Control for spatial autocorrelation
In common garden studies the spatial autocorrelation, or the probability that individuals growing closer together are more similar, of samples must be taken into account (Stopher et al., 2012). To account for geographic patterns within each of our gardens (Clatskanie and Corvallis), we used a thin-plate spline method (Blumstein et al., 2020; Evans et al., 2014)via the fields (9.6) (Nychka, Furrer, Paige, & Sain, 2017) package in R . This method fits an interpolated surface to the garden, which uncovers regions of each site that significantly differ from the mean. To correct these patterns of spatial concordance, we take the residuals from the thin plate spline and add them back to the model intercept, thus removing spatial trends and placing sample values back on a biologically meaningful scale. We did this for each of our metrics independently; sugar concentration, starch concentration, total nonstructural carbohydrate (TNC) concentration, the proportion of starch (starch / TNC), and diameter at breast height (DBH).