Evaluating ecosystem impacts of gear regulations in a data-limited fishery—comparing approaches to estimate predator–prey interactions in Ecopath with Ecosim
Bibliographic record
Abstract
Abstract Ecosystem models, such as Ecopath with Ecosim (EwE), are useful tools for developing ecosystem-based management strategies. Model development, however, requires data for the estimation of input parameters including time series for calibration. Most small-scale fisheries lack such information, making it difficult to reliably use ecosystem models for management strategy exploration. This is the case for the Chwaka Bay fishery (Zanzibar), where an increase in dragnets has led to concerns for unsustainable development. While EwE could help to explore the impacts of gear regulations in the bay, this is hampered by the lack of time series to estimate the predator–prey interactions (vulnerabilities) in the model. Here, we explore available approaches for estimating the vulnerabilities to simulate the effects of a dragnet prohibition with and without reallocation of fishing effort. Simulations suggest that banning dragnets would be beneficial for the fishing community judged by the increase in biomass of functional groups and fishers’ profits, but not if dragnet fishers were to continue fishing in the bay by reallocating to other gears, indicated by the reduced fish biomass and fishers’ profits. The overall trend (decreasing or increasing) in the changes of functional groups, ecosystem indicators, and profits were relatively consistent across vulnerability settings, illustrating that an unfitted EwE model can be used for qualitative management strategy exploration.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".