Predicting date-of-death
We predicted timing of death of trees as a secondary metric to assess
the importance of drought stress in driving beetle outbreaks by
including metrics related to tree size, vigor, and drought stress.
Predictions of date-of-death were conducted at the tree-level. More
specifically, tests of date-of-death used annually resolved
∆13C data averaged across all the trees by site, and
by time of death, whereas tree size (DBH) and growth rate (BAI) data
were used on the individual tree-level. ∆13C values
for each site and timing category were applied to all trees within the
site (e.g., all early-dying trees at the ASH site were given the same
∆13C value for a given year). Similar to the temporal
resolution of ∆13C, annual BAI values matching the
timeframe of ∆13C (1960-death) were used. A linear
mixed effects model was used to predict date-of-death per tree. Due to
the variability among plots, date-of-death was calculated as the number
of years from the year of peak mortality by site (positive values
indicate years of survival post peak mortality, and negative values,
years prior to the peak mortality that trees died, Table 1). The model
was fitted with all hypothesized main effects (BAI, DBH,
∆13C, and their interactions) and interpreted in terms
of significance at p < 0.05. Random effects were used
to account for the nested nature of years repeated for trees within
sites. All analyses were done using the “nlme” and “visreg” packages
in R (R Core Team 2016; Breheny and Burchett 2017; Pinheiro et
al. 2017).