Genomic variation among populations provides insight into the causes of metacommunity survival
Bibliographic record
Abstract
Predators and prey interact at small spatial scales, but during their lifetime disperse at much larger spatial scales. Trophic metacommunity theory proposes that dispersal is a critical process that determines food web structure at small and large scales. The application of metacommunity theory to empirical systems remains elusive because key parameters such as dispersal and interaction strengths have been very difficult to quantify. Here we develop a novel approach that combines population genomics with mesocosm experiments to parameterize a metacommunity model. Using genotyping-by-sequencing, we characterized the dispersal kernels of a predator-prey pair living in a phytotelm metacommunity. We found that the prey dispersed up to 25 km while the predator dispersed only 350 m. We then quantified a functional response for these species using feeding trials. Even without invoking differences in the abiotic niche, our empirically parameterized simulation model produced patterns of population survival and occupancy that were consistent with past observations on the natural system. Importantly we found that these patterns were more likely to be found with simulations based on our observed values than in other regions of potential parameter space. This suggests that the observed dispersal kernels contribute to the dynamics of these species in the metacommunity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".