Generalized linear and generalized additive models in studies of species distributions: setting the scene

By Antoine Guisan, THOMAS C. EDWARDS JR., Trevor J. Hastie. 2002

2,332 citations recorded by the source corpus.

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

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Citations in the corpus

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.”
Ecology of butterflies in Europe (2009, methodology): “For a preliminary analysis detecting the shape of the response variable predictor, we suggest to produce scatterplots of the response variable against each hypothesised predictor combined with univariate GAMs (Crawley, 2002; Guisan et al., 2002; Luoto et al., 2005) or LOWESS curves (cf.”
Selecting thresholds of occurrence in the prediction of species distributions (2005, methodology): “generalized linear models (GLM), generalized additive models (GAM), classification and regression trees (CARTs), principal components analysis (PCA), artificial neural networks (ANNs) (Guisan and Zimmermann 2000, Moisen and Frescino 2002, Guisan et al. 2002, Berg et al. 2004).”
Winter distribution of blue crab Callinectes sapidus in Chesapeake Bay : application and cross-validation of a two-stage generalized additive model (2005, background): “GAMs have become widely recognized as an important tool for understanding species distributions (reviewed in Guisan et al. 2002) because they effectively address many of the statistical challenges (e.g. non-linear responses, complex interactions, and counts that are zero-inflated or otherwise…”
Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models (2013, methodology): “Most of these applications are only explanatory models that seek to assess the relationship between the presence of species and a suite of one or more explanatory variables (e.g. precipitation, bathymetry, etc.) (Guisan et al 2002).”
GIS-based modeling of Javan Hawk-Eagle distribution using logistic and autologistic regression models (2008, methodology): “Generalized linear and additive models (GLM and GAM), implemented within a GIS, have become very popular for predicting species’ distributions (Guisan et al., 2002; Zaniewski et al., 2002; Engler et al., 2004).”
Linking temperate demersal fish species to habitat: scales, patterns and future directions (2013, background): “This approach has been criticized for shifting our focus away from hypothesis-driven science, and potentially ascribing coincidental correlations or indirect relationships as direct causal links (Guisan et al. 2002).”
Predicting climate‐induced range shifts: model differences and model reliability (2006, neutral): “Generalized linear models offer a slightly more flexible modeling framework than basic linear regression models as they allow for the modeling of alternative distributions in the response variable and nonconstant variance functions (Guisan et al., 2002).”
Differential effects of native and invasive algal wrack on macrofaunal assemblages inhabiting exposed sandy beaches (2008, positive): “This model is more flexible and better suited for analyzing ecological relationships that can be poorly represented by classical Gaussian distributions ( Guisan et al., 2002 and references therein).”