Assessing the impact of climate change on the habitat distribution of the giant panda in the Qinling Mountains of China

Synthesis of giant panda habitat suitability evaluations

Degradation, fragmentation, and habitat loss significantly threaten the survival of giant pandas (Ailuropoda melanoleuca). Habitat suitability evaluations (HSEs) represent a crucial component of giant panda habitat research. However, a systematic review of HSE research on giant pandas has not been conducted in recent years. To make up for that, we synthesised HSE research on giant pandas publicated from 2013 to 2022 and conducted a comprehensive analysis of the evaluation scale, evaluation methods, and research findings. We found a correlation between the geographical distribution of giant pandas and HSE-based studies on giant pandas. Furthermore, we observed a trend towards interdisciplinary and large-scale research. Although the evaluation accuracy has improved compared to that of earlier work, some limitations still remain, such as concentrated evaluation areas, incomplete indicators, and outdated data. Current HSE research on giant pandas helps determine suitable habitat distributions, facilitating protection strategies and management planning for protected areas. We suggest that future research should prioritize those unexplored or under-evaluated areas, incorporate a broader range of microenvironmental indicators, and update data resources and methodologies. This study bridges the gap in systematic reviews on HSEs of the giant panda and provides valuable references and recommendations for future research as well as the protection and management of giant panda habitats.

Assessing the effectiveness of protected areas for panda conservation under future climate and land use change scenarios

While the relatively stable land use and land cover (LULC) patterns is an important feature of protected areas (PAs), the influence of this feature on future species distribution and the effectiveness of the PAs has rarely been explored. Here, we assessed the role of land use patterns within PAs on the projected range of the giant panda (Ailuropoda melanoleuca) by comparing projections inside and outside of PAs for four model configurations: (1) only climate covariates, (2) climate and dynamic land use covariates, (3) climate and static land use covariates and (4) climate and hybrid dynamic-static land use covariates. Our objectives were twofold: to understand the role of protected status on projected panda habitat suitability and evaluate the relative efficacy of different climate modeling approaches. The climate and land use change scenarios used in the models include two shared socio-economic pathways (SSPs) scenarios: SSP126 [an optimistic scenario] and SSP585 [a pessimistic scenario]. We found that models including land-use covariates performed significantly better than climate-only models and that these projected more suitable habitat than climate-only models. Static land-use models projected more suitable habitat than both the dynamic and hybrid models under SSP126, while these models did not differ under SSP585. China's panda reserve system was projected to effectively maintain suitable habitat inside PAs. Panda dispersal ability also significantly impacted outcomes, with most models assuming unlimited dispersal forecasting range expansion and models assuming zero dispersal consistently forecasting range contraction. Our findings highlight that policies targeting improved land-use practices should be an effective means for offsetting some of the negative effects of climate change on pandas. As the effectiveness of PAs is projected to be maintained, we recommend the judicious management and expansion of the PA system to ensure the resilience of panda populations into the future.

Giant Panda Microhabitat Study in the Daxiangling Niba Mountain Corridor

Habitat reduction and increased fragmentation are urgent issues for the survival and recovery of the giant panda (Ailuropoda melanoleuca). However, changes in the distribution and microhabitat selection of giant panda habitats in different seasons in the same region have rarely been assessed. To further understand giant panda habitat requirements, this study analyzed the giant panda habitat selection characteristics and differences using the sample data of the giant panda occurrence sites collected during 2020-2022. The results showed that the giant panda in both seasons selected medium altitudes (2000-2400 m), southeastern slopes, slopes less than 15°, taller tree layers (8-15 m) with a larger diameter at breast height (17-25 cm) and medium density (25-55%), shorter shrub layers (<4 m) with sparse density (<30%), and taller bamboo (>2 m) with high density (>35%). The giant panda microhabitat survey in the Niba Mountain corridor clarified the characteristics of suitable habitat selection for the giant panda in the corridor. The findings of the study can provide scientific references for the development of practical habitat conservation and management measures for giant pandas in the study area.

