Assessing vulnerability of giant pandas to climate change in the Qinling Mountains of China

Modeling the Wintering Habitat Distribution of the Black Stork in Shaanxi, China: A Hierarchical Integration of Climate and Land Use/Land Cover Data

Species distribution models (SDMs) are effective tools for wildlife conservation and management, as they employ the quantification of habitat suitability and environmental niches to evaluate the patterns of species distribution. The utilization of SDMs at various scales in a hierarchical approach can provide additional and complementary information, significantly improving decision-making in local wildlife conservation initiatives. In this study, we considered the appropriate spatial scale and data resolution to execute species distribution modeling, as these factors greatly influence the modeling procedures. We developed SDMs for wintering black storks at both the regional and local scales. At the regional scale, we used climatic and climate-driven land use/land cover (LULC) variables, along with wintering occurrence points, to develop models for mainland China. At the local scale, we used local environmental variables and locally gathered wintering site data to develop models for Shaanxi province. The predictions from both the regional and local models were then combined at the provincial level by overlapping suitable areas based on climatic and local conditions. We compared and evaluated the resulting predictions using seven statistical metrics. The national models provide information on the appropriate climatic conditions for the black stork during the wintering period throughout China, while the provincial SDMs capture the important local ecological factors that influence the suitability of habitats at a finer scale. As anticipated, the national SDMs predict a larger extent of suitable areas compared to the provincial SDMs. The hierarchical prediction approach is considered trustworthy and, on average, yields better outcomes than non-hierarchical methods. Our findings indicate that human-driven LULC changes have a significant and immediate impact on the wintering habitat of the black stork. However, the effects of climate change seem to be reducing the severity of this impact. The majority of suitable wintering habitats lie outside the boundaries of protected areas, highlighting the need for future conservation and management efforts to prioritize addressing these conservation gaps and focusing on the protection of climate refuges.

Climatic factors and human disturbance influence ungulate species distribution on the Qinghai-Tibet Plateau

Due to human activities and climate change, the habitats of ungulate species on the Qinghai-Tibet Plateau have been greatly affected in recent decades. In this study, the distribution patterns of 19 ungulate species on the Qinghai-Tibet Plateau were identified based on MaxEnt model in the past (1960-1990) and current periods (2000-2015). Then the changes of their habitat distribution and the species richness in different periods were compared. Finally, the Zonation model was used to identify the key protected areas of ungulate species. The results show that the MaxEnt model can well predict the distribution of ungulate species. Most ungulate species are mainly distributed in the southeast of the Qinghai-Tibet Plateau. The distance to lakes and precipitation are the main factors affecting the distribution of most ungulate species. The habitats originally located in the southeast of the Qinghai-Tibet Plateau have mainly extended northeastwards, while the habitats originally located in the northwest has been largely lost. The changes in the habitats of ungulate species in the southeast and northwest are diametrically opposite. The key protected areas identified by Zonation model are mainly located in the southeast of the Qinghai-Tibet Plateau. The existing nature reserves can effectively protect the suitable habitats of the Tibetan antelope, Tibetan wild ass and wild yak. This research can provide scientific basis for coordinating the contradiction between development and protection and promoting the biodiversity conservation on the Qinghai-Tibet Plateau.

Coexistence patterns of sympatric giant pandas (Ailuropoda melanoleuca) and Asiatic black bears (Ursus thibetanus) in Changqing National Nature Reserve, China

Similar species may co-occur in sympatry because of the partitioning of habitat use and resources at different spatial and temporal scales. Understanding coexistence patterns of species may contribute to further uncovering the underlying coexistence mechanisms, and ultimately benefit the conservation of threatened species. In this study, camera trapping was used to investigate spatial and temporal activity patterns of sympatric giant pandas (Ailuropoda melanoleuca) and Asiatic black bears (Ursus thibetanus) in Changqing National Nature Reserve in Qinling Mountains, China. Our study obtained 281 independent detections of giant pandas and 185 of Asiatic black bears during 93,606 camera-trap days from April 2014 to October 2017. We performed occupancy modeling and temporal overlap analyses to examine the spatial-temporal relationships between pandas and bears, and results showed that: (1) giant pandas had higher detection probabilities than Asiatic black bears, while having lower occupancy probabilities; (2) Elevation positively predicted giant panda and negatively predicted Asiatic black bear occupancy, understory vegetation type negatively predicted giant panda occupancy, and distance to nearest settlement positively predicted Asiatic black bear occupancy; (3) giant pandas were more active in spring and winter, while Asiatic black bears were more active in summer, and the two species had low spatial overlap with one another throughout the year; (4) both giant pandas and Asiatic black bears showed mainly diurnal activity patterns, and had high temporal overlap with one another in spring and moderate temporal overlap with one another in autumn. Our results provide detailed information of the spatial and temporal ecology of sympatric giant pandas and Asiatic black bears in the Qinling Mountains of China, which could act as a guide to construct conservation priorities as well as design efficient management programs.

