Supporting marine spatial planning with an ecosystem model of Algoa Bay, South Africa
Bibliographic record
Abstract
The Ecopath with Ecosim (EwE) modelling framework was used to develop a model of Algoa Bay and test the ecosystem impacts of the implementation of the Addo Elephant National Park Marine Protected Area (MPA). The Ecopath model included 37 functional groups ranging from phytoplankton to top predators and was fitted to 12 and 14 time-series of biomass and landings, respectively, from 2010 to 2019 (calibration period), using Ecosim. Two scenarios representing different degrees of fisheries closures in the MPA were explored through a 30% and a 100% reduction in fishing effort. Temporal simulations were run until 2059. The fitting procedure identified the best-fit model as the one including the effects of fishing, the six most-sensitive predator–prey interactions, and environmental forcing (primary production anomaly on small phytoplankton). Overall, the predicted biomass and catch time-series reasonably reproduced the observed time-series for 2010–2019, with the biomass of sardine Sardinops sagax, round herring Etrumeus whiteheadi, and African penguins Spheniscus demersus showing the best fits to data. Both MPA scenarios resulted in higher total biomass compared with the baseline by the end of the simulation and decreased catches due to less fishing effort. The most profound biomass increases under the MPA scenarios were observed in apex and pelagic elasmobranchs, yellowtail Seriola lalandi and African penguins. Future research is needed to improve the more-uncertain model parameters and include other key sectors in Algoa Bay, such as shipping. However, this model provides a good foundation for future work including the application of spatially explicit modelling of the bay using Ecospace.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".