defence committee, and collaborators who provided me with data, assisted me with ...... threatened systems on Earth (Strayer and Dudgeon 2010; Carpenter et al. ...... correspondence analysis and was 5.97 SD (range 5.12 – 6.93), similar to the ...... Schindler, D.W., Beaty, K.G., Fee, E.J., Cruikshank, D.R., DeBruyn, E.R., ...
Quantifying the Resistance and Resilience of Freshwater Ecosystems to Anthropogenic Disturbance
by
Karl Andrew Lamothe
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Ecology and Evolutionary Biology University of Toronto
© Copyright by Karl A. Lamothe 2017
Quantifying the Resistance and Resilience of Freshwater Ecosystems to Anthropogenic Disturbance Karl Andrew Lamothe Doctor of Philosophy Ecology and Evolutionary Biology University of Toronto 2017
Abstract As many as 2 million lakes are estimated to be in Canada that provide beneficial ecosystem services to society such as clean drinking water and freshwater fisheries. However, anthropogenic disturbances on the landscape threaten the delivery of these services and pose questions regarding the maintenance of lake systems to future change. Understanding and quantifying the resistance and resilience of lake systems to disturbance is therefore a priority. The objectives of this thesis are to: 1) develop a quantitative framework for characterizing the resistance and resilience of ecosystems to disturbance; and, 2) quantify the relative resistance and resilience of freshwater lakes in Ontario to anthropogenic disturbance. I begin with a simulation study to demonstrate how distance-based measures in ordination space can provide a framework for characterizing the relative resistance and resilience of systems to disturbance. I then apply the distance-based approach to long-term monitoring data of crustacean zooplankton communities and associated water chemistry data from 19 lakes in Ontario subjected to varying levels of acidification. I show that most zooplankton communities lack resistance to change over time, whether affected by acidification or not, and that water chemistry is changing among all the lakes studied. Finally, I approach resilience from a functional diversity perspective and quantify the functional redundancy of Ontario lake fish communities across the province and relate these ii
patterns to biogeographic and environmental variables. My results demonstrate patterns of redundancy among freshwater fish communities provincially, however, these patterns varied regionally. Overall, this body of work provides a multidimensional approach for characterizing the resistance and resilience of freshwater ecosystems to anthropogenic disturbance that can be applied across systems (e.g., terrestrial, marine, freshwater) and scales (e.g., species, community, ecosystem).
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Acknowledgements Over the course of my post-secondary education, I have gained an immense support group that without, would have made completing my studies far more difficult. I first want to acknowledge my two Ph.D. supervisors at the University of Toronto, Don Jackson and Keith Somers. Don and Keith are experienced scientists, insightful mentors, and overall fun people to be around. I am extremely grateful for all the experiences they have shared and opportunities that they have provided, in which, have shaped the contents of this dissertation. I never imagined that I would have met so many scientists from across Canada and have all the experiences that I have had during my Ph.D., and I owe much of that to Don and Keith. Thank you. I would also like to thank members of my Ph.D. committee, Ph.D. appraisal committee, thesis defence committee, and collaborators who provided me with data, assisted me with chapters of my thesis, or participated in collaborative efforts: Karen Alofs, Elena Bennett, Andrew Chin, Cindy Chu, Roland Cormier, Irena Creed, Dak de Kerckhove, Rick Dong, Marie-Josée Fortin, Bill Keller, Dave Kreutzweiser, Hernán López-Fernández, Nick Mandrak, Camille Ouellet Dallaire, Mike Paterson, Jim Rusak, Fiona Schmiegelow, Óscar Senar, Brian Shuter, Ira Sutherland, Sonja Teichert, Stephanie Tomscha, Lisa Venier, Amy Villamagna, Rolf Vinebrooke, and Alex Yeung. This extensive list of top-notch scientists challenged my ideas with insight and overall have helped to make my science better. I am very grateful for the Jackson lab and extended members, who taught me something new every Friday morning and often had to listen to me ramble on about resilience or simulations: Abby, Allie, Andrew, Brad, Brian, Brie, Bronwyn, Chris, Cindy, Daisuke, Dana x2, Darren, David, Gabby, Georgina, Henrique, Jenn, Jun, Karen, Keenan, Lifei, Liset, Lucy, Mateus, Ruben, Simone, Tessa, Xijie, and Xuefeng. Further, the group of graduate students in the Department of Ecology and Evolutionary Biology at the University of Toronto have been an absolute blast and not only made me a better scientist but made the process of getting a graduate degree one that I will not forget. I gained countless experiences and long-lasting relationships through the Natural Sciences and Engineering Research Council Canadian Network for Aquatic Ecosystem Services (NSERC CNAES) that made my Ph.D. experience very rewarding. Working closely with students, postdocs, university faculty, government research scientists, and private sector scientists from across the country was an absolute privilege. Specifically, the Highly-Qualified Personnel Committee iv
members (Abby, Andrew, Camille, Chris, Francesco, Gillian, Gretchen, Ira, Kristin, Matt, Nicole, Stephanie, Vanessa) and the Science Committee members (Brian B, Brian S, Dave, Don, Jenn, John G, John R, Lucinda, Paul, Pedro) have been supportive and taught me a lot over the last four years. I look forward to continuing with the collaborations gained through this experience. My BSc and MSc supervisors and mentors, Patricia Szczys, Philip Elliott, and Ron Johnson, helped me towards getting my Ph.D. and their continued support is truly valued. Finally, I have an incredible group of supporters in life outside academics that have always been there for me through the good times and the bad. This includes my mother, father, brother, grandmother, and many aunts, uncles, cousins, and friends. My partner Sarah, who experienced the day-to-day graduate school life with me, always provided me with support, and for that, I am truly grateful.
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Table of Contents Abstract ........................................................................................................................................... ii Acknowledgements ........................................................................................................................ iv Table of Contents ........................................................................................................................... vi List of Tables .................................................................................................................................. x List of Figures ................................................................................................................................ xi List of Appendices ........................................................................................................................ xv Chapter 1 General Introduction ...................................................................................................... 1 References ....................................................................................................................................... 8 Chapter 2 Utilizing Gradient Simulations for Quantifying Community-Level Resistance and Resilience ................................................................................................................................. 15 2.1 Abstract .............................................................................................................................. 15 2.2 Introduction ........................................................................................................................ 15 2.3 Materials and Methods ....................................................................................................... 19 2.3.1 Terminology .............................................................................................................. 20 2.3.2 Step 1 Simulation coenoplanes .................................................................................. 20 2.3.3 Step 2 Sampling communities from the simulated coenoplanes ............................... 21 2.3.4 Step 3 Multivariate analyses ...................................................................................... 21 2.3.5 Step 4 Distance-based metrics ................................................................................... 22 2.3.6 Step 5 Comparing distance-based metrics between reference and impacted communities .............................................................................................................. 23 2.3.7 Characterizing the resistance and resilience of zooplankton communities to an invasion ..................................................................................................................... 24 2.4 Results ................................................................................................................................ 25 2.4.1 Example of a single analysis containing 50 reference and 10 impacted communities ................................................................................................................... 25 vi
2.4.2 Trends across community sample sizes..................................................................... 28 2.4.3 Characterizing the resistance and resilience of zooplankton communities to an invasion ..................................................................................................................... 29 2.5 Discussion .......................................................................................................................... 30 2.6 Acknowledgements ............................................................................................................ 32 References ..................................................................................................................................... 33 Supplementary Tables and Figures ............................................................................................... 39 Copyright Acknowledgements...................................................................................................... 41 Chapter 3 Resistance and Resilience of Freshwater Zooplankton Communities in Ontario, Canada to Changing Environmental Conditions ...................................................................... 42 3.1 Abstract .............................................................................................................................. 42 3.2 Introduction ........................................................................................................................ 42 3.3 Materials and Methods ....................................................................................................... 45 3.3.1 Study systems ............................................................................................................ 45 3.3.2 Multivariate analyses ................................................................................................. 48 3.3.3 Distance-based metric analyses ................................................................................. 50 3.4 Results ................................................................................................................................ 52 3.4.1 Zooplankton and water chemistry ordinations .......................................................... 52 3.4.2 Reference-lake zooplankton communities ................................................................ 52 3.4.3 Zooplankton communities from atmospherically acidified lakes ............................. 53 3.4.4 Zooplankton communities subjected to experimental acidification .......................... 55 3.4.5 Reference lake water chemistry................................................................................. 56 3.4.6 Atmospherically acidified lake chemistry ................................................................. 58 3.4.7 Water chemistry for the experimentally acidified lakes ............................................ 59 3.4.8 Comparison between water chemistry and zooplankton communities ..................... 59 vii
3.5 Discussion .......................................................................................................................... 60 3.6 Acknowledgements ............................................................................................................ 66 References ..................................................................................................................................... 67 Supplementary Tables and Figures ............................................................................................... 76 Chapter 4 Patterns of Functional Redundancy among Fish Communities in Ontario Lakes ....... 95 4.1 Abstract .............................................................................................................................. 95 4.2 Introduction ........................................................................................................................ 96 4.3 Materials and Methods ..................................................................................................... 100 4.3.1 Data collection ......................................................................................................... 100 4.3.2 Functional traits ....................................................................................................... 101 4.3.3 Functional diversity analysis ................................................................................... 102 4.3.4 Environmental gradient analysis ............................................................................. 105 4.4 Results .............................................................................................................................. 106 4.4.1 Observed trends from sampling............................................................................... 106 4.4.2 Functional trait space for provincial and regional pools ......................................... 107 4.4.3 Provincial functional metric analysis ...................................................................... 108 4.4.4 Regional functional metric analysis ........................................................................ 109 4.4.5 Environmental and geographic gradients ................................................................ 110 4.4.6 Species-level functional diversity metric analysis .................................................. 112 4.5 Discussion ........................................................................................................................ 113 4.6 Acknowledgements .......................................................................................................... 117 References ................................................................................................................................... 118 Supplementary Tables and Figures ............................................................................................. 129 Chapter 5 General Conclusion .................................................................................................... 145 viii
References ................................................................................................................................... 149 Appendix 1. Results across community sample sizes ................................................................. 151 Appendix 2. Description of minimally disturbed IISD-ELA reference lakes and taxonomic nomenclature .............................................................................................................................. 154 Appendix 3. Provincial fish community analysis ....................................................................... 157 Appendix 4. Northwestern fish community analysis .................................................................. 162 Appendix 5. Southeastern fish community analysis ................................................................... 167 Appendix 6. Northeastern fish community analysis ................................................................... 172
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List of Tables Table 2-1. Examples of techniques/surrogates used for quantifying resilience ............................ 17 Table 3-1. Lake region, surface area (A0, in ha), mean depth (Zmean, in m), maximum depth (Zmax, in m), pH range (minimum to maximum), and years when zooplankton were sampled for reference and impacted lakes .................................................................................................. 46 Table 3-2. Characterization of study lakes to the hypothetical scenarios presented by Matthews et al. (2013) .................................................................................................................. 55 Table 4-1. GAMs of functional diversity (FDis or FRic) versus species richness (R) residuals with maximum depth (Zmax), growing degree days (GDD), total dissolved solids (TDS), and surface area (SA). Models performed on scaled and centered variables. Significant coefficients (p < 0.05) indicated in bold................................................................... 111
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List of Figures Figure 1-1. Ball-in-cup conceptualizations of resilience. The circles represent a theoretical current ecosystem state. Dashed lines represent critical thresholds. Resilience is depicted with arrows as a combination of three properties: latitude (L), resistance (R), and precariousness (Pr; Walker et al. 2004). a) A resilient landscape and b) a hypothetical landscape subjected to environmental stressors .............................................................................. 2 Figure 2-1. A coenoplane showing the abundance relationships for 50 species along two orthogonal gradients (e.g., environmental gradients). Each numbered point represents an individual observation, where species within a community vary in their abundance................... 17 Figure 2-2. a-f) Conceptual reconfigurations of gradual and saltatorial community trajectories over two environmental gradients (adapted from Matthews et al. 2013). a) The trajectory of a reference community undergoing non-directional gradual change in composition. b-f) Impacted communities showing gradual (b, c) and saltatorial (d-f) responses. Saltatorial responses are depicted as having greater community turnover than gradual responses. Periods of relative constancy in community composition are depicted with filled circles. g) Space-fortime sampling approach where each trajectory represents a single community ........................... 19 Figure 2-3. a) Sampling coordinates from the coenoplane for the 60 communities within the single ordination. Point shapes refer to inset hypothetical trajectories. Lines connecting impacted community sampling coordinates show their individual temporal trajectories. One community undergoing saltatory directional change with recovery highlighted in red (Figure 2-2f). b) PCA ordination plot showing site scores for sampled communities. c) An enlarged view of one impacted community undergoing saltatorial directional change with recovery and one reference community (Figure 2-2a). Observations are connected in order of sampling. d) Distances to baseline centroids (white shapes) over time for both the single impacted and reference community. e) Distances to cumulative centroids (white shapes) over time for both the impacted and reference community. f) Distance to baseline centroid over time for both communities. Dashed line indicates significant regime shift (p < 0.01). g) Distance to cumulative centroid over time for both communities ................................................ 26
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Figure 2-4. a) Examples of the Euclidean distances to a baseline centroid from the PCA over time for one ordination containing 50 reference and 10 impacted communities. Plotted are representative examples of each inset scenario. Symbols match with inset scenarios. Mean distances over time are plotted for the reference communities with 95% confidence intervals (gray ribbon). b) Examples of the Euclidean distances to a cumulative centroid over time for the same representative examples ................................................................................................. 27 Figure 2-5. a) Principal component analysis ordination plot of the 10 zooplankton communities. Highlighted in red is the trajectory for the Harp Lake zooplankton community. Distances to b) baseline (dBaseline) and c) cumulative centroids (dCumulative) over time for the Harp Lake zooplankton community (red) and reference communities (white). Mean distances over time are plotted for the reference communities with 95% confidence intervals (gray ribbon). A significant shift in dBaseline occurred in 1993 for the Harp Lake zooplankton community (p < 0.01) ............................................................................................... 30 Figure 3-1. Hypothetical scenarios of community change depicted as trajectories through ordination space (adapted from Matthews et al. 2013). Scenarios are based on the magnitude (gradual versus saltatory) and directionality of change (non-directional, directional, or directional with recovery) between repeated observations of communities over time ................. 44 Figure 3-2. Map of the study locations in Ontario, Canada. Sampling locations of individual lakes are shown in the insets ........................................................................................ 47 Figure 3-3. Multivariate distance measures for quantifying the relative resistance and resilience of communities to disturbance using ordinations. a) An example of a single site undergoing directional change is highlighted with the principal component axis scores for two components shown. b) Distances between sequential observations are calculated to provide a measure of the relative magnitude of change year to year. Outlier-detection methods are used to determine whether distances indicate saltatorial or gradual change over time. c) Distances to a baseline centroid (dBase) calculated as the mean of the first two site scores. The triangle symbol represents the centroid. Plotted over time, dBase provides a measure of directionality relative to historical conditions. d) A Euclidean distance matrix is calculated from the principal component axis scores and the distances are plotted against time steps. A significant positive relationship indicates directional change over time ................ 49 xii
Figure 3-4. Distances to baseline centroids over time for the 10 impacted lake zooplankton communities (filled points; top two rows) and nine reference communities (filled points; bottom two rows). Mean reference values plotted (open points) with the 90th percentile forming the upper limit of the reference distribution (gray ribbon) ............................................. 53 Figure 3-5. Regressions of Euclidean distances between zooplankton PCA site scores and time steps for the 10 impacted (filled points; top two rows) and nine reference communities (filled points; bottom two rows). Mean reference values plotted (open points) with the 90th percentile forming the upper limit of the reference distribution (light gray ribbon). Best-fit models (solid lines) provided with 95% confidence intervals (dark gray ribbon). LOWESS models represented by dashed lines often overlapped the best-fit model ..................................... 54 Figure 3-6. Distances to baseline centroids over time for the 10 impacted lakes (filled points; top two rows) and 9 reference lakes (filled points; bottom two rows) for water chemistry. Mean reference values plotted (open points) with the 90th percentile forming the upper limit of the reference distribution (gray ribbon) .................................................................................... 57 Figure 3-7. Regression models of Euclidean distances between PCA site scores of annual water chemistry data and time-steps for all known impacted (filled points; top two rows) and reference communities (filled points; bottom two rows). Mean reference values plotted (open points) with the 90th percentile forming the upper limit of the reference distribution (light gray ribbon). Best-fit models (solid lines) provided with 95% confidence intervals (dark gray ribbon). LOWESS models represented by dashed lines, often overlapping best-fit model............................................................................................................................................. 57 Figure 3-8. Vector residuals from the comparison of crustacean zooplankton community composition with lake chemistry for impacted lakes (top two rows) and reference lakes (bottom two rows). No bars indicate missing years of matching data .......................................... 60 Figure 4-1. a) Linear, b) saturating, and c) nonlinear relationships between functional and species diversity. Adapted from Micheli and Halpern (2005) and Micheli et al. (2014) ............. 98 Figure 4-2. Sampling sites included in this study (n = 6,977; all points). Coloured points indicate subsamples for geographic analyses; black = southeastern (n = 1,325), blue = northeastern (n = 1,541); red = northwestern (n = 1,541)........................................................... 101 xiii
Figure 4-3. The occurrence of species included in the study. Inset) Frequency distribution of species richness values in lake communities .......................................................................... 107 Figure 4-4. a) Functional dispersion and b) functional richness versus species richness. Points indicate 6,977 lake fish communities. The solid line indicates a LOWESS smoothing algorithm applied to the provincial community values. The dashed line represents the mean null community values. The 95% confidence interval for the null models is shaded in gray .... 109 Figure 4-5. Functional dispersion and b) functional richness regressed against species richness. Colours represent measures for the three regions: northwestern (red), northeastern (gray), and southeastern (blue). Ribbons reflect 95% confidence intervals of null models. Solid lines indicate regional LOWESS models. Dashed lines indicate mean values for weighted null models .................................................................................................................. 110 Figure 4-6. Species-level a-c) distinctiveness, d-f) uniqueness, and g-i) distances to provincial centroid across thermal preference groups (warm, warm/cool, cool, cool/cold, cold), log10 transformed average total length (TL; cm), and log10 transformed species occurrence. Functional measures represent scaled and centered values. No significant relationships or differences were observed ................................................................................. 113
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List of Appendices Appendix 1. Results across community sample sizes ................................................................. 151 Appendix 2. Description of minimally disturbed IISD-ELA reference lakes and taxonomic nomenclature .............................................................................................................................. 154 Appendix 3. Provincial regional species pool trait ordinations .................................................. 157 Appendix 4. Northwest regional species pool trait ordinations .................................................. 162 Appendix 5. Southeast regional species pool trait ordinations ................................................... 167 Appendix 6. Northeast regional species pool trait ordinations ................................................... 172
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Chapter 1 General Introduction Freshwater environments provide a wide array of ecosystem services that benefit society (Aylward et al. 2005), but are also some of the most threatened systems on our planet (Strayer and Dudgeon 2010; Carpenter et al. 2011; Collen et al. 2014) often because of the cumulative effects of anthropogenic disturbances (Schindler 2001; Schindler and Smol 2006; Jackson et al. 2016). Here, disturbance describes a situation where the effects of an environmental stressor or stressors results in a change in state of that system (Niemi et al. 1990). Stressors can be localized, such as nutrient pollution (Smith et al. 2006) or overexploitation of local resources (Strayer and Dudgeon 2010), or more regional or global in their scale, such as global climate change (Woodward et al. 2010) and acid precipitation (Schindler 1988). Understanding and predicting how freshwater ecosystems respond to disturbances is challenging as environmental stressors rarely occur in isolation (Turner 2010) and their effects can vary spatially, temporally, and in magnitude (Lake 2000). Furthermore, stressors tend to impact freshwater ecosystems nonrandomly (Giller et al. 2004), often causing declines or exclusion of particular niches (e.g., impacts of dams on diadromous species) or body lengths (e.g., fishing for large-bodied species). A term commonly used to describe how freshwater ecosystems respond to disturbance is ‘resilience’. ‘Resilience’ has a long history of study in ecology, with Holling’s (1973) seminal paper on resilience providing the most commonly cited definition (Angeler and Allen 2016). Holling (1973, p. 17) defined resilience as “a measure of the ability of [these] systems to absorb changes of state variables, driving variables, and parameters, and still persist.” Later termed ‘ecological resilience’ (Holling 1996), this definition incorporates complexity and the possibility of alternative stable states (Holling 1973, 1996; Walker et al. 2004). Alternatively, ‘engineering resilience’ takes a stable equilibrium view of ecosystems, where the return time to equilibrium post-disturbance is used to describe resilience (i.e., the rate of return; Pimm 1984). Being conceptually similar to the engineering definition and somewhat nested within the ecological definition, a resistance-resilience approach considers two properties of ecosystems, resistance and resilience. Resistance describes the ability to persist during a disturbance event and resilience describes the ability to recover following a disturbance event (Nimmo et al. 2015).
