Effects of temporal and spatial variability in energy fluxes on phytoplankton
Bibliographic record
Abstract
Abstract Climate change has significantly altered the energy dynamics of lakes; however, little is known of how the temporal and spatial variation in energy fluxes impacts the structure and function of lake ecosystems. This study combined long‐term (2011–2018) measurements of lake energy fluxes with environmental, nutrient, and phytoplankton data at five stations to investigate the effects of variably energy fluxes on phytoplankton production and composition in a large, shallow, eutrophic lake (Lake Taihu, China). Overall, atmospheric warming increased heat storage and water temperatures, with energy fluxes exhibiting significant spatial heterogeneity. Specifically, faster rates of energy input and higher energy budgets increases were observed in the clear macrophyte‐rich regions of the lake compared to turbid hypereutrophic habitats. Temporal variation in energy fluxes was a strong predictor of primary production (as chlorophyll a ), the spatial extent of cyanobacterial blooms, and phytoplankton biodiversity at the whole‐lake level, whereas 42.4% of the variation in phytoplankton community composition was explained by a combination of energy fluxes, nutrients, and other environmental factors. Cyanobacterial taxa were significantly correlated with nutrients (total nitrogen and phosphorus), while green algal abundance was associated mainly with variations in the energy budget. These findings highlight the spatial variability of energy fluxes driven by local environmental conditions, underscoring the need for climate adaptation and mitigation strategies to account for heterogeneous energy effects on lake production and structure.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".