Mingjun Jiang , Yuan Tian , Yulei Sun , Xinqing Guo , Xinfei Zhao , Le Yin , Baolei Zhang
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引用次数: 0
Abstract
The establishment of nature reserves (NRs) is widely acknowledged as one of the most effective measures to mitigate the threats on habitat quality (HB) posed by human activities (HAs). Precise and scientific assessment of the effectiveness of NRs holds crucial significance in improving management and promoting conservation. In this study, key indicators were creatively selected and applied to the propensity score matching (PSM) model to comprehensively assess the variations in HAs and HB within national NRs on the Qinghai-Tibet Plateau. The results indicated that between 2000 and 2020, 67.4 % of the NR area experienced a decline in HA-related impacts, while 53.8 % of the area saw improvements in HB. Additionally, with the exclusion of external environmental factors, in 2020, the difference in HAs and HB between NRs and non-protected areas was −0.131 and 0.179, respectively. Finally, based on an assessment of the overall conservation effectiveness, seven NRs were classified as “Class I", 18 as “Class II", and another seven as “Class III". These results not only confirmed the effectiveness of national NRs in alleviating anthropogenic pressure and enhancing HB but also served as an important basis for accurately assessing the conservation effectiveness of other NRs and formulating more scientifically sound and appropriate management policies.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.