Year of Publication
2024
Journal
Ecological Modelling
Volume
498
Date Published
Jan-12-2024
Number of Pages
110865
DOI
10.1016/j.ecolmodel.2024.110865
URL
https://doi.org/10.1016/j.ecolmodel.2024.110865
ISSN Number
03043800
HCERES category
ACL - Articles in international or national peer-reviewed journals indexed by HCERES or in international databases
Abstract
Species distribution modeling (SDM) is widely used to predict past and future species distributions. However, absence data for species can be scarce or even nonexistent, necessitating the generation of pseudo-absences (PA). Traditionally, PA are generated based on geographic locations where the species is not observed, but this method can introduce biases related to environmental heterogeneity across geographic areas. To address these limitations, recent methods have shifted towards generating PA in ecological space rather than solely in geographic space.
Here, we introduce a methodological framework that strengthens the integration of ecological principles into the generation of PA. Our approach constructs an n-dimensional array, with each dimension representing an environmental predictor, and fills this array based on the density of species presences. By subtracting the presence density from the maximum density, we construct a 'reverse niche' from which PA are generated. We tested and validated our method by successfully reconstructing the response curves of a virtual species, demonstrating the potential of ecologically-based PA to better capture spatial patterns and enhance SDM accuracy. This method is available in an open-access, user-friendly R package, named EcoPA intended to serve as a valuable tool for researchers working with species distribution modeling in ecology, conservation, and related fields.
Here, we introduce a methodological framework that strengthens the integration of ecological principles into the generation of PA. Our approach constructs an n-dimensional array, with each dimension representing an environmental predictor, and fills this array based on the density of species presences. By subtracting the presence density from the maximum density, we construct a 'reverse niche' from which PA are generated. We tested and validated our method by successfully reconstructing the response curves of a virtual species, demonstrating the potential of ecologically-based PA to better capture spatial patterns and enhance SDM accuracy. This method is available in an open-access, user-friendly R package, named EcoPA intended to serve as a valuable tool for researchers working with species distribution modeling in ecology, conservation, and related fields.