EXPLORING GENERALITIES IN THE DRIVERS OF DIVERSITY PATTERNS IN
FRAGMENTED LANDSCAPES: MULTI-CONTINENTAL MODEL CROSS-
COMPARISONS USING BUTTERFLIES
By
NATALIE S ROBINSON
B.S., University of California, Berkeley, 2003
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirement for the degree of
Doctor of Philosophy
Department of Ecology and Evolutionary Biology
2014 This thesis entitled: Exploring Generalities in the Drivers of Diversity Patterns in Fragmented Landscapes: Multi- continental Model Cross-Comparisons Using Butterflies written by Natalie Suzanne Robinson has been approved for the Department of Ecology and Evolutionary Biology
______(Dr. M. Deane Bowers, Committee Co-chair)
______(Dr. Robert Guralnick, Committee Co-chair)
______(Dr. Kendi Davies)
______(Dr. Brett Melbourne)
______(Dr. Cesar Nufio)
______(Dr. Stefan Leyk)
Date______
The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. Robinson, Natalie S. (Ph.D., Ecology and Evolutionary Biology)
Exploring Generalities in the Drivers of Diversity Patterns in Fragmented Landscapes: Multi-
continental Model Cross-Comparisons Using Butterflies
Thesis directed by Professors M. Deane Bowers and Robert Guralnick
ABSTRACT
Landscape modification is leaving an irrevocable scar on the planet, most notably through habitat fragmentation. Fragmented landscapes are often unable to support communities that once inhabited them, leading to unprecedented rates of global biodiversity loss. As a result, substantial research effort focuses on investigating the drivers of species’ responses to habitat fragmentation, usually for one or a few species at select locations. This dissertation expands upon previous research in order to broaden understanding of the determinants of diversity patterns in fragmented landscapes. I modeled variation in among fragment butterfly diversity for entire communities, using both environmental attributes and species traits as predictors. I then compared models across three, widely separated fragmented landscapes. I found that patch area and water availability had consistent influences on butterfly diversity patterns; these factors may warrant inclusion into management policies for fragmented landscapes worldwide. Other predictors, e.g., butterfly wing length, had variable influences on diversity patterns, although results revealed similarities between certain study areas. For example, habitat heterogeneity influenced diversity patterns similarly in two study areas, possibly due to similarities in ecological and/or climatic characteristics (e.g., drought-prone summers). Furthermore, species traits played important, albeit inconsistent, roles in driving butterfly diversity patterns; this
iii pattern also potentially driven by among location ecological and/or climatic conditions. In all, this integrative data reuse analysis demonstrated patterns that may provide crucial information for better understanding wide-spread species responses to habitat fragmentation. The final component of this dissertation was an exploration of questions that arose from the data reuse strategy employed: how different are models constructed from datasets obtained via disparate levels of survey effort, and what implications does this have for data reuse analyses. I constructed a new model using data collected via 2/3 of the full sampling effort for one dataset.
The model was almost identical to that constructed from the full dataset, and the use of this
‘reduced sampling effort’ dataset would thus have had negligible impact on previous results.
This work provides insight into the sensitivity of downstream analyses to variation in survey methods, and substantiates the validity of analyses reusing datasets collected by different researchers.
iv ACKNOWLEDGEMENTS
I am not sure how I can put to words all of my appreciation for the myriad people whose support and understanding have helped me through this crazy journey. This is a long list, which starts with my major advisors, Deane Bowers and Rob Guralnick. Together you have given me zillions of hours of your time, through countless meetings, edits, and talks. You have been my cheerleaders, my mentors, my inspiration, and my friends. You have shaped both my dissertation research, and my ability to ask deeper questions, perform more focused investigations, and communicate more effectively than I ever thought possible. You took a naïve girl from a sheltered world and made her into a scientist, and all of my past and future successes are owed to you. Deane and Rob, I can never thank you enough for your intellectual stimulation, enthusiasm, tenacity, and encouragement. I would also like to sincerely thank my other committee members,
Kendi Davies, Stefan Leyk, Brett Melbourne, and Cesar Nufio, whose insights and feedback through the years have shaped my PhD into something truly unique and exciting. Finally, I feel lucky to have been accepted into a department of such amazing, supportive faculty and staff as are found in EEB. Special thanks to Jeff Mitton for encouraging me to explore my options and pursue my dreams, Yan Linhart for sharing his infectious love of science and seeing me through early days of my studies, and Mike Grant for your encouragement and intellectual support.
My ability to complete this PhD hinged upon help and support from a few key people. To my collaborators, Martin Konvicka, Tomas Kadlec, and Matt Williams, your willingness to share your hard-earned data and work with me to develop new ideas are at the heart of making this integrative study possible. I am forever grateful for having had the opportunity to work with other like-minded scientists who believe in the importance of sharing data, and who were willing
v to do whatever I needed in order to help me complete these analyses. Special thanks also to several people who helped me gather and synthesize additional information for this project through the years, Laura Tietz, Annie Frazier, Jan Koenig, and Monica Rother.
To my the friends, both biological and non-biological, you’ve seen me through so many years, supported me and understood what I was trying to do, expanded my ways of thinking, and accompanied me on countless adventures. In particular, to the girls, Susan Whitehead, Loren
Sackett, Kallin Tea, Se Jin Song, Mary Kay Herzenach, and Katherine McClure, our girls nights were a sustaining force during these many years in the Master’s and PhD programs, and I love you all for your commitment to our friendships. To my other biological friends, Carolina
Quintero, Ty Tuff, Evan Lampert, Amanda Williams, Niffer Wilkening, and so many more; your support and feedback have aided in this project in ways that you could not even know, thank you for every minute of it. I also wish to acknowledge the Bowers and Guralnick labs, past and present, including Caitlin Kelly, Megan Blanchard, Toby Hammer, Adrian Carper, Peri Mason,
Collin Schwantes, Carolina Quintero, Mary Jamieson, Katie Wolfson, Brian Stucky, Gaurav
Vaidya, Leisl and Peter Erb, Nate Kleist, Aiden Beers, and Aly Seeberger, thank-you for the stimulating conversation and amazing feedback throughout this process. And finally, to my non- biological friends, I would have gone crazy if you had not been there to remind me that there is more to life than just school. Special thanks to Amber Freeman and Cris Sturgis, my oldest and dearest friends whose support has been unbelievable. Thank-you also, and especially, to Jeremy
Lauffenburger, Phil Sonnenfeld, Truman Bradley, Taylor Chase, and Anna Jablonski for the late night Catan games, amazing hut trips, incredible raft trips, and other inspiring adventures.
Finally, I am unendingly grateful to my family, without whom I never would have had the strength to upend my life, start with a clean slate, and make a new and brighter future for
vi myself. To Jessica Robinson, you have been at times my sounding board, and at others my ear, and I appreciate your love and support over all of these years. To my Mom, Susan Robinson, you provided me with the one thing that has made every success in my life possible, a belief that I can do anything that I set my mind to. You taught me early to be strong, independent, tenacious, and confident in my ability to achieve any goal, no matter how challenging. I can never repay you, or express enough gratitude for you to understand that you are the foundation of everything that I am, and that I couldn’t be more proud to be your daughter. To my Dad, Bruce Robinson, you are my rock, my hero. I have always known that you believed in me, that your support for me could never be shaken, and that your love for me is completely unconditional. I walk with a higher step, a brighter smile, and a more compassionate heart because you are my father, and I will always strive to be, and to do, better, in the hopes that I can someday come even close to living up to your example. To Paula Connelly, you have supported me in so many ways, taking me to amazing places, giving me opportunities to have fun and enjoy this crazy ride, and helping me to travel around the world; thank-you. And finally, to Brooks Lustig, my partner, my best friend- I have taken you on a ride that neither one of us ever could have expected, and I do not think there are words enough in this world to express how much I appreciate your help in getting me to the finish line. Thank-you for supporting me when I needed help keeping my head up, making me laugh when I wanted to cry, and pushing me to always have fun and enjoy this life.
I am fortunate have received generous financial support for this work through funding from the University of Colorado Department of Ecology and Evolutionary Biology, the
University of Colorado Beverley Sears Grant, The University of Colorado Graduate School, and the University of Colorado Museum of Natural History.
vii CONTENTS
1 INTRODUCTION
1.1 Background and Research Rationale ...... 1 1.2 Chapter Overview...... 4
2 INTEGRATING SPECIES TRAITS AND HABITAT CHARACTERISTICS INTO MODELS OF BUTTERFLY DIVERSITY IN AN URBAN FRAGMENTED ECOSYSTEM
2.1. Abstract...... 9 2.2 Introduction...... 10 2.3 Methods...... 13 2.4 Results...... 26 2.5 Discussion...... 36
3 EXPLORING GENERALITIES IN THE DRIVERS OF DIVERSITY PATTERNS IN FRAGMENTED LANDSCAPES: A MODEL CROSS-COMPARISON ANALYSIS WITH BUTTERFLIES
3.1. Abstract...... 42 3.2 Introduction...... 43 3.3 Methods...... 47 3.4 Results...... 55 3.5 Discussion...... 66
4 THINKING OUTSIDE THE CONTINENT: EXPLORING THE DRIVERS OF DIVERSITY PATTERNS AMONG WIDELY SEPARATED FRAGMENTED ECOSYSTEMS
4.1. Abstract...... 72 4.2 Introduction...... 73 4.3 Methods...... 77 4.4 Results...... 83 4.5 Discussion...... 96
viii 5 PUTTING EFFORT TOWARD SURVEY EFFORT: AN EMPIRICAL INVESTIGATION OF THE INFLUENCE OF SURVEY EFFORT ON MODELING ANALYSES
5.1. Abstract...... 101 5.2 Introduction...... 102 5.3 Methods...... 105 5.4 Results...... 109 5.5 Discussion...... 113
6 SUMMARY AND CONCLUSIONS
6.1. Challenges of multi-species, multi-study comparisons ...... 118 6.2 Introduction...... 119 6.3 Methods...... 122
7 REFERENCES ...... 123
8 APPENDICES
A. Site-specific observed (dark grey bars) versus estimated butterfly population densities for the Prague full (light grey bars) and environment (white bars) models...... 135
ix LIST OF TABLES
Table 2.1: Quantification scheme for the measurement of edge permeability ...... 17
Table 2.2: Prague butterfly species, with total abundances, for the refined dataset ...... 27
Table 2.3: Averaged coefficient estimates and random effects variances for models examining the influences of environmental and/or trait predictor variables on proportions of butterfly species across sites, as fit by maximum likelihood...... 30
Table 3.1: Perth butterfly species, with total abundances, for the refined dataset...... 56
Table 3.2: Averaged coefficient estimates, relative importance during model averaging, and statistical significance for predictors of butterfly diversity patterns in Prague and Perth, and random effects variances for both models ...... 62
Table 4.1: Geo-locations, habitat characterizations, and climatic conditions of the three study areas used in this analysis...... 76
Table 4.2: Butterfly species, with total abundances, for the refined Colorado dataset...... 84
Table 4.3: Averaged coefficient estimates, relative importance during model averaging, and statistical significance for predictors of butterfly diversity patterns in Prague, Perth, and Colorado, and random effects variances for all models...... 89
Table 5.1- Parameter estimates, standard errors, relative importance to the models, and magnitudes of effect (p-values) for predictor variables in each of the full and subset models for Perth...... 111
x LIST OF FIGURES
Figure 1.1: Photographic examples of habitat fragments for each study area investigated in this dissertation ...... 4
Figure 2.1: Map of the Prague region, including the 20 habitat fragments (black circles) used for this study ...... 14
Figure 2.2: Modeling methods used in this study...... 23
Figure 2.3: Influences of individual environmental predictors on butterfly densities across sites in the Prague training dataset...... 32
Figure 2.4: Relative frequencies of occurrence of butterflies with different species trait values among patches in the Prague training dataset...... 33
Figure 2.5: Frequencies of occurrence of univoltine, bivoltine, and multivoltine butterfly species at patches in which water is absent and present, in the Prague training dataset ...... 34
Figure 2.6: Observed versus predicted butterfly population densities in the Prague validation dataset ...... 35
Figure 2.7: Site-specific observed (dark grey bars) versus estimated butterfly population densities for the full (light grey bars) and environment (white bars) models in Prague...... 36
Figure 3.1: Map of the Perth region, including the 19 habitat fragments (black circles) used for this study ...... 48
Figure 3.2: Ranges of variation for the response variable (A), and the environmental (B-H) and trait (I-N) predictor variables in the Perth and Prague datasets ...... 58
Figure 3.3: Observed (black dots), across sites in Perth, and predicted (gray triangles), by models CZfull (A), CZenvt (B), and CZtrait (C), butterfly population densities ...... 59
Figure 3.4: Influences of environmental predictors on butterfly population densities across sites, as identified in models AUfull (triangles and light grey boxes) and CZfull (circles and dark grey boxes)...... 63
Figure 3.5: Influences of species trait predictors on butterfly population densities across sites, as identified in models AUfull (light grey boxes) and CZfull (circles and white boxes...... 66
Figure 4.1: Study area maps, with black dots representing the 12 grassland habitat fragments in Colorado (A), 20 mixed-habitat fragments in the Prague (B), 19 woodland habitat fragments in Perth (C)...... 77
xi Figure 4.2: Ranges of variation for the predictor variables, with Colorado data represented by dark grey, Perth data by light grey, and Prague data by black bars...... 86
Figure 4.3: Influences of full model predictors on butterfly population densities across study areas, with Colorado data represented by dark grey dots and boxes, depending on the figure, Perth data represented by light grey squares and boxes, and Prague data represented by black triangles and white boxes ...... 90
Figure 5.1- Coefficient estimates for the predictor variables in each of the full (black circles), no water subset (light grey triangles), and go area subset (dark grey squares) models ...... 112
xii CHAPTER ONE
INTRODUCTION
1.1 Background and Research Rationale
We live in a human-dominated world (Vitousek et al., 1997), where continued population growth and expansion have led to anthropogenic influences being felt in essentially all ecosystems on the planet (Vitousek et al., 1997; Palmer et al., 2004). One result of this encroachment is that humans have now modified the majority of earth’s landscapes, through such activities as deforestation, agriculture, or the construction of urban features (Vitousek et al.,
1997). This landscape modification, in turn, results in the fragmentation of once continuous natural habitats into smaller, more isolated patches (Fahrig, 2003; Ewers and Didham, 2006).
Communities inhabiting these fragmented landscapes encounter altered landscape configuration, reduced habitat availability, and modified among patch environmental conditions (Tscharntke et al., 2002; Henle et al., 2004; Ewers and Didham, 2006), factors which contribute to the marked loss of global biodiversity that occurs in fragmented landscapes (Krauss et al., 2010). In order to curtail this escalating biodiversity loss (Butchart et al., 2010), it is important to establish a broader understanding of how species respond to habitat fragmentation, so that we may develop targeted management programs through which the factors that negatively impact the inhabitants of fragmented landscapes may be effectively, and efficiently, mitigated.
Recent years have seen a proliferation of research into how habitat fragmentation affects a wide range of species, including many plants, insects, amphibians, birds and mammals
(Saunders et al., 1991; Stuart et al., 2004; Ewers and Didham, 2006; Fischer and Lindenmayer,
2007) . Such ‘fragmentation effects’ are reflected by species’ distribution or diversity patterns
1 (e.g.,abundances, frequencies of occurrence, or population densities) in fragmented landscapes, as these patterns are influenced by factors that constrain their abilities to persist in certain habitat patches. To better understand the effects of habitat fragmentation, a common approach is thus to relate among fragment diversity patterns to the factors that may drive them, typically for one or a few species, using ecological models (Zanini et al., 2009). Unfortunately, while this approach has provided valuable information regarding the determinants of distribution or diversity patterns for many species, in locations all over the world, it has also served to demonstrate that species’ responses to habitat fragmentation vary widely among not only different kinds of organisms
(e.g.,amphibians, plants, arthropods), but also among different species in the same group
(e.g.,different species of butterflies) (Tscharntke et al., 2002; Krauss et al., 2003; Henle et al.,
2004; Hambäck et al., 2010). As a result, species-specific fragmentation studies provide limited contexts for a broader understanding of fragmentation effects.
In order to facilitate a broader understanding of the effects of habitat fragmentation, one way in which past research may be expanded is through evaluating the drivers of diversity patterns for not just single species, but entire communities. Modeling such multi-species systems, however, may require that the impacts of species traits on variation in among fragment diversity patterns be considered during the analyses. This is because trait-driven constraints limit individual community members’ abilities to occupy patches in which resource availability varies
(Suding et al., 2003; Summerville et al., 2006). Unfortunately, the inclusion of additional predictors, such as species traits, greatly adds to the already complex challenge of modeling diversity patterns in response to potentially large numbers of predictors. This complexity has likely discouraged widespread analyses of community-level environmental and species trait determinants of diversity patterns in fragmented landscapes.
2 A second way in which to expand upon previous research, and to broaden our understanding of how species respond to habitat fragmentation, is by comparing the determinants of diversity patterns across multiple, widely-separated fragmented landscapes. Such comparisons pave the way for identifying not only generalities regarding species’ responses to habitat fragmentation, but also ecosystem-specific abiotic or biotic factors that may govern these responses similarly or differently among fragmented landscapes. These multi-system studies also fit with a recent movement toward broader scale analyses in the field of ecology, where anthropogenic pressure on natural ecosystems has led to increasing demand for ecological investigations that are not only specific to particular locations, but also potentially useful for addressing widespread environmental issues (Thessen and Patterson, 2011; Hampton et al.,
2013).
The overarching goal of my dissertation was to expand on previous fragmentation research in order to broaden our understanding of the causes of species’ responses to habitat fragmentation. I thus addressed three main research questions: 1) What causes variation in butterfly diversity patterns among habitat fragments, at the level of not just individual species, but whole communities? 2) Are the drivers of these patterns consistent across widely-separated fragmented ecosystems?, and 3) If not, are there certain factors that contribute to similarities among locations, and that may facilitate a broader understanding of the causes of fragmentation effects among separate fragmented landscapes? I addressed these questions through an integrative data reuse analysis involving three datasets that describe butterfly abundances among habitat fragments in areas characterized by different ecological and climatic contexts (Figure
1.1). These data provided a means by which I could: (i) assess how diversity patterns for an entire butterfly community are influenced by both the environmental attributes of fragments and
3 the species traits of butterflies inhabiting them, in different fragmented landscapes, and (ii) compare the determinants of these diversity patterns among the three study locations. Finally, I explored one of the major challenges associated with multi-study data reuse analyses, that of integrating independently collected datasets in order to evaluate the potential influence of data collection methods on downstream secondary analysis results.
A) B) C)
Figure 1.1- Photographic examples of habitat fragments for each study area investigated in this dissertation. Patches in Prague, Czech Republic (A) consist of mixed-habitat vegetation; those in Perth, Western Australia (B) of predominantly Banksia and Eucalyptus woodland vegetation; and those in Colorado, USA (C) of open, grassland vegetation.
1.2 Chapter Overview
In chapter two, I assessed the determinants of variation in butterfly population densities and frequencies of occurrence among mixed-habitat fragments occurring near Prague, Czech
Republic (Figure 1.1A). This work was conducted in collaboration with Dr. Martin Konvicka
(Czech Institute of Entomology) and Dr. Tomas Kadlec (Czech University of Life Sciences).
Using their butterfly data, I first constructed a series of linear mixed effects models in order to assess the influences of environmental attributes, species traits, or both on among fragment diversity patterns for the entire Prague butterfly community. In addition, I tested a novel approach to variable selection for complex, multi-species models, in which the inclusion of too
4 many terms can lead to model overfitting (Zharikov et al., 2007). I found that butterfly community diversity patterns were modeled well by five key environmental attributes, habitat area, proportion of open habitat within patches, edge permeability, habitat heterogeneity, and within-site water availability. Furthermore, while two species traits, voltinism (number of annual broods) and average wing length, and one environment-trait interaction, water:voltinism, also influenced butterfly diversity patterns, their impacts were so negligible that their inclusion as predictors failed to improve model fit. This work thus suggested that species traits may play little role in determining butterfly community diversity patterns in this fragmented ecosystem. This manuscript is currently in press for Ecological Modelling.
In chapter three, I first tested the abilities of the models constructed using the Prague dataset to predict diversity patterns for a very different community of butterflies occurring in open woodland patches near Perth, Western Australia (Figure 1.1B). This work was a collaborative effort with Matt Williams (Perth Department of Parks and Wildlife). Good model prediction would have demonstrated that butterfly diversity patterns were determined by the same factors as they had been in Prague, and in the same ways. Prague models, however, poorly predicted diversity patterns for the Perth butterfly community. I then constructed new models for the Perth dataset, using the same methods used for Prague model construction, and compared the results from the two study areas. I found that the environmental attributes of habitat area, water availability, and edge permeability all had consistent influences on among fragment butterfly diversity patterns, although only habitat area was identified as a strong predictor in both locations. The other environmental attributes that had been identified as predictors in the Prague models were also identified as predictors in the models developed for Perth, although their influences on butterfly diversity patterns varied between the two study locations. Finally, species
5 traits had a much stronger influence on Perth butterfly diversity patterns than they had on those in Prague, with the best Perth model containing three total species trait predictors, two of which, butterfly flight period and diapause strategy, had particularly strong influences on butterfly diversity patterns (measured as the frequencies with which species with various traits occurred among patches). This work thus suggested that there are both consistencies and inconsistencies regarding the determinants of butterfly diversity patterns in different fragmented ecosystems, especially in relation to the importance of species traits, and these findings were suggested to result from variation in ecological and/or climatic characteristics between these two locations.
In chapter four I integrated a third dataset into the analyses described above; these data collected in grassland patches in the fragmented ecosystem surrounding Denver, Colorado, USA
(Figure 1.1C). This chapter was a collaborative effort with the three previously mentioned Czech and Australian collaborators, who generously provided data from their studies. Using the
Colorado data, I again fit models, and compared models among the three study areas for a full, multi-continental model cross-comparative analysis. Results showed that habitat area and water availability continued to have consistent impacts on butterfly diversity patterns in Colorado, although water availability had a much stronger impact on butterfly diversity patterns here in
Colorado than it had in either of the other two study areas. With regard to other model predictors, there were distinct similarities between pairs of models, particularly between those of Colorado and Perth, which suggested that certain shared ecological and/or climatic conditions may cause similarities regarding the determinants of butterfly diversity patterns, even in widely separated fragmented landscapes. Finally, the influences of species traits on butterfly diversity patterns were even stronger in Colorado than in Perth, as five species traits were identified as predictors of variation in butterfly frequencies of occurrence in Colorado, with four of these having
6 particularly strong impacts on these patterns. These results may be attributable to the limited habitat type and seasonal climate of Colorado, which may shape species trait pools in fragmented landscapes, as species adapt to the conditions of the region. This work helped to identify potentially important patterns with regard to how species in various settings respond to habitat fragmentation, although the patterns presented here clearly provide avenues for further research.
Additionally, this work highlighted the important roles of species traits in driving diversity patterns, particularly in certain fragmented landscapes.
Finally, in chapter five, I explored the impact of one important, but previously overlooked, factor that might influence the results of model cross-comparison analyses, that of survey methodology. Focusing specifically on survey effort, as defined by the intensity with which patches were surveyed, I compared models constructed from the original Perth dataset and a data subset, this representing a more restricted range of survey efforts than those represented by the original data. I found that the exclusion of ~21% of the original dataset resulted in a model that was almost identical to that constructed from the original data, and thus the use of the data subset in the original analyses would have changed the initial results very little. In addition, and due to correlations between predictors in the data subset, I also fit a second data subset model in which the highly multicolinear environmental attribute of habitat area was excluded as a possible predictor. This exercise revealed that habitat area has such a strong impact on among fragment butterfly diversity patterns that it actually suppresses the influences of other environmental variables, whose impact became much more pronounced in the model fit in the absence of habitat area. This work suggested that survey methodology may have less impact than assumed on downstream secondary analysis results, although additional investigations would help substantiate this conclusion.
7 Overall this work addressed a fundamental question in ecology: How consistent are the drivers of diversity patterns across dissimilar fragmented landscapes? The approach of re-using previously collected data to address this question was novel and valuable for multiple reasons.
First, it added new information to the growing body of knowledge on the causes of butterfly responses to habitat fragmentation, information which may aid in the development of more effective conservation planning and land-use management. Second, it fostered collaborations with colleagues on two other continents, which proved to be important for developing new insights into the causes and consequences of fragmentation effects. Finally, it facilitated a broad examination of the drivers of community-level responses to habitat fragmentation, as is impossible through single, case-by-case analyses. Continued studies of this nature may provide crucial information for land managers working to develop plans that may be broadly applied toward the conservation and protection of biodiversity, particularly in the face of limited conservation budgets.
8 CHAPTER TWO
INTEGRATING SPECIES TRAITS AND HABITAT CHARACTERISTICS INTO
MODELS OF BUTTERFLY DIVERSITY IN A FRAGMENTED ECOSYSTEM1
2.1 Abstract
The fragmentation of natural habitats has well-known impacts on biodiversity, though
responses to fragmentation are often community and/or species-specific. One potential cause for
this variation is that species traits may restrict their abilities to inhabit patches with certain
characteristics (e.g., small amounts of available habitat). Although assessing the influence of
both environmental attributes and species traits on diversity patterns across fragments is
analytically challenging, it is crucial for better understanding the consequences of increasing
anthropogenically-driven land-use change.
Using a multi-species, multi-site system, we explored the causes of variation in butterfly
diversity patterns in the fragmented landscape surrounding Prague, Czech Republic, and the
utility of a novel modeling approach for better understanding the drivers of these patterns.
Specifically, we integrated several environmental attributes and species traits predictors into a
single modeling framework, explored variable selection through an unusual application of a
secondary statistical technique, the fourth corner analysis, and then compared the fits of several
models in order to determine whether diversity patterns were best explained by environmental
attributes, species traits, or both.
We found that in this system, butterfly diversity patterns are most influenced by habitat
metrics, such as area and habitat heterogeneity. Species traits had only minor impacts on
1 This chapter was a collaborative effort between N. Robinson, T. Kadlec, M.D. Bowers, and R.P. Guralnick. Manuscript publication: June 2014, Ecological Modelling (http://dx.doi.org/10.1016/j.ecolmodel.2014.01.022) 9 diversity patterns. We suggest that this may be a result of the spatial scale and/or ecological context of this study system. The models, however, exhibited good fit to the data and showed strong predictive performance during model validation tests, suggesting that the techniques applied may provide useful tools for better understanding the effects of habitat fragmentation on natural communities.
2.2 Introduction
Human activities such as agriculture, forestry and urbanization have profound effects on natural environments (Vitousek et al., 1997), one of the most important of which is the fragmentation of landscapes (Ewers & Didham, 2006). Such fragmentation results in altered landscape structure, reduced habitat availability, and modified environmental conditions among habitat patches ( Tscharntke et al., 2002; Henle et al., 2004; Ewers and Didham, 2006). These transformations elicit unique responses from individual species (Steffan-Dewenter and
Tscharntke, 2000; Hambäck et al., 2010), which may then become differentially abundant among fragments. Identifying the factors that impact species’ abundances and distributions across patches is critical for better predicting continued changes to natural communities and the ecosystem services they provide. This is particularly for the conservation and protection of biodiversity in the face of escalating anthropogenically driven landscape modification (Newbold et al., 2013).
