Factors Influencing Ecological Metrics of Thermal Response in North American Freshwater Fish

Factors influencing ecological metrics of thermal response in North American freshwater fish

Sarah Hasnain

A thesis submitted in conformity with the requirements for the degree of Master's of Science Graduate Department of Ecology and Evolutionary Biology University of Toronto

Factors influencing ecological metrics of thermal response in North American freshwater fish

Sarah Hasnain

Master’s of Science

Ecology and Evolutionary Biology University of Toronto

2012 Abstract

Habitat temperature is a major determinant of performance and activity in fish. I examined the relationships between thermal response metrics describing growth (optimal growth temperature

[OGT] and final temperature preferendum [FTP]), survival (upper incipient lethal temperature

[UILT] and critical thermal maximum [CTMax]), and reproduction (optimum spawning [OS] and optimum egg development temperature [OE]) for 173 North American freshwater fish species. All metrics were highly correlated and associated with thermal preference class, reproductive guild and spawning season. Controlling for phylogeny resulted in an overall decrease in correlation strength, varying with metric pair relationship. ANCOVA and Bayesian hierarchical models were utilized to assess the influence of phylogeny on metric pair relationships. For both methods, FTP based metric pairs were weakly correlated within taxonomic family. Strong within family associations were found for reproduction metrics OS-

OE. These results suggest that evolutionary history plays an important role in determining species thermal response to their environment.

Acknowledgements

Firstly, I would like to thank my supervisors Prof. Brian Shuter (official) and Prof. Ken Minns (unofficial) for their support, guidance and assistance for the duration of my work at the University of Toronto. I would also like to thank Ken for giving me the opportunity to intern for him after my undergraduate degree and encouraging me to pursue my graduate studies and Brian for fostering my interest in thermal ecology research. I couldn’t have asked for better mentors. I would like to thank my supervisory committee, Dr. Michael Escobar and Dr. Don Jackson for their continued interest and assistance in my research. I would especially like to thank Michael for not only introducing me to Bayesian statistics and making it an enjoyable experience, but also all his help in conceptualising and constructing models for my thesis. I am greatly in his debt. I would like to thank Don for all of his helpful statistical discussions and guidance in general. My lab members (also friends) have played an instrumental role in the development of my thesis and I would like to thank Liset Cruz-Font, Dak de Kerckhove, Henrique Giacomini, and Jordan Pleet for all of their patience and help. I would like to thank Caroline Tucker for all of her assistance with phylogenetic comparisons and modelling in R. Dr. Helen Rodd, Dr. Nick Collin (EEB1310 was very helpful), and Dr. Ben Gilbert (statistical models) played an important role in the development of my thesis and I appreciate all of their guidance. I would also like to thank my fellow postdocs, Jaewoo Kim (conceptual support), Hawthorne Beyer (Bayesian models), and Karen Alofs (support with TAing) for all of their help. My thanks to fellow graduate students and friends, Robert Williamson, Emily Josephs, Aaron Hall, Caren Scott, Dorina Szurockzi, Bradley Murphy, Jun Cheng, Lifei Wang, Natalie Jones, Alex De Serrano, Deepthi Rajagopalan, Alivia Dey, Pasan Dissayanke, Rachel Germain, Kyle Turner, and Adam Cembrowski for an enjoyable experience at the EEB department. I would especially like to thank Anna Li and Lina Arcila-Hernandez for their emotional support and editing assistance. Finally, I would like to thank my friends and family for their encouragement and support, especially my mother whose hard work and perseverance has helped me achieve my goals.

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Table of Contents

Acknowledgements...... iii List of Tables...... v List of Figures...... vi List of Appendices ...... vii Chapter 1: Ecological thermal response metrics of North American freshwater fish ...... 1 1. Introduction...... 1 2. Methods...... 3 2.1 Data Collection...... 3 2.2 Statistical Analysis...... 5 3. Results...... 6 4. Discussion...... 7 References ...... 11 Tables...... 16 Figures ...... 18 Chapter 2: Application of Frequentist and Bayesian Statistical Approaches to Assess the Influence on Phylogeny on Thermal Response Metrics ...... 23 1. Introduction...... 23 2. Methods...... 24 2.1 Thermal response metrics...... 24 2.2 Frequentist Analyses ...... 25 2.3 Bayesian hierarchical models...... 25 2.4 MCMC Chains ...... 26 3. Results...... 27 4. Discussion...... 28 References ...... 33 Tables...... 41 Figures ...... 43 Appendices...... 50

List of Tables

Chapter 1

Table 1.1: List of thermal response metrics for growth, survival and reproduction assessed .... 16 Table 1.2: Sample sizes for statistical analyses employed to assess thermal response metrics of growth, survival and reproduction...... 16 Table 1.3: Summary statistics for growth, survival, and reproduction metrics for North American freshwater fish...... 16 Table 1.4: Mean, minimum, and maximum temperature (°C) values for the growth, survival, and reproduction metrics for taxonomic families (n>5, data available across all metrics)...... 17 Chapter 2

Table 2.1: Whole model ANCOVA statistics for the relationships between thermal metrics FTP- OGT, FTP-UILT, FTP-OS, and OS-OE ...... 41 Table 2.2: Summary statistics for ANCOVA for thermal metric pairs FTP-OGT, FTP-UILT, FTP-OS and OS-OE with factor taxonomic family...... 41 Table 2.3: βs and DIC values for Bayesian hierarchical models for thermal metric pairs FTP- OGT, FTP-UILT, FTP-OS and OS-OE...... 42 Table 2.4: Slope estimates generated using ANCOVA and Bayesian hierarchical models for thermal metric pairs FTP-OGT, FTP-UILT, FTP-OS, and OS-OE...... 42

List of Figures

Chapter 1

Figure 1.1: Scatterplot matrix showing relationships among growth, survival, and reproduction metrics ...... 18 Figure 1.2: Scatterplot matrix showing relationships among phylogenetically independent contrasts of growth, survival, and reproduction metrics...... 19 Figure 1.3: Growth, survival, and reproduction metric distributions across temperature preference class...... 20 Figure 1.4: Growth, survival, and reproduction metric distributions across reproductive guild .21 Figure 1.5: Growth, survival, and reproduction metric distributions across spawning season. .. 22

Chapter 2

Figure 2.1: Linear Regression depicting a positive relationship between FTP-OGT...... 43 Figure 2.2: Linear Regression depicting a positive relationship between FTP-UILT...... 44 Figure 2.3: Linear Regression depicting a positive relationship between FTP-OS ...... 45 Figure 2.4: Linear Regression depicting a positive relationship between OE-OS...... 46 Figure 2.5: Trace plots for the Bayesian-fitted parameter βs for FTP-OGT, FTP-UILT, FTP-OS, and OS-OE metric pair models ...... 47 Figure 2.6: αk estimates generated from the FTP-OGT Bayesian hierarchical model for North American freshwater fish species...... 48 Figure 2.7: Metric estimates for species with incomplete data generated by Bayesian hierarchical models for FTP-OGT, FTP-UILT, FTP-OS and OS-OE...... 49

List of Appendices

Table A.1: Mean OGT, FTP,UILT, CTMax, OS, and OE data for 173 North American freshwater fish species ...... 50 Table A.2: Pearson and Spearman correlation values for growth, survival, and reproduction metrics for North American freshwater fish species...... 55 Table A.3: Pearson correlation values for growth, survival, and reproduction metrics for freshwater fish species represented in the Canadian freshwater fish phylogeny...... 55

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Chapter 1: Ecological thermal response metrics of North American freshwater fish

1. Introduction

Temperature is one of the most important abiotic factors influencing fish survival and performance (Fry et al. 1947, Brett 1971, Magnuson et al. 1979, Christie and Regier 1988). As obligate poikilo-ectotherms, fish body temperatures are directly related to surrounding water temperatures through a combination of environmental and physiological factors, such as protein synthesis, metabolic rate, insulation, and the relationship between lamellar blood and water flow (McCarthy and Houlihan 1997, McCarthy et al. 1999, Beitinger et al. 2000). The nature of this relationship has been documented by a vast array of literature dating back to the 1800s, through a variety of field and laboratory methods. Most notably, Fry (1947, 1964, 1967, 1971) demonstrated the effects of temperature on oxygen consumption, growth, swimming performance and active metabolic rate. A thermal environment most suitable for physiological processes is a fundamental requirement for fish, with growth and survival dependant on the relationship between external temperature and the requirements of internal metabolic processes (Fry et al. 1947, Brett 1956, Brett 1971). For many critical processes, the reaction rates rise slowly as the preferred temperature is approached and drop rapidly as it is exceeded eventually reaching zero at the lethal temperature (Kling et al. 2003). Physiological performance is maximized within a narrow range of temperatures, with the optimal temperature centred around a species-specific value (Brett 1971, Hokanson 1977, Beitinger and Fitzpatrick 1979, Jobling 1981).

