Pouteau, Robin, et al. “Support vector machines to map rare and endangered native plants in Pacific islands forests.” Ecological Informatics 9 (2012): 37-46.
doi:10.1016/j.ecoinf.2012.03.003
Occurrence records are scarce for rare species, which results in small training sample available for species distribution models. Support Vector Machine (SVM) was traditionally used in remotely sensed data classification for classifying object reflectance, which is substantially the same than classifiers used in species distribution models. Since the decision made by SVM is solely based on few meaningful pixels, this method is much appropriate for predicting distribution of species with scarce occurrence records. Pouteau et. al. compared two machine-learning methods, random forest (RF) and SVM, to determine which method is the most relevant to map rare species and to predict potential habitat with their current observed range. The comparison was performed using three rare plants found at the island of Moorea. Biophysical variables including elevation, climate, geology, soil substrate, disturbance regime, floristic region, plant dispersal capacities, and ecological plant type and function. Their results showed that SVM preformed constantly better than RF in distribution prediction in terms of Kappa coefficient and the area under the curve (AUC). In this case, the predicted distribution generated from SVM has high enough accuracy with only 13 training pixels. This was contributed by the ability of SVM to train model with few meaningful pixels and fit limitation information and the ability to resist noise from insignificant pixels. By comparing species potential habitat with current observed range, we will be able to better understand the causes of the conservation status of the targeted species. So far, there are only limited applications of SVM for special distribution models. It would be interesting to repeat the application for other rare plants or animals.
