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A framework for the assessment of the spatial and temporal patterns of threatened coastal delphinids
Wang, J.; Yang, Y.; Yang, F.; Li, Y.; Li, L.; Lin, D.; He, T.; Liang, B.; Zhang, T.; Lin, Y.; Li Ping, P.; Liu, W. (2016). A framework for the assessment of the spatial and temporal patterns of threatened coastal delphinids. NPG Scientific Reports 6(19883): 12 pp. hdl.handle.net/10.1038/srep19883
In: Scientific Reports (Nature Publishing Group). Nature Publishing Group: London. ISSN 2045-2322, more
Peer reviewed article  

Available in Authors 

Keyword
    Marine

Authors  Top 
  • Wang, J.
  • Yang, Y.
  • Yang, F.
  • Li, Y.
  • Li, L.
  • Lin, D.
  • He, T.
  • Liang, B.
  • Zhang, T.
  • Lin, Y.
  • Li Ping, P.
  • Liu, W.

Abstract
    The massively accelerated biodiversity loss rate in the Anthropocene calls for an efficient and effective way to identify the spatial and temporal dynamics of endangered species. To this end, we developed a useful identification framework based on a case study of locally endangered Sousa chinensis by combining both LEK (local ecological knowledge) evaluation and regional boat-based survey methods. Our study investigated the basic ecological information of Sousa chinensis in the estuaries of eastern Guangdong that had previously been neglected, which could guide the future study and conservation. Based on the statistical testing of reported spatial and temporal dolphins sighting data from fishermen and the ecological monitoring analyses, including sighting rate, site fidelity and residence time estimations, some of the current Sousa chinensis units are likely to be geographically isolated and critically endangered, which calls for much greater conservation efforts. Given the accelerated population extinction rate and increasing budgetary constraints, our survey pattern can be applied in a timely and economically acceptable manner to the spatial and temporal assessment of other threatened coastal delphinids, particularly when population distributions are on a large scale and traditional sampling methods are difficult to implement.

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