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ECOLOGY OF THE IMPERILED GRASS PICKEREL (ESOX AMERICANUS VERMICULATUS LESUEUR) IN ONTARIO: DISTRIBUTION PATTERNS AND POPULATION DECLINE

Julia Elizabeth Colm

A thesis submitted to the Department of Biology

In conformity with the requirements for

the degree of Master of Science

Queen’s University

Kingston, Ontario, Canada

(September, 2015)

Freshwater fishes are among the most imperiled organisms on Earth and understanding their life history is vital to conservation efforts. Grass Pickerel, a top predator, is a nationally imperiled fish that is found at the northern edge of its range, has a highly patchy distribution, and can be rare to abundant where found in Canada. The distribution of Grass Pickerel within a watershed was explained using generalized linear models and is largely driven by site-scale variables such as ample submerged aquatic vegetation, channel cover, and associated wetlands in the floodplain, but regional factors, presumably climate, appeared important as well. Additionally, Grass Pickerel has declined in an eastern Ontario population since 1960 and forage fishes have increased in richness and abundance. Changes in the fish community were evaluated using non-metric multidimensional scaling and are likely due to combined effects of land use changes and alterations to stream hydrology from beaver activity and variable climatic conditions. The results from this study are informative for habitat protection and mitigating threats to this species, but also make a case for improved, standardized monitoring and baseline data. Grass Pickerel populations in

Ontario are at the periphery of the species’ range and are, perhaps, the most important for the species persistence in a changing world.

Acknowledgements

Firstly, I would like to thank my funding sources, the DFO Species at Risk program, Greenburg Family

Fund, and Queen’s University for allowing me to study my favourite organism, the Grass Pickerel, and allowing me the freedom to pursue questions I was most interested in. Fish community and reach-scale data were contributed by the OMNRF, and I greatly appreciate the time put in by Bastian Schmidt to help me understand the data set-up. Thanks as well to Nick Jones and Colleen Demille.

I am so grateful to my supervisors, Bruce Tufts and Nick Mandrak, for having faith in me and supporting me through this. Thanks to Nick for inspiring such a passion in ichthyology and conservation, for always asking questions to keep me thinking, and correcting my repeated comma offences with patience. I would also like to thank my committee members, Shelley Arnott and Laurene Ratcliffe for providing help with stats even at the most inconvenient times for you, providing a thoughtful perspective of broader biological principles, and for providing excellent role models for women in science. Thanks also to Mark Ridgeway for some thoughtful conversations.

I owe a huge amount of thanks to Dr. E.J. Crossman, without whom this study would not have been possible. It speaks volumes about him as a scientist that I was able to return to his notebooks over 50 years later and extract data to include in my analysis; his data management and organizational skills were incredible. It was a huge honour, not only to get to update sampling from the original population of my target species ever studied, but to follow in the footsteps of such a legendary fisheries biologist. I felt very privileged to work on this project, and I am tremendously grateful to Dr. Crossman for making it so easy to do so.

Next, I would like to thank colleagues from DFO, Jason Barnucz, Lynn Bouvier, Andrew Drake, and Bill

Glass for valuable advice, guidance, help with field work and stats, data management and sometimes life.

Thanks especially to Drake and Barnucz for getting me into the world of fisheries biology and taking a chance on me. I am forever indebted to you both for helping me find my path.

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Thanks to the Biodiversity Science lab at DFO, specifically Olivia Butty, Danielle Hosick, Alex Price, and Edyta Ratajczyk for the most fun field work and highest quality data collection. You will always be my dream team.

Thanks as well to the Freshwater Fisheries Conservation lab. Specifically, Paul Finigan for field work and many conversations and rants, you’ve been my partner in crime through this; Rachael Hornsby for keeping me grounded and laughing while neck deep in sludge; Eric Taylor for saving me hours of time using ArcGIS; Sean Bridgeman for providing comments on early drafts; Jessie Leudi for painful hours of data entry; Courtney Kolbe and Ben Labenski for field work help; Kathleen Allen, Mary Hanley, Dan

McCarthy, and Changhai Zhu and for suggestions and laughs along the way; and Randy Lindenblatt for helping with organization and procurement.

Lastly, thanks to my family and friends. Thank you Laurie and Brian for providing every possible avenue of support to me through this and all other years of my life. All of my successes in life I owe to you.

You’ve taught me how to work hard, care about what matters, stay positive and love what I do. You are the greatest humans I know. A big thanks to the Sarahs for being proud of me and acting interested, even when you weren’t. And thanks to J for all of the encouragement and spontaneous adventures through the end.

Table of Contents

Abstract ...... ii Acknowledgements ...... iii List of Figures ...... vi List of Tables ...... vii List of Abbreviations ...... viii Chapter 1 General Introduction and Literature Review ...... 1 Chapter 2 Local factors predict Grass Pickerel distribution ...... 7 Introduction ...... 7 Methods...... 8 Results ...... 12 Discussion ...... 15 Figures and Tables ...... 24 Chapter 3 Fish community change and decline of Grass Pickerel in Jones Creek, Ontario ...... 37 Introduction ...... 37 Methods...... 42 Results ...... 47 Discussion ...... 50 Figures and Tables ...... 61 Chapter 4 General Discussion ...... 67 Literature Cited ...... 72 Appendix A Additional Notes on Grass Pickerel Conservation ...... 84 Appendix B Supplemental Data ...... 87

List of Figures

Figure 1.1 North American range of Grass Pickerel...... 6 Figure 1.2 Distribution of Grass Pickerel in Ontario………………………………...……………………..6 Figure 2.1 Maps of sampling effort in each watershed ...... 24 Figure 2.2 Conductivity ...... 24 Figure 2.3 Mean baseflow index ...... 25 Figure 2.4 Non-metric multidimensional scaling by watershed...... 26 Figure 2.5 Non-metric multidimensional scaling by detection category...... 27 Figure 3.1 Non-metric multidimensional scaling- single seine data ...... 61 Figure 3.2 Non-metric multidimensional scaling- all data ...... 62 Figure 3.3 Land-use changes in the Jones Creek subwatershed ...... 63 Figure 3.4 Land-use changes in the 100m buffer around Jones Creek ...... 63

List of Tables

Table 2.1 Number of sites and reaches sampled by watershed and year ...... 28 Table 2.2 Site-scale variables per watershed...... 29 Table 2.3 Site-scale variables per detection category...... 30 Table 2.4 Reach-scale variables per watershed...... 31 Table 2.5 Reach-scale variables per detection category...... 32 Table 2.6 Catch per unit effort by watershed and year...... 32 Table 2.7 Most abundant and frequently observed species ...... 33 Table 2.8 Species captured...... 35 Table 3.1 Species detected in single seine and all gear sampling...... 64 Table 3.2 Similarity percentages for single seine data ...... 65 Table 3.3 Similarity percentages for all gear data...... 65

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List of Abbreviations

AEC – Aquatic Ecosystem Classification COSEWIC – Committee on the Status of Endangered Wildlife in Canada DFO – Fisheries and Oceans Canada DS – Detected at the Site (Grass Pickerel detection category) DW – Detected elsewhere in the Waterbody (Grass Pickerel detection category) FA – Fisheries Act GB – Georgian Bay watershed GIS – Geographic Information System GP – Grass Pickerel LOW – Lake Ontario West MANOSIM – Multivariate Analysis of Similarities ND – Not Detected (Grass Pickerel detection category) NMDS – Non-metric multidimensional scaling OMNRF- Ontario Ministry of Natural Resources and Forestry RCA – Reach Contributing Area SARA – Species At Risk Act SOLRIS – Southern Ontario Land Resource Information System StL – St. Lawrence watershed VHS – Viral Hemorrhagic Septicemia ZIP – Zero-Inflated Poisson

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Chapter 1

General Introduction and Literature Review

Aquatic species are some of the most imperiled animals in North America (Ricciardi and

Rasmussen 1999). Approximately 39% of known North American fishes are considered imperiled and the list is increasing (Jelks et al. 2008). Habitat degradation, invasive species, restricted ranges, pollution and overexploitation are considered the greatest threats to freshwater fishes in North America (Dextrase and Mandrak 2006, Jelks et al. 2008, McCune et al. 2013). In

Canada, 89 freshwater fish species have been assessed as at risk by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC). In Ontario alone, there are 30 freshwater fishes assessed as Endangered, Threatened, or Special Concern (COSEWIC 2015). With recent changes to the federal Fisheries Act (FA), species listed as Special Concern under the Species At Risk

Act (SARA) are arguably in the most danger of further declines as they already face threats that are unregulated and unmitigated, are afforded minimal protection by SARA, and may have reduced protection as a result of recent changes to the FA (Hutchings and Post 2013). One such species is the Grass Pickerel (Esox americanus vermiculatus).

The Grass Pickerel, currently assessed as Special Concern, qualified as Threatened under

COSEWIC guidelines in 2005 because of its small, declining Area of Occupancy in Ontario and

Quebec (Crossman and Holm 2005); however, the species is common across its range in the

United States (Nature Serve 2015) and is, thus, considered to have rescue potential from bordering populations. Currently, the biggest threat to the Grass Pickerel in Canada is from agricultural drain maintenance practices that destroy its habitat by removing aquatic and riparian vegetation where feeding and spawning occur, and increases turbidity, which reduces its ability to see prey (Crossman and Holm 2005). Invasive species, climate change, and disease are also considered threats of medium concern (Beauchamp et al. 2012). Grass Pickerel is often sampled incidentally as part of larger provincial and federal sampling programs (Beauchamp et al. 2012), but there have been a limited number of studies directly targeting this species (Crossman 1962,

Kramski 2014, Fisheries and Oceans Canada unpubl. data) and adequate data do not exist across its Canadian range to estimate population sizes or trends. Therefore, updated sampling of Grass

Pickerel is needed, particularly from populations that have not been recently sampled.

Grass Pickerel is the smallest member of the Pike family and is typically the top predator in the systems it inhabits (Crossman and Holm 2005). It occupies habitats that may be unsuitable for other piscivores (Cain et al. 2008) and is, therefore, integral in maintaining structure in the fish community. In a fisheries survey of a lake in Washington, it was suggested that the presence of

Grass Pickerel was responsible for maintaining healthy populations of panfishes by preventing stunting (Divens et al. 2001). Grass Pickerel is at the northern edge of its range in Ontario, most likely limited by climate (Crossman and Holm 2005). Range-edge populations are typically exposed to a wider range of environmental conditions (extremes and fluctuations) compared to populations at the centre of the range (Parsons 1991), which may make them better suited to adapt to changing climatic conditions (Fraser 2000). Grass Pickerel could be a suitable umbrella species for small stream systems as it requires naturalized riparian zones, aquatic vegetation, benthic invertebrates for the juvenile diet and forage fishes for the adult diet (Crossman 1962).

Protection of Grass Pickerel would indirectly benefit habitat and other aquatic organisms. Grass

Pickerel is functionally important to ecosystems, contributes to regional biodiversity, and

2 protection of its habitat would effectively protect the entire ecosystem it inhabits. Therefore, this species should be a conservation priority.

The life history and ecology of the Grass Pickerel is similar to that of the other, better-known members of the family such as Northern Pike (Esox lucius) and Muskellunge (Esox masquinongy). It spawns in flooded-over riparian vegetation in the spring (Kleinert and Mraz

1966) when water temperatures reach 8-12°C, and adhesive eggs are cast freely onto submerged aquatic vegetation (Crossman 1962, Crossman and Holm 2005). Young-of-the-year remain in these flooded spawning sites until melt water recedes and feed on benthic invertebrates. Grass

Pickerel shift to a diet composed of fish and crayfish at approximately 100mm total length

(Crossman 1962, Ming 1968). It is an ambush predator that requires clear water to see prey and uses aquatic vegetation to remain hidden while hunting. In Canada, it becomes sexually mature in its second year, reaches an average adult size of approximately 190mm total length (Canadian record is 328mm), and reach an age of up to seven years; females are typically larger and live longer than males (Crossman 1962, Crossman and Holm).

Many of the streams that Grass Pickerel inhabit have little aesthetic, commercial, or recreational value for humans and are, thus, largely ignored (Crossman 1962). Grass Pickerel was first documented in North America in the mid-1800s and appeared in fisheries surveys in Canada in the early 1900s by Reighard (1899), Nash (1908), Fowler (1915), and Toner (1937) (Crossman

1962), but the ecology of this species was not fully understood until an intensive survey of Grass

Pickerel was done in eastern Ontario in 1960. Crossman (1962) provided a comprehensive description of the species including morphology, distribution and habitat, feeding behaviours and

3 preferences, spawning and reproductive biology, and age and growth. Crossman (1966) was also integral in clarifying its classification as a subspecies of the Redfin Pickerel (Esox americanus).

A limited number of studies on Grass Pickerel have been carried out in recent years. It received moderate attention through the 1960’s as studies of its life history took place across its range in the United States, most notably by Kleinert and Mraz (1966) in Wisconsin and Ming (1968) in

Oklahoma. Since then, Grass Pickerel has received limited attention both in Canada and the

United States with research on predator-prey interactions (Foster 1979), prey handling (Hoyle and Keast 1988), diet (Weinman and Lauer 2007), muscle physiology (Hoyle et al. 1986,

Weatherly and Gill 1987), and habitat associations (Cain et al. 2008). Grass Pickerel has been used as a model for understanding all esocids because of its small size, ease of maintenance in captivity, and great degree of similarity in morphology and ecology with other members of the family (Scott and Crossman 1998).

Grass Pickerel is found in eastern North America west of the Appalachian Mountains (Figure

1.1). In the United States, it is found throughout the Mississippi River drainage and southern

Great Lakes basin and, in Canada, Grass Pickerel is found sporadically across southern and central Ontario and southwestern Quebec (Crossman 1962). Grass Pickerel habitat has been well described and is consistent across its range in North America. It lives in smaller creeks and associated wetlands or shallow, nearshore areas of lakes and rivers that are clear, heavily vegetated, slow moving, with mud or clay substrate (Crossman 1962, Kleinert and Mraz 1966,

Ming 1968). Grass Pickerel is common and widely distributed across its range in the United

States where suitable habitat exists (Page and Burr 2011), including systems with degraded water

4 quality as long as in-stream cover (macrophytes or woody debris) is present (Cain et al. 2008).

Its distribution across Ontario, however, is less consistent. Grass Pickerel is found in nine mostly isolated locations in Ontario (Figure 1.2) and can be rare to abundant (Crossman and Holm

2005). Although found in several watersheds in Ontario, it is not widely distributed through any of them despite areas of seemingly suitable habitat. Populations in southwestern Ontario have been more thoroughly and frequently sampled (Beauchamp et al. 2012), likely a result of the greater biodiversity, number of species at risk, and anthropogenic impact in that region (Chu et al. 2003). There are records of Grass Pickerel from elsewhere in the province over the last 50 years, but most have been from temporally scattered sampling events or incidental observations with a variety of gear types and effort employed (Beauchamp et al. 2012). The lack of consistency in sampling efforts makes it difficult to assess population trends and make meaningful comparisons through time.

This thesis examines the factors that best explain how Grass Pickerel is distributed within three watersheds in Ontario using biotic interactions and abiotic factors at several spatial scales

(Chapter 2). It also examines long-term trends from one of the (historically) largest and best- known Ontario populations, Jones Creek, by exploring changes in the fish community and evaluating potential drivers of these changes (Chapter 3). In addition, it addresses other research targets outlined in the species’ Management Plan (Beauchamp et al. 2012) including updating current distribution records, and determining the prevalence of other threats to the species where possible (Appendix A).

Figures and Tables

Figure 1.2 North American (global) range of Grass Pickerel. (Figure from Crossman and Holm 2005).

Figure 1.2. Distribution of Grass Pickerel in Ontario. (Figure from Colm and Mandrak (2014))

Chapter 2

Local factors predict Grass Pickerel distribution

Introduction

Most freshwater fishes occur in low abundance, are restricted in their range, and have patchy distributions (Sheldon 1988). Not all rare, spatially restricted fishes are considered imperiled, but most imperiled fishes are rare and are found in a small part of a geographic region (Jelks et al.

2008). Being rare may be acceptable as long as a species persists across its range (Yenni et al.

2012). Maintaining populations of rare species across a patchy range requires the factors that shape that distribution be maintained across the patches as well (Rosenfeld and Hatfield 2006).

However, determining important factors in the distribution of rare species is notoriously difficult to do because of the limited number of observations and inability to verify locations where a species is truly absent (Lomba et al. 2010). The Grass Pickerel (Esox americanus vermiculatus) is one such species that is generally rare where it occurs in Canada, is restricted in its range by the colder northern climate, and has a patchy distribution (Beauchamp et al. 2012).

The Grass Pickerel is assessed as Special Concern in Canada due to its relatively small range of occurrence and localized distribution where it is found (Crossman and Holm 2005). In the U.S.,

Grass Pickerel is common and widespread in the Mississippi River drainage and lower Great

Lakes region where suitable habitat exists (Page and Burr 2011, Beauchamp et al. 2012) including sites with degraded water quality, as long as in-stream cover objects are present (Cain et al. 2008). In Canada, Grass Pickerel is found across the southern portion of Ontario and extreme southwestern Quebec, but is patchy and can be rare to abundant (Crossman and Holm

2005). Even within watersheds where it occurs, it is restricted to a few waterbodies. This patchy 7 and locally restricted distribution occurs despite widespread areas of seemingly suitable habitat.

Thus, understanding factors that explain Grass Pickerel distribution locally within a watershed is important for making conservation decisions.

Grass Pickerel habitat at both site and reach scales has been well described across its range

(Crossman 1962, Kleinert and Mraz 1966, Ming 1966). It occupies shallow, low-gradient streams and nearshore areas of lakes and rivers with little flow, heavy vegetation, and usually over mud or clay substrates (Crossman and Holm 2005). Similar habitats can be found elsewhere in Ontario, but it is unclear which factors are most important for occupancy and provide the highest quality habitat. The only biotic interaction observed in Canada is an association with the

Central Mudminnow (Umbra limi), the main prey of adult Grass Pickerel (Crossman 1962); however, it is unclear whether a consistent fish community exists with Grass Pickerel. I hypothesize that site and reach scale habitat features and biotic interactions explain the distribution of Grass Pickerel within watersheds. If so, occupied sites and reaches will share similar physical and chemical habitat parameters and species assemblages, and will differ from unoccupied sites in the same watershed.

Methods

Site selection

Three watersheds were selected across the species range in Ontario that have not been recently sampled: Georgian Bay/South Simcoe (GB); southwestern Lake Ontario (LOW); and southwestern St. Lawrence (StL) (Figure 2.1). The GB, LOW, and StL watersheds drain areas of

10,609km2, 7544km2, and 4576km2, respectively. Waterbodies within a watershed were selected

8 where Grass Pickerel are known to occur (Beauchamp et al. 2012) and connected waterbodies not known to contain Grass Pickerel but appear to have similar habitat (i.e. silt or clay substrates, heavily vegetated, little or no flow). Sites were selected based on suitability and accessibility.

Sampling occurred in July-August 2013, and May-August 2014. Sampling in GB occurred only in 2014. In addition to sites being grouped by watershed, sites were also re-classified after sampling into detection categories where Grass Pickerel was detected at that site (DS), not detected at the site but detected elsewhere in the same waterbody (DW), and not detected (ND).

This allowed examination of differences from a presence/absence perspective, while partially accounting for “unverifiable absences” (Lomba et al. 2010). A total of 135 sites were sampled, and a summary of sampling effort by year, watershed, and detection category is presented in

Table 2.1.