Thermal environment also plays a central role in reproductive success. Spawning and egg development, for example occur under specific thermal conditions and are sensitive to water temperature perturbations (Alderdice et al. 1958, Rombough 1997, Van der Kraak and Pankhurst 1997). An increase in temperature of 2 ºC above the preferred range causes abnormal cleavage patterns in eggs leading to decreased hatching success(Van der Kraak and Pankhurst 1997). Changes in temperature have also been shown to arrest development in both previtellogenic and mature oocytes and shift the balance between environmental oxygen availability and metabolic

oxygen demand resulting in egg and/or embryo mortality (Alderdice et al. 1958, Rombough 1997, Chimlevsky 1999, Evans 2007).

Previous studies have suggested a genetic basis for species-specific patterns of thermal preference and tolerance (Beitinger and Fitzpatrick 1979, Johnson and Kelsch 1998). Some species with wide geographic distributions, such as Bluegill ( Lepomis macrochirus ) exhibit little variation in thermal preference (Beitinger and Fitzpatrick 1979). However, there is little evidence that this pattern is manifested generally across freshwater fish communities over large spatial scales. The relative roles of environment and trait inheritance on community assembly have been greatly debated in ecology (Diamond 1975, Ricklefs 1987, Hubbell 2001, Chase and Liebold 2003, Ricklefs 2004, Freckleton and Jetz 2009). Closely related species tend to be ecologically similar due to shared evolutionary history and therefore may occupy the same thermal niche (Freckleton and Jetz 2009, Cooper et al. 2011). Conversely, species inhabiting similar thermal environments may also exhibit similar thermal characteristics due to shared thermal conditions (Freckleton and Jetz 2009, Cooper et al. 2011), independent of their phylogenetic history.

In this study, I assessed the relationships between thermal metrics across three life stages: growth (optimal growth temperature [OGT] and final temperature preferendum [FTP]), survival (upper incipient lethal temperature [UILT] and critical thermal maximum [CTMax]) and reproduction (optimum spawning temperature [OS] and optimum egg development temperature [OE]) for North American freshwater fish species. To distinguish between the role of phylogeny and environment in the thermal response of freshwater fish, I tested for the effect of phylogeny within my analyses. I predicted that: 1) due to similarities in temperature requirements for each life history process, greatest positive correlations would be found between thermal metrics within a process, 2) thermal metric values would be associated with life history characteristics such as temperature preference class, reproductive guild and spawning season. I did not have any apriori expectations regarding the role of phylogeny in shaping thermal metric distributions.

2. Methods 2.1 Data Collection

I searched for growth, survival and reproduction temperature metric values across all North American freshwater fish species, excluding extinct and extirpated species as well as hybrid forms. Species were only evaluated if data were available for one or more thermal metrics. Life history data, the definition of which has been extended to include temperature preference class, reproductive guild and spawning season, were also collected.

Species-specific temperature metrics were first compiled from the following secondary literature sources Freshwater Fishes of Canada (Scott and Crossman 1973), Morphological and Ecological Characteristics of Canadian Freshwater Fishes (Coker et al. 2001), Temperature Relationships of Great Lakes Fishes: A Data Compilation (Wismer and Christie 1987), Temperature Requirements of Fishes from Eastern Lake Erie and the Upper Niagara River: A Review of Literature (Spotila et al. 1979), Temperature Tolerances and the Final Temperature Preferenda for the Assessment of Optimum Growth Temperature (Jobling 1981), Acute and Final Temperature Preferenda as Predictors of Lake St. Clair Fish Catchability (Danzman et al. 1991), Temperature Tolerances of North American Freshwater Fishes Exposed to Dynamic Changes in Temperature (Beitinger et al. 2000). Information from these sources was supplemented by species-specific primary and secondary literature cited in these references and primary literature sources published between 1970 and 2010, gathered through a literature search based on the ISI Web of Knowledge (http://isiwebofknowledge.com/). Additonal sources included peer-reviewed grey literature, government publications such as species reports, databases, recovery plans, and research theses.

For thermal measures related to growth, I collected Optimum Growth Temperature (OGT) and Final Temperature Preferendum (FTP) data. OGT is experimentally determined as the temperature supporting the highest growth rate when groups of fish are exposed to a set of constant temperatures under ad libitum feeding conditions (McCauley and Casselman 1980 cited

in Wismer and Christie 1987, Jobling 1981). FTP is defined as the temperature towards which fish gravitate when exposed to a large temperature range (Giattina and Garton 1982 cited in Wismer and Christie 1987). Two methods are used to determine FTP: the gravitation method and the acclimation method (Jobling 1981). FTP estimates obtained using both methods were compiled. For thermal measures related to survival, Upper Incipient Lethal Temperature (UILT) and Critical Thermal Maximum (CTMax) were used. UILT also referred to in the literature as IULT, is determined experimentally as the temperature at which 50% of fish survive for several hours (Fry et al. 1946, Spotila et al. 1979, Jobling 1981, Wismer and Christie 1987). CTMax is a measure of thermal resistance, the temperature at which a fish loses equilibrium - it's ability to maintain a 'normal' upright posture in water (Jobling 1981). I compiled Optimal Spawning temperature (OS) and Optimal Egg Development temperature (OE) data as thermal requirements for successful reproduction. OS is defined as the temperature at which peak spawning occurs (Wismer and Christie 1987). Data obtained through both experimental and field observations were utilized. OE is determined experimentally as the temperature at which highest rates of successful egg development occur.

Thermal preference class, spawning season and reproductive guild data were obtained for a subset of 91 species, due to limited life history information available. For each species, thermal preference class was determined based on Coker et al.'s (2001) classification, using preferred summer water temperature. Species were classified as warm (>25°C), cool (19 to 25°C) or cold (<19°C). A species occupied one of two intermediate classes, i.e., cool/cold or warm/cool if their preferred temperature overlapped boundaries of two preference classes. Species were designated as either spring (spawning between early April and late June) or fall spawner (spawning between early September to late October) using the spawning data cited in Scott and Crossman (1973). Spawning season data was not collected for species spawning multiple times within a year excluding one species, round goby, Neogobius melanostomus. Reproductive guilds were assigned to species based on spawning behaviour using Coker et al.'s (2001) application of Balon's (1975, 1981) classification system: A.1 = guarder broadcast spawners, A.2 = Non- guarder brood hiders, B.1 = guarder substratum choosers, B.2 = guarder nest spawners.

Data collected for OGT, FTP, UILT, CTMax and OE were limited to values derived from laboratory experiments. For OS, values from both field observations and laboratory experiments

were compiled. I did not attempt to assess the validity of the methods used to estimate each value for each metric, as I did not obtain enough inter-specific replication for estimates for species across all metrics. However, for FTP and UILT, intra-specific replication of estimates was sometimes high enough to identify clearly aberrant values. In these cases, I examined the original references to assess the reliability of the methods used to generate the estimates and I flagged those values (see Table A1.4 in the Appendix; 7 FTP values are flagged) where the methods did not match the requirements specified for the metric. These values were not included in the species-specific mean values used in the analyses described below. Although a similar assessment would have been ideal for the other metrics as well, typically the degree of intra- specific replication was insufficient to reliably identify apparently aberrant values.

2.2 Statistical Analysis

For each fish species, means and standard deviations were calculated from the individual estimates for each metric. If a range (instead of a single value) was specified then the mid-point was used in mean and standard deviation calculations. If only one estimate was available for a metric, this value was taken as the mean.

Phylogenetically independent contrasts (PICs) were performed for each metric to remove the effect of evolutionary history on species-specific thermal response data collected. For this analysis, a phylogenetic tree of 190 Canadian freshwater fish species based on DNA barcoding of the 652bp mitochondiral cytochrome c oxidase region was used (Hubert et al. 2008). Species without data for any of the six metrics were removed. For species represented by more than one individual within the phylogeny, the most phylogenetically conserved individual was chosen as representative of the species and all other individuals were removed. If all individuals were of equal phylogenetic distance, the representative was chosen at random. All changes to the Canadian freshwater fish phylogeny were made using Mesquite software, which allows the user to perform functions such as editing of phylogenetic trees, reconstruction of ancestral traits, simulation of character evolution and tree comparisons (Maddison and Maddison 2010).

Overall covariance between metrics was assessed using Pearson’s and Spearman’s rank correlation coefficients, both within and between life processes. For metric contrasts, we utilized

Spearman rank correlation to assess the relationships between metric pairs, as a non-parametric analysis would be less susceptible to the influence of outliers when determining associations between contrasts. For families represented by five or more species, mean, minimum and maximum values were determined from appropriate species values. For the 91 species that could be categorized according to temperature preference, reproductive guild and spawning season, box and whisker plots for each of the 6 metrics were used to compare observed variation within and across these categories. A one-way ANOVA was applied to the box and whisker plots to detect statistically significant differences. All statistical analyses were performed using R statistical software (R Development Core Team 2008).

3. Results

Of the 173 North American freshwater fish (23 families) assessed, growth, survival, and reproduction metric data were complete for 36 species. Complete FTP and OGT data were available for 55 species (Table 1.1). UILT and CTMax data were complete for 53 species and OS and OE were complete for 71 species. For a subset of 91 species within the database, temperature preference class, reproductive guild and spawning season data (excluding American eel, Anguilla rostrata , which spawns in the Sargasso Sea (Scott and Crossman 1973)) were complete. Among all of the metrics collected, FTP (109 species) and OS (110 species) were the most complete. UILT and OGT were the least complete with 71 and 66 species, respectively.

