Dispersal mediates trophic interactions and habitat connectivity to alter metacommunity composition
Bibliographic record
Abstract
Dispersal contributes vitally to metacommunity structure. However, interactions between dispersal and other key processes have rarely been explored, particularly in the context of multitrophic metacommunities. We investigated such a metacommunity in naturally fragmented habitats populated by butterfly species (whose dispersal capacities were previously assessed), flowering plants, and butterfly predators. Using data on butterfly species abundance, floral abundance, and predation (on experimentally placed clay butterfly models), we asked how dispersal ability mediates interactions with predators, mutualists, and the landscape matrix. In contrast to expectations, high densities of strong dispersers were found in more isolated sites and sites with low floral resource density, while intermediate dispersers maintained similar densities across isolation and floral gradients, and higher densities of poor dispersers were found in more connected sites and sites with higher floral density. These findings raise questions about how strong dispersers experience the landscape matrix and the quality of isolated and low-resource sites. Strong dispersers were able to escape habitat patches with high predation, while intermediate dispersers maintained similar densities along a predation gradient, and poor dispersers occurred at high densities in these patches, exposing them to interactions with predators. This work demonstrates that species that vary in dispersal capacities interact differently with predators and mutualist partners in a landscape context, shaping metacommunity composition.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".