Vegetation dynamics and their relationships with climatic factors in the "Golden Triangle" region

The "Golden Triangle" is located on the border between Myanmar, Laos, and Thailand, and slash-and-burn cultivation is an ancient and typical land type in this region. With the development of the "The Belt and Road" strategy of China and the climate change, the vegetation information is bound to change intensively under the combined influence of alternative plantation projects and economic policies. Here we used MOD13Q1-normalized differential vegetation index (NDVI) and meteorological data to analyze the variation of vegetation coverage and its correlation with climatic factors (temperature and precipitation) during the period of 2000-2018 by using trend analysis, stability analysis, and partial correlation analysis. The results showed that the overall vegetation coverage of this region exerted the trend of improvement and became more stable over time. Spatially, the agglomeration degree became weaker as time goes during 2000-2018. The precipitation was more closely correlated with NDVI than temperature, indicating that precipitation could be the main limiting factor influencing vegetation change in this area. The correlation between NDVI and climatic factors exhibited differences among different seasons, with NDVI being less correlated with temperature and precipitation in spring and summer and more correlated with them in autumn and winter. Investigating the long-term vegetation coverage of this region and analyzing the trend of climate change is beneficial to understand the development trend of the ecological environment and resource potential in this region. Simultaneously, it can provide a favorable ecological evaluation for The Belt and Road strategy and provide important scientific suggestions and guidance for the sustainable development of ecosystems and human society under the drastic environmental changes.

Elevated CO2 moderates the impact of climate change on future bamboo distribution in Madagascar

The distribution of bamboo is sensitive to climate change and is also potentially affected by increasing atmospheric CO₂ concentrations due to its C3 photosynthetic pathway. Yet the effect of CO₂ in climate impact assessments of potential changes in bamboo distribution has to date been overlooked. In this study, we proposed a simple and quantitative method to incorporate the impact of atmospheric CO₂ concentration into a species distribution modeling framework. To do so, we implemented 10 niche modeling algorithms with regionally downscaled climatic variables and combined field campaign observations. We assessed future climate impacts on the distribution of an economically and ecologically important and widely distributed bamboo species in Madagascar, and examined the effect of increasing CO₂ on future projections. Our results suggested that future climatic changes negatively impact potential bamboo distribution in Madagascar, leading to a decline of 34.8% of climatic suitability and a decline of 63.6 ± 3.2% in suitable areas towards 2100 under RCP 8.5. However, increasing atmosphere CO₂ offsets the climate impact for bamboo, and led to a smaller reduction of 19.8% in suitability and a potential distribution expansion of +111.6 ± 9.8% in newly suitable areas. We also found that the decline in climatic suitability for bamboo was related to increasing monthly potential evapotranspiration of the warmest quarter and minimum temperature of the warmest month. Conversely, the decreasing isothermality and increasing precipitation of the warmest quarter contributed to projected increase in bamboo-suitable areas. Our study suggested that elevated CO₂ may mitigate the decrease in climatic suitability and increase bamboo-suitable areas, through enhancing water use efficiency and decreasing potential evapotranspiration. Our results highlight the importance of accounting for the CO₂ effect on future plant species distributions, and provide a mechanistic approach to do so for ecosystems constrained by water.

The potential risks and exposure of Qinling giant pandas to polycyclic aromatic hydrocarbon (PAH) pollution

