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

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

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Dangosydd eitem ddigidol (DOI)

  • Owen Osborne
  • Henry G. Fell
    Nottingham University
  • Hannah Atkins
    Royal Botanic Garden Edinburgh
  • Jan van Tol
    Research Team Endless Forms, Naturalis Biodiversity Center, Darwinweg 2, Leiden 2333 CR, The Netherlands.
  • Daniel Phillips
  • Leonel Herrerra-Alsina
    University of Aberdeen
  • Poppy Mynard
    University of Aberdeen
  • Greta Bocedi
    University of Aberdeen
  • Cecile Gubry-Rangin
    University of Aberdeen
  • Lesley T. Lancaster
    University of Aberdeen
  • Simon Creer
  • Meis Nangoy
    Sam Ratulangi University
  • Fahri Fahri
    Tadulako University
  • Pungki Lupiyaningdyah
    Indonesian Institute of Sciences
  • I. Made Sudiana
    Indonesian Institute of Sciences
  • Berry Juliandi
    IPB University, Bogor, Indonesia
  • Justin Travis
    University of Aberdeen
  • Alexander S. T. Papadopulos
  • Adam C. Algar
    University of Nottingham
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.


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Rhif yr erthygle05880
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Dyddiad ar-lein cynnar5 Ebrill 2022
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - Gorff 2022

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