Agricultural and Forest Meteorology 221 (2016) 176–188
Contents lists available at ScienceDirect
Agricultural and Forest Meteorology
journal homepage: www.elsevier.com/locate/agrformet
Douglas-fir displays a range of growth responses to temperature,
water, and Swiss needle cast in western Oregon, USA
E. Henry Lee a,∗ , Peter A. Beedlow a , Ronald S. Waschmann a , David T. Tingey a ,
Charlotte Wickham b , Steve Cline a , Michael Bollman a , Cailie Carlile c
a
US Environmental Protection Agency, 200 SW 35th Street, Corvallis, OR 97333, United States
Oregon State University, Department of Statistics, Corvallis, OR 97332, United States
c
Missouri Department of Natural Resources, Jefferson City, MO 65102, United States
b
a r t i c l e
i n f o
Article history:
Received 19 October 2015
Received in revised form 11 January 2016
Accepted 6 February 2016
Available online 27 February 2016
Keywords:
Climate change
Dendrochronology
Douglas-fir
Pacific Decadal Oscillation
Pacific Northwest
Swiss needle cast
a b s t r a c t
Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco) growth in the Pacific Northwest is
affected by climatic, edaphic factors and Swiss needle cast (SNC) disease. We examine Douglas-fir growth
responses to temperature, dewpoint deficit (DPD), soil moisture, and SNC using time series intervention
analysis of intra-annual tree-ring width data collected at nine forest stands in western Oregon, USA.
Air temperature, previous-year DPD and SNC and their interactions were the primary factors influencing
tree growth at all sites, whereas other key seasonal climatic factors limiting growth varied by site. Winter
temperature was more important at high elevation cool sites, whereas summer temperature was more
important at warm and dry sites. Growth rate increased with summer temperature to an optimum (Topt )
then decreased at higher temperatures. At drier sites, temperature and water affected growth interactively such that Topt decreased with decreasing summer soil moisture. With increasing temperature due
to climate change, growth rates increased at high elevation sites and declined at mid-elevation inland
sites since ∼1990. Growth response to climate and SNC are confounded at all sites. We conclude that as
temperature rises and precipitation patterns shift toward wetter winters and drier summers, Douglas-fir
will experience greater temperature and water stress and an increase in severity of SNC.
Published by Elsevier B.V.
1. Introduction
By the end of the 21st century, climate models predict hotter,
drier summers and warmer, wetter winters in the Pacific Northwest (PNW), resulting in decreased snowpack, earlier snowmelt,
and increased summer water balance deficit (Mote and Salathé,
2010; Elsner et al., 2010). These changes will profoundly affect forest growth. Climate change is already affecting sensitive forested
ecosystems, raising concerns that forests are becoming increasingly susceptible to tree pathogens, phytophagous insects, and
fires (De Wolf and Isard, 2007; Dalton et al., 2013). Western North
American forests are experiencing large-scale, climate-related disturbances (i.e., diseases, insects, wildfires) at an unprecedented
scale (Raffa et al., 2008). Since the mid-1970s, Oregon forests are
experiencing changes in species composition, increased mortality
rates, and increased severity and frequency of forest diseases and
insects (Wickman, 1992; van Mantgem et al., 2009; Black et al.,
∗ Corresponding author.
E-mail address: lee.ehenry@epa.gov (E.H. Lee).
http://dx.doi.org/10.1016/j.agrformet.2016.02.009
0168-1923/Published by Elsevier B.V.
2010). PNWs diverse climate, landscape, and forested ecosystems
are host to many forest pathogens (e.g., Phaecryptopus gaeumannii, Arceuthobium spp., Armillaria, Phaseolus schweinitzii) and pests
(e.g., Dendroctonus ponderosae, Dendroctonus pseudotsugae, Choristoneura occidentalis Freeman, Orgyia pseudotsugata McDunnough)
but very little is known about the cumulative and interacting effects
of climate and forest diseases and pests on tree growth and mortality.
Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco)
is a dominant tree species in the PNW and economically important worldwide (Waring and Franklin, 1979). Understanding the
effects of temperature, soil moisture, and disturbance on its growth
is critical to understanding the vulnerability of the region’s forests
to increasing tree mortality, dieback, and growth loss due to climate change. Dendroecological studies in western North America
suggest that Douglas-fir growth is sensitive to temperature and
soil moisture depending on elevation (Case and Peterson, 2005),
topography, and soil characteristics (Brubaker, 1980; Watson and
Luckman, 2002). In warm, dry sites at low- to mid-elevations,
growth correlates positively with growing season soil moisture and
precipitation, and negatively with summer temperatures in both
E.H. Lee et al. / Agricultural and Forest Meteorology 221 (2016) 176–188
current and prior years (Brubaker, 1980; Watson and Luckman,
2002; Zhang and Hebda, 2004; Case and Peterson, 2005). At
high elevations, growth correlates positively with summer and
annual temperatures, winter snowpack, and growing season length
(Graumlich and Brubaker, 1986; Peterson and Peterson, 1994;
Nakawatase and Peterson, 2006; Littell et al., 2008).
But, analyzing the influence of climatic and soil factors on tree
growth is difficult because they are complex, nonlinear and strongly
interact with each other and with diseases and pests (Isebrands
et al., 2000; Lloyd et al., 2013). Climate affects forest diseases and
pests, which affect Douglas-fir growth (Alfaro et al., 1982, 2014;
Black et al., 2010; Lee et al., 2013) complicating the growth-climate
relation. Of the many forest disturbance agents of Douglas-fir,
Swiss needle cast (SNC) is particularly important because it occurs
wherever its host is found and reduces stem growth by reducing
photosynthetic capacity through stomatal occlusion and early needle abscission throughout the life of the tree (Shaw et al., 2011).
SNC, a climate-sensitive, foliar disease caused by the fungus P.
gaeumannii (Rhode) Petrak specific to Douglas-fir is particularly
important in western Oregon (Shaw et al., 2011) and New Zealand
(Kimberley et al., 2011; Watt et al., 2011). SNC is most severe on
the western slopes of the Oregon Coast range within the coastal fog
zone where warm winters and mild, humid summers are highly
favorable for the causal fungus, P. gaeumannii (Rosso and Hansen,
2003; Stone et al., 2008).
A previous study (Beedlow et al., 2013), using monthly growth
data, showed that temperature and soil moisture interactively
affected Douglas-fir growth in that the growth temperature optimum (Topt ) decreased with decreasing soil moisture. Lee et al.
(2013) found that annual variations in radial stem growth of
Douglas-fir at coastal sites were affected by both climatic and soil
conditions and SNC. Neither study quantified the interactions of
temperature, water, and SNC on Douglas-fir growth across the
diversity of ecoregions in the PNW ranging from wet maritime in
the Coast Range to dry Mediterranean in the Cascade Range.
To address this knowledge gap, we applied the time series intervention analysis of Beedlow et al. (2013) to test the hypotheses that
(1) low soil moisture exacerbates the effect of air temperature on
stem growth, (2) previous-year climatic conditions are as important as current-year conditions to radial stem growth (Fritts, 1965;
Waring and Franklin, 1979; Brubaker, 1980; Zhang and Hebda,
2004; Lloyd et al., 2013), (3) tree growth rates have changed in
response to increasing temperature, decreasing snowpack, and
early snowmelt in recent decades, which were the warmest on
record in the PNW (Kunkel et al., 2013), and (4) SNC modifies tree
growth response to temperature and soil moisture by impacting
physiological processes of carbon and water relations.
2. Methods
2.1. Study sites
Tree-ring chronologies of earlywood (EW) and latewood (LW)
widths at nine sites were developed from mature, closed-canopy
Douglas-fir stands located at low- to mid-elevations (<530 m) on
the western and eastern slopes of the Coast Range and Willamette
Valley, and mid- to high elevations (530–1200 m) on the western
slopes of the Cascade Mountains of Oregon (Table 1, Fig. 1). The nine
sites represent a range of climatic and edaphic conditions (Fig. S7).
