Comparing the suitability of classified land cover data and remote sensing variables for modeling distribution patterns of plants

Climate change and anthropogenic activities shrink the range and dispersal of an endangered primate in Sichuan Province, China

The golden snub-nosed monkey (Rhinopithecus roxellana) is a rare and endemic species in China. The population of golden snub-nosed monkeys in Sichuan Province has an isolated genetic status, large population size, and low genetic diversity, making it highly vulnerable to environmental changes. Our study aimed to evaluate the potential impact of climate and land-use changes on the distribution and dispersal paths of the species in Sichuan Province. We used three general circulation models (GCMs), three greenhouse gas emission scenarios, and three land-use change scenarios suitable for China to predict the potential distributions of the golden snub-nosed monkey in the current and 2070s using the MaxEnt model. The dispersal paths were identified by the circuit theory. Our results suggested that the habitats of the golden snub-nosed monkey were reduced under all three GCM scenarios. The suitable habitats for the golden snub-nosed monkey would be reduced by 82.67%, 82.47%, and 75.17% under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, compared to the currently suitable habitat area. Additionally, we found that the density of future dispersal paths of golden snub-nosed monkeys would decrease, and the dispersal resistance would increase. Therefore, relevant wildlife protection agencies should prioritize the climatically suitable distributions and key dispersal paths of golden snub-nosed monkeys to improve their conservation. We identified key areas for habitat preservation and increased habitat connectivity under climate change, which could serve as a reference for future adaptation strategies.

Ecological response of an umbrella species to changing climate and land use: Habitat conservation for Asiatic black bear in the Sichuan-Chongqing Region, Southwestern China

Climate and land use changes are increasingly recognized as major threats to global biodiversity, with significant impacts on wildlife populations and ecosystems worldwide. The study of how climate and land use changes impact wildlife is of paramount importance for advancing our understanding of ecological processes in the face of global environmental change, informing conservation planning and management, and identifying the mechanisms and thresholds that underlie species' responses to shifting climatic conditions. The Asiatic black bear (Ursus thibetanus) is a prominent umbrella species in a biodiversity hotspot in Southwestern China, and its conservation is vital for safeguarding sympatric species. However, the extent to which this species' habitat may respond to global climate and land use changes is poorly understood, underscoring the need for further investigation. Our goal was to anticipate the potential impacts of upcoming climate and land use changes on the distribution and dispersal patterns of the Asiatic black bear in the Sichuan-Chongqing Region. We used MaxEnt modeling to evaluate habitat vulnerability using three General Circulation Models (GCMs) and three scenarios of climate and land use changes. Subsequently, we used Circuit Theory to identify prospective dispersal paths. Our results revealed that the current area of suitable habitat for the Asiatic black bear was 225,609.59 km2 (comprising 39.69% of the total study area), but was expected to decrease by -53.1%, -49.48%, and -28.55% under RCP2.6, RCP4.5, and RCP8.5 projection scenarios, respectively. Across all three GCMs, the distribution areas and dispersal paths of the Asiatic black bear were projected to shift to higher altitudes and constrict by the 2070s. Furthermore, the results indicated that the density of dispersal paths would decrease, while the resistance to dispersal would increase across the study area. In order to protect the Asiatic black bear, it is essential to prioritize the protection of climate refugia and dispersal paths. Our findings provide a sound scientific foundation for the allocation of such protected areas in the Sichuan-Chongqing Region that are both effective and adaptive in the face of ongoing global climate and land use changes.

Optimal ranges of social-environmental drivers and their impacts on vegetation dynamics in Kazakhstan

