Predictions and their validation: Rare plants in the Central Highlands, Victoria, Australia

By Jane Elith, Mark A. Burgman. 2002

157 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/2be6941221aba29c0c2105135475eb7880f2c2a8

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Citations in the corpus

Does the interpolation accuracy of species distribution models come at the expense of transferability (2012, background): “(2007), and Elith and Graham (2009) where the good performance of MAXENT and GBM is concerned, which apparently includes also good transferability (Duncan et al.”
Presence-absence versus presence-only modelling methods for predicting bird habitat suitability (2004, methodology): “…adjustment to data and variation in data quality, habitat distribution modelling methods will likely cover different application areas and ECOGRAPHY 27:4 (2004) 445 it will be impossible to identify one among them as universally applicable (Elith and Burgman 2002a, b, Segurado and Araújo 2004).”
An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data (2004, background): “…relatively few predictive models have been applied to rare and endangered species (Miller 1986; Myatt 1987; Carey & Brown 1995; Godown & Peterson 2000; Elith & Burgman 2002), despite their potential in conservation management, for instance in identifying sites with high potential for colonization.”
Species-specific traits associated to prediction errors in bird habitat suitability modelling (2005, background): “In fact, predictive accuracy varies more among species than among modelling techniques (Elith and Burgman, 2002; Thuiller et al., 2003).”
Conservatism of ecological niche characteristics in North American plant species over the Pleistocene‐to‐Recent transition (2006, unknown): “While these tests in geographic space are standard means of validating such model predictions (Fielding & Bell, 1997; Elith & Burgman, 2002; Scott et al., 2002), ecological niche models should rightly be validated in ecological space to avoid pseudoreplication of test points (Soberón & Peterson,…”
A comparative evaluation of presence‐only methods for modelling species distribution (2007, methodology): “Similar results were reported in studies comparing other modelling techniques (e.g. Manel et al ., 1999; Elith & Burgman, 2002; Thuiller et al ., 2003; Berg et al ., 2004).”
Novel methods improve prediction of species' distributions from occurrence data (2006, unknown): “To date, few models have been validated via collection of new data (but see Ferrier and Watson 1997, Elith and Burgman 2002, Raxworthy et al. 2003, Peterson 2005).”
Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities (2011, background): “…planning, climate change adaptation, biosecurity (species invasion and disease), testing principles of theoretical ecology and understanding processes that drive biogeographic patterns (Elith & Burgman, 2002; Thomas et al., 2004; Pearson et al., 2007; Le Maitre et al., 2008; Kharouba et al., 2009).”
Evaluating predictive models of species’ distributions: criteria for selecting optimal models (2003, background): “…algorithms that identify only one solution (distributional prediction), such as generalized linear models, bioclimatic-envelope methods, and others (Busby, 1986; Nicholls, 1989; Walker and Cocks, 1991; Box et al., 1993; Carpenter et al., 1993; Jarvis and Robertson, 1999; Elith and Burgman, 2002).”
Predicting species distributions: a critical comparison of the most common statistical models using artificial species (2007, result): “As with other comparative studies (Franklin, 1998; Elith & Burgman, 2002; Thuiller et al., 2003; Segurado & Araújo, 2004), these two modelling strategies seem to represent a good trade-off between model complexity and performance for a diverse set of species.”
Habitat suitability for marine fishes using presence-only modelling and multibeam sonar (2010, background): “…compared more than 3 different methods on the same data, and the majority of these have focused on terrestrial, freshwater or simulated datasets (Elith & Burgman 2002, Ferrier et al. 2002b, Olden & Jackson 2002, Farber & Kadmon 2003, Loiselle et al. 2003, Brotons et al. 2004, Segurado & Araújo…”
Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models (2009, methodology): “Simply using the same data to build the model and evaluate predictions (resubstitution) leads to overly optimistic views of the model’s accuracy (Elith & Burgman, 2002).”
The derivation of species response curves with Gaussian logistic regression is sensitive to sampling intensity and curve characteristics (2006, background): “Indeed, knowing theminimum value for species occurrence is very important when dealingwith rare species (Wiser et al., 1998; Pearce and Ferrier, 2000a, 2000b; Elith and Burgman, 2002; Edwards et al., 2004).”