Habitat with small inter-structural spaces promotes mussel survival and reef generation
Bibliographic record
Abstract
Spatially complex habitats provide refuge for prey and mediate many predator–prey interactions. Increasing anthropogenic pressures are eroding such habitats, reducing their complexity and potentially altering ecosystem stability on a global scale. Yet, we have only a rudimentary understanding of how structurally complex habitats create ecological refuges for most ecosystems. Better informed management decisions require an understanding of the mechanisms underpinning the provision of physical refuge and this may be linked to prey size, predator size and predator identity in priority habitats. We tested each of these factors empirically in a model biogenic reef system. Specifically, we tested whether mortality rates of blue mussels ( Mytilus edulis ) of different sizes differed among: (i) different forms of reef structural distribution (represented as ‘clumped’, ‘patchy’ and ‘sparse’); (ii) predator species identity (shore crab, Carcinus maenas and starfish , Asterias rubens ); and (iii) predator size. The survival rate of small mussels was greatest in the clumped experimental habitat and larger predators generally consumed more prey regardless of the structural organisation of treatment. Small mussels were protected from larger A. rubens but not from larger C. maenas in the clumped habitats. The distribution pattern of structural objects, therefore, may be considered a useful proxy for reef complexity when assessing predator–prey interactions, and optimal organisations should be considered based on both prey and predator sizes. These findings are essential to understand ecological processes underpinning predation rates in structurally complex habitats and to inform future restoration and ecological engineering practices.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".