DNA barcode-based survey documents underestimated diversity and intricate phylogeographic patterns of aquatic Heteroptera in an endangered Balkan biodiversity hotspot: ancient Lake Skadar basin
Bibliographic record
Abstract
Abstract Lake Skadar with its surrounding springs, wetlands and larger affluents is among the most diverse freshwater ecosystems in the Mediterranean region and a key biodiversity/endemism hotspot in Europe. It is also highly endangered due to climate change and rapid tourism development in the area. Being abundant, diverse and mostly predatory, true aquatic bugs play an important role in the functioning of freshwater ecosystems and are used as indicators of aquatic habitat quality. Nevertheless, this taxonomic group has been scarcely studied in the area. Our survey provides the first comprehensive DNA barcode library for 24 out of 25 species of aquatic Heteroptera collected in the Skadar Lake basin and adjacent regions. By this, we extend the list of species known from the area by 60%. In the case of three species, Notonecta maculata , Hydrometra stagnorum and Nepa cinerea , we detected multiple highly divergent, and also new BINs indicating possible taxonomic inconsistencies, the potential for (pseudo)cryptic diversity and intricate phylogeographic patterns. We show that presumably well-known hotspots, such as Lake Skadar region, are heavily understudied regarding even the prominent insect taxa and, thus, particularly vulnerable to undocumented biodiversity loss. Finally, we underline the value of simple DNA-barcoding-based surveys for providing reference barcode libraries for effective biomonitoring and signalling taxonomic and biogeographic issues.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".