Demographic analysis of a declining pika <i>Ochotona collaris</i> population: linking survival to broad‐scale climate patterns via spring snowmelt patterns
Bibliographic record
Abstract
1. Demographic analysis is essential in order to determine which factors, such as survival, fertility and other life-history characteristics, have the greatest influence on a population's rate of growth (lambda). 2. We used life-table response experiments (LTREs) to assess the relative importance of survival and fertility rates for an alpine lagomorph, the collared pika Ochotona collaris, using 12 years (1995-2006) of census data. The LTRE analysis was repeated for each of three subpopulations within the main study site that were defined by aspect (east, west and south). 3. Across the entire study site, the survival and fertility of adults contributed 35.6 and 43.5%, respectively, to the variance observed in the projected population growth rate, V(lambda), whereas juvenile survival contributed 20.9%. Adult survival and fertility contributed approximately equal amounts for each subpopulation when considered separately, although their rank order varied spatially. 4. Adult survival across the entire site was positively correlated to the Pacific Decadal Oscillation (PDO) with a time lag of 1 year, and was uncorrelated to adult density. The PDO was negatively correlated to the timing of spring snowmelt at our site, implicating the importance of earlier spring conditions and plant phenology on the subsequent winter survival of adults and therefore, population growth. 5. When subpopulations were analysed separately, survivals and fertilities were variously correlated to lagged PDO and adult densities, but the patterns varied spatially. Therefore, the mechanisms underlying V(lambda) can vary substantially over relatively short distances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".