Vertical Niche Partitioning and the Performance of Mixotrophic Generalists against Autotrophic and Heterotrophic Specialists under Contrasting Light-Nutrient Supply Regimes
Bibliographic record
Abstract
AbstractA vertical separation in light and nutrient availability is observed in many terrestrial and aquatic ecosystems. In lakes and oceans, the opposing vertical gradients of light and nutrients typically observed are believed to promote phagomixotrophy, a generalist strategy that combines resource acquisition through photoautotrophic and phagoheterotrophic pathways. While phagomixotrophy is widespread, it is not well understood how this strategy performs against pure specialist strategies in a resource competition context. We simulate the dynamics of three competitors (pure photoautotroph, phagomixotroph, pure phagoheterotroph) and bacterial prey over the vertical dimension of a water column to investigate what conditions of resource availability favor mixotrophy and how the presence of the phagomixotroph alters community dynamics. Since mixotrophs can be more or less photoautotrophic, we incorporated this variability into our model. Under weak vertical mixing, mixotrophs persist under most light and nutrient conditions and negatively affect specialists. Mixotrophs can even be dominant competitors when they display an optimal degree of phototrophy, which is positively related to water transparency and negatively related to nutrient supply. The model indicates that the spatial organization of nanophytoplankton communities in water columns could arise through vertical niche partitioning of multiple resource acquisition strategies and that phagomixotrophy can promote overall community production.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".