Predation risk indirectly enhances survival of seaweed recruits but not intraspecific competition in an intermediate herbivore species
Bibliographic record
Abstract
Summary 1. Non‐consumptive predator effects are an important driver in predator–prey interactions. While indirect effects of predation risk shape interspecific species interactions, e.g. in fuelling trophic cascades, knowledge about non‐consumptive predator effects on intraspecific interactions among intimidated prey is scarce. 2. In laboratory experiments, we tested whether predation by male and female green crabs (Carcinus maenas) is stronger on small than on larger specimens of the periwinkle Littorina littorea, an important herbivore species in intertidal habitats of the temperate zone. Moreover, we asked whether size‐specific differences in predation‐induced mortality reflect as a stronger effect of predation risk (caused by nearby consuming crabs) on the foraging behaviour of more threatened periwinkles and ultimately on the recruitment success of the brown seaweed Fucus serratus. Furthermore, predator activity and prey abundance was measured in the field and the efficacy of predation risk on the survival of seaweed recruits was assessed in three field experiments with two different set‐ups (open plots and crab exclosures). 3. Male crabs consumed smaller periwinkles more often than larger conspecifics, while females refused periwinkles as food. In summer, 80–90% of periwinkles in the field attained a size at which crab predation was minimal in laboratory assays. The observed up to 15‐fold decrease in the consumption of seaweed recruits by periwinkles in the presence of nearby consuming crabs in laboratory assays was, however, independent of the size of periwinkles. Predation risk effects from the laboratory were only confirmed in field experiments using crab exclosures but not with open plots, suggesting an artefact due to trespassing by wild crabs in the open plot set‐up. 4. Synthesis. Predation risk may not change intraspecific competition for food in L. littorea. Yet, risk effects on foraging behaviour of periwinkles that experience a low threat of real predation indicate that non‐consumptive predator effects may affect prey population dynamics more strongly than consumptive predator effects. Thus predation risk effects may be indirectly beneficial for the survival of basal species like perennial seaweeds, as experimental evidence from field and laboratory experiments indicates.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".