Direct and Indirect Effects of the Invasive Orconectes Rusticus on Native O

Direct and indirect effects of the invasive Orconectes rusticus on native O. sanbornii

in Ohio streams

THESIS

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

Christopher Allen Johnson

Graduate Program in Environment & Natural Resources

The Ohio State University

2016

Master's Examination Committee:

Dr. Lauren M. Pintor, Advisor

Dr. Suzanne M. Gray

Dr. Stephen N. Matthews

Copyrighted by

Christopher Allen Johnson

2016

Abstract

While the direct effects of non-native species as predators and competitors have been widely studied, the indirect effects of invasive species as novel prey are relatively unexplored. Yet, these novel energetic pathways are of great importance to long-term community response to biological invasions. Novel prey, as supplemental resources for native predators, can positively influence predator abundances. These changes in predator populations, and the associated shifts in predation risk, may result in concomitant effects upon native prey. These indirect effects may impose significant consequences on native prey populations. Aquatic systems are among the most impacted by biological invasions. Globally, freshwater crayfish are among the taxa most at risk to the effects of invasive species. In Ohio, the invasive rusty crayfish (Orconectes rusticus) has been implicated in the decline of a native congener, O. sanbornii. This research aimed to evaluate the influence of indirect effects of invasive O. rusticus on patterns of replacement of the native O. sanbornii in Ohio streams. I used a combination of field observations and experimental manipulations to fulfill three objectives. First, I quantified population demographics of O. sanbornii from streams invaded and not yet invaded by

O. rusticus. I also measured and compared the body size of O. rusticus in streams where they currently co-occur with O. sanbornii or have replaced the native species. Second, I conducted a within stream tethering experiment to test for differences in predation risk between these two species. Finally, I used a laboratory experiment to explore the effect

ii of predation risk, competitor identity, and the interaction of the two factors on the behavior of both O. sanbornii and O. rusticus. Results of field surveys indicated a significantly smaller average O. sanbornii body size in streams where they are found co- occurring with O. rusticus. However, there was no detectable difference in O. rusticus body size between streams where they currently co-occur versus streams which they have displaced O. sanbornii. There were no detectable differences in sex ratios or risk of predation across populations of either species. Results of the laboratory experiment indicated that both species respond to increased risk of predation with increased refuge use and reduced measures of activity. However, risk effects depended upon competitor identity. When competing with conspecifics, O. sanbornii significantly increased its use of refuge with elevated predation risk, while decreasing measures of activity. However, when competing against invasive O. rusticus, O. sanbornii only increased refuge use at high predation risk and demonstrated increased activity at zero and low risk. In contrast,

O. rusticus decreased activity with elevated risk, an effect which was heightened when facing a heterospecific competitor. Native O. sanbornii suffers a tradeoff between managing the risk of predation versus cost of competing with a novel competitor.

However, O. rusticus responds to competition from O. sanbornii in ways which are advantageous in risky environments. These results suggest that the behaviors of O. rusticus may allow the species to minimize the trade-off between finding food and avoiding predators and out-compete native O. sanbornii.

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Acknowledgments

I’d like to first thank my advisor, Lauren Pintor, for providing me opportunity to embark on my graduate career under her mentorship. Some of my most enjoyable experiences pursuing this degree have been the discussion of research ideas and ecological theory in various meetings over these last few years. Through this, she has taught me the importance of seeing beyond initial appearances, to dig to discover the true nature being presented, but also to step back and see the “big picture”. I would also like to thank my graduate committee, Suzanne Gray and Stephen Matthews for their insight and patience as this work has processed through its numerous iterations.

Additionally, I would like to thank all the members of Pintor lab; Lauren Hostert for the encouragement and all the help that first year in acclimating to graduate life, and

Jenna Odegard for the never ending positivity and always willing hand whether it be cleaning years old muck out of tanks or impromptu trips to the field. I’d also like to thank Lynn and Doug McCready, Kay Stefanik, Richard Oldham for the all the help (and sunburn) in building, literally, the foundations for the initial iterations of this work. I would also like to thank my friends and fellow graduate students for the encouragement and good conversation.

I would like to give a special thanks to all the undergraduates that have aided me over these last few years, in particular, Erin O’Shaughnessey and Scott Meyer. Know

iv that this research would not have been completed without your help. Additionally, I’d like to thank my advisors and mentors from FMU, Jeff Steinmetz, Travis Knowles, and

Lorianne Turner. Each of you has played an instrumental role in getting me where I am today.

Lastly, I would like to thank my family for putting up with all the missed holidays and phone calls, and providing support and encouragement along the way.

Vita

May 2001 ...... Titusville High School, FL

May 2013 ...... B.S. Biology, Francis Marion University

August 2013 to present ...... Graduate Associate, School of Environment

and Natural Resources, The Ohio State

University

Publications

Johnson, C. A. and J. D. Camper. Geographic Distribution. Farancia erytrogramma erytrogramma. Herpetological Review, 2013

Fields of Study

Major Field: Environment and Natural Resources

Table of Contents

Abstract ...... ii Acknowledgments...... iv Vita ...... vi Publications ...... vi Fields of Study ...... vi Table of Contents ...... vii List of Tables ...... ix List of Figures ...... xi Chapter 1: Introduction to the effects of biological invasion ...... 1 The Invasion Pathway ...... 2 Consequences of Biological Invasion ...... 4 Impacts of Non-Native Prey ...... 5 Animal Behavior and Personality-Biased Invasion ...... 7 Indirect Effects of Novel Prey ...... 9 Study System ...... 10 Objectives ...... 12 Figures ...... 14 Chapter 2 : Direct and indirect effects of the invasive Orconectes rusticus on native O. sanbornii in Ohio streams ...... 15 Abstract ...... 15 Introduction ...... 16 Methods ...... 20 Field Survey...... 20 Statistical Methods ...... 22 Results ...... 23 Discussion ...... 23 Conclusions ...... 30 vii

Tables and Figures ...... 32 Chapter 3 : Evaluating the effects of indirect interactions on the behavior of a native and invasive crayfish...... 35 Abstract ...... 35 Introduction ...... 36 Methods ...... 40 Study System ...... 40 Animal Housing & Experimental Set-up ...... 41 Experimental Protocols ...... 42 Statistical Analysis ...... 45 Model Interpretation ...... 48 Results ...... 49 Predation Risk ...... 49 Competition ...... 52 Interaction of Predation Risk and a Heterospecific Competitor ...... 53 Individual Aggression ...... 53 Discussion ...... 54 Conclusions ...... 60 Tables and Figures ...... 62 References ...... 88 Appendix A: Additional Tables for Chapter 2 ...... 97 Appendix B: Additional Tables and Figures for Chapter 3 ...... 98 Appendix C: Considerations for Future Work in Assessing Behavioral Response of Native Orconectes sanbornii to Invasion ...... 107

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List of Tables

Table 2.1 Streams sampled for crayfish community observations...... 32 Table 2.2 Tethering trial overview...... 33 Table 2.3 Orconectes cephalothorax length (mm) by species and invasion status...... 34 Table 3.1 Definition of behaviors in experimental trials...... 62 Table 3.2 Sex pairing allocation per treatment...... 63 Table 3.3 Definition of agonistic levels (Modified from Karavanich and Atema 1998). Values on the negative scale indicate submissive behavior. Higher values represent an escalation of aggression...... 64 Table 3.4 Definition of agonistic behaviors for crayfishes (Modified from Karavanich and Atema 1998)...... 65 Table 3.5 Mean, range and frequency of proportional response values ...... 66 Table 3.6 Available parameters for modeling behavioral responses ...... 66 Table 3.7 Final parameters chosen for each level in post-predation models (immediately following application of predation risk treatment). Models represent the acute response to predation risk...... 67 Table 3.8 Final parameters chosen for each level in pre-predation models (immediately prior to application of predation risk treatment). Models represent the carry-over, or chronic effect of predation risk...... 68 Table 3.9 Estimated parameters for proportion of time in refuge immediately following application of predator treatment...... 69 Table 3.10 Estimated parameters for proportion of time in refuge immediately prior to predator treatment...... 70 Table 3.11 Estimates for proportion of time active immediately following predator treatment...... 71

Table 3.12 Estimates for proportion of time active immediately prior to predator treatment...... 72 Table 3.13 Estimates of parameters of time spent foraging immediately following predator treatment...... 73 Table 3.14 Estimates for proportion of time spent foraging immediately prior to predator treatment...... 74 Table 3.15 Estimates of parameters of time spent moving immediately following predator treatment ...... 75 Table 3.16 Estimates of parameters of time spent moving immediately prior to predator treatment...... 76 Table 3.17 Estimates of parameters of time spent engaged in agonistic interactions following predator treatment...... 77 Table 3.18 Estimates of parameters of time spent engaged in agonistic interactions prior to predator treatment...... 78 Table 3.19 Three-way ANOVA results for effect of species, predation risk and competition on aggression score following predator treatment...... 79 Table 3.20 Parameter estimates for mean aggression score following predator treatment...... 79 Table 3.21 Three-way ANOVA results for effect of species, predation risk and competition on aggression score prior to predator treatment...... 80 Table 3.22 Parameter estimates for mean aggression score prior to predator treatment. . 81 Table A.1 Abiotic parameters within sampled streams………… …….………….....97 Table B.1 Estimate for proportion of time active immediately following predator

treatment with O. rusticus as species reference…….…….………...………..… ……99 Table B.2 Estimates for proportion of time foraging immediately following predator treatment with O. rusticus as species reference……………………...………………100 Table C.1 Questions and comparisons to address response of native O. sanbornii to predation risk and competition………………………………………………….108

List of Figures

Figure 1.1 Schematic of the invasion pathway. Successful invasion demands a potential invader to overcome obstacles at each phase. Adapted from Moyle and Marchettie 2006...... 14 Figure 3.1 Mean ± SE proportion of time utilizing refuge for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry-over effect). “Conspec” and “Hetero” represent competitor identification...... 82 Figure 3.2 Mean ± SE proportion of time active for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry-over effect). “Conspec” and “Hetero” represent competitor identification. 83 Figure 3.3 Mean ± SE proportion of time foraging for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry-over effect). “Conspec” and “Hetero” represent competitor identification...... 84 Figure 3.4 Mean ± SE proportion of time moving for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry-over effect). “Conspec” and “Hetero” represent competitor identification...... 85 Figure 3.5 Mean ± SE proportion of time interacting for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation xi

risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry-over effect). “Conspec” and “Hetero” represent competitor identification...... 86 Figure 3.6 Mean ± SE aggression score for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry-over effect). “Conspec” and “Hetero” represent competitor identification...... 87 Figure B.1 Post predator treatment interaction between Predation Risk and Species on Refuge Use ……….…...………….…………………………………………………..101 Figure B.2 Pre predator treatment interaction between Predation Risk and Species on Refuge Use……………………….…………………………………………………...101 Figure B.3 Post predator treatment interaction between Predation Risk and Species on Activity.………………………….…………………………………………………...102 Figure B.4 Post predator treatment interaction between Predation Risk and Competitor Identity on Activity………………….………….…………………………………...102 Figure B.5 Pre predator treatment interaction between Competitor Identity and Species on crayfish Activity..….…….……………………………...... 103 Figure B.6 Post predator treatment interaction between Predation Risk and Species on crayfish Foraging.………………….……………………...... 103 Figure B.7 Post predator treatment interaction between Predation Risk and Species on

crayfish Movement..………...……...….……………………...... 104 Figure B.8 Post predator treatment interaction between Competitor Identity and Species on crayfish Movement….………….……………………...... 104 Figure B.9 Post predator treatment interaction between Predation Risk and Competitor Identity on crayfish Movement…...……………………...... 105 Figure B.10 Pre predator treatment interaction between Predation Risk and Competitor Identity on crayfish Movement…………………………...... 105 Figure B.11 Pre predator treatment interaction between Competitor Identity and Species on crayfish Movement...…..……………………...... 106

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Figure B.12 Interactions on crayfish aggression score. A) Competitor ID x Species, B) Predation Risk x Competitor ID, C) Competitor ID x Species...... 106

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Chapter 1: Introduction to the effects of biological invasion

Invasive species have been described as one of the most permanent forms of environmental pollution (Dodds 2002). Over 50,000 non-native species have been introduced to the United States with losses approximating $120 billion/year (Pimentel et al. 2005). The negative consequences of invasive species have been studied and published extensively. They function to alter ecosystems (Vitousek et al. 1996), act as vectors of disease (Rochlin et al. 2013), disrupt or damage infrastructure (Pimentel et al.

2005; Rodda and Savidge 2007) and have been implicated in severe loss of biodiversity of native wildlife (Rodda and Savidge 2007). Because of these impacts, invasive species research has been focused primarily on the negative, direct effects of invasive species as predators and competitors on native species. In contrast, relatively little research has been done to elucidate the effect of invasive species as prey and the subsequent indirect effects of novel predator-prey interactions on other species in the food web (Rodriguez

2006; Barber et al. 2008; Carlsson et al. 2009).

As novel prey, invasive species can elevate the abundance of native predator populations (King et al. 2006) and alter foraging behaviors (Rilov et al. 2002; Johnson et al. 2010). Evidence shows that invasive species can have a positive effect on native predator population abundance and distribution (Barber et al. 2008; Dijkstra et al. 2012).

This novel interaction, between native predator and invasive prey, subsequently generates a new pathway through which invasive species may impact food webs. Specifically,

1 invasive species can have negative effects on native competitors through the positive effect they have on a shared, native predator. These indirect effects of invasive species can have catastrophic consequences for native diversity (Roemer et al. 2002), yet empirical research on the impact of indirect interactions between native and non-native prey has been relatively unexplored (Noonburg and Byers 2005). This imbalance in our understanding of invasions, novel predator-prey interactions and the indirect effects of non-native species on native biota limits our ability to predict the long-term consequences of biological invasions.

The Invasion Pathway

Biological invasion represents the completion of a multistep pathway of species transport to an exotic range, and its subsequent establishment and spread (Mack et al.

2000). Each step represents a critical obstacle which a potential invader must overcome

(Figure 1; Adapted from Moyle and Marchetti, 2006). Here, I define an invasive species as a non-native species which has completed this process. A potential invader must first make their way into a transport vector. Transport may be an accidental event, or due to intentional introductions of human valued species (Hulme 2009). In this transport phase, organisms face heavy abiotic pressures, including temperature extremes and hypoxia, as well as potential biotic constraints such as resource scarcity leading to increased intraspecific competition (Chapple et al. 2012). Few individuals survive transport, and in this way this initial step often poses an insurmountable barrier, halting the process at its initiation (Mack et al. 2000). Individuals or populations that successfully survive transportation are immediately faced with the need to navigate and exploit a novel environment. Here, these populations become subject to the environmental conditions of

2 the receiving system, which may greatly differ from the source point. Additionally, they must persist novel biotic interactions in the new range (Sax and Brown 2000; Carlsson et al. 2009). Transported individuals must persist through these conditions to form self sustaining populations, at which point a non-native species can be said to have established within the novel system. Barriers to establishment are diverse. While the location of inoculation may be suitable to the novel species at the time of introduction, temporal variability may eliminate the colonizing population before it can become self- sustaining. Spatial stochasticity can inhibit multiple invasion stages through habitat heterogeneity (Sax and Brown 2000). A favorable site of introduction may sit within a patchwork of unfavorable sites. Thus, introduced populations carry some probability of restriction simply based on their opportunity to be introduced into a favorable vs. non- favorable habitat. Populations established within favorable systems may be restricted from spreading if surrounding systems are unfavorable (Sax and Brown 2000; Moyle and

Marchetti 2006). These unfavorable systems may function as a population sink, particularly in species with greater tendency to disperse. This may not only inhibit spread, but could function as a negative feedback on the previously established population, indirectly restricting growth (Sax and Brown 2000). Additionally, this may also render highly dispersing populations more vulnerable to severe environmental conditions. However, those populations that have surmounted these obstacles to establishment and spread, and which bring harm to the receiving communities, can be deemed invasive.