Rapid industrialization and urbanization have created a substantial urban-rural gradient for various pollutants. The Qinling Mountains are highly important in terms of biodiversity, providing habitat for giant pandas, which are endemic to China and are a widely recognized symbol for conservation. Whether polycyclic aromatic hydrocarbon (PAH) exposure risks regarding in situ animal conservation zones are affected by environmental pollution or even enhanced by the mountain-trapping effect requires further research. Our group carried out a large-scale investigation on the area ranging from Xi'an to Hanzhong across the giant panda habitat in the Qinling Mountains by collecting atmosphere, soil, bamboo, and fecal samples from different sites over a two-year period. The total toxicity of atmospheric PAHs and the frequencies of soil PAHs above effect range low (ERL) values showed a decreasing trend from urban areas to the central mountains, suggesting a distance effect from the city. The proportions of total 5- and 6-ring PAHs in the atmosphere were higher in the central mountainous areas than in the urban areas, while this difference was reversed in the soil. Health risk assessments showed that the incremental lifetime carcinogenic risks (ILCR) of PAH exposure by bamboo ingestion ranged from 2.16 × 10^-4 to 3.11 × 10^-4, above the critical level of 10^-4. Bamboo ingestion was the main driver of the PAH exposure risks. The concentration difference between bamboo and fecal samples provided a reference for the level of PAHs absorbed by the panda digestive system. Since the Qinling Mountains possess the highest density of giant pandas and provide habitats to many other endangered animal and plant species, we should not ignore the probability of health risks posed by PAHs. Monitoring the pollution level and reducing the atmospheric emissions of toxic pollutants are recommended actions. Further detailed research should also be implemented on pandas' health effects of contaminant exposure.

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.

Leukocyte and Platelet Characteristics of the Giant Panda (Ailuropoda melanoleuca): Morphological, Cytochemical, and Ultrastructural Features

The giant panda (Ailuropoda melanoleuca) is a vulnerable species and a charismatic member of zoological collections worldwide. Despite its importance as a representative species for global wildlife conservation efforts, no studies to date have described normal cell morphology or cytoplasmic constituents by traditional techniques such as cytochemical staining and evaluation of ultrastructural features. The objective of this study was to accurately identify and characterize the leukocytes and platelets of clinically healthy giant pandas using routine Wright-Giemsa stain, eight cytochemical stains, immunocytochemistry (CD3), and transmission electron microscopy (TEM) to further the collective understanding of normal cellular morphological features, cytochemical reactivity, and cytoplasmic contents found in health. Voluntary venipuncture was performed on four healthy individual animals (two adults and two juveniles), as part of routine preventive health evaluation. Blood was collected for routine and cytochemical stains, and into 2.5% glutaraldehyde for TEM. On Wright-Giemsa-stained blood films, leukocytes were differentiated into granulocytes (neutrophils, eosinophils, basophils) and mononuclear cells (lymphocytes, monocytes). Cytochemical staining revealed similar leukocyte and platelet staining patterns to those reported in other mammals, with some notable differences. By TEM, leukocytes with nuclear and cytoplasmic features of mononuclear cells were readily differentiated from granulocytes, and platelets had similar ultrastructural features to those reported in other mammals. Neutrophils were the predominant cell type followed by lymphocytes, while basophils were rare. Rare large or reactive lymphocytes, rare reactive monocytes, and rare large platelets were noted in apparently healthy giant pandas of this study. A unique mononuclear cell, with a moderately indented nucleus and shared cytochemical and ultrastructural characteristics of lymphocytes and monocytes, was discovered in this species. The combined cytochemical, immunocytochemical (CD3), and ultrastructural features of these unique cells more closely resemble those of monocytes, but the definitive cell lineage remains unknown at this time. This study provides novel information on giant panda leukocyte morphology and cellular constituents in health, shows the importance of manual blood film review, has important implications for hemogram interpretation in future clinical cases and research, and provides a baseline for future characterization and understanding of hemogram changes in response to disease.

Characteristics of heavy metal concentrations and risk assessment for giant pandas and their habitat in the Qinling Mountains, China