Habitat assessment

Site-scale habitat features were assessed during each sampling event. Physical measurements included maximum sampling depth, stream wetted width (lakes were arbitrarily assigned a width of 100m), percent channel cover (defined as the percent of the site covered by an object that blocks 75% of light from penetrating the water column), and bank slope. Substrate was assessed as percent composition of particle sizes based approximately on the Wentworth scale. Percent composition of aquatic vegetation and riparian vegetation was recorded. The dominant substrate type, aquatic vegetation type and riparian vegetation type was noted. Chemical parameters were also recorded, including water temperature and conductivity using a Hanna Instruments DiST5 short-range conductivity and temperature tester, and water clarity using a 1.2m secchi tube.

Additionally, stream reach variables were obtained from the Ontario Ministry of Natural

Resources and Forestry (OMNRF) Aquatic Ecosystem Classification (AEC) program. Reaches are defined as the length of stream found between two adjacent nodes established at tributary confluences (Melles et al. 2011). Each reach drains an area called the Reach Contributing Area

(RCA) from which terrestrial and aquatic variables are modelled and interpolated from

Geographic Information System (GIS) data. Sampling sites were mapped onto the AEC drainage network using ArcGIS (version 10.2.2, Environmental Systems Research Institute, Redlands

California) and habitat variables were extracted for each reach and RCA. Reach-scale variables were taken from the following categories: geomorphology; surficial geology; land-use types; and soil texture class. The 135 sampling sites fell into 70 reaches (Table 2.1) and variables were averaged when multiple sites fell within the same reach.

Fish sampling

The fish community was sampled using a 9.1m bag seine with 6.4mm mesh on the wing and bag.

The seine was stretched and deployed for a length of approximately 20m and, generally, three hauls were conducted (Reid and Hogg 2014). A seine net is one of the recommended gear types for detecting Grass Pickerel in Ontario (Portt et al. 2008). The edges of pools with heavy macrophyte cover were specifically selected at all sites to target Grass Pickerel (Crossman and

Holm 2005). At sites where Grass Pickerel was detected, it was first detected in the first haul

56% of the time, first detected in the second haul 22% of the time, and first detected in the third haul 15% of the time. The probability of detecting Grass Pickerel declined with subsequent

10 hauls, suggesting three hauls should be sufficient to detect it. All fishes captured were identified and tallied.

Statistical analysis

To determine if the targeted habitat was fundamentally different between watersheds and between sites where Grass Pickerel was detected and not detected, a series of linear models were used to test for differences in site-scale habitat variables and reach-scale habitat variables between watersheds and between Grass Pickerel detection categories. When significant differences existed, Tukey HSD tests were conducted to determine where pairwise differences fell within each group. Differences were considered significant at an α=0.05 level. Since each habitat variable was tested independently, a Bonferroni correction was applied so α=0.0025 for the site-scale habitat variables and α=0.0023 for the reach-scale habitat variables.

To broadly determine what habitat features predict Grass Pickerel abundance across the three watersheds, the data were fit with zero-inflated, Poisson distributed (ZIP) generalized linear models with a log link. Parameters were estimated by maximum likelihood. Abundance data were standardized by catch per unit effort. Dominant substrate, aquatic, and riparian vegetation types were used in the ZIP as the percent composition values co-vary. Site-scale and reach-scale variables were modelled separately to account for differences in scale (Guisan and Thuillier

2005), and watershed was included in both models to account for regional differences. All continuous variables were scaled within each model.

To investigate differences in fish community structure between watersheds and Grass Pickerel detection categories, non-metric multidimensional scaling (NMDS) was conducted using a Bray-

Curtis dissimilarity matrix accompanied by Adonis, a multivariate analysis of similarities

(MANOSIM) (Anderson 2001). The NMDS was run several times and similar solutions with a three-dimensional fit and medium stress (0.2> stress >0.1) were reached each time. Two thousand permutations of Adonis were done and significance was assessed at a level of α=0.05

(Anderson 2001). Lastly, a linear model was fit to see if the abundance of Central Mudminnow

(prey), Northern Pike (Esox lucius) (predator/competitor), or Common Carp (Cyprinus carpio)

(negative interactions via habitat alterations) predicted Grass Pickerel abundance (α =0.05). All statistical analyses were done using the R program for statistical computing (version 3.1.2).

Results

Habitat assessment: site scale

At the site scale, seven habitat variables differed significantly between watersheds (Table 2.2.).

Tukey HSD tests showed that water temperature was significantly lower in LOW than either GB or StL (p<0.0001). Conductivity was significantly different between all three watersheds

(p<0.006), highest in LOW and lowest in GB. The secchi depth was significantly lower in LOW than in either GB or StL (p<0.01). Stream width also differed significantly between all three watersheds (p<0.003), highest in GB and lowest in LOW. Channel cover was significantly lower in StL than in LOW (p<0.0001), but GB was not significantly different from either. The maximum depth was significantly lower in LOW than either GB or StL (p<0.01). The proportion of organic substrate was significantly higher in StL than in LOW (p=0.0001); while the proportion of clay substrate was significantly lower in StL than GB or LOW (p<0.006). The

12 proportion of floating vegetation was significantly higher in GB than either LOW or StL

(p<0.008), the proportion of submerged vegetation was significantly higher in StL than in either

GB or LOW (p<0.0001), and the proportion of open water significantly differed between all watersheds (p<0.03), highest in LOW and lowest in StL. Lastly, the proportion of deciduous vegetation was significantly lower in GB than LOW (p=0.007) but StL did not significantly differ from the other two watersheds.

Site-scale habitat variables are summarized by detection category (Table 2.3) and conductivity was the only site-scale habitat feature that differed significantly. The Tukey HSD test revealed that conductivity was significantly higher at sites where Grass Pickerel was found (DS) than either of the other two categories (p<0.05) (Figure 2.2).

Several site-scale variables were important in predicting Grass Pickerel abundance. Being in the

LOW watershed (z=2.767, p=0.006), channel cover (z=2.072, p=0.038), wetland floodplain use

(z=3.763, p=0.0002), and dominant submerged aquatic vegetation (z=-2.984, p=0.003) were all significant predictors of Grass Pickerel abundance. The site-scale ZIP model was significant overall (z =-3.392, p=0.0007).

Habitat assessment: reach scale

At the reach scale, eight habitat variables differed significantly between watersheds (Table 2.4)

Tukey HSD tests revealed the mean reach slope to be significantly lower in LOW than either GB or StL (p<0.0001). Baseflow index was significantly different between all watersheds (p<0.001), lowest in LOW and highest in GB. In terms of surficial geology, the proportion of Paleozoic

13 bedrock was significantly greater in StL than in GB or LOW (p<0.005), the proportion of organic deposits was significantly greater in StL than in GB or LOW (p<0.03), and the proportion of sand deposits was significantly greater in GB than in either LOW or StL (p<0.02).

The proportion of open water land cover was significantly higher in GB than in LOW (p<0.0001) but StL was not significantly different from GB or LOW; the proportion of treed land cover differed significantly between all three watersheds (p<0.0001), highest in GB and lowest in

LOW; and the proportion of agricultural land cover was significantly different between all watersheds (p<0.0001), highest in LOW and lowest in GB.

Only two reach-scale variables differed between detection categories (Table 2.5). Tukey HSD tests revealed that baseflow index was significantly lower at sites or waterbodies where Grass

Pickerel was detected than sites where it was not detected (p<0.002) (Figure 2.3). The proportion of agricultural land cover was significantly lower at sites where Grass Pickerel was not detected than sites where they were detected or detected in the waterbody (p<0.012). Mean reach slope

(marginally significant in the linear model) was significantly higher at sites where Grass Pickerel was not detected than where it was detected at the site or in the waterbody.

None of the reach-scale variables significantly predicted Grass Pickerel abundance, and the ZIP model was not significant overall (z=0.027, p=0.979).

Fish community

A total of 52 species were detected across all watersheds, and an average of 39.6 fishes were captured per haul. Catch per unit effort is summarized per year per watershed in Table 2.6. The

14 five most abundant species detected were Central Mudminnow (17.4%), Golden Shiner

(Notemigonus crysoleucas) (15.4%), Pumpkinseed (Lepomis gibbosus) (7.9%), Brook

Stickleback (Culaea inconstans) (7.6%), and Northern Redbelly Dace (Chrosomus eos) (7.4%).

Grass Pickerel was the 16th most abundant species, making up 1.7% of all fishes captured. The five most abundant species that co-occurred with Grass Pickerel were Golden Shiner (27.0%),

Central Mudminnow (16.4%), Pumpkinseed (10.3%), Emerald Shiner (Notropis atherinoides)

(7.3%), and Green Sunfish (Lepomis cyanellus) (4.8%). The five species that co-occurred with

Grass Pickerel most frequently were Pumpkinseed (75.9%), Golden Shiner (65.5%), Largemouth

Bass (Micropterus salmoides) (51.7%), Green Sunfish (48.3%), and Bluegill (Lepomis macrochirus) (46.6%). Table 2.7 summarizes the most frequently occurring and abundant species captured with Grass Pickerel and when Grass Pickerel was not detected.

The dissimilarity of the fish communities between watersheds (Figure 2.4) and detection categories (Figure 2.5) appears to be minimal, given the overlap between groups. However, the fish communities are significantly different between watersheds (p=0.001) and watershed explains approximately 13.3% of the variation. The fish communities are also significantly different between Grass Pickerel detection categories (p=0.001), and detection category explains

5.6% of the variation. The abundance of Central Mudminnow, Northern Pike, and Common

Carp did not significantly predict Grass Pickerel abundance in the linear model.

Discussion

The objective of this study was to determine which habitat features (at two spatial scales) and assemblage associations best predict Grass Pickerel distribution within a watershed in Ontario.

At the site scale, submerged aquatic vegetation, channel cover, and wetland floodplains were most important in predicting Grass Pickerel abundance. Similar associations with submerged aquatic vegetation and in-stream cover have been found for Grass Pickerel in Indiana (Cain et al.

2008), for closely-related Northern Pike in Ontario (Chapman and McKay 1984, Farrell et al.

2014), and other ambush piscivores like Spotted Gar (Lepisosteus oculatus) (Glass et al. 2012).

At the reach scale, none of the variables included in analysis were significant predictors of abundance. Although use of GIS data has made broader spatial-scale studies more feasible for modelling rare species distribution patterns (McKenna et al. 2012, McCusker et al. 2014), the site scale appeared to be more important than reach scale for Grass Pickerel here. Other species with specific habitat requirements have shown similar microhabitat associations to be more important than variables at broader scales (Glass et al. 2012, Dextrase et al. 2014).

It is not surprising that many site-scale and reach-scale variables differed between watersheds.

Features such as surficial geology and soil types are highly dependent on geological processes that were variable across Ontario, particularly from glacial deposits (Chapman and Putnam

1984). Land cover types would also differ across the province given patterns of surficial geology and human population densities and land uses (Shrestha et al. 2012, Salvati and Ferrara 2014).

Similarly, localized features such as water chemistry and aquatic vegetation (type and abundance) can be expected to vary (Karr and Schlosser 1978, Stuckey 1993, Zimmerman et al.

2003). Despite all of these differences, there are still similarities that allow for Grass Pickerel to survive and reproduce within each of these watersheds.

It is surprising, however, that there were few differences in site-scale or reach-scale habitat between detection categories. Conductivity (site scale) and agricultural land use (reach scale) were both higher at sites where Grass Pickerel was detected than where it was not detected, but this is likely an example of its ability to tolerate a wide range of environmental conditions (Cain et al. 2008) as opposed to an actual preference for these conditions. The baseflow index was lower at sites with Grass Pickerel and this is likely because Grass Pickerel’s thermal optimum is approximately 26°C (Scott and Crossman 1998) and, thus, they would avoid streams with high inputs of cold ground water. Although only marginally significant, sites with Grass Pickerel had lower slopes than sites without Grass Pickerel. Grass Pickerel is known only from low-gradient streams in Ontario (Crossman and Holm 2005) and was negatively associated with riffle areas in

Indiana streams (Cain et al. 2008), so perhaps the sites where we failed to detect the species had too high a gradient.

Overall, there were few differences in habitat between sites or reaches in different Grass Pickerel detection categories. It is possible that high baseflow is sufficient to deter Grass Pickerel from occupying otherwise suitable sites, or those sites may be unoccupied for other reasons. Sites where Grass Pickerel was not detected may have been suitable, but the species was historically excluded by access (i.e. fluvial geomorphological processes that occurred after post-glacial dispersal) or negative biotic interactions (Fraser et al. 1999). Alternatively, it may be subjected to a “dispersal limitation situation” (Guisan and Thuiller 2005) where, because of frequent extinction events coupled with limited dispersal abilities or routes to access and recolonize sites, the species is absent from these suitable sites (Svenning and Skov 2004). It is also possible that habitat variables that differentiate sites with Grass Pickerel from sites without Grass Pickerel

17 were not measured during this study. Lastly, Grass Pickerel could have been present at many of these sites but was not detected. Data supporting these hypotheses are unavailable and it remains unclear why Grass Pickerel is selecting some sites over others that appear suitable.

Predicting Grass Pickerel abundance with the ZIP models may help identify the highest quality habitat. Site-scale variables were generally important in predicting abundance. Being in the

LOW watershed predicted Grass Pickerel abundance likely because it is the most southerly watershed and has the most favourable climate for warmwater fishes like Grass Pickerel

(Mandrak 1995). However, there were also fewer sites that did not have Grass Pickerel in LOW, and the weight on habitat variables found to be important in this watershed may be over-inflated.

A higher proportion of channel cover and dominant submerged vegetation also predicted higher abundances of Grass Pickerel. Grass Pickerel is known to prefer sites with ample in-stream cover, such as aquatic macrophytes and woody debris (Crossman 1962, Cain et al. 2008). In an

Illinois stream, Grass Pickerel was always captured in, or leaving, dense submerged vegetation

(Larimore 1961). This association with cover objects and submerged vegetation was, therefore, expected. Wetland floodplains also promoted Grass Pickerel abundance and this is likely related to spawning behaviour. Grass Pickerel spawns in flooded-over riparian areas in the spring that then provide nursery habitat for the young until melt waters recede (Crossman 1962, Kleinert and Mraz 1968). Nearby wetlands in the floodplain would provide more spawning and nursery habitat for a longer period of time, thus, contributing to reproductive success.

Several site-scale variables were important in predicting Grass Pickerel abundance, but variables at the reach-scale were less important. Surprisingly, even watershed was not a significant

18 predictor of Grass Pickerel abundance when included with reach variables. Unfortunately, some sampling reaches were data deficient and the sample sizes for some variables may have been too small for an effect to be detected. Variables such as clay plains and soils, wetlands, and other natural land cover types were expected to be most important in predicting Grass Pickerel occupancy but had missing values. It is, therefore, possible that the variables with complete data were just not important at this scale.

It may be that the site scale is more important to Grass Pickerel life history than the reach scale.

Grass Pickerel do not move much daily or seasonally (Crossman 1962), often not leaving a pool for entire seasons (Fisheries and Oceans Canada, unpubl. data) and rarely undertaking long- distance migrations (Kramski 2014). Grass Pickerel, therefore, may only be influenced by habitat at this finer site scale, so site scale is more important than reach scale. The importance of different spatial scales differs between studies and regions (Weigel et al. 2006, Gido et al. 2006) and few studies have weighted the importance of spatial scales for single species (McKenna et al.

2013, Dextrase et al. 2014). In general, the site scale is considered more important in influencing fish community structure in minimally impacted systems, whereas, watershed scale is more important in degraded systems (Wang et al. 2006), but one scale alone is not always sufficient to explain a species distribution or community assemblage pattern (Matthews and Robison 1998,

Weigel et al. 2006).

Generally, species distribution models should represent the realized niche of a species; patterns of occurrence overlay favourable environmental conditions that have already been pruned by biotic interactions and resource availability (Guisan and Thuiller 2005). For species in highly

19 degraded systems, we may end up modelling the “forced niche”, where habitat is, or was, suitable but has been heavily impacted by anthropogenic disturbance and represents conditions at the species’ upper tolerance thresholds (Karr et al. 1986, Scheuerell and Schindler 2004).

Essentially, we are modelling the habitat it has been forced into but can still withstand, not the most suitable habitat. For example, Grass Pickerel was detected at sites with high conductivity and a high proportion of agricultural land use compared to sites where it was not detected. It makes sense that Grass Pickerel can tolerate high levels of conductivity, given that other members of the family, Redfin Pickerel (Esox americanus americanus) and Northern Pike are occasionally found in brackish waters (Scott and Crossman 1998), but there is no evidence to suggest that Grass Pickerel prefer high conductivity. There is evidence, however, to suggest that agricultural land uses negatively impact Grass Pickerel, particularly from agricultural drain maintenance (Trautman 1981, Coker et al. 2010, Beauchamp et al. 2012). Silt loads and turbidity are increased (Karr and Schlosser 1978), riparian vegetation is removed reducing channel cover and woody debris and increasing water temperature (Karr and Schlosser 1978, Zimmerman et al.

2003) and, most importantly, aquatic vegetation is removed, which is used for most life history stages (Cain et al. 2008, Coker et al. 2010). High conductivity and agricultural land use have not been found to be reliable predictors of Grass Pickerel occupancy elsewhere, and their influence is likely confounded by regional factors here.

Positive associations with factors found to be neutral or negative in other studies may be driven by regional processes. Lake Ontario West has the highest abundance of Grass Pickerel likely because of the more favourable climate, but also has the greatest anthropogenic disturbance and the most highly degraded habitat (Chu et al. 2015). The importance of these factors (e.g. high

20 conductivity and agricultural land use) are likely exaggerated because they happen to be more common where Grass Pickerel are more abundant. Ideally, a model would be fit for each watershed separately to determine whether important variables differed between watersheds in

Ontario; however, the sample size of sites with Grass Pickerel was too small in either GB or StL to test this.

There also appeared to be no strong species associations. Although the communities are significantly different whether comparing between watersheds or detection categories, there is substantial overlap in community structure in both cases. The differences in community structure were greater between watersheds than they were between detection categories suggesting regional differences are important in structuring these fish communities. Ming (1968) found evidence of a consistent fish community occurring with Grass Pickerel in Oklahoma, characterized by several sunfishes (Lepomis spp.), Orangebelly Darter (Etheostoma radiosum), and several minnows (Central Stoneroller (Campostoma anomalum), Bigeye Shiner (Notropis boops)), while sites without Grass Pickerel were characterized by larger-bodied fishes such as gars (Lepisosteus spp.), Bowfin (Amia calva) and buffaloes (Ictiobus spp.). Even Central

Mudminnow, always detected with Grass Pickerel in an eastern Ontario stream (Crossman

1962), was only found with Grass Pickerel 36% of the time in the present study. In this study, the most common species captured with Grass Pickerel are the same as the most common species captured when Grass Pickerel was not detected, further supporting that these assemblages are more driven by shared habitat preferences than by biotic interactions (Daniels 1993, Peres-Neto

2004). Lastly, the abundance of possible interacting species (Central Mudminnow, Northern

Pike, Common Carp) did not predict Grass Pickerel abundance suggesting no strong effects of biotic interactions.

There are two species that appeared to be negatively associated with Grass Pickerel in this study,

Northern Redbelly Dace and Brook Stickleback. Northern Redbelly Dace was never detected with Grass Pickerel but was the fifth most abundant and 14th most common (frequently occurring) species captured where Grass Pickerel was not detected. Brook Stickleback was only detected with Grass Pickerel twice, but was the fourth most abundant and tenth most common species captured where Grass Pickerel was not detected. These species can tolerate degraded water quality (Klinger 1982, France 1997) and, perhaps, are indicative of conditions beyond the tolerances of Grass Pickerel. Alternatively, these species may be heavily predated by Grass

Pickerel and habitat shifts have resulted from predation pressure (Tonn and Magnuson 1982,

Harvey 1991, Gilliam and Fraser 2001).

