Fitness implications of seasonal climate variation in Columbian ground squirrels
Bibliographic record
Abstract
The influence of climate change on the fitness of wild populations is often studied in the context of the spring onset of the reproductive season. This focus is relevant for climate influences on reproductive success, but neglects other fitness-relevant periods (e.g., autumn preparation for overwintering). We examined variation in climate variables (temperature, rainfall, snowfall, and snowpack) across the full annual cycle of Columbian ground squirrels (Urocitellus columbianus) for 21 years. We investigated seasonal climate variables that were associated with fitness variables, climate variables that exhibited directional changes across the study period, and finally observed declines in fitness (-0.03 units/year; total decline = 37%) that were associated with directional changes in climate variables. Annual fitness of adult female ground squirrels was positively associated with spring temperature (r = 0.69) and early summer rainfall (r = 0.56) and negatively associated with spring snow conditions (r = -0.44 to -0.66). Across the 21 years, spring snowmelt has become significantly delayed (r = 0.48) and summer rainfall became significantly reduced (r = -0.53). Using a standardized partial regression model, we found that directional changes in the timing of spring snowmelt and early summer rainfall (i.e., progressively drier summers) had moderate influences on annual fitness, with the latter statistically significant (ρ = -0.314 and 0.437, respectively). The summer period corresponds to prehibernation fattening of young and adult ground squirrels. Had we focused on a single point in time (viz. the onset of the breeding season), we would have underestimated the influences of climate change on our population. Rather, we obtained a comprehensive understanding of the influences of climate change on individual fitness by investigating the full lifecycle.
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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 | 0.000 | 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 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".