Environmental DNA-based biomonitoring of Cuban Crocodylus and their accompanying vertebrate fauna from Zapata Swamp, Cuba
Bibliographic record
Abstract
Crocodylians globally face considerable challenges, including population decline and extensive habitat modification. Close monitoring of crocodylian populations and their habitats is imperative for the timely detection of population trends, especially in response to management interventions. Here we use eDNA metabarcoding to identify the Critically Endangered Crocodylus rhombifer and the Vulnerable C. acutus, as well as vertebrate community diversity, in Cuba's Zapata Swamp. We tested four different primer sets, including those used previously in Crocodylus population genetic and phylogenetic research, for their efficiency at detecting crocodylian eDNA. We detected C. rhombifer eDNA in 11 out of 15 sampled locations within its historical geographic distribution. We found that data analyses using the VertCOI primers and the mBRAVE bioinformatics pipeline were the most effective molecular marker and pipeline combination for identifying this species from environmental samples. We also identified 55 vertebrate species in environmental samples across the four bioinformatics pipelines- ~ 85% known to be present in the Zapata ecosystem. Among them were eight species previously undetected in the area and eight alien species, including known predators of hatchling crocodiles (e.g., Clarias sp.) and egg predators (e.g., Mus musculus). This study highlights eDNA metabarcoding as a powerful tool for crocodylian biomonitoring within fragile and diverse ecosystems, particularly where fast, non-invasive methods permit detection in economically important areas and will lead to a better understanding of complex human-crocodile interactions and evaluate habitat suitability for potential reintroductions or recovery programs for threatened crocodylian species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".