Deep chlorophyll maxima, spatial overlap and diversity in phytoplankton exposed to experimentally altered thermal stratification
Bibliographic record
Abstract
Although theoretical mechanisms permitting phytoplankton co-existence have been extensively examined, few empirical field tests exist. Competition theory predicts greater diversity when species occupy heterogeneous habitats creating spatial niches. In a whole-lake experiment, we deepened thermoclines in two of three lake basins to examine alterations to the degree to which major algal groups showed spatial overlap (SO), in the vertical water column, and the consequences for diversity. Deeper thermoclines were expected to lead to less SO if species selected different depth positions in the water column in the absence of a thermal barrier, but to lead to greater SO if increased entrainment of phytoplankton in the mixed layer occurred. Increased diversity was expected to accompany less SO. Phytoplankton SO was determined using spectral group depth profiles from a FluoroProbe, and community diversity was estimated using high-performance liquid chromatography-estimated pigment diversity on daily samples taken over a 3-week focal period during the stratified summer period. SO declined and deep chlorophyll maxima were thicker when thermoclines were deepened. Unexpectedly, increases in SO preceded increased diversity by 5–7 days in all basins. This response likely arose from positive growth by most species within the unmanipulated deep chlorophyll maximum. The surprising result of our study, although supported by some theory, stresses the importance of large-scale field tests to validate hypotheses generated by resource-ratio theory.
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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 | 0.000 |
| 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) | 0.001 | 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".