The crucial role of the accessible area in ecological niche modeling and species distribution modeling

By N. Barve, Vijay Barve, A. Jiménez‐Valverde, A. Lira‐Noriega, S. Maher, A. Townsend Peterson, Jorge Soberón, F. Villalobos. 2011

1,747 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/9ff492947593b742eee9a5e2b25fb3ea42e4eca5

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In Ecological Modeling

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Citations in the corpus

Topoclimates, refugia, and biotic responses to climate change (2020, methodology): “…univariate generalized additive models (GAM) with binomial link functions, in which climate data at observation locations were combined with a random sample of 10,000 background points selected from an expanded convex hull around the species range (see Guisan et al. 2002; Barve et al. 2011).”
SDMtoolbox: a python‐based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses (2014, background): “…species with broad distributions or those existing in regions that do not conform well to rectilinear map layouts, rather they simply need to be more selective in the choice of background points in Maxent (and pseudo-absences in other SDM methods)(Barve et al. 2011; Merow, Smith & Silander 2013).”
Potential of remote sensing to predict species invasions (2015, background): “However, recent literature deals clearly with the risks of policy or management failures associated with heavy reliance on correlative approaches (Barve et al., 2011; Dawson et al., 2011; Guisan et al., 2012, 2014; Petitpierre et al., 2012; Roubicek et al., 2010; Sinclair et al., 2010; Veloz et…”
Niche breadth and geographic range size as determinants of species survival on geological time scales (2015, methodology): “Following preferred methodological protocol (Phillips et al., 2009; VanDerWal et al., 2009; Barve et al., 2011), we sought the union of the area sampled by researchers and that which was most likely accessible to the species during the Pliocene.”
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.”
Predictor complexity and feature selection affect Maxent model transferability: Evidence from global freshwater invasive species (2020, methodology): “On the other hand, the areas accessible to non-native populations were defined using distance buffers estimating maximum dispersal distance around known invasive occurrences (Barve et al., 2011; Figure S2.”
Two alternative evaluation metrics to replace the true skill statistic in the assessment of species distribution models (2019, background): “…be interpreted as the result of a complex interplay of artifacts (stemming from data or methods) and biological causes, since performance depends on modeller decisions, data availability and the underlying distribution of species based on dispersal from historical distributions (Barve et al. 2011).”
Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling (2012, background): “…probably the most important point raised by Lobo and collaborators was that the AUC value is sensitive and positively related to the spatial extent under consideration (see also Jiménez-Valverde et al., 2008, and Barve et al., 2011), which seriously places into doubt the robustness of SDM studies.”
No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation (2015, methodology): “Following our previous explorations (Barve et al. 2011; Saupe et al. 2012; Owens et al. 2013) and those of others (Hirzel, Helfer & Metral 2001; Elith & Graham 2009; Godsoe 2010), here, we use the powerful tool of simulated, virtual species as a means of testing, developing and improving…”
The area under the precision‐recall curve as a performance metric for rare binary events (2019, background): “…expands beyond the range margins of the focal species, more locations with correct predictions of low suitability are included, inflating AUCROC (Luoto, Pöyry, Heikkinen, & Saarinen, 2005; Elith et al., 2006; Lobo et al., 2008; Barve et al., 2011; Acevedo, Jiménez- Valverde, Lobo, & Real, 2012).”
Evaluating correlative and mechanistic niche models for assessing the risk of pest establishment (2014, methodology): “The background extent in MaxEnt was defined based on the Biotic-Abiotic-Mobility (BAM) diagram, a framework suggested by Soberon and Peterson (2005); and included regions that have been accessible to R. indifferens since 1900s (Barve et al. 2011, Saupe et al. 2012; Appendix: Fig.”
Species Distribution Modeling and Ecological Niche Modeling: Getting the Concepts Right (2012, methodology): “Our work was supported by a grant from Microsoft Research (#47780).”
Variation in niche and distribution model performance: The need for a priori assessment of key causal factors (2012, methodology): “Creating these scenarios involved selection of areas representing M, or the area accessible to the species (Soberón and Peterson, 2005; Barve et al., 2011).”
Hierarchical Species Distribution Models (2016, background): “How the study area A is defined has the potential to influence both prediction and inference [6, 96].”
What determines the climatic niche width of species? The role of spatial and temporal climatic variation in three vertebrate clades (2013, background): “…Kearney et al., 2009), and some species may be able tolerate climatic conditions beyond those where they currently occur (i.e. the fundamental climatic niche may be greater than the realized climatic niche, particularly if species ranges are set by non-climatic factors; e.g. Barve et al., 2011).”