Predicting plant–pollinator interactions: concepts, methods, and challenges

Our success in predicting general community-level interaction patterns contrasts with our limitations to predict pairwise plant–pollinator interactions.Limitations to predict pairwise interactions come from multiple gaps in our understanding of plant–pollinator interactions, model implementations, and data.Different phenomenological and mechanistic modeling approaches attempt to predict plant–pollinator pairwise interactions, although we still lack an equitable comparison between these different approaches to accurately determine differences in their predictive ability.Model predictive ability could be improved by accounting for heterogeneous detection probabilities of interactions resulting from sampling effects, estimating interaction predictors with greater accuracy and building models with more plausible assumptions. Plant–pollinator interactions are ecologically and economically important, and, as a result, their prediction is a crucial theoretical and applied goal for ecologists. Although various analytical methods are available, we still have a limited ability to predict plant–pollinator interactions. The predictive ability of different plant–pollinator interaction models depends on the specific definitions used to conceptualize and quantify species attributes (e.g., morphological traits), sampling effects (e.g., detection probabilities), and data resolution and availability. Progress in the study of plant–pollinator interactions requires conceptual and methodological advances concerning the mechanisms and species attributes governing interactions as well as improved modeling approaches to predict interactions. Current methods to predict plant–pollinator interactions present ample opportunities for improvement and spark new horizons for basic and applied research. Plant–pollinator interactions are ecologically and economically important, and, as a result, their prediction is a crucial theoretical and applied goal for ecologists. Although various analytical methods are available, we still have a limited ability to predict plant–pollinator interactions. The predictive ability of different plant–pollinator interaction models depends on the specific definitions used to conceptualize and quantify species attributes (e.g., morphological traits), sampling effects (e.g., detection probabilities), and data resolution and availability. Progress in the study of plant–pollinator interactions requires conceptual and methodological advances concerning the mechani