Predicting range shifts of the giant pandas under future climate and land use scenarios

Understanding and predicting how species will respond to global environmental change (i.e., climate and land use change) is essential to efficiently inform conservation and management strategies for authorities and managers. Here, we assessed the combined effect of future climate and land use change on the potential range shifts of the giant pandas (Ailuropoda melanoleuca) in Sichuan Province, China. We used species distribution models (SDMs) to forecast range shifts of the giant pandas by the 2050s and 2070s under four combined climate and land use change scenarios. We also compared the differences in distributional changes of giant pandas among the five mountains in the study area. Our SDMs exhibited good model performance and were not overfitted, with a mean Boyce index of 0.960 ± 0.015 and a mean omission rate of 0.002 ± 0.003, and suggested that precipitation seasonality, annual mean temperature, the proportion of forest cover, and total annual precipitation are the most important factors in shaping the current distribution pattern of the giant pandas. Our projections of future species distribution also suggested a range expansion under an optimistic greenhouse gas emission, while suggesting a range contraction under a pessimistic greenhouse gas emission. Moreover, we found that there is considerable variation in the projected range change patterns among the five mountains in the study area. Especially, the suitable habitat of the giant panda is predicted to increase under all scenarios in the Minshan mountains, while is predicted to decrease under all scenarios in Daxiangling and Liangshan mountains, indicating the vulnerability of the giant pandas at low latitudes. Our findings highlight the importance of an integrated approach that combines climate and land use change to predict the future species distribution and the need for a spatial explicit consideration of the projected range change patterns of target species for guiding conservation and management strategies.

The impact of climate change on three indicator Galliformes species in the northern highlands of Pakistan

The rise in global temperature is one of the main threats of extinction to many vulnerable species by the twenty-first century. The negative impacts of climate change on the northern highlands of Pakistan (NHP) could change the species composition. Range shifts and range reduction in the forested landscapes will dramatically affect the distribution of forest-dwelling species, including the Galliformes (ground birds). Three Galliformes (e.g., Lophophorus impejanus, Pucrasia macrolopha, and Tragopan melanocephalus) are indicator species of the environment and currently distributed in NHP. For this study, we used Maximum Entropy Model (MaxEnt) to simulate the current (average for 1960-1990) and future (in 2050 and 2070) distributions of the species using three General Circulation Models (GCMs) and two climate change scenarios, i.e., RCP4.5 (moderate carbon emission scenario) and RCP8.5 (peak carbon emission scenario). Our results indicated that (i) under all three climate scenarios, species distribution was predicted to both reduce and shift towards higher altitudes. (ii) Across the provinces in the NHP, the species were predicted to average lose around one-third (35%) in 2050 and one-half (47%) by 2070 of the current suitable habitat. (iii) The maximum area of climate refugia was projected between the altitudinal range of 2000 to 4000 m and predicted to shift towards higher altitudes primarily > 3000 m in the future. Our results help inform management plans and conservation strategies for mitigating the impacts of climate change on three indicator Galliforms species in the NHP.

Spatial-Temporal Evolution and Driving Forces of NDVI in China's Giant Panda National Park

Identifying the ecological evolution trends and vegetation driving mechanisms of giant panda national parks can help to improve the protection of giant panda habitats. Based on the research background of different geomorphological zoning, we selected the MODIS NDVI data from 2000 to 2020 to analyze the NDVI trends using a univariate linear model. A partial correlation analysis and multiple correlation analysis were used to reveal the influence of temperature and precipitation on NDVI trends. Fourteen factors related to meteorological factors, topographic factors, geological activities, and human activities were selected, and the Geographically Weighted Regression model was used to study the mechanisms driving NDVI change. The results were as follows: (1) The NDVI value of Giant Panda National Park has fluctuated and increased in the past 21 years, with an annual growth rate of 4.7%/yr. Affected by the Wenchuan earthquake in 2008, the NDVI value fluctuated greatly from 2008 to 2012, and reached its peak in 2018. (2) The NDVI in 94% of the study area improved, and the most significant improvement areas were mainly distributed in the northern and southern regions of Southwest Subalpine and Middle Mountain and the Xiaoxiangling area. Affected by the distribution of fault zones and their local activities, vegetation degradation was concentrated in the Dujiangyan-Anzhou area of Hengduan Mountain Alpine Canyon. (3) The Geographically Weighted Regression analysis showed that natural factors were dominant, with climate and elevation having a double-factor enhancement effect, the peak acceleration of ground motion and fault zone having a superimposed effect, and river density and slope having a double effect, all of which had a significant impact on the NDVI value of the surrounding area. To optimize the ecological security pattern of the Giant Panda National Park, we recommended strengthening the construction of ecological security projects through monitoring meteorological changes, preventing, and controlling geo-hazards, and optimizing the layout and intensity of human activities.

