I the Role of Functional Diversity in Biotic Resistance of Non-Native

Home , Agabus (beetle), Amnicola, Amnicola walkeri, Amnicolidae, Architaenioglossa, Belostoma flumineum

The role of functional diversity in biotic resistance of non-native fishes and invertebrates in Lake Erie coastal wetlands

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Masters of Science in the Graduate School of The Ohio State University

Jenna Lynn Odegard, B.S.

Graduate Program in Environment & Natural Resources

The Ohio State University

2017

Thesis Committee:

Dr. Suzanne M. Gray, Co-Advisor

Dr. Lauren M. Pintor, Co-Advisor

Dr. Christopher M. Tonra

Dr. Christopher J. Winslow

Jenna Lynn Odegard

2017

Abstract

Biological invasions are a leading cause of biodiversity declines and impairment of ecosystem function. Native assemblages that resist invasion by non-native species are frequently thought to be more diverse (i.e. diversity-invasibility hypothesis, DIH). This “biotic resistance” to non-natives by a more diverse assemblage of native species is thought to occur through increased interspecific competition, more fully used resources, and less available niche space. Evidence in support of the biotic resistance is mixed, suggesting that the DIH relationship depends on spatial scale (e.g. “invasion paradox”); however, another factor influencing the relationship between native and non-native species might be how diversity is measured. Most research that examines whether more diverse assemblages are more resistant to invasion has typically focused on measuring taxonomic biodiversity; however, functional diversity (e.g. feeding groups) might also be an important factor contributing to a native assemblage’s biotic resistance. In this study, I investigated if there is support for DIH in fish and invertebrate assemblages in coastal wetlands along the western basin of Lake Erie, according to taxonomic and functional richness and diversity. I sampled native and non-native fishes and invertebrates seasonally between 2013 and 2016. I expected to find a negative association between native and non-native organisms in support of DIH; however, I did not find significant within- taxonomic group relationships. In contrast, when investigating the association between fishes and non-native invertebrate presence across assemblage, I found a positive association. Explanations for these results might be related to spatial scale of the study, the possibility of abiotic factors or facilitation influencing invasion success, my approach to quantifying the biotic assemblage, time since invasion, and the statistical power. Assessing these biotic resistance trends is important for reducing costly impacts of invasion, prioritizing management efforts, and conserving native species.

Dedication

This work is dedicated to the love of nature, fresh air, calming waters, and funny-looking aquatic organisms. May we never cease to be amazed by life.

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Acknowledgments

Thanks to my advisors, Dr. Lauren Pintor and Dr. Suzanne Gray, for giving immense support and guidance during my graduate career. Thanks to my committee members, Dr. Chris Tonra and Dr. Chris Winslow, for sharing their ideas to develop and conduct this research project. Thanks to NOAA/Ohio Sea Grant and OSU for providing funding and employees and students to assist with field work, including Tory Gabriel, Matt Thomas, Kevin Hart, Chris Johnson, Richard Oldham, Erin O'Shaughnessey, Ryan Hudson, Alan Coburn, Martha Zapata, and Ross Standt. Thanks to Rhithron Associates, Angela Dripps (Environment Protection Agency), and Dr. Watters (Museum for Biological Diversity) for their professional assistance with identification of “mystery” macroinvertebrates. Finally, many thank to the numerous undergraduate students who assisted with processing of invertebrates samples in the laboratory. This army of undergraduate researchers includes Katie White, Elizabeth Bertoli, Victoria Stoodley, Callie Nauman, Grace Simpson, Alissa Finke, Ella Weaver, Kaylina Ruth, Mayim Hamblen, Alec Mell, Gemma Bush, Brin Kessinger, Taylor Gray, Scott Meyer, Dan Hribar, Katherine Denune, and Andy Opplinger. Without the involvement of all these people, this project would not have been possible!

Vita

May 2009 ...... Mahtomedi High School May 2013 ...... B.S. in Biology University of Wisconsin-River Falls July 2014 to present ...... Graduate Research and Teaching Associate, School of Environment and Natural Resources, The Ohio State University

Fields of Study Major Field: Environment and Natural Resources

Specialization Fisheries and Wildlife Science

Table of Contents

Abstract……………………………………………………………………...... ii Dedication……………………………………………………………………... iii Acknowledgements…………………………………………………………… iv Vita……………………………………………………………………...... v List of Tables………………………………………………………………….. vii List of Figures…………………………………………………………………. x Chapter 1: Literature review of invasion, biotic resistance, and functional diversity……………………………………………………………………...... 1 References…………………………………………………………………….. 11 Chapter 2: The role of functional diversity in biotic resistance of non-native fishes in Lake Erie coastal waters……………………………………………... 19 References...... 39 Chapter 3- The role of functional diversity of fishes and invertebrates in biotic resistance of non-native fishes in Ottawa National Wildlife Refuge…... 58 References...... 91 Appendix A. Native and non-native fishes found collected using fyke nets from 8 sites sampled in fall and spring 2013-2014 and their associated status, and feeding guilds (FG)…………………………………………. 124 Appendix B. Abiotic water sampling: averages of 3 water collections collected from each site were calculated to assess seasonal and yearly fluctuations.…………………………………………………………………… 126 Appendix C. Invertebrate subsampling method 1 used to randomly select material to search for invertebrates for samples larger than 250-mL. ………... 129 Appendix D. Fish species found, minnow trap mesh size they were collected in, Invader status according to GLANSIS, and associated feeding guild (FG)…..………………...... 130 Appendix E. Invertebrates found with Operational Taxonomic Units (OTUs), Invader status, and functional feeding group (FFG)…………………………... 131

List of Tables

Table 2.1. Sampling regime of fish sampling in fall and spring 2013 and 2014………………………………………………………………………... 46 Table 2.2. Non-native fishes found and percent relative abundance among non-native species found……………...…………………………………… 47 Table 2.3. Non-native fish taxonomic richness explained by native fish taxonomic richness, connection to Lake Erie, year, and season, shown by A) 15 candidate generalized linear mixed effect models and B) Relative Variable Importance (RVI) scores for the explanatory variables……….…. 48 Table 2.4. Non-native fish functional richness explained by native fish functional richness, connection to Lake Erie, year, and season, shown by A) Ranked generalized linear mixed effect models, and B) Relative Variable Importance (RVI) scores for the explanatory variables….………. 49 Table 2.5. Non-native fish taxonomic diversity according to Shannon- Weiner Diversity Index explained by native fish taxonomic diversity, connection to Lake Erie, year, and season, shown by A) Ranked linear mixed effect models, and B) Relative Variable Importance (RVI) scores for the explanatory variables…………………………...………………….. 50 Table 2.6. Non-native fish taxonomic diversity according to Simpson’s Index of Diversity explained by native fish taxonomic diversity, connection to Lake Erie, year, and season, shown by A) Ranked linear mixed effect models, and B) Relative Variable Importance (RVI) scores for the explanatory variables…………………….………………………… 51 Table 2.7. Non-native fish functional diversity according to Shannon- Weiner Index of Diversity explained by native fish functional diversity, connection to Lake Erie, year, and season, shown by A) Ranked linear

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mixed effect models, and B) Relative Variable Importance (RVI) scores for the explanatory variables…..…...…………………….………………... 52 Table 2.8. Non-native fish functional diversity according to Simpson’s Index of Diversity explained by native fish functional diversity, connection to Lake Erie, year, and season, shown by A) Ranked linear mixed effect models and B) Relative Variable Importance (RVI) scores for the explanatory variables……………………..………………...……… 53 Table 2.9. Area of sites not directly connected to Lake Erie………...……. 54 Table 3.1. Schedule of fish and invertebrate sampling conducted at Ottawa National Wildlife Refuge, OH…………………………………….. 98 Table 3.2. Taxonomic and functional richness and diversity averages +/- standard error fish and invertebrates samples across 7 sites in 2014 and 9 sites in 2015 in spring, summer and fall……...……………………………. 99 Table 3.3. Results of the generalized linear mixed effect regression model performed on the dependent variable, non-native fish abundance, to test its association with native fish……………………………………...…………. 100 Table 3.4. Results of generalized linear mixed effect regression model performed on the dependent variable, non-native fish presence, to test its association with native………..…………..…………………………...…… 102 Table 3.5. Results of the generalized linear mixed effect regression model performed on the dependent variable, non-native invertebrate richness, and its association with native invertebrates………..……………………... 104 Table 3.6. Results of generalized linear mixed effect regression model performed on the dependent variable, non-native invertebrate presence, and its association with native invertebrates……………………..………... 105 Table 3.7. Results of generalized linear mixed effect regression model and linear mixed effect regression model performed on the dependent variable, non-native invertebrate richness to test its association with native fish…………………………………………………………………………. 107

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Table 3.8. Results of the generalized linear mixed effect regression model 108 performed on the dependent variable, non-native invertebrate presence, to test its association with native fish…………………………………...…. Table 3.9. Results of the generalized linear mixed effect regression model performed on the dependent variable, non-native invertebrate presence, to test its association with total fish………………………...………………… 110

List of Figures Figure 1.1. Number of invasion studies conducted worldwide in various habitat types (Lowry et al., 2012)………………………………………….. 18 Figure 2.1. Sampling was conducted in the Western basin of Lake Erie, within Cedar Point National Wildlife Refuge, Ward’s Canal, and Ottawa National Wildlife Refuge……..……………………………………………. 55 Figure 2.2. Sampling sites in Cedar Point National Wildlife Refuge, Ward’s Canal, and Ottawa National Wildlife Refuge…………………….. 56 Figure 2.3. Number of native and non-native fish species categorized by feeding guild. Data includes fishes collected using fyke nets from all sites sampled in fall and spring 2013-2014……………….…………………….. 57 Figure 3.1. Previous research has found both a negative and positive association between native and non-native diversity………………..…..… 112 Figure 3.2. Ottawa National Wildlife Refuge (NWR), OH, located in the western basin of Lake Erie………………………………………………… 113 Figure 3.3. Ten wetland-sampling sites of Ottawa NWR...... 114 Figure 3.4. Number of native and non-native fish species categorized by functional guild collected using minnow traps sampling during spring, summer, and fall, 2015 and 2016 in Ottawa National Wildlife Refuge…… 115 Figure 3.5. Abundance of native and non-native fishes of each feeding guild (FG) collected using minnow traps sampling during spring, summer, and fall, 2015 and 2016 in Ottawa National Wildlife Refuge………....…... 116 Figure 3.6. Number of native and non-native invertebrate species categorized by functional feeding group……..……………………………. 117 Figure 3.7. Abundance of native and non-native invertebrates present in Crane Creek from Ekman and Dipnet sampling in 2014-2015 categorized by functional feeding group (FFG)………………………………………… 118

Figure 3.8. Water quality averages measured for water temperature, pH, water depth, turbidity, dissolved oxygen, and conductivity.………………. 119 Figure 3.9. Association between native fish diversity and non-native abundance…………………………………………………………….……. 121 Figure 3.10. Association between native and non-native invertebrates…... 122 Figure 3.11 Association between native fish and non-native invertebrates……...………………………………………………………… 123

Chapter 1: Literature Review of invasion, biotic resistance, and functional diversity Biodiversity and invasion Invasive species are one of the leading causes of declines of biodiversity and impairment of ecosystem function (Srivastava & Vellend, 2005; Havel et al., 2015). Biodiversity, defined broadly as the “degree of variation of life,” is indicative of ecosystem health (e.g. resilience to disturbance), function (e.g. wildlife habitat) and service (e.g. air and water purification; Woodward, 2009). For example, Tilman (2014) summarized 100 studies and found that biodiversity was positively correlated with productivity, indicating a strong relationship between biota and ecosystem processes. Therefore, changes in biodiversity frequently lead to a shift in ecosystem functions (Filip et al., 2014; Tilman, 2014) and services that humans rely on. Non-native species (i.e. organisms originating from a different geographical area) are termed invasive when they negatively affect native species, change the habitat, or cause cascading impacts to the ecosystem. Non- native, invasive species can negatively impact in several ways, such as by negatively impacting through interactions with native species, altering the habitat, and causing cascading impacts throughout the ecosystem. Non-natives can decrease native biodiversity directly through their interactions with resident species. This often occurs through increased competition for shared resources for food or space (Mallon et al., 2015). For example, when the herbivorous Rusty Crayfish (Orconectes rustics), which is native to Indiana and Ohio, invaded into northern Wisconsin, its’ establishment led to displacement of other crayfish species. It is likely that the competitive interactions and greater aggression displayed by Rusty Crayfish during diurnal feeding allowed it to outcompete the native crayfish (Lodge et al., 1998). Competition for habitat space was documented in an intertidal aquatic system in a before and after invasion study of a polychaete worm (Boccardia proboscidea),

which feeds on organic matter. Its invasion was attributed to an overall decline in native biodiversity of the epilithic intertidal community due displacement (i.e. lack of space to live) of formerly rich and diverse faunal community (Elias et al., 2015). Specifically, only the amphipod Monocorophium sp. was able to inhabit the B. proboscidea invaded area. In addition to competition for resources, other negative interactions such as predation can lead to native biodiversity loss. A pond study in Arizona demonstrated that invasion by the predatory American Bullfrog (Lithobates catesbeiana) led to decreased abundance and richness of native invertebrates (Hale et al., 2015). Furthermore, non-native species introductions can cause species to become globally extinct. For example, in the Great Lakes basin, human stocking of Common Carp (Cyprinus carpio) and Brown Trout (Salmo trutta) caused 3 taxa to go extinct, 18 to become extirpated, and 82 species to be added to the endangered species list in the Great Lakes basin (Mandrak & Cudmore, 2010). In addition to negative impacts on the native community, non-native species can also greatly alter the habitat of an invaded system by changing physical, chemical, and ecological conditions (Ross, 1991), thus altering the availability of resources. One such documented example comes from the Zebra Mussel (Dreissena polymorpha), a non-native in the Laurentian Great Lakes (Ricciardi, Whoriskey & Rasmussen, 1997). These quickly proliferating bivalves filter suspended particulate material from the water column, which in turn decreases turbidity, increases light penetration, and promotes macrophyte growth (reviewed in Crooks, 2002). Ultimately, these habitat changes cause cascading effects throughout the food web. Similarly, non-native Common Carp (Cyprinus carpio) and Goldfish (Carassius auratus) cause ecosystem-wide changes through their feeding behavior. They uproot submerged vegetation, which can increase turbidity and decreases macrophyte and planktonic standing stocks (reviewed in Crooks, 2002). Great changes of energy flow and habitat structure often cause homogenization of the biotic community (Ricciardi, 2001), which further limits biodiversity because a diverse biotic community requires habitat heterogenity. These negative impacts on habitat and biodiversity can furthermore cause great

disruptions throughout the ecosystem. Non-native species can influence the behavior and even morphology of native taxa. For example, competition for limited resources such as living space, light, water, heat; Jude & Pappas, 1992) can cause changes change migration habits or wandering behavior of natives (Jude & Pappas, 1992). Non-native species introduction can also influence morphological traits in native taxa. For example, introduced predatory fish can result in shifts to smaller invertebrates rendering them less vulnerable to fish predation (Fisk et al. 2007). These morphological and behavior shifts can cause cascading biotic and abiotic impacts throughout the ecosystem. Invasive species impacts on ecosystems are found to be difficult or impossible to reverse and therefore they have been listed as one of the greatest threats to biodiversity worldwide (Ellender et al., 2014). To avoid such negative impacts, a current dominant focus in invasion ecology is to better understand invasion and how to reduce it by identifying factors that might allow a native assemblage to be more resistant to invasion.

Biotic resistance of non-natives One factor that has been shown to influence biotic resistance (i.e. the ability of the resident assemblage to deter invasion of non-native species) and the subsequent impact of a non-native introduction on native biota is resource availability in an ecosystem. When resources are abundant (i.e. low competition for resources), invasion success is expected to be high (Ilheu, Matono & Bernardo, 2014). This is because underutilized resources and unsaturated niches likely create opportunities for non-natives to invade (Lowry et al., 2012). For example, the successful establishment of Nile Perch (Lattes niloticus) in Lake Victoria (East Africa) was likely facilitated by the perch’s ability to exploit abundant resources. Nile Perch are voracious piscivores and due to low competition for prey, the Nile Perch invasion contributed to the decline of haplochromine cichlid fishes (Downing et al., 2013). The importance of resource availability in invasion success has been shown in other studies, such as in a

microcosm study of a microbial assemblage, in which invasion success was greatest under high resource conditions (Jiang & Morin, 2004). Nutrient enrichment led to a positive relationship between diversity and invasibility. Invasion was also successful in the Long Island Sound marine system (Connecticut- New York, USA), in which the availability of open niche space dramatically increased the rate of settlement and recruitment of ascidian (sea squirt) invaders (Stachowicz et al., 2002). Surplus resources are primary requirements affecting invasibilty and will likely make an area more susceptible to invasion (Levine & D'Antonio, 1999); however, biotic resistance, or the ability of the resident assemblage to repel invaders, might also be an important factor (Fridley et al., 2007). The opportunity for invasion is likely influenced by the species composition of the assemblage being invaded; thus, biotic resistance has been researched by testing the diversity-invasibility hypothesis (DIH; Elton, 1958). DIH predicts that biodiversity of the invaded ecosystem is the main determinant of invasion and predicts that assemblages with greater biodiversity decrease the likelihood of non-native species establishing (Davis, 2011). The most commonly cited mechanism attributed to this is that more diverse assemblages can more fully make use of available resources, leaving few nutrients and little habitat available for an incoming species (Levine & D'Antonio, 1999; Tilman, 2014). While much research supports DIH, the negative association between native and non-native species has not always been found. Many fine-scale empirical (e.g. less than 10 m2) and theoretical studies support the idea that diverse native assemblages and communities more successfully resist invasion. For example, in freshwater systems, biotic resistance has been supported in fish assemblages in Mediterranean and temperate riverine reservoirs (Clavero et al., 2013), ostracods in rock pools (Beisner et al., 2006), microbes in microcosms (Jiang & Morin, 2004), sessile marine invertebrates (Stachowicz et al., 2002), and plants in experimental plots (McGradySteed, Harris & Morin, 1997; Fargione & Tilman, 2005). One small-scale study in rock pool mesocosms found that invertebrates and zooplankton communities with greater species richness

were less likely to be invaded by a midge species (Ceratoponidae: Dasyhelea sp.); likewise, communities with low diversity had lower biotic resistance (Romanuk & Kolasa, 2005). On the other hand, the opposite trend (i.e. a positive association) has also been found. For example, several coarse-scale examinations (i.e. 1 km2 or more) have observed greater non-native richness in systems with diverse and rich resident assemblages such as assemblages of microbes (Jiang & Morin, 2004) and plants (Levine & D'Antonio, 1999). For example, in a California riparian system, the most diverse natural assemblages were the most invaded by exotic plants (Levine, 2000). Native diversity again did not appear to provide biotic resistance to non-native invasions of fishes (Marchetti et al., 2004). According to their investigation of successful fish invaders in Californian watersheds, other variables such as fecundity, propagule pressure, and size of native range might be related to the success. The noted inconsistency about biodiversity’s conflicting influence in regards to invasion and the negative and positive relationship found is termed the “invasion paradox” (Fridley et al., 2007) and is thought to be dependent on the spatial scale of the research. In summary, small- scale, experimental studies show biodiversity is positively correlated with biotic resistance of non-natives (Levine & D'Antonio, 1999; Romanuk & Kolasa, 2005), while, in large-scale studies, biodiversity is negatively correlated with biotic resistance (Knops, Griffin & Royalty, 1995). This difference is thought to be due primarily to biotic interactions playing a large role in small-scale studies, and abiotic factors (e.g. resource availability and habitat heterogeneity) having dominant influence in large-scale studies (Levine & D'Antonio, 1999; Marchetti et al., 2004). For example, at small spatial scales, such as experimental quadrats (25 x 25 cm) and small sites (50 x 50 in) (Stachowicz et al., 2002), abiotic and physical factors are relatively homogeneous and as a result species interaction have predictable impacts on the relative abundance of native and non-native species. However, as you increase spatial scale, increased heterogeneity in environmental variables and biogeographic processes are more likely to determine community composition than species interaction (Fridley et al., 2007).

Thus, spatial scale has been noted as one factor that can influence the results in studies of DIH.

Axes of diversity Most invasion studies evaluating biodiversity have used taxonomic diversity (i.e. species richness) as the main measurement for evaluating biodiversity (Woodward, 2009). Such studies include research on invasion of non-native freshwater mollusk assemblages (Keller, Drake & Lodge, 2007), stability in freshwater fish assemblages (Franssen, Tobler & Gido, 2011), and respiration of aquatic microbial communities (McGradySteed, Harris & Morin, 1997). However, recent work has demonstrated that there are other axes of diversity that could be informative in such evaluations. One such relevant axis is functional diversity (Shea & Chesson, 2002; Bonada, Rieradevall & Prat, 2007; Schriever et al., 2015). This trait-based research focus came about as ecologists realized that not all species affect ecosystem processes equally or in the same manner; therefore, attention to what species ecologically do instead of simply what species taxonomically are (Woodward, 2009) has become a key component of biodiversity studies. Therefore, functional diversity likely offers benefits for researching specific aspects of species influence, assemblage structure, environmental processes, and invasion. First, functional studies have been beneficial in describing the biotic assemblage because of their informative and unique evaluation. Categorization has commonly used behavioral habits (e.g. activity mode; Merritt, 2008), morphological features (e.g. body size; Hein, Hou & Gillooly, 2012), habitat occupation (e.g. littoral zone), life history traits (e.g. reproduction times per year; Olden, Poff & Bestgen, 2006), and feeding groups (EPA, 2014). Such functional assessments can capture features of the biotic assemblage that would not be acknowledged with only taxonomic evaluations (Giller et al., 2004; Setala, 2002). Many traits can influence interactions between non-natives and resident species and thus influence invasion success (Nawrot, Chattopadhyay & Zuschin, 2015; Starzomski, Suen & Srivastava, 2010). Second, studying function is also

beneficial because it is indicative of ecosystem function (i.e. natural processes), service (i.e. functions that are beneficial to humans), and health (i.e. condition of ecosystem) (Gamfeldt & Hillebrand, 2008; Giller et al., 2004). This is because ecosystem-wide effects of biodiversity are thought to be more related to some functional attributes of species, rather than to species richness per se (Giller et al., 2004). For example, consumer performance was strongly affected by fluctuations in functional diversity (i.e. trait variation) in a microbial food web, yet taxonomic richness did not appear to have a significant effect (Filip et al., 2014). Likewise, categorizing bivalves, tubificid worms, and aquatic insect larvae by FFGs indicates that nutrient recycling is occurring through bioturbation of sediments (Covich, Palmer & Crowl, 1999). Other ecosystem qualities such as disturbance or impact can be highlighted with functional assessment compared to taxonomic assessment (Leduc, da Silva & Rosenfeld, 2015; Srivastava & Vellend, 2005). For example, in a study in an Argentinean river basin, functional groups of macroinvertebrates were investigated and related to impacts of land-use change, physio-chemical conditions, and spacio-temporal differences between sites (Miserendino & Pizzolon, 2003). Categorization of aquatic invertebrates according to their overall environmental tolerance indicated disturbance (Amalfitano et al., 2015). For example, sensitive species were limited in their ability to occupy harsh environmental conditions and thus categorizing species based on traits explained their absence from certain areas. Evidence drawn from an aquatic invertebrate study using FFGs revealed that carnivorous insects were found near the port where fishing/harbor activities took place, while suspension feeders, such as polychaetes, were found further away from the ports in the Gulf of Gabès (Aloui- Bejaoui & Afli, 2012). Exploration of functional diversity (e.g. feeding groups) allowed assemblage and community dynamics and structure to become more visible and eventually led to realization that anthropogenic disturbance, such as fishing, were influential in driving the distribution of organisms throughout the harbor (Aloui-Bejaoui & Afli, 2012). Taxonomic studies have not consistently revealed these conditions (Bonada, Rieradevall & Prat, 2007; Schriever et al.,

2015); therefore, assessing functional diversity is likely beneficial and should be included in biodiversity-focused studies. Functional categorization might be beneficial for investigating invasion and the DIH. According to Mack et al. (2000), “guilt by (taxonomic) association has proven imprecise at predicting invasion potential,” however, functional categorizations have been beneficially implemented to assess invasion. For example, by assessing life history characteristics of non-native frogs compared to the invaded system, non-native species might have been able to invade because of an open niche such as unique phylogenic characteristics from resident species (Escoriza & Ruhi, 2016). Feeding variation has also been found to be favorable when investigating differences between native and non-native feeding groups, such as generalist grazer snails, and for predicting biotic resistance (Nylund et al., 2012). For example, presence or abundance of stonefly larvae, which physically break down allochthonous material through feeding mechanisms, can reveal resource type and amount used. Assessing feeding groups can indicate level of competition between species more clearly than taxonomic assessments (Covich, Palmer & Crowl, 1999), and therefore might be beneficial in understanding DIH. Similar to the idea that species-rich assemblages use more resources (i.e. niche space and nutrients), a functionally diverse assemblage will use its resources fully (Mason et al., 2005), have fewer gaps in niche space (Dukes, 2001), and thus, it is thought, have greater biotic resistance. Fewer available resources limit the likelihood of invasion success and unsaturated feeding groups can be seen as unoccupied space for a non-native to invade. For example, in a ecosystem with many snails that act as scrapers, a non-native snail from that same functional group might be strongly resisted due to high competition for resources and, therefore, be unable to invade the system. However, a non-native from an uncommon functional group, perhaps a shredder, might be more able to invade due to decreased competition, open niche space, and increased resource availability. Functionally diverse assemblages often use more resources than non-

diverse assemblages. This finding has been empirically supported using terrestrial soil organisms which found that increased functional feed group diversity led to more efficient resource use, greater decomposition, and changes in primary productivity (Setala, 2002). This result is likely because there is a greater degree of niche differentiation (Mason et al., 2005) and functional redundancy (Schriever et al., 2015) in functionally diverse assemblages. More functionally diverse assemblages of plants have been found to be less invaded overall than functionally simple assemblages (Levine, 2000). There has also been evidence that a functional group in the extant assemblage can suppress invaders of that same functional group (Hooper & Dukes, 2010). Limiting similarity theory proposes that community assembly is not random, but instead that coexistence between functionally similar species is limited and that they should use resources similarly (Price & Partel, 2013). Therefore, resource partitioning and increased competition in a feeding niche might prevent establishment of non-native species. Ecologists need to strengthen their ability to decipher trait combinations that have successfully resisted non-natives to further avoid negative consequences of invasions (Mack et al., 2000). Functional diversity will aid us in understanding factors that reduce invasion success and should be used for further investigation of biotic resistance. Overall, assessment of functional diversity has great potential in furthering the field of invasion biology to promote and conserve resident species, habitats, and ecosystems.

