Hopes and challenges for giant panda conservation under climate change in the Qinling Mountains of China

One way that climate change will impact animal distributions is by altering habitat suitability and habitat fragmentation. Understanding the impacts of climate change on currently threatened species is of immediate importance because complex conservation planning will be required. Here, we mapped changes to the distribution, suitability, and fragmentation of giant panda habitat under climate change and quantified the direction and elevation of habitat shift and fragmentation patterns. These data were used to develop a series of new conservation strategies for the giant panda. Qinling Mountains, Shaanxi, China. Data from the most recent giant panda census, habitat factors, anthropogenic disturbance, climate variables, and climate predictions for the year 2050 (averaged across four general circulation models) were used to project giant panda habitat in Maxent. Differences in habitat patches were compared between now and 2050. While climate change will cause a 9.1% increase in suitable habitat and 9% reduction in subsuitable habitat by 2050, no significant net variation in the proportion of suitable and subsuitable habitat was found. However, a distinct climate change‐induced habitat shift of 11 km eastward by 2050 is predicted firstly. Climate change will reduce the fragmentation of suitable habitat at high elevations and exacerbate the fragmentation of subsuitable habitat below 1,900 m above sea level. Reduced fragmentation at higher elevations and worsening fragmentation at lower elevations have the potential to cause overcrowding of giant pandas at higher altitudes, further exacerbating habitat shortage in the central Qinling Mountains. The habitat shift to the east due to climate change may provide new areas for giant pandas but poses severe challenges for future conservation.     

气候变化背景下秦岭地区羚牛生境脆弱性评估及适应性保护对策

脆弱性是指物种受气候变化影响的程度,开展脆弱性评估工作有助于人类认识气候变化对野生动物的影响,为制定野生动物适应气候变化的保护对策提供科学依据。采用最大熵模型评估气候变化背景下秦岭地区羚牛（Budorcas taxicolor bedfordi）生境脆弱性。结果表明：（1）当前秦岭地区羚牛适宜生境总面积为6473 km²,到2050s年,预测秦岭地区羚牛适宜生境总面积为4217 km²,减少34.85%,羚牛适宜生境将向更高海拔地区转移,转移约210 m;（2）已建保护区覆盖49.82%当前羚牛适宜生境,尚有3248 km²的适宜生境处于保护区之外;到2050s年,保护区覆盖了43.87%适宜生境,尚有2367 km²的适宜生境未被保护;（3）到2050s年,当前分布在太白县、佛坪县、洋县和宁陕县等地区的3490 km²羚牛适宜生境将会成为生境脆弱区域,丧失53.92%;（4）分布在秦岭核心区域的2983 km²当前和2050s年保持不变适宜生境,将成为羚牛躲避气候变化的庇护所。基于研究结果,就未来羚牛应对气候变化的适应性保护对策提出以下几点建议：考虑将当前羚牛适宜生境纳入国家公园范围、构建适应性生态廊道、加强野生动物监测。     

Impact of road construction on giant panda’s habitat and its carrying capacity in Qinling Mountains

The Qinling giant panda (Ailuropoda melanoleuca) is an endangered endemic species to China. Despite ongoing efforts to ensure its conservation, concerns about maintaining its populations persist. We used GIS fed with data on land use including road network of 2001, third national giant panda survey, and a digital elevation model (DEM) to assess the impact of road construction on giant panda habitat, and estimate the carrying capacity of the Qinling Mountain area. We assessed habitat suitability with a mechanistic model, and conducted correlation analysis to evaluate relationship between the extent of giant panda habitat and amount of sites occupied by pandas within of 5 km × 5 km grid. We also estimated the carrying capacity of the Qinling Mountainous Area.
Our results revealed a significant correlation (R² = 0.447, P < 0.01) between the number of sites with signs left by giant panda and the extent of habitat within of 5 km × 5 km grid. The minimum habitat area that can support one panda was 10 km². Before the road network construction, the area of habitat suitable for the panda amounted about 1561 km² and that of marginally suitable habitat about 1499 km². The corresponding carrying capacity represented about 240 individuals. After the road network construction, the suitable habitat area was reduced by nearly 30% to 1093 km². Marginally suitable habitat and unsuitable habitat have both increased by 17% and 1%, respectively. As a result, the potential population size which the habitat could support was reduced to 217 individuals. The study results also suggested that most impacts on habitat from road construction took place in the high elevation areas above 1500 m. However, regarding the impact on the giant panda habitat, road networks developed much more inside the current nature reserves than outside of them.     

