PT  - JOURNAL ARTICLE
AU  - Owen G. Osborne
AU  - Henry G. Fell
AU  - Hannah Atkins
AU  - Jan van Tol
AU  - Daniel Phillips
AU  - Leonel Herrera-Alsina
AU  - Poppy Mynard
AU  - Greta Bocedi
AU  - Cécile Gubry-Rangin
AU  - Lesley T. Lancaster
AU  - Simon Creer
AU  - Meis Nangoy
AU  - Fahri Fahri
AU  - Pungki Lupiyaningdyah
AU  - I Made Sudiana
AU  - Berry Juliandi
AU  - Justin M.J. Travis
AU  - Alexander S.T. Papadopulos
AU  - Adam C. Algar
TI  - Fauxcurrence: simulating multi-species occurrences for null models in species distribution modelling and biogeography
AID  - 10.1101/2021.04.22.440999
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.04.22.440999
4099  - http://biorxiv.org/content/early/2021/04/23/2021.04.22.440999.short
4100  - http://biorxiv.org/content/early/2021/04/23/2021.04.22.440999.full
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.