Spatially-explicit estimation of population genetic boundaries to guide restoration decision-making

Daniel E. Winkler, Southwest Biological Science Center, U.S. Geological Survey, Moab, UT, Matthew R. Jones, Southwest Biological Science Center, US Geological Suvery, Flagstaff, AZ and Robert T. Massatti, Southwest Biological Science Center, U.S. Geological Survey, Flagstaff, AZ

Background/Question/Methods
Managers and practitioners are increasingly using genetic approaches in restoration and conservation but oftentimes rely on physiographic units, ecoregion boundaries, and climate data to define management units, develop seed transfer zones, and develop overall action plans. Explicitly incorporating population genetic information into materials development and selection can increase restoration success by revealing species-specific evolutionary patterns that do not correlate with broad-scale geographic or climatological patterns. The same is true in conservation, where molecular genetic approaches enable the delineation of management units for rare species, thereby informing monitoring efforts and the regulation of potential harmful human activity. To help guide restoration and conservation decision-making, we created POPMAPS (Population Management using Ancestry Probability Surfaces), a novel method implemented in an R package to estimate ancestry probability surfaces and delineate population boundaries across species’ ranges by combining empirical genetic and geospatial data.
Results/Conclusions
We successfully demonstrate the utility of popmaps for estimating population boundaries and management units in Hilaria jamesii (Poaceae, commonly called James’ galleta grass), a frequently used restoration species across the western United States, and Carex specuicola (Cyperaceae, commonly called Navajo sedge), a rare and threatened plant species in the desert southwest. In both instances, preliminary testing identified spatially explicit surfaces that explained empirical genetics patterns best. Using novel functions contained in our new R package POPMAPS, we used these surfaces to determine parameters that best estimated empirical genetic data, which then informed the estimation of ancestry probability surfaces. We discuss how species’ inherent biological characteristics may influence the estimation of ancestry probability surfaces, as well as how they can inform basic and applied evolutionary and ecological research. Finally, we emphasize how our method may improve decision-making about where to source seeds for native plant materials development and wildland restoration, how and where to define management unit boundaries for rare species, while simultaneously providing insightful information into the evolutionary histories and adaptations of common, workhorse restoration species and those of conservation concern.