Importance of researching aquatic ecosystems According to World Wide Fund for Nature, wild populations of freshwater species have declined by 76% between 1970 and 2010. Freshwater organisms are being impacted about two times the rate of marine and terrestrial species which have been declining at 39% (WWF, 2016). The amount of research studying invasive species has been covered unequally by ecosystem and experimental type. A high number of invasive species studies have been conducted in terrestrial systems compared to a limited number of studies in

aquatic environments with wetland systems having the fewest number of invasive species studies (Fig. 1.1; Lowry et al., 2012). Invasion studies in wetlands have been neglected even though freshwater, estuarine, and coastal wetlands are amongst the most invaded ecosystems (Bobbink et al., 2006). Additionally within wetland invasion studies, the majority of studies have been conducted in tropical systems rather than temperate zones (Havel et al., 2015). Wetland environments are complex, encompassing a combination of both aquatic and terrestrial habitat characteristics (Giller et al., 2004). For example, like terrestrial systems, wetlands are affected by soil, atmosphere, and adjacent ecosystems. Similarly to aquatic environments, wetlands are also influenced by nutrient flux from a wide variety of sources such as runoff, groundwater, as well as the transfer of nutrients via dispersal of organisms during various life stages (Bobbink et al., 2006). The many vectors of connection, including precipitation, runoff, and groundwater water transfer, might lead to a high rate of invasion of aquatic habitat. Greater invasion might also occur because of increased propagule pressure in coastal regions (Lowry et al., 2012). Empirical studies are needed to further study trends of invasion in these habitats, and therefore, this study aimed to contribute such information to the lack of knowledge. I sampled fish and invertebrate assemblages in wetlands and coastal waters of Lake Erie to contribute to our understanding of resistance to invasion. To investigate if native species might resist non-native species, I used the fish and invertebrate assemblages because they have often been used as indicators of biotic integrity (EPA, 2012; DNR, 2012) and fisheries species are listed among the 100 worst invasive organisms (Lowe et al., 2000). In addition, benthic invertebrates are listed as one of the most important components to freshwater ecosystems (Fierro et al., 2015) and most commonly used assemblage worldwide to assess environmental questions (Resh, 2008). Therefore, I assessed taxonomic and functional richness and diversity of the fish and invertebrates assemblages of coastal Lake Erie wetlands to determine if there is a negative association between natives and non-native organisms, and thus support of biotic resistance.

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Figures

Figure 1.1 Number of invasion studies conducted worldwide in various habitat types based on a systematic review of 1637 between 1966 and 2011. Figure modified from Lowry et al., 2012.

Chapter 2: Factors influencing biotic resistance of non-native fishes in Lake Erie coastal waters Introduction Invasive species are one of the leading causes of biodiversity declines and impairment of ecosystem function globally (Srivastava & Vellend, 2005; Havel et al., 2015). Invasive species have been shown to impact native species directly through predation and competitive displacement (Mallon et al., 2015; Britton, 2012), such as non-native fish which reduced the abundance of native fishes by out-competing them for prey (Fernie et al., 2008), and indirectly, by altering the abiotic and physical habitat in which they live (reviewed in Crooks, 2002; Ross, 1991). Invaders can also influence the traits (e.g. size, behavior, etc.) of native species, the outcome of species interactions, and overall influence food web dynamics (Jude & Pappas, 1992; reviewed in Crooks, 2002). Furthermore, the effects of invasive species in freshwater environments are often magnified by other historical and current anthropogenic factors such as habitat degradation, pollution, flow modification, and overexploitation (Alofs & Jackson, 2014; Dudgeon et al., 2006), and, thus, are even more problematic in recent years. These detrimental impacts have led non-native, invasive species to be considered one of the most serious threats facing natural ecosystems today. As such, a dominant focus in invasion biology is to identify general factors that might allow a native assemblage to be more resistant against invasion. Species richness and diversity of the native (i.e. resident) assemblage is one factor that has been shown to influence the resistance of an assemblage to invasion (i.e. biotic resistance) and subsequent impact of non-native species. Support for biotic resistance has been tested using the diversity-invasibility hypothesis (DIH; Elton, 1958), which predicts that greater native species richness and diversity increases biotic resistance against non-native species (Davis, 2011). Empirical support for the negative relationship between native and non-native diversity has been found in in both terrestrial and aquatic ecosystems. For example, in aquatic ecosystems, researchers have found that

higher native diversity has deterred invasions of freshwater fish (Clavero et al., 2013). Similar trends have been documented in marine assemblages of sessile invertebrates (Stachowicz et al., 2002), ostracods (Beisner et al., 2006), and microbes (Jiang & Morin, 2004). In an invertebrate and zooplankton community, invasion success of a midge species (Ceratoponidae: Dasyhelea sp.) was lower within high-diversity high-nutrient microcosms than in low-diversity high- nutrient microcosms (Romanuk & Kolasa, 2005). Thus, the support of the DIH pattern has been investigated further to better understand what factors might cause a negative association between native and non-native organisms. Resident diversity is thought to affect invasion resistance through various mechanisms. First, a diverse assemblage of native species is thought to use more of the available resources (Stachowicz et al., 2002), leaving few nutrients and habitat available for an incoming non-native species (Levine & D'Antonio, 1999; Tilman, 2014). For example, diverse plant assemblages used resources more fully, and results suggested that high competition for nitrogen limited invader success (Fargione & Tilman, 2005). Similarly, greater diversity in the assemblage or community can also reduce invasion success by increasing the likelihood of a native species acting as a predator (Elton, 1958). For example, high diversity communities contained more native mobile predators (e.g. crabs, echinoderms, limpets, and chitons) than low diversity communities. According to findings through lab and field research in rocky sub-tidal zones in California, high diversity areas had greater biotic resistance to non-native benthic sessile invertebrates (e.g. tunicates, bryzoans) (Rogers, Byrnes & Stachowicz, 2016). Overall, more diverse assemblages and communities are more likely to contain species that strongly interact in negative ways for the invader (e.g. competition, predation, parasitism; Havel et al., 2015) and result in biotic resistance to non- native species. Greater diversity of the native assemblage, however, has not always yielded resistance towards invasion of non-native species. In fact, some studies have found a positive association between native and non-native species (reviewed in Fridley et al., 2007; Levine & D'Antonio, 1999). For example, non-

native fish species have been shown to frequently invade diverse native fish assemblages in California (Marchetti et al., 2004). Similarly, a positive relationship has been found between native and non-native diversity of birds (White & Houlahan, 2007), microbes (Jiang & Morin, 2004), and plants (Levine, 2000). These positive relationships between the richness and diversity of native and non-native species could to be due to differences in the spatial scale of the study and abiotic factors having a stronger influence on diversity in contrast to species interactions (Fridley et al., 2007; Alofs & Jackson, 2014); however, there are other factors that might play a role in the difference of findings. One alternative factor that might explain a negative or positive in the relationship between native and non-native species richness and diversity could be the way by which richness and diversity were evaluated. For example, most studies testing DIH have primarily assessed taxonomic richness and diversity (i.e. species richness; Woodward, 2009; Keller, Drake & Lodge, 2007; Franssen, Tobler & Gido, 2011; McGradySteed, Harris & Morin, 1997); however, functional diversity (i.e. number of and abundance within functional roles) may also explain a system’s ability to resist invasion. For example, measuring functional diversity according to feeding categories (e.g. feeding guilds (FGs)) or feeding behavior (e.g. functional feeding groups (FFGs)) directly depicts resource use (Mason et al., 2005; Setala, 2002). Functional diversity also effectively assesses functional variation in the assemblage, instead of only the species present (Shea & Chesson, 2002); therefore it can depict how native and non-native species might use resources differently. For example, according to DIH, a high diversity of native fishes across FGs (e.g. Piscivore, Insectivore, and Omnivore) would indicate high competition and resource use, and therefore, might predict strong biotic resistance. Alternatively, low FG diversity and fish dominating in one FG (e.g. only Insectivore) might suggest low competition and open niches in the assemblage where non-native fishes can invade. Because DIH is related to competition and the availability of resources, incorporating feeding diversity into investigations of DIH might be a beneficial way to evaluate if biotic resistance is occurring.

Quantifying the functional diversity of a native assemblage has allowed researchers to better understand and integrate how particular traits (i.e. overlap of traits between the native and non-native species) of the non-native species might confer invasion success. For example, rock pool mesocosms with greater functional feeding group diversity of invertebrates and zooplankton were less likely to be invaded by a midge species (Ceratoponidae: Dasyhelea sp.) than communities with low functional feeding diversity (Romanuk & Kolasa, 2005). Measuring function of the invasive Cane Toad (Rhinella marina) and Bullfrog (Lithobates catesbeianus) relative to the functional diversity of the recipient assemblages revealed that these species were functionally distant to their recipient assemblages, according to life history and morphological traits (Escoriza & Ruhi, 2016). These studies demonstrate that functional diversity should be used in addition to taxonomic diversity, as investigating overlap in function of the native and non-native species could potentially help us to better understand biotic resistance (Shea & Chesson, 2002). The objective of this study was to examine if taxonomic and functional richness and diversity of native fishes in coastal wetlands along the western basin of Lake Erie (Fig. 2.1A; Fig. 2.1B) was associated with the taxonomic and functional richness and diversity of non-native fishes. Additionally, because many other factors have been shown to play a role in the invasion success of fishes, sampling season, sample year and connection to Lake Erie were also examined in combination with species richness and diversity. This research is apropos because coastal wetlands are among the most frequently invaded ecosystems (Bobbink et al., 2006); yet they have had the fewest number of invasion studies conducted in them (Lowry et al., 2012). Similarly, the coastal wetlands along the Western Lake Erie Basin have been relatively overlooked likely because of the more prominent impacts of invasive species within Lake Erie itself. Therefore, lack of research in this area presents an opportunity to determine what factors are most closely related to invasion.

Methods:

To investigate the relationship between native and non-native richness and diversity of fishes, I sampled fishes in coastal waters along the western Lake Erie basin near Cedar Point National Wildlife Refuge (NWR), Ward’s Canal, and Ottawa NWR (Logan County, OH). I sampled the fish assemblage within two sites at Cedar Point NWR (Pool 1 and Lake; Fig. 2.2A), one site at Ward’s Canal (Howard Farm; Fig. 2.2B), and six sites at Ottawa NWR (Pool 1 East, Pool 1 West, MS8A, East Ditch, West Ditch, and Pool 2C; Fig. 2.2C). These sites include managed wetland pools, ditches, and canals, some of which have direct connection to Lake Erie (e.g. Lake, Howard Farm, East Ditch, and West Ditch). I sampled these sites in conjunction with established monitoring to evaluate effects of a restoration project (i.e. hydrological connection) at these locations. The fish assemblage was sampled using a pair of fyke nets (1/2” and 3/16” mesh size). At each site, fyke nets were set and left immersed for approximately 24 hours. When nets were removed, fishes were identified to species according to “Fishes of Ohio” (Trautman, 1981; DNR, 2012), measured for total length (mm), weighed (g), and examined for DELTs (i.e. deformities, erosions, lesions, or tumors) before being returned to the water. Water quality parameters (pH, temperature, dissolved oxygen, turbidity, conductivity, and depth) were measured seasonally (e.g. spring, summer, and fall) in sites in other years (Appendix B) but not in conjunction with fyke net sampling; therefore, environmental parameters were not in analysis of the fish assemblage. Sites were sampled seasonally in fall (i.e. October and November) and spring (i.e. May and June) in either 2013 or 2014. Sites were sampled three times in Spring 2013, Spring 2014, and Fall 2014 and two times in Fall 2013. Although I recognize that differences in detectability of species could vary (Bayley & Peterson, 2001), I am confident that I was able to collect a representative sample of the taxonomic and functional groups present in the assemblage based on the sampling regime of repeated sample collection across multiple sites (Table 2.1).

Estimating taxonomic and functional richness and diversity

The native and non-native fish assemblages were quantified by summing the total number of species and/or functional groups present. Richness and diversity were calculated using data from each pair of fyke nets. Specifically, I combined the fishes found from each site and season to estimate native and non- native taxonomic and functional richness and diversity. Samples from Ottawa NWR Pool 1 East and Pool 1 West were collected from east and west sides of a single pool, and I treated data from both sides as one site for further statistical analysis. This resulted in a total of 42 sampling events from 8 sites in 2 seasons over 2 years. To quantify the taxonomic richness for each sampling event, I enumerated the number of fish species found in the pair of fyke nets. If the same species was found in both the large and small mesh fyke net, the species was only counted once. I categorized species as non-native if they were not historically found in the Lake Erie drainage (GLANSIS, 2016). To quantify the taxonomic diversity for each sampling event, I used species richness and abundance from each set of nets to calculate native and non-native diversity according to the Shannon–Weiner Diversity Index and Simpson’s Index of Diversity (Mason et al., 2005). Shannon-Weiner Diversity Index (H’) was calculated using the proportion of species i relative to the total number of species

(pi). It was then multiplied by the natural logarithm of this proportion, and the resulting product was summed across species and multiplied by -1.

The Simpson’s Index of Diversity (D) is the probability that two randomly selected individuals in an assemblage belong to different categories. It was calculated using the following equation where n is the total number of individuals of a particular species and N is the total number of individuals of all species.

To quantify the functional richness and diversity of native species and non-native species, fishes were classified into feeding guilds (FGs). Each species was assigned to one of eight FGs: Piscivore, Filter Feeder, Insectivore, Omnivore, Invertivore, Generalized Insectivore, Herbivore, and Carnivore (EPA, 2014). For hybridized fishes, presumed parental species had the same FG, and there was no conflict regarding assigning hybrids to feeding guilds. For example, Common Carp and Goldfish are both non-native Omnivores, and therefore Common Carp x Goldfish hybrids were categorized as Omnivores. For species not assigned a FG within the Ohio Environment Protection Agency (EPA) reference, I consulted other literature and experts knowledgeable in fish ecology in Ohio and the Great Lakes to identify fish FGs (Simon et al., 2016; Uzarski et al., 2005; Lunde & Resh, 2012). As with taxonomic analysis, the number of FGs was counted to calculate functional richness. Fish richness and abundance across 8 FGs were used to calculate functional diversity using Shannon-Weiner Diversity Index (FD) and Simpson’s Index of Diversity (D). To calculate Shannon-Weiner Diversity Index (H’), I calculated the proportion of individuals in FG i relative to the total number of individuals in all FGs (pi) and then multiplied it by the natural logarithm of this proportion. The resulting product was summed across functional groups and multiplied by -1.

pi = proportion of individuals within functional group i

FR = functional richness

Simpson’s Index of Functional Diversity (D) was also used to calculate functional diversity. This time I modified the equation for n to be the total number of individuals of a particular FG and N to be the total number of individuals of all FGs present.

Statistical analysis To evaluate how variation native taxonomic and functional richness and diversity, along with season, sampling year and connection to Lake Erie explained the richness or diversity of non-native fishes, I ran a series of candidate linear mixed effect regression and generalized linear mixed effect regression models including every combination of variables (native fish taxonomic richness or diversity, connection to Lake Erie, season, year). In the generalized linear effect models, I evaluated the Poisson family, which is often used with count data (Crawley, 2007). The dependent variable (non-native fish taxonomic and functional richness or diversity) was log-transformed to meet assumptions of normality when necessary, and a constant of 0.01, which is commonly used (Clark et al., 2016) was added to make the transformation possible when the dependent variable was zero. Sites varied in their connection to Lake Erie, which might increase a site’s likelihood of containing non-native specie, and because biotic sampling took place over time, season and year were also included as explanatory variables in the models. Samples were collected from the same sites multiple times; therefore, site was fitted as a random effect in each model. In the data set, I did not detect any outliers according to Cook’s Distance (values >1). There were no issues with multicollinearity (e.g. correlations between predictor variables) found in the models, assessed using the mean squared error values. I used Akaike’s Information Criterion adjusted for small sample size

(AICc) to select the most parsimonious models (Symonds & Moussalli, 2011) that best explained variation in non-native taxonomic and functional richness and diversity. ΔAICc scores were calculated using the difference between the AICc value of each model and the model with the lowest AICc. ΔAICc scores were then used to compute Akaike weights (wi) to rank for best-fit model. Models

with a ΔAICc < 2 were considered to be the best fitting models. All analyses were performed using statistical software R and linear models were run using lme4 package (Team, 2015; Bates et al., 2015). To further determine the relative importance of variables, I calculated the relative variable importance factor (RVI) for the 4 variables of all models and of the best fitting models (those with a ΔAICc value <2). Relative variable importance was calculated by summing

Akaike weights (wi) for all models containing a common variable (Burnham et al., 2011).

Results: Descriptive analysis In Crane Creek, Ward’s Canal, and Cedar Point using fyke nets from spring and fall in 2013 and 2014, I found a total of 10 orders, 16 families, and 50 species including 5 hybrids. These 50 species included 43 native species (including 4 native sunfish hybrids) and 7 non-native species (including 1 hybrid). The non-native species found were Round Goby, White Perch (Morone americana), Western Mosquitofish (Gambusia affinis), Smallmouth Buffalo (Ictiobus bubalus), Goldfish (Carassius auratus), Common Carp (Cyprinus carpio) and Common Carp x Goldfish. I considered hybrids as separate taxonomic categories because sunfish hybrids have been found to be partially viable (Lopez-Fernandez & Bolnick, 2007) and Common Carp x Goldfish hybrids can successfully reproduce up to 22 generations (Liu et al., 2016). Each mesh size of fyke net exclusively caught certain species. For example, Stonecat Madtom (Noturus flavus) was only found using small mesh fyke nets, while Longear Sunfish (Lepomis megalotis) and Silver Redhorse (Moxostoma anisurum) were only found using large mesh fyke nets. For each sampling event, the number of native fishes captured ranged from 2 to 21 species, and the number of non-native fishes captured ranged from 0 to 5 species. Native taxonomic diversity ranged from 0.26 to 2.23 and 0.11 to 0.99, according to Shannon’s Diversity Index and Simpson’s Diversity Index, respectively. Non- native taxonomic diversity ranged from 0 to 1.08 and 0 to 0.99. The most

commonly encountered native fishes were Bluegill (Lepomis macrochirus), Pumpkinseed (Lepomis gibbosus) and Black Crappie (Pomoxis nigromaculatus). Emerald Shiner (Notropis atherinoides) was the most abundant species. Out of 104 independent non-native fishes, I estimated that Western Mosquitofish were the most abundant and made up 32% of the non-native assemblage (Table 2.2). Based on the sampled areas, I estimated that non-native species make up approximately 14% of the species and 16% of the abundance of the fish assemblage. There were 5 FGs present (Appendix A). Native species contained 4 FGs, including Piscivore, Insectivore, Omnivore, and Carnivore. Non-native species made up 4 FGs, including Piscivore, Insectivore, Omnivore, and Generalized Insectivore groups (Fig. 2.3). There was FG overlap of native and non-native fishes in Piscivore, Insectivore, and Omnivore groups. Insectivore was the most abundant FG among all fishes. Native fishes were most commonly Insectivores while non-native fishes were most commonly Insectivores and Omnivores. For each sampling event, number of native functional groups ranged from 1 to 4, and number of non-native functional groups ranged from 0 to 3. Native functional diversity ranged from 0 to 1.31 and 0 to 0.81, according to Shannon’s Diversity Index and Simpson’s Diversity Index, respectively; exotic functional diversity ranged from 0 to 0.69 and 0 to 0.99.

Variables explaining non-native taxonomic richness

AICc model comparisons indicated that there were 8 top models with

ΔAICc < 2.0 explaining non-native fish taxonomic richness (Table 2.3A). The best 3 models included a two-variable model containing connection to Lake Erie and season, a three-variable model containing native fish taxonomic richness, connection to Lake Erie, and season, and a two-variable model containing native fish taxonomic richness and connection to Lake Erie. Connection to Lake Erie appears to be the most important variable included in our analysis having a relative importance value of 0.690 (Table 2.3B). Native fish taxonomic richness has a relative importance of 0.608. Connection to Lake Erie was included in 6 of

the 8 top models and native taxonomic richness is in 5 of the top models. Both are positively correlated, indicating that when a site is connected to Lake Erie and has greater native fish taxonomic richness, non-native fish taxonomic richness is greater.

Variables explaining non-native functional richness

AICc model comparisons indicated that there were 9 top models with

ΔAICc < 2.0 for explaining non-native fish functional richness (Table 2.4A). The best 3 models included a one-variable model including connection to Lake Erie, a one-variable model including season, and a two-variable model including connection to Lake Erie and season. Native fish functional richness was only in 2 of the top 9 models and had the lowest relative importance of any variable (0.406) (Table 2.4B). Connection to Lake Erie was included in 5 of the top 9 models and had the greatest relative importance of 0.569. When a site was connected to Lake Erie, the sample had greater non-native fish functional richness.

Variables explaining non-native taxonomic diversity

AICc model comparisons indicated that there were 6 top models with

ΔAICc < 2.0 for explaining non-native fish taxonomic diversity described using the Shannon-Weiner Diversity Index (Table 2.5A). The best 3 models included a 3-variable model including connection to Lake Erie, season, and year, a full model using native fish taxonomic diversity, connection to Lake Erie, season and year, and a two-variable model including connection to Lake Erie and year. Connection to Lake Erie was included in all 6 of the top 6 models and had a relative importance of 0.912 (table 2.5B). Native fish taxonomic diversity was in 3 of the top 6 models and had the lowest relative importance of all variables (0.423). When a site was connected to Lake Erie it had greater non-native taxonomic diversity.

AICc model comparisons indicated that there were 7 top models with

ΔAICc < 2.0 for explaining non-native fish taxonomic diversity described using

Simpson’s Index of Diversity (Table 2.6A). The 3 best models included a full 4- variable model including native fish taxonomic diversity, connection to Lake Erie, season and year, a 3-variable model including connection to Lake Erie, season and year, and a 3-variable model including native fish taxonomic diversity, connection to Lake Erie, and season. The connection to Lake Erie variable was included in all 7 of the 7 top models and had a relative importance of 0.813 (Table 2.6B). Native fish taxonomic diversity was included in 4 of 7 of the top models and had the lowest relative importance of any variable included in the model (0.572). Non-native taxonomic diversity was greater when a site was connected to Lake Erie, but lower when a site had greater native fish taxonomic diversity.

Variables explaining non-native functional diversity

AICc model comparisons indicated that all 15 models had a ΔAICc < 2.0 in explaining non-native fish functional diversity described using the Shannon- Weiner Index of Diversity (Table 2.7A). The best 3 models included a 3-variable model including connection to Lake Erie, season, and year, a 2 variable model including season and year, and a 2-variable model including connection to Lake Erie and season. Native functional diversity and connection to Lake Erie had approximately equal RVI of 0.495 (Table 2.7B). Year had the greatest RVI value of 0.644.

AICc model comparisons indicated that all 15 models had a ΔAICc < 2.0 in explaining non-native fish functional diversity described using the Simpson’s Index of Diversity (Table 2.8A). The 3 best models included a 3-variable model including native fish functional diversity, season, and year, a full 4-variable model including native fish functional diversity, connection to Lake Erie, season, and year, and a 2-variable model including native fish functional diversity and season. Native fish functional diversity was included in the 8 best models and had the highest RVI (0.615; Table 2.8B).

Discussion:

The objective of this study was to evaluate whether non-native taxonomic and functional richness and diversity was associated with native richness and diversity, as well as season, sampling year and connection to Lake Erie. Of particular interest in this study was evaluating whether there was a negative relationship between native and non-native taxonomic and functional richness and diversity. However, across all analyses, native fish taxonomic and functional richness or diversity did not explain variation in invasion success as indicated by the top models based on AICc model comparison. In fact, for each of the statistic models, there was very little variation in ΔAICc scores among the models.