Although reach scale variables and assemblage data were not explanatory of patterns of Grass

Pickerel distribution in Ontario, the use of these data in species distribution models has recently become more common because of improved statistical techniques and availability of spatial data

(Elith and Leathwick 2009). Using GIS data for modelling species distribution patterns is becoming an increasingly more common approach (Guisan and Thuiller 2005, McKenna et al.

2012, McCusker et al. 2014). Databases are increasing in size, allowing for more variables to be included in models at many spatial scales (McKenna and Johnson 2011). However, site-scale habitat assessments are still vital as some species have strong associations with localized conditions that are too variable through space and time to be accurately mapped or interpolated.

For example, Grass Pickerel is strongly associated with dense aquatic vegetation (Crossman

1962, Kleinert and Mraz 1968, this study), Eastern Sand Darter (Ammocrypta pellucida) with sand and gravel substrates and water clarity (Dextrase et al. 2014), and Spotted Gar with mixed aquatic macrophyte beds and specific depth profiles (Glass et al. 2012). Ultimately, the best scale and modelling design depend on the life history of the species and the questions being asked

(Elith and Leathwick 2009), but the increase in available data has broadened the scope of distribution models.

Conclusions

This study helps to explain the most important habitat features and species associations for Grass

Pickerel in Ontario. Site-scale variables, such as channel cover, submerged aquatic vegetation, and floodplains with associated wetlands, were most important for Grass Pickerel within a watershed. Being in the southern Lake Ontario West watershed was also a significant predictor of abundance emphasizing the importance of regional processes. Regional processes appeared important in terms of both habitat variables and fish community structure. Grass Pickerel may be avoiding sites with high groundwater input and medium to high gradients, explaining why some of the “seemingly suitable” habitat within a watershed is not actually suitable. These results can help to inform conservation goals by ensuring that required habitat parameters are protected across the species range (McKenna et al. 2013). They could also be useful for predicting potentially suitable sites where the species may occur undetected so sampling efforts can be updated, or sites that have the potential to support populations (Guisan and Thuiller 2005, Lomba et al. 2010). All of these actions will help ensure rare species persist and regional biodiversity is preserved.

Figures and Tables

Figure 2.1 Maps of sampling effort in each watershed. a) map of three selected watersheds in Ontario b) map of sampling sites in Georgian Bay South Simcoe c) map of sampling sites in Lake Ontairo West d) map of sampling sites in St. Lawrence. Sites where Grass Pickerel was detected are represented in green.

Figure 2.2 Conductivity (µs/cm) of sites a) per watershed [GB (n=24), LOW (n=69), StL (n=42)] and b) per Grass Pickerel detection category [DS (n=58), DW (n=53), ND (n=24)]. Conductivity is significantly higher in LOW (p<0.0006) and at sites where Grass Pickerel was detected (DS) (p<0.05).

Figure 2.3 Mean baseflow index of reaches a) per watershed [GB (n=19), LOW (n=31), StL (n=20)] and b) per Grass Pickerel detection category [DS (n=28), DW (n=25), ND (n=17)]. Mean reach slope is significantly different between all watersheds (p<0.001), but lowest in LOW; and significantly lower at sites where Grass Pickerel was detected at the site or in the waterbody (DS and DW) than where it was not detected (ND) (p<0.0002).

Figure 2.4 Non-metric multidimensional scaling by watershed (final stress=16.3%, dimensions=3). The fish communities are significantly different and watershed explains 13% of the variation in community structure (p=0.001, R2=0.1326). Sampling events from Georgian Bay South Simcoe (GB) are encompassed in the orange polygon, Lake Ontario West (LOW) are encompassed in the green and St. Lawrence (StL) are encompassed in blue. Species codes can be found in Table 2.8.

Figure 2.5 Non-metric multidimensional scaling by detection category (final stress=16.3%, dimensions=3). The fish communities are significantly different and detection category explains approximately 6% of the variation in community structure (p=0.001, R2=0.056). Sampling events when Grass Pickerel was detected at the site (DS) are encompassed in the orange polygon, when Grass Pickerel was detected in the waterbody but not at the site (DW) are encompassed in the green polygon, and events when Grass Pickerel was not detected (ND) are encompassed in the blue polygon. Species codes can be found in Table 2.8.

Table 2.1 Number of sites and reaches sampled by watershed and year, displayed by Grass Pickerel detection category (DS=detected at the site, DW=detected in the waterbody but not at the site, ND=not detected). Georgian Bay Lake Ontario West St. Lawrence South Simcoe 2013 2014 2013 2014 2013 2014 Number of sites DS NA 6 27 15 4 6 Number of sites DW NA 9 14 13 3 14 Number of sites ND NA 9 0 0 2 13 Total number of sites NA 24 41 28 9 33 Number of reaches DS 6 17 5 Number of reaches DW 6 14 5 Number of reaches ND 7 0 10 Total number of reaches 19 31 20

Table 2.2 Site-scale variables per watershed.

GEORGIAN BAY LAKE ONTARIO ST. LAWRENCE SOUTH SIMCOE WEST F-stat P-value Mean SD Mean SD Mean SD

(DF=2,132) Water Temperature (°C) 18.07 <0.0001 23.64 2.42 20.19 3.63 23.87 3.71 WATER Conductivity (µs/cm) 101.3 <0.0001 74.38 132.71 817.01 338.19 281.88 104.08 CHEMISTRY Secchi Tube (m) 10.45 <0.0001 0.69 0.22 0.41 0.47 0.76 0.43 Stream Width (m) 25.17 <0.0001 55.08 43.42 12.38 5.34 29.77 32.34 PHYSICAL Bank Slope (%) 17.17 22.54 30.06 23.46 17.40 24.50 SITE Channel Cover (%) 9.718 0.0001 13.75 10.03 23.26 22.01 8.21 12.92 Max Sampling Depth (m) 9.67 0.0001 1.07 0.17 0.84 0.26 0.98 0.24 Organic 18.47 <0.0001 28.75 19.80 17.12 16.36 43.21 29.85 Clay 10.3 <0.0001 58.13 24.26 45.84 23.45 30.48 27.05 Silt 5.00 7.80 11.64 12.92 10.83 19.47 Sand 0.63 2.24 0.43 2.68 3.45 9.78

SUBSTRATE Gravel 1.04 3.61 9.54 15.65 2.86 10.48 TYPE (%) Cobble 2.50 10.32 8.33 11.59 4.05 10.26 Boulder 0.83 2.82 1.01 2.79 2.62 5.76 Bedrock 2.92 10.83 4.57 13.50 2.50 7.91 Rubble 0.21 1.02 0.87 3.20 0.00 0.00 Concrete 0.00 0.00 0.65 3.20 0.00 0.00 Emergent 10.83 11.39 13.62 8.78 12.74 17.61 AQUATIC Floating 9.383 0.00015 20.00 23.87 6.30 8.86 9.64 11.12 VEGETATION Submerged 21.58 <0.0001 26.04 20.16 22.17 19.97 52.02 30.45 (%) Open Water 23.77 <0.0001 43.13 22.40 57.97 22.33 25.60 27.46 Deciduous 11.94 <0.0001 22.50 18.65 38.19 20.62 31.67 24.51

RIPARIAN Coniferous 15.21 24.25 0.14 1.20 7.38 16.02 VEGETATION Herbaceous 31.67 23.85 44.93 20.91 42.38 28.09 (%) Shrubs 26.67 25.78 14.06 15.75 14.76 19.60 None 0.00 0.00 1.74 5.20 0.24 1.54

Table 2.3 Site-scale variables per detection category. Asterisk (*) indicates variables that were marginally significantly different at the Bonferroni corrected p-value. DETECTED AT DETECTED IN NOT DETECTED SITE WATERBODY F-stat P-value Mean SD Mean SD Mean SD (DF=2,132) Water Temperature (°C) 6.148 *0.0028 20.67 4.12 22.72 3.27 23.35 3.84 WATER Conductivity (µs/cm) 12.35 <0.0001 674.55 453.39 476.51 340.69 234.13 169.64 CHEMISTRY Secchi Tube (m) 0.46 0.29 0.58 0.57 0.81 0.39 Stream Width (m) 19.68 25.46 26.01 29.64 37.77 38.23 PHYSICAL Bank Slope (%) 24.02 24.99 28.25 25.27 13.63 17.29 SITE Channel Cover (%) 20.43 21.69 14.72 17.14 13.13 14.58 Max Sampling Depth (m) 0.94 0.24 0.90 0.30 0.98 0.17 Organic 28.21 25.32 26.79 26.35 26.25 19.63 Clay 44.28 24.71 42.55 28.00 42.29 27.86 Silt 9.71 14.09 10.09 13.46 11.67 18.98 Sand 0.86 4.70 0.57 3.05 4.58 10.93 SUBSTRATE Gravel 5.83 13.05 7.08 13.67 3.75 12.70 TYPE (%) Cobble 5.09 7.69 7.36 14.03 5.00 1.33 Boulder 1.03 2.77 1.51 3.61 2.50 6.59 Bedrock 3.71 11.10 3.49 12.23 3.75 11.35 Rubble 0.69 3.03 0.38 1.92 0.21 1.02 Concrete 0.60 3.25 0.19 1.37 0.00 0.00 Emergent 13.97 9.12 9.91 8.17 16.67 22.83 AQUATIC Floating 7.33 9.47 12.74 17.91 9.17 13.57 VEGETATION Submerged 28.10 25.83 33.77 28.44 38.33 26.97 (%) Open Water 50.69 26.75 43.58 28.44 35.83 27.21 Deciduous 36.12 21.48 33.58 21.33 26.25 25.08

RIPARIAN Coniferous 3.19 12.34 5.85 16.04 7.92 16.15 VEGETATION Herbaceous 45.17 24.85 41.79 21.51 33.54 27.01 (%) Shrubs 12.84 15.68 16.51 17.72 25.42 27.89 None 1.38 4.06 0.94 4.50 0.00 0.00

Table 2.4 Reach-scale variables per watershed.

GEORGIAN BAY EAST LAKE ONTARIO ST. LAWRENCE WEST WEST F-stat P-value Mean SD Mean SD Mean SD (DF=2, 67) Reach Catchment Area 1674379 1531963 2426226 2431940 2083500 1825314 (m²)

GEOMOR- Channel area (m²) 62384 54715 114126 67222 79920 62059 PHOLOGY Channel slope (%) 0.0034 0.0023 0.0016 0.0015 0.0035 0.0045 : Channel length (m) 1662.27 1479.28 3111.01 1826.59 2105.63 1618.69 PHYSICAL Mean Slope (%) 26.94 <0.0001 5.55 2.18 1.98 0.79 4.80 2.51 Baseflow index 527.2 <0.0001 0.51 0.06 0.15 0.01 0.46 0.05 Stream power index 5260.55 14255.35 2770.83 3019.74 5628.67 13349.25 Paleozoic bedrock 8.678 0.0004 1.82% 7.92% 1.05% 3.25% 13.42% 18.68% Grave Sandy 1.68% 4.92% 0.18% 0.76% 1.62% 5.02% Organic 12.87 <0.0001 6.74% 7.79% 0.00% 0.00% 16.72% 20.19% GEOLOGY Sand 6.964 0.0018 13.87% 25.26% 0.00% 0.00% 1.26% 5.25% Sand silty 0.23% 0.98% 2.94% 6.68% 0.84% 3.75% Silt clayey 6.16% 12.15% 11.26% 11.45% 6.99% 11.16% Clay NA NA 5.76% 8.19% 6.65% 11.72% SOIL Clay loam NA NA 1.71% 4.84% 4.77% 11.33% CLASS Loam NA NA 1.45% 4.20% 27.95% 33.86% Silty loam NA NA 18.44% 28.53% 1.34% 3.59% Open Water 10.98 <0.0001 18.92% 20.87% 0.41% 0.79% 9.19% 15.56% LAND Wetland 6.15% 12.41% 10.53% 8.36% 15.49% 16.21% COVER Treed 71.26 <0.0001 62.08% 21.11% NA NA 34.30% 20.33% TYPE Infrastructure 1.61% 4.36% 6.44% 6.67% 5.62% 6.91% Agriculture 121.8 <0.0001 9.53% 10.84% 75.55% 11.93% 35.40% 21.31%

Table 2.5 Reach-scale variables per detection category. Asterisk (*) indicates variables that were marginally significantly different at the Bonferroni corrected p-value. DETECTED AT SITE DETECTED IN NOT DETECTED (ND) (DS) WATERBODY (DW) F-stat P-value Mean SD Mean SD Mean SD (DF=2, 67) Reach Contributing 2365939 2550748 2091888 1831442 1773688 1363856 Area (m²) Channel area (m²) 91061 62159 100728 78389 73747 48733 GEOMORPH Channel slope (%) 0.0023 0.0025 0.0027 0.0038 0.0031 0.0023 OLOGY: Channel length (m) 2460.21 1693.66 2717.89 2111.08 1959.05 1291.03 PHYSICAL Mean Slope (%) 6.478 *0.0027 3.02 2.39 3.45 2.00 5.43 2.35 Baseflow index 8.801 0.0004 0.29 0.18 0.30 0.18 0.48 0.06 Stream power index 2208.30 2975.11 2529.82 2979.68 10196.57 19682.82 Paleozoic bedrock 5.25% 11.60% 2.97% 10.88% 6.72% 14.88% Diamicton Sandy 3.46% 11.18% 1.16% 3.69% 10.65% 18.49% Gravel Sandy 0.92% 3.82% 0.41% 1.83% 2.01% 5.42% GEOLOGY Organic 5.35% 15.16% 3.99% 7.03% 12.52% 15.88% Sand 5.62% 20.92% 2.42% 6.96% 4.17% 9.34% Sand silty 2.83% 6.49% 0.66% 3.25% 0.99% 4.07% Silt clayey 8.55% 12.15% 9.47% 10.84% 7.64% 12.50% Open Water 6.19% 14.61% 4.77% 9.14% 15.49% 21.59% LAND Wetland 14.42% 14.53% 5.48% 5.33% 12.49% 14.17% COVER Treed NA NA 28.43% 27.81% 45.06% 23.47% TYPES Infrastructure 7.59% 8.75% 2.91% 2.69% 3.38% 4.30% Agriculture 7.09 0.0016 50.15% 30.74% 57.09% 32.41% 23.51% 20.50%

Table 2.6 Catch per unit effort by watershed and year.

GEORGIAN BAY LAKE ONTARIO WEST ST. LAWRENCE SOUTH SIMCOE 2013 2014 2013 2014 2013 2014 FISH /HAUL 0 16.64 31.63 31.50 170.21 41.85 GRASS PICKEREL /HAUL 0 0.14 1.66 0.43 0.92 0.07 SPECIES DETECTED 0 21 30 31 18 28

Table 2.7 Most abundant and frequently observed species with Grass Pickerel (DS) and not with Grass Pickerel (ND).

Species with Grass Pickerel Occurre Species with Grass Pickerel Relative Species when no Grass Occurrence Species when no Grass Relative nces (%) Abundan Pickerel detected s (%) Pickerel detected Abundance ce (%) (%)

Lepomis gibbosus 75.9% Notemigonus crysoleucas 27.4% Lepomis gibbosus 91.7% Umbra limi 24.4% Notemigonus crysoleucas 65.5% Umbra limi 16.6% Perca flavescens 66.7% Perca flavescens 13.0% Micropterus salmoides 51.7% Lepomis gibbosus 10.4% Micropterus salmoides 54.2% Lepomis gibbosus 9.9% Lepomis cyanellus 48.3% Notropis atherinoides 7.4% Umbra limi 50.0% Culaea inconstans 9.9% Lepomis macrochirus 46.6% Lepomis cyanellus 4.8% Esox lucius 41.7% Chrosomus eos 8.8% Ameiurus nebulosus 37.9% Micropterus salmoides 4.0% Notemigonus crysoleucas 37.5% Catostomus commersonii 6.3% Umbra limi 36.2% Pimephales notatus 3.8% Ambloplites rupestris 33.3% Lepomis macrochirus 6.1% Esox lucius 32.8% Lepomis macrochirus 3.5% Ameiurus nebulosus 29.2% Micropterus salmoides 5.9% Catostomus commersonii 27.6% Ameiurus nebulosus 2.9% Lepomis macrochirus 29.2% Pomoxis nigromaculatus 4.2% Pimephales notatus 27.6% Ambloplites rupestris 2.8% Culaea inconstans 20.8% Semotilus atromaculatus 1.9% Ambloplites rupestris 24.1% Pomoxis nigromaculatus 2.7% Fundulus diaphanus 20.8% Luxilus cornutus 1.7% Etheostoma nigrum 20.7% Catostomus commersonii 2.1% Pomoxis nigromaculatus 20.8% Chrosomus neogaeus 1.6% Noturus gyrinus 20.7% Perca flavescens 2.1% Catostomus commersonii 16.7% Notemigonus crysoleucas 1.5% Notropis atherinoides 19.0% Noturus gyrinus 1.9% Chrosomus eos 16.7% Ambloplites rupestris 1.0% Pomoxis nigromaculatus 19.0% Etheostoma nigrum 1.8% Luxilus cornutus 16.7% Esox lucius 0.86% Perca flavescens 15.5% Fundulus diaphanus 1.7% Pimephales promelas 16.7% Pimephales promelas 0.86% Cyprinus carpio 12.1% Esox lucius 1.0% Semotilus atromaculatus 16.7% Pimephales notatus 0.78% Ameiurus melas 8.6% Notropis heterolepis 1.0% Chrosomus neogaeus 12.5% Fundulus diaphanus 0.48% Notropis heterolepis 5.2% Semotilus atromaculatus 0.45% Pimephales notatus 12.5% Ameiurus nebulosus 0.45% Culaea inconstans 3.4% Culaea inconstans 0.27% Notropis heterodon 8.3% Percina caprodes 0.19% Etheostoma olmstedi 3.4% Ameiurus melas 0.25% Percina caprodes 8.3% Notropis heterodon 0.11% Fundulus diaphanus 3.4% Cyprinus carpio 0.18% Semotilus corporalis 8.3% Semotilus corporalis 0.11% Neogobius melanostomus 3.4% Notropis heterodon 0.11% Cyprinella spiloptera 4.2% Cyprinella spiloptera 0.04% Notropis heterodon 3.4% Etheostoma blennioides 0.09% Etheostoma nigrum 4.2% Etheostoma nigrum 0.04% Ameiurus natalis 1.7% Etheostoma olmstedi 0.09% Notropis bifrenatus 4.2% Notropis bifrenatus 0.04% Amia calva 1.7% Amia calva 0.05% Notropis heterolepis 4.2% Notropis heterolepis 0.04% Aplodinotus grunniens 1.7% Luxilus cornutus 0.05% Carassius auratus 1.7% Neogobius melanostomus 0.05% 33

Etheostoma blennioides 1.7% Ameiurus natalis 0.02% Hybognathus hankinsoni 1.7% Aplodinotus grunniens 0.02% Ictalurus punctatus 1.7% Carassius auratus 0.02% Luxilus chrysocephalus 1.7% Hybognathus hankinsoni 0.02% Luxilus cornutus 1.7% Ictalurus punctatus 0.02% Moxostoma valenciennesi 1.7% Luxilus chrysocephalus 0.02% Pimephales promelas 1.7% Moxostoma valenciennesi 0.02% Pomoxis annularis 1.7% Pimephales promelas 0.02% Sander vitreus 1.7% Pomoxis annularis 0.02% Scardinius erythrophthalmus 1.7% Sander vitreus 0.02% Semotilus atromaculatus 1.7% Scardinius 0.02% erythrophthalmus

Table 2.8 Species captured by scientific and common names, and codes found in NMDS plots.

