The global ocean is an ecosystem: simulating marine life and fisheries
Bibliographic record
Abstract
Abstract Aim There has been considerable effort allocated to understanding the impact of climate change on our physical environment, but comparatively little to how life on Earth and ecosystem services will be affected. Therefore, we have developed a spatial–temporal food web model of the global ocean, spanning from primary producers through to top predators and fisheries. Through this, we aim to evaluate how alternative management actions may impact the supply of seafood for future generations. Location Global ocean. Methods We developed a modelling complex to initially predict the combined impact of environmental parameters and fisheries on global seafood production, and initially evaluated the model's performance through hindcasting. The modelling complex has a food web model as core, obtains environmental productivity from a biogeochemical model and assigns global fishing effort spatially. We tuned model parameters based on M arkov chain random walk stock reduction analysis, fitting the model to historic catches. We evaluated the goodness‐of‐fit of the model to data for major functional groups, by spatial management units and globally. Results This model is the most detailed ever constructed of global fisheries, and it was able to replicate broad patterns of historic fisheries catches with best agreement for the total catches and good agreement for species groups, with more variation at the regional level. Main conclusions We have developed a modelling complex that can be used for evaluating the combined impact of fisheries and climate change on upper‐trophic level organisms in the global ocean, including invertebrates, fish and other large vertebrates. The model provides an important step that will allow global‐scale evaluation of how alternative fisheries management measures can be used for mitigation of climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".