Intraguild predation enables coexistence of competing phytoplankton in a well‐mixed water column
Bibliographic record
Abstract
Resource competition theory predicts that when two species compete for a single, finite resource, the better competitor should exclude the other. However, in some cases, weaker competitors can persist through intraguild predation, that is, by eating their stronger competitor. Mixotrophs, species that meet their carbon demand by combining photosynthesis and phagotrophic heterotrophy, may function as intraguild predators when they consume the phototrophs with which they compete for light. Thus, theory predicts that mixotrophy may allow for coexistence of two species on a single limiting resource. We tested this prediction by developing a new mathematical model for a unicellular mixotroph and phytoplankter that compete for light, and comparing the model's predictions with a laboratory experimental system. We find that, like other intraguild predators, mixotrophs can persist when an ecosystem is sufficiently productive (i.e., the supply of the limiting resource, light, is relatively high), or when species interactions are strong (i.e., attack rates and conversion efficiencies are high). Both our mathematical and laboratory models show that, depending upon the environment and species traits, a variety of equilibrium outcomes, ranging from competitive exclusion to coexistence, are possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".