Population viability analysis of Guadalupe fur seals Arctocephalus townsendi
Bibliographic record
Abstract
The Guadalupe fur seal Arctocephalus townsendi was intensively hunted and considered extinct by the end of the 1800s. However, small numbers (<20 seals) were reported on Guadalupe Island (Mexico) in the mid-1950s and were observed at a second location (San Benito Islands, Mexico) in the late 1990s. Sporadic counts at these 2 sites have followed a relatively rapid increase in numbers, but considerable uncertainty remains about the long-term viability of this recovering species. We performed a population viability analysis using historic seal counts from Guadalupe Island and the San Benito Islands to predict the probability of each colony (and both colonies combined) going extinct under 3 critical population threshold scenarios (100, 500, and 1000 seals). Using a diffusion approximation model, we found that the growth rates of the 2 colonies were similar (10-11% yr-1) and that the population totaled ~41000 individuals in 2017 (x̄ = 40614, 95% CI = 35779-46877). Guadalupe fur seals appear to be vulnerable to extreme climatic events. Of the 2 fur seal colonies, the San Benito Islands colony is less secure and is Endangered to Critically Endangered, depending on the quasi-extinction value used under the quantitative listing criteria established by the IUCN. In contrast, the Guadalupe Island colony and the 2 colonies combined meet the quantitative analysis criteria of Least Concern. Population viability analysis is an important component of assessing the status of wildlife populations and assisting nations and organizations in assigning appropriate categories of protection.
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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 | 0.001 |
| 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) | 0.001 | 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".