Community‐wide consequences of nonconsumptive predator effects on a foundation species
Bibliographic record
Abstract
Predators can exert nonconsumptive effects (NCEs) on prey, which often take place through prey behavioural adjustments to minimise predation risk. As NCEs are widespread in nature, interest is growing to determine whether NCEs on a prey species can indirectly influence several other species simultaneously, thus leading to changes in community structure. In this study, we investigate whether a predator can exert NCEs on a foundation species and indirectly affect community structure. Through laboratory experiments, we first tested whether the predatory marine snail Acanthina monodon exerts negative NCEs on larviphagy (consumption of pelagic larvae) and phytoplankton filtration rates of the mussel Perumytilus purpuratus, an intertidal foundation species. These hypotheses stem from the notion that mussels may decrease feeding activities in the presence of predator cues to limit detection by predators. Afterwards, a field experiment tested whether the presence of A. monodon near mussel beds leads to higher colonisation rates of invertebrates that reproduce through pelagic larvae (expected under a lower larviphagy in P. purpuratus) and to a lower algal biomass on P. purpuratus shells (expected under a lower metabolite excretion in the mussels), thereby changing the community structure of the species typically found in P. purpuratus beds. The laboratory experiments revealed that waterborne cues from A. monodon limit the larviphagy and filtration rates of P. purpuratus. In turn, the field experiment showed that A. monodon cues led to greater abundances of barnacles and bivalves and a lower algal biomass in P. purpuratus beds, thus altering community structure. Overall, this study shows that a predator can indirectly affect community structure through NCEs on an invertebrate foundation species. As invertebrate foundation species are ubiquitous worldwide, understanding predator NCEs on these organisms could help to better understand community regulation in systems structured by such species.
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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 | 0.000 | 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".