Population decline and demography of the world’s largest breeding population of huemul deer (<i>Hippocamelus bisulcus</i>) in Bernardo O'Higgins National Park, Chile
Bibliographic record
Abstract
Abstract The largest remaining populations of the endangered huemul deer (Hippocamelus bisulcus) are believed to be concentrated in the remote, rugged and largely uninhabited glacial valleys and fjords surrounding the Southern Patagonian Ice Field that lie within Chile's Bernardo O'Higgins National Park. On the Park's northwestern edge is Bernardo Fjord, an area home to the world's largest known breeding population of huemul. We present a demographic study of this population and examine its population trends since 2004. We conducted huemul transect surveys on‐foot during 16 expeditions to Bernardo Fjord from October 2014 to March 2020, during which we spent a total of 254 days in the field and documented 284 observations of huemul. This coastal population of huemul exhibited higher female proportions (mean 0.64 ± SD 0.05), lower fawn:female ratios (mean 0.18 ± SD 0.23 in March and mean 0.22 ± SD 0.07 in November), and similar group sizes (mean 1.77 ± 1.06 SD individuals) to previous studies. Huemul densities in Bernardo Valley encountered from 2016 to 2019 were 60% lower than densities observed in a previous study from 2004 to 2008. We produce a 2019 huemul population estimate for Bernardo Fjord of 84 (±SD 7) individuals including 44 observed individuals and 40 (±SD 10) individuals estimated to be undetected by our surveys, which comprises 6–8% of the IUCN global population estimate. Two of the surveyed sectors – Bernardo Valley and Pampa – are of critical conservation importance, given that the 5 km2 Pampa is home to 42% of the total huemul population and 41% of the fawn population of Bernardo Fjord. We examine potential drivers of this population decline, which may include poor recruitment, predation and high incidences of foot disease exacerbated by climatic conditions, and we offer recommendations for the conservation, monitoring and management of this and other coastal huemul populations.
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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 | 0.000 | 0.000 |
| 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".