The effects of productivity and seasonality on life history: comparing age at maturity among moose (<i>Alces alces</i>) populations
Bibliographic record
Abstract
Abstract The productivity hypothesis in respect of an animal species’ geographical range predicts that whereas higher productivity at the equatorial periphery of a species’ range favours superior competitors, lower productivity at the centre of a species’ range favours high reproduction and reduced competitive traits. I test whether life‐history patterns follow this hypothesis, using demographic data from 15 Canadian moose (Alces alces) populations. Two models are contrasted; the first assumes that intraspecific variation in age at maturity is explained proximately by density and juvenile mortality. Age at maturity was found to increase with decreasing juvenile mortality (P = 0.01) and increasing density (P = 0.006). To test the productivity hypothesis, the second model additionally included primary productivity and seasonality as geographical explanatory variables that would ultimately influence age at maturity via juvenile mortality and density. Path analysis indicated that including productivity and seasonality improved the model predictions of variation in age at maturity (Ra20.56 and 0.85). In bivariate comparisons, seasonality was negatively associated (P = 0.01) with age at maturity. In the best model, however, primary productivity was the environmental variable that explained 25% of the variance in age at maturity, and forest cover replaced seasonality as an explanatory variable. The positive association between primary productivity and age at maturity is consistent with the productivity hypothesis. Relative to populations that lived at the centre of the species’ range (51°N), moose populations living in relatively high productivity and low seasonality environments (equatorial periphery of species’ range; 48°N) experienced less juvenile mortality, more variable year‐to‐year density, higher relative density and slower life history (slower growth rate, later age at maturity, lower fecundity).
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".