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Key Uncertainties and Modeling Needs for Managing Living Marine Resources in the Future Arctic Ocean
Key Uncertainties and Modeling Needs for Managing Living Marine Resources in the Future Arctic Ocean
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Key Uncertainties and Modeling Needs for Managing Living Marine Resources in the Future Arctic Ocean
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Key Uncertainties and Modeling Needs for Managing Living Marine Resources in the Future Arctic Ocean
Key Uncertainties and Modeling Needs for Managing Living Marine Resources in the Future Arctic Ocean

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Key Uncertainties and Modeling Needs for Managing Living Marine Resources in the Future Arctic Ocean
Key Uncertainties and Modeling Needs for Managing Living Marine Resources in the Future Arctic Ocean
Journal Article

Key Uncertainties and Modeling Needs for Managing Living Marine Resources in the Future Arctic Ocean

2024
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Overview
Emerging fishing activity due to melting ice and poleward species distribution shifts in the rapidly‐warming Arctic Ocean challenges transboundary management and requires proactive governance. A 2021 moratorium on commercial fishing in the Arctic high seas provides a 16‐year runway for improved scientific understanding. Given substantial knowledge gaps, characterizing areas of highest uncertainty is a key first step. Marine ecosystem model ensembles that project future fish distributions could inform management of future Arctic fisheries, but Arctic‐specific variation has not yet been examined for global ensembles. We use the Fisheries and Marine Ecosystem Intercomparison Project ensemble driven by two Earth System Models (ESMs) under two Shared Socioeconomic Pathways (SSP1‐2.6 and SSP5‐8.5) to illustrate the current state of and uncertainty among biomass projections for the Arctic Ocean over the duration of the moratorium. The models generally project biomass increases in more northern Arctic ecosystems and decreases in southern ecosystems, but wide intra‐model variation exceeds projection means in most cases. The two ESMs show opposite trends for the main environmental drivers. Therefore, these projections are currently insufficient to inform policy actions. Investment in sustained monitoring and improving modeling capacity, especially for sea ice dynamics, is urgently needed. Concurrently, it will be necessary to develop frameworks for making precautionary decisions under continued uncertainty. We conclude that researchers should be transparent about uncertainty, presenting these model projections not as a source of scientific “answers,” but as bounding for plausible, policy‐relevant questions to assess trade‐offs and mitigate risks. Plain Language Summary As the Arctic Ocean gets warmer, melting ice is opening up new opportunities for fishing. However, we don't know where fish will go and how they can be managed sustainably. An important first step is to figure out which unknowns we can solve quickly with more research, and what is so uncertain that we will have to make decisions without ideal information. In this paper, we looked at uncertainty in a set of global models that predict how fish populations might shift in the next 10–25 years. Overall, these models show that fish populations might increase in the northern parts of the Arctic while decreasing in the south. But the models make very different predictions, and some disagree on whether fish populations will increase or decrease in certain areas. A major source of uncertainty is how sea ice will change, and how ocean life will respond. Therefore, this is a priority area to invest in long‐term research and better models. Overall, these models are too uncertain to rely on for specific management decisions about Arctic fishing. Instead, scientists and decision makers can use them to shape more informed discussions about potential trade‐offs and risks of future fishing in the Arctic. Key Points Variation and disagreement in marine ecosystem model projections are too high to be informative for near‐term Arctic fisheries management Insufficient inclusion and knowledge of sea ice cover and sea ice productivity dynamics are major drivers of uncertainty Researchers should be transparent about uncertainty and risk; present model projections as the basis for hypotheses and scenario planning