Habitat use and population status of Yellow-billed and Pacific loons in western Alaska, USA
Bibliographic record
Abstract
Effective conservation of sympatric avian populations depends on unbiased estimates of population size, distribution, and habitat use. For populations of Yellow-billed Loons (Gavia adamsii) and Pacific Loons (G. pacifica) co-occurring in Arctic wetland communities in Alaska, USA, such data are limited and difficult to obtain, hindering population assessments and decision making. The Yellow-billed Loon is also under consideration for additional protections under the Endangered Species Act due to small global population size, specific habitat requirements, and low fecundity, further increasing the need for information at the landscape scale. To help evaluate the population status and habitat use of both species, we used repeated aerial surveys and a dynamic multistate occupancy modeling approach to jointly estimate 1) probability of lake use and 2) probability of use for nesting for Yellow-billed and Pacific loon populations at the landscape scale on the Seward Peninsula and Cape Krusenstern, Alaska, in 2011 and 2013. We also estimated state-specific transition probabilities and degree of interspecific competition to assess population stability and degree of species interactions. We found that probability of site reuse (ϕYellow-billed = 0.73 [0.44–0.94]; ϕPacific = 0.86 [0.72–0.98]) or reuse for nesting (ϕYellow-billed = 0.72 [0.46–0.97]; ϕPacific = 0.59 [0.38–0.85]) in 2013 was high, as was overall use of lakes >7 ha by loons (>80%). These results suggested that lake habitats may have been saturated, and that populations of both species were stable over the two-year interval between surveys. Our estimates indicated that nesting populations in western Alaska were much larger than previously thought for both Yellow-billed (∼2.5 times larger) and Pacific loons (∼1.5–2.0 times larger). Together our results indicate that Arctic wetlands in western Alaska are important for both species and that loon populations in this area warrant additional consideration for conservation.
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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 | 0.000 | 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".