Consequences of Biological Invasion

The effects of a biological invader following a successful invasion can be diverse.

These effects include damage to infrastructure, such as the case with the invasive zebra and quagga mussels, which clog water intake and filtration systems as well as electrical generating facilities (Pimentel et al. 2005). Invasive species function to alter and degrade native biodiversity. The introduction of the invasive brown tree snake, Boiga irregularis, to the island of Guam, has resulted in the loss of multiple avian species, reduction in numerous lizard species and bats, and the associated decline in forest regeneration due to loss of seed dispersing organisms (Rodda and Savidge 2007). Human health is also directly impacted by biological invasions. The worldwide spread of the Asian tiger mosquito, Aedes albopictus, has been implicated in the spread of numerous arthropod- borne viruses, such as chikungunya in the Indian Ocean basin and Europe (Tsetsarkin et al. 2007; Grandadam et al. 2011), and West Nile Virus in North America (Turell et al.

2001; Farajollahi and Nelder 2009). Because of these potentially devastating consequences, it’s no surprise that research into the impacts of biological invasions have focused primarily on their negative impacts. Particularly with regards to native biodiversity and ecosystem function, this research has focused on the effects of biological invaders as novel predators of, or direct competitors to, native species. Examples include the predation of native, larval yellow perch, Perca flavescens by the invasive Alewife,

Alosa pseudoharengus, in Lake Ontario (Brandt et al. 1987) and the foraging competition of the invasive round goby, Neogobius melanostomus, with numerous native fishes

(French III and Jude 2001). However, due to the overwhelming focus placed on such direct, negative effects of invasive species, other avenues of research have been less

4 emphasized, if not overlooked. In particular, until recent years, the influence of animal behavior on biological invasions and the positive effects non-native species may have on a native community has received little attention.

Impacts of Non-Native Prey

While a great library of research has been developed revealing the effects of non- native species as predators or direct competitors in a system, little has been done to inform of the potential positive effects of non-native species, particularly in the form of novel pray (Rodriguez 2006; Barber et al. 2008; Carlsson et al. 2009). Novel prey can unlock new energetic pathways in a food web. This can have numerous consequences for native predators.

The Lake Erie Watersnake, Nerodia sepidon insularum, a non-venomous colubrid snake inhabiting the western basin of Lake Erie (Jones et al. 2009), had been prior to round goby invasion, a federally threatened-listed species. Its decline has been attributed to loss of native prey and human-induced mortality (King et al. 2006). However, within just a few generations following round goby invasion, King found that watersnake populations has undergone an extreme dietary shift, with round goby comprising up to

90% of waternsnake diet contents. The consequence of this being an improvement in both watersnake populations and observed individual biomass (King et al. 2006), resulting in increased reproductive fitness (King et al. 2008).

The dietary shift of the Lake Erie watersnake in response to an abundant, novel prey source and its subsequent population recovery is an extreme of a readily observable response to the watersnake populations. There are many examples of less obvious effects. For example, also in response to round goby invasion, double-crested cormorant,

(Phalacrocorax auritas), populations of the Pigeon and Snake Islands of Lake Ontario, demonstrated not only a diet shift to the abundant novel prey, but also an adaptive foraging behavior to take advantage of that prey (Johnson et al. 2010), as prior to invasion, cormorant diets consisted of relatively little consumption of benthic prey, such as the goby. Perhaps even less obvious is the response of native bloodstar (Henricia sanguinolenta) consumption of two non-native species of colonizing ascidians,

(Diplosoma listerianum) and (Didemnum vexillum) (Dijkstra et al. 2012). H. sanguinolenta is a predator known to heavily subsist on native sponge species (Sheild and Witman 1993). When consuming native prey, H. sanguinolenta individuals were shown to increase in mean mass, which did not occur when consuming the invasive ascidian prey. However, field observations confirmed the bloodstars subsisted heavily upon the novel prey source due to seasonal declines in the native sponge prey. Yet, despite an observed decrease in individual fitness due to less profitable prey, bloodstar populations increased following ascidian invasion. Dijkstra (2012) explains that while the invasive prey is not as profitable as the native sponges, they remain in abundance across seasons, thus providing a nutrient source over naturally resource scarce seasons.

The result being an increased baseline for subsequent growth seasons (Dijkstra et al.

2012).

While non-native species can function to negatively impact native populations through competition and predation, so too can they function to support or bolster native populations when able to be utilized as a novel prey source.

Animal Behavior and Personality-Biased Invasion

Research in animal behavior has, in recent years, made great strides in further elucidating the processes by which a biological invasion may unfold and detailing how certain traits may enhance species’ potential to invade novel systems. Animal behavior has been shown to influence multiple stages of the invasive process (Figure 1).

Behavioral traits such as boldness and reduced neophobia have been shown to have a strong influence over which individuals may be transported from a native population source (Chapple et al. 2011). Chapple showed that the invasive Australian delicate skink

(Lampropholis delicate) demonstrated a greater willingness to traverse novel obstacles to reach a resource than its non-invasive congener, the garden skink (Lampropholis guichenoti). Reduced neophobia within certain individuals can predict greater probability for those individuals to enter into human transport vectors. Additionally, because L. delicata was also shown to spend greater time in shelter when available, compared to L. guichenoti, it may be better suited to evade human inspection efforts aimed at detecting such stow-aways (Chapple et al. 2012). Given the tight association with traits such as activity, boldness and exploratory behavior (Chapple et al. 2012), it is possible that this suite of traits predisposes certain individuals to enter into human transport vectors. The implication of this discovery is that invasives may not simply be a random subset of a population from which the individuals derive, but a non-random population with a potentially selected suite of traits that enhance invasiveness, a phenomenon also known as Personality-Biased Invasion (Juette et al. 2014). Further, behaviors which may predispose certain individuals to enter into transport vectors may also enhance their survival. While boldness has been linked to increased individual

7 aggression (Chapple et al. 2012) and increased intraspecific competition (Pintor et al.

2009), the associated willingness to explore may allow for greater resource procurement and, when densities are low, decrease competitive interactions while in transit (Chapple et al. 2011), or decrease the consequences of these interactions (Pintor et al. 2009).

Alternatively, aggregations in social organisms may provide thermal inertia against extreme temperature regimes via shared body heat (Chapple et al. 2012). Thus, behavioral traits can predict both the probability of population transport and inform survival of that population to the novel environment.

However, behavior also can heavily influence the success of an exotic species once released into that novel environment, particularly in their ability to overcome novel biotic interactions with the native community. For example, invasive populations of rusty crayfish (Orconectes rusticus) have been shown to forage more aggressively than both native congeners and native range O. rusticus, and to aggressively steal food from competitors, when not first to recruit to bait (Pintor and Sih 2008). In this way, they may be better able to dominate resources over their native competitors, increasing their ability to compete in novel systems. Clearly behavioral traits can heavily influence the likelihood for a given population to enter into a novel system as well as the ultimate ability to proliferate in the new environment. However, dominance of one species implies submissiveness in another. With regards to behavior, little work has been done to explore how native species behavior may impact its ability to resist replacement by a novel competitor, particularly with regards to indirect relationships which may emerge in the recipient community such as those through apparent competition.

Indirect Effects of Novel Prey

Non-native prey affect native predator population abundance and behavior, and generate novel, indirect effects on native prey species (Holt and Lawton 1994). These novel, predator mediated interactions (i.e., apparent competition, indirect amensalism, etc.) have been shown to impose effects across diverse systems. For example, the arrival of the golden eagle (Aquila chrysaetos), an apex predator, on the California channel islands in response to the introduction of feral pigs (Sus scrofa), and the subsequent shifts in population densities between two endemic carnivores, illustrates the extreme effects of apparent competition across an entire simple food web (Roemer et al. 2002). Further, controlled experimental manipulation excluding direct competition has shown that apparent competition can lead to extinction of one competing species (Bonsall and

Hassell 1997). The previous examples, acting through shifts in population abundance, demonstrate lethal, density related effects. However, indirect effects of a non-native competitor on a species can also arise through phenotypic effects (behavior, morphology, etc.). These non-lethal, trait-mediated indirect interactions have been reviewed in previous literature (Werner and Peacor 2003) and shown experimentally to have significant contributions to community structures, such as the trophic cascade resulting from the interaction of the top predator, Nassau grouper (Epinephalus striatus) affecting the behavior and growth of two intermediate predator grouper species (Stallings 2008).

Such responses, often attributed to direct, lethal effects of predators, may actually be resulting from shifts in traits of surviving prey (Peacor and Werner 2001). Yet despite their potential significance, research upon these indirect effects is lacking with regard to biological invasions, particularly in reference to the non-lethal, trait-mediated effects.

This could be potentially significant in not only understanding long term consequences of invasion, but also the initial stages of species replacement. The increase in native prey mortality due to direct predation only comes to be significant once predator populations have sufficiently increased. Thus, the onset of significant density-mediated effects will be dependent upon the rate at which native predators can convert novel prey into predator biomass. However trait-mediated effects, such as changes in behavior, may occur even before a predation event has occurred (Peacor and Werner 2001) or before the accumulation of sufficient predator biomass to significantly increase prey mortality.

Thus, the onset of trait-mediated effects may occur at a lower conversion threshold. In this way, trait-mediated indirect interactions may be a more powerful driver of changes to community structure early in the invasion process.

Study System

The spread of the widespread, invasive rusty crayfish (Orconectes rusticus;

Faxon, 1884) into central and eastern Ohio streams provides an excellent opportunity to study the indirect effects of a biological invasion on a receiving community. Rusty crayfish, which has obtained the widest distribution of any Ohio crayfish, is an opportunistic forager subsisting on detritus, macrophytes, algae and animal matter

(Momot 1995) believed to have originated in the Great Miami and Scioto River basins of southwestern Ohio, southeastern Indiana and Northern Kentucky (Thoma and Jezerinac

2000; Lodge et al. 2012). Believed to have been introduced through release of live bait

(Mather and Stein 1993a), rusty crayfish have invaded multiple systems across the United

States (Taylor and Schuster 2004), including streams and lakes in at least 20 states and all

5 of the Laurentian Great Lakes, resulting in negative impacts to these systems (Lodge et

10 al. 2012), including the displacement of native species (Olsen et al. 1991). Reports of rusty crayfish outside of its native range appear as early as the early 1800’s (Perry et al.

2002), with the first record in the Great Lakes appearing in 1897 (Lodge et al. 2012), and in the Chagrin River system of northeastern Ohio in the 1930’s (Jezerinac 1982), the latter introduction likely resulting from a stocking program as food for game fishes. The species was first discovered within the Licking River drainage sometime between 1926 and 1967 surveys of the system (Butler 1988). Reported densities can vary significantly, ranging from 0.4-8.3 per (Mather and Stein 1993a; Taylor and Redmer 1996). The species can be found in both lentic and lotic habitat (Taylor and Redmer 1996; Lodge et al. 2012), typically within cobble or fractured concrete habitat at depths of less than 1m

(however, they have been reported as deep as 14.6m) (Taylor and Redmer 1996). Rusty crayfish spread has had profound impacts on native crayfish, resulting dominance of crayfish communities or localized extinction of natives in afflicted areas (Olsen et al.

1991; Taylor and Redmer 1996). Within Ohio, rusty crayfish range expansion has been implicated in the decline of the native Sanborn’s crayfish (O. sanbornii; Girard, 1852)

(Mather and Stein 1993a). Sanborn’s crayfish are native to the Ohio River drainage of northeastern Kentucky and extending into eastern Ohio along the Muskingum basin, the

Scioto and Lake Erie tributaries (Thoma and Jezerinac 2000) where it has reported densities ranging from 0.2-1.8 per (Adams et al. 2010). Due to similar preferences in habitat as the invasive O. rusticus, this species faces heavy competition within introduced ranges.

The consequences of O. rusticus range expansion have long been postulated

(Turner 1926). We now know that rusty crayfish spread is linked to population declines

11 in not only native crayfishes (Butler IV and Stein 1985; Lodge et al. 2012), but exhibit multi-trophic effects on native communities (Olsen et al. 1991; Nilssonn et al. 2012;

Lodge et al. 2012; Baldridge and Lodge 2013). Because of these impacts, several studies have been completed attempting to understand the mechanisms by which rusty crayfish may invade and replace native crayfishes. With few exceptions (Mather and Stein 1993a;

Hayes et al. 2009), this research has focused primarily on direct effects related to competitive interactions (Butler IV and Stein 1985), reproductive interference (Butler

1988), or abiotic factors of stream composition (Flynn and III 1984). Because the congeneric O. rusticus and O. sanbornii share similar life history traits such as diet and habitat selection, climate regimes in native ranges, and share a suite of predators ubiquitous throughout their ranges (Mather and Stein 1993a), it is believed these similarities also allow O. sanbornii to more effectively resist the invasion of O. rusticus, compared to other related species. Yet, evidence shows that O. rusticus is replacing O. sanbornii in Ohio streams. Thus, the decline in O. sanbornii due to O. rusticus invasion provides an excellent opportunity to explore potential alternative, indirect effects of a biological invasion on a native community.

Objectives

In this study, I examined the effect of the widespread, invasive rusty crayfish

(Orconectes rusticus) as novel prey upon native predators, and their subsequent indirect effects on the native Sanborn’s crayfish (O. sanbornii). Specifically, I evaluated the impact of invasive O. rusticus on native O. sanbornii by quantifying the demographic traits (size and sex ratio) of native O. sanbornii and invasive O. rusticus across invaded and non-invaded streams. Additionally, I measured predator induced mortality on O.

12 sanbornii and O. rusticus to compare predation risk across invaded and non-invaded streams. Lastly, I quantified the direct and indirect effects of predation risk, competitor identity, and the interaction of predation risk and competitor on the behavior of native O. sanbornii and invasive O. rusticus. This system provides an excellent opportunity to investigate how a novel interaction, between a native predator and exotic prey, may influence a native community, specifically native prey species, through indirect predator mediated interactions. These indirect effects, such as those mediated by a shared predator, can greatly impact native communities (Roemer et al. 2002), yet these indirect interactions in the context of biological invasions have been received little attention

(Noonburg and Byers 2005). This work focusing on these indirect effects of a novel prey contributes to a more balanced perspective on the direct and indirect effects of non-native species on native biodiversity, allowing conservation biologists and managers to institute more effective means of mitigating the consequences of biological invasions to native biodiversity.

Figures

Figure 1.1 Schematic of the invasion pathway. Successful invasion demands a potential invader to overcome obstacles at each phase. Adapted from Moyle and Marchettie 2006.

Chapter 2 : Direct and indirect effects of the invasive Orconectes rusticus on native

O. sanbornii in Ohio streams

Abstract

Biological invasions remain a prevalent threat to native ecosystems, with aquatic systems among the most at risk. Globally, crayfishes are both among the most invasive, and most vulnerable, taxa to the effects of biological invasions. Native crayfishes may be susceptible to both direct and indirect effects of competition with an invasive crayfish, including competition for resources and shelter, as well as indirect effects mediated through additional agents, such as shared predators. The widespread invasive rusty crayfish (Orconectes rusticus) has been shown to displace native North American crayfishes in introduced ranges. In Ohio, O. rusticus has been implicated in the decline of the native congener O. sanbornii. My work aimed to further elucidate the mechanisms by which O. rusticus is replacing O. sanbornii in Ohio streams. To do this, I utilized a combination of field surveys and within-stream manipulations to test for differences in demographic traits (body size and sex ratio) and risk of predation on native O. sanbornii in streams invaded by O. rusticus versus non-invaded streams. Additionally, I explored if body size, sex ratio and predation risk of invasive O. rusticus differed between streams where they are currently co-occurring with O. sanbornii versus those in which O. rusticus has displaced the native. Native O. sanbornii found co-occurring with invasive O.

