Herbivory in variable environments: an experimental test of the effects of vertical mixing and <i>Daphnia</i> on phytoplankton community structure
Bibliographic record
Abstract
Phytoplankton communities in lakes are exposed to different within-season frequencies of heterogeneity in resource supply because of wind-induced vertical mixing. Effects of such heterogeneity, in conjunction with herbivory, on phytoplankton community structure have rarely been simultaneously examined, despite the fact that each factor can have large effects on phytoplankton composition and diversity. This study uses replicated oligotrophic mesocosms to examine the effects of herbivory and different scales of temporal heterogeneity in deepwater mixing. The pattern of vertical mixing alone had minor effects on phytoplankton community diversity and composition. The herbivore Daphnia caused a shift in phytoplankton composition to less edible types, based mainly on morphological features (spiny shapes and trichomes on cell walls) rather than size structure alone. Phytoplankton richness depended jointly on mixing frequency and large Daphnia biomasses. When systems were well mixed, with high encounter rates between predator and prey populations, phytoplankton community richness was lowest. By contrast, the systems that were least often mixed had highest richness. These results are related to limited encounter rates with infrequent mixing and to the availability of refuges from predation. Responses to different scales of temporal heterogeneity in these oligotrophic phytoplankton communities depend more on Daphnia feeding than on resource pulsing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".