Habitat Suitability and Distribution Models: With Applications in R

By Antoine Guisan, Wilfried Thuiller, N. E. Zimmermann. 2017

1,315 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/922ebb51cf0535b97c514169692a6ffc73edc62e

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Citations in the corpus

A standard protocol for reporting species distribution models (2020, methodology): “For presence–absence data, we may distinguish threshold-independent measures such as the AUC (area under receiver-operating characteristic curve ROC, Swets 1988), explained deviance and log-likelihood, and threshold-dependent indices (Guisan et al. 2017).”
blockCV: an R package for generating spatially or environmentally separated folds for k-fold cross-validation of species distribution models (2018, background): “When evaluating SDM performance, it is common to test how well predictions match observations at a set of locations (Franklin, 2010; Guisan et al., 2017; Peterson et al., 2011).”
Large carnivore expansion in Europe is associated with human population density and land cover changes (2021, background): “Second, one of the main assumptions behind species distribution models is that species are in equilibrium (or pseudo-equilibrium) with the environment (Guisan et al., 2017); however, it is likely that large carnivores in Europe are in a non-equilibrium state.”
Use and categorization of Light Detection and Ranging vegetation metrics in avian diversity and species distribution research (2019, methodology): “…large‐scale studies, environmental information related to climate, productivity, topography and land cover is used to quantify predictor variables (Guisan et al., 2017; Hawkins et al., 2003), but information on fine‐scale habitat structure (e.g., density, cover and openness of vegetation) and 3‐”
High uncertainty in the effects of data characteristics on the performance of species distribution models (2021, background): “…SDMs, also known as Environmental Niche Models) include a set of algorithms and general data management procedures that seek to model the potential or realized geographic distributions of species, typically based on their known occurrences and several environmental predictors (Guisan et al., 2017).”
Using species distribution model to predict the impact of climate change on the potential distribution of Japanese whiting Sillago japonica (2019, methodology): “We randomly generated 10,000 pseudo-absence records within the study area, because some of these techniques require a set of environmental conditions to contrast with the ones where the species was found (Elith and Leathwick, 2009; Elith and Graham, 2009; Thuiller et al., 2014; Guisan et al., 2017).”
Comparative analysis of diversity and environmental niches of soil bacterial, archaeal, fungal and protist communities reveal niche divergences along environmental gradients in the Alps (2021, methodology): “be used to predict species distribution in a geographic space (Guisan et al., 2017).”
Insect responses to global change offer signposts for biodiversity and conservation (2020, background): “Bioclimate or species distribution models (SDMs) are based on the premise that the environmental niches of species include restricted climatic tolerances, which limit species’ geographic distributions (Guisan et al., 2017).”