Climate change in the rocky intertidal zone: predicting and measuring the body temperature of a keystone predator
Bibliographic record
Abstract
Forecasting the responses of populations and ecosystems to climate change requires that we understand both the direct effects of temperature on organism physiology and the indirect effects of temperature change on interactions such as predation and competition.The sea star Pisaster ochraceus is a keystone predator in the rocky intertidal zone with a broad geographic distribution along the west coast of North America.We developed a mechanistic heat budget model that uses environmental data to predict the body temperatures of P. ochraceus.Model accuracy was verified by comparing model output temperatures, generated using measured microclimatic data as input, to the temperatures of live P. ochraceus in the field.The average absolute errors between predicted and measured body temperatures were ~1°C.To continuously monitor sea star body temperatures in the field, we developed data loggers that thermally mimic P. ochraceus.Accuracy of these biomimetic loggers was tested by comparing logger temperatures to P. ochraceus body temperatures, and they were found to mimic body temperatures within ~1°C.Loggers were deployed at different tidal heights at a site in Bamfield, British Columbia, Canada, to investigate within-site body temperature variation.We also explored the relationship between body temperature and vertical distribution of P. ochraceus.A negative correlation was found between maximum body temperatures on Day n and the number of sea stars found in the intertidal on Day n + 1.These results suggest that temperatures reached during aerial exposure at low tide, at least in part, may determine where in the intertidal zone these sea stars are located, which could affect their foraging limits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".