Lü Yihe, Zhang Liwei, Zeng Yuan, Fu Bojie, Whitham Charlotte, Liu Shuguang, Wu Bingfang
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, 100085, China.
Joint Center for Global Change Studies, Beijing, 100875, China.
Conserv Biol. 2017 Aug;31(4):894-902. doi: 10.1111/cobi.12897. Epub 2017 Feb 20.
Traditional means of assessing representativeness of conservation value in protected areas depend on measures of structural biodiversity. The effectiveness of priority conservation areas at representing critical natural capital (CNC) (i.e., an essential and renewable subset of natural capital) remains largely unknown. We analyzed the representativeness of CNC-conservation priority areas in national nature reserves (i.e., nature reserves under jurisdiction of the central government with large spatial distribution across the provinces) in China with a new biophysical-based composite indicator approach. With this approach, we integrated the net primary production of vegetation, topography, soil, and climate variables to map and rank terrestrial ecosystems capacities to generate CNC. National nature reserves accounted for 6.7% of CNC-conservation priority areas across China. Considerable gaps (35.2%) existed between overall (or potential) CNC representativeness nationally and CNC representation in national reserves, and there was significant spatial heterogeneity of representativeness in CNC-conservation priority areas at the regional and provincial levels. For example, the best and worst representations were, respectively, 13.0% and 1.6% regionally and 28.9% and 0.0% provincially. Policy in China is transitioning toward the goal of an ecologically sustainable civilization. We identified CNC-conservation priority areas and conservation gaps and thus contribute to the policy goals of optimization of the national nature reserve network and the demarcation of areas critical to improving the representativeness and conservation of highly functioning areas of natural capital. Moreover, our method for assessing representation of CNC can be easily adapted to other large-scale networks of conservation areas because few data are needed, and our model is relatively simple.
评估保护区保护价值代表性的传统方法依赖于结构生物多样性的衡量指标。优先保护区在代表关键自然资本（即自然资本中重要且可再生的子集）方面的有效性在很大程度上仍不为人知。我们采用一种基于生物物理的新综合指标方法，分析了中国国家级自然保护区（即由中央政府管辖、在各省广泛分布的自然保护区）中关键自然资本保护优先区域的代表性。通过这种方法，我们整合了植被的净初级生产力、地形、土壤和气候变量，以绘制陆地生态系统产生关键自然资本的能力并进行排名。国家级自然保护区占全国关键自然资本保护优先区域的6.7%。全国总体（或潜在）关键自然资本代表性与国家级自然保护区中的关键自然资本代表性之间存在相当大的差距（35.2%），并且在区域和省级层面，关键自然资本保护优先区域的代表性存在显著的空间异质性。例如，区域层面最佳和最差的代表性分别为13.0%和1.6%，省级层面分别为28.9%和0.0%。中国的政策正在朝着生态可持续文明的目标转变。我们确定了关键自然资本保护优先区域和保护差距，从而有助于实现优化国家级自然保护区网络以及划定对提高自然资本高效功能区域的代表性和保护至关重要的区域这一政策目标。此外，我们评估关键自然资本代表性的方法可以很容易地应用于其他大型保护区网络，因为所需数据较少，且我们的模型相对简单。
