Identifying the demographic determinants of population growth rate: a case study of red‐billed choughs <i>Pyrrhocorax pyrrhocorax</i>
Bibliographic record
Abstract
Summary Identifying which age‐specific demographic rates underlie variation in a population's growth rate (λ) is an important step towards understanding the population's dynamics. Using data from a 20‐year study of marked individuals, we describe patterns of demographic variation and covariation in the Scottish red‐billed chough population ( Pyrrhocorax pyrrhocorax ), and investigate which demographic rates have the greatest projected and realized influence on λ. Survival, the probability of breeding and breeding success varied with age in this population. Data were sufficient to estimate year‐specific probabilities of first‐year, second‐year and adult (all ages over 2) survival and mean breeding success. A population trajectory modelled using these parameter estimates closely matched census data, suggesting that estimates and simplifying assumptions were sufficient to accurately describe important demographic processes. Elasticity analyses based on stage‐classes for which year‐specific survival was estimable suggested that λ was more elastic to variation in adult survival than first‐ or second‐year survival or breeding success. These ranks were consistent across all 15 years for which λ could be estimated directly, although the elasticity of adult survival declined with population growth. Survival and breeding success were positively correlated across years. λ remained most sensitive to adult survival when this demographic covariation was incorporated into elasticity analyses. However, elasticities calculated from a fully age‐structured model suggested that λ was more elastic to variation in first‐ and second‐year survival than to survival at any individual older age class. These ranks were robust to realistic demographic variation, but sensitive to postulated patterns of demographic covariation. We emphasize that covariation should be measured and incorporated into elasticity analyses, and that estimated elasticities must be interpreted in the context of the way in which stage‐classes are defined. Of the demographic rates in which we quantified between‐year variation, first‐year survival varied most, followed by second‐year survival, breeding success and adult survival. These rates consequently contributed more equally to variation in λ than elasticities predicted. Overall, variation in λ was caused primarily by variation in survival rather than breeding success, and variation in prebreeding survival accounted for 56% of the total variation in λ.
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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 | 0.001 | 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) | 0.000 | 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 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".