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Whereas ecological resilience is more often viewed at the ecosystem scale, the resistanceresilience framework can be applied at multiple biological levels (Nimmo et al. 2015). Resistance and resilience can be understood conceptually using a ball-in-cup diagram (Figure 11; Holling 1973; Scheffer et al. 2001; Folke et al. 2004; Walker et al. 2004). In this diagram, the ball represents the current ecosystem state, which is composed of all the biophysical structures and processes occurring in a system at a given space and time. The cups, or basins of attraction (Scheffer et al. 2012), represent all the possible states in which the ecosystem can persist. Natural variability among processes within the ecosystem allows the ball to roll around the cup and the cups are flexible in that environmental stressors can alter their shape, causing the cups to become more or less shallow (Figure 1-1b). Critical thresholds exist between each cup and when crossed can force a system into an alternative stable state characterized by abrupt changes in state variables (deYoung et al. 2008). This often leads to unknown, potentially undesirable long-term ecosystem states (Scheffer et al. 2009). Ecologists refer to systems that surpass a critical threshold as having undergone a regime shift (May 1977; Folke et al. 2004). Regime shifts have been demonstrated in freshwater ecosystems because of changes in water chemistry due to nutrient loading (Carpenter and Brock 2006) as well as from fish introductions (Batt et al. 2013). a.
b. L Pr R
Figure 1-1. Ball-in-cup conceptualizations of resilience. The circles represent a theoretical current ecosystem state. Dashed lines represent critical thresholds. Resilience is depicted with arrows as a combination of three properties: latitude (L), resistance (R), and precariousness (Pr; Walker et al. 2004). a) A resilient landscape and b) a hypothetical landscape subjected to environmental stressors. Researchers vary in their interpretation of the measurement of resistance and resilience using the ball-in-cup heuristic. Some studies have described resilience as the distance between stable states or thresholds (Gunderson 2000; van Nes and Scheffer 2007) and others describe it as a
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combination of the depth and width of the cup (Standish et al. 2014). Walker et al. (2004) described the resilience of ecosystems using the ball-in-cup conceptualization to be a function of three properties: 1) latitude (L) – the maximum amount a system can be changed before crossing a threshold, 2) resistance (R) – the ease or difficulty of changing the system, and 3) precariousness (Pr) – how close the current state of a system is to a threshold (Figure 1-1a). Although the ball-and-cup heuristic provides a useful tool for conceptualizing the resistance and resilience of ecosystems to disturbance, empirical measurements of these parameters are difficult and, therefore, emphasize the need for alternative measures. Species diversity has commonly been acknowledged as a key component for maintaining the resistance and resilience of ecosystems to disturbance (Darwin 1859; McNaughton 1977; Peterson et al. 1998; Yachi and Loreau 1999; Downing and Leibold 2010; Biggs et al. 2012; Timpane-Padgham et al. 2017). The insurance hypothesis of biodiversity states that greater levels of biological diversity provide greater insurance against disturbance events and the associated loss of ecosystem functions (Yachi and Loreau 1999). The rationale is that species likely show differences in response to disturbances and, therefore, the contribution of individual species to some ecosystem function may diminish, whereas other species contributions may increase (averaging effect; Yachi and Loreau 1999). This has been demonstrated empirically in terrestrial systems where more diverse plant communities showed greater productivity over time due to turnover in the dominant species (Allan et al. 2011) and in freshwater mesocosm experiments, where treatments with higher species richness showed greater stability in population and community biomass (Downing et al. 2014). The seeking of validation for the insurance hypothesis of biodiversity has helped to spark a whole subdiscipline in ecological research dedicated to understanding the biodiversity-ecosystem function (B-EF) relationship (reviews by Loreau et al. 2001; Hooper et al. 2005; Tilman et al. 2014). Functional diversity and response diversity play critical roles in the B-EF relationship and provide a mechanistic link between B-EF studies and resilience studies (Oliver et al. 2015, 2016). Functional diversity describes the range and values of organismal traits that contribute to ecosystem functioning (Tilman 2001) and response diversity describes the diversity in responses of organisms to environmental stressors among species that contribute to the same ecosystem function (Elmqvist et al. 2003). Ecosystem functions result from the expression of both species’ functional and response traits (Vaughn 2010).
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Functional redundancy is the existence of more than one species in a community performing the same ecological function (Rosenfeld 2002; Angeler and Allen 2016). Patterns of functional redundancy among ecological communities indicate that individual species may be substitutable with negligible impact on ecological functions (Rosenfeld 2002), therefore conferring resistance or resilience to environmental stressors (Walker 1995; Oliver et al. 2015; Truchy et al. 2015). However, as species will always differ to some degree in their functional role, redundancy does not indicate that individual species are expendable (Rosenfeld 2002). Differences in relative species abundances and distributions can occur among seemingly equivalent species, influencing interpretations of redundancy and potential compensatory abilities. True redundancy can only occur when functionally redundant taxa overlap in functional traits, demographic traits, and tolerances to environmental stressors (Rosenfeld 2002). Further, cross-scale redundancy describes the redundancy of species performing the same ecological function across ecological scales (Peterson et al. 1998; Truchy et al. 2015). In this context, scales represent redundancy among functional guilds chosen based on known, ecologically relevant, behavioral or morphological characteristics such as functional feeding groups (e.g., herbivore, carnivore, detritivore), or across body sizes. Investigating patterns of redundancy across body sizes allows for inferences at different geographic scales as larger organisms tend to necessitate greater resources and, therefore, tend to operate at larger geographic scales than smaller organisms (Nash et al. 2014; Angeler and Allen 2016). In some cases, over-redundancy can occur, which represents the case of an overrepresentation among some functional groups in terms of species richness (Mouillot et al. 2014). Studies on terrestrial biota have demonstrated that functionally redundant communities experience fewer losses in diversity following a disturbance event (Gerisch 2014), that environmental stressors can decrease overall levels of redundancy (Flynn et al. 2009; Laliberté et al. 2010; Gerisch 2014), and that the redundancy of ecosystems or communities can change depending on the disturbance that occurs (Fetzer et al. 2015). There have been relatively few studies on functional redundancy in freshwater systems compared to marine systems (e.g., Mouillot et al. 2014; Micheli and Halpern 2005); however, redundancy has been demonstrated in species-rich tadpole communities in Madagascar (Strauß et al. 2010) and Panamá (Colón-Gaud et al. 2010), and among aquatic invertebrates from south-eastern USA in experimental drought treatments (Boersma et al. 2013). Genetic diversity has also been implicated as important for maintaining ecosystem conditions
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when faced with disturbance (Chapin III et al. 1997). Multiple experimental manipulations have been performed in seagrass ecosystems demonstrating that plots with greater genetic diversity were more resistant to environmental extremes and recovered more quickly from grazing events (Hughes and Stachowicz 2004). In addition, plots with increased genetic diversity had greater biomass (Reusch et al. 2005) and density of seagrass shoots (Reusch et al. 2005; Reynolds et al. 2012), with greater invertebrate abundances (Reusch et al. 2005; Reynolds et al. 2012), and overall more nutrient retention (Reynolds et al. 2012). However, there has been far less research on how genetic diversity influences the resistance and resilience of freshwater ecosystems to disturbance. The pattern of biotic homogenization among freshwater ecosystems, or the gradual replacement of native communities by locally expanding non-native species, threatens potential stocks of genetic diversity (Olden et al. 2004; Petsch 2016). Biotic homogenization can lead to genetic homogenization, or an increased similarity among gene pools resulting from intra- and inter-specific hybridization (Olden and Rooney 2006), potentially limiting the ability of freshwater systems to adapt to novel or changing environmental conditions (Rahel 2002). Abiotic components have also been implicated as key factors shaping the resistance and resilience of freshwater ecosystems to disturbance. The extensive connectivity among freshwater ecosystems in a watershed makes understanding their resistance and resilience challenging (Fullerton et al. 2010; McCluney et al. 2014). Barriers of all sizes, from storm culverts to largescale dams, negatively influence the ability of freshwater systems to recover post-disturbance (Baxter 1977; Fausch et al. 2002; Blakely et al. 2006; Fullerton et al. 2010; Biggs et al. 2012). Furthermore, these barriers can restrict gene flow, leading to locally distinct gene pools (e.g., Faulks et al. 2011). Habitat complexity has been shown to increase the resistance of freshwater fish communities to flood events by increasing numbers and types of refugia (Pearsons et al. 1992; Kovalenko et al. 2012). In fact, refugia play a critical role in the spatial and temporal resistance and resilience of freshwater ecosystems, especially those that experience regular droughts (Magoulick and Kobza 2003) or flooding events (Rempel et al. 1999). Overall, understanding the resistance and resilience of freshwater ecosystems is challenging due to the complexity in how abiotic and biotic factors interact and respond when disturbance occurs. However, facing this challenge is critical as freshwater ecosystems are some of the most threatened systems on Earth (Strayer and Dudgeon 2010; Carpenter et al. 2011; Collen et al. 2014). My doctoral research investigates the resistance and resilience of Ontario freshwater lakes
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to disturbance in multiple systems and at multiple scales. Using an approach for simulating biomonitoring data, I first demonstrate how distances in ordination space can be used as metrics of resistance and resilience of ecosystems or communities to disturbance. I then apply my distance-based approach to quantify the relative resistance and resilience of Ontario zooplankton communities experiencing anthropogenic acidification, while integrating water chemistry parameters to better understand why zooplankton communities exhibit variable responses over time. To integrate a functional approach, I then investigate the redundancy in freshwater fish communities across multiple scales and various environmental gradients. I conclude with a general overview of the results from the three empirical chapters, reflect on their significance for understanding the resilience of Ontario freshwater ecosystems to disturbance, and provide some avenues for future research that build on the work presented in this thesis.
Research Overview The primary objective of my thesis is to provide a comprehensive, quantitative understanding of the resistance and resilience of Ontario freshwater ecosystems to disturbance. I begin in Chapter 2 by simulating community biomonitoring data and using these simulations to demonstrate how distance-based measures on multivariate ordinations of community data can provide a way to characterize the relative resistance and resilience of communities to disturbance. My simulation approach stems from the early work of Robert H. Whittaker (1960, 1967), who introduced ecologists to the concepts of ecological gradients and gradient analyses. Given that resistance and resilience are two multidimensional, multifaceted properties of ecosystems, quantifying them ultimately lends to multivariate approaches such as ordination techniques. The results of Chapter 2 demonstrate how distance-based approaches within multivariate ordinations can be used to characterize and visualize differences in the resistance and resilience of communities to disturbance. In Chapter 3, I use the distance-based approaches described in Chapter 2 to quantify the relative resistance and resilience of crustacean zooplankton communities in Ontario, Canada to anthropogenic acidification. In the late 1960s, it was discovered that acid rain was having profound consequences on freshwater ecosystems in Ontario (Beamish and Harvey 1972; Sprules 1975), which inevitably led to increased monitoring efforts in the region among acidified lakes as well as those unaffected by acidification (i.e., reference lakes; Yan et al. 2008; Palmer
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and Yan 2013). In addition to the monitoring efforts, two whole-lake experimental acidification trials with sampling of pelagic crustacean zooplankton communities at the Experimental Lakes Area (ELA and now known as the IISD-ELA) in northwestern Ontario provide controlled experimental studies of abiotic and biotic responses to acidification. I capitalize on these longterm monitoring programs and experiments to evaluate the overall variability in resistance and resilience to change among zooplankton communities experiencing anthropogenic acidification. My results show that the composition of most zooplankton communities included in the study have changed over the last few decades, whether they were impacted by anthropogenic acidification or not, coinciding with changing water chemistry over time. In Chapter 4, I take a functional diversity perspective to supplement the species-based approaches in Chapters 2 and 3 and investigate the functional redundancy of freshwater fish communities in Ontario lakes across environmental gradients. Despite that Ontario has over 150 species of freshwater fishes, most lakes in Ontario are relatively depauperate, and most fish species in Ontario are rare (low frequency of occurrence); of the 69 species included in the study, only four were present in greater than 25% of the 6,977 lakes. However, despite low species richness values across the province, relationships between functional diversity and species richness were saturating, indicating patterns of redundancy. I found differences in the shape of the functional diversity versus species richness relationship regionally; southeastern and northwestern fish communities showed saturating relationships, whereas northeastern communities had a linear relationship, indicating a lack of redundancy. Based on expectations of null models, functional diversity of most fish communities could be predicted based on species richness alone. Finally, using generalized additive models, I found nonlinear patterns between environmental variables related to lake size, productivity, and climate and the residual variation between functional diversity and species richness. In Chapter 5, I provide a general conclusion to my findings on the resilience of Ontario freshwater ecosystems and provide a perspective on future areas of research.
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Yan, N. D., Somers, K.M., Girard, R.E., Paterson, A.M., Keller, W.B., Ramcharan, C.W., Rusak, J.A., Ingram, R., Morgan, G.E., and Gunn, J.M. 2008. Long-term trends in zooplankton of Dorset, Ontario, lakes: the probable interact effects of changes in pH, total phosphorus, dissolved organic carbon, and predators. Can. J. Fish. Aquat. Sci. 65: 862–887.
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Chapter 2 Utilizing Gradient Simulations for Quantifying Community-Level Resistance and Resilience 2.1 Abstract Resilience is a complex, multidimensional property of ecosystems that relates to how ecosystems respond to disturbance and likely results from the interactions of species and their environments across temporal and spatial scales. Due to the complexity in how ecosystems function and respond to disturbance, measuring resilience is a challenge. Gradient analysis provides a familiar, yet somewhat neglected, framework for understanding and characterizing resilience. With simulations parameterized on existing biomonitoring data, I used distance-based measures in ordination space to characterize community-level resilience, here defined as a function of resistance and recovery. My simulations and analyses involved five steps: 1) I generated regional species pools by simulating species distributions across environmental gradients; 2) I sampled from these regional species pools to emulate temporal changes in reference (i.e., minimally disturbed) and impacted communities responding to disturbance; 3) I performed ordinations on observations from both impacted and reference communities to summarize multivariate data; 4) I calculated distance-based measures for individual community trajectories in the ordinations to quantify their relative resistance and resilience; and, 5) I compared these distance-based metrics between reference and impacted communities. I conclude with an empirical example demonstrating the lack of resistance of the Harp Lake (Ontario, Canada) zooplankton community to invasion relative to the changes observed among minimally disturbed reference communities. Overall, distance measures on ordinations provide a simple and effective visual framework to quantify the relative resistance and resilience of communities to disturbance and my simulation approach provides a novel technique to develop and evaluate quantitative metrics related to ecosystem or community-level processes.
2.2 Introduction ‘Resilience’ has become an increasingly important term and concept in ecology as evidenced by the increased frequency of citations in journal articles (Hodgson et al. 2015), discussion in books (e.g., Berkes and Folke 1998; Biggs et al. 2015), and the topic of special issues of academic
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journals (see Ecosystems, Carpenter et al. 2005; Journal of Applied Ecology, Angeler and Allen 2016). Because of its long-term use, there are many definitions of resilience and numerous techniques for quantifying resilience (Angeler and Allen 2016). Arguably the most common definition of resilience in the ecological literature is that of Holling (1973, p. 17), who defined resilience as “a measure of the ability of [these] systems to absorb changes of state variables, driving variables, and parameters, and still persist.” This definition, now termed ‘ecological resilience’, describes resilience as a property of ecosystems and assumes that ecosystems can persist in multiple states (i.e., stability domains; Holling 1996). In contrast, ‘engineering resilience’ describes the rate of return of an ecosystem to an equilibrium state following a disturbance event (Pimm 1984; Holling 1996). Conceptually similar to the engineering definition and somewhat nested within the ecological definition, ecologists have also advocated for a resistance-resilience framework, where resistance describes the ability to persist during a disturbance event and resilience describes the ability to recover following a disturbance event (Nimmo et al. 2015). Whereas ecological resilience is often viewed at the ecosystem scale, the resistance-resilience framework can be applied at multiple biological levels, in turn, informing us about the overall resilience of that system (Nimmo et al. 2015). A suite of novel quantitative approaches has been developed to characterize the resilience of systems to disturbance (e.g., Table 2-1). The variety of approaches developed has been fueled by differences in definition (Brand and Jax 2007; Myers-Smith et al. 2012), the scale at which studies take place (Angeler and Allen 2016), the specific questions being addressed, the stressors that are impacting the system (Carpenter et al. 2001), and the data available or required to answer the questions. One set of approaches that has been relatively underutilized for quantifying resilience are gradient analyses. Ecological gradient analyses were developed by plant community ecologists to understand patterns of species distributions and abundances along environmental gradients (Whittaker 1967). ‘Gradient’ is a general term that can be used to describe spatial gradients, environmental gradients, or abstract gradients that may not occur in nature (e.g., ordination axes, phase spaces that are not directly measurable during field sampling), but are useful for explaining community patterns (Austin 1985; ter Braak and Prentice 1988). A coenocline is a type of gradient that describes how species abundances change across a single environmental parameter, for example, elevation (Whittaker 1960, 1967). Similarly, a coenoplane describes how species abundances change across two orthogonal gradients (Figure 2-
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1). Simulations of coenoclines and coenoplanes based on patterns of species in nature have been used for testing the reliability of many quantitative techniques (e.g., Gauch and Wentworth 1976, Gauch and Whittaker 1976; Hirst and Jackson 2007). Table 2-1. Examples of techniques/surrogates used for quantifying resilience. Method Description Sample references Multiple species in a community perform the same Angeler and Allen 2016 Functional ecological function providing a form of insurance Laliberté et al. 2010 redundancy when a disturbance occurs. Angeler et al. 2013 Populations recover to historical population sizes or Dakos et al. 2008 Population biomass levels quickly from severe disturbance Scheffer et al. 2009 models events. Loss of resilience can be predicted using Dai et al. 2012 early warning signals. Diversity in the response of species to disturbances Elmqvist et al. 2003 Response maintains functional patterns and processes of Mori et al. 2013 diversity ecosystems. Baskett et al. 2014 Species rich communities can better buffer Species Yachi and Loreau 1999 environmental variability and are likely to contain richness Downing and Leibold 2010 species showing differing responses. When facing disturbance events, resilient Bennett et al. 2005 Systems ecosystems mimic those absent of disturbance Mumby and Anthony 2015 models events. Marzloff et al. 2015
Figure 2-1. A coenoplane showing the abundance relationships for 50 species along two orthogonal gradients (e.g., environmental gradients). Each numbered point represents an individual observation, where species within a community vary in their abundance.
