Relative importance of top‐down vs. bottom‐up control of lake phytoplankton vertical distributions varies among fluorescence‐based spectral groups
Bibliographic record
Abstract
Abstract The relative importance of top‐down vs. bottom‐up control of phytoplankton biomass in aquatic ecosystems has been long debated and studied. However, few studies have considered the relative importance of top‐down vs. bottom‐up control on phytoplankton vertical distributions and characteristics of deep chlorophyll maxima (DCMs), and fewer still have investigated the importance of these drivers for multiple phytoplankton groups. We examined depth profiles of four phytoplankton spectral groups and a suite of top‐down (zooplankton) and bottom‐up (nutrients, temperature, and light) drivers from 51 north temperate lakes varying on gradients of size, trophic state, light availability, and thermal stratification. We used regression trees to identify the most important drivers of different vertical distribution metrics for each phytoplankton spectral group. The relative importance of top‐down vs. bottom‐up control varied across spectral groups and was related to the characteristics of the dominant taxa within each spectral group, as assessed by microscope counts. Zooplankton biomass was the most important driver of brown algae vertical distributions, likely because this group contained highly edible taxa (primarily chrysophytes), while thermal stratification predicted vertical distributions of buoyancy‐regulating cyanobacteria. Our work highlights the importance of examining phytoplankton community composition to improve understanding of DCM characteristics and top‐down vs. bottom‐up control of phytoplankton in aquatic systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".