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

Abstract

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.

Red panda fine‐scale habitat selection along a Central Himalayan longitudinal gradient

“…Red pandas should have low metabolic rates to conserve heat and thermoregulation, especially during winter (Fei et al, ; Wei et al, ), but they also need more foraging time to maximize energy intake from less nutritious bamboo (McNab, ; Yonzon & Hunter, ). Liu, Guan, Dai, Li, and Gong () have reported temperature as the second most important determinant of giant panda distribution in the northern Minshan in China, similar to our findings.…”

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

“…In general, wildlife population ranges shift toward the poles and to higher elevations under climate change conditions (Hole et al., ). Our results are coincided with previous studies at the spatial shift of giant panda habitat in northward, upward, and northwestward (Fan et al., ; Liu et al., ; Tuanmu et al., ), but vary a great deal as following: (1) The significant loss of habitat was not detected compared to the results of Songer et al. () and Fan et al.…”

“…Further, to lessen the uncertainty of projection results by different GCMs, the value of climate variables for modeling in our study was averaged based on used GCMs. The high AUC value in the Maxent model proved our results were reliable, the variable contribution and permutation importance of habitat factors in our study were consistent with previous studies of climate impacts on giant pandas (Liu et al., ; Songer et al., ), and the results from giant panda habitat selection studies that found fidelity for specific slopes override elevation and aspect because of the need to maintain balance between energy intake and expenditure (Hu, ; Schaller, ; Zhang et al., ). Moreover, our study provides a methodological case to analyze future climate variables with better accuracy and can be applied to other climate change studies.…”

“…To minimize collinearity for modeling among 19 bioclimatic variables in the general circulation model (GCM), we employed eight climatic variables (Bio 2, Bio 4, Bio 10, Bio 11, Bio 15, Bio 17, Bio 18, and Bio 19; Table ) to represent climate conditions in 2050 by removing one variable when the correlation coefficient >|0.70| based on intercorrelation testing among two of the 19 bioclimatic variables (Li et al., ). Among 19 GCMs in WorldClim, BCC‐CSM1‐1, CCSM4, HadGEM2‐ES, and MIROC5 were adopted as future climate change projection models based on the GCMs used in previous studies of climate impacts on giant pandas (Li et al., ; Liu, Guan, Dai, Li, & Gong, ). To avoid the uncertainty of future climate projections from different GCMs, we averaged the value of eight climatic variables of 2050 projected under four selected GCMs using ARCGIS 10.0, and used the average value to form the future climatic raster with 30 × 30 m resolution and to construct climate models.…”

The minimum area requirements (MAR) for giant panda: an empirical study

“…Future work should analyze habitat connectivity and plan corridors to enhance the viability of giant panda populations76, particularly in southern mountain ranges where most habitat patches are smaller than the MAR. Because fragmentation of habitats may be aggravated in the future due to possible global climate change767778, it is necessary to evaluate their status within that context and investigate potential solutions.…”