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

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.     

Spatial conservation planning under climate change: Using species distribution modeling to assess priority for adaptive management of Fagus crenata in Japan

Protected areas are the basis of modern conservation systems, but current climate change causes gaps between protected areas and the species distribution ranges. To mitigate the impact of climate change on species distribution ranges, revision of protected areas are necessary. Alternatively, active management such as excluding competitive species or transplanting target species would be effective. In this study, we assessed optimal actions (revision of protected areas or active management) in each geographical region to establish an effective spatial conservation plan in Japan. Gaps between the protected areas and future potential habitats were assessed using species distribution models and 20 future climate simulations. Fagus crenata, an endemic and dominant species in Japan, was used as a target species. Potential habitats within the protected areas were predicted to decrease from 22,122 km² at present to 12,309 km² under future climate conditions. Sustainable potential habitats (consistent potential habitats both at present and in future) without the protected areas extended to 13,208 km², and were mainly found in northeast Japan. These results suggest that, in northeast Japan, revisions to protected areas would be effective in preserving sustainable potential habitats under future climate change. However, the potential habitats of southwestern Japan, in which populations were genetically different from northeastern populations, were predicted to virtually disappear both within and outside of protected areas. Active management is thus necessary in southwestern Japan to ensure intraspecific genetic diversity under future climate change.     

GIS-Based Multicriteria Evaluation and Fuzzy Sets to Identify Priority Sites for Marine Protection

There is an increasing momentum within the marine conservation community to develop representative networks of marine protected areas (MPAs) covering up to 30% of global marine habitats. However, marine conservation initiatives are perceived as uncoordinated at most levels of planning and decision-making. These initiatives also face the challenge of being in conflict with ongoing drives for sustained or increased resource extraction. Hence, there is an urgent need to develop large scale theoretical frameworks that explicitly address conflicting objectives that are embedded in the design and development of a global MPA network. Further, the frameworks must be able to guide the implementation of smaller scale initiatives within this global context. This research examines the applicability of an integrated spatial decision support framework based on geographic information systems (GIS), multicriteria evaluation (MCE) and fuzzy sets to objectively identify priority locations for future marine protection. MCE is a well-established optimisation method used extensively in land use resource allocation and decision support, and which has to date been underutilised in marine planning despite its potential to guide such efforts. The framework presented here was implemented in the Pacific Canadian Exclusive Economic Zone (EEZ) using two conflicting objectives - biodiversity conservation and fisheries profit-maximisation. The results indicate that the GIS-based MCE framework supports the objective identification of priority locations for future marine protection. This is achieved by integrating multi-source spatial data, facilitating the simultaneous combination of multiple objectives, explicitly including stakeholder preferences in the decisions, and providing visualisation capabilities to better understand how global MPA networks might be developed under conditions of uncertainty and complexity.     

Developing and testing a habitat suitability index model for Korean water deer (Hydropotes inermis argyropus) and its potential for landscape management decisions in Korea

Geographic information system (GIS) and landscape-level data offer a new opportunity for modeling and evaluating the quality of wildlife habitats. Models of habitat quality have not been developed for some species, and existing models could be improved by incorporating updated information on wildlife–habitat relationships and habitat variables. We developed a GIS-based habitat suitability index (HSI) model for the Korean water deer (Hydropotes inermis argyropus), which often causes human–wildlife conflicts in the Chungnam Province of Korea because of industrialization and urbanization. The model is based on logistic regression analysis, which addresses the impact of multiple habitat variables, such as habitat components, topographic characteristics, and human disturbances. The model yielded a p-value of .289 (χ²?=?9.672) and 65.4% correct prediction level with the overall observation–prediction comparison data. The model demonstrated that a large portion of the province (61.6%) could be regarded as a poor habitat (mean HSI value of the province?=?0.22), while the current habitats of the province could be considered of moderate quality (mean HSI value?=?0.31). In addition, the chance of observation of the deer increases as the HSI level increases, which means that the model yields a good predictive power. Lastly, we used the model to produce a habitat suitability map. Our HSI model enabled us to quantify habitat preferences, which could be the basis for decision-making on habitat protection, mitigation, and enhancement of the Korean water deer. The proposed model is also applicable for improving and enhancing the existing management practices, as well as for establishing an effective wildlife protection policy.