One approach to understanding distribution and diversity patterns within fragmented habitats is to explore the predictors of these patterns using statistical models (Fischer and
Lindenmayer, 2006). The development of such models is enormously challenging, however, in part because the breadth of potential variables that may be evaluated is constrained by trade-offs
10 between model complexity and accuracy (Hillborn and Mangel, 1997). The need to limit the number of possible predictor variables has resulted in most studies focusing solely on the roles of habitat metrics such as patch area or distance between fragments (see Debinski and Holt, 2000;
Elith and Leathwick, 2009). Although these studies have clearly demonstrated the importance of these environmental factors, they have typically overlooked species traits, which may also constrain occupancy in habitat fragments (Suding et al., 2003; Summerville et al., 2006).
Unfortunately, expanding models to include species trait variables greatly increases their complexity, particularly in multi-species, multi-site systems. Analyses of the effects of both environmental features and species traits on diversity patterns have thus been largely limited to either one or a few species (e.g.,Krauss et al., 2003a; Stoner and Joern, 2004) or restricted numbers of environmental and/or species trait variables (e.g., Krauss et al., 2003b; Ockinger et al., 2010; Ikin et al., 2012).
Modeling diversity patterns based on multiple environmental and species trait variables requires two essential elements: 1) a robust statistical framework, and 2) a method by which to limit initial model complexity (the number of predictor variables). The mathematical and computational challenges posed by the first element are met by mixed effects models, which provide flexible tools for modeling complex datasets (Jamil et al., 2012). The theoretical challenge posed by the second element has resulted in either a priori limitations of predictor variables, e.g.,through the exclusion of interaction terms from consideration (Soga and Koike,
2012), or a posteriori approaches to identifying predictors from a potentially large set of individual interaction terms. These include frequentist approaches, such as step-wise additions of interaction terms until the ‘best’ model is found (Jamil et al., 2012), and information theoretic approaches, such as model fit comparisons for models constructed using all possible
11 combinations predictors variables and their interactions (Azeria et al., 2011). There are, however, theoretical and/or computational disadvantages to each of these variable selection methods. For example, coefficient estimates in models derived through step-wise variable addition may be prone to bias (Whittingham et al., 2006), and comparisons among especially large numbers of candidate models, e.g.,those including all possible combinations of predictors and interactions, are computationally intensive. Continued experimentation with new techniques for handling the challenging issue of variable selection is essential.
One potential solution to variable selection in complex, multi-site, multi-species systems is the use of separate statistical analyses for the pre-selection of model interaction terms. Dray and Legendre’s (2008) fourth corner method, for example, measures correlations between the environmental features occurring at sites and the species traits of individuals that inhabit them.
Because these calculations are weighted by the relative abundances (or occurrences) of each species at each site, through the simultaneous analysis of multiple data matrices (see methods), environment-trait interactions are directly linked under observed diversity patterns (Dray and
Legendre, 2008; Aubin et al., 2009) Each correlation is then compared to a distribution of correlation statistics that were derived under a null hypothesis (e.g., that species’ abundances across sites are entirely random) in order to determine the relative strength with which the environment-trait interaction influences diversity patterns. This method may thus provide an elegant means of identifying important model inputs while limiting over-complexity. Such data exploration, while discouraged by some (e.g., Burnham and Anderson, 2002), may be essential for generating hypotheses regarding predictor variable inclusion in complex systems in which the a priori determination of the importance of interactions is difficult (Zurr et al., 2010).
12 Here, we use a comparative, multi-model approach to explore the causes of butterfly diversity patterns in an urbanized landscape. We first develop four separate linear mixed effects models to examine how butterfly population densities across sites (as a component of total patch diversity) are influenced by: (i) both environmental attributes and species traits (full model), (ii) local environmental attributes only (environment model), (iii) species traits only (trait model), or
(iv) the random effects of species and site identities alone (null model). Additionally, we include interaction terms that were identified as important in a fourth corner analysis in the full model, and thus also explore a novel application of this method for variable selection during statistical modeling. Together, these analyses answer the questions: 1) Do species traits strongly contribute to among site butterfly diversity patterns or are these patterns best explained by patch-level environmental features? And, which of these different types of factors influence butterfly diversity patterns most? 2) Can linear mixed effects models be used as predictive tools to determine expected butterfly population densities across fragmented habitats? This work thus helps to both establish the drivers of butterfly community response to habitat fragmentation, and verify whether integrating mixed effects models and the fourth corner method are useful for predicting diversity pattern across patches in a fragmented landscape.
2.3 Methods
Study system and survey methodology
Data for this study were previously collected in order to monitor butterfly (Lepidoptera;
Papilionoidea and Hesperioidea) and burnet moth (Lepidoptera; Zygaenoidea) population changes through time (Kadlec et al., 2008), and to assess responses to urbanization and the importance of natural reserves in the landscape around Prague, Czech Republic (latitude
13 50.09oN, longitude 14.42oE; Konvicka and Kadlec, 2011). We opted to re-use these data because: a) our questions are more focused on model construction methodologies than were those addressed in previous research, and b) the research presented here fits into a larger body of work comparing model results across multiple exemplar studies examining butterfly diversity in urban settings. This dataset was particularly useful in this context because its relatively large number of sites and habitats made it statistically robust and informative. Here, we used a subset of the original data, including butterfly (but not burnet moth) abundances across 20 protected reserves (but no urban parks) (Figure 2.1). Butterflies were chosen because: a) there is a great deal of available natural history data, and b) they are highly sensitive to environmental conditions in the landscapes they occupy (Debinski et al., 2006). This makes butterflies both convenient and informative, from a scientific as well as a management perspective. The sites included here were chosen because they are naturally vegetated, experience minimal management (e.g., limited mowing and/or grazing and scrub reduction to limit succession), represent a range of habitat patch sizes available to wildlife (from 0.74 to 125.28 ha), and encompass a variety of habitats including xeric grasslands, heathlands, deciduous forests and coniferous forests (see Konvicka and Kadlec (2011) for full details).
Figure 2.1- Map of the Prague region, including the 20 habitat fragments (black circles) used for this study. Individual patches range from less than one to greater than 100 ha in total area, 10 km and patch centers are separated by a minimum of 600m (figure adapted from Kadlec et al., 2008).
14 Butterflies were surveyed once per month from May to August of 2003 and 2004, for eight total surveys at each site. Due to Prague’s topography, which is characterized by numerous tall cliffs, surveys could not be performed using a transect protocol (e.g., the Pollard method
(Pollard, 1977)); instead, surveys were conducted in a time-specific manner in which all individuals were recorded as surveyors walked consistent routes that traversed the entire site.
Routes were walked at a slow, steady pace, and for area-proportional durations of time: 30 min for patches < one ha., 60 min for patches between one and ten ha., 90 min for patches between
11 and 100 ha., 180 min for patches > 100 ha.. Additionally, and in order to offset the potential under-sampling of large sites using the time-based approach, areas that were highly attractive to butterflies (e.g., those with particularly prevalent nectar resources) were preferentially visited during each survey. Surveys occurred during optimal butterfly flight conditions: between 10:00 am and 4:00 pm, on sunny days with temperatures ≥17oC and little wind (Konvicka and Kadlec,
2011). Upon the completion of all butterfly surveys, data were summed in order to eliminate the effect of year.
Environmental variables
Environmental variables used for this study include seven patch and landscape-level attributes that influence butterfly survival within, and movement between, patches (Sutcliffe et al., 1997; Grundel et al., 1998; Ries and Debinski, 2001; Schultz and Crone, 2001; Keller and
Yahner, 2002; Sisk and Haddad, 2002; Tscharntke et al., 2002; Bender et al., 2003; Krauss et al.,
2003b; Barbaro and van Halder, 2009; Dover and Settele, 2009; Robinson et al., 2012). These include: 1) water availability, 2) patch area, 3) shape complexity, 4) edge permeability, 5) habitat heterogeneity, 6) proportion of open habitat (e.g., grassland), and 7) amount of open habitat in a
15 buffer around the patch (hereafter OinB) (see details below). Prior to variable measurement, site
boundaries were digitized into ArcGIS (ESRI- http://www.esri.com/) polygons from geographic
coordinates at points occurring along patch perimeters (data from
http://geoportal.gov.cz/web/guest/map; 22 May, 2013). Polygons were then converted to Google
Earth (Google Inc., 2009) kml files, so that variables could be measured in both ArcGIS and
Google Earth as follows:
(1) Water availability (in the form of streams, ponds, etc.) was measured using a
combination of ground surveys and high resolution Google Earth satellite imagery (from
DigitalGlobe (http://www.digitalglobe.com/; 16 August, 2013) and/or GEODIS
(http://sluzby.geodis.cz); 16 August, 2013), and quantified as either absent or present.
(2) Patch areas (in ha) were calculated in ArcGIS.
(3) We measured shape complexity in ArcGIS, as the Number of Shape Characterizing
Points (NSCP), defined as vertices connecting two line segments by an angle ≤ 160o (Moser et al., 2002). Before calculating NSCP, we manually eliminated false vertices around each patch.
These occurred where patch boundaries lay within continuous natural vegetation (i.e., politically defined boundaries that represented a non-edge to patch inhabitants). This manual smoothing helped to avoid overestimation of shape complexity. For each vertex along the smoothed perimeter, we then calculated the smallest angle (between inner and outer) by which the line segments were connected. Vertices with angles ≤ 160o were then summed.
16 (4) To measure edge permeability, we first created 30m buffers around each habitat patch in ArcGIS. 30m was selected based on the distance that butterflies typically travel beyond a patch in search of food plants or oviposition sites (Pryke and Samways, 2001; Ross et al., 2005;
Vanreusel et al., 2007). These buffers were converted to kml and uploaded to Google Earth. For each of the e edges composing a site, we then quantified permeability as the degree to which the edge represented a barrier to butterfly dispersal within the 30m buffer. Permeability was quantified along an ordinal scale (Table 2.1), which was derived from previous studies of the dispersal limitation imposed by a wide variety of edge types (Leon-Cortes et al., 1999; Ries and
Sisk, 2008). We defined an ‘edge’ as any distance for which the permeability quantification was e e uniform, and total edge scores were then calculated as: EP= Σ (Pe x le)/ Σ (l e), where Pe is the 1 1 permeability quantification for edge e, and le is the length of edge e. This equation accounts for variability in both individual edge and total perimeter lengths among patches, and high edge permeability scores denote patches surrounded by poor quality matrix.
Table 2.1- Quantification scheme for the measurement of edge permeability. Category Quantification 1 Non-barrier (continuous, natural vegetation of similar type) 2 Minor barrier (continuous natural vegetation of dissimilar type) 3 Partial barrier (narrow road/non-sheer cliff + similar natural vegetation) 4 Medium barrier (narrow road/non-sheer cliff + dissimilar natural vegetation) 5 Significant barrier (narrow road/non-sheer cliff + agriculture, park, etc.) 6 Strong barrier (urban development + natural vegetation, or sheer cliff) 7 Almost complete barrier (urban development + landscaped vegetation) 8 Complete barrier (urban development + no vegetation)
(5) Habitat heterogeneity was measured in Google Earth, as the number of non-developed
(e.g., paved or containing buildings) land cover (LC) types within a patch. LC types were identified based on the classification scheme of the European Environment Agency’s Corine
17 Land Cover 2000 Program (CORINE2000), which describes 44 LC types. We made two modifications to the Corine2000 scheme: 1) we evaluated grasslands as a separate category
(from Corine2000’s ‘herbaceous vegetation’) due to the specific association of many species in this study with the grassland habitat type (Konvicka and Kadlec, 2011), and 2) we merged groomed vegetated surfaces like parks and golf courses (considered ‘artificial surfaces’ in the
Corine2000 scheme) with ‘agricultural areas’ in order to include all non-natural and maintained greenways in the same category. We therefore identified the presence of five main LC classes in our study sites: 1) natural grassland, 2) scrub/herbaceous vegetation, 3) forest, 4) bare ground, and 5) non-natural greenways.
(6) To measure the proportion of open habitat within patches, we first outlined all areas encompassing natural, open habitat (e.g., grasslands or grassland with few shrubs or trees) within patch boundaries, in Google Earth. These polygons were converted to shapefiles and uploaded to
ArcGIS. We then summed the open areas within each patch and divided by the patch area.
(7) OinB was calculated as an isolation metric, as this measure may outperform other such metrics at predicting animal dispersal between habitat fragments (Bender et al., 2003). We measured OinB similarly to proportion open habitat, although polygons were created in the landscape surrounding, as opposed to within, patches. We limited the analyses to open areas occurring within 500m buffers of each patch of interest, based on previous research into the distance that various butterfly species are able to fly in order to disperse among patches (Grundel et al., 1998; Baguette et al., 2000; Binzenhöfer et al., 2008). Finally, we measured OinB as the sum of all open area polygons within each patch buffer.
18 Species traits
Six species traits were incorporated into these analyses, all of which influence lepidopteran species’ survival in, and movement between, habitat patches (Stamp, 1980; Hill et al., 1999; Debinski and Holt, 2000; Steffan-Dewenter and Tscharntke, 2000; Stoner and Joern,
2004; Dennis et al., 2005; Pöyry et al., 2009; Hambäck et al., 2010; Ockinger et al., 2010). These include: 1) diet breadth, 2) average wing length, 3) voltinism (number of generations per year),
4) flight period, 5) diapause (in this case overwintering) strategy, and 6) eggs laid per batch (see details below). We deliberately excluded measures relating to species’ evolutionary relationships
(e.g., ‘family’) because while these relationships explain trait similarities among species, they are not, themselves, species traits. Butterfly species trait values were measured and quantified using the abundant available literature and online resources for butterflies (e.g., Benes et al., 2002), as well as methods described in previous studies (Brändle et al., 2002; Ohwaki et al., 2007; Pöyry et al., 2009; Stefanescu et al., 2011). Details of measurements and/or quantifications are as follows:
(1) Diet breadth was quantified as monophagous (feeding on one host plant species), oligophageous (feeding on several host plant species within a single family), or polyphageous
(feeding on host plants from several families).
(2) Average wing length data were obtained from literature and online resources, as the average length of a species’ leading forewing edge (in mm).
(3) Voltinism was categorized as univoltine (one generation per year), bivoltine (two generations per year), or multi-voltine (> 2 generations per year).
19 (4) Flight period was categorized based on the range of flight periods exhibited by species in this study as short (one to two months), medium (three to four months), or long (> four months).
(5) We classified diapause strategy based on the developmental stage at which diapause occurs: egg (or larva inside egg), larva, pupa, or adult (or no diapause).
(6) The number of eggs laid per batch was quantified as single, small (two to 50), medium (51 to 200), and large (> 200).
Data preparation
Before performing model analyses, we refined the dataset to exclude a) migratory species that appear inconsistently in the area, and b) species with fewer than ten total individuals recorded across surveys. This circumvented potentially disproportionate effects of rare species on models while still allowing small populations to inform the analyses. The minimum population size was chosen based on: a) a high incidence of species with fewer than ten individuals (leading to a heavily skewed community distribution), and b) minimum population cut-offs used by other researchers in community modeling studies (e.g., Luoto et al., 2005). With the remaining data, we then transformed continuous variables, where needed, to correct for non- linearity. We used a natural log transformation for patch area, shape complexity, OinB, and average wing length. The logit transformation was applied to proportion of open habitat.
Additionally, because the proportion of open habitat data contained a zero, we added the smallest
20 non-zero proportion open habitat value (0.015) to all measurements prior to transformation
(Warton and Hui, 2011). We then measured multicolinearity among variables of each type
(environmental or species trait), using a Variance Inflation Factor (VIF). Variables exhibiting
VIF >5 were excluded from further analyses in order to avoid confounded parameter weight estimates (Kati et al., 2012). Calculations were performed in the HH package in R (Heiberger,
2013; R Development Core Team 2013).
Model construction and fitting
Following data preparation, we selected interaction terms for inclusion in the full model via the fourth-corner method (Dray and Legendre, 2008; summary in Figure 2.2A-C). We used the ade4 package (Chessel et al., 2013) to identify environment-trait pairs that explained variation in butterfly abundances across sites. The response variable chosen was proportional population density (hereafter population density), measured as the number of individuals of a given species at a given site/all individuals of the species. This measure reflects the site-specific density of each species with reference to its total population, and variation in this measure across sites can be attributed to factors that promote or hinder patch inhabitance. In addition, as this measure standardizes the response variable across species, it eliminates situations where species with thousands of individuals are disproportionately weighted, during analyses, in comparison to those with hundreds of individuals (as occurs when using response variables such as abundance).
We next calculated a correlation statistic for each environment-trait pair as outlined in the introduction, and compared this to a distribution derived from permutation method 4 (Dray and
Legendre, 2008). Here, the null hypothesis states that species distributions across sites are driven by site preferences that are independent of species’ life-history traits, and environment-trait pairs
21 with significant correlations are those that provide evidence for the alternative hypothesis that distributions depend on species traits (Dray and Legendre, 2008). This method was chosen because it fit our hypothesis that species traits contribute to their densities across sites, and because it is not prone to the issues surrounding the other two methods that test trait-related hypotheses (methods 3 and 5, which are prone to species becoming void during calculations or to inflated type I errors, respectively (Dray and Legendre, 2008). Environment-trait pairs with statistically significant correlations were retained for further analyses (Figure 2.2D).
Following the identification of important environment-trait interactions, we constructed linear mixed effects models in which species’ population densities across sites were modeled by the full, environment, trait, or null model (Figure 2.2E). Only the full model included interaction terms, and only environment-trait interactions were considered. This was because environment- environment or trait-trait interactions could not be included given our lack of underlying hypotheses regarding their importance to the system (De Little et al., 2009).
22 Figure 2.2- Modeling methods used in this study. Important environment-trait interactions were first identified using the fourth corner method. Here, three matrices were constructed: L= abundances of each species at each site, R= environmental attribute values at each site, and Q= trait values for each species. For each environment-trait interaction, a correlation statistic (denoted as r1) was then computed, where the calculation was weighted by the abundances of species with that trait at sites with that environmental attribute (A). Matrix L was next reconstructed in accordance with the null hypothesis, here that species distributions across sites are driven by site preferences and not life-history traits, and a new r1 calculated under the new matrix (L). This was repeated 999 times and a null distribution of r1’s derived (B). Finally, the probability that each correlation statistic came from its null distribution was calculated, and those with a low probability (p<0.05) identified as important drivers of abundance patterns across sites and retained for further analyses (C). Next, the important environment-trait interactions (denoted as ex x ty) (D) were used in linear mixed effects models. Models were constructed from a training dataset (80% of the full dataset), for which butterfly population densities (p) across sites were modeled based on: random effects only (null model), random effects and environmental attributes (environment model), random effects and species traits (trait model), or random effects, environmental attributes, species traits, and important environment-trait interactions (full model) (E). Models were then used to estimate butterfly population densities in a validation dataset (remaining 20% of the full dataset), and correlations between estimated and observed population densities calculated for each model (F).
23 Initial (hereafter global) models were constructed based on a ‘training’ set of a randomly
selected 80% of the species x site occurrence records, and were specified using the lme4 package
(Bates et al., 2013) as:
p = βo + β1X1 + … + βnXn + εsite + εspecies + εobs
where p are population densities across sites, X1-n are fixed effects, and ε are Normal random deviations with mean zero. It should be noted that attempts to model butterfly abundances (rather than population densities) across sites, using the Poisson error family, resulted in poorly fitted error structures. This was due to: a) heterogeneous variances in abundances across sites, and b) the common observation that variances were an order of magnitude higher than means. We also attempted to model the data as occurrence probabilities, using the Binomial error family, and again found that error structures were poorly fitted.
In order for the specified model to fit Gaussian assumptions, and to create a link between the response variable and explanatory variables (Potts and Elith, 2006), the response variable was log transformed. Continuous predictor variables were then standardized to a mean of zero and standard deviation of 0.5, and binary variables to a mean of zero and a difference between their categories of one (Gelman et al., 2013). This standardization placed the parameter estimates on the same scale, thereby allowing for comparison. Standardization was performed using the arm package (Gelman et al., 2013). To control for the non-independence of site characteristics from site identification and species traits from species identification, site and species name were included in the models as random effects. Global models were then fit using maximum likelihood (ML), so that we could compare subsets of models with different fixed effects
(Verbeke and Molenberghs, 2000). Finally, model diagnostic plots were assessed to verify that the models exhibited appropriate fit to the data.
24 After specifying each global model, we performed model selection using the MuMIn
package (Barton, 2013). We first used the ‘dredge’ function to fit subset models, using all
possible combinations of predictors in the global model. For the ‘best’ model (e.g.,with the
lowest corrected (for finite sample sizes) Akaike Information Criteria (AICc) value), we then
assessed the overall fit, given both fixed and random effects, using a conditional R2 (sensu
Nakagawa and Schielzeth (2012)). Next, we obtained a group of closely equivalent models, those with ΔAICc < 2 from the best model, in keeping with the general ‘rule of thumb’ for equivalent
models in a set (Burnham & Anderson, 2004; Logan, 2011). Finally, we derived averaged
coefficient estimates across models in this groupt. These averaged coefficients represented
predictor variable weights in the final full, environment, and trait models.
Comparing model estimates to observed values
The last analyses involved using the final full, environment and trait models described
above to calculate estimated butterfly population densities in the ‘validation dataset’ (remaining
20% of the full dataset), and then comparing these to observed densities (Figure 2.2F). We first
re-fit each model, with data from the training dataset, using restricted maximum likelihood
(REML). Covariance parameter estimates calculated using REML were unbiased compared to
those calculated using ML, and standard errors for the fixed effects estimates also more accurate
when calculated using REML (Jamil et al., 2012); qualities that were desired for model testing.
We then used the fixed effects estimates as coefficients in model equations, from which we
calculated dependent variable estimates for the validation dataset. Here, the inclusion of different
variable types were as follows: 1) continuous and binary validation dataset variables were
transformed (where necessary) and standardized before being inserted into each model equation,
25 as described above, and 2) when the equation contained categorical variable coefficients, these
coefficients were multiplied by one when that variable level was represented by the species/site
in question, and zero otherwise (see Grueber et al., (2011) for full example). Finally, we back
transformed model outputs (exp(estimated value)) to obtain estimated population densities for all
species-site occurrences in the validation dataset. Population density estimates were also
calculated from models in which +/- 1 standard error had been added to fixed effects coefficients,
to calculate uncertainty around model estimates for graphical purposes. We then performed
correlation analyses to compare estimates from each model to observed population densities for
the validation dataset.
2.4 Results
Butterfly surveys
A total of 16,890 butterflies were recorded across survey sites and years, with 71 species
represented (see Konvicka and Kadlec, 2011). Removal of migratory species and those with
fewer than ten total individuals resulted in a dataset comprising 16,835 butterflies from 56
species (Table 2.2). Total butterfly abundances among the 20 sites varied from 188 to 3889
individuals per site, and this measure was highly correlated with patch area (r(18)=0.78, p<<
0.001). Butterfly species richness across sites ranged from 18 to 54 species, and species richness was also highly correlated with patch area (r(18)=0.80, p<< 0.001).
26 Table 2.2- Prague butterfly species, with total abundances, for the refined dataset. Total Species Family Abundance Aglais urticae (Linnaeus) Nymphalidae 94 Anthocharis cardamines (Linnaeus) Pieridae 203 Aphantopus hyperantus (Linnaeus) Nymphalidae 294 Araschnia levana (Linnaeus) Nymphalidae 180 Argynnis paphia (Linnaeus) Nymphalidae 34 Aricia agestis (Denis & Schiffermuller) Lycaenidae 287 Bolaria dia (Linnaeus) Nymphalidae 60 Callophyrus rubi (Linnaeus) Lycaenidae 235 Carterocephalus palaemon (Pallas) Hesperiidae 10 Coenonympha arcania (Linnaeus) Nymphalidae 27 Coenonympha pamphilus (Linnaeus) Nymphalidae 110 Colias alfacariensis Ribbe Pieridae 2961 Cupido minimus (Fuessly) Lycaenidae 254 Erebia medusa (Denis & Schiffermuller) Nymphalidae 101 Erynnis tages(Linnaeus) Hesperiidae 105 Fabriciana adippe (Denis & Schiffermuller) Nymphalidae 115 Gonepteryx rhamini (Linnaeus) Pieridae 30 Hesperia comma (Linnaeus) Hesperiidae 99 Hipparchia semele (Linnaeus) Nymphalidae 33 Inachis io (Linnaeus) Nymphalidae 90 Iphiclides podalirius (Linnaeus) Papilionidae 155 Issoria lathonia (Linnaeus) Nymphalidae 117 Lasiommata megera (Linnaeus) Nymphalidae 105 Leptidea reali Reissinger Pieridae 41 Leptidea sinapis (Linnaeus) Pieridae 16 Limenitis camilla (Linnaeus) Nymphalidae 16 Lycaena phlaeus (Linnaeus) Lycaenidae 74 Lycaena tityrus (Poda) Lycaenidae 46
27 Table 2.2 (continued) Total Species Family Abundance Lysandra coridon (Poda) Lycaenidae 1441 Maniola jurtina (Linnaeus) Nymphalidae 2480 Melanargia galathea (Linnaeus) Nymphalidae 1235 Melitaea athalia (Rottemburg) Nymphalidae 14 Neozephyrus quercus (Linnaeus) Lycaenidae 51 Ochlodes sylvanus (Esper) Hesperiidae 164 Papilio machaon Linnaeus Papilionidae 26 Pararge aegeria (Linnaeus) Nymphalidae 58 Pieris brassicae (Linnaeus) Pieridae 63 Pieris napi (Linnaeus) Pieridae 1330 Pieris rapae (Linnaeus) Pieridae 1538 Plebejusargyrognomon (Bergstrasser) Lycaenidae 140 Polygonia c album (Linnaeus) Nymphalidae 46 Polyommatus daphnis (Denis & Schiffermuller) Lycaenidae 45 Polyommatus icarus (Rotterburg) Lycaenidae 586 Pontia edusa (Fabricius) Pieridae 29 Pseudophilotes vicrama (Moore) Lycaenidae 67 Pyrgus carthamni (Hubner) Hesperiidae 198 Pyrgus malvae (Linnaeus) Hesperiidae 94 Satyrium acaciae (Fabricius) Lycaenidae 174 Satyrium pruni (Linnaeus) Lycaenidae 12 Satyrium spini (Denis & Schiffermuller) Lycaenidae 19 Scolitantides orion (Pallas) Lycaenidae 454 Spialia sertorius (Hoffmannsegg) Hesperiidae 25 Thymelicus acteon (Rottemburg) Hesperiidae 15 Thymelicus lineola (Ochsenheimer) Hesperiidae 478 Thymelicus sylvestris (Poda) Hesperiidae 118 Total no. individuals 16,835
28 Model construction and fitting
Predictor variable selection analyses revealed significant multicolinearity among the environmental variables of OinB and shape complexity, likely due to the correlation that both variables exhibited with patch area (r(18)= 0.75 and 0.81, respectively; p<< 0.001 in both cases).