FTP data collected also had the greatest within species replication, with a median of 10.5 estimates per species and fifty species with four or more estimates. Where sufficient replication was available, intra-specific variation among the estimates for a single metric was relatively similar for the six metrics: standard deviations ranged from 2.3°C for OGT and CTMax to 5.6°C for OE (Table 1.1). Replication within families was moderately high: six families (Catostomidae, Centrarchidae, Cyprinidae, Ictaluridae, Percidae and Salmonidae) had five or more species with values for at least one metric (Table 1.2).

Pair-wise correlation analysis revealed high correlation among all metric pairs (correlation values>0.5; Figure 1.1). Similar results were obtained for both Spearman and Pearson correlations and only Pearson correlation values will be referred to hereafter. The highest values

were obtained between the reproduction metrics, OS and OE (0.886) and the growth metrics, FTP and OGT (0.851) (Appendix: Table A1.2). Survival metrics UILT and CTMax had the lowest correlation among all the metrics (0.566). Correlations between all metrics were significant (p<0.01).

The strength of the correlations (Spearman) between most metrics was reduced when the effect of phylogeny was removed via phylogenetically independent contrasts. Correlations between metrics were reduced in range (0.3-0.7; Figure 1.2), with the highest observed between OS and OE (0.723) and the lowest between UILT and CTMax (0.301). Contrasts for growth and (OGT and FTP: 0.468) and survival (UILT and CTMax: 0.301) were weakly correlated with each other. A strong correlation was observed between reproduction metrics OS and OE (0.723). (Appendix Table A1.3).

Temperature metrics were also clearly grouped by temperature preference class, reproductive guild, and spawning season: the one-way ANOVA for these life history traits were all significant ( p<0.05). For temperature preference class, OGT, FTP, and ULIT data were well clustered within each class, with some overlap, and increased progressively from cold to warm (Figure 1.3). Values for CTMax, OS and OE consistently overlapped among all temperature preference classes. For OS and OE, no data was present for species classified as cool/warm. Thermal metrics were also distinctly grouped by reproductive guild, with a prominent clustering of low temperature values for guild A.2, (Figure 1.4). Only one species belonged to reproductive guild B.1, guarder substratum choosers. Significant overlap was observed between reproductive guilds A.1 and B.2 across all temperature metrics. Grouping by spawning season was also observed, with metric values clustered at lower temperature values for fall spawners and higher ones for spring spawners (Figure 1.5). Fall spawners exhibited a narrower range of values for all thermal metrics than spring spawners.

4. Discussion

This study is among the first to provide empirical evidence for overall relationships between all thermal metrics across growth, survival and reproduction processes. The strong correlations

between all metrics across all life processes strongly suggest that all metrics are interrelated and are physiologically relevant measures of thermal response in freshwater fish (Jobling 1981, Hasnain et al. 2010). The significant positive correlations between growth (OGT), preferred (FTP) and lethal (UILT, CTMax) temperatures confirm the results of Jobling (1981) and Beitinger and Fitzpatrick (1979) with a significantly larger dataset. The strong relationships that I observed between spawning/egg development temperatures (OS and OE) with preferred temperature resolve existing questions in the literature regarding the effect of thermal preferenda on spawning/embryogenesis temperature (Beitinger and Fitzpatrick 1979, Spotila et al. 1979).

It should be noted that the thermal response data collected for the six metrics were not controlled for within species variation due to differences in experimental procedures or regional acclimation. Additionally, I did not standardize across age, although only thermal data for adults were collected when age was specified. Despite the within species variability that is present due to these factors, the high degree of correlation observed between these metrics suggests that thermal metric pairs are highly associated with each other.

The relative low correlation observed between survival metrics UILT and CTMax relative to others can be attributed to the different responses that these metrics quantify (Beitinger et al. 2000). CTMax is a measure of the thermal point at which physiological processes start to breakdown and is determined using a constant linear temperature change upward from the acclimation temperature (Hutchison 1961, Reutter and Herdendorf 1976, Jobling 1981, Beitinger et al. 2000). Conversely, the UILT method quantifies temperature effects on mortality, using abrupt changes in temperature above acclimation (Fry et al. 1946, Spotila et al. 1979, Jobling 1981, Wismer and Christie 1987, Beitinger et al. 2000). There are a number of concerns associated with both methods. Time and temperature are confounded in the CTMax method resulting in difficulty when interpreting CTMax data, especially in the case of values determined with different rates of change and endpoints (Coutant 1969, Fry 1971, Hutchison 1976, Beitinger et al. 2000). This was not a concern for my study as the CTMax values compiled were determined at the same endpoint, loss of equilibrium. The UILT method has long been criticized in the past as an unrealistic measure of thermal regime changes in the field (Fry et al. 1946, Beitinger et al. 2000). As ectotherms, fish cannot physiologically respond to rapid fluctuations in environmental temperature, a major feature of the UILT method (Brett 1956, Beitinger et al.

2000). While CTMax approximates field conditions more accurately, I included UILT values in my study to provide an accurate reflection of thermal response measures used in literature.

The strong associations between growth, survival and reproduction metrics and other life history characteristics (temperature preference class, reproductive guild and spawning season) indicate interplay between species life history characteristics and ecological temperature metrics. The clear grouping of temperature preference class data across all metrics, especially OGT, FTP and UILT confirms that the thermal classification system of Coker et al. (2001) based on limited preferred temperature data holds true for this much larger dataset. Additionally, the results suggest strong thermal associations with nesting behaviour as categorized by Balon's (1975, 1981) reproductive guilds and spawning, especially for guild A.2, which is associated with low temperatures across all metrics. There are many examples of thermal effects on fish behaviour. Magnuson (1979) noted that thermal preference in Bluegill, Lepomis macrochirus and Green Sunfish, Lepomis cyanellus decreased in the presence of conspecifics and cofamilials due to competition. A 3ºC increase in water temperature has been shown to cause increased schooling behaviour in Trinidadian guppies, Poecilia reticulata (Weetman et al. 1998, 1999). Cutthroat trout, Oncorhychus clarkii also exhibited increased aggregation caused by changes in environmental temperature (Brown 1999). In Eastern Mosquito fish, Gambosia holbrooki , increasing temperatures have been shown to increase male coercive behaviour during mating (Wilson 2005). However, no general link has been actively demonstrated between measures of thermal response in freshwater fish (i.e. growth, survival and reproduction metrics) and reproductive behaviour. Given the high degree of delineation I observed for growth, survival and reproduction metrics with nesting behaviour and spawning season, I recommend the inclusion of thermal response metrics in future studies considering the impact of temperature on reproductive behaviour.

My results suggest that phylogeny acts as a strong influence on the response of fish to their thermal environment. The overall decrease in the strength of the correlation when phylogeny was removed from the analysis implies that evolutionary history is an important determinant of growth, survival and reproduction metrics for a species. It should be noted that correlation strength did not decline for all metric pairs in a consistent manner. Some metric pairs (OGT and FTP, UILT and CTMax) experienced greater declines in correlation strength than others (OS and

OE), indicating that the influence of phylogeny on metric distribution is variable and highly dependent upon the metrics themselves. The effect of phylogeny on thermal response metrics may be facilitated indirectly through other factors such as habitat choice. Related taxa may occupy habitats which are similar across a variety of environmental variables (i.e. depth, sediment type, turbidity) including temperature. Further analysis is needed to parse the relationship between phylogeny and these metrics before any definitive conclusions can be made.

The strong influence of phylogeny on the thermal response of North America freshwater fish raises some important questions about the interplay between evolutionary history and the environment in determining growth, survival and reproduction metrics for species, suggesting that species response to their thermal environment is largely conserved. The role of evolutionary history in determining species life history traits has been fairly well demonstrated across many systems (Winemiller and Rose 1992, Futuyma et al. 1993, Chown et al. 2002, Hazlett and McLay 2005, Friedman and Barrett 2008, Kellermann et al. 2009). My results suggest that this pattern also holds for thermal response in freshwater fish. However, the relative difference in the strength of phylogenetic effects observed across metric associations indicate that other biotic and abiotic factors (e.g. local thermal regime, competition, predator-prey dynamics and frequency of disturbance) should be considered when assessing these relationships. Further analysis is needed in order to understand the effects of ecological and evolutionary forces on thermal metric associations in North American freshwater fish.

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Tables

Table 1.1: List of thermal response metrics for growth, survival and reproduction assessed in this study.

Thermal Response Metric Life Process Acronym Description Optimum Growth Growth OGT Temperature at which maximum growth rate is Temperature recorded. Final Temperature Growth FTP Temperature which the fish gravitate towards when Preferendum provided with a broad range of temperatures Upper Incipient Lethal Survival UILT Temperature at which 50% mortality occurs in a Temperature population Critical Thermal Survival CTMax Temperature at which fish lose equilibrium Maximum Optimum Spawning Reproduction OS Temperature at which maximum spawning is recorded Temperature Optimum Egg Reproduction OE Temperature at which the rate of egg development is Development Temperature optimized.

