Delimiting the geographical background in species distribution modelling

By P. Acevedo, A. Jiménez‐Valverde, Jorge M. Lobo, Raimundo Real. 2012

173 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Genetics

View Paper: https://www.semanticscholar.org/paper/b99156076f4e49250f1050305bad5470955ad290

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Citations in the corpus

Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias (2014, background): “It has been argued that the selection of background points may strongly affect the resulting model [64–66].”
Discrimination capacity in species distribution models depends on the representativeness of the environmental domain (2013, background): “Most importantly, these authors stressed that higher discrimination values can be obtained simply by increasing the geographic extent of analysis (see also Barve et al., 2011; Acevedo et al., 2012), a fact that compromises the robustness of many SDM studies.”
The area under the precision‐recall curve as a performance metric for rare binary events (2019, result): “…ROC was higher for rarer species, in line with previous findings (Luoto et al., 2005; Elith et al., 2006; Evangelista et al., 2008; Jimenez- Valverde et al., 2008), and also generally confirmed increases in AUC- ROC with an increasing geographic extent (Lobo et al., 2008; Acevedo et al., 2012).”
Evaluating presence‐only species distribution models with discrimination accuracy is uninformative for many applications (2019, methodology): “This requires users to make decisions about the size of the appropriate study area for background samples (Acevedo et al., 2012), as well as the nature of sampling (e.g., random points or points from closely related species (Phillips et al., 2009)).”