Understanding the connections between species distribution models for presence-background data

Wang, Y and Stone, L 2019, 'Understanding the connections between species distribution models for presence-background data', Theoretical Ecology, vol. 12, pp. 73-88.


Document type: Journal Article
Collection: Journal Articles

Title Understanding the connections between species distribution models for presence-background data
Author(s) Wang, Y
Stone, L
Year 2019
Journal name Theoretical Ecology
Volume number 12
Start page 73
End page 88
Total pages 16
Publisher Springer
Abstract Models for accurately predicting species distributions have become essential tools for many ecological and conservation problems. For many species, presence-background (PB) data is the most commonly available type of spatial data. A number of important methods have been proposed to model PB data, and there have been debates on the connection between these seemingly disparate methods. The paper studies the close relationship between the LI (Lancaster and Imbens), LK (Lele and Keim), scaled binomial (SB), expectation-maximization (EM), partial likelihood based Lele method, MAXENT, and the point process models. We reveal that all these methods are the same in their ability to estimate the relative probability (or intensity) of presence from PB data, and the absolute probability of presence, when extra information of the species' prevalence is known. A new unified constrained LK (CLK) method is also proposed as a generalization of the better known existing approaches, with less theory involved and greater ease of implementation.
Subject Stochastic Analysis and Modelling
Conservation and Biodiversity
Keyword(s) Likelihood
Link function
Point process model
Presence-background
Prevalence
Probability of presence
Species distribution model
DOI - identifier 10.1007/s12080-018-0389-9
Copyright notice © The Author(s) 2018. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
ISSN 1874-1738
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