Aim
Advancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better‐surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo.
Location
Borneo, Southeast Asia.
Methods
We collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range‐restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north‐eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas.
Results
Spatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased.
Main Conclusions
We conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.
Knowledge of the distribution and habitat preferences of a species is of paramount importance when assessing its conservation status. We used accurately recorded occurrence records and ecological niche modelling to predict the distribution of two threatened and poorly known small carnivore species that occur in Southeast Asia, the banded civet ( Hemigalus derbyanus ) and Hose ' s civet ( Diplogale hosei ), and analysed their spatial niche differentiation for habitat and elevation. We then identified possible anthropogenic threats, and used our modelling predictions to recommend surveying priorities. The predicted distribution of the banded civet was principally in lowland evergreen forest in southern Myanmar/Thailand, Peninsular Malaysia, Sumatra, Borneo and three Mentawai Islands (Siberut, Sipora and South Pagai), and for Hose ' s civet in evergreen forest across the higher elevation regions of Borneo. Our niche analyses suggested that there is a tendency for these two species to separate spatially along an elevation gradient: the banded civet is mainly found in lowland areas, whereas Hose ' s civet primarily occurs at higher elevations. Our study strongly indicated that these two viverrids are forest-dependent species that may be threatened by forest loss, degradation and fragmentation. Field surveys should be prioritised in areas where each species is predicted to occur and no records currently exist.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.