These two predictors were thus excluded from further analyses. We found no multicolinearity among the species trait variables, and the fourth-corner analysis was thus performed using five environmental and six trait variables, for a total of 30 possible environment-trait pairs. Of these, four environment-trait interactions were identified as important drivers of butterfly diversity patterns: water:voltinism, water:flight period, area:voltinism, and area:flight period. Therefore, we parameterized global models as follows: the null model (as well as all others) included the random effects of species and site identity; full model fixed effects included the five retained environmental variables, the six retained species trait variables, and four interaction terms; environment model fixed effects included the five retained environmental variables; trait model fixed effects included the six trait variables. For all of these models, model diagnostics suggested that global models were appropriately parameterized, and exhibited good fit to the training dataset; residuals were relatively evenly scattered in comparison to fitted values (i.e., models lacked heteroscedasticity) and error distributions were close to normal.
The final models, following model averaging, included all five retained environmental variables (full and environment models), two species trait variables (full and trait models), and one environment-trait interaction (full model; Table 2.3). The traits of importance were voltinism and average wing length, and the environment-trait interaction was water:voltinism. Comparison among the final models revealed that variation in butterfly population densities across patches was equally well-explained by the full and environment models (AICc: 1408.2; conditional R2:
29 0.418 for both models). AICc calculation revealed that the trait model exhibited considerably poorer fit than the full model (AICc: 1426.6), and in fact provided no better explanation of variation in butterfly population densities than did the null model (AICc: 1426.6).
Table 2.3- Averaged coefficient estimates and random effects variances for models examining the influences of environmental and/or trait predictorss on butterfly diversity patterns, as fit by maximum likelihood
Parameter Estimate* SE Lower CI Upper CI Relative importance Pr(>|z|) Full Model: Fixed Effects Coefficientsa (Intercept) -3.0856 0.0993 -3.2801 -2.8911 1.00 <<0.001 Area (ha)b 1.1394 0.2261 0.6962 1.5826 1.00 <<0.001 Proportion open habitat b 0.3096 0.1589 -0.0019 0.6211 0.83 0.051 Edge permeability -0.2627 0.1735 -0.6028 0.0773 0.35 0.130 Habitat heterogeneity 0.2837 0.2436 -0.1937 0.7612 0.29 0.244 Water availability 0.0909 0.2340 -0.4069 0.5888 0.16 0.720 VoltinismMulti -0.0494 0.1596 -0.3622 0.2634 0.16 0.757 VoltinismUni 0.1544 0.1238 -0.0883 0.3971 0.16 0.213 Average wing length (mm)b -0.0471 0.1126 -0.2677 0.1735 0.07 0.676 Water:VoltinismMulti -0.0160 0.2059 -0.4196 0.3875 0.07 0.938 Water:VoltinismUni 0.3753 0.1719 0.0383 0.7123 0.07 0.029 Random Effects Variancesa Site 0.0866 Species name 0.0648
Environment Model : Fixed Effects Coefficientsa (Intercept) -3.0770 0.0937 -3.2607 -2.8934 1.00 <<0.001 Area (ha)b 1.1305 0.2298 0.6800 1.5809 1.00 <<0.001 Proportion open habitatb 0.3037 0.1595 -0.0090 0.6164 0.78 0.057 Edge permeability -0.2627 0.1735 -0.6028 0.0773 0.45 0.130 Habitat heterogeneity 0.2837 0.2436 -0.1937 0.7612 0.37 0.244 Water availability 0.1535 0.2382 -0.3134 0.6205 0.11 0.519 Random Effects Variancesa Site 0.0847 Species name 0.0891
Trait Model: Fixed Effects Coefficientsa (Intercept) -3.2246 0.1633 -3.5446 -2.9046 1.00 <<0.001 VoltinismMulti -0.0478 0.1644 -0.3701 0.2744 0.25 0.771 VoltinismUni 0.1702 0.1266 -0.0779 0.4183 0.25 0.179 Average wing length (mm)b -0.0383 0.1143 -0.2623 0.1857 0.21 0.738 Random Effects Variancesa Site 0.4246 Species name 0.0792 * Estimates are for standardized variables a Estimates are for global model b Variable is transformed
30 Influences of individual predictors
We next explored the influences of each individual variable on butterfly population densities across sites. The variable with single greatest impact was patch area, which appeared in every subset model, during model averaging, for both the full and environment models (relative importance of 1.00, low p-value; Table 2.3). Not surprisingly, greater population densities occurred in habitat patches with larger areas (positive coefficient estimates in Table 2.3; Figure
2.3A). Other important, though not statistically significant, variables were edge permeability, habitat heterogeneity, and proportion of open habitat (Table 2.3). Butterfly species occurred in larger densities in sites with more permeable edges (lower edge scores), and greater habitat heterogeneity and proportions of open habitat (Table 2.3, Figure 2.3B-D). The availability of water in sites also had a positive influence on butterfly population density (Table 2.3, Figure
2.3E), though this variable was less important to the models than the other environmental attributes (Table 2.3).
31 ) 0 0 0
s A) B) C) e i t
i -1 -1 -1 s n
e -2 -2 -2 D -3 -3 -3 y l f r
e -4 -4 -4 t t
u -5 -5 -5 B (
g -6 -6 -6 o l -7 -7 -7
-0.5 0.0 0.5 -1.0 -0.5 0.0 0.5 -1.0 -0.5 0.0 0.5
Area (ha)1 Edge Permeability Habitat Heterogeneity
) 0 0
s D) E) e i t
i -1 -1 s n
e -2 -2 D -3 -3 y l f r
e -4 -4 t t
u -5 -5 B (
g -6 -6 o l -7 -7 -1.0 -0.5 0.0 0.5 1.0 1.5 Absent Present
Proportion Open Habitat1 Water Availability
Figure 2.3- Influences of individual environmental predictors on butterfly densities across sites in the Prague training dataset. Data are standardized (mean=0, s.d=0.5 for continuous variables; mean=0, difference between categories=1 for binary variables) as they were for modeling analyses, and butterfly densities are natural log transformed. Boxplots are shown for categorical variables, with heavy lines indicating median values, lower and upper box edges representing first and third quartiles (respectively), and whiskers showing minimum and maximum values. Superscript indicates transformed data.
Given our choice of response variable, care is required in interpreting the meanings of
species trait effects in models. Specifically, by using a proportional response variable, with standardization by total population size for each species (i.e. standardized across species), the mean population density of all species is equal when unoccupied patches are included in the dataset. When these ‘zeros’ are excluded, as in our dataset, variation in mean population densities among species reflects differences in the frequencies with which species with different traits occurred among patches in the landscape. Because they relate to the relative frequencies of occurrence of species in the landscape, species trait parameters thus act as measures of occupancy in our analyses. Further, and again because of the standardization of population
32 density, coefficient estimates for species traits identified as model predictors indicate the inverse of the influences of those variables on butterfly diversity patterns (frequencies of occurrence).
For example, the species trait of voltinism had a relatively weak influence on butterfly diversity patterns (Table 2.3), with multivoltine species occurring with greater frequency among patches than bivoltine species (indicated by the negative coefficient estimate for multivoltinism), and univoltine species occurring with the lowest frequency throughout the landscape (Figure 2.4A).
The species trait of wing length also weakly influenced butterfly diversity patterns in this system, with longer winged species occurring slightly more frequently throughout the landscape than those with shorter wings (e.g.,a negative coefficient estimate; Table 2.3; Figure 2.4B).
Voltinism Avg. Wing Length (mm) A) B) e c
n 15 15 e r r u c
c 10 10 O
f o
.
q 5 5 e r F 0 0
Uni Bi Multi -0.5 0.0 0.5 1.0
Voltinism Avg. Wing Length (mm)1
Figure 2.4- Frequencies of occurrence of butterflies with different species trait values among all patches in the Prague training dataset. Graphs illustrate species traits that were identified as predictors in the full model. Boxplots are shown for the categorical variable of voltinism (A), with heavy lines indicating median values, lower and upper box edges representing first and third quartiles (respectively), and whiskers showing minimum and maximum values. Data are standardized to mean=0 and s.d=0.5 for the continuous variable of avg. wing length (B), as they were for modeling analyses. Superscript indicates transformed data.
Finally, the interaction of water:voltinism also had a weak, although significant (Table
2.3) influence on butterfly diversity patterns in this system. Here, the frequencies of univoltine, bivoltine and multivoltine species throughout the landscape were differentially influenced by the availability of water among patches; univoltine and multivoltine species occurred with relatively
33 equal frequencies in patches with water as in those without, while bivoltine species occurred
with greater frequencies in patches containing water (Figure 2.5).
UnivoltineUnivoltine Species Species BivoltineBivoltine Species MultivoltineMultivoltine Species Species 10 A) 10 B) 10 C) e
c 8 8 8 n e r r
u 6 6 6 c c O
f
o 4 4 4
. q e r 2 2 2 F
0 0 0 AbsentAbsent PresentPresent AbsentAbsent PresentPresent AbsentAbsent PresentPresent
Water Availability Water Availability Water Availability
Figure 2.5- Frequencies of occurrence for univoltine (A), bivoltine (B), and multivoltine (C) butterfly species at sites in which water was absent (unavailable) and present (available), in the training dataset. Heavy lines indicate median values, lower and upper box edges representing first and third quartiles (respectively), and whiskers show minimum and maximum values.
Comparing Model Estimates to Observed Values
Due to the relatively poor fit of the null and trait models, we did not compare butterfly
population density estimates for these models to observed densities across patches in the
validation dataset. For both the full and environment models, estimated and observed butterfly
population densities exhibited highly significant correlations across the validation dataset (r(129)=
0.516 and 0.530 for the full and environment models, respectively, p<<0.001 in both cases). The models, however, reached maximum density estimates of 0.14, and so were prone to underestimating the densities of butterflies occurring in large proportions at individual sites
(Figure 2.6). These underestimates occurred relatively consistently across sites, and we detected no environmentally driven patterns regarding the accuracy of estimates (e.g., the highly influential variable of patch area did not affect model accuracy; Figure 2.7; Appendix A) despite
34 noticeable site-specific differences among estimated values. Estimates for butterfly population densities at site Cimicke udoli, for example, ranged from ~0.030 to 0.111 (including uncertainty), but exhibited a much narrower range (~ 0.028 to 0.031) at the almost identically sized Branicke skaly (Figure 2.7C-D).
0.6 Figure 2.6- Observed versus predicted butterfly population densities in the Prague validation 0.5 dataset. Results shown are for the full model, but y
t are nearly identical for the environment model. i s
n 0.4 e D
d 0.3 e v r
e 0.2 s b
O 0.1
0.0
0.02 0.04 0.06 0.08 0.10 0.12 Predicted Density
35 OkrouhikOkrouhik ZlatniceZlatnice CimickeCimicke udoliudoli 0.030 A) 0.07 B) 0.14 C) Observed 0.06 0.12 Full 0.025 Environment y t
i 0.05 0.10 s 0.020 n e D
0.04 0.08 n
o 0.015 i t
a 0.03 0.06 l u
p 0.010 o 0.02 0.04 P
0.005 0.01 0.02
0.000 0.00 0.00 BranickeBranicke skaly skaly DivokaDivoka sarka TicheTiche udoliudoli 0.10 D) 0.20 E) 0.30 F)
0.25 0.08
y 0.15 t i s 0.20 n e 0.06 D
n
o 0.10 0.15 i t a
l 0.04 u
p 0.10 o
P 0.05 0.02 0.05
0.00 0.00 0.00 Species-site occurrence Species-site occurrence Species-site occurrence Figure 2.7- Site-specific observed (dark grey bars) versus estimated butterfly population densities for the full (light grey bars) and environment (white bars) models in Prague. Calculations were performed for a randomly selected validation dataset (20% of the full dataset), for which the numbers of species occurring at each site (species-site occurrences) are represented by the numbers of bars in that site’s subfigure. Error bars represent estimated population densities calculated for model in which +/- 1 standard error were added to fixed effects coefficients. Results are shown for a subset of the 20 sites represented in the validation dataset, for graphical clarity, with sites ordered from smallest to largest (in ha). Full graphical comparisons are available in Appendix A.
2.5 Discussion
Drivers of butterfly densities across habitat fragments
Results from this study provide empirical evidence that patch-level environmental conditions drive butterfly diversity patterns in the fragmented landscape surrounding Prague. In fact, given that no single analysis could encompass every factor influencing diversity patterns, butterfly population densities were remarkably well-estimated by models based solely on environmental variables. Moreover, environmental attributes influenced population densities so strongly that: a) species traits failed to improve either model fit or predictive ability, and b) the
36 interactions between these species traits and environmental features were both minimal and at best marginally influential on butterfly diversity patterns.
We were not surprised that environmental attributes heavily influenced butterfly densities across habitat fragments; several studies have verified the importance of these variables. For example, researchers have repeatedly demonstrated positive relationships between butterfly abundances and patch area, habitat heterogeneity, proportion of open habitat, and water availability (Krauss et al., 2003b; Luoto et al., 2005; Rundlöf and Smith, 2006; Robinson et al.,
2012). Additionally, the impacts of edge permeability on butterflies’ abilities to move between patches (which may affect population densities within those patches) is well reported (Ries and
Debinski, 2001; Ewers and Didham, 2006; Ries and Sisk, 2008). Our work suggests, however, that butterfly diversity patterns may be insufficiently explained by examining singular associative relationships between one or a few environmental factors and population measures.
In fact, we found that not only was every candidate environmental attribute influential to butterfly population densities, but that these densities were best explained and predicted when all environmental variables were integrated into a single analytical framework.
The roles of species traits
The biggest question that remains following these analyses is: Why did species traits have so little impact on the frequency with which butterflies occurred throughout the landscape?
This is particularly puzzling considering that species traits have often been shown to influence lepidopteran population densities and community membership across patches (e.g., Steffan-
Dewenter and Tscharntke, 2000; Summerville et al., 2006; Hambäck et al., 2010). Thus, while we avoided formulating specific hypotheses regarding the directionality of traits’ influences on
37 diversity patterns, given well-documented study-specific variation in trait-diversity relationships
(Summerville et al., 2006), we did expect that traits would be important in our models.
It may be that the detection of only weak influences of species traits on diversity patterns in this system is a result of the spatial scale and ecological context of this particular case study.
To start, this work was conducted over a local spatial extent of ~20 km. While such small spatial extents are commonly used to explore the local causes of ecological patterns (Elith and
Leathwick, 2009), the influences of species traits on relative densities may be negligible over these distances. This has been demonstrated for beetles, where relationships between body size and population measures (here abundance across sites) are rarely found at local spatial scales, but frequently at regional scales (Davies et al., 2000). Additionally, our data were gathered across patches encompassing many habitat types, and included species with diverse habitat associations
(26 woodland or mesic grassland specialists, 16 xeric grassland specialists, four preferring warm, dry locations, and 10 habitat generalists). The use of such an inclusive butterfly community may have effectively ‘washed out’ trait signals that would be key in a more limited context (e.g., grassland-only fragments). Average wing length, for example, might be important in communities where butterflies must travel long distances to find suitable grassland patches (as opposed to being able to utilize both open and forested areas). A possible continuation of this work would be to determine if traits have more influence on specialist than on generalist species’ diversity patterns.
Similarly, environment-trait interactions also played a surprisingly small role in driving these patterns. We posit that this may relate to certain methodological details of this study. For example, the use of number of land cover types to quantify habitat heterogeneity may have been too coarse a measure of this environmental attribute, such that some interactions between
38 butterflies and their environments were potentially missed. An alternate habitat heterogeneity measure, e.g.,number of plant functional groups, may be more applicable at the spatial scale considered here, and its use may result in interactions like habitat heterogeneity:diet breadth having more influence on butterfly diversity patterns.
We also acknowledge that the weak influences of both species traits and environment- trait interactions on butterfly diversity patterns in this study may also relate to our choice of statistical framework. We chose a linear mixed effects modeling framework, informed by a fourth corner analysis, because of the complex nature of this multi-site, multi-species system.
Linear mixed effects models are not only flexible and powerful tools for modeling complex systems, but are also technically well-developed and easily implemented. That said, the utilization of non-linear modeling may be a useful alternate approach to studies such as these, especially given that our models underestimated population densities in high density patches.
Although beyond the scope of this particular study, we see such alternate approaches as promising future directions for multi-site, multi-species fragmentation research.
Conclusions
This work addresses a fundamental question in ecology: what drives diversity patterns in fragmented landscapes? We addressed this question by incorporating both species traits and environmental attributes into a single statistical framework, using mixed effects modeling approaches that have only recently become popular among ecologists. Additionally, we explored a novel approach to variable selection for a complex dataset. We found that despite relatively large initial models, with up to 15 predictor variables, butterfly diversity patterns in this system were best explained five environmental predictors. Thus, while the expected influences of
39 species traits on diversity patterns were conspicuously absent from our results, the tools now exist to fully explore how a large array of factors may simultaneously affect diversity across habitat fragments.
The framework we have presented here moves past simple species-environment correlations, and toward a more mechanistic and integrated understanding of intrinsic and extrinsic factors that may drive diversity patterns. The continued application of such a framework in additional systems will contribute to the growing body of knowledge on the causes of diversity patterns in fragmented ecosystems. As further research is undertaken, the spatial scale and ecological context in which species traits influence diversity patterns may become better understood. Additionally, continued research may further illuminate the efficacy of novel applications of techniques such as the fourth corner method in complex, statistical analyses.
Our results have immediate relevance to conservation and land-use planning in the fragmented ecosystem surrounding Prague. First, the models could be used to better understand the consequences of landscape alteration on butterfly diversity in the region. If part of a reserve were under consideration for housing development, for example, the effects of habitat loss on butterfly diversity could be forecasted by inserting new environmental values into our models.
Such information is critical for impact assessments. Furthermore, this work could facilitate assessments of the generalizability of local-scale causes of butterfly diversity patterns in fragmented landscapes. If these models predicted butterfly population densities across patches in different urban areas, for example, it would provide demonstrable evidence that the drivers of species densities across habitat fragments are similar regardless of location. Otherwise, it would clarify the need to explore ecosystem-specific governing factors and/or continue to search for
40 general patterns. Such transferability analyses would lead to a deeper understanding of the generalizability of butterfly responses to habitat fragmentation across ecosystems and/or regions.
Finally, this work has important implications for conservation biology through its potential use with different taxa. While butterflies act as indicator species for the impacts of environmental change on many other organisms (Thomas et al., 2004), they are also characterized by life history strategies that undoubtedly dictated our results. For example, the holometabolous life-cycle of this group results in a relatively immobile larval stage, with a comparatively restricted diet breadth, and a highly mobile adult stage, with a much more general dietary requirement (nectar). Results of studies like these, from variables that were identified as important to predictive performances, may differ for organisms without complex life-cycles, or those that are sessile (e.g., plants) or very mobile (e.g., large mammals or birds). The methods applied here, however, could easily be extended to other taxa, and may highlight unexplored or unidentified environment-trait relationships that further our understanding of fragmentation effects across a wide variety of organisms.
41 CHAPTER THREE
EXPLORING GENERALITIES IN THE DRIVERS OF DIVERSITY PATTERNS IN
FRAGMENTED LANDSCAPES: A MODEL CROSS-COMPARISON ANALYSIS
WITH BUTTERFLIES2
3.1 Abstract
Habitat fragmentation through anthropogenic landscape modification poses significant
threats to global biodiversity. Understanding the determinants of species’ responses to habitat
fragmentation is critical for developing conservation and management policies to conserve and
protect biodiversity in human-dominated landscapes. The extent to which individual species’
responses to fragmentation can be studied, however, is limited by budgetary and time constraints.
A fundamental goal in fragmentation research is thus to identify generalities regarding the
drivers of diversity patterns in fragmented landscapes, knowledge of which may facilitate better
understanding of wide-spread fragmentation effects and inform broadly applicable conservation
and management policies.
We investigated the drivers of butterfly community diversity patterns in two fragmented
landscapes in different regions of the world. We first evaluated the transferability of models of
among fragment butterfly diversity patterns, in response to both environmental attributes and
species traits, in Prague, Czech Republic to Perth, Western Australia. Model transferability was
assessed as the accuracy with which Prague models predicted diversity patterns for the Perth
butterfly community, and was found to be poor. We then performed follow-up analyses in which
we fit new models in Perth, and compared model predictors among the two study locations.
2 This chapter was a collaborative effort among N. Robinson, M.R. Williams, M.D. Bowers and R.P. Guralnick, and is currently in preparation for submission to Ecography 42 Results suggested that: (i) there are key environmental attributes that consistently influence among fragment butterfly diversity patterns, regardless of the location of the fragmented landscape, (ii) additional environmental variables play important roles in driving butterfly diversity patterns in fragmented landscapes, though their influences vary depending on the study location, and (iii) species traits have differential influences on butterfly diversity patterns in different study areas. We suggest that ecosystem-specific ecological and/or climatic conditions may strongly influence the impacts of predictors on diversity patterns among study locations, and encourage future studies of the patterns described and potential influences of ecological and/or climatic conditions explored here.
3.2 Introduction
Habitat fragmentation from human land-use modification is an increasingly pervasive, global phenomenon. In every fragmented ecosystem, native communities face reduced habitat availability, altered landscape structure, and modified conditions within remaining patches
(Tscharntke et al., 2002; Henle et al., 2004; Ewers and Didham, 2006). Understanding how these transformations impact species’ abilities to occupy remnant patches is crucial for generating management policies to promote and maintain biodiversity in human modified ecosystems.
Widespread assessments of fragmentation effects, however, are constrained by budgetary and time restrictions that limit the extent to which individual species’ responses to fragmentation can be assessed. Thus, researchers often seek the development of management policies that are broadly applicable among widely separated and ecologically distinct fragmented landscapes.
Such a feat will only be possible if the factors that generally influence species diversity patterns across habitat fragments can be identified, regardless of where those fragments are located.
43 Ecological models, through which the drivers of distribution or diversity patterns among sites are identified (Zanini et al., 2009), may be useful tools for assessing generalities regarding species responses to habitat fragmentation. For example, if models constructed in one area could be extrapolated to predict distribution or diversity patterns in another, it would provide empirical evidence that the drivers of such patterns are broadly consistent across study locations. Such generalizable models are increasingly sought (Elith and Leathwick, 2009), particularly by researchers working with limited resources (Murray et al., 2011). This has resulted in several efforts to model variation in among site distribution or diversity patterns for one or a few species, based on environmental predictors, and then test these models’ abilities to predict such patterns for the same species but in separate study locations (Binzenhöfer, et al., 2005; Graf et al., 2006;
Menéndez and Thomas, 2006; Randin et al., 2006; Strauss and Biedermann, 2007; Vanreusel et al., 2007; McAlpine et al., 2008; Murray et al., 2011). While results from these species-habitat studies lend insight into similarities and differences in the determinants of diversity patterns among ecosystems, their applicability is limited to only a small number of species. If this work is to inform truly wide-spread assessments of such phenomena as habitat fragmentation, it must be expanded to include models constructed for entire communities that occupy diverse fragmented landscapes. In order to model such multi-species systems, however, it may be important to consider the additional influences of species traits on variation in diversity patterns among fragments. This is because trait-driven constraints limit individual community members’ abilities to occupy patches in which resource availability varies (Suding et al., 2003; Summerville et al.,
2006). Unfortunately, the inclusion of additional predictors comes with the side effect of increasing model complexity and specificity to the conditions under which construction occurred. Thus, while species traits are potentially important determinants of community
44 diversity patterns in fragmented landscapes, the additional complexity that accompanies their inclusion substantially reduces the potential for models to successfully extrapolate between regions (Binzenhöfer et al., 2005). This is a likely reason that examination of model generalizability for multi-species assessments remain underexplored.
Given that environment and species trait-based models may be essential for identifying the drivers of community diversity patterns in fragmented landscapes, but may also be too complex to transfer well among study locations, it is important to consider additional ways in which these models might provide information toward a broad assessment of fragmentation effects. For example, rather than focusing strictly on constructing spatially transferable models, we might instead build models for similar communities (in which species exhibit comparable trait variation) that occupy widely separated and ecologically distinct ecosystems, and then compare both the predictors among locations as well as their relative contributions in each area.
This would facilitate the identification of factors that: 1) uniformly affect diversity patterns
(e.g.,are equally important predictors in all models), 2) have similar effects on diversity patterns among locations, though with different relative influences depending on the ecological context of the study area, and 3) are idiosyncratic to place (sensu Billick and Price, 2010). The universally important factors might then be used as baseline criteria for management policies; policies which can be tailored to different ecosystems through the addition of factors that are important in an ecosystem-specific manner. Thus while it may be true that ‘nature is too complex and heterogeneous to be predicted accurately in every aspect of time and space from a single, although complex, model’ (Guisan and Zimmermann, 2000), model cross-comparisons may provide information that facilitates broader understanding of the causes of fragmentation effects
45 in diverse communities, and that potentially aid in the development of more efficient and effective conservation and management strategies for widely separated fragmented landscapes.
In this study, we investigate similarities and differences between models of butterfly community diversity patterns across patches in two fragmented ecosystems in different regions of the world. Communities were comprised of species with comparable ranges of phylogenetic and trait variation, though they occupy divergent ecological settings; one which is characterized by mixed-habitat fragments that lie in the climatically mild, warm-temperate region of Prague,
Czech Republic, and the other of which includes open woodland fragments that lie in the seasonally hot, drought-prone Mediterranean-type climatic region of Perth, Western Australia.
Using data collected for the same sets of predictors across study areas, we first compare predictor and response variable ranges in order to gauge whether models built in one location have the potential to be extrapolated to the other (Vanreusel et al., 2007). If variation is similar, we use models that were previously constructed to identify both environmental and species trait determinants of among patch butterfly diversity patterns in the first region (Chapter two) to answer the question: Do models for a group of species in one fragmented ecosystem predict diversity patterns for a taxonomically similar, but independent, group of species that occupy an ecologically and spatially distinct area? We predict that the best models from the first ecosystem will be unable to perfectly predict diversity patterns in the second, and thus perform model cross- comparisons to answer follow-up questions: Which factors, both environmental and species trait- based, consistently influence diversity patterns among habitat fragments, and which have location-specific directions and strengths of impact? Through comparing drivers of diversity patterns for entire communities, and in distinct ecosystem contexts, our goal is to identify
46 generalities that may lay the groundwork for more rapid assimilation of information on the
global influences of escalating fragmentation on communities.
3.3 Methods
Study system
The focal taxa for this study are butterflies and skippers (Lepidoptera: Papilionoidea and
Hesperoidea, hereafter ‘butterflies’). This group was chosen because their sensitivity to habitat
conditions (Debinski et al., 2006) makes them vulnerable to landscape change, such that
modeling butterfly diversity patterns in fragmented landscapes may be particularly informative
with regard to understanding the impacts of fragmentation on natural communities. Additionally,
successful model transfer has been realized for multiple species within this group (Binzenhöfer
et al., 2005; Vanreusel et al., 2007), suggesting that butterflies may be well-suited for model
transferability and cross-comparison analyses.
Data for this study were collected at: 1) 20 mixed-habitat sites occurring near Prague,
Czech Republic (latitude 50.09oN, longitude 14.42oE), where climate is characterized as warm temperate with warm summers and no dry seasons (Kottek et al., 2006), and 2) 19 open woodland sites occurring near Perth, Western Australia (latitude 31.95oS, longitude 115.86oE;
Figure 3.1), where climate is characterized as warm temperate with hot, dry summers (Kottek et al., 2006). Further details for each dataset are given below (for Perth) and in Chapter two (for
Prague).
