META-ANALYSIS OF COD-SHRIMP INTERACTIONS REVEALS TOP-DOWN CONTROL IN OCEANIC FOOD WEBS
META-ANALYSIS OF COD-SHRIMP INTERACTIONS REVEALS TOP-DOWN CONTROL IN OCEANIC FOOD WEBS
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鳕鱼-虾相互作用的元分析揭示了海洋食物网中自上而下的控制
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
R. Myers
中科院分区：
文献类型：
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作者：
B. Worm;R. Myers
在这里，我们提出了一种元分析方法来分析北大西洋上的种群相互作用。我们收集了一对有据可查的捕食者-猎物对--大西洋鳕鱼(Gadus Morhua)和北极虾(Pandalus Borealis)--的所有可用生物量时间序列，以测试这些种群的时间动态是否符合自上而下或自下而上的假设。九个地区中有八个地区显示鳕鱼和虾的生物量呈负相关，支持“自上而下”的观点。例外情况只发生在两个物种的南部活动范围界限附近。随机效应Meta分析表明，北大西洋对虾生物量与鳕鱼生物量呈显著负相关，而与海洋温度无关。相反，鳕鱼生物量与海洋温度呈正相关。然而，鳕鱼和虾的关系的强度随着平均温度的升高而下降。这些结果表明，捕食者种群的变化可以对海洋食物网中的猎物种群产生强烈的影响，这些相互作用的强度可能对平均海洋温度的变化很敏感。这意味着，海洋中过度捕捞的影响会向下延伸到较低的营养水平，就像以前在湖泊和沿海海域所显示的那样。为了进一步研究这些过程，我们建立了一个从时间序列数据分析物种相互作用的方法论框架。
Here we present a meta-analytic approach to analyzing population interac- tions across the North Atlantic Ocean. We assembled all available biomass time series for a well-documented predator-prey couple, Atlantic cod (Gadus morhua) and northern shrimp (Pandalus borealis), to test whether the temporal dynamics of these populations are con- sistent with the 'top-down' or the 'bottom-up' hypothesis. Eight out of nine regions showed inverse correlations of cod and shrimp biomass supporting the 'top-down' view. Exceptions occurred only close to the southern range limits of both species. Random-effects meta-analysis showed that shrimp biomass was strongly negatively related to cod biomass, but not to ocean temperature in the North Atlantic Ocean. In contrast, cod biomass was positively related to ocean temperature. The strength of the cod-shrimp relationship, how- ever, declined with increasing mean temperature. These results show that changes in predator populations can have strong effects on prey populations in oceanic food webs, and that the strength of these interactions may be sensitive to changes in mean ocean temperature. This means that the effects of overfishing in the ocean cascade down to lower trophic levels, as has been shown previously for lakes and coastal seas. In order to further investigate these processes, we establish a methodological framework to analyze species interactions from time series data.