Developing a novel approach to fill gaps in vital rates for sea duck management: breeding propensity of American common eider (Somateria molissima dresseri) hens determined from satellite telemetry movement patterns
Bibliographic record
Abstract
Waterfowl play key roles both ecologically, acting as biomonitors of ecosystem health, and anthropogenically, where most species are hunted and harvested. Sea ducks in particular are of great importance, specifically within Indigenous harvest and cultural practices, however, sea ducks generally tend to spend their time in habitats difficult to access and thus are understudied compared to other waterfowl. Population declines of several North American sea duck species were observed starting in the 1980s without known causes, and the Sea Duck Joint Venture (SDJV) was formed in response to concerns for those populations. The SDJV works in conjunction with partners to close knowledge gaps for better management and conservation of sea duck populations and habitats within Canada and the United States of America. To work towards a better understanding of population dynamics via population modelling, vital rates must be quantified. The American common eider (<em>Somateria mollisima dresseri</em>) is one such sea duck with recent concern for changing population trends, with population decreases in central areas of the range and increases or stability in northern areas. This subspecies of eider is long-lived and an intermittent breeder, such that females do not breed every year. Thus, a key vital rate to examine for a better understanding of current population trends for this species is breeding propensity, the proportion of sexually mature females that nest in a given year. This thesis is part of a large collaborative project that used satellite Platform Terminal Transmitter (PTT) devices deployed in female eiders across their range that collected data for up to three years. One hundred and thirteen hens transmitted locations over one to three years, resulting in movement data over 164 individual breeding seasons, which I used to develop a three-state hidden Markov model with a covariate for overlap with breeding locations. I assigned a breeding status to each hen (i.e., successful nesting attempt, failed nesting attempt, potential prospecting, skipped breeding) from which I determined overall breeding propensity and breeding propensity by region. While my findings fill an important knowledge gap for the American common eider, continued monitoring efforts are necessary for this species given conflicting changes in population trends across their range. Sea ducks in general are poorly studied, however, the methodology which I have developed for this well investigated species can be adapted and applied to other less studied sea duck species and contribute towards closing information gaps for conservation and management efforts.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".