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Simulations of coenoplanes coupled with a space-for-time substitution sampling approach can be used to develop data sets where communities undergo known changes in composition. Space-fortime substitution uses successive sampling from different spatial locations to represent changes in species composition that would occur over time (e.g., species succession; Pickett 1989). By placing a grid on a coenoplane, we create a spatial landscape that can be sampled using a spacefor-time approach (Figure 2-1). At each intersecting point along the gridded coenoplane is a snapshot of a community where abundances of individual species are known and vary across the coenoplane (Figure 2-1). Successive sampling at points of intersection along the gridded coenoplane results in a sequence of community composition data whereby abundances of species may vary over the order of observations (or time). Sampling repeatedly at a given point of intersection on the simulated coenoplane grid will result in identical species composition measurements over time representing a constant species composition and abundance. Alternatively, successive sampling of points that are more distant from each other will result in a sequence of observations that have less in common, both in terms of species abundance and occurrence (Figure 2-1). Matthews et al. (2013) described six hypothetical trajectories of temporal change in communities that can be represented spatially along a coenoplane (Figure 2-2). Community trajectory patterns were described as 1) saltatory or gradual, and 2) non-directional, directional, or directional followed by a return to a historical state. Small, gradual non-directional changes in communities can reflect trajectories of natural community variation exhibiting random increments of change in magnitude and direction (i.e., loose equilibrium; Matthews and Marsh-Matthews 2016; Figure 2-2a), whereas saltatorial, abrupt responses, can reflect the responses of communities to ‘pulse’ disturbances (Figure 2-2b) such as extreme hydroclimatic events or wildfires. Gradual and saltatorial directional responses can depict permanent changes in community composition (Figures 2-2b, e). In some cases, recovery to a historical state can occur (Figures 2-2c, f), but such recovery typically requires gradual steps (e.g., Keller et al. 2002). Using these six trajectories of change as a framework for the space-for-time substitution sampling on simulated coenoplanes (Figure 2-2g), we can mimic biomonitoring data sets where communities show differing trajectories of change in community composition and abundance (Figure 2-2), allowing comparisons of community-level resilience metrics.
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Figure 2-2. a-f) Conceptual reconfigurations of gradual and saltatorial community trajectories over two environmental gradients (adapted from Matthews et al. 2013). a) The trajectory of a reference community undergoing non-directional gradual change in composition. b-f) Impacted communities showing gradual (b, c) and saltatorial (d-f) responses. Saltatorial responses are depicted as having greater community turnover than gradual responses. Periods of relative stability in community composition are depicted with filled circles. g) Space-for-time sampling approach where each trajectory represents a single community. Here, I use coenoplane simulations and the hypothetical trajectories described by Matthews et al. (2013) to demonstrate a distance-based multivariate approach to quantify the relative resilience of communities to disturbance that embodies the properties of both resistance and recovery. The objective of my simulations is to represent actual community biomonitoring data to provide a practical approach for characterizing the relative resilience of communities to disturbances. I base the coenoplane simulations on long-term crustacean zooplankton community data from lakes in Ontario, Canada, where the sampling programs used a consistent sampling protocol, ensuring reliable comparisons of zooplankton communities over time (Yan et al. 2008; Palmer and Yan 2013; Table S2-1). I conclude with an empirical example, characterizing the resistance and resilience of the Harp Lake zooplankton community to invasion relative to minimally disturbed reference zooplankton communities.
2.3 Materials and Methods My simulations involved five steps. I first simulated the distributions of all species found in a region defined by two orthogonal gradients forming a coenoplane. I then sampled the coenoplane using a space-for-time substitution approach following the hypothetical trajectories described by Matthews et al. (2013; Figure 2-2). I characterize community trajectories as either reference
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communities exhibiting natural variability (Figure 2-2a) or impacted communities showing varying responses to disturbance (Figures 2-2b-f). The third step ordinated community data in a lower-dimensional space. Using the ordination results, I calculated distance-based metrics to characterize the relative resilience of individual communities. Finally, to follow a common approach in biomonitoring studies (Bowman and Somers 2005; Palmer et al. 2013), I compared these metrics between individual impacted communities and reference communities (see Figure S2-1 for a flow chart of steps taken).
2.3.1
Terminology
I adopt the following terminology to describe my simulations and analyses. Here, “sampling” describes the act of locating an intersecting point along the simulated coenoplane and extracting (with replacement) the underlying community composition snapshot, or “sample.” This sample is an “observation,” or a snapshot of community composition consisting of the abundances of all simulated species present at that location. Here, a “community” refers to 30 observations collected along a single trajectory based on Matthews et al. (2013). I refer to “reference communities” as minimally disturbed communities demonstrating gradual, non-directional trajectories (Figure 2-2a) and “impacted communities” as those showing some other response (Figures 2-2b-f). Finally, I used the term “constancy” to describe the relative consistency in community composition over time.
2.3.2
Step 1: Simulating coenoplanes
I simulated hypothetical species distributions along two orthogonal environmental gradients forming a coenoplane using the ‘coenoflex’ package (Roberts 2016) in the R statistical software (R Core Team 2016). I simulated 10 environmental coenoplanes, containing 50 species each and constructed to be 100 x 100 units in length-by-width. The centroids of individual species distributions (i.e., species optima) were randomly dispersed across each gradient with approximately constant density throughout the sample space. Species abundances were simulated to have a Gaussian response along each environmental gradient. This random distribution of species forms a mosaic of species richness levels across the grid. Centroids of species distributions did not always fall within the 100 x 100 grid and this represents the situation in nature where a species' optimum along an environmental gradient may fall beyond the range of
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conditions sampled. Each point of intersection within the grid represents a potential sampling location and represents a snapshot of species abundances with distinct species composition and potentially different richness. Differences in species composition across the simulated coenoplanes (i.e., gradient lengths) were assessed among coenoplanes using detrended correspondence analysis and was 5.97 SD (range 5.12 – 6.93), similar to the reported average of 4.10 (range 1.36 – 11.98) from terrestrial and aquatic field studies (Hirst and Jackson 2007). A measure of approximately 4 SD units represents complete species turnover between the two ends of the gradient (Gauch 1982).
2.3.3
Step 2: Sampling communities from the simulated coenoplanes
I sampled the coenoplanes to resemble the hypothetical trajectories of communities described by Matthews et al. (2013; Figure 2-2). Each scenario represents the trajectory of an individual community, and consists of 30 observations of species abundance data, consistent with freshwater zooplankton biomonitoring data from Ontario, Canada (i.e., 30 years; Table S2-1). For all six trajectories, communities were sampled to display 10 initial time-steps of relative constancy in composition (i.e., minimal changes in abundance) prior to showing a response. One hundred replicates of each of the community trajectories were sampled from each of the 10 coenoplanes, thereby providing a total of 1,000 possible replications of each scenario (Figures 22a-f). Trajectories of recovery were set to track back to historical centroids while incorporating noise into the sampled coordinates.
2.3.4
Step 3: Multivariate analyses
I used principal component analyses (PCAs) on Hellinger-transformed species abundance matrices to ordinate the species composition data in fewer dimensions. Hellinger transformations are a recommended distance measure for the ordination of species and were used to reduce the impact of rare species on ordination results (Legendre and Legendre 1998; Legendre and Gallagher 2001). For the ordinations, I consider situations that range from having many more reference communities (Figure 2-2a) to impacted communities (Figures 2-2b-f) to the reverse situation. For example, I performed 100 replicate ordinations where the analyses included 50 communities showing gradual, non-directional change (i.e., reference) and 10 communities showing any of the other five hypothetical trajectories (i.e., impacted). In this situation, the
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community data matrix contained 60 communities each consisting of 30 observations totaling 1800 observations. In the reverse situation, I performed a total of 100 replicate ordinations that each included 10 reference and 50 impacted communities. Finally, I performed 100 replicate ordinations of each of the ratios where the analyses contained: 20 reference communities: 4 impacted communities (20:4), 4:20, 30:30, and 10:10. Biomonitoring programs tend to vary in the number of reference and impacted community data sets available, and therefore understanding how the distance-based framework performs with differing numbers of communities is important. However, for the sake of brevity and clarity, I only present results for the situation where 50 communities showing gradual, non-directional change and 10 communities showing any of the other five hypothetical trajectories are considered (ideal scenario); results for differing sample size scenarios can be found in Appendix 1. Communities used in the ordination analyses were randomly selected from the communities described in Step 2. For example, in the analysis of 50 reference and 10 impacted communities, 50 reference communities were sampled without replacement from the 1,000 simulated communities undergoing gradual, non-directional change (Figure 2-2a) and 10 impacted communities were selected without replacement from any of the 5,000 simulated communities undergoing either gradual directional change, gradual directional change with recovery, saltatory non-directional change, saltatory directional change, and saltatory directional change with recovery (Figures 2-2b-f). Two principal component axes were extracted from each ordination because the simulations were based on two orthogonal gradients.
2.3.5
Step 4: Distance-based metrics
Distances between observations in an ordination represent differences between observations; greater distances indicate greater differences in species composition. Communities experiencing little change over time will travel relatively small distances in multivariate space over time compared to communities exhibiting major departures from a historical state (Vinebrooke et al. 2003a, b). Resistance and recovery were assessed by measuring the Euclidean distance of each observation relative to a baseline centroid (dBaseline; Anderson and Thompson 2004; Milner et al. 2016). I calculated distances to a baseline centroid of 10 observations for each individual community as each trajectory initially had 10-time steps of relative constancy in community
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composition prior to subsequent responses (Figure 2-2). Low dBaseline values over time indicate a community maintaining its composition around some equilibrial state or stability domain. Additionally, distances were calculated from each observation to the centroid defined by all prior observations within a trajectory (dCumulative; Anderson and Thompson 2004; Milner et al. 2016). Sequential distances to the cumulative centroid describe the magnitude of change in community composition in sequential time steps, with the potential for detecting the effects of ‘pulse’ disturbances (Anderson and Thompson 2004; Milner et al. 2016) and is used as a measure of relative resistance. Short distances to a cumulative centroid over time indicate slight changes in variation in community composition over time, whereas a large distance within a series of short distances can indicate the effects of a pulse disturbance impacting the system. To assess the ability of the ordinations to accurately capture changes in multivariate space, Euclidean distances to cumulative (dMV.Cumulative) and baseline (dMV.Baseline) centroids were calculated from the multivariate Hellinger-transformed abundance matrices. I then calculated Pearson correlations between these full multivariate distances and the distance-based metrics calculated from the PCA site scores (dBaseline and dCumulative) to assess any differences arising due to working in the reduced dimensionality of the PCA axes.
2.3.6
Step 5: Comparing distance-based metrics between reference and impacted communities
I compared results from distance-based metrics (dBaseline and dCumulative) for individual impacted communities with the results for communities undergoing gradual, non-directional change (i.e., reference communities). Ninety-five percent confidence intervals were calculated for the set of reference communities to define the boundary of distances traveled in multivariate space over time by reference communities. Distance values falling outside this reference distribution indicates changes in community composition that exceed expectations of natural variability and, therefore, a lack of resistance to change. To further evaluate deviations in dBaseline and dCumulative from historical conditions among individual communities, I applied the sequential regime shift detector (SRSD; Rodionov 2004). This methodology was developed to detect nontrivial shifts in time-series data using sequential ttests, where each additional value in a time series necessitates a new test of whether there is a
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significant difference between the new value and the previous l time steps. A regime-shift index (RSI) is calculated as the cumulative sum of normalized differences from the hypothetical mean for a new regime (Rodionov and Overland 2005). Positive RSI values indicate shifts away from a centroid and negative RSI values indicate shifts toward a centroid. Significant shifts to alternative states are largely determined by choices in the minimum length of l time steps to compare and by the chosen significance level (p). For the purposes of the simulations, I knew the length of community trajectories a priori and chose l = 5 and p = 0.01.
2.3.7
Characterizing the resistance and resilience of zooplankton communities to an invasion
I used long-term (1980-2012) crustacean zooplankton species abundance data from Ontario, Canada to demonstrate the utility of the distance-based approach for characterizing the resistance and resilience of communities to disturbance. Harp Lake is a relatively small lake (71.4 ha) located in southcentral Ontario (Yan et al. 2001). Crustacean zooplankton communities have been sampled from Harp Lake since the early 1980s (Yan and Pawson 1997). During this period, a nonnative zooplanktivore, Bythotrephes, appeared and has led to dramatic changes in the Harp Lake zooplankton community including a decline in species richness, reduction in body size, and decline in total zooplankton abundance (Yan and Pawson 1997; Yan et al. 2001; Yan et al. 2008). I combine data from Harp Lake with zooplankton monitoring data from two nearby minimally disturbed reference lakes (Red Chalk and Blue Chalk lakes) and seven minimally disturbed reference lakes from the Experimental Lakes Area (ELA now known as the IISD-ELA) in northwestern Ontario (lakes 224, 239, 240, 373, 375, 382, and 442). Reference lakes were chosen to reflect similar conditions to Harp Lake, being relatively free of impacts from anthropogenic stressors, particularly, the presence or invasion of Bythotrephes. Methods for sampling zooplankton differed between lakes, but were generally consistent within lakes over time. Several zooplankton species or taxonomic groups were combined due to differences in identification and changes in nomenclature over time (see Palmer et al. 2013 for details). I calculated a PCA on the covariance matrix of annual-average crustacean zooplankton species abundances for all lakes and years. Prior to the ordination, Hellinger transformations were performed to reduce the impact of rare species on the ordination results (Legendre and Gallagher 2001). The number of nontrivial components to retain was chosen using a permutation approach;
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I permuted each column of the Hellinger-transformed zooplankton annual average species abundance matrix and conducted a PCA 999 times. I retained a component if the proportion of variance explained in the empirical data exceeded 95% of the permuted PCAs for that component (Peres-Neto et al. 2003, 2005). I calculated and compared dBaseline and dCumulative over time between the Harp Lake zooplankton community and the group of nine reference sites. I used 95% percent confidence intervals from the set of reference communities to define the boundaries of reference-site distances traveled in ordination space. Furthermore, I applied the SRSD (Rodionov 2004) to test for detect nontrivial shifts in dBaseline and dCumulative over time using l = 5 and p = 0.01. All statistical analyses were conducted in base R (v 3.2.3; R Core Team 2016) using functions from the packages ‘coenoflex’ (Roberts 2016), ‘Hmisc’ (Harrell 2016), ‘vegan’ (Oksanen et al. 2016), and ‘ggplot2’ (Wickham 2009), or Excel (SRSD add-in; Rodionov 2004).
2.4 Results I performed 600 ordinations with differing numbers of ‘reference’ and ‘impacted’ communities (i.e., community sample sizes). Here, I present more detailed results of a single ordination containing 50 reference and 10 impacted communities and summarize the trends across community sample sizes. Detailed results on the various sample sizes can be found in Appendix 1. I report distance measures performed on ordination results because they allowed a graphical display of the relationships, and because there are strong correlations with the results from parallel distance-based analyses on multivariate data (dBaseline and dMV.Baseline range: 0.83 – 0.88; dCumulative and dMV.Cumulative range: 0.73 – 0.85).
2.4.1
Example of a single analysis containing 50 reference and 10 impacted communities
Site scores for communities showing gradual, non-directional change were typically clustered, whereas impacted communities were more widely scattered across the two components (Figure 2-3b). This outcome was expected and reflects the patterns of sampling (Figure 2-3a): sampling of impacted communities covered more of the coenoplane than reference communities showing gradual, non-directional change (Figure 2-3a). Looking more closely at individual communities,
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Figure 2-3. a) Sampling coordinates from the coenoplane for the 60 communities within the single ordination. Point shapes refer to inset hypothetical trajectories. Lines connecting impacted community sampling coordinates show their individual temporal trajectories. One community undergoing saltatory directional change with recovery highlighted in red (Figure 2-2f). b) PCA ordination plot showing site scores for sampled communities. c) An enlarged view of one impacted community undergoing saltatorial directional change with recovery and one reference community (Figure 2-2a). Observations are connected in order of sampling. d) Distances to baseline centroids (white shapes) over time for both the single impacted and reference community. e) Distances to cumulative centroids (white shapes) over time for both the impacted and reference community. f) Distance to baseline centroid over time for both communities. Dashed line indicates significant regime shift (p < 0.01). g) Distance to cumulative centroid over time for both communities. Figure 2-3c displays the trajectory of a single reference community and a single impacted community undergoing a saltatorial directional response with recovery. The reference community shows variation around a mean location in multivariate space, whereas the impacted community travels around the ordination. Figures 2-3d and 2-3e display information used in dBaseline and dCumulative calculations, respectively, for both the individual impacted and reference communities. Compared to the reference community, dBaseline of the impacted community deviated from the reference community trajectory at time-step 11 and showed a significant shift away from the initial community state (RSI = 1.414, p < 0.001; Figure 2-3f). The impacted
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community then showed a period of relative constancy in dBaseline measurements and subsequently returned to the reference community trajectory around time-step 24 with a significant shift back towards the reference (RSI = -1.152, p < 0.001). Alternatively, the impacted community showed a greater dCumulative over time than the reference community through the entire time series (Figure 2-3g). Reference communities exhibiting a gradual, non-directional response consistently showed low dBaseline and dCumulative over time (Figure 2-4). Low dBaseline and dCumulative values were expected as sampled reference communities lacked major change throughout the simulations. For dBaseline, deviations of impacted communities from the reference distribution occurred immediately following the initial period of relative constancy in composition (time steps 1-10), indicating a lack of resistance. Measures of dBaseline for communities showing directional trajectories (Figures 2-2b, e) remained outside the range of reference communities following the initial period of relative constancy in composition (Figure 2-4a), whereas communities showing resilience (Figures 2-2c, f) returned to their initial state (Figure 2-4a).
Figure 2-4. a) Examples of the Euclidean distances to a baseline centroid from the PCA over time for one ordination containing 50 reference and 10 impacted communities. Plotted are representative examples of each inset scenario. Symbols match with inset scenarios. Mean distances over time are plotted for the reference communities with 95% confidence intervals (gray ribbon). b) Examples of the Euclidean distances to a cumulative centroid over time for the same representative examples.
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Comparisons using dCumulative from the PCA ordinations indicated substantial change occurring among the differing impacted communities (Figure 2-4b). Impacted communities remained outside the 95% reference confidence interval for most the time series (Figure 2-4b). Distances to cumulative centroids of impacted communities were typically large during the initial 10-time steps of relative constancy in community composition, but then differed based on the community trajectory. Following the initial 10-years of constancy in community composition, communities showing gradual directional change (Figure 2-2b) tracked towards the centroid and, subsequently, deviated away (Figure 2-4b). Similarly, when communities showed patterns of recovery (Figures 2-2c, f), distances to cumulative centroids were reduced and followed by subsequent deviations from the centroid (Figure 2-4b). Patterns for communities undergoing saltatorial non-directional change (Figure 2-2d) peaked based on the magnitude of deviation from the cumulative centroid (Figure 2-4b). Finally, following the first 10 observations, communities undergoing saltatorial directional change (Figure 2-2e) tracked towards and then, subsequently, deviated from the cumulative centroid (Figure 2-4b). Further, measures of dCumulative were consistently lower in the second half of the time series compared to the first due to the longer period of constancy following the saltatorial change (18 times steps vs 10).
2.4.2
Trends across community sample sizes
Despite the differences in sample size across analyses, variation in dBaseline and dCumulative among reference communities remained low (Appendix 1). In contrast, variability among impacted communities increased as the number of impacted communities within the analysis decreased. However, despite the increase in variability across ordinations containing differing ratios of reference to impacted communities, trajectory patterns remained consistent for dBaseline and dCumulative measures, allowing for the consistent characterization of resistance and resilience. In all cases, impacted communities showed a period of relative constancy in dBaseline and dCumulative measures followed by consistent patterns based on the individual trajectories and distance metric (Appendix 1).
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2.4.3
Characterizing the resistance and resilience of zooplankton communities to an invasion
The zooplankton community from Harp Lake followed a similar trajectory to simulated communities undergoing saltatory, directional change over time (Figure 2-2e), beginning in the upper-left quadrant of ordination space in the early 1980s and finishing in the bottom right quadrant of ordination space during the late 2000s (Figure 2-5a). I observed a significant shift in dBaseline in 1993 (RSI = 2.70, p < 0.001) where the community deviated from the reference distribution and subsequently maintained that distance away from the historical centroid in ordination space (Figure 2-5b) indicating a lack of resistance to change. This is also the year in which Bythotrephes was first observed in Harp Lake. Furthermore, measures of dCumulative remained outside the reference distribution for most the sampling period (Figure 2-5c), with a decreasing trend over time. The decreasing trend in dCumulative over time is consistent with the patterns observed among simulated communities undergoing saltatory, directional change (e.g., Figure 2-4b). Similarly, the pulse disturbance of the invasion led to a substantial drop in dCumulative scores. This drop is indicative of the trajectory in ordination space; there was an initial 13 years of constancy in community composition followed by the invasion where the community deviated from the historical region in ordination space (i.e., upper-left quadrant) for approximately 3 years and then remained in the new region of ordination space (i.e., bottomright quadrant) for approximately 14 years (Figure 2-5a).