Typically, there is high variation in AICc score when the candidate models are under-fitting or over-fitting the data or a certain variable or group of variables explains a high degree of variation (Snipes & Taylor, 2014). The models we ran did not deviate much from the empty model, which suggests that our array of models and the predictor variables explored in this study in general did not explain variation in non-native fish taxonomic and functional richness and diversity (Anderson & Burnham, 2002). Overall, native fish richness and diversity, site connection to Lake Erie, season, and year of sampling may not adequately capture the true nature of what determines non-native fish richness and diversity in the wetland sites. It is certainly possible that other variables might be important in explaining non-native fishes. Below I discuss possible explanations for why invasion success of fishes within these coastal wetlands was not associated with native richness and diversity. Specifically, I discuss the following possible explanations: spatial scale of the study, the way I assessed and measured fish functional groups, the understanding of how diversity influences invasion, or because of low sample sizes.

Spatial scale of the study In contrast to many studies that have found support for DIH (Clavero et al., 2013; Stachowicz et al., 2002; Jiang & Morin, 2004; Beisner et al., 2006), other studies have found a positive association between native and non-native diversity (Levine, 2000; Levine & D'Antonio, 1999). For example, non-native

fishes were found to frequently invade watersheds in California containing high native fish diversity (Marchetti et al., 2004). Similarly, diverse non-native assemblages of microbes (Jiang & Morin, 2004) and plants (Levine, 2000) frequently are found in diverse assemblages of native species. The empirical support for both a negative and positive relationship between native and non- native species has been described as the “invasion paradox” and is thought to be related to spatial scale of the study (e.g. the size of area investigated; Fridley et al., 2007). In small-scale studies (<10 m2), native and non-native species richness are negatively associated (Levine & D'Antonio, 1999; Romanuk & Kolasa, 2005), while, in large-scale studies (1 km2 or more), native and non-native species richness are positively associated (Knops, Griffin & Royalty, 1995). The scale-dependent results could reflect differences in factors driving patterns of species diversity at different scales. For example, in small-scale studies, such as those conducted in experimental mesocosms, abiotic factors are relatively homogeneous, and therefore biotic interactions might better predict native and non-native species occurrence; however, as you increase spatial scale, environmental variables and biogeographic processes might be stronger factors determining community composition (Fridley et al., 2007). Overall, it is thought that biotic interactions deter invasions at small spatial scales, but greater resource availability and habitat heterogeneity increase the likelihood of invasion at large spatial scales (Levine & D'Antonio, 1999; Marchetti et al., 2004). Spatial scale might explain divergent patterns leading to the invasion paradox and also be a factor for the lack of a relationship I found in the study. The study area included sites that ranged widely in size. Sampled wetlands that are not connected to Lake Erie range between 0.19 and 0.98 km2 (Table 2.9). These would not be classified as either small (<10 m2) or large-scale (>1 km2), but as an intermediate size of area studied. The sampling sites might explain why native richness and diversity were not predictors of non-native fish richness and diversity. The lack of association I found might not be surprising and instead be evidence of a combination of biotic interactions and

environmental variables influencing the abundance of native and non-native fishes.

Assessing and measuring feeding guilds of fishes The lack of relationship between native and non-native functional richness and diversity in the study might be affected by the assessment of function. Categorization by feeding guild is effective in assessing resource use, which is believed to be an important factor of biotic resistance; however, the number of FGs I found in the study was relatively low (4 native FGs and 4 non- native FGs with overlap in 3 categories). Although dividing functions into too many groups can introduce subjectivity and arbitrariness to functional categorization (Micheli & Halpern, 2005), using only a small number of groups could limit the ability to tease apart the relationship between native vs. non- native functional richness and diversity. Refining the groups to create the right balance between too many or too few functional categorizations of fish functional guilds might allow more adequate variation along the diversity axis. The grouping of species could have large effects in results of how native and non-native fishes are associated. An additional complexity to consider is the plasticity of the species with respect to feeding guilds and behaviors (Funk et al., 2016). For example, within feeding guilds, diet preference and resource use of fishes can be variable based on life stage. Stomach content and isotope analyses have shown that many species have ontogenetic diet shifts (Pool et al., 2016), such as Longnose Gar (Lepisosteus osseus). This fish is an insectivore when it is a juvenile, and piscivore when it is an adult (Trautman, 1981). Several other fishes in Lake Erie are known to have similar diet shifts across life stage, including Gizzard Shad (Dorosoma cepedianum), Bowfin (Amia calva), Muskellunge (Esox masquinongy), Freshwater Drum (Aplodinotus grunniens), and Northern Pike (Esox lucius; Scott & Crossman, 1973). Like other studies that have found the refuges provide breeding habitat for adults and are nursery grounds for fry (Kowalski, Wiley & Wilcox, 2014), I found multiple life stages of fishes

inhabiting the coastal waters of Lake Erie. I accounted for life stage when categorizing FGs of fishes with ontogenetic shifts (e.g. Longnose Gar, Northern Pike, Gizzard Shad, and Freshwater Drum); however, within a feeding guild, fish will be eating a combination of smaller and larger prey as they grow. For example, Gizzard Shad shift from eating zooplankton to being Omnivorous (estimated at 22mm), however, the precision of fish length at which this transition occurs and how drastic the diet shift is could influence my assessment of functional diversity in the assemblage. Furthermore, there can be plasticity and variation of diet within a single species that could influence whether the individual fish was appropriately categorized in its correct functional group. For example, some species vary their FG based on resource availability or other species present in the community (Mayfield & Levine, 2010). The categorization of FGs I used might not adequately take into account how much of a specialist, generalist, or opportunistic feeder certain fishes are. Individual fish likely also have variable habits with respect to what they will eat, and competitive exclusion might depend on strength of these feeding niches (Mayfield & Levine, 2010). I was unable to assess the variation in fish FG and plasticity of diet, but with more research investigating feeding ecology, this could be evaluated more and incorporated to better assess FGs. The way functional diversity is estimated using various indices might influence results as well (Mason et al., 2013). I evaluated Shannon-Weiner Diversity Index and Simpson’s Index of Diversity because these are commonly used to calculate functional diversity (Zhang et al., 2016) and pair nicely in conjunction with the way we measured taxonomic diversity; however, there are additional methods to assess the functional assemblage that were not used in the study. For example, functional divergence variance (FDvar) is commonly used to measure the distance of species functional group relative to the rest of the assemblage. It is calculated using evenness and abundance and weighs the mean and deviations from it (Mason et al., 2003), and it has been beneficial because it discounts traits that are highly correlated with one another (Petchey & Gaston,

2009). Many invasion studies are interested in measuring the range of traits and there for will measure organism dissimilarity, such as by estimating evolutionary distances to evaluate phylogeny (Marx et al., 2016). Other methods include estimating Euclidian distances to measure functional originality (i.e. the distance between the invasive species and the centroid of the native assemblage) and functional uniqueness (i.e. the distance between the invasive species and the nearest neighbor distance; Escoriza & Ruhi, 2016). In conclusion, there are many methodological decisions regarding which and how many traits should be evaluated, how to relatively weight them, and how to combine functional traits that are measured on different scales (e.g. those qualitatively and quantitatively assessed; Leps et al., 2006). These decisions might influence the interpretation of the relationship that functional richness and diversity reveal between native and non-native groups. Focus on refining how to measure functional diversity would be beneficial for future studies. Finally, additional traits of fishes might be important to evaluate when investigating invasion. Research has suggested that there might be further hierarchical structure in groups, making certain invaders superior although they are functionally similar (Wedin & Tilman, 1993). Such hierarchal structure (e.g. life history traits, reproductive behavior, etc.) can be density dependent and has been seen to affect intraspecific competition of fishes (Foss-Grant et al., 2016). For example, fishes with a larger maximum body length typically have a higher number of recruits, and thus a larger non-native fish might have an advantage when invading an assemblage, irrespective of FG. There is no single index that can capture the entire scope of species traits and interactions (Giller et al., 2004), and my categorization of FG might be missing important traits that influence invasion. Not taking into account differences such as competitive ability between native and non-native species might explain why functional diversity did not predict invasion and incorporating such other methods would be a beneficial way to further investigate the DIH.

Understanding of how diversity influences invasion

The results of native taxonomic and functional richness and diversity not predicting non-native taxonomic and functional richness and diversity might not have been what I expected because of understanding of how the native assemblage affects the invasion. The invasion process includes 4 phases: introduction, establishment, spread, and impact (Mallon, van Elsas & Salles, 2015). The richness or diversity of the native assemblage has little to no control of the introduction stage (i.e. a non-native fish can be humanly introduced or via water body connection regardless of the resident assemblage), but the richness and diversity of the native assemblage is thought to greatly decrease the likelihood of establishment through more fully used resources. That is what we were testing here, however, other research has suggested that native species might influence the spread and impact stages of invasion greater than the establishment stage (Levine, Adler & Yelenik, 2004). Overall, Levine et al. (2004) suggested that resident species do not repel establishment but might reduce subsequent growth and impact. Instead of “biotic resistance,” a more accurate term to describe the resident assemblage’s influence might be “biotic containment.” Biotic containment could be investigated by researching how greater native richness and diversity prevent species from reaching proliferous population growth. My investigation of invasion by calculating richness and diversity of non-native species might tell about the establishment of non-natives but not reveal other signs of biotic resistance, such as native species constraining growth, containing spread, and minimizing impacts of non-native species.

Sample size Finally, the fourth possible explanation for the lack of an association between native and non-native taxonomic and functional richness and diversity in this study might be due to a small sample size. I had a sample size of 42 pairs of nets from 8 sites over 2 seasons and 2 years. The low number of samples and high site replication might restrict the ability to detect a significant trend over the coastal area of Lake Erie.

Importance of connection to Lake Erie I found that site connection to Lake Erie was a predictor of non-native taxonomic richness and diversity. Higher non-native taxonomic richness in sites that are connected to Lake Erie is likely because of the higher number of non- native fish species present there. There are 125 fish species in Lake Erie, ranging from rare or possibly extirpated to abundant (Simon et al., 2016), and non-native species comprise 24 of these (19.2 %). I found a total of 50 species (including 5 hybrids) in the sites (all overlapping with species in Lake Erie) and 7 non-native taxa (16.2 %) including White Perch, Common Carp, Goldfish, Common Carp x Goldfish hybrid, Western Mosquitofish, Smallmouth Buffalo and Round Goby. Other non-native species in Lake Erie but not found in the samples include Alewife (Alosa pseudoharengus), Bighead Carp (Hypophthalmichthys nobilis), Bigmouth Buffalo (Ictiobus cyprinellus), and Redear Sunfish (Lepomis microlophus; GLANSIS, 2016). I found 29% of the non-native fishes possible from Lake Erie. Connection to Lake Erie could increase propagule pressure, defined as the composite measure of the absolute number of individuals introduced into a region to which they are not native and the number of discrete release events (Lockwood, Cassey & Blackburn, 2005). Invasion is more likely when non-native fishes have more frequent and direct access from Lake Erie, and therefore, connection to Lake Erie might be a main driver of non-native taxonomic richness and diversity, irrespective of the biotic assemblage.

Conclusion: My results do not support DIH across the study sites nor that native fish taxonomic and functional richness and diversity predict non-native fish richness and diversity. This could be explained by variation in spatial scale, assessment and measurement of fish function, understanding how diversity influences invasion, and the low sample sizes used in this study. Future research should implement studies at intermediate-sized spatial scales to test what happens at the boundary between large and small-scale habitats, continue to use multiple axes of diversity (Giller et al., 2004), and investigate invasion at various stages of the

complex process. Ultimately, future insight into the relationship between native and non-native species will help us to better understand biotic resistance to reduce invasion and conserve native biodiversity.

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Tables

Table 2.1. Sampling regime of fish sampling including location, site, wetland site connection to Lake Erie and number of sampling events (i.e. number of pairs of fyke nets) seasonally in 2013-2014. Sampling Events Connection to Location Site Lake Erie Spring Fall Spring Fall 2013 2013 2014 2014 Ottawa Pool 1 East Unconnected 3 3 Ottawa Pool 1 West Unconnected 3 3 Ottawa Pool 2C Unconnected 3 2 Ottawa MS8A Unconnected 3 2 Ottawa East Ditch Connected 3 2 Ottawa West Ditch Connected 3 2 Cedar Point Pool 1 Unconnected 3 2 Cedar Point Lake Connected 3 2 Ward's Canal Howard Farm Connected 3 3

Table 2.2. Non-native fishes found and percent relative abundance among non- native species found. Data was from fish samples collected using fyke nets from 7 sites across 3 locations (Cedar Point, Howard Farm and Ottawa National Wildlife Refuge) in fall and spring 2013-2014.

Fish Common Name Scientific Name Percent Abundance White Perch Morone americana 17.301 Goldfish Carassius auratus 23.077 Common Carp x Goldfish Carassius auratus x Cyprinus carpio 5.633 Common Carp Cyprinus carpio 18.231 Western Mosquitofish Gambusia affinis 31.730 Smallmouth Buffalo Ictiobus bubalus 0.962 Round Goby Neogobius melanostomus 3.835

Table 2.3. Non-native fish taxonomic richness explained by native fish taxonomic richness, connection to Lake Erie, year, and season, shown by A) 15 candidate generalized linear mixed effect models and B) Relative Variable Importance (RVI) scores for the explanatory variables.

Independent Variables: K AICc ΔAICc w.AICc

1 connection to Lake Erie + 3 season 117.976 0.000 0.143 2 native species richness + 4 connection to Lake Erie + season 118.096 0.120 0.134 3 native species richness + 3 connection to Lake Erie 118.417 0.440 0.115 4 connection to Lake Erie 2 118.680 0.704 0.100 5 native species richness + 3 season 118.930 0.954 0.089 6 native species richness 2 119.047 1.071 0.084 7 native species richness + 5 connection to Lake Erie + year + season 119.736 1.760 0.059 8 connection to Lake Erie + year 4 + season 119.954 1.977 0.053 9 native species richness + 4 connection to Lake Erie + year 120.209 2.233 0.047 10 year + season 3 120.321 2.345 0.044 11 season 2 120.451 2.475 0.041 12 connection to Lake Erie + year 3 120.592 2.616 0.039 13 native species richness + year 3 120.666 2.690 0.037 14 year + season 3 122.435 4.459 0.015 15 year 2 123.074 4.977 0.012

Model parameters RVI of RVI of all models top models native species richness 0.608 0.480 habitat type 0.69 0.604 year 0.276 0.112 season 0.579 0.478

Table 2.4. Non-native fish functional richness explained by native fish functional richness, connection to Lake Erie, year, and season, shown by A) Ranked generalized linear mixed effect models, and B) Relative Variable Importance (RVI) scores for the explanatory variables.

Independent Variables: K AICc ΔAICc w.AICc 1 connection to Lake Erie 2 102.753 0.000 0.148 2 season 2 103.492 0.740 0.102 3 connection to Lake Erie + season 3 103.613 0.861 0.096 4 year 2 103.843 1.091 0.086 5 3 103.945 1.193 0.082 connection to Lake Erie + year 6 native functional richness 2 104.283 1.531 0.069 7 year + season 3 104.508 1.756 0.062 8 native functional richness + 4 104.567 1.815 0.060 connection to Lake Erie 9 connection to Lake Erie + year + 4 104.611 1.858 0.058 season 10 3 104.817 2.064 0.053 native functional richness + year 11 native functional richness + year + 4 104.917 2.165 0.050 season 12 native functional richness + season 3 104.940 2.188 0.050 13 native functional richness + 4 105.168 2.416 0.044 connection to Lake Erie + year 14 native functional richness + 4 105.312 2.560 0.041 connection to Lake Erie + season 15 native functional richness + 5 105.398 2.645 0.039 connection to Lake Erie + year + season

Model Parameter RVI of RVI of all models top models native functional richness 0.406 0.129 habitat type 0.569 0.444 year 0.473 0.287 season 0.499 0.318

Table 2.5. Non-native fish taxonomic diversity according to Shannon-Weiner Diversity Index explained by native fish taxonomic diversity, connection to Lake Erie, year, and season, shown by A) Ranked linear mixed effect models, and B) Relative Variable Importance (RVI) scores for the explanatory variables.

Independent Variables: k AICc ΔAICc w.AICc 1 connection to Lake Erie + year + 4 179.361 0.000 0.202 season 2 native taxonomic diversity + 5 179.862 0.501 0.158 connection to Lake Erie + year + season 3 connection to Lake Erie + year 3 180.260 0.899 0.129 4 connection to Lake Erie + season 3 180.263 0.903 0.129 5 native taxonomic diversity + 4 180.780 1.419 0.100 connection to Lake Erie + season 6 native taxonomic diversity + 4 181.182 1.822 0.081 connection to Lake Erie + year 7 connection to Lake Erie 2 181.514 2.153 0.069 8 native taxonomic diversity + 3 182.446 3.085 0.043 connection to Lake Erie 9 year + season 3 183.765 4.404 0.022

10 season 2 184.478 5.118 0.016 11 native taxonomic diversity + year + 4 184.523 5.162 0.015 season 12 year 2 184.570 5.209 0.015 13 native taxonomic diversity + season 3 185.240 5.879 0.011 14 native taxonomic diversity + year 3 185.517 6.156 0.009 15 native taxonomic diversity 2 186.487 7.126 0.006

Model Parameters RVI of RVI of all models top models native taxonomic diversity 0.423 0.339 habitat type 0.912 0.799 year 0.633 0.571 season 0.653 0.589

Table 2.6. Non-native fish taxonomic diversity according to Simpson’s Index of Diversity explained by native fish taxonomic diversity, connection to Lake Erie, year, and season, shown by A) Ranked linear mixed effect models, and B) Relative Variable Importance (RVI) scores for the explanatory variables.

Independent Variables: K AICc ΔAICc w.AICc

1 native taxonomic diversity + 5 177.440 0.000 0.176 connection to Lake Erie + year + season 2 connection to Lake Erie + year + 4 178.030 0.591 0.131 season 3 native taxonomic diversity + 4 178.150 0.710 0.123 connection to Lake Erie + year 4 native taxonomic diversity + 4 178.259 0.820 0.117 connection to Lake Erie + season 5 connection to Lake Erie + year 3 178.503 1.063 0.103 6 connection to Lake Erie + season 3 178.750 1.310 0.091 7 native taxonomic diversity + 3 179.206 1.766 0.073 connection to Lake Erie 8 connection to Lake Erie 2 179.537 2.097 0.062 9 native taxonomic diversity+ year + 4 181.106 3.667 0.028 season 10 year + season 3 181.613 4.173 0.022 11 native taxonomic diversity + year 3 181.624 4.184 0.022 12 native taxonomic diversity + season 3 181.797 4.357 0.020 13 year 2 182.026 4.586 0.018 14 season 2 182.264 4.824 0.016 15 native taxonomic diversity 2 182.520 5.080 0.014

Model Parameters RVI of RVI of all models top models native taxonomic diversity 0.572 0.488 habitat type 0.813 0.813 year 0.638 0.533 season 0.600 0.514

Table 2.7. Non-native fish functional diversity according to Shannon-Weiner Index of Diversity explained by native fish functional diversity, connection to Lake Erie, year, and season, shown by A) Ranked linear mixed effect models, and B) Relative Variable Importance (RVI) scores for the explanatory variables.

Independent Variables: K AICc ΔAICc w.AICc 1 connection to Lake Erie + year + 4 160.897 0.000 0.102 season 2 year + season 3 160.986 0.088 0.097 3 connection to Lake Erie + season 3 161.128 0.231 0.091 4 season 2 161.181 0.284 0.088 5 native functional diversity + 5 161.449 0.551 0.077 connection to Lake Erie + year + season 6 native functional diversity + year + 4 161.548 0.651 0.074 season 7 native functional diversity + 4 161.668 0.770 0.069 connection to Lake Erie + season 8 native functional diversity + season 3 161.735 0.837 0.067 9 connection to Lake Erie + year 3 161.926 1.029 0.061 10 year 2 162.013 1.116 0.058 11 connection to Lake Erie 2 162.049 1.151 0.057 12 native functional diversity + 3 162.164 1.267 0.054 connection to Lake Erie + year 13 native functional diversity + 3 162.234 1.337 0.052 connection to Lake Erie 14 native functional diversity + year 3 162.275 1.377 0.051 15 native functional diversity 2 162.313 1.415 0.050

Model Parameters RVI of RVI of all models top models native functional diversity 0.495 0.495 habitat type 0.495 0.495 year 0.644 0.644 season 0.597 0.597

Table 2.8. Non-native fish functional diversity according to Simpson’s Index of Diversity explained by native fish functional diversity, connection to Lake Erie, year, and season, shown by A) Ranked linear mixed effect models and B) Relative Variable Importance (RVI) scores for the explanatory variables.

Independent Variables: K AICc ΔAICc w.AICc

1 native functional diversity + year 4 160.517 0.000 0.082

+ season 2 native functional diversity + 5 160.528 0.011 0.081 connection to Lake Erie + year + season 3 native functional diversity + 3 160.598 0.081 0.079 season 4 native functional diversity + 4 160.631 0.114 0.077 connection to Lake Erie + season 5 native functional diversity 2 160.690 0.173 0.075 6 native functional diversity + 3 160.706 0.189 0.075 connection to Lake Erie

7 native functional diversity + 4 160.741 0.224 0.073

connection to Lake Erie + year

8 native functional diversity + year 3 160.748 0.232 0.073 9 year + season 3 160.792 0.275 0.071 10 connection to Lake Erie + year + 4 160.836 0.319 0.070 season 11 season 2 160.911 0.394 0.067 12 connection to Lake Erie + season 3 160.986 0.469 0.065

13 year 2 161.270 0.753 0.056

14 connection to Lake Erie + year 3 161.315 0.798 0.055

15 connection to Lake Erie 2 161.355 0.838 0.054

Model Parameters RVI of all RVI of top models models native functional diversity 0.615 0.615 habitat type 0.550 0.550 year 0.480 0.480 season 0.511 0.511

Table 2.9. Area of wetland sites sampled with fyke nets 2013-2014 that were not directly connected to Lake Erie.

Location Site Size (km2) Ottawa Pool 1 East/West 0.42 Ottawa Pool 2C 0.33 Ottawa MS8A 0.19 Cedar Point Pool 1 0.98

Figures

Figure 2.1. Fish sampling was conducted in spring and fall 2013-2014 in the A) Western basin of Lake Erie, within B) Cedar Point National Wildlife Refuge, Ward’s Canal, and Ottawa National Wildlife Refuge.

Figure 2.2. Sampling sites in A) Cedar Point National Wildlife Refuge: Pool 1 and Lake; B) Ward’s Canal: Howard Farm; and C) Ottawa National Wildlife Refuge: West Ditch, MS8A, East Ditch, Pool 2C, Pool 1 West and Pool 1 East.

30 25 20 15 10 5 Native 0 Non-native Number of Fish Species

Figure 2.3. Number of native (blue) and non-native (red) fish species categorized by feeding guild. Data includes fishes collected using fyke nets from all sites sampled in fall and spring 2013-2014.

