Digging up Diversity: An Assessment of DNA Barcoding and Metabarcoding Approaches to Survey Soil Arthropods, with an Emphasis on Mites
Bibliographic record
Abstract
Soils are one of the most biodiverse and least understood terrestrial habitats. Arthropods likely comprise the most important component of soil fauna diversity yet are so poorly known that estimates of their richness span two orders of magnitude. Comprehensive assessments of the fauna have been impeded by the prevalence of undescribed species and the scarcity of taxonomic expertise, combined with their small size and often cryptic morphology. In this thesis, I employ DNA barcoding and metabarcoding to advance understanding of soil arthropods and one of their most diverse factions, the mites (Arachnida: Acariformes, Parasitiformes). I first utilize DNA barcodes to assess a hemi-continental fauna—the mites of Canada. This survey uncovered 2.4x the number of species previously recorded in Canada and indicates that the fauna likely includes more than 30,000 species. Mite assemblages showed high β-diversity and were spatially structured, reflecting dispersal-limited, environmentally driven assembly. This survey generated the most comprehensive reference library for any national mite fauna, with coverage for all four orders and 60% of the families known from Canada. I then utilized this library to evaluate the accuracy of higher-taxonomic assignments based on DNA barcodes in mites, work that made it possible to develop family and ordinal identification thresholds for both full-length DNA barcodes and for the partial sequences used in metabarcoding. Lineage-specific thresholds and an expanded reference library improved the success of higher-taxonomic assignments, and more strict thresholds were needed to reduce assignment errors from short sequences. Finally, I assessed the efficacy of metabarcoding two types of bulk samples (specimens, soil) for surveying the soil arthropod community. Despite recovering different fractions of the fauna, specimen and soil analyses revealed similar patterns of α- and β-diversity. Expanded soil analysis also confirmed patterns of highly dissimilar, spatially structured arthropod distributions, demonstrating the ability of metabarcoding to enable rapid, robust surveys of the fauna. However, deep sequence coverage will be necessary to develop comprehensive baselines for the soil fauna, and reference library expansion is needed to improve the taxonomic resolution for these surveys. This thesis demonstrates the power of DNA barcoding and metabarcoding to advance knowledge of arthropod diversity and the processes that foster it.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".