The overlap of suitable tea plant habitat with Asian elephant (Elephus maximus) distribution in southwestern China and its potential impact on species conservation and local economy

The expansion of land being used for cash crop cultivation has threatened wildlife in recent decades. Tea has become the dominant cash crop in southwestern China. Unfortunately, tea plantations may threaten Asian elephant (Elephus maximus) populations via habitat loss and fragmentation. Identifying areas of suitable habitat for tea plant cultivation, and where this habitat overlaps with Asian elephant distribution, is vital for planning land use, managing nature reserves, shaping policy, and maintaining local economies. Here, we assess the potential impact of tea plantations on Asian elephants in southwestern Yunnan province, China. We used MaxEnt modeling with bioclimatic and environmental variables to identify suitable habitat for tea plant cultivation under the current climate scenario, and then overlapped this habitat with 9 known Asian elephant distribution areas (G1-G9) to determine "threatened areas." Our results showed that (1) annual precipitation (48.1% contribution), temperature constancy (29 % contribution), and slope (8.7 % contribution) were key in determining suitable habitat for tea plants; (2) the cumulative area of suitable habitat for tea plants was 13,784.88 km², mainly distributed in Menghai (3934.53 km²), Lancang (3198.67 km²), and Jinghong (2657.74 km²); (3) the distribution area of elephants was 943.75 km², and these areas overlapped with suitable tea plant habitat primarily located in G4 (379.40 km²), G3 (251.18), and G7 (168.03 km²); and (4) threatened areas in G1 and G7 were predominately located along the periphery of current nature reserves. Win-win solutions that work for elephant conservation and economic development include rescoping nature reserve boundaries, strengthening management on the periphery of nature reserves, establishing ecological corridors and new nature reserves within regions where elephants are currently distributed, planting alternative cash crops, and financial subsidies to farmers. This study improves understanding of human-elephant coexistence, and will assist in guiding land use policy for the future conservation outcomes seeking to promote responsible and profitable cash crop farming and elephant conservation.

Projected impacts of climate change on snow leopard habitat in Qinghai Province, China

Assessing species' vulnerability to climate change is a prerequisite for developing effective strategies to reduce emerging climate-related threats. We used the maximum entropy algorithm (MaxEnt model) to assess potential changes in suitable snow leopard (Panthera uncia) habitat in Qinghai Province, China, under a mild climate change scenario. Our results showed that the area of suitable snow leopard habitat in Qinghai Province was 302,821 km2 under current conditions and 228,997 km2 under the 2050s climatic scenario, with a mean upward shift in elevation of 90 m. At present, nature reserves protect 38.78% of currently suitable habitat and will protect 42.56% of future suitable habitat. Current areas of climate refugia amounted to 212,341 km2 and are mainly distributed in the Sanjiangyuan region, Qilian mountains, and surrounding areas. Our results provide valuable information for formulating strategies to meet future conservation challenges brought on by climate stress. We suggest that conservation efforts in Qinghai Province should focus on protecting areas of climate refugia and on maintaining or building corridors when planning for future species management.

Evaluating the Effects of Climate Change on Spatial Aggregation of Giant Pandas and Sympatric Species in a Mountainous Landscape

Understanding how climate change alters the spatial aggregation of sympatric species is important for biodiversity conservation. Previous studies usually focused on spatial shifting of species but paid little attention to changes in interspecific competitions under climate change. In this study, we evaluated the potential effects of climate change on the spatial aggregation of giant pandas (Ailuropoda melanoleuca) and three sympatric competitive species (i.e., black bears (Ursus thibetanus), golden takins (Budorcas taxicolor), and wild boars (Sus scrofa)) in the Qinling Mountains, China. We employed an ensemble species distribution modeling (SDM) approach to map the current spatial distributions of giant pandas and sympatric animals and projected them to future climate scenarios in 2050s and 2070s. We then examined the range overlapping and niche similarities of these species under different climate change scenarios. The results showed that the distribution areas of giant pandas and sympatric species would decrease remarkably under future climate changes. The shifting directions of the overlapping between giant pandas and sympatric species vary under different climate change scenarios. In conclusion, future climate change greatly shapes the spatial overlapping pattern of giant pandas and sympatric species in the Qinling Mountains, while interspecific competition would be intensified under both mild and worst-case climate change scenarios.

