Mapping Species Distributions: Spatial Inference and Prediction
Maps of species' distributions or habitat suitability are required for many aspects of environmental research, resource management and conservation planning. These include biodiversity assessment, reserve design, habitat management and restoration, species and habitat conservation plans and predicting the effects of environmental change on species and ecosystems. The proliferation of methods and uncertainty regarding their effectiveness can be daunting to researchers, resource managers and conservation planners alike. Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and questions being asked. The framework links theoretical ecological models of species distributions to spatial data on species and environment, and statistical models used for spatial prediction. Providing practical guidelines to students, researchers and practitioners in a broad range of environmental sciences including ecology, geography, conservation biology, and natural resources management.
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Why do We need species distribution models?
Ecological understanding of species distributions
The data needed for modeling species distributions
An overview of the modeling methods
Machine learning methods
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absence accuracy analysis approach Araujo atlas Austin binary biodiversity Biogeography biological bird categorical Chapter classiﬁcation climate change coefficients concept correlation datasets decision trees deﬁned described developed deviance Diversity and Distributions Ecological Applications Ecological Modelling ecological niche Elith ENFA environmental gradients environmental predictors error estimated evaluation example factors forest Franklin GAMs GARP genetic algorithm geographical global Guisan habitat models habitat suitability Hirzel inﬂuence interactions interpolation invasive species land cover landscape Leathwick linear model locations logistic regression machine learning Mahalanobis distance Maxent measures modeling methods multivariate observations ofthe parameters patterns performance Peterson plant species potential Predicting Species prevalence probability random forests region remote sensing resource response functions response variable sample SDM studies selection soil spatial prediction species data species distribution modeling species occurrence species presence species range species richness speciﬁcity statistical models suitable habitat survey temperature terrain threshold Thuiller tion values