Empirical dynamic programming for model‐free ecosystem‐based management
Abstract Quantitative ecosystem‐based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best‐studied systems. Here, we develop a management scheme that is based on predictive models driven by the observed dynamics. We show that near‐optimal m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-04-01
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| Series: | Methods in Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/2041-210X.14302 |
| _version_ | 1827295783058145280 |
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| author | Stephan B. Munch Antoine Brias |
| author_facet | Stephan B. Munch Antoine Brias |
| author_sort | Stephan B. Munch |
| collection | DOAJ |
| description | Abstract Quantitative ecosystem‐based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best‐studied systems. Here, we develop a management scheme that is based on predictive models driven by the observed dynamics. We show that near‐optimal management policies can be constructed from time‐series data by merging empirical dynamic modelling and stochastic dynamic programming. The Empirical Dynamic Programming approach performs well in cases we examined and outperformed a commonly used single‐species alternative. We expect model‐free ecosystem‐based management to be of use wherever ecosystem dynamics are uncertain or observations of the system do not cover all relevant species. |
| first_indexed | 2024-04-24T14:27:14Z |
| format | Article |
| id | doaj.art-c30d58d554064deb9e459f7b38b4994e |
| institution | Directory Open Access Journal |
| issn | 2041-210X |
| language | English |
| last_indexed | 2024-04-24T14:27:14Z |
| publishDate | 2024-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj.art-c30d58d554064deb9e459f7b38b4994e2024-04-03T04:38:58ZengWileyMethods in Ecology and Evolution2041-210X2024-04-0115476977810.1111/2041-210X.14302Empirical dynamic programming for model‐free ecosystem‐based managementStephan B. Munch0Antoine Brias1Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration Santa Cruz California USASouthwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration Santa Cruz California USAAbstract Quantitative ecosystem‐based management typically relies on hypothetical ecosystem models that are difficult to validate for all but the best‐studied systems. Here, we develop a management scheme that is based on predictive models driven by the observed dynamics. We show that near‐optimal management policies can be constructed from time‐series data by merging empirical dynamic modelling and stochastic dynamic programming. The Empirical Dynamic Programming approach performs well in cases we examined and outperformed a commonly used single‐species alternative. We expect model‐free ecosystem‐based management to be of use wherever ecosystem dynamics are uncertain or observations of the system do not cover all relevant species.https://doi.org/10.1111/2041-210X.14302approximate dynamic programmingecosystem managementGaussian process regressionnonlinear methodstemporal difference learningtime‐delay embedding |
| spellingShingle | Stephan B. Munch Antoine Brias Empirical dynamic programming for model‐free ecosystem‐based management Methods in Ecology and Evolution approximate dynamic programming ecosystem management Gaussian process regression nonlinear methods temporal difference learning time‐delay embedding |
| title | Empirical dynamic programming for model‐free ecosystem‐based management |
| title_full | Empirical dynamic programming for model‐free ecosystem‐based management |
| title_fullStr | Empirical dynamic programming for model‐free ecosystem‐based management |
| title_full_unstemmed | Empirical dynamic programming for model‐free ecosystem‐based management |
| title_short | Empirical dynamic programming for model‐free ecosystem‐based management |
| title_sort | empirical dynamic programming for model free ecosystem based management |
| topic | approximate dynamic programming ecosystem management Gaussian process regression nonlinear methods temporal difference learning time‐delay embedding |
| url | https://doi.org/10.1111/2041-210X.14302 |
| work_keys_str_mv | AT stephanbmunch empiricaldynamicprogrammingformodelfreeecosystembasedmanagement AT antoinebrias empiricaldynamicprogrammingformodelfreeecosystembasedmanagement |