Scientific Name Common Name Code Amia calva Bowfin Amca Ameiurus melas Black Bullhead Amme Ameiurus natalis Yellow Bullhead Amna Ameiurus nebulosus Brown Bullhead Amne Ambloplites rupestris Rock Bass Amru Aplodinotus grunniens Freshwater Drum Apgr Carassius auratus Goldfish Caau Catostomus commersonii White Sucker Caco Chrosomus eos Northern Redbellly Dace Cheo Chrosomus neogaeus Finescale Dace Chne Chrosomus eos X Chrosomus Northern Redbellly x Finescale Chxen neogaeus Dace hybrid Culaea inconstans Brook Stickleback Cuin Cyprinus carpio Common Carp Cyca Cyprinella spiloptera Spotfin Shiner Cysp Esox americanus vermiculatus Grass Pickerel Esamve Esox lucius Northern Pike Eslu Etheostoma blennioides Greenside Darter Etbl Etheostoma nigrum Johnny Darter Etni Etheostoma olmstedi Tesselated Darter Etol Fundulus diaphanus Banded Killifish Fudi Hybognathus hankinsoni Brassy Minnow Hyha Ictalurus punctatus Channel Catfish Icpu Lepomis cyanellus Green Sunfish Lecy Lepomis gibbosus Pumpkinseed Legi Lepomis hybrid Sunfish hybrid Lehy Lepomis macrochirus Bluegill Lema Lepomis sp Sunfish sp. Lesp. Luxilus chrysocephalus Striped Shiner Luch Luxilus cornutus Common Shiner Luco Margariscus margarita Northern Pearl Dace Mama Micropterus salmoides Largemouth Bass Misa Moxostoma sp Redhorse spp. Mosp. Moxostoma valenciennesi Greater Redhorse Mova Neogobius melanostomus Round Goby Neme Notropis atherinoides Emerald Shiner Noat Notropis bifrenatus Bridle Shiner Nobi Notemigonus crysoleucas Golden Shiner Nocr Noturus gyrinus Tadpole Madtom Nogy

Notropis heterolepis Blacknose Shiner Nohe Notropis heterodon Blackchin Shiner Nohed Notropis hudsonius Spottail Shiner Nohu Notropis stramineus Sand Shiner Nost Notropis volucellus Mimic Shiner Novo Percina caprodes Log Perch Peca Perca flavescens Yellow Perch Pefl Percina maculata Blackside Darter Pema Pimephales notatus Bluntnose Minnow Pino Pimephales promelas Fathead Minnow Pipr Pimephales sp Minnow spp. Pisp. Pomoxis annularis White Crappie Poan Pomoxis nigromaculatus Black Crappie Poni Pomoxis sp Crappie spp. Posp. Sander vitreus Walleye Savi Scardinius erythrophthalmus Common Rudd Scer Semotilus atromaculatus Creek Chub Seat Semotilus corporalis Fallfish Seco Umbra limi Central Mudminnow Umli

Chapter 3

Fish community change and decline of Grass Pickerel in Jones Creek, Ontario

Introduction

Streams are dynamic systems that offer habitat heterogeneity at several scales resulting in high species diversity (Gorman and Karr 1978). Patterns exist longitudinally in streams, where species diversity typically increases from headwaters to mouth (Sheldon 1968, Tramer and Rogers 1973,

Horowitz 1978). On a reach scale, habitat conditions vary between pools, riffles and runs, which results in locally variable species assemblages, where pools show greater species diversity than riffles as they can support more diverse trophic guilds and size classes (Schlosser 1982). These patterns are driven by variable depth, substrate and flow rates that exist through stream systems

(Gorman and Karr 1978). Understanding the mechanisms that structure stream fish communities is important to help maintain biodiversity. With 71 freshwater fishes being listed as at risk in

Canada (Dextrase and Mandrak 2006), understanding factors that destabilize community structure through time is important for conservation decisions, particularly when rare or imperiled species are present. Most stream fish communities change over time, but what drives these changes?

The biggest threat to freshwater fishes is habitat degradation or alteration (Jelks et al. 2008).

Increased urban development, for example, has been shown to increase homogenization of nearshore and wetland fish communities in the Great Lakes region, regardless of other land-use types in the watersheds (Trebitz et al. 2009, Chu et al. 2014). Similarly, agricultural land uses can negatively impact stream fish community structure by increasing nutrient loads and reducing

natural riparian vegetation cover (Wang et al. 1997). Wang et al. (1997) found a decline in biotic integrity (Karr et al. 1986) when urban land use exceeded 10% and agricultural land use exceeded 50% of the watershed area. Both urban and agricultural development tend to promote channelization of streams. This removes meander sequences, changes the gradient of pool habitats, and destabilizes or alters substrate types, all of which reduce the habitat diversity and buffering capacity of streams leading to reduced fish species diversity (Gorman and Karr 1978).

Anthropogenic or natural changes in the hydrology of a stream system can also have substantial negative impacts on the fish community through time, particularly when flow regimes are highly variable (Horowitz 1978). Common anthropogenic causes include increasing impervious surfaces in the watershed leading to greater runoff volume (Wang et al. 1997, Stanfield and

Kilgour 2013), and damming (Pringle et al. 2000). Although small, rural streams in Ontario are not typically affected by anthropogenic dams, they are frequently dammed by beavers, and the impacts can be similar: important species are excluded (Pringle et al. 2000); local hydrological patterns are altered by increasing water storage and reducing flow rates (Burchsted and Daniels

2014), which can affect fish movement patterns (Taylor and Cooke 2012); water temperatures typically increase while dissolved oxygen tends to decrease (Collen and Gibson 2001); substrate deposition is increased upstream (Naiman et al. 1988); aquatic vegetation increases in ponds

(Snodgrass and Meffe 1998); and seasonally flooded areas required by some species (i.e. esocids

(Scott and Crossman 1998)) can turn into permanently flooded areas upstream, while downstream flooding is eliminated (Pringle 2000, Naiman et al. 1988). Downstream fish assemblages dominated by traditionally lotic species requiring clear substrates and high dissolved oxygen are often replaced by traditionally lentic species after dams are built

(Fitzgerald et al. 1998). An important difference between beaver dams and anthropogenic dams on small streams is the number; many beaver dams longitudinally through a creek create habitat patches that are regulated by different fluvial processes that vary temporally as dams are built and abandoned (Snodgrass and Meffe 1998, Burchsted and Daniels 2014). Ultimately, any change in hydrology can result in changes in habitat structure and lead to variation in the fish community through time (Beugly and Pyron 2010).

Another potential cause for changes in fish community structure through time is stochastic events. Stochastic events often result in annual species abundances that are uncorrelated with those of previous years (Grossman et al. 1982, Magurran and Henderson 2010). These events can cause shifts in structure that are gradual or abrupt, directional or non-directional, and return to a previous state or not (Matthews et al. 2013). The most frequent disturbances to stream fishes include flood and drought events and these can be particularly devastating to surface-fed, warmwater systems (Horowitz 1978, Matthews et al. 2013). Floods have been shown to negatively impact stream fishes by displacing both adults and juveniles (Grossman et al. 1982).

They can also be inadvertently selective if the flooding happens during the peak spawning time of certain species, thereby, decreasing reproductive or recruitment success; this can result in an increased abundance of late-spawning fishes in subsequent years (Starrett 1951). The impacts of flood events tend to manifest in slight directional shifts, followed by subsequent (approximate) return to the pre-flood community (Grossman et al. 1982, Matthews et al. 2013).

Droughts, on the other hand, tend to have more persistent effects on stream community structure

(Matthews et al. 2013). Matthews et al. (2013) found two extreme drought events in their 27-year

study, both caused abrupt, directional shifts in community structure. Even species that have evolved under highly variable, drought-prone flow regimes are negatively impacted by drought events that may be increasing because of climate change (Ruhi et al. 2015). Douglas et al. (2003) suspected that an extreme drought event at the end of the Pleistocene reduced population sizes of many fishes in the Colorado River basin to such low levels that a genetic bottleneck occurred and resulted in low genetic diversity. Reductions in abundance that typically result during or immediately after drought events may be detrimental to fish communities in the short term, but may also impact their genetic structure and integrity in the longer term.

Disturbance events like floods and droughts can be beneficial to ecological communities when they are intermediate in frequency and intensity and can actually result in high species diversity

(Connell 1978). Occasional flooding and drying, even slightly more intense than seasonal patterns, may be beneficial for maintaining diversity in small streams. However, the frequency of extreme climatic events is likely to increase with climate change (IPCC 2014) and may result in stream fish communities dominated by a few early successional species (Schlosser and

Kallemeyn 2000) or invasive species that thrive during and after disturbances (Bêche et al.

2009).

Invasive species are the second largest threat to freshwater fishes in North America and can disturb community structure by altering biotic interactions and disrupting habitat (Jelks et al.

2008). Invasive species are a threat to approximately 63% of freshwater fishes at risk in Canada

(Dextrase and Mandrak 2006) and the greatest impacts to native fauna in stream systems are generally through competitive interactions or predation (Ross 1991). The addition of predators to

a stream system can cause reductions in forage fish abundance leading to trophic cascades, habitat shifts for forage fish away from open pools toward shallow areas (Harvey 1991), and restricted movement of fish longitudinally (Gilliam and Fraser 2001). In lakes, introductions of a single predator have caused drastic shifts in community structure, where small-bodied cyprinids are negatively affected (Jackson et al. 1992, Trumpickas et al. 2011).

Fish communities appear to have little chance of persisting over time in the face of many anthropogenic disturbances at several spatial scales. Understanding the drivers of community change through time will help determine where conservation efforts should be focused. Most studies examining changes in stream fish communities explore how a particular set of environmental variables or biotic interactions affect a fish community through time and/or space, but are not particularly interested in the individual species that make up the initial or final assemblage. This information is particularly important when species of conservation concern are present.

The objective of this study is to assess how the fish community in general, and Grass Pickerel

(Esox americanus vermiculatus) abundance specifically, has changed in Jones Creek after 50 years, and to identify drivers of any changes detected. I had a unique opportunity to return to a stream system studied in 1960 to assess the current status of the nationally imperiled Grass

Pickerel, a formerly abundant species in the system (Crossman 1962), and compare the fish community between time periods. Based on historical descriptions of the creek (Crossman 1962) and my preliminary observations, the habitat and land-use types appear to have changed minimally, general trends of warming temperatures resulting from climate change are predicted

to favour warmwater species like Grass Pickerel (Chu et al. 2005), and no new invasive species are expected to be present due to the isolated and non-navigable nature of Jones Creek. Given that none of these usual drivers seem to be relevant, I expect there to be no significant changes in the fish community structure in Jones Creek since 1960.

Methods

Study site

Jones Creek is a low-gradient stream located in the St. Lawrence River drainage near

Mallorytown, Ontario. It drains an area of approximately 233km2 (Figure 2.1(d)); this watershed also drains Lyn Creek and Michael Henry Creek (OMNRF 2002). Sections of the creek were channelized early in the 20th century for agricultural drainage (Crossman 1962). The headwaters of Jones Creek were connected to the Charleston Lake-Gananoque River system (Crossman

1962) but this connection is no longer apparent, although it may be ephemeral. The creek is approximately 19km long above an impassable waterfall, then continues for approximately 4km before ending at the St. Lawrence River. All sampling was conducted in the isolated portion above the falls.

The main channel of Jones Creek is approximately 1.5-6m wide (Crossman 1962) but is prone to flooding in spring making the wetted width much larger. There has been an increase in beaver activity since 1960 creating many large ponds. Three beaver dams were noted by Crossman in

Jones Creek in 1960 (Crossman, unpubl. field notes 1960), an Ontario Ministry of Natural

Resources and Forestry (OMNRF) survey revealed seven beaver dams in 1988 (within a 50m buffer of the creek) (OMNRF 1976), and I also noted seven in 2014, but was unable to access all

reaches to validate the total number. In dry years, the creek can be reduced to a few large, intermittent ponds 0.1-1.0m deep (Crossman 1962); while in wet years, the deepest part of the channel (thalweg) can remain close to 2.0m deep.

Although the hydrology of the system may have changed slightly at the headwaters and from beaver activity, the in-stream habitat remains markedly similar. Crossman (1962) described

Jones Creek as typical Grass Pickerel habitat. The substrate is mud, clay and rock, with abundant submerged, emergent and floating vegetation (see Crossman 1962 for species list of aquatic vegetation). In 1960, the riparian zone was mostly pasture land with “woody and shrubby margins” (Crossman 1962). Currently, there is little pasture land adjacent to the creek, although the surrounding land use remains predominantly agricultural. The water in Jones Creek is tea stained, had an average pH of 7.65 in 1960 and 7.87 in 2013, and an average water conductivity of 231µs/cm in 2014 across all sites. The peak water temperature was recorded as 29°C in July of 1960 and 28°C in July of both 2013 and 2014.

Data collection

Data collection occurred May-September 1960, July 2013, and June-August 2014. Sampling sites in 1960 were selected based on earlier works of George Toner, an observational pilot study, and access; most of these sites were resampled in 2013 and 2014. Three sites were sampled repeatedly each year and an additional three to five sites were sampled once. Three gear types were used in 1960: a seine net (size unknown, generally one haul per site); fyke net (size and effort unknown); and rotenone (volume unknown). A 9.1m bag seine was used in 2013 and 2014 with mesh size (bag and wing) of 3.2mm in 2013 and 6.4mm in 2014. Typically, three hauls

were done per site in 2013-2014. In 1960, all fishes captured were kept and sent to the Royal

Ontario Museum for verification. In 2013 and 2014, all fishes were identified and tallied in the field, and representatives of each species were kept for verification while the rest were returned live to the creek. Additionally, data was collected in Jones Creek in 1980 by the Ontario Ministry of Natural Resources and Forestry as part of the Aquatic Habitat Inventory (OMNRF, unpubl. data). These data were collected by angling, dip net, hoop net, seine, electrofishing, and sieve gear types but the effort and dimensions of each gear are unknown. It is unclear where these sites were located along the creek. The inventory project aimed to detect as many species as possible, and not to determine species abundances.

Fish community analyses

Analyses of the fish community were conducted on two data sets. First, fish abundances from the first seine haul from 1960, 2013 and 2014 sampling events were assembled and standardized by catch per unit effort. Generally, only one seine haul was conducted in 1960, so only the first haul from the 2013 and 2014 samples were used. This data set will be referred to as the “single-seine” data. A total of 22, 8, and 14 sampling events (and, thus, seine hauls) from 1960, 2013, and 2014, respectively, were used. Second, fish abundance data from all gear types and levels of effort from 1960, 1980, 2013, and 2014 were used and standardized by relative abundance. Species abundances were pooled by sampling event when multiple seine hauls were done or gear types used. This data set will be referred to as the “all-gear” data. A total of 23, 9, 8, and 14 sampling events from 1960, 1980, 2013, and 2014, respectively, were used here.

To visualize changes in the fish community for both data sets separately, Bray-Curtis dissimilarities were calculated between each pair of years and used for non-metric multidimensional scaling (NMDS). This ordination technique does not assume any particular distribution of the data and plots species and sampling events based on rank dissimilarities such that more dissimilar species assemblages appear farther apart. As my target species was the most abundant species in 1960 and one of the rarest in subsequent years, no data transformations were done to moderate the impact of very abundant or rare species. The NMDS was run several times and similar solutions with a three-dimensional fit and low stress (<0.1) were reached each time.

Although three-dimensions resulted in the lowest stress, a two-dimensional fit still revealed relatively low stress (stress=0.111) for the ‘single-seine’ data set and was used for ease of visualization (Trebitz et al. 2009). Clarke (1993) suggested that, if the same low stress value

(within three decimal places) is achieved after multiple runs, the NMDS configurations are likely indistinguishable.

A non-parametric multivariate analysis of similarities (MANOSIM) was conducted on each data set to test for a difference in the community structure between years. One thousand permutations were done and significance was assessed at a level of α=0.05 (Anderson 2001). Lastly, similarity percentages were calculated using the Bray-Curtis dissimilarity matrix to determine the average contribution of each species to the dissimilarity between groups by averaging the dissimilarity within each year (i.e. between sampling events). This identifies species that contribute at least

70% of the dissimilarity between years (Clarke 1993). All statistical analyses were conducted in the software program R, version 3.1.2 “Pumpkin Helmet”.

Land-use changes

To investigate whether land-use changes in the Jones Creek watershed affected the fish community, I used ArcGIS (version 10.2.2, Environmental Systems Research Institute, Redlands

California) to compare land-use types from the 1966 South Ontario Land Use layer (Ducks

Unlimited 1966) and the 2000 Southern Ontario Land Resource Information System layer

(SOLRIS) (OMNRF 2000). The SOLRIS layer was validated with the Agricultural Resource

Inventory layer (OMAFRA 2000) to help resolve some of the undifferentiated land uses in

SOLRIS.

As the 1966 and 2000 land-use inventories were conducted by different organizations, their classification systems differed slightly. I regrouped land-use types into eight categories that best described the original classifications: agricultural lands; built-up impervious; built-up pervious; extraction; forest; open water; swamp/marsh; and, unknown. Stanfield and Kilgour (2013) found no significant difference in explanatory power when assessing fish community change between a lower and higher resolution layer of land-use/cover types in Ontario. This suggests that pooling classifications into these broader categories should not significantly affect my results.

The percent change in land-use type was determined for the whole sub-watershed and for a 100m buffer around the main stem and mapped tributaries of Jones Creek (OMNRF 2002). Most studies suggest a 30m buffer is appropriate for assessing land use changes in stream systems

(Wang et al. 2003; Frimpong et al. 2005), but a 100m buffer was selected as Jones Creek often floods to greater than 30m wide and larger buffers have proven best for assessing impacts of

land-use changes on fish community structure in some stream types (Stanfield and Kilgour 2013;

Trebitz et al. 2009).

Results

Single-seine fish community

Sixteen species were detected in Jones Creek in the first haul of seining events, six species in

1960 and 13 species in both 2013 and 2014 (Table 3.1). All species detected in 1960 were detected in both 2013 and 2014, except Common Shiner (Luxilus cornutus) was only detected in

2014. The species composition was quite similar between 2013 and 2014, except that Brassy

Minnow (Hybognathus hankinsoni) was only detected in 2013, and Creek Chub (Semotilus atromaculatus) was only detected in 2014. The catch per unit effort differed substantially between years, with the greatest abundance found in 2013. The mean catch per haul was approximately 37±46 fishes in 1960, 300±301 in 2013, and 47±44 in 2014.

The ‘single-seine’ NMDS (Figure 3.1) reveals most of the 1960 sampling events grouped together and the 2014 sampling events grouped together, suggesting little difference in the fish community within those years, but large differences between years. The 2013 sampling events are widespread across the plot with certain species associating with specific events. Of the five species caught in all years, three of them (Brown Bullhead (Ameiurus nebulosus), Central

Mudminnow (Umbra limi), and Pumpkinseed (Lepomis gibbosus)) are grouped together in the centre of the ordination, suggesting similar abundances between years. Grass Pickerel is associated exclusively with 1960 sampling events.

There is a significant difference in the ‘single-seine’ fish communities between years (p<0.001).

Year of study explains approximately 29% (R2=0.2898) of the variation between the communities. The similarity percentages (Table 3.2) show that five species are responsible for

70% of the dissimilarity between all pairs of years. Notably, when comparing 1960 to 2014, only

Central Mudminnow and Grass Pickerel account for 70% of the difference, as Grass Pickerel declined and Central Mudminnow increased. It is mainly the average abundances of species that are responsible for the dissimilarity between the communities. The ratio of the average dissimilarity to the standard deviation is quite high (>1.0) for most species comparisons, suggesting those species are not consistently contributing to the dissimilarity. Blacknose Shiner

(Notropis heterolepis) was consistently more abundant in 2013 than other years and contributed substantially to the differences between years.

