Sequencing streams: a molecular approach for monitoring macroinvertebrate biodiversity and metacommunity dynamics in an agricultural landscape
Bibliographic record
Abstract
Freshwater habitats are under increased pressures from human-induced landscape changes, an aquatic biodiversity is declining at unprecedented rates. There are potentially millions of undescribed invertebrate taxa, and this massive proportion of unknown diversity can impede our understanding of community composition and the conservation of freshwater habitats. Aquatic macroinvertebrates are commonly used bioindicators of environmental condition, yet these groups present significant challenges for morphological identification. The incorporation of molecular identification (e.g., DNA metabarcoding) can provide greater resolution to detect patterns in taxonomic richness and species distributions. In this thesis, I evaluated the efficacy of \nmetabarcoding environmental DNA (eDNA) to capture the local community composition of aquatic macroinvertebrates in streams by comparing eDNA and tissue metabarcoding. I observed a significant lack of overlap between these two methods, suggesting that eDNA likely captures a different community altogether. Consequently, I used a bulk tissue approach to describe aquatic macroinvertebrate diversity in streams across a gradient of agricultural activity in Ontario, Canada. I observed over 1600 Operational Taxonomic Units (OTUs; a proxy for species) from 149 invertebrate families and described unprecedented taxonomic turnover amongst adjacent patches in the same stream. Samples collected only ten meters apart shared few OTUs and were largely comprised of rare taxa, suggesting that assembly of these communities is extremely variable, even at small distances. I built upon these results by incorporating DNA metabarcoding in a metacommunity ecology framework to describe the environmental and spatial factors which influence aquatic macroinvertebrate community composition. I found evidence that both environmental filtering and dispersal-based processes work in tandem to influence stream communities, though there was also a strong influence of stochastic assembly given the proportions of unexplained variation and extremely high turnover between streams. A temporal influence was also revealed since stream communities were more strongly influenced by agricultural activity in the spring. In general, agricultural land use influenced aquatic macroinvertebrate composition, yet these effects may be mitigated by a larger riparian buffer. This thesis demonstrates the utility of DNA metabarcoding for both biomonitoring and ecological applications and stresses the importance of integrative approaches for freshwater conservation and sustainable agricultural practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".