RT Journal Article SR Electronic T1 Fauxcurrence: simulating multi-species occurrences for null models in species distribution modelling and biogeography JF bioRxiv FD Cold Spring Harbor Laboratory SP 2021.04.22.440999 DO 10.1101/2021.04.22.440999 A1 Owen G. Osborne A1 Henry G. Fell A1 Hannah Atkins A1 Jan van Tol A1 Daniel Phillips A1 Leonel Herrera-Alsina A1 Poppy Mynard A1 Greta Bocedi A1 Cécile Gubry-Rangin A1 Lesley T. Lancaster A1 Simon Creer A1 Meis Nangoy A1 Fahri Fahri A1 Pungki Lupiyaningdyah A1 I Made Sudiana A1 Berry Juliandi A1 Justin M.J. Travis A1 Alexander S.T. Papadopulos A1 Adam C. Algar YR 2021 UL http://biorxiv.org/content/early/2021/04/23/2021.04.22.440999.abstract AB 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.Competing Interest StatementThe authors have declared no competing interest.