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Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection

Abstract : The use of naturalist mobile applications have dramatically increased during last years, and provide huge amounts of accurately geolocated species presences records. Integrating this novel type of data in species distribution models (SDMs) raises specific methodological questions. Presence-only SDM methods require background points, which should be consistent with sampling effort across the environmental space to avoid bias. A standard approach is to use uniformly distributed background points (UB). When multiple species are sampled, another approach is to use a set of occurrences from a Target-Group of species as background points (TGOB). We here investigate estimation biases when applying TGOB and UB to opportunistic naturalist occurrences. We modelled species occurrences and observation process as a thinned Poisson point process, and express asymptotic likelihoods of UB and TGOB as a divergence between environmental densities, in order to characterize biases in species niche estimation. To illustrate our results, we simulated species occurrences with different types of niche (specialist/generalist, typical/marginal), sampling effort and TG species density. We conclude that none of the methods are immune to estimation bias, although the pitfalls are different: For UB, the niche estimate fits tends towards the product of niche and sampling densities. TGOB is unaffected by heterogeneous sampling effort, and even unbiased if the cumulated density of the TG species is constant. If it is concentrated, the estimate deviates from the range of TG density. The user must select the group of species to ensure that they are jointly abundant over the broadest environmental sub-area.
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Submitted on : Thursday, May 28, 2020 - 11:50:18 AM
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Christophe Botella, Alexis Joly, Pascal Monestiez, Pierre Bonnet, François Munoz. Bias in presence-only niche models related to sampling effort and species niches: Lessons for background point selection. PLoS ONE, Public Library of Science, 2020, 15 (5), pp.e0232078. ⟨10.1371/journal.pone.0232078⟩. ⟨hal-02639237⟩

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