MOSAIC PATTERNS OF THERMAL STRESS IN THE ROCKY INTERTIDAL ZONE: IMPLICATIONS FOR CLIMATE CHANGE
Bibliographic record
Abstract
We explicitly quantified spatial and temporal patterns in the body temperature of an ecologically important species of intertidal invertebrate, the mussel Mytilus californianus, along the majority of its latitudinal range from Washington to southern California, USA. Using long-term (five years), high-frequency temperature records recorded at multiple sites, we tested the hypothesis that local “modifying factors” such as the timing of low tide in summer can lead to large-scale geographic mosaics of body temperature. Our results show that patterns of body temperature during aerial exposure at low tide vary in physiologically meaningful and often counterintuitive ways over large sections of this species' geographic range. We evaluated the spatial correlations among sites to explore how body temperatures change along the latitudinal gradient, and these analyses show that “hot spots” and “cold spots” exist where temperatures are hotter or colder than expected based on latitude. We identified four major hot spots and four cold spots along the entire geographic gradient with at least one hot spot and one cold spot in each of the three regions examined (Washington–Oregon, Central California, and Southern California). Temporal autocorrelation analysis of year-to-year consistency and temporal predictability in the mussel body temperatures revealed that southern animals experience higher levels of predictability in thermal signals than northern animals. We also explored the role of wave splash at a subset of sites and found that, while average daily temperature extremes varied between sites with different levels of wave splash, yearly extreme temperatures were often similar, as were patterns of predictability. Our results suggest that regional patterns of tidal regime and local pattern of wave splash can overwhelm those of large-scale climate in driving patterns of body temperature, leading to complex thermal mosaics of temperature rather than simple latitudinal gradients. A narrow focus on population changes only at range margins may overlook climatically forced local extinctions and other population changes at sites well within a species range. Our results emphasize the importance of quantitatively examining biogeographic patterns in environmental variables at scales relevant to organisms, and in forecasting the impacts of changes in climate across species ranges.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".