The overlooked role of biotic factors in controlling the ecological performance of artificial marine habitats
Bibliographic record
Abstract
Summary Artificial structures are sprawling in marine seascapes as a result of burgeoning coastal populations, increasing development and energy demand, and greater risks from climate change, storm surges and sea level rise. Interest in designing marine developments that maintain vital ecosystems and critical services is growing, but progress requires understanding the factors that influence the ecological performance of these novel artificial habitats. We combined field observations and experiments along 500 km of the North Adriatic coastline to analyse the performance of artificial substrata as habitats to support canopy‐forming algae belonging to the genus Cystoseira , among the most ecologically relevant foundation species along rocky Mediterranean coastlines. We aimed to: clarify the underlying factors controlling the growth of Cystoseira in the artificial habitat; contrast the relative importance of these factors between artificial and natural habitats; and test the generality of the results across different sites and species of Cystoseira . We found that: (i) the growth of canopy algae was significantly lower on artificial structures compared to rocky reefs; (ii) such lower growth of canopy algae was not related to less favourable abiotic conditions but to higher biotic disturbance from both consumptive and nonconsumptive interactions on the artificial structures compared to the natural reef; and iii) this was consistent across different study sites and canopy‐forming species. We conclude that biological factors influencing the growth of canopy algae, such as herbivory or other nonconsumptive disturbances, can differ substantially between artificial and natural habitats. The unusually large and previously unreported biotic pressure characterizing many artificial structures can negatively affect their performance as habitats to support ecologically relevant, foundation species. Synthesis and applications . While nearly all considerations to improve the ecological performance of hard marine infrastructures focus on abiotic factors (e.g. construction materials, surface texture, habitat complexity or water quality), careful consideration of critical biotic factors is also needed to further progress the green engineering of sprawling marine infrastructures.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".