High concentrations of heavy metals in the environment threaten the quality of ecosystems and the health of human beings and animals. Giant panda (Ailuropoda melanoleuca), which is endemic to China and a global conservation icon, has the largest density in the Qinling Mountains. This paper investigated the spatiotemporal variation of heavy metal concentrations in soil (N = 44) at the regional scale with three zones of urban areas, mountain edges, and central mountains, the temporal variation of heavy metal concentrations in three bamboo species (N = 19) and two types of feces (N = 10), and assessed the ecological risk and health risk for giant pandas and their habitat in the Qinling Mountains. The results showed that the median concentrations of studied eight heavy metals mercury (Hg), arsenic (As), copper (Cu), manganese (Mn), zinc (Zn), chromium (Cr), lead (Pb), and cadmium (Cd) in soil exceeded the background values of Shaanxi Province except Pb. The median concentrations of Hg, Zn, Cr, Pb, and Cd in bamboo surpassed the reference standard (RS) of national food safety limits in vegetables for human intake, but the concentration of Zn was within the nutrient range in the bamboo plants. Heavy metals were enriched more in feces of captive than the wild giant pandas, which illustrated either higher ingestion or lower digestibility for captive giant panda. Ecological risk assessment of soil by the geo-accumulation index (I_geo) and risk index (RI) showed strong pollution by Hg and moderate pollution by Cd. Health risk assessment by the hazard index (HI) showed a potential to strong risk for giant pandas exposed to Pb, As, and Hg. In addition, the concentrations of heavy metals in feces showed a higher exposure risk for captive giant pandas than wild giant pandas. We suggest that attention should be paid to and all effective measurements should be taken for reducing the emission of Hg, As, Pb, and Cd in the study area.

Comprehensive Breeding Techniques for the Giant Panda

The dramatic growth of the captive giant panda (Ailuropoda melanoleuca) population exemplifies how the application of scientific findings to animal care and reproductive management can improve conservation breeding outcomes. Detailed behavioral studies of giant panda estrus, pregnancy and cub rearing have demonstrated the importance of husbandry management that supports natural reproductive behavior to enhance breeding success. Natural breeding has been valuably augmented by the development of assisted reproductive techniques founded through detailed studies of the reproductive physiology of the giant panda and outlining fundamental information about reproductive seasonality, male fertility and characterization of the estrous cycle. The resultant holistic understanding of giant panda reproduction has improved reproductive success in the captive population to such an extent that it is now self-sustaining and provides surplus animals for reintroduction. Despite these significant advances, there are knowledge gaps and remaining challenges to be addressed. Pregnancy detection remains the single biggest challenge when determining if natural mating or assisted breeding have been successful. Because pregnancy can only be determined in the few weeks prior to parturition, there are gaps in understanding and detecting delayed implantation and early embryonic loss. Additionally, dynamic management practices and standard of care for reproductive assistance needs to be developed. Only large breeding centers in China have the ability to promote normal reproductive behaviors and allow mate choice for the giant panda. These challenges need to be addressed in the near future in order to maintain a self-sustaining, genetically diverse and behaviorally competent captive population. This chapter documents the development of successful giant panda managed breeding programs by focusing on three key areas, (1) the development of science-driven reproductive techniques to improve fecundity in a species where the mating system was poorly understood, (2) how targeted research and adaptive management of social settings surrounding estrus and breeding improved reproductive success, and (3) insights and solutions to challenges faced across the program's history with future directions for research.

Habitat selection and prediction of the spatial distribution of the Chinese horseshoe bat (R. sinicus) in the Wuling Mountains

Habitat selection by the Chinese horseshoe bat (Rhinolophus sinicus) in the Wuling Mountains was studied in this paper. Global positioning system (GPS), remote sensing (RS) and geographic information system (GIS) technologies were used to obtain ground survey data and analyse the habitat factors driving the distribution of R. sinicus. Based on these basic data, a binary logistic regression method was used to establish habitat selection models of R. sinicus. Then, the corrected Akaike information criterion (AIC_C) was used to screen an optimal model, and the Hosmer-Lemeshow test indicated that the optimal model has suitable goodness of fit. Finally, the optimal model was used to predict the spatial distribution of R. sinicus in the Wuling Mountains. Verification analysis showed that the overall accuracy of the model was 72.7% and that the area under the curve (AUC) value was 0.947, which indicated that the model was effective for predicting suitable habitat for R. sinicus. The model results also showed that the main factors that influenced habitat selection were slope, annual mean temperature and distances from roads, rivers and residential land. R. sinicus preferred areas far from roads and residential land and areas near rivers. Generally, higher values of slope and annual mean temperature were associated with a greater likelihood of R. sinicus presence. Therefore, the protection of the water bodies surrounding R. sinicus habitats and fully addressing the impacts of human activities on R. sinicus habitats are recommended to protect the survival and reproduction of the population.