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Figure 2-5. a) Principal component analysis ordination plot of the 10 zooplankton communities. Highlighted in red is the trajectory for the Harp Lake zooplankton community. Distances to b) baseline (dBaseline) and c) cumulative centroids (dCumulative) over time for the Harp Lake zooplankton community (red) and reference communities (white). Mean distances over time are plotted for the reference communities with 95% confidence intervals (gray ribbon). A significant shift in dBaseline occurred in 1993 for the Harp Lake zooplankton community (p < 0.01).
2.5 Discussion Given the inherent errors in sampling environmental data and lack of knowledge about how ecological communities respond to disturbance, comparing statistical methodologies with empirical field data may display significant differences in results, but the results may remain unclear about which methods best reflect true underlying relationships. Simulations provide the opportunity to evaluate quantitative approaches and aid in the development and assessment of novel metrics. Here, I used coenoplane simulations coupled with a space-for-time substitution approach for sampling to build community-level data sets and used these data to demonstrate a distance-based framework to characterize the relative resilience of potentially impacted communities to disturbance over time. Further, I applied this approach to zooplankton monitoring data from Ontario, Canada, and demonstrated a lack of resistance to change among the Harp Lake zooplankton community resulting from the invasion of Bythotrephes (Yan and Pawson 1997; Yan et al. 2001; Yan et al. 2008). Trajectories of distances from a historical state (dBaseline) over time provide a relative metric of both resistance and recovery, whereas distances to a community-level centroid (dCumulative) provide an understanding of the magnitude of change over time (Milner et al. 2016). Generally, communities showing the shortest distances traveled in ordination space and, therefore, having
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minimal changes in species composition were considered to represent the greatest level of resistance to change. In contrast, substantial deviations from historical ranges of natural variation, or alternatively the variation of reference communities, indicated a compositional change in the community and, therefore, a lack of resistance and resilience (Seidl et al. 2016). In the case of the Harp Lake zooplankton community, the trajectories of dBaseline over time prior to the invasion of Bythotrephes were consistent with reference communities. However, after the invasion of Bythotrephes, the composition of the community changed, evident from the sudden deviation from reference distribution in 1993, indicating a lack of resistance to change. Furthermore, we have not seen a recovery back to the historical composition post-invasion, indicating a lack of resilience to invasion. I was able to characterize the resistance and resilience among all the simulated communities despite varying the ratio of reference to impacted sites included in the analyses. This is an important finding, as empirical studies are often limited by the number of reference sites available. However, the empirical example comparing Harp Lake to the nine reference systems demonstrates that I may have underestimated the variability demonstrated by reference systems or, alternatively, that reference systems are changing and demonstrate patterns that are inconsistent with my expectations (i.e., gradual, non-directional). Regardless, the changes observed in dBaseline and dCumulative over time among the Harp Lake zooplankton community far exceeded that of the reference communities, providing a clear visual of the impact of invasion. The use of distance-based frameworks with ordinations is certainly not a novel approach in ecology (Holmes et al. 1979; Holmes and Recher 1986; Evans 1988; Hughes 1990; Boulton et al. 1992; Kilgour et al. 1998; Vinebrooke et al. 2003a, b; Anderson and Thompson 2004; Sponseller et al. 2010). In fact, distance-based frameworks have been used to develop a wealth of ecological metrics that have been described as important for resilience (Timpane-Padgham et al. 2017) including measures of functional diversity (Walker et al. 1999; Laliberté and Legendre 2010) and beta diversity (Anderson et al. 2006). Although I applied this approach towards understanding the resilience of biotic communities to change, these techniques can be similarly applied to replicated environmental monitoring data such as water-quality parameters or nutrient measures. To conclude, my approach of simulating coenoplanes and subsequently sampling using a spacefor-time substitution approach provides a novel means to generate community-level data that
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mimic that of biomonitoring programs like the freshwater zooplankton monitoring program in Ontario (Yan et al. 2008) or the stream fish monitoring in northwest Arkansas and Oklahoma (Matthews et al. 2013; Matthews and Marsh-Matthews 2016). I chose to simulate reference and impacted communities because the reference condition approach represents a common approach among biomonitoring programs (Bowman and Somers 2005; Palmer et al. 2013). By comparing distance measures of impacted community trajectories over time to those of reference communities, we can gain a better understanding of the resilience of these communities to disturbance and make inferences about how these communities may change in the future. Although this approach relies on the continuation of long-term monitoring programs, which can be difficult to fund and maintain, I am optimistic that the benefits of these programs for understanding the resilience of ecosystems to change will justify their perpetuation.
2.6 Acknowledgements I thank the members of the D.A. Jackson lab, B.J. Shuter, M.J. Fortin, and W.B. Keller, and two anonymous reviewers for comments on earlier drafts of this manuscript. I also thank W.B. Keller, M.J. Paterson, and J.A. Rusak for providing zooplankton community data that I used to parameterize my simulations and provide an empirical example.
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Seidl, R., Spies, T.A., Peterson, D.L., Stephens, S.L., and Hicke, J.A. 2016. Searching for resilience: addressing the impacts of changing disturbance regimes on forest ecosystem services. J. Appl. Ecol. 53: 120–129. Sponseller, R.A., Grimm, N.B., Boulton, A.J., and Sabo, J.L. 2010. Responses of macroinvertebrate communities to long-term variability in a Sonoran Desert system. Glob. Change Biol. 16: 2891–2900. ter Braak, C.J.F., and Prentice, C. 1988. A theory of gradient analysis. Adv. Ecol. Res. 34: 271– 317. Timpane-Padgham, B.L., Beechie, T., and Klinger, T. 2017. A systematic review of ecological attributes that confer resilience to climate change in environmental restoration. PLOS One. 12: e0173812. Turner, M.A., Findlay, D.L., Baulch, H.M., Armstrong, L.M., Kasian, S.E., McNicol, D.K., and Vinebrooke, R.D. 2009. Benthic algal communities: recovery from experimental acidification. Can. J. Fish. Aquat. Sci. 66: 1875–1891. Vinebrooke, R.D., Graham, M.D., Findlay, D.L., and Turner, M.A. 2003a. Resilience of epilithic algal assemblages in atmospherically and experimentally acidified boreal lakes. Ambio. 32: 196–202. Vinebrooke, R.D., Schindler, D.W., Findlay, D.L., Turner, M.A., Paterson, M., and Mills, K.H. 2003b. Trophic dependence of ecosystem resistance and species compensation in experimentally acidified Lake 302S (Canada). Ecosystems. 6: 101–113. Walker, B., Kinzig, A., and Langridge, J. 1999. Plant attribute diversity and ecosystem function: The nature and significance of dominant and minor species. Ecosystems. 2: 95–113. Whittaker, R.H. 1960. Vegetation of the Siskiyou Mountains, Oregon and California. Ecol. Mono. 30: 279–338. Whittaker, R.H. 1967. Gradient analysis of vegetation. Biol. Rev. 42: 207–264. Wickham, H. 2009. ggplot2: elegant graphics for data analysis. Springer-Verlag, New York. Yachi, S., and Loreau, M. 1999. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. PNAS. 96: 1463–1468. Yan, N.D., and Pawson, T. 1997. Changes in the crustacean zooplankton community of Harp Lake, Canada, following invasion by Bythotrephes cederstrœmi. Freshwater Biol. 37: 409–425. Yan, N.D., Blukacz, A., Sprules, W.G., Kindy, P.K., Hackett, D., Girard, R.E., and Clark, B.J. 2001. Changes in zooplankton and the phenology of the spiny water flea, Bythotrephes, following its invasion of Harp Lake, Ontario, Canada. Can. J. Fish. Aquat. Sci. 58: 2341– 2350.
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Yan, N.D., Somers, K.M., Girard, R.E., Paterson, A.M., Keller, W.B., Ramcharan, C.W., Rusak, J.A., Ingram, R., Morgan, G.E., and Gunn, J.M. 2008. Long-term trends in zooplankton of Dorset, Ontario, lakes: the probable interact effects of changes in pH, total phosphorus, dissolved organic carbon, and predators. Can. J. Fish. Aquat. Sci. 65: 862–887.
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Supplementary Tables and Figures Table S2-1. Three examples of the freshwater zooplankton community and water quality data collected over the past four decades in Ontario and used to provide a basis to the simulations. IISD = International Institute for Sustainable Development. Sampling Lake Location Reference Period Blue Chalk Lake Dorset, Ontario region 1980-2009 Rusak et al. 2008 Lake 302S IISD-Experimental Lakes 1983-2012 Turner et al. 2009 Area (Northwestern Ontario) Whitepine MacLeod Sudbury, Ontario region 1980-2006 Keller et al. 2002 Lake
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Figure S2-1. Flow chart summarizing steps of analysis.
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Copyright Acknowledgements Chapter 2 was previously published as an open access article in Ecosphere: Lamothe, K.A., Jackson, D.A., and Somers, K.M. 2017. Utilizing gradient simulations for quantifying community-level resistance and resilience. Ecosphere. 8(9): e01953.
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Chapter 3 Resistance and Resilience of Zooplankton Communities in Ontario, Canada to Changing Environmental Conditions 3.1 Abstract Understanding how communities respond to disturbance is a long-standing challenge in ecology and will remain a critical issue as human activity continues to alter environmental conditions. Resistance and resilience are two properties commonly used to describe how communities respond to disturbance, but quantifying these properties is challenging given the variability within and among systems over time. Using biomonitoring data from Ontario lakes, I quantify the relative resistance and resilience of crustacean zooplankton communities to change using distance measures in multivariate ordinations and characterize community trajectories based on their magnitude and directionality of change over time. Most zooplankton communities exhibited changes from historical conditions regardless of whether lakes were impacted by acidification. Zooplankton communities in experimentally acidified lakes showed patterns of resilience; however, these communities deviated from historical conditions post-manipulation. Most zooplankton communities from atmospherically acidified lakes showed gradual, directional trajectories of change over time, indicating a lack of resistance to change. Overall, changes among zooplankton communities likely reflect changes in water chemistry linked to the cumulative impacts of acidification, climate change, and regional land-use activities.
3.2 Introduction Environmental stressors are ubiquitous across the landscape and have long been recognized as factors shaping the structure and composition of ecosystems (Hutchinson 1948; White 1979; Pickett and White 1985). ‘Resilience’ has become a common term for describing the complex response of ecosystems to environmental stressors (Hodgson et al. 2015) and now forms the basis of many ecosystem management plans (Allen et al. 2011; Kingsford et al. 2011). Ecologists have viewed resilience from several different perspectives (see Angeler and Allen 2016 for a review). Ecological resilience describes the ability of an ecosystem to absorb the impacts from environmental stressors and maintain its current state (Holling 1973), measured as the magnitude of change needed to shift an ecosystem into an alternative state defined by novel structures and
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functions (Angeler and Allen 2016). Alternatively, others have characterized the responses of ecosystems to disturbance as a function of two components, resistance and resilience (Pimm 1984; Nimmo et al. 2015). Resistance describes the ability of a system to maintain its current state when faced with disturbance and resilience describes the ability to recover from an environmental stressor (Nimmo et al. 2015). Although the resistance-resilience framework neglects the assumptions underlying systems dynamics and behavior (Sundstrom et al. 2016), understanding and quantifying resistance and resilience provide useful information for managed systems subject to environmental stressors (Nimmo et al. 2015). Quantifying community responses to environmental stressors inherently lends itself to multivariate approaches. For example, Matthews et al. (2013) described six hypothetical trajectories of change that can be observed using ordinations of species-by-site matrices or environmental data matrices (Figure 3-1). These six scenarios are based on the magnitude and directionality of community change over time; trajectories can reveal gradual or substantial (saltatorial) changes in magnitude, and non-directional, directional, or directional change with recovery. Gradual change indicates slight changes from observation to observation, whereas saltatorial change describes leaps or large steps between some sequential observations. Directionality describes the overall trajectory of sample observations over time. Observations that are close together in ordination space indicate a relatively consistent community composition, whereas more distant points indicate greater differences in community composition. Gradual, non-directional trajectories can indicate natural community variation with random increments of change in magnitude and direction (Matthews and Marsh-Matthews 2016; Figure 3-1a). In contrast, saltatorial responses can be large and reflect the responses of communities to ‘pulse’ disturbances (Lake 2000; Figure 3-1d). Ecosystems or communities that resist change in response to an environmental stressor will display negligible movement in multivariate space with no directional pattern (Figure 3-1a), whereas systems that lack resistance are expected to show directional change over time (Figures 3-1b, e). Further, ecosystems or communities may vary in their rates of return to a historical state following a stressor event (e.g., Keller et al. 2002).
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Figure 3-1. Hypothetical scenarios of community change depicted as trajectories through ordination space (adapted from Matthews et al. 2013). Scenarios are based on the magnitude (gradual versus saltatory) and directionality of change (non-directional, directional, or directional with recovery) between repeated observations of communities over time. Freshwater environments are among the most threatened systems on Earth (Carpenter et al. 2011) as they are often subjected to the simultaneous effects of multiple environmental stressors (Schindler and Smol 2006; Jackson et al. 2016). In Ontario, many freshwater ecosystems are still recovering from the historical effects of acid precipitation (Jeffries et al. 2003). Acidified lakes in Ontario were first discovered during the late 1960s (Beamish and Harvey 1972; Sprules 1975), leading to increased attention and efforts to combat sulfur emissions (Brydges and Wilson 1990; Alm 1997) and propelling scientists to document and understand the impacts of acidification on freshwater environments (Galloway and Cowling 1978; Cowling 1982). In addition to the longterm monitoring efforts that followed the discovery of acidified lakes in Ontario, two whole-lake experimental acidification trials at the Experimental Lakes Area (ELA and now known as the IISD-ELA) in northwestern Ontario were performed to understand the abiotic and biotic responses to acidification (Schindler and Turner 1982; Rudd et al. 1990; Schindler et al. 1991). However, as these freshwater ecosystems have begun to recover from the effects of acidification (e.g., Keller et al. 2007), the effects of a changing climate have seemingly amplified. In Ontario, freshwater lakes are changing as average lake temperatures are rising (Schindler et al. 1996a, b), seasonal ice-on periods are shortening (Jensen et al. 2007), and overall water chemistry is changing (Edwards et al. 2009).
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I combine data from the experimental acidification trials with monitoring data from minimally disturbed reference lakes (sensu Stoddard et al. 2006) and atmospherically acidified lakes to quantify the relative resistance and resilience of pelagic zooplankton communities by characterizing temporal changes based on the hypothetical trajectories proposed by Matthews et al. (2013; Figure 3-1). I hypothesized that zooplankton communities in minimally disturbed reference lakes would display gradual, non-directional trajectories over time (Figure 3-1a), communities in the experimentally acidified lakes would show gradual, directional changes with recovery (Figure 3-1c), and finally, because the atmospherically acidified lakes had differing initial conditions, varied magnitudes of acid input, differential local effects across watersheds (e.g., liming, regional land-use), and therefore potentially different recovery rates, I hypothesized that these lakes would show variable responses over the study period (Figures 3-1b-f).
3.3 Materials and Methods 3.3.1
Study systems
I studied freshwater crustacean zooplankton communities and corresponding water chemistry for lakes from three regions of Ontario that vary in their exposure to acidification: Sudbury (n = 5 lakes), Dorset (n = 5 lakes), and the Experimental Lakes Area (IISD-ELA; n = 9 lakes; Table 31, Figure 3-2). Lakes from these regions were classified into reference or impacted categories based on historical water quality results (Schindler et al. 1985; Findlay and Kasian 1986, 1987; Rudd et al. 1990; Schindler 1990; Malley and Chang 1994; Hecky and Hesslein 1995; Yan et al. 1996; Keller et al. 2002; Vinebrooke et al. 2003a, b; Yan et al. 2008; Turner et al. 2009; Vinebrooke et al. 2009). Minimally disturbed reference lakes had pH levels above thresholds for acid-sensitive zooplankton species in Ontario (i.e., pH 6; Holt and Yan 2003; Holt et al. 2003; Figure S3-1), whereas impacted lakes exhibited pH levels below 6 at some point during the sampling period (Figure S3-1).
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Table 3-1. Lake region, surface area (A0, in ha), mean depth (Zmean, in m), maximum depth (Zmax, in m), pH range (minimum to maximum), and years when zooplankton were sampled for reference and impacted lakes. pH Lake Region A0 Zmean Zmax Years sampled range Reference Blue Chalk Dorset 52.4 8.9 23.0 6.47-6.80 1980-2009 Red Chalk Main Dorset 44.1 16.7 38.0 6.21-6.56 1980-2009 224 IISD-ELA 25.9 11.6 27.4 6.06-7.65 1982-83, 92-93, 99-2012 239 IISD-ELA 56.1 10.5 30.4 6.07-7.61 1980-2012 240 IISD-ELA 44.1 6.1 13.1 6.59-7.56 1988-92, 2000-2006 373 IISD-ELA 27.7 10.8 21.0 4.25-8.22 1983-1986, 88-2012 375 IISD-ELA 22.9 11.6 26.8 6.68-8.20 1987, 89-92, 2001-02 382 IISD-ELA 33.6 6.2 13.1 5.76-7.29 1985-1994 442 IISD-ELA 16.0 9.0 17.8 6.23-7.48 1988-1996, 1998-2012 Impacted 223 IISD-ELA 27.3 14.4 4.92-7.35 1974, 77-2004 302S IISD-ELA 10.9 5.1 10.6 4.41-7.53 1980-2004, 07-08, 10 Chub Dorset 34.4 8.9 27.0 5.51-5.76 1981-2009 Crosson Dorset 56.7 9.2 25.0 5.42-5.81 1981-2009 Plastic Dorset 32.1 7.9 16.3 5.50-5.84 1980-2009 Aurora Whitepine Sudbury 84.3 7.0 21.3 4.79-5.39 1987-1988, 1990-2006 Daisy Sudbury 36.1 5.2 14.8 4.82-6.71 1991-93, 95, 97-2006 Little Whitepine Sudbury 18.4 12.0 5.93-6.73 1987-88, 1990-2006 Sans Chambre Sudbury 14.5 5.6 15.0 5.15-6.61 1980-81, 83, 85, 88-2006 Whitepine MacLeod Sudbury 66.9 5.9 22.0 5.26-6.48 1980-2006 Note: pH ranges for Dorset lakes calculated from annual means; IISD-ELA and Sudbury lakes from monthly data. Zmean was not available for Lake 223 and Little Whitepine Lake.
The IISD-ELA consists of 58 small lakes and their watersheds designated for environmental research (Blanchfield et al. 2009). Two of the lakes, Lake 223 and the south basin of Lake 302 (i.e., 302S), were acidified (H2SO4 inputs) to understand the biological, physical, and chemical effects of acidification (Schindler et al. 1985, 1990; Rudd et al. 1990). In addition, seven reference lakes from the IISD-ELA were included in this study: 224, 239, 240, 373, 375, 382, and 442 (Table 3-1). These lakes have not been impacted by acidification and have undergone few manipulations or experiments. For more information on these reference sites, see Appendix 2.