15 rusticus were significantly smaller than those found in non-invaded streams. However, there was no detected difference in O. rusticus body size between streams where they have displaced or currently co-occur with O. sanbornii. Additionally, there were no detectable differences in sex ratios or risk of predation across populations. These results are consistent with research suggesting size plays an important role in the competitive replacement of O. sanbornii by the invasive O. rusticus in Ohio streams.

Introduction

Despite considerable research and management efforts, biological invasions remain one of the most prevalent forms of environmental pollution (Dodds 2002). The ecological and economic costs associated with biological invasions in the United States are approximately $120 billion/year (Pimentel et al. 2005). Aquatic systems are especially vulnerable. For example, the Laurentian Great Lakes alone have suffered introduction of 145 non-native species (Ricciardi and MacIsaac 2000). Consequences of biological invasions can be severe, including the alteration of ecosystem function

(Vitousek et al. 1996; Byers et al. 2014), damage to infrastructure (Pimentel et al. 2005;

Rodda and Savidge 2007), spread of disease to both humans (Rochlin et al. 2013) and wildlife (Geiger et al. 2005), and loss of native biodiversity (Rodda and Savidge 2007).

The threat biological invasions pose to native diversity has been a continued concern.

Considerable effort has been taken to understand the effects of biological invasions on native species, particularly when non-native species act as novel predators

(Brandt et al. 1987) or competitors (Salo et al. 2007; Carlsson et al. 2009; Pintor and

Byers 2015) within invaded systems. For example, classic examples of biological invasions include the large bodied common house gecko (Hemidactylus frenatus),

16 invasive to the Pacific islands, which has outcompeted mourning geckos (Lepidodactylus lugubris) for prey and has led to population declines of the native species (Hanley et al.

1998). The invasive Argentine ant (Linepithema humile) has been repeatedly shown to competitively displace native ants wherever introduced, largely due to their intense ability to rapidly locate and control resources (Holway 1999). Similarly, invasive round gobies (Neogobius melanostomus) have outcompeted several fish species in the Great

Lakes, including the native mottled sculpin (Cottus bairdi) by interfering with sculpin nest-guarding behavior and seizing spawning refuge (Janssen and Jude 2001). Relative reproductive potential between native and invasive competitors can be significant to invasive success. It has been shown in aquatic systems that invasive populations may be highly skewed female (Rumbold et al. 2016), enhancing short term population growth of the non-native. Further, direct interaction with competitively superior invasives has been shown to result in distorted adult sex ratios within native populations. This is due to sex- related differential survival (Barrientos 2015), potentially reducing native reproductive potential.

The effects of invasive species on native populations can range in severity.

Migration of the goldband goatfish (Upeneus moluccensis) into the Mediterranean following the opening of the Suez Canal led to localized displacement of the native red mullet (Mullus barbatus), the native being forced to shift into greater depths by the non- native (Spanier and Galil 1991). Alternatively, natives may be completely removed from a system, as was the case of the localized extinction of mottled sculpin resulting from round goby invasion (Janssen and Jude 2001). Taken together, many non-native species

17 have been frequently shown to directly outcompete native species for needed resources or shelter.

Although, direct competition between natives and non-natives has been widely studied, non-native species may also indirectly compete with natives. One way that non- native species may indirectly affect native species is by interactions mediated by a shared enemy (predator/pathogen) (Holt and Lawton 1994). For example, small-mouthed salamander (Ambystoma texanum) larvae were able to competitively exclude freshwater isopods (Lirceus fontinalis) from refuge in the presence of predatory sunfish (Lepomis cyanellis), increasing isopod mortality from sunfish predation (Huang and Sih 1990).

Interference competition, the prevention of a competitor from utilizing a resource, for shelter between native noble crayfish (Astacus astacus) and invasive signal crayfish

(Pacifasticus leniusculus) has led to declines due to increased predation risk on the native crayfish by European perch (Perca fluviatilis) (Söderbäck 1994). Mortality of the native water vole (Arvicola terrestris) was associated with home-range connectivity to that of the invasive European rabbit (Oryctolagus cuniculus). These species share a common predator in the also invasive American mink (Neovision vision) (Oliver et al. 2009).

When the two prey species maintained highly connected habitats, the highly mobile mink disproportionately affected water vole populations. These examples are illustrative of the potentially significant influence that indirect interactions, such as those mediated by a shared predator, have on the long term community response to biological invasions.

Within aquatic systems, crayfish are one of the most widely invasive taxa, with introductions occurring worldwide (Taylor and Redmer 1996; Geiger et al. 2005; Lodge et al. 2012). Introductions of non-native crayfish is also the leading cause of native

18 crayfish declines worldwide (Lodge et al. 2000). For example, invasive crayfish have been shown to dominate their native competitors in monopolizing resources (Pintor and

Sih 2008) and shelter (Söderbäck 1994). Competitive displacement of native crayfish by introduced species is thus a significant contribution to the continued biotic homogenization of aquatic systems.

The highly invasive rusty crayfish (Orconectes rusticus; Faxon, 1884) has obtained the widest distribution of any Ohio crayfish (Momot 1995). While its original range is believed to have been the Great Miami and Scioto River basins of southwestern

Ohio, southeastern Indiana and Northern Kentucky (Thoma and Jezerinac 2000; Lodge et al. 2012), O. rusticus been introduced and subsequently invaded multiple systems across the United States (Taylor and Schuster 2004). This includes streams and lakes in at least

20 states and all 5 of the Laurentian Great Lakes (Lodge et al. 2012). O. rusticus spread has had profound impacts on native crayfish, resulting in declines of natives in afflicted areas (Olsen et al. 1991; Taylor and Redmer 1996). Within Ohio, rusty crayfish range expansion has been implicated in the decline of the native Sanborn’s crayfish (O. sanbornii; Girard, 1852) (Mather and Stein 1993a). O. sanbornii is native to the Ohio

River drainage of northeastern Kentucky and extending into eastern Ohio along the

Muskingum basin, the Scioto and Lake Erie tributaries (Thoma and Jezerinac 2000).

Because both species share similar ecological characteristics, such as diet choice, habitat preferences, and a suite of predators common to both their native ranges; O. sanbornii faces heavy competition from O. rusticus within introduced ranges. In this study, I explored the direct and indirect effects of the invasive O. rusticus upon native O. sanbornii to elucidate mechanisms by which O. rusticus is replacing O. sanbornii in Ohio

19 streams. To this end, I sought to test three hypotheses. Specifically, I hypothesized that

O. sanbornii found within streams in which they co-occur with the invasive O. rusticus will be smaller than those found within non-invaded streams. However, there will be no difference in O. rusticus body size between streams in which they co-occur with native

O. sanbornii versus those in which they have displaced the native. Second, the sex ratios of O. sanbornii will be different between invaded and univaded streams. I expect sex ratios to also differ between O. rusticus populations where they currently co-occur or have displaced O. sanbornii. Lastly, I expect predation risk to be higher on O. sanbornii in streams invaded by O. rusticus.

Methods

Field Survey

Effects of invasive O. rusticus on native O. sanbornii

To evaluate whether the body size and sex ratio of native Orconectes sanbornii differ across non-invaded streams versus streams invaded by O. rusticus, I surveyed riffle sections within nine Ohio streams (N = 7 Non-invaded, N = 2 Invaded; Table 2.1) from two drainages, the Ohio River via Muskingum River in north-central Ohio, and the Ohio

River via Hocking River in southern Ohio. Sampling was conducted from 2 May to 14

August, 2015.

Crayfish were collected via quantitative kick seining. Within a riffle section, parallel transects (range = 45-65 meters in length) were sampled with one individual holding a 1.5-m wide seine while another individual kicked approximately 1-m upstream to turn over all rocks, dislodging crayfish (Mather and Stein 1993a). Parallel transects

20 were spaced 2-m apart to avoid overlap. All individuals were identified to species, sexed, and its cephalothorax length (anterior tip of rostrum to posterior edge of thorax) measured (millimeters) to quantify body size.

Population demographics of invasive O. rusticus

To compare body size and sex ratio of invasive O. rusticus from streams where they co-occur with O. sanbornii (N = 2) versus streams where they have displaced O. sanbornii (N = 6), I sampled crayfish from invaded Ohio streams from two drainages. To assess populations overlapping native O. sanbornii, I surveyed O. rusticus individuals from the Ohio River via Muskingum River. To assess O. rusticus populations that have excluded natives, I sampled six additional sites along the Scioto River drainage (Table

2.1).

Predation Rate

To assess predation risk within invaded versus non-invaded streams, I conducted tethering experiments across a subset of the previously described streams (Non-invaded,

N = 4; Invaded, N = 5). Experiments were conducted multiple times within streams from

3 May to 15 September, 2015 (Table 2.2). Because it was not possible to obtain measures of predation rate in streams with overlapping O. sanbornii and O. rusticus populations (ie. to test the hypothesis that predation risk on O. sanbornii will increase with presence of the O. rusticus within the community), I compared predation risk between non-invaded streams (dominated by O. sanbornii with no detected invasive crayfish) versus invaded streams in which O. rusticus populations have displaced the native O. sanbornii. Crayfish were tethered (restrained) within 1m2 quadrats

(1crayfish/quadrat) with a 20-cm length of 4lb-test monofilament fishing line tied around

21 an individual’s thorax between the 1st and 2nd pairs of walking legs and secured to a 23- cm steel tent stake that was embedded in the stream substrate. A 3.81-cm diameter fishing bobber was fastened to each stake in order to aid recovery. During each tethering event, six crayfish were tethered within a riffle section of the stream at a minimum of 6 meters apart and left for 18-168 hours (mean = 95 hours). Variation was due to initial difficulty in determining an optimal set time as predation rates are known to vary as the summer season progresses (Kuhlmann et al. 2007). Each tether was then checked for evidence of predation. Specifically, partially removed crayfish, cut tethers, empty loops or presence of exoskeletal fragments were considered predation events (Kuhlmann et al.

2007). Crayfish were considered “survivors” if found alive, or dead but fully intact. Due to environmental factors, not all tethers were able to be recovered following trials.

Therefore, mortality was calculated using only recovered tethers. Predation rate was calculated as mean mortality divided by trapping hours.

Statistical Methods

All analyses were performed using R statistical software (R Core Team 2015).

Responses were visually assessed for normality. Welch’s 2-sample t-tests were used to compare O. sanbornii cephalothorax lengths across non-invaded and invaded streams, and O. rusticus cephalothorax lengths across streams where they overlap with O. sanbornii versus streams with complete displacement of the native. To compare sex ratios of the two species, pooled 2-sample t-tests were used to compare the mean proportion of females in the previously mentioned O. sanbornii and O. rusticus populations. Finally, a pooled t-test was used to test for difference in mean predation rate across streams dominated by allopatric O. sanbornii versus O. rusticus populations.

Results

Mean native O. sanbornii CL in invaded streams was smaller compared to non- invaded (T35 = 4.359; P = 0.0001; 95% CI-Low: 2.62, High: 7.19). There was no evidence of a difference in mean CL for invasive O. rusticus across sympatric streams versus streams in which O. rusticus have replaced O. sanbornii (T20 = -0.843; P = 0.409;

95% CI-Low: -4.07, High: 1.72).

There was no evidence for a difference in sex ratio (the proportion of females) within populations for O. sanbornii (T12 = 1.231; P = 0.242; 95% CI-Low: -0.148, High:

0.531) across invaded versus non-invaded streams. One observation (Clear Fork, Licking

River; 23 May ‘15) held only one data point (a single female), and so was removed from the analysis. Additionally, there was no detectable difference between sympatric versus allopatric O. rusticus populations (T8 = 1.156; P = 0.281; 95% CI-Low: -0.149, High:

0.449).

Predation rate was calculated as the mean mortality rate adjusted for tether set hours in stream. There was no evidence for a difference in mean predation risk between invaded versus non-invaded streams (T7 = 1.493, P = 0.179).

Discussion

Presence of the invasive O. rusticus was found to correspond with smaller mean cephalothorax length of the native O. sanbornii in streams in which the native still persists alongside invasive O. rusticus. However, there was no evidence for a difference in sex ratio for either species. Further, there was no detectable difference in predation risk between O. sanbornii dominated versus O. rusticus dominated streams.

There was a detected difference in mean O. sanbornii cephalothorax length (CL).

Observed body sizes for the native were smaller within streams in which they were found to co-occur to the invasive O. rusticus. However, this result was not found across O. rusticus populations. Size is known to influence aggression and competitiveness in crayfish (Bovbjerg 1956; Mather and Stein 1993a), and could affect species replacement through resource competition. Comparing O. rusticus growth and foraging behavior to two related species, Pintor and Sih (2009) showed that invasive O. rusticus populations demonstrated greater foraging and recruitment to food than native O. cristavarius, but not

O. propinquus. However, in a reported 57% of instances in which O. propinquus was first to acquire food, invasive O. rusticus individuals demonstrated increased aggression in taking that food away from the competitor (Pintor and Sih 2008). Further, it has been shown in a comparison between invasive O. rusticus to native O. virilis populations that were either naïve (O. virilis individuals with no experience encountering O. rusticus) or experienced (O. virilis individuals from populations with experience competing against invasive O. rusticus) that naïve populations demonstrated decreases in body mass during field mesocosm trials. However, experienced O. virilis populations demonstrated increases in body mass (Hayes et al. 2009). These results seem to suggest O. rusticus competitively dominates inexperienced, native competitors through exploitation of consumable resources. However, when relating these results to other species facing O. rusticus invasion, it is important to note that O. sanbornii is thought to be more similar to the invasive crayfish than these other related species. This includes relative aggressiveness, and may be evident in the seeming greater ability for O. sanbornii to resist O. rusticus invasion (Mather and Stein 1993a). Indeed, as shown by behavioral

24 trials I present in Chapter 3, there are certain contexts in which O. sanbornii may demonstrate greater aggression than O. rusticus. Alternative to direct exploitation for resources, there exist potential behavioral differences which may contribute to greater O. rusticus size compared to native competitors. In field studies, O. rusticus demonstrated a greater preference for highly profitable food items compared to O. sanbornii (Olsen et al.

1991; Mather and Stein 1993b) and may forage more actively (Butler IV and Stein 1985).

This may be a general characteristic of O. rusticus displacement of natives, with evidence showing greater weight-specific feeding when comparing O. rusticus to other Orconectes crayfishes (Olsen et al. 1991). Further evidence against simple exploitation was provided by Butler and Stein (1985; and references within). Comparing stomach contents of O. sanbornii and O. rusticus across sympatric and allopatric sites, stomach fullness was greater for both species in sympatric sites, seemingly indicating little importance of exploitative interspecific competition (Butler IV and Stein 1985). However, O. sanbornii has been shown to decrease foraging activity when competing against larger O. rusticus individuals, more reflective of the size structure in natural systems (Mather and Stein

1993a). O. rusticus may also simply benefit from temporal differences in reproduction leading to greater size, with young juveniles escaping from females earlier in the season to colonize resources before than their competitors (Corey 1988).

In addition to competitive advantage, greater size also imparts benefits to resisting predation, particularly in aquatic systems where typical predators are often gape limited and demonstrate size selective prey choice (Stein and Magnuson 1976; Butler IV and

Stein 1985). Like many aquatic organisms, crayfish demonstrate microhabitat shifts in response to perceived predation risk (Mather and Stein 1993a), with risk being a function

25 of size and smaller crayfish retreating away from deeper pools and into riffle habitat when detecting presence of fish, while larger adults are less affected (Stein and

Magnuson 1976). However, in a comparison between O. rusticus to O. sanbornii, it was found that larger O. rusticus suppressed this habitat shift in O. sanbornii (Mather and

Stein 1993a). It should be noted, however, that even in sites with fish predators, more crayfish of either species remained within pools than riffles (Mather and Stein 1993b). It was proposed this may be due to greater energetic demands to maintain position within riffles with faster water velocities than slower flowing pools, as well as greater risk of displacement and passive drift, which can also increase risk of mortality (Mather and

Stein 1993b). Thus, while providing high energy resources, as well as escape from aquatic predators, these habitats also may impose energetic trade-offs which O. rusticus may be more suited to overcome.