Impact of road construction on giant panda’s habitat and its carrying capacity in Qinling Mountains

The Qinling giant panda (Ailuropoda melanoleuca) is an endangered endemic species to China. Despite ongoing efforts to ensure its conservation, concerns about maintaining its populations persist. We used GIS fed with data on land use including road network of 2001, third national giant panda survey, and a digital elevation model (DEM) to assess the impact of road construction on giant panda habitat, and estimate the carrying capacity of the Qinling Mountain area. We assessed habitat suitability with a mechanistic model, and conducted correlation analysis to evaluate relationship between the extent of giant panda habitat and amount of sites occupied by pandas within of 5 km × 5 km grid. We also estimated the carrying capacity of the Qinling Mountainous Area.
Our results revealed a significant correlation (R² = 0.447, P < 0.01) between the number of sites with signs left by giant panda and the extent of habitat within of 5 km × 5 km grid. The minimum habitat area that can support one panda was 10 km². Before the road network construction, the area of habitat suitable for the panda amounted about 1561 km² and that of marginally suitable habitat about 1499 km². The corresponding carrying capacity represented about 240 individuals. After the road network construction, the suitable habitat area was reduced by nearly 30% to 1093 km². Marginally suitable habitat and unsuitable habitat have both increased by 17% and 1%, respectively. As a result, the potential population size which the habitat could support was reduced to 217 individuals. The study results also suggested that most impacts on habitat from road construction took place in the high elevation areas above 1500 m. However, regarding the impact on the giant panda habitat, road networks developed much more inside the current nature reserves than outside of them.     

Identifying climate refugia and its potential impact on Tibetan brown bear (Ursus arctos pruinosus) in Sanjiangyuan National Park,China

Climate change has direct impacts on wildlife and future biodiversity protection efforts. Vulnerability assessment and habitat connectivity analyses are necessary for drafting effective conservation strategies for threatened species such as the Tibetan brown bear (Ursus arctos pruinosus). We used the maximum entropy (MaxEnt) model to assess the current (1950–2000) and future (2041–2060) habitat suitability by combining bioclimatic and environmental variables, and identified potential climate refugia for Tibetan brown bears in Sanjiangyuan National Park, China. Next, we selected Circuit model to simulate potential migration paths based on current and future climatically suitable habitat. Results indicate a total area of potential suitable habitat under the current climate scenario of approximately 31,649.46 km², of which 28,778.29 km² would be unsuitable by the 2050s. Potentially suitable habitat under the future climate scenario was projected to cover an area of 23,738.6 km². Climate refugia occupied 2,871.17 km², primarily in the midwestern and northeastern regions of Yangtze River Zone, as well as the northern region of Yellow River Zone. The altitude of climate refugia ranged from 4,307 to 5,524 m, with 52.93% lying at altitudes between 4,300 and 4,600 m. Refugia were mainly distributed on bare rock, alpine steppe, and alpine meadow. Corridors linking areas of potentially suitable brown bear habitat and a substantial portion of paths with low‐resistance value were distributed in climate refugia. We recommend various actions to ameliorate the impact of climate change on brown bears, such as protecting climatically suitable habitat, establishing habitat corridors, restructuring conservation areas, and strengthening monitoring efforts.     

Identifying the habitat suitability and built-in corridors for Asiatic black bear (Ursus thibetanus) movement in the northern highlands of Pakistan