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Figure 3-2. Map of the study locations in Ontario, Canada. Sampling locations of individual lakes are shown in the insets. I included two minimally disturbed reference and three impacted zooplankton community data sets from Dorset, Ontario (Yan et al. 2008; Table 3-1, Figure 3-2). Both reference systems are clear-water, dimictic lakes that have never exhibited a pH below 6 (Rusak et al. 2008), whereas the impacted lakes all showed average pH values below 6 throughout the sampling period (Table 3-1; Figure S3-1). In addition, five zooplankton communities were included from lakes in the Sudbury, Ontario region (Table 3-1). Sudbury has a long history of acidification (Beamish and Harvey 1972; Beamish et al. 1975), and many zooplankton communities have shown signs of recovery (Keller et al. 2002; Yan et al. 2004; Keller et al. 2007). Little Whitepine and Aurora Whitepine lakes are located within the same watershed ~110 km northeast of Sudbury (Keller et al. 2008). Little Whitepine Lake was limed in 1989 to increase pH levels and restore habitat for the Aurora Trout (Salvelinus fontinalis timagamiensis; Snucins et al. 1995; Keller et al. 2008), whereas Aurora Whitepine Lake was allowed to recover naturally. Similarly, Whitepine MacLeod Lake, which is ~20 km southwest from Little Whitepine and Aurora Whitepine lakes (Figure 3-2), was allowed to recover naturally. Sans Chambre Lake is ~20 km north of Sudbury and Daisy Lake is located within the City of Greater Sudbury (Figure 3-2). In 1994, 37 ha of land on the east side of Daisy Lake were limed as part of a local watershed restoration study (Gunn et al. 2001).
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Lakes were sampled during the ice-free months with, on average, nine sampling events occurring per year per lake (SD = 3.5). Water quality and zooplankton sampling methods differed among the regions, although the methods were usually consistent within lakes over time. However, different sampling techniques were used before and after 1988 in Lake 373. For taxonomic consistency across studies, several zooplankton species or taxonomic groups were combined (see Palmer et al. 2013) due to differences in identification and changes in nomenclature over time. For a detailed description of the sampling techniques and nomenclature, see Appendix 2.
3.3.2
Multivariate analyses
I calculated principal components analyses (PCAs) to reduce the community data dimensionality and provide a framework for characterizing the trajectories of zooplankton communities over time (Figure 3-3; Lamothe et al. 2017). That is, I completed a PCA on the covariance matrix of Hellinger-transformed (Legendre and Gallagher 2001), annual-average crustacean zooplankton species abundances for all lakes and years. I also calculated a PCA on the correlation matrix of water-quality measures (i.e., calcium [Ca], chlorophyll-a [Chl-a], chloride [Cl-], iron [Fe], potassium [K], magnesium [Mg], manganese [Mn], sodium [Na], pH, total phosphorus [TP], and sulfate [SO4]). Water-chemistry sample sizes were lower than the zooplankton sample sizes, as the water-chemistry variables were not measured on an annual basis in all lakes (Table S3-1). I identified the number of nontrivial components to retain using a permutation approach. For both the water chemistry and zooplankton data sets, I permuted each column of the data matrix and conducted a PCA 1000 times. A component was retained if the proportion of variance explained in the empirical data exceeded 95% of the permuted PCAs for that component (Peres-Neto et al. 2003, 2005). A resistant Procrustes analysis was used to reveal concordance between the zooplankton and water chemistry ordinations (Siegel and Benson 1982; Rohlf and Slice 1990). Procrustes analysis is an approach that compares two data sets (here, principal components ordinations) by scaling and rotating the observations to minimize differences between the two data sets (Peres-Neto and Jackson 2001). In a typical Procrustes analyses, this approach is performed by minimizing the sum of squared deviations between corresponding observations (Claude 2008). Because I was interested in knowing which individual years showed deviations between the two data sets, I used a median resistant-fitting algorithm to identify years with the greatest deviation (Siegel and
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Benson 1982; Rohlf and Slice 1990; Claude 2008; Zhao et al. 2008) and plotted these distances over time (hereafter referred to as vector residual plots). I evaluated the significance of the resistant Procrustes analysis by permuting the principal component axes scores for the zooplankton and water chemistry ordinations, performing the resistant Procrustes analysis, and extracting the median vector residuals 99 times. A significance value was then calculated as the number of median vector residuals less than or equal to the observed value (including the observed), divided by 100.
Figure 3-3. Multivariate distance measures for quantifying the relative resistance and resilience of communities to disturbance using ordinations. a) An example of a single site undergoing directional change is highlighted with the principal component axis scores for two components shown. b) Distances between sequential observations are calculated to provide a measure of the relative magnitude of change year to year. Outlier-detection methods are used to determine whether distances indicate saltatorial or gradual change over time. c) Distances to a baseline centroid (dBase) calculated as the mean of the first two site scores. The triangle symbol represents the centroid. Plotted over time, dBase provides a measure of directionality relative to historical conditions. d) A Euclidean distance matrix is calculated from the principal component axis scores and the distances are plotted against time steps. A significant positive relationship indicates directional change over time.
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3.3.3
Distance-based metric analyses
I used distances in ordination space (Figure 3-3a) to quantify the relative resistance and resilience of lakes and to characterize each trajectory according to the hypothetical scenarios proposed by Matthews et al. (2013; Figure 3-1; Lamothe et al. 2017). Distances (d) between principal component axis scores in ordination space represent differences in observations. First, distances between sequential annual observations were calculated for individual lake trajectories (dMag) providing a value of the relative magnitude of change from year-to-year (Figure 3-3b). Second, for each individual lake within the ordination, I calculated the Euclidean distance of each observation to the centroid for the first two observations (Anderson and Thompson 2004), here described as the distance to a baseline or historical centroid (dBase; Figure 3-3c). I then plotted dBase over time to show lake changes relative to a historical condition (Figure 3-3c). Consistently obtaining short distances from the baseline centroid over time indicates that the community composition has not changed over time, whereas repeated large distances indicate a deviation from the historical condition over time. Note that Lake 223 only had one year of zooplankton sampling data prior to acid-addition treatment and therefore distances were calculated using this single observation as the baseline centroid. To evaluate the directionality of zooplankton community trajectories in each lake, I first calculated Euclidean distance between each pair of observations (i.e., distances on the ordination between different sampling time periods) for an individual lake within an ordination (Figure 33d). Distances for individual lakes were then plotted against time lags (Collins et al. 2000). For example, a lake with 10 sequential annual observations would have a total of 9 one-year time lags (Year 1 – 2, Year 2 – 3, etc.), 8 two-year time lags (Year 1 – 3, 2 – 4, etc.), 7 three-year time lags, and so on, totaling 45 time lags (Figure 3-3d). I then fit regression models of the Euclidean distances against time lags for each lake. I used an AIC approach to determine the best-fit model for each lake from three candidate models: an intercept model, a linear relationship between Euclidean distance and time lags, and a 2nd order polynomial of Euclidean distance versus time lags. Best models were chosen to show the lowest AIC value among the three candidate models or the simplest model when ∆AIC < 2. A linear relationship between Euclidean distances and time steps indicates a change in community structure or water chemistry and, therefore, a lack of resistance to change (Collins et
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al. 2000). A relationship where a zero slope lies within the 95% confidence intervals indicates a consistent community composition over time. A negative polynomial relationship would indicate that a lake is returning to the historical state. As in Collins et al. (2000), I used these relationships as a tool to classify trends in community composition over time, but I discourage their use for assessing statistical significance due to the lack of independence among observations. To complement to the regression analysis, I have included locally weighted smoothing (LOWESS) functions for each trajectory (Cleveland et al. 1988). Due to the heteroscedasticity of the Euclidean distance measures, I have also investigated how values at large time steps influence my characterization of directionality by reanalyzing the data when the largest 20% and 40% of time steps were removed from the analyses (Tables S3-6, S3-7). Simple linear regression models of dBase over time were calculated using the principal component axis scores for the reference-lake water chemistry and zooplankton communities to track changes over time among reference sites. In addition, I compared dBase over time and the Euclidean distances versus time lags between individual impacted lakes with the group of reference sites. I used the upper 90th percentiles of reference-lake annual distances to define the boundaries of reference-site distances traveled in ordination space. Trajectories of lakes falling outside the upper bound of reference conditions are, therefore, exhibiting changes outside the bounds of minimally disturbed reference communities, indicating relatively low resistance to change over time. I used five outlier detection tests on dMag to distinguish between gradual and saltatorial changes for individual lakes. An extreme dMag value indicates a year where the change in average species’ abundances across the ice-free season was significantly larger relative to changes among other consecutive years. I applied square-root transformations to dMag for lakes that showed significant deviations from a normal distribution prior to performing outlier tests (Shapiro-Wilks test, p < 0.05), with most conforming to normality post-transformation (Tables S3-3, S3-5). The five outlier detection methods included the generalized extreme studentized deviate test (ESD test; Rosner 1983), Cook’s (1977) distance, Horn et al.’s (2001) method, Dixon’s Q (1950), and van der Loo’s (2010) method. I report results of the ESD test to characterize trajectories as saltatory or gradual (as in Matthews and Marsh-Matthews 2016), as that method represented a middle of the road approach between the most conservative and most liberal measures (Dixon: 1 outlier detected across lakes; Horn: 8; ESD test: 10; Cook: 37; van der Loo; 55; Tables S3-3, S3-5).
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Lakes with one or more outliers were characterized as undergoing saltatorial change during the sampling period; those lakes where no outliers were detected were considered as undergoing gradual changes over time. All analyses were conducted using the R software (R Core Team 2017) ‘vegan’ (Oksanen et al. 2016), ‘referenceIntervals’ (Finnegan 2014), and ‘ggplot2’ (Wickham 2009) packages.
3.4 Results 3.4.1
Zooplankton and water chemistry ordinations
Three components were extracted for both the zooplankton community ordination and water chemistry ordination explaining 28.2%, 15.4%, and 10.6%, and 40.0%, 24.6%, and 11.4% of the total variation, respectively. For both ordinations, reference and impacted lakes formed two relatively distinct groups along the first component, with impacted communities spanning most of the second component (Figure S3-2). Site scores were also partitioned along the first axis by region within the zooplankton ordination (Figure S3-2). Data from the Sudbury region, which only contained impacted lakes, were found on the negative end of the first zooplankton community axis, whereas the IISD-ELA and Dorset region data contained both reference and impacted lakes and were found in the middle and positive end of the first component, respectively. For clarity, I present the results of the zooplankton communities and water chemistry separately and close with a comparison of results.
3.4.2
Reference-lake zooplankton communities
Reference-lake zooplankton communities showed linear trends away from the baseline centroid over time (Figure S3-3) and a linear relationship between Euclidean distances and time steps (Figure S3-4) indicating a changing reference condition over time. Both reference lakes from the Dorset region (Blue Chalk and Red Chalk Main) showed gradual shifts away from the historical centroid over time (Figure 3-4). Variation was seen among the dBase distances for the IISD-ELA reference lakes. Lake 442 showed a small, but gradual increase in dBase over time, whereas Lake 382 showed an increase in dBase to 1989 followed by a return towards the historical centroid (Figure 3-4). Seven of the nine reference communities showed relationships between Euclidean distances and time steps; two communities showed positive linear trends, four showed negative
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polynomial functions, and one showed a positive polynomial trend (Figure 3-5; Table S3-2 for regression summaries). Only reference communities in lakes 240 and 375 showed no relationship in Euclidean distances against time steps (Table S3-2), indicating non-directional trends over time (Table 3-2). One outlier was detected in dMag for the Lake 240 zooplankton community from 1989-1990 indicating a saltatorial change (Table S3-3); the other reference communities all showed gradual patterns year-to-year (Table 3-2).
Figure 3-4. Distances to baseline centroids over time for the 10 impacted lake zooplankton communities (filled points; top two rows) and nine reference communities (filled points; bottom two rows). Mean reference values plotted (open points) with the 90th percentile forming the upper limit of the reference distribution (gray ribbon).
3.4.3
Zooplankton communities from atmospherically acidified lakes
All zooplankton communities from lakes impacted by acid precipitation showed either a directional or a directional-with-recovery trajectory (Table 3-2). Measures of dBase (Figure 3-4) and the Euclidean distances against time lags (Figure 3-5) were increasing for the Dorset zooplankton communities over time indicating a general lack of resistance to change. The Chub Lake zooplankton community experienced a shift in dBase in 1989, followed by a period of relative stability (Figure 3-4). Both Plastic and Crosson lakes zooplankton communities showed fluctuations in dBase over time, entering and exiting the reference distribution on multiple occasions (Figure 3-4). No outliers were detected between annual observations (dMag) for the any of the impacted-lake zooplankton communities indicating gradual changes over time (Table 3-2).
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Figure 3-5. Regressions of Euclidean distances between zooplankton PCA site scores and time steps for the 10 impacted (filled points; top two rows) and nine reference communities (filled points; bottom two rows). Mean reference values plotted (open points) with the 90th percentile forming the upper limit of the reference distribution (light gray ribbon). Best-fit models (solid lines) provided with 95% confidence intervals (dark gray ribbon). LOWESS models represented by dashed lines often overlapped the best-fit model. Among the Sudbury zooplankton communities, three communities showed directional change over time (Daisy, Little Whitepine, Sans Chambre) and two showed directional change with a return to a historical state (Aurora Whitepine, Whitepine MacLeod; Table 3-2). The trajectory for dBase over time (Figure 3-4) and the relationship between Euclidean distances and time lags (Figure 3-5) indicated that the Daisy Lake zooplankton community was changing throughout the sampling period. Little Whitepine zooplankton showed changes from the late 1980s into the late 1990s followed by a period of relative stability (1996 – 2006) away from the initial sampling observations (Figure 3-4). Distances to the historical centroid for the Sans Chambre Lake zooplankton community showed an initial spike in 1982 and then gradually increased over time (Figure 3-4). An increasing trend in Euclidean distance and time lag for the Sans Chambre community indicates a changing community; however, this change is consistent with changes in the reference communities (Figure 3-5).
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Table 3-2. Characterization of study lakes to the hypothetical scenarios presented by Matthews et al. (2013). Zooplankton Water Chemistry No. of No. of Site Trajectory Type Trajectory Type outliers outliers Reference Blue Chalk 0 Gradual, directional 0 Gradual, dir. w/ rec. Red Chalk 0 Gradual, directional 0 Gradual, dir. w/ rec. 224 0 Gradual, dir. w/ rec. 0 Gradual, directional 239 0 Gradual, directional 0 Gradual, dir. w/ rec. 240 1 Saltatory, non-directional 0 Gradual, directional 373 0 Gradual, dir. w/ rec. 0 Gradual, directional 375 0 Gradual, non-directional 2 Saltatory, directional 382 0 Gradual, dir. w/ rec. 0 Gradual, directional 442 0 Gradual, dir. w/ rec. 0 Gradual, directional Impacted 223 0 Gradual, dir. w/ rec. 1 Saltatory, dir. w/ rec. 302S 0 Gradual, dir. w/ rec. 0 Gradual, dir. w/ rec. Chub 0 Gradual, directional 0 Gradual, directional Crosson 0 Gradual, directional 2 Saltatory, dir. w/ rec. Plastic 0 Gradual, directional 0 Gradual, directional Aurora Whitepine 0 Gradual, dir. w/ rec. 0 Gradual, directional Daisy 0 Gradual, directional 0 Gradual, dir. w/ rec. Little Whitepine 0 Gradual, directional 2 Saltatory, directional Sans Chambre 0 Gradual, directional 0 Gradual, directional Whitepine MacLeod 0 Gradual, dir. w/ rec. 2 Saltatory, directional A negative polynomial function was observed for the Euclidean distances as a function of time lags for both the Aurora Whitepine and Whitepine MacLeod zooplankton communities (Figure 3-5) indicating a return to a historical condition over time. However, in contrast to the Aurora Whitepine trajectory, dBase over time for the Whitepine MacLeod zooplankton community indicates that this return may be incomplete. The most recent observations for the Aurora Whitepine zooplankton community indicate that they have not returned to the historical centroid. There were no outliers detected for either the Aurora Whitepine or Whitepine MacLeod zooplankton communities (Table S3-3) indicating gradual changes over time (Table 3-2).
3.4.4
Zooplankton communities subjected to experimental acidification
Both Lake 223 and Lake 302S zooplankton communities were characterized as undergoing gradual, directional change with recovery (Table 3-2). The Lake 223 zooplankton community showed an increase in dBase in 1981 and the community trajectory has slowly been returning to
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the reference condition (Figure 3-4). This change is consistent with the polynomial trajectory for Euclidean distances against time steps (Figure 3-5). Similarly, the trajectory of the Euclidean distances versus time steps for Lake 302S zooplankton communities was polynomial (Figure 35). However, the trajectory of dBase over time indicated that the Lake 302S community has further deviated from the historical state since 2003 (Figure 3-5).
3.4.5
Reference lake water chemistry
Water chemistry for the reference lakes showed a linear trend away from the historical centroid over time (Figure S3-5) and a linear relationship between Euclidean distances and time steps indicating changing lake water chemistry over time (Figure S3-6). The IISD-ELA showed the greatest deviation from the historical centroid over time (Figure 3-6), and overall, the greatest amount of change across the sampling period (Figure 3-7). All reference lakes showed declines in Ca, Cl-, K, Mg, Na, and SO4 during the sampling period, with fluctuations in Chl-a, Fe, Mn, and TP (Figures S3-7 – S3-10). All the reference lake systems showed linear or polynomial relationships between Euclidean distances and time steps (Figure 3-7; Table S3-4). The reference lakes from the Dorset region (Blue Chalk, Red Chalk Main), Lake 239, and Lake 375 were characterized as undergoing directional change with a return to a historical centroid, whereas lakes 224, 240, 373, 382, and 442 were all considered to be undergoing directional change. Two outliers were detected between annual observations of water chemistry for Lake 375 indicating saltatorial changes (Table S3-5); all the other reference lakes had no outliers indicating gradual changes over time (Table 3-2).
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Figure 3-6. Distances to baseline centroids over time for the 10 impacted lakes (filled points; top two rows) and 9 reference lakes (filled points; bottom two rows) for water chemistry. Mean reference values plotted (open points) with the 90th percentile forming the upper limit of the reference distribution (gray ribbon).
Figure 3-7. Regression models of Euclidean distances between PCA site scores of annual water chemistry data and time-steps for all known impacted (filled points; top two rows) and reference communities (filled points; bottom two rows). Mean reference values plotted (open points) with the 90th percentile forming the upper limit of the reference distribution (light gray ribbon). Best-fit models (solid lines) provided with 95% confidence intervals (dark gray ribbon). LOWESS models represented by dashed lines, often overlapping bestfit model.
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3.4.6
Atmospherically acidified lake chemistry
Water chemistry among Dorset lakes impacted by atmospheric acidification has continued to deviate from the historical condition since the initial observations (Figure 3-6). All impacted Dorset lakes showed declines in Ca, TP, and SO4 during the sampling period (Figures S3-7, S39, S3-10). Crosson Lake showed a consistent increase in dBase over time, except in 1990, which deviated substantially from the reference distribution (Figure 3-6). This jump corresponded with an outlier detected among distances between annual observations (Table S3-5) and a sharp increase in Chl-a concentration (Figure S3-7), with elevated TP levels (Figure S3-10). A negative polynomial relationship was found for the Crosson Euclidean distance and time step regression, indicating a return to a historical condition (Figure 3-7; Table S3-4). In contrast, Chub and Plastic lakes both showed linear relationships between Euclidean distance and time steps indicating a lack of resistance to change (Figure 3-7; Table S3-4). In addition to the declines in Ca, TP, and SO4 observed among the Dorset lakes, there were also declines in Fe, K, and Mg in Plastic Lake (Figures S3-7 – S3-10). Among the Sudbury lakes, dBase values steadily increased over time (Figure 3-6), indicating a continued change from the historical sampling period. Declines in Ca and SO4 were observed among all the study lakes from the Sudbury region (Figures S3-7, S3-9). In Sans Chambre Lake, declines were also observed in Fe, K, Mn, and Na, with a spike in Mg in 2002 (Figures S3-8, S39). Among the Whitepine lakes, declines in Cl-, K, Mg, and Mn were observed in Whitepine MacLeod, with a spike of Fe in 1984 that coincides with outlier detection methods (Table S3-5; Figures S3-7 – S3-10). There have also been declines K, Mg, and Mn in Aurora Whitepine Lake, as well as Chl-a since 1999 (Figures S3-7 – S3-9). Declines in Cl- and Mg were observed in Little Whitepine, with a spike in K and Na in 1994, consistent with outlier detection methods (Table S3-5; Figures S3-7, S3-8). Finally, patterns of water chemistry in Daisy Lake differed from the other Sudbury lakes, as declines in Ca have stabilized since the early 1990s and there have been increases in Chl-a, Cl-, Mg, and TP observed over time (Figures S3-7, S3-8, S3-10). Daisy Lake was the only Sudbury lake to show a negative polynomial function between Euclidean distance and time steps (Table S3-4); however, there were relatively few points for the calculation of this relationship. The remaining Sudbury lake trajectories all showed directional changes over time (Table 3-2).
