Multiscale drivers of phytoplankton communities in north‐temperate lakes
Bibliographic record
Abstract
Multiple factors operating across different spatial and temporal scales affect β-diversity, the variation in community composition among sites. Disentangling the relative influence of co-occurring ecological drivers over broad biogeographic gradients and time is critical to developing mechanistic understanding of community responses to natural environmental heterogeneity as well as predicting the effects of anthropogenic change. We partitioned taxonomic β-diversity in phytoplankton communities across 75 north-temperate lakes and reservoirs in Alberta, Canada, using data-driven, spatially constrained null models to differentiate between spatially structured, spatially independent, and spuriously correlated associations with a suite of biologically relevant environmental variables. Phytoplankton β-diversity was largely independent of space, indicating spatial processes (e.g., dispersal limitation) likely play a minor role in structuring communities at the regional scale. Our analysis also identified seasonal differences in the importance of environmental factors, suggesting a general shift toward greater relevance of local, in-lake (e.g., nutrients and Secchi depth) over regional, atmospheric and catchment-level (e.g., monthly solar radiation and grassland coverage) drivers as the open-water growing season progressed. Several local and regional variables explained taxonomic variation jointly, reflecting climatic and land-use linkages (e.g., air temperature and water column stability or pastureland and nutrient enrichment) that underscore the importance of understanding how phytoplankton communities integrate, and may serve as sentinels of, broader anthropogenic changes. We also discovered similar community composition in natural and constructed water bodies, demonstrating rapid filtering of regional species to match local environmental conditions in reservoirs comparable to those in natural habitats. Finally, certain factors related to human footprint (e.g., cropland development) explained the composition of bloom-forming and/or toxic cyanobacteria more than the overall phytoplankton community, suggesting their heightened importance to integrated watershed management.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".