47 Figure 3.1- Map of the Perth region, including the 19 habitat fragments (black circles) used for this study. Individual patches range from approximately 12 to 116 ha in total area, and patch centers are separated by a minimum of 1,066m. Figure is adapted from Williams (2011).
10 km
Data collection: Prague models
Prague models were previously constructed to identify both environmental and species trait determinants of among patch butterfly diversity patterns (population densities among patches and frequencies of occurrence throughout the landscape) in the fragmented ecosystem near Prague. Detailed methodologies for this study can be found in Chapter two. Briefly, data were collected at 20 mixed-habitat reserves, which experience minimal management and represent a range of habitat areas available to butterflies. Population densities for each species
(measured as the numbers of individuals of a given species at a given site/all individuals of that species) were then modeled based on environmental and/or species trait predictors in a linear mixed effects framework. Predictor variables considered included the following seven patch and landscape-level attributes, and six species traits: 1) water availability, 2) patch area, 3) shape complexity, 4) edge permeability, 5) habitat heterogeneity, 6) proportion of open cover type
(e.g.,grassland), 7) amount of open habitat in patch buffer (hereafter OinB), 8) diet breadth, 9) average wing length, 10) voltinism (number of generations per year), 11) flight period, 12) 48 diapause (in this case overwintering) strategy, and 13) eggs laid per batch. These variables were chosen because they have all been shown to affect the abilities of butterflies to inhabit habitat fragments, regardless of study location. Additionally, the same measurement and quantification schemes as used in initial model construction (details in Chapter two) could be broadly employed throughout different study areas. Three models were fit as follows: (i) a ‘full’ model (hereafter
CZfull) which included random effects of site and species identity, and fixed effects of environmental attributes, species traits, and environment-trait interactions that had been identified as important drivers of among fragment population densities (see Chapter two for details), (ii) an ‘environment’ model (hereafter CZenvt) which included the same random effects, and fixed effects of environmental attributes only, and (iii) a ‘trait’ model (hereafter CZtrait) which included the same random effects, and fixed effects of species traits only.
Data collection: Perth models
Data for Perth models were previously collected in order to assess the impacts of habitat quality and landscape metrics on butterfly and day-flying moth communities in the urban landscape of Perth (Williams, 2011). We chose to re-use these data here because: a) we wished to explore the influences of additional factors on butterfly diversity patterns, and b) the ecological context in which these data were collected (see below) differs from that characterizing the Prague dataset, such that model comparisons would highlight both commonalities and differences between the drivers of butterfly diversity patterns in markedly different fragmented landscapes.
Original surveys were performed at 46 remnant habitat patches, comprising primarily open Banksia and Eucalyptus woodlands, as described more fully in Williams (2011). These
49 sites are part of the ‘Bush Forever’ network (http://www.bushlandperth.org.au, accessed
September 2013) of reserves, through which Australian bushland is conserved along Perth’s
Swan Coastal Plain. Surveys were conducted following the Pollard method (Pollard, 1977), wherein observers walked transects at a slow, steady pace and recorded all individuals within 5m to each side and in front. One to three transects were positioned within each site, in order to sample as many vegetation types as possible, and transect length varied from 260 to 5,100m.
Additionally, transects were largely restricted to pre-existing paths and fire breaks, in order to minimize vegetation trampling and the spread of weeds or fungal pathogens within native vegetation. Surveys were performed every two weeks during the two main butterfly flight periods in Perth, those of Austral spring (late September to mid December) and autumn (late
February to early April), from 2003 to 2005. This resulted in a total of six to nine surveys per site, with data collection performed only when weather was optimal for butterfly flight: low wind speeds, low cloud cover, temperatures of at least 21oC.
For this study, we used a subset of the original data, including butterfly (but not moth) abundances from 19 habitat fragments. These data were selected in order to maximize consistency between the Prague and Perth datasets. For example, as no moth species were included during Prague model construction, these taxa were also excluded from the Perth dataset.
Regarding site selection, sites were only included if: 1) they were surveyed an equal number of total times, and 2) within site survey effort was within approximately the same range as that occurring in the Prague dataset. From the first criterion, we eliminated 17% of sites, for which total numbers of surveys differed from eight. For the second criterion, we first measured within site survey effort as the length of all transects within the site over that site’s area. Perth survey efforts ranged from 4.55 to 364.21 m/ha, an approximately 80-fold difference among sites. We
50 then compared this to the range of survey efforts in Prague. Here, surveys were time-based, due to the presence of cliffs that prevented the use of a transect protocol, with 30 to 180 minutes per survey, depending on area (Chapter two). Because time spent per site does not directly equate with the distance surveyed per unit area (e.g.,if surveyors spend time walking between areas in which they perform individual surveys), we could at best estimate differences in survey efforts from the total minutes spent among surveys. Thus, we estimated up to a 6-fold difference in survey effort among Prague sites. Finally, we refined the Perth dataset to include only sites for which: a) there was a 6-fold difference in among site survey effort, b) the range of survey effort was intermediate (e.g.,the most sparsely and intensely surveyed sites were not included), and c) the maximum patch area was similar to that found in the Prague dataset.
After site selection, and in keeping with methods used during Prague model construction, we further refined the Perth dataset to include: 1) only non-migratory species that appear consistently in the region, and 2) species for which at least five individuals were encountered across surveys. This minimum abundance requirement differed slightly from that used during
Prague model construction (where population minimums were set at ten individuals; Chapter two). This was because: a) total abundances for Perth butterfly species were small compared to those of Prague species, such that limiting the Perth dataset to species with ten or more total individuals would eliminate approximately one quarter of the records, and b) using only species with five or more total individuals corrected for skew in the Perth community distribution in a similar way to using species with ten or more individuals in Prague. Our minimum, however, was still similar to population cut-offs used by other researchers (e.g. Pöyry et al, 2008). Finally, and following previous methods, Perth butterfly abundances were summed across years to eliminate the effect of year on these analyses, and environmental and species trait variables
51 measured. Environmental variable collection was again performed using Google Earth and
ArcGIS, and species trait measurements obtained from the extensive literature on Australian butterflies (e.g., Braby, 2000; www.learnaboutbutterflies.com/).
Model Transferability Analyses
Following dataset refinement, we compared ranges of variation for the response and predictor variable measurements in Prague and Perth. We found that the ranges were similar (see results), so we then assessed the ability of Prague models to predict butterfly diversity patterns among sites in Perth. This involved first transforming the predictor variables in the Perth dataset that had: 1) been identified as important in the Prague models, and 2) required transformation during construction of these models (Chapter two). We used a natural log transformation for continuous variables, and a logit transformation for proportional data. Continuous and binary variable measures were next standardized to a mean of zero and standard deviation of 0.5 or difference between categories of one, respectively (Gelman et al., 2013), such that model transferability analyses would be performed on measurements that were of the same scale and general range as those used during Prague model construction. Calculations were performed using the arm package in R (Gelman et al., 2013; R Development Core Team 2013).
Next, we used the equations for models CZfull, CZenvt, and CZtrait to calculate predicted butterfly population densities for all species-site occurrences in the Perth dataset. For each model, this involved multiplying the coefficient estimates of each predictor by the applicable site attribute or species trait value in the Perth record. For categorical variables, coefficients were multiplied by ‘1’ if the level in question (e.g.,water= ‘present’) was represented in the species- site record, and ‘0’ otherwise. Because the dependent variables had been transformed during
52 Prague model construction (Chapter two), predicted values were then back transformed (epredicted value) in order to obtain population density estimates for all Perth species-site occurrences.
We next compared predicted butterfly population densities, for each of the Perth species-
site records, to actual data. We first computed a Pearson’s correlation coefficient (r) for the
predicted (by each model) versus observed butterfly population densities. As this statistic is only
a relative measure of how well observed and predicted values agree, and may provide
misinformation if predictions are inaccurate but in a consistently biased manner (Potts and Elith,
2006), we also calculated Spearman’s rank correlations (ρ). Here, ρ quantifies the degree to
which the ranks of observed and predicted values are linearly related, regardless of how different
the values actually are (Gauthier, 2001). Finally, we assessed actual error rates, between
observed and predicted butterfly population densities, via the root mean-square error (RMSE):
n 2 Σ (ŷi – yi) RMSE= i= 1 √ n where ŷ and y are predicted and observed values, respectively, and n is the sample size.
Model construction: Perth dataset
The full, environment, and trait models described above were fit for Perth data using the
same methods as described in Chapter two. This involved the following four steps:
(1) Predictor variables were transformed to correct for non-linearity, and then
multicolinearity measured among each variable type (environmental or species trait) using a
53 Variance Inflation Factor (VIF). VIF was calculated using the HH package in R (Heiberger,
2013), and variables with VIF<5 were retained for further analyses.
(2) Retained predictor variables were passed to a fourth corner analysis, through which we identified environment-trait interactions that influenced butterfly population densities across sites. Here, correlations were calculated for each of the environmental features occurring at sites and the species traits of individuals that inhabit them. Calculations were weighted by the population density of each species at each site, so that environment-trait interactions were directly linked under observed diversity patterns (Dray and Legendre, 2008; Aubin et al., 2009).
Each correlation was then compared to a distribution of correlation statistics derived under a null hypothesis (e.g., that species’ population densities across sites are random) in order to determine the relative strength with which the environment-trait interaction influenced diversity patterns.
Fourth corner analyses were executed using the ade4 package in R (Chessel et al., 2013), and each environment-trait pair’s correlation statistic was compared to a null distribution derived using permutation method 4 (Dray and Legendre, 2008; see Chapter two for further detail).
(3) Linear mixed effects models were fit to Perth data, as:
ln(p) = βo + β1X1 + … + βnXn + εsite + εspecies + εobs where p are butterfly population densities, X1-n are fixed effects, and ε are Normal random deviations with mean zero (random effects). Random effects for all models included site and species identity, and fixed effects were as follows: the full model included the retained environmental and species trait variables and interaction terms identified by the fourth corner analysis, the environment model included the retained environmental attributes, and the trait
54 model included the retained species trait variables. In keeping with methods used during Prague model construction (Chapter two), the response variable was log transformed, the models were fit using standardized continuous and binary predictor variables, and global models (those that were first fit using all candidate predictors as identified above) were fit using maximum likelihood (ML) so that subset models with different fixed effects could be compared. We then assessed model diagnostic plots, to verify that models exhibited appropriate fit to the data and that errors were distributed evenly and randomly. We also used Moran’s I to check for spatial autocorrelation among model residuals (Dormann et al., 2007). Here, we averaged the residuals from all estimated butterfly population densities, compared to expected values, across each of the
19 sites in the dataset. This resulted in an average residual per site (McAlpine et al., 2008), and these were then analyzed for spatial autocorrelation in ArcGIS (ESRI).
(4) For each of the Perth models, we used the ‘dredge’ function in the MuMIn package
(Barton, 2013) to fit subset models using all possible combinations of predictors in each global model. Next, we identified the ‘best’ of these subset models as that with the lowest corrected (for finite sample sizes) Akaike Information Criteria. Using the best model AIC as a baseline, we then obtained a subset of equivalent models (those with ΔAICc < 2), and calculated average coefficient estimates across models in this subset. This resulted in final full, environment, and trait models for the Perth dataset. The identities and coefficient estimates of model predictor variables could then be compared with those of the analogous Prague models.
3.4 Results
Butterfly Surveys
55 The dataset used during the construction of Prague models comprised 16,835 butterflies from 56 species (Chapter two). Species richness per site ranged from 18 to 54, resulting in a total of 659 species-site occurrence records. Species included members from four of the five families in Papilionoidea (excluding Riodinidae) as well as Hesperiidae. These same families were represented in Perth, with the exception of Papilionidae. Here the dataset (following removal of migratory species and those with fewer than five total individuals) comprised 3,043 individuals from 15 species (Table 3.1). Butterfly species richness at Perth sites ranged from four to 13, for a total dataset comprising 163 total species-site occurrence records.
Table 3.1- Perth butterfly species, with total abundances, for the refined dataset. Total Species Family Abundance Anisynta sphenosema (Meyrick & Lower) Hesperiidae 22 Geitoneura klugii (Guerin-Meneville) Nymphalidae 745 Geitoneura minyas (Waterhouse & Lyell) Nymphalidae 861 Heteronympha merope (Fabricius) Nymphalidae 78 Hypochrysops halyaetus Hewitson Lycaenidae 86 Lampides boeticus (Linnaeus) Lycaenidae 40 Mesodina cyanophracta Lower Hesperiidae 60 Motasingha trimaculata (Tepper) Hesperiidae 10 Nacaduba biocellata C. & R. Felder Lycaenidae 21 Neolucia agricola (Westwood) Lycaenidae 269 Pieris rapae Linnaeus Pieridae 169 Taractrocera papyria (Boisduval) Hesperiidae 52 Trapezites sciron Waterhouse & Lyell Hesperiidae 16 Vanessa kershawi (McCoy) Nymphalidae 543 Zizinia labrudus (Godart) Lycaenidae 71 Total no. individuals 3,043
56 Ranges of variation for predictor and response variables
Ranges of variation in response and predictor variables were similar between the Prague and Perth datasets (Figure 3.2). This was especially true for the response variable (where log- transformed proportional population densities were -6.76 to -0.15 in Perth, -6.89 to 0 in Prague;
Figure 3.2A). For the continuous predictors, ranges of variation were either essentially identical
(e.g.,Figure 3.2B) or strongly overlapping between datasets (e.g.,Figure 3.2E). For categorical predictors, similarities between datasets were assessed based on the proportions of times that each categorical level was represented in each dataset (e.g.,that water was ‘present’). Here, again, the ranges of variation were similar among datasets, with the possible exception of the species traits of voltinism and diapause strategy (Figure 3.2M-N). For each of these two predictors, one of the categorical levels was absent in the Perth dataset, where there were neither bivoltine species nor species undergoing diapause as pupae.
57 2 A) 150 B) 6 C) 0 5 100 -2 4 -4 50 3 -6 2 0 Perth Prague Perth Prague Perth Prague
log(Population Density) Area (ha) Habitat Heterogeneity
40 D) 1.0 E) 8 F) 7 30 0.8 6 0.6 20 5 0.4 4 10 0.2 3 0 0.0 2 Perth Prague Perth Prague Perth Prague
OpenOpen Habitat Area in in Buffer Buffer (ha) (ha) Proportion Open Habitat Edge Score
500 G) 0.6 H) 50 I) 0.5 400 40 0.4 300 0.3 30 200 0.2 20 100 0.1 10 0 0.0 Perth Prague Perth Prague Perth Prague
NSCP Sites with Available Water Avg. Wing Length (mm)
0.8 J) 0.8 K) 1.0 L) 0.6 0.6 0.8 0.6 0.4 0.4 0.4 0.2 0.2 0.2 0.0 0.0 0.0 Short Med Long Mono Oligo Poly Single Small Med Large
Flight Period Diet Breadth Eggs Per Batch
0.8 M) 0.8 N) 0.6 0.6
0.4 0.4
0.2 0.2
0.0 0.0 Uni Bi Multi Egg Instar Pupa Adult
Voltinism Diapause Strategy
Figure 3.2- Ranges of variation for the response variable (A), and the environmental (B-H) and trait (I-N) predictor variables in the Perth and Prague datasets. Perth data are represented by light gray bars, Prague data by dark gray bars. For continuous variables, data are presented as minimum and maximum values. For the binary variable of water availability, data are presented as the proportion of sites in which water was available. For categorical species trait variables, data are presented as the proportion of species for which trait level was represented. Predictor variables are untransformed for graphical clarity. 58 Model Transferability
As evidenced by Pearson’s and Spearman’s correlation results, butterfly population densities across patches in Perth could not be explained by Prague models (Pearson’s r(161)<0.15 and ρ(161)< ± 0.02, and p >0.05, in all cases). These results were supported by RMSE calculations, which suggested that error rates between predicted and observed butterfly population densities were high, and essentially equal, for all models (RMSE=0.1497, 0.1487, and
0.1492 for models CZfull, CZenvt, and CZtrait, respectively). These poor model transferability results were caused by the inability of Prague models to predict high butterfly population densities, where maximum predicted values were 0.15, 0.14, and a scant 0.05 for models CZfull,
CZenvt and CZtrait, respectively (Figure 3.3A-C).
Model CZfull Model CZenvt Model CZtrait 1.0 A) 1.0 B) 1.0 C) Observed Predicted 0.8 0.8 0.8 y t i s n e
d 0.6 0.6 0.6 n o i t a l
u 0.4 0.4 0.4 p o P 0.2 0.2 0.2
0.0 0.0 0.0 Dataset record Dataset record Dataset record
Figure 3.3- Observed (black dots), across sites in Perth, and predicted (gray triangles), by models CZfull (A), CZenvt (B), and CZtrait (C), butterfly population densities.
Model fitting: Perth
Before fitting Perth models, we log transformed the predictor variable of shape complexity, and logit transformed that of proportion of open habitat, in order to correct for nonlinearity. No other variables required transformation in this dataset. Next, we assessed multicolinearity among environmental and among trait variables, and found no evidence of
59 strong multicolinearity in either case (VIF<5 for all variables). Thus, the fourth corner analysis
was performed using all seven environmental, and all six species trait predictors, for a total of 42
environment-trait pairs. Of these, only Water:Voltinism was identified as influencing butterfly
population densities across sites. In addition to the random effects of site and species identity,
Perth models were thus fit with the following fixed effects: AUfull included all seven
environmental variables, all six species traits, and one environment-trait interaction, AUenvt
included all seven environmental variables, and AUtrait included all six species traits.
Of the global Perth models, both AUfull and AUenvt demonstrated good fit to the data. This
was evidenced by relatively evenly-scattered residuals compared to fitted values, error
distributions that were close to normal, and average per-site residuals that were neither clustered
nor overly dispersed in space (I= -0.34 and -0.33 for models AUenvt and AUfull, respectively, p
>0.05 in both cases). Residuals for model AUtrait, on the other hand, were spatially clustered (I=
0.47, p< 0.01). This was also true for the null model, and these two models were thus excluded from further analyses and model selection and averaging only performed for models AUfull and
AUenvt.
Following model selection and averaging, the predictors that were identified as drivers of
Perth butterfly diversity patterns were as follows: all seven environmental predictors as well as
the species traits of voltinism, flight period, and diapause strategy for model AUfull, and six of
the seven environmental predictors, excluding water availability, for model AUenvt. Of these
2 models, model AUfull fit the data best (AICc=481.8, conditional R = 0.45), and AIC comparisons
revealed that this fit was considerably better than was that of model AUenvt (AICc=486.9).
Following model selection and averaging, we performed one final set of comparisons in
order to verify our earlier finding that environment-trait interactions did not influence Perth
60 butterfly diversity patterns. Here, we fitted individual models in which fixed effects included all environmental and species trait predictors, as well as one environment-trait interaction. This resulted in a total of 42 single interaction models. Because model AUfull was a subset of each of these models, we then compared model fit using AIC. The lowest AICc of any single-interaction model (487.2) was substantially greater than that of model AUfull, demonstrating that the inclusion of any one interaction term failed to improve model fit, and supporting the results of the fourth corner analyses.
Predictors of butterfly diversity patterns among study locations
The effects of predictor variables on Perth and Prague butterfly communities are here compared for models AUfull and CZfull, given these model’s superior fits to the data in each study location. The only predictor to impact butterfly diversity patterns strongly and significantly across fragments in both locations was patch area (relative importance of 1.00 and p<0.001 in both cases; Table 3.2). Here, butterflies were consistently found in greater densities at larger sites
(Figure 3.4A-B). Two additional variables, water availability and edge permeability, also had similar impacts on butterfly diversity patterns in both locations, although their influences were weak in comparison to that of area (p> 0.05 in both cases; Table 3.2). Here, slightly greater butterfly population densities were found in sites with water than without, and in sites with greater edge permeability (lower edge scores; Figure 3.4C-F).
61 Table 3.2- Averaged coefficient estimates, relative importance during model averaging, and statistical significance for predictors of butterfly diversity patterns in Prague and Perth, and random effects variances for both models. For categorical variables, p-values indicate comparisons to the base level, which is the first in terms of alphabetical order. Predictors for which this applies include: voltinism (with levels of univoltine, bivoltine, and multivoltine), flight period (with levels of short, medium, and long), and diapause strategy (with levels of egg, larva, pupa, and adult). Dashes indicate that the variable was not identified as a predictor for the model in question. Bold face indicates statistical significance in at least one model. Parameter Estimate* SE Relative importance Pr(>|z|) Perth Prague Perth Prague Perth Prague Perth Prague Fixed Effects Coefficientsa (Intercept) -3.114 -3.086 0.193 0.099 1.00 1.00 <<0.001 <<0.001 Area (ha)b- for Prague model 0.631 1.139 0.246 0.226 1.00 1.00 0.010 <<0.001 Habitat heterogeneity -0.692 0.284 0.237 0.245 1.00 0.29 0.003 0.244 Proportion open habitat b -0.404 0.310 0.246 0.156 0.54 0.83 0.101 0.051 Water availability 0.088 0.091 0.417 0.234 0.14 0.16 0.833 0.720 Edge permeability score -0.226 -0.263 0.255 0.174 0.07 0.35 0.374 0.130
VoltinismUni -0.172 0.154 0.301 0.124 0.07 0.16 0.568 0.213
VoltinismMulti ------0.049 ----- 0.160 ----- 0.16 ----- 0.757
Flight periodShort 1.418 ----- 0.343 ----- 1.00 ----- <<0.001 ----- 2 6 Flight periodMedium 1.130 ----- 0.386 ----- 1.00 ----- 0.003 -----
Diapause strategyEgg -1.275 ----- 0.352 ----- 1.00 ----- <<0.001 -----
Diapause strategyLarva -0.068 ----- 0.273 ----- 1.00 ----- 0.803 ----- Avg. wing length (mm)b ------0.047 ----- 0.113 ----- 0.07 ----- 0.676 Open area in buffer -0.529 ----- 0.295 ----- 0.58 ----- 0.073 ----- Shape complexityb -0.384 ----- 0.231 ----- 0.22 ----- 0.096 -----
Water:VoltinismMulti ------0.016 ----- 0.206 ----- 0.07 ----- 0.938
Water:VoltinismUni ----- 0.375 ----- 0.172 ----- 0.07 ----- 0.029 Random Effects Variancesa Site 0.052 0.0866 Species name 0.056 0.0648 * Estimates are for standardized variables a Estimates are for global models b Variable is transformed 0 0 0 0 ) A) B) C) D) s e
i -1 -1 -1 -1 t i s
n -2 -2 -2 -2 e D -3 -3 -3 -3 y l f r -4 -4 -4 -4 e t t
u -5 -5 -5 -5 B (
g -6 -6 -6 -6 o l -7 -7 -7 -7
-1.0 -0.5 0.0 0.5 -1.0 -0.5 0.0 0.5 Abs Pres Abs Pres
Area (ha)1 Water Availability
0 0 0 0 ) E) F) G) H) s e
i -1 -1 -1 -1 t i s
n -2 -2 -2 -2 e D -3 -3 -3 -3 y l f r -4 -4 -4 -4 e t t
u -5 -5 -5 -5 B (
g -6 -6 -6 -6 o l -7 -7 -7 -7
-1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 -1.0 -0.5 0.0 0.5
Edge Score Habitat Heterogeneity
0 0 ) I) J) s e
i -1 -1 t i s
n -2 -2 e D -3 -3 y l f r -4 -4 e t t
u -5 -5 B (
g -6 -6 o l -7 -7
-1.0 -0.5 0.0 0.5 1.0 1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Proportion Open Habitat1
Figure 3.4- Influences of environmental predictors on butterfly population densities across sites, as identified in models AUfull (triangles and light grey boxes) and CZfull (circles and dark grey boxes). Data are standardized (mean=0, s.d=0.5 for continuous variables or difference between categories=1 for binary variables) as they were for modeling analyses, and butterfly densities are natural log transformed. Boxplots are shown for categorical variables, with heavy lines indicating median values, lower and upper box edges representing first and third quartiles (respectively), and whiskers showing minimum and maximum values. Superscript indicates transformed data.
63 All other predictors considered here had either contrasting influences on butterfly diversity patterns across sites in the two study areas, were only identified as influential in one location, or were not identified in either model (diet breadth and eggs per batch). For example,
Perth butterfly population densities decreased as habitat heterogeneity increased (Figure 3.4G), and this variable was identified in all models of the best model subset (relative importance of
1.00, p=0.003; Table 3.2). In contrast, Prague butterfly population densities increased with increasing habitat heterogeneity (Figure 3.4H), though the influence of this variable was not significant compared to those of other predictors (p=0.24; Table 3.2). Similarly, increasing proportions of open habitat within patches led to decreasing butterfly population densities among
Perth sites, but increasing population densities in Prague (Figure 3.4I-J). These trends were weak, however, as the influence of proportion of open habitat was only marginally significant compared to those of other predictors in Prague (p=0.053), and not significant in Perth
(p=0.101).
With regard to interpretations of the influences of species traits, care is required, due to our choice of response variable. Specifically, because we used a proportional response variable, with standardization by total population size for each species (i.e. standardized across species), the mean population density of all species would have been equal if we had included unoccupied patches in the dataset. That these ‘zeros’ were excluded resulted in variation in mean population densities among species reflecting differences in the frequencies with which species with different traits occurred among patches in the landscape. Thus, species trait parameters act as measures of occupancy in our analyses. Further, and again because of the standardization of population density, coefficient estimates for species trait predictors indicate the inverse of the influences of those variables on butterfly diversity patterns (frequencies of occurrence). Thus,
64 butterflies with fewer annual broods (univoltine species) occurred with slightly greater frequency in the Perth landscape than did those with more annual broods (indicated by the negative coefficient estimate for univoltine compared to multivoltine species in Table 3.2; Figure 3.5A).
In Prague, on the other hand, increasing voltinism had the opposite effect on frequencies of occurrence, as univoltine species occurred in the lowest, and multivoltine in the greatest, frequencies throughout the landscape (Table 3.2; Figure 3.5B; Chapter two). The influences of voltinism on butterfly diversity patterns, however, were non-significant in both study locations
(p>0.05 in both cases; Table 3.2).
For the other species traits, all of which were identified as predictors of butterfly diversity patterns in only one model, both flight period and diapause strategy influenced butterfly frequencies of occurrence among patches in Perth, while only average wing length influenced those in Prague. In Perth, butterfly flight period strongly influenced diversity patterns (relative importance of 1.00 and p<0.05 in both cases; Table 3.2), with species with short flight periods occurring with the least frequency across patches (large coefficient estimate in Table 3.2), while those with long flight periods occurred most frequently (Table 3.2, Figure 3.5C). Diapause strategy also had important influences on Perth butterfly diversity patterns (relative importance of 1.00, p<<0.001: Table 3.2). Here, species diapausing as eggs occurred in significantly greater frequencies throughout the landscape than did those diapausing as larvae or adults (low coefficient estimate in Table 3.2; Figure 3.5E). Larval and adult diapausers, on the other hand, occurred with essentially equal frequency among patches (Table 3.2; Figure 3.5E). Finally, the species trait of average wing length had a relatively minor influence on butterfly frequency of occurrence among Prague patches (Table 3.2; Figure 3.5H; Chapter two), although no influence of this predictor was found in the full Perth model.