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3.4.7
Water chemistry for the experimentally acidified lakes
Both lakes 223 and 302S showed negative polynomial relationships between Euclidean distance versus time step indicating lake chemistry recovering over time to earlier conditions (Table S3-4; Figure 3-7). However, recovery in Lake 302S is incomplete; dBase over time indicated that the water chemistry deviated from the historical state in 1982, began tracking back towards the historical state in 1991, and then began moving away from the historical state in 1995 (Figure 36). Among individual water chemistry parameters for Lake 302S, there were observed increases in Ca, Fe, K, Mg, Mn, and Na until the early 1990s with subsequent patterns of decline (Figures S3-7 – S3-9). For Lake 223, dBase immediately deviated from historical conditions following acidification in 1978, peaked in 1982, and then tracked back towards the historical condition, maintaining conditions consistent with reference lakes from 1992 onwards (Figure 3-6). Among individual water chemistry parameters, Ca and Mg increased in Lake 223 until the early 1990s with a subsequent decline to values below pre-acidification levels by 1998 (Figures S3-7, S3-8). Increases were observed in Na until 1985, followed by a period of decline (Figure S3-9). Similarly, declines in K have occurred since 1985 (Figure S3-8), whereas Cl- and Fe have generally declined over the entirety of the sampling period (Figures S3-8, S3-9). One outlier was detected for the Lake 223 water chemistry data that coincides with a spike in Mn observed in 1982 (Figure S3-9) indicating a period of saltatorial change (Table S3-5). No outliers were detected in Lake 302S water chemistry indicative of gradual change over time (Table 3-2).
3.4.8
Comparison between water chemistry and zooplankton communities
The was significant concordance among ordinations of water chemistry and zooplankton communities (Procrustes observed median: 0.043; nperm= 99; p ≤ 0.01). Reference lakes showed significantly greater average concordance between zooplankton and water chemistry ordinations compared to impacted lakes (t361.1 = -7.73, p < 0.001; Figure 3-8). Among the experimentally acidified lakes, the mismatch between zooplankton and water chemistry ordinations was greatest for observations obtained during the most acidic periods of sampling indicating an overall decoupling in responses (Figures 3-8, S3-1). For Lake 302S, the magnitude in the Procrustean vector residuals between the two ordinations follows a similar trajectory to dBase over time (Figures 3-4, 3-6); here, differences between ordinations are greater during periods when the lake
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has deviated from the historical condition and this is evidence of the zooplankton community and water chemistry patterns having different responses during this period of acidification. In Crosson Lake, a spike in the residuals between ordinations was observed in 1990, consistent with outlier detection methods, indicating a decoupling between zooplankton and environmental observations (Table S3-5). Similarly, two outliers were detected among distances between sequential observations for Whitepine MacLeod water chemistry (Table 3-2), consistent with the spike in residuals (Figure 3-8). However, two outliers were also detected among distances between water chemistry observations for Little Whitepine (Table S3-5), where patterns of residual variation between ordinations have increased over time with no obvious peak among years. Instead, years where outliers were detected (1993 – 1995) mark the beginning of an increasing trend in the magnitude of residuals between ordinations indicating a period of change across the sampling period.
Figure 3-8. Vector residuals from the comparison of crustacean zooplankton community composition with lake chemistry for impacted lakes (top two rows) and reference lakes (bottom two rows). No bars indicate missing years of matching data.
3.5 Discussion The objectives of this study were to characterize the resistance and resilience of zooplankton communities to environmental change and relate the trajectories of zooplankton communities over time to the hypothetical scenarios presented by Matthews et al. (2013; Figure 3-1). I found that the composition of zooplankton communities has changed among most of the studied lakes,
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whether they had experienced anthropogenic acidification or not. My expectation that reference communities would display gradual, non-directional trajectories over time was generally not met. Instead, three reference communities showed directional trends over time and four showed directional patterns with a return to a historical composition. Furthermore, one reference community showed a saltatorial response indicating large annual changes in community composition. Conversely, my expectations were met for zooplankton communities within the experimentally acidified lakes, which showed gradual, directional trajectories with subsequent recovery. As expected, crustacean zooplankton communities in lakes acidified by atmospheric inputs showed varying trajectories over time. I also found that water chemistry was changing among the study lakes, whether impacted by acidification or not, explaining the variable patterns of resistance and resilience to change in zooplankton communities. I found patterns of change over time among both reference and impacted zooplankton communities. However, zooplankton communities from lakes that were impacted by acidification generally exhibited greater magnitudes of change than minimally disturbed reference lakes. Furthermore, variation exists in the relative influence of anthropogenic acidification among the study lakes (Keller et al. 1992). Lakes in the Sudbury region likely experienced the greatest impact from acidification due to the proximity of lakes to the world’s largest source of sulfur dioxide (Beamish 1976) and length of exposure (mining operations began in late 19th century; Gunn 1996), whereas lakes in the Dorset region were primarily acidified through longer range transport of sulfur and nitrogen from southern regions of Canada and the Ohio River Valley (Kurtz et al. 1984). I chose to examine lakes that were relatively free of direct human disturbance (e.g., lakeshore development) to isolate the effects of acidification. However, regional activity on the landscape can also produce some of the changes observed (e.g., forestry practices; Keller et al. 2001; Watmough et al. 2003) and likely contributed to the cumulative effects observed from acidification and a changing climate. Furthermore, regional differences in the rates of change over time were likely influenced by differences in management strategies (e.g., liming; Gunn et al. 2001; Keller et al. 2008) and the overall regional buffering capacity (Keller and Gunn 1995). The experimentally acidified lake zooplankton communities (223 and 302S) both showed trajectories consistent with the manipulations. However, trajectories of the Lake 223 and Lake 302S zooplankton communities’ post-manipulation have since tracked away from the historical
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centroid. These directional patterns post-manipulation are consistent with the reference lakes, where warming of lakes (Schindler et al. 1996a, b; Guzzo and Blanchfield 2017), changes in dissolved organic carbon (Schindler et al. 1996a, b), and declines in Ca (Jeziorski et al. 2008, 2014) have contributed to alterations in the zooplankton community structure. Compared to the reference systems, the magnitude of change observed in the Lake 302S zooplankton community post-1995 surpasses expectations based on climate alone and may suggest lag effects from acidification. That is, declines in annual means of Ca, Cl-, Fe, K, Mg, Mn, Na, SO4, and Chl-a have occurred since 1995. Despite the observation that patterns of water chemistry in the experimental lakes showed relatively large changes over time compared to the other regions, the reference zooplankton communities showed less change over time. Two communities (lake 375 and 240) were characterized as undergoing non-directional change over time, three communities (lakes 373, 382, and 442) were characterized as undergoing gradual, directional change with recovery, and one community (Lake 239) was characterized as gradual, directional change over time. Because of the heteroscedastic nature of the Euclidean distance and time steps regression, we expected that the long time steps may be driving the conclusions on community trajectories. When removing observations having larger time steps in the zooplankton and water chemistry analyses, we did see changes in conclusions for lakes 224, 382, and 442, and lakes 239, 375, and 382, respectively (Tables S3-6 and S3-7). There were no changes observed for lakes 240 and 373 when we removed observations. Among the five Dorset lake zooplankton communities included in the study, all showed gradual, directional change over time (sensu Matthews et al. 2013) with water-chemistry patterns generally consistent across lakes. These zooplankton communities have all experienced Ca and TP declines (Redmond et al. 2016; Yan et al. 2008), changes in dissolved organic carbon (Yan et al. 2008), relatively stable pH (Figure S3-1), and changes in phytoplankton community composition (Paterson et al. 2008). Over the last 30 years, Red Chalk and Blue Chalk zooplankton species richness has increased (Yan et al. 2008), likely contributing to the changes seen in multivariate distance measures over time. Redmond et al. (2016) found that pH values in Crosson Lake are now approaching pre-industrial levels (pH 6). The observed outlier and spike in zooplankton abundance in 1990 likely resulted from a spike in TP. Total phosphorus peaked in 1990 relative to other sampling years, matching an increase in biomass of phytoplankton,
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particularly Synura spp. (Paterson et al. 2008) and zooplankton, including Diaphanosoma spp., Eubosmina spp., Diaptomus minutus, and Mesocyclops edax. In addition to the changes in water chemistry in Plastic Lake, drought-induced re-acidification has occurred during El Niño years, delaying the recovery of zooplankton communities from acidification (Dillon et al. 1997; Rusak et al. 2008). Given the history of acidification in the Sudbury region, I expected and observed variation in the resistance and resilience of lake zooplankton communities to change over time. Clear patterns of recovery in water chemistry were demonstrated by the reductions in SO4 concentrations and increases in pH resulting from reduced emissions in the region. Further, species richness has increased over time in all of the studied Sudbury zooplankton communities (Data not shown). Among the closest lakes to historical smelting operations, Daisy and Sans Chambre zooplankton communities both showed gradual, directional trajectories over time coinciding with changes in water chemistry. The pH in both lakes increased above the threshold for acid sensitive species with reductions in SO4 (Labaj et al. 2014; Gunn et al. 2016), but differed among other waterchemistry parameters (e.g., TP, Chl-a, Mg). Keller et al. (2002) found that, although species richness values were increasing over time in Sans Chambre Lake, the composition of zooplankton communities failed to reflect that of reference communities. The authors hypothesized that high densities of Chaoborus may be restricting other larger-bodied crustacean zooplankton (e.g., Daphnia spp.), therefore limiting patterns of recovery. Using long-term monitoring data and paleolimnological surveys, Labaj et al. (2014) pointed to the continued presence of Chydorus brevilabris as an indicator that the Daisy Lake zooplankton community was still in recovery; C. brevilabris is known to tolerate highly acidic (e.g., pH 3; Belyaeva and Deneke 2007) and metal-contaminated lakes (Cattaneo et al. 1998; Labaj et al. 2014). Farther north, the Whitepine MacLeod and Aurora Whitepine zooplankton communities both showed directional trajectories with subsequent patterns of return to a historical state, consistent with previous studies documenting initial stages of recovery (Gunn and Keller 1990; Schindler et al. 1991; Keller et al. 2002, Keller et al. 2007; Keller et al. 2008). Species richness in both lakes has increased over the period of sampling, while water chemistry continued to change (e.g., declines in Ca, K, Mg, Mn, SO4). Declines in Ca concentrations are widespread among Sudbury lakes, likely because of climate change, regional forest harvesting, and leaching due to acid deposition (Keller et al. 2001). Similarly, emissions reductions and a subsequent reduction in
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leaching can also lead to the observed declines in K, Mg, and Mn among lakes (Keller and Gunn 1995). The Little Whitepine zooplankton community showed directional change over time. In addition to the declines observed in Ca, Mg, and SO4 concentrations among all three Whitepine lakes (Keller et al. 2008), decreases in Cl- were also observed in Little Whitepine, however, the trajectory of the zooplankton community in ordination space has not returned toward the historical centroid. As the Sudbury zooplankton biomonitoring data used in this study date back to the late 1970s and early 1980s, trajectories of “recovery” reflect continued movement in ordination space towards conditions similar to that of a historical state. Activities on the landscape, such as forestry operations, may also be influencing water chemistry patterns and influencing zooplankton communities. Watmough et al. (2003) demonstrated that forest harvest scenarios in south central Ontario resulted in an overall removal of base cations leading to lower overall Ca concentrations in lakes. Calcium is a critical element for the development of zooplankton carapaces (Wærvågen et al. 2002) and declines in calcium can have negative consequences for freshwater zooplankton (Jeziorski et al. 2008), as well as other crustaceans (e.g., crayfishes; Edwards et al. 2009). In fact, all regions of Ontario have seen declines in Ca. Keller et al. (2001) attributed these declines to a combination of forestry practices, climate change, and leaching from the landscape due to acid deposition in the Sudbury region. Among the IISD-ELA lakes, the effects of forestry practices were not observed, and the declining Ca levels have likely resulted from the effects of drought and a changing climate (Jeziorski et al. 2014). Water chemistry among the Sudbury, Dorset, and IISD-ELA lakes all showed patterns of change that can at least partially be attributed to the effects of climate change. In Ontario, the effects of climate change are expected to vary among lakes and our overall knowledge of these effects is continuing to grow (Keller 2007). Projected impacts to lakes in general include changes in lake water levels (Gronewold et al. 2013), temperature (Palmer et al. 2014; O’Reilly et al. 2015), thermal stability (Hadley et al. 2014), ice phenology (Yao et al. 2013; Mason et al. 2016), transparency (Schindler et al. 1996a, b), community structure (Van Zuiden et al. 2016; Poesch et al. 2016), and habitat structure (Guzzo and Blanchfield 2017), among others (reviewed by Adrian et al. 2009). Furthermore, the effects of climate on lake systems will interact with other anthropogenic factors, for example, acidification and eutrophication (Keller 2007; O’Reilly et al. 2015). Continuing to monitor freshwater zooplankton and lake water chemistry into the future
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will provide important observational data on the interacting impacts of multiple stressors, including climate change, and the overall resistance and resilience of freshwater ecosystems. More broadly, quantifying the resistance and resilience of freshwater communities is an increasingly important challenge as environmental conditions continue to change. Resistance and resilience of ecosystems are multidimensional properties that result from interactions of the structures and components of the present-day systems, as well as the legacy effects from historical disturbances (e.g., Lake 2013). Approaches for quantifying resistance and resilience continue to be developed (e.g., Orwin and Wardle 2004; de Keersmaecker et al. 2014; Angeler and Allen 2016; Lamothe et al. 2017) and approaches often differ based on the system, question, and data available. The use of multivariate distance-based approaches for quantifying resistance and resilience I demonstrate provides a visual framework that can be applied to abiotic and biotic components of ecosystems (e.g., water chemistry, community composition) and compared between components (i.e., Procrustes analysis). Herein, I have demonstrated that several lake zooplankton communities from Ontario have shown a lack of resistance to change over time with variation in resilience. This change in zooplankton composition coincides with changes in the composition of other Ontario freshwater lake communities like phytoplankton (Findlay et al. 2001; Paterson et al. 2008; Hadley et al. 2013; Barrow et al. 2014) and freshwater fishes (Alofs et al. 2014; Poesch et al. 2016; Lynch et al. 2016). Characterizing patterns of resistance and resilience among lake communities would be a far greater challenge in the absence of the long-term monitoring programs that have collected data for over three decades. As management efforts take an ecosystem approach with aims for maintaining resistance and resilience of ecosystems to disturbance (e.g., Allen et al. 2011; Kingsford et al. 2011; Poesch et al. 2016), these data provide invaluable baseline conditions for comparisons with future freshwater communities. Although lake ecosystem conditions today are different than they were 30 years ago (Palmer et al. 2011), and likely how they will be in 30 years, maintaining freshwater community data collection efforts and performing experimental manipulations is essential for understanding and quantifying the relative resistance and resilience of freshwater ecosystems to future disturbance.
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3.6 Acknowledgements I would like to thank W.B. Keller, M.J. Paterson, and J.A. Rusak for providing the biomonitoring data from the Sudbury, IISD-ELA, and Dorset regions, respectively. W.B. Keller and M.J. Paterson also assisted with making the zooplankton taxonomy consistent across sites. I would also like to thank M.J. Fortin, W.B. Keller, M.J. Paterson, and B.J. Shuter for comments on earlier drafts and aiding in the conceptual development of the manuscript.
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Supplementary Tables and Figures Table S3-1. Years of sampling for water chemistry and zooplankton communities among reference and impacted lakes from the Dorset region, Sudbury region, and Experimental Lakes Area (IISD-ELA). Water Chemistry Zooplankton Lake Region Years sampled Years sampled Reference Blue Chalk Dorset 1981-2005 1980-2009 Red Chalk Main Dorset 1981-2005 1980-2009 224 IISD-ELA 1982-83, 92-93, 99-2008, 10 1982-83, 92-93, 99-2012 239 IISD-ELA 1980-2008, 10 1980-2012 240 IISD-ELA 1988-92, 2000-06 1988-92, 2000-2006 373 IISD-ELA 1983-86, 88-90, 92, 94-08, 10 1983-1986, 88-2012 375 IISD-ELA 1989-92, 2001, 04-08, 10 1987, 89-92, 2001-02 382 IISD-ELA 1985-1994 1985-1994 442 IISD-ELA 1994-96, 98-2008, 10 1988-1996, 1998-2012 Impacted 223 IISD-ELA 1977-99, 2001-2004 1974, 77-2004 302S IISD-ELA 1980-2004, 07-08 1980-2004, 07-08, 10 Chub Dorset 1981-2005 1981-2009 Crosson Dorset 1981-2005 1981-2009 Plastic Dorset 1981-2005 1980-2009 Aurora Whitepine Sudbury 1987-88, 1990-2006 1987-1988, 1990-2006 Daisy Sudbury 1992-93, 95, 97-2006 1991-93, 95, 97-2006 Little Whitepine Sudbury 1990-2006 1987-88, 1990-2006 Sans Chambre Sudbury 1983, 85, 88-2006 1980-81, 83, 85, 88-2006 Whitepine Sudbury 1982-2006 1980-2006 MacLeod
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Table S3-2. Summary of regressions of Euclidean distances and time steps for each zooplankton community. Lake Equation AIC |∆AIC| Reference lakes Intercept model -522.12 3465.26 Linear model -3987.39 0 Quadratic model -3985.39 2.00 Blue Chalk Intercept model 67.01 782.70 Linear model -715.69 0 Quadratic model -716.44 0.75 Red Chalk Main Intercept model 138.57 988.92 Linear model -842.40 7.95 Quadratic model -850.35 0 Lake 224 Intercept model -116.99 239.77 Linear model -340.27 16.49 Quadratic model -356.76 0 Lake 239 Intercept model -96.97 884.86 Linear model -981.83 0 Quadratic model -979.86 1.97 Lake 240 Intercept model 10.04 100.28 Linear model -90.24 0 Quadratic model -89.07 1.17 Lake 373 Intercept model -644.12 778.18 Linear model -1405.54 16.76 Quadratic model -1422.30 0 Lake 375 Intercept model -35.59 49.01 Linear model -84.60 0 Quadratic model -82.77 1.83 Lake 382 Intercept model -13.02 81.31 Linear model -89.88 4.45 Quadratic model -94.33 0 Lake 442 Intercept model -297.24 536.70 Linear model -831.74 2.2 Quadratic model -833.94 0 Imp223 Intercept model 569.00 836.19 Linear model -233.64 33.55 Quadratic model -267.19 0 Imp302S Intercept model 501.02 689.92 Linear model -168.74 19.98 Quadratic model -188.72 0 Chub Intercept model 271.10 763.86 Linear model -492.76 0 Quadratic model -491.07 1.69 Crosson Intercept model 422.57 732.05 Linear model -309.48 0 Quadratic model -307.56 1.92
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Table S3-2 cont. Lake Plastic
Aurora Whitepine
Daisy
Little Whitepine
Sans Chambre
Whitepine MacLeod
Equation Intercept model Linear model Quadratic model Intercept model Linear model Quadratic model Intercept model Linear model Quadratic model Intercept model Linear model Quadratic model Intercept model Linear model Quadratic model Intercept model Linear model Quadratic model
AIC 160.00 -613.52 -612.68 84.64 -210.88 -237.55 84.29 -129.60 -130.18 172.32 -192.25 -190.32 -89.39 -583.49 -584.40 465.60 -270.12 -278.21
|∆AIC| 773.52 0 0.84 322.19 26.67 0 213.89 0 0.58 364.57 0 1.93 494.10 0 0.91 743.81 8.09 0
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Table S3-3. Years of outliers detected from zooplankton data using five different outlier detection methods: Horn et al. (2001), Cook (1977), Dixon (1950), van der Loo (2010), and the generalized extreme studentized deviate test (ESD; Rosner 1983). Horn Cook Dixon van der Loo ESD Reference Blue Chalk 2007-2008 2007-2008 Red Chalk Main 224 1992-1993 239 240 1989-1990 1989-1990 1 373 1985-1986 1985-1986 375 1987-1989 1989-1990 382 1989-1990 442 Impacted Lake 223 1978-1979 1978-1979, 1981-1982 Lake 302S Chub 1988-1989 1988-1989 1995-1996 1995-1996 Crosson 2008-2009 2007-2008 2006-2007, 2008-2009 2007-2008, 2008-2009 Plastic 1982-1983 1982-1983 1982-1983 1986-1987 1986-1987 1985-1986 1986-1987 Aurora Whitepine 1996-1997 1996-1997, 1997-1998 Daisy 2004-2005 1991-1992, 2004-2005 Little Whitepine 1994-1995 1994-1995 Sans Chambre 1997-1998 1997-1998 Whitepine MacLeod 1982-1983 1982-1983 Note: *Data do not conform to normality after square-root transform.
