Evaluation of statistical models used for predicting plant species distributions: Role of artificial data and theory

By Michael P. Austin, L. Belbin, Jacqui A. Meyers, M. Doherty, Miska Luoto. 2006

231 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/f509bcda9c2a080850e0201689792474a122f638

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, background): “…the prevalence of species (Leathwick et al. 2006, Meynard and Quinn 2007, Syphard and Franklin 2009, Santika 2011, Madon et al. 2013) and the strength and shape of the environmental gradient (Thuiller et al. 2003, Austin et al. 2006, Santika and Hutchinson 2009, Hoffman et al. 2010, Santika 2011).”
Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias (2014, background): “The principle of SDM is to relate known locations of a species with the environmental characteristics of these locations in order to estimate the response function and contribution of environmental variables [12], and predict the potential geographical range of a species [13].”
Equilibrium or not? Modelling potential distribution of invasive species in different stages of invasion (2012, background): “…models differ in their performance and go back to the basic ecological principles of this discipline, rather than continue to apply descriptive models to multiple species using black-box approaches (Austin et al., 2006; JimenezValverde et al., 2008; Elith & Graham, 2009; Sutherst & Bourne, 2009).”
Assessing the environmental requirements of invaders using ensembles of distribution models (2011, result): “Still, previous studies have shown that correlative models can fail to detect the correct relationships between the species and the environment (Austin et al., 2006).”
Predicting distributions of invasive species (2013, background): “More attention should be given to the problem of evaluation, including how to simulate data that is useful for model testing (Austin et al., 2006; Fensterer, 2010).”
Predicting impacts of climate change on biodiversity: a role for semi‐mechanistic community‐level modelling (2011, background): “Non-mechanistic approaches could be further applied to populate the required parameters of a mechanistic model with simulated values for species or community attributes, generated from known statistical distributions (Austin et al., 2006; Gotelli et al., 2009).”
Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling (2011, methodology): “As a result, simulated data have increasingly been used as a tool for developing and evaluating SDMs (Austin et al., 2006; Meynard & Quinn, 2007; Elith & Graham, 2009; JiménezValverde et al., 2009; Santika & Hutchinson, 2009; Beale et al., 2010).”
The effect of species response form on species distribution model prediction and inference (2009, methodology): “Simulated data have increasingly been used for evaluating the performance of species distribution modelling methods (Hirzel et al., 2001; Austin et al., 2006; Meynard and Quinn, 2007).”
The application of predicted habitat models to investigate the spatial ecology of demersal fish assemblages (2010, methodology): “The use of AIC in model selection procedures produces models with more variables (Boyce et al. 2002) and may include random and non-signiWcant predictors ( Austin et al. 2006 ), especially in data with large absences (Zuur et al. 2007).”