Migratory behaviour of eastern whip-poor-wills (Antrostomus vociferus): quantifying return rates and the effects of artificial light on flight paths
Bibliographic record
Abstract
Migration poses significant challenges for organisms, especially those traveling long distances. It is hypothesized that despite these challenges, migration evolved as a method of resource maximization and competition reduction. Along with morphological adaptations, species often develop life history traits in complement with their migratory habits. Urbanization and artificial light have further complicated migration patterns for many species. This thesis was aimed at addressing gaps in our understanding of the life history of the Eastern Whip-poor-will (Antrostomus vociferus) in relation to their migratory behaviour and to determine the impact of artificial light on their migratory routes. I captured whip-poor-wills at their breeding locations in southern Manitoba and north-western Ontario and used direct-tracking technologies (archival GPS units; radio telemetry tags) to collect data on timing, routes, and return rates. I used the resulting migration tracks and Bayesian generalized linear models to test for an effect of artificial light levels on route tortuosity along fall migratory pathways. I found that whip-poor-wills took more indirect flight paths on nights when a direct path would result in exposure to more intense artificial light, suggesting they sacrifice efficiency for light avoidance. Next, I used 6 years of capture data, a displacement experiment, and automated radio-telemetry to quantify recapture rates and site-fidelity at breeding territories. I found evidence for high survival rates and site fidelity: annual recapture rates ranged from 50 – 80% and average recapture of birds on the same territory in subsequent years was 75%, 75% of birds returned after displacement, and 90% of radio-tagged birds survived to be detected by receiver towers along the spring migratory routes in the year following tagging birds. Since whip-poor-wills have a longer than average life span and lower annual clutch sizes for a small migratory land bird, my results showing high site fidelity and return rates align with predictions based upon these traits. My results demonstrating sensitivity to artificial light and high site fidelity could be incorporated into conservation and management planning for this threatened species.
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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 | 0.000 |
| 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) | 0.001 | 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".