Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus)

Abstract

Aim Data on geographical ranges are essential when defining the conservation status of a species, and in evaluating levels of human disturbance. Where locality data are deficient, presence‐only ecological niche modelling (ENM) can provide insights into a species’ potential distribution, and can aid in conservation planning. Presence‐only ENM is especially important for rare, cryptic and nocturnal species, where absence is difficult to define. Here we applied ENM to carry out an anthropogenic risk assessment and set conservation priorities for three threatened species of Asian slow loris (Primates: Nycticebus).Location Borneo, Java and Sumatra, Southeast Asia.Methods Distribution models were built using maximum entropy (MaxEnt) ENM. We input 20 environmental variables comprising temperature, precipitation and altitude, along with species locality data. We clipped predicted distributions to forest cover and altitudinal data to generate remnant distributions. These were then applied to protected area (PA) and human land‐use data, using specific criteria to define low‐, medium‐ or high‐risk areas. These data were analysed to pinpoint priority study sites, suitable reintroduction zones and protected area extensions.Results A jackknife validation method indicated highly significant models for all three species with small sample sizes (n = 10 to 23 occurrences). The distribution models represented high habitat suitability within each species’ geographical range. High‐risk areas were most prevalent for the Javan slow loris (Nycticebus javanicus) on Java, with the highest proportion of low‐risk areas for the Bornean slow loris (N. menagensis) on Borneo. Eighteen PA extensions and 23 priority survey sites were identified across the study region.Main conclusions Discriminating areas of high habitat suitability lays the foundations for planning field studies and conservation initiatives. This study highlights potential reintroduction zones that will minimize anthropogenic threats to animals that are released. These data reiterate the conclusion of previous research, showing MaxEnt is a viable technique for modelling species distributions with small sample sizes.

Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China

“…The ENMs were developed to understand environmental variation associated with the distribution of infected rodent reservoirs. 21 A recent introduced presence-only distribution modeling technique-the Maximum Entropy approach, was applied in various domains and achieved high predictive accuracy, [22][23][24][25][26][27][28] and showed the best predictive power across all sample sizes. 3,25,[29][30][31] Detailed descriptions of the Maximum Entropy program (MAXENT, version 3.3.1) can be found in References 25 and 32.…”

Bringing the Hutchinsonian niche into the 21st century: Ecological and evolutionary perspectives

“…For instance, for rare or poorly understood organisms, SNMs provide a starting point for projecting impacts of environmental change (e.g., ref. 28) and crafting efficient survey designs (25,28). One welcome recent trend is that multiple approaches are being applied simultaneously (29,30).…”

Assessing range-wide habitat suitability for the Lesser Prairie-Chicken

“…However, this is only possible if data to inform conservation needs are available at the time decisions are being made. Models of species distribution and habitat suitability can be created using fewer than 25 locations to successfully guide future field surveys (Pearson et al 2007) and can inform conservation decisions related to land-use conversion (Marini et al 2009, Thorn et al 2009). …”