Reducing uncertainty in projections of extinction risk from climate change

By Miguel B. Araújo, Robert J. Whittaker, Richard J. Ladle, M. Erhard. 2005

563 citations recorded by the source corpus.

Subfields: Ecological Modeling, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/2c29cf2644ed230e36d2a94335a5ec08325a0644

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In Ecological Modeling

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

The importance of biotic interactions for modelling species distributions under climate change (2007, background): “In other words, both the ‘explanatory’ (in calibration) and ‘predictive’ power of models (in non-independent validation, sensu Araújo et al., 2005a) were greater with climate than with host plant models, but models were significantly improved (except when using BIC) when host plants were considered…”
Spatial autocorrelation and the selection of simultaneous autoregressive models (2007, methodology): “This should include an evaluation of the predictive performance and accuracy of spatial models under changing environmental conditions such as climate change (for good examples with non-spatial methods see Araújo et al., 2005a, b; Hijmans & Graham, 2006).”
Ecological niche modelling of montane mammals in the Great Basin, North America: examining past and present connectivity of species across basins and ranges (2009, background): “Recent studies have advised a consensus approach in ENM development, in which multiple algorithms are used to infer niche dimensions (Araújo et al., 2005; Pearson et al., 2006, 2007).”
Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? (2007, background): “Hortal & Lobo (2006) also noted that errors in species distribution models are often spatially aggregated across species (see also Thuiller et al., 2004; Araújo et al., 2005).”
Climate warming and the decline of amphibians and reptiles in Europe (2006, methodology): “The modelling approach adopted in this study is cuttingedge in the field of bioclimatic ‘envelope’ modelling (Thuiller, 2004; Araújo et al., 2005b).”
Uncertainty associated with survey design in Species Distribution Models (2014, background): “The accuracy of consensus predictions is highly constrained by the quality of individual predictions (Ara ujo et al., 2005).”
Adapting the IUCN Red List criteria for invertebrates (2011, background): “…2006) is often considered one of the most accurate (Elith et al., 2006), but ensemble forecasting, a technique that combines a variable number of modelling techniques and reaches a consensus distribution of species, may in many cases be more accurate (Araújo et al., 2005; Thuiller et al., 2009).”
How biotic interactions may alter future predictions of species distributions: future threats to the persistence of the arctic fox in Fennoscandia (2012, background): “…of a species correlates positively with abundance (Borregaard & Rahbek, 2010), changes in the geographic distribution might be used as a surrogate for species abundance (Thomas et al., 2004; but see Araújo et al., 2005) and, in the present case, availability of prey and presence of competitors.”
When and how should biotic interactions be considered in models of species niches and distributions? (2017, background): “In addition, it is healthy to recognize that the representation of the biotic interactor carries inherent—but typically uncharacterized— uncertainty (Ara ujo et al., 2005; Pearson et al., 2006; Zurell et al., 2012).”
Palaeoclimatic models help to understand current distribution of Caucasian forest species (2012, background): “Species may be absent from suitable habitats because of limited dispersal ability, preventing full recolonization after historical reduction of their ranges (Pulliam, 2000; Araújo et al., 2005; Guisan & Thuiller, 2005; Marsico, 2009).”
Accounting for uncertainty when mapping species distributions: The need for maps of ignorance (2011, methodology): “It is well known that using exactly the same data with different SDM techniques provides quite different results (e.g. Araújo and New, 2007; Araújo et al., 2005; Loiselle et al., 2003; Wisz et al., 2008).”