A simple null model predicts fruit‐frugivore interactions in a temperate rainforest
Bibliographic record
Abstract
Studies of seed dispersal mutualisms typically test for adaptive relationships between fruits and frugivores. Here, I take the opposite approach, and ask what relationships would be expected based on chance encounters between mutualists. I derived a simple null model to predict pair‐wise relationships between fruit and frugivore species. I assumed that all frugivores had identical fruit preferences, but allowed for interspecific variation in plant abundances, frugivore abundances and fruit phenologies. Under these conditions, the number of visits each frugivore species makes to each plant species results from the product of plant abundances and frugivore abundances when each plant species produces fruit. I then tested null model predictions with observations in a temperate rainforest in British Columbia, Canada. I measured the abundance and phenology of seven bird‐dispersed plant species, seasonal changes in the abundances of six avian frugivore species and 212 interactions between them. Empirical results were generally consistent with predictions, suggesting that relationships between birds and fruits are structured randomly. However, some variation in relationships between observations and predictions was observed, suggesting deterministic processes may also be important. Overall results illustrate that predictable relationships between fruit and frugivore species can result from random encounters between mutualists.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".