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.

CzechGlobe Global Change Research Institute The Czech Academy of Sciences Bělidla 986 4a 60300 Brno Czech Republic

Department of Geography Tourism and Environment College of Shaanxi Normal University Xi'an Shaanxi 710119 China

Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing 100101 China

State Engineering Laboratory of Southern Forestry Applied Ecology and Technology Central South University of Forestry and Technology Changsha Hunan 410004 China

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 University of Chinese Academy of Sciences Beijing 100049 China

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