Improving generalized regression analysis for the spatial prediction of forest communities

By R. Maggini, Anthony Lehmann, N. E. Zimmermann, Antoine Guisan. 2006

206 citations recorded by the source corpus.

Subfields: Ecology, Economics and Econometrics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/0c3ee1802c750b403c007f3172e25764126629d7

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Citations in the corpus

Climate warming and the decline of amphibians and reptiles in Europe (2006, background): “However there are still important model uncertainties and cautious interpretation of model results is required (for discussion see Araújo et al., 2005a; Maggini et al., 2006; Pearson et al., 2006; Randin et al., 2006).”
Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data (2009, background): “Random stratified samples may have improved this further, as they typically result in models that predict with higher accuracy [25] however these are infrequently used in practice [35,36].”
Effects of sample and grid size on the accuracy and stability of regression‐based snow interpolation methods (2009, unknown): “Predictors were selected using a newly developed method for GAMs (Lehmann et al., 2002) called ‘crossselection,’ as implemented in the software generalized regression analysis and spatial prediction (GRASP) (Lehmann et al., 2002; Maggini et al., 2006).”
The ghost of past species occurrence: improving species distribution models for presence-only data (2006, background): “‘naughty noughts’; Thuiller et al. 2004; Maggini et al. 2006 ) influence species’ response curves and thus spatial predictions.”
A study on the effects of unbalanced data when fitting logistic regression models in ecology (2018, unknown): “Maggini et al. (2006) assessed the effect of weighting absences when modelling forest communities by generalized additive models.”
Predicting species distributions: a critical comparison of the most common statistical models using artificial species (2007, methodology): “…that an implementation of GAM with flexible degrees of freedom, equivalent to sGAM here, performs similarly to other traditional methods (e.g. GAM, GLM) in terms of predictive ability, and fails to select the meaningful model factors into the models (Elith et al., 2006; Maggini et al., 2006).”
Are niche‐based species distribution models transferable in space? (2006, methodology): “Testing of model transferability could prove particularly powerful for complementing standard procedures of model evaluation (for best practice techniques see Maggini et al., 2006, this issue ).”
Niche based distribution modelling of an invasive alien plant: effects of population status, propagule pressure and invasion history (2009, methodology): “prevalence was fixed to 0.5 (cf. Guisan et al. 2006; Maggini et al. 2006 ).”
Determinants of the biogeographical distribution of butterflies in boreal regions (2006, methodology): “GAMs were performed using GRASP (generalized regression analysis and spatial prediction) in S-Plus (Version 6.1 for Windows; Insightful Corp., Seattle, WA, USA) (Lehmann et al., 2002; Maggini et al., 2006).”
Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges (2010, background): “201 neity in topography, climate, geology and land cover maps (e.g. Hortal et al., 2004; Maggini et al., 2006; Wohlgemuth et al., 2008) instead of using continuous spectral information.”
Understanding what controls the spatial distribution of fish populations using a multi-model approach (2011, background): “A very rich and recent literature, which reviews, critically assesses and compares all these techniques is available (Maggini et al., 2006, Moisen and Frescino, 2002, Thuiller, 2003, Segurado and Araújo, 2004, Moisen et al., 2006, Elith and Graham, 2009, Dormann, 2007b, Heikkinen et al., 2006,…”