Rates of increase in age-structured populations: a lesson from the European harbour seals
Bibliographic record
Abstract
Behavioural differences among population segments coupled with the transient dynamics of perturbed population structures lead to severely biased estimates of the intrinsic rates of increase in natural populations. This phenomenon is expected to occur in most populations that are structured by age, sex, state, or rank. The 1988 epizootic in European harbour seals (Phoca vitulina) perturbed the population composition radically. Detailed documentation of mass mortality, 20 years of population surveys, and data on age- and sex-specific behaviour were used to quantify biases in the observed rate of increase (λ obs. ), which in many areas substantially exceeded the "maximum rate of increase". This is serious, since λ obs. is a key parameter, for example, in estimating potential biological removal or modelling population dynamics. For populations where the underlying age and sex composition is unknown, we suggest that data on fecundity and survival rates be used to find the upper theoretical rate of population increase. We found that the intrinsic rates of increase (λ 1 ) in populations of true seals with even sex ratios and stable age structures cannot exceed 13% per year (λ 1max. = 1.13). Frequently reported larger values are indicative of nonstable population structures or populations affected by migrations.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".