Effects of species’ ecology on the accuracy of distribution models

By J. McPherson, Walter Jetz. 2007

345 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/8173030d65eac630a9003c89b52daff6f223e114

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In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Assessing the location and stability of foraging hotspots for pelagic seabirds: An approach to identify marine Important Bird Areas (IBAs) in Spain (2012, background): “Fourthly, the usefulness of the MPA approach could differ between species, as suggested by the different performance of the models presented here (AUC values), probably linked to the particular ecological requirements of each species (McPherson and Jetz, 2007).”
Climatic niche shifts between species' native and naturalized ranges raise concern for ecological forecasts during invasions and climate change (2014, background): “Weedy, dominant species with large range sizes are, however, more likely than other species to be at climatic equilibrium in the native range (McPherson & Jetz, 2007; Marmion et al., 2009).”
Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California (2009, background): “Some studies have also examined the effect of other species traits, such as species mobility and rarity (Pearce & Ferrier, 2000; McPherson & Jetz, 2007), on the performance of SDMs.”
Predicting species distributions across the Amazonian and Andean regions using remote sensing data (2008, background): “Because the usefulness of remote sensing data in distribution modelling may be dependent on a species’ ecology (Hernandez et al., 2006; McPherson & Jetz, 2007), these species were chosen to capture a variety of ecological traits such as range size and habitat specificity.”
Selecting predictors to maximize the transferability of species distribution models: lessons from cross‐continental plant invasions (2017, methodology): “The usual approach by which a 369 subsample of the calibration area is used as an independent dataset for model evaluation may be 370 biased by spatial correlation with the calibration dataset (McPherson & Jetz, 2007; Bahn & McGill, 371 2013).”
AUC: a misleading measure of the performance of predictive distribution models (2008, background): “Third, and related to the second point, AUC weights omission and commission errors equally, while in many applications of distribution modelling, omission and commission errors may not have the same importance (Fielding & Bell, 1997; Peterson, 2006).”
Climatic Risk Atlas of European Butterflies (2008, background): “• Species’ ecology: Model performance may be infl uenced in a variety of ways by species characteristics (McPherson & Jetz 2007).”
Testing the predictive performance of distribution models (2013, neutral): “…(Elith et al. 2006, Garzon et al. 2006, Prasad et al. 2006, Cutler et al. 2007), influence of ecology/life-history on models (Austin 2007, McPherson and Jetz 2007), scale (Storch and Gaston 2004, Araújo et al. 2005b, Betts et al. 2006), and spatio-temporal variability/population…”
Methods to account for spatial autocorrelation in the analysis of species distributional data : a review (2007, background): “Both simulation and empirical studies also indicate that autocovariate models achieve better fit than equivalent models lacking the autocovariate term (Augustin et al. 1996, Hoeting et al. 2000, Osborne et al. 2001, He et al. 2003, McPherson and Jetz 2007).”
Modelling invasion for a habitat generalist and a specialist plant species (2008, background): “Only a few studies on these relationships have been conducted in any depth (Chong et al., 2006; McPherson & Jetz, 2007), leaving a large gap in our understanding of the effects that autecology has on predictive modelling.”
Assessing the vulnerability of European butterflies to climate change using multiple criteria (2010, methodology): “…based on the modelling technique adopted (Lawler et al. 2006; Pearson et al. 2006), the choice of future climate scenarios (Beaumont et al. 2007), and the life-history traits and geographical characteristics of the modelled species (Luoto et al. 2005; McPherson and Jetz 2007; Pöyry et al. 2008).”
Within‐taxon niche structure: niche conservatism, divergence and predicted effects of climate change (2010, background): “While ecological correlates of intraspecific niche differentiation may be elusive (McPherson and Jetz 2007), deep phylogeographic divisions within species indicate the potential for intraspecific niche differentiation and suggest the need for definitive field experiments.”
Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors (2009, background): “…(the proportion of species’ presences in the training data) may be related to habitat specificity (Stockwell and Peterson 2002, Segurado and Araújo 2004, Luoto et al. 2005, Elith et al. 2006, McPherson and Jetz 2007), which could constrain the potential area to be predicted as suitable habitat.”
Minimum required number of specimen records to develop accurate species distribution models (2016, result): “2–4), which is in line with results reported by others (Manel et al. 2001, McPherson and Jetz 2007, Mateo et al. 2010, Lobo and Tognelli 2011).”