Factors affecting the accuracy of molecular delimitation in minute herbivorous mites (Acari: Eriophyoidea)
Bibliographic record
Abstract
Abstract Single‐locus molecular delimitation plays a key role in meeting the need to expedite the exploration and description of the species on our planet. Multiple methods have been developed to aid data interpretation over the past 20 years, but species delimitation remains difficult due to their varying performance. In this study, we examine the accuracy of five widely used delimitation methods (i.e. BIN, ABGD, ASAP, GMYC and mPTP) in analysing 63 empirical data sets that included 1850 mitochondrial COI sequences derived from eriophyoid mites assigned to 456 morphospecies. Our results establish that all five methods resolve approximately 90% of morphospecies. We investigated some factors which might affect the species delimitation results, that is taxonomic rank, number of haplotypes per species, mean number of host plants per species, and geographical distance among sampling sites. We found complex interactions between these factors which affected delimitation effectiveness. An increase in haplotype number negatively affected delimitation accuracy, while increased geographical distance improved delimitation accuracy. BIN was influenced by the number of host plants per species as cryptic speciation linked to host plant usage might be prevalent in eriophyoid mites, while ABGD was not significantly impacted by other factors. Our results highlight multiple factors that affect molecular species delimitation and underline the value of employing multiple analytical approaches to aid species delimitation.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".