Spatial ecotoxicology: what we know about the relationship between avian movements and contaminant levels
Bibliographic record
Abstract
Birds are widely used as indicators of environmental contamination because they integrate signals across space and time. However, this advantage also means that locating the source of pollution can be challenging, especially for species that migrate large distances. Historically, the assignment of contaminant burden to particular life stages or locations has been made by sampling tissues with varying time signals and associating those signals with locations assigned from band recoveries or stable isotope analyses. Unfortunately, these assignments are geographically coarse. More recently, tracking devices have been increasingly used to determine more precisely where and when contamination is likely to occur. Here, we review current knowledge on using tracking devices to examine the relationship between avian movements and contaminant loads. We found 42 published articles with samples collected from 1986 to 2021 that used tracking devices to examine the relationship between avian movement and contaminant loads, where studies were primarily concentrated in the northern hemisphere, notably in the North Atlantic. Tracking methodology varied widely across studies to date, but the use of radiotransmitters and satellite tags generally decreased as newer technologies, such as global positioning systems and geolocation sensors, were developed. Blood and feathers were the most commonly used tissues to assess contaminant concentrations, but sample sizes were often low (<40, the recommended sample size for some tracking or contaminant studies), and one-quarter of studies did not statistically test the relationship between tracking data and contaminant concentrations. Moving forward, studies should (1) consider statistical power; (2) consider tissue turnover rates, contaminant turnover rates, and tracking device resolution; (3) examine tissues that inform different time scales; 4) explore the impacts of contaminants on behaviour and movement; and 5) increase collaboration, standardization, and information sharing across existing tracking or contaminant monitoring programs. Focusing on these aspects will enhance our ability to identify the source, transport, and fate of contaminants in avian species across the globe.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".