Identifying habitat suitability and potential corridors are important tools for biodiversity conservation in the face of climate change. We modeled habitat suitability and simulated possible corridors for movement and gene flow among the Asiatic black bear (Ursus thibetanus) population in the Northern Highlands of Pakistan (NHP). Results indicated that the areas of 13,923 km² and 21,931 km² suitable for the Asiatic black bear under current and future scenarios respectively. Under the future scenario, we found an area of 12,657 km² (57.21%) as increase in suitable habitat (ISH_f) and 4649 km² (33.39%) area as a decrease in current suitable habitat (DSH_c). Our model predicted that about >65% (9274 km²) of the current suitable habitat as a climate refugia which is projected from the center to southeast east and northwest of the NHP primarily in the Khyber Pakhtunkhwa (KPK) and Pakistan Administered Kashmir (PAK). The attitudinal range of refugia was projected from 688 m to 4483 m with >56% at the elevations between 2001 m to 3000 m. A very small portion of suitable habitats (current suitable habitat = 2.75%, future suitable habitat = 5.11%) were projected under the protected areas. Maps connecting suitable habitats identified different regions delineated as important for the dispersal of Asiatic black bears, which mainly distributed in the PAK and KPK. Our results help informs conservation strategies and management plans for mitigating the impacts of climate change on Asiatic black bears in the NHP.     

Synergistic effects of anthropogenic disturbances on giant pandas

Livestock grazing and the collection of bamboo shoots are the main threats to giant panda (Ailuropoda melanoleuca) habitat in the Liangshan Mountains in China. It is important to clarify the effect of these disturbances to the giant panda to formulate targeted management policies. Based on species distribution models and daily activity models, we investigated the effects of livestock grazing and bamboo shoot collection on giant pandas from May 2021 to July 2022. Our results indicated the giant panda's suitable habitat in the reserve covered 51.83 km² (15.02% of the reserve area). Grazing and bamboo shoot collection led to losses of 19.08 km² and 7.68 km² of suitable habitat, respectively. Together, the 2 activities resulted in a loss of 28.35 km² of suitable habitat, which was more than half of the area of panda habitat. The areas of suitable habitat for giant pandas significantly overlapped with the areas affected by both disturbances. Giant pandas did not show significant differences in daily activity rhythms under a single disturbance, but the daily activity rhythms of giant pandas differed when we compared the area combining the 2 disturbances with the undisturbed area. Our study reveals that the anthropogenic disturbances in the reserve have varying effects on the suitable habitat range and daily activity rhythm of giant pandas and evidence of a synergistic effect. Therefore, when formulating relevant conservation policies, it is important to fully evaluate the extent and characteristics of anthropogenic disturbances in shaping the population distribution and habitat preferences of the giant panda and other wildlife to enhance the efficacy of conservation management practices.     

An ecophysiological perspective on likely giant panda habitat responses to climate change

Threatened and endangered species are more vulnerable to climate change due to small population and specific geographical distribution. Therefore, identifying and incorporating the biological processes underlying a species’ adaptation to its environment are important for determining whether they can persist in situ. Correlative models are widely used to predict species’ distribution changes, but generally fail to capture the buffering capacity of organisms. Giant pandas (Ailuropoda melanoleuca) live in topographically complex mountains and are known to avoid heat stress. Although many studies have found that climate change will lead to severe habitat loss and threaten previous conservation efforts, the mechanisms underlying panda's responses to climate change have not been explored. Here, we present a case study in Daxiangling Mountains, one of the six Mountain Systems that giant panda distributes. We used a mechanistic model, Niche Mapper, to explore what are likely panda habitat response to climate change taking physiological, behavioral and ecological responses into account, through which we map panda's climatic suitable activity area (SAA) for the first time. We combined SAA with bamboo forest distribution to yield highly suitable habitat (HSH) and seasonal suitable habitat (SSH), and their temporal dynamics under climate change were predicted. In general, SAA in the hottest month (July) would reduce 11.7%–52.2% by 2070, which is more moderate than predicted bamboo habitat loss (45.6%–86.9%). Limited by the availability of bamboo and forest, panda's suitable habitat loss increases, and only 15.5%–68.8% of current HSH would remain in 2070. Our method of mechanistic modeling can help to distinguish whether habitat loss is caused by thermal environmental deterioration or food loss under climate change. Furthermore, mechanistic models can produce robust predictions by incorporating ecophysiological feedbacks and minimizing extrapolation into novel environments. We suggest that a mechanistic approach should be incorporated into distribution predictions and conservation planning.     

