Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 112, Issue 13, Pages E1569-E1576Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1417063112
Keywords
ecosystem forecasting; fisheries ecology; physical-biological interactions; empirical dynamic modeling; nonlinear dynamics
Categories
Funding
- National Science Foundation [DEB-1020372]
- Foundation for the Advancement of Outstanding Scholarship
- Ministry of Science and Technology of Taiwan
- National Science Foundation-National Oceanic and Atmospheric Administration Comparative Analysis of Marine Ecosystem Organization (CAMEO) Program [NA08OAR4320894/CAMEO]
- National Science Foundation Graduate Research Fellowship
- Sugihara Family Trust
- Deutsche Bank-Jameson Complexity Studies Fund
- McQuown Chair in Natural Science
- Division Of Environmental Biology
- Direct For Biological Sciences [1020372] Funding Source: National Science Foundation
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It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon (Oncorhynchus nerka) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner-recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts.
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