Predators regulate prey species sorting and spatial distribution in microbial landscapes
Bibliographic record
Abstract
The role of predation in determining the metacommunity assembly model of prey communities is understudied relative to that of interspecific competition among prey. Previous work on metacommunity dynamics of competing species has shown that sorting by habitat patch type and spatial patterning can be affected by disturbances. Microcosms offer a useful model system to test the effect of multi-trophic interactions and disturbance on metacommunity dynamics. Here, we investigated the potential role of predators in enhancing or disrupting sorting and spatial pattern among prey in experimental landscapes. We exposed multi-trophic protist microcosm landscapes with one predator, two competing prey, two patch resource types, and localized dispersal to three disturbance regimes (none, low, and high). Then, we used variation partitioning and spatial clustering analysis to analyse the results. In contrast with previous experiments that did not manipulate predators, we found that patch type did not structure prey communities very well. Instead, we found that it was the distribution of the predator that most strongly predicted the composition of the prey community. The predator impacted species sorting by (1) preferentially consuming one prey, thereby acting as a strong local environmental driver, and by (2) indirectly magnifying the impact of patch food resources on the less preferred prey. The predator also enhanced spatial signal in the prey community because of its limited dispersal. Our results indicate that predators can strongly influence prey species sorting and spatial patterning in metacommunities in ways that would otherwise be attributed to stochastic effects, such as dispersal limitation or demographic drift. Therefore, whenever possible, predators should be explicitly included as separate explanatory factors in variation partitioning analyses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".