Chapter 3: The taxonomic and functional association between native and non-native fishes and invertebrates in Ottawa National Wildlife Refuge

Introduction: Invasive species are one of the leading causes of biodiversity declines and impairment of ecosystem function globally (Srivastava & Vellend, 2005; Havel et al., 2015). They are considered key drivers of species loss and replacement due to direct predation (Mallon et al., 2015; Britton, 2012), changing of physical, chemical, and ecological conditions (reviewed in Crooks, 2002; Ross, 1991), and habitat homogenization (Ricciardi, 2001). The detrimental impacts have led non-native, invasive species to be considered one of the most serious threats facing natural ecosystems today. To achieve worldwide goals of conserving biodiversity and reducing species loss, a central research focus of ecology today is to determine factors that prevent biological invasions. The biological invasion process includes successful introduction, establishment, spread, and impact (Mallon, van Elsas & Salles, 2015). The invasion process is thought to be mainly influenced by three factors: 1) the invasiveness of the non-native species, 2) propagule pressure, and 3) the native assemblage. Invasiveness, assessed by traits of non-native species making them successful (e.g. greater fecundity, larger body size, bold behavior, etc.; van der Gaag et al., 2016) and propagule pressure (e.g. the number of discrete invasion events and number of individuals involved; Lockwood, Cassey & Blackburn, 2005) are factors ecologists can attempt to reduce such as with pesticides and barriers, but another way to limit invasion success might be to promote the diversity and health of the native community. Understanding how the resident community might influence invasion success is a major focus of invasion biology research. Biotic resistance theory suggests that the native assemblage has the ability to resist invasion (e.g. biotic resistance theory; Levine, Adler & Yelenik,

2004). Support for biotic resistance has been tested using the diversity- invasibility hypothesis (DIH), which states that more species-rich and diverse assemblages ought to be better equipped to resist invasion (Elton, 1958), and predicts that greater native species richness and diversity increases biotic resistance against non-native species (Fig. 3.1A; Davis, 2011). Support for DIH (i.e. a negative association between natives and non-native richness and diversity) has been found in aquatic assemblages such as freshwater fishes (Clavero et al., 2013), sessile invertebrates (Stachowicz et al., 2002), ostracods (Beisner et al., 2006), and marine microbes (Jiang & Morin, 2004). For example, in a rock pool mesocosm experiment, the invasion success of focal invader, a midge species (Ceratoponidae: Dasyhelea sp.), was tested in an invertebrate and zooplankton community (Romanuk & Kolasa, 2005). The native community was altered to contain between 0 to 16 species to test how biodiversity would influence invasion. Invasion success was lower within high-diversity microcosms than in low-diversity microcosms, suggesting that greater diversity might be able to reduce invasion. Overall, according to a recent meta-analysis, there is broad support for DIH (Stotz, Pec & Cahill, 2016), but reasons why are still being researched. The most commonly attributed explanation for the DIH is that in a more diverse assemblage there are fewer unused resources (e.g. niche space and nutrients) available for incoming organisms (Tilman, 2014; Levine & D'Antonio, 1999). Given that resources are typically limited, greater competition for those resources in diverse assemblages might deter non-native species from successfully establishing (i.e. colonizing and reproducing). Additionally, diverse assemblages are more likely to contain species that strongly interact in negative ways with the invader, for example through competition, predation, or parasitism (Havel et al., 2015). Greater negative biotic interactions than positive interactions (e.g. facilitation, commensalism, mutualism) could also explain support for DIH. Although DIH has served as a key guiding hypothesis in the field of invasion biology, the empirical support for this theory has been mixed. The

negative and positive relationship between diversity of the native assemblage and invasion success has been found to vary across systems and habitat type (Alofs & Jackson, 2014). For example, DIH has been supported more in marine and terrestrial systems than in freshwater, and within freshwater systems support is greater in lentic than lotic habitats (Alofs & Jackson, 2014). In some cases, there is evidence of the opposite relationship, in which a positive association between native and non-native species richness has been found (Fig. 3.1B; Levine, 2000; Levine & D'Antonio, 1999). One example of this positive association comes from fish assemblages in which invasion of non-native fishes occurred in Californian watersheds that had high native fish diversity (Marchetti et al., 2004) Potential factors that explain the conflicting results included differences in spatial scale, experimental design, and abiotic factors; however, another important factor not evaluated within the studies was how biodiversity is evaluated. DIH has most commonly been tested using taxonomic species richness and diversity (Woodward, 2009; Keller, Drake & Lodge, 2007; Franssen, Tobler & Gido, 2011; McGradySteed, Harris & Morin, 1997); however, recent work has also incorporated other axes of diversity that might be involved in invasion success. One such axis is functional diversity, which categorizes species by their ecological traits, and interactions with other species and their environment (Woodward, 2009). Functional axes can assess species characteristics such as body size, tolerance to environmental conditions, mobility, habitat occupation, breeding guild, and feeding behavior (Shea & Chesson, 2002; Bonada, Rieradevall & Prat, 2007; Schriever et al., 2015; Woodward, 2009). As an example, functional richness and diversity assessments have been found to effectively predict invasion in aquatic systems in a fish assemblage in the Colorado River Basin (Olden, Poff & Bestgen, 2006). This study assessed morphological, behavioral, physiological, feeding, and life- history traits of all fish species found in pools in the Colorado River Basin. Specifically using life history traits (e.g. time of maturation, juvenile survivorship, fecundity, etc.), they found that native species that show the

greatest distributional declines are separated into those exhibiting strong functional overlap with non-native species (evidence for negative biotic interactions). Furthermore they found that rapidly spreading non-native fishes generally occupied "vacant" niche positions in life-history space, which is associated either with "niche opportunities" or with minimal overlap with native life-history strategies (consistent with biotic resistance theory). According to a meta-analysis of experimental studies, non-native species are most successful in assemblages where its own functional group was absent, but functionally similar plant assemblages can limit coexistence of introduced species and reduce invader colonization and performance (Price & Partel, 2013). There are several reasons why functional diversity might effectively predict invasion. DIH is expected to depend on competition among species for resources, and therefore, functional assessments that describe the similarity of species traits associated with resource use among species could be effective at predicting invasion success (Romanuk et al., 2009). Functionally diverse assemblages, by definition, contain a larger suite of traits (e.g. body size, life history, feeding behavior, etc.) and more completely fill niches than less diverse assemblages (Dukes, 2001; Mason et al., 2005; Schriever et al., 2015; Amalfitano et al., 2015), which might make invasion more challenging for non- native species. Second, if a native assemblage is functionally diverse, there is a higher likelihood that it will include traits and mechanisms for combating invasion compared to an assemblage that is only taxonomically diverse. Traits promoting the ability of the native assemblage to combat invasion include higher tolerance to environmental variables, greater fecundity, and flexibility in onset and duration of reproduction (Olden, Poff & Bestgen, 2006). In addition, using feeding groups and behavior to evaluate DIH might be beneficial because they can account for resource use and highlight open niches for non-natives to invade. A fish assemblage containing Muskellunge (Esox masquinongy), Bowfin (Amia calva) and Yellow Perch (Perca flavescens) would have high taxonomic diversity because the species are from taxonomically distinct categories; however, it would have no functional diversity because the fishes are all

piscivores and fill the same functional role. According to DIH, I would expect that the native assemblage would not be equipped to resist invasion because there is low resource competition of nutrients across the fish assemblage and open niche space in other FGs (e.g. invertivore, carnivore, etc.) for a non-native to invade (Romanuk et al., 2009). Overall, the association between native and non- native organisms using functional categorization might be more related to DIH than taxonomy and an apt predictor of invasion. Support for the DIH might also depend on how broadly I assess the native assemblages. DIH is typically evaluated within a single trophic level (i.e. invasion as a function of the diversity of native competitors; Pintor & Sih, 2011) or within one assemblage (i.e. invasion as a function of the diversity of native species; Henriksson et al., 2016; Britton, 2012); however, I know that natives and non-natives also influence each other more broadly across the community (Kuehne, Olden & Rubenson, 2016). Such interactions and their relationship to invasion have been researched between an invasive exotic bryozoan (Watersipora subtorquata) and native sea otters, seas stars, and mussels (Needles et al., 2015). An experiment placing cages of the invasive bryozoan with native species in a natural ecosystem was conducted to determine direct and indirect effects of the native community on a non-native species. Consideration of the whole community has claimed to be necessary because both the ecological and evolutionary aspects of community assembly are critical to understanding invasion patterns (Fitzgerald, Tobler & Winemiller, 2016). Similarly to within assemblage studies, community-wide diversity studies can describe traits, evolutionary histories, and interactions that strongly influence invasion (Fitzgerald, Tobler & Winemiller, 2016) but can do so more thoroughly than studies in one assemblage. For example, lab and field research in rocky subtidal zones in California investigating DIH found that a community with higher diversity had greater biotic resistance to non-native benthic sessile invertebrates (e.g. tunicates, bryozoans). This finding was likely because of a greater number of native mobile predators such as crabs, echinoderms, limpets, and chitons that acted as benthic consumers, which might have reduced abundance and invasion

success of the non-native invertebrates (Rogers, Byrnes & Stachowicz, 2016). Similar to diverse assemblages, diverse communities are more likely to contain native species that will be able to resist non-natives (Elton, 1958). Another study researching community-wide functional characteristics in rocky subtidal communities, which are relatively free of introduced species, found that richness of native fish decreased invasion of a non-native Mediterranean mussel (Mytilus galloprovincialis; Shinen, Morgan & Chan, 2009). Three of the native fish predators preferred the exotic mussels compared to native mussels. Greater predation in diverse communities was found, but fish predation is not always adequate to control non-native species such as ascidians on the coast of Brazil (Kremer & da Rocha, 2016). Furthermore, greater diversity across the community can increase positive interactions such as facilitation and lead to greater invasion success (Needles et al., 2015). An aquatic community is a complex web with many interactions that occur among assemblages, not only a linear cascade (Leroy et al., 2016); therefore, across-assemblage interactions might be an important component to include when investigating DIH. Studying aquatic community more completely can lead to important insights of the abiotic and biotic factors influencing invasion success as well. The role of community diversity in biotic resistance was researched in mesocosm experiments using native and non-natives in plant and zooplankton communities (Viana et al., 2016). Invasion success of non-native plants decreased with increasing resident species richness, and biotic resistance constrained the invasibility of aquatic plant communities and eventually determined their diversity. On the other hand, zooplankton did not appear to be strongly affected by resident species richness but instead affected by plant composition. These findings might be attributed to the differences in trophic statuses (e.g. primary producers vs. planktivores) and dissimilar growing rates and composition changes. For example, the plants typically grew more later in the season but with little changes in composition, whereas zooplankton community composition changed throughout the growing season. Because these two assemblages were studied in the same mesocosm, factors that influence invasion could be

researched simultaneously without great influence of variations that occur across studies. Overall, community-wide studies are beneficial for determining factors that influence invasion and may explain the conflicting relationships between native and non-native species richness across spatial scale (Needles et al., 2015; Fitzgerald, Tobler & Winemiller, 2016). Here, I quantified the taxonomic and functional richness and diversity of fish and macroinvertebrate community within managed coastal wetlands at the Ottawa National Wildlife Refuge (ONWR) within the western basin of Lake Erie (Fig. 3.2). Freshwater and estuarine coastal wetlands are among the most frequently invaded ecosystems (Bobbink et al., 2006). However, wetlands have been understudied relative to other ecosystems when it comes to the causes and consequences of biological invasions (Lowry et al., 2012). Furthermore, plant invasions are much more commonly studied than those involving other biota (Gao et al., 2015). Through surveys of these coastal wetlands, I specifically examined three sets of relationships between native and non-native species within these wetlands: 1) native vs. non-native fishes, 2) native vs. non-native macroinvertebrates, and 3) native fishes vs. non-native macroinvertebrates. I examined the relationship between native fishes and non-native macroinvertebrates because between-assemblage interactions in the food web could also influence invasion success. I evaluated each relationship (e.g. native vs. non-native fishes) using both taxonomic and functional diversity, where functional diversity was defined using feeding guilds of fish and functional feeding groups of invertebrates. I predicted that higher richness and diversity of native fish and macroinvertebrates (either expressed as taxonomic or functional diversity) would be negatively associated with non-native richness, diversity and presence in the managed coastal wetlands in support of DIH (Fig 3.1A). An alternative prediction would be to find a positive association between native and non-native species and functional groups in support of abiotic factors or positive biotic interactions driving the relationship between native and non-native fish and invertebrates (Fig. 3.1B).

Methods: To assess the fish and invertebrate community, I sampled a total of 10 Lake Erie coastal wetland pools within the Ottawa National Wildlife Refuge (NWR; Fig. 3.3). Some waterbodies were hydrologically connected to Lake Erie (East Ditch, West Ditch, MS8B), others were unconnected (Pool 1 East, Pool 1 West, MS5), and others were changed from hydrologically unconnected to connected as part of a restoration project during the sampling (Pool 2A in 2011, Pool 2B in May 2015, Pool 2C in May 2016, and MS8A in August 2015). Sampling took place between June and October in 2014-2016. I sampled invertebrates in 7 sites in 2014, invertebrates and fish in 9 sites in 2015, and fish in 9 sites in 2016. To assess sampling period, I categorized June as spring, July as summer, and August-October as fall (Table 3.1) under the rationale that there are seasonal differences. To sample the fish assemblage, I used minnow traps of two mesh sizes (1/4” and 1/8”). I lined half of the 1/4" mesh minnow traps with 1/8” nylon mesh and attached it with zip ties to create a finer meshed minnow trap and increase likelihood of catching the diversity of species present in the wetland. 10 large- mesh (1/4”) traps and 10 small-mesh (1/8”) minnow traps were distributed at least 5m apart throughout shore and open water habitat in each site and left immersed overnight (~ 20 hours). This amount of time was decided upon based on preliminary sampling in the previous year and hypoxic conditions (minimum D.O. = 0.51 mg/L, Appendix B). All fish captured were identified to species (Trautman, 1981), measured for total and standard length (mm), and weighed (g) before being returned to the water. To sample the macroinvertebrate assemblage, I collected 3 Ekman grab samples and 3 D-frame dipnet samples at each site and sampling event. Each Ekman grab sample collected a 0.1m2 area of wetland bottom and targeted benthic organisms. Each dipnet sample contained 10 1-m passes to gather organisms from various microhabitats, specifically pelagic and vegetation zones near shore. After briefly cleaning the sample and draining the water, I placed contents containing invertebrates, soil, and vegetation into a labeled 500-mL

bottle and preserved it in 90% ethanol (diluted to 70% with organic contents). I brought samples to the Ohio State laboratory for further processing. There, using Ohio EPA benthic macroinvertebrate processing protocols (EPA, 2012), samples were rinsed in a 250-µm mesh sieve. Large organic material (e.g. whole leaves, twigs, etc.) was discarded after rinsing and visual inspection. I picked organisms from the substrate and vegetation and placed them into vials of 70% ethanol. Next, I identified organisms to furthest resolution (typically genus, or species when possible; Merritt, 2008) with aid of a Nikon SMZ745T microscope. A subset of samples taken in 2015 (26 dipnet samples) was sent to a professional freshwater taxonomy service, Rhithron Associates, Inc., to be identified. This collection and a reference set of 6 samples from Ottawa NWR and Cedar Point NWR in 2013 (3 dipnet and 3 Hester-Dendy samples) were used to assist with accurate invertebrate identification of invertebrates from 2014 and 2015. In addition to biotic sampling, water quality parameters (e.g. temperature, dissolved oxygen, turbidity, pH, conductivity, and water depth) were measured three times within each wetland site using an YSI instrument, which measures water quality (Fig. 3.8A-F). I also documented weather conditions (e.g. cloud cover, time of day, season) to monitor abiotic variation across sites, seasons, and years (Appendix B).

Invertebrate Subsampling I used a subsampling technique to reduce time required for invertebrate sorting and another for invertebrate identification. Each technique was used exclusively (i.e. subsampling technique 1 and 2 were never used on the same sample). Subsampling method 1 was used on 96 samples (61 Ekman samples and 35 dipnet samples) to reduce material processed by half for bottles that yielded a sample size greater than 250-mL. First, I cleaned the entire sample in the 500-µm mesh sieve as described above. Next, I homogenized material by mixing it well in the sieve and distributed the contents into a tray that was divided evenly into 8 sections. Using a random number chart, I randomly selected one section to remove invertebrates from (i.e. process), and continued to select random sections

until I processed half of the contents from the bottle. Finally, all macroinvertebrates were picked from the subsampled material and identified. To validate the subsampling protocol, I compared the invertebrate species composition between the subsample and the entire sample from 2 Ekman grab and 4 dipnet samples across 5 sites. I found that subsampling method 1 generally resulted in a similar composition of species and concluded that the subsampling by half would be representative of the entire sample (Appendix C). Subsampling method 2 was used for 18 dipnet samples that were especially dense with organisms (i.e. over 600 organisms). Samples were entirely searched for invertebrates, but I selected a subsample of invertebrates to identify. First, organisms thoroughly mixed in their vial(s) and poured out evenly into a gridded tray divided by 30 squares. Next, individual squares, each approximately 6 cm by 6 cm, were randomly selected until at least 300 organisms were selected. The number of squares used was recorded (i.e. number of squares/30) and all organisms randomly selected were identified.

Characterizing Functional of Fishes and Invertebrates To investigate the functional richness and diversity of fish and invertebrate assemblages, I assigned each species to a feeding guild (FG) for fishes and functional feeding group (FFG) for invertebrates. Fishes are typically assigned to FGs, which describe what material is consumed, whereas, invertebrates are assigned to FFGs, which describe how material is consumed. I assigned fish species to one of eight FGs: Piscivore, Filter Feeder, Insectivore, Omnivore, Invertivore, Generalized Insectivore, Herbivore, and Carnivore (EPA, 2014; Appendix D). If a species was not assigned a FG from descriptions outlined by the Ohio Environment Protection Agency (EPA), then I consulted other local references (Uzarski et al., 2005; Lunde & Resh, 2012). I assigned invertebrates to one of eight FFGs: shredder, scraper, gather-collector, filter- collector, gathering-collector, piercer, parasite, and predator (Merritt, 2008; EPA, 2012).

Assessing the Native and Non-native status of Fish and Macroinvertebrates I consulted the Great Lakes Aquatic Nonindigenous Species Information System (GLANSIS) species list to identify fishes and invertebrates non-native to the Lake Erie drainage (GLANSIS, 2016). It is possible that there were additional non-native invertebrate species present but because I had limited taxonomic resolution (i.e. challenges due to the small size of aquatic invertebrates, limitations of the taxonomic literature, availability of expertise, need for intensive preparation or rearing, and the limited time and resources), I was unable to always identify macroinvertebrates to the taxonomic resolution needed to determine if they were native or non-native to the system and therefore I may have used a more conservative estimate of non-native abundance, richness, diversity, and presence of than is actually present in the wetlands.

Quantifying taxonomic and functional richness and diversity I used the most specific category of taxonomic resolution possible to quantify taxonomic and functional richness (i.e. number of OTUs or FFGs), diversity, and presence of non-native fishes and invertebrates. I was confronted with a challenge about how to estimate richness and diversity of invertebrates because there was a wide discrepancy of taxonomic resolution, ranging from order down to species. How to handle various taxonomic resolutions is debated (Bouchard, Genet & Chirhart, 2014). A number of recent studies analyzing different levels of taxonomic resolution in macroinvertebrates have found that less specific taxonomic identification (e.g. genus level) can provide sufficient information (Da Silva Giehl et al., 2014); however, others recommend furthest identification because when species are aggregated on higher taxonomic level, important environmental and biological information is lost (Jiang et al., 2013). The balance between these opposing tendencies is thought to be a judgment call and should be decided upon with the purpose of the study.

I decided to preserve taxonomic categorization of invertebrates to their furthest resolution category (i.e. Operational taxonomic unit, OTU) to minimize information lost. Each unique identification category was assigned an OTU, regardless of if there was another identification category at fine resolution. For example, the family Crangonidae was assigned an OTU and a genus within Crangonidae, Crangonyx, was assigned to a different OTU. Other studies have implemented OTUs, such as for chironomids and ostracods diversity in rock pool mesocosms (Romanuk & Kolasa, 2005) and macroinvertebrates in Thai streams (Boonsoong, Sangpradub & Barbour, 2009). To assess fish richness and diversity, I pooled data of fishes collected from 10 large-mesh and again for 10 small-mesh minnow traps because I tested for differences in fish richness between trap mesh size and found no significant difference (t (113) = 0.095; p = 0.925). To assess invertebrate richness, and diversity, I kept each of 3 Ekman grab and 3 dipnet samples separate because I found significant difference in invertebrate richness between sample type (t (308) = =2.69; p = 0.007). When I assessed the association between non-native invertebrates and native fishes, I averaged the 3 Ekman grab samples and the 3 dipnet samples (i.e. I averaged within but not across sampling gear). Samples from Ottawa NWR Pool 1 East and Pool 1 West were collected from two sides of a single pool, and therefore, I treated data from these sampling locations as one site for further statistical analysis. To calculate the taxonomic diversity for each sample using OTUs, I used statistical program PC-ORD (version 5.0) to calculate native and non-native diversity according to the Shannon–Weiner Diversity Index and Simpson’s Index of Diversity (Mason et al., 2005). Shannon-Weiner Diversity Index (H’) was calculated using the proportion of species i relative to the total number of species

(pi). It was then multiplied by the natural logarithm of this proportion, and the resulting product was summed across species and multiplied by -1.

The Simpson’s Index of Diversity (D) is the probability that two randomly selected individuals in an assemblage belong to different categories. It was calculated with the following equation where n is the total number of individuals of a particular species and N is the total number of individuals of all species.

I counted the number of unique FFGs present to quantify the native and non-native functional richness. I calculated native and non-native functional diversity (FD) using Shannon-Weiner Diversity Index and Simpson’s Index of Diversity (D). To calculate Shannon-Weiner Diversity Index, I calculated the proportion of individuals in FFG i relative to the total number of individuals in all FFGs (pi) and then multiplied it by the natural logarithm of this proportion. The resulting product was summed across functional groups and multiplied by -1.

pi = proportion of individuals within functional group i

FR = functional richness

Simpson’s Index of Functional Diversity (D) was also used to calculate functional diversity. In this case, I modified the equation for n to be the total number of individuals of a particular FFG and N to be the total number of individuals of all FFGs present.

I summarized seasonal and yearly shifts of taxonomic and functional richness and diversity of all fishes (Table 3.2A) and total invertebrates (Table 3.2B) by averaging samples collected.

Statistical Analyses

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To investigate the association between native species and non-native species, the non-native fish richness or diversity was the dependent variable and native fish richness or diversity was an independent variable (Table 2.2A). The independent native richness or diversity term was always included in the final model to assess how it performed at predicting non-native species and if it explained significant variation in non-native richness or diversity. All analyses were performed using statistical software R and linear models were run using lme4 package (Team, 2015; Bates et al., 2015). Site was fitted as a random effect to account for the repeated sampling that occurred at each sample site and was always included in the models. I used manual backward selection of linear mixed effect regression and generalized linear mixed effect regression models to determine which other variables needed to be accounted for in this relationship, such as connection to Lake Erie, sampling gear, season, and an interaction term between season and sampling gear. Dependent variables were transformed when necessary (i.e. to meet a normal distribution assumption). I treated year as a factor in the models. I kept these explanatory variables in the model if they had an alpha-level of p < 0.05. The model output of a linear mixed effect model does not give true degrees freedom but instead estimates the degrees freedom based on the random effect term. True degrees freedom were presented in tables.

Relationship between native and non-native fish Although my goal was to examine the relationship between native and non-native richness and diversity of fish in coastal wetlands, I only identified one non-native fish species in the samples, the Common Carp (Cyprinus carpio). Therefore, I modified the analyses to examine the relationship between native fish richness and the presence/absence of non-native Common Carp and diversity and the abundance of non-native Common Carp. Specifically, I analyzed a generalized linear mixed effect model to examine the relationship between native fish richness and diversity (taxonomic and functional, analyzed separately) and the presence/absence or abundance of the non-native common carp. I used Poisson distribution for modeling abundance and a binomial distribution when

modeling presence/absence of the non-native Common Carp. Explanatory variables I considered in fish analyses included connection to Lake Erie, sampling gear (e.g. large or small meshed minnow traps), season, year, and an interaction term between season and sampling gear. Site was our random effect term used to account for repeated sampling at each site.

Relationship between native and non-native macroinvertebrates I analyzed a linear mixed effects model to examine the relationship between native macroinvertebrate richness (and diversity) and non-native macroinvertebrate richness and diversity (taxonomic and functional, analyzed separately). I also analyzed a generalized linear mixed effect regression model to examine the relationship between native macroinvertebrate richness (diversity) and non-native macroinvertebrate presence. To analyze the relationship between native and non-native macroinvertebrates, similarly to the fish analyses in this chapter, I included the same additional explanatory variables in the model (e.g. connection to Lake Erie, sampling gear (i.e. Ekman grab or dipnet), season, year, season x sampling gear). Site was fitted as a random effect to account for repeated sampling at each site.

Relationship between native fish and non-native macroinvertebrates I analyzed generalized linear mixed effect regression and linear mixed effect regression models to examine data collected in 2015 to answer whether non-native macroinvertebrate richness and presence, respectively, influenced the richness and diversity (taxonomic and functional) of native fishes. Similar to the analyses above, site was fitted as a random effect to account for repeated sampling at each site, and I included the same additional explanatory variables in the model (connection to Lake Erie, sampling gear (e.g. minnow trap mesh size, invertebrate sample type), season, and season x sampling gear). Only data from 2015 was analyzed for this across assemblage relationship because that is when invertebrates and fish were sampled in conjunction with each other.

Results: Description of the fish assemblage At the Ottawa NWR, I caught 3,461 fish using minnow traps between 2015 and 2016, representing 22 taxa (i.e. 21 species and 1 sunfish hybrid). There were 13 taxa found using both large and small-mesh minnow traps, 6 taxa found exclusively in large-mesh minnow traps, and an additional 3 taxa found only using small-mesh minnow traps (Appendix C). Bluegill (Lepomis macrochirus) and Central Mudminnow (Umbra limi) were the most common species found. Based on 73 individual non-native Common Carp found, I estimate that non- natives make up 4.5 % richness (1 non-native species/22 total species) and 2.1% abundance (73 non-native fish/3,461 total fish) of the fish assemblage. Fishes were comprised of 5 FGs. The native fish assemblage included Piscivore, Insectivore, Omnivore, Generalized Insectivore, and Carnivore feeding guilds; the non-native fish was an Omnivore (Fig. 3.4). Insectivores were the most common and abundant FG found and made up 89% of the assemblage (Fig. 3.5).

Description of the invertebrate assemblage From dipnet and Ekman grab sampling, I identified 50,290 organisms. I identified these to 408 OTUs (Appendix E). The most commonly collected invertebrate family was Chironomidae followed by the annelid family, Naididae. The most commonly found Genera were Physella sp. from family Physidae, and Caenis sp. from family Caenidae. Four non-native species found included two gastropods (e.g. Faucet Snail (Bithynia tentacula), and Asian Mystery Snail (Viviparus japonica)) and two mussels (e.g. Zebra Mussel (Dreissena polymorpha), and Freshwater Clam (Corbicula fluminea)). Based on 276 non- native individuals collected, I estimated that Zebra Mussels make up 78% of the non-native assemblage, while non-natives as a whole make up 0.549% of the total assemblage. There were 8 FFGs present within the invertebrate assemblage: filter- collector, gatherer-collector, omnivore, piercer, predator, parasite, scraper, and

shredder. Predator was the most speciose FFG (Fig. 3.6), and they were encountered 32% of time among all functional feeding groups; however gatherer/collecter was the most dominant FFG (i.e. most commonly encountered and most abundant). It made up 69% of the invertebrate assemblage (Fig. 3.7). Predatory invertebrates were the second most abundant FFG found. There were 2 non-native species present in the scrapper FFG and filter-collecter FFG.

