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Mapping Species Distributions: Spatial Inference and Prediction
TLDR
This chapter discusses the history and Ecological Basis of Species' Distribution Modeling, and the design and implementation of species' distribution models.Abstract:
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.read more
Citations
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Journal ArticleDOI
Species Distribution Models: Ecological Explanation and Prediction Across Space and Time
Jane Elith,John R. Leathwick +1 more
TL;DR: Species distribution models (SDMs) as mentioned in this paper are numerical tools that combine observations of species occurrence or abundance with environmental estimates, and are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time.
Journal ArticleDOI
A statistical explanation of MaxEnt for ecologists
TL;DR: A new statistical explanation of MaxEnt is described, showing that the model minimizes the relative entropy between two probability densities defined in covariate space, which is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts.
Journal ArticleDOI
Predicting species distributions for conservation decisions
Antoine Guisan,Reid Tingley,John B. Baumgartner,Ilona Naujokaitis-Lewis,Patricia Sutcliffe,Ayesha I. T. Tulloch,Tracey J. Regan,Lluís Brotons,Eve McDonald-Madden,Eve McDonald-Madden,Chrystal Mantyka-Pringle,Chrystal Mantyka-Pringle,Tara G. Martin,Tara G. Martin,Jonathan R. Rhodes,Ramona Maggini,Samantha A. Setterfield,Jane Elith,Mark W. Schwartz,Brendan A. Wintle,Olivier Broennimann,Mike P. Austin,Simon Ferrier,Michael R. Kearney,Hugh P. Possingham,Hugh P. Possingham,Yvonne M. Buckley,Yvonne M. Buckley +27 more
TL;DR: It is proposed that species distribution modellers should get involved in real decision-making processes that will benefit from their technical input and have the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.
Journal ArticleDOI
Selecting thresholds for the prediction of species occurrence with presence‐only data
TL;DR: In this paper, the authors investigate mathematically and empirically which of the existing threshold selection methods can be used confidently with presence-only data and show that Max SSS is a promising threshold selection method for threshold selection when only presence data are available.
Journal ArticleDOI
Where is positional uncertainty a problem for species distribution modelling
TL;DR: It is proposed that local spatial association is a way to identify the species occurrence records that require treatment for positional uncertainty and developed and presented a tool in the R environment to target observations that are likely to create error in the output from SDMs as a result of positional uncertainty.
Related Papers (5)
Novel methods improve prediction of species' distributions from occurrence data
Jane Elith,Catherine H. Graham,Robert P. Anderson,Miroslav Dudík,Simon Ferrier,Antoine Guisan,Robert J. Hijmans,Falk Huettmann,John R. Leathwick,Anthony Lehmann,Jin Li,Lúcia G. Lohmann,Bette A. Loiselle,Glenn Manion,Craig Moritz,Miguel Nakamura,Yoshinori Nakazawa,Jacob C. M. Mc Overton,A. Townsend Peterson,Steven J. Phillips,Karen Richardson,Ricardo Scachetti-Pereira,Robert E. Schapire,Jorge Soberón,Stephen E. Williams,Mary S. Wisz,Niklaus E. Zimmermann +26 more