Kazakhstan is part of the Eurasian Steppes, the world's largest contiguous grassland system. Kazakh grassland systems are largely understudied despite being historically important for agropastoral practices. These grasslands are considered vulnerable to anthropogenic activities and climatic variability. Few studies have examined vegetation dynamics in Central Asia owing to the complex impacts of moisture, climatic and anthropogenic forcings. A comprehensive analysis of spatiotemporal changes of vegetation and its driving factors will help elucidate the causes of grassland degradation. Here, we investigated the individual and pairwise interactive influences of various social-environmental system (SES) drivers on greenness dynamics in Kazakhstan. We sought to examine whether there is a relationship between peak season greenness and its drivers - spring drought, preceding winter freeze-thaw cycles, percent snow cover and snow depth - for Kazakhstan during 2000-2016. As hypothesized, snow depth and spring drought accounted for 60 % and 52 % of the variance in the satellite-derived normalized difference vegetation index (NDVI) in Kazakhstan. The freeze-thaw process accounted for 50 % of NDVI variance across the country. In addition, continuous thawing during the winter increased vegetation greenness. We also found that moisture and topographic factors impacted NDVI more significantly than socioeconomic factors. However, the impacts of socioeconomic drivers on vegetation growth were amplified when they interacted with environmental drivers. Terrain slope and soil moisture had the highest q-values or power of determinant, accounting for ~70 % of the variance in NDVI across the country. Socioeconomic drivers, such as crop production (59 %), population density (48 %), and livestock density (26 %), had significant impacts on vegetation dynamics in Kazakhstan. We found that most of the pairwise interactive influences of the drivers exhibited bi-factor enhancement, and the interaction between soil moisture and elevation was the largest (q = 0.92). Our study revealed the optimal ranges and tipping points of SES drivers and quantified the impacts of various driving factors on NDVI. These findings can help us identify the factors causing grassland degradation and provide a scientific basis for ecological protection in semiarid regions.

Analysis of Conservation Gaps and Landscape Connectivity for Snow Leopard in Qilian Mountains of China

Human modification and habitat fragmentation have a substantial influence on large carnivores, which need extensive, contiguous habitats to survive in a landscape. The establishment of protected areas is an effective way to offer protection for carnivore populations by buffering them from anthropogenic impacts. In this study, we used MaxEnt to model habitat suitability and to identify conservation gaps for snow leopard (Panthera uncia) in the Qilian Mountains of China, and then assessed the impact of highways/railways and their corridors on habitat connectivity using a graph-based landscape connectivity model. Our results indicated that the study area had 51,137 km2 of potentially suitable habitat for snow leopards and that there were four protection gaps outside of Qilian Mountain National Park. The findings revealed that the investigated highway and railway resulted in a decrease in connectivity at a regional scale, and that corridor development might enhance regional connectivity, which strengthens the capacity of central habitat patches to act as stepping stones and improve connections between western and eastern habitat patches. This study emphasized the need for assessing the impact of highways and railways, as well as their role in corridor development, on species’ connectivity. Based on our results, we provide some detailed recommendations for designing protection action plans for effectively protecting snow leopard habitat and increasing habitat connectivity.

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.

Modelling Potential Distribution of Snow Leopards in Pamir, Northern Pakistan: Implications for Human–Snow Leopard Conflicts

The snow leopard (Panthera uncia) is a cryptic and rare big cat inhabiting Asia’s remote and harsh elevated areas. Its population has decreased across the globe for various reasons, including human–snow leopard conflicts (HSCs). Understanding the snow leopard’s distribution range and habitat interactions with human/livestock is essential for understanding the ecological context in which HSCs occur and thus gives insights into how to mitigate HSCs. In this study, a MaxEnt model predicted the snow leopard’s potential distribution and analyzed the land use/cover to determine the habitat interactions of snow leopards with human/livestock in Karakoram–Pamir, northern Pakistan. The results indicated an excellent model performance for predicting the species’ potential distribution. The variables with higher contributions to the model were the mean diurnal temperature range (51.7%), annual temperature range (18.5%), aspect (14.2%), and land cover (6.9%). The model predicted approximately 10% of the study area as a highly suitable habitat for snow leopards. Appropriate areas included those at an altitude ranging from 2721 to 4825 m, with a mean elevation of 3796.9 ± 432 m, overlapping between suitable snow leopard habitats and human presence. The human encroachment (human settlements and agriculture) in suitable snow leopard habitat increased by 115% between 2008 and 2018. Increasing encroachment and a clear overlap between snow leopard suitable habitat and human activities, signs of growing competition between wildlife and human/livestock for limited rangeland resources, may have contributed to increasing HSCs. A sound land use plan is needed to minimize overlaps between suitable snow leopard habitat and human presence to mitigate HSCs in the long run.