Crayfish should also moderate location within a microhabitat, choosing substrates such as pebble versus sand which provide greater protection as risk increases (Stein and

Magnuson 1976). However, O. rusticus has been shown to utilize refuge more readily than native competitors. When compared to native O. virilis, which were naïve to invasive O. rusticus, it was found that O. rusticus spent 24% more time in shelter than the inexperienced O. virilis (Hayes et al. 2009). This was supported by my behavioral trials presented in chapter 3. While this may simply be a function of competition, it is typically the more competitive male crayfish which are found exposed, rather than monopolizing shelter, and so it would seem predation, rather than competition, motivates shelter use in crayfish (Stein and Magnuson 1976). In addition to more limited opportunity to be preyed upon through refuge use choices, size as a refuge itself, could favor O. rusticus

(Mather and Stein 1993a), with fewer O. rusticus consumed in experimental manipulations than O. sanbornii when size differences matched those found in situ.

Further, a study of crayfish predation in North Turtle Lake, WI showed that predator diets consisted of invasive O. rusticus at levels lower than would be predicted based on relative prey availability, compared to O. propinquus and O. virilis populations (Roth and

Kitchell 2005). In this case, it was not simply size proposed as the causative mechanism, but greater chelae size, specifically. This would indicate morphological advantages favoring the invasive O. rusticus (Roth and Kitchell 2005). Comparing invasive O. rusticus to native O. sanbornii, Butler (1988) found that chelae differences were more exaggerated in sympatric populations than allopatric (Butler 1988). Overall, greater size appears central to the greater ability for O. rusticus to both compete, and avoid predation, than native O. sanbornii. Further, it is likely these greater size attainments on part of O. rusticus are resultant from a convergence of multiple factors, external and internal, rather than any single specific cause.

As an alternative to exploitative competition or predator mediated species replacement, it has been proposed that O. rusticus may displace native species through reproductive interference (Butler 1988). Crayfish typically demonstrate high conspecific fidelity in mate choice (Butler 1988). However, O. rusticus has been documented hybridizing with related species following introduction (Tierney and Dunham 1982).

Previous work has shown that males of both species will preferentially mate with O. rusticus females (Butler IV and Stein 1985). While this may provide resistance to invasion through decreased invasive recruitment, size selectivity may overcome this resistance. Crayfish mating is highly aggressive, indistinguishable from a fight. Males

27 must be sufficiently large to subdue females in copulatory events (Berrill and Arsenault

1984). Smaller O. sanbornii size in sympatric populations should reduce probability for

O. sanbornii males to negatively impact O. rusticus breeding efficiency, while O. sanbornii females may be more vulnerable (Butler 1988). Consequences of these mating interactions can be severe (Butler IV and Stein 1985), however, because of larger size and increased fecundity of the invasive females, O. rusticus may be better able to persist through such negative impacts (Butler IV and Stein 1985).

Reproductive potential can have significant consequences on success of invasion

(Lanna et al. 2015; Howeth et al. 2016), with high fecundity enhancing invasive potential as populations struggle to overcome stochasticity resisting establishment (Sax and Brown

2000). O. rusticus females, due to size, often have higher reproductive potential than competing natives, including O. sanbornii (Butler IV and Stein 1985). Additionally, in aquatic systems, invasive potential has been associated with a large proportion of females constituting invasive populations (Rumbold et al. 2016), presumably by further enhancing short term population growth needed to establish self sustaining populations.

Further, invasives have been shown to impose adult sex ratio distortion, skewing native population demographics, potentially reducing reproductive potential (Barrientos 2015).

Thus, quantifying adult sex ratio is important in assessing consequences of a biological invasion. However, this study found no evidence to support the hypothesis that sex ratios were different in populations of O. sanbornii across non-invaded versus invaded streams.

Nor was there evidence of differences in sex ratios in O. rusticus comparing sympatric versus allopatric populations. This may be due to low sample size as co-occurring populations were only found within two invaded streams. O. sanbornii populations were

28 skewed slightly towards females in non-invaded streams (1.4 F : 1.0 M) and slightly male within invaded streams (1.6 M : 1.0 F). While O. rusticus slightly favored females in sympatric streams (1.4 F : 1.0 M) versus males in allopatric streams (1.3 M : 1.0 F).

Observable sex ratios may seasonally vary greatly in crayfishes, as reproduction is associated with temperatures and photoperiod (Berrill and Arsenault 1982; Berrill and

Arsenault 1984), and more vulnerable females typically remain hidden after egg extrusion (Berrill and Arsenault 1982). However, surveys in this project were carried out after hatching and release of young-of-year, and these estimates reflect those previously reported for these species (Butler IV and Stein 1985).

Lastly, there was no observable difference in risk of predation between streams dominated by O. sanbornii versus those dominated by O. rusticus. Due to low water levels under summer conditions at locations studied, there was an absence of aquatic predators within sampled areas. There was, however, qualitative evidence of terrestrial predation in the study, as tether traps were discovered removed and carried up the stream bank in two locations (Brushy Fork and Olentangy River at Mingo Park) with presence of chew marks on three of the bobbers found in this manner. Additionally, presence of terrestrial predators (raccoon tracks along streamside and direct observation of predatory birds) was noted at numerous locations. Again, sampling size may have limited the ability to detect differences in predation. Alternatively, terrestrial predation within streams may not be strongly tied to prey densities, rather driven by other environmental characteristics. However, previous work has found that in streams containing high densities of smallmouth bass (Micropterus dolomieu), both species were found to be suppressed by predation (Mather and Stein 1993a). As it was not possible to obtain

29 relative risk in sympatric populations, comparative risk between the two species cannot be addressed in my study. However, comparing consumption of O. rusticus to native O. propinquus and O. virilis, Roth and Kitchell (2005) found that native predators consumed

O. rusticus in fewer proportions that should be predicted through optimal foraging based on relative abundances of the crayfishes alone (Roth and Kitchell 2005). Size selective predation has been associated with replacement of native crayfishes by O. rusticus

(DiDonato and Lodge 1993). It is likely a similar mechanism is at work in the replacement of native O. sanbornii by O. rusticus (Mather and Stein 1993a).

Conclusions

This work supports previous findings regarding the replacement of native crayfish by the invasive Orconectes rusticus whereby there were significant consequences to native O. sanbornii sizes in response to the introduction of O. rusticus. However, no evidence was found to support differences in sex ratios, nor was risk of predation found to be different between the two species. Thus, it is not possible to say from these field observations if species replacement is influenced by predator mediated indirect interactions, such as apparent competition. While O. rusticus appears to be competitively dominant, evidence from previous research does not seem to support direct exploitative competition influencing native fitness, as resources do not appear to be limiting. The negative association between native O. sanbornii sizes and presence of invasive O. rusticus then suggests alternative effects, such as behavioral differences, may be leading to decreased native condition. It appears likely then that species replacement is resultant from a composite of effects leading to greater growth of the invasive, while degrading

30 native health, and lending to possible interference competition leading to localized displacement of native O. sanbornii following introduction.

Tables and Figures

Table 2.1 Streams sampled for crayfish community observations.

Stream Inv Status Order Drainage Brushy Fork NI 2nd Muskingum River Clear Fork NI 2nd Muskingum River Fork River, Mansfield NI 2nd Muskingum River Homer Run NI 2nd Muskingum River Lobdell Creek NI 1st Muskingum River North Fork Licking NI 2nd Muskingum River Raccoon Creek NI 2nd Muskingum River Clear Creek Inv-S 2nd Ohio River Kokosing River Inv-S 3rd Muskingum River Alum Creek Inv-A 2nd Scioto River Big Walnut Inv-A 3nd Scioto River Culver Creek Inv-A 2nd Scioto River Olentangy Campus Inv-A 5th Scioto River Olentangy Mingo Park Inv-A 5th Scioto River Reynolds Run Inv-A 2nd Scioto River Invasion status represents presence of degree of invasion of O. rusticus. NI = Non- invaded: No detection of O. rusticus, Inv-S = Invaded-Sympatric: O. rusticus detected sympatric with native O. sanbornii, Inv-A = Invaded-Allopatric: No detected O. sanbornii, complete replacement by O. rusticus.

Table 2.2 Tethering trial overview.

Stream Date Set Inv. Status Tethers Recovered Notes Brushy Fork 9-Jun-15 Non-Inv 6 6 Brushy Fork 15-Jun-15 Non-Inv 6 0 Flood, Excluded Brushy Fork* 21-Jul-15 Non-Inv 5 5 Brushy Fork 31-Jul-15 Non-Inv 6 6 Clear Fork* 2-May-15 Non-Inv 3 3 Homer Run* 2-May-15 Non-Inv 5 5 Homer Run 23-May-15 Non-Inv 6 6 Homer Run 8-Jun-15 Non-Inv 6 3 Not all tethers recovered Homer Run 15-Jun-15 Non-Inv 6 0 Flood, Excluded Homer Run 21-Jul-15 Non-Inv 6 6 Homer Run 29-Jul-15 Non-Inv 6 6

Homer Run 5-Aug-15 Non-Inv 6 5 1 Unrecovered tether, bobber found chewed Lodbell Creek 5-Sep-15 Non-Inv 6 5 Big Walnut 3-May-15 Invaded 6 5 Not all tethers recovered Big Walnut 22-May-15 Invaded 6 0 No tethers recovered, Excluded Culver Creek 27-Jul-05 Invaded 6 6 Culver Creek 31-Jul-15 Invaded 6 6 Culver Creek 5-Aug-15 Invaded 6 6 Olentangy-OSU Campus 3-May-15 Invaded 6 4 Not all tethers recovered Olentangy-Mingo Park 6-Jun-15 Invaded 6 6 Olentangy-Mingo Park 1-Aug-15 Invaded 6 3 Not all tethers recovered Olentangy-Mingo Park 10-Aug-15 Invaded 6 5 Not all tethers recovered Reynolds Run 5-Sep-15 Invaded 6 5 Not all recovered, 3 bobbers with signs of chewing

In three trials there were less than 6 tethers set (Indicated by *). Not all tethers were recovered following trials. Mortality was calculated only from recovered tethers. Three trails were excluded (See Notes)

Table 2.3 Orconectes cephalothorax length (mm) by species and invasion status.

Overall NI Inv-S Inv-A Species CL SD CL SD CL SD CL SD OS 26.12 4.42 26.75 3.70 21.84 6.31 OR 27.54 5.38 26.52 5.88 27.61 5.34 Total 26.87 4.99 23.55 6.51

Non-Invaded (NI) streams are defined as those with no detectable presence of invasive O. rusticus (OR). Sympatric (Inv-S) streams are those in which both native O. sanbornii (OS) and invasive O. rusticus were found co-occurring. Allopatric, invaded (Inv-A) streams are those which once held native O. sanbornii, however, none were detected alongside invasive O. rusticus populations.

Chapter 3 : Evaluating the effects of indirect interactions on the behavior of a native

and invasive crayfish.

Abstract

While the direct effects of non-native species as predators and competitors have been widely studied the indirect effects of invasive species as novel prey are relatively unexplored, despite their importance on long-term community response to invasion.

Novel prey, as a supplemental resource for native predators, can positively influence predator abundances, resulting in concomitant effects upon native prey. These indirect interactions may induce both consumptive and non-consumptive effects upon native prey, negatively impacting populations. In Ohio, the invasive rusty crayfish (Orconectes rusticus) has been implicated in the gradual decline of native Sanborni’s crayfish (O. sanbornii). My study aimed to evaluate the indirect effects of invasive O. rusticus on native O. sanbornii. Specifically, I used a lab experiment to compare the effect of simulated predation risk on the behavior of native O. sanbornii and invasive O. rusticus.

Additionally, I examined whether the response to predation risk was affected by whether crayfish were competing with a conspecific or heterospecific competitor, and whether the response differed between the two species. Results suggest that both species respond to increased risk of predation by increasing refuge use and decreasing activity when exposed. However, the response to predation risk for the native O. sanbornii differed

35 when competing with a conspecific versus heterospecific competitor. Specifically, when competing against O. rusticus, O. sanbornii did not increase refuge use and was more active as risk increased, compared to when competing against conspecifics. In contrast, the response of O. rusticus to predation risk remained consistent regardless of competitor.

Together these results suggest that native O. sanbornii may face a greater tradeoff between managing risk and avoiding predators when competing with a novel competitor.

In contrast, invasive O. rusticus is able to reduce its risk of predation while still outcompeting O. sanbornii for resources and may experience a smaller trade-off between food and safety than O. sanbornii. These results suggest that understanding the behavioral mechanisms underlying these novel species interactions are integral to predicting the impact of invasive species.

Introduction

Much of the study of biological invasions has focused on the effect of non-native species as predators or direct competitors of native species (Salo et al. 2007; Weis 2011).

However, non-native species may equally affect native species by functioning as novel prey, generating new pathways through which they may impact food webs (Rodriguez

2006; Barber et al. 2008; Carlsson et al. 2009). For example, when incorporated into native predator diets, non-native prey often increase abundances of native predator populations across terrestrial and aquatic systems (King et al. 2006; Pintor and Byers

2015). An increase in the abundance of a native predator population through this novel predator-prey interaction can generate an indirect, negative effect of a non-native species on a native species if the predator consumes both (i.e. apparent competition). The effects of these indirect interactions on community structure have been shown with field

36 observations (Stallings 2008) and experimental manipulations (Peacor and Werner 2001;

Morris et al. 2004). However, with few exceptions (Mather and Stein 1993a; Roemer et al. 2002; Hayes et al. 2009), little empirical work has been done to distinguish between the direct and indirect effects of competition by a non-native species (White et al. 2006).

This resultant imbalance in our knowledge of the effects of non-native species on native food webs limits our ability to estimate the net effects of biological invasions (Parker et al. 1999)

Indirect relationships may take many forms (Werner and Peacor 2003; White et al. 2006). If predator populations are stable, inclusion of novel prey may function to decrease predation upon native prey, due to effects of predator satiation or prey switching, resulting in positive effects on native prey (Holt and Lawton 1994; Abrams and Matsuda 1996). However, assuming predator populations are limited by prey availability, ecological theory predicts a numerical response whereby predator density should increase as a function of an increase in prey density. Thus, reduced predation on native prey might be short lived. Once native predator populations respond to additional resources, an indirect, negative effect on prey populations may be generated via apparent competition (Holt and Lawton 1994). For example, the introduction of the Asian nest mussel (Arcuatula senhousia) has been linked to increased predation upon native bivalves in Southern California estuaries through attraction of native predators (Castorani and Hovel 2015), demonstrating indirect competition between an native and non-native prey. These relationships may have profound effects on community structure with consequences ranging from predator mediated coexistence (Oliver et al. 2009) to

37 asymmetrical responses resulting in elimination of the less fit competitor (Chaneton and

Bonsall 2000; Noonburg and Byers 2005).

Often the indirect effects of elevated predator abundances are described or studied as changes in direct mortality through increased predation, which in turn impacts the abundance of native prey. However, we know that, in general, trait-mediated indirect effects can be as powerful as effects on density (Preisser et al. 2005). For example, increased risk of predation has been shown to decrease prey growth rates in non-lethal manipulations (Peacor and Werner 1997). Sub-lethal effects a predator may impose upon its prey can have significant influence on prey response, such as change in distribution

(Miller et al. 2014; Basille et al. 2015), activity and habitat use (Mather and Stein 1993a), or growth rates (Peacor and Werner 1997).

