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Species-Habitat Associations Species-Habitat associations Spatial Data • Predictive Models • Ecological Insights Jason Matthiopoulos • John Fieberg • Geert Aarts 2 Suggested Citation: Matthiopoulos, Jason; Fieberg, John; Aarts, Geert. (2020). Species-Habitat Associations: Spatial data, predictive models, and ecological insights. University of Minnesota Libraries Publishing. Retrieved from the University of Minnesota Digital Conservancy, http://hdl.handle.net/11299/217469. Related Works: A copy of the book, which we plan to continuously update (with new versions in the future) can be accessed in gitbook format at: https://bookdown.org/jfieberg/SHABook/. Cover photograph: Guanacos, a camelid native to South America, grazing in Torres del Paine National Park in the Pantagonia region of Chile. ©Gary R. Jensen, www.GaryRobertPhotography.com. License: This work, other than the cover photo, is licensed under a Creative Commons Attribution 4.0 International License. ISBN: 978-1-946135-68-1 Edition 1 Contents 5 About the Authors 6 Jason Matthiopoulos . .6 John Fieberg . .6 Geert Aarts . .6 Acknowledgments 7 Abbreviations 8 Glossary 9 Notation 15 Preface 16 0.1 A “live” project . 16 0.2 Audience . 16 0.3 Objectives . 17 0.4 Why is this book unique? . 17 0.5 Why model species habitat associations? . 18 I Fundamental concepts and methods 20 1 The ecology behind species-habitat-association models 21 1.1 Objectives . 21 1.2 How do living beings “see” the world around them? . 21 1.3 What is a habitat? . 23 1.4 What is a species-habitat association? . 24 1.5 What mechanisms drive habitat-mediated changes in species densities? . 26 1.6 When is species density a reliable reflection of habitat suitability? . 28 1.7 When are the null model assumptions violated? . 33 1.8 The SHA models we wish for and the SHA models we have . 38 1.9 A puritan taxonomy of SDMs . 38 1.10 Hybridisation of SDMs . 40 1.11 Concluding remarks . 41 2 Modelling Species-Habitat Associations 42 2.1 Objectives . 42 2.2 You are here . 42 2.3 Formalising the association between habitat and species . 42 2.4 The meaning of the SHA model output . 45 3 4 CONTENTS 2.5 The process model, as a black box: Inputs and outputs . 45 2.6 Usage in Geographical and Environmental spaces: Model transferability . 46 2.7 Lifting the black box lid: Habitat usage and habitat availability . 47 2.8 Thinking inside the box: Mathematical formulations . 49 2.9 Spatial intensity from habitat preference . 51 2.10 Thinking outside the box: Biological mechanism in process models . 52 2.11 Concluding remarks . 61 3 Observation models for different types of usage data 64 3.1 Objectives . 64 3.2 You are here . 64 3.3 Homogeneous Point-Process Model . 66 3.4 The Inhomogeneous Poisson Point-Process Model . 67 3.5 Weighted Distributions: Connecting SHA Process Models to the IPP . 69 3.6 Simulation Example: Fitting the IPP model . 70 3.7 Fitting IPP models: Special Cases . 75 3.8 Imperfect Detection and Sampling Biases . 77 3.9 Looking Forward: Data Integration for Addressing Sampling Biases and Imperfect Detection 81 3.10 Looking Forward: Relaxing the Independence Assumption . 82 3.11 Telemetry data . 83 3.12 Camera Traps . 87 3.13 Software for fitting IPP models . 88 3.14 Concluding remarks . 88 References 90 5 About the Authors Jason Matthiopoulos Jason Matthiopoulos is Professor of Spatial and Population Ecology at the University of Glasgow, Scotland. He has a joint degree in mathematics and zoology, a PhD in population ecology, qualifications in statistics, and almost 30 years’ experience in teaching mathematics, statistics and computing to biology students at all levels. His research interests revolve around population dynamics and spatial ecology and he has published widely on taxa as diverse as invertebrates, fish, birds and mammals. He lives with his wife and son in suburban Glasgow and enjoys listening loudly to music that most people hate and playing the bass poorly along with music most people like. John Fieberg John Fieberg is an Associate Professor of Quantitative Ecology and McKnight Presidential Fellow in the Department of Fisheries, Wildlife, and Conservation Biology at the University of Minnesota where he teaches undergraduate and graduate-level courses in statistics. Prior to joining the faculty at the University of Minnesota, he worked for ten years as a research statistician with the Minnesota Department of Natural Resources and two and a half years as a Biometrician with the Northwest Indian Fisheries Commission. In these positions, he had the pleasure of collaborating with many highly engaged fisheries, wildlife, and conservation biologists on a wide range of applied problems. These experiences have shaped his approach to problem solving and helped develop his ability to communicate difficult concepts to