气候变化对邛崃山系大熊猫主食竹和栖息地分布的影响

气候变化对生物多样性的影响,特别是珍稀濒危物种的影响是当前的研究热点。全球气候变化对大熊猫的影响一直受到广泛关注。根据野外调查的大熊猫活动痕迹点、竹类分布点和主食竹扩散距离数据,采用Maxent模型,利用植被、地形、气候等因素,在RCP8.5下分析了2050年和2070年邛崃山系大熊猫主食竹分布及栖息地变化趋势。结果显示:(1)未来大熊猫适宜生境及主食竹气候适宜区面积均有所减少,到2070年分别减少37.2%和4.7%;(2)未来主食竹分布范围总体向高海拔扩展,但面积持续减少,到2070年分布面积比当前减少8.3%;(3)大熊猫栖息地未来有向高海拔扩张的趋势,在低海拔地区退缩明显,到2070年较当前减少27.2%;但到2070年大熊猫栖息地面积加上非栖息地有主食竹分布的面积,较现有大熊猫栖息地面积大1.5%;(4)受气候变化影响较严重的区域是邛崃山系南部以及低海拔地区,其余区域所受影响相对较小;(5)未来需要加强对受气候变化影响严重区域的监测与保护,特别是邛崃山系中部的大熊猫集中分布区。     

Habitat Assessment for Giant Pandas in the Qinling Mountain Region of China

ABSTRACT Because habitat loss and fragmentation threaten giant pandas (Ailuropoda melanoleuca), habitat protection and restoration are important conservation measures for this endangered species. However, distribution and value of potential habitat to giant pandas on a regional scale are not fully known. Therefore, we identified and ranked giant panda habitat in Foping Nature Reserve, Guanyinshan Nature Reserve, and adjacent areas in the Qinling Mountains of China. We used Mahalanobis distance and 11 digital habitat layers to develop a multivariate habitat signature associated with 247 surveyed giant panda locations, which we then applied to the study region. We identified approximately 128 km²of giant panda habitat in Foping Nature Reserve (43.6% of the reserve) and 49 km²in Guanyinshan Nature Reserve (33.6% of the reserve). We defined core habitat areas by incorporating a minimum patch-size criterion (5.5 km²) based on home-range size. Percentage of core habitat area was higher in Foping Nature Reserve (41.8% of the reserve) than Guanyinshan Nature Reserve (26.3% of the reserve). Within the larger analysis region, Foping Nature Reserve contained 32.7% of all core habitat areas we identified, indicating regional importance of the reserve. We observed a negative relationship between distribution of core areas and presence of roads and small villages. Protection of giant panda habitat at lower elevations and improvement of habitat linkages among core habitat areas are important in a regional approach to giant panda conservation.     

Habitat assessment of Marco Polo sheep (Ovis ammon polii) in Eastern Tajikistan: Modeling the effects of climate change

Identifying the factors predicting the high‐elevation suitable habitats of Central Asian argali wild sheep and how these suitable habitats are affected by the changing climate regimes could help address conservation and management efforts and identify future critical habitat for the species in eastern Tajikistan. This study used environmental niche models (ENMs) to map and compare potential present and future distributions of suitable environmental conditions for Marco Polo argali. Argali occurrence points were collected during field surveys conducted from 2009 to 2016. Our models showed that terrain ruggedness and annual mean temperature had strong correlations on argali distribution. We then used two greenhouse gas concentration trajectories (RCP 4.5 and RCP 8.5) for two future time periods (2050 and 2070) to model the impacts of climate change on Marco Polo argali habitat. Results indicated a decline of suitable habitat with majority of losses observed at lower elevations (3,300–4,300 m). Models that considered all variables (climatic and nonclimatic) predicted losses of present suitable areas of 60.6% (6,928 km²) and 63.2% (7,219 km²) by 2050 and 2070, respectively. Results also showed averaged habitat gains of 46.2% (6,106 km²) at much higher elevations (4,500–6,900 m) and that elevational shifts of habitat use could occur in the future. Our results could provide information for conservation planning for this near threatened species in the region.     

