Macroinvertebrate Communities and Food Web Structure in Tundra Streams are Shaped by Substrate Size and Beaver Impoundments
Bibliographic record
Abstract
North American beavers (Castor Canadensis) are expanding their range into the Arctic tundra as climate change drives earlier ice and snowmelt, and increased shrub cover. As ecosystem engineers, beavers build dams that alter water chemistry by creating impoundments within streams, trapping sediment and organic matter, and modifying nutrient cycling. This thesis evaluates how beaver impoundments interact with natural geomorphic variation in tundra streams and how impoundments affect tundra freshwater ecosystems in the western Canadian Arctic. In Chapter 2, I compared water chemistry and benthic macroinvertebrate (BMI) community composition across 16 stream reaches, including those with and without beaver dams, with gravel and sand substrates. Sand-dominated streams exhibited significantly higher dissolved organic carbon and mercury concentrations and supported a higher relative abundance of disturbance-tolerant BMI taxa compared to gravel-dominated sites. Beaver-impacted sites had higher downstream mercury concentrations, but did not affect BMI community composition. In Chapter 3, I used stable isotope analysis to derive Layman metrics of food web structure across 15 streams. Gravel streams showed broader trophic diversity and higher niche overlap compared to sand sites. Beaver impoundments had minimal influence on any food web metric. Together, these findings demonstrate that physical stream characteristics have a greater influence on water chemistry, BMI communities and food web structure compared to beaver impoundments. By highlighting geomorphology as a key driver of benthic invertebrate communities and food web structure, this work informs monitoring and stewardship efforts in the Inuvialuit Settlement Region, where healthy stream ecosystems support food and water security, and cultural well-being under rapid environmental change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".