Spatially explicit modelling of kelp-grazer interactions: a seascape approach for effective habitat enhancement using artificial reefs
Bibliographic record
Abstract
Abstract Artificial structures are sprawling along the coast affecting the aspect and the functioning of shallow coastal seascapes. For years, the ecology of artificial structures has been investigated mainly in contrast to natural coastal habitats. However, it is increasingly emerging that structuring processes, such as trophic interactions, can depend on properties of the surrounding landscape. Heterogeneity of coastal seafloor and habitats could thus play a major role in determining the variability of ecological outcomes on artificial structures. Artificial reefs are being used in coastal areas in attempts to restore and enhance marine habitats and communities, including large brown seaweed (“kelp”), to offset habitat loss and mitigate coastal development impacts. The outcome of enhancement projects using artificial reefs have not always been either consistent or positive. Overlooking the effect of strong ecological interactions adds a high level of uncertainty and can undermine the success of these efforts. In Eastern Canada, top-down control exerted by green sea urchins ( Strongylocentrotus droebachiensis ) can seriously compromise the success of artificial reefs for kelp enhancement. Importantly, urchin interactions with macroalgae are likely to be influenced by the bottom composition. A seascape approach could thus integrate behavior and habitat heterogeneity. We investigated whether the local seascape could create zones of differential grazing risk for kelp outplanting kelp ( Alaria esculenta ) on artificial blocks on an heterogenous bottom. Adopting a spatially explicit framework, we determined how seascape affected the urchin use of the habitat and used this information to map the grazing risk throughout the area. Kelp survival was a function of frequency of urchin presence throughout the study site. While urchins avoided sandy patches, bottom composition and algal cover modulated the within-patch urchin use of the habitat. This translated in the heterogeneity of grazing risk intensity. Synthesis and applications . The presence of discrete seascape features locally increased the grazing risk for kelp by differentially affecting the urchin’s usage of the habitat, even within the same bottom patch. Incorporating this information when planning artificial reefs could minimize the detrimental grazing risk thus increasing the rate of success and ensuring lasting results.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".