Prediction of habitat suitability,connectivity, and corridors in the future to conserve roe deer (Capreolus capreolus) as a locally endangered species in northern Iran

Roe deer is a protected species in Iran as its population and distribution in the country have considerably declined. Roe deer are threatened by several factors such as habitat fragmentation and road mortality, so studying their distribution and movement through the increasing habitat destruction and fragmentation is necessary. This will become increasingly important because climate change will transform the species’ future habitat and connectivity patterns. We evaluated the roe deer’s potential distribution range in northern Iran and, for the first time, developed connectivity models and designed corridors for the present and future to make better conservation plans. We collected 91 points indicating the presence of roe deer in the study region. After developing ensemble models using six species distribution algorithms, we defined high-ranked habitat cores using the concept of landscape suitability prioritization. From these, we designed connectivity and corridors in two time-frames with the help of least-cost paths and circuit theories to predict the potential movement throughout the study area. We estimated that the overall core habitats for roe deer in the present and future periods are, respectively, around 1200 km² and 2600 km², corresponding to 2 and 4 percent of the whole area. This suggests that the habitat core will expand in the future as a result of climate change. Similarly, the connectivity among the cores will strengthen. We also conclude that the temperature-driven and anthropogenic variables significantly affect the distribution of roe deer in northern Iran. It is necessary that conservationists and managers consider the designed corridors in the present study while planning conservation strategies.     

BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change

A new computation framework (BIOMOD: BIOdiversity MODelling) is presented, which aims to maximize the predictive accuracy of current species distributions and the reliability of future potential distributions using different types of statistical modelling methods. BIOMOD capitalizes on the different techniques used in static modelling to provide spatial predictions. It computes, for each species and in the same package, the four most widely used modelling techniques in species predictions, namely Generalized Linear Models (GLM), Generalized Additive Models (GAM), Classification and Regression Tree analysis (CART) and Artificial Neural Networks (ANN). BIOMOD was applied to 61 species of trees in Europe using climatic quantities as explanatory variables of current distributions. On average, all the different modelling methods yielded very good agreement between observed and predicted distributions. However, the relative performance of different techniques was idiosyncratic across species, suggesting that the most accurate model varies between species. The results of this evaluation also highlight that slight differences between current predictions from different modelling techniques are exacerbated in future projections. Therefore, it is difficult to assess the reliability of alternative projections without validation techniques or expert opinion. It is concluded that rather than using a single modelling technique to predict the distribution of several species, it would be more reliable to use a framework assessing different models for each species and selecting the most accurate one using both evaluation methods and expert knowledge.     

基于BIOMOD的青海湖流域高原鼠兔分布模拟

随着统计模型及空间信息数据的不断发展和完善,物种分布模型已经成为全球变化背景下研究大尺度物种分布情况的重要工具。高原鼠兔(Ochotona curzoniae)是青藏高原特有的关键物种,在青藏高原生态系统中占有重要地位。通过采集高原鼠兔的分布点数据及环境变量数据,基于R语言中BIOMOD包中的7个模型对其在青海湖流域的分布进行了模拟。结果表明,高原鼠兔主要分布于青海湖西岸和北岸、天峻县周边及布哈河流域上游,影响高原鼠兔分布的主要环境因子为距道路距离、距居民点距离、最暖月最高气温、NDVI标准差、最冷季和最干季降水量。BIOMOD组合模型中,推进式回归树模型(GBM)和最大熵模型(MAXENT)的模拟效果最好,广义线性回归模型(GLM)结果较差。而优化后的结果显示,模拟结果的集成和筛选能有效提高模型的精度和效果。     

Likelihood of changes in forest species suitability,distribution, and diversity under future climate: The case of Southern Europe

Forest conservation strategies and plans can be unsuccessful if the new habitat conditions determined by climate change are not considered. Our work aims at investigating the likelihood of future suitability, distribution and diversity for some common European forest species under the projected changes in climate, focusing on Southern Europe. We combine an Ensemble Platform for Species Distribution Models (SDMs) to five Global Circulation Models (GCMs) driven by two Representative Concentration Pathways (RCPs), to produce maps of future climate‐driven habitat suitability for ten categories of forest species and two time horizons. For each forest category and time horizon, ten maps of future distribution (5 GCMs by 2 RCPs) are thus combined in a single suitability map supplied with information about the “likelihood” adopting the IPCC terminology based on consensus among projections. Then, the statistical significance of spatially aggregated changes in forest composition at local and regional level is analyzed. Finally, we discuss the importance, among SDMs, that environmental predictors seem to have in influencing forest distribution. Future impacts of climate change appear to be diversified across forest categories. A strong change in forest regional distribution and local diversity is projected to take place, as some forest categories will find more suitable conditions in previously unsuitable locations, while for other categories the same new conditions will become less suited. A decrease in species diversity is projected in most of the area, with Alpine region showing the potentiality to become a refuge for species migration.     