65 20 A) 20 B) 20 C) 20 D) e c n
e 15 15 15 15 r r u c c
O 10 NA 10 10 10 N/A
f o
. q
e 5 5 5 5 r F 0 0 0 0
Uni Bi Multi Uni Bi Multi Short Medium Long Short Medium Long
Voltinism Flight Period
20 E) 20 F) 20 G) 20 H) e c n
e 15 15 15 15 r r u c c
O 10 NA 10 N/A 10 N/A 10
f o
.
q 5 5 e 5 5 r F 0 0 0 0
Egg Larva Pupa Adult Egg Larva Pupa Adult -1.0 0.0 0.5 1.0 1.5 -1.0 0.0 0.5 1.0 1.5
Diapause Strategy Avg. Wing Length (mm)1
Figure 3.5- Influences of species trait predictors on butterfly frequencies of occurrence across sites, as identified in models AUfull (light grey boxes) and CZfull (circles and white boxes). Data are standardized (mean=0, s.d=0.5 for continuous variables or difference between categories=1 for binary variables) as they were for modeling analyses. Boxplots are shown for categorical variables, with heavy lines indicating median values, lower and upper box edges representing first and third quartiles (respectively), and whiskers showing minimum and maximum values. Superscript indicates transformed data.
3.5 Discussion
A fundamental goal in fragmentation research is to identify generalities in the drivers of diversity patterns across fragmented landscapes, and to use this knowledge to better understand wide-spread fragmentation effects and inform broadly applicable conservation and management policies. The work presented here represents a novel approach to addressing this goal, by not only evaluating how both species traits and environmental attributes influence diversity patterns at the whole community-level, but also comparing the drivers of these patterns among widely separated and ecologically distinct fragmented landscapes. Our results suggest that there may exist key factors, such as patch area and water availability, that generally drive butterfly diversity
66 patterns, even for entire communities that inhabit very different fragmented ecosystems and assembled independently over evolutionary time. As importantly, we found that species traits significantly improved model fit in Perth but not Prague, suggesting that the influence of traits on diversity patterns may vary depending on the ecological context of the study area.
Transferring models between study locations
Given that our response and predictor variables had relatively similar ranges of variation among our study areas, an important prerequisite for attempting model transfer (Zhu et al.,
2012), we anticipated that Prague models might predict among patch Perth butterfly population densities reasonably well. That we failed to see even moderately accurate predictions may relate to the fact that while we considered local environmental variables and species traits predictors of diversity patterns, our models did not account for among location variation in broad ecological and climatic conditions. Such conditions may play substantial roles in determining butterfly diversity patterns among different fragmented landscapes. For example, the negative influence of increasing proportions of open habitat on Perth butterfly population densities may indicate that greater amounts of open habitat represent reductions in remnant Banksia and Eucalyptus habitat availability for these woodland-adapted species. Given the very different ecological conditions in
Prague, however, it is perhaps not surprising that greater proportions of open habitat positively influenced butterfly population densities, as such open habitat may provide additional resources for the mixed-habitat associated species. Such contextual dissimilarities among study locations, although not captured in local-scale environmental measurements, may greatly influence among location diversity patterns and lend deeper understanding to why ‘nature is too complex and heterogeneous to be predicted accurately in every aspect of time and space from a single,
67 although complex, model’ (Guisan and Zimmermann, 2000). Given the apparent infeasibility of extrapolating models between different ecoregions, in widely separated geographic locations, the question then becomes: what can we learn by comparing similarities and dissimilarities regarding the drivers of diversity patterns in different study areas?
Model comparisons
Three important results arose from our follow-up model cross-comparison analyses. First, despite the complexities of factors determining butterfly diversity patterns in our two ecologically distinct study areas, three predictors, patch area, water availability, and edge permeability, consistently influenced these patterns, in both directionality and strength of impact.
Second, additional variables were identified as predictors in both study areas, although, as discussed above regarding proportion of open habitat, they had different directionalities and strengths of impact. These variables provide grounds for generating new hypotheses regarding the determinants of diversity patterns across different fragmented landscapes, and additional research may ultimately provide important information regarding differences in species compositions among wide-ranging, fragmented habitats. Third, butterfly species traits were much more important determinants of diversity patterns in Perth than in Prague, as illustrated by trait predictors appearing differentially in the models and having different strengths of impact on diversity patterns.
In terms of the first result, the finding that increasing patch area had strong positive impacts on butterfly population densities among habitat fragments in both of our study locations was not unexpected, given the abundance of information regarding the positive influence of greater habitat area on butterfly populations (Debinski and Holt, 2000; Fahrig, 2003; Ewers and
Didham, 2006; Dover and Settele, 2009). Perhaps more informative was that in our ecologically
68 distinct study areas, with complex sets of determinants of diversity patterns in each, both water availability and overall edge permeability also consistently influenced butterfly population densities among habitat fragments. In both cases, the impacts of water availability and edge permeability were relatively weak, but their consistent identification as predictors in best fitting models suggests that they may be important components of explaining variation in butterfly diversity patterns in any fragmented ecosystem. Although we caution against overgeneralization, we suggest that these three variables receive particular attention in future studies of butterfly diversity in fragmented ecosystems.
The other important, and equally interesting, result from model cross-comparisons was that species traits have significantly more impact on butterfly diversity patterns in Perth than in
Prague. This was first demonstrated by within study area model fit comparisons, where the inclusion of species trait predictors failed to improve model fit in Prague, compared to that of the environment only model (Chapter two), while substantially improving the fit of the full Perth model. Additionally, species traits consistently appeared in models of Perth’s ‘best model subset’. This raises the question: why do species traits have more influence on butterfly diversity patterns in some areas than in others? Again, the answer likely lies in sources of variation not considered here, such as ecological and/or climatic conditions, which may markedly influence species’ abilities to persist in certain landscapes. For example, although it is not well understood why the species trait of flight period would influence the frequencies with which butterflies occur throughout certain fragmented landscapes, but not others, it has been shown that species with shorter flight periods are generally more sensitive to unfavorable habitat conditions (Nilsson et al., 2008) and at greater risk of extinction than are those with longer flight periods (Kotiaho et al., 2005; Nilsson et al., 2008). Thus, we posit that in the single habitat setting of Perth, the
69 combination of resource reduction due to habitat fragmentation with resource scarcity that accompanies summer droughts may be particularly challenging for species with short flight periods, to the point that these species are found in the least frequency throughout the landscape.
In contrast, the mixed habitat and non-drought prone setting of Prague may not induce this same pattern with regard to frequencies of occurrence of species with various flight periods.
We also acknowledge that location specific ecological and/or climatic conditions may shape species trait pools over long time periods, through local adaptation, to the point that they drive diversity patterns even more strongly than does habitat fragmentation itself. For example, forest-associated lepidopterans that overwinter as eggs or larvae tend to complete only one generation per year (Koricheva et al., 2012), regardless of the fragmentation status of the habitats they occupy. Given that univoltine species were slightly more frequent among patches in
Perth, where 2/3 of the woodland-associated species overwinter as either eggs or larvae, the species trait pool appears to play a strong role in driving diversity patterns throughout the region, and is perhaps even more influential to diversity patterns than is habitat fragmentation itself.
Similarly, multivoltinism in butterflies tends to be associated with environments that are not characterized by marked seasonality, as such seasonality selects against multivoltinism (Dennis,
1993). This pattern is consistent with the trend of increasing frequency of occurrence of species with larger numbers of annual generations in the seasonally mild environment of Prague, again suggesting that diversity patterns may be more strongly influenced by species trait pools that are shaped by conditions that are unrelated to habitat fragmentation.
Overall, these results support our earlier suggestion that the consideration of species traits may be crucial to modeling diversity patterns in fragmented ecosystems, as other researchers have demonstrated (e.g., Ockinger et al., 2010), though the relative importance of these traits
70 may depend on location-specific climatic conditions. Thus, while explorations of climatic influences on diversity patterns were beyond the scope of this study, further research may help identify why species traits play variable roles in driving diversity patterns in climatically diverse ecosystems, particularly if comparisons include areas with both similar and dissimilar climatic characterizations.
Conclusions and further considerations
We compared the influences of environmental and trait predictors on community-wide butterfly diversity patterns in widely separated and ecologically distinct fragmented landscapes, and explored similarities among models constructed in each location. Three environmental variables, patch area, water availability, and overall edge permeability, consistently influenced diversity patterns across study locations. Other variables had ecosystem-specific influences on diversity patterns, including contrasting directionalities and magnitudes of influence, or impacts in only one study location. We believe that an important source of this variation was the different ecological and climatic conditions characterizing our two study areas. As is typical, this study does not, and could not, claim to consider all possible predictors of distribution or diversity patterns (Barry and Elith, 2006), and we thus encourage future research to build on this work through the implementation of focused and semi-quantitative comparative assessments of similarities and differences among models constructed along climatic and ecological gradients.
Such work may contribute additional knowledge to generalities in species responses to habitat fragmentation in diverse ecosystems, and also further explicate the conditions under which species traits significantly impact diversity patterns in human-modified landscapes.
71 CHAPTER FOUR
THINKING OUTSIDE THE CONTINENT: EXPLORING THE DRIVERS OF
DIVERSITY PATTERNS AMONG WIDELY SEPARATED FRAGMENTED
ECOSYSTEMS3
4.1 Abstract
Recent years have seen major revolutions regarding approaches to ecological research,
including: (i) growing numbers of collaborations, and (ii) increasing realizations that re-using
previously collected data to address questions that lie outside of the context of past studies is a
valuable research strategy. As a result, ecological studies have expanded into broad, integrative
investigations by which patterns and processes can be broadly assessed, new questions
addressed, and strategies developed for managing landscapes in the face of burgeoning human
impacts on natural ecosystems.
In this study, we employed a collaborative, data reuse scheme in order to model butterfly
community diversity patterns across habitat fragments in different parts of the world, and
assessed how similar the environmental and species trait determinants of these patterns were
among widely separated fragmented landscapes. Because our different study sites occurred in
areas characterized by varying degrees of ecological and climatic consistencies, we also
evaluated how these factors may lead to similarities in among-location fragmentation effects. We
found evidence that some environmental attributes, such as patch area and the availability of
water, consistently influence butterfly community diversity patterns, in all fragmented
landscapes. In addition, results underscored ecological and climatic characteristics, such as
3 This chapter was a collaborative effort among N. Robinson, T. Kadlec, M.R. Williams, M.D. Bowers and R.P. Guralnick, and is currently in preparation for submission to Global Ecology and Biogeography 72 restricted habitats and seasonality, which may lead to consistencies regarding the determinants of butterfly diversity patterns among certain study areas. Finally, we illustrated that species traits play a stronger role in determining butterfly community diversity patterns in some fragmented landscapes than in others, and discussed the potential influence of among-location ecological and/or climatic context on this pattern.
Results from this study underscore the complexity of factors influencing butterfly diversity patterns in fragmented ecosystems, but also pave the way for future research through which we can better understand global fragmentation effects, and potentially use this information to help develop broadly applicable conservation and management regimes.
4.2 Introduction
In the last decade, ecological research has rapidly expanded from fine-scale, individually conducted and analyzed case-studies to broad, integrative investigations in which large amounts of data are used to assess wide-ranging ecological patterns and processes (Reichman et al.,
2011). This movement has eliminated boundaries that historically restricted the spatial and temporal scales of ecological studies (Michener, 2006; Zimmerman, 2008), and allowed researchers to address new questions regarding the causes of phenomena such as globally escalating biodiversity loss (Butchart et al., 2010), and the best strategies for managing landscapes in the face of burgeoning human impacts on natural ecosystems (Vitousek et al.,
1997; Palmer et al., 2004).
At the cornerstone of this new era of ecology are two major revolutions regarding approaches to ecological research. First, ecologists have recognized the value of collaborations, through which researchers from diverse theoretical backgrounds work together to conceptualize
73 problems, pool resources and divide labor to study these problems (Palmer et al., 2004; Leimu and Koricheva, 2005), and interpret results (Tenopir et al., 2011). In addition to the time and cost savings that can be achieved though such collaborations (Leimu and Koricheva, 2005), the results of collaborative studies are viewed as more impactful than are those of non-collaborative investigations (Leimu and Koricheva, 2005). Second, researchers are increasingly recognizing the value of data reuse strategies, in which previously collected data are applied toward answering questions that lie beyond the context of the studies for which they were originally intended (Zimmerman, 2008). Such integrative analyses are frequently tied to the previously mentioned collaborative research approach, given that significant researcher cooperation and interfacing are often required to ensure that the influences of such details as collection methodologies and ecological contexts are properly accounted for in re-analysis schemes.
Landscape change, especially human-caused habitat fragmentation, is a global issue that has been predominantly investigated at local scales, given the logistic and resource challenges associated with broader-scale examinations (Erb et al., 2012). Local-scale analyses have convincingly shown that habitat fragmentation is a key driver of biodiversity loss within human- modified landscapes (Fahrig, 2003; Foley et al., 2005; Fischer and Lindenmayer, 2007), and that the anthropogenic modification of earth’s ecosystems has reached into all environments on the planet (Vitousek et al., 1997; Palmer et al., 2004). What we do not know is the degree to which habitat fragmentation impacts communities similarly or differently among diverse fragmented ecosystems. For example, are the abiotic and biotic drivers of among fragment community diversity patterns consistent across different, widely separated fragmented landscapes? If not, are there predictable differences that can be explained by such factors as habitat and climate? Such wide-ranging assessments of fragmentation effects are only possible through collaborative efforts
74 and the integration of several datasets from diverse locations, by which cross-comparative analyses of location-specific determinants of diversity patterns may be performed.
In this study, we assess similarities and differences regarding the drivers of butterfly diversity patterns among fragments in three widely separated, fragmented landscapes. In previous analyses, we modeled butterfly diversity patterns among fragments located near Prague,
Czech Republic, based on both environmental and species trait predictors (Chapter two), and then tested the abilities of these models to predict diversity patterns for a separate butterfly community occupying the fragmented landscape of Perth, Western Australia (Chapter three).
Butterfly diversity patterns in Perth were not well predicted by Prague models. However, three important patterns arose from subsequent model cross-comparison between those two sites: 1) patch area, the availability of water, and edge permeability had consistent influences on butterfly diversity patterns between the two study areas, in both their directions and magnitude of effect,
2) habitat heterogeneity, the proportion of open habitat within patches, and the species trait of voltinism were identified as predictors in both Prague and Perth models, though these had varying directionalities and strengths of influence, and 3) species traits, such as flight period and diapause strategy, affected butterfly diversity patterns much more strongly in Perth than in
Prague. These differences may have arisen from disparities in the ecological and/or climatic characterizations of each study area.
Here, we utilize a third dataset, collected from habitat fragments near Denver, Colorado
USA, whose climatic and habitat characteristics differ from those of the other study locations, although with certain similarities to both Prague and Perth. Specifically, the ecological conditions in Colorado are like those of Perth in that the fragments in both study areas comprise predominantly single habitat ecosystems, although Colorado fragments consist of open grassland
75 vegetation while those in Perth comprise woodland vegetation (Table 4.1). In contrast, Prague
fragments are characterized by mixed habitats, with both grassland and woodland vegetation
present (Table 4.1). Additionally, Colorado’s climate is somewhat similar to that of Prague in
that winters are cold and freeze-prone in both study areas, but also comparable to Perth’s climate
in that summers are hot and drought-prone (Table 4.1). Assimilating this new study system into
the comparative analysis thus allows us to: 1) determine if results from a two-system comparison
extend to a third fragmented ecosystem, on another continent, and 2) investigate additional
among location similarities, regarding the determinants of butterfly diversity patterns, that may
be driven by ecological and/or climatic conformities. In addition, we are interested in further
exploring our previous conclusion, from Chapter three, that species traits may be particularly
important for explaining butterfly diversity patterns in more drought-prone environments. Our
working prediction is that butterfly diversity patterns in Colorado will be determined in large part
by species traits, as they were in Perth.
Table 4.1- Geo-locations, habitat characterizations, and climatic conditions of the three study areas used in this analysis. Geo-location data are provided in the WGS 84 coordinate reference system, and climatic conditions are exemplified by average maximum and minimum temperatures in each region (data obtained from World Weather Online (http://www.worldweatheronline.com/, accessed November, 2013)).
Avg. Avg. Low High Study Area Lat/Lon Habitat Temperature Temperature
Prague, CZ 50.09oN/14.42oE Mixed-habitat 25oC -3oC
Single-habitat: Perth, WA 31.95oS/115.86oE Banksia & 32oC 7oC Eucalyptus woodland Denver, Single-habitat: 39.74oN/104.98oW 33oC -15oC Colorado USA grasslands
76 4.3 Methods
Study system
Butterfly survey data were collected at: 1) 20 mixed-habitat sites occurring near Prague,
Czech Republic (hereafter the ‘Prague’ dataset; Table 4.1; Figure 4.1; Kadlec et al., 2008), where climate is characterized as warm temperate with warm summers and no dry seasons (Kottek et al., 2006), 2) 19 Banksia and Eucalyptus woodland-dominated sites occurring in near Perth,
Western Australia (hereafter the ‘Perth’ dataset; Table 4.1; Figure 4.1; Williams, 2011), where climate is characterized as warm temperate with hot, dry summers (Kottek et al., 2006), and 3)
12 grassland fragments located near Denver, Colorado, USA (hereafter the ‘Colorado’ dataset;
Table 4.1; Figure 4.1), where climate is characterized as arid steppe with hot, dry summers and cold winters (Kottek et al., 2006).
A)
10 km C)
10 km B) 10 km
Figure 4.1- Study area maps, with black dots representing the 12 grassland habitat fragments in Colorado (A), 20 mixed-habitat fragments in the Prague (B), 19 woodland habitat fragments in Perth (C). Figure B was adapted from (Kadlec et al., 2008), and figure C from (Williams, 2011).
77 Butterfly surveys: Prague and Perth
Detailed methodologies for Prague butterfly surveys can be found in Kadlec et al. (2008) and those for Perth can be found in Williams (2011). Briefly, Prague data were collected via surveys performed once per month from May to August of 2003 and 2004, for a total of eight surveys at each habitat fragment. Surveyors walked routes that traversed patches for area- dependent ranges of time, due to the presence of cliffs that prevented the use of a transect-based survey methodology. Time spent ranged from 30 min for patches < 1 ha, to 60 min for patches between one and ten ha, 90 min for patches between 11 and 100 ha, and 180 min for patches
>100 ha. Additionally, and in order to offset the potential under-sampling of large patches, areas that were particularly attractive to butterflies (e.g.,those with prevalent nectar resources) were preferentially visited during surveys. Surveys occurred during times of locally optimal butterfly flight, between the hours of 10:00 am and 4:00 pm, on sunny days with temperatures ≥ 17o C.
In Perth, surveys were conducted every two weeks during the two main butterfly flight periods, late September to mid-December (Austral spring) and late February to early April
(Austral autumn) of 2003-2005, for a total of eight surveys per habitat fragment. Surveys were performed using the Pollard method (Pollard, 1977), by which surveyors walked transects at a slow, steady pace, and recorded butterflies within 5m to each side and in front. Transects ranged from one to three per patch, and from 260 to 5,100m in length. Here too, data were collected only during times of locally optimal butterfly flight, on days with low wind speeds, low cloud cover, and temperatures ≥ 21o C.
Butterfly surveys: Colorado
Data for Colorado models were collected from 12 fragments occurring in protected sites that are part of the Open Space and greenway networks in the Denver metro area counties of
78 Adams, Arapahoe, Boulder, Broomfield, and Jefferson. Sites are predominantly characterized by grassland vegetation, with occasional strips of deciduous or coniferous forest vegetation occurring along waterways, paths, or urban interfaces. Habitat fragments were chosen because they: (i) experience minimal management (e.g.,mowing only along path edges, pesticide application prohibited or constrained to path edges, etc), and (ii) represent a range of available urban fragment sizes in this region (from 2.52 to 56.46 ha).
Butterfly abundance data were collected once in June and twice in July and August of
2009 and 2010, for a total of 10 surveys per site. Surveys were conducted along 100-m transects, which varied in number in accordance with patch size, with one transect per every six ha of habitat. Surveys were performed following the Pollard method (Pollard, 1977), and again occurred only during locally optimal butterfly flight conditions: between 9:00 am and 3:00 pm on days with wind speeds ≤ 15 mph, cloud cover ≤ 30%, and temperatures of at least 24oC.
Following previous methods (Chapter three), Colorado butterfly data were summed across years to eliminate the effect of year on these analyses.
In keeping with methods used during previous analyses (Chapter three), the Colorado dataset was refined to include: 1) only non-migratory species that appear consistently in the region, and 2) species for which at least five individuals were encountered across all surveys.
Data collection: Colorado predictor variables
Detailed methods for the quantification and measurement of environmental and species trait data used in this analysis are available in Chapter two. Briefly, predictor variables included the following seven patch and landscape-level attributes, and six species traits: 1) water availability, 2) patch area, 3) shape complexity, 4) edge permeability, 5) habitat heterogeneity, 6)
79 proportion of open cover type (e.g.,grassland), 7) amount of open habitat in patch buffer
(hereafter OinB), 8) diet breadth, 9) average wing length, 10) voltinism (number of generations per year), 11) flight period, 12) diapause strategy, and 13) eggs laid per batch. These variables were chosen because they have all been shown to affect the abilities of butterflies to inhabit habitat fragments, regardless of study location (Stamp, 1980; Sutcliffe et al., 1997; Grundel et al., 1998; Hill et al., 1999; Debinski and Holt, 2000; Steffan-Dewenter and Tscharntke, 2000;
Ries and Debinski, 2001; Schultz and Crone, 2001; Keller and Yahner, 2002; Sisk and Haddad,
2002; Tscharntke et al., 2002; Bender et al., 2003; Krauss et al., 2003b; Stoner and Joern, 2004;
Dennis et al., 2005; Barbaro and van Halder, 2009; Dover and Settele, 2009; Pöyry et al., 2009;
Hambäck et al., 2010; Ockinger et al., 2010; Robinson et al., 2012). Additionally, the same measurement and quantification schemes could be broadly employed throughout the different study areas. For environmental predictors, data collection was mostly performed using Google
Earth and ArcGIS. Species trait measurements were obtained from similar literature sources as were used to collect Czech and Australian species trait data (e.g., Braby, 2000; Benes et al.,
2002), although this time specific to North American species (e.g.,Opler, 1999; http://www.butterfliesandmoths.org, http://www.butterfliesofamerica.com/).
Data comparisons
As a final step before constructing Colorado models, we visualized all data, from all study areas, in order to determine whether our three datasets were comparable enough that three- way model comparisons would be sensible. We thus plotted ranges of variation for all variables in the three study areas, where continuous data were plotted as minimum to maximum values, and categorical data as proportions of times that each level of each categorical variable
80 (e.g.,water availability= ‘present’) occurred in each dataset. As discussed in the results, we found broad comparability in measurements across the three different study locations.
Colorado model construction and comparisons
Colorado models were fit following the same methods as had been used during previous modeling analyses, and detailed methodology is available in Chapters two and three. Briefly, three linear mixed effects models were fit in each of the Prague and Perth study areas, in which among fragment butterfly population densities were modeled based on: 1) environmental attributes (‘environment’ models), 2) species traits (‘trait’ models), or 3) both environmental attributes and species traits, as well as interactions between the two (‘full’ models). For this study, we fit these models to the Colorado dataset, with the exception of a trait model, which we omitted because we were not interested in the influences of only species traits for this analysis.
Prior to Colorado model construction, and in keeping with previous analyses (Chapter two), we transformed predictor variables in order to correct for nonlinearity. We then calculated
Variance Inflation Factor’s (VIF’s), using the HH package in R (Heiberger, 2013), in order to check for multicolinearity among environmental or species trait variables. Any variable exhibiting a VIF>5 was excluded from further analyses (Kati et al., 2012). Next, we selected environment-trait interactions for inclusion in the full model, using a permutation test called a fourth corner analysis (Dray and Legendre, 2008). This procedure tests whether an environment- trait interaction leads to greater species’ population densities than would be expected under a null hypothesis (e.g., that among fragment population densities are unrelated to either the environmental attributes of a patch or the traits of the species inhabiting it). Interactions that were associated with greater than expected population densities were considered potentially
81 important drivers of butterfly diversity patterns, and these were then included as predictors in the
full model. Further details for the methods used to perform the fourth corner analysis for this
study are available in Chapter two.
A full and environment model were next fit to the Colorado data, as:
ln(p) = βo + β1X1 + … + βnXn + εsite + εspecies + εobs
where p are population densities across sites, X1-n are fixed effects, and ε are Normal random
deviations with mean zero. Random effects for both models included site and species identity,
and fixed effects were as follows: the full model included the retained environmental and species
trait variables and interaction terms identified by the fourth corner analysis, and the environment
model included the retained environmental attributes. Additionally, and in keeping with methods
used during previous model construction (Chapter two), the models were fit using maximum
likelihood (ML), so that subset models with different fixed effects could be compared, and
standardized continuous and binary predictor variables. We then assessed model diagnostic plots
to verify that models exhibited appropriate fit to the data and that errors were distributed evenly
and randomly. We also used Moran’s I to check for spatial autocorrelation among model residuals (Dormann et al., 2007). Here, we averaged the residuals from all estimated butterfly population densities, compared to actual values, at each of the 12 sites in the Colorado dataset.
This resulted in an average residual per site (McAlpine et al., 2008), and these were then analyzed for spatial autocorrelation in ArcGIS (ESRI).
After fitting global full and environment models (e.g.,those in which all retained variables and interactions were included as model predictors), we performed model selection, through multi-model inference, and model averaging to obtain the final models for this study.
For each global model, we first used the ‘dredge’ function in the MuMIn package in R (Barton,
82 2013) to compare corrected (for finite sample sizes) Akaike Information Criterion (AICc) values among all possible subsets of the global model, and identify the ‘best’ model, or that with the lowest AICc. We next obtained a subset of equivalent models, or those with ΔAICc < 2 from the best model, and calculated average coefficient estimates across models in this subset to obtain parameter estimates for the final full and environment models for the Colorado dataset.
Following Colorado model construction, and in order to determine if: 1) results from the previous two-system comparison extend to a third fragmented ecosystem, and 2) there were additional among location similarities regarding the determinants of butterfly diversity patterns, we performed model cross-comparisons between the full Colorado, Prague, and Perth models.
Comparisons included evaluations of among model similarities with regard to: (i) which variables were identified as predictors, (ii) the direction of influence (+/- parameter estimate) of each predictor, (iii) each predictor’s relative importance during model averaging, and (iv) the magnitude of impact (p-value) of each predictor. Finally, and in order to address the prediction that species traits have particularly strong influences on Colorado butterfly diversity patterns, we used AIC to determine whether the full Colorado model, in which species traits were included as predictors, exhibited superior fit to the environment model, in which they were excluded.
4.4 Results
Butterfly Surveys
The datasets used during Prague and Perth model construction comprised 16,835 butterflies from 56 species, and 3043 individuals from 15 species, respectively (Chapters two and three). In Perth, three of the five families of Papilionoidea (excluding Riodinidae and
Papilionidae) were represented, along with Hesperiidae. In Prague, only members of the
83 Riodinidae were absent from the dataset, as was also true in Colorado. In Colorado, the entire dataset (after excluding migratory species and those for which fewer than five total individuals were encountered) encompassed 1867 butterflies from 16 species (Table 4.2). The relatively low numbers of individuals in Colorado were likely due to: a) natural community demography, b) the existence of fewer study sites here than in Prague or Perth, and c) the absence of very large sites in Colorado (where maximum patch area was 56.46 ha compared to 125.38 ha and 115.84ha in
Prague and Perth, respectively). Butterfly species richness at Colorado sites ranged from four to
14, and this resulted in a dataset comprising 102 species-site occurrence records, compared to
659 and 163 records in Prague and Perth, respectively (Chapters two and three).