All-gear fish community

Twenty-nine species were detected in Jones Creek across all years and gear types; 19 of these were detected in 1960; 18 species in 1980; 13 species in 2013; and, 17 species in 2014 (Table

3.1). Six species were detected in all years, including Brown Bullhead, Central Mudminnow,

Fathead Minnow (Pimephales promelas), Golden Shiner, Pumpkinseed, White Sucker

(Catostomus commersonii); four others were detected in three of four years, including Brook

Stickleback (Culaea inconstans), Common Shiner, Creek Chub, and Grass Pickerel. These 10 species remained consistently present over the sampling period, but there was a loss of benthic invertivores (Johnny Darter (Etheostoma nigrum), Fantail Darter (E. flabellare), Logperch

(Percina caprodes)) after 1960, large-bodied piscivores (Fallfish (Semotilus corporalis),

Largemouth Bass (Micropterus salmoides), Northern Pike (Esox lucius), Rockbass (Ambloplites

rupestris), Smallmouth Bass (M. dolomieu)) after 1980, and an increase in richness and abundance of small-bodied species (Banded Killifish (Fundulus diaphanus), Blackchin Shiner

(Notropis heterodon), Blacknose Shiner, and Brassy Minnow) after 1980 (Goldstein and Smith

1999).

The most notable change is the decline of Grass Pickerel through the study period. It was the most abundant species in 1960, making up approximately 43% of all fishes captured that year, but was found in very low numbers or not detected at all in subsequent years (0-0.56% of all fishes captured).

The ‘all-gear’ NMDS (Figure 3.2) places the 1960 fish community far to the left of all subsequent samples with no overlap. Much like the ‘single-seine’ NMDS, most 1960 sampling events group together and Grass Pickerel is strongly associated with those samples. There is overlap between the 1980, 2013, and 2014 sampling events, and some species group between the

1960 and 1980 communities, suggesting that the 1980 community was somewhat transitional with some of the large-bodied piscivores still present, but an increase in minnow diversity as well. In this ordination, the 2013 and 2014 communities appear quite similar. The six to 10 species common to all years are grouped approximately in the centre of the plot, but are shifted towards sampling events where they were captured in greater numbers.

There was a significant difference (p=0.001) in the fish community between years when all gears are used. The year of study explains approximately 24% (R2=0.2373) of the variation in the fish community. The similarity percentages (Table 3.3) show that average abundances of common

species are generally responsible for the dissimilarity between years. Pair-wise comparisons between 1960, 2013 and 2014 tend to show a similar trend as with the ‘single-seine’ data where a few species (three to four) are contributing the most difference. The ratios of average abundance to standard deviation are relatively high (>1.0) for most of these comparisons suggesting these species are not consistently contributing to the dissimilarity between years. Comparisons with

1980 samples, however, show a greater number of species (five or more) contributing to the dissimilarity between years. The ratio of average abundance to standard deviation is much lower

(<1.0) for these species comparisons though, suggesting they are consistently contributing to the difference in community structure between years. Central Mudminnow contributes the greatest dissimilarity in community structure between all pairs of years except between 1960 and 1980 when Grass Pickerel is responsible for the greatest dissimilarity.

Land-use changes

The land use has changed slightly between 1966 and 2000 (Figures 3.3 and 3.4). The dominant land-use types in the subwatershed were 55% agricultural and 40% forest in 1966, and 45% agricultural, 36% forest and 14% swamp/marsh in 2000. Similarly, within the 100m buffer of the creek, the dominant land use types were 53% agricultural and 43% forest in 1966, and 48% agricultural, 40% forest and 11% swamp/marsh in 2000.

Discussion

The fish community changed significantly in Jones Creek over 50 years despite minimal direct anthropogenic impacts. Grass Pickerel went from the most abundant species in the system to one of the rarest, and forage fishes increased in richness and abundance. Similar shifts in fish

community structure have been observed following experimental removal of piscivores in lakes

(Demers et al. 2001). The changes observed in Jones Creek can be partially attributed to changes in land use. Land use has shifted from agriculture towards increased wetland cover. Additionally, increased beaver activity appears to have changed the hydrology of the system by separating the stream into impassable sections with minimal flow and more water storage compared to historical descriptions (Crossman 1962, Collen and Gibson 2001). These changes in land use and hydrology may favour small-bodied fishes more typical of wetlands than streams (Knudsen

1962, Harpur 2010).

Overall, there was a significant change in the fish community in Jones Creek from 1960 to 2014.

Statistically, fluctuations in the abundances of species common through the study period are driving the significant differences in the fish community but, biologically, the loss of trophic groups is probably more important. Species lost belong to the piscivore guild and benthic invertivore guild, and species gained belong to generalist or omnivore guilds (Goldstein and

Simon 1999). The loss of predators likely resulted in an increase in both abundance and richness of forage fishes (Power et al 1985, Paine 1966). For example, Brassy Minnow is often an indicator of low predator conditions (Radford and Sullivan 2014) and it appeared after 1980 when most predators were in decline.

The decline of Grass Pickerel is the most notable change in the Jones Creek fish community, and may be responsible for the observed shift towards more abundant prey fishes (Tonn and

Magnuson 1982, Robinson and Tonn 1989, Demers et al. 2001). Grass Pickerel was the most abundant species in 1960, making up almost half of all fishes. Although it is possible that the

large 1960 population of Grass Pickerel was unusual or unsustainable, the species can be occasional to abundant in small streams in Ontario (Crossman and Holm 2005) and elsewhere within its range (Kleinert and Mraz 1966, Ming 1968). Large Grass Pickerel made up 20-90% of fishes captured in Illinois stream reaches (Angermeier 1987). As a top predator, it may have acted as a keystone species in Jones Creek responsible for regulating community structure as it does in other communities (Crossman and Holm 2000, Divens et al. 2000). The 1980 fish community was somewhat transitional in terms of species composition, having some large- bodied piscivores and an increase in forage fish richness, so the community may have begun shifting as a result of Grass Pickerel decline after 1960. Additionally, the decline of Grass

Pickerel is particularly alarming because of its conservation status in Canada. It has a patchy range and can be abundant to rare, depending on location; Jones Creek was one of the most abundant populations known in Canada until recently (Crossman and Holm 2005).

Fluctuations in abundances of forage fishes were seen between all years of the study. These fluctuations may be the result of variable environmental conditions (Grossman et al. 1998,

Menge and Sutherland 1987) or resource availability (Schlosser 1982). Magurran and Henderson

(2010) noted that estuarine fish abundances varied asynchronously from year to year, likely because of variability in the environment and resources affecting niches differently. The largest number of fishes was captured in the summer of 2013, when catch per unit effort was six times larger than in either 1960 or 2014. Low-gradient, predominantly surface-fed streams like Jones

Creek can experience fluctuations in community structure because of seasonal variation in flow

(Schlosser 1982). Grossman et al. (1998) found an increase in abundance and richness of fishes in their samples during low-flow years, which may have been due to reduced water velocity being more favourable for some downstream residents by increasing foraging success and 52

decreasing energy expense. Temporal changes in flow may be responsible for the changes in abundance of fishes observed here, although discharge data are lacking.

There are several possible explanations for the high abundance of all fishes observed in 2013. It is possible that higher abundances are now typical in Jones Creek with few predators in the system (Demers et al. 2001), but a disturbance event may have occurred before 2014 sampling that may have reduced abundances of all fishes (Menge and Sutherland 1987). Alternatively, density-dependent factors (i.e. competition for space or food) could have resulted in a thinning of population sizes by the end of 2013 back to more sustainable levels in 2014 (Lobón-Cerviá 2012,

Forrester and Steele 2000, Hixon and Jones 2005). Finally, this difference between 2013 and

2014 could represent ‘normal’ fluctuations in species abundance through time (Grossman et al.

1982, Matthews et al. 2013). This emphasizes the need for longer-term standardized sampling to fully understand what was, and is, now ‘typical’ in temporally variable systems like Jones Creek.

The increased abundance of certain species may be indicative of environmental stressors. Five species (Central Mudminnow, Golden Shiner, Brook Stickleback, Blacknose Shiner, Northern

Redbelly Dace) were caught in substantially greater numbers in 2013 than other years. All of these species are tolerant of low dissolved oxygen (Scott and Crossman 1998) and many are known to tolerate winterkill conditions (Klinger 1982). This could be indicative of changes in flow regimes (France 1997), possibly related to beaver activity, as low dissolved oxygen is generally rare in lotic systems. Low dissolved oxygen could also explain the loss of large-bodied species (Knudsen 1962, Harpur 2010) and the darters. Darters are often used as indicators of high biotic integrity as their benthic lifestyle makes them sensitive to changes in silt loads and

dissolved oxygen (Karr et al. 1986). The loss of all three darter species suggests a decline in habitat quality. Small-bodied species increased in abundance in Jones Creek, but additions of new species also occurred over time.

At least four species of forage fishes appeared only in more recent samples. These species

(Banded Killifish, Blackchin Shiner, Blacknose Shiner, Brassy Minnow) may have been newly detected for several reasons. It is possible that they were always present, but were kept in low densities by predators and, thus, were too rare to be detected (Paine 1966, Radford and Sullivan

2014). New species could have colonized Jones Creek from the Charleston Lake-Gananoque system when the connection was intact (i.e. prior to beaver dams). Alternatively, these species could have been introduced to Jones Creek through accidental or intentional baitfish introductions (Drake and Mandrak 2014). All of these pathways of introduction appear to be equally probable here and may have all been important.

All of these observed changes in the fish community, including the decline of Grass Pickerel, are likely a result of physical changes occurring in the watershed. The land use changed minimally around Jones Creek 1966-2000 and is still mostly agricultural with ample forest cover. However, there was a substantial increase in swamp and marsh lands. This is likely a combined effect of decommissioned agricultural lands that are no longer being drained during the growing season and an increase in beaver damming causing the fields to flood (Collen and Gibson 2001).

Although agricultural lands have decreased in this watershed in favour of wetland habitats,

Maloney and Weller (2011) showed that land uses from 1952 had persistent effects on stream habitat and biotic communities 50 years later. Even in cases of land improvements (i.e.

reforested agricultural lands), there may be a latent effect of historic land uses on stream biota

(Harding et al. 1998). The impacts of ditching in Jones Creek may have permanently affected the

Grass Pickerel population. Grass Pickerel was once abundant in Ohio drainages, but declined dramatically in the early 1900s as a result of channelization and dredging for agricultural practices (Trautman 1981). This is a common threat to Grass Pickerel across Ontario

(Beauchamp et al. 2012) and may be responsible for the observed decline in this species. The final change in land use that may have affected the Jones Creek fish community is the increase in development (built-up impervious land use) in the sub-watershed from 1966 to present (Maloney and Weller 2011). Although only a slight increase here, increases in developed land uses have been shown to increase homogenization of nearshore fish communities in Ontario by excluding sensitive and rare species like Grass Pickerel (Chu et al. 2014).

Tied to changes in land use and cover types is the increase in beaver activity noted in Jones

Creek through the study period. Beaver dams are capable of altering many physicochemical parameters of lotic systems and changing the suite of species able to best use the habitat (Collen and Gibson 2001). The number of beaver dams increased through the creek and the impact of each dam may be greater now. No impassable barriers to fishes were noted in 1960 (Crossman, unpubl. field notes 1960), but all dams observed in 2014 had created substantial changes in elevation that would be impassable for fishes throughout most of the year. Harpur (2010) noted changes in the fish community of an Ontario lake system between the 1970s and 2000s and attributed at least some of the observed shifts to beaver activity creating an assemblage dominated by small-bodied fishes more indicative of wetlands than lakes. France (1997) found an increased abundance of fishes surrounding beaver lodges compared to areas nearby in the

same lakes, and these areas were dominated by Northern Redbelly Dace (Chrosomus eos) and

Finescale Dale (C. neogaeus). Although cyprinids, in particular, seem to benefit from increased beaver activity, the effects on piscivores can vary greatly across species and systems, sometimes positive, sometimes negative (Collen and Gibson 2001). Beaver dams often shift the hydrology of streams from lotic to functionally lentic systems (Collen and Gibson 2001). This is particularly harmful to species that require flow to maintain clear, unsilted substrates and well- oxygenated water for eggs or other life history stages (Knudsen 1962, Harpur 2010), which could help to explain the loss of the black basses and darters from Jones Creek. Although the loss of flow is unlikely to affect Grass Pickerel as it is tolerant of low dissolved oxygen conditions and inhabits low-gradient streams with little flow (Scott and Crossman 1998), the increased water level associated with damming could negatively impact nursery habitat by increasing accessibility of temporarily flooded nursery areas to larger predators, including adult Grass

Pickerel (Kleinert and Mraz 1966). Additionally, the creation of shallow, lentic ponds could result in more frequent exposure to winterkill conditions particularly harmful to large-bodied species (Keast and Fox 1990), again explaining the apparent loss of both Micropterus species.

Dams can also reduce recolonization rates of fishes and restrict habitat use (Keast and Fox 1990).

The increased number of impassable dams on Jones Creek may be responsible for reducing or eliminating the connection to the Charleston Lake-Gananoque system that may have acted as a source population for Grass Pickerel (or a refuge during drought events), or simply segmenting the creek into isolated populations, eliminating reach-scale metapopulation dynamics. Grass

Pickerel in a southern Ontario creek moved almost exclusively into a tributary with little movement out towards the main branch, suggesting this tributary may be more favourable, or

may function as a sink (Kramski 2014). Fitzgerald et al. (1998) found that connections to higher order streams were critical in providing refuge and recolonization opportunities to the fish communities in smaller tributaries, allowing some community persistence through time.

Northern Pike has been observed to accumulate below beaver dams in spring when access to upstream spawning sites is blocked (Knudsen 1962). Grass Pickerel tend to spawn more locally than Northern Pike and do not undertake such extensive spawning migration runs (Scott and

Crossman 1998). However, they still show some preference for spawning sites within a creek

(Fisheries and Oceans Canada, unpubl. data), so recruitment could be compromised if they are forced to spawn in less ideal areas (Foust and Haynes 2007).

Climate change is another potential threat facing aquatic ecosystems globally (IPCC 2014).

Although I was unable to evaluate potential impacts from climate change directly, it is likely that the observed changes in land use and beaver activity coupled with changes in climatic conditions have caused shifts in the fish community in Jones Creek. In general, it is expected that increasing temperatures should favour warmwater species like Grass Pickerel by increasing the length of the growing season, thus, improving recruitment, and/or allowing some species to expand their range northwards (Shuter et al. 2002, Chu et al. 2005). Congeners of the Grass Pickerel, the

Northern Pike and Chain Pickerel (Esox niger), appear to be benefitting from increasing temperatures (Casselman 2002, Hoyle and Lake 2011). However, as a spring spawner, Grass

Pickerel recruitment could be negatively impacted by variable spring warming conditions

(Mortsch et al. 2006). This has been observed for other warmwater piscivores such as Walleye in

Lake Erie (Shuter et al. 2002). The potential impacts on Grass Pickerel from changing

temperatures are still somewhat unclear, but a greater threat to this species may be from increases in extreme weather events, particularly droughts.

Extreme weather events are expected to increase as a result of climate change (IPCC 2014), and changes in the hydrology of freshwater systems could include increased flooding from extreme precipitation events, and increased water losses from evaporation (Magnuson 2002). Grass

Pickerel is somewhat drought tolerant (Crossman 1962); however, prolonged summers, increased temperature, and evaporation can have several direct and indirect effects on Grass Pickerel and other aquatic organisms (Lake 2003). Available habitat and connectivity to refugia are diminished during droughts, which could reduce abundance (Matthews and Marsh-Matthews

2003). Water quality can decline and water temperatures could exceed the lethal thermal limit of this species (Cowx et al. 1984). Other organisms (e.g. benthic invertebrates, forage fishes) may also be affected by drought, which could alter food-web dynamics and intensify biotic interactions (Labbe and Fausch 2000, Lake 2003). Predation risk from terrestrial animals can increase as well, since fishes are concentrated in pool refugia (Lake 2003). Extreme climatic events can also facilitate invasion by non-native species that can have lasting effects on community structure (Bêche et al. 2009). Grass Pickerel should, in theory, be able to recover from the impacts of droughts if spring flooding returns connectivity to pools and replenishes spawning habitat. In Jones Creek, connectivity and prevalence of spring melt waters appear to be altered by beaver damming, which may hinder recolonization and recruitment following extreme droughts.

Conclusions

The significant change in the fish community, particularly the decline of Grass Pickerel, in Jones

Creek between 1960 and 2014 is likely due to changes in land use and hydrology resulting from beaver activity and climate change. My null hypothesis may have overlooked the importance of beaver damming and apparent shift towards a lentic habitat type, and the drought aspect of climate change that is more important in streams than lakes. Since the fish community did shift dramatically, my data may be better explained by either (or both) of two hypotheses put forth by

Horowitz (1978) to explain temporal changes in stream fish communities: the competition- trophic structure hypothesis, or extermination hypothesis. The competition-trophic structure hypothesis predicts that temporally variable habitat structure and food sources will favour generalist species, while the extermination hypothesis predicts that temporally variable flow rates in streams may increase extermination rates of fishes, particularly during droughts (Horowitz

1978). Large-bodied piscivores (including Grass Pickerel) in Jones Creek may have been exterminated as a result of highly variable flow rates or drought events; this loss of trophic complexity and increased environmental variability resulted in variable food resources that caused fluctuations in abundances of generalist forage fishes.

This study emphasizes the need for consistent, long-term data sets on biotic communities and their associated local habitat and regional environmental conditions (Poff and Ward 1989,

Fitzgerald et al. 1998). Matthews et al. (2013) noted that if they had stopped their study a few years sooner, they would have seen an abrupt, directional shift in community structure and missed the approximate return towards the pre-disturbance community that was observed in recent years. A similar note of caution should be taken here, as my samples represent four

snapshots in time of the fish community with no information on the trajectory between years.

Nonetheless, the results are valuable for understanding drivers of stream fish community change temporally and are particularly important for Grass Pickerel conservation, as this is the first example of long-term data on a population of this species in Canada. More standardized monitoring of this species is required across its range in Canada to determine if similar trends of declining abundance are occurring elsewhere, and to provide a reliable baseline of habitat conditions and community structure so that drivers of change can be soundly evaluated.

Figures and Tables

Figure 3.1 Non-metric multidimensional scaling- single seine data (final stress=10.3%, dimensions=2). The fish communities are significantly different and sampling year explains 29% of the variation in community structure (p=0.001, R2=0.2898). Sampling events from 1960 are encompassed in the pink polygon, 2013 in blue, and 2014 in the green polygon. Grass Pickerel (Esamve) groups exclusively with 1960 sampling events. Data are standardized by catch per unit effort. Species codes can be found in Table 3.1.

Figure 3.2 Non-metric multidimensional scaling- all data (final stress=6.3%, dimensions=3). The fish communities are significantly different and sampling year explains 24% of the variation in community structure (p=0.001, R2=0.2373). Sampling events from 1960 are encompassed in the pink polygon, 1980 in yellow (OMNRF, unpubl. data), 2013 in blue, and 2014 in the green polygon. Grass Pickerel (Esamve) groups exclusively with 1960 sampling events. Data are standardized by relative abundance. Species codes can be found in Table 3.1.

Figure 3.3 Land-use changes in the Jones Creek subwatershed from 1966-2000. Land-use data obtained from the Ducks Unlimited South Ontario Land Use (1966) and OMNRF Southern Ontario Land Resource Information System (2000).

Figure 3.4 Land-use changes in the 100m buffer around Jones Creek from 1966-2000. Land-use data obtained from the Ducks Unlimited South Ontario Land Use (1966) and OMNRF Southern Ontario Land Resource Information System (2000).

