Key Abiotic and Biotic Variables Influencing Lake Plankton Communities Across Canada
Bibliographic record
Abstract
ABSTRACT While previous studies have investigated environmental and biotic variables influencing plankton community composition, comprehensive analyses across multiple trophic levels and broad spatial scales remain limited. Using a Joint Species Distribution Modelling framework, we identified key abiotic and biotic variables, including body size and fish presence that shape variation in plankton food webs across Canadian lakes. We analyzed the joint responses of plankton biomass to lake morphometry, water physico‐chemistry, and fish species presence, using data collected from 301 lakes spanning the four main national continental catchments. We also examined how the body size trait modulated plankton food web associations with these variables. Results showed that lake nutrient and ion status were the most important factors explaining variation in plankton community composition, particularly in the Arctic and Hudson continental basins, which exhibited large environmental gradients. Lake morphometry also played an important role, especially in shaping communities in large, shallow lakes. Plankton body sizes modulated some niche responses, but these effects varied across continental basins and plankton trophic levels. Little residual variation in the models indicated limited roles of plankton species interactions or unmeasured environmental variables. While fish presence only explained small amounts of variation, further studies should instead assess and incorporate fish biomass data. Our findings suggest that future national strategies for the study of Canadian freshwaters should combine a continental‐scale perspective (e.g., gradients across ecozones, climate regions, biogeographic zones) with regionally focused monitoring programs to better capture critical factors influencing different lake ecosystems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".