Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia

Abstract

Distribution data on threatened and endangered species are often sparse and clustered making it difficult to model their suitable habitat distribution using commonly used modeling approaches. We used a novel method called maximum entropy distribution modeling or Maxent for predicting potential suitable habitat for Canacomyrica monticola, a threatened and endangered tree species in New Caledonia, using small number of occurrence records (11). The Maxent model had 91% success rate (that is, a low omission rate) and was statistically significant. The approach presented here appears to be quite promising in predicting suitable habitat for threatened and endangered species with small sample records and can be an effective tool for biodiversity conservation planning, monitoring and management.