Power source, data retrieval method, and attachment type affect success of dorsally mounted tracking tag deployments in 37 species of shorebirds
Bibliographic record
Abstract
Animal‐borne trackers are commonly used to study bird movements, including in long‐distance migrants such as shorebirds. Selecting a tracker and attachment method can be daunting, and methodological advancements often have been made by trial and error and conveyed by word of mouth. We synthesized tracking outcomes across 2745 dorsally mounted trackers on 37 shorebird species around the world. We evaluated how attachment method, power source, data retrieval method, relative tracker mass, and biological traits affected success, where success was defined as whether or not each tag deployment reached its expected tracking duration (i.e. all aspects succeeded for the intended duration of the study: attachment, tracking, data acquisition, and bird survival). We conducted separate analyses for tag deployments with remote data retrieval (‘remote‐upload tag deployments') and those that archived data and had to be recovered (‘archival tag deployments'). Among remote‐upload tag deployments, those that were a lighter mass relative to the bird, were beyond their first year of production, transmitted data via satellite, or were attached with a leg‐loop harness were most often successful at reaching their expected tracking duration. Archival tag deployments were most successful when applied at breeding areas, or when applied to males in any season. Remote‐upload tag deployments with solar power, satellite data retrieval, or leg‐loop harnesses continued tracking for longer than those with battery power, other types of data retrieval, or glue attachments. However, the majority of tag deployments failed to reach their expected tracking duration (71% of remote‐upload, 83% of archival), which could have been due to tracker failure, attachment failure, or bird mortality. Our findings highlight that many tag deployments may fail to meet the goals of a study if tracking duration is crucial. Using our results, we provide guidelines for selecting a tracker and attachment to improve success at meeting study goals.
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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 | 0.000 |
| 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 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".