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Efficient Modelling of Presence-Only Species Data via Local Background Sampling

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  • Jeffrey Daniel

    (University of Guelph)

  • Julie Horrocks

    (University of Guelph)

  • Gary J. Umphrey

    (University of Guelph)

Abstract

In species distribution modelling, records of species presence are often modelled as a realization of a spatial point process whose intensity is a function of environmental covariates. One way to fit a spatial point process model is to apply logistic regression to an artificial case–control sample consisting of the observed presence records combined with a simulated pattern of background points, usually a uniform random sample from within the study’s spatial domain. In this paper we propose local background sampling as an alternative to uniform background sampling when using logistic regression to fit spatial point process models to data. Our method is similar to the local case–control sampling procedure of Fithian and Hastie (Ann Appl Stat 42:1693–1724, 2014), but differs in that background points are sampled with probability proportional to an initial intensity estimate based on a pilot point process model. We compare local background sampling with uniform background sampling in a simulation study and in an example modelling the distributions of bumble bees (genus Bombus) in Ontario, Canada. Our results show local background sampling to be more efficient than uniform background sampling in all simulated settings and across all species analysed. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Jeffrey Daniel & Julie Horrocks & Gary J. Umphrey, 2020. "Efficient Modelling of Presence-Only Species Data via Local Background Sampling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(1), pages 90-111, March.
    • Handle: RePEc:spr:jagbes:v:25:y:2020:i:1:d:10.1007_s13253-019-00380-4
    DOI: 10.1007/s13253-019-00380-4
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    References listed on IDEAS

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    1. Mark Berman & T. Rolf Turner, 1992. "Approximating Point Process Likelihoods with Glim," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 31-38, March.
    2. Peter Diggle, 1985. "A Kernel Method for Smoothing Point Process Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 138-147, June.
    3. Ian W. Renner & David I. Warton, 2013. "Equivalence of MAXENT and Poisson Point Process Models for Species Distribution Modeling in Ecology," Biometrics, The International Biometric Society, vol. 69(1), pages 274-281, March.
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