diverse audiences. When not at work, he enjoys playing ultimate frisbee with his wife, watching his girls play hockey, playing guitar with his son (a drummer), and traveling and camping with his family. Geert Aarts Geert Aarts is a researcher at Wageningen Marine Research and the Department of Wildlife Ecology and Conservation of Wageningen University, and he is a postdoctoral researcher at the Royal Netherlands Institute for Sea Research. His main field of interest is quantitative marine ecology and wildlife conservation, with a special interest in studying marine mammals. When not at work, he enjoys being outdoors in nature and to (trail)run, cycle, canoe or swim. He lives with his family on the Wadden Sea island Texel, located in the Netherlands. 6 Acknowledgments The book has been supported by insightful comments from Christine Beardsworth, Allert Bijleveld, Fergus Chadwick, Jan van Gils, Yacob Haddou, Grant Hopcraft, Jana Jeglinski, Paul Johnson, Roger Kirkwood, Freja Larsen, Heather McDevitt, Tom Morrison, Louise Riotte-Lambert and Andrew Seaton. We thank Jeroen Hoekendijk and Gary R. Jensen for their photo contributions. We thank Shane Nackerud for his assistance with the publishing process. John Fieberg received partial salary support from the Minnesota Agricultural Experimental Station. 7 Abbreviations Term Abbreviation Generalised Additive Model GAM Generalised Linear Mixed effects Model GLMM Generalised Linear Model GLM Geographic Information System GIS Homogeneous Poisson Process HPP Ideal-Free Distribution IFD Inhomogenenious Poisson Process IPP Kernel Density Estimator KDE Maximum Entropy MaxEnt Principal Components Analysis PCA Resource-Selection Function RSF Species-Habitat Association SHA Species Distribution Model SDM Step-Selection Function SSF 8 Glossary Accuracy: The lack of bias in an estimator of a model parameter or model prediction. Allee effects: A discounting of population growth rates in small populations. It is a form of density dependence (often known as depensatory density dependence) which populations can overcome when they benefit from a critical size. For instance, in small (or low-density) populations, Allee effects may originate from the inability of individuals to encounter suitable breeding mates, or to form efficient hunting packs. Antagonistic resources: Two resources that, when taken together, offset the benefits of each other. Available locations or Background locations: Locations in geographic or environmental space that are assumed to be accessible to individuals under study. These are locations that moving animals or dispersing plants can reach but may not eventually be found in. Camera trap: A stationary, autonomous camera, triggered by movement. Climate envelope models: A model of the distribution of a species with particular applications to climate change. Model predictions are based on a species’ tolerances to different climate covariates (e.g. temperature or precipitation). Tolerances are represented as deterministic or probabilistic ranges (or, envelopes). Colonization credit: The opposite of extinction debt. The number of species or members of a species that can reestablish in a region, and will do so given enough time. Complementary resources: Two resources that, when taken together, have a more beneficial effect (per resource) than when taken individually. Condition: Environmental variable (such as ambient temperature, humidity, salinity or pressure) that influences an organism’s functioning. Extreme values of conditions (e.g. very high or very low temperatures) are usually detrimental to an organism, so responses to conditions are often modeled with non-linear (unimodal) functions. Demography: The study of birth, death, immigration and emigration rates at the level of populations. Demographic sorting: The process of habitat-driven changes in the vital rates (i.e. survival, growth and reproduction) that lead to differences in species density between habitats. Density, species: The observed or expected number of individuals per unit of area. See also intensity function. Density estimation method: A statistical method for estimating the number organisms per unit of area in a landscape, often relying on smoothing or spatial interpolation between observations. Depletion: The reduction of resource density caused by the action of an organism (e.g. through consumption or occupation). Design matrix: A matrix containing the values of explanatory variables for a set of observations. Each row represents a different observation and the columns contain the specific values of the explanatory variables associated with that observation. The design matrix provides a convenient shorthand for describing and implementing regression models. 9 10 CONTENTS Deterministic variable: A variable whose exact value
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