Across taxa, crayfish among the most threatened by the impacts of biological invasions (Lodge et al. 2012). Competition with invasive crayfish has been shown to impart both direct and indirect effects on native crayfishes. The invasive rusty crayfish

(Orconectes rusticus) demonstrates greater foraging activity and greater growth than its native competitors in Wisconsin lakes (Olsen et al. 1991; Roth and Kitchell 2005), and is competitively superior in bouts for resources (Pintor and Sih 2008). Interference competition has been proposed to explain decline of the noble crayfish (Astacus astacus) following the invasion of the North American signal crayfish (Pacifastacus leniusculus)

(Söderbäck 1994). Within Ohio streams, the decline of native Sanborn’s crayfish

(Orconectes sanbornii) has been attributed to the gradual spread of invasive rusty crayfish. While it has been long proposed that the replacement of O. sanbornii appears to be the ultimate fate of this invasion (Turner 1926), O. sanbornii has shown more resistant

38 to direct competition than other, related crayfishes facing the spread of O. rusticus

(Mather and Stein 1993a). Because of this, this system provides a useful opportunity to study the indirect effects of a novel prey on a native competitor.

Here, I aimed to evaluate the effect of increased predation risk on the competitive interactions between native O. sanbornii and invasive O. rusticus. There were three objectives associated with this study. First, I simulated the increased risk of predation that may result from a numerical increase in a native predator population to a novel prey to quantify the sub-lethal effects of increased risk of predation on native and invasive crayfish behavior. Second, I evaluated whether the effect of predation risk differed depending on whether crayfish were competing with a conspecific or heterospecific competitor and whether this effect differed between the two species. To this end, I measured the behavioral responses (See Table 3.1 for definitions) and aggressiveness of native Orconectes sanbornii and invasive O. rusticus crayfishes under different levels of predation risk and when competing with a heterospecific or conspecific competitor.

Through these treatment combinations I addressed the following hypotheses. First, I predicted that increased risk of predation will result in increased use of refuge but decreased activity, foraging, movement and frequency of competitive interactions, and decreased aggressiveness of both crayfish species. However, as invasive species have been shown to demonstrate greater boldness (defined here as willingness to engage in risk prone behavior) (Chapple et al. 2012; Juette et al. 2014), I predicted that invasive O. rusticus will not change their behavior as much as native O. sanbornii under predation risk. Second, I predicted that the effect of predation risk on native O. sanbornii will depend on the identity of its competitors. Specifically, I predict that when competing with

39 an invasive O. rusticus, O. sanbornii’s behavior will be different in comparison to when it’s competing with a conspecific. In contrast, I do not expect the response of O. rusticus to predation risk will differ when competing with a heterospecific or conspecific competitor. The difference in predicted response of O. sanbornii and O. rusticus is expected to reflect a trade-off in managing risk associated with elevated predation risk and the threat of a novel competitor.

Methods

Study System

Orconectes rusticus (Faxon, 1884), native to the Great Miami and Scioto River basins of southwestern Ohio, southeastern Indiana and northern Kentucky, have obtained the widest distribution of any Ohio crayfish (Thoma and Jezerinac 2000; Lodge et al.

2012). Largely due to release of live individuals from fishing bait buckets (Mather and

Stein 1993a), O. rusticus has been introduced into at least 20 states and all 5 of the

Laurentian Great Lakes (Taylor and Schuster 2004; Lodge et al. 2012). It has been implicated in the decline of the native Orconectes sanbornii (Girard, 1852) (Olsen et al.

1991), a native to the Ohio River drainage of northeastern Kentucky and extending into eastern Ohio along the Muskingum basin, the Scioto and Lake Erie tributaries (Thoma and Jezerinac 2000).

Crayfish used in the experiment were collected between July and August 2014 via seining riffle sections of Ohio streams. Native O. sanbornii (n = 54) individuals were collected from North Fork of the Licking River (Knox and Licking Counties). The

Licking River is a central Ohio tributary of the Muskingum River. While the invasive O. rusticus was discovered in the Licking River drainage sometime between 1926 and 1967

(Butler 1988), there is no evidence of invasion in North Fork Licking River. Therefore, all O. sanbornii are assumed naïve to invasive O. rusticus. O. rusticus (n = 54) individuals were collected from Big Walnut Creek (Delaware Co.), north of Hoover

Reservoir, and Olentangy River (Franklin Co.), south of the Dodridge Road low-head dam. Big Walnut Creek is part of the Scioto River system, flowing southward from

Delaware Co. into the Hoover Reservoir (constructed 1955) before discharging into the

Scioto River in southern Franklin Co. Olentangy River is a Scioto River tributary originating in Crawford Co., flowing southward into Delaware State Park Reservoir

(constructed 1951) before merging into Scioto River in Columbus, Ohio. All individuals were identified to species, sexed and sized [cephalothorax length (anterior tip of rostrum to posterior edge of thorax); range: O. sanbornii=18.24-28.63 mm, mean=23.22 mm; O. rusticus=17.57-30.40 mm, mean=21.75 mm]. Females and non-reproductive, Form-I males, were used in experiments.

Animal Housing & Experimental Set-up

All individuals were housed under laboratory conditions with a 13-hour photoperiod to mimic summer conditions in Ohio. Prior to behavioral assays, individuals were housed in individual 500-ml perforated containers placed within 39L (88.6cm x

42.2cm x 15.6cm) aerated tanks (15-18 individuals per tank). Therefore, all individuals were physically separated from each other. Individuals were fed 2-3 pellets (Hikari Crab

Cuisine, Hikari Brand Fish Food) every other day. Behavioral assays were conducted within 40-L aquaria aerated with a single airstone. Each tank was covered on all sides with black plastic to prevent outside influence on behavior. Along the front face of each tank a small viewing window was cut to allow viewing during experimental intervals.

Experimental Protocols

I used a 2x3 factorial design to quantify the effects of predation risk, competitor identity, and the interaction between competitor identity and risk on the behavior (e.g. refuge use, foraging, aggression) of native O. sanbornii and invasive O. rusticus (N = 6 replicates per treatment combination). Specifically, competitor identity was evaluated by manipulating the species which the focal individual interacted with (conspecific versus heterospecific competitor). The effect of predation risk was manipulated by simulating three levels of predator attack (None-no attack, Low-5 second attack, High-20 second attack) with a model fish (see below). Multiple behavioral responses of the crayfish were measured including the proportion of time using refuge, active, foraging, moving and engaged in agonistic interactions (see Table 3.1 for definitions).

Prior to the start of the experiment focal individuals of each species were semi- randomly assigned to a treatment pair (either a single conspecific or heterospecific competitor). Because crayfish aggression and dominance is strongly related to size

(Bovbjerg 1956; Mather and Stein 1993a), pairs were matched within 3-mm of cephalothorax length. While it was not possible to restrict all experimental units to a single sex, effort was made to utilize an equivalent number of sex pairing combinations per treatment combination (Table 3.2). Pairs were assigned to an experimental tank and given a 24-hour acclimation period during which they were freely interacting.

Experimental pairs were randomly assigned to a predation risk treatment.

Predation risk was simulated by mimicking a predator attack with a 7.62-cm plastic model fish (Micropterus spp.) fixed to a handle. Low predation risk was simulated by chasing and attacking (physically touching the model to the individual) for a 5 second

42 period of time. High predation risk was simulated by chasing and attacking the crayfish for a 20 second period of time, during which each crayfish received 3 chase/taps. If an individual crayfish was occupying refuge, the model was presented to both openings of the shelter, as it was not possible to determine crayfish orientation.

Trials were video recorded to minimize observer effects on behavior. Each trial lasted 10 minutes (600 seconds). Each trial began with the addition of 12-14 bloodworms suspended in 5-ml of water. Thus, daily crayfish feeding coincided with the presentation of the chemical food cue to initiate crayfish activity. Crayfish behavior was recorded for 5 minutes. Then, after the initial 5 minutes (300 seconds), the predation risk treatment was applied to the experimental tank, as described above. Crayfish behavior following the application of the predation risk treatment was recorded for an additional five minutes. Therefore, each trial was divided into a 5 minute pre and 5 minute post predation risk treatment observation period. These 10 minute trials were repeated five times (once daily) for each experimental pair to test for an effect of previous treatment applications over time. Crayfish were held in their test aquaria over the duration of the six days. Each experimental morning, all tanks were siphon vacuumed free of organic material and received a 30% water change to prevent accumulation of additional food, as crayfish are opportunistic omnivores and will consume detrital matter.

Quantifying Crayfish Behavior

Crayfish behavior was recorded every 5 seconds for the duration of the 600 second trial following the One-Zero Interval sampling method (Martin and Bateson

2007). This 5 second duration was chosen to reduce the frequency with which multiple, contradictory behaviors might occur within a single interval. Following video analysis,

43 the number of intervals in which a given behavior was displayed was summed and converted to an estimated proportion of time for each behavior, standardized to 300 seconds (the pre/post duration).

Aggression has been shown to be indicative of a competitive advantage

(Vorburger and Ribi 1999), thus, may be informative in assessing dominance in direct interspecific competition. Additionally, aggression may also prove an additional useful indicator of indirect effects of competitive arrangement, particularly under the influence of sub-lethal effects of predation risk. Aggressiveness was measured by quantifying the number of physical interactions observed between individual crayfish, the proportion of time crayfish spent in physical interactions and by scoring the level of aggressiveness observed during an interaction.

Aggression scoring (Table 3.3) followed procedures in previously published literature (Karavanich and Atema 1998). An interaction was initiated whenever individuals came within 1.5 body lengths distance. In the event that distance could not be determined (for example, one individual was masking the view of the other), any obvious agonistic behavior constituted indication of an interaction. Interactions were terminated when individuals separated to greater than 1.5 body lengths or individuals maintained no obvious agonistic behavior for a complete 5second interval. In the event an individual exhibited multiple agonistic behaviors within an interval (no more than 2 were observed); the interval score was taken to be the average of the 2 behaviors. Aggression scores were then calculated as the average of all interval scores for that individual, giving two averaged scores per individual/trial (one each for pre- and post- predator treatment application). Average aggression scores were then used to 1) determine the dominant

44 individual for each observational period before and after application of simulated predation treatment and 2) compare treatment effects on mean aggression for O. sanbornii and O. rusticus. In the event of a tie (equal average aggression score), no individual in the pair was declared dominant.

Statistical Analysis

To test for the effect of predation risk, competitor identity and the interaction between predation risk and competitor identity on each of the behavioral responses measured (proportion of time using refuge, active, foraging, moving, engaged in interactions), I used zero- and zero-and-one beta regressions (Ospina and Ferrari 2010).

Beta regression models are tailored for use in modeling responses that assume values along the standard unit interval (0,1) (Ferrari and Cribari-Neto 2004). In general, a beta regression provides several advantages when data are expressed as a proportion of time in a set interval in contrast to standard linear regressions of transformed data (Ferrari and

Cribari-Neto 2004). However, beta regression models do not allow for response values falling at exactly zero or one. Therefore, if response variables include values at zero and/or one (Table 2.5), modified versions of the beta regression, the zero inflated (BEZI) or zero-and-one inflated beta regression (BEINF) (Ospina and Ferrari 2010) should be used. These models are a composite of either three (for zero-and one-inflated) or two

(zero-inflated) sub-models. In a zero-and-one inflated beta regression, a mixed continuous-discrete distribution is used to model responses. For values along the continuous interval, a beta distribution is used for regression; otherwise a Bernoulli distribution is used. These models have been used to successfully model ecological data constituting proportions with values at zero and/or one (Scott-Hayward et al. 2014;

Kuijper et al. 2015). Analyses were performed with R statistical software (R Core Team

2015) using Generalized Additive Models for Location, Scale, and Shape (GAMLSS)

(Stasinopoulos and Rigby 2007; Stasinopoulos et al. 2008).

GAMLSS are semi-parametric regression type models designed to overcome the limitations associated with linear regression methods and generalized additive models

(Stasinopoulos and Rigby 2007). In GAMLSS, the response variable is assumed to follow a parametric distribution but modeling of the distribution parameters, as functions of the explanatory variables, allows for use of non-parametric smoothing functions.

Additionally, the parametric distribution does not need to belong to the exponential family distribution, favoring a generalized family distribution, allowing for modeling of highly skewed or kurtotic distributions, including both continuous and discrete distributions. Thus, the GAMLSS external package provides a framework for user implementation of the zero-inflated family procedures within R.

GAMLSS models assume that independent observations , with probability

density function , are conditional on a vector of four distribution parameters,

. Each of these four parameters may be functions of the explanatory variables (Stasinopoulos and Rigby 2007). Parameters and represent location (i.e. mean, median, etc.) and scale, respectively. The two remaining parameters are shape parameters, representing skew and kurtosis. It is noted that a user does not necessarily need to define all parameters (Stasinopoulos and Rigby 2007).

Model parameters are estimated using Maximum Likelihood. Default link functions were used; µ-logit, σ-logit, ν-log, τ-log. Parameter estimates were calculated from the inverse of the respective link functions. However, σ-level of the model was not

46 parameterized based on explanatory variables, but instead given a constant. This was due to the inability of the model to converge to a solution when attempting to fit explanatory variables at this level. Variable selection was performed by using Akaike’s Information

Criteria (AIC) (Akaike 1973; Burnham et al. 2011). Backward stepwise variable selection was used to exclude non-treatment covariates and insignificant terms, when appropriate. While the GAMLSS model carries few statistical assumptions, multi- collinearity may adversely affect interpretation. Thus, subsequent to backward variable selection, forward stepwise selection was used to build a model from a reduced

(intercept) model to the final model from the previous, backward selection to assess for any issues of collinearity among predictors, evidenced by unjustified inflation of error terms. In the event AIC values would dictate removal of a significant parameter of interest (i.e. treatment parameter), selection favored inclusion of the parameter.

Retention of such parameters of a priori interest were deemed justified by arguments in previous literature (Burnham et al. 2011). Because individuals were repeatedly observed for five consecutive days, all models contained a random effect for the repeated measure on the same individual and a parameter to measure the effect of the day of observation on behavior. Also, as noted previously, crayfish behavior was measured for five minutes before and after the application of the predator treatment. Differences in behavior post- predator application represent the acute response to predation risk. Differences in behavior immediately prior to the application of the predation risk treatment would suggest a carry-over, or potentially chronic effect of predation risk on crayfish behavior from previous applications of the risk treatment. Responses recorded before and after predation risk treatments were modeled independently for each response variable.

Finally, a three-way ANOVA was used to test for difference in average aggression scores between O. sanbornii and O. rusticus with predation risk and competitor identification as factors. Analyses were performed with R statistical software (R Core Team 2015).

Model Interpretation

Because the predictors are categorical, regression parameters assume binary place-holder variables in the analysis (i.e., dummy variables). Model parameter estimates must therefore be interpreted with these contrasts in mind. Each parameter estimate is representative of the mean difference in response between the comparison group and the reference group, for example, O. rusticus (comparison group X = 1) compared to O. sanbornii (reference group X = 0). The intercept in the model is then indicative of the baseline mean (i.e., the average response of the overall reference level, when all dummy variables = 0). In this model, the overall reference is representative of Species = O. sanbornii, Predation risk = Zero Risk and Competitor = Conspecific. Interaction terms are interpreted as the mean difference in response to one parameter based on the level of the associated 2nd parameter. For example, an interaction between Species and Predation

Risk indicates the difference in the effect of increased predation risk between species

(i.e., the mean difference in response between O. rusticus [comparison group] and O. sanbornii [reference group] to increased risk). Interaction terms must be interpreted in relation to main terms; a non-significant interaction term indicates a lack of evidence of a difference in response between the comparison and reference group. In other words, a non-significant interaction between Species and Predation Risk indicates that the response of O.rusticus is not significantly different from that of O. sanbornii. For example, should O. sanbornii demonstrate a significant response to Predation Risk, there

48 is then evidence (by lack of difference between species) that O. rusticus responded similarly. However, interactions cannot be interpreted in isolation and it is not definitely possible to state that O. rusticus demonstrated a significant response without assessing the model with alternative contrasts (models with alternative contrasts are provided in

Appendix B).

