Sound attenuation in forest and roadside environments: Implications for avian point-count surveys
Bibliographic record
Abstract
Point counts are one of the most common ways of collecting data to determine the relative abundance of birds. Many studies and monitoring programs, including the North American Breeding Bird Survey, use relative differences in counts of birds to assess changes in abundance over time and space. Many factors influence whether relative differences in counts of birds between various environmental conditions are reflective of actual differences in bird density. A major assumption of relative abundance is that birds with different song frequencies and amplitudes are heard at the same distances in different environmental conditions. We compared sound transmission in forest habitats and along low-use forestry roads, and calculated detection radius for different species to test the assumption that differences in bird counts between forest interior and roadside locations reflect actual differences in bird abundance. A playback–recording experiment was used to broadcast sounds through forest interior, along a forest edge, and down forestry roads in conifer and deciduous forests to determine whether sound propagation differed across environments. Sound attenuated significantly faster in forests than along roads or forest edges. Similarly, the distance at which bird songs could be detected was significantly shorter in forest than along the road or forest edge for 20 of 25 species. We found the area surveyed to be up to twice as large on road compared to within forests, which suggests that roadside surveys might inflate avian density estimates in comparison to off-road counts. Local atmospheric conditions also influenced detection probability, but the magnitude of the effect was weaker than the land-cover effect. Major differences in detection between roads and interior forest suggest that comparisons of surveys conducted along roadsides and in forest areas should be done carefully if the goal is to make direct comparisons of abundance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".