Evaluating the impacts of metabarcoding primer selection on DNA characterization of diet in an aerial insectivore, the Purple Martin
Bibliographic record
Abstract
Abstract DNA metabarcoding is a molecular technique frequently used to characterize diet composition of insectivorous birds. However, results are sensitive to methodological decisions made during sample processing, with primer selection being one of the most critical. The most frequently used DNA metabarcoding primer set for avian insectivores is ZBJ. However, recent studies have found that ZBJ produces significant biases in prey classification that likely influence our understanding of foraging ecology. A new primer set, ANML, has shown promise for characterizing insectivorous bat diets with fewer taxonomic biases than ZBJ, but ANML has not yet been used to study insectivorous birds. Here, we evaluate the ANML primer set for use in metabarcoding of avian insectivore diets through comparison with the more commonly used ZBJ primer set. Fecal samples were collected from both adult and nestling Purple Martins (Progne subis subis) at 2 sites in the USA and 1 site in Canada to maximize variation in diet composition and to determine if primer selection impacts our understanding of diet variation among sites. In total, we detected 71 arthropod prey species, 39 families, and 10 orders. Of these, 40 species were uniquely detected by ANML, whereas only 11 were uniquely detected by ZBJ. We were able to classify 54.8% of exact sequence variants from ANML libraries to species compared to 33.3% from ZBJ libraries. We found that ANML outperformed ZBJ for PCR efficacy, taxonomic coverage, and specificity of classification, but that using both primer sets together produced the most comprehensive characterizations of diet composition. Significant variation in both alpha- and beta-diversity between sites was found using each primer set separately and in combination. To our knowledge, this is the first published metabarcoding study using ANML primers to describe avian diet, and also the first to directly compare results returned by ANML and ZBJ primer sets.
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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 | 0.001 | 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".