Pollinator size and its consequences: Predictive allometry for pollinating insects

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Authors Liam L.K. Kendall, Romina Rader, +16 authors Ignasi Bartomeus
Journal/Conference Name bioRxiv
Paper Category
Paper Abstract 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. Predictive allometric models that consider biogeography, phylogenetic relatedness and intraspecific variation are urgently required to ensure greater accuracy. Here, we measured body size, as dry weight, and intertegular distance (ITD) of 391 bee species (4035 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 (OLS) regression to assess intraspecific dry weight - ITD relationships for 10 bee and five hoverfly species. Including co-variates led to more robust interspecific body size predictions for both bees (Bayesian R2: 0.946; Delta-R2 0.047) and hoverflies (Bayesian R2: 0.821; Delta-R2 0.058) relative to models with ITD alone. In contrast, at the intraspecific level, our results demonstrate that ITD is an inconsistent predictor of body size for bees (R2: 0.02 - 0.66) and hoverflies (R2: -0.11 - 0.44). Therefore, predictive allometry is more suitable for interspecific comparative analyses than assessing intraspecific variation. Collectively, these models form the basis of the dynamic R package, 9pollimetry9, which provides a comprehensive resource for allometric research concerning insect pollinators worldwide.
Date of publication 2018
Code Programming Language R
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