Environmental variability and phenology evolution: impacts of climate change and spring onset on reproductive timing in a small mammal
Bibliographic record
Abstract
The phenology, or timing of life history events, of organisms affects both ecological and evolutionary dynamics. Recent work has illustrated the effects of climate change on the phenology for many species. Changing selective pressures on phenology can have consequences for species if the reliability of phenological cues decreases or if climate change affects interacting species differentially. There are now numerous examples, in which earlier mean timing of spring has selected for earlier phenology of organisms. However, much less is known about how changes in the variability of spring — and consequently the reliability of cues — might affect species. We built a general model of animal population dynamics to study both the ecology and evolution of phenological events under climate change. We parameterized this model for a population of the collared pika (Ochotona collaris) found in the Yukon, Canada. In line with past work, we show that an earlier timing of spring snowmelt will select for an earlier timing of reproduction. In addition, we show that variability in the onset of spring also selects for earlier reproduction. However, evolution or plasticity in juvenile mortality, due to late snowmelt, can lead to later reproduction. These results highlight the importance of looking at the variability, and not only the mean, in spring onset. The specific relationship between the mean and variability of spring onset coupled with the ability of a population to be plastic or adaptable will determine the long-term effects of climate change on the phenology of species.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".