The Structure of Freshwater and Diadromous Fish Assemblages

THE STRUCTURE OF FRESHWATER AND DIADROMOUS FISH ASSEMBLAGES

IN NOVA SCOTIA RIVERS AND LAKES AND THE CONTRIBUTION OF

NON-NATIVE PISCIVOROUS FISH TO FISH ASSEMBLAGE STRUCTURE USING

HISTORICAL DATA

Danielle E. A. Quinn

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science (Biology)

Acadia University Spring Convocation 2013

This thesis by Danielle E. A. Quinn was defended successfully in an oral examination on March 6, 2013.

The examining committee for the thesis was:

______Dr. John Colton, Chair

______Dr. Kurtis Trzcinski, External Reader

______Dr. Mark Mallory, Internal Reader

______Dr. Trevor Avery, Supervisor

______Dr. Jamie Gibson, Supervisor

______Dr. Soren Bondrup-Nielsen, Department Head

This thesis is accepted in its present form by the Division of Research and Graduate Studies as satisfying the thesis requirements for the degree of Master of Science (Biology).

I, Danielle E. A. Quinn, grant permission to the University Librarian at Acadia University to reproduce, loan or distribute copies of my thesis in microform, paper or electronic formats on a non-profit basis. I, however, retain the copyright in my thesis.

______Author

______Supervisors

______Date

iii

Table of Contents

List of Tables...... ix

List of Figures ...... xii

Abstract ...... xx

List of Abbreviations ...... xxi

Acknowledgements ...... xxvii

Chapter One ...... 1

Community Structure and Biodiversity: Introduction to Fish Assemblages in Nova

Scotia and Their Spatial and Temporal Variability ...... 1

Characterizing Fish Assemblages Using Ecological Community Indices ...... 3

Fish Assemblages in Nova Scotia ...... 7

Non-Native Species ...... 9

Modeling Abundance Trends in Ecological Data ...... 14

Model Selection ...... 17

Issues with Ecological Data ...... 18

Data Sources ...... 19

Chapter Two ...... 33

Trends in Fish Species Abundance and Characteristics of Fish Assemblages and

Structure ...... 33

Introduction ...... 33

Methods...... 35

Standardization and Filtration of Data ...... 35

Data Filtration...... 36

Resolution ...... 37

Modeling CPUA ...... 37

Model Testing...... 38

Model Selection Process ...... 40

Calculating Ecological Indices ...... 41

Results ...... 43

Modeling CPUA ...... 43

Model Selection ...... 43

River and Species Trends ...... 44

Statistical Versus Biological Significance ...... 46

Calculating Ecological Indices ...... 48

Species Richness and Diversity ...... 48

Homogenization and Differentiation ...... 49

Discussion ...... 49

Using Models in Ecological Studies ...... 49

Changes in Catch-Per-Unit-Area ...... 50

At-Risk Species ...... 50

Other Freshwater and Diadromous Fish Species ...... 51

Changes in Ecological Indices ...... 53

Species Richness and Diversity ...... 53

Homogenization and Differentiation ...... 54

Changes in Nova Scotia Fish Assemblage Structure ...... 57

Chapter Three ...... 77

Potential Contributions of Smallmouth Bass Presence and Establishment to Freshwater

and Diadromous Fish Assemblage Structure...... 77

Introduction ...... 77

Methods...... 78

Standardization and Filtration of Data ...... 78

Comparing Abundance and Rank ...... 78

Characterizing Fish Assemblages ...... 79

Comparing Abundance and Rank ...... 79

Catch-Per-Unit-Area (CPUA) ...... 79

Species Ranks ...... 80

Characterizing Fish Assemblages ...... 81

Species Richness and Diversity ...... 81

Species Composition, Homogenization, and Differentiation ...... 81

Results ...... 82

Comparing Abundance and Rank ...... 82

Atlantic Salmon ...... 83

American Eel ...... 83

Brook Trout ...... 84

White Sucker ...... 84

Other Species ...... 84

Characterizing Fish Assemblages ...... 85

Species Richness and Diversity ...... 85

Species Composition ...... 86

Homogenization and Differentiation ...... 86

Discussion ...... 87

Smallmouth Bass in LaHave River ...... 87

Influence of Smallmouth Bass on Native Fish ...... 87

Atlantic Salmon ...... 87

Brook Trout ...... 88

Small-Bodied Fish Species ...... 89

Other Species ...... 89

Influence of Smallmouth Bass on Fish Assemblage Characteristics ...... 90

Portfolio Effect ...... 93

Assessment of Results ...... 93

References ...... 108

Appendix A: Assessment of Data Sources and Preliminary Data Restructuring 121

Introduction ...... 121

Description of Data Sets ...... 121

Objectives ...... 123

Methods...... 123

Results ...... 124

Formatting ...... 124

Filtering Surveys for All Analyses...... 125

Editing Abundances ...... 125

Discussion ...... 126

vii

Appendix B: Model Selection and Testing ...... 130

viii

List of Tables

Table 1. Common categories of variations in the structure of fish assemblages, and associated metrics...... 23

Table 2. Common freshwater and diadromous fish species found in Nova Scotia rivers and lakes...... 24

Table 3. Numbers of native, non-native and total freshwater fish species in the 13 provinces and territories of Canada (Taylor 2004)...... 25

Table 4. Jaccard’s faunal similarity coefficients for native freshwater fish faunas among

13 provinces and territories of Canada estimated from species presence-absence data

(Taylor 2004)...... 26

Table 5. Terms and definitions based on an ecological approach to fish introduction

(Gozlan et al. 2010)...... 27

Table 6. Sanctioned releases of smallmouth bass (Micropterus dolomieu) in Nova Scotia

(LeBlanc 2010)...... 28

Table 7. Modeled percent annual change of catch-per-unit-area (CPUA) of select species in 12 rivers in Nova Scotia. Red indicates significant decline (p < 0.05, negative coefficient), green indicates significant increase (p < 0.05, positive coefficient), blue indicates no significant change, and black indicates a lack of data (i.e. no successful model)...... 58

Table 8. Observed and estimated species richness of freshwater fish assemblages in Nova

Scotia rivers and watersheds (lakes). The annual percent change in each richness index, at each resolution, was found using negative binomial generalized linear models. . 59

Table 9. Simpson and Shannon diversity indices of freshwater fish assemblages in Nova

Scotia rivers. The annual percent change in each diversity index, at each resolution, was found using negative binomial generalized linear models...... 60

Table 10. Summary statistics of smallmouth bass in LaHave River, as reported in the

Federal River Surveys data set. Catch-per-unit-area (CPUA) is defined as the number of individuals per 100 m2. The number of sites with smallmouth bass recorded, percent of total sites surveyed each year with smallmouth bass recorded, mean CPUA and range of

CPUA are included...... 95

Table 11. Annual change (%) in CPUA of select species between 2000 - 2010 in LaHave

River sites with no established smallmouth bass population (non-influenced) and sites in which smallmouth bass eventually become established (influenced). Change was estimated using negative binomial GLMs which modeled CPUA over time and used site type (non-influenced and influenced) as an interaction term. Brown trout, common shiner, and threespine stickleback had insufficient data for the model. Standard errors

(SE) were calculated as percent annual change in CPUA. No significant values at α =

0.05 were associated with any model...... 96

Table 12. Comparison of catch-per-unit-area (CPUA) of select species between survey types in LaHave River. Non-influenced sites are those in which smallmouth bass did not establish. Influenced sites are those in which smallmouth bass did establish. Early surveys of non-influenced sites occurred prior to 2000. Early surveys of influenced sites occurred prior to smallmouth bass establishment. Late surveys of non-influenced sites occurred between 2000 - 2010. Late surveys of influenced sites occurred post- establishment. Brown trout, common shiner, and threespine stickleback had insufficient

data for the analysis. Symbols indicate significantly higher (+) or lower (-) CPUA in survey type (a) than (b), using a Mann-Whitney test at α = 0.05. Double symbols indicate p < 0.01. Blanks represent no significant difference at α = 0.05...... 97

Table 13. Comparison of AP1 ranks (i.e. smallmouth bass and chain pickerel could occupy ranks) (A) and AP2 ranks (i.e. smallmouth bass and chain pickerel could not occupy ranks) (B) of select species between LaHave River survey types. Non-influenced sites are those in which smallmouth bass did not establish. Influenced sites are those in which smallmouth bass did establish. Early surveys of non-influenced sites occurred prior to 2000. Early surveys of influenced sites occurred prior to smallmouth bass establishment. Late surveys of non-influenced sites occurred between 2000 - 2010. Late surveys of influenced sites occurred post-establishment. Brown trout, common shiner, and threespine stickleback had insufficient data for the analysis. Symbols indicate significantly higher (+) or lower (-) CPUA in survey type (a) than (b), using a Mann-

Whitney test at α = 0.05. Single symbols indicate p < 0.05. Double symbols indicate p <

0.01. Blanks represent no significant difference at α = 0.05...... 98

List of Figures

Figure 1. Preliminary ranking of watersheds in Nova Scotia, based on a sum of rankings for all watershed impact indicators. Major and residual watershed groups are ranked separately. A low ranking does not indicate the absence of watershed impacts

(Hydrologic Systems Research Group 2012)...... 29

Figure 2. Global to local factors that influence the composition of fish assemblages

(Matthews 1998)...... 30

Figure 3. Theoretical view of changing ecosystem state over time, using an ecological metric. State A represents initial stable state, State B represents stable state resulting from a transition period. Critical slowing down (CSD) occurs prior to a trigger event (black point), which pushes the ecosystem into a state of transition, resulting in highly variable metric values...... 31

Figure 4. Example of a Gaussian (mean = 3, standard deviation = 1) (A) and Poisson

(mean = 1, standard deviation = 1) (B) distribution...... 32

Figure 5. Hierarchical algorithm used to calculate missing electrofishing site area values in the Federal River Surveys (FRS) data set. Missing area values refer to records in which area is not available. Applicable area values refer to the previously unavailable area values which have been calculated in each stage of the algorithm. The number of total applicable area values is the total number of area values available after each stage of the algorithm...... 61

xii

of successful models (b) associated with each species. Dashed line placed at a coefficient value of 0. Annotated values indicate the number of points outside the range of the x- axis...... 62

Figure 7. Fitted negative binomial GLMs for Atlantic salmon (A) and American eel (B) catch-per-unit-area (CPUA) in East River (Chester) between 1980 - 2010. Negative binomial GLMs also fitted for CPUA before and after 1995 (dashed line), by species (C,

D). Annual change in CPUA (%) as estimated by each model included, with decline shown in red. Double asterisks (**) indicate p < 0.01, and single asterisk (*) indicates p <

0.05. Of 189 electrofishing surveys of East River (Chester), 123 records of Atlantic salmon CPUA were 0, and ten records of American eel were 0. In addition, 29 records of

Atlantic salmon CPUA were less than 1, and seven records of American eel were less than 1...... 63

Figure 8. Fitted negative binomial GLMs for Atlantic salmon (A) and American eel (B) catch-per-unit-area (CPUA) in Ingram River between 1980 - 2010. Negative binomial

GLMs also fitted for CPUA before and after 1995 (dashed line), by species (C, D).

Annual change in CPUA (%) as estimated by each model included, with decline shown in red. Double asterisks (**) indicate p < 0.01, and single asterisk (*) indicates p < 0.05.

...... 64

Figure 9. Fitted negative binomial GLMs for Atlantic salmon (A) and American eel (B) catch-per-unit-area (CPUA) in LaHave River between 1980 - 2010. Negative binomial

GLMs also fitted for CPUA before and after 1995 (dashed line), by species (C, D).

Annual change in CPUA (%) as estimated by each model included, with decline shown in

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red. Double asterisks (**) indicate p < 0.01, and single asterisk (*) indicates p < 0.05.

...... 65

Figure 10. Observed species richness of 42 selected lakes in Nova Scotia between 1980 -

2010 (top). Individual lakes represented by arrows indicating change in observed species richness between surveys (bottom). Red arrows indicate a change in observed species richness...... 66

Figure 11. Observed species richness of nine select rivers in Nova Scotia, between 1980

- 2010. Fitted values of negative binomial generalized linear models overlaid by river

(blue), and p-value included...... 67

Figure 12. Observed species richness of watersheds (A) and rivers (B). The plotted values represent the richness of each site by year, grouped by river, and the number of years each river or watershed was surveyed included on the secondary y-axis.... 68

Figure 13. Simpson diversity of nine select rivers in Nova Scotia, between 1980 - 2010.

Fitted values of negative binomial generalized linear models overlaid by river (blue), and p-value included...... 69

Figure 14. Simpson (A) and Shannon (B) diversity indices of each river in the Federal

River Surveys (FRS) data set. The plotted values represent the diversity of each site by year, grouped by river, and the number of years each river was surveyed included on the secondary y-axis...... 70

Figure 15. Comparison of Jaccard’s coefficient of similarity (J) between watersheds in periods 1980 - 1990, and 2000 - 2010, using the Provincial Lake Surveys (PLS) data set.

Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not

xiv

change. Diagonal line represents a state of no change. Percent of total comparisons indicated for increasing and declining points. Data allowed for pairwise comparison (n =

378) of 28 of 49 watersheds...... 71

Figure 16. Comparison of Jaccard’s coefficient of similarity (J) between rivers in periods

1980 - 1990, and 2000 - 2010, using the Federal River Survey (FRS) data set. Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not change.

Diagonal line represents a state of no change. Percent of total comparisons indicated for increasing and declining points. Data allowed for pairwise comparison (n = 378) of 28 of

70 rivers...... 72

Figure 17. Comparison of Jaccard’s coefficient of similarity (J) between lakes in periods

1980 - 1990, and 2000 - 2010, using the Provincial Lake Surveys (PLS) data set. Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not change. Diagonal line represents a state of no change. Percent of total comparisons indicated for increasing and declining points. Data allowed for pairwise comparison (n =

861) of 42 of 163 lakes...... 73

Figure 18. Comparison of Jaccard’s coefficient of similarity (J) between sites in periods

1980 - 1990, and 2000 - 2010, using the Federal River Surveys (FRS) data set. Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not change. Diagonal line represents a state of no change. Percent of total comparisons

indicated for increasing and declining points. Data allowed for pairwise comparison (n =

3,321) of 82 of 650 sites...... 74

Figure 19. Comparison of Jaccard’s coefficient of similarity (J) between sites in periods

1980 - 1990, and 2000 - 2010, using St. Mary’s River data from the Federal River Survey

(FRS) data set. Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not change. Diagonal line represents a state of no change. Percent of total comparisons indicated for increasing and declining points. Data allowed for pairwise comparison (n = 78) of 13 of 90 sites...... 75

Figure 20. Comparison of Jaccard’s coefficient of similarity (J) between sites in periods

1980 - 1990, and 2000 - 2010, using Annapolis River data from the Federal River

Surveys (FRS) data set. Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not change. Diagonal line represents a state of no change. Percent of total comparisons indicated for increasing and declining points. Data allowed for pairwise comparison (n = 15) of six of 29 sites...... 76

Figure 21. Ranks of select species in LaHave River Surveys (LHS) without (W/O)

(black) and with (W) (red) smallmouth bass present using AP1 (i.e. smallmouth bass and chain pickerel could occupy ranks) (top) and AP2 (i.e. smallmouth bass and chain pickerel could not occupy ranks) (bottom). Double asterisks (**) indicate a Mann-

Whitney test result of p < 0.01. Single asterisk (*) indicates a Mann-Whitney test result of p < 0.05...... 99

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Figure 22. Observed species richness in LaHave River Surveys (LHS, 1978 - 2009) and

Provincial Lake Surveys (PLS, 1942 - 2008) without (W/O) (black) and with (W) (red) smallmouth bass. Double asterisks (**) indicate a Mann-Whitney test result of p < 0.01.

...... 100

Figure 23. Observed species richness in LaHave River surveys in relation to smallmouth bass establishment. Black plots represent non-influenced sites (in which smallmouth bass never established), and blue plots represent influenced sites (in which smallmouth bass eventually established). ‘Early’ refers to surveys of non-influenced sites prior to 2000, and surveys of influenced sites prior to smallmouth bass establishment. ‘Late’ refers to surveys of non-influenced sites between 2000 - 2010, and surveys of influenced sites after smallmouth bass establishment. Double asterisks (**) indicate a Mann-Whitney test result of p < 0.01...... 101

Figure 24. Shannon and Simpson diversity in LaHave River surveys without (W/O)

(black) and with (W) (red) smallmouth bass. Asterisk (*) indicates a Mann-Whitney test result of p < 0.05...... 102

Figure 25. Shannon (top) and Simpson (bottom) diversity in LaHave River surveys in relation to smallmouth bass establishment. Black plots represent non-influenced sites (in which smallmouth bass never established), and blue plots represent influenced sites (in which smallmouth bass eventually established). ‘Early’ refers to surveys of non- influenced sites prior to 2000, and surveys of influenced sites prior to smallmouth bass establishment. ‘Late’ refers to surveys of non-influenced sites between 2000 - 2010, and surveys of influenced sites after smallmouth bass establishment. Double asterisks (**)

xvii

indicate a Mann-Whitney test result of p < 0.01.Single asterisk (*) indicates a Mann-

Whitney test result of p < 0.05...... 103

Figure 26. Jaccard’s coefficient of similarity (J) at non-influenced sites (sites in which smallmouth bass never established) (bottom) before and after 2000 (n = 8), and influenced sites (sites in which smallmouth bass eventually established) (top) before and after smallmouth bass establishment (n = 14). Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not change. Diagonal line represents a state of no change. Percent of total comparisons indicated for increasing and declining points.

...... 104

Figure 27. Percent of LaHave River non-influenced sites (in which smallmouth bass never established) (black/grey) and influenced sites (in which smallmouth bass eventually established) (blue/light blue) with select species present. Only sites with both early

(black/blue) and late (grey/light blue) surveys were included (non- influenced n = 8, influenced n = 14)...... 105

Figure 28. Jaccard’s coefficient of similarity (J), observed species richness (S), Shannon diversity (Shan), and Simpson diversity (Simp) in site LHav110, LaHave River, between

1980 - 2010. Similarity coefficients calculated by comparing species presence between surveys. Red dashed line indicates the first record of smallmouth bass presence for site

LHav110. Black dashed lines are possible boundaries of ecological stable states, indicated as State A and State B, and were estimated visually. Smallmouth bass were not included in species richness or diversity measures...... 106

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Figure 29. Occurrence of smallmouth bass in watersheds and lakes in Nova Scotia, 1942

- 1993 (McNeill 1995)...... 107

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Abstract

The primary objectives of this study were to quantify changes in catch-per-unit-area

(CPUA) of freshwater and diadromous fish species, characterize freshwater and diadromous fish assemblages and evaluate the effects of non-native piscivores on fish assemblage structure in Nova Scotia using historical data provided by the Department of

Fisheries and Oceans Canada and Nova Scotia Department of Fisheries and Aquaculture.

Changes in CPUA of freshwater and diadromous fish species were modeled over time using negative binomial generalized linear models. Assemblages were characterized using ecological community indices, and non-parametric statistical analyses were used to assess potential contributions of smallmouth bass (Micropterus dolomieu) to assemblage structure. Nearly 40% of rivers analyzed had significant declines in CPUA of several species, with American eel (Anguilla rostrata) and Atlantic salmon (Salmo salar) accounting for more than 50% of all declines. Smallmouth bass may negatively influence

Atlantic salmon, chub and killifish CPUA and Atlantic salmon rank (of abundance) over time. Observed species richness and diversity were significantly lower in LaHave River surveys without smallmouth bass than surveys with them. Freshwater fish assemblages in the rivers and lakes of Nova Scotia have significantly changed over time. The significance of small-bodied species may be overlooked in the literature, and their influence on overall ecosystem processes should be examined more closely. The data sets are thought to underestimate smallmouth bass distribution in both rivers and lakes in

Nova Scotia.

