Patterns in taxonomic and functional diversity of lake phytoplankton
Bibliographic record
Abstract
Summary 1. Patterns in phytoplankton diversity in lakes and their relationships with environmental gradients have been traditionally based on taxonomic analyses and indices, even though measures of functional diversity (FD) might be expected to be more responsive to such gradients. 2. We assessed the influence of water column physical structure, and other components of the overall environment, on lake phytoplankton diversity using two taxonomically based indices [species richness ( S ) and the Shannon index ( H ’)] and a FD index, to determine whether these different measures respond in similar ways to habitat structure. The study encompassed 45 lakes in Eastern Canada, within two lake districts [the Eastern Townships Region (ETR) and Laurentians Region (LR)] that vary in geology and landscape and in lake morphometry and chemistry. 3. Across all lakes, S and H ’ were higher in lakes having greater vertical temperature heterogeneity and higher susceptibility to wind mixing. In addition, H ’ declined with total phosphorus concentration. FD was only related to maximum lake depth, a variable that integrates many other habitat features. 4. Further insight into the factors affecting phytoplankton diversity was obtained by contrasting the two regions. The taxonomically based diversity measures differed little between the regions, while FD was higher in the ETR where more trait variants were present and more evenly distributed amongst species. Whereas factors driving S did not differ between the regions, we found region‐dependent patterns in the relationships of H ’ and FD with maximum lake depth: both indices decreased with maximum depth in the region with lakes more exposed to wind (ETR) but increased in the more hilly landscape where lakes are more sheltered from wind mixing (LR). 5. Our study demonstrates that, for phytoplankton communities, a FD index can show simpler and stronger responses to environmental drivers than a taxonomically based index, while shedding further light onto the functional traits that are important in particular lake categories.
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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 | 0.000 |
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".