Table 3.1 Species detected in single seine and all gear sampling. Species codes refer to NMDS plots. P indicates the species was present in that year. Single Seine All Gear Species Species 1960 2013 2014 1960 1980 2013 2014 Code Ambloplites rupestris Amru P P Ameiurus nebulosus Amne P P P P P P Amia Calva Amca P Catostomus commersonii Caco P P P P P P Chrosomus eos Cheo P P P P P Chrosomus neogaeus Chne P Culaea inconstans Cuin P P P P P P Esox americanus vermiculatus Esamve P P P P P P Esox lucius Eslu P P Etheostoma flabellare Etfl P Etheostoma nigrum Etni P Fundulus diaphanus Fudi P P P P Hybognathus hankinsoni Hyha P P P Lepomis gibbosus Legi P P P P P P P Luxilus cornutus Luco P P P P P Micropterus dolomieu Mido P P Micropterus salmoides Misa P Notemigonus crysoleucas Nocr P P P P P P P Notropis heterodon Nohed P P Notropis heterolepis Nohel P P P P Notropis volucellus Novo P P Percina caprodes Peca P Pimephales notatus Pino P Pimephales promelas Pipr P P P P P P Pomoxis annularis Poan P Semotilus atromaculatus Seat P P P P Semotilus corporalis Seco P Umbra limi Umli P P P P P P P

Table 3.2 Similarity percentages for single seine data. Species listed account for 70% of difference between pairs of years.

Average Standard Ratio of Average Average Cumulative contribution deviation avg. abundance abundance contribution to contr.:SD in year A in year B dissimilarity

1960-2013 Umbra limi 0.357 0.305 1.17 0.746 7.66 0.387 Notemigonus crysoleucas 0.182 0.239 0.76 0.083 8.64 0.585 Notropis heterolepis 0.114 0.243 0.468 0 4.7 0.708 1960-2014 Umbra limi 0.464 0.278 1.666 0.746 2.24 0.497 Esox americanus vermiculatus 0.262 0.23 1.14 0.855 0.005 0.778 2013-2014 Umbra limi 0.258 0.24 1.07 7.66 2.24 0.324 Notemigonus crysoleucas 0.18 0.228 0.789 8.64 0.5 0.55 Notropis heterolepis 0.11 0.234 0.471 4.7 0.041 0.689 Chrosomus eos 0.086 0.147 0.585 7.81 0.061 0.797

Table 3.3 Similarity percentages for all gear data. Species listed account for 70% of difference between pairs of year.

Average Standard Ratio of Average Average Cumulative contribution deviation avg. abundance abundance contribution to contr.:SD in year A in year B dissimilarity 1960-1980 Esox americanus vermiculatus 0.23 0.231 0.996 1.88 0 0.254 Umbra limi 0.18 0.291 0.617 1.18 1.209 0.42 Catostomus commersonii 0.13 0.237 0.551 0.007 3.72 0.554 Lepomis gibbosus 0.068 0.157 0.436 0.018 0.418 0.699 Micropterus dolomieu 0.063 0.127 0.5 0.073 0.976 0.764 1960-2013 Umbra limi 0.275 0.215 1.28 1.18 2.75 0.293 Esox americanus vermiculatus 0.189 0.184 1.03 1.88 0.071 0.496 Notemigonus crysoleucas 0.153 0.188 0.813 0.194 2.42 0.658 Notropis heterolepis 0.084 0.187 0.448 0 0.966 0.747 1960-2014 Umbra limi 0.418 0.291 1.44 1.18 3.96 0.447 Esox americanus vermiculatus 0.215 0.177 1.21 1.88 0.013 0.676 Notemigonus crysoleucas 0.096 0.14 0.683 0.194 1.02 0.778 1980-2013 Umbra limi 0.205 0.195 1.05 1.21 2.75 0.229 Notemigonus crysoleucas 0.141 0.165 0.852 1.58 2.41 0.386 Catostomus commersonii 0.109 0.204 0.534 3.72 0.055 0.507 Chrosomus eos 0.079 0.116 0.683 0.511 2.83 0.595 Culaea inconstans 0.066 0.128 0.517 0 2.76 0.669 N. heterolepis 0.062 0.149 0.419 0 0.966 0.738 65

1980-2014 Umbra limi 0.296 0.261 1.13 1.21 3.96 0.344 Catostomus commersonii 0.119 0.211 0.563 3.72 0.107 0.483 Notemigonus crysoleucas 0.106 0.14 0.758 1.58 1.02 0.606 Semotilus atromaculatus 0.061 0.094 0.641 1.4 0.309 0.677 Ameiurus nebulosus 0.06 0.112 0.539 0.465 0.661 0.747 2013-2014 Umbra limi 0.22 0.194 1.133 2.75 3.96 0.31 Notemigonus crysoleucas 0.141 0.147 0.96 2.42 1.02 0.509 Culaea inconstans 0.084 0.126 0.664 2.76 0.193 0.627 Chrosomus eos 0.077 0.121 0.631 2.83 0.124 0.734

Note: This chapter was produced by Julia Colm under Licence with the Ontario Ministry of Natural Resources and Forestry © Queen’s Printer for Ontario 2015.

Chapter 4

General Discussion

The processes that shape species distribution patterns are ultimately the same processes that structure fish communities. Viewing these processes as a series of filters has been proposed, where species are distributed according to their ability (physical and/or physiological) to pass through a series of filters at finer scales, and communities are formed by the collection of species that passed through the same filters at approximately the same place and time (Smith and Powell

1971, Tonn 1990, Poff 1997). At the broadest regional scale, fishes are distributed based on their zoogeographical history (Smith and Powell 1971). In Ontario, fish distributions are predominantly driven by dispersal following the Wisconsinan glaciation. Southwestern Ontario has the highest fish species diversity because it was closest to the Mississipian refugium, which supported the largest species pool during the glaciation, had the most number of outlets, and outlets that were open for the longest period of time connecting back into freshly melted Ontario waters (Mandrak and Crossman 1992). Climate is the next coarse regional filter that defines species distribution patterns. Temperature, precipitation and number of growing degree days are considered the most important climatic variables in temperate freshwater ecosystems, further explaining why southern Ontario has the highest species diversity (Mandrak 1995). Abiotic factors provide a finer-scale filter by which a species distribution is constrained. Features such as habitat complexity and hydrological variability structure communities of lakes and streams based on seasonal habitat needs and tolerances to variability in flows (Gorman and Karr 1978,

Horowitz 1978). Dissolved oxygen and pH are considered the two most important physicochemical parameters that structure fish communities that depend on the physiological thresholds of individual species (Jackson et al. 2001). Biotic interactions form the next finer filter 67

(Poff 1997) where competition and predation work to exclude species entirely from a system or a component of the habitat (Harvey 1991, Gilliam and Fraser 2001). In reality, these filters do not operate in a linear fashion, and their relative importance for each species and, therefore, each community depends on other factors such as waterbody type (i.e. lake or stream), the amount of environmental variability (Menge and Sutherland 1987, Jackson et al. 2001), and the disturbance regime (Connell 1978, Grossman et al. 1982, Matthews et al. 2013).

Exploring these processes helps us investigate one of the most fundamental ecological questions: why are species found in some places but not in others (Krebs 1978, Caughley et al. 1988)? This can also help uncover why species distributions and community assemblages can change over time. With biodiversity declining around the globe and across taxa (IUCN 2015), understanding the factors that best explain the distribution of rare or imperiled species and that destabilize community structure are becoming two very important pieces of conservation planning. These are the two topics I have explored for Grass Pickerel in Ontario.

When modelling Grass Pickerel distribution within a watershed in Ontario in Chapter 2, patterns are best explained by site-scale habitat variables such as ample submerged aquatic vegetation and channel cover, and wetlands within the floodplain but, being found in the more southern

Lake Ontario West watershed, was important as well. Grass Pickerel may be avoiding sites that appear suitable but have intermediate gradients and higher groundwater inputs relative to surface water inputs. Grass Pickerel also appeared to be positively associated with conductivity and agricultural land uses. Although, these associations are more likely explained by a greater importance on regional factors such as a warmer, wetter climate in southern Ontario that is

favourable for both warmwater fishes and for agricultural crops than by a true preference for those conditions.

Investigating possible causes of Grass Pickerel population decline through changes in the whole community (Chapter 3) revealed an additive problem of historical land uses, altered hydrology from beaver activity and (likely) an increase in extreme weather events via climate change. In

1960, Jones Creek was dominated by Grass Pickerel, the most abundant species present at the time (Crossman 1962), but was one of the rarest species in 2013 and barely detectable in 2014.

Unfortunately, site-scale habitat data are lacking from 1960 to determine whether habitat has changed at this scale, so community data were used as some species are good indicators of changes in conditions and may be useful for determining broader habitat trends (Karr et al.

1986). In Jones Creek, multiple beaver dams appear to have changed the habitat to a more lentic ecosystem dominated by habitat and diet generalists and have blocked connectivity between different reaches of the creek, possibly preventing access to better spawning grounds or refuge areas during the recently more frequent and extreme droughts.

The results presented in this thesis and in the additional notes addressing issues in the species

Management Plan found in Appendix A can help inform conservation planning related to Grass

Pickerel. Ensuring in-stream cover remains intact, both aquatic vegetation and natural riparian zones that can provide woody debris objects has been addressed in the mitigation guide for drain maintenance (Coker et al. 2010) and the federal Management Plan (Beauchamp et al. 2012) and the importance of these is emphasized again here. Increasing the width of riparian buffer zones, particularly for low gradient streams where spring melt waters flood out instead of up, would

help protect the quality and quantity of Grass Pickerel spawning and nursery habitat. Managing beaver dams to remain passable to fishes so that stream-dwelling Grass Pickerel have access to preferred spawning habitat in the spring and refuge areas during extreme droughts would be ideal. The most substantial declines in Grass Pickerel abundance have been observed in two creeks (Twenty Mile Creek and Jones Creek (Crossman 1962, Beauchamp et al. 2012)) that both lack connections to larger waterbodies suggesting there may be an association between connectivity and population persistence (Keast and Fox 1990). Although Grass Pickerel appear unaffected by high levels of conductivity, other fishes they share habitat with may not and this could have impacts on food-web dynamics. Additionally, juvenile Grass Pickerel consume benthic invertebrates that are negatively affected by high conductivity and this could negatively affect recruitment (Crossman 1962, Crowther and Hynes 1977).

The Grass Pickerel is functionally important to the ecosystems it inhabits as the top predator, is important to regional biodiversity in Canada in terms of species richness and genetic diversity, and has inherent value that makes it an important species to protect. It is also tolerant of a wide range of environmental conditions and can inhabit highly degraded systems as long as cover is present (Cain et al. 2008). Few other piscivores have such a wide tolerance to low dissolved oxygen and ability to thrive in heavily vegetated habitats, making it a non-substitutable species in many systems (Dudgeon et al. 2006). Despite its ability to withstand environmental variability, it still appears to be declining in Ontario, suggesting that threats and impacts are already quite advanced. What does this mean for more sensitive species?

Freshwater biodiversity across all taxa is declining worldwide from habitat degradation, pollution, flow regulation, overexploitation and invasive species (Dudgeon et al. 2006). Most of these issues are expected to become exaggerated under most climate change scenarios (Mortsch et al. 2006). In addition, climate change will further threaten access to water for humans in a world where water conflicts are ever increasing (Dudgeon et al. 2006). Freshwater organisms are already the last to be considered in conflicts over water (Poff et al. 2003) and threats to aquatic ecosystems are easier for people to ignore, as they cannot see changes occurring below the surface (Tufts et al. 2015). All of these issues suggest that, now more than ever, science should be informing policy decisions (Dudgeon et al. 2006) yet, in Canada, there has been a decline in protection afforded to aquatic ecosystems through legislative changes (Hutchings and Post 2013,

Winegardner et al. 2015). If rare or sensitive species such as Grass Pickerel are expected to persist and recover, a greater political focus on aquatic organisms and ecosystems is needed to preserve these natural resources.

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Appendix A Additional Notes on Grass Pickerel Conservation

In addition to providing information on summer habitat needs of Grass Pickerel and exploring trends of a well-known population through time, I addressed a few other research interests from the federal Management Plan for the species (Beauchamp et al. 2012). I updated distribution records in the three watersheds, identified a new threat in one watershed, and commented on the degree of other threats. We detected Grass Pickerel in a new waterbody, Grass Lake, in the

Georgian Bay-South Simcoe watershed. Given the connectivity of the Severn River system from

Georgian Bay proper through Lake Couchiching, it is likely the species is more widespread through the watershed than what was previously known. In 2014, we detected Grass Pickerel in the mid-lower reaches of Twenty Mile Creek (east of Smithville, ON), where it had not been detected in several years (Beauchamp et al. 2012). Additionally, we failed to detect Grass

Pickerel in Michael Henry Creek, where it was detected in 1960 (Crossman 1962), although it was unclear if we sampled the same reaches as the habitat did not seem suitable.

Impacts from agricultural drain maintenance are considered the biggest threat to Grass Pickerel in Ontario (Beauchamp et al. 2012); however, Grass Pickerel in the Georgian Bay watershed are found in the heart of the Muskoka region where agriculture is not nearly as prevalent as cottage development and shoreline alterations. Habitat degradation, particularly the removal of aquatic vegetation, is addressed in the Management Plan for Grass Pickerel, and the impacts of shoreline alterations and land development may be similar to those of drain maintenance. But the outreach process, target audience, and mitigation of shoreline alterations would be substantially different and these should be considered in future revisions of the Management Plan.

The impacts of climate change on freshwater fishes are still largely unknown, as several scenarios are possible and the effects will differ across regions and species (Mortsch et al. 2006).

It is possible that climate change will benefit warmwater species like Grass Pickerel currently at the northern edge of their range in southern Ontario by expanding locations that meet their thermal optima (Chu et al. 2005). An increase in temperature may be beneficial for lake-dwelling

Grass Pickerel, but less so for stream-dwelling Grass Pickerel. Lakes are better buffered against extreme weather events such as droughts and floods, while these can be particularly damaging in streams (Matthews et al. 2013) where Grass Pickerel are more common. Negative impacts in streams may be occurring already; declines have been observed in several historically abundant

Ontario stream populations (Twenty Mile Creek, Jones Creek), both of which lack a connection to a larger waterbody to provide refuge or recolonization potential. But Grass Pickerel in lakes and larger rivers appear to be stable or possibly increasing (this study, Oldenburg and Gilbert

2013, J.M. Casselman, personal communication). In a Great Lakes coastal wetland report, Grass

Pickerel was ranked the 26th most vulnerable species of 99 lacustrine fishes, placing them in the high risk category of climate change impacts largely due to it being a late spring spawner that inhabits shallow, vegetated habitats (Mortsch et al. 2006). Lakes and streams in the Experimental

Lakes Area showed similar trends as a result of climatic changes, where streamflow was reduced in streams and water temperature and clarity increased in lakes (Schindler et al. 1996). It is unclear how Grass Pickerel in Canada will be affected by climate change, but it is clear that more regular monitoring of Grass Pickerel in lakes and streams across its range in Ontario is required to observe population changes before it’s too late to mitigate impacts

Invasive species are another threat considered to be of medium concern to Grass Pickerel in

Canada (Beauchamp et al. 2012). Only two invasive species were detected with Grass Pickerel in this study, the Round Goby and Common Carp, both detected in the LOW watershed, and already established. Common Carp may negatively interact with Grass Pickerel through removal of aquatic vegetation and increases in turbidity (Jolley and Willis 2008). Although no other invasive species were observed here, impacts from Phragmites australis are a concern to most wetland fishes in other watersheds (Glass and Mandrak 2015), and if Asian carps and the Chain

Pickerel (Esox niger) invade, these could have devastating effects on Grass Pickerel populations through habitat destruction (Cudmore and Mandrak 2004), and competition and predation (Hoyle and Lake 2011), respectively.

Lastly, disease could be a threat to Grass Pickerel (Beauchamp et al. 2012). None of the Grass

Pickerel I captured appeared to be diseased (i.e. none were captured with lesions or appeared abnormal in any way). In the LOW watershed, almost all of the Northern Pike captured were infected with blackspot disease (the digenean parasite), but none of the Grass Pickerel, even ones captured in the same haul as infected Northern Pike, showed signs of infection. It is unclear if

Grass Pickerel is affected by incoming diseases like viral hemorrhagic scepticaemia (VHS), but continued monitoring of populations including body condition of individuals will help determine if diseases become threatening.

Appendix B Supplemental Data

Detection. Field.Number Year Waterbody.Name Waterbody.Type Watershed Category Effort GP 2013-GP-ND020713-001A 2013 Beaver Creek Stream LOW DS 3 5 2013-GP-ND020713-002A 2013 Beaver Creek Stream LOW DW 3 0 2013-GP-ND050713-001A 2013 Beaver Creek Stream LOW DS 3 26 2013-GP-ND060813-001A 2013 Beaver Creek Stream LOW DS 3 9 2013-GP-ND090813-001A 2013 Beaver Creek Stream LOW DS 3 10 2013-GP-ND090813-002A 2013 Beaver Creek Stream LOW DS 3 2 2013-GP-ND090813-003A 2013 Beaver Creek Stream LOW DS 3 4 2013-GP-ND140513-001A 2013 Beaver Creek Stream LOW DS 3 1 2013-GP-ND160513-001A 2013 Beaver Creek Stream LOW DS 3 3 2013-GP-ND160513-002A 2013 Beaver Creek Stream LOW DW 3 0 2013-GP-ND230513-001A 2013 Beaver Creek Stream LOW DW 3 0 2013-GP-ND230513-002A 2013 Beaver Creek Stream LOW DS 3 32 2013-GP-ND230513-003A 2013 Beaver Creek Stream LOW DS 3 1 2013-GP-ND240513-001A 2013 Beaver Creek Stream LOW DS 3 21 2013-GP-ND240613-001A 2013 Beaver Creek Stream LOW DS 3 21 2013-GP-ND250613-001A 2013 Beaver Creek Stream LOW DS 3 3 2013-GP-ND250613-002A 2013 Beaver Creek Stream LOW DS 3 4 2013-GP-ND250613-003A 2013 Beaver Creek Stream LOW DS 3 15 2013-GP-ND260613-001A 2013 Beaver Creek Stream LOW DS 3 1 2013-GP-ND260613-003A 2013 Beaver Creek Stream LOW DS 3 11 2013-GP-ND270513-001A 2013 Beaver Creek Stream LOW DS 3 2 2013-GP-ND270513-002A 2013 Beaver Creek Stream LOW DS 3 1 2013-GP-ND270513-003A 2013 Beaver Creek Stream LOW DS 3 2 2013-GP-ND270513-004A 2013 Beaver Creek Stream LOW DW 3 0 2013-GP-ND270613-001A 2013 Beaver Creek Stream LOW DS 3 6 2013-GP-ND280513-001A 2013 Beaver Creek Stream LOW DW 3 0 2013-GP-ND280513-002A 2013 Beaver Creek Stream LOW DS 3 5 2013-GP-ND300513-001A 2013 Beaver Creek Stream LOW DS 3 1 2013-GP-ND300513-002A 2013 Beaver Creek Stream LOW DW 3 0 2013-GP-ND310513-001A 2013 Beaver Creek Stream LOW DS 3 7 2013-GP-ND310513-002A 2013 Beaver Creek Stream LOW DW 3 0 2013-GP-TMC070813-001A 2013 Twenty Mile Creek Stream LOW DS 3 8 2013-GP-TMC070813-002A 2013 Twenty Mile Creek Stream LOW DW 3 0 2013-GP-TMC070813-003A 2013 Twenty Mile Creek Stream LOW DW 3 0 2013-GP-TMC080813-001A 2013 Twenty Mile Creek Stream LOW DW 3 0 2013-GP-TMC080813-002A 2013 Twenty Mile Creek Stream LOW DS 3 2