Five of the study sites (Cascade Head, Falls Creek, Moose Mountain,
Soapgrass Mountain, Toad Creek) were established in the mid1990s, and local climatic and edaphic conditions are monitored at
5-min intervals and averaged hourly (Lee et al., 2007; Beedlow et al.,
2013). The two Horse Creek Trail sites are located in the Siuslaw
National Forest on the west slopes of the Coast Range within 2 km
177
distance but at different elevations. These two sites and the Cascade
Head site contain trees affected by SNC which reduced annual radial
stem growth 18–31% averaged across the diseased years (Lee et al.,
2013). The remaining two sites (Woods Creek, Jackson Place) represent drier valley conditions (Fig. S7) and contain mature Douglas-fir
lacking obvious disturbances. The sites will be referred to by their
abbreviations as follows: CH = Cascade Head; HCTL & HCTU = Horse
Creek Trail Lower & Upper, WC = Woods Creek, JP = Jackson Place,
FC = Falls Creek, MM = Moose Mountain, SG = Soapgrass, TC = Toad
Creek.
Needle samples collected in 2014 confirmed the presence of P.
gaeumannii at all study sites. Evidence of SNC was also indicated
by sparse canopies, loss of older needles, and chlorotic needles on
Douglas-fir trees at all sites in the summer of 2014.
2.2. Intra-annual tree-ring data and cross-dating
We collected tree cores at all sites between 1997 and 2013. Tree
core samples from dominant and co-dominant Douglas-fir trees
were taken at approximately breast height (1.4 m) using a 5-mm
diameter Swedish increment borer. The number of trees sampled
at each site ranged from 16 to 34 depending upon stand density and
difficulty of coring. One or two cores were taken from each tree.
Tree cores were air-dried, mounted, sanded, and scanned using
an Epson Expression 10000XL flatbed scanner to generate a digital image of the cellular structure for dendrochronological analysis.
We measured earlywood (EW) and latewood (LW) ring-widths to
the nearest 0.01 mm using the WinDENDRO 2008 g tree-ring measuring system (Regent Instruments Inc., Quebec, Canada). The EW
and LW ring-widths allow the time of cell formation to be dated, and
hence, associated with seasonal climatic factors (Meko and Baisan,
2001; Watson and Luckman, 2002).
Following measurement, the tree-ring data were cross-dated by
matching patterns of narrow and wide rings between cores from
the same tree or from different trees within and between sites.
We also used several available Douglas-fir chronologies from this
region as master chronologies for cross-dating (Black et al., 2010).
As the SNC impacts in 1984–1986 were unprecedented and synchronous in the Coast Range of Oregon (Black et al., 2010; Lee
et al., 2013), the narrow rings in 1984–1986 were used as key
growth patterns for cross-dating. The University of Arizona Laboratory of Tree-Ring Research software program COFECHA Version
6.06P (Holmes, 1983) was used to confirm assignment of the correct
calendar year to each growth increment.
Prior to cross-dating, the data were log-transformed, detrended,
and deseasonalized using a cubic spline with a 50% frequency
response of 32 years. Log transformation was used to infer the multiplicative effects of climate and associated disturbances on growth
and to stabilize the variance of ring-widths (Cook, 1987). Within
each site, every detrended series was correlated with the mean of
all other detrended series to check for potential errors due to missing or false rings and to calculate the interseries correlation. The
high-frequency variations of every detrended, non-logged series
was examined by calculating the growth difference between consecutive years in proportion to their mean and averaged across
years to calculate the mean sensitivity (Fritts, 1976). Mean sensitivity values >0.2 indicate growth sensitivity to climate whereas
values <0.10 indicate complacency.
2.3. Master chronologies of earlywood and latewood ring-widths
Initially, a cubic spline smoother with a 50% frequency response
of 32 years was applied to the entire series to remove the agerelated trends for cross-dating. But, a comparison of the detrended
and raw chronologies indicated that radial growth had a prominent age-related trend prior to ∼1960 and a multi-decadal climate
178
E.H. Lee et al. / Agricultural and Forest Meteorology 221 (2016) 176–188
Table 1
Site and tree core sampling information and dendrochronology summary statistics for Douglas-fir.