Assessing sustainable development prospects through remote sensing: A review

The Earth's ecosystems face severe environmental stress from unsustainable socioeconomic development linked to population growth, urbanization, and industrialization. Governments worldwide are interested in sustainability measures to address these issues. Remote sensing allows for the measurement, integration, and presentation of useful information for effective decision-making at various temporal and spatial scales. Scientists and decision-makers have endorsed extensive use of remote sensing to bridge gaps among disciplines and achieve sustainable development. This paper presents an extensive review of remote sensing technology used to support sustainable development efforts, with a focus on natural resource management and assessment of natural hazards. We further explore how remote sensing can be used in a cross-cutting, interdisciplinary manner to support decision-making aimed at addressing sustainable development challenges. Remote sensing technology has improved significantly in terms of sensor resolution, data acquisition time, and accessibility over the past several years. This technology has also been widely applied to address key issues and challenges in sustainability. Furthermore, an evaluation of the suitability and limitations of various satellite-derived indices proposed in the literature for assessing sustainable development goals showed that these older indices still perform reasonably well. Nevertheless, with advancements in sensor radiometry and resolution, they were less exploited and new indices are less explored.

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Effectiveness of remote sensing tools to address sustainability issues.

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Decadal changes in remote sensing research to address various challenges.

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There is a need to explore new indices with the development of new satellite sensors.

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Remote sensing-based information to policymakers for decision making.

Mapping Seriphium plumosum encroachment and interaction with wildfire and environmental factors in a protected mountainous grassland

Accurate information on the distribution of invasive native species could provide important and effective procedures for managing savannah environment, especially in sensitive mountainous grasslands. The study detected and mapped Seriphium plumosum within a mountainous landscape and linked the georeferenced occurrence data with the corresponding site-specific environmental factors to predict the locations of unknown populations using a MaxEnt niche model. We also explored the relative contribution in terms of species interaction with its surrounding biophysical environment. The AUC value of 0.876 estimated for the species distribution is an indication of a good model fit. Our findings indicated that Seriphium plumosum preferred areas with higher temperature associated with recurrence fire events and limited soil moisture. It was concluded that the projected conditions of increasing temperature and fire events could promote widespread gain of niche space for Seriphium plumosum while at the same time altering community structure and composition, hydrological properties, and other vital ecosystem services in the study area.

Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data

Mountain ecosystems are biodiversity hotspots that are increasingly threatened by climate and land use/land cover changes. Long-term biodiversity monitoring programs provide unique insights into resulting adverse impacts on plant and animal species distribution. Species distribution models (SDMs) in combination with satellite remote sensing (SRS) data offer the opportunity to analyze shifts of species distributions in response to these changes in a spatially explicit way. Here, we predicted the presence probability of three different rove beetles in a mountainous protected area (Gran Paradiso National Park, GPNP) using environmental variables derived from Landsat and Aster Global Digital Elevation Model data and an ensemble modelling approach based on five different model algorithms (maximum entropy, random forest, generalized boosting models, generalized additive models, and generalized linear models). The objectives of the study were (1) to evaluate the potential of SRS data for predicting the presence of species dependent on local-scale environmental parameters at two different time periods, (2) to analyze shifts in species distributions between the years, and (3) to identify the most important species-specific SRS predictor variables. All ensemble models showed area under curve (AUC) of the receiver operating characteristics values above 0.7 and true skills statistics (TSS) values above 0.4, highlighting the great potential of SRS data. While only a small proportion of the total area was predicted as highly suitable for each species, our results suggest an increase of suitable habitat over time for the species Platydracus stercorarius and Ocypus ophthalmicus, and an opposite trend for Dinothenarus fossor. Vegetation cover was the most important predictor variable in the majority of the SDMs across all three study species. To better account for intra- and inter-annual variability of population dynamics as well as environmental conditions, a continuation of the monitoring program in GPNP as well as the employment of SRS with higher spatial and temporal resolution is recommended.

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 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.

Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France

Continuous-based predictors of habitat characteristics derived from satellite imagery are increasingly used in species distribution models (SDM). This is especially the case of Normalized Difference Vegetation Index (NDVI) which provides estimates of vegetation productivity and heterogeneity. However, when NDVI predictors are incorporated into SDM, synchrony between biological observations and image acquisition must be questionned. Due to seasonal variations of NDVI during the year, landscape patterns of habitats are revealed differently from one date to another leading to variations in models’ performance. In this paper, we investigated the influence of acquisition time period of NDVI to explain and predict bird community patterns over France. We examined if the NDVI acquisition period that best fit the bird data depends on the dominant land cover context. We also compared models based on single time period of NDVI with one model built from the Dynamic Habitat Index (DHI) components which summarize variations in vegetation phenology throughout the year from the fraction of radiation absorbed by the canopy (fPAR). Bird species richness was calculated as response variable for 759 plots of 4 km2 from the French Breeding Bird Survey. Bird specialists and generalists to habitat were considered. NDVI and DHI predictors were both derived from MODIS products. For NDVI, five time periods in 2010 were compared, from late winter to begin of autumn. A climate predictor was also used and Generalized Additive Models were fitted to explain and predict bird species richness. Results showed that NDVI-based proxies of dominant habitat identity and spatial heterogeneity explain more bird community patterns than DHI-based proxies of annual productivity and seasonnality. We also found that models’ performance was both time and context-dependent, varying according to the bird groups. In general, best time period of NDVI did not match with the acquisition period of bird data because in case of synchrony, differences in habitats are less pronounced. These findings suggest that the most powerful approach to estimate bird community patterns is the simplest one. It only requires NDVI predictors from a single appropriate time period, in addition to climate, which makes the approach very operational.

Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling

Global environmental changes are rapidly affecting species' distributions and habitat suitability worldwide, requiring a continuous update of biodiversity status to support effective decisions on conservation policy and management. In this regard, satellite-derived Ecosystem Functional Attributes (EFAs) offer a more integrative and quicker evaluation of ecosystem responses to environmental drivers and changes than climate and structural or compositional landscape attributes. Thus, EFAs may hold advantages as predictors in Species Distribution Models (SDMs) and for implementing multi-scale species monitoring programs. Here we describe a modelling framework to assess the predictive ability of EFAs as Essential Biodiversity Variables (EBVs) against traditional datasets (climate, land-cover) at several scales. We test the framework with a multi-scale assessment of habitat suitability for two plant species of conservation concern, both protected under the EU Habitats Directive, differing in terms of life history, range and distribution pattern (Iris boissieri and Taxus baccata). We fitted four sets of SDMs for the two test species, calibrated with: interpolated climate variables; landscape variables; EFAs; and a combination of climate and landscape variables. EFA-based models performed very well at the several scales (AUCmedian from 0.881±0.072 to 0.983±0.125), and similarly to traditional climate-based models, individually or in combination with land-cover predictors (AUCmedian from 0.882±0.059 to 0.995±0.083). Moreover, EFA-based models identified additional suitable areas and provided valuable information on functional features of habitat suitability for both test species (narrowly vs. widely distributed), for both coarse and fine scales. Our results suggest a relatively small scale-dependence of the predictive ability of satellite-derived EFAs, supporting their use as meaningful EBVs in SDMs from regional and broader scales to more local and finer scales. Since the evaluation of species' conservation status and habitat quality should as far as possible be performed based on scalable indicators linking to meaningful processes, our framework may guide conservation managers in decision-making related to biodiversity monitoring and reporting schemes.

Contemporary Remotely Sensed Data Products Refine Invasive Plants Risk Mapping in Data Poor Regions

Invasive weeds are a serious problem worldwide, threatening biodiversity and damaging economies. Modeling potential distributions of invasive weeds can prioritize locations for monitoring and control efforts, increasing management efficiency. Forecasts of invasion risk at regional to continental scales are enabled by readily available downscaled climate surfaces together with an increasing number of digitized and georeferenced species occurrence records and species distribution modeling techniques. However, predictions at a finer scale and in landscapes with less topographic variation may require predictors that capture biotic processes and local abiotic conditions. Contemporary remote sensing (RS) data can enhance predictions by providing a range of spatial environmental data products at fine scale beyond climatic variables only. In this study, we used the Global Biodiversity Information Facility (GBIF) and empirical maximum entropy (MaxEnt) models to model the potential distributions of 14 invasive plant species across Southeast Asia (SEA), selected from regional and Vietnam's lists of priority weeds. Spatial environmental variables used to map invasion risk included bioclimatic layers and recent representations of global land cover, vegetation productivity (GPP), and soil properties developed from Earth observation data. Results showed that combining climate and RS data reduced predicted areas of suitable habitat compared with models using climate or RS data only, with no loss in model accuracy. However, contributions of RS variables were relatively limited, in part due to uncertainties in the land cover data. We strongly encourage greater adoption of quantitative remotely sensed estimates of ecosystem structure and function for habitat suitability modeling. Through comprehensive maps of overall predicted area and diversity of invasive species, we found that among lifeforms (herb, shrub, and vine), shrub species have higher potential invasion risk in SEA. Native invasive species, which are often overlooked in weed risk assessment, may be as serious a problem as non-native invasive species. Awareness of invasive weeds and their environmental impacts is still nascent in SEA and information is scarce. Freely available global spatial datasets, not least those provided by Earth observation programs, and the results of studies such as this one provide critical information that enables strategic management of environmental threats such as invasive species.

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.