*: outlier in year 1960 removed
Fig. 1 Conceptual diagram of the relationships that possibly
influence D. rufipennis outbreaks with particular attention to
factors that could influence whether drought stress has played a role in
weakening tree resistance to beetle attacks. Box colors; purple
represents bark beetle population dynamics across a landscape, blue
represents factors affecting water supply, pink represents factors
affecting water demand, orange represents regional to local factors
driving drought stress, green represents ecophysiological
characteristics that vary among trees, gray represents stand- site- or
microsite-factors that can modify drought stress and/or likelihood of
bark beetle host selection, white represents the likelihood of survival
or death for a given timestep during a bark beetle outbreak.
Relationship directionality is indicated by symbols in arrows. The
shading of arrows reflects the degree to which the relationship has been
previously demonstrated, with an emphasis on D. rufipennis and
its hosts across North America; dark arrows are more strongly
established and lighter arrows are more weakly established or in some
cases simply hypothesized. Specific aspects of this system that our
study addresses are given in boldface type.
Footnotes: 1Macroclimate evaporative demand
is influenced primarily by warm season temperatures and secondarily by
cool season precipitation and temperature; by their influence on
snowpack persistence into spring or summer months.2Meteorological drought severity is quantified in this
study using mean monthly climatic moisture deficit (CMD) across June to
August. 3In montane forest of the Rocky Mountains,
forest structure can have a strong influence on soil water via snow
interception and ablation; or conversely, how the presence of gaps in
forests can locally increase snowpack (Hart and Lomas 1979; Hubbart et
al. 2015). 4Drought stress is quantified in this study
by the sensitivity of latewood carbon isotope discrimination to CMD.5Bark beetle populations, including D.
rufipennis , are influenced by many factors not represented here, but
likely the single greatest effect is how growing season temperatures
modify the proportion of beetles that can reproduce in 1- vs 2-yr
lifecycles; for more details see (Hansen et al. 2001; Hansen and Bentz
2003; Berg et al. 2006; Raffa et al. 2008; Bentz et al. 2010).6Stand structure, in this case, reflects the relative
abundance of large trees within a stand that D. rufipennis prefer
if tree vigor is low enough and/or if beetle populations are high enough
to overwhelm tree defenses (Massey and Wygant 1954; Hard 1983, 1985;
Doak 2004). 7Drought can affect tree resistance to
bark beetles through various mechanisms; for more details see Anderegg
et al. (2015). 8Many traits factor into tree defense;
for P. engelmannii , the frequency of traumatic resin duct
formation appears to be critical for survival (DeRose et al. 2017).9For this study we use the timing of death within a
stand as a surrogate for tree/host resistance to bark beetles.
Fig. 2 Conceptual diagram displaying a demographic sampling
approach designed to contrast trees that died early during an epidemic
(red bars), to those that died late during an outbreak (blue bars). This
approach specifically allows for comparisons of early-dying trees that
had lower resistance to incipient bark beetles populations (i.e.,
building from endemic to outbreak), versus late-dying trees from the
same stands that resisted pressure by bark beetle attacks for a longer
time. If drought stress contributed to the initiation of a beetle
outbreak by constraining carbon gain available for defenses, sensitivity
to drought stress should be greater in early-dying compared to
late-dying trees.
Fig. 3 The six sites where tree cores were collected on the
Markagunt Plateau in southern Utah are shown as yellow circles overlaid
on a regional map showing the extent of canopy P. engelmanniimortality (red), P. tremuloides or mixed P.
tremuloides -conifer (green) meadows (pink), lava fields and other
non-vegetated lands (dark purple and white, respectively) are after
DeRose et al. (2011). A broader context for the sampling is shown by
maps of climatic moisture deficit (CMD; after Wang et al. 2016) for the
period of 1961-1990 (left) and projected for 2050 (right) across the
distribution of P. engelmannii .
Fig. 4 Predicted relationship between ∆13C
values and climatic moisture deficit averaged across June, July, and
August (CMD JJA) colored by early/late timing of mortality from repeated
measures mixed effects model showing no significant interaction between
timing of death (early vs. late) and CMD JJA (model estimate = 0.000268,p = 0.76; Appendix S1: Table S4). Dots represent partial
residuals with colors indicating the timing of mortality: red dots/line
indicate early-dying trees; blue dots/line indicate late-dying trees.
Fig. 5 Visualization of relationship between raw
∆13C values and climatic moisture deficit averaged
across June, July, and August (CMD JJA) for the six sampling sites
individually (noted by the three letter code at the top of each panel;
Table 1). Linear regression lines have been added for ease of
interpretation. Dots represent actual data points with colors indicating
timing of mortality: red and blue points/linear regression lines
represent early- or late-dying trees, respectively. Gray bands represent
confidence intervals.