 1991, Houde 1969). High flow events are relatively uncommon in OWC as the main OWC estuary area has a very low flow rate due to a sprawling inundated area, low-volume upstream input, and the frequent presence of a barrier beach at the estuary mouth. These factors provide a habitat that can retain larval fish that spawn in the estuary and larvae that are displaced from upstream habitats. With low-velocity flow comes low suspended sediments and discharge, which can injure or kill larvae

(Mion et al. 1998).

It appears that the main driving forces behind seasonality of larval fish abundance are time of year and water temperature (which were strongly correlated), as demonstrated by similar trends in abundance across the two sampling seasons. This is supported by existing literature,

81 which documents that spawning seasons for many species begin between April and June (Auer

1982), but it is likely that fish spawn in response to changes in water temperature that occur seasonally (Wismer and Christie 1987, Blaxter 1991). In fact, peaks in spawning activity in single spawning habitats are generally predictable and consistent between spawning seasons.

This trend holds up in a variety of ecosystem types, including river and marine systems (Cushing

1969, Humphries et al. 2002). More generally, spawning periods appeared to be quite similar across species using this wetland, with some evidence for more of a protracted spawning for goldfish.

Larval fish abundance followed similar patterns in each sampling year, although sampling in 2015 was abbreviated. Species richness was higher later into 2015 than 2016, indicated more protracted spawning in 2015. Spawning occurred in two main patterns: explosive spawning, in which intense spawning took place over a short period, and protracted spawning, which is spawning that extended across the growing season. Larval fish distribution and abundance can be affected by many abiotic and biotic factors. In this freshwater estuary, it appears that water clarity, dissolved oxygen, and pH alter the structure of the larval fish community structure. Further, larval fishes were found in high abundance and diversity in vegetation that provides cover from above (SAV and water lily and lotus). To better understand these associations, it would be useful to determine preferred breeding habitats of adult fishes with respect to where larval fish are actually found.

All but one (white crappie) of most abundant species of larvae in my study were classified as tolerant species in indices of biotic integrity, meaning that these tolerant species were the most successful reproducers in the estuary. This implies that OWC is somewhat degraded, making it a sink habitat for more desirable species, such as sport fishes. Further, the

82 highest abundance of larvae was captured in cattail patches, and these larvae were dominated by cyprinids. The cyprinids in my study were mainly goldfish and common carp, which are highly tolerant species that also contribute to habitat degradation. The most common species of cattail in OWC is the invasive narrow leaf cattail. To manage the estuary for more desirable fish species, I recommend the following: begin excluding adult carp and goldfish from the estuary by way of barrier or physical removal at the estuary mouth. I also suggest the replacement of narrow leaf cattail with other plant species, such as Myriophyllum spp., which is known to serve as important habitat for northern pike (Esox lucius), which are valuable species for the regional ecosystem and economy (McCarraher and Thomas 1972). These recommendations would serve to improve water quality, increase abundance of more desirable species of fishes, and remove the invasive cattail species from the estuary.

83

CHAPTER 4 - CONCLUSIONS

General Importance of Spatial and Temporal Patterns in Fish Abundance, Diversity, and

Spawning

Understanding various patterns in ichthyofaunal abundance, diversity, and reproduction is of great importance, as these help us get a sense for aquatic biology at the population, community, and ecosystem levels. The spatial and temporal patterns can aid in the monitoring of fish populations for species of interest, be they of economic importance (e.g. sport fishes, such as walleye), ecological relevance (e.g. prolific regulators such as common carp), or invasive species

(e.g. round goby). Tracking the movements and estimated abundances for these various classes of species can inform fisheries and wildlife managers alike, as well as following the spread of invasive species in efforts to control or eradicate problem populations.

Understanding patterns in reproduction and spawning can help anticipate future changes in populations of these species, allowing for us to plan for and curb the effects of any dramatic changes in populations. Managers and habitat rehabilitators can also employ this knowledge to maintain or even bolster populations if they have a strong understanding of population-specific habitat requirements and tolerances to various conditions of water quality.

The same information is importance at the community scale as well. Spawning patterns impact the food web in the present and in the future, because larvae are prey, but eventually grow to be herbivores or predators. This can affect diversity and abundance within an entire

84 community, and can have lagging effects on future generations. For example, if fish are very successful in reproduction and a high percentage of larvae survive and grow one year, there will be much more herbivore and predator biomass in the following year. These grown fishes will then reduce macrophyte growth, which limits cover for the next cohort of larvae. There is then a synergistic effect, as reduced cover and increased predator biomass can combine to reduce the larval population. This can then lead to reduced herbivore and predator biomass in the subsequent year, and so on. Further understanding of spatial distributions and community response to abiotic variables can also inform new policies on land use surrounding specific habitats. Finally, Tracking changes in community composition can aid fisheries by helping them decide which species to target, and may even be able to help identify communities at risk of invasion.

Because fish are known to regulate ecosystem function from the top down, to understand fish is to understand their ecosystems. Great Lakes fishes span multiple trophic levels, and move up trophic levels as they grow. Each species affects nutrient cycling and primary production in different ways at each level, and to truly understand how well a wetland ecosystem performs, one must understand the fishes in that ecosystem, as well as how they are distributed through space and time.

