Community‐wide seed dispersal distances peak at low levels of specialisation in size‐structured networks
Bibliographic record
Abstract
Network approaches provide insight into the complex web of interspecific interactions that structure ecological communities. However, because data on the functional outcomes of ecological networks are very rarely available, the effect of network structure on ecosystem functions, such as seed dispersal, is largely unknown. Here, we develop a new approach that is able to link interaction networks to a trait‐based seed–dispersal model to estimate community‐wide seed dispersal distances. We simulated networks, using a niche model based on size‐matching between plants and birds, that varied in the degree of niche partitioning, i.e. the overlap in interaction partners between coexisting species. We found that community‐wide dispersal distances were longest when networks had low degrees of niche partitioning. We further found that dispersal distances of plant species with small fruits peaked in models without niche partitioning, whereas dispersal distances of medium and large‐fruited plants peaked at low degrees of niche partitioning. Our simulations demonstrate that the degree of niche partitioning between species is an important determinant of the ecological functions derived from ecological networks and that simulation approaches can provide new insights into the relationship between the structural and functional components of ecological networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".