Figs and frugivores in the Afrotropics:inferring biotic interactions in a seed-dispersal meta-network
Bibliographic record
Abstract
Natural and anthropogenic climate change influence the geographical range and survival of species and can lead to new or lost species interactions, eventually re-organizing entire biological communities into new novel communities. However, species networks are inherently complex and difficult to fully characterize, thus we often have an incomplete picture of all potential interactions in a community. Machine learning has proven useful for inferring biotic interactions in ecological networks, thereby filling the gap of unobserved but potential interactions. Here we develop a macro-ecological framework for inferring seed-dispersal interactions. Specifically, we gathered data on mutualistic interactions between Afrotropical figs (Ficus) and frugivorous animals which consume figs, dispersing their seeds. Based on 734 studies, we compiled a database of 4570 unique empirical interactions between 106 fig species and 492 frugivore species (271 birds and 214 mammals). Here we show how these data are taxonomically and geographically biased toward highly studied families and geographic areas, highlighting the need for unbiased predictions of potential species interactions. We also elucidate how these observed interactions can be combined with functional traits of both the figs and frugivores in machine-learning algorithms for classifying novel interactions. By understanding how functional traits drive seed dispersal interactions on a macro-scale, it is possible to model lost or acquired interactions as well as extinction velocity and sensitivity as species move in response to global change. The proposed framework can ultimately provide new insights into the stability of ecological communities on a continental scale, and the importance of specific functional traits in seed dispersal networks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".