Metabarcoding metacommunities: time, space, and land use interact to structure aquatic macroinvertebrate communities in streams
Bibliographic record
Abstract
Abstract There is an increasing need to move beyond evaluating the effect of land use on stream communities by only studying local variables, and instead incorporate a metacommunity perspective which integrates environmental and spatial factors across larger spatial scales. The use of molecular tools (DNA metabarcoding) to identify bioindicator groups, such as aquatic macroinvertebrates, can provide greater taxonomic resolution to explore patterns in stream metacommunities. In this study, we collected aquatic macroinvertebrates from streams in southern Ontario which spanned a gradient of agricultural disturbance and used DNA metabarcoding to identify the species composition from these samples. We address a significant knowledge gap in previous stream aquatic macroinvertebrate metacommunity studies by incorporating molecular identification as well as a temporal component. We observed that a combination of local habitat conditions, regional agricultural land use, and spatial position influenced aquatic macroinvertebrate community composition, suggesting there is an interaction between environmental filtering and dispersal processes that structures these communities. However, aquatic macroinvertebrate communities were also highly dissimilar between streams and composed of many rare species, and a large percentage of unexplained variation suggests that there is a strong stochastic component to community assembly. We also observed that there is a seasonal component to metacommunity dynamics, with different water quality variables being significant to community composition in each sampling month. While we expected that an increased percentage of surrounding agricultural land use would result in more homogenous macroinvertebrate communities, we only detected this relationship in May and found evidence that a larger riparian buffer width can mitigate the effects of agricultural land use. We demonstrate the utility of DNA metabarcoding for revealing patterns in metacommunity dynamics that may not be detectable using coarse taxonomic identifications, and reveal the importance of incorporating a seasonal component when evaluating the influence of land use on community composition.
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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 | 0.001 | 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".