Relationship between native and non-native fishes I tested the association between native and non-native richness of fishes (for both taxnonomic and functional richness), but this test was equivalent to non-native presence/absence because there was only one non-native fish found (e.g. Common Carp); therefore, I did not include a separate richness analysis (see presence/absence analysis in paragraph below). When investigating the relationship between non-native Common Carp abundance and native taxonomic diversity according to the Shannon-Weiner Index (Fig. 3.9A), results of the generalized linear mixed effect regression model indicated that native diversity was negatively associated with non-native abundance (Estimate - 3.151, p < 0.001). In addition, season (Estimate -2.1836, p = 0.005) and connection to Lake Erie (Estimate 2.6527, p < 0.001) were associated with non-native abundance (Table 3.3A). When investigating the relationship between non-native Common Carp abundance and native taxonomic diversity according to the Simpson’s Index of Diversity (Fig. 3.9B), results of the generalized linear mixed effect regression model indicated native diversity was negatively associated with non- native abundance (Estimate -5.716, p < 0.001). In addition, season (Estimate - 2.107, p = 0.007) and connection to Lake Erie (Estimate 2.6102, p < 0.001) was associated with non-native abundance (Table 3.3B). When investigating the relationship between non-native Common Carp abundance and native functional diversity (Fig. 3.9C), results of the generalized linear mixed effect regression model indicated that native diversity described using the Shannon-Weiner Diversity Index was negatively associated with non- native abundance (Estimate -4.692, p = 0.040). In addition, year (Estimate -

0.797, p = 0.043), season (Estimate -1.871, p = 0.015), and connection to Lake Erie (Estimate 1.232, p = 0.039) were associated with non-native abundance (Table 3.3C). When investigating the relationship between non-native Common Carp abundance and native functional diversity described using the Simpson’s Index of Diversity (Fig. 3.9D), results of the generalized linear mixed effect regression model indicated that native diversity was not associated (Estimate - 7.109, p = 0.057); however, year (Estimate -0.813. p = 0.040), season (Estimate - 1.843, p = 0.017) and connection to Lake Erie (Estimate 1.2450, p = 0.037) were associated with non-native abundance across wetlands (Table 3.3D). When investigating the relationship between non-native fish presence and native fish taxonomic richness, results of the generalized linear mixed effect regression model indicated that native fish taxonomic richness was not associated with non-native Common Carp presence (Estimate -0.183, p = 0.521; Table 3.4A). Similarly, when investigating the relationship between non-native fish presence and native fish functional richness, results of the generalized linear mixed effect regression model indicated that native fish functional richness was not associated with non-native Common Carp presence (Estimate 0.935, p = 0.286); however, season was positively associated with non-native Common Carp presence across wetlands (Estimate 2.033, p = 0.045; Table 3.4B). When investigating the relationship between the non-native fish presence and native fish taxonomic diversity, results of the generalized linear mixed effect regression models indicated that native fish taxonomic diversity described using the Shannon-Weiner Diversity Index was not associated with non-native

Common Carp presence (Estimate -1.026, p = 0. 291; Table 3.4C). Similarly, native fish taxonomic diversity described using the Simpson’s Index of Diversity was not associated with non-native Common Carp presence across wetlands (Estimate -1.978, p = 0.241; Table 3.4D). No other predictor/explanatory variables explained signficant variation in non-native invertebrate presence. When investigating the relationship between non-native fish presence and native fish functional diversity, results of the generalized linear mixed effect regression model indicated that native fish functional diversity according to

Shannon-Weiner Diversity Index was not associated with non-native Common Carp presence (Estimate -2.986, p = 0.193); however, season was positively associated with non-native Common Carp presence (Estimate 2.184, p = 0.030; Table 3.4E). Similarly, native fish functional diversity according to Simpson’s Index of Diversity was not associated with non-native common carp presence (Estimate -4.624, p = 0.191); however, season was positively associated with non-native Common Carp presence across wetlands (Estimate 2.216, p = 0.028; Table 3.4F). There was a significant relationship between non-native fish abundance amd native taxonomic diversity described using Shannon-Weiner Index and Simpson’s Index of diversity and functional diversity described using Shannon- Weiner Index (Table 3.3). There were no significant relationships between non- native fish presence and native fish richness or diversity across the coastal wetlands sampled (Table 3.4). In other words, native fish taxnomic richness, functional richness, taxonomic and functional Shannon-Weiner Diversity Index and taxonomic Simpson’s Index of Diversity did predict the abundance but not the presence of non-native Common Carp across the coastal wetlands sampled.

Relationship between native and non-native invertebrates When investigating the relationship between non-native invertebrate taxonomic richness and native invertebrate taxonomic richness, results of the generalized linear mixed effect regression model indicated that native invertebrate richness was not associated with the richness of non-native invertebrates across coastal wetlands (Estimate 0.004, p = 0.733; Table 3.5A). When investigating the relationship between non-native invertebrate functional richness and native invertebrate functional richness, results of the generalized linear mixed effect regression model indicated that native invertebrate richness was not associated with the richness of non-native invertebrates across coastal wetlands (Estimate -0.055, p = 0.467; Table 3.5B). No other predictor/explanatory variables explained signficant variation in non-native invertebrate presence.

When investigating the relationship between non-native invertebrate taxonomic diveristy and native invertebrate taxonomic diversity described using the Shannon-Weiner Index, results of the linear mixed effect regression model indicated that native invertebrate diversity was not associated with native invertebrate taxonomic diversity (Estimate 0.000, p = 0.975; Table 3.5C). Likewise, when investigating the relationship between non-native invertebrate functional diveristy and native invertebrate functional diversity described using the Shanon-Weiner Index, results of the linear mixed effect regression model indicated that native invertebrate diversity was not associated with the diversity of non-native invertebrates across wetlands (Estimate 0.000, p = 0.583; Table 3.5D). No other predictor/explanatory variables explained signficant variation in non-native invertebrate presence. When investigating the relationship between non-native invertebrate presence and native invertebrate taxonomic richness, results of the Generalized linear mixed effect regression model indicated that native taxonomic richness was not associated with non-native invertebrate presence (Estimate 0.008, p = 0.583); however, connection to Lake Erie was positively associated with non- native invertebrates presence across coastal wetlands (Estimate 0.995, p = 0.019; Table 3.6A). When investigating the relationship between non-native invertebrate presence and native invertebrate functional richness, results of the generalized linear mixed effect regression model indicated that native invertebrate functional richness was not associated with non-native invertebrate functional richness (Estimate 0.029, p = 0.740); however, connection to Lake Erie was positively associated with non-native invertebrate functional richness across coastal wetlands (Estimate 1.003, p = 0.018; Table 3.6B). When investigating the relationship between non-native invertebrate presence and native invertebrate taxonomic diversity, results of the generalized linear mixed effect regression model indicated that native invertebrate taxonomic diversity described using the Shannon-Weiner Diversity Index was not associated with non-native invertebrate presence (Estimate 0.016, p = 0.968); however, connection to Lake Erie was positively associated with non-native

invertebrate presence across coastal wetlands (Estimate 1.005, p = 0.017; Table 3.6C). When investigating the relationship between non-native invertebrate presence and native invertebrate taxonomic diversity, results of the generalized linear mixed effect regression model indicated that native invertebrate diversity described using the Simpson’s Index of Diversity was not associated with non- native invertebrate presence (Estimate 0.034, p = 0.968); however, connection to Lake Erie was positively associated with non-native invertebrate presence (Estimate 1.006, p = 0.017; Table 3.6D). When investigating the relationship between non-native invertebrate presence and native invertebrate functional diversity, results of the generalized linear mixed effect regression model indicated that native invertebrate functional diversity described using the Shannon-Weiner Diversity Index was not associated with non-native invertebrate presence (Estimate -0.533, p = 0.205); however, connection to Lake Erie was positively associated with non-native invertebrate presence across coastal wetlands (Estimate 1.028, p = 0.014; Table 3.6E). When invertebrate functional diversity was described using the Simpson’s Index of Diversity, native invertebrate functional diversity was not associated with non-native invertebrate presence (Estimate -1.145, p = 0.141); however, connection to Lake Erie was positively associated with non-native invertebrates presence across coastal wetlands (Estimate 1.017, p = 0.015; Table 3.6F). When investigating the relationship between native and non-native invertebrates (Fig. 3.10), there were no significant relationships between native richness and diversity and non-native invertebrate richness, diversity, and presence across the coastal wetlands sampled (Table 3.5, 3.6). In other words, native invertebrate taxnomic richness, functional richness, Shannon-Weiner Diversity and Simpson’s Diversity (for both taxonomic and functional diversity) did not significanlty predict the non-native invertebrate richness, diversity, or presence across the coastal wetlands sampled.

Relationship between native fishes and non-native invertebrate richness and diversity

When investigating the relationship between non-native invertebrate taxonomic richness and native fish taxonomic richness, results of the generalized linear mixed effect regression model indicated that native taxonomic richness was positively associated with the non-native invertebrate richness across coastal wetlands (Estimate 0.200, p = 0.031; Table 3.7A). When investigating the relationship between non-native invertebrate functional richness and native fish functional richness, results of the generalized linear mixed effect regression model indicated that native functional richness was not associated with the non- native invertebrate richness across coastal wetlands (Estimate 0.370, p = 0.141; Table 3.7B). No other predictor/explanatory variables explained signficant variation in non-native invertebrate presence. When investigating the relationship between non-native invertebrate taxonomic diversity and native fish taxonomic diversity described using the Shannon-Weiner Diversity Index, results of the Linear mixed effect regression model indicated that native taxonomic diversity was not signifcanly associated with the non-native invertebrate diversity across coastal wetlands (Estimate 0.251, p = 0.326; Table 3.7C). When investigating the relationship between non- native invertebrate functional diversity and native fish functional diversity described using the Shannon-Weiner Diversity Index, results of the Linear mixed effect regression model indicated that native functional diversity was not signifcanly associated with the non-native invertebrate diversity across coastal wetlands (Estimate -0.178, p = 0.343; Table 3.7D). No other predictor/explanatory variables explained signficant variation in non-native invertebrate presence. When investigating the relationship between non-native invertebrate and native fish richness and diversity (Fig. 3.11), there were no significant relationships found across the coastal wetlands sampled (Table 3.7). In other words, native fish taxnomic richness, functional richness, Shannon-Weiner Diversity and Simpson’s Diversity (for both taxonomic and functional diversity) did not significanlty predict the richness, or diversity of non-native invertebrates.

Relationship between native fishes and non-native invertebrate presence When investigating the relationship between non-native invertebrate presence and native fish taxonomic richness, results of the generalized linear mixed effect regression model indicated that native taxonomic richness was positively associated with non-native invertebrate presence across coastal wetlands (Estimate 0.622, p = 0.001; Table 3.8A). When investigating the relationship between non-native invertebrate presence and native fish functional richness, results of the generalized linear mixed effect regression model indicated that native functional richness was positively associated with non- native invertebrate presence across coastal wetlands (Estimate 0.947, p = 0.019; Table 3.8B). No other predictor/explanatory variables explained signficant variation in non-native invertebrate presence. When investigating the relationship between non-native invertebrate presence and native fish taxonomic diversity, results of the generalized linear mixed effect regression model indicated that native taxonomic diversity described using the Shannon-Weiner Index was positively associated with non- native invertebrate presence across coastal wetlands (Estimate 1.718, p = 0.004; Table 3.8C). When investigating the relationship between non-native invertebrate presence and native fish taxonomic diversity, results of the generalized linear mixed effect regression model indicated that native taxonomic diversity described using the the Simpson’s Index of Diversity was positively associated with non-native invertebrate presence across coastal wetlands (Estimate 2.710, p = 0.007; Table 3.8D). No other predictor/explanatory variables explained signficant variation in non-native invertebrate presence. When investigating the relationship between non-native invertebrate presence and native fish functional diversity, results of the generalized linear mixed effect regression model indicated that native functional diversity described using the Shannon-Weiner Index was not associated with non-native invertebrate presence across coastal wetlands (Estimate 1.542, p = 0.132; Table 3.8E). When investigating the relationship between non-native invertebrate presence and native fish functional diversity, results of the generalized linear

mixed effect regression model indicated that native taxonomic diversity described using the Simpson’s Index of Diversity was not associated with non- native invertebrate presence across coastal wetlands (Estimate 2.067, p = 0.167; Table 3.8F). No other predictor/explanatory variables explained signficant variation in non-native invertebrate presence.

Relationship between total fishes and non-native invertebrate presence When investigating the relationship between non-native invertebrate presence and total fish taxonomic richness (i.e. summed number of native and non-native fish species), results of the generalized linear mixed effect regression model indicated that total fish taxonomic richness was positively associated with non-native invertebrate presence across coastal wetlands (Estimate 0.636, p = 0.001; Table 3.9A). When investigating the relationship between non-native invertebrate presence and total fish functional richness (i.e. summed number of native and non-native fish FGs), results of the generalized linear mixed effect regression model indicated that total fish functional richness was positively associated with non-native invertebrate presence across coastal wetlands (Estimate 0.864, p = 0.016; Table 3.9B). No other explanatory variables explained significant variation in non-native invertebrate presence. When investigating the relationship between non-native invertebrate presence and total fish taxonomic diversity (i.e. diversity of native and non- native fish species), results of the generalized linear mixed effect regression model indicated that total fish taxonomic diversity described using Shannon- Weiner Diversity Index was positively associated with non-native invertebrate presence across coastal wetlands (Estimate 1.800, p = 0.002; Table 3.9C). When investigating the relationship between non-native invertebrate presence and total fish taxonomic diversity described using the Simpson’s Index of Diversity, results of the generalized linear mixed effect regression model indicated that total fish taxonomic diversity was positively associated with non-native invertebrate presence across coastal wetlands (Estimate 2.894, p = 0.003; Table 3.7D).

When investigating the relationship between non-native invertebrate presence and total fish functional diversity (i.e. diversity of native and non-native fish FGs), results of the generalized linear mixed effect regression model indicated that total fish functional diversity described using Shannon-Weiner Diversity Index was positively associated with non-native invertebrate presence across coastal wetlands (Estimate 1.873, p = 0.042; Table 3.9E). When investigating the relationship between non-native invertebrate presence and total fish functional diversity described using Simpson’s Index of Diversity, results of the generalized linear mixed effect regression model indicated that total fish functional diversity was positively associated with non- native invertebrate presence across coastal wetlands (Estimate 1.873, p = 0.042; Table 3.9F). There were significant relationships between native richness and diversity and non-native presence across the coastal wetlands sampled (Table 3.3, 3.4). Specifically, native fish taxnomic richness, functional richness, and Shannon- Weiner Diversity and Simpson’s Diversity Index for taxonomic diversity were positively associated with non-native invertebrate presence ; however, total fish taxnomic richness, functional richness, and Shannon-Weiner Diversity and Simpson’s Diversity Index for both taxonomic and functional diversity were positively associated with non-native invertebrate presence across the coastal wetlands sampled.

Discussion: I expected to find a negative association between native and non-native taxonomic and functional richness and diversity within the fish assemblage, invertebrate assemblage, and between the native fish and non-native invertebrate assemblages in support of DIH; however, this was not supported by our results. Specifically, within the fish and invertebrates assemblages, I did not find an association between native and non-native using taxonomic or functional richness and diversity in the fish or invertebrate assemblage. In the modified analyses of only one non-native fish collected, I found a negative association

between native fish taxonomic and functional diversity and non-native Common Carp abundance; however because diversity and abundance were tested for association, this would not specifically be support for DIH. Between assemblages, non-native invertebrates and native fish were positively associated, such that non-native invertebrates were present at sites with higher taxonomic and functional richness of fishes. Overall, I did not find support for the DIH using taxonomic richness or diversity (e.g. no relationship within fish and invertebrate assemblages and a positive relationship between). The results might be explained by my approach to quantifying the biotic assemblages, the duration of time since invasion, abiotic factors influencing the relationship, or the possibility of facilitation influencing invasion success.

Assessing the biotic assemblages The lack of relationship found between native and non-native taxonomic and functional richness and diversity in the fish and invertebrate assemblages might be attributed to the way I categorized the biotic assemblages. Specifically, the results might be influenced by factors such as the strength of taxonomic and functional categorizations, assessment of the biotic assemblages using richness and diversity, and lack of community-wide research of DIH. First, strength of taxonomic and functional categorization could have influenced the results. For example, among invertebrates, I assigned OTUs for all organisms from a unique taxonomic classification, which might have resulted in redundant taxonomic categories. Although I chose this method to preserve taxonomic resolution, treating each level of identification as a separate OTU resulted in a less conservative estimate of richness and diversity (e.g. 408 OTUs used) and could reduce clarity in the relationship between native and non-native taxa by increasing variation too much. Furthermore, variability of resource use within feeding categories could also influence the results. Species that have ontogenetic shifts or are generalists in their diet preferences might compete differently than specialist that needs one specific resource. Species tolerations to fluctuations in resources availability might influence invasion success. Overall,

the way taxonomic and functional groups were categorized might have resulted in a weak relationship between native and non-natives in the fish assemblage, invertebrate assemblage, and between the two. My assessment of the biotic assemblages using richness and diversity could also influence the results. There are many metrics that can be used to evaluate support of DIH. DIH can be tested according to several common methods, including assessing presence/absence of key species, species richness, weighted species richness (i.e. a weighted sum of the number of species, where each species’ weight describes its contribution to resistance), and community saturation (i.e. the number of resident species relative to the maximum number of species that can be supported). Henriksson et al. (2016) tested these hypotheses using data on the success of 571 introductions of four non-native freshwater fishes (e.g. Arctic Char (Salvelinus alpinus), Tench (Tinca tinca), Zander (Sander lucioperca), and Whiteﬁsh (Coregonus lavaretus)) into lakes throughout Sweden and found weighted species richness was the most effective at predicting invasion resistance. Weighted species richness has been found to be beneficial because it accounts for each resident species contribution to biotic resistance by incorporating factors thought to influence invasion (e.g. commonness, FG, biotic interactions, etc.). Weighted species richness might also be beneficial because it recognizes that some species can have a positive effect and allows resistance (i.e. weight values calculated based on modeling of the probability each species has in predicting invasion of the non-native) to vary in both sign and strength, in contrast to species richness that assumes all species have a negative effect on non-native species. A community includes a combination of predatory, competitive, and facilitative interactions (Simberloff & Von Holle, 1999; Stachowicz et al., 2002); however, the way I quantified taxonomic and functional richness and diversity does not account for positive or mutualistic interactions. This may furthermore explain why DIH is not strongly supported in the study. We used taxonomic and functional richness and Shannon-Weiner and Simpson’s Diversity Indices to use similar analyses across taxonomic and functional metrics; however, testing how other measures of functional diversity

reveal the native vs. non-native relationship would be a beneficial way to further investigate DIH in future studies. Finally, DIH is typically evaluated within a single trophic level (Pintor & Byers, 2015) or assemblage (Olden, Poff & Bestgen, 2006); however, incorporating the taxonomic and functional richness and diversity across these levels to assess feeding relationships within the entire community could more effectively reveal how natives and non-natives are associated. For example, holistically evaluating taxonomic and functional richness and diversity of native and non-native fish and invertebrate community could allow better interpretation of DIH. This is because there are often feeding behavior similarities and complementary resource use. For example, feeding guild overlap between assemblages is found between piscivorous fish and predatory invertebrates, such as the Belostomatidae beetle, which can pierce and prey on fishes. This community-wide assessment might not be commonly done because of differences in common categorizations of assemblages (e.g. FG of fish or FFG of invertebrates) and challenges in accounting for wide variability in resource quantity used by different assemblages (Woodward, 2009); however, implementing a community wide-assessment from combining assemblages would benefit understanding of how resources are used and might provide deeper insight into how native species might impact biotic resistance (Hornung & Foote, 2006).

Time since invasion Time since invasion is an important factor to take into account for the lack of relationships I found. Non-native Common Carp have been present in Lake Erie drainage since 1941 (GLANSIS, 2016), and therefore, biotic resistance might no longer be a strong force influencing the fish assemblage. Between the fish and invertebrate assemblages, a positive association might be found between native fish and non-native invertebrates because non-native invertebrates have been found for many decades as well. Specifically, documentation of the presence of Zebra Mussels has occurred since 1986, Asian Clams since 1980,

Japanese Mystery Snails since 1940, and Faucet Snails since 1871. The extended period of inhabitation of these non-native species in surrounding waters of Ottawa NWR hinders the knowledge of whether biotic resistance did occur at one time in these wetland pools and effects have since faded. For example, DIH might have initially reduced invasion of these species, but, over time, great propagule pressure could have led to successful invasion and naturalization (i.e. the process of non-natives species functioning as residents of the community; Marx et al., 2016). Because of an extended period of time since invasion, biotic resistance might not be detected by the research.

Influence of abiotic factors The influence of abiotic factors could explain the positive relationship I found. When a positive relationship is found between native and non-native organisms, experts recommend investigation of abiotic and environmental conditions when interpreting the direction and strength of the association between invaders and resident species (Stotz, Pec & Cahill, 2016). At times abiotic factors can be larger influencers in the native and non-native species composition than biotic interactions such as in communities containing fish (Marchetti et al., 2004), microbes (Jiang & Morin, 2004), and plants (Levine, 2000). For example, water depth and water quality might determine species present. Hydrologic gradients are often found to influence the functional composition of invertebrate assemblages (Schriever et al., 2015). The positive association I found could reflect environmental factors influencing native fish and non-native invertebrates. Other abiotic factors such as time of connection or disturbance of restoration activities could limit the ability to detect biotic resistance. Ottawa NWR sites were sampled at various points during restoration (i.e. hydrological connection of wetlands to Lake Erie) in hopes of improving water quality and increasing spawning area for fishes. Specifically, some sites have always been connected to Lake Erie (e.g. East and West Ditch), others have only been connected recently (e.g. Pool 2a in 2011, Pool 2b in May 2015, MS8A in August

2015, and Pool 2C in May 2016), while others were not connected during the sampling period (e.g. Pool 1 and Pool MS5). Time of wetland site connection could have increased access of fishes and invertebrates to Lake Erie and the native assemblages might have limited influence with great propagule pressure. Furthermore, for sites restored during the sampling period, substantial disturbance to the sites could be affecting the biotic community and abiotic features of the site such as resource and refuge availability. The restoration activities might explain why I found a lack of relationship between native and non-native richness and diversity of fish and invertebrates within assemblage and a positive association between assemblages.

Biotic facilitative interactions A positive association between native and non-native fishes and invertebrates could suggest that facilitation is occurring between fish and invertebrates (Lopez Van Oosterom, 2013) or other factors unmeasured. Biotic facilitation is a beneficial interaction in which one species positively influences survival of another, either directly or indirectly (Simberloff & Von Holle, 1999). Facilitation can occur for example when facilitative effects of shredder and scraper detritivores create material that can be easily used for collector species (Starzomski, Suen & Srivastava, 2010). Ecosystem engineers are oftentimes facilitators as well because they directly alter the habitat (Altieri et al., 2010; reviewed in Crooks, 2002). Facilitation, also termed biotic assistance and defined as the opposite of biotic resistance (Stotz, Pec & Cahill, 2016), could be occurring between native and non-native species. This interaction between native and non-native taxa has recently been researched to elucidate how non-natives might facilitate each other in an invasion scenario (Simberloff, 2006). Overall, facilitation can occur between species and across assemblages, regardless of native or non-native status (DeVanna et al., 2011). In addition to the possibility of fish and invertebrates facilitating each other, another factor could be facilitating them as well, such as the plant assemblage or prey resources. Many fish and invertebrates in the wetlands rely

heavily on plants. For example, studies have revealed that invertebrate abundance, taxonomic composition, and FFG distribution are more greatly influenced by macrophytes than by fish presence (Hornung & Foote, 2006). According to a field study investigating the relationship between species richness and invasion success across the intertidal landscape of southern New England cobble beaches, a community-wide facilitative cascade was found between native (e.g. and ribbed mussels (Guekensia demissa)) and non-native (e.g. Asian shore crab (Hemigrapsus sanguineus)) species due positive effects of the Atlantic Cordgrass (Spartina alterniflora; Altieri et al., 2010). Additional studies have found that prey biomass can be influential in invasion success. For example, when investigating the relationship between non-native predator, non- native Signal Crayfish (Pacifastacus leniusculus), and native prey, macroinvertebrates and snails, prey biomass was a larger driver of invasion success than prey diversity at small-scale stream transects (Pintor & Sih, 2011). Investigating the plant assemblage, prey biomass, or other features of the wetlands beyond season and connection to Lake Erie might be important for understanding the positive association I observed and data regarding such factors could be valuable when attempting to explain invasion success of non-native fish and invertebrates. Facilitation is not as common of a phenomenon as biotic resistance; however, the findings could be explained by positive interactions that commonly occur across feeding categories in the community (Altieri et al., 2010). Therefore, facilitation cascade might be another explanation for the positive significant relationship between native fish taxonomic and functional richness and taxonomic diversity and non-native invertebrate presence, and furthermore explain why functional diversity becomes significant of non-native invertebrates when I included all fishes in the system. From the field study, I cannot conclude if facilitation is occurring, nor can I determine the causality of the association (e.g. non-native invertebrates is influencing fish richness and diversity, or fish diversity is positively influencing non-native invertebrate presence), but more

controlled experiments could be implemented to research the associations further.