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.

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.

Quantifying the evidence for co-benefits between species conservation and climate change mitigation in giant panda habitats

Conservationists strive for practical, cost-effective management solutions to forest-based species conservation and climate change mitigation. However, this is compromised by insufficient information about the effectiveness of protected areas in increasing carbon storage, and the co-benefits of species and carbon conservation remain poorly understood. Here, we present the first rigorous quantitative assessment of the roles of giant panda nature reserves (NRs) in carbon sequestration, and explore the co-benefits of habitat conservation and climate change mitigation. Results show that more than 90% of the studied panda NRs are effective in increasing carbon storage, with the mean biomass carbon density of the whole NRs exhibiting a 4.2% higher growth rate compared with lands not declared as NRs over the period 1988-2012, while this effectiveness in carbon storage masks important patterns of spatial heterogeneity across the giant panda habitats. Moreover, the significant associations have been identified between biomass carbon density and panda's habitat suitability in ~85% NRs and at the NR level. These findings suggest that the planning for carbon and species conservation co-benefits would enhance the greatest return on limited conservation investments, which is a critical need for the giant panda after its conservation status has been downgraded from "endangered" to "vulnerable".

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.

Bamboo Classification Using WorldView-2 Imagery of Giant Panda Habitat in a Large Shaded Area in Wolong, Sichuan Province, China

This study explores the ability of WorldView-2 (WV-2) imagery for bamboo mapping in a mountainous region in Sichuan Province, China. A large area of this place is covered by shadows in the image, and only a few sampled points derived were useful. In order to identify bamboos based on sparse training data, the sample size was expanded according to the reflectance of multispectral bands selected using the principal component analysis (PCA). Then, class separability based on the training data was calculated using a feature space optimization method to select the features for classification. Four regular object-based classification methods were applied based on both sets of training data. The results show that the k-nearest neighbor (k-NN) method produced the greatest accuracy. A geostatistically-weighted k-NN classifier, accounting for the spatial correlation between classes, was then applied to further increase the accuracy. It achieved 82.65% and 93.10% of the producer’s and user’s accuracies respectively for the bamboo class. The canopy densities were estimated to explain the result. This study demonstrates that the WV-2 image can be used to identify small patches of understory bamboos given limited known samples, and the resulting bamboo distribution facilitates the assessments of the habitats of giant pandas.

Predictions of potential geographical distribution and quality of Schisandra sphenanthera under climate change

Climate change will significantly affect plant distribution as well as the quality of medicinal plants. Although numerous studies have analyzed the effect of climate change on future habitats of plants through species distribution models (SDMs), few of them have incorporated the change of effective content of medicinal plants. Schisandra sphenanthera Rehd. et Wils. is an endangered traditional Chinese medical plant which is mainly located in the Qinling Mountains. Combining fuzzy theory and a maximum entropy model, we obtained current spatial distribution of quality assessment for S. spenanthera. Moreover, the future quality and distribution of S. spenanthera were also projected for the periods 2020s, 2050s and 2080s under three different climate change scenarios (SRES-A1B, SRES-A2 and SRES-B1 emission scenarios) described in the Special Report on Emissions Scenarios (SRES) of IPCC (Intergovernmental Panel on Climate Change). The results showed that the moderately suitable habitat of S. sphenanthera under all climate change scenarios remained relatively stable in the study area. The highly suitable habitat of S. sphenanthera would gradually decrease in the future and a higher decline rate of the highly suitable habitat area would occur under climate change scenarios SRES-A1B and SRES-A2. The result suggested that in the study area, there would be no more highly suitable habitat areas for S. sphenanthera when the annual mean temperature exceeds 20 °C or its annual precipitation exceeds 1,200 mm. Our results will be influential in the future ecological conservation and management of S. sphenanthera and can be taken as a reference for habitat suitability assessment research for other medicinal plants.