Table 1.2: Number of species in data sets used in statistical analyses employed to assess thermal response metrics of growth, survival and reproduction.

Statistical Analyses Number of species Pearson and Spearman Correlation (Overall) 173 Spearman Correlation (Overall and PICs for species 116 within Canadian freshwater fish phylogeny) One-way ANOVA (Life History traits) 91

Table 1.3: Summary statistics for growth (optimum growth temperature [OGT] and final temperature preferendum [FTP]), survival (upper incipient lethal temperature [UILT] and critical thermal maximum [CTMax]), and reproduction (optimum spawning temperature [OS] and optimum egg development temperature [OE]) for 173 species of North American freshwater fish.

Growth Survival Reproduction Summary Statistics OGT FTP UILT CTMax OS OE Total number of species present 66 111 72 93 111 81 Median number of values per species present 3.5 10.5 9.5 4.5 2.5 2 Total number of species with n ≥4 values 7 50 27 11 10.0 3.0 Median standard deviation with n ≥4 values 2.3 3.2 2.8 2.3 2.1 5.6

Table 1.4: Mean, minimum, and maximum temperature (°C) values for the growth (optimum growth temperature [OGT] and final temperature preferendum [FTP]), survival (Upper incipient lethal temperature [UILT] and critical thermal maximum [CTMax]), and reproduction (Optimum spawning temperature [OS] and optimum egg development temperature [OE]) metrics for taxonomic families (n>5, data available across all metrics).

Growth Survival Reproduction Family Name Temperature (°C) OGT FTP ULIT CTMax OS OE Catostomidae mean 25.5 21.5 29.9 34.2 15.6 16.7 min 25.5 11.1 26.8 30.8 10.0 12.5 max 25.6 26.8 35.2 37.9 21.7 20.5 Centrarchidae mean 25.3 25.0 34.4 36.6 21.1 22.5 min 18.0 19.1 31.7 32.8 17.0 18.2 max 28.4 30.2 40.0 40.2 25.0 28.0 Cyprinidae mean 25.7 23.1 31.7 34.1 19.5 21.2 min 21.7 15.3 27.8 28.6 11.4 15.6 max 28.9 27.9 38.0 39.0 24.5 25.0 Ictaluridae mean 29.8 24.3 33.9 35.5 24.2 23.9 min 29.4 18.6 33.2 29.0 21.1 22.8 max 30.0 28.3 35.4 37.9 27.8 25.0 Percidae mean 24.7 20.8 27.6 31.1 14.9 17.4 min 22.1 17.8 25.6 23.4 7.7 12.2 max 28.0 24.6 30.5 35.0 22.5 22.8 Salmonidae mean 14.5 12.6 24.5 28.1 6.4 7.2 min 10.0 4.2 21.4 22.1 2.8 3.0 max 30.0 27.1 27.8 33.8 10.7 12.8

Figures

Figure 1.1: Scatterplot matrix showing relationships among growth (optimum growth temperature [OGT] and final temperature preferendum [FTP]), survival (upper incipient lethal temperature [UILT] and critical thermal maximum [CTMax]) and reproduction metrics (optimum spawning temperature [OS] and optimum egg development temperature [OE]. Pearson (blue) and Spearman (red) correlation values for corresponding metric pairs are shown right of the diagonal. Correlation strength increases with colour intensity (pink>0.69, light blue 0.68-0.66, yellow<0.66). All correlations were significant (p<0.01)

Figure 1.2: Scatterplot matrix showing relationships among phylogenetically independent contrasts of growth (optimum growth temperature [OGT] and final temperature preferendum [FTP]), survival (upper incipient lethal temperature [UILT] and critical thermal maximum [CTMax]) and reproduction metrics (optimum spawning temperature [OS] and optimum egg development temperature [OE]). Bar plots left of the diagonal show differences between Spearman correlation values between metric relationships with (red) and without the effects of phylogeny (blue). All correlations were significant (p<0.01)

Figure 1.3: Growth ( optimum growth temperature [OGT] and final temperature preferendum [FTP]), survival (upper incipient lethal temperature [UILT] and critical thermal maximum [CTMax]) and reproduction (optimum spawning temperature [OS] and optimum egg development temperature [OE]) metric distributions across five temperature preference classes: cold (<19°C, n=27), cold/cool (n=6), cool (19-25°C, n=23), cool/warm (n=6) and warm (>25°C, n=28). All metric distributions were significantly different ( p<0.05, p=2.26x10 -16 ) across preference classes.

Figure 1.4: Growth ( optimum growth temperature [OGT] and final temperature preferendum [FTP]), survival (upper incipient lethal temperature [UILT] and critical thermal maximum [CTMax]) and reproduction (optimum spawning temperature [OS] and optimum egg development temperature [OE]) metric distributions across four reproductive guild categories; A.1 (Non-guarder broadcast spawners, n=50), A.2 (Non-guarder brood hiders, n=16), B.1 (guarder substratum choosers, n=1) and B.2 (guarder nest spawners, n=23). All metric distributions were significantly different (p<0.05, p=2.2x10 -6) across guild categories.

Figure 1.5: Growth ( optimum growth temperature [OGT] and final temperature preferendum [FTP]), survival (upper incipient lethal temperature [UILT] and critical thermal maximum [CTMax]) and reproduction (optimum spawning temperature [OS] and optimum egg development temperature [OE]) metric distributions for two spawning seasons; fall (September to early November, n=17); spring (April to late June, n=74). All metric distributions were significantly different (p<0.05, p= 2.2x10 -6) across spawning season categories.

Chapter 2: Application of Frequentist and Bayesian Statistical Approaches to Assess the Influence on Phylogeny on Thermal Response Metrics

1. Introduction

As one of the most important determinants of life history in fish, temperature has been shown to directly impact growth, survival and reproduction (Fry et al. 1947, Brett 1971, Magnuson et al. 1979, Christie and Regier 1988). The thermal environment plays an important role in determining a number of physiological processes such as protein synthesis and metabolic rate, as well as the development of previtellogenic and mature oocytes during reproduction (Alderdice et al. 1958, McCarthy and Houlihan 1997, Rombough 1997, Chimlevsky 1999, McCarthy et al. 1999, Beitinger et al. 2000, Evans 2007). For fish, the physiological optimum is achieved within a narrow range of temperatures that are often species-specific.

The responses of fish to their thermal environment across growth, survival and reproduction processes are linked with each other. The review of thermal metrics of growth, survival and reproduction for 173 species of North American freshwater fish in Chapter 1 showed that all metrics are highly correlated with each other and linked with thermal preference class, spawning season and reproductive guild. Additionally, phylogeny was shown to influence the relationships between these metrics at varying levels, depending on the specific metric pairs themselves.

The effect of phylogenetic relatedness in determining species response to their thermal environment has been demonstrated in a variety of systems. In cyanobacteria, genus Synechococcus , taxa with high thermal tolerance share a common lineage (Miller and Castenholz 2000). For bees, phylogeny plays an important role in explaining the variation in warm-up rate and in flight thoracic temperatures between species (Stone and Willmer 1989). Sprinting speed and thermal preference in Australian skinks (genus: Lygosominae) are phylogenetically clustered; genera with higher thermal preference select temperatures that enable sprinting at maximal speeds and genera with low thermal preference prefer temperatures sub-optimal for sprinting (Huey and Bennett 1987). The insect-pathogenic fungus Beauveria bassiana exhibits thermal growth preferences that are closely associated with distinct genetic groups (Bidochka et

al. 2002). However, phylogeny has also been shown to play little or no role in determining thermal preference and tolerance in other systems. In Eastern Pacific porcelain crabs (genus: Petrolisthes), upper thermal tolerance is highly associated with maximal microhabitat temperatures, regardless of phylogeny (Stillman and Somero 2000). Population level differences in the cold hardening response in Collembola are influenced by thermal habitat conditions with no phylogenetic signal (Bahrndorff et al. 2009).

In this chapter, I will assess the effect of phylogenetic relatedness on the relationships between thermal metrics of growth, survival and reproduction using two methods: 1) Analysis of Covariance (ANCOVA) and 2) Bayesian hierarchical models. The objectives of this chapter are three-fold: 1) to investigate the relationships between these metrics at the taxonomic level of family using ANCOVA and Bayesian hierarchical models, 2) to compare Bayesian and ANCOVA estimates of covariance between these metrics and 3) to assess Bayesian hierarchical models as tools for estimating missing values in incomplete datasets. I expect that both methods would provide similar results for metric relationships at the level of family and that these results would be metric specific, as seen in Chapter 1. Additionally, I predict that estimates for missing data generated from Bayesian hierarchical models would fit with the existing dataset, as estimates are drawn from defined prior distributions based on this dataset

2. Methods 2.1 Thermal response metrics

Species-specific values for six thermal response metrics of growth, survival and reproduction (OS and OE) were collected for 173 North American freshwater fish species, excluding extinct and extirpated species as well as hybrid forms. For growth, I collected optimum growth temperature (OGT) and final temperature preferendum values (FTP). Upper incipient lethal temperature (UILT) and critical thermal maximum (CTMax) values were collected as thermal response metrics for survival. For reproduction metrics, optimum spawning temperature (OS) and optimum egg development temperature (OE) were collected. Thermal response data were collected from a variety of peer reviewed and grey literature sources. All species-specific thermal

response data collected were limited to values derived from experimentation, excepting OS for which both field and experimental data was compiled.

