Linking demographic transitions to population dynamics in a fluctuating environment
Bibliographic record
Abstract
Recruitment has been linked to decreases in the ratio of age-specific mortality (M′) to mass-specific growth (G′), and year-class strength may be predicted by the age when M′/G′ = 1. Hydrological stress adversely affects these parameters for species inhabiting floodplains; however, the relationship between M′ and G′ in hydrologically variable environments is poorly understood. We evaluated age-specific mortality for six species from a 20-year time series and growth curves from otolith length-at-age data. We assessed the effect of hydrology on the transitional age (age M′/G′ = 1) at 21 sites representing a hydrological gradient. Disturbance intensity influenced age-specific mortality but had no effect on mass-specific growth. The transitional age was inversely correlated with annual density, but weakly associated with population biomass. Hydrological disturbance shifted the transitional age to older ages, reducing recruitment overall. We demonstrated that the M′/G′ transition was affected adversely by hydrological stress and can be applied to a diverse group of taxa. Growth, survivorship, and the transitional age should be evaluated to improve population modelling efforts used to predict the influence of future restoration actions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".