Predictability and empirical dynamics of fisheries time series in the North Pacific
Previous studies have documented a strong relationship between marine ecosystems and large-scale modes of sea surface height (SSH) and sea surface temperature (SST) variability in the North Pacific such as the Pacific Decadal Oscillation and the North Pacific Gyre Oscillation. In the central and wes...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2022.969319/full |
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| author | Gian Giacomo Navarra Emanuele Di Lorenzo Ryan R. Rykaczewski Antonietta Capotondi Antonietta Capotondi |
| author_facet | Gian Giacomo Navarra Emanuele Di Lorenzo Ryan R. Rykaczewski Antonietta Capotondi Antonietta Capotondi |
| author_sort | Gian Giacomo Navarra |
| collection | DOAJ |
| description | Previous studies have documented a strong relationship between marine ecosystems and large-scale modes of sea surface height (SSH) and sea surface temperature (SST) variability in the North Pacific such as the Pacific Decadal Oscillation and the North Pacific Gyre Oscillation. In the central and western North Pacific along the Kuroshio-Oyashio Extension (KOE), the expression of these modes in SSH and SST is linked to the propagation of long oceanic Rossby waves, which extend the predictability of the climate system to ~3 years. Using a multivariate physical-biological linear inverse model (LIM) we explore the extent to which this physical predictability leads to multi-year prediction of dominant fishery indicators inferred from three datasets (i.e., estimated biomasses, landings, and catches). We find that despite the strong autocorrelation in the fish indicators, the LIM adds dynamical forecast skill beyond persistence up to 5-6 years. By performing a sensitivity analysis of the LIM forecast model, we find that two main factors are essential for extending the dynamical predictability of the fishery indicators beyond persistence. The first is the interaction of the fishery indicators with the SST/SSH of the North and tropical Pacific. The second is the empirical relationship among the fisheries time series. This latter component reflects stock-stock interactions as well as common technological and human socioeconomic factors that may influence multiple fisheries and are captured in the training of the LIM. These results suggest that empirical dynamical models and machine learning algorithms, such as the LIM, provide an alternative and promising approach for forecasting key ecological indicators beyond the skill of persistence. |
| first_indexed | 2024-04-11T07:09:14Z |
| format | Article |
| id | doaj.art-b6c3901f89234422aedc5d45781f8379 |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-04-11T07:09:14Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-b6c3901f89234422aedc5d45781f83792022-12-22T04:38:16ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452022-11-01910.3389/fmars.2022.969319969319Predictability and empirical dynamics of fisheries time series in the North PacificGian Giacomo Navarra0Emanuele Di Lorenzo1Ryan R. Rykaczewski2Antonietta Capotondi3Antonietta Capotondi4Program in Ocean Science & Engineering, Georgia Institute of Technology, Atlanta, GA, United StatesDepartment of Earth and Atmospheric Sciences, Brown University, Providence, RI, United StatesNOAA Pacific Islands Fisheries Science Center, Ecosystem Sciences Division, Honolulu, HI, United StatesNOAA Earth System Research Laboratory, Physical Sciences Division, Boulder, CO, United StatesCooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, United StatesPrevious studies have documented a strong relationship between marine ecosystems and large-scale modes of sea surface height (SSH) and sea surface temperature (SST) variability in the North Pacific such as the Pacific Decadal Oscillation and the North Pacific Gyre Oscillation. In the central and western North Pacific along the Kuroshio-Oyashio Extension (KOE), the expression of these modes in SSH and SST is linked to the propagation of long oceanic Rossby waves, which extend the predictability of the climate system to ~3 years. Using a multivariate physical-biological linear inverse model (LIM) we explore the extent to which this physical predictability leads to multi-year prediction of dominant fishery indicators inferred from three datasets (i.e., estimated biomasses, landings, and catches). We find that despite the strong autocorrelation in the fish indicators, the LIM adds dynamical forecast skill beyond persistence up to 5-6 years. By performing a sensitivity analysis of the LIM forecast model, we find that two main factors are essential for extending the dynamical predictability of the fishery indicators beyond persistence. The first is the interaction of the fishery indicators with the SST/SSH of the North and tropical Pacific. The second is the empirical relationship among the fisheries time series. This latter component reflects stock-stock interactions as well as common technological and human socioeconomic factors that may influence multiple fisheries and are captured in the training of the LIM. These results suggest that empirical dynamical models and machine learning algorithms, such as the LIM, provide an alternative and promising approach for forecasting key ecological indicators beyond the skill of persistence.https://www.frontiersin.org/articles/10.3389/fmars.2022.969319/fullempirical dynamical modelfishery indicatorsclimate variabilityclimate changeforecastbiomass anomalies |
| spellingShingle | Gian Giacomo Navarra Emanuele Di Lorenzo Ryan R. Rykaczewski Antonietta Capotondi Antonietta Capotondi Predictability and empirical dynamics of fisheries time series in the North Pacific Frontiers in Marine Science empirical dynamical model fishery indicators climate variability climate change forecast biomass anomalies |
| title | Predictability and empirical dynamics of fisheries time series in the North Pacific |
| title_full | Predictability and empirical dynamics of fisheries time series in the North Pacific |
| title_fullStr | Predictability and empirical dynamics of fisheries time series in the North Pacific |
| title_full_unstemmed | Predictability and empirical dynamics of fisheries time series in the North Pacific |
| title_short | Predictability and empirical dynamics of fisheries time series in the North Pacific |
| title_sort | predictability and empirical dynamics of fisheries time series in the north pacific |
| topic | empirical dynamical model fishery indicators climate variability climate change forecast biomass anomalies |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2022.969319/full |
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