Advancing bioenergetics-based modeling to improve climate change projections of marine ecosystems
Bibliographic record
Abstract
Climate change has rapidly altered marine ecosystems and is expected to continue to push systems and species beyond historical baselines into novel conditions. Projecting responses of organisms and populations to these novel environmental conditions often requires extrapolations beyond observed conditions, challenging the predictive limits of statistical modeling capabilities. Bioenergetics modeling provides the mechanistic basis for projecting climate change effects on marine living resources in novel conditions, has a long history of development, and has been applied widely to fish and other taxa. We provide our perspective on 4 opportunities that will advance the ability of bioenergetics-based models to depict changes in the productivity and distribution of fishes and other marine organisms, leading to more robust projections of climate impacts. These are (1) improved depiction of bioenergetics processes to derive realistic individual-level response(s) to complex changes in environmental conditions, (2) innovations in scaling individual-level bioenergetics to project responses at the population and food web levels, (3) more realistic coupling between spatial dynamics and bioenergetics to better represent the local- to regional-scale differences in the effects of climate change on the spatial distributions of organisms, and (4) innovations in model validation to ensure that the next generation of bioenergetics-based models can be used with known and sufficient confidence. Our focus on specific opportunities will enable critical advancements in bioenergetics modeling and position the modeling community to make more accurate and robust projections of the effects of climate change on individuals, populations, food webs, and ecosystems.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".