GRASP: generalized regression analysis and spatial prediction

By Anthony Lehmann, J. Overton, J. Leathwick. 2002

519 citations recorded by the source corpus.

Subfields: Ecological Modeling, Economics and Econometrics, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/29604242454522e2442a32180bc55a3291e0e17b

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Citations in the corpus

Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France (2006, methodology): “Hundreds of studies have been conducted since the end of the 1990s (Franklin, 1995; Guisan & Zimmermann, 2000; Guisan et al., 2002; Lehmann et al., 2002b; Scott et al., 2002; Rushton et al., 2004; M.P. Austin, in review).”
Determinants of the biogeographical distribution of butterflies in boreal regions (2006, methodology): “Because the response variables represent binary data (presence or absence of species), a binomial distribution of error via a logistic link function was applied, as suggested by Lehmann et al. (2002).”
The influence of spatial errors in species occurrence data used in distribution models (2007, methodology): “Techniques included those that have been used commonly in species distributional modelling for presence-only data, such as BIO-CLIM (Busby 1991) and DOMAIN (Walker & Cocks 1991; Carpenter, Gillison & Winter 1993); regression-based techniques which use background (also referred to as pseudoabsence) i”
Modelling species diversity through species level hierarchical modelling (2005, methodology): “In particular, the recently proposed generalized regression analysis and spatial prediction methodology as in Lehmann et al. (2002) appends a spatial prediction technique onto a generalized additive model.”
Improving generalized regression analysis for the spatial prediction of forest communities (2006, methodology): “The use of GAMs was made easier through the development of grasp (generalized regression analysis and spatial predictions), a collection of s-plus functions with a graphical user interface (Lehmann et al., 2002c).”
A generalized additive model for the spatial distribution of snowpack in the Spanish Pyrenees (2005, methodology): “…Tibshirani, 1987) has been used in environmental modelling, mainly in ecology and biogeography studies related to the prediction and explanation of distinct features of biodiversity: species presence/absence data or species richness (Yee and Mitchell, 1991; Bio et al., 1998; Lehmann et al., 2002).”
Comparison of six correlative models in predictive vegetation mapping on a local scale (2012, methodology): “…a “local” model is fitted to only a particular region of the space, but the model form can differ in different regions of the sample space, as e.g., MARS (Munoz and Felicisimo 2004), classification techniques, CART (Segurado and Araujo 2004; Miller and Franklin 2002), GAM (Lehmann et al. 2002).”
Modeling spatial distribution of amphibian populations: a GIS approach based on habitat matrix permeability (2002, methodology): “Binomial models were selected to fit this presence–absence data, using S-Plus (Mathsoft Inc., Seattle) and the GRASP library developed by Lehmann et al. (2002).”
Multi-scale effect of landscape processes and habitat quality on newt abundance: Implications for conservation (2006, methodology): “We used the Generalized Regression Analysis and Spatial Prediction library (GRASP: Lehmann et al. , 2002b) in Splus (Insightful) to select, validate and interpret models.”
Prospective sampling based on model ensembles improves the detection of rare species (2010, methodology): “Generalized Additive Models (GAMs) were fitted, for each species, with the available presence and absence data, using the GRASP package (Lehmann et al. 2002).”
Species traits are associated with the quality of bioclimatic models (2008, methodology): “…butterfly species We first developed bioclimatic envelope models for individual butterfly species using (generalized regression analysis and spatial prediction) in S-Plus (version 6.1 for Windows, Insightful Corp., Seattle, WA, USA) with standard GAM and custom functions (Lehmann et al ., 2003).”
GIS-based modeling of Javan Hawk-Eagle distribution using logistic and autologistic regression models (2008, background): “Thus, models that ignore the spatial autocorrelation may be inappropriate because they might overestimate the importance of environmental variables (Lichstein et al., 2002).”
Habitat suitability modelling and niche theory (2008, background): “Lehmann and colleagues (Lehmann 1998; Lehmann, Overton & Leathwick 2002 ) provided further striking examples of complex response curves.”