List of Abbreviations

i. ALE Alewife

ii. AIC Akaike Information Criterion

iii. AME American Eel

iv. AMS American Shad

v. ANP Annapolis River

vi. ANN Annis River

vii. AP1 Rank Approach 1

viii. AP2 Rank Approach 2

ix. ARG Argyle River

x. ASI Atlantic Silverside

xi. ATS Atlantic Salmon

xii. AWH Atlantic Whitefish

xiii. BAK Banded Killifish

xiv. BEA Bear River

xv. BEL Belliveau River

xvi. BLB Black’s Brook

xvii. BND Blacknose Dace xviii. BNS Blacknose Shiner

xix. BRB Brown Bullhead

xx. BRT Brook Trout

xxi. BWT Brown Trout

xxii. CCH Creek Chub

xxi

xxiii. CDS Coldstream

xxiv. CHG Chegoggin River

xxv. CHP Chain Pickerel

xxvi. CHZ Chezzetcook River

xxvii. CLH Cole Harbour

xxviii. CLY Clyde River

xxix. CNH Country Harbour

xxx. COSEWIC Committee on the Status of Endangered Wildlife in Canada

xxxi. CPUA Catch-Per-Unit-Area

xxxii. CSD Critical Slowing Down

xxxiii. CSH Common Shiner

xxxiv. DFO Department of Fisheries and Oceans Canada

xxxv. Shannon’s Index of Diversity

xxxvi. Simpson’s Index of Diversity

xxxvii. EBP East Brook (Porter’s Lake) xxxviii. ECH East River (Chester)

xxxix. ECS Ecum Secum River

xl. ELO East River (Lockeport)

xli. ESH East River (Sheet Harbour)

xlii. ESM East River (St. Margaret’s)

xliii. ETB East Taylor Bay

xliv. FIS Fivespine Stickleback

xlv. FOS Fourspine Stickleback

xxii

xlvi. FRS Federal River Surveys xlvii. GGB Gegogan Brook xlviii. GISIN Global Invasive Species Information Network

xlix. GISP Global Invasive Species Programme

l. GLM Generalized Linear Model

li. GOF Goldfish

lii. GOL Gold River

liii. GSB Gaspereaux Brook

liv. GSH Golden Shiner

lv. GVB Granite Village Brook

lvi. HWB Halfway Brook

lvii. IDG Indian River (Guysborough)

lviii. IHL Indian Harbour Lakes

lix. IND Indian River

lx. ING Ingram River

lxi. IOBC International Organization for Biological Control of Noxious Animals and

Plants

lxii. ISH Isaac’s Harbour

lxiii. ISSG Invasive Species Specialist Group

lxiv. J Jaccard’s Coefficient of Similarity

lxv. JOR Jordan River

lxvi. LHV LaHave River lxvii. LIS Liscomb River

xxiii

lxviii. LTW Little West River

lxix. MAR Martin’s River

lxx. MED Medway River

lxxi. MER Mersey River

lxxii. MCG Mummichog

lxxiii. MDC Middle River (Chester)

lxxiv. MLE Maximum Likelihood Estimate

lxxv. MOS Moser River

lxxvi. MSQ Musquodoboit River

lxxvii. MUS Mushamush River

lxxviii. NNM Nine Mile River

lxxix. NNS Ninespine Stickleback

lxxx. NSDFA Nova Scotia Department of Fisheries and Aquaculture

lxxxi. NWH New Harbour

lxxxii. LAT Landlocked Salmon

lxxxiii. LCH Lake Chub

lxxxiv. LWH Lake Whitefish

lxxxv. PET Petite River

lxxxvi. PLS Provincial Lake Surveys lxxxvii. PRB Purney Brook lxxxviii. PRD Pearl Dace

lxxxix. QRB Quarterway Brook

xc. QUO Quoddy River

xxiv

xci. RBD Redbelly Dace

xcii. RBT Rainbow Trout

xciii. RDB Rodney Brook

xciv. RMV Roman Valley River

xcv. ROH Round Hill River

xcvi. ROS Roseway River

xcvii. SAB Sable River xcviii. SAC Sackville River

xcix. SDI Salmon River (Digby)

c. SFH St. Francis Harbour

ci. SGU Salmon River (Guysborough)

cii. SHA Salmon River (Halifax)

ciii. SHH Ship Harbour

civ. Estimated Species Richness

cv. Chao1 Estimate of Species Richness

cvi. Chao2 Estimate of Species Richness

cvii. Observed Species Richness

cviii. SLA Sea Lamprey

cix. SLM Salmon River (Lake Major)

cx. SLW Salmon River (Lawrencetown)

cxi. SMB Smallmouth Bass

cxii. SMI Smith Brook

cxiii. SML Smelt

xxv

cxiv. SMY St. Mary’s River

cxv. SPD Salmon River (Port Dufferin)

cxvi. SSB Sissibo River

cxvii. STB Striped Bass

cxviii. TAN Tangier River

cxix. TBB Taylor Bay Brook

cxx. THS Threespine Stickleback

cxxi. TID Tidney

cxxii. TMC Tomcod

cxxiii. TUS Tusket

cxxiv. WBP West Brook (Porter’s Lake)

cxxv. WHP White Perch

cxxvi. WSH West River (Sheet Harbour)

cxxvii. WSU White Sucker cxxviii. WTB West Taylor Bay

cxxix. YWP Yellow Perch

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Acknowledgements

I must express heartfelt gratitude to my supervisors Dr. Trevor Avery (Acadia

University) and Dr. Jamie Gibson (DFO) for all of their guidance, expertise, and patience. Trevor, I would have withered away without you providing me with fresh tomatoes and a sleeping bag for my office, and I will do my best to pay it all forward.

Thank you to the Department of Fisheries and Oceans, as well as the Nova Scotia

Department of Fisheries and Aquaculture for providing enormous data sets for me to explore, as well as financial support for this project. Thank you to Jason LeBlanc

(NSDFA) for his time and knowledge of the provincial lake data, and all those who spent countless hours digitizing electrofishing surveys.

Finally, and most importantly, thank you to my always supportive family.

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Chapter One

Community Structure and Biodiversity: Introduction to Fish Assemblages in Nova

Scotia and Their Spatial and Temporal Variability

As the world experiences biodiversity declines, environmental changes, and increased pressure on ecosystems and resources, there is a growing need to understand the complex linkages that exist at community, ecosystem, and even global scales (Kodric-

Brown and Brown 1993). The intricacies of these links are made even more challenging to decipher because natural variation at such scales causes a wide range of analytical problems. Characterizing changes in ecosystems through measures of species abundance and species assemblage features (e.g. diversity) are early steps to investigate these links.

Additional forces, such as establishment of non-native species, damming of waterways, urban population growth, or increased agricultural runoff can lead to various changes in all structural levels of ecosystems; including community and species assemblage structure (e.g. Bowlby et al. 2012).

Freshwater ecosystems are home to some of the world’s most threatened species, and have the highest extinction rates in North America (Taylor 2004). In Nova Scotia, four fish species, Atlantic salmon (Salmo salar), American eel (Anguilla rostrata), striped bass (Morone saxatilis), and Atlantic whitefish (Coregonus huntsmani), have been designated as species-at-risk by the Committee on the Status of Endangered Wildlife in

Canada (COSEWIC); Atlantic salmon as Endangered (COSEWIC 2010a), American eel upgraded from Special Concern (COSEWIC 2006) to Threatened in May, 2012, striped bass as Threatened (COSEWIC 2005) and upgraded to Endangered in May 2012, and

Atlantic whitefish as Endangered (COSEWIC 2010b). It is likely that changes in

freshwater ecosystems will also influence other freshwater and diadromous species, even if these species have not been assessed or are not at risk. In Nova Scotia, freshwater ecosystems are impacted by acidification, water control structures, roads, human land use and water withdrawal, among others (Figure 1) (Hydrologic Systems Research Group

2012). An additional, and growing, threat to the structure of freshwater ecosystems is the introduction of non-native species (Gozlan et al. 2010).

Statistical modeling and visualizations were used to observe underlying trends in characteristics of freshwater and diadromous fish assemblages of Nova Scotia, Canada.

Two data sets were analyzed to answer general and specific questions about spatial and temporal changes in fish assemblages and, where data were sufficient, characterize the effects of invasive aquatic species on these fish assemblages. In general:

1) How do fish assemblages in Nova Scotia vary spatially and temporally?

2) How do non-native piscivores affect the structure of fish assemblages in Nova

Scotia both spatially and temporally?

To answer these overarching questions, specific analyses were completed:

1) Evaluation of the utility of existing freshwater and diadromous fish assemblage

data for assessing fish assemblage structure in rivers and lakes (Chapter 2).

2) Modeling of changes in standardized relative abundance for riverine fish both

spatially and temporally using generalized linear models (GLMs) (Chapter 2).

3) Characterization of the riverine and lake fish assemblages using ecological

community indices (e.g. diversity indices, species richness, abundance, etc.) and

model temporal and spatial changes, where possible (Chapter 2).

4) Where possible, conduction of statistical analyses comparing the fish assemblages

in rivers and lakes with and without chain pickerel (Esox niger) and/or

smallmouth bass (Micropterus dolomieu) (Chapter 3).

Characterizing Fish Assemblages Using Ecological Community Indices

An assemblage is defined as a taxonomic subset of an ecological community, and may include a broad range of organisms. A fish assemblage is part of a larger community which may include amphibians, mammals, insects, plants, and other life forms present in a typical aquatic ecosystem. The relative importance of physical and biological factors in structuring fish assemblages is greatly debated. An extremely complex series of factors from global to local scales influence assemblage structure (Figure 2). In general, most researchers agree that assemblage structure is primarily related to the abiotic environment, species interactions and biogeographical events (Matthews 1998, Robillard and Fox 2006, Rodriguez and Lewis 2006, Reyjol et al. 2008). Significant variations in fish assemblage structure can be seen on local and regional scales, and may be grouped into categories reflecting how variation is qualified. Commonly studied categories include quantitative aspects of variation, such as abundance, and taxonomic variation, but depending on what questions are being considered, the scale of inquiry, and the availability of appropriate data, many more categories may be constructed (Table 1).

In simple terms, fish assemblages can be characterized by species richness and diversity. The basic measure of species richness ( ) is the number of species observed in an assemblage (Gotelli and Colwell 2011). Species richness can provide vital information about biodiversity by evaluating rarefaction. Rarefaction involves calculating a rarefied curve by averaging the accumulation of over random sites. This can be

used to compare among sites that have been sampled using different sampling efforts (Hughes et al. 2001). However, estimating the number of species actually present in an assemblage ( ) is more difficult to determine (Gotelli and Colwell 2001).

Richness estimators are used to estimate , and often involve extrapolation from species accumulation curves. Because the actual species richness of a given site is finite, species accumulation curves, which show how increases as sampling effort increases, eventually result in an asymptote (Hughes et al. 2001). Walther and Moore

(2005) found that estimator methods involving extrapolated asymptotes are inferior to non-parametric estimators, particularly Chao, which are better suited for and perform better with most data of this type. These estimators can be used in two ways: (a) as applied to abundance data (Chao1), or (b) as applied to replicate incidence data (Chao2)

(Gotelli and Colwell 2011). Using each of these methods, a correction factor is applied to

to find , by identifying singleton and doubleton species (Hughes et al. 2001;

Gotelli and Colwell 2011). Singleton species, in the context of abundance data, are those species for which only a single individual was found in a particular survey. Doubleton species are those species for which two individuals were found. When applied to replicate incidence data, these values are based on frequencies of species represented in only one or two surveys. The Chao1 estimate is calculated with:

where is the estimated species richness, is the observed species richness, is the number of singleton species, and is the number of doubleton species. The Chao2 estimator is found with:

( ) ( )

where is the estimated species richness, is the observed species richness, is the number of surveys, is the number of species encountered in one survey, and is the number of species encountered in two surveys (Gotelli and Colwell 2011).

Species richness is a fundamental way of describing assemblages and, thus, communities, and forms the basis of ecological conservation strategies, which often aim to maximize species richness (Gotelli and Colwell 2001). However, the limitation of using only species richness to characterize fish assemblages is that while the number of species can be estimated, there is no information gained about the relative abundances of these species or the species assemblage. More intricate diversity measures are based on functions of both species richness and evenness, which is the measure of how different abundances in assemblages are from each other (Maurer and McGill 2011). For example, assemblages would be considered ‘even’ if every species had the same abundance.

Shannon diversity ( ) is an index commonly used to characterize species diversity in assemblages, and is found with:

∑

where is the proportion of abundance for species i. Inverse Simpson diversity ( ), hereafter referred to as Simpson diversity, is the probability that two individuals drawn from an infinite assemblage would be different species, and is found with:

∑ where is the proportion of abundance for species i (Maurer and McGill 2011). These measurements of assemblage structure reflect both abiotic (e.g. water temperature) and

biotic (e.g. predator-prey relationships) factors, and may be additionally influenced by various disturbances, such as the acidification of water, or establishment of invasive piscivores.

While Simpson diversity, and related measures, allow for a more in-depth look at relative abundance of species present in assemblages, it is not informative about which species are actually present. Examining and comparing species composition, including what species are present, and in what relative or absolute abundances, gives additional insight into the structure of fish assemblages. When dealing with a large number of samples (surveys), it is practical to compare species composition by using a similarity (or dissimilarity) matrix. Such a matrix will act as a means of organizing pairwise comparisons of species composition in each sample, with each comparison presented as a coefficient of similarity.

Jaccard’s coefficient of similarity ( ), can be used to compare the incidence of species across multiple assemblages, with:

where is the number of shared species between any two assemblages being compared,

is the number of species found in only the first assemblage, and is the number of species found in only the second assemblage (Taylor 2004, Magurran and McGill 2011).

Similarity matrices can be used to quantify homogenization, or species assemblages becoming more similar over time. If homogenization occurs, it represents a clear loss of biodiversity across the areas being examined (Taylor 2004).

Ecological systems are dynamic, but also balanced, so they often experience gradual changes in abundance or composition of species and rates of ecological processes

in response to environmental conditions. Thus, most systems have the capacity to be stable in multiple states such as alternating between high and low, yet stable, population levels (Heath 2006, Bestelmeyer et al 2011). The balance aspect mitigates or buffers change, but only until a threshold is reached. Trigger events or pulse disturbances are caused by changing ecological drivers to the point of reaching critical thresholds, or singular events such as invasive species introduction or fire; any of which may lead to tipping points that shift populations into a different stable state or an unstable state.

Positioned between triggers and alternate stable states can be highly variable transition periods, which may be detected using various metrics (Bestelmeyer et al. 2011). In addition, as a some critical point is approached, systems may experience a period of time referred to as ‘critical slowing down’, which may be detected with early warning signals

(Dakos et al. 2011) (Figure 3). During the period of critical slowing down, the rate of recovery of a system from small disturbances decreases (Drake and Griffen 2010,

Scheffer 2010, Carpenter et al. 2011). Carpenter et al. (2011) found that early warning signals could be used to predict regime shifts in temperate lake ecosystems when top predators were gradually added to the system. However, without reliable baseline data, both critical slowing down and tipping point detection is difficult (Drake and Griffen

2010), and the use of mathematical modeling in non-experimental scenarios may be limited (Carpenter et al. 2011).

Fish Assemblages in Nova Scotia

Measures of species richness, diversity, and similarity have been used to describe freshwater and diadromous fish assemblages across North America (Rodriguez and

Lewis 2006; Long et al. 2012), Canada (Matthews 1998; Taylor 2004), and Nova Scotia

(Gilhen 1974). In Canada, trends are evident from local to national scales. Nova Scotia freshwater fish assemblages commonly consist of a subset of 39 freshwater and/or diadromous fish species (Table 2). Species richness and diversity are reported to be highest in Cumberland County, and steadily decrease along the northeast and southwest extremes of the province (Gilhen 1974; Davis and Browne 1998). For example, blacknose dace (Family: Cyprinidae) are historically found only in Cumberland County, and lake chub (Family: Cyprinidae) are not found in Cape Breton (Gilhen 1974). Gilhen

(1974) suggested that this fragmentation was a result of fewer freshwater pathways in the northeast and southwest areas of Nova Scotia. The number of freshwater fish species present in each of Canada’s provinces and territories was summarized by Taylor (2004), and Nova Scotia was found to have ten fewer species than New Brunswick, which is comparable in both total lakes and rivers and water area, but has more freshwater connectivity (Table 3). Ontario and Quebec had 154 and 113 extant species, respectively.

Using Jaccard’s similarity coefficient, Taylor (2004) found that in comparison with Nova

Scotia, the similarity of native freshwater fish assemblages decreased in a northwest pattern across Canada (Table 4). For example, fish assemblages in Nova Scotia were more similar to those found in New Brunswick ( than those species found in

British Columbia ( . When non-native species were included in the analysis, it was found that 64% of pairwise comparisons showed an increase in similarity. This quantification of homogenization provided evidence for the hypothesis that Canada’s freshwater fish assemblages, on a regional scale, are becoming less diverse as a result of the introduction of non-native species (Taylor 2004).

Non-Native Species

When discussing the subject of non-native species, it is essential that relevant terms are defined clearly (Table 5). A native species is one which occurs naturally in a specific body of water, while a non-native species (also known as an exotic, non- indigenous, or alien species) is one which has been introduced, or released into a habitat outside of its natural range or distribution. Non-native species are classified as invasive if the introduced species reproduces, forms self-sustaining populations, increases its range rapidly, and presents a risk to native species (Gozlan et al. 2010). Essentially, if a non- native species has the ability to successfully integrate itself into an area outside of its natural range it is possible that it will have a detrimental effect of some degree on native species (Gozlan 2009).

At a global scale, the number of non-native species has doubled in the past thirty years. In 2010, over 1,400 species were identified as non-native in Canada alone

(Federal, Provincial and Territorial Governments of Canada 2010). An increase in human mobility, primarily in relation to global trade, has amplified the vectors by which these species move or are transported (Gozlan et al. 2010). Currently, there are dozens of international organizations committed to working endlessly on the prevention, eradication, and monitoring of non-native species. These include the Global Invasive

Species Information Network (GISIN), the International Organization for Biological

Control of Noxious Animals and Plants (IOBC), the Invasive Species Specialist Group

(ISSG), and the Global Invasive Species Programme (GISP). Worldwide, fish species are commonly introduced, with an estimated 624 non-native fish species currently (Gozlan et al. 2010). Although only 8% of aquatic species introductions are accidental, many of

these instances are thought to be a direct result of dispersal through ballast water of ships

(Ruiz et al. 1997; Wonham et al. 2000). This often means that species are being carried over long distances, even overseas, before being released. It has been estimated that over

10,000 species are carried around the world every week (Gozlan et al. 2010).

At a more local scale, sanctioned introductions of fish species have been common, with over half of such instances related to food aquaculture (Casal 2006). The introduction of non-native species is also closely linked with recreational fisheries, which may be sanctioned or unsanctioned, either intentionally or unintentionally (Cambray

2003; Gozlan et al. 2010). For example, government stocking of lakes for sport fishing purposes, the intentional release of non-native species by anglers, and the accidental escapement of juvenile non-native bait fish are all vectors by which non-native species enter and move through regional waters. The consequences of these releases are difficult, if not impossible, to accurately predict.

