Examination of the role of the microbial loop in regulating lake nutrient stoichiometry and phytoplankton dynamics
Bibliographic record
Abstract
Abstract. The recycling of organic material through bacteria and microzooplankton to higher trophic levels, known as the "microbial loop", is an important process in aquatic ecosystems. Here the significance of the microbial loop in influencing nutrient supply to phytoplankton has been investigated in Lake Kinneret (Israel) using a coupled hydrodynamic–ecosystem model. The model was designed to simulate the dynamic cycling of carbon, nitrogen and phosphorus through bacteria, phytoplankton and zooplankton functional groups, with each pool having unique C : N : P dynamics. Three microbial loop sub-model configurations were used to isolate mechanisms by which the microbial loop could influence phytoplankton biomass, considering (i) the role of bacterial mineralisation, (ii) the effect of micrograzer excretion, and (iii) bacterial ability to compete for dissolved inorganic nutrients. The nutrient flux pathways between the abiotic pools and biotic groups and the patterns of biomass and nutrient limitation of the different phytoplankton groups were quantified for the different model configurations. Considerable variation in phytoplankton biomass and dissolved organic matter demonstrated the sensitivity of predictions to assumptions about microbial loop operation and the specific mechanisms by which phytoplankton growth was affected. Comparison of the simulations identified that the microbial loop most significantly altered phytoplankton growth by periodically amplifying internal phosphorus limitation due to bacterial competition for phosphate to satisfy their own stoichiometric requirements. Importantly, each configuration led to a unique prediction of the overall community composition, and we conclude that the microbial loop plays an important role in nutrient recycling by regulating not only the quantity, but also the stoichiometry of available N and P that is available to primary producers. The results demonstrate how commonly employed simplifying assumptions about model structure can lead to large uncertainty in phytoplankton community predictions and highlight the need for aquatic ecosystem models to carefully resolve the variable stoichiometry dynamics of microbial interactions.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".