Conclusion: The conducted research investigating biotic resistance by testing DIH of the native and non-native fish and invertebrate of the Lake Erie wetlands did not provide support for biotic resistance theory. Specifically, I found no significant association between native and non-native species and functional groups in the fish and invertebrate assemblages; however, I found a positive association between non-native invertebrate presence and native taxonomic and functional richness and diversity of fishes. The factors explaining these findings could be related to my approach to quantifying the biotic assemblages, the duration of time since invasion, abiotic factors influencing the relationship, or the possibility of facilitation influencing invasion success. Ultimately, results from this research reveal that taxonomic and functional richness and diversity of natives can predict non-native invertebrate presence, but in opposite direction of DIH. Researching biotic resistance of invasive species should continue and invasion ecology would benefit from implementation of various functional metrics at intermediate spatial scales and of assessments of the entire aquatic community. This will allow researchers to determine which communities are most at risk and predict susceptibility to invasion to prioritize management efforts, to minimize invasion, and to conserve native diversity. Although I did not find support for DIH, wetland connection to Lake Erie was positively associated with non-native fish and invertebrates. Specifically, non-native taxonomic richness and abundance, and non-native invertebrate presence were greater in sites that were connected to Lake Erie. The wetlands are being hydrologically connected to Lake Erie as part of a restoration plan to improve water quality and increase spawning habitat for fishes of Lake Erie; however, this restoration might have large effects on the successful invasion of non-native fishes and invertebrates and could impact the native community. I recommend continuation of monitoring of the fish and invertebrate community in

the Ottawa NWR wetlands to assess the outcomes of such hydrological connection restoration plans. Management plans should evaluate the short and long-term benefits and costs of such restoration efforts and continue to adapt and modify actions to maximize conservation of native biodiversity.

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Tables

Table 3.1. Schedule and site location of fish and invertebrate sampling conducted at Ottawa National Wildlife Refuge (Logan County, OH).

Biotic Spring Summer Fall Spring Summer Fall Spring Summer Fall Sampling 2014 2014 2014 2015 2015 2015 2016 2016 2016 Pool 1 East Pool 1 West Pool 2A Pool 2B Pool 2C MS5 MS8A MS8B East Ditch West Ditch

Key: Invertebrate sampling

Fish sampling

Invertebrate and fish sampling

Table 3.2. Taxonomic and functional richness and diversity averages +/- standard error A) of fish from 115 sets of 10 minnow traps (large and small mesh pooled) across 9 sites at Ottawa National Wildlife Refuge collected in spring, summer and fall 2015-2016; and B) of invertebrates from 313 Ekman and dipnet samples across 7 sites in 2014 and 9 sites at Ottawa National Wildlife Refuge in spring summer and fall 2015.

Year Season N Taxonomic Functional Taxonomic Taxonomic Functional Functional Richness Richness Shannon Simpson's Shannon Simpson's Diversity Index of Diversity Index of Index Diversity Index Diversity 2015 Spring 20 1.211 +/- 0.181 1.000 +/- 0.153 0.150 +/- 0.181 0.0976 +/- 0.041 0.050 +/- 0.036 0.0305 +/- 0.024 2015 Summer 19 2.250 +/- 0.260 1.500 +/- 0.115 0.480 +/- 0.260 0.2767 +/- 0.059 0.220 +/- 0.059 0.141 +/- 0.040 2015 Fall 20 3.200 +/- 0.345 1.500 +/- 0.138 0.675+/- 0.345 0.3875 +/- 0.047 0.231 +/- 0.061 0.1501 +/- 0.044 2016 Spring 18 2.150 +/- 0.221 1.45 +/- 0.114 0.371+/- 0.221 0.220 +/-0.050 0.193 +/- 0.056 0.1229 +/- 0.038

2016 Summer 20 3.500 +/- 0.305 1.889 +/- 0.111 0.797+/- 0.305 0.434 +/- 0.0.053 0.419 +/- 0.065 0.2716 +/- 0.045

2016 Fall 18 3.444 +/- 0.283 1.611 +/- 0.143 0.748 +/- 0.283 0.423 +/- 0.052 0.188 +/- 0.051 0.1108 +/- 0.033 B)

Year Season N Taxonomic Functional Taxonomic Taxonomic Functional Functional Richness Richness Shannon's Simpson's Shannon's Simpson's Diversity Diversity Diversity Diversity Index Index Index Index 2014 Spring 47 10.702 +/- 0.868 3.787 +/- 0.233 1.482 +/- 0.075 0.649 +/- 0.024 0.651 +/-0.052 0.348 +/- 0.029 2014 Summer 43 14.221 +/- 0.797 4.182 +/- 0.133 1.618 +/-0.050 0.671 +/-0.016 0.785 +/-0.027 0.422 +/-0.014 2014 Fall 42 16.333 +/- 1.561 4.238 +/- 0.261 1.857 +/-0.086 0.753 +/-0.019 0.808 +/-0.054 0.439 +/-0.028 2015 Spring 60 13.333 +/- 1.218 4.167 +/- 0.198 1.700 +/-0.081 0.709 +/-0.019 0.859 +/-0.043 0.467 +/-0.022 2015 Summer 61 13.492 +/- 1.371 3.967 +/- 0.237 1.622 +/- 0.096 0.671 +/-0.032 0.768 +/-0.049 0.411 +/-0.026 2015 Fall 60 15.850 +/- 1.533 4.417 +/- 0.254 1.532 +/-0.085 0.633 +/- 0.029 0.728 +/-0.046 0.389 +/-0.026

Table 3.3. Results of the generalized linear mixed effect regression model performed on the dependent variable, non-native fish abundance, to test its association with native fish A) taxonomic Shannon-Weiner Diversity Index; B) taxonomic Simpson’s Index of Diversity; C) functional Shannon-Weiner Diversity Index; and D) functional Simpson’s Index of Diversity. Signif. codes: * = 0.05. Analysis included fish data collected using minnow traps during spring summer and fall 2015 from 9 sites at Ottawa National Wildlife Refuge (Logan County, OH).

A) Fixed Effects Estimate Std. Error df z value p-value Intercept -3.816 1.259 109 -3.032 0.002* Native Taxonomic -3.151 0.712 109 1.903 0.057* Shannon-Weiner Diversity Index Season -2.184 0.772 109 -2.830 0.005*

Connection to Lake 2.652 0.726 109 4.845 0.000* Erie

B) Fixed Effects Estimate Std. Error df z value p-value Intercept -3.798 1.256 109 -3.023 0.003* Native Taxonomic -5.716 1.224 109 -4.671 0.000* Simpson’s Index of Diversity Season -2.1065 0.7819 109 -2.694 0.007*

Connection to Lake 2.610 0.704 109 3.708 0.000* Erie

C) Fixed Effects Estimate Std. Error df z value p- value Intercept - 2.788 1.0475 108 - 2.662 0.008* Native Functional 4.6923 2.2845 108 2.054 0.040* Shannon-Weiner Diversity Index Year -0.797 0.394 108 -2.024 0.043*

Season -1.871 0.772 108 - 2.424 0.015*

Connection to Lake 1.2324 0.596 108 2.068 0.039* Erie Continued

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Table 3.3 continued D) Fixed Effects Estimate Std. Error df z value p-value Intercept -2.8884 1.0657 108 - 2.710 0.007* Native Functional - 7.1087 3.7302 108 -1.906 0.057 Simpson’s Index of Diversity Year - 0.8126 0.400 108 -2.054 0.039*

Season - 1.843 0.772 108 -2.386 0.017*

Connection to Lake 1.245 0.598 108 2.081 0.037* Erie

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Table 3.4. Results of generalized linear mixed effect regression model performed on the dependent variable, non-native fish presence, to test its association with native fish A) taxonomic richness; B) functional richness; C) taxonomic Shannon-Weiner Diversity Index; D) taxonomic Simpson’s Index of Diversity; E) functional Shannon-Weiner Diversity; F) functional Simpson’s Index of Diversity. Signif. codes: * = 0.05. Analysis included fish data collected using minnow traps during spring summer and fall 2015 and 2016 from 9 sites at Ottawa National Wildlife Refuge (Logan County, OH).

A) Fixed Effects Estimate Std. Error df z value p-value Intercept -2.574 1.064 112 -2.420 0.016* Native Taxonomic -0.183 0.286 112 -0.642 0.521 Richness

Fixed Effects Estimate Std. Error df z value p-value Intercept - 2.74 1.823 110 -1.503 0.133 Native Functional 0.935 0.876 110 -1.067 0.286 Richness

Season 2.033 1.013 110 2.006 0.045*

C) Fixed Effects Estimate Std. Error df z value p-value Intercept - 2.551 0.888 112 -2.872 0.004* Native Taxonomic -1.026 0.972 112 -1.056 0.291 Shannon-Weiner Diversity Index

D) Fixed Effects Estimate Std. Error df z value p-value Intercept - 2.515 0.877 112 - 2.866 0.004* Native Taxonomic - 1.978 1.687 112 - 1.173 0.241 Simpson’s Index of Diversity

Continued

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Table 3.4 Continued E) Fixed Effects Estimate Std. Error df z value p-value Intercept - 3.609 1.256 110 - 2.874 0.004* Native Functional -2.986 2.299 110 - 1.299 0.193 Shannon-Weiner Diversity Index Season 2.184 1.006 110 2.171 0.030*

F) Fixed Effects Estimate Std. Error df z value p-value Intercept -3.653 1.247 110 - 2.930 0.003* Native Functional - 4.624 3.538 110 - 1.307 0.191 Simpson’s Index of Diversity Season 2.216 1.008 110 2.199 0.028*

103

Table 3.5. Results of the generalized linear mixed effect regression model performed on the dependent variable, non-native invertebrate richness, and its association with native invertebrate A) taxonomic richness B) functional richness; and Results of Linear mixed effect regression model performed on the dependent variable, non-native invertebrate diversity, to test its association with native invertebrate C) taxonomic Shannon-Weiner Diversity Index; D) functional Shannon-Weiner Diversity Index. Signif. codes: * = 0.05. Analysis included fish and invertebrate data collected using minnow traps, Ekman grabs, and dipnets during spring summer and fall 2015 from 9 sites at Ottawa National Wildlife Refuge (Logan County, OH).

Fixed Effects Estimate Std. Error df z value p-value Intercept 1.765 0.299 310 - 5.903 0.000* Native Taxonomic 0.004 0.012 310 0.341 0.733 Richness

Fixed Effects Estimate Std. Error df z value p-value Intercept -1.961 0.390 310 - 5.030 0.000* Native Functional 0.055 0.076 310 0.728 0.467 Richness

Fixed Effects Estimate Std. Error df t value p-value Intercept 0.000 0.000 310 0.734 0.465 Native Taxonomic -0.000 0.000 310 -0.032 0.975 Shannon-Weiner Diversity Index

Fixed Effects Estimate Std. Error df t value p-value Intercept 0.000 0.000 310 0.078 0.938 Native Functional -0.000 0.000 310 0.550 0.583 Shannon-Weiner Diversity Index

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Table 3.6. Results of generalized linear mixed effect regression model performed on the dependent variable, non-native invertebrate presence, and its association with native invertebrate A) taxonomic richness; B) functional richness; C) taxonomic Shannon’s Diversity Index of invertebrates; D) taxonomic Simpson’s Index of Diversity; E) functional Shannon’s Diversity Index; F) functional Simpson’s Index of Diversity. Signif. codes: * = 0.05. Analysis included fish and invertebrate data collected using minnow traps, Ekman grabs, and dipnets during spring summer and fall 2015 at Ottawa National Wildlife Refuge (Logan County, OH). A) Fixed Effects Estimate Std. Error df z value p-value Intercept -2.199 0.411 309 - 5.350 0.000* Native Taxonomic 0.008 0.015 309 0.550 0.583 Richness Connection to Lake 0.995 0.424 309 2.340 0.019* Erie

B) Fixed Effects Estimate Std. Error df z value p-value Intercept -2.207 0.499 309 -4.420 0.000* Native Functional 0.029 0.088 309 0.330 0.740 Richness Connection to Lake 1.003 0.426 309 2.360 0.018* Erie

Fixed Effects Estimate Std. Error df z value p-value

Intercept -2.092 0.526 309 -3.980 0.000*

Native Taxonomic 0.016 0.244 309 0.060 0.968 Shannon-Weiner Diversity Connection to Lake Erie 1.005 0.420 309 2.390 0.017*

Continued

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Table 3.6 Continued D) Fixed Effects Estimate Std. Error df z value p-value Intercept -2.091 0.670 309 - 3.120 0.002* Native Taxonomic 0.034 0.835 309 0.040 0.968 Simpson’s Index of Diversity Connection to Lake Erie 1.006 0.420 309 2.400 0.017*

E) Fixed Effects Estimate Std. Error df z value p-value Intercept -1.685 0.459 309 -3.670 0.000* Native Functional -0.533 0.420 309 -1.270 0.205 Shannon-Weiner Diversity Connection to Lake 1.028 0.419 309 2.460 0.014* Erie

F) Fixed Effects Estimate Std. Error df z value p-value Intercept -1.619 0.459 309 - 3.530 0.000* Native Functional -1.145 0.777 309 -1.470 0.141 Simpson’s Index of Diversity Connection to Lake Erie 1.017 0.417 309 2.440 0.015*

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Table 3.7. Results of generalized linear mixed effect regression model performed on the dependent variable, non-native invertebrate richness, and its association with native fish A) taxonomic richness; B) functional richness; and results of Linear mixed effect regression model performed on the dependent variable, non-native invertebrate diversity, to test its association with native fish; C) taxonomic Shannon-Weiner Diversity Index; D) functional Shannon-Weiner Diversity Index. Signif. codes: * = 0.05. Analysis included fish and invertebrate data collected using minnow traps, Ekman grabs, and dipnets during spring summer and fall 2015 at Ottawa National Wildlife Refuge (Logan County, OH).

A) Fixed Effects Estimate Std. Error df z value p-value Intercept -1.287 0.341 115 -3.778 0.000* Native Taxonomic 0.200 0.093 115 2.156 0.031* richness

B) Fixed Effects Estimate Std. Error df z value p-value Intercept -1.340 0.419 115 -3.197 0.001* Native Functional 0.370 0.252 115 1.472 0.141 richness

C) Fixed Effects Estimate Std. Error df t value p-value Intercept -4.444 0.181 115 24.550 0.000* Native Taxonomic 0.251 0.255 115 0.986 0.326 Shannon-Weiner Diversity Index

D) Fixed Effects Estimate Std. Error df t value p-value

Intercept -4.458 0.117 115 -37.955 0.000*

Native Functional -0.178 0.187 115 -0.952 0.343 Shannon-Weiner Diversity Index

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Table 3.8. Results of the generalized linear mixed effect regression model performed on the dependent variable, non-native invertebrate presence, to test its association with native fish A) taxonomic richness; B) functional richness; C) taxonomic Shannon-Weiner Diversity Index; D) taxonomic Simpson’s Index of Diversity; E) functional Shannon- Weiner Diversity Index; and F) functional Simpson’s Index of Diversity. Signif. codes: * = 0.05. Analysis included fish and invertebrate data collected using minnow traps, Ekman grabs, and dipnets during spring summer and fall 2015 at Ottawa National Wildlife Refuge (Logan County, OH). A) Fixed Effects Estimate Std. Error df z value p-value Intercept -1.582 0.614 115 - 2.576 0.010* Native Taxonomic 0.622 0.184 115 3.381 0.001* Richness

B) Fixed Effects Estimate Std. Error df z value p-value Intercept -1.428 0.695 115 - 2.054 0.040* Native Functional 0.947 0.404 115 2.346 0.019* Richness

C) Fixed Effects Estimate Std. Error df z value p-value Intercept -0.935 0.512 115 -1.826 0.068* Native Taxonomic 1.718 0.595 115 2.888 0.004* Shannon-Weiner Diversity Index

D) Fixed Effects Estimate Std. Error df z value p-value Intercept -0.876 0.510 115 -1.716 0.086 Native Taxonomic Simpson’s 2.710 0.997 115 2.717 0.007* Index of Diversity

E) Fixed Effects Estimate Std. Error df z value p-value Intercept -0.437 0.468 115 -0.935 0.350 Native Functional Shannon-Weiner 1.542 1.024 115 1.507 0.132 Diversity Index Continued

108

Table 3.8 Continued F) Fixed Effects Estimate Std. Error df z value p-value Intercept -0.408 0.466 115 -0.875 0.382 Native Functional 2.067 1.495 115 1.383 0.167 Simpson’s Index of Diversity

109

Table 3.9. Results of the generalized linear mixed effect regression model performed on the dependent variable, non-native invertebrate presence, to test its association with total fish A) taxonomic richness; B) functional richness; C) taxonomic Shannon-Weiner Diversity Index; D) taxonomic Simpson’s Index of Diversity; E) functional Shannon- Weiner Diversity Index; F) functional Simpson’s Index of Diversity. Signif. codes: * = 0.05. Analysis included fish and invertebrate data collected using minnow traps, Ekman grabs, and dipnets during spring summer and fall 2015 at Ottawa National Wildlife Refuge (Logan County, OH).

A) Fixed Effects Estimate Std. Error df z value p-value Intercept - 1.689 0.611 115 - 2.765 0.006* Native Taxonomic Richness 0.636 0.183 115 3.480 0.001*

B) Fixed Effects Estimate Std. Error df z value p-value Intercept - 1.421 0.665 115 - 2.137 0.033* Native Functional Richness 0.864 0.359 115 2.407 0.016*

C) Fixed Effects Estimate Std. Error df z value p-value Intercept -1.037 0.497 115 - 2.086 0.037* Native Taxonomic Shannon- 1.800 0.574 115 3.135 0.002* Weiner Diversity Index

D) Fixed Effects Estimate Std. Error df z value p-value Intercept -0.989 0.495 115 - 1.999 0.046* Native Taxonomic Simpson’s 2.894 0.958 115 3.021 0.003* Index of Diversity

E) Fixed Effects Estimate Std. Error df z value p-value Intercept -0.563 0.459 115 - 1.225 0.221 Native Functional Shannon- 1.873 0.920 115 2.037 0.042* Weiner Diversity Index

Continued

110

Table 3.9 Continued F) Fixed Effects Estimate Std. Error df z value p-value Intercept -0.537 0.458 115 - 1.171 0.242 Native Functional 2.683 1.345 115 1.995 0.046* Simpson’s Index of Diversity

111

Figures

A) B)

native diversity native diversity - - Non Non

Native diversity Native diversity

Figure 3.1. Previous research has found both A) a negative association between native and non-native diversity and B) a positive association between native and non-native diversity.

112

Figure 3.2. Ottawa National Wildlife Refuge (Logan County, OH) located in the western basin of Lake Erie where sampling took place during spring, summer, and fall 2014-2016.

113

Figure 3.3. Nine wetland sites sampled at Ottawa National Wildlife Refuge (Logan County, OH) including MS5, West Ditch, MS8A, Pool 2A, Pool 2B East Ditch, Pool 2 C, MS8B, and Pool 1 (Pool 1 West and Pool 1 East samples combined).

114

8 Native

6 Non-native

Number of Species 4

0 Carnivore Insectivore Omnivore Piscivore Generalized Invertivore

Figure 3.4. Number of native (blue) and non-native (red) fish species categorized by FG collected using minnow traps (small and large mesh pooled) sampling during spring, summer, and fall, 2015 and 2016 in Ottawa National Wildlife Refuge (Logan County, OH).

115

3500

3000

2500

2000 Native 1500 Non-native

Number of Fishes 1000

500

0 Carnivore Insectivore Omnivore Piscivore Generalized Invertivore

Figure 3.5. Abundance of native (blue) and non-native (red) fishes of each feeding guild (FG) collected using minnow traps (small and large mesh pooled) during spring, summer, and fall, 2015 and 2016 in Ottawa National Wildlife Refuge (Logan County, OH).

116

160 140 120 100 80 60 Native 40

Number of Species Non-native 20 0

Figure 3.6. Number of native and non-native invertebrate species operational taxonomic units (OTUs) categorized by functional feeding group (FFG). Invertebrates collected using 3 Ekman grab and 3 dipnet samplers in spring summer and fall 2014 and 2015 in 9 sites at Ottawa National Wildlife Refuge (Logan County, OH).

117

40000 35000 30000 25000 20000 Native 15000 Non-native Abundance 10000 5000 0 FC GC OM PI PR SC SH PA

Figure 3.7. Abundance of native (blue) and non-native (red) invertebrates present in 9 sites at Ottawa National Wildlife Refuge (Logan County, OH). Pooled data from 3 Ekman and 3 Dipnet samples taken in spring, summer, and fall 2014-2015. Invertebrates categorized by functional feeding group (FFG): FC=filter/collector; GC- gatherer/collector; OM=omnivore; PI=piercer; PR=predator; SH=shredder; SC=scraper; PA-parasite.

118

35.00

C) East Ditch ° 30.00 MS5

25.00 MS8A MS8B 20.00 Pool 1 East/West 15.00 Pool 2A Pool 2B Water Temperature ( 10.00 Pool 2C West Ditch

9.50 9.00 East Ditch 8.50 MS5 8.00 MS8A 7.50 MS8B pH 7.00 Pool 1 East/West 6.50 Pool 2A 6.00 Pool 2B 5.50 Pool 2C West Ditch

800.00 East Ditch 700.00 MS5 600.00 MS8A 500.00 400.00 MS8B 300.00 Pool 1 East/West 200.00 Pool 2A 100.00 Pool 2C 0.00 Conductivity (µS/cm) West Ditch Pool 2B

Figure 3.8. Water quality averages measured for A) Water temperature, B) pH, C) Water depth, D) Turbidity, E) Dissolved oxygen, and F) Conductivity. Samples were taken 3 times during spring, summer and fall within 7 sites in 2014, 9 sites in 2015, and 9 sites in 2016 (Pool 1 East and Pool 1 West pooled) in Logan County, OH. Continued

119

Figure 3.8 Continued D)

70.00 East Ditch 60.00 50.00 MS5 40.00 MS8A 30.00 MS8B 20.00

Water depth (in) Pool 1 East/West 10.00 0.00 Pool 2A Pool 2B

Pool 2C

80.00 East Ditch 70.00 MS5 60.00 MS8A 50.00 MS8B 40.00 Pool 1 East/West 30.00 Pool 2A Turbidity (NTU) 20.00 Pool 2B 10.00 Pool 2C 0.00 West Ditch

14.00 East Ditch 12.00 MS5 10.00 MS8A 8.00 MS8B 6.00 Pool 1 East/West 4.00 2.00 Pool 2A Dissolved Oxygen (mg/L) 0.00 Pool 2B Spring Summer Fall 2014 Spring Summer Fall 2015 Fall 2016 2014 2014 2015 2015 Pool 2C

120

A) B)

C) D)

Figure 3.9. Association between native fish diversity and non-native fish abundance according to A) Taxonomic Shannon-Weiner Diversity Index (p<0.000); B) Taxonomic Simpson’s Index of Diversity (p<0.000); C) Functional Shannon-Weiner Diversity Index (p = 0.040); and D) Functional Simpson’s Index of Diversity (p = 0.057). Fish were collected using minnow traps and invertebrates were collected using Ekman grab and dipnet samplers in spring summer and fall 2015 in 9 sites at Ottawa National Wildlife Refuge (Logan County, OH).

121

A) B)

C) D)

Figure 3.10. Association between native and non-native invertebrates according to A) Taxonomic Richness (p=0.733); B) Functional Richness (p=0.467); C) Taxonomic Shannon-Weiner Diversity Index (p=0.975); and D) Functional Shannon-Weiner Diversity Index (p=0.583). Fish were collected using minnow traps and invertebrates were collected using Ekman grab and dipnet samplers in spring summer and fall 2014 and 2015 in 9 sites at Ottawa National Wildlife Refuge (Logan County, OH).

122

A) B)

C) D)

Figure 3.11 Association between native fish and non-native invertebrates according to A) Taxonomic Richness (p=0.031); B) Functional Richness (p=0.370); C) Taxonomic Shannon-Weiner Diversity Index (p=0.326); and D) Functional Shannon-Weiner Diversity Index (p=0.343).