Aquatic invasive and introduced species may drastically influence the community structure of ecosystems they enter through predation, increased competition for resources, habitat alterations, hybridization, and introduction of diseases and/or parasites (Gozlan et al. 2010). Predation is thought to be a major factor contributing to changes in fish assemblage structure (Brazner and Beals 1997; MacRae and Jackson 2001; Kraft 2009), and small-bodied fish are often most vulnerable to non-native piscivores due to piscivoran size-selective feeding. Populations of predation-intolerant species are often severely compromised (MacRae and Jackson 2001). The consequence of non-native piscivores becoming established in freshwater systems is often rapid population increases of introduced fishes followed by declines and/or extirpation of local or native fishes

(Kraft 2009). Primarily, these piscivores influence native fish assemblage structure by significantly changing or simplifying local food webs (Molles 2005). This

“simplification” results in less diverse communities (Brown et al. 2009). For example, small-bodied fishes such as dace (Family: Cyprinidae) often become reduced or absent from watersheds in which large invasive predators such as smallmouth bass (Family:

Centrachidae) or chain pickerel (Family: Esocidae) have become abundant (Brazner and

Beals 1997). Homogenization can result.

Two non-native piscivores that have been hypothesized to affect fish assemblages in Nova Scotia rivers and lakes are smallmouth bass (Micropterus dolomieu) and chain pickerel (Esox niger). Both are known to be invasive and introduced, and are considered alien as they were not historically present in Nova Scotia. The focus of this study is made feasible by the presence of historical data regarding the presence and distribution in Nova

Scotia of these two species. A historical perspective is warranted.

Smallmouth bass first arrived in Nova Scotia in 1942 via a sanctioned release into

Bunker Lake, Yarmouth County, Nova Scotia (McNeill 1995). Livingstone (1952) did not list smallmouth bass as a species in Nova Scotia suggesting they were virtually unknown as a local species at that time. Legal introductions of smallmouth bass occurred into an additional 16 lakes until 1984 (Table 6; LeBlanc 2010). Since they were first introduced, many unauthorized introductions into other watersheds has led to 188 rivers

(as of 2008) and lakes in Nova Scotia having successfully established and actively colonizing populations of smallmouth bass (LeBlanc 2010). As of October, 2010, records indicate 241 lakes with smallmouth bass populations (Department of Fisheries and

Aquaculture 2010), indicating the range of these species continues to grow. While

smallmouth bass are an invasive species with the potential to negatively impact local fishes and community structure, anglers report smallmouth bass to be among their favorite sport fish, and contribute an estimated $7 million to the economy annually through their sportfishing activities (LeBlanc 2010). This dichotomy leads to social and economic friction between anglers and those involved in conservation and/or management.

When smallmouth bass are introduced and become established, they are thought to prey heavily on small fishes. In many rivers in Nova Scotia, these small fishes are often cyprinid species including dace, shiners and chub, or may be other species including perch (Family: Percidae), sculpins (Family: Cottoidae), and trout (Family:

Salmonidae) (Chaput and Caissie 2010). Ontario lakes containing non-native smallmouth bass populations are home to an average of 2.3 fewer small-bodied species than those without (MacRae and Jackson 2001). As well, there is potential for competition for habitat between juvenile smallmouth bass and juvenile Atlantic salmon, and Atlantic salmon smolt are likely preyed upon by larger smallmouth bass during seaward migration

(Chaput and Caissie 2010). Chaput and Caissie (2010) report the overall risk of smallmouth bass populations to changes in aquatic biota (fish assemblages) in lakes to be

“high with low uncertainty”, and in rivers to be “moderate with high uncertainty”. They hypothesized that smallmouth bass become dominant predators and cause reductions in native fishes.

The history of chain pickerel in Nova Scotia is not as well documented.

Compared with the information available regarding smallmouth bass in Nova Scotia, relatively little is known about the introduction, distribution, and potential impacts of

chain pickerel. Chain pickerel were illegally introduced into three lakes in 1945 (Mitchell

2012; Mitchell et al. 2012), with the intention of creating an alternative recreational fishery (Mitchell 2012). The species are now known to be present in 95 locations in Nova

Scotia, with most records in the southern portion of the province (Mitchell 2012). Like smallmouth bass, the spreading of chain pickerel is likely through illegal movement of live fish by anglers (Mitchell 2012, Mitchell et al. 2012).

Chain pickerel may greatly threaten native fish populations (Allen 2007); maybe more so than smallmouth bass due to their aggressiveness and comparably larger size.

Fierce predators, chain pickerel have been found to prey upon all other fish when introduced to water bodies, and reduce once complex fish assemblages to simply those individuals (and species) too large to be eaten (Mitchell et al. 2012). Once this complex food web has been destroyed, adult chain pickerel feed primarily on insects and/or become cannibalistic (Mitchell 2012). Warner et al. (1968) noted chain pickerel to be a common predator of Atlantic salmon in Maine, demonstrating the potential impacts on at- risk species.

As fish communities are altered by the establishment and colonization of smallmouth bass and chain pickerel, the impacts of species assemblage changes may be extensive. If nodes of a complex ecosystem food web are removed at the aquatic vertebrate level, it may lead to a cascading effect, in which many or all nodes of the food web are affected, and thus, the entire ecosystem is affected (Gozlan et al. 2010;

Wasserman et al. in press). For example, nutrient cycling, phytoplankton and zooplankton production and feeding habits of avian and mammalian fish predators could all be impacted (Mitchell 2012). The additive and multiplicative properties of these

changes may pose a significant threat to communities or ecosystems. The indication of a significant ecological change occurs when individual or cascading changes resulting from the presence of non-native species leads to an overall decrease in ecosystem function, either gradually or abruptly. This leads to ecosystems with decreased resilience, and ecosystem components, such as individual species, that are vulnerable to disturbance also decrease in abundance or become extirpated (Gozan et al. 2010). In the context of ecosystem change, the introduction of an invasive species may lead to a period of critical slowing down, followed by a tipping point and abrupt changes in fish assemblage structure or processes, or may act as a trigger event itself.

If long-term data sets are available, the contributions of invasive piscivores to freshwater and diadromous fish assemblages may be characterized, especially if such data have enough samples collected before and after invasive species establishment. By examining abundance trends over time of each species in an assemblage, and comparing species abundance, richness, diversity, and composition, the overall effects of non-native species may be differentiated from other factors, such as changes in water quality or watershed connectivity.

Modeling Abundance Trends in Ecological Data

In quantitative ecology, the dependent (or response) variable can consist of counts, or standardized counts (e.g. density). Count data do not follow a Gaussian

(normal) distribution, and are better described with the non-normal Poisson or a related distribution (Figure 4) (Dobson and Barnett 2008). As well, Poisson data can be positive or right skewed because of increased zero values (Zuur et al. 2009). Least-squares linear regression is rarely appropriate for modeling the relationship between dependent and

independent variables if data do not follow a normal distribution, where relationships are non-linear, or where factors are categorical. In these cases generalized linear models

(GLMs) are well-tested and may be appropriate (Cameron and Trivedi 2001; Wood 2006;

Zuur et al. 2009). GLMs are an extension of linear models which allow the response variable to have a non-normal distribution and independent variables can take a continuous or categorical form. The basic structure of a GLM consists of two parts:

where , and ~ exponential family distribution, and:

where g is a link function, is the ith row of a model matrix X, and β is a vector of unknown parameters (Wood 2006). The link function is used to define the relationship between the predictor and the distribution, and the canonical (standard) link function employed differs based on the distribution of the data (Dobson and Barnett 2008; Zuur et al. 2009). Despite the flexibility of GLMs, there are instances in which count data are not effectively modeled using the Poisson distribution. An assumption of the Poisson distribution is that the mean and variance are equal. However, if the variance is larger than expected, this assumption is violated and the data are said to be overdispersed. The dispersion parameter ( ) is found with:

where is the dispersion parameter, D is the deviance, which is the fit of the observed values to the expected values, , and is the degrees of freedom.

Overdispersion often arises because of a poor model fit, caused by covariates or variable interactions not accounted for in the model. Other times, overdispersion is

considered “real”, that is, not caused by covariates or interactions, but caused by either count data having more zeros than expected, or skewed data. If such data are modeled using the Poisson distribution, model results show deflated standard errors and inflated t- statistics (Cameron and Triveldi 2001). When is calculated to be between approximately 1.5 and 15, a quasi-Poisson model can be used to correct the deflated standard errors. If is greater than 15, then Zuur et al. (2009) suggest using other methods, such as the negative binomial model, which allows for the use of an extra parameter to adjust the variance independently of the mean (Cameron and Triveldi 2001).

The negative binomial model is recognized as a descriptor for animal distribution patterns, especially in areas with low densities (Hubert and Fabrizio 2007). If the negative binomial GLM is to be compared to the Poisson GLM, the same link function must be used for both. The canonical (standard) link function used in Poisson models is log, defined as:

Using this link function allows the negative binomial GLM to be nested within the

Poisson GLM. Nested models are characterized by the ability of complex models (e.g. multifactorial GLM) to be reduced to simple models (e.g. linear model) if specific components of their equations are set to zero. This quality facilitates the creation of multiple models in which distributions and dispersion parameters vary because the complex models converge to the simple models as necessary (Dobson and Barnett 2008;

Zuur et al. 2009).

The key values in the output of these models are the coefficients, intercept ( ) and slope ( ) values. The slope value is used to determine rates of change characterized

by fitted model. In the context of species abundance, a rate of change is interpreted as the percent decline (if is negative) or increase (if is positive). Conveniently, the exponential structure of these models is inherently related to the biological processes involved in changes in species abundance. This interpretation of the parameters is relevant because organizations such as COSEWIC use percent decline in abundance as a metric of identifying species at risk.

Model Selection

Models containing multiple parameters are often evaluated by finding the maximum likelihood estimate (MLE), which determines the set of parameters that gives the observed data the highest probability of having occurred (Bolker 2008). While actual values of MLEs do not give information about models, likelihood ratio tests are used to compare fits of nested models by calculating how more likely observed data are using one set of parameters than another. Likelihood ratio tests are limited by the fact that as additional parameters are added, the likelihood will increase because of the additional parameters. As well, as complexity of the model increases, accuracy may not. A common method used to evaluate the balance of accuracy and complexity in the models is the

Akaike Information Criterion (AIC):

where L is the log-likelihood and k is the number of parameters in the model (Bolker

2008; Zuur et al. 2009). Like MLE, AIC values can be used to compare truly nested models (i.e. models incorporating the same data points). There are no associated critical or p-values, but generally a difference of greater than four indicates a distinguishable difference between model fits (Bolker 2008).

One challenge in ecological statistics is that selecting the best fitting model statistically may or may not result in a model that is biologically relevant. When modeling ecological data, it is essential that models are examined visually, and biological factors that cannot be included in the model (e.g. because of lack of data) are recognized.

Because ecological data is inherently variable, both quantity and quality of data are important. Models often fit best when the data are filtered to increase data quality at each factor, but this may reduce the amount of data available to construct models, rendering them impractical or uninformative. Increasing parameters (factors) within models also exacts a penalty on data quantity across factors. AIC values essentially strive to solve these issues, but, alone, they may not be sufficient in determining which model should be used when dealing with ecological information. Therefore, the process of model selection must include data visualization and biological interpretation.

Issues with Ecological Data

It is important to observe and record information (“data”) at spatial and temporal scales that match those of the processes that underpin the dynamics of fish assemblages, or that provide conditions for the colonization of invasive aquatic species (Swetnam et al.

1999). Long-term response data captures natural variation within processes occurring over long periods such as changes in fish assemblages, increases or decreases in species abundances, and/or regime shifts within populations (e.g. Capitán and Cuesta 2010).

Drivers that affect natural variation are also collected over long periods, but may not link directly with the response of interest. In the case of this study, the presence and abundances of two widespread invasive species means that the long-term data must span multiple sites, such as rivers and sampling sites within rivers, to investigate responses on

broad spatial and temporal scales. Further, sites with and without these invasive species are required to investigate the effects of their colonization. These spatially and temporally matched data should increase the predictive ability of models and their usefulness in comparisons.

Generally, long-term data do not come from one source or are not collected using one protocol; rather they must be compiled from past research from various sources. It is rarely a viable option to collect long-term data from scratch, even though doing so would allow the inclusion of factors of interest. Indeed, long-term data from many sources is often necessary when dealing with a subject matter as multifaceted as ecosystem dynamics especially in a retrospective or historical analysis. It is helpful if the data vary not only temporally and spatially, but also at various resolutions that bridge multiple facets of the ecosystem (e.g. biological, physical, and chemical data). This is especially true when considering long periods over which many natural processes occur. Long-term data potentially can provide reliable baseline (before) data for before-and-after comparisons of established invasive species. It is common for this type of data to come from various sources and collections including research laboratories, government archives, and published and unpublished literature (Kodric-Brown and Brown 1993).

Data Sources

To undertake studies which depend on compiled data (“data sets”), particularly those from long-term, historical data, three tasks are presented:

1) Collect data sets or original recording logs,

2) Organize and compile all data into a data set or multiple data sets in a meaningful

format, and

3) Identify potential gaps, errors or biases prior to analysis so that, if possible, gaps

can be filled, errors corrected and biases mitigated.

These processes can be tedious and time consuming, as well as potentially influential to study outcomes if done without due care. Therefore, analysis soundness is directly based upon the quality of the data. Without a sound data set, analyses run the risk of having fundamental biases (Kodric-Brown and Brown 1993).

The primary source of data for this study, the Federal River Survey data set

(FRS), was provided by the Department of Fisheries and Oceans Canada (DFO).

Electrofishing involves sending electric currents through the water that influence fish behavior either by causing oscillotaxis (forced thrashing motion) or electrotaxis (forced swimming motion), which leads to capture (Reynolds 1996). This is an effective method for capturing fish from streams and littoral zones of lakes (Hubert and Fabrizio 2007).

The FRS data set has electrofishing survey records from 644 sites, dating from July 1965 to September 2009. A second source of data is the Provincial Lake Survey data set (PLS), which was provided by the Nova Scotia Department of Fisheries and Aquaculture

(NSDFA), and was extracted from the FINS database. The PLS data set has few sites with multiple records, but is used in complementary analyses throughout this study.

These extensive data allow for the exploration of the establishment and potentially colonization of smallmouth bass and (potentially) chain pickerel and their effects on native or well-established fish assemblages within both rivers and lakes, and for explorations of temporal and spatial trends in fish assemblages and/or specific species.

Arguably one of the most essential aspects of experimental design and data analysis is the standardization of sampling effort and sampling protocols (Peterson and

Paukert 2009). Variable sampling effort within and between data collection events may result in biases during analysis, which may lead to abundance patterns, population trends, or community characteristics that are not representative of the actual ecosystem (Kodric-

Brown and Brown 1993; Williams et al. 2002). Many large-scale data sets compiled from taxonomic and biogeographic literature face the problem of variable sampling effort which is not only compounded as the number of contributing data sets increases, but further complicated as the effectiveness of a standardization technique decreases (Kodric-

Brown and Brown 1993; Bonar et al. 2009). In cases of biological sampling, both sampling effort and sampling protocols may vary over time, perhaps as gear and, consequently, survey efficiency changes, personnel skill and effectiveness fluctuates, and overall research objectives are modified (McLaughlin et al. 2001). In addition, field observations are often opportunistic, or a result of focused effort by the observer based on expected results. Count data of multiple species is often biased against species with certain morphological, behavioral or ecological characteristics (Kodric-Brown and

Brown 1993; Williams et al. 2002). Temporal and spatial gaps in data are also common, and hinder comparisons over long periods where noncontiguous data exists or where biological characteristics vary across environmental gradients (McLaughlin et al. 2001;

Williams et al. 2002). Swetnam et al. (1999) explain that historical data tend to experience a ‘fading record’ problem, in which the reliability of the data decreases with age due to a lack of preservation, degradation, or loss of evidence through time.

Fortunately, the primary data set used in this study was collected by a single organization, which reduces the severity of some of these issues. For one, sampling protocols have changed very little, and because electrofishing areas and shocking times are reported,

standardizing abundance values is relatively simple. Thus, to calculate catch-per-unit- area (CPUA), an appropriate index of abundance, standardizing relative abundance values based on the initial electrofishing pass of a particular survey is straightforward. As well, the method of electrofishing – using a pulsed current – is known to result in highly representative data, because it is a non-selective method of capture (Rodriguez and Lewis

2006).

Practical or typographic problems with compiling data sets include formatting issues, such as variations in species codes or date structure, and the need to evaluate the quality of the data from various sources (i.e. “primary data quality”). Williams et al.

(2002) identifies two areas of concern. First, variables are often misidentified due to shared area (or site) names. Second, the precision of species identification often varies.

To combat formatting and precision issues, multiple standardization treatments are likely necessary. Data filtration allows for identification of data that contributes strong bias, and also allows building of data subsets for specific analyses. Identification and subsequent mitigation of biased data will strengthen the credibility of analyses both herein and for future studies. Once identified, bias can be tested statistically as well.

Table 1. Common categories of variations in the structure of fish assemblages, and associated metrics.

Category of Variation Metric Quantitative Abundance Relative Abundance Diversity Size (Life Stage, Length)

Taxonomic Species Richness Family Representation Morphology Native/Non-Native Species

Behavioral Characteristics Diurnal/Nocturnal Benthic/Pelagic Migratory/Stationary Level of Aggression or Territorialism Schooling/Solitary

Trophic Guild Predatory Insectivorous Herbivorous Generalists Bottom Feeders Detritivorous

Tolerance Dissolved Oxygen Dissolved Solids Environmental Contaminants Predation

Table 2. Common freshwater and diadromous fish species found in Nova Scotia rivers and lakes.

Family Common Name Scientific Name Anguillidae American Eel Anguilla rostrata Acipenseridae Atlantic Sturgeon Acipenser oxyrhynchus Catostomidae White Sucker Catostomus commersonii Centrachidae Smallmouth Bass Micropterus dolomieu Clupeidae Alewife Alosa Pseudoharengus American Shad Alosa sapidissima Blueback Herring Alosa aestivalis Cottoidea Sculpin Not Specified Cyprinidae Creek Chub Semotilus atromaculatus Lake Chub Couesius plumbeus Blacknose Dace Rhinichthys atratulus Redbelly Dace Chrosomus eos Pearl Dace Margariscus margarita Common Shiner Luxilus cornutus Golden Shiner Notemigonus crysoleucas Blacknose Shiner Notropis heterolepis Fallfish Semotilus corporalis Esocidae Chain Pickerel Esox niger Fundulidae Banded Killifish Fundulus diaphanus Mummichog Fundulus heteroclitus Gadidae Atlantic Tomcod Microgadus tomocod Gasterosteidae Fourspine Stickleback Apeltes quadracus Ninespine Stickleback Pungitius pungitius Threespine Stickleback Gastrosteus aculeatus Fivespine (Brook) Stickleback Culaea inconstans Ictaluridae Brown Bullhead Ameiurus nebulosus Lotidae Burbot Lota lota Moronidae Striped Bass Morone saxatilis White Perch Morone Americana Osmeridae Rainbow Smelt Osmerus mordax Percida Yellow Perch Perca flavescens Petromyzontidae Sea Lamprey Petromyzon marinus Salmonidae Atlantic Salmon Salmo salar Brook Trout Salvelinus fontinalis Brown Trout Salmo trutta Rainbow Trout Oncorhynchus mykiss Lake Trout Salvelinus namaycush Atlantic Whitefish Coregonus canadensis Lake Whitefish Coregonus clupeaformis

Table 3. Numbers of native, non-native and total freshwater fish species in the 13 provinces and territories of Canada (Taylor 2004).

