Body size is an integral functional trait that underlies pollination-related ecological processes, yet it is often impractical to measure directly. Allometric scaling laws have been used to overcome this problem. However, most existing models rely upon small sample sizes, geographically restricted sampling and have limited applicability for non-bee taxa. Allometric models that consider biogeography, phylogenetic relatedness, and intraspecific variation are urgently required to ensure greater accuracy. We measured body size as dry weight and intertegular distance (ITD) of 391 bee species (4,035 specimens) and 103 hoverfly species (399 specimens) across four biogeographic regions: Australia, Europe, North America, and South America. We updated existing models within a Bayesian mixed-model framework to test the power of ITD to predict interspecific variation in pollinator dry weight in interaction with different co-variates: phylogeny or taxonomy, sexual dimorphism, and biogeographic region. In addition, we used ordinary least squares regression to assess intraspecific dry weight ~ ITD relationships for ten bees and five hoverfly species. Including co-variates led to more robust interspecific body size predictions for both bees and hoverflies relative to models with the ITD alone. In contrast, at the intraspecific level, our results demonstrate that the ITD is an inconsistent predictor of body size for bees and hoverflies. The use of allometric scaling laws to estimate body size is more suitable for interspecific comparative analyses than assessing intraspecific variation. Collectively, these models form the basis of the dynamic R package, “pollimetry,” which provides a comprehensive resource for allometric pollination research worldwide.
Bibliographical noteFunding Information:
This study was funded by a University of New England PhD scholarship and a CSIRO PhD top up scholarship to LKK, an Australian Research Council Discovery Early Career Researcher Award (DE170101349) and an Ian Potter Foundation grant (Ref: 20160225/ RME20044) to RR, a NERC Knowledge Exchange Fellowship (NE/M006956/1) to KCRB, a Productivity Research Grant from CNPq‐Brazil #308948/2016‐5 to BMF, a Marie Curie Fellowship (MSCA FOMN:705287) to LR and a MSCA grant (BeeFun: PCIG14‐GA‐2013‐631653) to IB.
- R package
- body size
- dry weight
- predictive models