A seascape approach for guiding effective habitat enhancement
Bibliographic record
Abstract
Kelp habitats are threatened across the globe, and because of their ecological importance, active conservation and restoration solutions are needed. The use of man-made structures as artificial reefs is one way to enhance kelp habitat by providing suitable substrata, but in the past the ecology of artificial structures has been investigated mainly in contrast to natural coastal habitats, not as elements integrated into the seascape. Indeed, it is now emerging that structuring processes, including ecological interactions (e.g., herbivory), can depend on properties of the surrounding seascape. In Eastern Canada, grazing by the green sea urchin can jeopardize the success of artificial reefs for kelp enhancement. Urchin activity is, however, likely to be influenced by the bottom composition, and thus a seascape approach is needed to integrate urchin behavior and habitat heterogeneity. Adopting a spatially explicit framework, we investigated whether the seascape creates areas of differential grazing risk for kelp by affecting urchin habitat use. Specifically, we transplanted kelp onto modules of artificial substrata distributed on a heterogeneous area that we mapped for bottom type and algal cover. After following kelp survival and urchin distribution over time, we modeled kelp survival as function of urchin metrics and coupled it to urchin use of the habitat models to map grazing risk in the area. Kelp survival was a function of the frequency of the urchins presence. Urchins avoided sandy patches, while bottom composition and algal cover modulated the within-patch urchin use of the habitat, creating heterogeneity in grazing risk. Discrete seascape features (boulders) also increased the grazing risk locally. The heterogeneity of coastal seafloor can thus play a major role in determining the ecological outcomes on artificial structures. Incorporating this information when planning artificial reefs could minimize the detrimental grazing risk, thereby increasing the success of artificial reefs for kelp habitat enhancement.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".