2.2 Frequentist Analyses

Species-specific means and standard deviations were calculated for each fish species from individual metric estimates collected from the literature. Overall covariance between metrics was assessed using Pearson and Spearman correlation analysis. Analysis of covariance was used to examine the role of phylogeny in determining the strength of the pairwise associations for each of the four metric pairs with the highest Pearson correlation coefficients. This was done by including taxonomic family as a qualitative ‘treatment’ variable in the ANCOVA for each of the metric pairs. All analyses were performed using R software version 2.12.1 (R Development Core Team 2008).

2.3 Bayesian hierarchical models

Bayesian statistical models were constructed for the 4 metric pairs with the greatest Pearson correlation coefficients. Two levels of hierarchical structures were used to assess the relationships between each metric pair: species and family. At the species level, all models were constructed for the most data deficient metrics from each metric pair: OGT (FTP-OGT), UILT (FTP-UILT), OS (FTP-OS) and OE (OS-OE). The relationship between each metric pair was specified using a linear model of the following form:

M1 ik = αk + βs*M2 ik

where M1 ik is the observed value of metric 1 for species i from family k, αk is the family specific intercept for M1, βs represents the species-level slope linking metric 1 and metric 2, and M2 ik is

the observed value of metric 2 for species i from family k. The family specific intercept αk is

assumed to be drawn from a normal distribution whose mean value ( µα k ) is given by:

µα k = δ + βf * µM2k

where µM2k is the family level mean for metric 2, βf is the estimate of the difference between the family-level slope and βs, and δ is the intercept for the family-level relationship linking µα k and

µM2k The model structure permits separate values to be estimated for βs and βf. Based on earlier analyses of the thermal response metrics of North American freshwater fish, the following priors were assigned to this model: Parameters

M1 ik ~ normal( µM1ik, τ M1ik )

M2 ik ~ normal ( µM2k, τM2ik )

βs ~ normal (C, 0.1)

βf ~ normal (C, 0.1) δ ~ normal (D, 0.01) Means

µM2k ~ normal ( µ0M2k, τM2 )

µ0M2k ~ normal (20.0 , 0.4) Precision ( 1/ σ2)

τM1ik ~ gamma (2, 20)

τM2ik ~ gamma (2, 20)

ταk ~ gamma (2, 20) with C as the slope and D as the intercept from the regression analysis for each metric pair.

2.4 MCMC Chains

Monte Carlo Markov Chain simulations were used to sample the posterior distribution of parameters for all individual species and families for each metric pair relationship. Five chains with different initial parameter sets were used in all simulations.. For each chain, 45000 iterations were run with the initial 15000 iterations discarded in the burn-in period. The Gelman- Rubin convergence diagnostic (Gelman and Rubin 1992, Spiegelhalter et al. 2002), an approach for assessing convergence among multiple MCMC chains was used to assess the convergence in my model. This diagnostic utilizes a variance-ratio method based on the ANOVA and sampling from normal distributions, to compare the within and between chain variance for each parameter. This is used to estimate the Potential Scale Reduction Factor (PSRF), a measure of chain convergence. The closer the PSRF is to 1, the closer the chains are to the target distribution. The posterior distributions generated for each metric pair were assessed using the deviance 26

information criterion (DIC), a measure of the relative goodness of fit of the posterior distribution of Bayesian statistical models obtained using MCMC simulations. DIC is calculated as the sum of the expectation and the effective number of parameters in a model (Spiegelhalter et al. 2002). Models with smaller DIC values indicate a stronger fit as compared to models with larger DIC values. All MCMC simulations were performed using WinBUGS14 software (Imperial Council and MRC UK 2007), which utilizes Gibbs sampling; a special case of the Metropolis-Hastings algorithm, which generates a sequence of samples from the joint probability distribution of two or more random variables (Gilks 1996, Spiegelhalter et al. 2002).

3. Results

Among all of the 15 metric pairs assessed, four pairs exhibited the greatest correlations: FTP- OGT (0.846), OS-OE (0.846), FTP-UILT (0.777) and FTP-OS (0.692) (Figures 2.1-2.4). The relationship between each metric pair was assessed using an ANCOVA with family as a factor. Significant within-family associations between metric pairs were observed between FTP-UILT (p<0.05), FTP-OS (p<0.05) and OS-OE (p< 0.01) (Table 2.1). However for the FTP-OGT metric pair, no significant within family relationship was observed. Significant p-values were obtained for family-specific intercepts across all metrics pairs, confirming the existence of significant differences in metrics between families (Table 2.2). No significant interactions were observed between the factor (taxonomic family) and covariates for all metric pairs assessed.

For all metric pair models, all five chains converged and mixed well (Fig 2.5). No significant auto-correlation was observed within any of the chains for all metric pairs (autocorrelation at the

20th lag for each parameter was reduced to 0). Gelman-Rubin convergence diagnostics for αk

and βs for all models revealed a PSRF equal to 1, suggesting that all chains converged well. Thus, the posterior distribution was utilized from all chains.

M1 and M2 estimates across all models fell within biologically reasonable ranges for species with missing data; αk estimates for all metric pairs were clearly delineated by family with varying means and standard deviations (Figure 2.6). Estimates for βs differed between metrics pairs, with the highest value for OS-OE and the least for FTP-OS (Table 2.3). Similar βs estimates were obtained for FTP-OGT, FTP-UILT and FTP-OS. For all metric pair models, lower DIC values were associated with estimation using the most complete metric (M2) parameters (FTP, for all

FTP based pairs and OS for OS-OE), indicating a greater level of uncertainty in inverse estimations using M1 parameters (Table 2.3). For FTP-OS, DIC scores for the two metrics were similar.

ANCOVA and Bayesian hierarchical models showed some variation in terms of the within- family slope estimates generated (Table 2.4). Slope estimates for OS-OE from both methods were the highest among all metric pairs, indicating strong associations between OS and OE at the level of family. For both methods, within-family slopes were similar for FTP-OGT, FTP-UILT and FTP-OS, with the highest value observed for FTP-UILT. Among the Bayesian estimates, the lowest value was obtained for FTP-OS; for the ANCOVA estimates the lowest was obtained for FTP-OGT. There were consistent differences between the ANCOVA and Bayesian estimates for the FTP within-family slopes: the ANCOVA estimates for all pairs were substantially lower. For OS-OE, the ANCOVA slope was marginally higher.

Across all metric pairs assessed, no metric was complete for all 173 North American freshwater fish species. The most complete metrics were OS and FTP, with data present for 110 and 111 species respectively. OGT was the least complete metric with data present for 65 species. Bayesian hierarchical models generated estimates for species with incomplete data for each metric pair. All estimated data points fell within the range of values of the data points provided (Figure 2.7).

4. Discussion

As demonstrated in Chapter 1, phylogeny is an important factor in determining the thermal response of North American freshwater fish, especially for thermal metrics of growth and survival. ANCOVA and Bayesian hierarchical models confirmed this result at the taxonomic level of family, in four metric pairs with the highest Pearson correlation coefficients: FTP-OGT, FTP-UILT, FTP-OS and OS-OE, with varying degrees of phylogenetic influence. Outputs from both methods were somewhat comparable to each other, with OS-OE exhibiting the lowest phylogenetic signal among all metric pairs. Both methods however, contrasted in terms of slope estimates obtained for FTP metric pairs. The Bayesian model generated higher within-family slope estimates ( βs) as compared to the ANCOVA. Additionally, there were differences between the ANCOVA within-family slope estimates and the Bayesian within-family slope ( βs) estimates

obtained for these metrics (e.g. FTP-OGT has the lowest slope estimate, but FTP-OS has the lowest βs estimate).

The similarities and differences observed in ANCOVA and Bayesian within-family slope estimates across these metric pairs are indicative of the methodological differences between these analyses. ANCOVA utilizes a combination of linear regression and ANOVA to determine significant differences in the means of the dependent variable (M1) between groups (factor: taxonomic family) after removing the variance associated with the covariate (M2) (Rutherford 2001, 2011). The statistical model for ANCOVA consists of four terms:

M1 ik = µM1+ αk + βs *(M2 ik – µM2) + εik

where µM1 is the mean of metric 1 across all individuals and families and αk is the family specific offset from the mean (Elashoff 1969). The variance between M1 and M2 is divided into

two parts: 1) variance associated with differences in taxonomic family αk (factor) and 2) variance associated with differences in M2 (covariate) for individual species within a family. The within family variance for the dependent variable is further divided into variance associated with the linear dependence of M1 (dependent variable) on M2 (covariate), which is included as a residual in the model (M2 ik – µM2), and εik the unexplained variance, which is assumed to be an independent random value, drawn from a Normal distribution with mean 0. In the Bayesian

approach, αk is the family specific intercept determined from a linear regression between the family specific M1 and M2 means. Additionally, variance is integrated within the model via

precision parameters τM1ik and τM2ik in a similar manner as εik,, but no exact equivalent of the residual term (M2 ik – µM2) is applied. Differences in the parameterization of both models may account for the differences observed in the outputs between both methods. The use of the

residual in the ANCOVA for M2 in contrast to overall precision parameters τM1ik and τM2ik might explain the lower values for ANCOVA slope estimates as compared to the Bayesian model. While both methods produced similar patterns in slope estimates, due to the inclusion of between family effects, Bayesian hierarchical models may provide a more realistic output, especially for metric pairs with a combination of within family and between family associations.

