Is individual heterogeneity in growth rates relevant to population dynamics of long‐lived reptiles?
Bibliographic record
Abstract
Many populations show pronounced individual heterogeneity in traits such as somatic growth rates, but the relevance of this heterogeneity to population dynamics remains unclear. Individual heterogeneity may be particularly relevant to long-lived organisms for which vital rates (survival and reproduction) increase with adult growth, as subtle differences in growth rates can have major fitness consequences. Previous analysis of data for snapping turtles (Chelydra serpentina) in Algonquin Park, Canada, from 1972 to 2012 showed that individual heterogeneity in growth rates and size-specific reproductive rates of adult females led to eightfold variation in lifetime reproductive output. Here, we test whether this individual heterogeneity affects population dynamics by comparing projections from alternative integrated population models (IPMs) where: (1) vital rates of adult females increase with size and there is individual heterogeneity in their adult growth and reproduction parameters as well as their ages at maturity; (2) vital rates increase with size but there is no individual heterogeneity; or (3) vital rates are assumed equal among adult females. The three IPMs all integrated component models for growth, reproduction, and survival, incorporated random annual variation in rates, and used data augmentation to model unobserved individuals including future recruits. The data augmentation approach allowed the individual heterogeneity in parameters to be extrapolated from observed to unobserved individuals under Model 1. Models 1 and 2 produced similar annual estimates of recruitment, mortality, and abundance from 1972 to 2012 and similar projections for the next 10 years. Those projections had wide prediction intervals (5% increase to 73% decline) due to annual variation in rates but were consistent with the 59% decline estimated based on new data collected from 2012 to 2022. The projected decline reflected predicted decreases in survival and recruitment due to a decrease in the average body size of adult females. Consequently, Model 3 gave more optimistic projections that were inconsistent with the observed decline. The results therefore showed that the size composition of adult females, and therefore their overall somatic growth rate, was important to the dynamics of the population. However, the results also indicated that the pronounced individual heterogeneity in growth rates observed was irrelevant to population dynamics.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".