Plankton community structure in fluctuating environments and the role of productivity
Bibliographic record
Abstract
Environmental variability in space and time can have significant influences on community structure. Temporal heterogeneity in nutrient supply has been shown in laboratory studies to have strong impacts on the diversity and composition of phytoplankton communities, depending on the scale of fluctuations. This paper extends the work in chemostats in a number of ways: using large‐scale field mesocosms with natural plankton communities exposed to various frequencies of vertical mixing, modifying environmental productivity and incorporating higher trophic levels. The first major question and experiment focus on whether vertical mixing at various frequencies, and the associated nutrient pulse, has similar effects in nutrient‐rich and nutrient‐poor environments for predominantly single trophic level systems. The results indicate that the temporal scale of fluctuation is more of a structuring factor for phytoplankton communities in enriched enclosures, with little response under oligotrophic conditions. The second experiment examines the responsiveness of entire plankton communities (three trophic levels). Major shifts in community structure were absent under both nutrient‐rich and nutrient‐poor conditions. Responses were seen only in the demography of the top trophic level ( Chaoborus flavicans ). It appears from these experiments that the spatial disruption that accompanies mixing events may be more important than the temporal component (nutrient pulses) for phytoplankton. This appears to be the case only under conditions where natural spatial heterogeneity is high as it is when systems are enriched. When nutrient pulses are small, as they are in oligotrophic systems where recycling is efficient, little phytoplankton community response is observed. Finally, the inclusion of entire plankton food webs here suppressed the effects of the scale of intermittency in water column mixing at both low and high nutrient levels for all but the highest trophic level.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".