Differences were observed between OS-OE and FTP-OGT, FTP-UILT, FTP-OS in terms of the influence of taxonomic family on metric pair relationships. The observation of weak relationships in all FTP metric pairs at the species level (within family) suggests a strong influence of phylogeny on the thermal response of North American freshwater fish in terms of growth and survival. The stronger relationship observed between reproduction metrics (OS-OE) at the species level (within family), suggests that phylogeny is a less important factor in determining the relationship between reproductive metrics. Egg development (OE) is restricted in terms of the thermal environment that supports it as the lack of mobility of eggs precludes the possibility of behavioural thermoregulation. Therefore, the thermal environment in which spawning occurs determines the thermal environment in which egg development occurs, resulting in strong associations between spawning (OS) and egg development temperatures (OE) within family. Behavioural thermoregulation plays an important role in the seeking out and maintaining of temperatures that are physiologically favourable and can allow ectotherms to buffer the effects of a changing thermal environment (Neill and Magnuson 1974, Beitinger 1975, McCauley and Huggins 1975, Reynolds and Casterlin 1975, Stuntz and Magnuson 1975, Wrenn 1975, Casterlin and Reynolds 1979, Bryan et al. 1990, Beitinger et al. 2000, Fangue et al. 2009, Reyes et al. 2011). In a number of fish species, growth (OGT), preferred (FTP), survival (UILT) and spawning (OS) temperatures have been shown to be maintained by behavioural thermoregulation (Beitinger 1975, Reynolds and Casterlin 1975, Reutter and Herdendorf 1976, Reynolds and Casterlin 1976, Casterlin and Reynolds 1979, Stauffer 1981, Cincotta and Stauffer 1984, Deacon et al. 1987, Berman and Quinn 1991, Newell and Quinn 2005). The ability of fish to thermally regulate each of these processes through its behaviour may permit OGT, UILT and OS to vary somewhat independently of FTP.

Evolutionary biologists and physiologists have long asserted that species thermal response, especially preferred temperatures are expected to undergo strong selection pressures (Angilletta Jr et al. 2002, Angilletta Jr et al. 2006), a claim which has been supported and opposed by a number of studies. Experimental data for Bluegill Lepomis macrochirus and Smallmouth bass Micropterus dolomieui suggests that thermal preference is conserved and exhibits little variation regardless of geographic distribution or thermal history (Reynolds and Casterlin 1976, Beitinger and Fitzpatrick 1979). In contrast, thermal preference in White Perch Morone americana (Hall et al. 1978) and Coho Salmon Oncorhynchus kisutch (Konecki et al. 1995) varies according to

geographic distribution, with preferred temperature ranges linked with habitat temperatures. For Banded killifish Fundulus heteroclitus , a countergradient variation in thermal preference is observed at the population level, with individuals from colder clines preferring higher temperatures than those from warmer clines (Fangue et al. 2009). Contrary to predictions of local thermal adaptations, this study suggests that species growth, preferred, survival and spawning temperatures are determined by family specific thermal adaptations as seen by the general association between FTP-OGT, FTP-UILT and FTP-OS metric pairs shown in Chapter 1 and the lack of within family associations between these metrics. Interestingly, phylogenetic inheritance of thermal adaptations plays a less important role in some aspects of species thermal response. The strong within family association observed between reproduction metrics OS and OE indicate the possibility of linked local thermal adaptations in both spawning and egg development, although considering the βs parameter values for this metric pair, family specific adaptations also exert some influence as the strength of this association is reduced to some extent within the family.

The Bayesian hierarchical models developed in this study were used to estimate thermal response metrics for species without data present within the literature. Estimated metric values generated from these models fell within the range of temperature values obtained from the literature for each metric, indicating the relative accuracy of estimates generated by this model. However, the higher DIC values for inverse estimations of missing predictor variable (M2) values, generated from dependent variable (M1) suggest a poorer fit to the dataset as compared to estimations of M1 value from M2 values. Lack of fit for values generated using inverse estimation poses important questions about the utility of this model in estimating both missing predictor and independent variables and should be addressed in future work involving Bayesian estimation techniques.

The fit of estimated thermal metric values generated using a Bayesian approach with data collected from the literature suggests the utility of this model not only for incomplete datasets, but also in cases where thermal response data cannot be experimentally obtained. This is particularly relevant for determining thermal response metrics for growth, survival and reproduction for rare, threatened or endangered species as the mortality associated with such labratory experiments is unacceptable. This is especially true for survival metrics UILT and

CTMax, thermal response measures that are determined through large changes in the thermal environment that, by necessity, induce near lethal stress levels. In this context spawning temperature (OS), which can be measured when peak spawning is observed in the field, can be used, within this model structure, to estimate other thermal response metrics that cannot be determined without experimental methods. However, it is important to note that the Bayesian hierarchical model developed in this chapter provides an exploratory analysis of the influence of phylogeny on the thermal response of North American freshwater fish and a possible method for the estimation of missing thermal response metric values within the literature. Future work in Bayesian approaches to estimation will compare different estimation models in terms of their fit with thermal metric data obtained from the literature.

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40 Tables

Table 2.1: Whole model ANCOVA statistics for the relationship between four thermal response metric pairs: FTP-OGT (ºC), FTP-UILT (ºC), FTP-OS (ºC) and OS-OE (ºC). Significant p values (p<0.05) are indicated by (*). DF refers to degrees of freedom for the whole model.

Metric Pair Slope SE t-value p-value DF Whole Model R 2 FTP-OGT 0.1590 0.135 1.180 0.246 36 0.989 FTP-UILT* 0.2742 0.109 2.521 0.0153 45 0.993 FTP-OS* 0.2626 0.096 2.734 0.0084 55 0.964 OS-OE* 0.6114 0.091 6.725 1.15x10 -8 54 0.985

Table 2.2: Summary statistics for ANCOVA of metric pairs: FTP-OGT, FTP-UILT, FTP-OS and OS-OE (ºC) with factor taxonomic family. Family intercepts significantly different from global intercepts for each metric pair (p<0.05) are indicated by (*). A dash (-) indicates lack of data.

Intercept Family FTP-OGT FTP-UILT FTP-OS OS-OE Acipenseridae 22.9399* - 9.44507* 7.3003* Anguillidae 21.8366* - 13.56522* 14.4466* Atherinopsidae - 23.8823* 8.81473* 7.3617* Catostomidae 21.5441* 22.5124* 14.36245* 9.5574* Centrachidae 21.2724* 27.0163* 13.00006* 8.2989* Clupidae 15.5615* 25.0762* 3.66259* 5.5805* Cottidae 22.4917* 19.821* 13.88521* 8.3064* Cyprinidae 20.3259* 24.9725* 16.96794* 8.8978* Cyprinodontidae 20.3859* 23.0693* 4.47915* 4.4222* Esocidae 14.5017* 26.7559* -2.32215* 6.7969* Gadidae 15.1129* 19.6806* 8.38157* 10.8154* Gastrosteidae 25.4977* 24.2661* 18.61398* 8.7255* Ictaluridae 22.0444* 25.8153* 17.34931* 14.4466* Lepisosteidae 23.7628* - 5.45242* - Osmeridae 21.5441* 18.629* 9.00676* 9.1646* Percichthyidae - 26.9218* 7.71651* 8.6126* Percidae 20.0033* 22.1524* 12.6382* 8.8017* Petromyzontidae 15.8627* 28.5758* 8.81473* 9.1156* Poecillidae 25.692* - - - Salmonidae 12.3755* 20.9531* 3.06234* 3.0856* Sciaenidae 18.0895* 26.0548* 14.54242* 11.0614*

Table 2.3: βs and DIC values for all Bayesian hierarchical models for four metric pairs: FTP-OGT, FTP- UILT, FTP-OS and OS-OE. Lower and Upper CI refer to the lower (2.5%) and upper (97.5%) confidence intervals for estimated βs values.

DIC DIC Model βs Lower CI Upper CI (forward estimation) (Inverse estimation) FTP-OGT 0.3382 0.1336 0.5452 314.010 638.084 FTP-UILT 0.3834 0.1685 0.5852 371.498 640.180 FTP-OS 0.3043 0.1086 0.4087 615.248 629.551 OS-OE 0.6054 0.483 0.7734 383.957 623.628

Table 2.4: Within family slope estimates generated using ANCOVA and Bayesian hierarchical models for four metric pairs: FTP-OGT, FTP-UILT, FTP-OS and OS-OE.

