Calibrating ecosystem models to support ecosystem-based management of marine systems
Bibliographic record
Abstract
Abstract Ecosystem models, such as Ecopath with Ecosim (EwE), provide a platform to simulate intricate policy scenarios where multiple species, pressures, and ecosystem services interact. Complex questions often return complex answers, necessitating evidence and advice to be communicated in terms of trade-offs, risks, and uncertainty. Calibration procedures for EwE, which can act as a source of uncertainty and bias in model results, have yet to be explored in a comprehensive way that communicates how sensitive model outputs are to different calibration approaches. As the EwE community has grown, multiple divergent approaches have been applied to calibrate models through the estimation of vulnerability multipliers: parameters that augment the consumption rate limits of predators. Here we explore the underlying principles of vulnerability multipliers as well as existing calibration approaches and their justification. Two case studies are presented: the first explores how vulnerability multipliers emerge based on the chosen calibration approach using simulated data, while the second takes two operational EwE models (Irish Sea and Northwest Atlantic Continental Shelf) and compares their outputs when calibrated following alternate calibration approaches. We show how calibration approaches can impact model-derived advice and provide a list of best practice recommendations for EwE calibration.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".