%0 Journal Article %1 Guisan2007 %A Guisan, A. %A Zimmermann, N. E. %A Elith, J. %A Graham, C. H. %A Phillips, S. %A Peterson, A. T. %D 2007 %J Ecological Monographs %K grainsize methods presence-only scale niche-basedmodeling Switzerlandnativetreespecies speciestraits samplesize treeoccurrences locationerror datatreatment modelperformance %N 4 %P 615--630 %R doi:10.1890/06-1060.1 %T What matters for predicting the occurrences of trees: techniques, data, or species' characteristics? %U http://www.esajournals.org/doi/abs/10.1890/06-1060.1 %V 77 %X Data characteristics and species traits are expected to inuence the accuracy with which species' distributions can be modeled and predicted. We compare 10 modeling techniques in terms of predictive power and sensitivity to location error, change in map resolution, and sample size, and assess whether some species traits can explain variation in model performance. We focused on 30 native tree species in Switzerland and used presence- only data to model current distribution, which we evaluated against independent presence- absence data. While there are important differences between the predictive performance of modeling methods, the variance in model performance is greater among species than among techniques. Within the range of data perturbations in this study, some extrinsic parameters of data affect model performance more than others: location error and sample size reduced performance of many techniques, whereas grain had little effect on most techniques. No technique can rescue species that are difcult to predict. The predictive power of species- distribution models can partly be predicted from a series of species characteristics and traits based on growth rate, elevational distribution range, and maximum elevation. Slow-growing species or species with narrow and specialized niches tend to be better modeled. The Swiss presence-only tree data produce models that are reliable enough to be useful in planning and management applications.

@article{Guisan2007, abstract = {Data characteristics and species traits are expected to inuence the accuracy with which species' distributions can be modeled and predicted. We compare 10 modeling techniques in terms of predictive power and sensitivity to location error, change in map resolution, and sample size, and assess whether some species traits can explain variation in model performance. We focused on 30 native tree species in Switzerland and used presence- only data to model current distribution, which we evaluated against independent presence- absence data. While there are important differences between the predictive performance of modeling methods, the variance in model performance is greater among species than among techniques. Within the range of data perturbations in this study, some extrinsic parameters of data affect model performance more than others: location error and sample size reduced performance of many techniques, whereas grain had little effect on most techniques. No technique can rescue species that are difcult to predict. The predictive power of species- distribution models can partly be predicted from a series of species characteristics and traits based on growth rate, elevational distribution range, and maximum elevation. Slow-growing species or species with narrow and specialized niches tend to be better modeled. The Swiss presence-only tree data produce models that are reliable enough to be useful in planning and management applications.}, added-at = {2010-01-14T17:44:45.000+0100}, author = {Guisan, A. and Zimmermann, N. E. and Elith, J. and Graham, C. H. and Phillips, S. and Peterson, A. T.}, biburl = {https://www.bibsonomy.org/bibtex/248bd56a0f724b7c77d322eacb7ca2232/uvesco}, doi = {doi:10.1890/06-1060.1}, file = {:Guisan2007.pdf:PDF}, interhash = {8d25f2f51f4a5113da7a0a6a6db9b374}, intrahash = {48bd56a0f724b7c77d322eacb7ca2232}, journal = {Ecological Monographs}, keywords = {grainsize methods presence-only scale niche-basedmodeling Switzerlandnativetreespecies speciestraits samplesize treeoccurrences locationerror datatreatment modelperformance}, number = 4, pages = {615--630}, timestamp = {2010-01-14T17:44:45.000+0100}, title = {What matters for predicting the occurrences of trees: techniques, data, or species' characteristics?}, url = {http://www.esajournals.org/doi/abs/10.1890/06-1060.1}, volume = 77, year = 2007 }