Empirical test of increasing genetic variation via inter‐population crossing for native plant restoration in variable environments. Issue 8 (4th March 2022) - Goebl, April M.; Doak, Daniel F.; Kane, Nolan C.
Abstract : Restoring native plant populations is an essential component of conserving biodiversity, ecological function, and ecosystem services. Restoration using local, ecotypic source materials is largely acknowledged as best practice; however, local populations are not always available or adapted to current or future site conditions. A major challenge in restoration comes from increasingly variable and unpredictable environmental conditions that impose selective pressures and threaten restoration success. Understanding how to conserve and restore populations under changing environments requires attention to within‐species genetic diversity, which can exert a number of population‐level effects. However, given that these effects can be positive and negative, it remains unclear how increasing genetic diversity via mixing distinct populations may ultimately affect restoration in variable environments. To empirically investigate these effects, we established lineages of a native forb, Helianthus petiolaris (prairie sunflower), with higher and lower individual‐ and population‐level genetic diversity by crossing plants using seed from four distinct locations. We planted and tracked a total of 3, 200 individual seeds across all lineages in replicated plots in two common gardens representing a range of environmental conditions, and measured fitness components throughout the growing season. We found that populations with increased genetic diversity had intermediate emergence and reproduction, improved survival in the poorest quality plots, and were moderately buffered against environmental variability. Overall, higher diversity led to high or intermediate and stable performance across environments. Our findings support a strategy of increasing genetic diversity when restoring populations, in particular when a lack of information hampers selection of an optimal source population.