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Appendix A. Native and non-native fishes found collected using fyke nets from 8 sites sampled in fall and spring 2013-2014, associated status: N-Native; NN-Non-native; and feeding guild (FG): I-Insectivore; P-Piscivore; O-Omnivore (including plant material); G- Generalized Insectivore; C-Carnivore (Fish, Large Invertebrate; EPA, 2014). Family Latin name Common name Status FG Amiidae Amia calva Bowfin N P Atherinopsidae Labidesthes sicculus Brook Silverside N I Catostomidae Carpiodes cyprinus Quillback N O Catostomidae Catostomus commersonii White Sucker N O

Catostomidae Ictiobus bubalus Smallmouth Buffalo NN I Catostomidae Minytrema melanops Spotted Sucker N I Catostomidae Moxostoma anisurum Silver Redhorse N I Centrarchidae Ambloplites rupestris Rock Bass N C Centrarchidae L. cyanellus x L. gibbosus Green x Pumpkinseed N I Centrarchidae L. macrochirus x L. cyanellus Bluegill x Green N I Centrarchidae L. macrochirus x L. gibbosus Bluegill x Pumpkinseed N I Centrarchidae Lepomis cyanellus Green Sunfish N I Centrarchidae Lepomis gibbosus Pumpkinseed Sunfish N I Centrarchidae Lepomis humilis Orangespotted Sunfish N I Centrarchidae Lepomis macrochirus Bluegill N I Centrarchidae Lepomis peltastes Northern Longear Sunfish N I Centrarchidae Lepomis sp. Sunfish Hybrid N I Centrarchidae Micropterus dolomieu Smallmouth Bass N C Centrarchidae Micropterus salmoides Largemouth Bass N C Centrarchidae Pomoxis annularis White Crappie N C Centrarchidae Pomoxis nigromaculatus Black Crappie N I

Continued

124

Appendix A Continued

Family Latin name Common name Status FG Clupeidae Dorosoma cepedianum Gizzard Shad N O Cyprinidae C. carpio x C. auratus Common Carp x Goldfish NN O Cyprinidae Carassius auratus Goldfish NN O Cyprinidae Cyprinella spilopterus Spotfin Shiner N I Cyprinidae Cyprinus carpio Common Carp NN O Cyprinidae Luxilus cornutus frontalis Common Shiner N I Cyprinidae Notemigonus crysoleucas Golden Shiner N I

Cyprinidae Notropis atherinoides Emerald Shiner N I Cyprinidae Notropis hudsonius Spottail Shiner N I Cyprinidae Notropis stramineus Sand Shiner N I Cyprinidae Notropis v. volucellus Mimic Shiner N I Cyprinidae Pimephales notatus Bluntnose Minnow N O Cyprinidae Semotilus atromaculatus Creek Chub N I Esocidae Esox lucius Northern Pike N P Fundulidae Fundulus diaphanus menona Western Banded Killifish N I Gobiidae Neogobius melanostomus Round Goby NN G Ictaluridae Ameiurus melas Black Bullhead N I Ictaluridae Ameiurus natalis Yellow Bullhead N I Ictaluridae Ameiurus nebulosus Brown Bullhead N I Ictaluridae Ictalurus punctatus Channel Catfish N I Ictaluridae Noturus flavus Stonecat Madtom N I Ictaluridae Noturus gyrinus Tadpole Madtom N I Lepisostedidae Lepisosteus osseus Longnose Gar N P Moronidae Morone americana White Perch NN P Moronidae Morone chrysops White Bass N P Percidae Perca flavescens Yellow Perch N P Poeciliidae Gambusia affinis Western Mosquitofish NN I Sciaenidae Aplodinotus grunniens Freshwater Drum N I Umbridae Umbra limi Central Mudminnow N I

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Appendix B. Abiotic water quality parameters sampled in spring summer and fall of 2014-2016 in conjunction with fish and invertebrate sampling. Three water samples from each site were averaged.

Year Season Site D.O. Water Turbidity % Cloud Temp. Conductivity pH (mg/L) Depth (in) (NTU) Cover (°C) (µS/cm) 2014 Fall Pool 1 5.78 47.00 11.11 90.00 16.47 190.63 8.38 2014 Fall Pool 2A 5.81 13.42 22.63 100.00 19.27 391.17 8.07 2014 Fall Pool 2B 2.79 24.83 5.23 66.67 14.73 314.07 7.93 2014 Fall Pool 2C 8.06 6.17 22.86 70.00 11.50 246.92 8.53 2014 Fall West Ditch 10.14 9.83 12.18 100.00 14.20 546.67 8.39 2014 Fall East Ditch 2.41 10.30 3.18 96.00 15.53 456.67 8.14 2014 Spring MS8A 8.45 27.73 19.07 30.83 25.05 265.17 8.47 2014 Spring Pool 1 4.45 47.22 10.85 100.00 22.97 200.33 8.33 2014 Spring Pool 2A 8.80 32.30 30.95 100.00 21.38 300.25 8.31 2014 Spring Pool 2B 5.40 36.20 14.80 100.00 22.02 457.83 7.96 2014 Spring Pool 2C 5.97 14.17 33.80 100.00 21.24 300.00 9.03 2014 Spring West Ditch 7.85 35.80 40.07 4.17 25.83 674.50 8.14 2014 Spring East Ditch 3.94 34.32 18.23 100.00 23.54 467.67 7.82 2014 Summer MS8A 8.24 16.17 25.30 5.00 23.90 276.10 8.08 2014 Summer Pool 1 3.85 60.00 8.28 0.00 22.53 210.63 7.60 2014 Summer Pool 2A 9.98 5.00 31.53 11.67 31.03 406.63 8.23 2014 Summer Pool 2B 12.68 31.67 11.70 5.00 25.73 324.10 8.82 2014 Summer Pool 2C 4.00 15.75 52.87 90.00 21.87 366.53 7.51 2014 Summer West Ditch 6.14 35.17 18.17 0.00 22.47 460.87 7.77 2014 Summer East Ditch 2.07 31.50 3.93 30.00 22.67 496.00 7.44 2015 Fall MS 5 5.95 21.67 38.60 31.67 26.99 368.33 7.94 2015 Fall MS8A 1.28 28.50 15.07 100.00 24.73 261.33 7.23 2015 Fall MS8B 9.10 42.33 5.14 25.00 28.57 309.67 8.20 2015 Fall Pool 1 3.40 52.25 6.51 16.67 24.91 228.17 8.25

Continued

126

Appendix B Continued

Water % D.O. Temp. Conductivity Year Season Site Depth Turbidity Cloud pH (mg/L) (°C) (µS/cm) (in) Cover 2015 Fall Pool 2A 5.1 49 6.81 88.33 26.64 421.67 8.24 2015 Fall Pool 2B 4.8 39.17 14.07 81.67 26.55 393.67 8.25 2015 Fall Pool 2C 0.51 12.83 5.35 5 21.52 381.33 7.21 West 2015 Fall 0.82 35.33 13.67 100 25.23 550.67 7.54 Ditch East 2015 Fall 5.89 44 17.41 100 26.76 441 7.77 Ditch 2015 Spring MS 5 5.85 16.14 25.47 50 21.14 365.33 8.13 2015 Spring MS8A 9.13 33.57 19.87 93.33 15.57 227 8.08 2015 Spring MS8B 8.81 26.5 3.96 95.67 20.03 309 8.29 2015 Spring Pool 1 9.74 59.39 9.1 91.67 17.52 193.67 8.2 2015 Spring Pool 2A 7.38 40.69 11.06 5 16.95 422.67 8.24 2015 Spring Pool 2B 10.3 41.35 6.07 5 17.07 414 7.27 2015 Spring Pool 2C 5.92 16.1 4.02 18.33 15.16 272 7.72 West 2015 Spring 6 47.57 58.33 9.33 18.34 336.67 7.79 Ditch East 2015 Spring 2.93 43.98 54.97 0 15.49 249.33 7.76 Ditch 2015 Summer MS 5 8.12 24.5 33.43 50 21.97 276.67 8.22 2015 Summer MS8A 2.61 47.83 8.74 48.33 21.76 202 6.99 2015 Summer MS8B 7.07 38.5 4.61 56.67 24.55 240 7.45 2015 Summer Pool 1 7.81 51.17 4.23 0.83 22.96 191.67 8.4 2015 Summer Pool 2A 6.46 53.37 7.52 90 22.94 393 7.58 2015 Summer Pool 2B 6.58 44 9.44 91.67 22.87 369 7.73 2015 Summer Pool 2C 0.83 20 3.38 10 18.33 226.33 7.42 West 2015 Summer 6.29 48 16.37 0 23.4 412 7.6 Ditch East 2015 Summer 0.62 41.83 6.75 5.33 21.91 243.67 7.48 Ditch 2016 Fall MS5 10.36 8.5 71.52 0.5 32.71 396 7.85 2016 Fall MS8A 7.82 13.05 22.94 35 28.05 389 7.86 2016 Fall MS8B 5.78 12.25 9.48 0 26.64 414 7.52 2016 Fall Pool 1 8.07 12.4 39.81 10 33.2 355 7.95 2016 Fall Pool 2A 2.72 29.3 8.81 20 26.66 520 7.25 2016 Fall Pool 2B 2.71 33.15 7.1 60 26.15 342 7.49 Continued 127

Appendix B Continued

Water % D.O. Temp. Conductivity Year Season Site depth Turbidity Cloud pH (mg/L) (°C) (µS/cm) (in) Cover 2016 Fall Pool 2C 0.69 27.85 3.53 25 25.52 324 7.33 West 2016 Fall 5.78 26 26.23 70 28.03 419 7.38 Ditch East 2016 Fall 1.32 56.1 9.02 6.5 25.77 327 7.67 Ditch 2016 Spring MS5 * 11.8 42.03 80 27.46 167.53 9.19 2016 Spring MS8A * 19.13 8.24 81.67 22.97 133.13 8.71 2016 Spring MS8B * 16.33 6.5 31.67 21.05 113.63 8.82 2016 Spring Pool 1 * 15 16.15 68.33 23.71 124.62 8.34 2016 Spring Pool 2A * 36.17 4.93 90 22.08 196.47 8.19 2016 Spring Pool 2B * 31.17 3.27 89.33 21.23 192.33 8.23 2016 Spring Pool 2C * 28.27 2.98 55 24.01 136.33 8.43 West * 2016 Spring 34.83 23.73 71.67 23.71 247 8.15 Ditch East * 2016 Spring 41.67 7.72 75 24.9 191.33 8.32 Ditch 2016 Summer MS5 * 9.5 18.38 25 28.73 258.13 8.47 2016 Summer MS8A * 8.5 6.26 30 26.98 174 7.32 2016 Summer MS8B * 11.75 5.47 30 24.38 158.08 7.22 2016 Summer Pool 1 * 13.6 19.45 15 25.77 140 5.86 2016 Summer Pool 2B * 30 5.89 40 25.21 353 7.52 2016 Summer Pool 2C * 25.75 2.96 30 25.34 62.88 7.88 West * 2016 Summer 26.75 17.5 15 26.47 156.88 7.16 Ditch *Dissolved oxygen readings missing in spring and summer 2016 due to probe error.

128

Appendix C. Invertebrate subsampling method 1 used to randomly select material to search for invertebrates for samples larger than 250-mL. Table showing cumulative number and abundance of invertebrates found (identified to operational taxonomic units (OTUs)) in each subsampled section of material.

Sample Location Site Sample Date Subsample OTU OTU Type Size richness abundance 1 Crane Creek East Ditch Ekman 10/4/14 13% 5 12 Crane Creek East Ditch Ekman 10/4/14 25% 5 18 Crane Creek East Ditch Ekman 10/4/14 38% 6 27 Crane Creek East Ditch Ekman 10/4/14 50% 7 31 Crane Creek East Ditch Ekman 10/4/14 63% 8 35 Crane Creek East Ditch Ekman 10/4/14 75% 9 41 Crane Creek East Ditch Ekman 10/4/14 88% 10 57 Crane Creek East Ditch Ekman 10/4/14 100% 11 62 2 Crane Creek Pool 2B Dipnet 10/4/14 25% 16 55 Crane Creek Pool 2B Dipnet 10/4/14 50% 17 171 Crane Creek Pool 2B Dipnet 10/4/14 75% 20 315 Crane Creek Pool 2B Dipnet 10/4/14 100% 20 419 3 Crane Creek Pool 2C Dipnet 10/4/14 75% 26 120 Crane Creek Pool 2C Dipnet 10/4/14 88% 28 172 Crane Creek Pool 2C Dipnet 10/4/14 100% 29 226 4 Crane Creek Pool 2B Ekman 10/4/14 25% 12 40 Crane Creek Pool 2B Ekman 10/4/14 50% 16 66 Crane Creek Pool 2B Ekman 10/4/14 75% 18 104 Crane Creek Pool 2B Ekman 10/4/14 100% 20 154 5 Crane Creek West Ditch Dipnet 10/4/14 50% 25 357 Crane Creek West Ditch Dipnet 10/4/14 75% 27 637 Crane Creek West Ditch Dipnet 10/4/14 100% 27 938 6 Cedar Point Lakeside Dipnet 10/5/14 13% 8 25 Cedar Point Lakeside Dipnet 10/5/14 25% 13 51 Cedar Point Lakeside Dipnet 10/5/14 38% 19 70 Cedar Point Lakeside Dipnet 10/5/14 50% 21 113 Cedar Point Lakeside Dipnet 10/5/14 63% 22 151 Cedar Point Lakeside Dipnet 10/5/14 75% 24 193 Cedar Point Lakeside Dipnet 10/5/14 88% 24 227 Cedar Point Lakeside Dipnet 10/5/14 100% 25 282

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Appendix D. Fish species found in 9 sites of Ottawa National Wildlife Refuge using minnow traps between 2015-2016 in spring, summer, and fall; minnow trap mesh size they were collected in: L-large; S-small; B-both large and small; Invader status according to GLANSIS: N-Native; NN-Non-Native; and associated feeding guild (FG): I- Insectivore; P-Piscivore; O-Omnivore (including plant material); C-Carnivore (Fish, Large Invertebrate); G=Generalized Insectivore.

Family Latin Name Common Name Mesh size Status FG Amiidae Amia calva Bowfin S N P Centrarchidae Lepomis cyanellus Green Sunfish B N I Centrarchidae Lepomis gibbosus Pumpkinseed Sunfish B N I Centrarchidae Lepomis humilis Orangespotted Sunfish B N I Centrarchidae Lepomis Bluegill B N I macrochirus Centrarchidae L. macrochirus x Bluegill x Green B N I L. cyanellus Centrarchidae Micropterus Largemouth Bass B N C salmoides Centrarchidae Pomoxis Black Crappie S N I nigromaculatus Cyprinidae Cyprinus carpio Common Carp B NN O Cyprinidae Notemigonus Golden Shiner L N I crysoleucas Cyprinidae Notropis Emerald Shiner B N I atherinoides Centrarchidae Pimephales notatus Bluntnose Minnow L N O Cyprinidae Pimephales Fathead Minnow L N O promelas Cyprinidae Semotilus Creek Chub L N G atromaculatus Esocidae Esox masquinongy Muskellunge L N P Ictaluridae Ameiurus melas Black Bullhead B N I Ictaluridae Ameiurus natalis Yellow Bullhead B N I Ictaluridae Noturus flavus Stonecat Madtom B N I Ictaluridae Noturus gyrinus Tadpole Madtom B N I Percidae Percina caprodes Northern Logperch S N I Percidae Perca flavescens Yellow Perch L N P Umbridae Umbra limi Central Mudminnow B N I

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Appendix E. Invertebrates found with Operational Taxonomic Units (OTUs), Invader status code: N-Native; NN- Non-native; and functional feeding group (FFG): GC- gatherer/collector; SH=shredder; PI=piercer; PR=predator; OM=omnivore; PA-parasite; SC=scraper; FC=filter/collector

Phylum Class Order Family Genus Species OTU Status FFG

Annelida Clitellata Arhynchobdellida Erpobdellidae Erpobdella Erpobdella N PR

Annelida Clitellata Arhynchobdellida Erpobdellidae Erpobdellidae N PR Annelida Clitellata Arhynchobdellida Hirudinidae Haemopis Haemopis N PR

Annelida Clitellata Enchytraeida Enchytraeidae Fridericia Fridericia N SC

Annelida Clitellata Lumbriculida Lumbriculidae Lumbriculidae N GC Annelida Clitellata Oligochaeta Oligochaeta N GC

Annelida Clitellata Oligochaeta Naididae Dero digitata Dero digitata N GC Annelida Clitellata Oligochaeta Naididae Dero furcata Dero furcata N GC

Annelida Clitellata Oligochaeta Naididae Dero Dero N GC 1

31 Annelida Clitellata Rhynchobdellida Glossiphoniidae Glutops N PR

Annelida Clitellata Rhynchobdellida Glossiphoniidae Helobdella stagnalis Helobdella stagnalis N PR

Annelida Clitellata Rhynchobdellida Glossiphoniidae Helobdella Helobdella N PR

Annelida Clitellata Rhynchobdellida Glossiphoniidae Placobdella Placobdella N PR

Annelida Clitellata Tubificida Naididae Tubificinae N GC

Annelida Clitellata Tubificida Naididae Bratislavia Bratislavia N GC Annelida Clitellata Tubificida Naididae Chaetogaster Chaetogaster N GC

Annelida Clitellata Tubificida Naididae Haemonais waldvogeli Haemonais waldvogeli N SC

Annelida Clitellata Tubificida Naididae Nais Nais N GC Annelida Clitellata Tubificida Naididae Ophidonais serpentina Ophidonais serpentina N GC

Annelida Clitellata Tubificida Naididae Pristina Pristina N GC Annelida Clitellata Tubificida Naididae Slavina appendiculata Slavina appendiculata N GC

Annelida Clitellata Tubificida Naididae Stylaria lacustris Stylaria lacustris N GC

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Annelida Clitellata Tubificida Naididae Naididae N GC

Annelida Clitellata Hirudinea N PR

Annelida Clitellata Homoptera N GC

Annelida Trepaxonem- Trepaxonemata N PR ata Annelida Annelida N GC

Arthropoda Arachnida Sarcoptiformes Oribatida N PR

Arthropoda Arachnida Trombidiformes Arrenuridae Arrenurus Arrenurus N PR

Arthropoda Arachnida Trombidiformes Hydrachnidae Hydrachna Hydrachna N PR Arthropoda Arachnida Trombidiformes Limnesiidae Limnesia Limnesia N PR

Arthropoda Arachnida Trombidiformes Pionidae Piona Piona N PR

Arthropoda Arachnida Trombidiformes Unionicolidae Koenikea Koenikea N PR Arthropoda Arachnida Trombidiformes Unionicolidae Neumania Neumania N PR

Arthropoda Arachnida Trombidiformes Unionicolidae Unionicola Unionicola N PR 1

32 Arthropoda Arachnida Trombidiformes Unionicolidae Unionidae N FC

Arthropoda Collembola Entomobryomorpha Paronellidae Cyphoderus Cyphoderus N GC

Arthropoda Collembola Entomobryomorpha Entomobryidae Seira Seira N GC

Arthropoda Collembola Entomobryomorpha Entomobryidae Semicerura Semicerura N OM

Arthropoda Collembola Entomobryomorpha Entomobryidae Entomobryidae N GC

Arthropoda Collembola Entomobryomorpha Isotomidae Isotomurus Isotomurus N GC Arthropoda Collembola Entomobryomorpha Isotomidae Isotomidae N OM

Arthropoda Collembola Entomobryomorpha Poduridae Podura Podura N GC

Arthropoda Collembola Symphypleona Sminthuridae Bourletiella Bourletiella N SH

Arthropoda Insecta Coleoptera Hydroscaphidae Hydroscapha N SC

Arthropoda Insecta Coleoptera Amphizoidae Amphizoa Amphizoa N PR Arthropoda Insecta Coleoptera Chrysomelidae Chrysomelidae N SH

Arthropoda Insecta Coleoptera Curculionidae Curlicionidae N SH

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Arthropoda Insecta Coleoptera Dytiscidae Agabates Agabates N PI

Arthropoda Insecta Coleoptera Dytiscidae Agabus Agabus N PR Arthropoda Insecta Coleoptera Dytiscidae Celina Celina N PR Arthropoda Insecta Coleoptera Dytiscidae Colymbetes sculptilis Colymbetes sculptilis N PR

Arthropoda Insecta Coleoptera Dytiscidae Copelatus Copelatus N PI

Arthropoda Insecta Coleoptera Dytiscidae Desmopachria Desmopachria N PR Arthropoda Insecta Coleoptera Dytiscidae Dytiscus Dytiscus N PR

Arthropoda Insecta Coleoptera Dytiscidae Eretes Eretes N PR

Arthropoda Insecta Coleoptera Dytiscidae Helochares Helochares N OM Arthropoda Insecta Coleoptera Dytiscidae Helocombus Helocombus N SH

Arthropoda Insecta Coleoptera Dytiscidae Helophorus Helophorus N SH

Arthropoda Insecta Coleoptera Dytiscidae Hydaticus Hydaticus N PR Arthropoda Insecta Coleoptera Dytiscidae Hydrotrupes Hydrotrupes N PR

Insecta Coleoptera Dytiscidae Hygrotus Hygrotus N PR

Arthropoda 133 Arthropoda Insecta Coleoptera Dytiscidae Laccophilus Lacccophilis N PR

Arthropoda Insecta Coleoptera Dytiscidae Laccornis Laccornis N PR

Arthropoda Insecta Coleoptera Dytiscidae Matus Matus N PR Arthropoda Insecta Coleoptera Dytiscidae Rhantus Rhantus N PI

Arthropoda Insecta Coleoptera Dytiscidae Uvarus Uvarus N PI Arthropoda Insecta Coleoptera Dytiscidae Dytiscidae N PR

Arthropoda Insecta Coleoptera Elmidae Ancyronyx Ancyronyx N OM

Arthropoda Insecta Coleoptera Elmidae Dubiraphia Dubiraphia N GC

Arthropoda Insecta Coleoptera Elmidae Elimia Elimia N SC

Arthropoda Insecta Coleoptera Elmidae Macronychus glabratus Macronychus glabratus N OM

Arthropoda Insecta Coleoptera Elmidae Macronychus Macronychus N OM Arthropoda Insecta Coleoptera Elmidae Elmidae N GC

Arthropoda Insecta Coleoptera Gyrinidae Dineutus Dineutus N PR

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Arthropoda Insecta Coleoptera Gyrinidae Gyrinus Gyrinus N PR

Arthropoda Insecta Coleoptera Haliplidae Brychius Brychius N SC Arthropoda Insecta Coleoptera Haliplidae Haliplus Haliplus N SH Arthropoda Insecta Coleoptera Haliplidae Peltodytes Peltodytes N SH Arthropoda Insecta Coleoptera Haliplidae Haliplidae N SH Arthropoda Insecta Coleoptera Hydrophilidae Hydrophilus N PR

Arthropoda Insecta Coleoptera Hydrophilidae Berosus Berosus N PI

Arthropoda Insecta Coleoptera Hydrophilidae Enochus Enochrus N GC Arthropoda Insecta Coleoptera Hydrophilidae Helobata Helobata N OM

Arthropoda Insecta Coleoptera Hydrophilidae Hydraena Hydraena N PR

1 Arthropoda Insecta Coleoptera Hydrophilidae Hydrobius Hydrobius N PR 34 Arthropoda Insecta Coleoptera Hydrophilidae Hydrochara Hydrochara N PR

Arthropoda Insecta Coleoptera Hydrophilidae Hydrochus Hydrochus N SH

Arthropoda Insecta Coleoptera Hydrophilidae Hydrometra Hydrometra N PR Arthropoda Insecta Coleoptera Hydrophilidae Paracymus Paracymus N PR

Arthropoda Insecta Coleoptera Hydrophilidae Tropisternus Tropisternus N PR

Arthropoda Insecta Coleoptera Lampyridae Lampyridae N PR

Arthropoda Insecta Coleoptera Noteridae Hydrocanthus Hydrocanthus N OM

Arthropoda Insecta Coleoptera Noteridae Notomicrus Notomicrus N PR

Arthropoda Insecta Coleoptera Noteridae Suphisellus Suphisellus N OM Arthropoda Insecta Coleoptera Noteridae Noteridae N PR

Arthropoda Insecta Coleoptera Scirtidae Cyphon Cyphon N SC

Arthropoda Insecta Coleoptera Scirtidae Elodes Elodes N SC

Arthropoda Insecta Coleoptera Scirtidae Scirtes Scirtes N SC Arthropoda Insecta Coleoptera Staphylinidae Philonthus Philonthus N PR

Arthropoda Insecta Coleoptera Staphylinidae Staphylinidae Staphylinidae N PR

Arthropoda Insecta Coleoptera Coleoptera N FC

134

Arthropoda Insecta Diptera Blephariceridae Blephariceridae N SC

Arthropoda Insecta Diptera Canacidae Canace Canace N SC

Arthropoda Insecta Diptera Canacidae Canacidae N PR Arthropoda Insecta Diptera Ceratopogonidae Atrichopogon Atrichopogon N PR

Arthropoda Insecta Diptera Ceratopogonidae Atrichopogon websteri Atrichopogon websteri N PR Arthropoda Insecta Diptera Ceratopogonidae Bezzia Bezzia N PR Arthropoda Insecta Diptera Ceratopogonidae Forcipomyia Forcipomyia N PR

Arthropoda Insecta Diptera Ceratopogonidae Ceratopogonidae N PR

Arthropoda Insecta Diptera Ceratopogonidae Ceratopogoninae N PR Arthropoda Insecta Diptera Chaoboridae Chaoborus Chaoborus N PR

Arthropoda Insecta Diptera Chironomidae Chironomini N GC Arthropoda Insecta Diptera Chironomidae Tanytarsini N FC Arthropoda Insecta Diptera Chironomidae Ablabesmyia Ablabesmyia N GC

1 Arthropoda Insecta Diptera Chironomidae Acricotopus Acricotopus N PR 35 Arthropoda Insecta Diptera Chironomidae Apedilum Aplexa N SC

Arthropoda Insecta Diptera Chironomidae Chironomus Chironomus N GC

Arthropoda Insecta Diptera Chironomidae Cladopelma Cladopelma N GC

Arthropoda Insecta Diptera Chironomidae Corynoneura Corynoneura N GC

Arthropoda Insecta Diptera Chironomidae Cricotopus Cricotopus N SH

Arthropoda Insecta Diptera Chironomidae Cricotopus Cricotopus (Isocladius) N SH

Arthropoda Insecta Diptera Chironomidae Dicrotendipes Dicrotendipes N GC

Arthropoda Insecta Diptera Chironomidae Endochironomus Endochironomus N SH

Arthropoda Insecta Diptera Chironomidae Glyptotendipe Glyptotendipes N PR -s Arthropoda Insecta Diptera Chironomidae Glyptotendipes Glyptotendipes N FC