Table 4.2- Butterfly species, with total abundances, for the refined Colorado dataset.
Total Species Family Abundance Cercyonis pegala (Fabricius) Nymphalidae 8 Colias eurytheme Boisduval Pieridae 351 Colias philodice Godart Pieridae 103 Epargyreus clarus (Cramer) Hesperiidae 10 Lycaena dione (Scudder) Lycaenidae 14 Papilio multicaudata W. F. Kirby Papilionidae 9 Papilio polyxenes Fabricius Papilionidae 6 Phyciodes tharos (Drury) Nymphalidae 14 Pieris rapae (Linnaeus) Pieridae 805 Plebejus melissa (W. H. Edwards) Lycaenidae 5 Poanes taxiles (W. H. Edwards) Hesperiidae 5 Polites peckius (W. F. Kirby) Hesperiidae 46 Polites mystic Edwards Hesperiidae 11 Pyrgus communis (Grote) Hesperiidae 173 Pontia protodice (Boisduval & Leconte) Pieridae 293 Strymon melinus Hübner Lycaenidae 15 Total no. individuals 1,867
84 Model construction: Colorado
Ranges of variation for the potential predictor variables considered in this analysis exhibited considerable similarity among the three study areas (Figure 4.2). Regarding continuous variables, ranges of variation strongly overlapped between the three datasets in all cases
(e.g.,Figure 4.2A). For categorical predictors, each level of most categorical variables occurred in relatively similar proportions among the three datasets (e.g.,Figure 4.2B). For a few of these variables, such as voltinism (Figure 4.2E), one level was not represented in one of the three datasets. We deemed this level of variability acceptable, however, given that similarities in the other levels would still allow for among- dataset comparisons of the variables’ influence.
85 150 1.0 A) B) 1.0 C) 0.8 0.8 100 0.6 0.6 0.4 50 0.4 0.2 0.2 0 0.0 0.0 Colorado Perth Prague Colorado Perth Prague Colorado Perth Prague Area (ha) Sites with Available Water Proportion Open Habitat
6 D) 0.8 E) 8 F) 7 5 0.6 6 4 0.4 5 4 3 0.2 3 2 0.0 2 Colorado Perth Prague Uni Bi Multi Colorado Perth Prague
Habitat Heterogeneity Voltinism Edge Score
0.8 G) 0.8 H) 60 I) 50 0.6 0.6 40 0.4 0.4 30 0.2 0.2 20
0.0 0.0 10 Short Med Long Egg Instar Pupa Colorado Perth Prague Flight Period Diapause Strategy Avg. Wing Length (mm)
500 40 1.0 J) K) L) 400 0.8 30 300 0.6 20 0.4 200 10 0.2 100 0.0 0 0 Single Small Med Large Colorado Perth Prague Colorado Perth Prague
Eggs Per Batch NSCP Open Habitat in Buffer (ha)
0.8 M) 0.6
0.4
0.2
0.0 Mono Oligo Poly Diet Breadth
Figure 4.2- Ranges of variation for the predictor variables. Continuous variables (A, C-D, F, I, K-L) are represented as minimum to maximum values, and categorical variables (B, E, G-H, J, M) as proportions of times that each level of each variable (e.g.,water availability= ‘present’) was represented in each dataset. Colorado data are represented by dark grey, Perth data by light grey, and Prague data by black bars.
86 Before fitting models to the Colorado data, we log transformed the predictor variables of patch area, shape complexity, and average wing length, and logit transformed that of proportion of open habitat, in order to correct for nonlinearity. We then assessed multicolinearity, and found that only shape complexity exhibited a high enough VIF (> 5) to warrant exclusion. Following variable selection, we therefore performed the fourth corner analysis using six environmental and six species trait variables, for 36 environment-trait pairs. As previously found in Perth (Chapter three), no environment-trait interactions were identified as influential to butterfly abundances across sites in Colorado. Along with the random effects of site and species identity, the full
Colorado model was thus fit with all six retained environmental variables and all six species traits as fixed effects, and the environment model fit with the six environmental variables as fixed effects.
Model fit diagnostics demonstrated that the global full and environment models fit the
Colorado data well, as evidenced by relatively evenly scattered residuals compared to fitted values, and error distributions that were close to normal. Additionally, model residuals did not violate the assumption of independence (Moran’s I=0.079 and 0.108 for the full and environment models, respectively, and p>>0.05 in both cases). We then performed model selection and averaging in order to identify the model subsets which best fit the data, and the following factors were identified as predictors for this ‘best’ Colorado models (hereafter models COfull and COenvt): four of the six environmental attributes (excluding edge permeability and open area in patch buffers) and five of the six species traits (excluding diet breadth) for model COfull, and five of the six environmental factors (excluding open area in site buffers) for model COenvt. These models fit the data considerably better than did the global models in both cases (∆AICc= 5.5 and 2.4 for models COfull and COenvt, respectively).
87 Model comparisons: environmental influences
Of the three predictors that had been previously found to have consistent impacts on butterfly diversity patterns in Prague and Perth, those of patch area, water availability, and edge permeability, only area influenced Colorado butterfly diversity patterns in the same manner
(Table 4.3). Increasing patch area led to significant increases in butterfly population densities among fragments in all study areas (Table 4.3, Figure 4.3A). The availability of water in sites, however, also led to consistently greater among fragment butterfly population densities in all three study areas (Table 4.3, Figure 4.3B), although this variable’s strength of impact was only significant in Colorado. Finally, edge permeability did not predict Colorado butterfly diversity patterns, although decreasing edge permeability (increasing edge scores) had slightly negative impacts on population densities among fragments in both Prague and Perth (Table 4.3, Figure
4.3C).
88 Table 4.3- Averaged coefficient estimates, relative importance during model averaging, and statistical significance for predictors of butterfly diversity patterns in Colorado, Perth, and Prague, and random effects variances for all models. For categorical variables, p-values indicate comparisons to the base level, which is the first in terms of alphabetical order. Predictors for which this applies include: voltinism (with levels of univoltine, bivoltine, and multivoltine), diapause strategy (with levels of egg, larva, pupa, and adult), eggs per batch (with levels of single and small), and flight period (with levels of short, medium, and long). Dashes indicate that the variable was not identified as a predictor for the model in question. Parameter Estimate* Relative importance Pr(>|z|) Colorado Perth Prague Colorado Perth Prague Colorado Perth Prague Fixed Effects Coefficientsa (Intercept) -1.483 -3.114 -3.086 1.00 1.00 1.00 0.011 <<0.001 <<0.001 Area (ha)b- for Prague model 0.853 0.631 1.139 1.00 1.00 1.00 0.002 0.010 <<0.001 Habitat heterogeneity -0.442 -0.692 0.284 0.78 1.00 0.29 0.037 0.003 0.244 Proportion open habitat b -0.344 -0.404 0.310 0.27 0.54 0.83 0.177 0.101 0.051 Water availability 1.248 0.088 0.091 1.00 0.14 0.16 0.001 0.833 0.720 Edge permeability score ------0.226 -0.263 ----- 0.07 0.35 ----- 0.374 0.130
VoltinismUni 0.704 -0.172 0.154 0.87 0.07 0.16 0.029 0.568 0.213
VoltinismMulti -0.131 ------0.049 0.87 ----- 0.16 0.609 ----- 0.757
Flight periodShort 0.625 1.418 ----- 0.13 1.00 ----- 0.118 <<0.001 ----- 9
Flight periodMedium 0.550 1.130 ----- 0.13 1.00 ----- 0.024 0.003 ----- 8
Diapause strategyEgg ------1.275 ------1.00 ------<<0.001 -----
Diapause strategyPupa -1.214 ------0.86 ------0.003 ------
Diapause strategyLarva -1.023 -0.068 ----- 0.86 1.00 ----- 0.013 0.803 ----- Avg. wing length (mm)b 0.372 ------0.047 0.62 ----- 0.07 0.053 ----- 0.676 Eggs per batch 0.720 ------0.22 ------0.149 ------Open area in buffer ------0.529 ------0.58 ------0.073 ----- Shape complexityb ------0.384 ------0.22 ------0.096 -----
Water:VoltinismMulti ------0.016 ------0.07 ------0.938
Water:VoltinismUni ------0.375 ------0.07 ------0.029 Random Effects Variancesa Site 0.0287 0.052 0.0866 Species name 0.0186 0.056 0.0648 * Estimates are for standardized variables a Estimates are for global models b Variable is transformed ) s e i
t 0
i A) s -1 n e -2 D -3 y l f -4 r e t -5 t u -6 B ( -7 g o l -1.0 -0.5 0.0 0.5 -0.5 0.0 0.5 -0.5 0.0 0.5 Area (ha)1 ) s e i
t 0
i B) s -1 n
e -2 D -3 y l
f -4 r e
t -5 t
u -6 B ( -7 g o l Abs Pres Abs Pres Abs Pres Water Availability ) s e i
t 0
i C) s -1 n e -2 D -3 y l N/A f -4 r e t -5 t u -6 B ( -7 g o l -1.0 -0.5 0.0 0.5 -0.5 0.0 0.5 -1.0 -0.5 0.0 0.5 Edge Permeability ) s e i
t 0
i D) s -1 n e -2 D -3 y l f -4 r e t -5 t u -6 B ( -7 g o l -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 -1.0 -0.5 0.0 0.5 Habitat Heterogeneity
90 ) s e i
t 0
i E) s -1 n
e -2 D -3 y l
f -4 r e
t -5 t
u -6 B ( -7 g o l -1.0 -0.5 0.0 0.5 -0.5 0.0 0.5 1.0 -1.0 -0.5 0.0 0.5 1.0 1.5 Proportion Open Habitat1 e c 20 F) n e r r 15 u c c 10 N/A O f o
5 . q e r 0 F Short Medium Long Short Medium Long Short Medium Long Flight Period e c 20 G) n e r r 15 u c c 10 N/A N/A N/A O f o
5 . q e r 0 F Egg Larva Pupa Adult Egg Larva Pupa Adult Egg Larva Pupa Adult Diapause Strategy e c 20 H) n e r r 15 u c c 10 NA O f o
5 . q e r 0 F Uni Bi Multi Uni Bi Multi Uni Bi Multi Voltinism
91 e
c 20 I) n e r
r 15 u c c 10 N/A O
f o
5 . q e r 0 F
-1.0 -0.5 0.0 0.5 1.0 1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Avg. Wing Length (mm)1 e
c 20 J) n e r r 15 u c c 10 N/A N/A O
f o
5 . q e r 0 F Single Small Medium Large Single Small Medium Large Single Small Medium Large Eggs per Batch
Figure 4.3- Influences of full model predictors on butterfly population densities across study areas. Data for Colorado are represented by dark grey dots and boxes, depending on the figure, for Perth are represented by light grey squares and boxes, and for Prague are represented by black triangles and white boxes. “N/A” indicates that the variable was not identified as a model predictor. Data are standardized (mean=0, s.d=0.5 for continuous variables or difference between categories=1 for binary variables) as they were for modeling analyses, and butterfly densities are natural log transformed. Boxplots are shown for categorical variables, with heavy lines indicating median values, lower and upper box edges representing first and third quartiles, and whiskers showing minimum and maximum values. Superscript indicates transformed data.
For the other environmental variables that were previously identified as predictors in both
Prague and Perth, those of habitat heterogeneity and proportion of open area in patches (Chapter three), both were identified as predictors of Colorado butterfly diversity patterns as well. In both cases, however, the variables’ influences were only similar between Colorado and Perth.
Increasing habitat heterogeneity (number of land cover types) had significantly negative impacts on among fragment butterfly population densities in Colorado and Perth, although the opposite pattern was seen in Prague (Table 4.3, Figure 4.3D). Similarly, increasing proportions of open habitat had weakly negative impacts on among fragment butterfly population densities in
92 Colorado and Perth, while having a marginally significant positive impact on those in Prague
(Table 4.3; Figure 4.3E).
The importance of species traits among ecosystems
Species traits had substantial influence on butterfly diversity patterns in Colorado. This was first demonstrated by model fits, as the full Colorado model fit the data considerably better than did the environment model (AICc=267.3 and 270.8 for models COfull and COenvt, respectively). This result was similar to that found in the other single-habitat, drought-prone ecosystem considered here, Perth (Chapter three). Additionally, five species traits were identified as predictors of butterfly diversity patterns in Colorado, with three having a strong magnitude of impact (Table 4.3). This result was relatively similar to that found in Perth (Chapter three), where three species traits were identified as predictors of butterfly diversity patterns, with two having a strong magnitude of impact (Table 4.3). In contrast, only two species traits were identified as predictors in the mixed-habitat, non-drought prone setting of Prague (Chapter three), although none had a strong magnitude of impact (Table 4.3).
Additional similarities were found between Colorado and Perth butterfly communities, with regard to which species trait predictors influenced diversity patterns, and how. Specifically, both flight period and diapause strategy influenced butterfly diversity patterns in Colorado and
Perth, while having no impact on those in Prague (Table 4.3, Figure 4.3F-G). It should be noted that because we used: 1) a proportional response variable, which was standardized by total butterfly population size, and 2) a dataset in which butterfly absences were excluded, species trait parameters act as a measure of occupancy in our analyses, with influences relating to the frequencies of occurrence of species with various traits throughout the landscape (see Chapters
93 two for additional details). Furthermore, the standardization of our response variable resulted in the coefficient estimates for species trait predictors reflecting the inverse of the influences of those variables on butterfly frequencies of occurrence. Thus, species with long flight periods were found with significantly greater frequency throughout the Colorado and Perth landscapes that were those with medium flight periods (as indicated by the positive coefficient estimate for
Flight periodMedium in Table 4.3), which occurred more frequently than did those with short flight periods in both study areas (and significantly so in Perth; Table 4.3, Figure 4.3F). Regarding diapause strategy, while this species trait was identified as a predictor in both Colorado and Perth models, it had opposing influences on diversity patterns in the two study areas. In Colorado, species diapausing in consecutively later developmental stages were found with increasing frequency throughout the landscape; egg diapausers occurred significantly less frequently among patches than did larval or pupal diapausers, and pupal diapausers occurred with the greatest frequency (Table 4.3, Figure 4.3G). In Perth, on the other hand, species diapausing as eggs occurred with the greatest frequency throughout the landscape, and these species were found with significantly greater frequency among patches than were those diapausing as adults (Table
4.3, Figure 4.3G).
For the other species traits identified as predictors of butterfly diversity patterns in at least one study area, those of voltinism, average wing length, and eggs laid per batch, the variables’ influences were wither similar between Colorado and Prague or unique to individual study areas.
For example, in both Colorado and Prague, univoltine species were found in the lowest frequencies throughout the landscape, a pattern which was significant in Colorado, and frequency of occurrence then increased with subsequently greater numbers of generations per year (Table 4.3, Figure 4.3H). In Perth, on the other hand, the reverse pattern was seen:
94 univoltine species occurred with greater frequency among patches than did multivoltine species, although this trend was statistically weak (Table 4.3, Figure 4.3H). The species trait of average wing length was also found to be a predictor of butterfly diversity patterns in both Colorado and
Prague, although the influences of this variable differed between the two study areas. Here, butterflies with increasingly longer wings were found with marginally decreasing frequencies among patches in Colorado, but with increasing frequencies among those in Prague (although this trend was statistically weak; Table 4.3, Figure 4.3I). Finally, the species trait of eggs per batch was only identified as a predictor of butterfly diversity patterns in Colorado, where species laying eggs singly were found with slightly greater frequency among patches than were those laying eggs in small batches (Table 4.3, Figure 4.3J).
4.5 Discussion
This study provides an example of how collaboration and data reuse may facilitate greater understanding of the drivers of species’ responses to habitat fragmentation across disparate ecosystems. Our research approach is especially informative to this goal, given that we:
(i) assessed the determinants of community-wide diversity patterns, such that results are more widely applicable than are those of species-specific modeling analyses, (ii) incorporated both environmental and species traits predictors, thereby accounting for the potentially important influences of species traits on community members’ abilities to persist in fragmented landscapes, and (iii) compared models constructed in ecosystems with ecological and climatic consistencies, to assess how similarities in these factors contribute to comparable among-location fragmentation effects. We found evidence that certain environmental attributes generally drive butterfly community diversity patterns, in all fragmented landscapes. Our results also highlighted
95 various ecological and/or climatic characteristics that may lead to consistency regarding the determinants of butterfly diversity patterns among certain study areas. Furthermore, among location ecological and/or climatic conditions may also influence the degree to which species traits influence diversity patterns, with such traits being especially important in restricted-habitat, drought-prone ecosystems.
Toward a broader understanding of drivers of fragmentation effects
The factor that is found to be most important in most studies examining environmental influences on species’ responses to habitat fragmentation is patch area. Large habitat patches have repeatedly been shown to contribute to biodiversity maintenance in fragmented ecosystems, and this has been demonstrated for a wide range of taxa (e.g.,plants (Bastin andThomas, 1999), arthropods (Debinski and Holt, 2000), amphibians (Vallan, 2000), and birds and mammals
(Andren, 1994)) in a wide variety of ecosystems. Results from this study thus further confirm that the maintenance of large habitat patches within fragmented networks should be a universal management objective (Fischer and Lindenmayer, 2007). Our findings also suggest, however, that it may be important for land-managers to ensure the sustained availability of accessible water within habitat fragments, particularly in regions characterized by open habitats and arid climatic conditions.
The finding that water availability had a disproportionately strong impact on butterfly diversity patterns in the highly seasonal, drought-prone setting of Colorado exemplifies how the determinants of diversity patterns may be influenced by among location ecological and/or climatic contexts. For example, the shade limited context of Colorado’s open, grassland patches
(personal observation), may combine with the drought-prone climate to amplify the importance
96 of water availability for maintaining large among fragment butterfly population densities, although this environmental characteristic may be less critical in less shade limited and/or arid ecosystems. Similarly, increasing numbers of land cover types in areas characterized by restricted ecological contexts, such as the predominantly single habitats of Colorado and Perth, may negatively influence butterfly population densities if they represent resource loss for habitat- adapted butterflies (e.g.,the presence of shrub cover may limit the availability of grassland resources for grassland-adapted Colorado species (personal observation), and woodland resources for woodland-adapted Perth species (personal observation)). In the less ecologically restricted setting of Prague, however, increasing numbers of land cover types likely result in greater resource availability for mixed-habitat adapted species, thus leading to greater butterfly population densities in fragments with greater habitat heterogeneity.
The results described above suggest that there may be marked similarities regarding the determinants of butterfly diversity patterns among widely separated fragmented landscapes. We acknowledge, however, that these results are only preliminary, and additional research is needed in order to further explore the patterns that began to emerge from this three-ecosystem study.
Such research may be particularly important given that ecological and climatic conditions do not operate independently, and that their combined influences may confound overly simplistic generalizations. For example, while the proportion of open habitat within patches might be expected to positively influence among fragment butterfly population densities in the grassland ecosystem of Colorado, where species are adapted to open habitat, the finding of the reverse pattern may be the product of combined ecological and climatic conditions in the region; increasing open habitat is associated with reduced shade resources that may impose restrictions on butterfly population densities in patches within this drought-prone ecosystem. Such a
97 hypothesis may also explain the negative influence of increasing proportions of open habitat within patches on butterfly diversity patterns in Perth, or, alternately, the influence of this environmental variable could simply relate to a reduction in woodland habitat availability for the woodland adapted species.
In addition to similarities with regard to the influences of predictor variables on butterfly diversity patterns among the three study areas, there were also notable differences. Most conspicuously, although we may have anticipated similarities with regards to the influences of species trait predictors on diversity patterns between what are arguably the two most ecologically and climatically similar ecosystems, Colorado and Perth, both diapause strategy and voltinism defied this expectation. In addition, that the species trait of voltinism influenced butterfly frequency of occurrence similarly in the predominantly single habitat, drought-prone region of
Colorado as in the mixed habitat, non-drought-prone ecosystem of Prague was also unexpected.
We recognize, however, that this research was conducted across widely separated ecosystems that reside in distinct ecological and evolutionary contexts, and that this may lead to natural variation in the mechanisms by which environmental and species trait predictors influence butterfly diversity patterns. Clearly, additional research is needed in order to resolve the causes of various factors’ influences on butterfly diversity patterns among disparate ecosystems.
Importance of considering species trait predictors of butterfly diversity patterns
Regardless of the directions and/or magnitudes of effect, one of the most important results of this study was that it further supported and strengthened our previous argument that the inclusion of species trait predictors may be particularly important for models constructed in regions characterized by ecological habitat-restrictions and drought-prone climates (Chapter
98 three). Given the results from this study, we posit that the consideration of species traits in models constructed in highly seasonal settings may also be crucial. This is evidenced by the fact that the study area in which species traits played the largest and most influential roles in determining butterfly diversity patterns was that in which butterfly communities not only occupy single-habitat fragments, but are also subjected to harsh climatic conditions in both drought- prone summers and freeze-prone winters. Furthermore, species traits were also strongly influential in the second most ecologically and climatically limited ecosystem analyzed here,
Perth.
Conclusions and further considerations
Results from this study underscore the complexity of factors influencing butterfly diversity patterns in fragmented ecosystems. Such patterns may be driven by large numbers of environmental and/or species trait predictors, and the influences of these predictors may also be mediated by location-specific ecological and/or climatic factors. While this study provides preliminary evidence of ecologically and/or climatically driven similarities regarding the determinants of butterfly diversity patterns among widely separated fragmented landscapes, additional comparisons, e.g., with locations sampled with respect to latitudinal and longitudinal gradients, are clearly needed.
We see two main ways in which future research on this topic may be beneficial to conservation biologists. First, additional studies would further resolve patterns regarding the determinants of butterfly diversity patterns in fragmented landscapes, potentially facilitating the development of broadly applicable management recommendations. Such recommendations might include that management policies for drought-prone ecosystems preferentially focus on
99 species with long flight periods, which may be particularly susceptible to habitat fragmentation, and work toward the conservation of large patches with available water and intermediate levels of shaded habitat. Furthermore, the principles applied during this study would be useful for any taxa, and may pave the way for valuable insight regarding more broad-scale conservation and management plans for wide varieties of taxa in fragmented landscapes around the world.
100 CHAPTER FIVE
PUTTING EFFORT TOWARD SURVEY EFFORT: AN EMPIRICAL INVESTIGATION OF
THE INFLUENCE OF SURVEY EFFORT ON MODELING ANALYSES4
5.1 Abstract
Recent decades have seen increasing demand for ecological studies that address local-
scale environmental issues, and that can also be scaled up to inform and predict over broad
geographic scales. Such broad-scale evaluations require that individual, previously collected
datasets are brought together into meta-analyses or data reuse schemes through which ecological
patterns and processes may be extensively examined. Such analyses are challenging, in large part
because independent datasets are often collected via non-standardized sampling or survey
schemes, which may bias both the datasets themselves and downstream results of secondary
analyses in which they are used.
In this study, we investigated the impact of survey effort on downstream data reuse
analyses by evaluating how differences between models of butterfly diversity patterns in
fragmented landscapes, which were constructed from datasets representing different survey
efforts, would have altered previously conducted model comparisons. We found striking
similarities between the different models, leading us to conclude that the results of the previous
analyses would have remained largely unchanged if we had used a dataset representing a
narrower range of survey efforts than those represented in the original data. This suggests that
data reuse analyses may be less sensitive to variation in the survey efforts used to collect the
original datasets than anticipated. In addition, and as a product of additional model comparisons
4 This chapter was a collaborative effort among N. Robinson, M.D. Bowers and R.P. Guralnick, and is currently in preparation for submission to Ecology 101 conducted in this study, this work also highlighted the contributions of particular variables analyzed, e.g.,patch area and certain species traits, to diversity patterns in fragmented landscapes, and thus confirmed several conclusions drawn during previous model comparison analyses.
5.2 Introduction
Recent decades have seen increasing demand for ecological studies that not only address local-scale environmental issues, but that can be scaled up to inform and predict over broad, sometimes even global, geographic scales (Hampton et al., 2013). Such broad-scale evaluations are only possible through comparative analyses of multiple, individual studies, in which patterns and processes may be examined more extensively than is possible through case-by-case investigations (Osenberg et al., 1999). This has precipitated two important approaches to broader investigations. The first is a meta-analytic approach, in which results of multiple case studies are summarized and then analyzed together in order to explore patterns at wider extents or over broader spatial or temporal scales (Osenberg et al., 1999). The second, and the focus here, is a data reuse/secondary use (sensu Zimmerman, 2008) approach, in which the data, as opposed to the results, are integrated into new analyses aimed at addressing different questions than those originally investigated. Such integrative, analytical strategies pave the way for a more robust and broader understanding of ecological patterns and processes, and may be extremely valuable for furthering ecological theory and advancing global scale conservation and management.
Both of the approaches to broad-scale analyses described above pose enormous challenges to researchers. This has been most extensively discussed with regard to meta- analyses, where the trustworthiness of comparing results from studies based on independently
102 collected datasets, for which methodologies are rarely standardized, has been repeatedly questioned (e.g., Stewart, 2010). The following issues are of particular concern for data usage during meta-analytical investigations (i) differences in spatial and temporal scale among studies
(Stewart, 2010); (ii) different experimental manipulation schemes (Osenberg et al., 1999); and
(iii) irregular sampling or survey strategies (Stewart, 2010). As these factors also relate to the data themselves, and not just to the results of individual studies, researchers attempting to reuse data for new analyses face these same issues. In this paper, we focus on the issue of bringing together data collected via different sampling or survey methodologies into reuse analyses, and on the sensitivity of downstream results to variation in the manners in which individual datasets were originally collected.
A lack of uniformity with regard to data collection protocols, especially in field-based ecological studies (Zimmerman, 2003; Stewart, 2010), is one of the greatest challenges faced by researchers attempting to assimilate multiple, previously collected datasets in order to address broad scale ecological questions. This is because inconsistent data collection methods, including those relating to the frequency and/or duration of field surveys, may lead to dataset bias
(Osenberg et al., 1999). Variation in data collection schemes does not necessarily reflect a lack of methodological rigor, but rather may be caused by differences in the initial research motivations, logistical and/or time constraints, or even area-specific landscape configurations
(e.g.,geological features that restrict movement within study areas) by which otherwise standard protocols cannot be followed (e.g.,Chapter two). As a result, variation in survey methodologies is an unavoidable characteristic of data reuse analyses, and the assimilation of these datasets must be carefully considered in order to balance the potential impacts of separate methodological approaches on the results of downstream secondary analyses.
103 One way to minimize the potentially negative downstream impacts of reusing data collected via inconsistent methodologies is for researchers to define a narrow sampling universe
(sensu Stewart (2010)) into which data must fit in order to be used in secondary analyses. For example, primary datasets may be refined into subsets that represent a specified range of sampling or survey effort, thereby standardizing such effort among datasets to be included in the secondary analysis. A priori, this seems like a good approach to managing the assimilation of multiple datasets for reuse. However, the actual need for such procedures is unknown, as the influence of variable sampling or survey efforts on downstream results has not been explicitly tested.
