Fishing during extreme heatwaves alters ecological interactions and increases indirect fishing mortality in a ubiquitous nearshore system
Bibliographic record
Abstract
Heatwaves may have multifaceted ecological impacts; however, field studies assessing the ecological ramifications of nearshore fishing during heatwaves are rare. We leverage a field experiment simulating clam fishing to document such effects on a ubiquitous ecological system at the land-sea interface. During monthly field trials from May-September 2024, we experimentally fished clams at low tide and tracked reburrowing and mortality rates of marked, sub-legal sized clams returned to the sediment. Half of the clams were protected from crab predation and estimates of predator and scavenger activity were recorded. Clams typically reburrowed quickly and mortality was low. During the heatwave, however, clams appeared unhealthy, failed to reburrow, and suffered near-complete mortality. Predator activity in experimental plots was >4× higher during the heatwave compared to other months. Clam mortality during the heatwave was likely a combined result of physiological death and increased predation. When put into the context of air temperature during fishing, there was a clear ecological shift at 30 °C, whereby clam reburrowing plummeted, and predator/scavenger activity and clam mortality dramatically increased. These results provide in situ documentation of human-climate interactions influencing indirect fishing mortality and altering ecological dynamics, ultimately generating pertinent information for ecosystem-based fisheries management.
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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 | 0.000 |
| 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) | 0.001 | 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".