Utilizing benthic habitat maps to inform biodiversity monitoring in marine protected areas
Bibliographic record
Abstract
Abstract The designation of marine protected areas (MPAs) requires the development of a monitoring design to assess the effectiveness of the closure in meeting its conservation objectives. Natural variability should be considered in the design, ideally determined using baseline information collected at the scale of the closure. Monitoring benthos informs on general ecosystem state. Benthic habitat maps are widely used as surrogates of benthos in marine spatial planning, and could potentially be used to inform monitoring by minimizing confounding habitat effects. Here, epibenthic diversity was assessed in the St. Anns Bank MPA in Atlantic Canada, the first assessment of benthic patterns at the scale of this closure. Epibenthic assemblages were determined using a photographic camera system along a single transect (~100–150 m in length) at 44 locations in 2013 and 2014 (with 10 or 11 images per location, providing a total of 438 images), prior to the designation of the MPA. Epibenthic patterns were correlated with a previously developed benthic habitat (benthoscape) map to determine the potential of using benthoscape classes as units for monitoring. Hierarchical agglomerative clustering of epibenthic assemblages and similarity profile analysis revealed five clusters of assemblages in the MPA ( P < 0.01), each of which were associated with specific indicator taxa. Some clusters of assemblages correlated well with distinct benthoscape classes representing either hard/coarse (gravel) or soft sediment (sand and mud), whereas clusters associated with mixed sediment segregated spatially. The within‐cluster variability in assemblages between locations was lower overall than within the management zones, but differed between clusters. Similarities were detected with previous coarser‐scale assessments of epibenthic diversity in the St. Anns Bank MPA, but this study revealed a more complex benthic structure than previously thought. A monitoring design should thus consider this natural variability to reliably monitor change and aid in determining the effectiveness of the MPA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".