Metric Pair ANCOVA Bayesian Hierarchical Models FTP-OGT 0.1590 0.3382 FTP-UILT 0.2742 0.3834 FTP-OS 0.2626 0.3043 OS-OE 0.6114 0.6054

Figures

Figure 2.1: Linear Regression depicting a positive relationship between OGT (°C) and FTP (°C)for 55 North American freshwater fish species.

Figure 2.2: Linear Regression depicting a positive relationship between UILT(°C) and FTP (°C) for 62 North American freshwater fish species.

Figure 2.3: Linear Regression depicting positive relationship between OS(°C) and FTP (°C) for 75 North American freshwater fish species

Figure 2.4: Linear Regression depicting positive relationship between OE (°C) and OS (°C) for 71 North American freshwater fish species.

Figure 2.5: Trace plots for the parameter βs for four metric pair models: FTP-OGT (n= 124, 23 families), FTP-UILT (n=119, 25 families), FTP-OS (n=148, 26 families) and OS-OE (n=118, 22 families) estimated by five parallel chains for 173 North American freshwater fish species. αk parameters show the same convergence properties across all families

Figure 2.6: αk estimates (family specific OGT means) generated from the FTP-OGT Bayesian hierarchical model for North American freshwater fish species (n =124). Families are coded numerically in alphabetical order (1= Sturgeons (Acipenseridae), 2= Bowfin (Amiidae), 3= Eels (Anguillidae), 4= Neotropical silversides (Atherinopsidae), 5= Suckers (Catostomidae), 6= Sunfish (Centrarchidae), 7= Herrings (Clupidae), 8= Sculpins (Cottidae), 9= Minnows (Cyprinidae), 10= Killifish (Cyprinodontidae), 11= Pike (Esocidae), 12= Burbot (Lotidae), 13= Stickleback (Gastrosteidae), 14=Mooneyes (Hiodontidae), 15= Catfish (Ictaluridae), 16= Gars (Lepisostidae), 17= Temperate Perch (Percicthyidae), 18= Osmeridae (Smelts), 19= Perch (Percidae), 20= Trout-Perch (Percopsidae), 21= Lamprey (Petromyzontidae), 22= Mosquitofish (Poecillidae), 23= Salmon and Trout (Salmonidae), 24 =

Freshwater drum (Scianidae)). Family specific distributions were obtained for αk estimates across all metric pair models

Figure 2.7: Metric estimates for species with incomplete data generated by Bayesian hierarchical models across all four metric pairs: FTP-OGT, FTP-UILT, FTP-OS and OS-OE. Data indicates metric values obtained from the literature review. Estimate indicates metric estimates generated by metric pair specific Bayesian models

Appendices

Table A.1: Mean optimum growth temperature (OGT), final temperature preferendum (FTP), upper incipient lethal temperature (UILT), critical thermal maxima (CTMax), optimal spawning temperature (OS), and optimum egg development temperature (OE) data for 173 North A American freshwater fish species. Species are arranged alphabetically within families.

Temperature ( °C) Family Scientific Names Common Name OGT FTP ULIT CTMax OS OE Achiridae Trinectes maculatus Hogchoker 22.5 Acipenseridae Acipenser fulvescens Lake Sturgeon 11.0 15.0 14.5 Acipenser medirostris Green Sturgeon 20.8 17.0 34.0 12.1 15.0 Shortnose Acipenser brevistronum 27.1 26.2 34.4 11.7 15.3 Sturgeon Acipenser oxyrinchus Atlantic Sturgeon 16.0 15.6 18.6 oxyrinchus Acipenser transmontanus White Sturgeon 21.0 14.7 14.5 Scaphirhynchus Shovelnose 20.7 11.7 15.3 platorynchus Sturgeon Amiidae Amia calva Bowfin 30.3 37.0 Anguillidae Anguilla rostrata American Eel 25.0 19.9 Atherinopsidae Labidesthes sicculus Brook Silverside 24.5 30.6 20.0 Menidia beryllina Inland silverside 31.0 13.5 22.7 Catostomidae Carpiodes carpio River Carpsucker 35.2 21.7 Bigmouth Ictiobus cyprinellus 19.8 17.0 20.5 Buffalo Catostomus catostomus Longnose Sucker 11.1 26.8 10.0 12.5 Northern Hog Hypentelium nigricans 25.6 27.0 29.8 30.8 17.5 17.4 Sucker Carpiodes cyprinus Quillback 20.5 37.2 Minytrema melanops Spotted Sucker 21.8 31.0 Catostomus commersonii White Sucker 25.5 23.4 27.8 31.6 15.8 15.0 Catostomus discobolus Bluehead Sucker 20.1 20.5 discobolus Flannelmouth Catostomus latipinnis 25.9 34.1 13.8 Sucker Chasmistes liorus liorus June Sucker 13.0 15.7 Moxostoma anisurum Silver Redhorse 13.4 Moxostoma Shorthead 26.8 14.0 15.6 macrolepidotum Redhorse Moxostoma erythrurum Golden Redhorse 35.4 Xyrauchen texanus Razorback sucker 37.9 Catostomus clarkii Desert Sucker 17.5 35.6 Centrachidae Pomoxis nigromaculatus Black Crappie 18.0 23.4 33.3 34.9 19.2 18.2 Lepomis macrochirus Bluegill 29.2 30.2 32.2 40.2 25.0 23.0 Lepomis cyanellus Green Sunfish 28.0 25.4 40.0 36.0 21.9 29.1 Micropterus salmoides Largemouth Bass 26.6 28.6 31.9 38.4 19.2 20.0 Lepomis gibbosus Pumpkinseed 25.0 27.7 31.7 37.6 26.0 28.0

Temperature ( °C) Family Scientific Names Common Name OGT FTP ULIT CTMax OS OE Ambloplites rupestris Rock Bass 28.4 24.9 33.9 36.0 22.2 22.5 Micropterus dolomieui Smallmouth Bass 26.0 25.0 36.0 36.3 18.0 21.0 Pomoxis annularis White Crappie 22.5 19.1 32.8 17.0 19.2 Lepomis microlophus Redear Sunfish 23.9 23.1 23.1 Lepomis gulosus Warmouth 25.0 21.0 Ambloplites constellatus Ozark Bass 21.1 21.0 Orangespotted Lepomis humilis 20.5 sunfish Lepomis megalotis Longear sunfish 22.5 Bluespotted Enneacanthus gloriosus 29.5 36.0 37.5 sunfish Clupidae Alosa pseudoharengus Alewife 20.1 16.9 23.1 31.3 13.8 17.8 Alosa aestivalis Blueback Herring 24.4 32.9 20.8 23.0 Alosa mediocris Hickory Shad 19.5 19.5 Alosa sapidissima American Shad 15.3 30.0 31.8 15.8 13.0 Dorosoma cepedianum Gizzard Shad 17.0 20.7 35.5 31.7 22.0 22.2 Dorosoma petenense Threadfin Shad 29.3 23.8 25.0 Myoxocephalus Deepwater Cottidae 5.0 thompsoni Sculpin Myoxocephalus Fourhorn Sculpin 5.0 1.0 quadricornis Cottus bairdii Mottled Sculpin 16.2 24.3 30.9 11.4 12.6 Cottus cognatus Slimy Sculpin 11.0 22.8 26.1 7.3 Spoonhead Cottus ricei 6.0 5.0 Sculpin Cottus hypselurus Ozark Sculpin 28.8 Cottus carolinae Banded Sculpin 31.8 Cottus tallapoosae 31.4 Cyprinidae Notropis heterodon Blackchin Shiner 38.0 32.8 Rhinichthys atratulus Blacknose Dace 19.6 28.6 30.2 Bluntnose Pimephales notatus 26.2 24.1 31.5 29.9 Minnow Cyprinus carpio Carp 27.3 27.7 34.5 39.0 24.0 21.0 Central Campostoma anomalum 24.8 23.9 31.0 34.3 Stoneroller Luxilus cornutus Common Shiner 21.9 30.4 31.2 Semotilus atromaculatus Creek Chub 24.9 29.1 33.0 Notropis atherinoides Emerald Shiner 25.7 19.3 27.4 28.6 24.0 23.9 Semotilus corporalis Fallfish 22.0 Pimephales promelas Fathead Minnow 25.8 26.6 31.3 34.1 19.5 25.0 Chrosomus neogaeus Finescale Dace 24.1 30.3 32.2 18.5 20.0 Notemigonus crysoleucas Golden Shiner 25.0 21.8 32.0 33.4 20.3 20.0 Carassius auratus Goldfish 26.6 27.4 34.9 35.8 21.1 17.0 Rhinichthys cataractae Longnose Dace 15.3 31.4 11.7 15.6 Northern Chrosomus eos 25.3 29.2 29.0 Redbelly Dace Notropis anogenus Pugnose Shiner 16.5 Notropis rubellus Rosyface Shiner 25.5 25.3 33.0 33.6 24.3 21.1 Cyprinella spiloptera Spotfin Shiner 28.9 27.5 36.0 Notropis hudsonius Spottail Shiner 27.3 16.6 33.0 33.2 19.0 20.0

