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| Autori principali: | , , , , , , , , , , , , , , , , , |
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| Natura: | Artículo científico |
| Lingua: | en |
| Pubblicazione: |
PloS one
2025
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| Accesso online: | https://pubmed.ncbi.nlm.nih.gov/40478913/ |
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| _version_ | 1868266193699209218 |
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| author | Ferriss, Bridget E Hunsicker, Mary E Ward, Eric J Litzow, Michael A Rogers, Lauren Callahan, Matt Cheng, Wei Danielson, Seth L Drummond, Brie Fergusson, Emily Gabriele, Christine Hebert, Kyle Hopcroft, Russell R Nielsen, Jens Spalinger, Kally Stockhausen, William T Strasburger, Wesley W Whelan, Shannon |
| author_facet | Ferriss, Bridget E Hunsicker, Mary E Ward, Eric J Litzow, Michael A Rogers, Lauren Callahan, Matt Cheng, Wei Danielson, Seth L Drummond, Brie Fergusson, Emily Gabriele, Christine Hebert, Kyle Hopcroft, Russell R Nielsen, Jens Spalinger, Kally Stockhausen, William T Strasburger, Wesley W Whelan, Shannon Ferriss, Bridget E Hunsicker, Mary E Ward, Eric J Litzow, Michael A Rogers, Lauren Callahan, Matt Cheng, Wei Danielson, Seth L Drummond, Brie Fergusson, Emily Gabriele, Christine Hebert, Kyle Hopcroft, Russell R Nielsen, Jens Spalinger, Kally Stockhausen, William T Strasburger, Wesley W Whelan, Shannon |
| collection | PubMed - marine biology |
| contents | Identifying common trends and ecosystem states to inform Gulf of Alaska ecosystem-based fisheries management. Ferriss, Bridget E Hunsicker, Mary E Ward, Eric J Litzow, Michael A Rogers, Lauren Callahan, Matt Cheng, Wei Danielson, Seth L Drummond, Brie Fergusson, Emily Gabriele, Christine Hebert, Kyle Hopcroft, Russell R Nielsen, Jens Spalinger, Kally Stockhausen, William T Strasburger, Wesley W Whelan, Shannon Fisheries Ecosystem Animals Alaska Conservation of Natural Resources Fishes Pacific Ocean Biomass Markov Chains Ecosystem-based fisheries management requires the successful integration of ecosystem information into the fisheries management process. In the Northeast Pacific Ocean, ecosystem data collection and accessibility have achieved successful milestones, yet application to the harvest specification process remains challenging. The synthesis, interpretation, and application of ecosystem information to groundfish fisheries management in the Gulf of Alaska (GOA) can be supported by the identification of common ecosystem trends and ecosystem states across a diverse set of indicators. In this study, we used Dynamic Factor Analysis (DFA) and hidden Markov models (HMM) to analyze 92 indicators in climate, lower-trophic, mid-trophic, and seabird models for the western and eastern GOA marine ecosystems. Time series ranged from 25 to 52 years in length, analyzed through 2022. The DFA identified common trends across indicators and groups of covarying indicators (e.g., biomass of zooplankton species), highlighting opportunities to streamline communication of these data to management. Non-stationarity analyses revealed past changes in relationships, and can provide early warnings in future annual updates if previously identified correlations change. The HMM identified two to three ecosystem states in each sub-model that largely aligned with previously observed long- and short-term shifts in ecosystem dynamics in the region (i.e., shifts starting in 1975, 1988, and 2014). Annually updating these analyses, within an existing framework of reporting ecosystem information to management bodies, can streamline communication and improve early warning of changes in ecosystem dynamics. These tools can provide ecosystem support to management decisions relative to groundfish productivity and resulting harvest specifications. |
| format | Artículo científico |
| id | pubmed_40478913 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | PloS one |
| record_format | pubmed |
| spellingShingle | Identifying common trends and ecosystem states to inform Gulf of Alaska ecosystem-based fisheries management. Ferriss, Bridget E Hunsicker, Mary E Ward, Eric J Litzow, Michael A Rogers, Lauren Callahan, Matt Cheng, Wei Danielson, Seth L Drummond, Brie Fergusson, Emily Gabriele, Christine Hebert, Kyle Hopcroft, Russell R Nielsen, Jens Spalinger, Kally Stockhausen, William T Strasburger, Wesley W Whelan, Shannon Fisheries Ecosystem Animals Alaska Conservation of Natural Resources Fishes Pacific Ocean Biomass Markov Chains Identifying common trends and ecosystem states to inform Gulf of Alaska ecosystem-based fisheries management. Ferriss, Bridget E Hunsicker, Mary E Ward, Eric J Litzow, Michael A Rogers, Lauren Callahan, Matt Cheng, Wei Danielson, Seth L Drummond, Brie Fergusson, Emily Gabriele, Christine Hebert, Kyle Hopcroft, Russell R Nielsen, Jens Spalinger, Kally Stockhausen, William T Strasburger, Wesley W Whelan, Shannon Fisheries Ecosystem Animals Alaska Conservation of Natural Resources Fishes Pacific Ocean Biomass Markov Chains Ecosystem-based fisheries management requires the successful integration of ecosystem information into the fisheries management process. In the Northeast Pacific Ocean, ecosystem data collection and accessibility have achieved successful milestones, yet application to the harvest specification process remains challenging. The synthesis, interpretation, and application of ecosystem information to groundfish fisheries management in the Gulf of Alaska (GOA) can be supported by the identification of common ecosystem trends and ecosystem states across a diverse set of indicators. In this study, we used Dynamic Factor Analysis (DFA) and hidden Markov models (HMM) to analyze 92 indicators in climate, lower-trophic, mid-trophic, and seabird models for the western and eastern GOA marine ecosystems. Time series ranged from 25 to 52 years in length, analyzed through 2022. The DFA identified common trends across indicators and groups of covarying indicators (e.g., biomass of zooplankton species), highlighting opportunities to streamline communication of these data to management. Non-stationarity analyses revealed past changes in relationships, and can provide early warnings in future annual updates if previously identified correlations change. The HMM identified two to three ecosystem states in each sub-model that largely aligned with previously observed long- and short-term shifts in ecosystem dynamics in the region (i.e., shifts starting in 1975, 1988, and 2014). Annually updating these analyses, within an existing framework of reporting ecosystem information to management bodies, can streamline communication and improve early warning of changes in ecosystem dynamics. These tools can provide ecosystem support to management decisions relative to groundfish productivity and resulting harvest specifications. |
| title | Identifying common trends and ecosystem states to inform Gulf of Alaska ecosystem-based fisheries management. |
| topic | Fisheries Ecosystem Animals Alaska Conservation of Natural Resources Fishes Pacific Ocean Biomass Markov Chains |
| url | https://pubmed.ncbi.nlm.nih.gov/40478913/ |