Ordinal response regression models in ecology.

By Antoine Guisan, Frank E. Harrell. 2000

194 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/eff5e7f2e2182e408da79a2dc88de64879776346

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In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

AGRICULTURAL PONDS SUPPORT AMPHIBIAN POPULATIONS (2004, methodology): “We evaluated our final models for each species with 2001 data using Somer’s D, percentage concordant, and statistics as criteria for each model’s2Rmax predictive ability (Somers 1980, Guisan and Harrell 2000, Mitchell et al. 2001).”
Species distribution models and ecological theory: A critical assessment and some possible new approaches (2007, background): “However, Guisan and Harrell (2000) have shown that the cover/abundance scales used for estimating species abundance in relevees require ordinal regression techniques as the values are ranks not continuous variates.”
Evaluating resource selection functions (2002, methodology): “This weighted Kappa technique has proven to be useful in assessing thematic maps where the categories are ordered or classified according to a nominal or ordinal scale (Naesset, 1996; Guisan and Harrell, 2000).”
A regional impact assessment of climate and land‐use change on alpine vegetation (2003, methodology): “Proportional odds models are based on the cumulative probabilities of the classes, stemming from successive logistic regression models (McCullagh & Nelder, 1991; Guisan & Harrell, 2000; Harrell, 2001).”
Predictive habitat distribution models in ecology (2000, methodology): “With ordinal regression models, partial residual plots can be used to check the ordinality of the response variable y against each explanatory variable xi (Guisan and Harrell, 2000).”
Changes in the spatio-temporal patterns and habitat preferences of Ambrosia artemisiifolia during its invasion of Austria. (2009, methodology): “With a bootstrap-validated Somers’ Dxy of 0.85 and a R2 of 0.41, the calibrated SDM is a good predictor ( Guisan & Harrell 2000 ).”
Spatial prediction of species distribution: an interface between ecological theory and statistical modelling (2002, background): “Guisan and Harrell (2000) indicate that better statistical models (ordinal regression) are required for use with cover/abundance data.”
Improving generalized regression analysis for the spatial prediction of forest communities (2006, methodology): “K-fold cross-validation (Franklin et al., 2000) and bootstrap methods (Efron & Tibshirani, 1993; Guisan & Harrell, 2000) are often seen as the best compromise because they allow model stability to be assessed without losing information to calibrate the final model.”