Environmental stochasticity: empirical estimates of prairie vole survival with implications for demographic models
Bibliographic record
Abstract
A rich theory exists regarding the potential impact of correlations among vital rates on population projections derived from demographic models. However, relatively little is known about the magnitude of correlations among vital rates in natural populations, particularly in mammals. We used 30 years of mark–recapture data from a population of prairie voles ( Microtus ochrogaster (Wagner, 1842)) to test for differences in survival among mass classes and sexes, in correlations among vital rates, in correlations between vital rates and environmental factors, and in autocorrelation in vital rates. Estimated monthly survival rates did not differ significantly among mass classes and there were no significant cross-correlations among mass classes. Survival of large prairie voles increased in mild winters (i.e., warm temperatures and low snowfall). Survival rates of medium and large voles were negatively autocorrelated at time lags of 9–12 months, and survivals of large voles were positively autocorrelated for time lags of <3 months. These autocorrelations were not explained by patterns of temperature or precipitation. The observed degree of autocorrelation in vital rates is sufficient to affect projections from demographic models, particularly in short-lived taxa that require seasonal or monthly estimation of vital rates.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".