Assessing the effects of pseudo-absences on predictive distribution model performance

By Rosa M. Chefaoui, Jorge M. Lobo. 2008

413 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/0e071b12912442ff74b59398cf5609302ff0062c

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Not as good as they seem: the importance of concepts in species distribution modelling (2008, background): “Here, absence data must come from environmental conditions that are known to be unsuitable for the species (Chefaoui & Lobo, 2008).”
Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. (2012, background): “Key words: AUC; Bioclim; cross-validation; MaxEnt; model evaluation; niche model; pairwise distance sampling; spatial autocorrelation; spatial sorting bias; species distribution model.”
Using presence‐only modelling to predict Asian elephant habitat use in a tropical forest landscape: implications for conservation (2010, methodology): “Because niche models are calculated using raw presence data collected in the field, they are approximations of the realized niche which can substantially differ from the fundamental ecological niche as described by Hutchinson (1957) (Chefaoui & Lobo, 2008).”
CliMond: global high‐resolution historical and future scenario climate surfaces for bioclimatic modelling (2012, background): “Methods in Ecology and Evolution 2011 British Ecological Society, Methods in Ecology and Evolution, 3, 53–64 are specified (Chefaoui & Lobo 2008; VanDerWal et al. 2009).”
Correction note: Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology (2010, methodology): “…it is unclear how pseudo-absences should be chosen [Elith and Leathwick ( 2007); Guisan et al. (2007); Zarnetske, Edwards and Moisen (2007); Phillips et al. (2009)], and one can obtain qualitatively diﬀerent re-sults depending on the method of choice of pseudo-absences [Chefaoui and Lobo (2008)].”
More complex distribution models or more representative data (2008, background): “Including absences from a priori favorable environmental localities will inevitably diminish the predicted range size, so that the modeled distribution approaches the realized one (see Chefaoui & Lobo 2008).”
Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates (2011, methodology): “There is a debate on how to select pseudo-absences, and the choice of selection method is dependent on the purpose of the study (Chefaoui & Lobo, 2008).”
Creating individual accessible area hypotheses improves stacked species distribution model performance (2018, positive): “Many algorithms can be used to provide such indices of environmental similarity (Chefaoui & Lobo, 2008; Elith et al., 2006, 2011; Peterson et al., 2011; Phillips et al., 2009), as reviewed by Franklin (2010), which in turn can then be thresholded to create binary correlative models that predict a…”
Using species distribution models in paleobiogeography: A matter of data, predictors and concepts (2011, background): “should be used with caution (Lobo and Tognelli, 2011) because the way they are selected has a great influence on the geographic representation of the distribution of the species that is finally obtained (Chefaoui and Lobo, 2008; Anderson and Raza, 2010; Lobo et al., 2010).”
A framework for species distribution modelling with improved pseudo-absence generation (2015, background): “…terms, the variability in the predicted probabilities is related to314 19 the ability of the SDMs to represent realized vs. potential species distribu-315 tions, lying spatially wider predicted distributions closer to the fundamental316 niche of the target species (Chefaoui and Lobo, 2008).”
Differential impact of landscape transformation on pumas (Puma concolor) and jaguars (Panthera onca) in the Upper Paraná Atlantic Forest (2011, background): “…models could overestimate the amount of suitable habitat because they ignore absence information (Brotons et al., 2004; Engler et al., 2004; Chefaoui & Lobo, 2008) and because some of the presence records may not come from truly suitable areas 430 Diversity and Distributions, 17, 422–436,…”
Habitat suitability modelling and niche theory (2008, neutral): “2006; Chefaoui & Lobo 2008).”