Predicted declines in suitable habitat for greater one‐horned rhinoceros (Rhinoceros unicornis) under future climate and land use change scenarios

Rapidly changing climate is likely to modify the spatial distribution of both flora and fauna. Land use change continues to alter the availability and quality of habitat and further intensifies the effects of climate change on wildlife species. We used an ensemble modeling approach to predict changes in habitat suitability for an iconic wildlife species, greater one‐horned rhinoceros due to the combined effects of climate and land use changes. We compiled an extensive database on current rhinoceros distribution and selected nine ecologically meaningful environmental variables for developing ensemble models of habitat suitability using ten different species distribution modeling algorithms in the BIOMOD2 R package; and we did this under current climatic conditions and then projected them onto two possible climate change scenarios (SSP1‐2.6 and SSP5‐8.5) and two different time frames (2050 and 2070). Out of ten algorithms, random forest performed the best, and five environmental variables—distance from grasslands, mean temperature of driest quarter, distance from wetlands, annual precipitation, and slope, contributed the most in the model. The ensemble model estimated the current suitable habitat of rhinoceros to be 2610 km², about 1.77% of the total area of Nepal. The future habitat suitability under the lowest and highest emission scenarios was estimated to be: (1) 2325 and 1904 km² in 2050; and (2) 2287 and 1686 km² in 2070, respectively. Our results suggest that over one‐third of the current rhinoceros habitat would become unsuitable within a period of 50 years, with the predicted declines being influenced to a greater degree by climatic changes than land use changes. We have recommended several measures to moderate these impacts, including relocation of the proposed Nijgad International Airport given that a considerable portion of potential rhinoceros habitat will be lost if the airport is constructed on the currently proposed site.     

Shifting ranges and conservation challenges for lemurs in the face of climate change

Geospatial modeling is one of the most powerful tools available to conservation biologists for estimating current species ranges of Earth's biodiversity. Now, with the advantage of predictive climate models, these methods can be deployed for understanding future impacts on threatened biota. Here, we employ predictive modeling under a conservative estimate of future climate change to examine impacts on the future abundance and geographic distributions of Malagasy lemurs. Using distribution data from the primary literature, we employed ensemble species distribution models and geospatial analyses to predict future changes in species distributions. Current species distribution models (SDMs) were created within the BIOMOD2 framework that capitalizes on ten widely used modeling techniques. Future and current SDMs were then subtracted from each other, and areas of contraction, expansion, and stability were calculated. Model overprediction is a common issue associated Malagasy taxa. Accordingly, we introduce novel methods for incorporating biological data on dispersal potential to better inform the selection of pseudo‐absence points. This study predicts that 60% of the 57 species examined will experience a considerable range of reductions in the next seventy years entirely due to future climate change. Of these species, range sizes are predicted to decrease by an average of 59.6%. Nine lemur species (16%) are predicted to expand their ranges, and 13 species (22.8%) distribution sizes were predicted to be stable through time. Species ranges will experience severe shifts, typically contractions, and for the majority of lemur species, geographic distributions will be considerably altered. We identify three areas in dire need of protection, concluding that strategically managed forest corridors must be a key component of lemur and other biodiversity conservation strategies. This recommendation is all the more urgent given that the results presented here do not take into account patterns of ongoing habitat destruction relating to human activities.     

Predicting the future of forests in the Mediterranean under climate change,with niche‐ and process‐based models: CO2 matters!

Assessing the potential future of current forest stands is a key to design conservation strategies and understanding potential future impacts to ecosystem service supplies. This is particularly true in the Mediterranean basin, where important future climatic changes are expected. Here, we assess and compare two commonly used modeling approaches (niche‐ and process‐based models) to project the future of current stands of three forest species with contrasting distributions, using regionalized climate for continental Spain. Results highlight variability in model ability to estimate current distributions, and the inherent large uncertainty involved in making projections into the future. CO₂ fertilization through projected increased atmospheric CO₂ concentrations is shown to increase forest productivity in the mechanistic process‐based model (despite increased drought stress) by up to three times that of the non‐CO₂ fertilization scenario by the period 2050–2080, which is in stark contrast to projections of reduced habitat suitability from the niche‐based models by the same period. This highlights the importance of introducing aspects of plant biogeochemistry into current niche‐based models for a realistic projection of future species distributions. We conclude that the future of current Mediterranean forest stands is highly uncertain and suggest that a new synergy between niche‐ and process‐based models is urgently needed in order to improve our predictive ability.