Detection range of songbirds using a stopover site by automated radio‐telemetry
Bibliographic record
Abstract
A major uncertainty in automated radio-telemetry studies of small birds is the detection range of receiving antennas. We compared simultaneous daytime detections (± 30 s) by automated and manual radio-telemetry to assess detection probability and the proportion of transmissions detected for birds on migratory stopover as a function of distance, foraging guild (Black-throated Blue Warblers, Setophaga caerulescens, and Yellow-rumped Warblers, Dendroica coronata coronata, represented mid-canopy foliage gleaners and White-throated Sparrows, Zonotrichia albicollis, represented a ground forager), habitat type, meteorological variables, tower antenna number (1–4), and the position of a bird relative to the receiving antenna's bearing (offset angle). Our study was conducted at a migratory stopover site in southern Ontario, Canada. Most detections were in dense to sparse forest, and all individuals were within 1.03 km of the automated receiving station. Daily detection probability was near 100% for both foraging guilds. However, within 30 s before and after a manual radio-telemetry location was made, detection probability and the proportion of transmissions detected by automated radio-telemetry declined with distance, was higher for warblers than sparrows, and was lowest for 90° offset angles. Our results suggest that when research goals do not require detections with high temporal frequency, e.g., estimation of departure date or daily departure probability, our study design had an effective detection range of at least 1 km. However, where temporal precision is required, e.g., to investigate movements and changes in activity levels during stopover, detection range was ~300 m for ground-foraging sparrows and 600 m for mid-canopy foraging warblers, which is much lower than the presumed detection range of antennas under optimal conditions (15 km). This corresponds to a spatial area of coverage for forest-dwelling birds of ~0.3–1.1 km2. Our results suggest that to optimally configure an automated radio-telemetry array at the regional scale, investigators should carefully consider detection range and its underlying covariates, including species type, the habitat matrix, and the orientation of antennas relative to preferred habitat.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".