The stars are out: Predicting the effect of seawater freshening on the ecological impact of a sea star keystone predator
Bibliographic record
Abstract
Predicting the myriad effects of climate change on ecological communities is a major challenge for scientists, and to date relatively few studies have focused on the effects of sea freshening on species interactions. In particular, changes in keystone species predatory effects could be pervasive. Here, we assess the consequences of decreasing salinity on the ecological impact exerted by a keystone predatory sea star, Asterias rubens . We quantified sea star functional responses (FRs; per capita predation as a function of prey density) under decreasing salinity treatments aligned with climate change projections (18ppt, 15ppt, 12ppt). Furthermore, we combined FRs with larval recruitment estimates, i.e. ecological “Impact Potential”, to act as an ecological indicator of predator population-level responses under this environmental change. Attack and maximum feeding rates of sea stars were reduced by decreasing salinities, with no instances of predation found at 12ppt. Given that decreasing salinities also reduced larval sea star recruitment, the overall Impact Potential of this keystone predator species was lessened by decreased salinity. Sea freshening projections by the end of this century could thus drive significant decreases in the effects of this keystone predator, with serious implications for the structuring and functioning of ecological communities.
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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 | 0.001 |
| 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".