Inferring the foraging ranges of social bees from sibling genotypes sampled across discrete locations
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A knowledge of the distances regularly travelled by foraging bees is essential to understanding the movement of pollen across landscapes, and has implications for the conservation of both pollinators and plants. Unfortunately, the movements of bees are difficult to measure directly at ecologically relevant scales. A common strategy for quantifying the foraging ranges of social bees is to sample the genotypes of foragers across a landscape. Individual foragers can be assigned to colonies with polymorphic genetic markers, and the dispersion of siblings in space can be used to make inference about colony locations and foraging movements. Several previous studies have sampled sibling genotypes at discrete locations (for example, at regular points along a transect), rather than in continuous space. Restricting the collection of bees to discrete locations presents a number of considerations for sampling design and data analysis. In this paper, we develop a spatially-explicit, model-based framework for the simulation and estimation of foraging ranges. Using these tools, we simulated experiments to characterise the efficacy of different sampling strategies, and provide an example with actual data that demonstrates the advantages of our method over an approach based on regression.
KeywordsSocial bees Foraging range estimation Sibship reconstruction Sampling design Spatial analysis
We would like to thank members of the Jha lab at the University of Texas at Austin, attendees of the 2015 Ecological Society of America symposium “Conservation genetics of bee pollinators”, and three anonymous referees for useful comments and criticisms. We acknowledge the Texas Advanced Computing Center (TACC, tacc.utexas.edu) at the University of Texas at Austin for providing HPC resources that have contributed to the research results reported within this paper. This work was supported by the University of Texas at Austin, the National Science Foundation (NSF DEB 1148679), and the Army Research Office. NP is supported in part by an NSF predoctoral fellowship (NSF DEB 1110007).
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