Biomonitoring 2.0 Refined: observing local change through metaphylogeography using a community-based eDNA metabarcoding monitoring network
Bibliographic record
Abstract
BACKGROUND: Biological data at different levels of organization is essential to support actions to mitigate the current biodiversity crisis. DNA metabarcoding is an established method to detect species/genus level taxa from bulk samples leading the way for a Biomonitoring 2.0 framework. Biomonitoring 2.0 Refined adds another dimension to Biomonitoring 2.0-high-throughput, scalable DNA metabarcoding with a higher resolution at the intraspecific level. Intraspecific diversity is key to understanding the distribution and movement of local populations for conservation efforts. Gaining reliable intraspecific information from metabarcoding data, however, is challenging due to qualitative/quantitative issues that can impact validity of the inference. RESULTS: Samples collected for the STREAM community-based monitoring project were used to perform an intraspecific genetic variation analysis on benthic arthropods. We targeted two non-overlapping cytochrome c oxidase subunit 1 mitochondrial DNA amplicons to assess the reproducibility of our results. Samples from the Rocky Mountains were grouped into four regions separated by mountain ranges. Significant separation (PERMANOVA, p value < 0.05) of Sørensen dissimilarity between regions was observed for community and intraspecific levels, fitting the expectation that mountains are barriers to dispersal. Two of the regions showed significant spatial structuring (Mantel test, p value < 0.05) at the intraspecific level, while all regions showed significant structuring at the community level. Results were consistent across both amplicons. CONCLUSIONS: We show that DNA metabarcoding is applicable to intraspecific diversity analysis and it is robust to different amplicons. This paves the way for Biomonitoring 2.0 Refined, which can provide much needed fine-scale biodiversity data for ecological assessments and conservation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".