Comparing population growth rates between census and recruitment‐mortality models
Bibliographic record
Abstract
ABSTRACT In forested ecosystems, estimating the abundance or trend of most wildlife populations is difficult. Therefore, vital rates are often used to model population change, but validating such models is important. Using data from woodland caribou (Rangifer tarandus), we compared estimates of population change (λ) based on vital rate models to λ based on aerial censuses. We modeled λ using Hatter and Bergerud's (1991) recruitment‐mortality (R‐M) equation (λ = survival/[1 − recruitment]). We estimated survival and recruitment from a sample of 317 radio‐collared caribou from 9 subpopulations in British Columbia, Canada. In this ecosystem, woodland caribou have high sightability (>85%) in winter and thus are easy to census compared to most forest wildlife. We found that the R‐M equation overestimated λ compared to census‐based λ across most of the observed range of data (e.g., if R‐M estimated λ of 1.1, census‐based λ was 0.99, and if R‐M was 0.90, census‐based λ was 0.89). We then assessed whether recruitment, survival, a linear model of both parameters, or the R‐M equation best predicted census‐based λ. The R‐M equation explained 60% of the variation in census‐based λ, more than double the next‐best approach (i.e., the simple linear model), even though identical parameters were included. Further, we simulated variability due to the unknown sex (M:F) ratio in the sample, and found that the R‐M equation remained the best predictor of census‐based λ. Although the R‐M equation was the most precise and accurate approach, our results reaffirm that it is important to periodically validate trend estimates based on vital rate models with estimates of absolute abundance, particularly for species of management concern. © 2016 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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".