ObjectiveDistributions of air temperatures at high temporal and spatial resolution are the basis to model the suitable distribution range of species closely. The distribution range of giant panda is one of the 35 world's biodiversity hotspots, and the terrain of this region is heterogeneous and typical mountainous.

MethodTo reduce the error induced by elevation effects on temperature interpolation effectively, we used the linear lapse rate adjustment method (LLRA) to interpolate the daily maximum temperature (dT_max), daily minimum temperature (dT_min), and daily mean temperature (dT_mean) of 42 meteorological stations throughout the distribution range of giant panda during 1960-2010. We assessed the accuracy of LLRA, and analyzed the pattern of warming in the distribution range of giant panda over the past half century.

ResultWe found that the accuracy of interpolation using the local lapse rate adjusted by LLRA was higher than the global mean lapse rate and unadjusted local lapse rate. dT_max, dT_min, and dT_mean increased significantly simultaneously in nearly 90% of the distribution range of giant panda, and the area with the highest increasing rate mainly concentrated in the Qinling and northwestern Minshan Mountains. The median rate of significant increasing dT_max, dT_min and dT_mean was 1.68, 2.32, and 1.77 every hundred years, respectively. More than 3/4 of the distribution range of giant panda was with a greater rate in dT_min than dT_max. When the elevation was lower than 3 500 m, the representative elevations of the meteorological stations, the change rate of dT_min and dT_mean showed significant increasing trend with elevation, and the trend of dT_min was stronger than that of T_mean. We confirmed that LLRA method can deal with elevation effects and improve the accuracy when interpolating temperature at high temporal and spatial resolution in mountainous areas.

ConclusionThe distribution range of giant panda experienced significant warming over the past half century, and the daily temperature maps from LLRA interpolation can be the basis for assessing the effects of warming on the distribution of rare and endangered species, and developing corresponding conservation plan.