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• W3159106018 abstract "Mutualistic bee–plant interaction networks are a vital part of terrestrial ecosystems. They frequently arise through co-evolutionary processes, which match the traits of both partners, facilitating their interaction. Insights in these interaction mechanisms are vital to be able to manage changing ecosystems. This entails the need for models to predict species interaction networks in general and pollination networks in particular. We show how kernel-based pairwise learning can predict bee–plant interactions based on the traits and the phylogeny of the plant and bee species. The traits and the phylogeny of the plant and bee species proved to be highly predictive. Although the traits were slightly more informative compared to the phylogeny, the best results were obtained by combining both the traits and the phylogeny in the model Notably, the model performance varied greatly depending on whether the goal was to pinpoint missing interactions in the network or to predict interactions for new bee species, new plant species, or both. This issue highlights the importance of proper stratification when fitting biological network prediction models. Our model, however, showed the capacity to generalize beyond the original dataset provided by FlorAbeilles. The model was validated by predicting potentially interacting plant species for the invasive bee species Megachile sculpturalis. Four out of the five plant species identified by the model could be validated based on literature. Our results indicate that pairwise learning has potential as a general method for supervised species interaction prediction. Caution should be taken to validate such models correctly." @default.
• W3159106018 created "2021-05-10" @default.
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• W3159106018 date "2021-07-01" @default.
• W3159106018 modified "2023-10-15" @default.
• W3159106018 title "Pairwise learning for predicting pollination interactions based on traits and phylogeny" @default.
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• W3159106018 doi "https://doi.org/10.1016/j.ecolmodel.2021.109508" @default.
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