The importance of correcting for sampling bias in MaxEnt species distribution models

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.publishe

Targeted Conservation to Safeguard a Biodiversity Hotspot from Climate and Land-Cover Change

Responses of biodiversity to changes in both land cover and climate are recognized [1] but still poorly understood [2]. This poses significant challenges for spatial planning as species could shift, contract, expand, or maintain their range inside or outside protected areas [2, 3 and 4]. We examine this problem in Borneo, a global biodiversity hotspot [5], using spatial prioritization analyses that maximize species conservation under multiple environmental-change forecasts. Climate projections indicate that 11%–36% of Bornean mammal species will lose ?30% of their habitat by 2080, and suitable ecological conditions will shift upslope for 23%–46%. Deforestation exacerbates this process, increasing the proportion of species facing comparable habitat loss to 30%–49%, a 2-fold increase on historical trends. Accommodating these distributional changes will require conserving land outside existing protected areas, but this may be less than anticipated from models incorporating deforestation alone because some species will colonize high-elevation reserves. Our results demonstrate the increasing importance of upland reserves and that relatively small additions (16,000–28,000 km2) to the current conservation estate could provide substantial benefits to biodiversity facing changes to land cover and climate. On Borneo, much of this land is under forestry jurisdiction, warranting targeted conservation partnerships to safeguard biodiversity in an era of global change