Fine‐scale environmental variation in species distribution modelling: regression dilution, latent variables and neighbourly advice

By Greg J. McInerny, Drew Purves. 2011

85 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/5333bc145c69b6f6de4352d618f9a7d1dd03cfdb

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Predicting the distribution of vulnerable marine ecosystems in the deep sea using presence-background models (2014, background): “…data sets containing sample selection bias (Araújo & Guisan, 2006; Phillips et al., 2009; Cabral and Schurr, 2010), autocorrelation (Dormann, 2007a; Legendre, 1993; Segurado et al., 2006) and imperfect measurement (Barry and Elith, 2006; Clark, 2005; McInerny and Purves, 2011; Menke et al., 2009).”
Testing the ability of species distribution models to infer variable importance (2019, background): “As a result, SDMs cannot reliably measure variable importance when grain size is coarser than the scale at which the species responds to the environment and environmental spatial autocorrelation is low (McInery & Purvis 2011; Mertes & Jetz 2017).”
Accounting for spatially biased sampling effort in presence‐only species distribution modelling (2015, background): “…2008; Boakes et al., 2010; Peterson et al., 2011), only more recently have solutions been explored for identifying (e.g. herbarium bias; Loiselle et al., 2008) or accounting for effort bias within the modelling framework itself (Beale & Lennon, 2011; McInerny & Purves, 2011; Fourcade et al., 2014).”
A climate of uncertainty: accounting for error in climate variables for species distribution models (2015, methodology): “…a hierarchical Bayesian framework using a Gibbs sampler where uncertainty in the explanatory variables was accounted for using a validation data set; McInerny & Purves (2011) investigated uncertainty in explanatory variables attributed to fine-scale environmental variation, and proposed a general…”
Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity (2015, background): “The processes of adaptation and microevolution influence the Eltonian and Hutchinsonian shortfalls, given the central role of natural selection in mediating abiotic and biotic interactions (McLachlan & Ladle 2011) combined with the shuffling of species co-occurrences over time.”
Stacking species distribution models and adjusting bias by linking them to macroecological models (2014, background): “The observed bias in S-SDMs may have nothing to do with SDM model accuracy or neglecting species interactions during stacking, but may instead be caused by ‘regression dilution’ or ‘attenuation’ (Madansky, 1959; MacMahon et al., 1990; McInerny & Purves, 2011).”
What do we gain from simplicity versus complexity in species distribution models (2014, background): “Parametric, latent variable models can help to deal with this imprecision (Mcinerny and Purves 2011).”