Effects of Vertical Spatial Overlap on Phytoplankton Diversity under Experimentally Altered Lake Stratification Regimes
Bibliographic record
Abstract
In phytoplankton communities, competitive exclusion might occur when functionally similar species are impeded from regulating their positions along light and nutrient gradients to reduce niche overlap. Greater spatial overlap (SO) between species due to water column mixing could thus promote competitive exclusion, reducing community taxonomic diversity. However, greater SO could also promote coexistence of functionally different taxa. Using data from a whole-lake experiment, we investigated the effects of SO and other relevant environmental factors on phytoplankton diversity across the water columns of lake basins with different thermocline manipulations. We estimated SO using an in situ fluorometer, and overall community diversity microscopically. Using structured equation models, we estimated directional relationships between phytoplankton diversity, SO, the lake physical structure and the zooplankton community. No significant effect of SO on phytoplankton taxonomic diversity was observed, but higher SO was associated with greater functional diversity. Change in lake physical structure and in the zooplankton community also affected diversity, with a negative response to increased top-down interactions. Overall, despite the fact that the alteration of water column stratification structure and top-down interactions were stronger drivers of phytoplankton diversity in our system, some effect of spatial overlap on the outcome of inferred competitive interactions were observable.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".