Ecotoxicological Assessment of Microplastics and Cellulose Particles in the Galápagos Islands and Galápagos Penguin Food Web
Bibliographic record
Abstract
Abstract Microplastic pollution threatens some of the world's most iconic locations for marine biodiversity, including the remote Galápagos Islands, Ecuador. Using the Galápagos penguin (Spheniscus mendiculus) as a sentinel species, the present study assessed microplastics and suspected anthropogenic cellulose concentrations in surface seawater and zooplankton near Santa Cruz and Galápagos penguin colonies (Floreana, Isabela, Santiago), as well as in penguin potential prey (anchovies, mullets, milkfish) and penguin scat. On average, 0.40 ± 0.32 microplastics L−1 were found in surface seawater (<10 μm; n = 63 L), while 0.003, 0.27, and 5.12 microplastics individual−1 were found in zooplankton (n = 3372), anchovies (n = 11), and mullets (n = 6), respectively. The highest concentration (27 microplastics individual−1) was observed in a single milkfish. Calculations based on microplastics per gram of prey, in a potential diet composition scenario, suggest that the Galápagos penguin may consume 2881 to 9602 microplastics daily from prey. Despite this, no microplastics or cellulose were identified in 3.40 g of guano collected from two penguins. Our study confirms microplastic exposure in the pelagic food web and endangered penguin species within the UNESCO World Heritage site Galápagos Islands, which can be used to inform regional and international policies to mitigate plastic pollution and conserve biodiversity in the global ocean. Environ Toxicol Chem 2024;43:1442–1457. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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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 | 0.001 | 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) | 0.000 | 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".