Common milkweed (Asclepias syriaca) is a clonal plant of conservation concern with a large range that can reproduce both sexually and asexually via root buds. Currently it is unclear the extent to which clonal plants like A. syriaca remain integrated. To determine clone size and integration in A. syriaca we ask whether spatial structure in demographic trait data aligns with spatial structure in genetic data? We have trait data over five years at ten different sites, with 59 total unique year and transect combinations. For each ramet, we measured demographic traits including size such as apical height, stem diameter, and leaf area; reproduction, including flowering and pod production; herbivory severity; and plant functional traits such as cardenolides, leaf mass area, leaf N, cellulose, and lignin. For each of these traits we created a distance matrix that we correlated with a physical distance matrix using a mantel correlation. Using a correlogram we identified the critical distance at which plants are similar to create hypotheses about genetic similarity. We tested these hypotheses using microsatellite data from the same individuals to determine number and spatial structure of genets along each transect as well as how well the phenotypic data predicts genetic identity.
Results/Conclusions
We found significant spatial structure in A. syraica demographic traits that varied across space but was consistent across time within location. Traits that measured growth tended to correlate with distance more often than reproductive traits. We hypothesize that transects with a high number of traits that correlate with distance consist of a small number of clones. For example, at one transect, apical height (r = 0.55, P = 0.003), stem diameter (r = 0.51, P = 0.003), and number of inflorescences (r = 0.59, P = 0.002), all correlated with distance across years. In contrast, at another transect within the same site, traits never correlated with distance over three years of trait data. This indicates that clone size and potentially physiological integration for A. syriaca varies across patches. Using the spatial patterns in trait data we can make specific predictions about the number and spatial structure of the genets in a patch and test these predictions using genetic data. Information on clonality and integration will improve our understanding of population dynamics for A. syriaca and other clonal plant species.