Salinity has a greater effect on sea star righting time than temperature
Bibliographic record
Abstract
ABSTRACT Acute increases in temperature linked with marine heatwaves have been occurring with increasing frequency around New Zealand. Additionally, as rainfall rates vary in association with climate change, the salinity regimes in estuaries are changing, often with more frequent pulses of low salinity events. These physical changes in the environment can lead to sublethal stress and changes in the distribution of marine organisms. Echinoderms play a key role in the structuring of many inter‐ and subtidal ecosystems and can exert top‐down control, which promotes biodiversity and overall ecosystem function. Here, we investigated the impacts of high temperature, low salinity and their combined effect on behavioural responses of Coscinasterias muricata , a keystone predator. We examined how these two climatic variables, individually and together, would affect righting time, a proxy for fitness, over a one‐week period. We found that only salinity significantly affected righting times, with those exposed to ambient salinity treatments righting an average of 47% faster than those in low salinities, across all days. Neither temperature nor duration of exposure significantly affected righting time. These findings suggest, because keystone species may be affected, that future changes in salinity can have drastic effects on coastal ecosystems and should be further investigated.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".