A DNA barcoding and photo-documentation resource of water mites (Acariformes, Hydrachnidia) of Siberia: Accurate species identification for global climate change monitoring programs
Bibliographic record
Abstract
Water mites (Hydrachnidia) are good model organisms for the assessment and long-term monitoring of the biological impacts of natural and human-induced environmental changes in freshwater ecosystems, including those related to global climate change. However, monitoring programs using water mites as bioindicators may be impeded by difficulties associated with species identification. Here we integrate conventional morphology, DNA sequence data (using the COX1 barcoding locus) and extensive voucher photo documentation to create and validate a tool for accurate species identification of water mites in Western Siberia (including a dedicated reference climate monitoring and research site). Using this approach, we detected a total of 95 species, of which, one was a conventional new species; 14 taxa were cryptic species having large among-species COX1 K2P distances but lacking any apparent morphological differences. Our a priori species delimitation was successfully validated a posteriori. An automatic species delimitation algorithm (ASAP) identified exactly the same set of 95 species, with a species delimitation threshold of 6.1%. This result agrees with previous works suggesting a large threshold of 5.6–6.0% for water mites, but contrasts with the BOLD approach which uses a much lower threshold to identify BINs (1%). Furthermore, by comparing our identified sequences with GenBank data, we expanded known geographic ranges of several water mite species. Using extensive GenBank data on mites in Canadian waters, four species were detected to be Holarctic rather than Palaearctic as thought previously (Lebertia obscura, Limnesia undulatoides, Oxus nodigerus and Arrenurus papillator). Four species, Lebertia obscura, Torrenticola brevirostris, Hygrobates limnocrenicus and Unionicola parvipora, were recorded for the first time in Russia. We provide an annotated species checklist reporting the distribution, ecology, bioindiocation potential, and COX1 barcode sequence data along with high-resolution photographs of each DNA voucher. Future ecological and biodiversity studies will benefit from using molecular tools for accurate identification of useful mesofaunal bioindicator organisms, such as water mites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".