Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education), China West Normal University, Nanchong, China; Institute of Ecology, China West Normal University, Nanchong, China; Liziping Giant Panda's Ecology and Conservation Observation and Research Station of Sichuan Province, China.
Conservation Science and Wildlife Health, San Diego Zoo Wildlife Alliance, Escondido, CA, USA.
J Environ Manage. 2023 Sep 15;342:118319. doi: 10.1016/j.jenvman.2023.118319. Epub 2023 Jun 6.
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.
虽然相对稳定的土地利用和土地覆盖（LULC）模式是保护区（PA）的一个重要特征，但这种特征对未来物种分布和 PA 的有效性的影响很少被探索。在这里，我们通过比较四个模型配置（1）仅气候协变量，（2）气候和动态土地利用协变量，（3）气候和静态土地利用协变量和（4）气候和混合动态-静态土地利用协变量，评估了 PA 内土地利用模式对大熊猫（Ailuropoda melanoleuca）预测范围的作用。我们的目标有两个：了解保护状况对预测大熊猫栖息地适宜性的作用，并评估不同气候建模方法的相对效果。模型中使用的气候和土地利用变化情景包括两个共同社会经济途径（SSP）情景：SSP126（乐观情景）和 SSP585（悲观情景）。我们发现，包括土地利用协变量的模型明显优于仅气候模型，并且这些模型预测的适宜栖息地比仅气候模型更多。在 SSP126 下，静态土地利用模型比动态和混合模型预测的适宜栖息地更多，而在 SSP585 下，这些模型没有差异。中国的熊猫保护区系统预计将有效地维持保护区内的适宜栖息地。熊猫的扩散能力也显著影响了结果，大多数模型假设无限扩散预测范围扩大，而假设零扩散的模型则一致预测范围收缩。我们的研究结果强调，针对改善土地利用实践的政策应该是抵消气候变化对熊猫负面影响的有效手段。由于预计 PA 的有效性将得到维持，我们建议审慎管理和扩大 PA 系统，以确保熊猫种群在未来具有弹性。
