Assessing the effect of density on population growth when modeling individual encounter data
Bibliographic record
Abstract
The relative role of density-dependent and density-independent variation in vital rates and population size remains largely unsolved. Despite its importance to the theory and application of population ecology, and to conservation biology, quantifying the role and strength of density dependence is particularly challenging. We present a hierarchical formulation of the temporal symmetry approach, also known as the Pradel model, that permits estimation of the strength of density dependence from capture-mark-reencounter data. A measure of relative population size is built in the model and serves to detect density dependence directly on population growth rate. The model is also extended to account for temporal random variability in demographic rates, allowing estimation of the temporal variance of population growth rate unexplained by density dependence. We thus present a model-based approach that enable to test and quantify the effect of density-dependent and density-independent factors affecting population fluctuations in a single modeling framework. More generally, we use this modeling framework along with simulated and empirical data to show the value of including density dependence when modeling individual encounter data without the need for auxiliary data.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 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".