The trophic-level-based ecosystem modelling approach: theoretical overview and practical uses
Bibliographic record
Abstract
Abstract Gascuel, D., Guénette, S., and Pauly, D. 2011. The trophic-level-based ecosystem modelling approach: theoretical overview and practical uses. – ICES Journal of Marine Science, 68: 1403–1416. A trophic-level (TL)-based ecosystem modelling approach is presented, where ecosystem functioning is modelled as a flow of biomass up the foodweb through predation and ontogenetic processes. The approach, based on simple equations derived from fluid dynamics, provides insights into ecosystem functioning and the impact of fishing. A virtual ecosystem is simulated and the model shown to be capable of mimicking the effects of various exploitation patterns on ecosystem biomass expected from the theory. It provides the theoretical basis to explain complex patterns, such as cascading effects, maximum sustainable ecosystem yield, and fishing down the foodweb. The utility of the TL-based approach as a practical tool for determining fishing impacts in specific ecosystems is illustrated using the Guinean ecosystem as a case study, showing how current fishing effort levels led to full exploitation of higher TLs, confirming and generalizing previous single-species assessment results. Finally, catch trophic spectrum analysis is presented to show that it provides reliable biomass estimates when catches per TL and primary production are known.
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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 | 0.003 | 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".