Comparing three common nest survey methods, using double-crested cormorants as a proposed sentinel for monitoring plastic pollution in freshwater environments
Bibliographic record
Abstract
Many bird species use plastic as nest-building material, and nest surveys represent a unique opportunity to monitor environmental plastic pollution. However, the current literature lacks consistent, repeatable methodologies, making comparison across studies challenging. This study evaluated three common nest survey methods to assess the use of nest debris: photographic assessment, visual assessment, and nest deconstruction. We applied these methods to double-crested cormorant nests ( Nannopterum auritum ) at two locations on Lake Ontario, Canada. We found that for this species, nest deconstruction yielded the greatest accuracy and detail for detecting debris abundance, type, and colour. Of the two non-invasive methods, visual surveys outperformed photographic surveys across all metrics assessed. In our mainland colony, 100% of nests contained debris based on visual assessment, while at our offshore colony only 37.4% of nests contained debris, suggesting that location influences nest debris for this species. We detected Personal Protective Equipment (PPE)-related debris in 20% of all nests across survey years at our mainland colony (2022 and 2023), representing the first time that PPE has been documented in the nests of this species and demonstrating that nest surveys can be a useful tool for capturing changes in an evolving pollution landscape. This study contributes evidence that double-crested cormorants may be a useful sentinel species for monitoring plastic pollution in understudied, freshwater environments. Our results also demonstrate that the chosen method can greatly impact the results of a nest survey, and careful consideration of methodology should be undertaken before implementing a nest survey to monitor plastic pollution.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".