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Developing a rapid method for undertaking the World Ocean Assessment in data-poor regions - A case study using the South China Sea Large Marine Ecosystem

机译:开发一种在数据贫乏地区进行世界海洋评估的快速方法-以南中国海大型海洋生态系统为例

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摘要

The United Nations Environment Program, World Ocean Assessment, requires rapid assessment of the state of marine ecosystems at regional scales, yet a practical method for achieving this in data-poor regions has not been developed. We present a method that is capable of synthesising information across a broad range of ecosystem components in a timely manner, while also overcoming the paucity of empirical data available at large spatial scales. We develop a hierarchical and adaptable assessment framework that encompasses 'Biodiversity', 'Ecosystem health ' and 'Environmental pressure' components, which are further divided into parameters (representing the structure and/or function of each component) and indicators (which indicate the spatial distribution of the assessed parameter). We argue that this framework is best utilised within an expert elicitation process. To determine the validity of this framework in undertaking a WOA, this approach was applied to the South China Sea Large Marine Ecosystem during a 3-day pilot workshop held in Bangkok, Thailand, 2012. Forty-five experts from 11 countries in the region participated in assessing the biodiversity, ecosystem health and environmental pressures structuring 104 pre-identified ecosystem variables. The majority of areas within the SCS were graded as 'Poor' in terms of their biodiversity, with biodiversity in decline within 64% of these variables. In contrast, most areas were graded as 'Good' for ecosystem health, with 51% of variables considered stable in terms of ecosystem health. However, most areas were graded as 'Poor' for ecosystem pressures, with pressures either stable or increasing. Ecosystem variables in 'Poor' or 'Very Poor' condition in Most (80%) of areas were identified as conservation priorities. These variables were primarily associated with groups of taxa, including elasmobranch fauna, inner-shelf demersal fishes, squid and large invertebrate species inhabiting reefs (e.g., giant clam). A range of iconic species (e.g., dugong), key habitats (e.g., coral reefs), and key ecosystem processes (e.g., benthic productivity) were also graded as 'Poor' in Most (80%) of areas. Confidence in condition and trend scores was assessed to be medium or high for 86% of variables assessed. The rapid method developed here assessed and conveyed to policy-makers a broad-scale overview of the condition, trends and issues in the SCS. The method provides a way forward for future World Ocean Assessments of large data-poor marine ecosystems.
机译:联合国环境规划署的《世界海洋评估》要求在区域范围内快速评估海洋生态系统的状况,但尚未开发出在数据贫乏地区实现这一目标的实用方法。我们提出了一种方法,该方法能够及时地综合范围广泛的生态系统组成部分的信息,同时还克服了在大空间尺度上可获得的经验数据的不足。我们开发了一种分级且适应性强的评估框架,涵盖了“生物多样性”,“生态系统健康”和“环境压力”组件,这些组件又进一步分为参数(代表每个组件的结构和/或功能)和指标(表示空间评估参数的分布)。我们认为该框架最好在专家启发过程中利用。为了确定该框架在实施WOA方面的有效性,在2012年于泰国曼谷举行的为期3天的试点研讨会上,将该方法应用于了南中国海大型海洋生态系统。该地区11个国家的45位专家参加了该研讨会。在评估生物多样性,生态系统健康和环境压力方面,构建了104个预先确定的生态系统变量。根据其生物多样性,SCS内的大多数地区都被评为“较差”,其中生物多样性的下降幅度在这些变量的64%之内。相反,大多数地区在生态系统健康方面被评为“良好”,其中51%的变量在生态系统健康方面被认为是稳定的。但是,大多数地区的生态系统压力被评为“较差”,压力要么稳定要么上升。在大多数(80%)地区,处于“差”或“极差”状态的生态系统变量被确定为保护重点。这些变量主要与分类群有关,包括弹bra动物群,内层深层鱼类,鱿鱼和居住在礁石上的大型无脊椎动物(例如巨蛤)。在大多数地区(80%)中,一系列标志性物种(例如儒艮),主要栖息地(例如珊瑚礁)和主要生态系统过程(例如底栖生物生产力)也被评为“较差”。对于86%的变量,对状态和趋势评分的置信度被评估为中或高。这里开发的快速方法评估并向决策者传达了SCS的状况,趋势和问题的广泛概述。该方法为未来对大数据匮乏的海洋生态系统的世界海洋评估提供了前进的方向。

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  • 来源
    《Ocean & coastal management》 |2014年第7期|129-137|共9页
  • 作者单位

    School of the Environment, University of Technology, 123 Broadway, Sydney, NSW 2007, Australia;

    School of the Environment, University of Technology, 123 Broadway, Sydney, NSW 2007, Australia;

    School of the Environment, University of Technology, 123 Broadway, Sydney, NSW 2007, Australia,Greenward Consulting, Perth, WA, Australia;

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