Functional connectivity of animal-dispersed plant communities depends on the interacting effects of network specialization and resource diversity
Bibliographic record
Abstract
Plant functional connectivity-the dispersal of plant propagules between habitat patches-is often ensured through animal movement. Yet, there is no quantitative framework to analyse how plant-animal interactions and the movement of seed dispersers influence community-level plant functional connectivity. We propose a trait-based framework to quantify plant connectivity with a model integrating plant-frugivore networks, animal-mediated seed-dispersal distances and the selection of target patches by seed dispersers. Using this framework, we estimated how network specialization, between-patch distance and resource diversity in a target patch affect the number and diversity of seeds dispersed to that patch. Specialized networks with a high degree of niche partitioning in plant-frugivore interactions reduced functional connectivity by limiting the diversity of seeds dispersed over long distances. Resource diversity in the target patch increased both seed number and diversity, especially in specialized networks and within short and intermediate distances between patches. Notably, resource diversity was particularly important at intermediate distances, where the number and diversity of seeds reaching a patch increased more strongly with resource diversity than at longer distances. Using a trait-based framework, we show that resource diversity in the target patch is a major driver of connectivity in animal-dispersed plant communities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".