Decomposing the variation in population growth into contributions from multiple demographic rates
Bibliographic record
Abstract
Summary The decomposition of variation in population growth into the relative contributions from different demographic rates has multiple uses in population, conservation and evolutionary biology. Recent research has favoured methods based on matrix models termed ‘life‐table‐response experiments’ or more generally ‘the retrospective matrix method’, which provide an approximation of a complete demographic decomposition. The performance of the approximation has not been assessed. We compare the performance of the retrospective matrix method to a complete decomposition for two bighorn sheep populations and one red deer population. Different demographic rates make markedly different contributions to variation in growth rate between populations, because each population is subject to different types of environmental variation. The most influential demographic rates identified from decomposing observed variation in population growth are often not those showing the highest elasticity. Consequently, those demographic rates most strongly associated with deterministic population growth are not necessarily strongly associated with temporal variation in population growth. The retrospective matrix method provides a good approximation of the demographic rate associated most strongly with variation in population growth. However, failure to incorporate the contribution of covariation between demographic rates when decomposing variation in population growth can lead to spurious conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".