Meta-analysis of cod-shrimp interactions reveals top-down control in oceanic food webs
Here we present a meta-analytic approach to analyzing population interactions 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...
| Main Authors: | , |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Wiley
2003
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| Subjects: | |
| Online Access: | https://oceanrep.geomar.de/id/eprint/4711/ https://oceanrep.geomar.de/id/eprint/4711/1/ecy2003841162.pdf |
| Summary: | Here we present a meta-analytic approach to analyzing population interactions 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 consistent 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, however, 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. |
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