Are Point Counts of Boreal Songbirds Reliable Proxies for More Intensive Abundance Estimators?
Bibliographic record
Abstract
Abstract Point counts are often used to provide information on abundance of songbirds. If data from point counts are to be compared in space or time, however, any bias in the estimate should be consistent and linearly related to the true abundance. Several studies have suggested that this assumption may be violated for songbirds. Here, we used double sampling to test whether point counts are linearly related to true abundance, as estimated from spot mapping, for 12 songbird species in the boreal mixed-wood forest of northern Alberta, Canada. We found that total abundance of birds across several point-count stations was positively correlated with the number of territories and confirmed that point counts were linearly related to spot-mapping abundance for the species tested. However, large sampling errors masked this relationship at the scale of a single point-count station (100-m fixed-radius plot). Double-sampling models that accounted for differences in abundance between spot-mapping grids using random effects improved prediction for most species. We found no year effect on detectability. Maximum abundance over point-count rounds was a more sensitive index of abundance than mean abundance and tended to produce better-fitting models. Point-count abundance was more closely related to true abundance in species with relatively small territories, or those with large spatial or temporal variation in density. Our results further suggest that point-count abundance may be proportional to the total length of territorial boundaries in the plot rather than the total fraction of territories in the plot. Our analysis suggests that point counts provide a reasonable index of abundance, even though individual point-count stations are not consistently effective in estimating the density of territorial individuals. Est-ce que les Points D'Écoute de Passereaux Boréaux Constituent une Méthode Fiable pour L'Obtention D'Estimateurs D'Abondance Plus Avancés?
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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".