Improving Generalized Regression Analysis for the Spatial Prediction of Forest Communities. Maggini, R., Lehmann, A., Zimmermann, N. E., & Guisan, A. 33(10):1729–1749. ![Improving Generalized Regression Analysis for the Spatial Prediction of Forest Communities [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment.
@article{magginiImprovingGeneralizedRegression2006,
title = {Improving Generalized Regression Analysis for the Spatial Prediction of Forest Communities},
author = {Maggini, Ramona and Lehmann, Anthony and Zimmermann, Niklaus E. and Guisan, Antoine},
date = {2006-10},
journaltitle = {Journal of Biogeography},
volume = {33},
pages = {1729--1749},
issn = {0305-0270},
doi = {10.1111/j.1365-2699.2006.01465.x},
url = {http://mfkp.org/INRMM/article/835166},
abstract = {Aim\hspace{0.6em} This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location\hspace{0.6em} State of Vaud, western Switzerland. Methods\hspace{0.6em} Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results\hspace{0.6em} Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions\hspace{0.6em} Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-835166,~to-add-doi-URL,bias-correction,bioclimatic-predictors,correlation-analysis,forest-resources,habitat-suitability,statistics,uncertainty,weighting},
number = {10}
}
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E.","Guisan, A."],"bibdata":{"bibtype":"article","type":"article","title":"Improving Generalized Regression Analysis for the Spatial Prediction of Forest Communities","author":[{"propositions":[],"lastnames":["Maggini"],"firstnames":["Ramona"],"suffixes":[]},{"propositions":[],"lastnames":["Lehmann"],"firstnames":["Anthony"],"suffixes":[]},{"propositions":[],"lastnames":["Zimmermann"],"firstnames":["Niklaus","E."],"suffixes":[]},{"propositions":[],"lastnames":["Guisan"],"firstnames":["Antoine"],"suffixes":[]}],"date":"2006-10","journaltitle":"Journal of Biogeography","volume":"33","pages":"1729–1749","issn":"0305-0270","doi":"10.1111/j.1365-2699.2006.01465.x","url":"http://mfkp.org/INRMM/article/835166","abstract":"Aim This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location State of Vaud, western Switzerland. Methods Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. However, one should not neglect to consider the shapes and interpretability of response curves, as well as the resulting spatial predictions in the final assessment.","keywords":"*imported-from-citeulike-INRMM,~INRMM-MiD:c-835166,~to-add-doi-URL,bias-correction,bioclimatic-predictors,correlation-analysis,forest-resources,habitat-suitability,statistics,uncertainty,weighting","number":"10","bibtex":"@article{magginiImprovingGeneralizedRegression2006,\n title = {Improving Generalized Regression Analysis for the Spatial Prediction of Forest Communities},\n author = {Maggini, Ramona and Lehmann, Anthony and Zimmermann, Niklaus E. and Guisan, Antoine},\n date = {2006-10},\n journaltitle = {Journal of Biogeography},\n volume = {33},\n pages = {1729--1749},\n issn = {0305-0270},\n doi = {10.1111/j.1365-2699.2006.01465.x},\n url = {http://mfkp.org/INRMM/article/835166},\n abstract = {Aim\\hspace{0.6em} This study used data from temperate forest communities to assess: (1) five different stepwise selection methods with generalized additive models, (2) the effect of weighting absences to ensure a prevalence of 0.5, (3) the effect of limiting absences beyond the environmental envelope defined by presences, (4) four different methods for incorporating spatial autocorrelation, and (5) the effect of integrating an interaction factor defined by a regression tree on the residuals of an initial environmental model. Location\\hspace{0.6em} State of Vaud, western Switzerland. Methods\\hspace{0.6em} Generalized additive models (GAMs) were fitted using the grasp package (generalized regression analysis and spatial predictions, http://www.cscf.ch/grasp). Results\\hspace{0.6em} Model selection based on cross-validation appeared to be the best compromise between model stability and performance (parsimony) among the five methods tested. Weighting absences returned models that perform better than models fitted with the original sample prevalence. This appeared to be mainly due to the impact of very low prevalence values on evaluation statistics. Removing zeroes beyond the range of presences on main environmental gradients changed the set of selected predictors, and potentially their response curve shape. Moreover, removing zeroes slightly improved model performance and stability when compared with the baseline model on the same data set. Incorporating a spatial trend predictor improved model performance and stability significantly. Even better models were obtained when including local spatial autocorrelation. A novel approach to include interactions proved to be an efficient way to account for interactions between all predictors at once. Main conclusions\\hspace{0.6em} Models and spatial predictions of 18 forest communities were significantly improved by using either: (1) cross-validation as a model selection method, (2) weighted absences, (3) limited absences, (4) predictors accounting for spatial autocorrelation, or (5) a factor variable accounting for interactions between all predictors. The final choice of model strategy should depend on the nature of the available data and the specific study aims. Statistical evaluation is useful in searching for the best modelling practice. 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