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 risk regions of house break-ins caused by Tibetan brown bears (Ursus arctos pruinosus) in the Sanjiangyuan region, China

Damage to homesteads by brown bears (Ursus arctos) has become commonplace in Asia, Europe, and the Americas. Science-based solutions for preventing damages can contribute to the establishment of mechanisms that promote human-bear coexistence. We examined the spatial distribution patterns of house break-ins by Tibetan brown bears (U. a. pruinosus) in Zhiduo County of the Sanjiangyuan region in China. Occurrence points of bear damage were collected from field surveys completed from 2017 to 2019. The maximum entropy (MaxEnt) model was then used to assess house break-in risk. Circuit theory modeling was used to simulate risk diffusion paths based on the risk map generated from our MaxEnt model. The results showed that (a) the total risk area of house break-ins caused by brown bears was 11,577.91 km2, accounting for 29.85% of Zhiduo County, with most of the risk areas were distributed in Sanjiangyuan National Park, accounting for 58.31% of the total risk area; (b) regions of alpine meadow located in Sanjiangyuan National Park with a high human population density were associated with higher risk; (c) risk diffusion paths extended southeast to northwest, connecting the inside of Sanjiangyuan National Park to its outside border; and (d) eastern Suojia, southern Zhahe, eastern Duocai, and southern Jiajiboluo had more risk diffusion paths than other areas examined, indicating higher risk for brown bear break-ins in these areas. Risk diffusion paths will need strong conservation management to facilitate migration and gene flow of brown bears and to alleviate bear damage, and implementation of compensation schemes may be necessary in risk areas to offset financial burdens. Our analytical methods can be applied to conflict reduction efforts and wildlife conservation planning across the Qinghai-Tibet Plateau.

Identifying potential refugia and corridors under climate change: A case study of endangered Sichuan golden monkey (Rhinopithecus roxellana) in Qinling Mountains, China

Climate change threatens endangered species and challenges current conservation strategies. Effective conservation requires vulnerability assessments for species susceptible to climate change and adaptive strategies to mitigate threats associated with climate. In this paper, we used the Maxent to model the impacts of climate change on habitat suitability of Sichuan golden monkey Rhinopithecus roxellana. Our results showed that (i) suitable habitat for Sichuan golden monkey was predicted to decrease by 37% in 2050s under climate change; (ii) the mean elevations of suitable habitat in the 2050s was estimated to shift 160 m higher; (iii) nature reserves protect 62% of current suitable habitat and 56% of future suitable habitat; and (iv) 49% of current suitable habitat was predicted to be vulnerable to future climate change. Given these results, we proposed conservation implications to mitigate the impacts of climate change on Sichuan golden monkey, including adjusting range of national park, establishing habitat corridors, and conducting long‐term monitoring.

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.

Gap analysis and implications for seasonal management on a local scale

BACKGROUND

Identifying biodiversity hotspots on a local scale, using multiple data sources, and ecological niche modeling, has the potential to contribute to more effective nature reserve management.

METHODS

In this study, we used infrared-triggered camera trapping, field surveys, and interviews to create a dataset on the distribution of species (mammals and birds) in Hebei Wulingshan Nature Reserve (Hebei Province, China).

RESULTS

We identified 101 species (14 orders, 38 families), 64 of which (2,142 effective records) were selected for environmental niche modeling. All results were reclassified into three groups: "priority areas" (areas including the potential distributions of over 80% of species), "important areas" (those with 50% of species), and "normal areas" (all other areas). Our results show that priority areas (1.31-1.82 km2) and important areas (7.73-21.44 km2) for conservation were mainly covered by the core and experimental zones of the reserve; additionally, a kilometer-wide margin around the outside of the nature reserve seems to be important to maintaining biodiversity.

DISCUSSION

We close by suggesting some actions for enhancing conservation of biodiversity in the reserve, including monitoring, strengthen law enforcements, introducing popular science, and co-operating with local people.

Uncertainty of future projections of species distributions in mountainous regions

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

Assessing vulnerability of giant pandas to climate change in the Qinling Mountains of China

Climate change might pose an additional threat to the already vulnerable giant panda (Ailuropoda melanoleuca). Effective conservation efforts require projections of vulnerability of the giant panda in facing climate change and proactive strategies to reduce emerging climate-related threats. We used the maximum entropy model to assess the vulnerability of giant panda to climate change in the Qinling Mountains of China. The results of modeling included the following findings: (1) the area of suitable habitat for giant pandas was projected to decrease by 281 km² from climate change by the 2050s; (2) the mean elevation of suitable habitat of giant panda was predicted to shift 30 m higher due to climate change over this period; (3) the network of nature reserves protect 61.73% of current suitable habitat for the species, and 59.23% of future suitable habitat; (4) current suitable habitat mainly located in Chenggu, Taibai, and Yangxian counties (with a total area of 987 km²) was predicted to be vulnerable. Assessing the vulnerability of giant panda provided adaptive strategies for conservation programs and national park construction. We proposed adaptation strategies to ameliorate the predicted impacts of climate change on giant panda, including establishing and adjusting reserves, establishing habitat corridors, improving adaptive capacity to climate change, and strengthening monitoring of giant panda.