Pattern of NDVI-based vegetation greening along an altitudinal gradient in the eastern Himalayas and its response to global warming

The eastern Himalayas, especially the Yarlung Zangbo Grand Canyon Nature Reserve (YNR), is a global hotspot of biodiversity because of a wide variety of climatic conditions and elevations ranging from 500 to > 7000 m above sea level (a.s.l.). The mountain ecosystems at different elevations are vulnerable to climate change; however, there has been little research into the patterns of vegetation greening and their response to global warming. The objective of this paper is to examine the pattern of vegetation greening in different altitudinal zones in the YNR and its relationship with vegetation types and climatic factors. Specifically, the inter-annual change of the normalized difference vegetation index (NDVI) and its variation along altitudinal gradient between 1999 and 2013 was investigated using SPOT-VGT NDVI data and ASTER global digital elevation model (GDEM) data. We found that annual NDVI increased by 17.58% in the YNR from 1999 to 2013, especially in regions dominated by broad-leaved and coniferous forests at lower elevations. The vegetation greening rate decreased significantly as elevation increased, with a threshold elevation of approximately 3000 m. Rising temperature played a dominant role in driving the increase in NDVI, while precipitation has no statistical relationship with changes in NDVI in this region. This study provides useful information to develop an integrated management and conservation plan for climate change adaptation and promote biodiversity conservation in the YNR.

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.

Progress in the ecology and conservation of giant pandas

Giant panda (Ailuropoda melanoleuca) conservation is a possible success story in the making. If extinction of this iconic endangered species can be avoided, the species will become a showcase program for the Chinese government and its collaborators. We reviewed the major advancements in ecological science for the giant panda, examining how these advancements have contributed to panda conservation. Pandas' morphological and behavioral adaptations to a diet of bamboo, which bear strong influence on movement ecology, have been well studied, providing knowledge to guide management actions ranging from reserve design to climate change mitigation. Foraging ecology has also provided essential information used in the creation of landscape models of panda habitat. Because habitat loss and fragmentation are major drivers of the panda population decline, efforts have been made to help identify core habitat areas, establish where habitat corridors are needed, and prioritize areas for protection and restoration. Thus, habitat models have provided guidance for the Chinese governments' creation of 67 protected areas. Behavioral research has revealed a complex and efficient communication system and documented the need for protection of habitat that serves as a communication platform for bringing the sexes together for mating. Further research shows that den sites in old-growth forests may be a limiting resource, indicating potential value in providing alternative den sites for rearing offspring. Advancements in molecular ecology have been revolutionary and have been applied to population census, determining population structure and genetic diversity, evaluating connectivity following habitat fragmentation, and understanding dispersal patterns. These advancements form a foundation for increasing the application of adaptive management approaches to move panda conservation forward more rapidly. Although the Chinese government has made great progress in setting aside protected areas, future emphasis will be improved management of pandas and their habitat.

Plant bioclimatic models in climate change research

Bioclimatics is an ancient science that was once neglected by many ecologists. However, as climate changes have attracted increasing attention, scientists have reevaluated the relevance of bioclimatology and it has thus become essential for exploring climate changes. Because of the rapidly growing importance of bioclimatic models in climate change studies, we evaluated factors that influence plant bioclimatology, constructed and developed bioclimatic models, and assessed the precautionary effects of the application of the models. The findings obtained by sequentially reviewing the development history and importance of bioclimatic models in climate change studies can be used to enhance the knowledge of bioclimatic models and strengthen their ability to apply them. Consequently, bioclimatic models can be used as a powerful tool and reference in decision-making responses to future climate changes. The objectives of this study were to (1) understand how climatic factors affect plants; (2) describe the sources, construction principles, and development of early plant bioclimatic models (PBMs); and (3) summarize the recent applications of PBMs in climate change research.