Results

Predation Risk

Post-predation risk treatment

Final model parameters for each behavioral response are shown in Tables 3.7 and

3.8. Mean proportion of time (µ level of the model) in each behavior is given in Table

3.5. Again, observations following application of the predation risk treatment represent the immediate response to a predator. Low predation risk did not change the proportion of time that O. sanbornii (Est. = 0.310, P = 0.286) or O. rusticus (Est. = -0.342, P =

0.400) spent in refuge (Table 3.9) relative to the no predation risk treatment. However, under high predation risk O. sanbornii significantly increased the proportion of time it spent in refuge compared to no predation risk (Est. = 1.285, P < 0.0001), but O. rusticus did not change its use of refuge (Est. = -1.56, P = 0.0004). Rather, O. rusticus spent a significantly greater proportion of time in refuge than O. sanbornii across all predation risk conditions (Est. = 1.25, P = 0.0001).

Similarly, the effect of predation risk on activity (Table 3.11) depended on level of risk and species identity. At low risk, both O. sanbornii (Est. = -0.507, P = 0.006) and

O. rusticus (Est. = 0.021, P = 0.934) decreased activity to the same degree compared to

49 zero risk. However, at high risk of predation, only O. sanbornii decreased activity compared to zero risk control (Est. = -0.863, P < 0.0001). There was no change in O. rusticus compared to O. sanbornii (Est. = 0.800, P = 0.002). Additionally, low predation risk coincided with both a decrease in the probability of zero, or no activity, (Est. =

-1.904, P = 0.004) and a decrease in the probability of 100% activity (Est. = -1.879, P =

0.004) for both species. At high risk, there was no change in the probability of zero activity (Est. = -0.769, P = 0.085), however there was a significant decrease in the probability of 100% activity for both species (Est. = -1.562, P = 0.008).

The proportion of time spent foraging (Table 3.13) depended on the level of risk and species identity, but also on the day of observation. In general, both species reduced the mean proportion of time spent foraging at both low and high risk, compared to zero risk control. Under low predation risk, both O. sanbornii (Est. = -0.395, P = 0.004) and

O. rusticus (Est. = 0.035, P = 0.870; indicating lack of significant difference in response compared to O. sanbornii) decreased foraging activity to the same degree relative to zero risk control. Under high risk of predation, O. sanbornii (Est. = -0.725, P < 0.0001) reduced foraging activity to a significantly greater degree than O. rusticus (Est. = 0.551,

P = 0.025). Both species demonstrated a trend to increase foraging with each subsequent day of trial (Est. = 0.13, P = 0.0001). Additionally, O. rusticus demonstrated a greater probability to demonstrate zero foraging in a trial (Est. = 0.493, P = 0.036). At low risk, there was no difference in the probability of zero foraging for either species (Est. = -

0.132, P = 0.656), however, at high risk of predation, there was an increase in the probability of zero foraging in both species (Est. = 0.854, P = 0.003).

In general, both species reduced their proportion of time spent moving across both predation risk treatments, although O. rusticus reduced its movement to a smaller degree than O. sanbornii (Table 3.15). Specifically, at low risk, both O. sanbornii (Est. = -0.443,

P = 0.006) and O. rusticus (Est. = -0.171, P = 0.361) decreased proportion of time moving compared to zero risk. At high predation risk, O. sanbornii significantly decreased movement (Est. = -0.881, P < 0.0001), as did O. rusticus although to a lesser degree (Est. = 0.592, P = 0.002). There was a decrease in the probability of zero movement at both low (Est. = -1.407, P = 0.001) and high risk of predation (Est. = -

0.704, P = 0.049).

Pre-predation risk treatment

Observations made prior to the application of simulated predation represent the potential carryover, or chronic effect, of previous risk applications. The carryover effect of the predation risk treatment was dependent upon the species identity and level of risk.

Refuge (Table 3.10) use by O. sanbornii was not affected by the prior application of the low (Est. = 0.250, P = 0.42) or high (Est. = 0.378, P = 0.314) predation risk treatments compared to zero risk. However, O. rusticus refuge use decreased at both predation risk levels in comparison to the zero risk control (Low: Est. = -1.072, P = 0.018, High: Est. =

-1.076, P = 0.025). Results again showed that O. rusticus utilized refuge for a greater proportion of time than O. sanbornii (Est. = 1.156, P = 0.0001) at zero risk.

Additionally, prior applications of the predation risk treatment resulted in decreased foraging which varied by risk level (Table 3.14). While low risk did not produce a significant effect (Est. = -0.201, P = 0.093) compared to zero risk, high risk

51 resulted in significantly decreased foraging (Est. = -0.277, P = 0.019). These results were consistent for both species.

Competition

The effect of competitor identity on behavior varied between species and specific behaviors. Model results for the presence of a heterospecific competitor from observations immediately following predation risk treatments reveal that refuge use significantly increased in the presence of a heterospecific competitor relative to conspecific (Est. = 0.363, P = 0.029) for both species (Table 3.9). However, the effect of the identity of the competitor on activity was dependent on species. O. rusticus decreased its activity (Est. = -0.719, P < 0.001) (Table 3.11) and movement (Est. = -0.697, P <

0.0001) (Table 3.15) in the presence of O. sanbornii relative to its activity with conspecifics. The activity of O. sanbornii was greater in treatments competing with O. rusticus versus those with conspecifics (Est. = 0.411, P = 0.005). Finally, both species decreased the proportion of time spent interacting (Table 3.17) with heterospecific competitor relative to a conspecific competitor (Est. = -0.175, P = 0.05).

As previously mentioned, observations made prior to each day’s predator risk treatment represent a carry-over, or potentially chronic, effect of risk. Model results for the effect of a heterospecific competitor under this carry-over effect reveal an increase in refuge use seen in both species, however, this effect was not significant at the α = 0.05 level (Est. = 0.369, P = 0.054) (Table 3.10). Similar to observations made following risk treatments, the effect of the identity of the competitor on activity was, again, dependent on species. O. rusticus decreased activity in the presence of O. sanbornii (Est. = -0.694,

P = 0.001) (Table 3.12). However, there was no significant change noted in overall O.

52 sanbornii activity in the presence of O. rusticus versus an O. sanbornii competitor (Est. =

0.237, P = 0.126). Yet, there was a suggestive, though not significant, increase in O. sanbornii movement (Est. = 0.318, P = 0.054) (Table 3.16).

Interaction of Predation Risk and a Heterospecific Competitor

Across all response variables, there was only one instance of an interaction effect between risk and presence of a heterospecific competitor. While predation risk alone caused a decrease in movement (for O. sanbornii) when competing with conspecifics, the presence of a heterospecific competitor caused movement to increase during both low risk (Est. = 0.481, P = 0.01) and high risk (Est. = 0.442, P = 0.022) (Table 3.15).

Conversely, while there was evidence of a carry-over effect of predation risk on movement, there was a significant interaction decreasing movement when risk was high and in the presence of a heterospecific competitor (Est. = -0.414, P = 0.041) (Table 3.16).

Individual Aggression

Post-predation treatment

Mean aggression was 0.774 ± 1.21 (O. sanbornii = 0.925 ± 1.16 SD; O. rusticus =

0.615 ± 1.24 SD). O. rusticus exhibited significantly lower aggressiveness than O. sanbornii (Est. = -0.5915, P = 0.029) (Table 3.20) when there was no, or low, risk of predation. However, when risk was high, a cross-over effect occurred whereby O. sanbornii decreased aggression (Est. = -0.785, P = 0.006) and O. rusticus increased aggression (Est. = 0.935, P = 0.005).

Pre-predation treatment

The chronic effect of predation risk upon aggression depended on species and the effect of competitor identity depended on predation risk (Table 3.21). At low predation risk, O. rusticus demonstrated significantly lower aggression than O. sanbornii (Est. = -

1.021, P = 0.007) (Table 3.22). In the presence of heterospecific competition and high predation risk, aggression was intensified for both species (Est. = 1.1273, P = 0.002), however, the effect was more pronounced for O. rusticus (Figure 3.6).

Discussion

Overall, results suggest that the response of crayfish to increased predation risk and competitor identity depended upon species identity and the level of risk. As predicted, high predation risk generally increased the use of refuge, and decreased activity, foraging and movement by both species of crayfish. However, the effect of a heterospecific competitor relative to a conspecific on behavior was highly dependent upon species. O. sanbornii increased activity and movement in response to a heterospecific competitor (vs. a conspecific). O. rusticus demonstrated an increase in refuge use with decreased activity and movement. Finally, there was an interaction between risk of predation and competitor identification, with presence of heterospecific competition causing an increase in movement, even under heightened risk of predation.

However, this too was dependent upon species, affecting only O. sanbornii. Individual aggression was also shown dependent upon species and the level of risk. While O. sanbornii was more aggressive than O. rusticus at zero and low predation risk, at high risk levels O. rusticus increased its aggressiveness and was significantly more aggressive than O. sanbornii. Overall, these results suggest that the response to predation risk and

54 competitor identity differed between the species. In general, while increased risk increased O. sanbornii refuge use while decreasing activity. However, when in the presence of an O. rusticus competitor, O. sanbornii maintained lower refuge use and increased activity than when competing against a conspecific. Conversely, O. rusticus responded to increased risk of predation and the presence of a heterospecific competitor in similar ways.

Predation risk caused predictable changes in behavior; however, the invasive O. rusticus generally exhibited more frequent predator avoidance behaviors regardless of risk. For example, whereas O. sanbornii increased its use of refuge under predation risk,

O. rusticus already spent a higher proportion of time in refuge regardless of risk treatment. While this might seem contrary to expectations given that O. rusticus is frequently cited as a bold and aggressive species, our result is not without precedent.

Previous work comparing the behavior of invasive O. rusticus populations to native

Orconectes virilis, from a population naïve to O. rusticus, showed that O. rusticus demonstrated 24% greater refuge use than its inexperienced competitor (Hayes et al.

2009). Greater refuge use by O. rusticus could also have been a difference between species in response to a novel environment or structure. For example, Chapple (2011) found that the invasive delicate skink, Lampropholis delicata, made greater use of novel structures as refuge in comparison to the non-invasive L. guichenoti (Chapple et al.

2011). This would be an advantageous response to predation in a novel environment.

Previous research has shown that in field observations, the presence of smallmouth bass

(Micropterus dolomieu) will suppress the abundance of both Orconectes crayfishes within invaded ranges, though greater preference was given to consuming O. sanbornii

(Mather and Stein 1993a). This preference was described as a result of the greater sizes

O. rusticus obtains in situ. However, greater use of refuge in novel environments may also provide an establishing population a necessary buffer from predation until individuals reach sizes large enough to allow them to escape predation (i.e., refuge size).

Alternatively, O. sanbornii’s lower use of refuge may be due to alternative response options available, as individuals were observed flattening into a lower profile posture or retreating into corners of the aquaria. All of these options may be more advantageous, particularly in a risky environment isolated from shelter, than attempting to locate, and have to potentially compete for, refuge (Mather and Stein 1993a).

Risk-induced changes in refuge use coincided with decreases in the proportion of time spent engaged in other activities, such as foraging or movement. Following expectations, activity levels for O. sanbornii were most affected by predation risk, which decreased uniformly across foraging, movement and interaction. Conversely, while O. rusticus did demonstrate a decrease in activity levels during low risk of predation, at high risk, O. rusticus activity was not significantly different than under zero risk. Overall, these results follow expected crayfish responses to immediate threat (Stein and

Magnuson 1976). “Risky times hypothesis” (Creel et al. 2008) dictates that an individual should respond to immediate risk with increased vigilance, at the cost of other activities, such as foraging or reproduction. However, my results do contrast previous work on these species. O. rusticus have shown to be more active foragers (Butler IV and Stein

1985) that consume greater quantities of resource than their competitors (Olsen et al.

1991), including O. sanbornii (Mather and Stein 1993b). It must be noted that while in my study, O. rusticus did decrease activity, there was not an associated change in time

56 spent foraging. In fact, under the highest risk, O. rusticus spent a greater amount of time foraging than O. sanbornii, the latter significantly decreasing all activities in response to risk. This is a significant result, as greater foraging efficiency has been proposed in explaining the greater sizes obtained by O. rusticus in field observations (Butler IV and

Stein 1985). There is the possibility that high risk, as defined in these trials, is actually more approximate to perceived risk in situ, and the results here are more indicative of reality. Nevertheless, these results demonstrate the potential for less energetically costly responses to risk for O. rusticus, which may contribute to an overall pattern of larger sizes demonstrated by the invasive crayfish.

This growth itself may be a defense mechanism (Mather and Stein 1993b), particularly in aquatic systems in which predators are typically gape limited (Stein and

Magnuson 1976; Mather and Stein 1993a). Faster growth rates and greater sizes have been linked to reduced crayfish susceptibility to predation (Mather and Stein 1993a).

Alternative mechanisms allowing O. rusticus to obtain greater sizes in situ have also been proposed, such as earlier juvenile escape from females (Corey 1988). Additionally, O. rusticus have demonstrated a greater preference for more profitable food sources (Mather and Stein 1993b). Results in this study add to work in showing that invasive O. rusticus populations are more willing to engage in foraging behavior, while heightened risk impedes this in other related species.

Presence of a heterospecific competitor resulted in opposing effects across species. Whereas O. sanbornii decreased refuge use while increasing activity and movement, O. rusticus responded by increased use of refuge coinciding with a significant decrease in activity. Why such a difference, particularly if O. rusticus is the more bold

57 and aggressive competitor? Observations of O. sanbornii against a conspecific competitor can be thought of as demonstrative of the normal response to environmental stimuli, such as predation risk. In this light, the effect of a heterospecific competitor becomes pronounced. O. rusticus represents a disruptive presence to the appropriate response of O. sanbornii to environmental conditions. Similar effects have been proposed with other novel crayfish interactions. For example, the signal crayfish

(Pacifastacus leniusculus), a native to Northwest United States and British Columbia,

Canada, was introduced into Sweden in the 1960’s, where it has now become widespread

(Söderbäck 1994), and has been associated with the gradual decline in Sweden’s only native crayfish, Astacus astacus. Here, evidence points to enhanced predation on the native crayfish resulting from interference competition for limited shelter. When comparing O. rusticus to O. sanbornii at relative sizes reflective of natural conditions, presence of larger juvenile O. rusticus were associated with a reduced frequency for O. sanbornii to shift from pools to safer riffle habitat in response to presence of predators

(Mather and Stein 1993a; Mather and Stein 1993b). Conversely, in a study comparing predator induced mortality of O. virilis across lakes invaded by O. rusticus, Peters and

Lodge (2013) found that O. virilis was able to persist in lakes with reduced cobble habitat and increased density of vegetated habitat (Peters and Lodge 2013). They were able to further quantify that O. virilis was not able to persist in any lake with greater than 33% cobble habitat. In absence of the congeneric competitor, both species will use all habitats available. However, when sympatric, O. rusticus dominates the cobble habitat.

Coexistence of the two species is a result of the capacity for O. virilis to shift to vegetated habitat to escape predation (Peters and Lodge 2013). Given this, it is possible that O.

58 sanbornii response to an O. rusticus competitor is also driven by an attempt to avoid costs of competition with a potentially superior competitor. Interestingly, however, was the large number of observations in which neither individual used any amount of refuge.

In fact, 63% of observations produced no refuge use. In heterospecific trials, this was split nearly even between the two species. Further, qualitative observations suggest a tendency for O. sanbornii individuals to attempt to climb experimental aquaria, indicating that O. sanbornii may be more driven to seek exclusive habitat than compete for established territory. This may, unfortunately, incur greater fitness costs as movement enhances risk of predation (Mather and Stein 1993a).

Two key points may be gleaned from incorporating my results with those of previous researchers in this system. First, crayfish predation is unquestionably linked to size selective predatory behavior. O. rusticus benefits from reduced predation that results from a combination of effects which lend to greater sizes attained than their native competitors. O. rusticus, intentionally or unintentionally, exhibits behaviors which favor energy efficiency, while at the same time reducing risk of predation. Second, this increased efficiency lends to greater competitiveness against natives via greater size.

This allows O. rusticus to further to tip the scales by dominating valuable shelter, possibly enhancing mortality on native crayfishes by disrupting their ability to respond to predation.