No. of Species Historical Extant Region Native Non-Native Extirpated Total Extant Yukon Territory 31 0 0 31 Northwest Territory 48 1 0 49 Nunavut Territory 25 0 0 25 British Columbia 67 15 0 82 Alberta 52 11 0 63 Saskatchewan 59 9 0 68 Manitoba 77 12 0 89 Ontario 131 28 5 154 Quebec 102 8 0 113 New Brunswick 45 5 0 50 Nova Scotia 33 7 0 40 Prince Edward Island 23 3 0 23 Newfoundland/Labrador 25 3 0 28

Table 4. Jaccard’s faunal similarity coefficients for native freshwater fish faunas among

13 provinces and territories of Canada estimated from species presence-absence data

(Taylor 2004).

Region YT NWT NUT BC ALT SASK MAN ONT QUE NB NS PEI

NWT 0.58 - NUT 0.51 0.49 - BC 0.40 0.46 0.30 - ALT 0.32 0.45 0.28 0.47 - SASK 0.21 0.37 0.25 0.27 0.56 - MAN 0.17 0.32 0.23 0.24 0.43 0.70 - ONT 0.10 0.19 0.13 0.16 0.25 0.40 0.53 - QUE 0.12 0.23 0.17 0.18 0.29 0.43 0.54 0.64 - NB 0.13 0.21 0.21 0.16 0.18 0.27 0.30 0.23 0.40 - NS 0.07 0.11 0.12 0.10 0.10 0.18 0.18 0.16 0.29 0.66 - PEI 0.04 0.04 0.07 0.05 0.06 0.07 0.09 0.09 0.20 0.45 0.56 - NFL 0.19 0.18 0.25 0.14 0.15 0.14 0.16 0.13 0.25 0.52 0.42 0.50

Table 5. Terms and definitions based on an ecological approach to fish introduction

(Gozlan et al. 2010).

Term Definition Native A species that occurs naturally in a specific water body (i.e. rivers, lakes, ponds, etc.). Non-native A species introduced outside its natural range. Range The geographical distribution of a species. Introduction The deliberate or accidental release into the wild of a non-native species. Translocation The human-assisted movement of fish within a specific water body. Stocking The release of a species into a specific water body following its initial introduction. Establishment The process whereby an introduced species reproduces and forms self-sustaining populations (i.e. not relying on further introductions). Naturalization The process whereby an established species develops persistent populations. Dispersal The natural dissemination (i.e. non-human assisted) of a species from its point of introduction). Colonization The natural dispersal of an established population resulting in its range expansion. Spreading A species expanding its range (can be human assisted or natural). Invasion The process whereby an introduced species has established populations, spreads rapidly and presents a risk to native species. Lag Phase The delay between introduction and invasion.

Table 6. Sanctioned releases of smallmouth bass (Micropterus dolomieu) in Nova Scotia

(LeBlanc 2010).

Year(s) Lake County 1942 Bunkers Lake Yarmouth 1944 & 1946 Micmac Lake Halifax 1945 & 1946 Laytons Lake Cumberland 1947 Victoria Lake Queens 1948 Lily Lake Hants 1950 Blair Lake Cumberland 1951 Elliott Lake Annapolis 1952 & 1953 Cantelope Lake Lunenburg 1952 Fishermans Harbour Lake Guysborough 1953 Awalt Lake Lunenburg 1967 Black River Lake Kings 1967 Dean Chapter Lake Kings 1967 Four Mile Lake Kings 1967 Gaspereau Lake Kings 1967 Little River Lake Kings 1967 Lumsden Lakes Kings 1967 Methals Lake Kings 1971 Spectacle Lake Digby 1984 Murphy Lake Kings

(Hydrologic Systems Research Group 2012).

Figure 2. Global to local factors that influence the composition of fish assemblages

(Matthews 1998).

Figure 4. Example of a Gaussian (mean = 3, standard deviation = 1) (A) and Poisson

(mean = 1, standard deviation = 1) (B) distribution.

Chapter Two

Trends in Fish Species Abundance and Characteristics of Fish Assemblages and

Structure

Introduction

The structure of local fish assemblages is influenced by multiple factors, both biotic and abiotic (see Chapter 1). Specific threats to freshwater fish habitat in Nova

Scotia have been studied extensively, including habitat alteration (East Coast Aquatics

2006; Floyd et al. 2009), acidification (Lacroix 1989; Department of Fisheries and

Oceans 2000), and establishment of invasive species (Chaput and Caissie 2010; LeBlanc

2010), among others. Likely there are a variety of factors at work simultaneously (see

Figure 2), and determining the degree of impact an individual factor has on freshwater fish assemblage structure is complicated because one or more factors may mask the impact of others (Neill et al. 1994). For example, the influence of an increase in food availability on a predatory species population would not be observed if an increase in stream turbidity prevented the predatory species from finding its prey.

Studies of fish assemblage structure commonly use species richness (Tonn et al.

1990; Grenouillet et al. 2004; Kanno and MacMillan 2004; Thornbrugh and Gido 2010;

Armstrong et al. 2011; Long et al. 2012), species composition (Tonn et al. 1990;

Thornbrugh and Gido 2010; Balik et al. 2011), and abundance indices (Thornbrugh and

Gido 2010; Armstrong et al. 2011; Balik et al. 2011) to describe spatial patterns. Relative to spatial analyses, few studies use such metrics to characterize temporal changes in assemblage structure (but see Bronte et al. 2003; Robillard and Fox 2006; Winfield et al.

2011).

One approach to temporal analysis is to fit models to long-term data in addition to spatial data, thus accounting for both spatial and temporal data simultaneously.

Mathematical models are an important tool in the field of applied ecology, but they must be used and interpreted carefully to avoid misrepresenting data. The complexity of ecological systems complicates model construction and may result in models not converging. In this case, no set of parameters would be returned and thus, no model created. Most importantly, biological significance must be considered in addition to statistical outcomes while interpreting the output of models.

One way to examine changes in ecosystems is to characterize changes in species assemblages. In the case of freshwater fish assemblages, one of the areas of interest is species abundance, and the method of evaluating change is to estimate annual changes in abundance. The Federal River Survey (FRS) data set provides an opportunity to examine long-term change in abundance of multiple freshwater and diadromous fish species found in Nova Scotia, as well as explore traditional analyses using community indices such as species richness, diversity and composition. Examining these changes across a relatively large spatial and temporal range could provide insights into community structure across scales.

These indices act as surrogates for multiple, more complex indices (see Table 1), and because of this, it is hypothesized that the structure of freshwater and diadromous fish assemblages in Nova Scotia Southern Upland rivers will experience change, as measured using ecological indices. Lentic habitats should reflect similar trends. In addition, models that take into account temporal and spatial differences in sampling as

well as models incorporating multiple rivers, should be able to detect changes in relative abundance over time. These hypotheses will be approached using the following methods:

1) Modeling Abundance: Model changes in standardized relative abundance (CPUA)

for riverine fish both spatially and temporally using generalized linear models

(GLMs) fit to the FRS data, and

2) Characterizing Fish Assemblages: Characterize lotic and lentic fish assemblages

using ecological community indices (e.g. species richness, species diversity, and

species composition), fit to the FRS and Provincial Lake Survey (PLS) data sets,

and model changes where possible.

Methods

Standardization and Filtration of Data

Armstrong et al. (2011) noted that data obtained during single-pass electrofishing could be effectively used to describe fish assemblage structure; a method used commonly in juvenile Atlantic salmon studies (Gibson et al. 2003; Gibson et al. 2009). Variation in sampling effort was accounted for by using the electrofishing area for each site to standardize abundance to catch-per-unit-area (CPUA) in the form of number of individuals per 100 m2 (density) for each species. Each record reported electrofishing area as “site area”, or “site length and width”. Where provided, area was used directly to standardize abundance. Where areas were not provided (n = 1,046, 23% of records), they were calculated using a hierarchical approach (Figure 5). Standardization allows for general comparison of CPUA across years, rivers and sites both within and among species. Shock period was also reported for each electrofishing pass, but was not used in these analyses, as no relationship between shock period and abundance was noted.

When possible, analysis was completed by species. However, several species or groups were excluded for various reasons. Lake and creek chub abundances were summed with unidentified chub abundances because the two species are inconsistently identified in the surveys. Banded killifish and mummichog abundances were combined because the hybridization of these species is common and identifying young or smaller fish to species is difficult. Sea lamprey abundances were excluded from all analyses because a specialized electrofishing protocol is required to effectively capture this species. Including sea lamprey would lead to increases in false zero values. Blacknose dace abundances were not included in any analyses because they are typically not found in the Southern Upland region (Gilhen 1974). Alosa abundances were not included in the analysis because they are highly migratory species, and are not residents in particular sites.

Data Filtration

Describing population trends using a modeling approach required that the FRS data set undergo additional filtration and editing based on the requirements of the analysis. Abundances reported as ‘-99’ (n = 100 occurrences) were replaced with NA to avoid skewing models and misrepresenting summary statistics. NA values are automatically excluded from GLM routines in R. Rivers in which surveys only occurred within a single year were removed (n = 17 rivers) because modeling requires at least two unique values of any independent variable. To calculate ecological community indices for fish assemblages included in the FRS and PLS data sets, abundances of ‘-99’ (n = 100 occurrences) were replaced with 1 to allow the calculation of species richness, species diversity and species composition.

Resolution

The data were analyzed at various resolutions for each data set, depending on the objective of analysis. The FRS data set, at the coarsest resolution, could be pooled across sites to examine abundance or community indices at the scale of entire rivers, and at the finest resolution, variation among individual sites could be analyzed. Similarly, the PLS data set, at the coarsest resolution, could be pooled across lakes to examine entire watersheds, and at the finest resolution, individual lakes could be considered. However, comparing resolutions between data sets may be difficult. For example, St. Mary’s River drains 1,350 km2 of land (St. Mary’s River Association 2008), and is considered an entire watershed within itself, while multiple lakes are contained within an individual watershed. Therefore, it may be illogical to directly compare attributes of lakes and rivers, such as species richness. In general, rivers (FRS) and watersheds (PLS) are comparable, and sites (FRS) and lakes (PLS) are comparable.

Modeling CPUA

Negative binomial generalized linear models (GLMs) were used to quantify changes in CPUA over time for each river and species combination. Negative binomial models were chosen because data were count data, and, during preliminary model building, overdispersion prevented the effective use of Poisson models (Appendix B). In addition, for trends to be effectively compared, all models need to be based on the same distribution and model design. Therefore, negative binomial models were deemed the best solution for consistency because they effectively reduced overdispersion (Appendix

B, Figure B1). Recall that dispersion parameters less than 1.5 are considered not overdispersed (see Chapter 1).

The general equation of the models is:

where was the predicted CPUA at time t, is the intercept estimate, and is the year coefficient. The coefficient values were interpreted within the context of CPUA trends over time for each species within rivers (i.e. each species and river combination):

where is the percent change in CPUA after time t in years, and is the instantaneous rate of change as provided by model coefficients. Negative coefficient values represent declines in CPUA.

R is an open source software environment and language used for customized statistical analysis and data manipulation and includes packages for many statistical functions (R Development Core Team 2011 version 2.14). The glm.nb() function in the R package MASS was used to complete negative binomial GLMs (Venables and Ripley

2002). The output of these models provides coefficients (slope and intercept), residuals, deviance, Akaike’s Information Criterion (AIC), log likelihood, and other useful statistics.

Model Testing

The data set was filtered according to rivers, sampling sites, and period (years) into multiple subset data sets in a hierarchical fashion to accomplish the analyses as follows. Model series were identified with labels according to their data subset

(Appendix B, Table B1) with the suffix of ‘Y1’ indicating all data years (e.g. GLM1Y1).

Models were further tested after removing rivers which were surveyed across fewer than ten years to avoid poorly fitting models due to a lack of data, and the revised models were

identified with the tag ‘Y10’ (e.g. GLM1Y10). Species that accounted for less than 0.1% of the total relative abundance (Appendix B, Table B2) were removed to mitigate or remove the effect of rarely occurring species that would have many zero values, and, thus, may result in overdispersion. These model revisions were identified with the additional tag ‘S01’ (e.g. GLM1Y10S01). Known gaps in the data prior to 1980 warranted creating a series of models using only records spanning the period 1980 –

2010. The Y10 year and S01 species filters were also applied to this series of models and they were identified with the tag ‘REP’ (e.g. GLM1REP). REP models were also used to compare models using data split into 15-year periods (1980 – 1995, 1996 – 2010) and to compare changes in CPUA during these periods.

A second body of models were created in which survey site was added as a factor.

These models were identified with the label ‘GLM2’ (e.g. GLM2Y1). Within this second body, three additional model series were created in which the data were subset to only include those sites surveyed a minimum of two, five, and ten times, and these were identified as ‘GLM2a’, ‘GLM2b’, and ‘GLM2c’, respectively (e.g. GLM2aY1). All models were summarized, and evaluated in the context of their effectiveness in characterizing responses and CPUA trends. Temporal resolution was assessed where possible.

It was essential to recognize that statistical significance of these models may or may not reflect underlying biological or ecological relationships. Therefore, coefficients and statistical significance were also considered from an ecological point of view.

Models and abundances were examined visually to assess discrepancies between model output and plotted data (biological samples). In addition, as more complex models are

constructed, the number of model parameters increases and model interpretation is subjected to increased complexity. In concert, customized filters result in smaller data subsets and, therefore, fewer observations upon which to create models. To explore this trade off of increased model complexity and decreased data, models were arranged by increasing complexity to examine patterns that emerge from summary statistics such as number of successful (convergent) models, model significance, and model trends

(Appendix B, Table B3).

Model Selection Process

Trends were evaluated within model series to identify model fit to ultimately select one model form to be used for comparative analyses. Y1 and Y10 models were compared to examine if overall CPUA trends were better characterized by models that considered only rivers for which long time series existed. The use of Y10 models resulted in narrower ranges of significant coefficients (when α = 0.05) for those species most commonly found (Atlantic salmon, American eel, brook trout, and others) (Appendix B,

B2). Although fewer total surveys were incorporated into Y10 models, the success rate of these models, defined as the percentage of potential models that successfully converged

(‘successful models’), was higher than when using Y1 models. Because each model is guaranteed at least ten surveys, the likelihood of the common species being found in multiple surveys is higher in Y10 models than in Y1 models, and therefore Y10 models are based on more data points, which is reflected in the greater number of successful models. Model success was highest in the Y10S01 models mainly because the elimination of rare species accounting for less than 0.1% of the total relative abundance resulted in fewer failed models. When compared to Y10S01 models, REP models

(truncated to exclude surveys prior to 1980) were found to have slightly lower success rates, and fewer successful models.

Increasing the minimum number of surveys per site from two (GLM2a) to five

(GLM2b) surveys resulted in fewer successful models and a noted decrease in model success rate. GLM2c models (minimum ten surveys per site) were sometimes noted to have an increase in success rate, however, this was merely a reflection of a drastic decrease in potential models as sites surveyed for ten or more years were infrequent.

Exclusion of rare species (S01 models) and exclusion of rare species with only surveys after 1980 (REP models) were further evaluated using AIC. AIC values of REP models with site included as a factor (GLM2) were compared to models without sites included

(GLM1) using a paired Student’s t-test.

Calculating Ecological Indices

Observed species richness ( ) and Chao2 species richness ( ) were calculated using presence-absence data. Chao1 species richness ( ) was calculated using original (non-standardized) abundances. was found for each river and pooled across sites for each year, and for each site within rivers for each year, using the FRS data set. Also, was calculated for each watershed and pooled across lakes for each year, and for each lake within watersheds for each year, using the PLS data set. Negative binomial GLMs were used to model changes in over years in each of these groups, with river included as a factor when using the FRS data set, and with watershed included as a factor when using the PLS data set. was calculated for each river and pooled across sites for each year, and for each watershed and pooled across lakes for each year.

Negative binomial GLMs were used to model changes in over years in each of

these groups, with river included as a factor when using the FRS data set, and with watershed included as a factor when using the PLS data set. Abundances were summed to calculate for each river and pooled by site for each year. Because the PLS data set only consists of presence-absence data, could not be calculated for lake surveys.

Negative binomial GLMs were used to model changes in over years in rivers pooled by site, with river included as a factor when using the FRS data set.

Shannon ( ) and Simpson ( ) diversity indices were calculated for each river and pooled across sites for each year, and for each site within rivers for each year, using the FRS data set. Negative binomial GLMs were used to model diversity index changes over years for each of these groups, with river included as a factor. Boxplots were used to visualize diversity indices and compare them at different resolutions. and were also modeled over time in nine example rivers: St. Mary’s, LaHave,

Sackville, East (Chester), Ingram, Middle (Chester), Gold, Salmon (Lawrencetown), and

Musquodoboit, using data collected between 1980 – 2010.

Pairwise calculations of Jaccard’s similarity index ( ) were used to compare species composition of lakes grouped by watershed. Jaccard’s similarity index uses presence-absence data. Surveys completed between 1980 – 1990 were used to calculate

, which resulted in a comparison of species composition between 28 of 49 watersheds; the remaining 21 watersheds did not have adequate data for analysis. This analysis was repeated using surveys completed between 2000 – 2010 and the two sets of coefficients ( and ) were compared to characterize homogenization of freshwater lake fish assemblages. Homogenization was quantified based on the percentage of similarity coefficients that increased between the two periods. Where data

were available, this analysis was conducted using the species composition of specific lakes (PLS), rivers (FRS) and sites (FRS) between each time period.

Specialized plots were created to visualize homogenization and differentiation of species composition at various spatial scales between the two time periods (1980 – 1990 and 2000 – 2010). were plotted along the x-axis, and were plotted on the y-axis. A diagonal line representing a state of no overall change was included.

Points falling in the upper left quadrant represented sites that became more compositionally similar (homogenization), while points falling in the lower right quadrant represented sites that became less compositionally similar (differentiation).

Results

Modeling CPUA

Model Selection

Regardless of which model series was used, between 70 and 88% of the total number of statistically significant coefficients indicated a decline in CPUA (Appendix B,

Table B3). In other words, irrespective of the data filtration method, if change in CPUA of a particular species was found to be statistically significant, it was nearly always experiencing decline. Inclusion (or exclusion) of species identified as rare did not affect the number of successful models, but did affect model success rate simply because the number of potential models differed. Models of rare species (n = 14 species) were seldom successful because of sparse data and were excluded from further CPUA model analyses.

In contrast, Atlantic salmon and American eel were candidates for in-depth analyses because they both had more data available than rare species. In addition, brook trout,

brown trout, killifish, chub, threespine stickleback, and white sucker had enough data to successfully model, and warrant closer examination.

Biologically, variation among sites is expected and a site factor should be included in CPUA analyses because environment variables often shape fish assemblage structure and composition, and it is expected that environment variables are site dependent. Logically, using a block of years across which models can be compared and removing rare species (REP models) should result in models with sufficient data and fewer zeros. Adding site as a factor caused 14% of AIC values (n = 10) to change by less than five, and 13% (n = 9) to change by more than 100 (Appendix B, Figure ). AIC values indicated improvement in 51% of the nested models, and there was no significant difference between AIC values of REP models with and without site included as a factor

(paired t-test, p = 0.956). Even so, some of these models are based on very few data points, and overall trends were subject to potentially being driven by a single site or river, or very few of either. Despite these issues, models based on data from 1980 on (REP models) were determined to be the best candidates for analyses because species trends were evaluated on a per river basis and sufficient data exist to use more complex models.

Therefore, GLM2REP models, which use rivers with at least ten years of survey data, and exclude rare species and surveys prior to 1980, were selected to be used in all analyses because these models were determined to be reasonably balanced between relevance, complexity, and fit.

