The potential of aquatic bloodfeeding and nonbloodfeeding leeches as a tool for iDNA characterisation
Bibliographic record
Abstract
Leeches play important roles in food webs due to their abundance, diversity and feeding habits. Studies using invertebrate-derived DNA (iDNA) extracted from leech gut contents to target vertebrate DNA have focused on the Indo-Pacific region and mainly leveraged the leech family Haemadipsidae, composed of bloodfeeding terrestrial leeches, while predatory, fluid/tissue-feeding and aquatic bloodfeeding species have been largely disregarded. While there is some general knowledge regarding the taxonomic groups that leeches prefer to feed on, detailed taxonomic resolution is missing and, therefore, their potential use for monitoring animals is unknown. In this study, 116 leeches from 12 species (six families) and spanning the three feeding habits were collected in Mexico and Canada. We used DNA metabarcoding to investigate their diet and assess their potential use for biodiversity monitoring. We detected vertebrates from five orders including fish, turtles and birds in the diet of aquatic bloodfeeding leeches; eight invertebrate orders of annelids, arthropods and molluscs in leeches that feed on body fluids and tissues; and 10 orders of invertebrates belonging to Arthropoda and Annelida, as well as one vertebrate and one parasitic nematode, in predatory leeches. These results show the potential use of iDNA from aquatic bloodfeeding leeches for retrieving vertebrate taxa, and from predatory and fluid-feeding leeches for invertebrates. Our study provides information about the dietary range of freshwater leeches and one terrestrial leech and contributes proof-of-concept for the use of these leeches for animal monitoring, expanding our knowledge of the use of iDNA from leech gut contents to North America.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".