Light, but Not Nutrients, Drives Seasonal Congruence of Taxonomic and Functional Diversity of Phytoplankton in a Eutrophic Highland Lake in China
Bibliographic record
Abstract
Information on temporal dynamics of phytoplankton communities and their responses to environmental factors can provide insights into mechanisms driving succession of phytoplankton communities that is useful in programs to manage and or remediate undesirable assemblages. Populations of phytoplankton can be controlled by bottom-up factors such as nutrients and temperature or top-down such as predation by zooplankton. Traditionally, taxonomic diversity based on morphologies has been the measure used for analysis of responses to environmental factors. Recently, according to functional groupings, including functional groups (FG), morpho-functional groups (MFG), and morphology-based functional groups (MBFG), functional diversity has been used to represent functional aspects of phytoplankton communities. However, to what extent these taxonomic and functional groupings are congruent at seasonal time-scales and the main environmental factors, which drive succession, have remained less studied. Here, we analyzed absolute and relative proportions of a phytoplankton community during a 3-year period in Lake Erhai, a eutrophic highland lake in China. Alpha diversity and beta diversity, as measured by Shannon-Wiener and Bray-Curtis indices of taxonomic grouping and three functional groupings (FG, MFG, and MBFG) were applied to investigate environmental factors determining diversity. Significant, positive relationships were observed between taxonomic diversity and functional diversity that were strongly linked through seasons. Considering that the tolerance of dominant species to some extreme conditions affected associations, when dominant species were excluded, residual communities still exhibited similar patterns of succession. This synchronous temporal pattern was not principally driven by the dominant genera (Microcystis, Psephonema and Mougeotia). Instead, the entire phytoplankton community assemblages were important in the pattern. Most diversity indices of taxonomic and functional groupings were significantly correlated with solar irradiance, but not nutrient concentrations. Because the lake is eutrophic and there were already sufficient nutrients available, additional nutrients had little effect on seasonal taxonomic and functional diversity of phytoplankton in Lake Erhai.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".