Using eDNA to biomonitor the fish community in a tropical oligotrophic lake
Bibliographic record
Abstract
Environmental DNA (eDNA) is an effective approach for detecting vertebrates and plants, especially in aquatic ecosystems, but prior studies have largely examined eDNA in cool temperate settings. By contrast, this study employs eDNA to survey the fish fauna in tropical Lake Bacalar (Mexico) with the additional goal of assessing the possible presence of invasive fishes, such as Amazon sailfin catfish and tilapia. Sediment and water samples were collected from eight stations in Lake Bacalar on three occasions over a 4-month interval. Each sample was stored in the presence or absence of lysis buffer to compare eDNA recovery. Short fragments (184-187 bp) of the cytochrome c oxidase I (COI) gene were amplified using fusion primers and then sequenced on Ion Torrent PGM or S5 before their source species were determined using a custom reference sequence database constructed on BOLD. In total, eDNA sequences were recovered from 75 species of vertebrates including 47 fishes, 15 birds, 7 mammals, 5 reptiles, and 1 amphibian. Although all species are known from this region, six fish species represent new records for the study area, while two require verification. Sequences for five species (2 birds, 2 mammals, 1 reptile) were only detected from sediments, while sequences from 52 species were only recovered from water. Because DNA from the Amazon sailfin catfish was not detected, we used a mock eDNA experiment to confirm our methods would enable its detection. In summary, we developed protocols that recovered eDNA from tropical oligotrophic aquatic ecosystems and confirmed their effectiveness in detecting fishes and diverse species of vertebrates.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".