Application of ecological-niche factor analysis in habitat assessment of giant pandas

Ecological-niche factor analysis (ENFA) is a multivariate approach to study geographic distribution of species on a large scale with only “presence” data. It has been widely applied in many fields including wildlife management, habitat assessment and habitat prediction. In this paper, this approach was applied in habitat suitability assessment for giant pandas in Pingwu County, Sichuan Province, China. With “presence” data of giant pandas and remote sensing data, habitat suitability of pandas in this county was evaluated based on ENFA model, and spatial distribution pattern of nature reserves and conservation gaps were then evaluated. The results show that giant pandas in this county prefer high-elevation zones (> 2128 m) dominated by coniferous forest, and mixed coniferous and deciduous broadleaf forest, and avoid deciduous broadleaf forest and shrubs. Pandas avoid staying at habitats with human disturbances. Farmland is a major factor threatening panda habitat. Panda habitat is mainly distributed in north and west of Pingwu with a total area of 234033 hm², 106345hm² for suitable habitat and 127688 hm² for marginally suitable habitat). 3 nature reserves were located in Pingwu, covering over 49.2% of total suitable habitat and 45.6% of total marginally suitable habitat. Although 47.2% of panda habitat was in reserves under protection, connectivity between reserves was weak and a conservation gap existed in the north part of Pingwu. Thus, a new nature reserve in Baima and Mupi should be established to link the isolated habitats.     

Integrating population size analysis into habitat suitability assessment: implications for giant panda conservation in the Minshan Mountains,China

Compared to conventional approaches, the integration of population size analysis with habitat suitability assessment on a large scale can provide more evidence to explain the mechanisms of habitat isolation and fragmentation, and thus make regional conservation plans. In this paper, we analyzed the habitat suitability for giant pandas in the Minshan Mountains, China, using the ecological-niche factor analysis (ENFA) method, and then evaluated the current conservation status of this endangered species. The results showed that (1) giant pandas were distributed in a narrow altitudinal range in which vegetation cover was dominated by coniferous forest, mixed coniferous and deciduous broadleaf forest, and deciduous broadleaf forest with scattered bamboo understory, and (2) roads and human settlements had strong negative effects on the panda habitat selection. According to habitat analysis, the total habitat area of giant panda in the Minshan Mountains was 953,173 ha, which was fragmented into 12 habitat units by major roads, rivers, and human settlements. The habitat of the mid-Minshan was less fragmentized, but was seriously fragmented in the north. The panda population size estimation showed that 676 individuals inhabited the study area, and 94.53% of them were in the mid-Minshan, but small panda populations less than 30 individuals inhabited the isolated and fragmented habitat patches in the north. The nature reserves in the Minshan Mountains have formed three conservation groups, which covered 41.26% of panda habitat and protected 70.71% of panda population of the study area, but there still exists two conservation gaps, and the connectivity among these reserves is still weak. Due to habitat isolation and extensive human disturbances, giant pandas in the north (i.e., Diebu, Zhouqu, and Wudou) are facing threats of local extinction. In order to protect pandas and their habitats in this area, some effective conservation approaches, such as establishing new reserves in gap areas, creating corridors among patches, and seasonally controlling traffic flux in key roads, should be implemented in the future to link these isolated habitats together.     

Impacts of temperature on giant panda habitat in the north Minshan Mountains

Understanding the impacts of meteorological factors on giant pandas is necessary for future conservation measures in response to global climate change. We integrated temperature data with three main habitat parameters (elevation, vegetation type, and bamboo species) to evaluate the influence of climate change on giant panda habitat in the northern Minshan Mountains using a habitat assessment model. Our study shows that temperature (relative importance = 25.1%) was the second most important variable influencing giant panda habitat excepting the elevation. There was a significant negative correlation between temperature and panda presence (ρ = −0.133, P < 0.05), and the temperature range preferred by giant pandas within the study area was 18–21°C, followed by 15–17°C and 22–24°C. The overall suitability of giant panda habitats will increase by 2.7%, however, it showed a opposite variation patterns between the eastern and northwestern region of the study area. Suitable and subsuitable habitats in the northwestern region of the study area, which is characterized by higher elevation and latitude, will increase by 18007.8 hm² (9.8% habitat suitability), while the eastern region will suffer a decrease of 9543.5 hm² (7.1% habitat suitability). Our results suggest that increasing areas of suitable giant panda habitat will support future giant panda expansion, and food shortage and insufficient living space will not arise as problems in the northwest Minshan Mountains, which means that giant pandas can adapt to climate change, and therefore may be resilient to climate change. Thus, for the safety and survival of giant pandas in the Baishuijiang Reserve, we propose strengthening the giant panda monitoring program in the west and improving the integrity of habitats to promote population dispersal with adjacent populations in the east.     

