Temperature rise improves harvest sustainability in a model system despite reduction in carrying capacity
Bibliographic record
Abstract
Stage-structured population models parameterized from benchtop trials of individual growth, reproduction and survival predicted that temperature rise should make populations of Daphnia magna more resilient to periodic harvest perturbation, despite reduced stock abundance. We tested these predictions under controlled laboratory conditions on 24 populations maintained under constant levels of food abundance, but subjected to weekly harvest events over 10 weeks. As predicted, unperturbed Daphnia populations raised at 15◦C were substantially more abundant by the end of 10 week trials than those raised at 25◦C, but abundance declined sharply with harvest intensity and Daphnia populations collapsed entirely at the highest harvest rate, whereas those populations raised at 25◦C were little affected by perturbation. Our findings suggest that projected patterns of climate change should tend to make populations whose growth rates and rate of maturation increase with temperature better capable of coping with periodic harvest perturbation, despite declining levels of abundance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".