A new primer for metabarcoding of spider gut contents
Bibliographic record
Abstract
Abstract As a key predator group, spiders have received a lot of attention by food web ecologists. The difficulty involved in studying their diet has led to the use of new technologies such as metabarcoding of gut contents. The amplification of a broad range of spider prey without amplifying spiders themselves is challenging. Until now, an efficient universal primer for this purpose was not available. We developed a novel forward primer (NoSpi2) targeting the COI gene. The primer was designed not to amplify spiders ofPardosagenus while amplifying most other invertebrates. NoSpi2 was tested together with the reverse primer BR2 in silico, in vitro on single specimens of prey and spiders, on mock and malaise trap communities, and in an ecological application. In silico evaluation predicted high primer bias forPardosaspecies and more generally for spiders of the oval calamistrum clade (Lycosidae and closely related species) and low bias for other invertebrates. These results were confirmed by in vitro tests. Additionally, some spider families were not amplified contrary to our expectations. We demonstrated a high efficiency for the primer pair NoSpi2/BR2 which recovered 94% of taxa in the mock community and 85% of the taxa detected by the best invertebrate primer pair known for the malaise trap community. The field experiment showed that Lycosidae (Hygrolycosa,Pardosa,Piratula,Trochosa) DNA is not amplified by NoSpi2/BR2. It demonstrated a broad range of detectable prey species (12 orders, 67 families, 117 species). The ability of NoSpi2/BR2 primer to reliably amplify prey species, without amplifying any predator DNA, makes it an ideal choice for gut content analysis for lycosid species and related species, even enabling the homogenization of entire specimens without dissection. Given that the detected prey species included other spiders and carabid beetles, this primer could be also used to study intraguild predation.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".