Effects of restricting environmental range of data to project current and future species distributions

By Wilfried Thuiller, Lluís Brotons, Miguel B. Araújo, S. Lavorel. 2004

641 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/54d6e5a01d38e780ca81b4e87a38ecaaf35aff3f

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In Ecological Modeling

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In Nature and Landscape Conservation

Citations in the corpus

Consensual predictions of potential distributional areas for invasive species: a case study of Argentine ants in the Iberian Peninsula (2009, methodology): “Differences between the eastern and western sides of the Iberian Peninsula were expected, since models were calibrated in different areas (Van Horne 2002; Thuiller et al. 2004; Pearson et al. 2006).”
A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants (2020, methodology): “…and fitting include model comparisons for specific scenarios e.g. fitting a small number of models within a cross-validation framework (Norberg et al., 2019) or ensemble modelling methods combining individual models to create one predictive output (Marmion et al., 2009; Thuiller et al., 2004).”
Potential Impacts of Climate Change on the Distribution of North American Trees (2007, background): “…in which populations are able to migrate entirely into their future climate habitat, and a “no dispersal” situation, in which they are unable to migrate quickly enough and thus survive only in areas that overlap with their current climatic range (Peterson et al. 2002, Thuiller et al. 2006).”
Using species distribution models in paleobiogeography: A matter of data, predictors and concepts (2011, background): “…of PSDMs. Failing to include data from the entire geographic distribution of a species can result in sampling a spuriously narrow range of environmental tolerance, which ultimately results in an underestimation of its potential distribution (Thuiller et al., 2004; Sánchez-Fernández et al., 2011).”
Exploring the effects of quantity and location of pseudo-absences and sampling biases on the performance of distribution models with limited point occurrence data (2011, background): “In contrast, Thuiller et al. (2004) proposed including a large part of the environmental combinations where the species currently occur or not.”
Model‐based uncertainty in species range prediction (2006, background): “The problem of extrapolation into 'unknown' niche space is rarely considered in model applications (Pearson & Dawson, 2003; Thuiller et al., 2004b) yet we demonstrate the uncertainty that can arise if models are naively applied without consideration of the effect that extrapolation will have on…”
Assessing the effects of pseudo-absences on predictive distribution model performance (2008, methodology): “Previous results (A. Jiménez-Valverde, J.M. Lobo, J. Hortal, unpublished data) clearly demonstrate that it is necessary to use as much good absence data as possible, especially when dealing with small numbers of presences, to correctly classify the absence zone (see also Thuiller et al., 2004).”
Here be dragons: a tool for quantifying novelty due to covariate range and correlation change when projecting species distribution models (2014, methodology): “The application of an SDM beyond the niche breadth used to formulate it is very risky (Elith et al., 2010) and might be ecologically and statistically invalid (Davis et al., 1998; Thuiller et al., 2004b; Dormann, 2007; Fitzpatrick & Hargrove, 2009).”
How much do we overestimate future local extinction rates when restricting the range of occurrence data in climate suitability models (2010, background): “…barriers and where species occurrences are known across these limits, distribution modeling can lead to a truncated climatic niche, unless outside presences occur within the climatic niche defined by the presences under study (Van Horn 2002, Thuiller et al. 2004, Guisan and Thuiller 2005).”