Identifying refugia and corridors under climate change conditions for the Sichuan snub‐nosed monkey (Rhinopithecus roxellana) in Hubei Province,China

Using a case study of an isolated management unit of Sichuan snub‐nosed monkey (Rhinopithecus roxellana), we assess the extent that climate change will impact the species’ habitat distribution in the current period and projected into the 2050s. We identify refugia that could maintain the population under climate change and determine dispersal paths for movement of the population to future suitable habitats. Hubei Province, China. We identified climate refugia and potential movements by integrating bioclimatic models with circuit theory and least‐cost model for the current period (1960–1990) and the 2050s (2041–2060). We coupled a maximum entropy algorithm to predict suitable habitat for the current and projected future periods. Suitable habitat areas that were identified during both time periods and that also satisfied home range and dispersal distance conditions were delineated as refugia. We mapped potential movements measured as current flow and linked current and future habitats using least‐cost corridors. Our results indicate up to 1,119 km² of currently suitable habitat within the study range. Based on our projections, a habitat loss of 67.2% due to climate change may occur by the 2050s, resulting in a reduced suitable habitat area of 406 km² and very little new habitat. The refugia areas amounted to 286 km² and were located in Shennongjia National Park and Badong Natural Reserve. Several connecting corridors between the current and future habitats, which are important for potential movements, were identified. Our assessment of the species predicted a trajectory of habitat loss following anticipated future climate change. We believe conservation efforts should focus on refugia and corridors when planning for future species management. This study will assist conservationists in determining high‐priority regions for effective maintenance of the endangered population under climate change and will encourage increased habitat connectivity.     

Anticipated climate and land‐cover changes reveal refuge areas for Borneo's orang‐utans

Habitat loss and climate change pose a double jeopardy for many threatened taxa, making the identification of optimal habitat for the future a conservation priority. Using a case study of the endangered Bornean orang‐utan, we identify environmental refuges by integrating bioclimatic models with projected deforestation and oil‐palm agriculture suitability from the 1950s to 2080s. We coupled a maximum entropy algorithm with information on habitat needs to predict suitable habitat for the present day and 1950s. We then projected to the 2020s, 2050s and 2080s in models incorporating only land‐cover change, climate change or both processes combined. For future climate, we incorporated projections from four model and emission scenario combinations. For future land cover, we developed spatial deforestation predictions from 10 years of satellite data. Refuges were delineated as suitable forested habitats identified by all models that were also unsuitable for oil palm – a major threat to tropical biodiversity. Our analyses indicate that in 2010 up to 260 000 km² of Borneo was suitable habitat within the core orang‐utan range; an 18–24% reduction since the 1950s. Land‐cover models predicted further decline of 15–30% by the 2080s. Although habitat extent under future climate conditions varied among projections, there was majority consensus, particularly in north‐eastern and western regions. Across projections habitat loss due to climate change alone averaged 63% by 2080, but 74% when also considering land‐cover change. Refuge areas amounted to 2000–42 000 km² depending on thresholds used, with 900–17 000 km² outside the current species range. We demonstrate that efforts to halt deforestation could mediate some orang‐utan habitat loss, but further decline of the most suitable areas is to be expected given projected changes to climate. Protected refuge areas could therefore become increasingly important for ongoing translocation efforts. We present an approach to help identify such areas for highly threatened species given environmental changes expected this century.     

Model‐based conservation planning of the genetic diversity of Phellodendron amurense Rupr due to climate change

Climate change affects both habitat suitability and the genetic diversity of wild plants. Therefore, predicting and establishing the most effective and coherent conservation areas is essential for the conservation of genetic diversity in response to climate change. This is because genetic variance is a product not only of habitat suitability in conservation areas but also of efficient protection and management. Phellodendron amurense Rupr. is a tree species (family Rutaceae) that is endangered due to excessive and illegal harvesting for use in Chinese medicine. Here, we test a general computational method for the prediction of priority conservation areas (PCAs) by measuring the genetic diversity of P. amurense across the entirety of northeast China using a single strand repeat analysis of twenty microsatellite markers. Using computational modeling, we evaluated the geographical distribution of the species, both now and in different future climate change scenarios. Different populations were analyzed according to genetic diversity, and PCAs were identified using a spatial conservation prioritization framework. These conservation areas were optimized to account for the geographical distribution of P. amurense both now and in the future, to effectively promote gene flow, and to have a long period of validity. In situ and ex situ conservation, strategies for vulnerable populations were proposed. Three populations with low genetic diversity are predicted to be negatively affected by climate change, making conservation of genetic diversity challenging due to decreasing habitat suitability. Habitat suitability was important for the assessment of genetic variability in existing nature reserves, which were found to be much smaller than the proposed PCAs. Finally, a simple set of conservation measures was established through modeling. This combined molecular and computational ecology approach provides a framework for planning the protection of species endangered by climate change.     