River and Species Trends

Unless otherwise noted, any discussion of CPUA refers to modeled (estimated) values. Of the 80 successful models, 26% (n = 21) indicated a statistically significant

decline in CPUA (Figure 6). The degree and direction (i.e. decrease or increase in CPUA) of change is variable within and among fish species (Figure 6). Atlantic salmon declines accounted for 14% (n = 3) of significantly declining models (n = 21). Atlantic salmon were found to be declining in three of 11 rivers (with successful models, of 12 rivers with potential models), with annual decline rates between 3.9 and 7.7% (Table 7). Another

38% (n = 8) of the significant negative coefficients were declines in American eel CPUA, which was found to be declining in six of 12 rivers (of 12 rivers). The largest decline rate

(11% per year) was in West River (Sheet Harbour) with a CPUA decline from three to zero individuals per 100 m2 over a 25 year period, beginning from the first record of

American eel in this river in 1985. In a single river, East River (Sheet Harbour),

American eel CPUA increased.

Trends in white sucker abundance varied between rivers. Four rivers (33%) had declining white sucker CPUA, ranging from 3.9 to 15% annually, while three rivers

(25%) had increasing CPUA, ranging from 6.1 to 72% annually. Small-bodied fish, including chub, common shiner, killifish, and threespine stickleback also had varying abundance trends across rivers. Killifish, common shiner and threespine stickleback

CPUA did not significantly increase or decrease across 75% of rivers. Chub abundances declined in two rivers (11 and 27% annually), and increased in two rivers (6.2 and 8.3% annually).

Of all rivers (n = 12), 42% (n = 5) showed statistically significantly decreasing

CPUA for multiple species (Table 7). Several non-significant coefficients indicated growth or decline unreasonably larger or smaller (i.e. coefficient greater than 1 or less than -1) than what should be expected in this type of biological system. Coefficients of 1

or -1 are nonsensical because they represent unrealistic changes in fish populations; either an unrealistic population explosion (1) or a nearly immediate extirpation (-1). Several species were data deficient in a high proportion of rivers: common shiner (67% , n = 8 rivers), threespine stickleback (67% , n = 8 rivers), smallmouth bass (75% , n = 9 rivers) and brown trout (83% , n = 10 rivers) out of 12 rivers (Table 7).

Statistical Versus Biological Significance

Visualization of models resulted in the identification of models that appeared to fit very well, and others in which problems were apparent. For example, American eel

CPUA in East River (Sheet Harbour) was found to have a significant positive coefficient of 0.2, indicating a 22% annual increase. However, upon visual examination of model outcomes, it was apparent that there were simply not enough data points to fit a model well or reflect biological samples. The influence of few high CPUA values in combination with many low values was not always apparent until the data were visualized.

Peak abundances occurred at approximately 1995 in most rivers. For example,

East River (Chester) experienced high CPUA values during the mid-1990s across nearly all species. However, in four species (Atlantic salmon, American eel, brook trout, and killifish), a high frequency of low CPUA values resulted in non-statistically significant models with fitted lines at, essentially, zero. Running additional models on data recorded before and after 1995 revealed that these trends may not represent the actual changes in

CPUA. For example, Atlantic salmon were found to be declining at a rate of 23% annually since 1995 (Figure 7A and B). American eel CPUA declined by 5.0% per year since 1996, and although this was not a statistically significant decline (p = 0.522), it is

more relevant to the overall examination of trends in CPUA than the 2.9% increase reported by the model spanning 1980 – 2010 (Figure 7C and D).

Also, plotted model lines of Ingram River Atlantic salmon and American eel

CPUA between 1980 – 2010 may not reflect observed trends in these populations, because there were noted peaks in the early to mid-1990s. Atlantic salmon CPUA declined by 2.7% annually and American eel declined by 6.0% annually (Figure 8A and

B). Constraining models to data recorded before and after 1995 revealed CPUA increased for Atlantic salmon prior to 1995 at an annual rate of 9.8%, and decreased for American eel by 4.8% annually. Between 1996 – 2010, American eel CPUA continued to decline, but at a greater rate of 8.2% per year. However, Atlantic salmon experienced a decline of

26% per year, a decline which was not described to the same degree by the model spanning the entire data series (1980 – 2010) (Figure 8C and D).

Overall trends (1980 – 2010) may not hold during pre- and post-1995 in some cases, and models with many zero values of CPUA coupled with few high values lead to disconnect between modeled and visual results. Fitted annual decline rates for many of the models also visually fit the data and the biological implications drawn from the models are considered sound. For example, LaHave River models fit the data very well visually and were not changed considerably pre- or post-1995 although the rate of decline of both Atlantic salmon and American eel increased after 1995 (Figure 9). Although

CPUA did not significantly increase or decrease over time for other common species, such as brook trout and white sucker, it is important to consider that the observed changes in CPUA may still be biologically significant (Table 7).

Calculating Ecological Indices

Species Richness and Diversity

Rivers (FRS dataset) generally had fewer fish species observed than watersheds

(PLS dataset, pooled by lakes), and experienced no marked change in observed or estimated richness over time (Table 8). Watersheds (PLS dataset, pooled by lakes) and lakes (PLS dataset, not pooled) experienced a significant decline in richness over time, ranging from 0.9% to 2.0% annually, depending on richness index (all p < 0.01) (Table

8). However, this decline in species richness was found to be an artifact of inconsistent sampling where many lakes were sampled only once or a few times, but generally not in both the decades of 1980 and 2000. Of 1,163 lakes included in the PLS data set, only 42 lakes (3.6%) were sampled at least once in both decades, and no decline in species richness occurred over time in these lakes. In fact, the observed species richness ( ) of

98% of these lakes did not change between the two time periods (Figure 10). Models revealed a significant increase of in St. Mary’s River and Salmon River

(Lawrencetown) (n = 2 of 9 rivers) between 1980 – 2010, with annual increases of 0.8% and 3.4%, respectively (both p < 0.015) (Figure 11). Visualization of revealed variation was high across sites among rivers (FRS dataset) and across lakes among watersheds (PLS dataset), and variable within rivers (FRS dataset) and watersheds (PLS dataset). More variation was seen within watersheds (mean SD = 3.0) than rivers (mean

SD = 1.2) (t-test, p < 0.01) (Figure 12).

Calculated diversity indices were not influenced by the resolution (river or site scale) of the data (Table 9). Neither models of nor significantly changed over time in rivers (both p > 0.227) or sites (both p > 0.433). Models also revealed no

significant changes in in example rivers between 1980 – 2010 (all p > 0.117)

(Figure 13). Variation in diversity indices across sites and years among rivers was high, and within rivers was variable (Figure 14).

Homogenization and Differentiation

Examples of both differentiation (decrease in J) and homogenization (increase in

J) occurred in both lakes and rivers between the decades of 1980 and 2000. Only a proportion of the total surveys could be included in the analysis, depending on gaps in the data and the scale of inquiry. At the lowest resolution, pairwise calculations (n = 378) of

increased among 24% of watersheds (28 analyzed of 49 total, PLS data set), with points in the upper left quadrant of plots, indicating homogenization (Figure 15). Using rivers

(28 analyzed of 70 total, FRS data set), 56% of comparisons (n = 378) had increased between the decades of 1980 and 2000 (Figure 16). No evidence of homogenization was found using individual lakes (42 analyzed of 1163 total, PLS data set), as no points (n =

861) fell into the upper left quadrant, but 2% of lakes showed differentiation (Figure 17).

At the sites level, pairwise calculations (n = 3,321) of increased among 41% of river sites (82 analyzed of 650 total, FRS data set) (Figure 18).

Discussion

Using Models in Ecological Studies

The FRS data set produced the best fitting models when data were filtered to exclude rare species (accounting for less than 0.1% of total relative abundance), rivers sampled across fewer than ten years, and surveys prior to 1980, and when models were created with site considered as a factor. However, 70 – 88% of the models identified as

experiencing a statistically significant change in CPUA were declining, regardless of the data filtration method used, and whether or not site was included in the model implying an inherent data robustness, increasing confidence in the analysis.

Over 7,300 potential models were created using various model equations, but only a portion of these (n = 2,209 models) converged. Therefore, it became extremely important to summarize model outcomes, visually examine selected models, and assess their biological significance. Models may or may not be sensitive to real events, which can be difficult to deduce from both a statistical and biological viewpoint. For example,

1984 marked the end of coastal commercial salmon fishing across Nova Scotia, including

East River, Chester. Models corresponding to this river did not fit the data appropriately potentially because of increases in the abundance of several species (e.g. Atlantic salmon and American eel) shortly after this closure. Soon after this increase, these species sharply declined. Initially, the inclination is to assume that increases in abundances were related to the fishery closure. However, a similar pattern was seen in the abundance of various species in several other rivers during the same time period. This discrepancy indicates that these trends may be a result of broader forces.

Changes in Catch-Per-Unit-Area

At-Risk Species

Atlantic salmon and American eel accounted for 14 and 38% of the models with significantly declines, respectively , and both species experienced these declines across multiple rivers during the same time period. Declines of these species are well documented (Haro et al. 2000; Cairns et al. 2008; Gibson et al. 2008, Gibson et al. 2009), and this is reflected in decisions by the Committee on the Status of Endangered Wildlife

in Canada which designated the Nova Scotia Southern Upland population of Atlantic salmon as Endangered (COSEWIC 2010a), and upgraded American eel from Special

Concern to Threatened in May, 2012.

Haro et al. (2000) found no significant change in American eel abundance in East

River (Sheet Harbour) between 1984 – 1995, as measured using elver trap abundances. A significant increase was found herein, but the increase was a result of insufficient data and poor model fit rather than a biological effect. While Haro et al. (2000) did not analyze eel abundance data from other Nova Scotia rivers, they did find significant declines in Lake Ontario (Ontario), St. Lawrence River (Quebec), Hudson River (New

York), and North Anna River (Virginia).

Barriers, including hydroelectric dams, may pose a threat to both Atlantic salmon and American eel populations (Bowlby et al. in press; COSEWIC 2012). However, both

Atlantic salmon and American eel populations declined in St. Mary’s River, which does not contain barriers suggesting other factors are causing declines. For example, Mitchell

(2009) noted that St. Mary’s River experiences both flooding and low flow periods, which likely impact the fish populations residing within it, and suggests that these impacts will be intensified by climate change. Future evaluation of changes in fish assemblage structure should include habitat characteristics, especially potential threats to freshwater fish populations.

Other Freshwater and Diadromous Fish Species

Although Atlantic salmon and American eel accounted for over half of declines described by models herein, it is important to realize that other common freshwater fish species have experienced similar declines, or other changes in CPUA, over the same

period. Many of these species are classified as small-bodied fish, including shiners, chub, killifish, and stickleback, which are often found in river and lake surveys (Rachlin and

Warkentine 2012; Teather et al. 2012). Unfortunately, information regarding population dynamics of small-bodied fishes is notably absent from the scientific literature and other reports. These small-bodied species often are overlooked because they have no directed fisheries, are not economically important, and/or are not otherwise seen as ‘important’.

This lack of attention results in infrequent collection of population data. However, as freshwater habitats and communities continue to change, it is essential that researchers look at the bigger picture, and consider changes in all aspects of ecosystems.

As discussed by Eby (2006), trophic models show top-down effects involving small-bodied fish species leading to changes in ecosystem processes. For example, the decline of small-bodied fish may lead to increased abundance of zooplankton, resulting in a decline in phytoplankton abundance, which then has the potential to impact multiple ecosystem processes. These interactions are often complex and difficult to identify, underlining the need to better understand complex links in ecosystem dynamics at multiple scales. The underlying cause of these observed changes in CPUA of multiple species in accordance with the decline of Atlantic salmon and American eel is unclear.

These changes may be the result of trophic cascade interactions, or a result of overarching ecosystem drivers also impacting Atlantic salmon and American eel.

Potential ecosystem drivers include invasive species, climate change, and land use change, and the effects of such drivers may change over both spatial and temporal scales

(Bohensky et al. 2005), or interact with other factors or drivers (McEwan et al. 2011).

Changes in Ecological Indices

Species Richness and Diversity

Taylor (2004) reported seven non-native species in Nova Scotia, with zero province-wide extirpations of native species. Therefore, a general increase in species richness may be expected as non-native species are added to assemblages. However, a significant increase in observed species richness was only noted in two of ten rivers with substantial data (St. Mary’s River and Salmon River). In fact, the addition of non-native species has been linked to declines in species richness (Strecker et al. 2006). The link between non-native species and Nova Scotia fish assemblage structure will be examined more in Chapter 3.

One approach to interpreting temporal and spatial trends in observed species richness is to consider the relationship between regional species richness, phenotypic plasticity – in terms of niche requirements, and available habitat heterogeneity. As discussed by Kanno and Beazley (2004), freshwater fish zoogeography in Nova Scotia, driven by the colonization of freshwater fish after the last glaciation, determines the regional species pool from which fish assemblages can be formed. In addition, many of these species have a degree of plasticity in life history strategy (Hutchings 1996; Kanno and Beazley 2004), diet (Fraser 1978), and habitat requirements (Scott et al. 2006;

Svanback and Schulter 2012). This plasticity, recently referred to as the portfolio effect

(Greene et al. 2010; Schindler et al. 2010), in addition to the relatively low regional species richness, means that under-utilized niches may support various species during periods of fish assemblage change or increases/decreases in abundances. Thus, although abundances of each species and overall fish assemblage structure may change over time,

observed species richness may not reflect this change. This effect may not occur, or occur to a lesser degree in areas with fewer available habitat types, such as lakes, as compared to rivers. In general, species richness may need to be considered at higher resolutions to evaluate changes in fish assemblage structure. At the current resolution, it may be more valuable to examine the variability in species richness within and among sites over time rather than at the level of rivers, lakes, and watersheds. Unfortunately, only in a few cases do sufficient data exist to carry out such analyses.

Like species richness, Shannon and Simpson diversity indices may not reflect overall changes in the structure of fish assemblages. Individually, Shannon and Simpson diversity indices describe different aspects of diversity of an assemblage. Unlike the

Shannon index of diversity, which weights species by their frequencies, and therefore does not favor dominant or rare species, Simpson diversity is measured using the sum of squares of frequencies resulting in lower values, but that favor dominant species.

Nagendra (2002) has demonstrated that the difference between the Shannon and Simpson diversity indices provides a metric of dominance or unevenness. Essentially, this means that Shannon diversity can be used as a basic measure of diversity over time, while

Simpson diversity can be used to evaluate changes in common species within the assemblage. However, no changes in either measure of diversity was found in rivers or lakes of Nova Scotia, but the data may be re-evaluated where higher resolution data exist.

Homogenization and Differentiation

Examples of both homogenization and differentiation exist at various spatial scales of rivers and lakes of Nova Scotia, and spatial scales varied in terms of homogenization and differentiation patterns across Canada. Taylor (2004) compared

pairwise calculations of Jaccard’s similarity index of freshwater fish assemblages across

Canada, based on inclusion of non-native and extinct or extirpated species. At a national scale, assemblages became more similar (homogenization) with the inclusion of all species, while aquatic regions within British Columbia became less similar

(differentiation), and specific areas within regions of British Columbia also experienced homogenization. The presence of non-native species was not used as a grouping method in this study, as only a single non-native species (smallmouth bass) had sufficient data, and were reported in a limited number of rivers and sites. Instead, a temporal factor was applied. Jacquemin and Pyron (2010) used a similar method to quantify changes in species composition in Indiana streams, and noted homogenization at a sub-basin scale.

Comparisons of pairwise calculations of for both watersheds and lakes revealed little evidence of homogenization between the decades of 1980 and 2000. However, when rivers were examined, neither river nor site-level species composition showed clear patterns of homogenization or differentiation. By analyzing changes in at the site scale on a per-river basis, patterns become apparent. For example, pairwise calculations of between St. Mary’s River electrofishing sites illustrate that both homogenization and differentiation may have occurred over time (Figure 19). However, sites in this river system during both periods (1980 – 1990 and 2000 – 2010) tended to be compositionally similar. In contrast, sites in the Annapolis River system show strong indications of homogenization, with 80% of comparisons of increasing between the two periods

(Figure 20). As well, it is noted that sites in this river system tended to be very dissimilar between 1980 – 1990, with sharp increases in similarity between sites from 2000 to 2010.

Here, the degree of homogenization is more extreme than evidence of homogenization seen in St. Mary’s River.

Generally, Taylor (2004) noted that whether or not non-native and extinct or extirpated species were included in analyses, similarity between areas appeared to be correlated to physical distance between the areas. For example, Nova Scotia fish assemblages had a calculated of 0.7, 0.2, and 0.1, when compared to New Brunswick,

Saskatchewan, and Yukon Territories, respectively. Applying this technique to spatial scale, where rivers are closer in proximity within a watershed than watersheds are among each other, Nova Scotia fish assemblages at the watershed level had maximum of 0.8, while some comparisons of fish assemblages at a river level resulted in as high as 1.0.

That is, similarity among watersheds (regardless of watershed proximity) is less than among rivers (regardless of river proximity).

Buisson and Grenouillet (2009) predicted a global increase in species similarity between assemblages in relation to climate change based on pairwise analyses. However, they found that upstream assemblages were likely to differentiate in response to climate change, while mid- and downstream assemblages may experience homogenization. This location effect may explain some of the variability found in plots of pairwise calculations of between time periods at various spatial scales herein. Stream position of individual sites was not included in this analysis although, in some rivers, this analysis may be possible.

Additional environmental factors, such as watershed impact indices, could be used to explore changes in compositional similarity. Jacquemin and Pyron (2010) identified habitat alteration as one driver of homogenization and differentiation. Various

measures of habitat disturbance are available for Nova Scotia watersheds and rivers. For example, the Nova Scotia Watershed Assessment Program found that the Annapolis

River watershed ranked much higher (i.e. was subject to higher watershed impact indices such as road density, acid rain, and dam density) than the St. Mary’s River watershed

(Figure 1) (Hydrologic Systems Research Group 2012). This may explain differences in patterns of homogenization and differentiation in these systems. Annapolis River sites experienced drastic homogenization of species composition between the decades of 1980 and 2000, while St. Mary’s experienced both homogenization and differentiation.

Changes in Nova Scotia Fish Assemblage Structure

Freshwater fish assemblages in the rivers and lakes of Nova Scotia have significantly changed over time and in recent history. While this chapter does not attempt to identify the causes of these changes, their combined effect appears to be impacting assemblages across the province in a similar manner, whether at a species, fish assemblage, river, lake, or watershed level. In fact, 42% of rivers showed a significant decline in CPUA for multiple species, suggesting that the overarching cause of these declines is occurring at a regional scale. Essentially, the results suggest an ecosystem- based problem that has been occurring over time and over large spatial scales, and impacts many species. It is hypothesized that the introduction of invasive species in Nova

Scotia, particularly smallmouth bass, may be contributing to this problem. The FRS and

PLS data sets allow for a limited examination of the contributions of establishment of smallmouth bass in Nova Scotia rivers and lakes to fish assemblage structure, and this inquiry is addressed in Chapter 3.

Table 7. Modeled percent annual change of catch-per-unit-area (CPUA) of select species in 12 rivers in Nova Scotia. Red indicates

significant decline (p < 0.05, negative coefficient), green indicates significant increase (p < 0.05, positive coefficient), blue indicates

no significant change, and black indicates a lack of data.