2013-GP-TMC080813-003A 2013 Twenty Mile Creek Stream LOW DS 3 1 2013-GP-TMC290813-001A 2013 Twenty Mile Creek Stream LOW DW 3 0 2013-GP-TMC290813-001B 2013 Twenty Mile Creek Stream LOW DW 3 0 2013-GP-TMC290813-002A 2013 Twenty Mile Creek Stream LOW DW 3 0 2013-GP-TMC290813-002B 2013 Twenty Mile Creek Stream LOW DW 3 0 2013-GP-JC160713-001A 2013 Jones Creek Stream StL DS 3 9 2013-GP-JC160713-001B 2013 Jones Creek Stream StL DW 3 0 2013-GP-JC160713-002A 2013 Jones Creek Stream StL DS 1 2 2013-GP-JC160713-002B 2013 Jones Creek Stream StL DS 3 2 2013-GP-JC160713-003B 2013 Jones Creek Stream StL DW 3 0 2013-GP-JC180713-001A 2013 Jones Creek Stream StL DW 2 0 2013-GP-JC180713-002A 2013 Jones Creek Stream StL DS 3 9 Michael Henry 2013-GP-JC170713-001A 2013 Creek Stream StL ND 3 0 Michael Henry 2013-GP-JC170713-002A 2013 Creek Stream StL ND 3 0 2014-GP050714-001A 2014 Gartersnake Creek Stream GB ND 3 0 2014-GP060714-001A 2014 Buck Lake Stream GB ND 3 0 2014-GP060714-003A 2014 Grass Lake Stream GB DS 5 3 2014-GP060714-002A 2014 Kahshe River Stream GB DW 3 0 2014-GP090714-003A 2014 Kahshe River Stream GB DW 3 0 2014-GP100714-001A 2014 Hoaglands Marsh Stream GB ND 3 0 2014-GP100714-002A 2014 Severn River Stream GB ND 3 0 2014-GP110714-001A 2014 Kahshe River Stream GB DW 3 0 2014-GP140714-001A 2014 Buck Lake Lake GB ND 3 0 2014-GP140714-002A 2014 Buck Lake Lake GB ND 3 0 2014-GP150714-003A 2014 Bass Lake Lake GB DS 3 1 2014-GP150714-004A 2014 Bass Lake Lake GB DW 3 0 2014-GP150714-001A 2014 Gartersnake Creek Stream GB ND 3 0 2014-GP150714-002A 2014 Gartersnake Creek Stream GB ND 3 0 2014-GP160714-001A 2014 Kahshe Lake Lake GB DW 3 0 2014-GP160714-002A 2014 Kahshe Lake Lake GB DW 3 0 2014-GP160714-003A 2014 Kahshe Lake Lake GB DS 3 2 2014-GP170714-001A 2014 Kahshe Lake Lake GB DS 3 1 2014-GP170714-002A 2014 Kahshe Lake Lake GB DW 3 0 2014-GP170714-005A 2014 Kahshe Lake Lake GB DW 3 0 2014-GP170714-003A 2014 Kahshe River Stream GB DS 3 1 2014-GP170714-004A 2014 Kahshe River Stream GB DW 3 0 2014-GP180714-001A 2014 Grass Lake Lake GB DS 3 2 2014-GP180714-002A 2014 Sparrow Lake Lake GB ND 3 0 2014-GP120814-001A 2014 Twenty Mile Creek Stream LOW DS 3 4 2014-GP120814-002A 2014 Twenty Mile Creek Stream LOW DW 3 0 2014-GP120814-003A 2014 Twenty Mile Creek Stream LOW DS 2 1 2014-GP130814-001A 2014 Twenty Mile Creek Stream LOW DS 2 2 88

2014-GP130814-002A 2014 Twenty Mile Creek Stream LOW DW 3 0 2014-GP140814-001A 2014 Twenty Mile Creek Stream LOW DS 3 3 2014-GP140814-002A 2014 Twenty Mile Creek Stream LOW DS 3 1 2014-GP140814-003A 2014 Twenty Mile Creek Stream LOW DS 3 7 2014-GP140814-004A 2014 Twenty Mile Creek Stream LOW DS 3 6 2014-GP150814-001A 2014 Twenty Mile Creek Stream LOW DW 3 0 2014-GP150814-002A 2014 Twenty Mile Creek Stream LOW DS 3 1 2014-GP150814-003A 2014 Twenty Mile Creek Stream LOW DW 3 0 2014-GP180814-001A 2014 Twenty Mile Creek Stream LOW DS 3 2 2014-GP180814-002A 2014 Twenty Mile Creek Stream LOW DS 3 1 2014-GP180814-003A 2014 Twenty Mile Creek Stream LOW DS 3 1 2014-GP180814-004A 2014 Welland River Stream LOW DW 3 0 2014-GP190814-001A 2014 Welland River Stream LOW DW 3 0 2014-GP190814-002A 2014 Welland River Stream LOW DS 3 1 2014-GP190814-003A 2014 Welland River Stream LOW DW 3 0 2014-GP190814-004A 2014 Welland River Stream LOW DW 3 0 2014-GP200814-004A 2014 Twenty Mile Creek Stream LOW DW 3 0 2014-GP200814-001A 2014 Welland River Stream LOW DW 3 0 2014-GP200814-002A 2014 Welland River Stream LOW DS 3 2 2014-GP200814-003A 2014 Welland River Stream LOW DS 3 1 2014-GP210814-001A 2014 Twenty Mile Creek Stream LOW DS 3 2 2014-GP210814-002A 2014 Welland River Stream LOW DW 3 0 2014-GP210814-003A 2014 Welland River Stream LOW DW 3 0 2014-GP210814-004A 2014 Welland River Stream LOW DW 3 0 Michael Henry 2014-GP290514-001A 2014 Creek Stream StL ND 3 0 Michael Henry 2014-GP290514-002A 2014 Creek Stream StL ND 3 0 2014-GP300514-001A 2014 Leeders Creek Stream StL DW 3 0 2014-GP040614-001A 2014 Jones Creek Stream StL DW 3 0 2014-GP040614-002A 2014 Jones Creek Stream StL DS 3 1 2014-GP050614-001A 2014 Jones Creek Stream StL DS 3 1 2014-GP050614-002A 2014 Jones Creek Stream StL DW 3 0 2014-GP060614-001A 2014 Lambs Pond Stream StL ND 3 0 North Wiltse 2014-GP060614-002A 2014 Creek Stream StL ND 3 0 2014-GP100614-001A 2014 Jones Creek Stream StL DW 3 0 2014-GP100614-002A 2014 Jones Creek Stream StL DW 3 0 2014-GP110614-001A 2014 Leeders Creek Stream StL DW 3 0 2014-GP300614-001A 2014 Gananoque River Stream StL ND 3 0 2014-GP300614-002A 2014 Grippen Creek Stream StL ND 3 0 2014-GP020714-001A 2014 Gananoque River Stream StL ND 3 0 2014-GP030714-001A 2014 Gananoque River Stream StL ND 3 0 2014-GP030714-002A 2014 Gananoque River Stream StL ND 3 0

2014-GP220714-001A 2014 Jones Creek Stream StL DS 3 1 2014-GP220714-002A 2014 Jones Creek Stream StL DW 3 0 2014-GP240714-003A 2014 Elbe Creek Stream StL ND 3 0 2014-GP240714-001A 2014 Jones Creek Stream StL DW 3 0 2014-GP240714-002A 2014 Jones Creek Stream StL DW 3 0 2014-GP280714-001A 2014 Leeders Creek Stream StL DS 3 1 2014-GP010814-001A 2014 Jones Creek Stream StL DW 3 0 2014-GP010814-002A 2014 Jones Creek Stream StL DW 3 0 2014-GP010814-003A 2014 Jones Creek Stream StL DW 3 0 2014-GP050814-001A 2014 Temperance lake Lake StL ND 3 0 2014-GP050814-002A 2014 Temperance lake Lake StL ND 3 0 2014-GP080814-001A 2014 Graham Lake Lake StL DS 3 1 2014-GP080814-002A 2014 Graham Lake Lake StL DS 3 2 2014-GP080814-003A 2014 Graham Lake Lake StL DW 3 0 2014-GP280814-001A 2014 Jones Creek Stream StL DW 2 0 Michael Henry 2014-GP280814-002A 2014 Creek Stream StL ND 3 0

Water.Tem Conductivit Secchi. Stream.Widt Bank.Slop Channel.Cov Stream.dept Field.Number p y m h e er h 2013-GP-ND020713- 001A 18.21 488 0.276 12 5 45 1.05 2013-GP-ND020713- 002A 18.66 408 0.46 11.5 5 30 1.25 2013-GP-ND050713- 001A 21.42 515 0.34 12.5 2 50 0.88 2013-GP-ND060813- 001A 17.15 1285 0.128 11 2 0 1.28 2013-GP-ND090813- 001A 18.69 1145 0.556 6 2 5 0.94 2013-GP-ND090813- 002A 19.91 1081 0.07 11 75 0 0.77 2013-GP-ND090813- 003A 23.96 1350 0.7 10.5 2 20 0.97 2013-GP-ND140513- 001A 9.17 1939 0.43 10 5 15 0.9 2013-GP-ND160513- 001A 14.24 978 0.47 10.5 5 50 0.82 2013-GP-ND160513- 002A 13.1 998 0.23 9 5 10 0.64 2013-GP-ND230513- 001A 19 1076 0.7 12 30 60 1.2 2013-GP-ND230513- 002A 19.97 1194 0.37 10 45 40 0.7 2013-GP-ND230513- 003A 22.54 1287 0.48 12.5 5 70 0.84 2013-GP-ND240513- 001A 14.4 1005 0.33 12.7 5 70 0.74 2013-GP-ND240613- 001A 21.27 8.38 0.21 10 5 75 0.93 2013-GP-ND250613- 001A 22.09 1017 0.26 8 45 50 0.85 2013-GP-ND250613- 002A 20.63 1006 0.36 8 5 35 0.97 2013-GP-ND250613- 003A 21.4 574 0.35 13 45 70 0.8 2013-GP-ND260613- 001A 21.03 533 0.12 8.5 5 10 0.75 2013-GP-ND260613- 003A 22 2019 0.47 10 2 20 0.96 2013-GP-ND270513- 001A 13.49 745 0.14 11 20 40 0.76 2013-GP-ND270513- 002A 14.08 730 0.22 13 20 20 0.78 2013-GP-ND270513- 003A 16.25 7.57 0.38 11 30 80 0.79 2013-GP-ND270513- 004A 21.11 725 0.05 10 20 40 0.31 2013-GP-ND270613- 001A 19.82 806 0.268 11 5 20 1.1 2013-GP-ND280513- 001A 13.5 927 3.9 6 40 0 0.76 2013-GP-ND280513- 002A 13.3 1249 0.06 11 2 0 1.2

2013-GP-ND300513- 001A 17.6 341 0.118 11 5 20 0.98 2013-GP-ND300513- 002A 19.07 544 0.367 8.5 5 5 0.91 2013-GP-ND310513- 001A 18.56 864 0.405 10 5 5 0.74 2013-GP-ND310513- 002A 22.75 474 0.194 13 5 70 1 2013-GP-TMC070813- 001A 18.99 674 0.22 8 2 5 0.49 2013-GP-TMC070813- 002A 19.96 650 0.212 17.5 30 10 0.76 2013-GP-TMC070813- 003A 20.38 635 0.7 13.5 40 10 0.75 2013-GP-TMC080813- 001A 21.83 693 0.266 19.5 40 5 0.72 2013-GP-TMC080813- 002A 21.17 899 0.462 23.5 5 5 0.89 2013-GP-TMC080813- 003A 21.55 698 0.7 10 10 20 0.57 2013-GP-TMC290813- 001A 22.77 791 0.242 18.1 20 60 1.21 2013-GP-TMC290813- 001B 23.16 715.8 0.33 24.5 30 5 0.92 2013-GP-TMC290813- 002A 26.87 983.1 0.204 17.1 50 50 0.21 2013-GP-TMC290813- 002B 26.87 983.1 0.21 32.5 60 0 0.29 2013-GP-JC160713- 001A 24.8 299 0.26 12 50 0 1.62 2013-GP-JC160713- 001B 24.8 144 0.126 7 60 0 1.14 2013-GP-JC160713- 002A 32.9 215 0.05 23 2 5 1.52 2013-GP-JC160713- 002B 26.08 306 0.13 4 0 0 1.15 2013-GP-JC160713- 003B 26.33 475 0.451 15 2 5 0.78 2013-GP-JC180713- 001A 25.55 503 0.872 8 10 0 0.85 2013-GP-JC180713- 002A 27.01 314 0.852 12 2 0 0.89 2013-GP-JC170713- 001A 25.35 487 0.57 11.5 35 20 0.64 2013-GP-JC170713- 002A 26.64 511 0.541 13.5 5 0 0.93 2014-GP050714-001A 23.2 22 0.41 17.5 15 5 1.13 2014-GP060714-001A 18.3 35 0.6 7.5 5 40 1 2014-GP060714-003A 22 611 0.98 4 50 5 1.05 2014-GP060714-002A 24 34 1 11 40 5 1.37 2014-GP090714-003A 22.3 22 0.38 4 95 15 1.34 2014-GP100714-001A 22.1 24 0.42 50 0 15 1.24 2014-GP100714-002A 23.5 184 0.82 22 2 5 1.1 2014-GP110714-001A 21.6 28 0.87 9 5 15 1.26 2014-GP140714-001A 28 30 0.62 100 5 5 1.1 92

2014-GP140714-002A 26.7 23 0.725 100 5 15 1.05 2014-GP150714-003A 25 25 0.44 100 5 20 1 2014-GP150714-004A 23.1 28 0.37 100 10 5 1.05 2014-GP150714-001A 20.7 27 0.7 6 5 15 1 2014-GP150714-002A 21.6 25 0.7 11 5 5 1 2014-GP160714-001A 22 28 0.56 100 5 20 1.05 2014-GP160714-002A 22.3 23 0.56 100 20 30 1.16 2014-GP160714-003A 21.7 27 0.54 100 40 30 1.17 2014-GP170714-001A 22.3 25 0.97 100 50 5 0.55 2014-GP170714-002A 25.5 21 1.1 100 10 5 1.12 2014-GP170714-005A 26.4 23 0.83 100 20 5 0.7 2014-GP170714-003A 26.2 22 0.82 25 5 15 1.1 2014-GP170714-004A 25.4 21 0.45 40 5 20 1.05 2014-GP180714-001A 25.6 280 0.84 15 5 25 1.05 2014-GP180714-002A 27.8 197 0.97 100 5 5 1.15 2014-GP120814-001A 20.4 710 0.41 8 20 10 0.75 2014-GP120814-002A 23.6 710 0.26 10 50 15 1.05 2014-GP120814-003A 24.2 685 0.28 10 50 20 1.1 2014-GP130814-001A 19.7 715 0.59 11 40 15 0.95 2014-GP130814-002A 21.8 700 0.51 14 30 35 1.1 2014-GP140814-001A 16.9 705 0.62 8 70 50 0.66 2014-GP140814-002A 17.7 702 0.52 7 45 20 1.05 2014-GP140814-003A 16.7 655 0.77 5 40 15 1.1 2014-GP140814-004A 18.3 975 0.7 9 60 10 1.05 2014-GP150814-001A 17.7 885 0.23 13 30 10 0.35 2014-GP150814-002A 20.5 865 0.7 18 30 5 0.3 2014-GP150814-003A 26.8 607 0.28 30 40 5 1 2014-GP180814-001A 18.7 864 0.19 12 60 20 1.1 2014-GP180814-002A 19.6 976 0.285 18 65 5 1.15 2014-GP180814-003A 19.7 1265 0.45 11 40 10 0.4 2014-GP180814-004A 25.8 675 0.1 20 70 5 0.8 2014-GP190814-001A 17.7 425 0.17 11 30 15 0.8 2014-GP190814-002A 21.4 425 0.11 15 80 20 1.05 2014-GP190814-003A 23.1 910 0.16 7 50 40 0.9 2014-GP190814-004A 24.4 760 0.48 10 60 15 0.35 2014-GP200814-004A 22.9 930 0.7 6 30 5 0.35 2014-GP200814-001A 22.1 789 0.23 13 20 5 0.4 2014-GP200814-002A 22.9 382 0.7 10 50 5 1.05 2014-GP200814-003A 23.3 527 0.13 25 90 5 1.05 2014-GP210814-001A 20.1 775 0.32 8 40 15 1.1 2014-GP210814-002A 23.9 1131 0.22 10 40 15 0.9 2014-GP210814-003A 23.8 745 0.7 10 40 10 1 2014-GP210814-004A 27.3 840 0.24 17 60 10 1.05 93

2014-GP290514-001A 15.2 332 1.3 12 40 10 0.89 2014-GP290514-002A 15.3 341 1.3 7 5 5 1.1 2014-GP300514-001A 19.4 252 1.3 9.6 10 0 1 2014-GP040614-001A 21.5 288 0.92 20.7 5 0 0.61 2014-GP040614-002A 22.8 220 0.53 9.7 5 0 1 2014-GP050614-001A 18.7 239 1.3 27 0 0 0.95 2014-GP050614-002A 17.8 243 1.3 5 0 0 1 2014-GP060614-001A 17.9 430 1.3 4 20 10 0.84 2014-GP060614-002A 20.3 388 1.3 13.6 5 40 0.84 2014-GP100614-001A 23 204 1.3 15 100 10 1.05 2014-GP100614-002A 27.6 196 0.57 8.7 30 30 0.66 2014-GP110614-001A 22.7 287 0.65 45 0 0 0.64 2014-GP300614-001A 26.6 254 0.43 40 10 5 0.7 2014-GP300614-002A 25.3 353 1.3 15.9 10 0 1 2014-GP020714-001A 27.1 230 0.25 100 20 15 0.54 2014-GP030714-001A 25.5 257 1.3 18 20 15 1.22 2014-GP030714-002A 26 252 0.29 35 5 10 0.85 2014-GP220714-001A 27.7 282 0.95 30 5 5 1.15 2014-GP220714-002A 26.2 246 0.9 20 5 0 1.1 2014-GP240714-003A 24.5 430 1.3 10 10 60 1 2014-GP240714-001A 24.1 233 0.24 17 5 10 1.35 2014-GP240714-002A 23.6 218 0.24 7 5 5 1.15 2014-GP280714-001A 19.4 252 1.3 9 20 0 1.17 2014-GP010814-001A 19.7 207 0.64 10 5 5 1.3 2014-GP010814-002A 25.1 199 0.635 80 5 10 0.75 2014-GP010814-003A 26.6 208 0.64 50 5 0 0.86 2014-GP050814-001A 24.4 158 0.5 100 10 0 1.05 2014-GP050814-002A 27.3 141 0.49 100 5 0 1.1 2014-GP080814-001A 26.7 150 0.38 100 5 10 0.55 2014-GP080814-002A 26.7 193 0.39 100 5 0 1.2 2014-GP080814-003A 26.4 160 0.49 100 90 40 1.2 2014-GP280814-001A 21 254 1.01 13 20 5 1 2014-GP280814-002A 21 488 1.3 12 80 15 1