Species Distribution Modelling predicts habitat suitability and reduction of suitable habitat under future climatic scenario for Sclerophrys perreti: A critically endangered Nigerian endemic toad

Sclerophrys perreti is a critically endangered Nigerian native frog currently imperilled by human activities. A better understanding of its potential distribution and habitat suitability will aid in conservation; however, such knowledge is limited for S. perreti. Herein, we used a species distribution model (SDM) approach with all known occurrence data (n = 22) from our field surveys and primary literature, and environmental variable predictors (19 bioclimatic variables, elevation and land cover) to elucidate habitat suitability and impact of climate change on this species. The SDM showed that temperature and precipitation were the predictors of habitat suitability for S. perreti with precipitation seasonality as the strongest predictor of habitat suitability. The following variable also had a significant effect on habitat suitability: temperature seasonality, temperature annual range, precipitation of driest month, mean temperature of wettest quarter and isothermality. The model predicted current suitable habitat for S. perreti covering an area of 1,115 km². However, this habitat is predicted to experience 60% reduction by 2050 owing to changes in temperature and precipitation. SDM also showed that suitable habitat exists in south-eastern range of the inselberg with predicted low impact of climate change compared to other ranges. Therefore, this study recommends improved conservation measures through collaborations and stakeholder's meeting with local farmers for the management and protection of S. perreti.     

Applying a framework for landscape planning under climate change for the conservation of biodiversity in the Finnish boreal forest

Conservation strategies are often established without consideration of the impact of climate change. However, this impact is expected to threaten species and ecosystem persistence and to have dramatic effects towards the end of the 21st century. Landscape suitability for species under climate change is determined by several interacting factors including dispersal and human land use. Designing effective conservation strategies at regional scales to improve landscape suitability requires measuring the vulnerabilities of specific regions to climate change and determining their conservation capacities. Although methods for defining vulnerability categories are available, methods for doing this in a systematic, cost‐effective way have not been identified. Here, we use an ecosystem model to define the potential resilience of the Finnish forest landscape by relating its current conservation capacity to its vulnerability to climate change. In applying this framework, we take into account the responses to climate change of a broad range of red‐listed species with different niche requirements. This framework allowed us to identify four categories in which representation in the landscape varies among three IPCC emission scenarios (B1, low; A1B, intermediate; A2, high emissions): (i) susceptible (B1 = 24.7%, A1B = 26.4%, A2 = 26.2%), the most intact forest landscapes vulnerable to climate change, requiring management for heterogeneity and resilience; (ii) resilient (B1 = 2.2%, A1B = 0.5%, A2 = 0.6%), intact areas with low vulnerability that represent potential climate refugia and require conservation capacity maintenance; (iii) resistant (B1 = 6.7%, A1B = 0.8%, A2 = 1.1%), landscapes with low current conservation capacity and low vulnerability that are suitable for restoration projects; (iv) sensitive (B1 = 66.4%, A1B = 72.3%, A2 = 72.0%), low conservation capacity landscapes that are vulnerable and for which alternative conservation measures are required depending on the intensity of climate change. Our results indicate that the Finnish landscape is likely to be dominated by a very high proportion of sensitive and susceptible forest patches, thereby increasing uncertainty for landscape managers in the choice of conservation strategies.     

Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya

Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator–prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy‐deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate‐only model shows that only 11.64% (17,190 km²) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km² (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate‐only model. It is predicted that future climate may alter the predator–prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards – a species already facing energetic constraints due to the limited dietary resources in its alpine habitat. Our findings provide valuable information for extension of protected areas in future.