engleski

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

A computational framework to map species' distributions 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 Nether- lands, 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-absence points fall further away from the occurrence points in both feature and geographical spaces. After the pseudo-absences have been produced, they are combined with occurrence locations and used to build regression-kriging prediction models. The output of prediction are either probability of species' occurrence or density measures. Addition of the pseudo-absence locations has proven e ective | 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 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.

spatial prediction; pseudo-absence; R; adehabitat; gstat; spatstat

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