The effect of species response form on species distribution model prediction and inference

By Truly Santika, Michael F. Hutchinson. 2009

59 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/43dcd2cc1296e653ca4e84ec310fd967195d885e

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Equilibrium or not? Modelling potential distribution of invasive species in different stages of invasion (2012, methodology): “To create a realistic species, we used two types of functions to model the responses of P. virtualis to environmental factors (Hirzel et al., 2001; Santika & Hutchinson, 2009): (1) a Gaussian response with a symmetrically decreasing probability of occurrence around median optimum (for PSI and…”
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).”
Accounting for multi‐scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM) (2012, methodology): “…of Biogeography 39, 42–55 ª 2011 Blackwell Publishing Ltd structure in the response variable is dependent on spatial structure in predictor variables, a properly specified model fitted with correct covariates would reduce the need to account for SAC (Austin, 2002; Santika & Hutchinson, 2009).”
The effect of sample size on the accuracy of species distribution models: considering both presences and pseudo‐absences or background sites (2019, unknown): “2009, Santika and Hutchinson 2009, Chefaoui et al.”
What do we gain from simplicity versus complexity in species distribution models (2014, background): “For example, Santika and Hutchinson (2009) showed that using only linear responses in logistic regression reduced the model performance by misleadingly introducing”
A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling (2013, methodology): “This accords with recommendations in several distribution modelling studies by logistic regression, to consider carefully which transformation is likely to optimise the modelʼs predictive ability (e.g., Santika & Hutchinson 2009, Gastón & García-Viñas 2011, Michel et al. 2011).”
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).”