Historical dynamics of the Australian fur seal population: evidence of regulation by man?
Bibliographic record
Abstract
The Australian fur seal (Arctocephalus pusillus doriferus) was severely over-exploited in the 18th and 19th centuries and until relatively recently its population had remained steady at well below estimated presealing levels. However, the population is now increasing rapidly (6%20% per annum) throughout its range and there is a need to understand its dynamics in order to assess the potential extent and impact of interactions with fisheries. Age distribution (n = 156) and pregnancy rate (n = 110) were determined for adult females collected at a breeding colony on Seal Rocks, southeast Australia, in 19711972. Mean ± SE and maximum observed ages were 9.37 ± 0.41 and 20 years (n = 1), respectively. A stochastic modelling approach was used to fit an age distribution to the observed age-structure data and calculate rates of recruitment and adult survival. Annual adult female survival and recruitment rates between 1954 and 1971 were 0.478 ± 0.029 (mean ± SE) and 0.121 ± 0.007, respectively, suggesting that the population was experiencing a decline during the 1960s. The pregnancy rate increased from 78% at 3 years of age to an average of 85% between 413 years of age before significantly decreasing in older females (the oldest was 19 years of age). There was no significant effect of body mass or condition on the probability of a female being pregnant (P > 0.5 in both cases) and the nutritional burden of lactation did not appear to affect pregnancy rates or gestational performance. These findings suggest that the low survivorship was due to density-independent effects such as mortality resulting from interactions with fishers, which are known to have been common at the time. The recent increase in the population is consistent with anecdotal evidence that such interactions have decreased as fishing practices have changed.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".