Hidden diversity: DNA metabarcoding reveals hyper-diverse benthic invertebrate communities
Bibliographic record
Abstract
BACKGROUND: Freshwater ecosystems, such as streams, are facing increasing pressures from agricultural land use and recent literature stresses the importance of robust biomonitoring to detect trends in insect decline globally. Aquatic insects and other macroinvertebrates are often used as indicators of ecological condition in freshwater biomonitoring programs; however, these diverse groups can present challenges to morphological identification and coarse-level taxonomic resolution can mask patterns in community composition. Here, we incorporate molecular identification (DNA metabarcoding) into a stream biomonitoring sampling design to explore the diversity and variability of aquatic macroinvertebrate communities at small spatial scales. While individual stream reaches can be very heterogenous, most community ecology studies focus on larger, landscape-level patterns of community composition. A high degree of community variability at the local scale has important implications for both biomonitoring and ecological research, and the incorporation of DNA metabarcoding into local biodiversity assessments will inform future sampling protocols. RESULTS: We sampled twenty streams in southern Ontario, Canada, for aquatic macroinvertebrates across multiple time points and assessed local community variability by comparing field replicates taken ten meters apart within the same stream. Using bulk-tissue DNA metabarcoding, we revealed that aquatic macroinvertebrate communities are highly diverse at small spatial scales with unprecedented levels of local taxonomic turnover. We detected over 1600 Operational Taxonomic Units (OTUs) from 149 families, and a single insect family, the Chironomidae, contained over one third of the total number of OTUs detected in our study. Benthic communities were largely comprised of rare taxa detected only once per stream despite multiple biological replicates (24-94% rare taxa per site). In addition to numerous rare taxa, our species pool estimates indicated that there was a large proportion of taxa that remained undetected by our sampling regime (14-94% per site). Our sites were located across a gradient of agricultural activity, and while we predicted that increased land use would homogenize benthic communities, this was not supported as within-stream dissimilarity was unrelated to land use. Within-stream dissimilarity estimates were consistently high for all levels of taxonomic resolution (invertebrate families, invertebrate OTUs, chironomid OTUs), indicating stream communities are very dissimilar at small spatial scales.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".