Environmental drivers of taxonomic and functional variation in zooplankton diversity and composition in freshwater lakes across Canadian continental watersheds
Bibliographic record
Abstract
ABSTRACT Canada is home to more lakes than any other nation, but there is a fragmented and limited understanding of the ecological status of these water bodies. Zooplankton are excellent bioindicators of lake health, given their central food web position. To date, many studies have investigated the effect of individual stressors on zooplankton communities, mediated through changes in water quality (e.g., macronutrients, temperature, or chemicals). Increasingly, stressors act simultaneously in lakes, often over extended periods of time. As part of the NSERC Canadian Lake Pulse Network project, pelagic zooplankton were sampled in 624 lakes across Canada, spanning six continental drainage basins. We evaluated the effect of 40+ environmental variables on zooplankton diversity and community composition, considering both taxonomic and functional approaches. We also tested specific hypotheses on the relationships between zooplankton communities and environmental conditions, including eutrophication, calcium, chloride, and fish predation. We found that lake morphometry variables were among the most important predictors of zooplankton diversity, while water quality metrics were more critical in explaining variation in community composition. Our results also reveal significant heterogeneity across Canada, with contrasting effects of environmental drivers among continental watersheds, highlighting that response models cannot be assumed to apply universally.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".