INDEPENDENT GRADIENTS OF PRODUCER, CONSUMER, AND MICROBIAL DIVERSITY IN LAKE PLANKTON
Bibliographic record
Abstract
Interactions between trophic levels during food web assembly can drive positive correlations in diversity between producers, consumers, and decomposers. However, the contribution of trophic interactions relative to local environmental factors in promoting species diversity is poorly understood, with many studies only considering two trophic levels. Here we examine correlations in diversity among zooplankton, phytoplankton, and bacteria in the pelagic zone of 31 lakes in British Columbia, Canada. We sampled species diversity of zooplankton and phytoplankton through morphological identification, and bacterial genetic diversity was estimated by denaturing gradient gel electrophoresis (DGGE) of 16S rDNA polymorphisms. We looked for correlations in diversity that were independent of the abiotic environment by statistically controlling for 18 limnological variables. No significant correlations were found between the diversity of zooplankton, phytoplankton, and bacteria. In addition, the physical factors that were associated with species composition in one trophic level were independent of those that were important for another. Our results provide no support for the importance of direct feedbacks between producers, consumers, and decomposers in maintaining diversity. Zooplankton, phytoplankton, and bacterial diversity and composition are regulated independently from one another and respond to different environmental variables. These results suggest that species of lake plankton show loose trophic associations with one another due to broad diets in consumers and decomposers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".