While interference competition is likely a significant driver in O. sanbornii replacement by O. rusticus, native predators are highly adapted to consuming both species (Mather and Stein 1993a). When novel prey function additively to native prey, that is, as a supplemental resource, native predator populations should respond

59 numerically with increased abundances (Pintor and Byers 2015). In the event that predation risk remains asymmetrical, favoring consumption of natives through predator choice or competitive influence of the invasive (such as interference competition) then the increase in predator abundances may exacerbate declines of native prey (i.e. apparent competition or apparent amensalism) (Chaneton and Bonsall 2000; Noonburg and Byers

2005). Further, these indirect effects of novel prey on native prey densities may also coincide with sub-lethal trait changes, such as reduced foraging in response to elevated perception of risk (Werner and Peacor 2003). Given the importance of size in direct competition, escape from predation, and reproductive potential, these sub-lethal effects may be important drivers in species replacement, functioning to reinforce effects of direct competition. However, indirect competition, such as apparent competition, presents a pathology that is often indistinguishable from direct competition, such as interference in shelter acquisition.

Conclusions

Through controlled experimental manipulation, this study provides findings regarding the response of a native prey species to trait effects related to predation risk and competition with a novel competitor. Here, I simulated the increased risk of predation which may be associated with introduction of a novel prey. Increased predation risk alone produced predictable responses in the native and invasive prey, with both seeking to minimize the threat of predation by increasing refuge use and decreasing activity when exposed. However, competition against a heterospecific produced dichotomous effects between species; functioning to increase the risk imposed upon native prey while functioning synergistically to enhance invasive fitness. Given the results in Chapter 2,

60 more work is needed to confirm an indirect relationship of predator mediated competition, such as apparent competition, in this system. However, the results presented here demonstrate behavioral mechanisms by which the effect of predation risk, competition with a novel competitor, and the interaction between these two forces, may influence the replacement of native Orconectes sanbornii in the face of invasion by O. rusticus. Further, these results function to unify previous work by establishing species- specific mechanisms by which indirect effects of risk and competition may lead to the reported effects of direct competition, such as interference in predator avoidance or exploitation of resources. In this way, this work aids in providing a more balanced understanding of the role of biological invasions in shaping affected communities.

Further it reiterates the need to consider behavioral effects, and the complex network of species interactions which may give rise to them, when seeking to assess the potential threat of a biological invasion.

Tables and Figures

Table 3.1 Definition of behaviors in experimental trials.

Refuge: Utilizing provided refuge. An individual was considered exposed (not in refuge) if either the carapace was fully exposed or if individual was exposed enough to demonstrate clear actions (such as foraging) Activity: Any activity, regardless of nature (foraging, movement, cleaning, etc.)

Foraging: Demonstrating any action indicative of foraging. Visual confirmation of food in chelae was not determined as view could be obscured (crayfish facing away) and food particles may be too small to be captured by video (for example, items removed from carapace and eaten during cleaning activity). If occurred in conjunction with movement, foraging was considered the primary behavior and that interval was scored as foraging

Movement: Any directed motion (walking or swimming) of greater than 1.5 times individual's body length. If occurred in conjunction with foraging, movement was considered a subordinate behavior and was not counted during that interval

Interaction: Agonistic interactions. Interactions were considered any occurrence in which pair moved to within 1.5 body lengths from one another. Interaction was ruled concluded when either individuals separated beyond 1.5 times body length or individuals were within interaction distance but a full interval (5-sec) has transpired with no visible agonistic behavior (see Table 3.4 for agonistic behaviors) Responses were proportion of time during observation periods that an individual demonstrated any given behavior. Behaviors may occur simultaneously.

Table 3.2 Sex pairing allocation per treatment.

Treatment MM FF MF OS, Conspec, Zero 3 1 2 OS, Conspec, Low 2 2 2 OS, Conspec, High 2 2 2 OR, Conspec, Zero 2 2 2 OR, Conspec, Low 2 2 2 OR, Conspec, High 3 2 1 Hetero, Zero 2 2 2 Hetero, Low 2 1 3 Hetero, High 1 2 3 (OS=O. sanbornii, OR=O. rusticus, Risk=Zero, Low, or High, MM=Male-Male, FF=Female-Female, MF=Male- Female).

Table 3.3 Definition of agonistic levels (Modified from Karavanich and Atema 1998). Values on the negative scale indicate submissive behavior. Higher values represent an escalation of aggression.

Level Behavior Definition -2 Fleeing Jumping away, tail flip -1 Avoiding Low posture, submissive posture, turning away, walking away, walking backward 0 Neutral Separation, non-interactive, unaware 1 Initiation (No physical Turning toward, facing, approaching, following contact 2 Threat display (No physical High posture, claw presentation (up and/or contact) forward), antenna point, lunge 3 Physical contact (Claws not Antenna tapping, antenna touching, antenna used to grasp) whipping, claw touching, claw tapping, claw pushing, claw boxing 4 Physical contact (Claws Claw holding, claw lock grasping) 5 Unrestrained use of claws Claw ripping, claw snapping See Table 3.4 for definitions of behaviors

Table 3.4 Definition of agonistic behaviors for crayfishes (Modified from Karavanich and Atema 1998).

Antenna point: holding antenna(e) parallel to body axis and directed towards opponent Antenna tapping: antenna(e) repeatedly tapping opponent's antenna(e) or body Antenna touching: antenna(e) continuously touching opponent's body Antenna whipping: lashing opponent's body with antenna(e) in sweeping motion Approaching: Walking toward opponent (speed less than 1 body length/5 s) reducing separation to within 1.5 body length Claw boxing: rapid jabbing or touching of chela(e) to opponent's body Claw holding: grasping with chela(e) to opponent's body or legs Claw lock: chela(e) used to grasp opponent's claws Claw presentation: either/both large chelae(e) directed forward, up, or spread extending up and out from the body Claw pushing: pressing chela(e) against opponent's body and walking forward or attempting to displace opponent Claw ripping: motion in which crayfish grasps a part of opponent’s body with its chela(e) and pulls back rapidly or jumps away Claw snapping: rapidly opening and closing chela(e), directed towards opponent's body Claw tapping: discontinuous touching of chela(e) to opponent's body Claw touching: continuous contact of chela(e) to opponent's body Facing: crayfish are within 1.5 body length and positioned such that rostrums are facing each other Following: walking toward or stalking opponent while opponent is walking away High posture: body is raised above substrate on extended walking legs, large chela(e) raised off substrate Jumping away: short but rapid retreat by springing backward or to the side Low posture: body low, ventrally touching substrate, chela(e) resting on substrate Lunge: thrusting forward at opponent or charging, usually covering short range (speed greater than 1 body length/5’s), often seen as an intermediate between approach and claw pushing/touching Non-interactive: crayfish are within 1.5 body length with no visible agonistic behavior for a duration of at least 5’s Separation: a threat neutral (i.e. non-retreating) separation of distance to greater than 1.5 body length Submissive posture: a non-fighting posture in response to aggressive physical contact from opponent, accepting of manipulation by opponent, may include low posture, most often seen in female during mating Tail flip: rapid contraction of tail causing crayfish to propel itself backwards, away from opponent Turning away: crayfish rotating such that it is no longer facing opponent Turning toward: crayfish rotating such that it is facing opponent Unaware: crayfish within 1.5 body length and seemingly unaware or unconcerned with opponents agonistic behavior, often seen when opponent is approaching or stalking from behind Walking Away: walking forward, increasing distance from opponent Walking backward: retreating while facing opponent

Table 3.5 Mean, range and frequency of proportional response values

Refuge Use Activity Foraging Movement Interacting Mean 0.15 0.64 0.14 0.22 0.14 OS 0.11 0.69 0.16 0.23 0.15 OR 0.18 0.59 0.12 0.20 0.13 Range 0.00 - 1.00 0.00 - 1.00 0.00 - 0.85 0.00 - 0.80 0.00 - 1.00 0 < y < 0 231 (32%) 600 (83%) 492 (68%) 597 (83%) 532 (74%) y = 0 452 (63%) 64 (9%) 228 (32%) 123 (17%) 182 (25%) y = 1 37 (5%) 56 (8%) 0 0 6 (~ 1%)

(OS = Orconectes sanbornii , OR = Orconectes rusticus ). Frequencies depict the proportion of values on the continuous distribution, or discrete distribution at either boundary.

Table 3.6 Available parameters for modeling behavioral responses

Parameter Unit Day 1 to 5 Species OS, OR Competitor Conspecific, Heterospecific Predation Risk Zero, Low, High Predator Application (Chase) Pre, Post

Table 3.7 Final parameters chosen for each level in post-predation models (immediately following application of predation risk treatment). Models represent the acute response to predation risk.

Response Model Level Covariate Refuge Use µ Day, Species, Pred Risk, Competitor ID, Species*Pred

p0 Day

p1 Intercept Activity µ Species, Pred Risk, Competitor ID, Species*Pred, Species*Comp

p0 Pred Risk, Competitor ID

p1 Pred Risk Foraging µ Day, Species, Pred Risk, Species*Pred

p0 Species, Pred Risk

p1 NA Movement µ Species, Pred Risk, Competitor ID, Pred*Comp, Species*Pred, Species*Comp

67 p0 Pred Risk

p1 NA Interaction µ Competitor ID

p0 Day

p1 Day

µ represents the mean of the continuous (beta) distributed values, represents the probability of a response to take the value of zero, represents the probability of a response to take the value of one. Each level is fit in a hierarchical fashion, beginning with the mean. All models (excluding for foraging and movement) contain a random effect for individual. There were no observed values of 1 for foraging and movement.

Table 3.8 Final parameters chosen for each level in pre-predation models (immediately prior to application of predation risk treatment). Models represent the carry-over, or chronic effect of predation risk.

Response Model Level Covariate Refuge Use µ Day, Species, Pred Risk, Competitor ID, Species*Pred

p0 Day

p1 Competitor ID Activity µ Species, Competitor ID, Species *Competitor

p0 Competitor ID

p1 Day Foraging µ Day, Pred Risk

p0 Intercept

p1 NA Movement µ Species, Pred Risk, Competitor ID, Pred*Comp, Species*Comp

p0 Competitor ID

p1 NA Interaction µ Day

p0 Competitor ID

p1 Day µ represents the mean of the continuous (beta) distributed values, represents the probability of a response to take the value of zero, represents the probability of a response to take the value of one. Each level is fit in a hierarchical fashion, beginning with the mean. All models (excluding for foraging and movement) contain a random effect for individual. There were no observed values of 1 for foraging and movement.

Table 3.9 Estimated parameters for proportion of time in refuge immediately following application of predator treatment.

Parameter Estimate SE T P µ (Intercept) -1.67236 0.30655 -5.455 9.97E-08 *** Day -0.02447 0.05843 -0.419 0.67562 Species (O. rusticus) 1.24976 0.31796 3.931 0.000104 *** Pred Risk (Low) 0.31024 0.29041 1.068 0.28623 Pred Risk (High) 1.28529 0.32079 4.007 7.71E-05 *** Competition (Hetero) 0.36309 0.16557 2.193 0.029046 * Species : Pred (Low) -0.34218 0.4062 -0.842 0.400221 Species : Pred (High) -1.55987 0.43156 -3.614 0.000351 *** p 0 (Intercept) 0.8198 0.26457 3.099 0.00212 ** Day -0.12435 0.07912 -1.572 0.11703 p 1 (Intercept) -2.0843 0.2402 -8.677 <2e-16 *** This represents the acute response to risk of predation. Mean level of refuge use (µ), absence of use (p0) and probability of 100% refuge use (p1) represent the different levels of the model. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05.

Table 3.10 Estimated parameters for proportion of time in refuge immediately prior to predator treatment.

Parameter Estimate SE T P µ (Intercept) -1.49526 0.32163 -4.649 4.90E-06 *** Day -0.06552 0.06709 -0.977 0.32951 Species (O. rusticus) 1.15554 0.29567 3.908 0.000114 *** Pred Risk (Low) 0.24996 0.30951 0.808 0.41993 Pred Risk (High) 0.3778 0.37496 1.008 0.314419 Competition (Hetero) 0.36927 0.19133 1.93 0.054502 . Species : Pred (Low) -1.07213 0.45085 -2.378 0.017997 * Species : Pred (High) -1.07615 0.4777 -2.253 0.024955 * p 0 (Intercept) 1.59715 0.29034 5.501 7.70E-08 *** Day -0.1972 0.08471 -2.328 0.0205 * p 1 (Intercept) -2.3493 0.458 -5.13 4.84E-07 *** Competition (Hetero) 0.9137 0.5443 1.679 0.0941 . This represents estimated carry-over effect of predation risk on refuge use. Mean level of refuge use (µ), absence of use (p0) and probability of 100% refuge use (p1) represent the different levels of the model. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05, ‘.’ 0.1.

Table 3.11 Estimates for proportion of time active immediately following predator treatment.

Parameter Estimate SE T P µ (Intercept) 1.0821 0.1528 7.082 1.06E-11 *** Species (O. rusticus) -0.3077 0.2236 -1.376 0.169875

Pred Risk (Low) -0.5074 0.183 -2.773 0.005903 ** Pred Risk (High) -0.8632 0.1829 -4.719 3.68E-06 *** Competition (Hetero) 0.4107 0.1453 2.826 0.005032 ** Species (OR) : Pred (Low) 0.0213 0.2588 0.082 0.934442

Species (OR) : Pred (High) 0.8002 0.2621 3.053 0.002471 ** Species (OR) : Comp -0.719 0.2059 -3.492 0.000554 *** p 0 (Intercept) -2.3263 0.3868 -6.014 4.66E-09 *** Pred Risk (Low) -1.9039 0.6506 -2.926 0.00366 ** Pred Risk (High) -0.769 0.4457 -1.725 0.08538 . Competition (Hetero) 0.9076 0.4344 2.089 0.03743 * p 1 (Intercept) -1.8052 0.2762 -6.535 2.30E-10 *** Pred Risk (Low) -1.8793 0.6476 -2.902 0.00395 ** Pred Risk (High) -1.5616 0.5815 -2.686 0.00759 ** This represents the acute response to risk of predation. Mean level of activity (µ), absence of activity (p0) and probability of 100% activity (p1) represent the different levels of the model. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05, ‘.’ 0.1.

Table 3.12 Estimates for proportion of time active immediately prior to predator treatment.

Parameter Estimate SE T P µ (Intercept) 0.9405 0.1049 8.967 < 2e-16 *** Species (O. rusticus) -0.1528 0.1485 -1.029 0.30427 Competition (Hetero) 0.2369 0.1545 1.534 0.12621 Species (OR) : Comp (Het) -0.6937 0.2159 -3.213 0.00146 ** p 0 (Intercept) -2.6556 0.3033 -8.756 <2e-16 *** Competition (Hetero) 0.7479 0.3788 1.974 0.0492 * 5.60E- (Intercept) -2.8899 0.4831 -5.982 *** p1 09 Day 0.18 0.1357 1.327 0.186

This represents estimated carry-over effect of predation risk on activity. Mean level of activity (µ), absence of activity (p0) and probability of 100% activity (p1) represent the different levels of the model. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05, ‘.’ 0.1.

Table 3.13 Estimates of parameters of time spent foraging immediately following predator treatment.

Parameter Estimate SE T P µ (Intercept) -1.97623 0.14579 -13.556 < 2e-16 *** Day 0.12962 0.03327 3.896 0.000121 ***

Species (O. rusticus) -0.21705 0.14739 -1.473 0.141928 Pred Risk (Low) -0.39488 0.13711 -2.88 0.004268 ** Pred Risk (High) -0.72503 0.17837 -4.065 6.17E-05 *** Species : Pred (Low) 0.0352 0.21523 0.164 0.870188

Species : Pred (High) 0.55086 0.24411 2.257 0.024762 * p 0 (Intercept) -1.0611 0.2421 -4.383 1.59E-05 *** Species (O. rusticus) 0.4933 0.2341 2.107 0.03587 * Pred Risk (Low) -0.1317 0.2957 -0.445 0.65643

Pred Risk (High) 0.8541 0.2806 3.044 0.00253 ** This represents the acute response to risk of predation. Mean foraging (µ) and absence of foraging (p0) represent the different levels of the model. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05.

