The choice of DNA extraction protocol affects the quantification of gut microbiomes in two passerines
Bibliographic record
Abstract
There is ever increasing need for the robust characterization of the microbial communities of wild animals. DNA extraction from bird feces is challenging and to date, no protocol has proven to be efficient with all bird feces samples. Thus, there is a need to test different extraction protocols for a variety of bird species. We compared five commercial kits and four protocols to extract DNA from black-capped chickadee and blue tit feces. We found that all kits and methods allowed the study of the bacterial microbiota of black-capped chickadee feces, but the choice of kit influenced the measured diversity and composition of microbiota communities. Only two kits out of five allowed the recovery of DNA from blue tit feces. We recommend using PowerSoil by Qiagen or QuickDNA by Zymo Research with black-capped chickadee feces, and MagMAX by Fisher for blue tit feces. Our study highlights the difficulty of extracting microbial DNA from bird feces, points out the limits of comparing bacterial communities across studies using different methods, and proposes optimized efficient protocols to extract microbial DNA from feces of two commonly studied bird species for the study of bacterial microbiota.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".