Diatom species responses along gradients of dissolved inorganic carbon, total phosphorus, and lake depth from lakes across Canada
Bibliographic record
Abstract
Abstract Diatoms are key components of freshwater ecosystems and are regularly used for paleolimnological reconstructions, in which defining species optima and tolerances is fundamental for interpreting assemblage shifts in a sediment record. Here, we examined responses of diatoms across three major environmental gradients—dissolved inorganic carbon (range: 0.1–230.5 mg · L−1), total phosphorus (range: 3–326 μg · L−1), and maximum lake depth (range: 0.9–55.0 m)—taken from 158 lakes from across Canada. The lakes were sampled as part of the LakePulse Network, which conducted a standardized sampling of lakes spanning 12 Canadian ecozones. Hierarchical logistic regression was used to model the species responses of 37 common taxa, and species optima and tolerances were calculated with weighted average modeling. The most common response detected was the symmetrical unimodal model, suggesting we likely captured the full environmental ranges for many species, although skewed unimodal responses were also common. Indicator species analyses identified taxa with high predictive values and fidelities to particular ecozones, with high‐nutrient‐adapted taxa such as Stephanodiscus spp. and Cyclotella meneghiniana characteristic of the agriculturally productive Prairie region. The Prairies stood out in the dataset as the region with the most unique flora from the local contribution to beta diversity analysis. Overall, the autecological data provided by our study will allow for improved interpretations of paleolimnological records and other biomonitoring efforts, addressing management concerns and contributing to a better understanding of our changing environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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 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".