Predicting interaction frequency in plant-pollinator networks

Abstract

Flowers and their pollinators represent a bipartite interaction system, whose links are hypothesised to be related to species traits. To explore whether we can predict the weight of this link, i.e. the frequency of interactions, in an validation network, we analysed 14 studies of pollinator-flower visitation network from around the world. We used information on species abundances, their traits and their phylogenetic (for plants) or taxonomic (for animals) position as predictors of interaction frequency, and fitted different statistical modelling approaches. We expected to see prediction quality on validation data to decay with spatial and temporal distance to the training networks. Similarly, we expect that changes in pollinator or plant composition will negatively affect predictive performance. Using the best-predicting modelling approach (randomForest), we indeed see a slight decay in predictive quality with plant and pollinator compositional distance. Temporal distance played little role, although predictions for one year ahead (or back) were better than across the season or across multiple years. The overall predictive power of our models was low (Spearman’s rho of approximately 0.4), suggesting a very noisy system. Also, the most important predictor was abundance, as revealed by a parameter-free benchmark model that only used the cross-product of abundances to predict interaction frequency. Trait and phylogenetic information did not substantially improve predictive performance beyond abundance-based predictions. Across all studies, we failed to confirm a substantial contribution of ecological characteristics to pollinator-flower interaction frequency. One reason why predictions were relatively poor is that sampling effort is not standardised, and thus networks differed substantially in the observed number of interactions, network size, and interaction density. Also the pooling of networks across space or across time may have diluted preferences in the data, reducing their explanatory value. Finally, the majority of species in each network are rare, and the interaction information they provide may be much less relevant that that of common species. At present, we conclude that the frequencies of interactions are very difficult to predict, and using traits we cannot really do better than simply using abundance information.