Linking climate stressors to ecological processes in ecosystem models, with a case study from the Gulf of Alaska
Bibliographic record
Abstract
Abstract As climate stressors are impacting marine ecosystems and fisheries across the world, ecosystem models that incorporate environmental variables are increasingly used to inform ecosystem-based fisheries management. The assumptions around the mechanistic links between climate stressors and the biological processes in these models are important, but the implications for model outcomes of which stressors are captured and how they affect modeled biological processes are seldom explored. Using a whole-ecosystem model (Atlantis) for the Gulf of Alaska, we explore the effects of capturing physical (increased temperature) and biogeochemical (decreased low trophic level productivity) climate stressors, and disentangle the effects of each stressor on the productivity of forage fish, groundfish, and fish-eating seabirds. We then test the effects of alternative model specifications of temperature-driven habitat determination and bioenergetics. Increased temperature resulted in increased weight-at-age and higher natural mortality, while decreased productivity resulted in decreased weight-at-age and higher natural mortality. Model specification of temperature dependence of movement and spawning influenced model outcomes, and decoupling these processes from temperature led to overly optimistic biomass predictions. As the use of ecosystem models to inform fisheries management becomes more operational, we illustrate that the assumptions around the links between climate stressors and ecological processes influence model outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".