Spatial prediction of species’ distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging Export

@article{citeulike:5255475, abstract = {A computational framework to map species' distributions (realized density) using occurrence-only data and environmental predictors is presented and illustrated using a textbook example and two case studies: distribution of root vole ( Microtes oeconomus ) in the Netherlands, and distribution of white-tailed eagle nests ( Haliaeetus albicilla ) in Croatia. The framework combines strengths of point pattern analysis (kernel smoothing), Ecological Niche Factor Analysis (ENFA) and geostatistics (logistic regression-kriging), as implemented in the spatstat , adehabitat and gstat packages of the R environment for statistical computing. A procedure to generate pseudo-absences is proposed. It uses Habitat Suitability Index (HSI, derived through ENFA) and distance from observations as weight maps to allocate pseudo-absence points. This design ensures that the simulated pseudo-absences fall further away from the occurrence points in both feature and geographical spaces. The simulated pseudo-absences can then be combined with occurrence locations and used to build regression-kriging prediction models. The output of prediction are either probabilitiesy of species' occurrence or density measures. Addition of the pseudo-absence locations has proven effective — the adjusted R -square increased from 0.71 to 0.80 for root vole (562 records), and from 0.69 to 0.83 for white-tailed eagle (135 records) respectively; pseudo-absences improve spreading of the points in feature space and ensure consistent mapping over the whole area of interest. Results of cross validation (leave-one-out method) for these two species showed that the model explains 98\% of the total variability in the density values for the root vole, and 94\% of the total variability for the white-tailed eagle. The framework could be further extended to Generalized multivariate Linear Geostatistical Models and spatial prediction of multiple species. A copy of the R script and step-by-step instructions to run such analysis are available via contact author's website.}, author = {Hengl, Tomislav and Sierdsema, Henk and Radovi\'{c}, Andreja and Dilo, Arta}, citeulike-article-id = {5255475}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.ecolmodel.2009.06.038}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0304380009004438}, day = {23}, doi = {10.1016/j.ecolmodel.2009.06.038}, issn = {03043800}, journal = {Ecological Modelling}, keywords = {distribution-modeling, enfa, presence-only}, month = {July}, posted-at = {2009-07-24 23:37:59}, priority = {2}, title = {Spatial prediction of species' distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging}, url = {http://dx.doi.org/10.1016/j.ecolmodel.2009.06.038}, year = {2009} }

TY - JOUR ID - citeulike:5255475 L3 - citeulike-article-id:5255475 N2 - A computational framework to map species’ distributions (realized density) using occurrence-only data and environmental predictors is presented and illustrated using a textbook example and two case studies: distribution of root vole ( Microtes oeconomus ) in the Netherlands, and distribution of white-tailed eagle nests ( Haliaeetus albicilla ) in Croatia. The framework combines strengths of point pattern analysis (kernel smoothing), Ecological Niche Factor Analysis (ENFA) and geostatistics (logistic regression-kriging), as implemented in the spatstat , adehabitat and gstat packages of the R environment for statistical computing. A procedure to generate pseudo-absences is proposed. It uses Habitat Suitability Index (HSI, derived through ENFA) and distance from observations as weight maps to allocate pseudo-absence points. This design ensures that the simulated pseudo-absences fall further away from the occurrence points in both feature and geographical spaces. The simulated pseudo-absences can then be combined with occurrence locations and used to build regression-kriging prediction models. The output of prediction are either probabilitiesy of species’ occurrence or density measures. Addition of the pseudo-absence locations has proven effective — the adjusted R -square increased from 0.71 to 0.80 for root vole (562 records), and from 0.69 to 0.83 for white-tailed eagle (135 records) respectively; pseudo-absences improve spreading of the points in feature space and ensure consistent mapping over the whole area of interest. Results of cross validation (leave-one-out method) for these two species showed that the model explains 98% of the total variability in the density values for the root vole, and 94% of the total variability for the white-tailed eagle. The framework could be further extended to Generalized multivariate Linear Geostatistical Models and spatial prediction of multiple species. A copy of the R script and step-by-step instructions to run such analysis are available via contact author’s website. JF - Ecological Modelling SN - 03043800 TI - Spatial prediction of species’ distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging KW - distribution-modeling KW - enfa KW - presence-only AU - Hengl, Tomislav AU - Sierdsema, Henk AU - Radović, Andreja AU - Dilo, Arta PY - 2009/07/23/ UR - http://dx.doi.org/10.1016/j.ecolmodel.2009.06.038 ER -