Species-Habitat Associations: Spatial data, predictive models, and ecological insights

Abstract

Ecologists develop species-habitat association (SHA) models to understand where species occur, why they are there and where else they might be. This knowledge can be used to designate protected areas, estimate anthropogenic impacts on living organisms and assess risks from invasive species or disease spill-over from wildlife to humans. Here, we describe the state of the art in SHA models, looking beyond the apparent correlations between the positions of organisms and their local environment. We highlight the importance of ecological mechanisms, synthesize diverse modelling frameworks and motivate the development of new analytical methods. Above all, we aim to be synthetic, bringing together several of the apparently disconnected pieces of ecological theory, taxonomy, spatiotemporal scales, and mathematical and statistical technique in our field. The first edition of this ebook reviews the ecology of species-habitat associations, the mechanistic interpretation of existing empirical models and their shared statistical foundations that can help us draw scientific insights from field data. It will be of interest to graduate students and professionals looking for an introduction to the ecological and statistical literature of SHAs, practitioners seeking to analyse their data on animal movements or species distributions and quantitative ecologists looking to contribute new methods addressing the limitations of the current incarnations of SHA models.

Solving the fourth‐corner problem: forecasting ecosystem primary production from spatial multispecies trait‐based models

“…This also emphasizes the importance of showing extreme caution when using species distributions as a proxy for fitness, particularly in highly dynamic ecosystems such as the Sundarbans (Matthiopoulos et al. 2015, 2020).…”

“…Our approach is an extension of the multivariable regression models typically used to quantify species–habitat associations (Matthiopoulos et al. 2020) on the basis of one response and multiple explanatory variables. We have augmented this approach by allowing it to model several interdependent response variables (multiple functional traits from multiple species), using a Bayesian hierarchical modeling framework.…”

Distribution and habitat use of the Madagascar Peregrine Falcon: first estimates for area of habitat and population size

“…In particular, species distributions and range limits in the Tropics are often unknown or little studied, especially for raptors (Buechley et al 2019). To address distribution knowledge gaps, Species Distribution Models (SDMs) have become a widely used tool to infer species-habitat associations and identify environmental range limits (Elith & Leathwick 2009;Franklin 2009;Matthiopoulos et al 2020). SDMs are statistical methods that correlate the underlying environmental conditions from known species occurrences and predict where similar environmental conditions should exist for a given species (Scott et al 2002;Pearce & Boyce 2006).…”

“…Here, we use point process logistic regression and environmental ordination in an SDM framework as described by Sutton et al (2021b) using Resource Selection Functions (RSFs) and Habitat Suitability Models (HSMs). Both RSFs and HSMs are conceptually the same method, under the general SDM analytical paradigm of predicting species distributions based on species-habitat associations (Boyce & McDonald 1999;Kearney 2006;Matthiopoulos et al 2020). Specifically, our aims are to address three significant knowledge gaps for the Madagascar Peregrine: (1) provide the first detailed distribution map and area of habitat, (2) define habitat requirements across the current known range, and ( 3) calculate a first estimate of population size based on inferred habitat area.…”

“…2019). To address distribution knowledge gaps, Species Distribution Models (SDMs) have become a widely used tool to infer species-habitat associations and identify environmental range limits (Elith & Leathwick 2009; Franklin 2009; Matthiopoulos et al . 2020).…”