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Seabirds as environmental indicators: the advantages of combining data sets

机译:海鸟作为环境指标:结合数据集的优势

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ABSTRACT: Breeding performance of seabirds reflects conditions in the marine environment, and seabirds are often considered suitable indicators because they are sensitive to variations in food supply and relatively easy to observe. However, any individual parameter (e.g. breeding success of a particular species at one site) may also be affected by drivers other than food supply, and selecting a suitable univariate indicator can be difficult, particularly if independent estimates of food availability on the appropriate scale are unavailable. We propose combining several data sets to overcome this limitation: if a given temporal pattern occurs for several parameters measured at one site, or for the same parameter measured at several sites, it is likely to reflect important spatiotemporal environmental variation, probably linked to food supply. Multivariate statistical techniques, such as principal component analysis (PCA), can be used to extract common signals from a number of intercorrelated time series. Examples from seabirds in the North Sea demonstrate that such common signals are correlated with physical and biological environmental variables. We propose a preliminary ‘North Sea seabird index’ and discuss how this index could be used in ecosystem-based management.
机译:摘要:海鸟的繁殖表现反映了海洋环境的状况,海鸟通常被认为是合适的指标,因为它们对食物供应的变化敏感并且相对容易观察。但是,任何单独的参数(例如,某一地点某个物种的成功繁殖)也可能受到食物供应以外的驱动因素的影响,因此选择合适的单变量指标可能很困难,尤其是如果要对适当规模的食物可得性进行独立估计的话不可用。我们建议合并多个数据集以克服此限制:如果给定的时间模式在一个站点上测量的多个参数或在多个站点上测量的同一参数发生,则很可能反映出重要的时空环境变化,可能与粮食供应有关。多元统计技术,例如主成分分析(PCA),可用于从多个相互关联的时间序列中提取公共信号。来自北海海鸟的例子表明,这种共同的信号与物理和生物环境变量相关。我们提出了一个初步的“北海海鸟指数”,并讨论了该指数如何在基于生态系统的管理中使用。

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