Prospects for using DNA barcoding to identify spiders in species-rich genera
Bibliographic record
Abstract
While previous research has indicated the utility of DNA barcoding in identifying spider species sampled from a localized region, the effectiveness of this method over a broader geographic scale and with denser taxon sampling has not yet been extensively considered. Using both new and published data from 1801 individuals belonging to 361 morphospecies, this study examined intra- and interspecific divergences for 19 genera that were each represented by at least 10 morphospecies. We particularly focused on increasing species-level sampling in order to better characterize levels of interspecific divergence within species-rich genera and to examine the prevalence of a “barcode gap” (discontinuity between intra- and interspecific divergences). Overall, the mean intraspecific divergence value was found to be 2.15%, the average maximum intraspecific divergence was 3.16%, while the mean divergence between nearest interspecific neighbours was 6.77%, demonstrating the typical presence of a barcode gap. Of the 66% of morphospecies that formed monophyletic sequence clusters, the majority (92.5%) possessed a barcode gap. We also examine possible biological explanations for the large proportion of paraphyletic and polyphyletic clusters and discuss the need for further taxonomic investigations. The overlap between intra- and interspecific divergences was not unexpected for some ‘species’, such as Pardosa groenlandica, since prior morphological studies have suggested that it is an example of a species complex. However, other cases of high intraspecific divergences may reflect cryptic species diversity, indicating the need for a taxonomic approach that combines both morphological and molecular methods. The list of the species, COI sequences, and source references used in the analysis is published as a dataset under doi: 10.3897/zookeys.16.239.app.A.ds. The list of analyzed species, mean and maximum intraspecific divergences, distances to the nearest neighbouring species in its genus, general localities, and lifestyle characteristics is published as a dataset under doi: 10.3897/zookeys.16.239.app.B.ds.
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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 | 0.041 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".