In this study, we ask the question: does variation in among dataset survey effort affect conclusions drawn from data reuse analyses? We address this question through an empirical investigation of the influence of survey effort on covariate estimates for models of butterfly diversity patterns in relation to environmental and species trait predictors in fragmented ecosystems. Specifically, we first take a dataset that was previously used in a comparative modeling analysis of the drivers of diversity patterns in widely separated fragmented landscapes, and reconstruct the model using a data subset that represents a narrower range of survey efforts than those encompassed by the original dataset. Next, we compare differences in the directions and magnitudes of influence of predictors in models from different subsets of the same data.
Finally, we discuss how the use of the data subset might have altered the outcome of the previously conducted comparative analysis. This assessment is particularly well-suited for evaluating the impacts of variation in survey effort among datasets on downstream analyses because estimates of model parameter or effect size are especially sensitive to data bias
(Osenberg et al., 1999), such as that which may arise from certain survey methodologies.
104 5.3 Methods
The primary dataset for this analysis was collected in Perth, Western Australia (latitude
31.95oS, longitude 115.86oE), and detailed information about the data and collection methods can be found in Chapter three. Briefly, this ‘full dataset’ was comprised of butterfly abundance records for a community inhabiting 19 remnant Banksia and Eucalyptus woodland habitat fragments in the area surrounding Perth. Butterfly data were collected along transects, and via the Pollard method (Pollard, 1977). The full Perth dataset was then assimilated with two other butterfly community abundance datasets, these having been previously collected in the fragmented ecosystems around Prague, Czech Republic (latitude 50.09oN, longitude 14.42oE) and Denver, Colorado, USA (latitude 39.74oN, longitude 104.98oW), and all three used in a comparative analysis of the determinants of butterfly diversity patterns in different fragmented landscapes (Chapter four). Predictor variables for all models included seven environmental attributes and six species traits that have all been previously found to influence butterfly persistence in, and movement between, patches in fragmented landscapes (see Chapter two), including: 1) water availability, 2) patch area, 3) shape complexity, 4) edge permeability, 5) habitat heterogeneity, 6) proportion of open habitat (e.g., grassland), 7) amount of open habitat in a buffer around the patch (hereafter OinB), 8) diet breadth, 9) average wing length, 10) voltinism
(number of generations per year), 11) flight period, 12) diapause strategy, and 13) eggs laid per batch. Data collection for these variables involved a combination of Google Earth satellite imagery, ArcGIS (ESRI), and available printed and online literature (e.g.,Opler, 1999; Braby,
2000; Benes et al., 2002; http://www.butterfliesandmoths.org; www.learnaboutbutterflies.com/; http://www.lepidoptera.cz/motyli/; details in Chapters two through four).
105 Data refinement
For the purposes of this analysis, the original Perth dataset was next refined to a data subset that represented a different range of survey efforts than those encompassed by the full dataset. Survey effort is here defined as the relative intensity with which a habitat fragment was surveyed, measured as meters surveyed per hectare of patch area, in contrast to the total number of surveys conducted, which was equal among all patches in the initial dataset. For the full dataset, survey efforts among fragments ranged from 16 to 96 m/ha. For the analysis presented here, the range of allowed survey efforts was narrowed to four-fold, from 16 and 64 m/ha, and the most intensely surveyed sites excluded from the dataset. This reduced the original Perth dataset by ~21%, and this new data subset comprised records representing a more limited range of survey efforts than those accounted for in the original data.
Model construction
Following dataset refinement, and in accordance with previous methods (Chapter two), butterfly population densities for the Perth data subset were next modeled based on the previously mentioned environmental and species trait predictors, using a linear mixed effects framework. Complete details for model fitting can be found in Chapter two, and included the following four steps:
(1) Predictor variables were transformed to correct for non-linearity, and then multicolinearity measured among each variable type (environmental or species trait) using a
Variance Inflation Factor (VIF). VIF was calculated using the HH package in R (Heiberger,
2013), and variables with VIF<5 were retained for further analyses.
106 (2) Retained predictor variables were passed to a permutation test called a fourth corner analysis (Dray and Legendre, 2008), in order to identify the environment-trait interactions that appeared to influence butterfly population densities, measured as the number of individuals of a given species at a given site/all individuals of the species, across sites and that might be useful to consider as model predictors. Here, correlations were calculated for each of the environmental features occurring at sites and the species traits of individuals that inhabit them. Calculations were weighted by the relative population densities of each species at each site, so that environment-trait interactions were directly linked under observed density patterns (Dray and
Legendre, 2008; Aubin et al., 2009). Each correlation was then compared to a distribution of correlation statistics derived under a null hypothesis (i.e., that species’ densities across sites are random) in order to determine the relative strength with which the environment-trait interaction influenced population densities. Fourth corner analyses were executed using the ade4 package in
R (Chessel et al., 2013), and each environment-trait correlation statistic was compared to a null distribution derived using permutation method 4 (Dray and Legendre, 2008).
(3) A linear mixed effect models was fit as:
p = βo + β1X1 + … + βnXn + εsite + εspecies + εobs
where p are population densities across sites, X1-n are fixed effects, and ε are Normal random deviations with mean zero. Random effects included site and species identity, and fixed effects included the retained environmental and species trait variables and interaction terms identified by the fourth corner analysis. In keeping with methods used during previous model construction (Chapter two), the model was fit using standardized continuous and binary predictor
107 variables, and maximum likelihood (ML; so that subset models with different fixed effects could be compared).
(4) The ‘best’ model was next identified, as that with the lowest corrected (for finite sample sizes) Akaike Information Criterion (AICc). This was performed via model selection in the MuMIn package (Barton, 2013). We then obtained a subset of equivalent models (those with
ΔAICc < 2), and calculated average coefficient estimates across models in this subset. This resulted in a final model for the Perth data subset.
Finally, and due to finding that VIF was greater than five for both water availability and patch area (see results), we fit two separate models to the Perth data subset. The first model
(hereafter model SNoWater) included all potential environmental and species trait predictors except water availability, and represented the model that we would have chosen during our previous comparative analysis (given the well demonstrated impacts of patch area on species’ responses to habitat fragmentation (Debinski and Holt, 2000; Fahrig, 2003; Ewers and Didham, 2006)), had we been required to exclude either of these two variables. The second model (hereafter model
SNoArea) was fit using all potential predictors except patch area, such that we could explore the effects of excluding this environmental attribute on parameter estimates for the remaining predictors.
108 5.4 Results
Perth data subset
After refining the Perth data to include only records for which survey effort fell within a four-fold range, we were left with a dataset comprising 15 of the original 19 sites, and 2,467 butterflies from 15 species (out of 3,043 individuals from the same 15 species in the original dataset (Chapter three)). Patches in the data subset covered the same range of patch areas as were found in the original dataset, as neither the largest nor the smallest fragments were dropped during data refinement (i.e., survey effort was not greatest at the smallest Perth sites, etc.). We then transformed predictor variables in the data subset, in order to correct for non-linearity, with transformations being applied to the same variables as had been transformed during the previous analysis. Thus, we logit transformed the environmental attribute of proportion of open habitat within sites, and natural log transformed that of shape complexity. Next, we performed multicolinearity analyses, and found that both patch area and water availability exhibited high multicolinearity (VIF’s of 5.41 and 5.84, respectively), but that no other environmental or species trait variables were multicolinear. Finally, we performed the fourth corner analysis, and found that no environment-trait interactions were identified as influencing among patch butterfly population densities within the Perth data subset.
Comparisons among full and subset models
The environmental and species trait variables identified as predictors in model SNoWater were almost identical to those of the previously constructed Perth model (hereafter ‘full model’), in terms of not only identities, but also directions of influence (+/- parameter estimates), relative importance to the models, and magnitudes of impact (p-values). In fact, nine of the ten variables
109 that had been both evaluated during the construction of both models (thus excluding water availability), and identified as predictors in the full model, were also identified as predictors in model SNoWater. The only exception was edge permeability, which was a weak predictor in the full model but was not identified as a predictor in model SNoWater (Table 5.1). For all other variables, however, the directions of influence, relative importance to the models, and magnitudes of impact were similar, or even equal, between the two models. This includes diapause strategy, where diapause at the level of larva appeared to have a different direction of influence in the two models (Table 5.1), but for which strongly overlapping standard errors around the parameter estimates (Figure 5.1) suggested otherwise. The only minor source of variation between the full and SNoWater models was that the relative importance of proportion of open habitat decreased from 0.54 to 0.16, and that of shape complexity increased slightly from
0.22 to 0.40, in the full and SNoWater. models, respectively (Table 5.1).
110 Table 5.1- Parameter estimates, standard errors, relative importance to the models, and magnitudes of effect (p-values) for predictor variables in each of the full and subset models for Perth.
Parameter Estimate* SE Relative importance Pr(>|z|) Full SNoWater SNoArea Full SNoWater SNoArea Full SNoWater SNoArea Full SNoWater SNoArea Habitat -0.692 -0.870 -1.133 0.237 0.292 0.287 1.00 1.00 1.00 0.003 0.003 <<0.001 heterogeneity
Flight periodshort 1.418 1.619 1.679 0.343 0.446 0.448 1.00 1.00 1.00 <<0.001 <0.001 <0.001 Flight 1.130 1.439 1.484 0.386 0.464 0.472 1.00 1.00 1.00 0.003 0.002 0.002 periodmedium Diapause -1.275 -1.531 -1.587 0.352 0.422 0.425 1.00 1.00 1.00 <0.001 <0.001 <0.001 strategyegg Diapause -0.068 0.067 0.072 0.273 0.308 0.314 1.00 1.00 1.00 0.803 0.827 0.819 strategylarva Proportion of -0.404 -0.436 -0.516 0.246 0.247 0.242 0.54 0.16 0.49 0.100 0.077 0.033 open habitata 1
Open habitat in 1
-0.529 -0.560 -0.669 0.295 0.305 0.292 0.58 0.46 0.75 0.073 0.066 0.022 1 buffer Shape -0.384 -0.399 -0.621 0.231 0.248 0.236 0.22 0.40 0.75 0.096 0.107 0.009 complexitya
Voltinismuni -0.172 -0.280 ----- 0.301 0.361 ----- 0.07 0.09 ----- 0.568 0.437 -----
Area (ha) 0.631 0.639 ----- 0.246 0.262 ----- 1.00 0.90 ----- 0.010 0.015 ----- Water 0.088 ----- 0.763 0.417 ----- 0.282 0.14 ----- 1.00 0.833 ----- 0.007 availability Edge -0.226 ------0.255 ------0.07 ------0.374 ------permeability * Estimates are for standardized variables a Variable is transformed Model 2 Full SNoWater SNoArea 1 e t a m i t s e
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Figure 5.1- Coefficient estimates for the predictor variables in each of the full (black circles), no water subset (light grey triangles), and go area subset (dark grey squares) models. Error bars represent +/- 1 standard error around the coefficient estimates.
Model SNoArea was less similar to the full model than was model SNoWater. This
dissimilarity was not related to the identities or directions of influence of the model predictors,
which were again remarkably similar between the full and subset models (Table 5.1). In fact,
only the variables of edge permeability and voltinism were not identified as predictors in model
SNoArea, although they had been identified as predictors in the full model. The dissimilarity
between the full and SNoArea models arose instead from differences in the relative importance and magnitude of effect of several shared predictors between these two models. For model SNoArea,
112 almost all environmental predictors, with the exception of habitat heterogeneity, had strikingly greater relative importance and magnitudes of effect than were seen in the full model (Table 5.1).
In fact, the environmental predictors of proportion of open habitat, open habitat in patch buffers, shape complexity, and water availability, all went from having relatively weak influences on butterfly diversity patterns in the full model to very strong influences on diversity patterns in model SNoArea (Table 5.1).
5.5 Discussion
Simply stated, data reuse analyses are difficult, particularly when they involve datasets collected by multiple, independent researchers, who have employed idiosyncratic and incongruous data collection methods. Such variation in methods may have particularly strong influences on modeling studies, as survey efforts within and among sites may lead to data biases that then influence downstream comparisons of model parameters and/or effect sizes. This issue has led to a great deal of consideration of such factors as the amount of survey effort that is required for the unbiased detection of certain species (Garrard et al., 2008), and the effects of different survey designs on the accuracy of ecological models (e.g.,Reese et al., 2005). These efforts, however, all focus on the better development of individual studies from the ground up, rather than on dealing with preexisting datasets for which survey designs and efforts are already fixed. In this study, we investigated the impact of survey effort on downstream data reuse analyses, by evaluating how differences between models constructed from data subsets, which represented different survey efforts, would have altered previously conducted model comparison analyses. We showed that the results of the previous work would have remained largely unchanged, even if we had used a dataset collected via a different range of survey efforts.
113 The influence of survey effort on a secondary data reuse analysis
Perhaps the most important result of this work was that the only real difference between the original model and that constructed using the data subset (model SNoWater) was that one of the least influential predictors from the full model, edge permeability, was absent from the subset model. This finding suggests one of two things: 1) the determinants of among fragment butterfly diversity patterns in Perth are so stable that they apply regardless of the number of fragments included in the analysis, or how thoroughly they were surveyed, or 2) data collected via a smaller range of survey efforts are just as accurate as those collected using a larger range of survey efforts, such that excluding the sites in which the greatest total survey effort was employed does not alter modeling outcomes. In either case, the use of the subset model from this analysis would have resulted in almost no change to our previous conclusions, with two minor exceptions: (i) we would have had less evidence for the previous assertion that water availability was a potentially important factor to preserve in fragmented ecosystems (Chapters three and four), and (ii) we would have been unable to say that edge permeability influenced butterfly diversity patterns similarly in the fragmented landscapes of Prague and Perth (Chapter three).
Additional implications of this work
In addition to addressing how variation in survey effort affects conclusions drawn from data reuse analyses, two other important results arose as a product of this analysis: 1) the environmental attribute of patch area plays a dominant role in determining butterfly diversity patterns in fragmented landscapes, to the point that this predictor may overwhelm the influences of other predictors during modeling analyses, and 2) as previously argued, studies aimed at better
114 understanding the determinants of butterfly diversity patterns in fragmented landscapes are remiss if the influences of species traits are not considered (Chapters two through four), all other factors aside.
Regarding the first result, we were less surprised to find that patch area plays a strong role in determining butterfly diversity patterns, which we already knew (see methods), than we were to see just how influential it was. Excluding this variable clearly demonstrated that the influences of several other environmental predictors on butterfly diversity patterns in fragmented landscapes are suppressed by that of patch area, for although the exclusion of area did not lead to a decreased number of environmental predictors, these factors showed a much stronger effect in its absence. Thus, these results support our previous argument (Chapter two) that examinations of the effects of habitat fragmentation on the landscape inhabitants, particularly at the community level, must consider not just among fragment patch area, or area and one or two other environmental attributes (e.g.,Krauss et al., 2003b; Prugh et al., 2008), but several simultaneously operating factors.
Regarding the second conclusion, results from this study support and reaffirm our previous point that species traits play critical roles in determining butterfly community diversity patterns in fragmented landscapes, and thus should not be ignored in studies of fragmentation effects. Indeed, all of the analyses presented in earlier chapters, from individual model explorations (Chapter two) through three-way model cross-comparisons (Chapter four), validate the importance of including species traits in such models. The results from these studies provides unequivocal evidence that improving understanding of species’ responses to habitat fragmentation requires the consideration of how their traits mediate their abilities to persist in fragmented landscapes.
115 Limitations of this study
One potential limitation of this work is that we performed comparisons among models constructed using datasets collected via ranges of survey effort that were not enormously divergent. This was due to dataset availability for this study. For example, while we considered refining the original Perth dataset to one which had been collected via the same range of survey efforts to those employed during Colorado data collection, we found that this would have limited the Perth data subset to only seven sites, and thus have been too restrictive. Thus, while we contend that a two-fold difference in survey effort was substantial enough to address our initial research question, these are preliminary findings that would benefit from further exploration in which comparisons are made using datasets collected over more divergent levels of survey effort.
Such sensitivity analyses, across a broader range of survey effort differentials, would help to elucidate the point at which model parameter estimates begin to diverge as a result of among dataset sampling bias and/or incompleteness as opposed to inherent patterns in the data.
Conclusions
We assessed the influence of survey effort on downstream data reuse analyses for a model cross-comparison study of the determinants of butterfly diversity patterns in widely separated fragmented landscapes. We found that models constructed for one of the datasets were almost identical whether the dataset included a wider or narrower range of survey efforts. We thus argue that downstream results of data reuse analyses may be somewhat less sensitive than generally assumed to among dataset variation in such factors as data collection protocols, at least with regard to the amount of survey effort applied. We also acknowledge, however, that
116 additional work, focusing on broader ranges of survey efforts and across various taxa (for which detection probabilities differ), will be required in order to substantiate this conclusion. In addition, this work further highlighted the importance of considering the influences of not only several environmental attributes, but also of species traits, on species’ responses to habitat fragmentation if fragmentation effects are to be broadly understood.
117 CHAPTER 6
CONCLUSIONS
6.1 Challenges of multi-species, multi-study comparisons
The analyses that form this dissertation provide first steps toward a broader understanding of the factors that drive butterfly diversity patterns in fragmented landscapes around the world. Each step of this work was characterized by complex analytical challenges, which were crucial components of the synthetic and comparative conceptual framework. For example, I assessed the determinants of diversity patterns for not just one or a few butterfly species, but for entire communities. This required: a) that I collect a large amount of data, for my own dataset as well as those generously provided by other researchers, b) employing techniques designed to identify interaction terms for inclusion during modeling analyses, in the absence of a priori knowledge regarding their potential importance, and c) working through model fitting and selection given a large number of potential predictors. Such community-wide analyses may facilitate a better understanding of fragmentation effects in a way that may be important for the development of widely applicable recommendations for mitigating negative responses to habitat fragmentation. This may be particularly true if some of the variability that characterizes species- specific fragmentation research, such as that arising when different species are found to respond to fragmentation effects in different ways (e.g.,Debinski and Holt, 2000; Fahrig, 2003; Henle et al., 2004; Ewers and Didham, 2006), can be reduced by broadening assessments to the community level.
The other primary challenge faced throughout this dissertation related to the comparisons of patterns found using multiple datasets, which were obtained from independent researchers
118 who had employed different data collection methodologies. In order to overcome this issue, I attempted to standardize survey effort to the level that it was possible and sensible, and also employed a modeling method that may have mitigated the remaining effects of different among- dataset survey methodologies. This is because modeling frameworks in which random effects are included as predictors are thought to control for among location sampling or survey inconsistencies that arise from methodological differences (Ockinger et al., 2010). That said, the incorporation of independently collected data into new analytical frameworks is an issue for which there is no universal ‘patch’. My experience and view is that the ecological community must not be daunted by this challenge, but continue to collaborate, share data, and search for better techniques with which to perform comparisons and analyses using data that are already available and that may provide crucial information toward a broader understanding of ecological issues such as habitat fragmentation.
6.2 Future directions
There are several key next steps that come to mind given the results presented in this dissertation. First, although the environmental attributes measured here all influence the suitability of habitat patches for butterflies in fragmented landscapes, these are not the only potentially important factors. Other metrics, such as elevation, aspect, and slope (Weiss &
Murphy, 1990; Rich and Weiss, 1991; Dennis et al., 2003) may also play key roles in defining suitable habitat fragments, and thus be important drivers of diversity patterns in fragmented landscapes. The incorporation of such factors into future investigations may provide additional, and important, information regarding how butterfly communities respond to habitat fragmentation. It may even be theoretically possible to integrate many of these factors into a
119 single measure, e.g.,a habitat suitability index (Roloff and Kernohan, 1999), which could then be incorporated as a potential predictor of diversity patterns. Continued explorations of this nature will provide crucial additional information toward a more robust understanding of what drives butterfly responses to habitat fragmentation.
In addition to incorporating additional, and/or different, sets of potential predictors, future analyses could investigate the determinants of among fragment diversity patterns using alternative statistical methods. Linear mixed effects models provide powerful and flexible tools for analyzing complex, multi-species, multi-system datasets (Jamil et al., 2012) and so were a natural choice for this study. In addition, statistical packages implementing these models are readily available and growing in sophistication (e.g., in the R statistical language). I do think, however, that alternate methods, such as non-linear models, may fit these data well, and potentially even uncover some new results or provide a basis of comparison with the linear mixed effects approach. I thus suggest that future studies explore additional methods by which to compare the determinants of butterfly diversity patterns.
Furthermore, the work presented here could also be extended to other groups of organisms that inhabit fragmented landscapes. Such analyses would require taxon-specific adjustments, such as altering potential predictors to include factors that have previously been shown to impact diversity patterns for particular taxa of interest, but the principles applied during the course of this study are nonspecific and thus generally applicable to any group.
Perhaps most importantly, I foresee expanding this work into broader theoretical, comparative, and statistical modeling frameworks. This might involve, for example, an approach similar to that of Bamford et al. (2009), who fit species distribution models for two species of south African vultures using: (i) data from one study area, and (ii) data from two or three study
120 areas. In the case of that work, models constructed using data from multiple study areas
exhibited the lowest fit to any one area, but the greatest propensity for predicting vulture
occurrences in locations outside of those used to construct the models (Bamford et al., 2009). I
posit that a similar approach could be adapted in order to construct not just species-specific
habitat suitability models, but also be widely applicable community-level models that are useful
in the context of broader-scale fragmentation analyses. For example, if additional comparisons,
using more study areas, revealed that the same predictors consistently drive butterfly diversity
patterns among fragments in drought-prone fragmented landscapes, these predictors could
perhaps be used to fit a ‘drought-prone ecosystem model’. Such a model might not only fit
individual study areas well, but potentially be more generalizable, and able to adequately predict
butterfly diversity patterns in other drought-prone ecosystems around the world.
In considering ways in which this dissertation research might be further expanded and
adjusted in order to facilitate broader-scale evaluations of fragmentation effects, I also recognize
that scaling to the level of ‘global’ may be particularly difficult given the already daunting
challenge of scaling up to any level. An alternative approach may thus be to apply the abovementioned concepts toward finer-scale analyses, such as those focused at the level of regions. For example, rather than attempting to fit a ‘drought-prone ecosystem model’ using data collected in fragmented landscapes around the world, effort could instead be applied toward fitting a ‘North American cold desert model’. Such an ecoregion-specific model may even be fit using additional predictors that help quantify the ecological and/or climatic characterizations of the individual study areas incorporated into the analyses, such as relative summer heat index.
Furthermore, this work could be readily performed through the use of data that are already, or soon to be, available from continental-scale data collection programs such as the US Long Term
121 Ecological Research program (LTER; Hobbie et al., 2003), and the National Ecological
Observatory Network (NEON; Keller et al., 2008). Such data collection and dissemination efforts, which greatly ease the burden of assimilating data for broad-scale analyses, may provide grounds for facilitating a broader understanding of fragmentation effects that are just as crucial as that aimed toward a more global-scale analyses.
6.3 Conclusions
Habitat fragmentation as a result of human landscape modification is a pervasive and escalating problem that is contributing to global biodiversity loss. Understanding the causes of species’ responses to habitat fragmentation is crucial for mitigating its impacts, but is also inherently challenging. This is particularly true for assessments of the determinants of fragmentation effects for entire communities, and across widely-separated fragmented ecosystems. Such analyses, however, are crucial for providing new insights into the causes of species’ responses to habitat fragmentation, and thus broadening our understanding of fragmentation effects in a world where human influence is reaching into all ecosystems on the planet.
122 REFERENCES
Andren, H. (1994). Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos, 71(3), 355–366.
Aubin, I., Ouellette, M.-H., Legendre, P., Messier, C., & Bouchard, A. (2009). Comparison of two plant functional approaches to evaluate natural restoration along an old-field - deciduous forest chronosequence. Journal of Vegetation Science, 20, 185–198. doi:10.1111/j.1654-1103.2009.05513.x
Aubin, I., Ouellette, M., Legendre, P., Messier, C., Bouchard, A. (2009). Comparison of two plant functional approaches to evaluate natural restoration along an old-field–deciduous forest chronosequence. Journal of Vegetation Science, 20(2), 185–198. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1654-1103.2009.05513.x/full
Azeria, E. T., Ibarzabal, J., Hebert, C., Boucher, J., Imbeau, L., & Savad, J. P. L. (2011). Differential response of bird functional traits to post-fire salvage logging in a boreal forest ecosystem. Acta Oecologica, 37(3), 220–229.
Baguette, M., Petit, S., & Queva, F. (2000). Population spatial structure and migration of three butterfly species within the same habitat network: consequences for conservation. Journal of Applied Ecology2, 37(1), 100–108.
Bamford, A. J., Monadjem, A., Anderson, M. D., Anthony, A., Borello, W. D., Bridgeford, M., … Hardy, C. (2009). Trade-offs between specificity and regional generality in habitat association models: a case study of two species of African vulture. Journal of Applied Ecology, 46(4), 852–860. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1365- 2664.2009.01669.x/full
Barbaro, L., & van Halder, I. (2009). Linking bird, carabid beetle and butterfly life-history traits to habitat fragmentation in mosaic landscapes. Ecography, 32(2), 321–333. doi:10.1111/j.1600-0587.2008.05546.x
Barry, S., & Elith, J. (2006). Error and uncertainty in habitat models. Journal of Applied Ecology, 43(3), 413–423. doi:10.1111/j.1365-2664.2006.01136.x
Barton, K. (2013). MuMIn: Multi-model inference. R package version 1.9.5.
Bastin, L., & Thomas, C. D. (1999). The distribution of plant species in urban vegetation fragments. Landscape Ecology, 14(5), 493–507.
Bates, A. D., Bolker, B., Maechler, M., & Walker, S. (2013). lme4: Linear mixed-effects models using Eigen and S4. R package version 1.0-4.
Bender, D. J., Tischendorf, L., & Fahrig, L. (2003). Using patch isolation metrics to predict animal movement in binary landscapes. Landscape Ecology, 18(1), 17–39.
123 Benes, J., Konvicka, M., Dvorak, J., Fric, Z., Havelda, Z., Pavlicko, A., … Weidenhopper, W. (Eds.). (2002). Butterflies of the Czech Republic: distribution and conservation (Volume II.). Prague, Czech Republic: SOM Praha.
Billick, I., & Price, M. V (Eds.). (2010). The ecology of place: contributions of place-based research to ecological understanding. Chicago, Illinois: University of Chicago Press.
Binzenhöfer, B., Biedermann, R., Settele, J., & Schröder, B. (2008). Connectivity compensates for low habitat quality and small patch size in the butterfly Cupido minimus. Ecological Research, 23(2), 259–269. doi:10.1007/s11284-007-0376-x
Binzenhöfer, B., Schröder, B., Strauss, B., Biedermann, R., & Settele, J. (2005). Habitat models and habitat connectivity analysis for butterflies and burnet moths–the example of Zygaena carniolica and Coenonympha arcania. Biological Conservation, 126(2), 247–259. Retrieved from http://www.sciencedirect.com/science/article/pii/S0006320705002259
Braby, M. F. (2000). Butterflies of Australia: their identification, biology and distribution. Victoria, Australia: CSIRO Publishing.
