Estimating seed and pollen movement in a monoecious plant: a hierarchical Bayesian approach integrating genetic and ecological data.

Published

Journal Article

The scale of seed and pollen movement in plants has a critical influence on population dynamics and interspecific interactions, as well as on their capacity to respond to environmental change through migration or local adaptation. However, dispersal can be challenging to quantify. Here, we present a Bayesian model that integrates genetic and ecological data to simultaneously estimate effective seed and pollen dispersal parameters and the parentage of sampled seedlings. This model is the first developed for monoecious plants that accounts for genotyping error and treats dispersal from within and beyond a plot in a fully consistent manner. The flexible Bayesian framework allows the incorporation of a variety of ecological variables, including individual variation in seed production, as well as multiple sources of uncertainty. We illustrate the method using data from a mixed population of red oak (Quercus rubra, Q. velutina, Q. falcata) in the NC piedmont. For simulated test data sets, the model successfully recovered the simulated dispersal parameters and pedigrees. Pollen dispersal in the example population was extensive, with an average father-mother distance of 178 m. Estimated seed dispersal distances at the piedmont site were substantially longer than previous estimates based on seed-trap data (average 128 m vs. 9.3 m), suggesting that, under some circumstances, oaks may be less dispersal-limited than is commonly thought, with a greater potential for range shifts in response to climate change.

Full Text

Duke Authors

Cited Authors

  • Moran, EV; Clark, JS

Published Date

  • March 2011

Published In

Volume / Issue

  • 20 / 6

Start / End Page

  • 1248 - 1262

PubMed ID

  • 21332584

Pubmed Central ID

  • 21332584

Electronic International Standard Serial Number (EISSN)

  • 1365-294X

International Standard Serial Number (ISSN)

  • 0962-1083

Digital Object Identifier (DOI)

  • 10.1111/j.1365-294x.2011.05019.x

Language

  • eng