Birds as Bioindicators: Revealing the Widespread Impact of Microplastics
Bibliographic record
Abstract
The global crisis of plastic pollution, particularly involving microplastics (MPs) and nanoplastics (NPs), has profound ecological implications. Birds, serving as bioindicators, are especially susceptible to these pollutants. This systematic review synthesizes the current research on the presence, distribution, and impact of MPs and NPs on avian species, alongside advancements in detection methodologies. MPs and NPs have been identified in over 200 bird species across 46 families, encompassing several ecosystems, from Antarctica to Labrador, including Australia, China, and South Europe. Seabirds such as penguins, gulls, and shearwaters exhibit a high burden of MPs in tissues and feces due to fishing debris, while terrestrial species face contamination from urban and agricultural sources. Depending on their composition, MPs can cause gastrointestinal damage, oxidative stress, and bioaccumulation of toxic chemicals, particularly polyethylene and polypropylene. However, challenges in detection persist due to methodological inconsistencies, though advances in spectroscopy and flow cytometry offer improved accuracy. Addressing this pollution is vital for bird conservation and ecosystem health, requiring international collaboration and standardized research protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".