Testing the match–mismatch hypothesis in bighorn sheep in the context of climate change
Bibliographic record
Abstract
In species with long gestation, females commit to reproduction several months before parturition. If cues driving conception date are uncoupled from spring conditions, parturition could be mistimed. Mismatch may increase with global change if the rate of temporal changes in autumn cues differs from the rate of change in spring conditions. Using 17 years of data on climate and vegetation phenology, we show that autumn temperature and precipitation, but not vegetation phenology, explain parturition date in bighorn sheep. Although autumn cues drive the timing of conception, they do not predict conditions at parturition in spring. We calculated the mismatch between individual parturition date and spring green-up, assessed whether mismatch increased over time and investigated the consequences of mismatch on lamb neonatal survival, weaning mass and overwinter survival. Mismatch fluctuated over time but showed no temporal trend. Temporal changes in green-up date did not lead to major fitness consequence of mismatch. Detailed data on individually marked animals revealed no effect of mismatch on neonatal or overwinter survival, but lamb weaning mass was negatively affected by mismatch. Capital breeders might be less sensitive to mismatch than income breeders because they are less dependent on daily food acquisition. Herbivores in seasonal environments may access sufficient forage to sustain lactation before or after the spring 'peak' green-up, and partly mitigate the consequences of a mismatch. Thus, the effect of phenological mismatch on fitness may be affected by species life history, highlighting the complexity in quantifying trophic mismatches in the context of climate change.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".