Functional traits are the primary biotic component driving organism influence on ecosystem functions; in consequence, traits are widely used in ecological research. However, most animal trait-based studies use easy-to-measure characteristics of species that are at best only weakly associated with functions. Animal-mediated pollination is a key ecosystem function and is likely to be influenced by pollinator traits, but to date no one has identified functional traits that are simple to measure and have good predictive power. Here, we show that a simple, easy to measure trait (hairiness) can predict pollinator effectiveness with high accuracy. We used a novel image analysis method to calculate entropy values for insect body surfaces as a measure of hairiness. We evaluated the power of our method for predicting pollinator effectiveness by regressing pollinator hairiness (entropy) against single visit pollen deposition (SVD) and pollen loads on insects. We used linear models and AICC model selection to determine which body regions were the best predictors of SVD and pollen load. We found that hairiness can be used as a robust proxy of SVD. The best models for predicting SVD for the flower species Brassica rapa and Actinidia deliciosa were hairiness on the face and thorax as predictors (R2
= 0.98 and 0.91 respectively). The best model for predicting pollen load for B. rapa was hairiness on the face (R2
= 0.81). Accordingly, we suggest that the match between pollinator body region hairiness and plant reproductive structure morphology is a powerful predictor of pollinator effectiveness. We show that pollinator hairiness is strongly linked to pollination -an important ecosystem function, and provide a rigorous and time-efficient method for measuring hairiness. Identifying and accurately measuring key traits that drive ecosystem processes is critical as global change increasingly alters ecological communities, and subsequently, ecosystem functions worldwide.
Journal Paper -
no. e2433v1, PeerJ Preprints, 2016 PEERJ
DOI: 10.7287/peerj.preprints.2433v1 » doi