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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.

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Journal ArticleDOI

Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

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

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.
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