Declining survival of black brant from subarctic and arctic breeding areas
Bibliographic record
Abstract
ABSTRACT Since the mid 1990s, the number of black brant ( Branta bernicla nigricans ; brant) nests on the Yukon‐Kuskokwim Delta (YKD), Alaska, USA, the historically predominant breeding area of brant, has declined steadily. This has caused researchers and managers to question if arctic breeding populations can compensate for the reduction in brant nests on the YKD. An important component of the assessment of brant population dynamics is having current estimates of first‐year and adult survival. We banded brant at 4 locations in Arctic Alaska and western Canada, and at 1 location in the subarctic, the Tutakoke River (TR) colony on the YKD, 1990–2015. We used joint live and dead mark‐recapture models to estimate first‐year and adult (≥1 yr old) survival of brant. We also used band recovery rates from a Brownie model to assess temporal trends in band recovery rates of adult brant. First‐year survival of brant hatched at TR declined from approximately 0.60 to <0.20 and, although first‐year survival generally was higher for goslings marked in the Arctic, their survival declined from approximately 0.70 in the early 1990s to ≤0.45 in the 2010s. Annual survival of adult females decreased from an average of 0.881 (95% CI = 0.877–0.885) to 0.822 (95% CI = 0.815–0.829) at TR and from 0.851 (95% CI = 0.843–0.860) to 0.821 (95% CI = 0.805–0.836) in the Arctic, from 1990 to 2014. Band recovery rates of adults generally were <1.25% until the last several years of study, when they reached ≤3.5%. Although the current harvest rates may be partially additive to natural mortality, we do not believe that harvest is the main influence on the declines in survival. The general decline in survival rates of brant breeding across a large geographic area may be influenced by a reduction in the quality of migration and wintering ground habitats. We suggest an analysis of seasonal survival of brant to test the hypothesis that declining habitat quality on wintering or spring migration areas is reducing survival. Our results suggest that the number of breeding pairs at TR will continue to decline and also brings into question the ability of arctic breeding populations to grow at a rate necessary to offset the declines on the YKD. Researchers should continue to closely monitor survival and harvest rates of brant, and assess methods currently used to monitor their abundance. © 2017 The Wildlife Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".