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Global hotspots of coherent marine fishery catches - PubMed

Global hotspots of coherent marine fishery catches

Joyce J L Ong et al. Ecol Appl. 2021 Jul.

Abstract

Although different fisheries can be tightly linked to each other by human and ecosystem processes, they are often managed independently. Synchronous fluctuations among fish populations or fishery catches can destabilize ecosystems and economies, respectively, but the degree of synchrony around the world remains unclear. We analyzed 1,092 marine fisheries catch time series over 60 yr to test for the presence of coherence, a form of synchrony that allows for phase-lagged relationships. We found that nearly every fishery was coherent with at least one other fishery catch time series globally and that coherence was strongest in the northeast Atlantic, western central Pacific, and eastern Indian Ocean. Analysis of fish biomass and fishing mortality time series from these hotspots revealed that coherence in biomass or fishing mortality were both possible, though biomass coherence was more common. Most of these relationships were synchronous with no time lags, and across catches in all regions, synchrony was a better predictor of regional catch portfolio effects than catch diversity. Regions with higher synchrony had lower stability in aggregate fishery catches, which can have negative consequences for food security and economic wealth.

Keywords: fishery catches; global hotspots; marine fisheries; phase relationships; portfolio effects; synchrony; wavelet coherence.

© 2021 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of Ecological Society of America.

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Figures

Fig. 1
Fig. 1

Illustration of coherence and phase relationships comparing wavelet approaches and Pearson’s correlation tests. (A) Two time series with an in‐phase relationship that shows both coherence (P = 0.001) and correlation (P = 2.2 × 10−16). (B) Two time series with a phase‐lagged relationship that shows coherence (P = 0.001) but statistically nonsignificant correlation (P = 0.662). (C) Two time series with no significant coherence (P = 0.437) or correlation (P = 0.648). Units for signal and time are arbitrary.

Fig. 2
Fig. 2

Histograms of coherent relationships in fishery catches across spatial scales. (A, D, and G) Number of significant coherent relationships (count) observed (red line) and expected from a null model (gray histogram) globally (A), within ocean basins (D), and within FAO regions (G). (B, E, and H) Average coherence values observed (red line) and expected from a null model (gray histogram) globally (B), within ocean basins (E), and within FAO regions (H). (C, F, and I) Circular histogram of average phase relationships among the significant, coherent relationships globally (C), within ocean basins (F), and within FAO regions (I). For all plots, significance was defined with a false discovery rate of <20%. See Appendix S1: Fig. S2 for an alternative null model.

Fig. 3
Fig. 3

Global coherence hotspots and relationship to portfolio effects for fishery catches. (A) Map of FAO regions ranked by confidence (P) that the observed coherence (in terms of average coherence values or the percentage of coherent relationships) is greater than that expected from the null model. Larger sizes and warmer colors represent regions with stronger evidence for coherence. The number of fisheries in each region is written below each dot. (B) Scatterplot showing that the strength of the portfolio effect is weaker (y‐axis) in FAO regions with a higher percentage of in‐phase relationships (x‐axis). (C) Density plot of mean phase relationships within eastern Indian Ocean (IOE) and the northeast Atlantic (ANE), the two regions with the lowest and the highest portfolio effect, respectively. A, Atlantic; P, Pacific; IO, Indian Ocean; MBS, Mediterranean and Black Sea; N, S, E, W are North, South, East, West, respectively; C, central.

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