Fauxcurrence: simulating multi-species occurrences for null models in species distribution modelling and biogeography

Abstract

Defining appropriate null expectations for species distribution hypotheses is important because sampling bias and spatial autocorrelation can produce realistic, but ecologically meaningless, geographic patterns. Generating null species occurrences with similar spatial structure to observed data can help overcome these problems, but existing methods focus on single or pairs of species and do not incorporate between-species spatial structure that may occlude comparative biogeographic analyses. Here, we describe an algorithm for generating randomised species occurrence points that mimic the within- and between-species spatial structure of real datasets and implement it in a new R package - fauxcurrence. The algorithm can be implemented on any geographic domain for any number of species, limited only by computing power. To demonstrate its utility, we apply the algorithm to two common analysis-types: testing the fit of species distribution models (SDMs) and evaluating niche-overlap. The method works well on all tested datasets within reasonable timescales. We found that many SDMs, despite a good fit to the data, were not significantly better than null expectations and identified only two cases (out of a possible 32) of significantly higher niche divergence than expected by chance. The package is user-friendly, flexible and has many potential applications beyond those tested here, such as joint SDM evaluation and species co-occurrence analysis, spanning the areas of ecology, evolutionary biology and biogeography.

Fauxcurrence: simulating multi‐species occurrences for null models in species distribution modelling and biogeography

“…To cite Fauxcurrence or acknowledge its use, cite this Software note as follows, substituting the version of the application that you used for 'version 1.0': Osborne, O. G. et al 2022. Fauxcurrence: simulating multispecies occurrences for null models in species distribution modelling and biogeography.…”

“…Data are available from the Dryad Digital Repository: <https:// doi.org/10.5061/dryad.gtht76hp8> (Osborne et al 2022).…”

Biotic factors limit the invasion of the plague pathogen (Yersinia pestis) in novel geographical settings

“…To guard against this, we used a null modelling procedure. Following Beale et al (2008), Algar et al (2013) and Nunes and Pearson (2017), we generated a set of null occurrences for each host species with the same number of points and the same degree of spatial clustering as the observed species, using the "fauxcurrence" package (v.1.1.0) in R (Osborne et al, 2021; for details on the null modelling algorithm, see Supporting Information Appendix S1). Our algorithm also preserved spatial clustering of interspecies centroid distances to account for the fact that host species might be found in similar environments (see Supporting Information Appendix S1).…”

How to scale up from animal movement decisions to spatio-temporal patterns: an approach via step selection

“…Understanding and predicting the spatio-temporal distribution of individuals, populations and communities is a fundamental aim of ecological research. Key drivers of these spatial dynamics are the movement decisions of individuals in response to individual and local environmental conditions and resources (Nathan et al , 2008; Fryxell et al , 2008), the distribution of conspecific and heterospecific individuals (Osborne et al , 2022) and environmental change (Tuomainen & Candolin, 2011) and disturbance (Courbin et al , 2022). Animal movements fundamentally affect other ecological processes, including population (Hamilton & May, 1977) and community dynamics (Costa-Pereira et al , 2022), transport processes (Abbas et al , 2012), disease spread (Merkle et al , 2018), and ecosystem processes (Doughty et al , 2016).…”