Brändle, M., Öhlschläger, S., & Brandl, R. (2002). Range sizes in butterflies: correlation across scales. Evolutionary Ecology Research, 4(7), 993–1004.
Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: a practical information-theoretic approach (Second.). Berlin, Germany: Springer.
Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods & Research, 33(2), 261–304. doi:10.1177/0049124104268644
Butchart, S. H. M., Walpole, M., Collen, B., van Strien, A., Scharlemann, J. P. W., Almond, R. E. a, … Watson, R. (2010). Global biodiversity: indicators of recent declines. Science, 328(5982), 1164–1168. doi:10.1126/science.1187512
Chessel, D., Darfour, A. B., & Dray, S. (2013). ade4: Analysis of ecological data: exploratory and Euclidean methods in environmental sciences. R package version 1.5-2.
CORINE land cover. (n.d.).
Davies, K. F., Margules, C. R., & Lawrence, F. (2000). Which traits of species predict population declines in experimental forest fragments? Ecology, 81(5), 1450–1461.
De Little, S. C., Bowman, D. M. J. S., Whelan, P. I., Brook, B. W., & Bradshaw, C. J. A. (2009). Quantifying the drivers of larval density patterns in two tropical mosquito species to maximize control efficiency. Environmental Entomology, 38(4), 1013–1021.
124 Debinski, D., & Holt, R. (2000). A survey and overview of habitat fragmentation experiments. Conservation biology, 14, 342–355. Retrieved from http://onlinelibrary.wiley.com/doi/10.1046/j.1523-1739.2000.98081.x/full
Debinski, D. M., & Holt, R. D. (2000). A Survey and overview of habitat fragmentation experiments. Conservation Biology, 14(2), 342–355. doi:10.1046/j.1523-1739.2000.98081.x
Debinski, D. M., Vannimwegen, R. E., & Jakubauskas, M. E. (2006). Quantifying relationships between bird and butterfly community shifts and environmental change. Ecological applications: a publication of the Ecological Society of America, 16(1), 380–393. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/16705987
Dennis, R. L. H. (1993). Butterflies and climate change. Manchester, UK: Manchester University Press.
Dennis, R. L. H., Shreeve, T. G., & Dyck, H. Van. (2003). Towards a functional resource-based concept for habitat: a butterfly biology viewpoint. Oikos, 102(2), 417–426.
Dennis, R. L., Shreeve, T. G., Arnold, H. R., & Roy, D. B. (2005). Does diet breadth control herbivorous insect distribution size? Life history and resource outlets for specialist butterflies. Journal of Insect Conservation, 9(3), 187–200. Retrieved from http://link.springer.com/article/10.1007/s10841-005-5660-x
Dormann, C. F., McPherson, J. M., Araujo, M. B., Bivand, R., Bollinger, J., Carl, G., … Wilson, R. (2007). Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography, 30(5), 609–628. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.2007.0906-7590.05171.x/full
Dover, J., & Settele, J. (2009). The influences of landscape structure on butterfly distribution and movement: a review. Journal of Insect Conservation, 13(1), 3–27. doi:10.1007/s10841-008- 9135-8
Dray, S, & Legendre, P. (2008). Testing the species traits-environment relationships: the fourth- corner problem revisited. Ecology, 89, 3400–3412. Retrieved from http://www.esajournals.org/doi/abs/10.1890/08-0349.1
Dray, Stéphane, & Legendre, P. (2008). Testing the species traits-environment relationships: the fourth-corner problem revisited. Ecology, 89(12), 3400–3412. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/19137946
Elith, J., & Leathwick, J. R. (2009). Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics, 40(2009), 677–697. doi:10.1146/annurev.ecolsys.110308.120159
Erb, P. L., McShea, W. J., & Guralnick, R. P. (2012). Anthropogenic influences on macro-level mammal occupancy in the Appalachian Trail corridor. PloS one, 7(8), e42574.
125 Ewers, R. M., & Didham, R. K. (2006). Confounding factors in the detection of species responses to habitat fragmentation. Biological Reviews, 81(1), 117–142. doi:10.1017/S1464793105006949
Fahrig, L. (2003). Effects of habitat fragmentation on biodiversity. Annual review of ecology, evolution, and systematics, 34, 487–515. Retrieved from http://www.jstor.org/stable/10.2307/30033784
Fahrig, Lenore. (2003). Effects of habitat fragmentation on biodiversity. Annual Review of Ecology, Evolution, and Systematics, 34, 487–515. doi:10.1146/132419
Fischer, J, & Lindenmayer, D. (2007). Landscape modification and habitat fragmentation: a synthesis. Global Ecology and Biogeography, 16(3), 265–280. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1466-8238.2007.00287.x/full
Fischer, Joern, & Lindenmayer, D. B. (2006). Beyond fragmentation: the continuum model for fauna research and conservation in human modified landscapes. Oikos, 112, 473–480. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.0030-1299.2006.14148.x/full
Foley, J. a, Defries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., … Snyder, P. K. (2005). Global consequences of land use. Science, 309(5734), 570–574. doi:10.1126/science.1111772
Garrard, G. E., Bekessy, S. a., McCarthy, M. a., & Wintle, B. a. (2008). When have we looked hard enough? A novel method for setting minimum survey effort protocols for flora surveys. Austral Ecology, 33(8), 986–998. doi:10.1111/j.1442-9993.2008.01869.x
Gauthier, T. (2001). Detecting trends using Spearman’s rank correlation coefficient. Environmental Forensics, 2(4), 359–362. doi:10.1006/enfo.2001.0061
Gelman, A., Yajima, M., Hill, J., Pittau, M. G., Kerman, J., Zheng, T., & Dorie, V. (2013). arm: Data analysis using regression and multilevel/hierarchical models. R package version 1.6- 06.-1.
Graf, R. F., Bollmann, K., Sachot, S., Suter, W., & Bugmann, H. (2006). On the generality of habitat distribution models: a case study of capercaillie in three Swiss regions. Ecography, 29(3), 319–328.
Grueber, C. E., Nakagawa, S., Laws, R. J., & Jamieson, I. G. (2011). Multimodel inference in ecology and evolution: challenges and solutions. Journal of Evolutionary Biology, 24(4), 699–711. doi:10.1111/j.1420-9101.2010.02210.x
Grundel, R., Pavlovic, N. B., & Sulzman, C. L. (1998). Habitat use by the endangered Karner blue butterfly in oak woodlands: the influence of canopy cover. Biological Conservation1, 85(1), 47–53.
126 Guisan, A., & Zimmermann, N. E. (2000). Predictive habitat distribution models in ecology. Ecological Modelling, 135(2), 147–186. doi:10.1016/S0304-3800(00)00354-9
Hambäck, P. a., Bergman, K.-O., Bommarco, R., Krauss, J., Kuussaari, M., Pöyry, J., & Öckinger, E. (2010). Allometric density responses in butterflies: the response to small and large patches by small and large species. Ecography, 33(6), 1149–1156. doi:10.1111/j.1600- 0587.2009.06125.x
Hampton, S. E., Strasser, C. a, Tewksbury, J. J., Gram, W. K., Budden, A. E., Batcheller, A. L., … Porter, J. H. (2013). Big data and the future of ecology. Frontiers in Ecology and the Environment, 11(3), 156–162. doi:10.1890/120103
Heiberger, R. M. (2013). HH: Statistical analysis and data display: Heiberger and Holland. R package version 2.3-37 (p. http://CRAN.R–project.org/web/packages/HH).
Henle, K., Davies, K. F., Kleyer, M., Margules, C., & Settele, J. (2004). Predictors of species sensitivity to fragmentation. Biodiversity and Conservation, 13(1), 207–251. doi:10.1023/B:BIOC.0000004319.91643.9e
Hill, K., Thomas, D., & Blakeley, S. (1999). Evolution of flight morphology in a butterfly that has recently expanded its geographic range. Oecologia, 121(2), 165–170.
Hillborn, R., & Mangel, M. (1997). The ecological detective: confronting models with data. Pinceton, New Jersey: Princeton University Press.
Hobbie, J. E., Carpenter, S. R., Grimm, N. B., Gosz, J. R., & Seastedt, T. R. (2003). The US Long Term Ecological Research program. BioScience, 53(1), 21–32. doi:10.1641/0006- 3568(2003)053[0021:TULTER]2.0.CO;2
Ikin, K., Knight, E., Lindenmayer, D. B., Fischer, J., & Manning, A. D. (2012). Linking bird species traits to vegetation characteristics in a future urban development zone: implications for urban planning. Urban Ecosystems, 15(4), 961–977. doi:10.1007/s11252-012-0247-2
Jamil, T., Opdekamp, W., van Diggelen, R., & ter Braak, C. J. F. (2012). Trait-environment relationships and tiered forward model selection in linear mixed models. International Journal of Ecology, 2012(2012), 1–12. doi:10.1155/2012/947103
Kadlec, T., Benes, J., Jarosik, V., & Konvicka, M. (2008). Revisiting urban refuges: changes of butterfly and burnet fauna in Prague reserves over three decades. Landscape and Urban Planning, 85(1), 1–11. doi:10.1016/j.landurbplan.2007.07.007
Kati, V., Zografou, K., Tzirkalli, E., Chitos, T., & Willemse, L. (2012). Butterfly and grasshopper diversity patterns in humid Mediterranean grasslands: the roles of disturbance and environmental factors. Journal of Insect Conservation, 16(6), 807–818. doi:10.1007/s10841-012-9467-2
127 Keller, M., Schimel, D. S., Hargrove, W. W., & Hoffman, F. M. (2008). A continental strategy for the National Ecological Observatory Network. Frontiers in Ecology and the Environment, 6(5), 282–284.
Konvicka, M., & Kadlec, T. (2011). How to increase the value of urban areas for butterfly conservation? A lesson from Prague nature reserves and parks. European Journal of Entomology, 108(2), 219–229.
Koricheva, J., Klapwijk, M. J., & Bjorkman, C. (2012). Insect Outbreaks Revisited. (P. Barbosa, D. Letourneau, & A. Agrawal, Eds.) (p. 189). Chichester, West Sussex: John Wiley & Sons.
Kotiaho, J. S., Kaitala, V., Komonen, A., & Palvinen, J. (2005). Predicting the risk of extinction from shared ecological characteristics. PNAS, 102(6), 1963–1967.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World Map of the Köppen- Geiger climate classification updated. Meteorologische Zeitschrift, 15(3), 259–263. doi:10.1127/0941-2948/2006/0130
Krauss, J., Bommarco, R., Guardiola, M., Heikkinen, R. K., Helm, A., Kuussaari, M., … Steffan-Dewenter, I. (2010). Habitat fragmentation causes immediate and time-delayed biodiversity loss at different trophic levels. Ecology letters, 13(5), 597–605. doi:10.1111/j.1461-0248.2010.01457.x
Krauss, J., Steffan-Dewenter, I., & Tscharntke, T. (2003). Local species immigration, extinction, and turnover of butterflies in relation to habitat area and habitat isolation. Oecologia, 137(4), 591–602. doi:10.1007/s00442-003-1353-x
Krauss, J., Steffan-dewenter, I., & Tscharntke, T. (2003). How does landscape context contribute to effects of habitat fragmentation on diversity and population density of butterflies? Journal of Biogeography, 30(6), 889–900.
Leather, S. R., Walters, K. F. A., & Bale, J. S. (1993). The ecology of insect overwintering. (S. R. Leather, Ed.). Cambridge, England: Cambridge University Press.
Leimu, R., & Koricheva, J. (2005). Does scientific collaboration increase the impact of ecological articles? BioScience, 55(5), 438–443. doi:10.1641/0006- 3568(2005)055[0438:DSCITI]2.0.CO;2
Leon-Cortes, J. L., Cowley, M. J. R., & Thomas, C. D. (1999). Detecting decline in a formerly widespread species: how common is the common blue butterfly Polyommatus icarus? Ecography, 22(3), 643–650.
Logan, M. (2011). Biostatistical design and analysis using R: a practical guide. Wiley.com Google ebook.
128 Luoto, M., Pöyry, J., Heikkinen, R. K., & Saarinen, K. (2005). Uncertainty of bioclimate envelope models based on the geographical distribution of species. Global Ecology and Biogeography, 14(6), 575–584. doi:10.1111/j.1466-822x.2005.00186.x
McAlpine, C. a., Rhodes, J. R., Bowen, M. E., Lunney, D., Callaghan, J. G., Mitchell, D. L., & Possingham, H. P. (2008). Can multiscale models of species’ distribution be generalized from region to region? A case study of the koala. Journal of Applied Ecology, 45(2), 558– 567. doi:10.1111/j.1365-2664.2007.01431.x
Menéndez, R., & Thomas, C. D. (2006). Can occupancy patterns be used to predict distributions in widely separated geographic regions? Oecologia, 149(3), 396–405. doi:10.1007/s00442- 006-0466-4
Michener, W. K. (2006). Meta-information concepts for ecological data management. Ecological Informatics, 1(1), 3–7. doi:10.1016/j.ecoinf.2005.08.004
Moser, D., Zechmeister, H. G., Plutzar, C., Sauberer, N., & Grabherr, G. (2002). Landscape patch shape complexity as an effective measure for plant species richness in rural landscapes. Landscape Ecology, 17(7), 657–669.
Murray, J. V., Low Choy, S., Mcalpine, C. a., Possingham, H. P., & Goldizen, a. W. (2011). Evaluating model transferability for a threatened species to adjacent areas: Implications for rock-wallaby conservation. Austral Ecology, 36(1), 76–89. doi:10.1111/j.1442- 9993.2010.02122.x
Nakagawa, S., & Schielzeth, H. (2012). A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4(2), 133–142.
Newbold, T., Scharlemann, J. P. W., Butchard, S. H. M., Sekercioglu, C. H., Alkemadeo, R., Booth, H., & Purves, D. W. (2013). Ecological traits affect the response of tropical forest bird species to land-use intensity. Proceedings of the Royal Society B: Biological Sciences, 280(1750), 2012–2031.
Nilsson, S. G., Franzén, M., & Jönsson, E. (2008). Long-term land-use changes and extinction of specialised butterflies. Insect Conservation and Diversity, 2008(1), 197–207. doi:10.1111/j.1752-4598.2008.00027.x
Ockinger, E., Schweiger, O., Crist, T. O., Debinski, D. M., Krauss, J., Kuussaari, M., … Bommarco, R. (2010). Life-history traits predict species responses to habitat area and isolation: a cross-continental synthesis. Ecology letters, 13(8), 969–79. doi:10.1111/j.1461- 0248.2010.01487.x
Ohwaki, A., Nakamura, K., & Tanabe, S. (2007). Butterfly assemblages in a traditional agricultural landscape: importance of secondary forests and conserving diversity, life history specialists and endemics. Biodiversity and Conservation2, 16(5), 1521–1539.
129 Opler, P. A. (1999). A field guide to western butterflies (2nd ed.). New York, New York: Houghton Mifflin Company.
Osenberg, C. W., Sarnelle, O., Cooper, S. D., & Holt, R. D. (1999). Resolving ecological questions through meta-analysis: goals, metrics, and models. Ecology, 80(4), 1105–1117.
Palmer, M. A., Bernhardt, E. S., Chornesky, E. A., Collins, S. L., Dobson, A. P., Duke, C. S., … Turner, M. G. (2004). Ecological science sustainability for a crowded planet. Report from the Ecological Society of America (p. 55). Retrieved from http://www.esa.org/ecovisions
Pollard, E. (1977). A method for assessing changes in the abundance of butterflies. Biological Conservation, (12), 115–134.
Potts, J. M., & Elith, J. (2006). Comparing species abundance models. Ecological Modelling, 199(2), 153–163. doi:10.1016/j.ecolmodel.2006.05.025
Pöyry, J., Luoto, M., Heikkinen, R. K., Kuussaari, M., & Saarinen, K. (2009). Species traits explain recent range shifts of Finnish butterflies. Global Change Biology, 15(3), 732–743. doi:10.1111/j.1365-2486.2008.01789.x
Pöyry, J., Luoto, M., Heikkinen, R. K., & Saarinen, K. (2008). Species traits are associated with the quality of bioclimatic models. Global Ecology and Biogeography, 17(3), 403–414. doi:10.1111/j.1466-8238.2007.00373.x
Prugh, L. R., Hodges, K. E., Sinclair, A. R. E., & Brashares, J. S. (2008). Effect of habitat area and isolation on fragmented animal populations. Proceedings of the National Academy of Sciences of the USA, 105(52), 20770–20775. doi:10.1073/pnas.0806080105
Pryke, S. R., & Samways, M. J. (2001). Width of grassland linkages for the conservation of butterflies in South African afforested areas. Biological Conservation, 101(1), 85–96.
Randin, C. F., Dirnböck, T., Dullinger, S., Zimmermann, N. E., Zappa, M., & Guisan, A. (2006). Are niche-based species distribution models transferable in space? Journal of Biogeography, 33(10), 1689–1703. doi:10.1111/j.1365-2699.2006.01466.x
Reese, G. C., Wilson, K. R., Hoeting, J. A., & Flather, C. H. (2005). Factors affecting species distribution predictions: a simulation modeling experiment. Ecological Applications, 15(2), 554–564.
Reichman, O. J., Jones, M. B., & Schildhauer, M. P. (2011). Challenges and opportunities of open data in ecology. Science, 331(6018), 703–705. doi:10.1126/science.1197962
Rich, P. M., & Weiss, S. B. (1991). Spatial models of microclimate and habitat suitability: lessons from threatened species. In Proceedings of the Eleventh Annual ESRI User Conference (pp. 95–99). Palm Springs, CA.
130 Ries, L., & Debinski, D. M. (2001). Butterfly responses to habitat edges in the highly fragmented prairies of Central Iowa. Journal of Animal Ecology, 70(5), 840–852.
Ries, L., & Sisk, T. D. (2008). Butterfly edge effects are predicted by a simple model in a complex landscape. Oecologia, 156(1), 75–86. doi:10.1007/s00442-008-0976-3
Robinson, N., Armstead, S., & Deane Bowers, M. (2012). Butterfly community ecology: the influences of habitat type, weather patterns, and dominant species in a temperate ecosystem. Entomologia Experimentalis et Applicata, 145(1), 50–61. doi:10.1111/j.1570- 7458.2012.01308.x
Robinson, N., Bowers, M. D., Kadlec, T., & Guralnick, R. P. (n.d.). Integrating species traits and habitat characteristics into models of butterfly diversity in an urban fragmented ecosystem. Ecological Modelling.
Roloff, G. J., & Kernohan, B. J. (1999). Evaluating reliability of habitat suitability index models. Wildlife Society Bulletin, 27(4), 973–985.
Ross, A. J., Matter, S. F., & Roland, J. (2005). Edge avoidance and movement of the butterfly Parnassius smintheus in matrix and non-matrix habitat. Landscape Ecology, 20(2), 127– 135.
Rundlöf, M., & Smith, H. G. (2006). The effect of organic farming on butterfly diversity depends on landscape context. Journal of Applied Ecology, 43(6), 1121–1127. doi:10.1111/j.1365- 2664.2006.01233.x
Saunders, D. A., Hobbs, R. J., & Margules, C. R. (1991). Biological consequences of ecosystem fragmentation: a review. Conservation biology, 5(1), 18–32.
Schultz, C. B., & Crone, E. C. (2001). Edge-mediated dispersal behavior in a prairie butterfly. Ecology, 82(7), 1879–1892.
Sisk, T. D., & Haddad, N. M. (2002). Incorporating the effects of habitat edges into landscape models: effective area models for cross-boundary management. Integrating landscape ecology into natural resource management. (J. Lui & W. Taylog, Eds.). Cambridge, MA: Cambridge University Press.
Soga, M., & Koike, S. (2012). Life-history traits affect vulnerability of butterflies to habitat fragmentation in urban remnant forests. Ecoscience, 19(1), 11–20.
Stamp, N. E. (1980). Egg deposition patterns in butterflies: why do some species cluster their eggs rather than deposit them singly? The American Naturalist, 115(3), 367–380.
Stefanescu, C., Carnicer, J., & Penuelas, J. (2011). Determinants of species richness in generalist and specialist Mediterranean butterflies: the negative synergistic forces of climate and habitat change. Ecography2, 34(3), 353–363.
131 Steffan-Dewenter, I., & Tscharntke, T. (2000). Butterfly community structure in fragmented habitats. Ecology Letters, 3(5), 449–456. doi:10.1046/j.1461-0248.2000.00175.x
Stewart, G. (2010). Meta-analysis in applied ecology. Biology letters, 6(1), 78–81. doi:10.1098/rsbl.2009.0546
Stoner, K. J. L., & Joern, A. (2004). Landscape vs. lobal habitat scale influences to insect communities from tallgrass prairie remnants. Ecological Applications, 14(5), 1306–1320.
Strauss, B., & Biedermann, R. (2007). Evaluating temporal and spatial generality: how valid are species–habitat relationship models? Ecological Modelling, 204(1), 104–114. doi:10.1016/j.ecolmodel.2006.12.027
Stuart, S. N., Chanson, J. S., Cox, N. a, Young, B. E., Rodrigues, A. S. L., Fischman, D. L., & Waller, R. W. (2004). Status and trends of amphibian declines and extinctions worldwide. Science, 306(5702), 1783–1786. doi:10.1126/science.1103538
Suding, K. N., Goldberg, D. E., & Hartman, K. M. (2003). Relationships among species traits: separating levels of response and identifying linkages to abundance. Ecology, 84(1), 1–16.
Summerville, K. S., Conoan, C. J., & Steichen, R. M. (2006). Species traits as predictors of lepidopteran composition in restored and remnant tallgrass prairies. Ecological Applications, 16(3), 891–900. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/16826989
Tenopir, C., Allard, S., Douglass, K., Aydinoglu, A. U., Wu, L., Read, E., … Frame, M. (2011). Data sharing by scientists: practices and perceptions. PloS one, 6(6), e21101. doi:10.1371/journal.pone.0021101
Thessen, A. E., & Patterson, D. J. (2011). Data issues in the life sciences. ZooKeys, 150(2011), 15–51. doi:10.3897/zookeys.150.1766
Thomas, J. A., Telfer, M. G., Roy, D. B., Preston, C. D., Greenwoon, J. J. D., Asher, J., … Lawton, J. H. (2004). Comparative losses of British butterflies, birds, plants and the global extinction crisis. Science, 303(5665), 1879–1881.
Tscharntke, T., Steffan-Dewenter, I., Kruess, A., & Thies, C. (2002). Contribution of small habitat fragments to conservation of insect communities of grassland-cropland landscapes. Ecological Applications, 12(2), 354–363.
Vallan, D. (2000). Influence of forest fragmentation on amphibian diversity in the nature reserve of Ambohitantely, highland Madagascar. Biological Conservation, 96(1), 31–43.
Vanreusel, W., Maes, D., & Van Dyck, H. (2007). Transferability of species distribution models: a functional habitat approach for two regionally threatened butterflies. Conservation Biology, 21(1), 201–12. doi:10.1111/j.1523-1739.2006.00577.x
132 Verbeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data. New York, New York: Springer.
Vitousek, P. M., Mooney, H. A., Lubchenco, J., & Melillo, J. M. (1997). Human domination of earth’s ecosystems. Science, 277(5325), 494–499.
Warton, D. I., & Hui, F. K. (2011). The arcsine is asinine: the analysis of proportions in ecology. Ecology, 92(1), 3–10.
Weiss, S. B., & Murphy, D. D. (1990). “Thermal microenvironments and the restoratin of rare butterfly habitat.” Environmental Restoration (pp. 50–60). Covelo, CA: Island Press.
Whittingham, M. J., Stephens, P. A., Bradbury, R. B., & Freckleton, R. P. (2006). Why do we still use stepwise modelling in ecology and behaviour? The Journal ofAnimal Ecology, 75(5), 1182–1189. doi:10.1111/j.1365-2656.2006.01141.x
Williams, M. R. (2011). Habitat resources, remnant vegetation condition and area determine distribution patterns and abundance of butterflies and day-flying moths in a fragmented urban landscape, south-west Western Australia. Journal of Insect Conservation, 15 1- 2(2011), 37–54. doi:10.1007/s10841-010-9307-1
Zanini, F., Pellet, J., & Schmidt, B. R. (2009). The transferability of distribution models across regions: an amphibian case study. Diversity and Distributions, 15(3), 469–480. doi:10.1111/j.1472-4642.2008.00556.x
Zharikov, Y., Lank, D. B., & Cooke, F. (2007). Influence of landscape pattern on breeding distribution and success in a threatened Alcid, the marbled murrelet: model transferability and management implications. Journal of Applied Ecology, 44(4), 748–759. doi:10.1111/j.1365-2664.2007.01319.x
Zhu, G., Petersen, M. J., & Bu, W. (2012). Selecting biological meaningful environmental dimensions of low discrepancy among ranges to predict potential distribution of bean plataspid invasion. PloS one, 7(9), e46247. doi:10.1371/journal.pone.0046247
Zimmerman, A. S. (2003). Data sharing and secondary use of scientific data: experiences of ecologists. University of Michigan.
Zimmerman, A. S. (2008). New knowledge from old data: the role of standards in the sharing and reuse of ecological data. Science, Technology & Human Values, 33(5), 631–652. doi:10.1177/0162243907306704
Zurr, A. F., Leno, E. N., & Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution, 1(1), 3–14.
133 APPENDIX
Appendix A- Site-specific observed versus estimated butterfly population densities for the Prague full and environment models. Calculations were performed for a randomly selected validation dataset (20% of the full dataset), for which the numbers of species occurring at each site (species-site occurrences) are represented by the numbers of bars in that site’s subfigure. Error bars represent estimated population densities calculated for model in which +/- 1 standard error were added to fixed effects coefficients. Sites are ordered from smallest to largest (in ha).
Podbabske skaly Jablonka Trojska 0.04 A) 0.20 C) 0.14 B) Observed 0.12 Full y 0.03 0.15 t
i Environment s 0.10 n e D
n 0.08
o 0.02 0.10 i t a
l 0.06 u p o
P 0.01 0.04 0.05
0.02
0.00 0.00 0.00 Jeneralka Nad mlynem Havranka 0.04 D) 0.10 E) 0.10 F)
0.08 0.08
y 0.03 t i s n e 0.06 0.06 D
n
o 0.02 i t a
l 0.04 0.04 u p o
P 0.01 0.02 0.02
0.00 0.00 0.00 Bohnicke udoli Dolni sarka Baba 0.05 G) 0.14 I) 0.15 H) 0.12 0.04 y t
i 0.10 s n e 0.03 0.10 D
0.08 n o i t
a 0.06 l 0.02 u
p 0.05 o 0.04 P 0.01 0.02
0.00 0.00 0.00 Barrandovske skaly Dalejsky profil Lochkovsky profil 0.5 K) 0.30 L) 0.20 J) 0.25 0.4 y t i s 0.15 0.20 n e 0.3 D
n
o 0.15 i
t 0.10 a
l 0.2 u
p 0.10 o P 0.05 0.1 0.05
0.00 0.0 0.00 Species-site occurrence Species-site occurrence Species-site occurrence
134 Appendix A (cont.)
Prokopske udoli Zmrzlik/Radotinske udoli M) 0.6 N) 0.4 0.5 y t i 0.3 s 0.4 n e D
n
o 0.3 i 0.2 t a l u
p 0.2 o P 0.1 0.1
0.00 0.00 Species-site occurrence Species-site occurrence
135