Predictive mapping of reproductive fish habitats to aid marine conservation planning
Bibliographic record
Abstract
Postsettlement spillover from marine protected areas (MPAs) can support adjacent fished populations and has been subject of many scientific studies. The larval subsidy effect, on the contrary, is more challenging to study and less demonstrated, although it, arguably, provides key benefits for fisheries. We modeled and predicted the spatial distribution of fish spawning biomass and fecundity across a temperate insular MPA network (Azores archipelago, Northeast Atlantic) and identified potential single- and multispecies reproductive habitats (RHs) in shallow reefs. Reproductive strategies or skewed sex ratios influenced spatial patterns of potential spawning biomass and fecundity. Predicted multispecies RHs covered 5%–20% of the studied reef habitat. Given their potentially high reproductive output, we argue that such sites should be considered in marine conservation planning to increase chances of achieving fisheries and conservation benefits. Spatial patterns of the reproductive output may function as surrogates for larval subsidy when limited or no larval connectivity information is available and also may assist in identifying potential larval sources and priority sites for conservation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".