NGS barcoding reveals high resistance of a hyperdiverse chironomid (Diptera) swamp fauna against invasion from adjacent freshwater reservoirs
Bibliographic record
Abstract
BACKGROUND: Macroinvertebrates such as non-biting midges (Chironomidae: Diptera) are important components of freshwater ecosystems. However, they are often neglected in biodiversity and conservation research because invertebrate species richness is difficult and expensive to quantify with traditional methods. We here demonstrate that Next Generation Sequencing barcodes ("NGS barcodes") can provide relief because they allow for fast and large-scale species-level sorting of large samples at low cost. RESULTS: We used NGS barcoding to investigate the midge fauna of Singapore's swamp forest remnant (Nee Soon Swamp Forest). Based on > 14.000 barcoded specimens, we find that the swamp forest maintains an exceptionally rich fauna composed of an observed number of 289 species (estimated 336 species) in a very small area (90 ha). We furthermore barcoded the chironomids from three surrounding reservoirs that are located in close proximity. Although the swamp forest remnant is much smaller than the combined size of the freshwater reservoirs in the study (90 ha vs. > 450 ha), the latter only contains 33 (estimated 61) species. We show that the resistance of the swamp forest species assemblage is high because only 8 of the 314 species are shared despite the close proximity. Moreover, shared species are not very abundant (3% of all specimens). A redundancy analysis revealed that ~ 21% of the compositional variance of midge communities within the swamp forest was explained by a range of variables with conductivity, stream order, stream width, temperature, latitude (flow direction), and year being significant factors influencing community structure. An LME analysis demonstrates that the total species richness decreased with increasing conductivity. CONCLUSION: Our study demonstrates that midge diversity of a swamp forest can be so high that it questions global species diversity estimates for Chironomidae, which are an important component of many freshwater ecosystems. We furthermore demonstrate that small and natural habitat remnants can have high species turnover and can be very resistant to the invasion of species from neighboring reservoirs. Lastly, the study shows how NGS barcodes can be used to integrate specimen- and species-rich invertebrate taxa in biodiversity and conservation research.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".