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Representation of critical natural capital in China
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Representation of critical natural capital in China
Representation of critical natural capital in China
Journal Article

Representation of critical natural capital in China

2017
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Overview
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-conservationpriority 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. Los medtos tradicionales para evaluar la representatividad de los valores de conservación en las áreas protegidas dependen de las medidas de la biodiversidad estructural. La efectividad de las áreas de conservación prioritarias por representar al capital natural crítico (CNC) (es decir, un subconjunto esencial y renovable de capital natural) todavía permanece desconocida en su mayoría. Analizamos la representatividad de las áreas de conservación prioritarias de CNC en las reservas naturales nacionales (es decir, reservas naturales bajo jurisdicción del gobierno central con una gran distribución espacial a lo largo de las provincias) de China con una nueva estrategia indicadora basada en componentes biofísicos. Con esta estrategia, integramos la producción primaria neta de vegetación, la topografía, el suelo y las variables climáticas al mapa y clasificamos las capacidades de los ecosistemas terrestres para generar CNC. Las reservas naturales nacionales representaron el 6.7 % de las áreas de conservación prioritarias de CNC en toda China. Existieron vacíos considerables (35.2 %) entre la representatividad general (o potencial) de CNC a nivel nacional y la representación de CNC en las reservas nacionales, y hubo una heterogeneidad espacial significativa de la representatividad en las áreas de conservación prioritarias de CNC en los niveles regionales y provinciales. Por ejemplo, la mejor y la peor representación fueron, respectivamente, 13.0 % y 1.6% regionalmente y 28.9 % y 0.0 % provincialmente. La política en China está en transición hacia la meta de una civilización sustentable ecológicamente. Identificamos las áreas de conservación prioritarias de CNC y los vacíos de conservación y con esto contribuimos a las metas políticas de optimizar la red de reservas naturales nacionales y la demarcación de las áreas cruciales para mejorar la representatividad y conservación de las áreas de capital natural altamente funcionales. Además, nuestro método para evaluar la representación del CNC puede adaptarse fácilmente a otras redes a gran escala de áreas de conservación porque se requieren pocos datos y nuestro modelo es relativamente simple.