River Atlantic Salmon American Eel Brook Trout Brown Trout White Sucker Smallmouth Bass Chub Killifish Common Shiner Threespine Stickleback East (Chester) 4.1 3.0 4.1 6.2 6.2 4.1 East (Sheet Harbour) 27.1 22.1 24.6 15.0 Gold -6.8 -8.6 -3.9 -100.0 -7.7 >100.0 -1.0 4.1 -18.9 58 Ingram -3.0 -5.8 -10.4 -14.8 -100.0 9.4 LaHave -7.7 -4.9 2.0 -2.0 -4.9 20.9 -11.3 -8.6 15.0 20.9 Liscomb -3.9 -4.9 7.3 8.3 >100.0 Middle (Chester) -4.9 0.0 25.9 8.3 8.3 6.2 Musquodoboit -3.0 0.0 8.3 -2.0 -27.4 7.3 -3.9 -39.3 Salmon (Lake Major) -6.8 7.3 -6.8 -8.6 Salmon (Lawrencetown) 10.5 -3.9 -3.9 18.5 10.5 12.7 St Mary’s -3.9 -7.7 -2.0 6.2 -3.9 1.0 4.1 -4.9 8.3 West (Sheet Harbour) 13.9 -11.3 -20.5 71.6 25.9 69.9

Table 8. Observed and estimated species richness of freshwater fish assemblages in Nova

Scotia rivers and watersheds (lakes). The annual percent change in each richness index, at each resolution, was found using negative binomial generalized linear models.

Resolution/ Annual Richness Index Range Mean Change (%) Rivers By Site ns Observed 0 – 9 3 0.2 By River Observed 0 – 10 4 0.3ns Chao2 0 – 16 5 0.5ns Chao2 0 – 15 4 0.3ns

Watersheds By Lake

Observed 0 – 12 3 -2.0** By Watershed Observed 0 – 16 5 -1.0** Chao2 0 – 27 6 -0.9** ns - not significant at α = 0.05 ** - p < 0.01

Table 9. Simpson and Shannon diversity indices of freshwater fish assemblages in Nova

Scotia rivers. The annual percent change in each diversity index, at each resolution, was found using negative binomial generalized linear models.

Resolution/ Annual Diversity Index Range Mean Change (%) Rivers By Site Simpson 0.00 – 1.00 0.39 0.1ns ns Shannon 0.00 – 1.66 0.68 0.2 By River Simpson 0.00 – 1.00 0.41 0.7 ns Shannon 0.00 – 1.72 0.74 0.8 ns ns - not significant at α = 0.05

Missing Areas Missing Areas Replaced Total Records With Areas

1044 3474 Area = Mean(Area of Same Site: Same Year)

997 47 3521 Area = Mean(Area of Same Site: Different Years)

197 800 4321 Area = Mean(Area of All Sites: Same River, Same Year)

6 191 4512

Area = Mean(Area of All Sites: Same River, Different Years) 2 4 4516

Area = Mean(Area of All Sites) 0 2 4518

Figure 5. Hierarchical algorithm used to calculate missing electrofishing site area values in the Federal River Surveys (FRS) data set. ‘Missing Areas’ refer to the number of records in which area is not available. ‘Missing Areas Replaced’ refer to the number of previously missing area values which have been calculated in each stage of the algorithm.

‘Total Records With Areas’ refer to the number of records with areas after each stage of the algorithm.

Figure 6. Model coefficients of catch-per-unit-area (CPUA) over time, grouped by species of interest. The number of rivers included in the models indicate by n. Values on the right hand y-axis (a/b) indicate the number of significant models (a) and the number of successful models (b) associated with each species. Dashed line placed at a coefficient value of 0. Annotated values indicate the number of points outside the range of the x- axis.

Figure 7. Fitted negative binomial GLMs for Atlantic salmon (A) and American eel (B) catch-per-unit-area (CPUA) in East River (Chester) between 1980 – 2010. Negative binomial GLMs also fitted for CPUA before and after 1995 (dashed line), by species (C,

D). Annual change in CPUA (%) as estimated by each model included, with decline shown in red. Double asterisks (**) indicate p < 0.01, and single asterisk (*) indicates p <

0.05. Of 189 electrofishing surveys of East River (Chester), 123 records of Atlantic salmon CPUA were zero, and ten records of American eel were zero. In addition, 29 records of Atlantic salmon CPUA were less than one, and seven records of American eel were less than one.

Figure 8. Fitted negative binomial GLMs for Atlantic salmon (A) and American eel (B) catch-per-unit-area (CPUA) in Ingram River between 1980 – 2010. Negative binomial

GLMs also fitted for CPUA before and after 1995 (dashed line), by species (C, D).

Annual change in CPUA (%) as estimated by each model included, with decline shown in red. Double asterisks (**) indicate p < 0.01, and single asterisk (*) indicates p < 0.05.

Figure 9. Fitted negative binomial GLMs for Atlantic salmon (A) and American eel (B) catch-per-unit-area (CPUA) in LaHave River between 1980 – 2010. Negative binomial

GLMs also fitted for CPUA before and after 1995 (dashed line), by species (C, D).

Annual change in CPUA (%) as estimated by each model included, with decline shown in red. Double asterisks (**) indicate p < 0.01, and single asterisk (*) indicates p < 0.05.

Figure 10. Observed species richness of 42 selected lakes in Nova Scotia between 1980 –

2010 (top). Individual lakes represented by arrows indicating change in observed species richness between surveys (bottom). Red arrows indicate a change in observed species richness.

Figure 11. Species richness of nine select rivers in Nova Scotia, between 1980 – 2010.

Observed values shown as boxplots. Fitted values (line) of negative binomial generalized linear models overlaid by river (blue), and p-value included.

Figure 12. Species richness of watersheds (A) and rivers (B). The plotted values represent the richness of each site by year, grouped by river, and the number of years each river or watershed was surveyed included on the secondary y-axis.

Figure 13. Simpson diversity of nine select rivers in Nova Scotia, between 1980 – 2010.

Fitted values (line) of negative binomial generalized linear models overlaid by river

(blue), and p-value included.

Figure 14. Simpson (A) and Shannon (B) diversity indices of each river in the Federal

River Surveys (FRS) data set. The plotted values represent the diversity of each site by year, grouped by river, and the number of years each river was surveyed included on the secondary y-axis.

Figure 15. Comparison of Jaccard’s coefficient of similarity (J) between watersheds in periods 1980 – 1990, and 2000 – 2010, using the Provincial Lake Surveys (PLS) data set.

Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not change. Diagonal line represents a state of no change. Percent of total comparisons indicated for increasing and declining points. Data allowed for pairwise comparison (n =

378) of 28 of 49 watersheds.

Figure 16. Comparison of Jaccard’s coefficient of similarity (J) between rivers in periods

1980 – 1990, and 200 – 2010, using the Federal River Survey (FRS) data set. Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not change.

Diagonal line represents a state of no change. Percent of total comparisons indicated for increasing and declining points. Data allowed for pairwise comparison (n = 378) of 28 of

70 rivers.

Figure 17. Comparison of Jaccard’s coefficient of similarity (J) between lakes in periods

1980 – 1990, and 2000 – 2010, using the Provincial Lake Surveys (PLS) data set. Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not change. Diagonal line represents a state of no change. Percent of total comparisons indicated for increasing and declining points. Data allowed for pairwise comparison (n =

861) of 42 of 163 lakes.

Figure 18. Comparison of Jaccard’s coefficient of similarity (J) between sites in periods

1980 – 1990, and 2000 – 2010, using the Federal River Surveys (FRS) data set. Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not change. Diagonal line represents a state of no change. Percent of total comparisons indicated for increasing and declining points. Data allowed for pairwise comparison (n =

3,321) of 82 of 650 sites.

Figure 19. Comparison of Jaccard’s coefficient of similarity (J) between sites in periods

1980 – 1990, and 2000 – 2010, using St. Mary’s River data from the Federal River

Survey (FRS) data set. Blue points indicate similarity coefficients that decreased, red points indicate similarity coefficients that increased, and black points indicate similarity coefficients that did not change. Diagonal line represents a state of no change. Percent of total comparisons indicated for increasing and declining points. Data allowed for pairwise comparison (n = 78) of 13 of 90 sites.

Figure 20. Comparison of Jaccard’s coefficient of similarity (J) between sites in periods

1980 – 1990, and 2000 – 2010, using Annapolis River data from the Federal River

Chapter Three

Potential Contributions of Smallmouth Bass and/or Chain Pickerel Presence and

Establishment to Freshwater and Diadromous Fish Assemblage Structure

Introduction

The introduction and establishment of non-native species, particularly invasive piscivorous species, can influence the structure of freshwater and diadromous fish assemblages by contributing to changes in the characteristics of complex aquatic ecosystems (as discussed in Chapter 1). These introductions may influence assemblage structure directly or indirectly and/or lead to tipping point scenarios affecting multiple aspects of the assemblage, community, or ecosystem, perhaps in irreversible ways.

Essentially, if a non-native species has the ability to successfully integrate itself into an area outside of its natural range it is possible that it will have a negative effect of some degree on native species (Gozlan 2009).

It is hypothesized that the introduction and establishment of invasive species, particularly smallmouth bass, will influence catch-per-unit-area (CPUA) and abundance rank of freshwater and diadromous fish species, and these changes will be reflected in various ecological indices. This hypotheses will be approached using the following methods:

1) Comparing Abundance and Rank: Compare trends in abundance (e.g. CPUA) and

rank of freshwater and diadromous fish species in assemblages with and without

invasive species establishment, and

2) Characterizing Fish Assemblages: Characterize riverine and lake fish

assemblages using ecological community indices (e.g. species richness, species

diversity, and species composition), and compare these indices in assemblages

with and without invasive species establishment.

Methods

Standardization and Filtration of Data

The Federal River Surveys (FRS) and Provincial Lake Surveys (PLS) data sets were subset, filtered and standardized as described in Chapter 2 and Appendix A. The presence or absence of smallmouth bass during each survey was defined as a new variable where 1 indicated presence and 0 indicated absence. All smallmouth abundances reported as -99 were interpreted as presence, and all electrofishing passes of each survey were used to determine presence/absence of smallmouth bass. Sites (FRS) and lakes

(PLS) were subset into two groups: non-influenced sites and lakes, at which smallmouth bass were never recorded, and influenced sites and lakes, at which smallmouth bass were recorded in any year. This grouping factor was referred to as ‘site type’ and ‘lake type’, respectively. Each survey was classified as being ‘early’ or ‘late’. For influenced sites, early surveys were those that occurred prior to smallmouth bass establishment, and late surveys were those that occurred after establishment. Non-influenced sites were classified using an approximation of the time period during which smallmouth bass establishment was occurring at influenced sites; 89% of cases had smallmouth bass establishment occurring after 1999.Thus, for non- influenced sites, early surveys were those that occurred prior to 2000, and late surveys were those that occurred between 2000 – 2010.

Comparing Abundance and Rank

CPUA and rank trends were compared in relation to smallmouth bass establishment using LaHave River, Nova Scotia, as a case study. Smallmouth bass were

reported in six rivers in the FRS data set, but only LaHave River had sufficient data to produce a successful model (Table 7) and have smallmouth bass recorded at 14 sites since 1995. The model indicated the establishment of a smallmouth bass population as an increase in CPUA of 21% annually (Table 7). This distribution of data allows for the examination of CPUA and rank of species before and after smallmouth bass colonization, and so the FRS data set was subset to include only LaHave River surveys for analyses.

This subset will be referred to as the LaHave Surveys (LHS) data set. Abundances reported as ‘-99’ were replaced with NA.

Characterizing Fish Assemblages

Because of a lack of chain pickerel data in the FRS data set (only one record for chain pickerel), the PLS data set was used in addition to the FRS data set to increase the spatial distribution of this species, as well as enhance smallmouth bass analyses. The PLS data set was used to elucidate trends in changes in fish assemblages that may be transferable to riverine data. The PLS data set, which has multiple records of smallmouth bass, was used as a means of qualitative comparisons when interpreting overall results.

Abundances of ‘-99’ were replaced with 1 to facilitate the calculation of species richness, species diversity and species composition.

Comparing Abundance and Rank

Catch-Per-Unit-Area (CPUA)

Negative binomial GLMs were used to model annual percent change in CPUA of target species (i.e. Atlantic salmon, American eel, brook trout, brown trout, white sucker, chub, killifish, common shiner, and threespine stickleback) in LaHave River between

2000 – 2010, with site type (non- influenced and influenced) as an interaction term.

Changes in CPUA of smallmouth bass at each influenced site were modeled over time by site to characterize variability in the rates of change of smallmouth bass populations.

A secondary approach was developed by which CPUA of target species were grouped by site type and time period (early and late) and compared using the non- parametric Mann-Whitney test. These analyses examine the contribution of smallmouth bass to CPUA of target species while reducing or eliminating confounding temporal effects. In summary, CPUA of species were compared in:

1) Early surveys of non- influenced sites and early surveys of influenced sites,

2) Early surveys of non- influenced sites and late surveys of non- influenced sites,

3) Early surveys of influenced and late surveys of influenced sites, and

4) Late surveys of non- influenced sites and late surveys of influenced sites.

Species Ranks

CPUA in the LHS data set were converted to abundance rankings by survey, with the most common species in each survey being assigned the highest rank (one). Non- parametric Mann-Whitney tests were used to determine if there was a significant difference in the ranks of each species found when grouped by smallmouth bass presence.

Two approaches to this analysis were undertaken. The first (AP1) involved testing the differences in ranks of each species between non- influenced and influenced sites when smallmouth bass occupy a rank within the fish assemblage. The second (AP2) removed the potential bias created when these invasive species occupy a rank (i.e. its presence creates an effect) by identifying cases in which species ranked lower than smallmouth bass, and increasing their rank by one. In other words, if smallmouth bass become the dominant species, all other species will decrease in rank by one, potentially biasing the

results by creating an artificial position (rank) change. In concert, these complementary analyses allowed for the relationship between changing species rank and CPUA to be examined; i.e. did species rank change independently of CPUA?

Characterizing Fish Assemblages

Species Richness and Diversity

Non-parametric Mann-Whitney tests were used to determine if there was a significant difference in observed species richness of surveys grouped by smallmouth bass presence, for both the LHS and PLS data sets, and did not include smallmouth bass or chain pickerel when calculating species richness. Species richness of surveys completed in LaHave river were also compared when grouped by site type and time period. Mann-Whitney tests were used to determine if there was a significant difference in observed species richness of PLS surveys grouped by chain pickerel presence.

Simpson and Shannon diversity indices were calculated for each survey of the LHS data set. Mann-Whitney tests were used to determine if there was a significant difference in the diversity of LHS surveys when grouped by (a) smallmouth bass presence, and (b) site type and time period.

Species Composition, Homogenization, and Differentiation

The percentage of LaHave River sites with each species present was calculated by site type (non- influenced and influenced), using only those sites with both early and late surveys available. Influenced LaHave River sites which had both early and late surveys completed were identified, and pairwise calculations of Jaccard’s similarity index (J) were used to compare species composition of these sites, grouped by site type. The resulting similarity matrix was used to examine changes in similarity between surveys

prior to and post establishment by comparing each pairwise calculation of J for early surveys of influenced sites with the corresponding pairwise calculation of J for late surveys of influenced sites (e.g. similarity index of site A and site B prior to smallmouth bass establishment compared to similarity index of site A and site B after establishment).

This was repeated using non- influenced LaHave River sites which had both early and late surveys completed.

Results

Comparing Abundance and Rank

Smallmouth bass were first recorded in LaHave River in the FRS data set in 1995

(Table 10). Of 67 sites surveyed in LaHave River between 1978 – 2009, 21% (n = 14) were eventually found to have established smallmouth bass populations, and were identified as ‘influenced’. Annual increases of smallmouth bass CPUA at these sites ranged from 3.3 to 99%, in addition to three cases in which model coefficients were unrealistically large (i.e. greater than 1). While only one site shows a statistically significant increase in smallmouth bass CPUA, other, non-significant increases may be due to low sample size. Biologically, the establishment of smallmouth bass at these sites may influence native fish species regardless of statistical significance. Negative binomial

GLMs were used to model changes in CPUA of various species between 2000 – 2010 including site type with very limited success. No significant values at α = 0.05 were associated with any models, and calculated standard errors were often larger than the predicted change in annual CPUA (Table 11).

Atlantic Salmon

Atlantic salmon ranked significantly lower in surveys without smallmouth bass than surveys with smallmouth bass when species ranks were calculated using both AP1

(i.e. smallmouth bass and chain pickerel could occupy ranks) and AP2 (i.e. smallmouth bass and chain pickerel could not occupy ranks) (both p < 0.017) (Figure 21). Atlantic salmon CPUA (Table 12) and rank (both AP1 and AP2) (Table 13) were significantly lower in early surveys of non- influenced sites than early surveys of influenced sites (all p

< 0.01). Both CPUA and AP1 rank were higher in late surveys of non- influenced sites than late surveys of influenced sites (all p < 0.037), but this trend was apparent using

AP2 rank (p = 0.224). Neither CPUA nor rank (both AP1 and AP2) differed significantly over time (early to late surveys) in non- influenced sites (all p > 0.076), but all experienced significant decline over time (early to late surveys) in influenced sites (all p

< 0.01).

American Eel

American eel ranked significantly lower in surveys without smallmouth bass than surveys with smallmouth bass when species ranks were calculated using AP2 (p < 0.01), but not when ranks were calculated using AP1 (p = 0.101) (Figure 21). American eel

CPUA was significantly higher in early surveys of both influenced and non- influenced sites, than late surveys (both p < 0.01) (Table 12). However, CPUA did not differ based on site type (non- influenced or influenced) in early (p = 0.653) or late (p = 0.530) surveys. Rank (AP1 and AP2) did not significantly differ over time (early to late) in non- influenced sites (both p > 0.739), but AP2 rank of American eel was significantly lower in early than late surveys of influenced sites (p < 0.01) (Table 13).

Brook Trout

Brook trout ranks (both AP1 and AP2) did not significantly differ in surveys with or without smallmouth bass (both p > 0.212) (Figure 21). Brook trout CPUA was significantly higher in both early and late surveys of non- influenced sites than influenced sites (both p < 0.01). However, CPUA did not significantly change over time (early to late surveys) in either site type (both p > 0.456) (Table 12). Brook trout rank (both AP1 and AP2) was significantly higher in early surveys of non- influenced sites than influenced sites (both p < 0.032), but did not change over time (early to late surveys) in either site type (all p > 0.475) (Table 13).

White Sucker

White sucker ranked significantly lower in surveys without smallmouth bass than surveys with smallmouth bass when species ranks were calculated using AP2 (p = 0.014), but not when ranks were calculated using AP1 (p = 0.529) (Figure 21). CPUA significantly declined over time (early to late surveys) in influenced sites (p < 0.01), but not non- influenced sites (p = 0.242) (Table 12). White sucker AP1 ranks did not significantly differ based on time period (early and late) or site type (non- influenced and influenced) (all p > 0.102) (Table 13). However, AP2 ranks were significantly lower in early than late surveys of influenced sites (p = 0.014).

Other Species

Chub ranked significantly lower in surveys with smallmouth bass than surveys without smallmouth bass when species ranks were calculated using AP1 (p < 0.01), but not when ranks were calculated using AP2 (p = 0.078) (Figure 21). Killifish ranks did not significantly differ based on smallmouth bass presence using either AP1 or AP2 (both p >

0.601). Brown trout, common shiner, and threespine stickleback did not have sufficient data to complete an analysis. Both influenced and non- influenced sites had significantly higher CPUA of chub and killifish in early surveys than late surveys (all p < 0.01) (Table

12). Chub ranks (both AP1 and AP2) were significantly higher in early surveys of non- influenced sites than early surveys of influenced sites (both p < 0.01) (Table 13), but did not change over time (early to late) in either site type (all p > 0.200). Killifish ranks (both

AP1 and AP2) did not significantly differ based on time period (early and late) or site type (non- influenced and influenced) (all p > 0.078) (Table 13). Brown trout, common shiner, and threespine stickleback did not have sufficient data to complete comparisons of

CPUA or rank by survey type.

