A SYSTEMATIC ANALYSIS OF FACTORS AFFECTING THE PERFORMANCE OF CLIMATIC ENVELOPE MODELS

By R. Kadmon, Oren Farber, A. Danin. 2003

260 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/585696588d6b689a57cdbb44eb10df6410ecee26

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Citations in the corpus

Taxonomist survey biases and the unveiling of biodiversity patterns (2009, background): “…the information compiled on species distributions presents a marked pattern of spatial and environmental biases and survey incompleteness (Dennis and Hardy, 1999; Dennis et al., 1999; Soberón et al., 2000; Lobo et al., 2007; Hortal et al., 2008; Kadmon et al., 2003, 2004; Kéry et al., 2008).”
Novel methods improve prediction of species' distributions from occurrence data (2006, unknown): “…indicate that small sample size negatively influences modelling success (Pearce and Ferrier 2000a, Harrell 2001, Stockwell and Peterson 2002, Kadmon et al. 2003), this was not evident here, perhaps because in general we had adequate localities for modelling (mean number of modelling records…”
Species Distribution Modeling in the Tropics: Problems, Potentialities, and the Role of Biological Data for Effective Species Conservation (2009, background): “However, an inevitable tension arises when data-driven models are applied to conservation problems.”
A comparative evaluation of presence‐only methods for modelling species distribution (2007, background): “Some studies documented positive effects (Boone & Krohn, 1999; Manel et al ., 2001; Kadmon et al ., 2003; Berg et al ., 2004), other studies documented negative effects (Pearce et al ., 2001; Stockwell & Peterson, 2002; Guisan & Hofer, 2003; Rouget et al ., 2004; Seguardo & Araujo, 2004), in some…”
Predicting species distributions for conservation decisions (2013, background): “Many methodological factors, such as error in locational or temporal accuracy, or biased data, also potentially affect SDMs and their predictions (Kadmon et al. 2003; Cayuela et al. 2009; Appendix S1).”
Five (or so) challenges for species distribution modelling (2006, background): “Others have regarded models as providing a spatial representation of the realized niche (e.g. Austin et al., 1990; Guisan & Zimmermann, 2000; Pearson & Dawson, 2003) on the grounds that the observed species’ spatial distributions that are utilized 1Department of Biodiversity and Evolutionary”
Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data (2011, background): “…inclusion of predictors able to represent the role played by the processes that hinder the presence of a species under environmentally favourable conditions can improve the results of the models under these circumstances (Kadmon et al. 2003; Guo et al. 2005; Chefaoui & Lobo 2008; Lobo et al. 2010).”
Species traits are associated with the quality of bioclimatic models (2008, methodology): “…on climatic envelope models have shown that species with small ranges, strict ecological requirements and marginal niche positions in relation to the prevailing climates are usually modelled more accurately than other species (Kadmon et al., 2003; Segurado & Araújo, 2004; Luoto et al., 2005).”
Species distribution models and ecological theory: A critical assessment and some possible new approaches (2007, background): “Kadmon et al. (2003, 2004) have examined number of presences, and climatic and roadside biases on the performance of a climatic envelope model, i.e. using presence-only data.”
The importance of correcting for sampling bias in MaxEnt species distribution models (2013, background): “The consequences of sampling bias on SDM performance have increasingly been acknowledged (Kadmon et al., 2003, 2004; Reese et al., 2005; Araujo & Guisan, 2006), but so far only a few studies have attempted to address sampling bias in MaxEnt models (occurrence record manipulation: Pearson et al.,…”
Effects of sample size on the performance of species distribution models (2008, methodology): “Few records may suffice to characterize distributions of species with narrow environmental tolerances, especially compared to those with broader tolerances (Kadmon et al ., 2003).”
Species-specific traits associated to prediction errors in bird habitat suitability modelling (2005, methodology): “Nevertheless, little is known about whether ecological traits of species may predict the errors in habitat suitability modelling (but see,Boone and Krohn, 1999; Kadmon et al., 2003).”
Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? (2007, result): “A more consistent result in our study was that model performance was affected by the number of training points used and the range size of the species (see also Manel et al., 2001; Kadmon et al., 2003; Thuiller et al., 2004; Elith et al., 2006; Hernandez et al., 2006).”