The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela

By R. P. Anderson, Ali Raza. 2010

575 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a724a8246ee26d83496261cfb4406d9de61b4de9

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Citations in the corpus

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).”
Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates (2011, background): “…the median and (in brackets) the lower and upper quartiles of the proportions of the total sum of squares attributed to each factor over the study area, and are shown for both mid-and late-century response curves are high or increasing where truncated (Anderson & Raza, 2010; Webber et al. , 2011).”
Species-specific tuning increases robustness to sampling bias in models of species distributions: An implementation with Maxent (2011, methodology): “…tuning experiments, researchers should take into ccount principles for selecting an appropriate study region where iotic interactions and dispersal limitations do not (or are less likely o) lead to non-equilibrium distributions that violate assumptions f modelling (Anderson and Raza, 2010).”
On the brink of extinction? How climate change may affect global chelonian species richness and distribution (2012, background): “Selection of appropriate background data is a crucial step during model building (Phillips et al., 2009; Anderson & Raza, 2010; Barve et al., 2011).”
The challenge of modeling niches and distributions for data‐poor species: a comprehensive approach to model complexity (2018, methodology): “The WorldClim data describe aspects of temperature and precipitation, and have been shown to produce informative niche models of non-volant mammals (Jezkova et al. 2009, Anderson and Raza 2010).”
Delimiting the geographical background in species distribution modelling (2012, methodology): “Both Anderson & Raza (2010) and Barve et al. (2011) recognized that delimiting the appropriate GB is generally not feasible because the biological information required for this purpose is rarely available for most species.”
Spatial filtering to reduce sampling bias can improve the performance of ecological niche models (2014, methodology): “The 0 km distance was chosen based on the high spatial heterogene- ty of the mountains in Madagascar, and the same distance has een used in previous studies in mountainous areas with high delling 275 (2014) 73– 77 geographical heterogeneity (Pearson et al., 2007; Anderson and Raza, 2010).”
Mechanistic and Correlative Models of Ecological Niches (2015, background): “By this means, but under rather restrictive and too-often-ignored constraints (Anderson et al. 2003; Anderson & Raza 2010; Barve et al. 2011; Saupe et al. 2012; Owens et al. 2013), it is possible to identify sets of conditions under which the species is able to maintain populations.”
Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes (2013, methodology): “The WorldClim variables ave been broadly used for generating ENMs, including successful se with small non-volant mammals in the northern Neotropcs (Anderson and Gonzalez, 2011; Anderson and Raza, 2010).”
SDMtoolbox 2.0: the next generation Python-based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses (2017, unknown): “As a result, the ‘best’ performing model tends to be over179 fitted because the selection criterion favors a model that fails to predict the species in the un-colonized 180 climatically suitable habitat (Anderson & Raza 2010, Barbet-Massin et al. 2012).”
Environmental filtering improves ecological niche models across multiple scales (2019, methodology): “Following previous recommendations (Anderson & Raza, 2010), we determined the study extent for each species by increasing the extent of a convex hull of all species occurrences by one degree.”
Predicting species distribution at range margins: testing the effects of study area extent, resolution and threshold selection in the Sahara–Sahel transition zone (2014, background): “In fact, small study regions led to more realistic estimates of species’ potential distributions when compared with larger areas (Anderson & Raza, 2010), given that at range margins, the most suitable habitats present in core range areas become scarcer and species may occupy suboptimal habitats…”