Table 3.14 Estimates for proportion of time spent foraging immediately prior to predator treatment.

Parameter Estimate SE T P µ (Intercept) -1.5259 0.1356 -11.249 < 2e-16 *** Day 0.192 0.0351 5.471 9.47E-08 *** Pred Risk (Low) -0.2011 0.1193 -1.686 0.0929 . Pred Risk (High) -0.2766 0.1172 -2.36 0.0189 * p 0 (Intercept) -1.1623 0.1267 -9.176 <2e-16 *** This represents estimated carry-over effect of predation risk on foraging. Mean foraging (µ) and absence of foraging (p0) represent the different levels of the model. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05, ‘.’ 0.1.

Table 3.15 Estimates of parameters of time spent moving immediately following predator treatment

Parameter Estimate SE T P µ (Intercept) -0.65149 0.12061 -5.401 1.39E-07 *** Species (O. rusticus) -0.02216 0.15585 -0.142 0.88704

Pred Risk (Low) -0.44305 0.16136 -2.746 0.00642 ** Pred Risk (High) -0.8813 0.16383 -5.379 1.56E-07 *** Competition (Hetero) 0.13493 0.15233 0.886 0.37648

Species (OR) : Pred (Low) -0.1713 0.1874 -0.914 0.36143

Species (OR) : Pred (High) 0.59204 0.19168 3.089 0.00221 ** Species (OR) : Comp -0.69659 0.15376 -4.53 8.67E-06 *** Comp : Pred (Low) 0.48108 0.18673 2.576 0.01049 * Comp : Pred (High) 0.44182 0.19145 2.308 0.02173 * p 0 (Intercept) -1.3097 0.2247 -5.828 1.33E-08 *** Pred Risk (Low) -1.4069 0.4318 -3.258 0.00124 ** Pred Risk (High) -0.7041 0.357 -1.973 0.04937 * This represents the acute response to risk of predation. Mean movement (µ) and absence of movement (p0) represent the different levels of the model. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05.

Table 3.16 Estimates of parameters of time spent moving immediately prior to predator treatment.

Parameter Estimate SE T P

µ (Intercept) -1.4135 0.11486 -12.306 <2e-16 *** Species (O. rusticus) 0.05388 0.11291 0.477 0.6336

Pred Risk (Low) 0.05386 0.1407 0.383 0.7021

Pred Risk (High) 0.2112 0.13647 1.548 0.1228

Competition (Hetero) 0.31769 0.16402 1.937 0.0537 .

Species : Comp -0.42089 0.16609 -2.534 0.0118 *

Comp : Pred (Low) -0.04975 0.20308 -0.245 0.8067

Comp : Pred (High) -0.4142 0.2014 -2.057 0.0406 * p0 (Intercept) -1.8036 0.212 -8.509 6.34E-16 *** Competition (Hetero) 0.6047 0.2767 2.186 0.0296 *

This represents estimated carry-over effect of predation risk on movement. Mean movement (µ) and absence of movement (p0) represent the different levels of the model. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05, ‘.’ 0.1.

Table 3.17 Estimates of parameters of time spent engaged in agonistic interactions following predator treatment.

Parameter Estimate SE T P µ (Intercept) -1.47486 0.06058 -24.34 <2e-16 *** Competition (Hetero) -0.17489 0.0888 -1.97 0.0498 * p 0 (Intercept) -0.8901 0.318 -2.799 0.00542 ** Day -0.2752 0.1054 -2.611 0.00945 **

(Intercept) -1.7331 1.333 -1.3 0.194 p1 Day -1.4444 0.8931 -1.617 0.107

This represents the acute response to risk of predation. Mean time interacting (µ), absence of interaction (p0) and probability of 100% of time interacting (p1) represent the different levels of the model.. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05.

Table 3.18 Estimates of parameters of time spent engaged in agonistic interactions prior to predator treatment.

Parameter Estimate SE T P

µ (Intercept) -1.70449 0.12885 -13.228 <2e-16 *** Day 0.07377 0.03829 1.927 0.055 . p0 (Intercept) -1.0375 0.1729 -6.002 5.24E-09 *** Competition (Hetero) 0.6048 0.2342 2.583 0.0102 * (Intercept) -1.6554 1.3304 -1.244 0.214 p1 Day -1.3927 0.8909 -1.563 0.119

This represents estimated carry-over effect of predation risk. Mean time interacting (µ), absence of interaction (p0) and probability of 100% of time interacting (p1) represent the different levels of the model.. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05, ‘.’ 0.1.

Table 3.19 Three-way ANOVA results for effect of species, predation risk and competition on aggression score following predator treatment.

Parameter df MS F P Species 1 9.1167 6.7725 0.009735 ** Pred Risk 2 0.0421 0.0313 0.969211 Competition 1 0.0267 0.0198 0.888115 Species : Pred Risk 2 11.1599 8.2903 0.000316 *** Competition : Pred Risk 2 2.6448 1.9647 0.142063 Species : Competition 1 0.4651 0.3455 0.557129 Residuals 289 1.3461 Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05, ‘.’ 0.1.

Table 3.20 Parameter estimates for mean aggression score following predator treatment.

Parameter Estimate SE T P (Intercept) 1.203131 0.199803 6.022 5.23E-09 *** Species (O. rusticus) -0.59145 0.269611 -2.194 0.02905 * Pred Risk (Low) -0.00349 0.275309 -0.013 0.98989 Pred Risk (High) -0.78454 0.280812 -2.794 0.00556 * * Competition (Hetero) -0.41363 0.267834 -1.544 0.12359 Species (OR) : Pred (Low) -0.37111 0.32844 -1.13 0.25945 Species (OR) : Pred (High) 0.935089 0.333449 2.804 0.00538 ** Comp : Pred (Low) 0.463943 0.328292 1.413 0.15867 Comp : Pred (High) 0.629057 0.333682 1.885 0.06041 . Species (OR) : Comp (Hetero) 0.158228 0.26919 0.588 0.55713 Intercept represents the reference level (Species = O. sanbornii, Predation risk = Zero, Competition = Conspecific). Interactions indicated by “:” (F9,289=5.079; P=0.00152). Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05, ‘.’ 0.1.

Table 3.21 Three-way ANOVA results for effect of species, predation risk and competition on aggression score prior to predator treatment.

Parameter df MS F P Species 1 4.0233 3.0634 0.081411 . Pred Risk 2 2.3539 1.7923 0.168909 Competition 1 2.8525 2.172 0.141918 Species : Pred Risk 2 10.526 8.0147 0.000432 *** Competition : Pred Risk 2 10.2667 7.8173 0.00052 *** Species : Competition 1 0.107 0.0815 0.775572 Residuals 229 1.3133 Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05, ‘.’ 0.1.

Table 3.22 Parameter estimates for mean aggression score prior to predator treatment.

Parameter Estimate SE T P (Intercept) 1.00546 0.22949 4.381 1.80E-05 *** Species (O. rusticus) -0.15668 0.29072 -0.539 0.59044 Pred Risk (Low) 0.27124 0.31242 0.868 0.38619 Pred Risk (High) -0.6434 0.29236 -2.201 0.02876 * Competition (Hetero) -0.19036 0.29648 -0.642 0.52147 Species (OR) : Pred (Low) -1.02091 0.37598 -2.715 0.00713 ** Species (OR) : Pred (High) 0.35123 0.35297 0.995 0.32076 Comp : Pred (Low) -0.1885 0.37779 -0.499 0.61829 Comp : Pred (High) 1.12728 0.35381 3.186 0.00164 ** Species (OR) : Comp (Hetero) 0.08524 0.29865 0.285 0.77557 Intercept represents the reference level (Species = O. sanbornii, Predation risk = Zero, Competition = Conspecific). Interactions indicated by “:” (F9,229=4.507; P=1.81). Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05.

Figure 3.1 Mean ± SE proportion of time utilizing refuge for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry-over effect). “Conspec” and “Hetero” represent competitor identification.

Figure 3.2 Mean ± SE proportion of time active for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry-over effect). “Conspec” and “Hetero” represent competitor identification.

Figure 3.3 Mean ± SE proportion of time foraging for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry- over effect). “Conspec” and “Hetero” represent competitor identification.

Figure 3.4 Mean ± SE proportion of time moving for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry- over effect). “Conspec” and “Hetero” represent competitor identification.

Figure 3.5 Mean ± SE proportion of time interacting for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry- over effect). “Conspec” and “Hetero” represent competitor identification.

Figure 3.6 Mean ± SE aggression score for O. sanbornii and O. rusticus. “Post” indicates observations immediately following application of the predation risk treatment. “Pre” represents observations prior to predation risk treatment (i.e. the carry-over effect). “Conspec” and “Hetero” represent competitor identification.

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Appendix A: Additional Tables for Chapter 2

Table A.1 Abiotic parameters within sampled streams.

Depth Velocity Invasion Status (cm) (m/s) Temp (°C) pH Cond. O2 Order Non-invaded (7) 21.79 0.32 21.24 7.93 0.68 8.56 1.80 Active (2) 14.83 0.58 21.17 8.03 0.54 8.89 2.50 Displaced (6) 23.49 0.37 19.57 7.90 0.57 8.67 3.17 Overall mean 20.04 0.42 20.66 7.95 0.60 8.70 2.49 (N) indicates number of streams sampled in each invasion category. Minimum of three measures/parameter/stream taken along transverse path across stream width. Measurements taken concurrent to crayfish sampling throughout season.

Appendix B: Additional Tables and Figures for Chapter 3

Table B.1 Estimate for proportion of time active immediately following predator treatment with O. rusticus as species reference. Parameter Estimate SE T P µ (Intercept) 0.77442 0.16325 4.744 3.28E-06 *** Species (O. sanbornii) 0.30767 0.2236 1.376 0.169875 Pred Risk (Low) -0.48612 0.183 -2.656 0.008332 ** Pred Risk (High) -0.06302 0.18768 -0.336 0.737262 Competition (Hetero) -0.30832 0.1459 -2.113 0.035433 * Species (OS) : Pred (Low) -0.0213 0.25877 -0.082 0.934442 Species (OS) : Pred (High) -0.80022 0.26208 -3.053 0.002471 ** Species (OS) : Comp 0.71904 0.20593 3.492 0.000554 *** p 0 (Intercept) -2.3263 0.3868 -6.014 4.66E-09 *** Pred Risk (Low) -1.9039 0.6506 -2.926 0.00366 ** Pred Risk (High) -0.769 0.4457 -1.725 0.08538 . Competition (Hetero) 0.9076 0.4344 2.089 0.03743 * p 1 (Intercept) -1.8052 0.2762 -6.535 2.30E-10 *** Pred Risk (Low) -1.8793 0.6476 -2.902 0.00395 ** Pred Risk (High) -1.5616 0.5815 -2.686 0.00759 ** This represents the acute response to risk of predation. Mean level of activity (µ), absence of activity (p0) and probability of 100% activity (p1) represent the different levels of the model. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05, ‘.’ 0.1.

Table B.2 Estimates for proportion of time foraging immediately following predator treatment with O. rusticus as species reference. Parameter Estimate SE T P µ (Intercept) -2.19327 0.15297 -14.338 < 2e-16 *** Day 0.12962 0.03327 3.896 0.000121 *** Species (O. sanbornii) 0.21705 0.14739 1.473 0.141928 Pred Risk (Low) -0.35968 0.16589 -2.168 0.03094 * Pred Risk (High) -0.17417 0.16685 -1.044 0.297377 Species (OS) : Pred (Low) -0.0352 0.21523 -0.164 0.870188 Species (OS) : Pred (High) -0.55086 0.24411 -2.257 0.024762 * p 0 (Intercept) 2.64578 0.08656 30.56 <2e-16 *** p1 (Intercept) -0.5677 0.2333 -2.434 0.01548 * Species (O. sanbornii) -0.4933 0.2341 -2.107 0.03587 * Pred Risk (Low) -0.1317 0.2957 -0.445 0.65643 Pred Risk (High) 0.8541 0.2806 3.044 0.00253 ** This represents the acute response to risk of predation. Mean foraging (µ), absence of foraging (p0) and probability of 100% foraging (p1) represent the different levels of the model. Intercept represents the reference level of the model (Species = O. sanbornii, Competition = Conspecific, Predation = Zero). Interactions shown with “:”. Significance: ‘***’ 0.001, ‘**’ 0.01, ‘*’ 0.05.

100

Figure B.1 Post predator treatment interaction between Predation Risk and Species on Refuge Use.

Figure B.2 Pre predator treatment interaction between Predation Risk and Species on Refuge Use.

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Figure B.3 Post predator treatment interaction between Predation Risk and Species on Activity.

Figure B.4 Post predator treatment interaction between Predation Risk and Competitor Identity on Activity.

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Figure B.5 Pre predator treatment interaction between Competitor Identity and Species on crayfish Activity.

Figure B.6 Post predator treatment interaction between Predation Risk and Species on crayfish Foraging.

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Figure B.7 Post predator treatment interaction between Predation Risk and Species on crayfish Movement.

Figure B.8 Post predator treatment interaction between Competitor Identity and Species on crayfish Movement.

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Figure B.9 Post predator treatment interaction between Predation Risk and Competitor Identity on crayfish Movement.

Figure B.10 Pre predator treatment interaction between Predation Risk and Competitor Identity on crayfish Movement.

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Figure B.11 Pre predator treatment interaction between Competitor Identity and Species on crayfish Movement.

A B C

Figure B.12 Interactions on crayfish aggression score. A) Competitor ID x Species, B) Predation Risk x Competitor ID, C) Competitor ID x Species

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Appendix C: Considerations for Future Work in Assessing Behavioral Response of

Native Orconectes sanbornii to Invasion

The behavioral trials described in Chapter 3 present distinct species differences in the behavioral responses to predator induced risk and competition. There, I compared the behavior of O. sanbornii and O. rusticus when subject to increased predation risk and intra- versus interspecific competition treatments. Future work will isolate the effects of elevated predation risk on O. sanbornii behavior only. Additionally, I will evaluate how intra- versus interspecific competition might affect O. sanbornii’s response to increased predation risk.

Specifically, future work will reframe and reanalyze the data presented in chapter three to address the following questions (Table C.1): 1) how is native O. sanbornii’s behavior

(refuge use, activity, aggressiveness) affected by an intraspecific verses interspecific competitor?, 2a) does predation risk alter the behavior of O. sanbornii?, 2b) how does increasing risk of predation affect O. sanbornii behavior, and 3) does O. sanbornii respond differently to predation risk when competing against a conspecific versus heterospecific competitor?

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Table C.1 Questions and comparisons to address response of native O. sanbornii to predation risk and competition.

Research Question Comparison 1) How is native O. sanbornii’s behavior affected by an Intra + Zero Risk vs. Inter + intra- vs. interspecific competitor? Zero Risk 2a) Does predation risk alter the behavior of O. sanbornii? Intra + Zero Risk vs. Intra + High Risk 2b) How does increasing risk of predation affect O. Intra + Low Risk vs. Intra + sanbornii behavior (apparent competition)? High Risk 3) Does O. sanbornii respond differently to risk when Intra + High Risk vs. Inter + competing against invasive O. rusticus? High Risk Model factors; Predation Risk levels: Zero, Low, and High. Competition levels: Intra- Intraspecific, Inter-Interspecific. The reference level of the model is designated as Predation Risk = High and Competition = Intraspecific.

Methods

From the existing data set, focal O. sanbornii individuals will be randomly selected for analysis. All behavioral responses (see Table 3.1) will be analyzed against all predictors of interest (predation risk, competitor identity, and the interaction between risk and competitor) using Zero-and-One Beta Regression (Ospina and Ferrari 2010) via the GAMLSS package (Stasinopoulos and Rigby 2007) in R statistical software (R Core

Team 2015).

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