Estimating ungulate recruitment and growth rates using age ratios
Bibliographic record
Abstract
Abstract Trends in population growth can be monitored with data for key vital rates without knowledge of abundance. Although adult female survival has the highest elasticity for ungulate population dynamics, the more variable recruitment rates are commonly monitored to track local variation in growth rates. Specifically, recruitment is often measured using late winter young:adult age ratios, though these age ratios are difficult to reliably interpret given the contribution of multiple vital rates to annual ratios. We show that the supplementation of age ratio data with concurrent radio‐telemetry monitoring of adult female survival allows both retrospective estimation of empirical population growth rates and the decomposition of recruitment‐specific vital rates. We demonstrate the estimation of recruitment and population growth rates for 1 woodland caribou population using these methods, including elasticity and life‐stage simulation analysis of the relative contribution of adult female survival and recruitment rates to variation in population growth. We show, for this woodland caribou population, that adult female survival and recruitment rates were nearly equivalent drivers of population growth. We recommend the concurrent monitoring of adult female survival to reliably interpret age ratios when managing caribou and other ungulates. © 2011 The Wildlife Society.
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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 | 0.005 |
| 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) | 0.001 | 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".