Temperature ( °C) Family Scientific Names Common Name OGT FTP ULIT CTMax OS OE Gila elegans bonytail 38.1 Gila robusta roundtail chub 23.8 33.3 17.7 Gila intermedia gila chub 26.0 37.0 18.0 Gila boraxobius borax lake chub 35.0 Hybognathus placitus plains minnow 39.9 Lepidomeda mollispinis virgin spinedace 33.4 Lythrurus umbratilis redfin shiner 20.5 21.0 Nocomis micropogon river chub 21.7 30.9 20.6 Notropis topeka topeka shiner 27.0 39.8 Couesius plumbeus lake chub 27.0 14.5 Ctenopharyngodon idella grass carp 27.1 38.8 26.4 Hypophthalmichthys Bighead carp 25.4 39.0 23.0 nobilis Iotichthys phlegethontis least chub 21.7 16.0 20.0 Northern Snyderichthys copei 22.7 29.0 32.8 Leatherside Chub Hybognathus hankinsoni Brassy Minnow 11.4 Clinostomus elongatus Redside Dace 17.6 Notropis percobromus Carmine Shiner 24.5 21.1 Cyprinella analostana Satinfin Shiner 22.5 25.0 Cyprinodontidae Fundulus diaphanus Banded Killifish 23.0 31.7 23.0 24.4 Fundulus heteroclitus Mummichog 24.3 25.0 27.6 39.8 Cyprinodon macularius Desert Pupfish 25.3 28.0 24.3 Fundulus zebrinus Plains Killifish 41.3 20.0 21.5 Esox americanus Esocidae Grass Pickerel 25.7 9.5 8.4 vermiculatus Esox masquinongy Muskellunge 25.1 25.4 32.2 32.0 12.8 13.5 Esox lucius Northern Pike 23.0 20.7 31.0 11.5 12.1 Esox niger Chain Pickerel 25.5 36.7 9.7 Esox americanus Redfin Pickerel 10.0 americanus Gobiidae Neogobius melanostomus Round Goby 31.6 17.5 Gadidae Lota lota Burbot 16.6 13.2 23.3 1.2 7.5 Brook Gastrosteidae Culaea inconstans 21.3 30.6 13.1 18.3 Stickleback Ninespine Pungitius pungitius 16.5 Stickleback Threespine Gasterosteus aculeatus 17.1 12.5 27.2 28.7 12.5 19.0 Stickleback Hiodontidae Hiodon tergisus Mooneye 28.0 Hiodon alosoides Goldeye 11.4 Ictaluridae Ameiurus melas Black Bullhead 35.4 37.5 Ameiurus nebulosus Brown Bullhead 30.0 26.2 33.4 37.9 21.1 22.8 Ictalurus punctatus Channel Catfish 29.5 27.3 32.9 36.7 25.0 22.9 Noturus flavus Stonecat 15.3 29.0 27.8 Ameiurus natalis Yellow Bullhead 28.2 36.4 Noturus albater Ozark Madtom 25.0 Noturus miurus Bridled Madtom 25.0 Northern Noturus stigmosus 23.0 Madtom 52

Temperature ( °C) Family Scientific Names Common Name OGT FTP ULIT CTMax OS OE Lepisosteidae Lepisosteus osseus Longnose Gar 26.4 27.4 22.6 Lepisosteus oculatus Spotted Gar 16.0 23.5 Percichthyidae Morone chrysops White Bass 27.3 33.5 35.3 15.5 17.5 Morone americana White Perch 28.5 29.8 36.0 17.5 20.0 Morone saxatilis Striped Bass 28.8 25.9 Hypomesus Osmeridae Delta smelt 25.4 transpacificus Osmerus mordax Rainbow Smelt 11.2 21.7 8.4 14.3 Eastern Sand Percidae Ammocrypta pellucida 24.6 Darter Ethestoma carolineum Rainbow Darter 19.9 32.1 Sander canadensis Sauger 22.0 19.6 10.3 13.5 Sander vitreus Walleye 22.1 22.5 29.7 23.4 7.7 12.2 Perca flavescens Yellow Perch 25.4 17.6 25.6 35.0 9.1 15.0 Eastern Sand Ammocrypta pellucida 24.6 Darter Etheostoma akatulo Bluemask darter Ethestoma carolineum Rainbow Darter 19.9 32.1 20.0 Greenbreast Etheostoma jordani 22.5 Darter Greenthroat Etheostoma lepidum 21.5 Darter Tessellated Etheostoma olmstedi 22.8 15.5 Darter Etheostoma gracile Slough Darter 22.8 Etheostoma nigrum Johnny Darter 22.8 32.4 16.5 Etheostoma percnurum Duskytail Darter 20.5 22.2 Waccamaw Etheostoma perlongum 21.5 Darter Etheostoma barbouri Teardrop Darter 14.0 Gymnocephalus cernua Ruffe 16.0 Percina pantherina Leopard Darter 14.5 Percina peltata Shield Darter 12.8 Sander lucioperca Pike-perch 28.0 9.0 15.8 Etheostoma cragini Arkansas Darter 36.7 Orangethroat Etheostoma spectabile 26.0 31.4 Darter Etheostoma flabellare Fantail Darter 33.4 Etheostoma blennioides Greenside darter 32.2 Percopsidae Percopsis omiscomaycus Trout-Perch 13.4 22.9 Petromyzontidae Petromyzon Marinus Sea Lamprey 17.5 10.3 31.4 15.4 18.5 Northern Brook Ichthyomyzon fossor 17.9 Lamprey Southern Brook Ichthyomyzon gagei 19.0 Lamprey Lampetra tridentata Pacific Lamprey 12.5 Poecillidae Gambusia affinis Mosquitofish 30.0 27.1 37.8 Eastern Gambusia holbrooki 36.4 40.0 Mosquitofish

Temperature ( °C) Family Scientific Names Common Name OGT FTP ULIT CTMax OS OE Thymallus arcticus Salmonidae Arctic Grayling 26.9 6.0 12.8 arcticus Salvelinus alpinus Arctic Char 11.0 10.5 22.0 33.8 alpinus Oncorhynchus gilae Gila Trout 8.0 Salvelinus confluentus Bull Trout 22.2 27.6 Salmo salar Atlantic Salmon 13.6 15.3 27.6 32.8 Coregonus hoyi Bloater 18.6 8.5 26.5 Salvelinus fontinalis Brook Trout 14.2 14.8 24.9 29.3 10.7 6.1 Salmo trutta Brown Trout 12.6 15.7 25.0 28.3 7.8 7.5 Oncorhynchus Chinook Salmon 14.3 13.8 23.5 25.1 tshawytscha Oncorhynchus keta Chum Salmon 13.0 14.1 Oncorhynchus kisutch Coho Salmon 13.6 14.4 24.3 27.6 6.1 7.2 Oncorhynchus clarkii Cutthroat Trout 17.8 14.9 21.4 28.0 Lake Herring, Coregonus artedii 18.1 12.4 23.9 3.3 5.6 Cisco Coregonus kiyi Kiyi 4.2 2.8 Salvelinus namaycush Lake Trout 10.0 11.8 24.3 Coregonus clupeaformis Lake Whitefish 14.7 12.7 23.9 3.3 5.0 Oncorhynchus gorbuscha Pink Salmon 15.5 13.0 10.0 7.3 Oncorhynchus mykiss Rainbow Trout 15.6 15.5 25.0 22.1 7.0 8.9 Proposium cylindraceum Round Whitefish 8.3 3.8 3.0 Oncorhynchus nerka Sockeye Salmon 15.0 13.7 27.8 8.6 8.3 Sciaenidae Aplodinotus grunniens Freshwater Drum 22.0 24.6 32.8 34.0 21.0 23.9 Central Umbridae Umbra limi 33.5 Mudminnow

Table A.2: Pearson and Spearman correlation values for growth, survival, and reproduction metrics for 173 North American freshwater fish species. Pearson correlation values are located left of the diagonal. Spearman correlation values are located right of the diagonal.

Growth Survival Reproduction OGT FTP UILT CTMax OS OE OGT 1 0.8508 0.6859 0.6245 0.6652 0.7117 FTP 0.8343 1 0.7807 0.6850 0.6622 0.6435 UILT 0.7300 0.7757 1 0.5664 0.6927 0.6880 CTMax 0.6563 0.7310 0.6411 1 0.5998 0.6084 OS 0.6334 0.6058 0.6871 0.5444 1 0.8886 OE 0.6377 0.6068 0.6615 0.5694 0.9013 1

Table A.3: Spearman correlation values for growth, survival, and reproduction metrics for 116 freshwater fish species represented in the Canadian freshwater fish phylogeny. Spearman correlation values for all metric pairs are located right of the diagonal. Spearman correlation values for phylogenetically independent contrasts for metric pairs are located right of the diagonal.

Growth Survival Reproduction OGT FTP UILT CTMax OS OE OGT 1 0.8508 0.6859 0.6245 0.6652 0.7117 FTP 0.4677 1 0.7807 0.6850 0.6622 0.6435 UILT 0.3390 0.4189 1 0.5664 0.6927 0.6880 CTMax 0.2900 0.4145 0.6411 1 0.5998 0.6084 OS 0.2526 0.5480 0.6871 0.5444 1 0.8886 OE 0.1940 0.5679 0.4650 0.2697 0.7260 1