Abundance of Long-billed Curlews on military lands in the Columbia Basin
Bibliographic record
Abstract
Long-billed Curlews (<em>Numenius americanus</em>) are declining throughout North America, and the loss of grassland breeding habitat is one of the primary threats to the species. Intermountain West, in particular, has been identified as the most important region in North America for breeding curlews. Nevertheless, the density and abundance of Long-billed Curlews in this region is not well understood. Lands managed for military training can provide habitat for wildlife species of conservation concern, and increasingly these lands are becoming relevant to sustaining biodiversity. We conducted point count surveys of Long-billed Curlews on Department of Defense lands in the Columbia Basin near Boardman, Oregon, USA during two consecutive breeding seasons. We used multinomial-Poisson mixture models to estimate detection probability and density of curlews and to investigate environmental correlates of those metrics. Mean detection probability at a distance of 400 m was 0.45 and 0.61 in 2015 and 2016, respectively. In 2015, the clarity of skies increased detection probability, but in 2016, none of the variables we measured influenced detection probability. Mean predicted density was 3.3 (95% confidence interval: 2.4–4.7) and 1.8 (1.2–2.7) curlews/km² in 2015 and 2016, respectively. In both years, curlew density was higher in lower-elevation or topographically smoother areas. Estimated abundance of curlews in the study area was 639 (456–912) and 350 (237–520) birds in 2015 and 2016, respectively. The number of curlews appeared to fluctuate across the two years of our study, a demographic trend that may have been influenced by a wildfire in our study area in June 2015. The results of our study indicate that federal grasslands, including areas where military operations are conducted, can provide conservation benefit to breeding Long-billed Curlews.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".