Key Findings from these Studies

Adult Fishes

I found that the type of macrophytes available in our estuary were not important factors in the spatial distribution of adult fishes, and that the fish community used open water areas nearly as much as vegetated areas. This was despite the availability of food resources, such as

85 macroinvertebrates, which are known to be in higher abundance in vegetated areas as well. I also found that fish abundance slightly decreased at deeper water levels, which may have important climate change implications because water levels in the Great Lakes are expected to increase.

However, this relationship was weak at best. This still could prove to limit suitable habitat for fishes and change spatial distributions in the future, which could have dramatic impacts on ecosystem function.

I also found that fish abundance and species richness decreased over our sampling periods, which we attributed to the mass-migrations that both precede and follow spawning. This provides a better idea of when and where these migrations occur, which has important reproductive implications.

Fish also grew fairly rapidly, showing that Great Lakes coastal wetlands, such as the

OWC estuary provides suitable conditions and resources to aid fish survival and reproduction, even for fishes that migrate away from the estuary following their nursery periods. However, many the species I captured in this study were associated with degraded habitats, so OWC may only be suitable habitat for highly tolerant species to reproduce and survive at this time.

Larval Fishes

I found larval fishes to be relatively unaffected by the abiotic variables that we measured.

This may have been due to similar conditions that were likely tolerable throughout the estuary.

The abiotic variables that did influence community assembly, such as secchi depth (cm), which is a proxy measurement for water clarity, have been shown to influence larval fish distribution in previous studies. This is mainly because of high predatory success in clearer waters, so larvae either actively avoided clear waters or were eaten before we were able to catch them ourselves.

These findings show that larvae may be more tolerant to abiotic conditions than we initially

86 believed due to their apparent fragility and lack of development. These findings are optimistic because they show that despite highly agricultural land use upstream of the estuary, fish can successfully reproduce, which is positive for the Lake Erie fish populations, even if only tolerant species are able to survive.

Vegetation was found to be more important in the spatial distribution of larval fishes than their adult counterparts. It is possible that these patterns are due to habitat selection by their parent generation, as opposed to the larvae themselves, although this is unclear, as my study did not attempt to answer that question. However, it is unlikely that larvae were physically developed to the point that they could make habitat choices.

Temporal patterns in spawning fell into two main categories: explosive spawning and protracted spawning. Explosive spawning species (e.g. white bass) peaked on a specific date, with nearly 100% of all representative individuals being collected on one or two sampling dates.

Such species peaked on various dates throughout the summer, some in May, some in mid-June, some in August. Protracted spawning species (e.g. goldfish) began spawning earlier than most species, being collected in fewer numbers over a longer set of sampling dates. Patterns in total length of larvae collected throughout the season indicated that several species spawned multiple cohorts, as average lengths had periods of growth and decline from May – August 2016. Finally, it appears that species-specific spawning dates are somewhat predictable from year to year.

How these Findings Fit in the Bigger Picture

I confirmed, to some extent, what is known about the fish-macrophyte relationship, which has important management implications. Managers and habitat rehabilitators who have the goal of remediating wetlands to provide habitat for fishes simply must include vegetation to

87 accomplish that goal. Further, it may help decision-making on remediation projects that are faced with multiple problems. For example, if a wetland was experiencing a macrophyte invasion and rapidly decreasing fish abundance, knowing that fish prefer habitats with moderately dense vegetation may help managers weigh the remediation options, or better yet, find a way to mitigate both problems.

I improved understanding of another key factor effecting the distribution of adult fishes: water depth. This may prove to be vital in predicting the full impact of global climate change in the Great Lakes. Science has been focused on the potential effects of climate change for nearly three decades. It is of great importance that research aimed at fully understanding the breadth of its impact continues to be done so that policies and regulations can change to mitigate future catastrophe. However, I caution future studies to not oversimplify the effect of water depth, as there are likely other keys factors that would limit fish habitat as it pertains to water depth.

I also identified spawning periods that were roughly consistent between two sampling seasons. This has been a conflicted topic in the literature, with some reports claiming that there are dramatic variations in spawning seasons in the same wetland from year to year, and others showing relative consistency. This may be a phenomenon specific to OWC or the two years in which we conducted our sampling. However, this helps science better understand behavior associated with reproduction at multiple life stages (e.g. mass-migrations in adults and hatching periods in eggs and larvae).

These studies can orient future studies on spawning patterns of Great Lakes fishes, and provide provocative questions to be answered in these studies. It is unclear whether the spatial distribution of larval fishes is because of the parent generation or the larvae themselves. If larvae are responsible, then it tells us more about the level of parental involvement that fishes exhibit.

88

The tolerance that larvae and eggs have for various conditions is also unclear, which could have implications reaching into hatching, growth and development, and population dynamics. This also poses the question of whether reproduction (i.e. the physical acts of egg laying and fertilization) is affected by abiotic conditions, and if so, the magnitude of these effects. Finally, the different spawning patterns (i.e. explosive vs. protracted) presents an interesting life history attribute that could have implications on competition. Protracted spawners seem to be “temporal generalists,” meaning that they spawn indiscriminately throughout a larger spawning period.

This is not an area rich in research, but it is intuitive that fish that can spawn early in the season would have an advantage in the competition for spawning habitat and resources available to larvae following hatching. This could also be beneficial in years with harsh conditions early in the season, as there would be more of a likelihood that at least some individuals would hatch in more favorable if spawning spanned multiple months.

89

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