Computer Science

It is critical to know accurately the ecological and geographic range of rare and endangered species for biodiversity conservation and management. In this study, we used support vector machines (SVM) for modeling rare species distribution and we compared it to another emerging machine learning classifier called random forests (RF). The comparison was performed using three native and endemic plants found at low-to mid-elevation in the island of Moorea (French Polynesia, South Pacific) and considered rare because of scarce occurrence records: Lepinia taitensis (28 observed occurrences), Pouteria tahitensis (20 occurrences) and Santalum insulare var. raiateense (81 occurrences). We selected a set of biophysical variables to describe plant habitats in tropical high volcanic islands, including topographic descriptors and an overstory vegetation map. The former were extracted from a digital elevation model (DEM) and the latter is a result of a SVM classification of spectral and textural bands from very high resolution Quickbird satellite imagery. Our results show that SVM slightly but constantly outperforms RF in predicting the distribution of rare species based on the kappa coefficient and the area under the curve (AUC) achieved by both classifiers. The predicted potential habitats of the three rare species are considerably wider than their currently observed distribution ranges. We hypothesize that the causes of this discrepancy are strong anthropogenic disturbances that have impacted low-to midelevation forests in the past and present. There is an urgent need to set up conservation strategies for the endangered plants found in these shrinking habitats on the Pacific islands.