Variability and stability in predation landscapes: A cross‐ecosystem comparison on the potential for predator control in temperate marine ecosystems
Bibliographic record
Abstract
Abstract Predation can play an important role in population, community and ecosystem processes. When predator guilds are diverse, fluctuations in individual predator populations may have small influences on the guild at large, suggesting that predator diversity stabilizes the amount of predation prey experience. The strength of this phenomenon depends on how synchronously populations within predator assemblages vary and whether all predators are equally important consumers. We utilized a database of biomasses of fish species across ten temperate marine ecosystems paired with diet composition and feeding rate information from mass‐balance food web models to develop a predation index that weights the importance of predators on a prey based on predator feeding habits. Using the index, we asked how diverse sources of predation in these ecosystems are and how much diversity stabilizes variability in predation. Predator assemblages displayed a wide range of diversity; in one‐third of assemblages, a single predator group accounted for over half of all predation. Abundances of predator populations within assemblages generally varied independently of one another (neither synchronously nor asynchronously), implying an intermediate stabilization on predation intensity by predator diversity. Accounting for interaction strength (versus mere presence) is critical for interpreting the predator landscape; doing so led to a wider range in predator assemblage diversity and less synchronous assemblages. This work challenges conventional notions that marine food webs are diverse and therefore less susceptible to predator control. Future work should consider the temporal variability of the predator assemblages and account for differences in mortality induced by each predator population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".