Organi Grave Cobbl Boulde Bedroc Rubbl Concret Field.Number c Clay Silt Sand l e r k e e 2013-GP-ND020713-001A 80 10 10 0 0 0 0 0 0 0 2013-GP-ND020713-002A 5 70 0 0 15 0 0 0 10 0 2013-GP-ND050713-001A 10 25 15 0 50 0 0 0 0 0 2013-GP-ND060813-001A 60 20 0 0 0 20 0 0 0 0 2013-GP-ND090813-001A 0 40 10 0 50 0 0 0 0 0 2013-GP-ND090813-002A 15 60 0 0 25 0 0 0 0 0 2013-GP-ND090813-003A 15 70 0 0 0 15 0 0 0 0 2013-GP-ND140513-001A 31 33 33 0 3 0 0 0 0 0 2013-GP-ND160513-001A 15 75 10 0 0 0 0 0 0 0 2013-GP-ND160513-002A 0 70 10 0 0 0 0 0 10 10 2013-GP-ND230513-001A 30 20 30 0 20 0 0 0 0 0 2013-GP-ND230513-002A 0 30 0 20 20 10 0 0 20 0 2013-GP-ND230513-003A 15 55 15 0 0 15 0 0 0 0 2013-GP-ND240513-001A 10 70 10 0 10 0 0 0 0 0 2013-GP-ND240613-001A 10 50 40 0 0 0 0 0 0 0 2013-GP-ND250613-001A 10 55 20 0 0 15 0 0 0 0 2013-GP-ND250613-002A 0 50 10 0 35 0 0 0 5 0 2013-GP-ND250613-003A 10 40 0 0 50 0 0 0 0 0 2013-GP-ND260613-001A 10 40 20 0 0 0 0 0 10 20 2013-GP-ND260613-003A 30 0 70 0 0 0 0 0 0 0 2013-GP-ND270513-001A 10 90 0 0 0 0 0 0 0 0 2013-GP-ND270513-002A 10 70 0 0 0 0 0 20 0 0 2013-GP-ND270513-003A 15 65 0 0 0 0 0 20 0 0 2013-GP-ND270513-004A 30 40 10 0 0 0 0 20 0 0 2013-GP-ND270613-001A 10 80 10 0 0 0 0 0 0 0 2013-GP-ND280513-001A 20 50 0 10 20 0 0 0 0 0 2013-GP-ND280513-002A 30 30 20 0 0 20 0 0 0 0 2013-GP-ND300513-001A 10 60 10 0 0 0 0 0 5 15 2013-GP-ND300513-002A 5 65 10 0 20 0 0 0 0 0 2013-GP-ND310513-001A 25 40 15 0 20 0 0 0 0 0 2013-GP-ND310513-002A 10 60 20 0 10 0 0 0 0 0 2013-GP-TMC070813-001A 30 70 0 0 0 0 0 0 0 0 2013-GP-TMC070813-002A 0 0 30 0 70 0 0 0 0 0 2013-GP-TMC070813-003A 30 30 40 0 0 0 0 0 0 0 2013-GP-TMC080813-001A 0 0 30 0 40 30 0 0 0 0 2013-GP-TMC080813-002A 0 100 0 0 0 0 0 0 0 0 2013-GP-TMC080813-003A 50 20 20 0 0 10 0 0 0 0 2013-GP-TMC290813-001A 60 10 20 0 10 0 0 0 0 0 2013-GP-TMC290813-001B 50 20 30 0 0 0 0 0 0 0 2013-GP-TMC290813-002A 0 10 0 0 40 50 0 0 0 0 2013-GP-TMC290813-002B 20 20 40 0 0 10 10 0 0 0 95

2013-GP-JC160713-001A 60 0 0 30 0 0 10 0 0 0 2013-GP-JC160713-001B 90 0 0 0 0 0 10 0 0 0 2013-GP-JC160713-002A 90 0 0 0 0 0 10 0 0 0 2013-GP-JC160713-002B 100 0 0 0 0 0 0 0 0 0 2013-GP-JC160713-003B 80 0 10 0 0 0 5 5 0 0 2013-GP-JC180713-001A 40 0 40 0 20 0 0 0 0 0 2013-GP-JC180713-002A 40 60 0 0 0 0 0 0 0 0 2013-GP-JC170713-001A 10 30 0 40 0 20 0 0 0 0 2013-GP-JC170713-002A 15 25 25 25 0 10 0 0 0 0 2014-GP050714-001A 25 75 0 0 0 0 0 0 0 0 2014-GP060714-001A 60 40 0 0 0 0 0 0 0 0 2014-GP060714-003A 40 40 10 0 10 0 0 0 0 0 2014-GP060714-002A 0 40 0 0 0 50 10 0 0 0 2014-GP090714-003A 20 80 0 0 0 0 0 0 0 0 2014-GP100714-001A 5 0 0 5 15 10 10 50 5 0 2014-GP100714-002A 20 70 10 0 0 0 0 0 0 0 2014-GP110714-001A 0 100 0 0 0 0 0 0 0 0 2014-GP140714-001A 20 80 0 0 0 0 0 0 0 0 2014-GP140714-002A 30 70 0 0 0 0 0 0 0 0 2014-GP150714-003A 60 30 10 0 0 0 0 0 0 0 2014-GP150714-004A 20 80 0 0 0 0 0 0 0 0 2014-GP150714-001A 30 50 20 0 0 0 0 0 0 0 2014-GP150714-002A 30 50 20 0 0 0 0 0 0 0 2014-GP160714-001A 30 70 0 0 0 0 0 0 0 0 2014-GP160714-002A 40 40 0 0 0 0 0 20 0 0 2014-GP160714-003A 60 40 0 0 0 0 0 0 0 0 2014-GP170714-001A 50 30 20 0 0 0 0 0 0 0 2014-GP170714-002A 10 90 0 0 0 0 0 0 0 0 2014-GP170714-005A 10 90 0 0 0 0 0 0 0 0 2014-GP170714-003A 60 40 0 0 0 0 0 0 0 0 2014-GP170714-004A 50 50 0 0 0 0 0 0 0 0 2014-GP180714-001A 10 80 10 0 0 0 0 0 0 0 2014-GP180714-002A 10 60 20 10 0 0 0 0 0 0 2014-GP120814-001A 50 50 0 0 0 0 0 0 0 0 2014-GP120814-002A 10 50 20 0 20 0 0 0 0 0 2014-GP120814-003A 20 60 10 0 0 10 0 0 0 0 2014-GP130814-001A 10 70 10 0 0 10 0 0 0 0 2014-GP130814-002A 10 50 10 0 0 20 10 0 0 0 2014-GP140814-001A 0 90 0 0 0 10 0 0 0 0 2014-GP140814-002A 20 55 10 0 0 15 0 0 0 0 2014-GP140814-003A 20 60 10 0 0 10 0 0 0 0 2014-GP140814-004A 15 45 10 0 15 10 5 0 0 0 2014-GP150814-001A 0 20 0 0 0 20 0 60 0 0 96

2014-GP150814-002A 0 20 0 0 0 20 5 55 0 0 2014-GP150814-003A 20 50 0 0 0 10 0 20 0 0 2014-GP180814-001A 10 60 0 0 0 30 0 0 0 0 2014-GP180814-002A 20 55 10 0 0 15 0 0 0 0 2014-GP180814-003A 10 20 0 0 0 20 10 40 0 0 2014-GP180814-004A 10 60 10 0 0 20 0 0 0 0 2014-GP190814-001A 10 50 0 0 25 10 5 0 0 0 2014-GP190814-002A 20 30 10 0 30 5 5 0 0 0 2014-GP190814-003A 10 55 10 0 20 5 0 0 0 0 2014-GP190814-004A 10 55 10 0 0 25 0 0 0 0 2014-GP200814-004A 0 10 15 0 10 55 10 0 0 0 2014-GP200814-001A 0 20 0 0 0 20 0 60 0 0 2014-GP200814-002A 20 20 10 0 10 20 0 20 0 0 2014-GP200814-003A 45 45 0 0 0 0 10 0 0 0 2014-GP210814-001A 20 50 10 0 10 10 0 0 0 0 2014-GP210814-002A 20 70 10 0 0 0 0 0 0 0 2014-GP210814-003A 10 80 10 0 0 0 0 0 0 0 2014-GP210814-004A 20 50 10 0 10 10 0 0 0 0 2014-GP290514-001A 10 80 5 0 0 5 0 0 0 0 2014-GP290514-002A 20 80 0 0 0 0 0 0 0 0 2014-GP300514-001A 20 30 50 0 0 0 0 0 0 0 2014-GP040614-001A 40 60 0 0 0 0 0 0 0 0 2014-GP040614-002A 55 40 0 0 0 5 0 0 0 0 2014-GP050614-001A 100 0 0 0 0 0 0 0 0 0 2014-GP050614-002A 70 15 0 0 0 0 15 0 0 0 2014-GP060614-001A 40 40 20 0 0 0 0 0 0 0 2014-GP060614-002A 20 10 10 0 60 0 0 0 0 0 2014-GP100614-001A 100 0 0 0 0 0 0 0 0 0 2014-GP100614-002A 20 60 20 0 0 0 0 0 0 0 2014-GP110614-001A 40 30 30 0 0 0 0 0 0 0 2014-GP300614-001A 40 40 20 0 0 0 0 0 0 0 2014-GP300614-002A 20 70 10 0 0 0 0 0 0 0 2014-GP020714-001A 75 0 20 0 0 0 5 0 0 0 2014-GP030714-001A 0 0 10 0 15 45 30 0 0 0 2014-GP030714-002A 10 0 90 0 0 0 0 0 0 0 2014-GP220714-001A 20 10 65 0 0 0 5 0 0 0 2014-GP220714-002A 20 70 10 0 0 0 0 0 0 0 2014-GP240714-003A 50 45 0 0 0 0 5 0 0 0 2014-GP240714-001A 40 60 0 0 0 0 0 0 0 0 2014-GP240714-002A 0 45 0 0 25 25 5 0 0 0 2014-GP280714-001A 10 70 0 0 0 0 0 20 0 0 2014-GP010814-001A 50 50 0 0 0 0 0 0 0 0 2014-GP010814-002A 30 70 0 0 0 0 0 0 0 0 97

2014-GP010814-003A 60 40 0 0 0 0 0 0 0 0 2014-GP050814-001A 30 50 0 0 0 0 0 20 0 0 2014-GP050814-002A 60 40 0 0 0 0 0 0 0 0 2014-GP080814-001A 40 20 0 0 0 0 0 40 0 0 2014-GP080814-002A 50 30 20 0 0 0 0 0 0 0 2014-GP080814-003A 50 0 0 20 0 30 0 0 0 0 2014-GP280814-001A 100 0 0 0 0 0 0 0 0 0 2014-GP280814-002A 0 10 0 30 0 30 10 20 0 0

sh Emerg Decid Conifero Herbace ru Non Field.Number ent Floating Submerged Open.Water uous us ous bs e 2013-GP-ND020713-001A 20 0 10 70 80 0 20 0 0 2013-GP-ND020713-002A 30 0 30 40 20 0 50 30 0 2013-GP-ND050713-001A 25 0 10 65 60 0 30 10 0 2013-GP-ND060813-001A 10 10 0 80 30 0 60 5 10 2013-GP-ND090813-001A 20 0 15 65 15 0 75 10 0 2013-GP-ND090813-002A 15 5 0 80 10 0 90 0 0 2013-GP-ND090813-003A 15 10 0 75 60 0 25 15 0 2013-GP-ND140513-001A 15 0 5 80 30 0 40 30 0 2013-GP-ND160513-001A 30 0 45 25 30 0 60 10 0 2013-GP-ND160513-002A 5 0 15 80 30 0 40 30 0 2013-GP-ND230513-001A 20 0 5 75 25 0 60 5 10 2013-GP-ND230513-002A 10 0 30 60 10 0 0 70 20 2013-GP-ND230513-003A 20 5 50 30 35 0 35 20 10 2013-GP-ND240513-001A 25 0 15 60 45 0 25 20 10 2013-GP-ND240613-001A 45 0 30 25 30 0 60 10 0 2013-GP-ND250613-001A 30 0 10 60 80 0 20 0 0 2013-GP-ND250613-002A 20 0 10 70 50 0 15 35 0 2013-GP-ND250613-003A 10 0 10 80 80 0 20 0 0 2013-GP-ND260613-001A 5 0 10 85 10 0 80 10 0 2013-GP-ND260613-003A 15 5 10 70 35 0 30 30 5 2013-GP-ND270513-001A 10 0 0 90 50 0 50 0 0 2013-GP-ND270513-002A 5 5 0 90 50 0 50 0 0 2013-GP-ND270513-003A 15 15 0 70 50 0 50 0 0 2013-GP-ND270513-004A 15 0 45 40 50 0 50 0 0 2013-GP-ND270613-001A 20 0 20 60 10 0 80 10 0 2013-GP-ND280513-001A 5 0 20 75 10 0 60 0 30 2013-GP-ND280513-002A 20 0 0 80 15 0 80 5 0 2013-GP-ND300513-001A 20 0 10 70 80 0 20 0 0 2013-GP-ND300513-002A 20 0 30 50 10 0 70 10 10 2013-GP-ND310513-001A 10 0 30 60 30 0 25 30 15 2013-GP-ND310513-002A 10 0 10 80 80 0 10 10 0 2013-GP-TMC070813- 001A 20 20 60 0 0 0 100 0 0 2013-GP-TMC070813- 002A 20 10 0 70 30 0 70 0 0 2013-GP-TMC070813- 003A 10 30 30 30 45 0 20 35 0 2013-GP-TMC080813- 001A 5 15 50 30 30 0 60 10 0 2013-GP-TMC080813- 002A 5 30 45 20 60 0 20 20 0 2013-GP-TMC080813- 003A 0 10 20 70 80 0 20 0 0

2013-GP-TMC290813- 001A 30 30 10 30 70 0 30 0 0 2013-GP-TMC290813- 001B 5 10 15 70 10 0 10 80 0 2013-GP-TMC290813- 002A 0 0 85 15 40 0 60 0 0 2013-GP-TMC290813- 002B 5 0 30 65 40 0 30 30 0 2013-GP-JC160713-001A 20 0 0 80 0 0 90 10 0 2013-GP-JC160713-001B 10 5 50 35 40 0 40 20 0 2013-GP-JC160713-002A 5 15 50 30 20 0 80 0 0 2013-GP-JC160713-002B 0 0 100 0 30 60 10 0 0 2013-GP-JC160713-003B 10 0 0 90 40 10 50 0 0 2013-GP-JC180713-001A 15 5 70 10 0 0 100 0 0 2013-GP-JC180713-002A 20 5 45 30 10 0 80 0 10 2013-GP-JC170713-001A 15 0 10 75 85 0 5 10 0 2013-GP-JC170713-002A 70 0 0 30 20 0 50 30 0 2014-GP050714-001A 15 0 25 60 0 0 70 30 0 2014-GP060714-001A 0 10 25 65 5 0 15 80 0 2014-GP060714-003A 40 10 20 30 10 0 60 30 0 2014-GP060714-002A 0 15 25 60 30 20 50 0 0 2014-GP090714-003A 0 0 30 70 0 0 50 50 0 2014-GP100714-001A 0 5 20 75 5 10 5 5 0 2014-GP100714-002A 20 10 65 5 5 5 80 10 0 2014-GP110714-001A 15 0 10 75 15 10 70 5 0 2014-GP140714-001A 40 0 15 45 10 0 0 90 0 2014-GP140714-002A 10 40 10 40 35 5 15 45 0 2014-GP150714-003A 10 10 25 55 70 0 10 20 0 2014-GP150714-004A 5 30 40 25 15 70 15 0 0 2014-GP150714-001A 10 0 50 40 30 20 40 10 0 2014-GP150714-002A 5 5 70 20 15 15 60 10 0 2014-GP160714-001A 10 70 10 10 20 10 0 70 0 2014-GP160714-002A 0 90 5 5 10 85 5 0 0 2014-GP160714-003A 0 5 40 55 20 65 15 0 0 2014-GP170714-001A 15 15 15 55 20 5 30 45 0 2014-GP170714-002A 0 25 10 65 35 35 15 15 0 2014-GP170714-005A 0 50 0 50 5 0 35 40 0 2014-GP170714-003A 15 45 20 20 35 10 25 30 0 2014-GP170714-004A 15 30 5 50 50 0 25 25 0 2014-GP180714-001A 15 15 20 50 50 0 40 10 0 2014-GP180714-002A 20 0 70 10 50 0 30 20 0 2014-GP120814-001A 10 30 20 40 20 0 40 40 0 2014-GP120814-002A 0 10 20 70 60 0 30 10 0 2014-GP120814-003A 15 5 10 70 70 0 20 10 0 2014-GP130814-001A 10 0 40 50 40 0 40 10 0 100

2014-GP130814-002A 10 10 50 30 30 0 40 20 0 2014-GP140814-001A 15 5 0 80 30 10 60 0 0 2014-GP140814-002A 10 5 15 70 40 0 50 0 0 2014-GP140814-003A 10 5 30 55 30 0 60 0 0 2014-GP140814-004A 10 20 15 55 40 0 60 0 0 2014-GP150814-001A 10 0 0 90 40 0 30 30 0 2014-GP150814-002A 5 0 20 75 50 0 50 0 0 2014-GP150814-003A 10 25 50 15 40 0 50 10 0 2014-GP180814-001A 10 10 20 60 35 0 60 5 0 2014-GP180814-002A 10 15 55 20 40 0 30 30 0 2014-GP180814-003A 10 5 10 75 40 0 50 10 0 2014-GP180814-004A 0 0 25 75 30 0 40 30 0 2014-GP190814-001A 15 0 25 60 70 0 20 10 0 2014-GP190814-002A 5 0 65 30 30 0 40 30 0 2014-GP190814-003A 10 5 5 80 50 0 40 10 0 2014-GP190814-004A 10 0 55 35 35 0 35 30 0 2014-GP200814-004A 10 10 10 70 10 0 70 20 0 2014-GP200814-001A 10 0 15 75 30 0 50 20 0 2014-GP200814-002A 15 0 75 10 10 0 65 15 0 2014-GP200814-003A 5 20 25 50 20 0 70 10 0 2014-GP210814-001A 25 5 15 55 40 0 40 10 0 2014-GP210814-002A 35 0 0 65 50 0 50 0 0 2014-GP210814-003A 10 20 40 30 20 0 50 20 0 2014-GP210814-004A 10 20 0 70 30 0 60 10 0 2014-GP290514-001A 10 0 30 60 50 0 20 30 0 2014-GP290514-002A 30 0 0 70 0 0 95 5 0 2014-GP300514-001A 10 5 80 5 0 0 50 50 0 2014-GP040614-001A 5 5 85 5 55 5 15 20 0 2014-GP040614-002A 0 5 90 5 40 35 25 0 0 2014-GP050614-001A 15 0 55 30 10 0 90 0 0 2014-GP050614-002A 10 0 70 20 25 0 50 25 0 2014-GP060614-001A 10 0 60 30 0 0 35 65 0 2014-GP060614-002A 10 15 65 10 40 0 30 30 0 2014-GP100614-001A 10 30 55 5 30 0 35 30 0 2014-GP100614-002A 5 5 10 80 20 0 70 10 0 2014-GP110614-001A 10 5 80 5 25 15 50 10 0 2014-GP300614-001A 10 30 50 10 70 0 20 10 0 2014-GP300614-002A 0 10 90 0 10 0 50 40 0 2014-GP020714-001A 0 50 50 0 25 25 40 10 0 2014-GP030714-001A 0 5 55 40 35 50 15 0 0 2014-GP030714-002A 0 15 40 45 20 0 80 0 0 2014-GP220714-001A 15 20 65 0 40 0 40 10 0 2014-GP220714-002A 5 5 80 10 50 20 20 10 0 101

2014-GP240714-003A 5 0 45 50 80 0 10 0 0 2014-GP240714-001A 5 5 90 0 10 0 80 10 0 2014-GP240714-002A 10 20 70 0 20 0 75 5 0 2014-GP280714-001A 5 5 90 0 10 0 50 20 0 2014-GP010814-001A 5 15 70 10 90 0 10 0 0 2014-GP010814-002A 5 25 60 10 60 0 40 0 0 2014-GP010814-003A 15 20 35 30 50 20 20 10 0 2014-GP050814-001A 10 20 70 0 10 0 20 0 0 2014-GP050814-002A 95 5 0 0 20 60 10 0 0 2014-GP080814-001A 0 25 60 15 40 0 0 60 0 2014-GP080814-002A 15 10 75 0 40 0 60 0 0 2014-GP080814-003A 0 0 80 20 80 0 20 0 0 2014-GP280814-001A 30 20 0 50 20 10 40 10 0 2014-GP280814-002A 15 0 5 80 10 0 10 80 0

102