What drove giant panda Ailuropoda melanoleuca expansion in the Qinling Mountains? An analysis comparing the influence of climate, bamboo, and various landscape variables in the past decade

Abstract

The role of climate and aclimatic factors on species distribution has been debated widely among ecologists and conservationists. It is often difficult to attribute empirically observed changes in species distribution to climatic or aclimatic factors. Giant pandas (A. melanoleuca) provide a rare opportunity to study the impact of climatic and aclimatic factors, particularly the food sources on predicting the distribution changes in the recent decade, as well-documented information on both giant panda and bamboos exist. Here, we ask how the climate metrics compare to the bamboo suitability metric in predicting the giant panda occurrences outside the central areas in the Qinling Mountains during the past decade. We also seek to understand the relative importance of different landscape-level variables in predicting giant panda emigration outside areas of high giant panda densities. We utilize data from the 3rd and 4th National Giant Panda Surveys (NGPSs) for our analysis. We evaluate the performance of the species distribution models trained by climate, bamboo suitability, and the combination of the two. We then at 4 spatial scales identify the optimal models for predicting giant panda emigration between the 3rd and the 4th NGPSs using a list of landscape-level environmental variables. Our results show that the models utilizing the bamboo suitability alone consistently outperform the bioclimatic and the combined models; the distance to high giant panda density core area and bamboo suitability show high importance in predicting expansion probability across all four scales. Our results also suggest that the extrapolated bamboo distribution using bamboo occurrence data can provide a practical and more reliable alternative to predict potential expansion and emigration of giant panda along the range edge. It suggests that restoring bamboo forests within the vicinity of high giant panda density areas is likely a more reliable strategy for supporting shifting giant panda populations.

Giant pandas are losing their edge: Population trend and distribution dynamic drivers of the giant panda

“…Traditional frequentist data models often have difficulty addressing the complexity of distribution dynamics (Olden et al, 2008). The XGBoost offers a better framework to explore mechanistic relationships between variables and species distribution and outperforms traditional statistical methods (Cushman et al, 2017; Mi et al, 2017) and the most commonly used methods (e.g., the maximum entropy model and habitat suitability index model; Bai et al, 2018; Huang et al, 2020; Hull et al, 2015). The XGBoost is a boosting ‐based ensemble machine learning technique (Chen & Guestrin, 2016) with several features intended to improve its scalability and control over‐fitting (Prasad, 2018).…”

“…However, significant uncertainty remains because of inconsistent study methods, and there is substantial variation within and between panda populations (Connor et al, 2016). For example, the bamboo occurrence was the most influential factor for giant panda distribution dynamics in the Qinglin Mountains (Huang et al, 2020) but not in the Minshan Mountains (forest coverage) (Wang et al, 2022), whereas temperature was most influential for giant pandas across the entire species population (i.e., larger scale) (Tang et al, 2020).…”

Big data-big problems? How to circumvent problems in biodiversity mapping and ensure meaningful results

“…Even when more carefully delineated ranges (IUCN, birdlife, GARD: http://www.gardinitiative.org/) are likely to overestimate the degree of protection, their area is still smaller than an MCP, especially if a habitat filter is not applied (Table 1). We used a general habitat filter, so more specialist filters and other steps outlined throughout could greatly improve range estimates and make them more similar to those in expert range maps (de Barros et al, 2021;Huang et al, 2020;Xu et al, 2022). In all cases, the lack of filtering means ranges are projected as many times larger than they are likely to be.…”

Understanding Species–Habitat Associations: A Case Study with the World’s Bears

“…It now seems clear that a reduction in human activities, grazing of livestock, and road building would be a top priority for improving panda habitat and thus benefiting its conservation, at least in the short term [150,151]. Longer term challenges remain insofar as effects of climate change, both on the availability of bamboo and also possibly on temperature effects on pandas directly [152], although model predictions differ greatly [153,154]. However, it must be remembered that just a few centuries ago, giant pandas occupied lowland habitats, where temperatures were higher than in the mountains where they live now, and where they consumed different kinds of bamboo and other plants [155].…”