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Conservation of Terrestrial Vertebrates in a Global Hotspot of Karst Area in Southwestern China.

ABSTRACT: The karst area of southwest China (KASC) is the largest piece of karst landscape on the earth and a global biodiversity hot-spot with high concentrations of endemic species. Although a number of nature reserves (NRs) have been established across the region, the representativeness of biodiversity of the NR system is still unknown. Based on comprehensive literature and field surveys, and intensive consultations with zoologists and wildlife managers, we compiled distributions of 1,204 terrestrial vertebrate species and 271 NRs in KASC. We found Jinxiu, Mengla, Hekou, and Jinghong have the richest amphibian species; Jinxiu has the highest species richness of reptiles; Jinghong, Menghai, and Mengla have the largest numbers of avian species; whereas, Mengla, Longzhou, and Ningming have the greatest mammalian diversity in the region. Gap analysis among NR system, species richness pattern, and five biogeographic indicators found insufficient representation of the NR system on territorial vertebrate diversity. The conservation effectiveness in Guizhou Province was much lower than that in Guangxi and Yunnan Provinces. Under-representation and over-representation simultaneously occurred in many of the ecoregions, elevation classes, vegetation types, landcover categories, and human disturbance intensity gradients. For conservation of terrestrial vertebrates in KASC, several suggestions were presented in this study.

SUBMITTER: Luo Z

PROVIDER: S-EPMC4881395 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Project description:Area of habitat (AOH) is defined as the "habitat available to a species, that is, habitat within its range" and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.

Dataset Information

Conservation of Terrestrial Vertebrates in a Global Hotspot of Karst Area in Southwestern China.

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