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Table S3-4. Summary of regressions of Euclidean distances and time steps for lake water chemistry. Lake Equation AIC |∆AIC| Reference lakes Intercept model 6950.10 2534.10 Linear model 4416.00 0 Quadratic model 4417.41 1.41 Blue Chalk Intercept model 565.46 525.40 Linear model 64.27 24.21 Quadratic model 40.06 0 Red Chalk Main Intercept model 624.12 617.87 Linear model 15.18 8.93 Quadratic model 6.25 0 Lake 224 Intercept model 875.54 851.10 Linear model 24.44 0 Quadratic model 23.14 1.3 Lake 239 Intercept model 1908.11 1290.19 Linear model 642.15 24.23 Quadratic model 617.92 0 Lake 240 Intercept model 617.81 396.70 Linear model 221.11 0 Quadratic model 223.04 1.93 Lake 373 Intercept model 881.26 530.47 Linear model 350.79 0 Quadratic model 352.78 1.99 Lake 375 Intercept model 108.65 41.44 Linear model 67.21 0 Quadratic model 67.81 0.6 Lake 382 Intercept model 131.39 78.18 Linear model 53.21 0 Quadratic model 54.83 1.62 Lake 442 Intercept model 311.35 186.16 Linear model 125.19 0 Quadratic model 126.08 0.89 Imp223 Intercept model 1938.19 560.91 Linear model 1396.21 18.93 Quadratic model 1377.28 0 Imp302S Intercept model 2259.35 625.82 Linear model 1689.86 56.33 Quadratic model 1633.53 0 Chub Intercept model 955.32 485.92 Linear model 469.40 0 Quadratic model 471.03 1.63 Crosson Intercept model 1255.00 203.22 Linear model 1056.14 4.36 Quadratic model 1051.78 0
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Table S3-4 cont. Lake Plastic
Aurora Whitepine
Daisy
Little Whitepine
Sans Chambre
Whitepine MacLeod
Equation Intercept model Linear model Quadratic model Intercept model Linear model Quadratic model Intercept model Linear model Quadratic model Intercept model Linear model Quadratic model Intercept model Linear model Quadratic model Intercept model Linear model Quadratic model
AIC 683.52 58.13 59.12 566.55 127.99 126.63 407.28 192.03 179.87 548.86 390.95 392.92 987.88 624.62 626.19 1263.22 859.29 859.88
|∆AIC| 625.39 0 0.99 438.56 0 1.36 227.41 12.16 0 157.91 0 1.97 363.26 0 1.57 403.93 0 0.59
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Table S3-5. Years of outliers detected from water chemistry data using five different outlier detection methods: Horn et al. (2001), Cook (1977), Dixon (1950), van der Loo (2010), and the generalized extreme studentized deviate test (ESD; Rosner 1983). Horn Cook Dixon van der Loo ESD Reference Blue Chalk 1995-1996 1995-1996 1998-1999 1998-1999 Red Chalk Main 1985-1986 2004-2005 2003-2004 2004-2005 2004-2005 224 1994-1995 1994-1995 239 1990-1991 1990-1991 1996-1997 1996-1997 1997-1998 240 2005-2006 1991-1992 2005-2006 373* 2006-2007 2006-2007 2007-2008 2007-2008 375 1991-1992 2 382 1985-1986 1987-1988 442 1999-2000 2000-2001 Impacted Lake 223 1981-1982 1981-1982 1981-1982 1 1993-1994 Lake 302S Chub 1982-1983 1982-1983 2003-2004 2003-2004 Crosson* 1989-1990 1989-1990 2 1990-1991 1990-1991 1996-1997 Plastic 1998-1999 1998-1999 Aurora Whitepine 1991-1992 1991-1992 Daisy 1999-2000 1999-2000 Little Whitepine* 1998-1999 1993-1994 1993-1994 2 1994-1995 1994-1995 Sans Chambre 1992-1993 1992-1993 Whitepine 1983-1984 1984-1985 1983-1984 2 MacLeod* 1984-1985 1984-1985 Note: *Data do not conform to normality after square-root transform.
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Table S3-6. Differences in the characterization of zooplankton community directionality when removing the largest ~20% and ~40% of time steps from regression analyses. For example, the maximum time step for Aurora Whitepine was 19. 19-(19*.2) = 15.2 = 15 time steps included in the analysis. Original 20% removal 40% removal TimeTimeTimeLake Directionality Directionality Directionality steps steps steps Reference Blue Chalk 29 Directional 23 Directional 17 Directional Red Chalk 29 Directional 23 Directional 17 Directional Main Lake 224 30 Dir. w/ rec. 24 Dir. w/ rec. 18 Directional Lake 239 32 Directional 26 Directional 19 Directional Lake 240 18 Non-directional 14 Non-directional 11 Non-directional Lake 373 29 Dir. w/ rec. 23 Dir. w/ rec. 17 Dir. w/ rec. Lake 375 15 Non-directional 12 Non-directional 9 Non-directional Lake 382 9 Dir. w/ rec. 7 Non-directional 5 Non-directional Lake 442 24 Dir. w/ rec. 19 Dir. w/ rec. 14 Directional Impacted 223 30 Dir. w/ rec. 24 Dir. w/ rec. 18 Directional 302S 30 Dir. w/ rec. 24 Dir. w/ rec. 18 Dir. w/ rec. Chub 28 Directional 22 Directional 17 Dir. w/ rec. Crosson 28 Directional 22 Directional 17 Directional Plastic 29 Directional 23 Directional 17 Directional Aurora 19 Dir. w/ rec. 15 Dir. w/ rec. 11 Dir. w/ rec. Whitepine Daisy 15 Directional 12 Dir. w/ rec. 9 Directional Little 19 Directional 15 Directional 11 Directional Whitepine Sans 26 Directional 21 Directional 16 Directional Chambre Whitepine 26 Dir. w/ rec. 21 Dir. w/ rec. 16 Directional MacLeod
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Table S3-7. Differences in the characterization of water chemistry directionality when removing the largest ~20% and ~40% of time-steps from regression analyses. For example, the maximum time-step for Aurora Whitepine was 19. 19-(19*.2) = 15.2 = 15 time-steps included in the analysis. Original 20% removal 40% removal TimeTimeTimeLake Directionality Directionality Directionality steps steps steps Reference Blue Chalk 24 Dir. w/ rec. 19 Dir. w/ rec. 14 Dir. w/ rec. Red Chalk 24 Dir. w/ rec. 19 Dir. w/ rec. 14 Directional Main Lake 224 28 Directional 22 Directional 17 Directional Lake 239 30 Dir. w/ rec. 24 Directional 18 Directional Lake 240 18 Directional 14 Directional 11 Directional Lake 373 27 Directional 22 Directional 16 Directional Lake 375 13 Directional 10 Non-directional 8 Non-directional Lake 382 9 Directional 7 Directional 5 Non-directional Lake 442 16 Directional 13 Directional 10 Directional Impacted 223 27 Dir. w/ rec. 22 Directional 16 Directional 302S 28 Dir. w/ rec. 22 Dir. w/ rec. 17 Dir. w/ rec. Chub 24 Directional 19 Directional 14 Directional Crosson 24 Dir. w/ rec. 19 Dir. w/ rec. 14 Non-directional Plastic 24 Directional 19 Dir. w/ rec. 14 Dir. w/ rec. Aurora 19 Directional 15 Dir. w/ rec. 11 Directional Whitepine Daisy 14 Dir. w/ rec. 11 Dir. w/ rec. 8 Dir. w/ rec. Little 16 Directional 12 Directional 8 Directional Whitepine Sans 23 Directional 18 Directional 14 Directional Chambre Whitepine 24 Directional 19 Directional 14 Directional MacLeod
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Figure S3-1. Average annual pH values for the 19 study lakes. Impacted lakes are shown in the top two rows, reference lakes in bottom two rows. Red line at pH 6 represents the threshold for acid-sensitive zooplankton species.
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Figure S3-2. a) Principal component axes 1 and 2, and b) axes 2 and 3 of the zooplankton community data. c) PCA axes 1 and 2, and d) axes 2 and 3 of the water chemistry data. Each point represents one lake-year. Filled points show impacted sites (n = 10 lakes). Open points show reference sites (n = 9 lakes). Circled are 95% confidence intervals for Dorset (solid), IISD-ELA (dotted), and Sudbury (dashed) regions.
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Figure S3-3. Distance to a baseline centroid for all reference zooplankton communities combined. 95% confidence interval for the best-fit model provided (gray ribbon) and the red line showing LOWESS smoothing model.
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Figure S3-4. Euclidean distances versus time lag for combined reference zooplankton data. The black line shows the best-fit model (Table A2) with 95% confidence interval (gray ribbon) and the red line showing LOWESS smoothing model.
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Figure S3-5. Distance to a baseline centroid for all reference lake water chemistry data combined. 95% confidence interval for best-fit model provided (gray ribbon) and the red line shows the LOWESS smoothing model.
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Figure S3-6. Euclidean distances versus time lag for combined reference water quality data. The black line shows the best-fit model (Table A4) with a 95% confidence interval (gray ribbon) and the red line showing LOWESS smoothing model.
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Figure S3-7. Annual averages of calcium (mg/L), chlorophyll-a (μg/L), and chloride (mg/L) measurements for (a, c, e) reference and (b, d, f) impacted lakes.
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Figure S3-8. Annual averages of iron (μg/L), potassium (mg/L), and magnesium (mg/L) measurements for (a, c, e) reference and (b, d, f) impacted lakes.
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Figure S3-9. Annual averages of manganese (μg/L), sodium (mg/L), and sulfate (mg/L) measurements for (a, c, e) reference and (b, d, f) impacted lakes.
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Figure S3-10. Annual average total phosphorus (μg/L) measurements for a) reference and b) impacted lakes.
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Chapter 4 Patterns of Functional Redundancy among Fish Communities in Ontario Lakes 4.1 Abstract Aim Species richness and functional diversity have been described as important components of ecosystem resilience, as greater numbers of taxa and functional groups among taxa provide a greater probability that ecosystem conditions will be maintained post-disturbance. Functional redundancy, or when more than one species perform the same ecological function, may be important in situations where communities are relatively depauperate in richness. However, few studies have investigated patterns of redundancy among depauperate communities such as freshwater fish communities of temperate lakes. Location 6,977 lakes in Ontario, Canada. Methods Fish biomonitoring data and trait data related to species’ niches were used to quantify the relationship between functional diversity and species richness across geographical scales with generalized additive models (GAMs). I compared functional diversity measures of fish communities to expectations from null models to test whether fish communities had less functional diversity (i.e., greater redundancy) than expected from a random community assembly. I then used GAMs to test if biogeographic and environmental variables explained significant residual variation in the functional diversity versus species richness relationships. Finally, I compared species-level metrics related to functional rarity between fish thermalpreference groups, across body sizes, and across species occurrence rates. Results The relationship between functional diversity and species richness among fish communities was saturating at the provincial scale but varied regionally. Southeastern and northwestern Ontario fish communities showed the greatest redundancy compared to northeastern communities. Variables related to climate, productivity, and lake size were significant predictors of the residual variation in functional diversity versus species richness relationships, however, GAMs explained relatively little of the total variation (R2 range: 0.11-
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0.24). Furthermore, few differences were observed in species-level functional rarity measures between thermal-preference groups, across body sizes, or across species occurrence rates. Main conclusions Even though lakes in this study were relatively depauperate of species, Ontario fish communities exhibited functional redundancy across the province. Regional variation existed in this relationship, particularly at low species richness. In depauperate systems, such as the temperate fish communities of Ontario, functional redundancy may play a key role in providing resilience to communities against future disturbance.
4.2 Introduction The concept of ‘resilience’ has been applied to a wide-variety of systems (e.g., coral reefs, forests, lakes) since its introduction to the ecological literature (Holling 1973) and now forms the basis of many ecosystem management plans (Kingsford et al. 2011; Pope et al. 2014; Carlson et al. 2017). The term ‘resilience’ has been defined several ways (Myers-Smith et al. 2012; Hodgson et al. 2015; Angeler and Allen 2016), but generally refers to the ability of an ecosystem to absorb disturbance and remain relatively unchanged (Holling 1973; Walker et al. 2004). Biodiversity is a common consideration among resilience discussions (e.g., Downing and Leibold 2010; Biggs et al. 2012; Oliver et al. 2015) and early studies seeking to quantify resilience often focused on species-level measures of diversity (e.g., Shannon-Weiner diversity; Thomson and Lehner 1976). However, resilience is complex, involving interactions between species and the environment at multiple temporal and spatial scales, and therefore quantifying resilience has been a long-standing challenge in ecology (Holling 1973; Pimm 1984; Ives 1995; Carpenter et al. 2005; Angeler and Allen 2016). One approach that incorporates this complexity for quantifying resilience is the use of functional diversity metrics (Angeler and Allen 2016). Functional diversity describes the range and values of organismal traits that contribute to ecosystem functioning (Tilman 2001). To supplement species-based analyses of resilience, measures of functional diversity relate the characteristics of individual organisms or species (i.e., functional traits) to their role in maintaining ecosystem conditions (Mouillot et al. 2013). Differences in species’ traits can influence how ecosystems respond when faced with environmental stressors (Haddad et al. 2008; Mouillot et al. 2013). Functionally diverse communities among systems are expected to better maintain ecosystem structures and functions
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when faced with environmental stressors (Peterson et al. 1998; Standish et al. 2014). Related, functional redundancy describes the situation where more than one species perform the same ecological function in a community or ecosystem (Walker 1992; Lawton and Brown 1993; Rosenfeld 2002). Like the insurance hypothesis of biodiversity, which states that higher species diversity provides a greater probability that ecosystem conditions will be maintained when disturbances occur (Yachi and Loreau 1999), communities with greater functional redundancy are thought to be more resilient to environmental stressors. Approaches for quantifying functional redundancy continue to be developed (Bruno et al. 2016; Ricotta et al. 2016), but typically require community abundance or presence-absence data and a matrix of morphological, behavioral, or life-history traits (Rosenfeld 2002). Using these data, one approach for quantifying functional redundancy is to regress measures of functional diversity against species richness (Micheli and Halpern 2005; Sasaki et al. 2009; Guillemot et al. 2011; Figure 4-1). Common measures of functional diversity include Rao’s quadratic entropy (Rao’s Q; Rao 1982) and functional dispersion (FDis; Anderson 2006; Laliberté and Legendre 2010), both calculated as distances in multivariate trait space constructed from the chosen morphological, behavioral, or life-history traits (Laliberté and Legendre 2010; Schleuter et al. 2010; Schmera et al. 2017). Shapes of functional diversity and species richness regressions will vary depending on the chosen metrics of functional and species diversity (Cadotte et al. 2011; Schmera et al. 2017), the number of species and traits within the analysis (Guillemot et al. 2011), as well as across environmental (Mason et al. 2008) and disturbance gradients (Guerrero et al. 2014). Conceptually, a one-to-one relationship (Figure 4-1a) between functional diversity and species richness would indicate that for each added species, there was an increase in functional diversity of equal size, indicating a lack of redundancy among communities. Generally, functionally redundant communities are expected to show a saturating relationship between functional diversity and species richness (Micheli and Halpern 2005; Figure 4-1b); as species richness increases, functional diversity increases at a greater rate and plateaus, indicating redundancy (i.e., saturation) across communities. Finally, nonlinear patterns can occur (e.g., Sasaki et al. 2009), where redundancy is observed at high and low levels of species richness, but communities with fewer species show relatively lower levels of functional diversity (Figure 41c).
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Figure 4-1. a) Linear, b) saturating, and c) nonlinear relationships between functional and species diversity. Adapted from Micheli and Halpern (2005) and Micheli et al. (2014). Interpretations of the functional diversity and species richness relationship can vary based on the types of traits used in the analysis. For studies that focus on characteristics of organisms that relate to ecosystem functions, processes, or services (i.e., effect traits; Goodness et al. 2016), functional redundancy provides a measure of stability or risk of losing an ecosystem function (Mumme et al. 2015). If the measured traits describe how the individual species or organism might respond to environmental stressors (i.e., response traits; Elmqvist et al. 2003), functional redundancy provides a measure of resilience. However, characterizing traits as either “effect” or “response” can be challenging given the complexity in which individual species or functional groups respond to environmental stressors (Loreau and de Mazancourt 2013; Tomimatsu et al. 2013). Therefore, arguments can be made for and against including “effect” and “response” traits in analyses simultaneously (Tomimatsu et al. 2013). Many of the developments in functional trait-based approaches, including functional redundancy, have come from studies of marine systems (e.g., Bellwood et al. 2003; Micheli and Halpern 2005; Hoey and Bellwood 2009). Much less research has been directed towards understanding redundancy in freshwater ecosystems, particularly in temperate or Arctic regions. Therefore, I set out to quantify the functional diversity and species richness relationship (Figure 4-1) among temperate, North American lake fish communities. I approach this objective using historical fish community data from Ontario, Canada. Ontario spans an area of over 10,000,000 ha (approximately the combined area of France and Germany) with approximately 250,000
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freshwater lakes that are morphologically diverse (Jackson and Mandrak 2002; Lester et al. 2003) and influenced by different climatic effects occurring across Ontario. Differences in species composition and lake characteristics exist across Ontario (Jackson and Harvey 1989) that can influence the functional diversity and subsequent redundancy of communities. Most freshwater fish species in Ontario colonized from the southern and southwestern refugia (Mississippi and Missourian) or southeastern (Atlantic) refugium after the Wisconsinan ice sheet receded (Mandrak and Crossman 1992), contributing to greater overall species richness in the south than the north. In addition, species richness in southern lakes may be influenced by greater productivity due to warmer temperatures and higher nutrient levels (Jackson and Harvey 1989). I hypothesized that the influence of historical biogeography and abiotic factors on species distributions would contribute to variation in the functional diversity and redundancy of freshwater fish communities among different regions of the province. Specifically, if redundancy were to exist among Ontario fishes, I expected it to be among communities in the southeastern region of the province simply because of the greater number of species in this regional lacustrine pool and, therefore, the more densely populated multivariate trait space. In this study, I aimed to identify patterns of functional redundancy among freshwater fish communities in Ontario, and understand how several factors, specifically species richness, historical biogeography, and environmental variation, can contribute to such patterns. Previous work has demonstrated that lake area, depth, temperature, and productivity are related to local species richness of freshwater fish communities (Matuszek and Beggs 1988; Minns 1989; Dodson et al. 2000). I hypothesized that, after accounting for differences in species richness, the largest, deepest, and warmest lakes in Ontario would harbor the most functionally diverse fish communities. Greater diversity of habitat and available dietary items in these lakes should provide more ecological niches. Dodson et al. (2000) demonstrated that the relationships between productivity and species richness were unimodal among lake systems; the most and least productive lake systems had the fewest species. I hypothesized that the lower species richness levels at high and low levels of productivity consist of more functionally redundant species through processes of habitat filtering, whereas patterns of redundancy will vary among moderately productive lake systems. High productivity can often lead to decreased oxygen levels, potentially placing a constraint on species that require elevated oxygen-rich
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environments, whereas lakes with low levels of productivity offer fewer available resources, which could also inhibit redundancy among species (Waide et al. 1999). Together these lines of investigation should provide insight into factors influencing patterns of functional redundancy, allowing us to better predict how freshwater fish communities may change in the future.
