Quantitative Methods for Modeling Species Habitat: Comparative Performance and an Application to Australian Plants

By Jane Elith. 2000

375 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/9cb33f55db83b4674f7880c894e23a43e06e11a4

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Citations in the corpus

Five (or so) challenges for species distribution modelling (2006, background): “…recognition that different techniques yield different results, even when models are calibrated with the same response and predictor variables (e.g. Elith, 2000; Olden & Jackson, 2002; Thuiller et al., 2003; Brotons et al., 2004; Segurado & Araújo, 2004; Araújo et al., 2006; Elith et al., 2006;…”
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation (2008, background): “…curves where Maxent was not performing well: log loss b-curves where Maxent never reached performance below 14.2 (the log loss of the uniform distribution) and AUC curves that remained below 0.7 (based on the recommendations of Elith (2002) that values above 0.75 are considered potentially useful).”
Connectivity, Probabilities and Persistence: Comparing Reserve Selection Strategies (2006, methodology): “Since these measures are affected by prevalence and sensitive to the arbitrarily chosen threshold value, we also used ROC curves and derived AUC indices (Fielding and Bell 1997; Elith 2000; Pearce and Ferrier 2000; Manel et al. 2001).”
Competing roles for landscape, vegetation, topography and climate in predictive models of bird distribution (2004, background): “…predictors is considerably larger than the set of potential topo-climatic predictors (24 versus 5 predictors,Table 1), and thus the vegetation models could be better, on average, because they consider as potential candidates a wider set of measures of environmental conditions (Elith, 2000).”
Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela (2003, background): “…using environmental characteristics of localities of known occurrence represent a great advance in the production of range maps (e.g. Busby, 1986; Walker & Cocks, 1991; Carpenter et al., 1993; Skov, 2000; 2003 Blackwell Publishing Ltd, Journal of Biogeography, 30, 591–605 Peterson, 2001; Anderson…”
Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France (2006, methodology): “Many statistical techniques have been developed to model the response of species to environmental conditions (Yee & Mitchell, 1991; Huisman et al., 1993; Elith, 2000; Vayssières et al., 2000; Gelfand et al., 2003; Robertson et al.,”
Niche‐based modelling as a tool for predicting the risk of alien plant invasions at a global scale (2005, methodology): “The use of GAM in biogeographical studies is not new, and they have been widely tested and compared with other distribution models (Elith, 2000; Thuiller, 2003; Segurado & Araújo, 2004).”
Modelling geographic distribution and detecting conservation gaps in Italy for the threatened beetle Rosalia alpina (2013, background): “In general, models whose AUC > 0.75 are regarded as reliable (Elith 2002).”
Avoiding Pitfalls of Using Species Distribution Models in Conservation Planning (2003, background): “Little attention has been paid to how robust these different species-distribution models (SDMs) are and whether the use of these models in reserve-selection processes lead conservation policymakers to different outcomes (e.g., Elith 2000).”
An evaluation of a GARP model as an approach to predicting the spatial distribution of non‐vagile invertebrate species (2006, result): “Stockwell and Peterson (2002) claimed that GARP produced more accurate models than logistic regression, but other studies have shown no significant differences in model accuracy between GARP, BIOCLIM, and GLM models (Elith, 2000; Loiselle et al ., 2003).”
WHAT MATTERS FOR PREDICTING THE OCCURRENCES OF TREES: TECHNIQUES, DATA, OR SPECIES' CHARACTERISTICS? (2007, result): “This result confirms results from previous studies (e.g., Austin and Meyers 1995, Ferrier and Watson 1997, Elith 2000, Moisen and Frescino 2002, Thuiller 2003), but across a larger sample of techniques and with a focus on presence-only data.”
INFERRING PROCESS FROM PATTERN: CAN TERRITORY OCCUPANCY PROVIDE INFORMATION ABOUT LIFE HISTORY PARAMETERS? (2001, methodology): “The ROC curve is a measure borrowed from the medical literature, and is a plot of the sensitivity vs. 1-specificity at all possible threshold probabilities for a positive prediction; Hanley and McNeil 1982, Elith 2000).”
Predicting the distribution of four species of raptors (Aves: Accipitridae) in southern Spain: statistical models work better than existing maps (2004, methodology): “Only the statistical mixed models (those including both topography and land-use/land-cover: TU, TUC and TUCS) for the common buzzard are satisfactory in terms of predictive ability (the threshold of practical utility is AUC ¼ 0.75 or AUC ¼ 0.70 as given, respectively, by Elith, 2000; Harrell, 2001).”
Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? (2002, methodology): “…for using eld-survey data to measure the accuracy of mapped land classi cations (e.g., Congalton, 1991; Edwards et al., 1998; Stehman and Czaplewski, 1998) or the accuracy of modeled species distributions (e.g., Flather and King, 1992; Edwards et al., 1996; Elith, 2000; Pearce and Ferrier, 2000).”