Opportunities for behavioral rescue under rapid environmental change
Bibliographic record
Abstract
Laboratory measurements of physiological and demographic tolerances are important in understanding the impact of climate change on species diversity; however, it has been recognized that forecasts based solely on these laboratory estimates overestimate risk by omitting the capacity for species to utilize microclimatic variation via behavioral adjustments in activity patterns or habitat choice. The complex, and often context-dependent nature, of microclimate utilization has been an impediment to the advancement of general predictive models. Here, we overcome this impediment and estimate the potential impact of warming on the fitness of ectotherms using a benefit/cost trade-off derived from the simple and broadly documented thermal performance curve and a generalized cost function. Our framework reveals that, for certain environments, the cost of behavioral thermoregulation can be reduced as warming occurs, enabling behavioral buffering (e.g., the capacity for behavior to ameliorate detrimental impacts) and "behavioral rescue" from extinction in extreme cases. By applying our framework to operative temperature and physiological data collected at an extremely fine spatial scale in an African lizard, we show that new behavioral opportunities may emerge. Finally, we explore large-scale geographic differences in the impact of behavior on climate-impact projections using a global dataset of 38 insect species. These multiple lines of inference indicate that understanding the existing relationship between thermal characteristics (e.g., spatial configuration, spatial heterogeneity, and modal temperature) is essential for improving estimates of extinction risk.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".