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).