Arthropoda Insecta Diptera Chironomidae Guttipelopia Guttipelopia N PR Arthropoda Insecta Diptera Chironomidae Kiefferulus Kiefferulus N GC Arthropoda Insecta Diptera Chironomidae Labrundinia Labrundinia N PR

Arthropoda Insecta Diptera Chironomidae Larsia Larsia N PR

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Arthropoda Insecta Diptera Chironomidae Micropsectra Micropsectra N GC

Arthropoda Insecta Diptera Chironomidae Nanocladius Nanocladius N GC

Arthropoda Insecta Diptera Chironomidae Orthocladiinae Orthocladiinae N GC Arthropoda Insecta Diptera Chironomidae Orthocladius Orthocladius N GC

Arthropoda Insecta Diptera Chironomidae Parachironomus Parachironomus N PR Arthropoda Insecta Diptera Chironomidae Parakiefferiella Parakiefferiella N GC

Arthropoda Insecta Diptera Chironomidae Paratanytarsus Paratanytarsus N GC Arthropoda Insecta Diptera Chironomidae Phaenopsectra Phaenopsectra N GC Arthropoda Insecta Diptera Chironomidae Polypedilum Polypedilum N GC Arthropoda Insecta Diptera Chironomidae Psectrocladius Psectrocladius N GC Arthropoda Insecta Diptera Chironomidae Pseudochironomus Pseudochironomus N GC Arthropoda Insecta Diptera Chironomidae Rheotanytarsus Rheotanytarsus N FC

Arthropoda Insecta Diptera Chironomidae Tanypodinae Tanypodinae N PR Arthropoda Insecta Diptera Chironomidae Tanypus Tanypus N PR 1 36 Arthropoda Insecta Diptera Chironomidae Tanytarsus Tanytarsus N PR

Arthropoda Insecta Diptera Chironomidae Thienemanniella Thienemanniella N GC

Arthropoda Insecta Diptera Chironomidae Zavreliella marmorata Xenylla N GC Arthropoda Insecta Diptera Chironomidae Zavreliella marmorata Zavreliella marmorata N PR

Arthropoda Insecta Diptera Chironomidae Zavrelimyia Zavrelimyia N PR

Arthropoda Insecta Diptera Chironomidae Chironomidae N GC Arthropoda Insecta Diptera Culicidae Aedes Aedes N FC

Arthropoda Insecta Diptera Culicidae Anopheles Anopheles N FC Arthropoda Insecta Diptera Culicidae Anopheles Apedilum N GC Arthropoda Insecta Diptera Culicidae Culex Culex N FC Arthropoda Insecta Diptera Culicidae Culiseta Culiseta N GC

Arthropoda Insecta Diptera Culicidae Uranotaenia sapphirina Uranotaenia sapphirina N GC

Arthropoda Insecta Diptera Culicidae Uranotaenia Uranotaenia N GC

Arthropoda Insecta Diptera Culicidae Culicidae N GC

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Arthropoda Insecta Diptera Dixidae Dixella Dixella N GC Arthropoda Insecta Diptera Dixidae Dixidae N GC

Arthropoda Insecta Diptera Dolichopodidae Hydrophorus Hydrophorus N OM

Arthropoda Insecta Diptera Empididae Empididae N PR Arthropoda Insecta Diptera Ephydridae Ephydra Ephydra N GC

Arthropoda Insecta Diptera Ephydridae Setacera Setacera N SH

Arthropoda Insecta Diptera Ephydridae Ephydridae N GC Arthropoda Insecta Diptera Muscidae Limnocoris Limnocoris N PR

Arthropoda Insecta Diptera Muscidae Limnophora Limnophora N PR

Arthropoda Insecta Diptera Pediciidae Pediciidae Dicranota N PR Arthropoda Insecta Diptera Phoridae Phoridae N GC

Arthropoda Insecta Diptera Psychodidae Maruina Maruina N SC Arthropoda Insecta Diptera Psychodidae Pericoma Pericoma N GC Arthropoda Insecta Diptera Psychodidae Psychoda Psychoda N GC

Arthropoda Insecta Diptera Sciomyzidae Dictya Dictya N PR

1 Arthropoda Insecta Diptera Sciomyzidae Sciomyzidae N PR 37 Arthropoda Insecta Diptera Stratiomyidae Euparyphus Euparyphus N GC

Arthropoda Insecta Diptera Stratiomyidae Nemotelus Nemotelus N GC

Arthropoda Insecta Diptera Stratiomyidae Odontomyia Odontomyia N GC Arthropoda Insecta Diptera Stratiomyidae Stratiomys Stratiomys N FC

Arthropoda Insecta Diptera Stratiomyidae Odontomyia / N GC Hedriodiscus Arthropoda Insecta Diptera Stratiomyidae Stratiomyidae N GC

Arthropoda Insecta Diptera Tabanidae Silvius Silvius N PR

Arthropoda Insecta Diptera Tabanidae Tabanus Tabanus N PR

Arthropoda Insecta Diptera Tipulidae Limnophorus Limnoporus N PR Arthropoda Insecta Diptera Tipulidae Limonia Limnophila N PR Arthropoda Insecta Diptera Tipulidae Limonia Limonia N SH

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Arthropoda Insecta Diptera Tipulidae Phalacrocera Phalacrocera N SH Arthropoda Insecta Diptera Tipulidae Pilaria Pilaria N PR Arthropoda Insecta Diptera Tipulidae Tipula Tipula N SH

Arthropoda Insecta Diptera Tipulidae Tipulidae N SH Arthropoda Insecta Diptera Brachycera N OM Arthropoda Insecta Diptera Diptera N GC Arthropoda Insecta Ephemeroptera Baetidae Baetis Baetis N GC Arthropoda Insecta Ephemeroptera Baetidae Baetisca Baetisca N GC Arthropoda Insecta Ephemeroptera Baetidae Callibaetis Callibaetis N GC Arthropoda Insecta Ephemeroptera Baetidae Cloeon dipterum Cloeon dipterum N GC Arthropoda Insecta Ephemeroptera Baetidae Procloeon Procloeon N OM Arthropoda Insecta Ephemeroptera Baetidae Baetidae N GC Arthropoda Insecta Ephemeroptera Caenidae Caenis Caenis N GC

Arthropoda Insecta Ephemeroptera Caenidae Caenidae N GC

Arthropoda Insecta Ephemeroptera Ephemerellidae Ephemerella Ephemerella N GC Arthropoda Insecta Ephemeroptera Ephemerellidae Ephemerellidae N GC Arthropoda Insecta Ephemeroptera Ephemeridae Hexagenia Hexagenia N GC 1

38 Arthropoda Insecta Ephemeroptera Neoephemeridae Neoephemera Neoephemera N GC

Arthropoda Insecta Ephemeroptera Ephemeroptera N GC Arthropoda Insecta Hemiptera Belostomatidae Belostoma flumineum Belostoma flumineum N PR

Arthropoda Insecta Hemiptera Belostomatidae Belostoma Belostoma N PR Arthropoda Insecta Hemiptera Belostomatidae Belostomatidae N PR

Arthropoda Insecta Hemiptera Cicadellidae Cicadellidae N PI

Arthropoda Insecta Hemiptera Corixidae Corixidae N PR

Arthropoda Insecta Hemiptera Corixidae Corisella Corisella N PI Arthropoda Insecta Hemiptera Corixidae Hesperocorixa Hesperocorixa PI Arthropoda Insecta Hemiptera Corixidae Palmacorixa Palmacorixa N PI

Arthropoda Insecta Hemiptera Corixidae Ramphocorixa Ramphocorixa N PR

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Arthropoda Insecta Hemiptera Corixidae Trichocorixa Trichocorixa N PR

Arthropoda Insecta Hemiptera Gelastocoridae Gelastocoris Gelastocoris N PR

Arthropoda Insecta Hemiptera Gerridae Gerridae N PR Arthropoda Insecta Hemiptera Gerridae Glossiphoniidae N PR

Arthropoda Insecta Hemiptera Gerridae Neogerris Neogerris N PI

Arthropoda Insecta Hemiptera Gerridae Rheumatobates Rheumatobates N PR Arthropoda Insecta Hemiptera Gerridae Trepobates Trepobates N PR

Arthropoda Insecta Hemiptera Hebridae Hebrus Hebrus N PR

Arthropoda Insecta Hemiptera Hebridae Lipogomphus Lipogomphus N PR Arthropoda Insecta Hemiptera Hebridae Merragata Merragata N PI

Arthropoda Insecta Hemiptera Hebridae Hebridae N PR

Arthropoda Insecta Hemiptera Hydrometridae Hydrometra Hydrophilidae N PR Arthropoda Insecta Hemiptera Macrovelidae Oravelia Oravelia N PI

Arthropoda Insecta Hemiptera Macroveliidae Macrovelia Macrovelia N PR

Arthropoda Insecta Hemiptera Macroveliidae Macroveliidae N PR 1

39 Arthropoda Insecta Hemiptera Melyridae Melyridae N SC

Arthropoda Insecta Hemiptera Mesoveliidae Mesovelia Mesovelia N PI

Arthropoda Insecta Hemiptera Naucoridae Ambryous Allognosta N GC

Arthropoda Insecta Hemiptera Naucoridae Ambryous Ambryous N PI

Arthropoda Insecta Hemiptera Naucoridae Ambrysus crista Amiger crista N SC

Arthropoda Insecta Hemiptera Naucoridae Naucora Naucora N PR

Arthropoda Insecta Hemiptera Naucoridae Pelocoris Pelocoris N PR

Arthropoda Insecta Hemiptera Naucoridae Naucoridae N PR

Arthropoda Insecta Hemiptera Nepidae Curicta Curicta N PI Arthropoda Insecta Hemiptera Nepidae Nepa Nepa N PR

Arthropoda Insecta Hemiptera Nepidae Ranatra Ranatra N PR

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Arthropoda Insecta Hemiptera Nepidae Nepidae N PR

Arthropoda Insecta Hemiptera Notonectidae Buenoa Buenoa N PR

Arthropoda Insecta Hemiptera Notonectidae Notonecta Notonecta N PI Arthropoda Insecta Hemiptera Notonectidae Notonectidae N PR

Arthropoda Insecta Hemiptera Pleidae Neoplea striola Neoplea striola N PI

Arthropoda Insecta Hemiptera Pleidae Neoplea Neoplea N PI Arthropoda Insecta Hemiptera Pleidae Pleidae N PI

Arthropoda Insecta Hemiptera Saldidae Micracanthia Micracanthia N PI

Arthropoda Insecta Hemiptera Saldidae Saldula Saldula N PI Arthropoda Insecta Hemiptera Saldidae Saldidae N PI

Arthropoda Insecta Hemiptera Veliidae Microvelia Microvelia N PI

Arthropoda Insecta Hemiptera Veliidae Platyvelia Platyvelia N PI

Arthropoda Insecta Hemiptera Veliidae Veliidae N PR

Arthropoda Insecta Hemiptera Hemiptera N PR

1 Arthropoda Insecta Lepidoptera Crambidae Acentria Acentria N SH 40

Arthropoda Insecta Lepidoptera Crambidae Paraponyx Paraponyx N SH

Arthropoda Insecta Lepidoptera Crambidae Petrophila Petrophila N OM

Arthropoda Insecta Lepidoptera Crambidae Crambidae N SH Arthropoda Insecta Lepidoptera Noctuidae Noctuidae N SH Arthropoda Insecta Megaloptera Corydalidae Chauliodes Chauliodes N PR

Arthropoda Insecta Megaloptera Corydalidae Corydalidae N PR

Arthropoda Insecta Megaloptera Sialidae Sialis Sialis N PR

Arthropoda Insecta Odonata Anisoptera N PR Arthropoda Insecta Odonata Aeshinidae Anax Anax N PR

Arthropoda Insecta Odonata Aeshnidae Aeshna Aeshnidae N PR

Arthropoda Insecta Odonata Aeshnidae Basiaeschna Basiaeschna N PR

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Arthropoda Insecta Odonata Aeshnidae Boyeria Boyeria N PR

Arthropoda Insecta Odonata Aeshnidae Nasiaeschna pentacantha Nasiaeschna pentacantha N PR

Arthropoda Insecta Odonata Aeshnidae Aeshna N FC Arthropoda Insecta Odonata Coenagrionidae Coenagrionidae N PR

Arthropoda Insecta Odonata Coenagrionidae Amphiagrion Amphiagrion N PR Arthropoda Insecta Odonata Coenagrionidae Argia Argia N PR Arthropoda Insecta Odonata Coenagrionidae Chromagrion conditum Chromagrion conditum N PR Arthropoda Insecta Odonata Coenagrionidae Chromagrion Chromagrion N PR Arthropoda Insecta Odonata Coenagrionidae Enallagma Enallagma N PR Arthropoda Insecta Odonata Coenagrionidae Ischnura Ischnura N PR

Arthropoda Insecta Odonata Corduliidae Epitheca Epitheca N PR

Arthropoda Insecta Odonata Corduliidae Neurocordulia Neurocordulia N PR Arthropoda Insecta Odonata Corduliidae Corduliidae N PR

Arthropoda Insecta Odonata Gomphidae Lanthus Lanthus N PR

141 Arthropoda Insecta Odonata Gomphidae Progomphus Progomphus N PR

Arthropoda Insecta Odonata Lestidae Lestes Lestes N PR

Arthropoda Insecta Odonata Libellulidae Libellula N PR Arthropoda Insecta Odonata Libellulidae Erythemis simplicicollis Erythemis simplicicollis N PR Arthropoda Insecta Odonata Libellulidae Erythemis Erythemis N PR

Arthropoda Insecta Odonata Libellulidae Erythrodiplax Erythrodiplax N PR

Arthropoda Insecta Odonata Libellulidae Leucorrhinia Leucorrhinia N PR Arthropoda Insecta Odonata Libellulidae Libellula Libellulidae N PR

Arthropoda Insecta Odonata Libellulidae Pachydiplax longipennis Pachydiplax longipennis N PR Arthropoda Insecta Odonata Libellulidae Pachydiplax Pachydiplax N PR

Arthropoda Insecta Odonata Libellulidae Perithemis Perithemis N PR Arthropoda Insecta Odonata Libellulidae Sympetrum Sympetrum N PR

Arthropoda Insecta Odonata Libellulidae Tramea Tramea N PR

Arthropoda Insecta Odonata Libellulidae Lestidae N PR

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Arthropoda Insecta Odonata Zygoptera N PR

Arthropoda Insecta Orthoptera Tettigoniidae Merioptera sphagnorum Merioptera sphagnorum N PR Arthropoda Insecta Orthoptera Tettigoniidae Tettigidea Tettigidea N PR

Arthropoda Insecta Plecoptera Chloroperlidae Alloperla Alloperla N PR Arthropoda Insecta Pterygota Ceratopogonidae Dasyhelea Dasyhelea N GC

Arthropoda Insecta Pterygota Dytiscidae Demopachria Demopachria N PR

Arthropoda Insecta Pterygota Ephemerellidae Dannella simplex Dannella simplex N GC Arthropoda Insecta Trichoptera Hydroptilidae Agraylea Agraylea N PI

Arthropoda Insecta Trichoptera Hydroptilidae Hydroptila Hydroptila N SC

Arthropoda Insecta Trichoptera Hydroptilidae Leucotrichia pictipes Leucotrichia pictipes N SC Arthropoda Insecta Trichoptera Hydroptilidae Leucotrichia Leucotrichia N SC

Arthropoda Insecta Trichoptera Hydroptilidae Neotrichia Neotrichia N SC

Arthropoda Insecta Trichoptera Hydroptilidae Ochrotrichia Ochrotrichia N GC Arthropoda Insecta Trichoptera Hydroptilidae Orthotrichia Orthotrichia N GC

Arthropoda Insecta Trichoptera Hydroptilidae Oxyethira Oxyethira N PI 1

42 Arthropoda Insecta Trichoptera Hydroptilidae Hydroptilidae N PI Arthropoda Insecta Trichoptera Leptoceridae Ceraclea Ceraclea N GC

Arthropoda Insecta Trichoptera Leptoceridae Leptocerus americanus Leptocerus americanus N GC

Arthropoda Insecta Trichoptera Leptoceridae Nectopsyche Nectopsyche N SH

Arthropoda Insecta Trichoptera Leptoceridae Oecetis Oecetis N PR

Arthropoda Insecta Trichoptera Leptoceridae Triaenodes Triaenodes N SH

Arthropoda Insecta Trichoptera Leptoceridae Tribelos N GC

Arthropoda Insecta Trichoptera Leptoceridae Leptoceridae N GC

Arthropoda Insecta Trichoptera Lestidae Archilestes Archilestes grandis N PR

Arthropoda Insecta Trichoptera Limnephilidae Phanocelia canadensis Phanocelia canadensis N SH

Arthropoda Insecta Trichoptera Odontoceridae Psilotreta Psilotreta N SC Arthropoda Insecta Trichoptera Philopotamidae Wormaldia Wormaldia N FC

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Arthropoda Insecta Trichoptera Phryganeidae Agrypnia Agrypnia N SH

Arthropoda Insecta Trichoptera Phryganeidae Phryganea Phryganea N SH

Arthropoda Insecta Trichoptera Phryganeidae Ptilostomis Ptilostomis N SH Arthropoda Insecta Trichoptera Phryganeidae Phryganeidae N SH

Arthropoda Insecta Trichoptera Polycentropodidae Cernotina Cernotina N PR

Arthropoda Insecta Trichoptera Polycentropodidae Neureclipsis Neureclipsis N FC Arthropoda Insecta Trichoptera Polycentropodidae Nyctiophylax Nyctiophylax N FC

Arthropoda Insecta Trichoptera Polycentropodidae Polycentropus Polycentropus N PR

Arthropoda Insecta Trichoptera Polycentropodidae Polycentropodidae N FC Arthropoda Insecta Trichoptera Psychomyiidae Lype diversa Lype diversa N SC

Arthropoda Insecta Trichoptera Psychomyiidae Lype Lype N SC Arthropoda Insecta Trichoptera Rhyacophilidae Rhyacophila Rhyacophila N PR Arthropoda Insecta Trichoptera Trichoptera N GC

Arthropoda Insecta Trombidiformes Hydrachnidae Hydrachnidae N PR Arthropoda Insecta Polymitarcyidae Polymitarcyidae N GC Arthropoda Insecta Hymenoptera N PA 1

43 Arthropoda Malacostraca Amphipoda Amphipoda N PR

Arthropoda Malacostraca Amphipoda Crangonyctidae Crangonyx Crangonyx N GC

Arthropoda Malacostraca Amphipoda Crangonyctidae Crangonyctidae N GC Arthropoda Malacostraca Amphipoda Gammaridae Gammarus Gammarus N OM

Arthropoda Malacostraca Amphipoda Gammaridae Gammaridea N SC

Arthropoda Malacostraca Amphipoda Hyalellidae Hyalella azteca Hyalella azteca N GC Arthropoda Malacostraca Amphipoda Hyalellidae Hyalella Hyalella N GC

Arthropoda Malacostraca Amphipoda Gammaridae N SC

Arthropoda Malacostraca Decapoda Cambaridae Orconectes immunis Orconectes immunis N SH Arthropoda Malacostraca Decapoda Cambaridae Orconectes Orconectes N SH

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Arthropoda Malacostraca Decapoda Palaemonidae Palaemon Palaemon N OM

Arthropoda Malacostraca Decapoda Palaemonidae Palaemonetes kadiakensis Palaemonetes kadiakensis N OM

Arthropoda Malacostraca Decapoda Palaemonidae Palaemonidae N OM Arthropoda Malacostraca Isopoda Asellidae Caecidotea Caecidotea N GC

Arthropoda Malacostraca Isopoda Asellidae Asellidae N GC

Mollusca Bivalvia Pectinida Dryopidae Helichus Helichus N FC

Mollusca Bivalvia Veneroida Corbiculidae Corbicula Corbicula N FC Mollusca Bivalvia Veneroida Corbiculidae Corbicula fluminea Corbicula fluminea NN FC

Mollusca Bivalvia Veneroida Dreissenidae Dreissena Dreissena N FC

Mollusca Bivalvia Veneroida Dreisseniidae Dreissena polymorpha Dreissena polymorpha NN FC Mollusca Bivalvia Veneroida Pisidiidae Musculium Musculium N FC

Mollusca Bivalvia Veneroida Sphaeriidae Sphaeriidae N FC

Mollusca Bivalvia Veneroida Sphaeriidae Pisidium Pisidium N FC

1 Mollusca Bivalvia Veneroida Sphaeriidae Sphaerium Sphaerium N FC 44

Mollusca Bivalvia Veneroida Sphaeriidae Sphaerium Stactobiella N SH

Mollusca Gastropoda Architaenioglossa Viviparidae Cipangopaludi Cipangopaludina N SC -na Mollusca Gastropoda Basommatophora Ancylidae Ferrissia Ferrissia N SC

Mollusca Gastropoda Basommatophora Ancylidae Laevapex fuscus Laevapex N SC

Mollusca Gastropoda Basommatophora Ancylidae Laevapex Laevapex fuscus N SC

Mollusca Gastropoda Basommatophora Armiger Armiger N PR

Mollusca Gastropoda Basommatophora Arrachnidae Arrachnidae N PR

Mollusca Gastropoda Basommatophora Lymnaeidae Lymnaeidae N SC

Mollusca Gastropoda Basommatophora Lymnaeidae Galba Galba N SC

Mollusca Gastropoda Basommatophora Lymnaeidae Lymnaea Lymnaea N SC

Mollusca Gastropoda Basommatophora Lymnaeidae Stagnicola Stagnicola N SC

Mollusca Gastropoda Basommatophora Physidae Aplexa Archilestes N PR

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Mollusca Gastropoda Basommatophora Physidae Physella Physella N SC

Mollusca Gastropoda Basommatophora Planorbidae Armiger crista Armiger crista N SC Mollusca Gastropoda Basommatophora Planorbidae Gyralus Gyraulus parvus N SC Mollusca Gastropoda Basommatophora Planorbidae Helisoma anceps Helisoma N SC

Mollusca Gastropoda Basommatophora Planorbidae Helisoma Heliosoma anceps N SC

Mollusca Gastropoda Basommatophora Planorbidae Menetus Menetus N SC Mollusca Gastropoda Basommatophora Planorbidae Micronectus Micromentus N SC Mollusca Gastropoda Basommatophora Planorbidae Planorbella Planorbella N SC

Mollusca Gastropoda Basommatophora Planorbidae Planorbula Planorbula N SC

Mollusca Gastropoda Basommatophora Planorbidae Planorbula armigera Planorbula armigera N SC

Mollusca Gastropoda Basommatophora Planorbidae Promenetus exacuous Promenetus exacuous N SC

Mollusca Gastropoda Basommatophora Planorbidae Promenetus Promenetus N SC Mollusca Gastropoda Basommatophora Planorbidae Gyraulus N SC Mollusca Gastropoda Basommatophora Planorbidae Planorbidae N SC

Mollusca Gastropoda Littorinimorpha Amnicolidae Amnicola limosa Amnicola limosa N SC 1 45 Mollusca Gastropoda Littorinimorpha Amnicolidae Amnicola Amnicola N SC

Mollusca Gastropoda Littorinimorpha Amnicolidae Amnicolidae N SC Mollusca Gastropoda Littorinimorpha Hydrobiidae Pyrgulopsis lustricus Pygulopsis lustricus N SC

Mollusca Gastropoda Littorinimorpha Hydrobiidae Pyrgulopsis Hydrobiidae N SC Mollusca Gastropoda Littorinimorpha Hydrobiidae Pyrgulopsis Pyrugulopsis N SC

Mollusca Gastropoda Neotaenioglossa Bithyniidae Bithynia tentaculata Bithynia tentaculata NN SC

Mollusca Gastropoda Neotaenioglossa Bithyniidae Bithynia Bithynia N SC

Mollusca Gastropoda Neotaenioglossa Bithyniidae Bithyniidae N SC

Mollusca Gastropoda Neotaenioglossa Pleuroceridae Pleurocera lustricus Pleurocera lustricus N SC Mollusca Gastropoda Neotaenioglossa Pleuroceridae Pleurocera Pleurocera N SC

Mollusca Gastropoda Neotaenioglossa Pleuroceridae Pleuroceridae N SC

Mollusca Gastropoda Stylommatophora Succineidae Succineidae N SC

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Mollusca Gastropoda Stylommatophora Succinidae Pseudosuccinea Pseudosuccinea N SC

Mollusca Gastropoda Valvatidae Valvata sincera Valvata sincera N SC

Mollusca Gastropoda Valvatidae Valvata tricarinata Valvata tricarinata N SC Mollusca Gastropoda Valvatidae Valvata Valvata N SC

Mollusca Gastropoda Valvatidae Valvatidae N SC

Mollusca Gastropoda Viviparidae Viviparus japonica Viviparus japonica NN SC

Mollusca Gastropoda Viviparidae Viviparus Viviparus N SC Mollusca Gastropoda Viviparidae Viviparidae N SC Mollusca Gastropoda Gastropoda N SC

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