Effects of iron deficiency on the growth and photosynthesis of three bloom‐forming cyanobacterial species isolated from Lake Taihu
Bibliographic record
Abstract
SUMMARY Cyanobacterial blooms are found in many freshwater ecosystems around the world, but the effect of environmental factors on their growth and the proportion of species still require more investigation. In this study, the physiological responses of bloom‐forming cyanobacteria M icrocystis aeruginosa FACHB 912, M icrocystis flos‐aquae FACHB 1028 and P seudanabaena sp. FACHB1 282 to iron deficiency were investigated. Their specific growth rates were found to decrease as the available iron concentration decreased. At low available iron concentrations of 1 × 10 −7 M (pFe 21.3) and 5 × 10 −8 M (pFe 21.6), M . aeruginosa had the lowest specific growth rate among three studied species. The cell sizes of M . flos‐aquae and Pseudanabaena sp. were significantly smaller under the lowest iron concentration. The chlorophyll a content of the three species decreased at the lowest iron concentration. The maximal relative electron transport rate, photosynthetic efficiency, and light‐saturation parameter of M . aeruginosa were lower than the other two cyanobacteria at pFe 21.3. Therefore, M . aeruginosa was the least able to adapt to iron deficiency. Under iron deficiency, the functional absorption cross‐section of PSII and electron transport rate on the acceptor side of PSII decreased in M . aeruginosa , while the connectivity factor between individual photosynthetic units increased in M . flos‐aquae , and the electron transport rate on the acceptor side of PSII and between PSII and PSI decreased in P seudanabaena sp. The ability to store iron was highest in M . flos‐aquae , followed by P seudanabaena sp. and M . aeruginosa . Thus, these results provide necessary information for detecting the role of iron in the succession of cyanobacterial species in Lake Taihu, the third largest freshwater lake in China, because all three species were isolated from this lake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 | 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 teacher head, 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".