Characterizing Fish Assemblages

Species Richness and Diversity

LaHave River surveys had significantly fewer observed species in surveys with smallmouth bass present than in surveys without smallmouth bass (p < 0.01). However,

PLS surveys showed the opposite trend with significantly more observed species in sites with smallmouth bass present than in sites where they were absent (p < 0.01) (Figure 22).

PLS surveys also had significantly more observed species in surveys with chain pickerel present ( ) than without ( ) (p < 0.01). Species richness of early surveys of influenced and non- influenced LaHave River sites was not significantly different (p = 0.312). Influenced sites had significantly more observed species prior to smallmouth bass establishment than after (p < 0.01), but species richness was not significantly different in non- influenced sites over the same time period (early to late) (p

= 0.317) (Figure 23).

Shannon diversity was significantly lower in LaHave river surveys with smallmouth bass present than without (p = 0.031). Simpson diversity tended to be in the same direction, but was marginally significant (p = 0.076) (Figure 24). Both Simpson and

Shannon diversity in LaHave River surveys were significantly lower in early surveys of influenced sites than non- influenced sites (both p < 0.01), but did not differ in late surveys of each site type (both p > 0.210). Although Simpson diversity significantly declined (early to late surveys) in non- influenced sites (p = 0.027), neither index of diversity differed over time (early to late surveys) in influenced sites (both p > 0.645)

(Figure 25).

Species Composition

American eel, yellow perch and brown bullhead presence did not differ between early and late surveys at either influenced or non- influenced sites. Atlantic salmon and golden shiner presence was lower in late surveys of non- influenced sites than early surveys, but no difference was found between influenced sites. White sucker were found in fewer early than late surveys of influenced sites, but did not differ in presence in non- influenced sites. Brook trout and chub were present in more late surveys of non- influenced than early surveys of non-influenced sites, but fewer were found in late than early surveys of influenced sites. The number of sites with killifish present was lower in late surveys of both non- influenced and influenced sites, but by a higher degree in influenced sites (n = 8) than non- influenced sites (n = 1) (Figure 27).

Homogenization and Differentiation

Overall, Jaccard’s similarity coefficients ( ) comparing non- influenced LaHave

River sites (eight analyzed of 53 total), 64% of comparisons (n = 28) indicated an

increase in similarity (homogenization), and 29% indicated a decrease in similarity

(differentiation) between the decades of 1980 and 2000. Analysis of influenced sites (14 analyzed of 14 total) resulted in evidence of homogenization in 36%, and differentiation in 52% of comparisons (n = 91) (Figure 26).

Discussion

Smallmouth Bass in LaHave River

Smallmouth bass populations were established in 21% of LaHave River sites, and although the data do not support a thorough examination of changes in CPUA, estimates of increases in abundance range from 3.3 to 99%, annually. McNeill (1995) noted that sportfishing surveys reported 32 times the number of smallmouth bass caught in 1990 than what was estimated for 1974. This roughly translates to an annual increase of nearly

26%. Although this increase occurred during an earlier time period and on a larger scale than increases calculated herein, it does provide evidence of the high rate of smallmouth bass population increase in Nova Scotia. The high fecundity and the protection of eggs and fry by adult males is also thought to contribute to the high reproductive potential of smallmouth bass (Brown et al. 2009).

Influence of Smallmouth Bass on Native Fish

Atlantic Salmon

The establishment of smallmouth bass may negatively impact Atlantic salmon populations in LaHave River. This is demonstrated by the decline in Atlantic salmon

CPUA between early and late surveys of influenced sites, a trend not seen in non- influenced sites. Furthermore, the decrease in Atlantic salmon CPUA at influenced sites

is pronounced by the fact that while early surveys found a significantly greater CPUA in influenced sites, late surveys found a significantly greater CPUA in non- influenced sites.

This pattern was also noted using rank as a metric. Declines in salmonid populations have been noted with the establishment of smallmouth bass in multiple cases (see Brown et al.

2000), and likely are a result of both increased competition for food resources, and predation on juvenile Atlantic salmon by smallmouth bass (Weidel et al. 2000). The establishment of smallmouth bass at sites with high Atlantic salmon CPUA may reflect shared areas with overlapping or adjacent habitats (Chaput and Cassie 2010). This overlap, in conjunction with the apparent negative influence of smallmouth bass on

Atlantic salmon CPUA is noteworthy because Southern Upland Atlantic salmon is currently designated as Endangered.

Brook Trout

Results of the study herein suggest that the establishment of smallmouth bass does not influence brook trout populations. However, based on findings by several researchers, brook trout often appear to be negatively influenced by smallmouth bass presence (see

Brown et al. 2009, LeBlanc 2010). For example, LeBlanc (2010) noted a decline in

CPUE of brook trout, and increase in the CPUE of invasive smallmouth bass in Lake

Ainslie, Cape Breton, over the same time period. Although fewer brook trout were reported in influenced LaHave River sites than non- influenced sites, this did not appear to be in response to smallmouth bass establishment because early surveys of influenced sites (i.e. prior to smallmouth bass establishment) also noted significantly fewer brook trout at these sites. This suggests the influence of a habitat or ecosystem-based factor to smallmouth bass establishment success. However, this may not be the case. Although

many case studies of interactions between brook trout and smallmouth bass exist (see

Brown et al. 2009), the tendency for smallmouth bass to establish at sites with few brook trout may not be a result of purely biological processes. The spread of smallmouth bass is commonly attributed to illegal introductions by anglers with the intention of ‘replacing’ dwindling trout populations in Nova Scotia (Marriner 2005). If so, it is hypothesized that anglers are less likely to introduce smallmouth bass into areas that still support brook trout populations.

Small-Bodied Fish Species

The data were insufficient for quantifying any influence of smallmouth bass establishment on CPUA of small-bodied fish species (i.e. chub and killifish). Mann-

Whitney tests indicated a significant decline of both species over time in both influenced and non- influenced sites (early to late surveys), but model coefficients related to percent annual change in CPUA of both species in each site type had standard errors too high for the models to be considered relevant. Chub AP2 rank was noted to be significantly lower in early surveys of influenced sites than late surveys. Because AP2 rank does not include smallmouth bass during rank calculation, this implies that chub rank did not decrease simply as a result of high numbers of smallmouth bass. Rather, it suggests that the decrease in rank in these sites over time (early to late surveys) could be an indirect result of high smallmouth bass abundance. The means of impact is likely predation, as suggested by multiple studies (see Brazner and Beals 1997; Chaput and Caissie 2010).

Other Species

Like chub and killifish, American eel CPUA declined in both influenced and non- influenced sites when grouped by time period, but model results of changes in CPUA

over time in each site type were inconclusive. White sucker experienced decline in CPUA in influenced sites, and may be a result of multiple factors related to smallmouth bass.

Competition for benthic invertebrates may be partly responsible, but sucker species have been found in the stomachs of smallmouth bass (Brown et al. 2009). Interestingly, both

American eel and white sucker AP2 ranks were higher in late surveys of influenced sites than early surveys. In the case of American eel, it is hypothesized that this is a result of competition with smallmouth bass for prey. As small-bodied species experience decline, likely in response to smallmouth bass predation, it leads to American eel both increasing in rank (as a direct result of changing abundance), and decreasing in CPUA (as a result of prey becoming more scarce).

Influence of Smallmouth Bass on Fish Assemblage Characteristics

Influenced sites in LaHave River were noted to experience a decline in observed species richness between early and late surveys. As indicated by the distribution of species in influenced sites in early and late surveys, this is most often linked to the loss of killifish and/or chub. This link is supported by the findings of MacRae and Jackson

(2001), who noted that lakes with smallmouth bass had an average of 2.3 fewer small- bodied fish species than lakes without smallmouth bass. As discussed, Simpson diversity tends to describe diversity among the most common species in an assemblage. Shannon diversity was significantly lower in surveys with smallmouth bass than surveys without, while Simpson diversity was only marginally significantly lower. This suggests that low diversity measures in surveys with smallmouth bass are influenced by both common species, and uncommon or rare species.

Comparisons of between influenced LaHave River sites are clustered in the upper right hand corner of the plot, demonstrating that sites in which smallmouth bass became established were compositionally similar prior to establishment (Figure 26). In contrast, comparisons of between non- influenced sites illustrate that these sites were less compositionally similar, although homogenization was shown to have occurred.

Analysis of compositional similarity can also be used to evaluate ecological change. While homogenization is evidence of a loss of biodiversity (Taylor 2004), the implication that influenced sites did not experience homogenization when smallmouth bass became established is actually evidence of a high degree of change in species composition among sites. For example, consider two sites, A and B. Prior to establishment, both sites have a value of zero for smallmouth bass, indicating absence, and values of zero or one, indicating absence or presence of various other species (e.g. site , site , smallmouth bass absence indicated in bold). Once smallmouth bass become established, their presence is noted in each site (e.g. site

, site , smallmouth bass presence indicated in bold). Because

Jaccard’s similarity coefficient calculation does not include those species present at neither site (e.g. smallmouth bass at and ), then it can be determined that if the composition of species other than smallmouth bass remains constant, should be larger than , simply for mathematical reasons.

However, in influenced LaHave River sites, 53% of similarity coefficients instead decreased after smallmouth bass became established. This indicates a high degree of change in species composition among sites, and may be a result of fish assemblages experiencing critical slowing down, nearing or reaching tipping points, or moving

through a transition phase toward a new stable state as a result of smallmouth bass establishment.

If smallmouth bass indeed lead to tipping point scenarios, or act as trigger events in freshwater and diadromous fish assemblages of Nova Scotia, abundance (i.e. CPUA), species richness, diversity, and similarity indices are all metrics which may be used as indicators of ecological change. Because stable ecological states, critical slowing down, tipping points, and transition periods will likely reflect localized conditions, visualizing these metrics over time on a per-site basis may be useful. Many sites included in the FRS data set do not have sufficient data. There are few cases which can be examined in more detail, although boundaries between stable states and transition periods are estimated visually, and therefore subjective. For example, site LHav110, in LaHave River, has adequate data to estimate periods of stable states, and evaluate which ecological metrics change between these states (Figure 28). Jaccard’s similarity coefficient suggests that the stable state of this site’s fish assemblage changed between roughly 1986 – 2003, as species composition varied during this period. However, it does not indicate what types of changes were actually occurring. Examining species richness and diversity measures over the same time period infers that while diversity stabilizes at the same level, species richness decreases; chub are no longer present. Of course, these observations are simply suggestions given that only one species was lost, and are subjective to individual researchers, as well as influenced by multiple environmental factors, and sampling biases.

The evaluation of stable states, critical slowing down, and transition periods may be a valuable tool in management strategies if enough data can be included in analyses.

Portfolio Effect

The likelihood of a non-native species to negatively influence a native fish assemblage may be linked to life-history and fundamental niche characteristics of the assemblage members. In this context, the fundamental niche refers to a species’ niche in the absence of any interspecific competition, while the realized niche occurs in the presence of competing species (Roughgarden 1974). The portfolio effect, as described in

Chapter 2, considers the plasticity of various aspects of a species, population, or individual in various situations. Essentially, these two concepts are interconnected, and may help to explain observed or predicted differences in the response of fish assemblages to the establishment of smallmouth bass. For example, brook trout did not appear to be negatively influenced by the establishment of smallmouth bass in LaHave River. LeBlanc

(2010) noted a decline in brook trout CPUE in a Cape Breton lake over the same time period as an increase in smallmouth bass CPUE was reported. It is hypothesized that inconsistencies in observations such as these be examined in the context of the portfolio effect and/or fundamental niche. It is possible, or likely, that both the plasticity of brook trout (as a species or as individual populations), as well as the habitat and niche availability in Nova Scotia lakes is notably different than in rivers.

Assessment of Results

Past studies and results of this thesis agree that smallmouth bass have expanded their distribution across Nova Scotia lakes and rivers since initial introduction in 1942

(Figure 29). Six rivers (8.6%) and 60 lakes (5.2%) had smallmouth bass populations established, according to the current FRS and PLS data sets. However, past studies indicate that the FRS and PLS data sets underestimate the distribution and abundance of

both smallmouth bass and chain pickerel (McNeill 1995; Marriner 2005; Brown et al.

2009). To determine the actual distribution of these species, other sources of data should be included, such as angler surveys or other recreational fishing records.

This statement highlights a much larger problem when dealing with many historical data sets. Data analyzed at low resolution (e.g. considering many rivers over large spatial scales), may show overall trends regardless of issues with gaps in the data or ill-fitting models of individual species or rivers (see Chapter 3). However, when the same data are analyzed at a higher resolution (e.g. consideration of a single river), either by design or necessity, results become difficult to interpret. While the FRS and PLS data sets contain large amounts of data, the amount of information that can be garnered is currently limited. In some cases it would be relatively easy to increase data resolution; for example, a bevy of lake surveys to identify smallmouth bass presence or absence. In other cases, further evaluations of historical events are necessary to bridge gaps and mitigate bias.

Compositional similarity analyses may be built upon by including environmental variables such as land use and dam density, and these and other factors such as water chemistry may be added to various analyses and models. Mixed effect models will likely allow for the exploration of information in areas where data deficiency prevented analysis with GLMs. Finally, data regarding the distribution of smallmouth bass and chain pickerel in Nova Scotia is likely available through various sources such as recreational fishing records, and this information can be used to further explore the contributions of non-native piscivores to the structure of fish assemblages.

Table 10. Summary statistics of smallmouth bass in LaHave River, as reported in the

CPUA are included.

Year No. Sites % Sites Mean CPUA CPUA Range 1995 1 2.6 0.1 - 1996 0 0.0 - - 1997 2 14.3 0.1 0.1 - 0.2 1998 1 50.0 0.3 - 1999 2 15.3 2.8 0.2 – 5.5 2000 5 27.8 2.3 0.3 – 5.9 2001 5 41.7 1.2 0.3 – 2.5 2002 5 35.7 0.6 0.1 – 1.1 2003 4 40.0 0.4 0.1 – 0.7 2004 4 28.6 0.3 < 0.01 – 0.8 2005 8 50.0 0.7 < 0.01 – 2.3 2006 6 54.5 0.2 0.7 – 0.3 2007 5 83.3 1.0 0.3 – 1.6 2008 3 33.3 0.5 0.2 – 0.9 2009 6 66.7 0.3 0.1 – 0.4

Table 11. Annual change (%) in CPUA of select species between 2000 – 2010 in LaHave

River sites with no established smallmouth bass population (non- influenced) and sites in which smallmouth bass eventually become established (influenced). Change was estimated using negative binomial GLMs which modeled CPUA over time and used site type (non- influenced and influenced) as an interaction term. Brown trout, common shiner, and threespine stickleback had insufficient data for the model. Standard errors

(SE) were calculated as percent annual change in CPUA. No significant values at α =

0.05 were associated with any model.

Annual Change in CPUA (%) ± SE Species Non-Influenced Sites Influenced Sites Atlantic salmon -5.0 ± 5.4 -8.8 ± 6.5 American eel -7.1 ± 11.4 -21.4 ± 14.2 Brook trout -13.1 ± 16.6 -28.1 ± 95.7 White sucker -14.4 ± 9.9 2.6 ± 13.8 Chub -19.6 ± 15.7 -11.2 ± 20.0 Killifish -63.9 ± 102.3 -35.6 ± 103.8

Table 12. Comparison of catch-per-unit-area (CPUA) of select species between survey types in LaHave River. Non- influenced sites are those in which smallmouth bass did not establish. Influenced sites are those in which smallmouth bass did establish. Early surveys of non- influenced sites occurred prior to 2000. Early surveys of influenced sites occurred prior to smallmouth bass establishment. Late surveys of non- influenced sites occurred between 2000 – 2010. Late surveys of influenced sites occurred post- establishment. Brown trout, common shiner, and threespine stickleback had insufficient data for the analysis. Symbols indicate significantly higher (+) or lower (-) CPUA in survey type (a) than (b), using a Mann-Whitney test at α = 0.05. Double symbols indicate p < 0.01. Blanks represent no significant difference at α = 0.05.

Sites Compared (a) Early Early Early Late Non-Influenced Non-Influenced Influenced Non-Influenced Species (b) Early Late Late Late Influenced Non-Influenced Influenced Influenced Atlantic salmon -- ++ ++ American eel ++ ++ Brook trout ++ ++ White sucker ++ ++ Chub ++ ++ Killifish ++ ++

Table 13. Comparison of AP1 ranks (i.e. smallmouth bass and chain pickerel could occupy ranks) (A) and AP2 ranks (i.e. smallmouth bass and chain pickerel could not occupy ranks) (B) of select species between LaHave River survey types. Non- influenced sites are those in which smallmouth bass did not establish. Influenced sites are those in which smallmouth bass did establish. Early surveys of non- influenced sites occurred prior to 2000. Early surveys of influenced sites occurred prior to smallmouth bass establishment. Late surveys of non- influenced sites occurred between 2000 – 2010. Late surveys of influenced sites occurred post-establishment. Brown trout, common shiner, and threespine stickleback had insufficient data for the analysis. Symbols indicate significantly higher (+) or lower (-) CPUA in survey type (a) than (b), using a Mann-

Whitney test at α = 0.05. Single symbols indicate p < 0.05. Double symbols indicate p <

0.01. Blanks represent no significant difference at α = 0.05.

A Sites Compared (a) Early Early Early Late

Non-Influenced Non-Influenced Influenced Non-Influenced (b) Early Late Late Late Species Influenced Non-Influenced Influenced Influenced Atlantic salmon -- ++ + American eel Brook trout + White sucker Chub ++ Killifish

B Sites Compared (a) Early Early Early Late

Non-Influenced Non-Influenced Influenced Non-Influenced (b) Early Late Late Late Species Influenced Non-Influenced Influenced Influenced Atlantic salmon -- ++ American eel -- - Brook trout + White sucker - Chub ++ Killifish

Figure 21. Ranks of select species in LaHave River Surveys (LHS) without (W/O)

Whitney test result of p < 0.01. Single asterisk (*) indicates a Mann-Whitney test result of p < 0.05.

Figure 22. Observed species richness in LaHave River Surveys (LHS, 1978 – 2009) and

Provincial Lake Surveys (PLS, 1942 – 2008) without (W/O) (black) and with (W) (red) smallmouth bass. Double asterisks (**) indicate a Mann-Whitney test result of p < 0.01.

100

Figure 23. Species richness in LaHave River surveys in relation to smallmouth bass establishment. Black plots represent non- influenced sites (in which smallmouth bass never established), and blue plots represent influenced sites (in which smallmouth bass eventually established). ‘Early’ refers to surveys of non- influenced sites prior to 2000, and surveys of influenced sites prior to smallmouth bass establishment. ‘Late’ refers to surveys of non- influenced sites between 2000 – 2010, and surveys of influenced sites after smallmouth bass establishment. Double asterisks (**) indicate a Mann-Whitney test result of p < 0.01.

101

Figure 24. Shannon and Simpson diversity in LaHave River surveys without (W/O)

(black) and with (W) (red) smallmouth bass. Asterisk (*) indicates a Mann-Whitney test result of p < 0.05.

