Making better biogeographical predictions of species’ distributions

By Antoine Guisan, Anthony Lehmann, Simon Ferrier, Michael P. Austin, J. Overton, Richard J. Aspinall, Trevor J. Hastie. 2006

524 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/d2f07fd064af10b18f68b2ce5817113d64993b92

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Citations in the corpus

Spatial autocorrelation and the selection of simultaneous autoregressive models (2007, background): “This is not simply a statistical discussion but has profound implications for biogeography, macroecology and global change research because biased estimates and incorrect model specifications will influence the testing of hypotheses and the prediction of species distributions (e.g. Diniz-Filho et al”
Adaptation, migration or extirpation: climate change outcomes for tree populations (2008, methodology): “…or presence only data for species distributions, and temperature and precipitation interpolations from existing weather station data adjusted using digital elevation models for environmental conditions, but levels of sophistication are increasing rapidly as the field matures (Guisan et al. 2006).”
Applications and limitations of museum data for conservation and ecology, with particular attention to species distribution models (2010, background): “For example, one study (Guisan et al., 2006a) used records of the occurrence of alpine sea holly (Eryngium alpinum Linnaeus) in Switzerland, collected by volunteers, to guide a survey to search for more populations; the resulting survey led to the discovery of seven new populations (Guisan et al.,…”
A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels (2019, background): “…studies suggest that how ecological processes depend on one covariate may depend on the value of other covariates (Harpole et al. 2011), including interactions among covariates increases model complexity and, therefore, the risk of statistical overfitting (Guisan et al. 2006, Merow et al. 2014).”
WHAT MATTERS FOR PREDICTING THE OCCURRENCES OF TREES: TECHNIQUES, DATA, OR SPECIES' CHARACTERISTICS? (2007, background): “The various human-induced threats imposed on nature have recently reinvigorated the study of species’ distributions, and more particularly the use of predictive models to describe and quantify them (Guisan and Thuiller 2005, Guisan et al. 2006).”
Ecology of butterflies in Europe (2009, methodology): “There is a rapid development of statistical methods applicable for species distribution modelling (Guisan & Zimmermann, 2000; Guisan et al., 2002; Guisan et al., 2006; Latimer et al., 2006).”
Mapping avian distributions: the evolution of bird atlases (2007, methodology): “A further contribution to the decline in numbers of atlases may be that other, less labour-intensive methods are being used to describe or predict distributions, though these methods as currently used face their own shortcomings (Guisan et al. 2006).”
Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data (2009, methodology): “They include classical regression-based approaches such as generalised linear and additive models [9,10] but also more recent and robust techniques such as boosted regression trees (e.”
Where will conflicts between alien and rare species occur after climate and land-use change? A test with a novel combined modelling approach (2011, methodology): “Modelling species distribution is a challenging procedure due to the complex nature of species range dynamics and of the related factors acting at multiple spatial scales ( Guisan and Zimmermann 2000; Guisan and Thuiller 2005; Elith and Leathwick 2007; Murphy and Lovett-Doust 2007; Randin et al. 20”
Moving beyond static species distribution models in support of conservation biogeography (2010, background): “Species distribution modelling (SDM) has exploded in recent years in the literature and in practice (Scott et al., 2002; Guisan et al., 2006; Elith & Leathwick, 2009; Franklin, 2009).”
Benthos distribution modelling and its relevance for marine ecosystem management (2015, background): “According to Guisan et al. (2006b), models using spatially and temporally heterogeneous samples contain an unknown level of bias and error.”
Climate Change Impacts on Seagrass Meadows and Macroalgal Forests: An Integrative Perspective on Acclimation and Adaptation Potential (2018, background): “Although the need for an integrative approach is well recognized (Guisan and Thuiller, 2005; Guisan et al., 2006; Lavergne et al., 2010; Sinclair et al., 2010; Franco et al., 2018), implementation is largely unexplored and lacks consensus.”