%0 Journal Article %1 Elith2002 %A Elith, Jane %A Burgman, Mark A. %A Regan, Helen M. %D 2002 %J Ecological Modelling %K Prediction Model Generalizedlinearmodels Visualization Confidenceintervals Vagueness Logisticregression Epistemicandlinguisticuncertainty %N 2-3 %P 313 - 329 %R DOI: 10.1016/S0304-3800(02)00202-8 %T Mapping epistemic uncertainties and vague concepts in predictions of species distribution %U http://www.sciencedirect.com/science/article/B6VBS-470V3VF-9/2/d6d3fe86e925f7acfa038020adb32aad %V 157 %X Most habitat maps are presented as if they were a certain fact, with no indication of uncertainties. In many cases, researchers faced with the task of constructing such maps are aware of problems with the modelling data and of decisions that they make within the modelling process that are likely to affect the output, but they find it difficult to quantify this information. In some cases they attempt to evaluate the modelled predictions against independent data, but the summary statistics have no spatial component and do not address errors in the predictions. It is proposed that maps of uncertainty would help in the interpretation of these summaries, and to emphasize patterns in uncertainty such as spatial clustering or links with particular covariates. This paper reviews the aspects of uncertainty that are relevant to habitat maps developed with logistic regression, and suggests methods for investigating and communicating these uncertainties. It addresses the problems of subjective judgement, model uncertainty and vague concepts along with the more commonly considered uncertainties of random and systematic error. Methods for developing realistic confidence intervals are presented along with suggestions on how to visualize the information for use by decision-makers.

@article{Elith2002, abstract = {Most habitat maps are presented as if they were a certain fact, with no indication of uncertainties. In many cases, researchers faced with the task of constructing such maps are aware of problems with the modelling data and of decisions that they make within the modelling process that are likely to affect the output, but they find it difficult to quantify this information. In some cases they attempt to evaluate the modelled predictions against independent data, but the summary statistics have no spatial component and do not address errors in the predictions. It is proposed that maps of uncertainty would help in the interpretation of these summaries, and to emphasize patterns in uncertainty such as spatial clustering or links with particular covariates. This paper reviews the aspects of uncertainty that are relevant to habitat maps developed with logistic regression, and suggests methods for investigating and communicating these uncertainties. It addresses the problems of subjective judgement, model uncertainty and vague concepts along with the more commonly considered uncertainties of random and systematic error. Methods for developing realistic confidence intervals are presented along with suggestions on how to visualize the information for use by decision-makers.}, added-at = {2010-01-14T17:43:58.000+0100}, author = {Elith, Jane and Burgman, Mark A. and Regan, Helen M.}, biburl = {https://www.bibsonomy.org/bibtex/281606b96c1600fa6ef836a8a4af8fbd3/uvesco}, doi = {DOI: 10.1016/S0304-3800(02)00202-8}, file = {:Elith2002.pdf:PDF}, interhash = {3b9bb68864928be04fb8bbe8a0b307d2}, intrahash = {81606b96c1600fa6ef836a8a4af8fbd3}, issn = {0304-3800}, journal = {Ecological Modelling}, keywords = {Prediction Model Generalizedlinearmodels Visualization Confidenceintervals Vagueness Logisticregression Epistemicandlinguisticuncertainty}, number = {2-3}, pages = {313 - 329}, timestamp = {2010-01-14T17:43:58.000+0100}, title = {Mapping epistemic uncertainties and vague concepts in predictions of species distribution}, url = {http://www.sciencedirect.com/science/article/B6VBS-470V3VF-9/2/d6d3fe86e925f7acfa038020adb32aad}, volume = 157, year = 2002 }