Species distribution models and ecological theory: A critical assessment and some possible new approaches

By Michael P. Austin. 2007

1,546 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/f178c3e2e67618292828b92fa2bc298642384bcd

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Citations in the corpus

Spatial prediction of demersal fish distributions: enhancing our understanding of species–environment relationships (2009, methodology): “…assessing the area under the curve (AUC) of the receiver-operating characteristic (ROC) plot, model sensitivity (percentage of correctly predicted presences) and specificity (percentage of correctly predicted absences) using the Pfair threshold (Fielding & Bell 1997, Austin 2007, Lobo et al. 2007).”
Applications of species distribution modeling to paleobiology (2011, methodology): “cally Weighted Regression (Austin, 2007), although these have”
Comparison of six correlative models in predictive vegetation mapping on a local scale (2012, background): “(2006) and Austin (2007) and suggest that AUC and other model success indices need to be re-examined.”
Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models (2010, background): “…will be more accurate, more biologically informative and more generalizable than models developed with variables that have only an indirect effect on distributions, through correlations with variables that have a direct effect (Austin and Meyers, 1996; Austin, 2002; 2007; Austin et al., 2006).”
Extinction debt and colonization credit delay range shifts of eastern North American trees (2017, neutral): “conditions, where the species is (1) currently present and predicted to be present at equilibrium (continued presence; blue), (2) currently absent but”
sdm: a reproducible and extensible R platform for species distribution modelling (2016, background): “variables (e.g. whether predictor contributions are weighted, and whether they allow for interactions among variables) (Segurado and Araújo 2004, Elith et al. 2006, Austin 2007, Naimi et al. 2011, Peterson et al. 2011).”
Ensemble modelling of species distribution: the effects of geographical and environmental ranges (2011, background): “SDMs may vary in how they model the shape, nature and complexity of species responses (i.e. realized niche), select predictor variables, weight variable contributions, or allow for interactions (Guisan and Zimmermann 2000, Elith et al. 2006, Austin 2007).”
What do we gain from simplicity versus complexity in species distribution models (2014, background): “Smooth responses, characterized by simpler models, are to be expected at large spatial extents and coarse resolution that smooth over the confounding processes that affect finer resolution occurrence patterns (Austin 2007).”
Climatic predictors of species distributions neglect biophysiologically meaningful variables (2019, background): “Proximal variables represent a direct link between climate and physiol‐ ogy (Austin, 2002; Jackson et al., 2009) and as physiological lim‐ its are inherent traits, their influences on a species' distribution are more likely to be conserved in time and space (Austin, 2002, 2007).”
Modelling and predicting fungal distribution patterns using herbarium data (2008, methodology): “Relying on the AUC as a sufficient criterion for model performance is not recommended by Austin (2007) or Lobo et al. (2008).”
Assessing species climatic requirements beyond the realized niche: some lessons mainly from tree species distribution modelling (2017, background): “See Austin (2007), and Soberón and Nakamura (2009) for more about niches and distributional areas.”