Quantifying seasonal succession of phytoplankton trait‐environment associations in human‐altered landscapes
Bibliographic record
Abstract
Abstract Integration of species traits in direct gradient analysis generates insights into how communities are assembled and may respond to environmental changes. We investigated phytoplankton trait‐environment relationships for over 300 taxa during the open‐water season across 75 north‐temperate lakes and reservoirs in Alberta, Canada. An innovative, data‐driven approach was applied using iterative model selection for RLQ optimization to reveal key monthly associations. Fourth‐corner analysis then tested the significance of relationships using spatially and phylogenetically constrained null models derived by Moran spectral randomization. Both local‐ and regional‐scale drivers of succession were found with evidence of deterministic filtering by traits increasing in mid‐summer. Trait associations to land‐use and water quality highlighted potential anthropogenic cross‐scale interactions, such as logging and pasture development affecting phytoplankton via allochthonous nutrient inputs. Biogeographic factors (e.g., elevation and habitat size) and associated temperature and chemical gradients (e.g., pH and bicarbonate) were also linked to multiple morphological, physiological, and behavioral traits, including potential toxin production. Several correlated traits emphasized importance of trait syndromes corresponding to distinct taxonomic groups (e.g., cyanobacteria and green algae). However, rather than clustering species with shared ecological preferences or roles, our study builds on past trait‐based approaches to phytoplankton by testing for explicit trait associations with a range of environmental factors. Thus, we provide a novel, quantitative means of revealing environmental constraints on communities and translating their compositional changes under climate and other human influences into functional impacts on freshwater ecosystems.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".