102

Figure 25. Shannon (top) and Simpson (bottom) diversity in LaHave River surveys in relation to smallmouth bass establishment. Black plots represent non-influenced sites (in which smallmouth bass never established), and blue plots represent influenced sites (in which smallmouth bass eventually established). ‘Early’ refers to surveys of non- influenced sites prior to 2000, and surveys of influenced sites prior to smallmouth bass establishment. ‘Late’ refers to surveys of non-influenced sites between 2000 – 2010, and surveys of influenced sites after smallmouth bass establishment. Double asterisks (**) indicate a Mann-Whitney test result of p < 0.01.Single asterisk (*) indicates a Mann-

Whitney test result of p < 0.05.

103

104

(black/blue) and late (grey/light blue) surveys were included (non-influenced n = 8, influenced n = 14).

105

Figure 28. Jaccard’s coefficient of similarity (J), observed species richness (S), Shannon diversity (Shan), and Simpson diversity (Simp) in site LHav110, LaHave River, between

1980 – 2010. Similarity coefficients calculated by comparing species presence between surveys. Red dashed line indicates the first record of smallmouth bass presence for site

106

Figure 29. Occurrence of smallmouth bass in watersheds and lakes in Nova Scotia, 1942

– 1993 (McNeill 1995).

107

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Appendix A: Assessment of Data Sources and Preliminary Data Restructuring

Introduction

As discussed in Chapter 1, it is essential to normalize and standardize data prior to analyses, especially when data consist of multiple and/or compiled data sets. Diligence during this process will improve the quality of analyses, and increase confidence in the results. By evaluating the utility of the data, future sampling and analyses can be strategized with potential sources of bias identified. The first objective of this study was to normalize, standardize, and evaluate the utility of existing freshwater and diadromous fish assemblage data for assessing fish assemblage structure in rivers and lakes.

Description of Data Sets

The original Federal River Surveys (FRS) data set contains 5,063 abundance survey records from 2,479 backpack electrofishing surveys of Nova Scotia rivers completed by the Department of Fisheries and Oceans Canada (DFO), focusing on the

Southern Upland region of the province and complied into an Excel spreadsheet. Similar and/or overlapping electrofishing survey data for juvenile Atlantic salmon have been used to estimate abundance via capture-mark-recapture studies (Gibson et al. 2003;

Gibson et al. 2009), calculate catchability (Gibson et al. 2003; Gibson et al. 2009), and measure rates of change in abundance between 2000 – 2008 in several Southern Upland rivers (Gibson et al. 2009). A total of 72 unique rivers are included encompassing 664 unique sampling sites labeled with unique site codes (Figure A1). Coordinates are defined as conventional latitude and longitude or UTMs consisting of easting and northing values. Survey dates ranged from July 1965 to September 2009. The length, width and area of each electrofishing site, in addition to the time spent actively

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electrofishing each site were not always reported. Each survey consisted of multiple passes, each represented by a record in the data set. Crew leaders are identified, and notes and comments are included throughout the spreadsheet. The data are broken down into 39 taxonomic groups and include abundance records; 29 groups are defined at the species level and used in analyses (Table A1). Nine higher taxonomic levels (perch, Alosa, shiner, chub, stickleback, dace, trout, cyprinid, and sculpin) and one unidentified species group are included in the data set, but filtered out of the analyses unless otherwise noted.

Gaspereau and alewife are reported separately, although ‘gaspereau’ may refer to either alewife (Alosa pseudoharengus) or blueback herring (Alosa aestivalis) (Chaput et al.

2001); therefore, these groups were summed and referred to as ‘Alosa’. Atlantic salmon abundances are recorded in two life stages (fry and parr), as well as the total count of

Atlantic salmon; only the total count is used in analyses.

The Provincial Lake Surveys (PLS) data set contains 2,244 records from surveys of Nova Scotia lakes between August 1942 – September 2008. Unlike the FRS data set, each PLS record reports the presence of up to 14 species rather than abundance.

Collection methods included the use of minnow traps, beach seines, and 100 ft multi- panel gill nets with panels of various mesh sizes. Details of which methods were employed at which site are not readily available although multi-panel gill nets predominated (Jason LeBlanc, pers. comm.). A total of 1,015 lakes are included from across all 18 counties in Nova Scotia (Figure A2). Watershed and site codes are reported, as well as the latitude and longitude of each survey site. In total, 34 taxa consisting of freshwater and anadromous fish species were identified and used in analyses (Table A1).

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Two groups identified to higher taxonomic levels (stickleback and flounder) and one group that encompassed all species as unidentified were not used.

Objectives

The objectives are to:

1) Assess the existing diadromous/freshwater fish community data for potential

biases, formatting issues, and missing information,

2) Develop and apply general filtration algorithms to the existing data to address

issues identified in objective 1, and

3) Evaluate the utility of filtered data for assessing fish assemblage structure in

rivers (FRS) and lakes (PLS).

Methods

Each data set was first assessed for usefulness in each analysis, and thoroughly inspected for common data set issues described here and in Chapter 1. Then, each data set was carefully examined for inconsistencies in sampling protocol and effort, formatting errors, and missing information. These examinations were done via custom filtration routines, visualizations (plotting summary statistics and exploring nonsensical data points), and/or editing of the data to create a revised version of each data set that were then used to complete the objectives of the study. In each instance, variations in specific filtration methods were used based on the needs of the analysis.

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Results

Formatting

To avoid issues with the way statistical programs such as R (S Plus) interpret special characters and to ease importing character strings from comma delimited file formats, punctuation was removed from river names of the FRS data set including commas, periods, and apostrophes to simplify character strings and to provide easier names to use in analyses. In many cases, this was a matter of personal coding practice, such as the removal of parentheses in river names; sixteen rivers had county or general location names included in brackets after the river name as a result of shared river names.

Parentheses were removed, but additional location names were kept (eg. ‘East (Chester)’ became ‘East Chester’). Extra whitespaces at the end of river names were truncated. Sites with no site codes associated with them were changed to ‘XXXunknown’, where ‘XXX’ were abbreviations of river names (eg. an unknown site on Sackville River became

‘SACunknown’). Infrequently, latitude and longitude values were switched, or exchanged with easting northing values, and these values were corrected and verified by plotting coordinates using QGIS (Quantum GIS Development Team 2012 version 1.7.4).

For the PLS data set, minor edits were required for fewer than ten dates, which were either missing or formatted improperly. Simple spelling issues, extra whitespace, and unnecessary punctuation were corrected. The dates in Excel were split into three independent variables (day, month, and year) to avoid date formatting issues and peculiarities of Excel dates. Unnecessary punctuation was removed from lake names, and all lake names were examined to ensure that lakes which shared names, or had known aliases, were properly identified and labeled. Nearly 80 lakes were noted in which it was

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believed that their proximity to county borders may have caused them to be listed under multiple counties; these data were annotated. As well, 49 missing site codes were given values suitable for future data filtering.

Filtering Surveys for All Analyses

FRS surveys specifically noted to have been ‘salmon-only’ (n = 543 records) were removed for analyses because the overall research focus is on fish assemblages and electrofishing surveys focused on only salmon were assumed to be biased. These surveys would result in false zeros in the abundances of other species, misrepresentation of summary statistics, and skewed model outcomes. While these surveys could be used for analyses specific to Atlantic salmon, there is potential for changes in catchability of other species when researchers are focused on collecting Atlantic salmon thereby introducing a catchability bias for which it would be difficult to control during analyses. Fifty-seven surveys were removed from the data set because they were classified as ‘Gene Bank

Collection’, ‘Disease Testing’, ‘Mortality Check’, ‘Tissue Collection’, or ‘Re-Sweep’ type surveys, and protocols likely differed from standard surveys.

Editing Abundances

Over 185,000 missing abundances in the FRS data set were evaluated to determine if they represent true zeros (i.e. a species was not present). With the exception of those surveys targeting Atlantic salmon, which were removed from the data set, these missing abundances were determined to represent the true absence of particular species.

Therefore, these values were changed to 0, with one further exception: records in which

Atlantic salmon were reported with no abundance of fry or parr. In this case, fry and parr abundances were recorded as ‘NA’; the missing value placeholder in R and many other

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statistical analysis applications. Atlantic salmon abundances were revised to be the sum of parr plus fry, because this was the case in 99.6% of the data set.

Abundances recorded as ‘-99’, indicating species presence with no count available, were edited individually. Of the 138 occurrences, 38 were associated with explanatory comments from the original field sheets which included estimates of abundance. When available, these estimates were used to replace the ‘-99’ value. The remaining ‘-99’ values were left unchanged as various analyses may require different treatment of these values. For example, if presence/absence data are analyzed, any value other than zero would be taken as presence whereas if actual abundances are required for analysis, -99 records would be dropped from the analysis.

Discussion

The process of carefully revising and filtering both the FRS and PLS data sets will allow future analyses, albeit cautiously, because additional filtration and standardization likely will be required on a per-analysis basis. For example, abundance (FRS) or presence/absence (PLS) of rare species will need to be identified and treated appropriately. While variation in sampling protocols between the two data sets restricts joining the FRS and PLS data sets, they may each be used in complementary or supportive analyses. The challenges of working with compiled historical data sets are omnipresent and appropriate treatments must be applied at every stage of analysis. This sentiment applies within this study as well and further analysis treatments are applied within Chapters 2 and 3. Nonetheless, the prospect of using comprehensive data sets spanning a significant portion of the province has the potential to facilitate large-scale ecosystem dynamics research beyond that presented in this study.

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Table A1. Fish species recorded in biological surveys of Nova Scotia lakes (1942-2008) and rivers (1965-2009), including taxonomic family, scientific name, and common name.

Species found in the FRS and/or PLS data set are indicated.

Family Species Common Name FRS PLS Anguillidae Anguilla rostrata American Eel X X Atherinopsidae Menidia menidia Atlantic Silverside X Catostomidae Catostomus commersonii White Sucker X X Centrarchidae Micropterus dolomieu Smallmouth Bass X X Clupeidae Alosa pseudoharengus Alewife X X Alosa sapidissima American Shad X X Cyprinidae Chrosomus eos Redbelly Dace X X Couesius plumbeus Lake Chub X X Luxilus cornutus Common Shiner X X Notemigonus crysoleucas Golden Shiner X X Notropis heterolepis Blacknose Shiner X Rhinichthys atratulus Blacknose Dace X X Margariscus margarita Pearl Dace X Semotilus atromaculatus Creek Chub X X Carassius spp. Goldfish X Esocidae Esox niger Chain Pickerel X X Fundulidae Fundulus diaphanous Banded Killifish X X Fundulus heteroclitus Mummichog X X Gadidae Microgadus tomcod Tomcod X Gasterosteidae Apeltes quadracus Fourspine Stickleback X X Gasterosteus aculeatus Threespine Stickleback X X Pungitius pungitius Ninespine Stickleback X X Culaea inconstans Fivespine Stickleback X Ictaluridae Ameiurus nebulosus Brown Bullhead X X Moronidae Morone americana White Perch X X Morone saxatilis Striped Bass X X Osmeridae Osmerus mordax Smelt X X Percidae Perca flavescens Yellow Perch X X Petromyzontidae Petromyzon marinus Sea Lamprey X Salmonidae Coregonus clupeaformis Lake Whitefish X Coregonus huntsman Atlantic Whitefish X Salmo gairdneri Rainbow Trout X X Salmo salar Atlantic Salmon X X Salmo salar Landlocked Salmon X Salmo trutta Brown Trout X X Salvelinus fontinalis Brook Trout X X

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Figure A1. Locations of Nova Scotia rivers included in the Federal River Surveys (FRS) data set (1965-2009).

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Figure A2. Distribution of Nova Scotia lakes included in the Provincial Lake Surveys (PLS) data set (1942-2008). The number of lakes surveyed in each county is indicated.

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Appendix B: Model Selection and Testing

Table B1. Generalized linear model labels and description of filtering to create subset data.

Model Label Description GLM1 Changes in CPUA over time were modeled using negative binomial generalized linear models based only on rivers regardless of the number of sampling sites or frequency of sampling. GLM2 Electrofishing site within each river was added to GLM1 models as a factor regardless of the frequency of samples taken at each site. GLM2a Electrofishing sites sampled more than once. GLM2b Electrofishing sites sampled five or more times. GLM2c Electrofishing sites sampled ten or more times. Y1 Rivers sampled in more than one year. Y10 Rivers sampled in ten or more years. S01 Species accounting for 0.1% or more of the total relative abundance.

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Table B2. The percent of total relative abundance of each species or group in the Federal

River Surveys data set. Those included in models tagged as 'S01' are indicated with an asterisk (*).

Species/Group % Total *Atlantic Salmon 44.87 *American Eel 31.36 *Brook Trout 9.49 *White Sucker 5.89 *Chub 4.77 *Common Shiner 0.96 *Killifish 0.90 *Brown Trout 0.35 *Smallmouth Bass 0.15 *Threespine Stickleback 0.12

Ninespine Stickleback 0.08 Yellow Perch 0.06 Brown Bullhead 0.04 Fivespine Stickleback 0.02 Redbelly Dace 0.01 Golden Shiner 0.01 American Shad 0.01 Rainbow Trout <0.01 Pearl Dace <0.01 Fourspine Stickleback <0.01 White Perch <0.01 Rainbow Smelt <0.01 Striped Bass <0.01 Chain Pickerel <0.01

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Table B3. Summary of results of negative binomial generalized linear models evaluated for use in catch-per-unit-area (CPUA) trend

analysis for each species. Model output data arranged by increasing complexity, with model formula and data filters described. Model

output listed, including the number of rivers, species and sites analyzed, number of potential models, and number and percent of

successful models, significant models, species successfully modeled, and species with significant models. Species with non-successful

(*) or non-significant models listed, and the number and percent of models with significantly declining and increasing CPUA.

Significance is determined at α = 0.05.

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dels with dels

Model Name Model Formula Data Filters

GLM1Y1 CPUA ~ Year rivers >1 year 52 25 622 1300 303 58 241 11acd 42 16 (23.1) (19.1) (96.0) (45.8) (72.4) (27.6) GLM1Y10 CPUA ~ Year rivers >10 years 13 25 622 325 123 44 2012 11afgi 31 13 (37.9) (35.8) (80.0) (55.0) (70.5) (29.6)

GLM2Y1 CPUA ~ Year + Site rivers >1 year 52 25 622 1300 277 50 241 10abcd 38 12 (21.3) (18.1) (96.0) (41.7) (76.0) (24.0)

GLM2Y10 CPUA ~ Year + Site rivers >10 years 13 25 392 325 112 34 2012 10abcg 25 9 (34.5) (27.9) (80.0) (50.0) (73.5) (26.5)

dels with dels

Model Name Model Formula Data Filters

GLM2aY10 CPUA ~ Year + Site rivers >10 years 13 25 260 325 110 23 2012 9abcgj 18 5 sites >1 year (33.9) (20.9) (80.0) (45.0) (78.3) (21.7) GLM2bY1 CPUA ~ Year + Site rivers >1 year 16 25 130 400 104 20 19123 9abcj 17 3 sites >4 years (26.0) (19.2) (76.0) (47.4) (85.0) (15.0) GLM2bY10 CPUA ~ Year + Site rivers >10 years 13 25 126 325 99 20 19123 9abcj 17 3 sites >4 years (30.5) (20.2) (76.0) (47.4) (85.0) (15.0) 133 GLM2cY1 CPUA ~ Year + Site rivers >1 year 9 25 54 225 57 14 181234 7abe 11 3

sites >9 years (25.3) (24.6) (72.0) (38.9) (78.6) (21.4) GLM2cY10 CPUA ~ Year + Site rivers >10 years 9 25 54 225 57 14 181234 7abe 11 3 sites >9 years (25.3) (24.6) (72.0) (38.9) (78.6) (21.4) GLM1Y10S01 CPUA ~ Year rivers >10 years 13 11 392 143 90 43 11 10f 35 13 species >0.1% (62.9) (47.8) (100) (90.9) (69.8) (30.2) GLM2Y10S01 CPUA ~ Year + Site rivers >10 years 13 11 392 143 82 33 11 9b 24 9 species >0.1% (57.3) (40.2) (100) (81.8) (72.7) (27.3) GLM2aY10S01 CPUA ~ Year + Site rivers >10 years 13 11 260 143 82 23 11 9b 18 5 species >0.1% (57.3) (28.0) (100) (81.8) (78.2) (21.7) site >1 year GLM2bY10S01 CPUA ~ Year + Site rivers >10 years 13 11 126 143 77 20 11 9b 17 3 species >0.1% (53.9) (26.0) (100) (81.8) (85.0) (15.0) sites >4 years GLM2cY10S01 CPUA ~ Year + Site rivers >10 years 9 11 54 99 43 14 11 7bfk 11 3 species >0.1% (43.4) (32.6) (100) (63.6) (78.6) (21.4) sites >9 years

dels with dels

Model Name Model Formula Data Filters

No. Rivers No. Species No. Sites Potential No. Models No. Successful (%) Models Models No. (%) Significant No. Species Successfully (%) Modeled No. Species Modeled (%) Significant Mo No. (%) CPUA Declining with Models No. (%) CPUA Increasing GLM1REP CPUA ~ Year years 1980-2010 13 11 342 143 88 27 11 10f 20 7 rivers >10 years (61.5) (30.7) (100) (90.9) (74.0) (25.9) species >0.1% GLM2REP CPUA ~ Year + Site years 1980-2010 13 11 342 143 80 22 11 9b 16 6 rivers >10 years (55.9) (27.5) (100) (81.8) (72.7) (27.3) species >0.1% GLM2aREP CPUA ~ Year + Site years 1980-2010 13 11 236 143 80 22 11 9b 16 6 rivers >10 years (55.9) (27.5) (100) (81.8) (72.7) (27.3) species >0.1% 134 site >1 year b GLM2bREP CPUA ~ Year + Site years 1980-2010 12 11 124 132 72 17 11 9 15 2 rivers >10 years (54.6) (23.6) (100) (81.8) (88.2) (11.8) species >0.1% site >4 year GLM2cREP CPUA ~ Year + Site years 1980-2010 9 11 54 99 43 14 11 8bk 11 3 rivers >10 years (43.4) (32.6) (100) (72.7) (78.6) (21.4) species >0.1% site >9 year + Species which did not produce successful models: 1 - rainbow trout 2 - chain pickerel, fourspine stickleback, redbelly dace, striped bass 3 - smelt 4 - white perch o Species which did not produce significant models: a – American shad , brown bullhead, fivespine stickleback, golden shiner, yellow perch b - brown trout, common shiner c - pearl dace, white perch d - chain pickerel, fourspine stickleback, redbelly dace

e - alewife, ninespine stickleback, pearl dace, threespine stickleback f - alewife g - smelt h - striped bass i - white perch j - ninespine stickleback k - threespine stickleback

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Figure B1. Frequency histogram of dispersion parameters for Poisson generalized linear models (A) and negative binomial generalized linear models (B) using the GLM1Y10 subset of the Federal River Surveys data set.

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Figure B2. Coefficients of successful GLM1Y1 and GLM1Y10 models, grouped by species. Annotated values specify the number of coefficients falling outside of the range of the plot. The number of rivers included in the models indicate by n. Values on the right hand y-axis (a/b) indicate the number of significant models (a) and the number of successful models (b) associated with each species. Dashed line placed at a coefficient value of 0.

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Figure B3. Comparison of Akaike Information Criterion (AIC) values of GLM1REP and

GLM2REP models (left). Line segments indicate change in paired AIC values of nested models (n = 71). Magnification of lower range of AIC values indicated (red = 0 to 1,000, blue = 0 to 400). Magnitude of change between nested models GLM1REP and

GLM2REP AIC values (right) included.

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