Modelling microplastic bioaccumulation and biomagnification potential in the Galápagos penguin ecosystem using Ecopath and Ecosim (EwE) with Ecotracer
Bibliographic record
Abstract
Bioaccumulation and biomagnification of anthropogenic particles are crucial factors in assessing microplastic impacts to marine ecosystems. Microplastic pollution poses a significant threat to iconic and often endangered species but examining their tissues and gut contents for contaminant analysis via lethal sampling is challenging due to ethical concerns and animal care restrictions. Incorporating empirical data from prey items and fecal matter into models can help trace microplastic movement through food webs. In this study, the Galápagos penguin food web served as an indicator species to assess microplastic bioaccumulation and biomagnification potential using trophodynamic Ecopath with Ecosim (EwE) modelling with Ecotracer. Empirical data collected from surface seawater near Galápagos penguin colonies, zooplankton, penguin prey, and penguin scat in October 2021 were used to inform the ecosystem model. Multiple scenarios, including a 99% elimination rate, were employed to assess model sensitivity. Model predictions revealed that microplastics can bioaccumulate in all predator-prey relationships, but biomagnification is highly dependent on the elimination rate. It establishes the need for more research into elimination rates of different plastics, which is a critical missing gap in current microplastic ecotoxicological and bioaccumulation science. Compared to empirical data, modelling efforts underpredicted microplastic concentrations in zooplankton and over-predicted concentrations in fish. Ultimately, the ecosystem modelling provides novel insights into potential microplastics' bioaccumulation and biomagnification risks. These findings can support regional marine plastic pollution management efforts to conserve native and endemic species of the Galápagos Islands and the Galápagos Marine Reserve.
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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 | 0.000 | 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) | 0.000 | 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 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".