Spatial overlap in lake phytoplankton: Relations with environmental factors and consequences for diversity
Bibliographic record
Abstract
Opposing gradients of light and nutrients can create a variety of niche opportunities for lake phytoplankton. Theory predicts that phytoplankton vertical distribution should be associated with these gradients as different taxa maximize resource access, while minimizing competitive interactions using niche partitioning. We examined the relationships between spatial overlap (SO) of four major phytoplankton spectral groups with key biogeochemical and morphometric environmental parameters across 52 north temperate lakes. SO decreased primarily with greater thermal stratification, and where more light was available and nutrient levels lower. When greater SO did occur in highly stratified lakes, taxonomic diversity was favored through increased species evenness, but not richness. Taxonomic richness, on the other hand, increased in lakes with greater light availability, coincident with low SO. Similarly, niche partitioning associated with greater functional diversity in the range of traits present in the communities was detected when SO was low in clear lakes. Overall, our results indicate that the ability of phytoplankton to spatially separate and utilize large portions of the water column is important for augmenting species and functional trait richness related to motility and resource acquisition. However, our study also suggests that community evenness is favored when phytoplankton distributions overlap in deep chlorophyll maxima associated with stratified lakes.
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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 | 0.002 |
| 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 | 0.001 |
| 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".