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

Owen Osborne* (Corresponding Author), Henry Fell, Hannah Atkins, Jan Vantol , Daniel Phillips, Leonel Herrera Alsina, Poppy Mynard, Greta Bocedi, Cecile Gubry-Rangin, Lesley Lancaster, Simon Creer, Meis Nangoy , Justin Travis, Alexander Papadopulos, Adam C. Algar, Fahri Fahri, Pungki Lupiyaningdyah , I. M. Sudiana, Berry Juliandi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
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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.
Original languageEnglish
Article numbere05880
Number of pages7
Issue number7
Early online date5 Apr 2022
Publication statusPublished - 1 Jul 2022

Bibliographical note

The work was funded by Newton Fund (UK)/NERC (UK)/RISTEKDIKTI (Indonesia) grants awarded to JT, BJ, ACA, ASTP, CG-R, GB and LTL (grant no.: NE/S006923/1, NE/S006893/1, 2488/IT3.L1/PN/2020 and 3982/IT3.L1/PN/2020). GB and CG-R are funded by Royal Society Univ. Research Fellowships (UF160614 and UF150571 respectively).

Data Availability Statement

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

Supporting information
The supporting information associated with this article is available from the online version.


  • environmental niche model
  • joint species distribution modelling
  • niche conservatism
  • niche divergence
  • niche overlap
  • null biogeographical model


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