4.3 Materials and Methods 4.3.1
Data collection
I used data collected under the Ontario Ministry of Natural Resources and Forestry Aquatic Habitat Inventory (AHI) survey for this study. Sampling methods are described in Dodge et al. (1985) and generally consisted of 3- to 5-day surveys of lakes between the late 1960s and early 1980s. Fishes were sampled using different-sized mesh gillnets, seine nets, and baited minnow traps (Matuszek and Beggs 1988). Additionally, habitat measures were taken that included surface area (SA; ha), maximum depth (Zmax; m), and total dissolved solids (TDS; mg/L). Previous studies have demonstrated that the AHI program under-sampled small fishes and northern Boreal lakes (Bowlby and Green 1985). For the purposes of this study, I use the term “community” to refer to the species composition of a single lake. The AHI consists of the presence or absence of 99 fish species for approximately 10,000 lakes in Ontario. Thirty species were found in fewer than 0.1% of the lakes (i.e., less than 10 lakes); lakes where these species occurred were removed from the analyses (provincial species pool) so that results from multivariate statistical analyses would not be heavily influenced by rare species. The 69 fish species retained consisted of 18 warm-water species, 8 warm/cool-water species, 22 coolwater species, 5 cold/cool-water species, and 16 cold-water species (Coker et al. 2001; Table S41). Subsequently, I eliminated any lakes with fewer than four fish species due to analytical requirements, as this minimum allows for calculations of convex hull volumes in 3-dimensions of trait space (see: Functional diversity analysis below), leaving a total of 6,977 fish communities in the analysis (Figure 4-2). I subsampled the lakes based on watershed distributions and characterized them geographically as either southeastern (n = 1,325), northeastern (n = 1,365), or northwestern (n = 1,541) to investigate regional differences in functional redundancy (Figure 42). From the provincial species pool of 69 species, 18 species were absent in northeastern lakes, five species in southeastern lakes, and nine in northwestern lakes (Table S4-1).
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Figure 4-2. Sampling sites included in this study (n = 6,977; all points). Coloured points indicate subsamples for geographic analyses; black = southeastern (n = 1,325), blue = northeastern (n = 1,541); red = northwestern (n = 1,541).
4.3.2
Functional traits
Fish traits were gathered from multiple sources including FishTraits database (Frimpong and Angermeier 2009), Freshwater Fishes of Ontario (Holm et al. 2009), Morphological and Ecological Characteristics of Canadian Freshwater Fishes (Coker et al. 2001), and ontariofishes.ca (Eakins 2017). Twenty-six traits (variables) were chosen to characterize the role of species within communities (Givan et al. 2017) and generally related to adult species’ niches including modes of reproduction, species-substrate associations, components of the diet, and species-habitat associations (Frimpong and Angermeier 2009; Table S4-2). Modes of reproduction for each species were characterized by two binary variables, 1) guarders vs. nonguarders and 2) species that reproduce on open substratum vs. those that hide their brood or use nests. Species-substrate associations consisted of seven binary variables indicating use vs. nonuse of 1) muck, 2) clay/silt, 3) sand, 4) gravel, 5) cobble, 6) boulder, and 7) bedrock. Dietary components consisted of five binary variables indicating consumption of 1) algae or phytoplankton, including filamentous algae, 2) macrophytes and vascular plants, 3) detritus or
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unidentifiable vegetative matter, 4) fish, crayfish, and frogs, and 5) eggs. Eleven binary habitat variables were included that indicate use of 1) vegetation, 2) organic debris or detrital substrates, 3) large woody debris, 4) open water, 5) slow currents, 6) moderate currents, 7) fast currents, 8) large rivers, 9) small rivers, 10) creeks, and 11) lakes (Table S4-2). In addition, temperature preferences (cold-water, cold/cool-water, cool-water, cool/warm-water, and warm-water species) and average total lengths (TL) were retrieved from Coker et al. (2001) and Holm et al. (2009), respectively, and used to describe species post-analyses (see: Functional diversity analysis below; Table S4-1).
4.3.3
Functional diversity analysis
To quantify functional diversity, I first combined the traits described above to represent ecological niche dimensions associated with reproduction, diet, habitat, and substrate use with separate principal component analyses (PCAs) of traits from each of the four trait categories (Mouillot et al. 2013). By reducing the number of traits into respective trait dimensions, I am assuming that traits have more of less equal weights. Prior to each PCA, Hellinger transformations were performed (Legendre and Gallagher 2001). Computing a Hellinger transformation on species-presence absence data is mathematically similar to using the Ochiai similarity coefficient (Ochiai 1957; Hubálek 1982); dividing Hellinger transformed presenceabsence data by the square-root of 2 is equal to the square root of 1 - Ochiai similarity (Borcard et al. 2011, pg. 37). Jackson et al. (1989) demonstrated that applying an Ochiai coefficient is useful in reducing the importance of “size” (i.e., frequency of species occurrence) and summarizes the “shape” of assemblages (i.e., interspecific associations independent of their frequency of occurrence). Based on ease of interpretation of niche axes and overall explanatory power, I retained only the first axis of the PCAs of reproductive mode, species-substrate associations, and dietary components, and the first two axes for species-habitat associations, totaling five dimensions for each species. I then performed a principal coordinates analysis (PCoA) on Euclidean distances of the five trait variables extracted from the PCAs to define the functional trait space (Laliberté et al. 2010). I calculated three measures of functional diversity for each the 6,977 fish communities: functional richness (FRic; Villéger et al. 2008; Schleuter et al. 2010; Boersma et al. 2014), functional dispersion (FDis; Anderson 2006; Laliberté and Legendre 2010), and Rao’s quadratic
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entropy (Rao’s Q; Rao 1982; Botta-Dukát 2005). FRic describes the volume of multivariate trait space occupied by a set of species (Villéger et al. 2008) calculated as the convex hull volume in ordination space (Cornwell et al. 2006). I used the first three PCoA axes to calculate FRic since my species richness was limited to four or more species. FDis and Rao’s Q were calculated from the Euclidean distance trait matrix. FDis describes the mean distance in multivariate trait space of each species in a community to the centroid of all species in a community (Laliberté and Legendre 2010). Communities showing relatively large FDis values contain a more diverse set of species trait combinations. Rao’s Q describes the average functional distance between two randomly chosen species in a community (Schleuter et al. 2010; Schmera et al. 2017). However, due to strong correlations of Rao’s Q with FDis (r > 0.98; Table S4-3), I only present results of FDis. I used null models of the relationship between functional diversity measures (FDis and FRic) and species richness to test whether functional redundancy of fish communities differed from a random assembly of species at both the provincial and regional scales (Gerisch 2014). For each level of species richness from n = 4 to 30, I randomly sampled n species from the provincial or regional species pool 10,000 times, and calculated functional diversity metrics for each randomly assembled community. I weighted the probability of individual species being sampled by their frequency of occurrence within the matrix of provincial or regional lakes. Weighting species by their frequency of occurrence increases the probability that sampled communities better represent actual combinations of observed species occurrences. A saturating pattern between functional diversity and species richness would indicate functional redundancy at the provincial or regional scale (Figure 4-1b). Lake communities outside the 95% confidence interval of the null model would indicate significantly different (higher = more distinct; lower = more redundant) functional diversity levels than expected based on species richness alone. Due to the heteroscedastic nature of the data, I also compared the functional diversity and species richness relationships among geographical locations using locally weighted scatterplot smoothing (LOWESS; Cleveland et al. 1988). In addition to provincial and regional patterns of redundancy among fish communities, I calculated species-level measures of functional rarity (Violle et al. 2017). Functional rarity measures describe the extent to which functional traits are distinct or redundant among assemblages and include measures of functional distinctiveness (D; Violle et al. 2017),
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functional uniqueness (U; Violle et al. 2017), and the distances to provincial (dP) or regional centroids (dR) for each species. Functional distinctiveness and uniqueness (D and U) were calculated from the Euclidean distance trait matrix. Distinctiveness describes the average functional distance of each species to all other species within a community (Violle et al. 2017), which I averaged across lakes within provincial or regional pools; species with low D indicates a more average species and, therefore, contribute more to the redundancy of traits. Uniqueness (U) describes the functional distance to the nearest neighbor within the regional species pool (Buisson et al. 2013; Mouillot et al. 2013; Violle et al. 2017); species that are “less unique,” or are more similar functionally to other species, also contribute to the redundancy of species. Distances to global centroids (dP or dR) in trait space provide an indication of the influence of individual species on functional diversity measures. Species on the periphery of ordination space likely increase convex hull volumes. Environmental stressors are known to impact freshwater ecosystems non-randomly, often causing declines or exclusion of particular niches (Giller et al. 2004). I therefore tested for differences in D, U, dP¸ and dR among thermal preference groups (cold-water, cold/cool-water, cool-water, cool/warm-water, warm-water; Coker et al. 2001) with ANOVAs and against body sizes (average total length, TL; Holm et al. 2009) with least-squares linear regression. Due to the location and large geographic scale of Ontario, lake conditions span the northern or southern thermal boundaries for many fish species (Shuter et al. 1980; Jackson et al. 2001). Furthermore, these boundaries are changing due to a changing climate (Alofs et al. 2014), which lends to the question: do fish species that vary in their thermal tolerance show differences in functional-rarity measures? Body size is also a key variable that relates to many aspects of fish ecology including reproduction (Blueweiss et al. 1978) and the likelihood to disperse (Radinger and Wolter 2014), among others (Woodward et al. 2005; Alofs 2016). Finally, as geographically limited species are more likely to be extirpated than ubiquitous species over time (Harnik et al. 2012), I tested for relationships between functional rarity measures and the frequency of occurrence of species (i.e., the proportion of lakes present) with least-squares linear regressions. I log10 transformed the average TL and frequency of occurrence values to conform to normality.
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4.3.4
Environmental gradient analysis
I used generalized additive models (GAMs) to investigate functional redundancy across environmental gradients and geographic scales. First, I extracted the residual variation of the functional diversity (FDis and FRic) and species richness relationships using single predictor GAMs (see Figures S4-1 and S4-2). Positive residuals indicate that functional diversity levels are greater than expected after accounting for species richness, negative residuals indicate functional diversity levels below expectations, and 0 indicates that species richness accurately predicts functional diversity. I then examined how maximum depth (Zmax), surface area (SA), climate (growing degree days; GDD), and productivity (total dissolved solids; TDS) were associated with the residuals of functional diversity and species richness models using GAMs. Although using the residual variation as a dependent variable can lead to biased parameter estimates due to a lack of independence among predictor variables (Freckleton 2002), the nonlinear, heteroscedastic properties of the data do not allow for use of multiple regression. Separate GAMs were run across geographical scales (i.e., provincial, northeastern, northwestern, and southeastern). Variables were centered and scaled prior to the analysis by subtracting the variable means and dividing them by their standard deviation. I used restricted maximum likelihood procedures for selecting smoothing parameters with an automatic term-selection procedure (Marra and Wood 2011). I removed four lakes from the provincial analysis, eight lakes from the northwest analysis, and one from the northeast analysis due to extreme SA and TDS values relative to other lakes. Among my predictor variables, Zmax and SA have previously been described as key factors related to the structure of fish communities (Harvey 1975; Tonn and Magnuson 1982; Jackson and Harvey 1993; Mehner et al. 2007), as larger lakes can provide more structural habitat diversity (Eadie et al. 1986; Minns 1989) while deeper lakes provide a greater diversity of thermal niches (Chu et al. 2008) and reduce the likelihood of winterkill (Casselman and Harvey 1975; Harvey 1978, 1982). Similarly, species distributions are also influenced by temperature, which plays a fundamental role in fish growth and maturity (Venturelli et al. 2010). Like Alofs et al. (2014), I estimated the mean number of GDD above 5˚C air temperature between 1961 and 1990 for each lake using thin spline smoothing algorithms from the Canadian Forestry Service (https://cfs.nrcan.gc.ca/projects/3). Lake temperatures are known to be influenced by a suite of factors including climate, lake morphology, and watershed topography (Sharma et al. 2008);
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here, the mean number of GDD above 5˚C air temperature represents our best estimate of climate conditions at the time of sampling as well as a base temperature for fish growth (particularly Walleye; Venturelli et al. 2010). Finally, I used TDS as my best available proxy for overall productivity in these lakes at the time of sampling. All analyses were performed in the R Statistical Software (R Core Team 2017) with the ape (PCoA; Paradis et al. 2004), vegan (transformation; Oksanen et al. 2016), FD (functional diversity metrics; Laliberté and Legendre 2010, Laliberté et al. 2014), psych (correlations; Ravelle 2016), funrar (functional rarity metrics; Grenié et al. 2016), ggplot2 (graphing; Wickham 2009), and mgcv (GAMs; Wood 2011) packages.
4.4 Results 4.4.1
Observed trends from sampling
Most of the species included in the study were infrequent; 35 species were found in less than 5% of the 6977 lakes (Figure 4-3). Similarly, most lakes had few observed species (Figure 4-3 inset). White Sucker (83% of lakes), Yellow Perch (72% of lakes), and Northern Pike (54% of lakes) were the most ubiquitous species. The most common species combination was found in 63 lakes and contained four species: Northern Pike, Walleye, White Sucker, and Yellow Perch.
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Figure 4-3. The occurrence of species included in the study. Inset) Frequency distribution of species richness values in lake communities.
4.4.2
Functional trait space for provincial and regional pools
Single PCA axes were extracted for the reproduction traits, species-substrate associations, and dietary components from PCAs performed on provincial and regional species-trait pools with consistent patterns across scales (See Appendices 3-6 for detailed descriptions of each ordination with biplots). The first axis for reproduction ranged from brood-guarding species on the negative end to open-substrate spawners on the positive end, explaining between 82.7% and 85.2% among ordinations. The single substrate preference axes explained between 34.1% and 37.3% across scales, with associations with larger substrates showing positive scores and associations with smaller substrates with negative scores. The diet axis showed more piscivorous species on the positive end and more herbivorous species on the negative end, explaining between 38.4% and 40.0% of the variation. Two axes were extracted from the species-habitat associations PCA
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explaining differing components of habitat diversity; the first axis summarized lotic vs. lentic traits explaining between 21.6% and 24.3% of the variation. The second axis summarized habitat size, explaining between 16.1% and 17.1% of the variation. Three axes were extracted from all four PCoAs (provincial, northwestern, northeastern, and southeastern) of the five functional trait axes with a high reduced space quality (cumulative proportion of eigenvalues: provincial: 0.821; northwestern: 0.844; northeastern: 0.825; southeastern: 0.807).
4.4.3
Provincial functional diversity metric analysis
Consistent with the findings of Laliberté and Legendre (2010), FDis and Rao’s Q were highly correlated (Pearson’s r = 0.989, Spearman’s ρ = 0.990, p < 0.001; Table S4-3); therefore, I only present results of FDis. Expectations from the weighted null models of FDis against species richness showed a saturating relationship, whereby FDis increased at a lesser rate than species richness, reaching a plateau at higher levels of species richness (Figure 4-4a). In contrast, the expected relationship between FRic and species richness was less saturating (Figure 4-4b). Most lake fish communities fell within the 95% confidence bounds of the null models (Figure 4-4; Appendix 3), with most fish communities falling below mean null expectations (FDis = 61%; FRic = 64%; Appendix 3). Only 376 of the 6,977 lake communities showed lower FDis than the 95% confidence intervals of the weighted null model and 306 showed less FRic than expected (Appendix 3). LOWESS smoothing models of empirical data for both FDis and FRic regressed against species richness at the provincial scale crossed the mean null values at species richness levels of approximately 18 (Figure 4-4); lakes with greater than 18 species generally showed greater functional diversity than mean null expectations indicating less redundancy than expected simply due to random community assembly (Figure 4-4; Appendix 3).
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Figure 4-4. a) Functional dispersion and b) functional richness versus species richness. Points indicate 6,977 lake fish communities. The solid line indicates a LOWESS smoothing algorithm applied to the provincial community values. The dashed line represents the mean null community values. The 95% confidence interval for the null models is shaded in gray.
4.4.4
Regional functional diversity metric analysis
The relationship between FDis and species richness at the regional scale indicated redundancy (i.e., saturated) for southeastern and northwestern Ontario lake fish communities, whereas the northeastern communities showed a linear trend indicating a lack of redundancy (Figure 4-5a). Similarly, southeastern and northwestern communities showed saturating patterns in FRic versus species richness, whereas the northeastern communities showed linear trends (Figure 4-5b). Based on null expectations, northeastern Ontario communities were expected to show the greatest level of redundancy in FDis and FRic (Figure 4-5; gray ribbon). However, southeastern communities and northwestern communities showed the most saturating relationships between functional diversity and species richness (Figure 4-5). Northwestern Ontario had the greatest proportion of communities falling below the 95% confidence limits with FDis having 12% and FRic having 5% less than expected by species richness alone. Furthermore, northwestern communities had the greatest proportion of communities falling below the mean null functional diversity levels per species richness (FDis: 76%; FRic: 68%; Appendix 4). Southeastern Ontario had the second greatest proportion of fish communities with significantly less diversity than expected by species richness (FDis: 6%; FRic: 5% falling below the confidence limits), with most communities falling below mean expectations (FDis: 54%; FRic: 55%; Appendix 5). Finally, northeastern Ontario showed the smallest proportion of communities falling below the null model expectations (FDis: 3%; FRic: 3% falling below the confidence limits), however,
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showed more communities falling below the mean null values (FDis: 60%; FRic: 56%; Appendix 6) than southeastern communities.
Figure 4-5. a) Functional dispersion and b) functional richness regressed against species richness. Colours represent measures for the three regions: northwestern (red), northeastern (gray), and southeastern (blue). Ribbons reflect 95% confidence intervals of null models. Solid lines indicate regional LOWESS models. Dashed lines indicate mean values for weighted null models.
4.4.5
Environmental and geographic gradients
Models of FRic regressed against species richness showed an overall better fit (R2 range: 0.68 0.81) than FDis versus species richness models (R2 range: 0.09 - 0.17; Table S4-4; Figures S4-1, S4-2). Environmental variables generally explained a small proportion of the variance among residuals of the functional diversity versus species richness relationships (R2 range: 0.11 - 0.24; Table 4-1). Among most models, all four environmental predictor terms were significant (Zmax, GDD, TDS, and SA; Table 4-1). The model explaining the greatest amount of variation from all comparisons was the southeastern FRic versus species richness residual model, where Zmax, GDD, SA, and TDS were significant predictor variables. Similar relationships were observed between environmental variables and the FDis and FRic residuals (Figures S4-3, S4-4), with some variation regionally. With increasing SA, I found functional diversity levels that were less than expected based on species richness (negative residuals) indicative of greater redundancy among communities in larger lakes. In contrast, I observed positive residuals with increasing Zmax, indicating that deeper lakes had greater functional diversity than expected based on species richness. Confidence intervals mostly overlapped zero when relating TDS to the residuals of
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functional diversity and species richness (Figures S4-3, S4-4). Relationships between GDD and the residuals of functional diversity versus species richness were nonlinear (edf; Table 4-1); redundancy (i.e., negative residuals) was observed among communities experiencing a relatively moderate climate, whereas communities in the warmest or the coldest regions had positive residuals, indicating greater functional diversity than expected (Figures S4-3, S4-4). Table 4-1. GAMs of functional diversity (FDis or FRic) versus species richness (R) residuals with maximum depth (Zmax), growing degree days (GDD), total dissolved solids (TDS), and surface area (SA). Models performed on scaled and centered variables. Significant coefficients (p < 0.05) indicated in bold. Region Response Predictor edf F p Adj. R2 Dev. Exp. Provincial FDis-R GDD 7.09 51.40
