An evaluation of methods for modelling species distributions

By Pedro Segurado, Miguel B. Araújo. 2004

927 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/531ba84202ecf54e45dc749c99b744bf65045310

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In Ecological Modeling

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In Nature and Landscape Conservation

Citations in the corpus

Species-specific traits associated to prediction errors in bird habitat suitability modelling (2005, methodology): “These techniques have been compared in terms of predictive accuracy and ease of use (Guisan et al., 1999; Manel et al., 1999; Olden and Jackson, 2002; Segurado and Araújo, 2004; Yen et al., 2004), with the conclusion that there is not a single best method.”
Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors (2009, background): “…because prevalence (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…”
Species’ Distribution Modeling for Conservation Educators and Practitioners (2010, background): “For example, artificial neural networks have shown good predictive ability (e.g., Pearson et al., 2002; Segurado and Araújo, 2004; Thuiller, 2003), but identifying the relative contribution of each input variable to the prediction is difficult (sensitivity analysis may be used, but this requires…”
Where will species go? Incorporating new advances in climate modelling into projections of species distributions (2007, background): “The accuracy of different models may also vary with the characteristics of individual species (Thuiller, 2003; Segurado & Araújo, 2004).”
Predicting worldwide invasiveness for four major problematic decapods: an evaluation of using different calibration sets (2011, methodology): “ANN is a method used regularly in NBM and has also been recognized as one of the best performing techniques (Segurado and Araújo 2004).”
Spatial autocorrelation and the selection of simultaneous autoregressive models (2007, methodology): “We propose to extend the ongoing comprehensive tests of non-spatial methods for modelling species distributions (e.g. Segurado & Araújo, 2004; Elith et al., 2006) with a comprehensive assessment and full comparison of the various spatial modelling techniques (Cressie, 1993; Haining, 2003; Rangel et…”
Different climatic envelopes among invasive populations may lead to underestimations of current and future biological invasions (2009, background): “To an extent, the accuracy of different models may vary with the characteristics of individual species (Thuiller, 2003; Segurado & Araújo, 2004; Luoto et al., 2005), and small differences between current distributions derived from alternative models can be magnified when projecting ranges under…”
Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling (2011, background): “Many models are now available to explore this relationship, although these techniques may differ in their ability to summarize useful relationships between response and predictor variables (Segurado & Araújo, 2004).”
Climate warming and the decline of amphibians and reptiles in Europe (2006, methodology): “…studies have used this analytical tool to examine distributions of herptile species in relation to current climate (e.g. Brito et al., 1999; Guisan & Hofer, 2003; Segurado & Araújo, 2004), and some have used scenarios to project distributions under future climates (Teixeira & Arntzen, 2002).”
Spatial modelling of biodiversity at the community level (2006, background): “However, while many recent studies have modelled large numbers of species (Bakkenes et al. 2002; Segurado & Araujo 2004), surprisingly few of these have proceeded to apply any community-level analysis to the resulting stack of species’ distributions.”
Home away from home — objective mapping of high‐risk source areas for plant introductions (2007, methodology): “The use of GAM in biogeographical studies is not new, and they have been widely tested and compared to other distribution models (Thuiller et al ., 2003; Thuiller, 2003; Segurado & Araújo, 2004; Elith et al ., 2006).”
Potential impacts of climate change on the distributions and diversity patterns of European mammals (2007, result): “Species with low prevalence generally had better modelling performance than species with high prevalence, corresponding to the findings of some recent studies (Segurado and Araújo 2004; Luoto et al. 2005).”
Modelling stream fish species distribution in a river network: the relative effects of temperature versus physical factors (2008, background): “…in modelling species responses and it has already been shown that modelling techniques able to summarise non-linear and complex relationships have greater efficiency than linear approaches (Brosse & Lek 2000; Bio et al. 2002; Olden & Jackson 2002; Segurado & Araujo 2004; Araujo et al. 2005).”