Trophic cascades and connectivity in coastal benthic marine ecosystems: a meta-analysis of experimental and observational research
Bibliographic record
Abstract
Predators can exert top-down control on lower trophic levels, such that their removal or addition may trigger trophic cascades. Despite coastal ecosystems containing well known trophic cascades, there remains uncertainty about the abiotic and biotic factors governing the occurrence and strength of these cascades. Here, we sought to explain the variability of trophic cascades in benthic marine ecosystems by conducting a meta-analysis of experimental (n = 17) and observational (n = 22) studies that recorded herbivore and producer populations in the presence and absence of a predator. From these data (147 predator-herbivore-producer measurements), we show that predators decreased herbivore populations between 2.1-4.76 times and increased producer populations by 1.62-2.83 times their original biomass, abundance, or density. Contrary to past research, these values are comparable to other ecosystems. Biotic factors related to species body size were most influential in determining herbivore population responses to the presence of predators, while abiotic factors, including nutrient concentration, best determined producer population responses. Our results also show that producers responded more strongly to changes in herbivore populations in high-nutrient and low-temperature environments. We found that herbivore populations in marine reserves were 2.83 times lower on average compared to areas outside the reserve, while producer populations were on average 1.90 times higher. Overall, this work advances understanding of factors modulating trophic cascade strength, demonstrates that reserves can have ecosystem-wide impacts, and provides new information about the average strength of trophic cascades in benthic marine ecosystems.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".