Tropical invertebrate response to marine reserves varies with protection duration, habitat type, and exploitation history
Bibliographic record
Abstract
Abstract Macroinvertebrates play a critical role in marine processes, are important in global fisheries, and make up the vast majority of ocean biodiversity, yet are largely overlooked in fisheries stock assessment and conservation. Marine reserves are a heavily advocated method for promoting recovery of marine biodiversity, but the design of reserves and the methods for evaluating their performance often neglect invertebrate taxa, instead assessing changes in fish abundance or biomass. The purpose of this study is to (1) measure the impacts of no‐take marine reserves on marine macroinvertebrates, (2) identify the correlates of changes to macroinvertebrate abundance, and (3) determine if the typical taxa used to measure reserve success (finfish) can predict changes in invertebrate abundance. Non‐coral, non‐sponge, macroinvertebrates were sampled inside and outside of 10 community‐managed marine reserves in the Central Philippines and compared with abundances found at distant fished sites. Using generalized linear mixed effects models with multimodel inference, positive reserve effects were found in exploited invertebrate taxa both inside and outside of reserves (1.5–2.3 times greater abundances), but no effect was found in unfished taxa. Habitat composition and complexity were consistently associated with higher invertebrate abundance. Most surprisingly, invertebrate abundance was not consistently predicted by that of fish. These results indicate fish, in isolation, may not be an ideal indicator for biodiversity response to reserves, and habitat considerations are important when creating reserves that support invertebrates. These results are particularly relevant to practitioners in developing regions, where community‐managed reserves are common and invertebrates are important in fisheries.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".