Patterns and uncertainties of species' range shifts under climate change

By Wilfried Thuiller. 2004

926 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/c85cabdd1ac3446f9a481c7244d0357f9b2b6e12

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Citations in the corpus

Biotic interactions influence the projected distribution of a specialist mammal under climate change (2012, result): “Although different modelling algorithms can yield differing results (Munoz & Felicisimo, 2004; Thuiller, 2004; Elith et al., 2006; Pearson et al., 2006; Elith & Graham, 2009), our approach was to test whether neglecting ecologically meaningful mechanisms, such as biotic interactions, can also yield…”
Consequences of spatial autocorrelation for niche‐based models (2006, background): “This problem may be greater than previously anticipated, as illustrated by studies showing high levels of intermodel variability in projections of species’ range shifts under climate change scenarios (Thuiller 2004; Thuiller et al. 2004; Araújo et al. 2005b; Araújo, Thuiller & Pearson 2006).”
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…”
Evaluation of consensus methods in predictive species distribution modelling (2009, methodology): “Two methods (Median(All) and Mean(All)) are based on global (i.e. output of all eight single-models) median and mean functions, whereas Weighted Average (WA), Best, and Median(PCA) methods preselect the single-models based on certain predefined criteria.”
From climate change predictions to actions – conserving vulnerable animal groups in hotspots at a regional scale (2010, methodology): “…mostly related to: (a) the statistical tool used for modelling species distributions; (b) the global circulation models (GCMs) used to predict future climate conditions (Thuiller, 2004; Pearson et al., 2006; Beaumont et al., 2008) and (c) uncertainty derived from scale effects (Seo et al., 2009).”
Identifying priority areas for reducing species vulnerability to climate change (2012, methodology): “Other studies have used different strategies for selecting species for SDM under climate change including using large numbers of species (Bakkenes et al., 2002; Peterson et al., 2002; Thuiller, 2004), endemic species (Thomas et al.”
Spatially autocorrelated sampling falsely inflates measures of accuracy for presence‐only niche models (2009, background): “, 2007) and to predict the effects of climate change on species distributions (Thuiller, 2004).”
Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta‐analysis (2012, background): “A plethora of modelling studies have already shown the potential impacts of climate change on the distributions and abundances of species (e.g. Easterling et al., 2000; Berry et al., 2002; Midgley et al., 2003; Thomas et al., 2004; Thuiller, 2004; Harrison et al., 2006; Márquez et al., 2010).”
Potential impacts of climate change on the distributions and diversity patterns of European mammals (2007, background): “…et al. 2002; Berry et al. 2002; Midgley et al. 2002; Araújo et al. 2004; Skov and Svenning 2004; Elith et al. 2006), although the validity of these has recently been questioned (Davis et al. 1998; Pearson and Dawson 2003; Thuiller 2004; Hampe 2004; Ibáñez et al. 2006; Araújo and Rahbek 2006).”
The projection of species distribution models and the problem of non-analog climate (2009, background): “…online: 14 April 2009 © US Government 2009 By 2100, a quarter or more of the Earth’s land surface may experience climatic conditions that have no modern analog, with novel climates predicted to arise primarily in regions that currently support high levels of biodiversity (Williams et al. 2007).”
Effects of sample size on the performance of species distribution models (2008, background): “While some models appear intrinsically capable of making such predictions (Hijmans & Graham, 2006), results can remain controversial because of uncertainty associated with predicting distributions for current and hence future situations (Thuiller, 2004).”
Projecting global marine biodiversity impacts under climate change scenarios (2009, background): “as a good measure of biodiversity and ecosystem perturbation (Peterson et al. 2002; Thuiller 2004).”
Model complexity affects species distribution projections under climate change (2019, background): “…as econom‐ ics and climate science, projection ensembles were introduced to species distribution modelling relatively recently (Araújo & New, 2007; Thuiller, 2004), but gained popularity since specialized mod‐ elling platforms became available—such as the R‐package ‘biomod2’ (Thuiller,…”
The ability of climate envelope models to predict the effect of climate change on species distributions (2006, background): “The degree to which different statistical models may be affected by these problems has proved difficult to determine, but large variability among different CEM approaches used to predict species distributions under a projected future climate have been reported ( Thuiller, 2003, 2004; Pearson et al.”
Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change (2009, methodology): “Rather than evaluating each species’ ensemble distribution independently, we calculated species turnover for each combination of SDM, AOGCM, and scenario, which was based on the number of potential species gained (G) or lost (L) within each cell and given by (G L)/(S G) (Thuiller 2004).”
Bioclimatic velocity: the pace of species exposure to climate change (2014, methodology): “Species distribution modelling (SDM) is the most widespread technique used to assess species exposure to future climate change impacts (e.g. Thomas et al., 2004; Thuiller, 2004; Ara ujo et al., 2011).”