Name
Latitude,
longitude
Cascade Head (CH)
N45◦ 02
W123◦ 54
N44◦ 29
W123◦ 54
N44◦ 28.5
W123◦ 55
N44◦ 32
W123◦ 33
N44◦ 36.7
W123◦ 17.5
N44◦ 40
W122◦ 37
N44◦ 25
W122◦ 24
N44◦ 21
W122◦ 17
N44◦ 26
W122◦ 02
Horse Creek Trail Lower (HCTL)
Horse Creek Trail Upper (HCTU)
Woods Creek (WC)
Jackson Place (JP)
Falls Creek (FC)
Moose Mountain (MM)
Soapgrass Mountain (SG)
Toad Creek (TC)
Elevation
(m)
Aspect
Slope (%)
Pith date
No. trees
cored
Basal area
density
(m2 /ha)
Interseries
correlationa
Mean
sensitivityb
150
80◦ (E)
19
1870
17
88d
0.46
0.26
280
320◦ (NW)
29
0.50
0.26
310◦ (NW)
26
9
8
16
69e
470
1586c
1860c
1859
98e
0.47
0.20
e
◦
525
330 (NW)
6
1873
20
72
0.52
0.16
180
30◦ (NE)
28
1874
19
67e
0.54
0.19
530
45◦ (NE)
8
1890
26
60d
0.53
0.16
658
◦
250 (SW)
17
1903
24
d
61
0.51
0.17
1190
250◦ (SW)
6
1547
29
148d
0.51
0.18
1198
100◦ (E)
12
1811
18
85d
0.55
0.16
a
The average correlation between each detrended time series and the mean of all other detrended time series.
The mean absolute first difference of each detrended time series relative to its running mean value. The index ranges from 0 (i.e., no variability) to 2 (i.e., high variability
with periodicity of 2 years).
c
The forest stand contained two different-aged cohorts of Douglas-fir growing in adjacent sections.
d
Basal area density is based on a complete plot survey of individual trees at these permanent field sites.
e
Basal area density is based on multiple stand measurements using a wedge prism that has a basal area factor of either 20 or 30.
b
Fig. 1. Tree core samples were collected from nine field sites located in mature Douglas-fir stands on the west and east sides of the Coast Range, in the Willamette Valley,
and on the west side of the Cascade Mountains.
trend after ∼1980 which was removed by the cubic spline smoother
(see Figure S1 in Supplementary Material). A known weakness of
detrending each series individually with a cubic spline smoother
or negative exponential curve is a failure to preserve the long-term
climate trends (Lloyd et al., 2013). To overcome this problem of
end effects after the data were cross-dated, we used spline regression to detrend and deseasonalize the EW and LW width series
by partitioning each whole series into three segments. We used a
cubic spline smoother with a 50% frequency response of 32 years
to remove the age-related trend in the first segment and a horizontal trend line in the latter two segments so as to be continuous
in the join year (i.e., last year of first segment and first year of second segment) and preserve as much of the low-frequency variation
as possible. The first join year for each site was determined visually by examining when the cubic spline smoother had zero slope
and the detrended and raw chronologies initially converged after
E.H. Lee et al. / Agricultural and Forest Meteorology 221 (2016) 176–188
1950. We used the same join year for both EW and LW series for
all trees within a site. The first join year ranged between 1960 and
1970, which was prior to 1975 when the warming trend associated
with climate change began to accelerate (Kunkel et al., 2013). The
median across trees was then used to produce the master chronologies of EW and LW widths for each site. The climate trend in the
master chronologies is examined in the statistical modeling section.
2.4. Climate data
We obtained monthly temperature, dew-point temperature,
and precipitation data from the PRISM Climate Group at Oregon State University, available at their Web site (http://prism.
oregonstate.edu). Single grid-point PRISM data (2.5 arcmin) of
1895–2012 mean monthly temperature variables for the specific
study site locations were adjusted for bias to the elevation of interest. The PRISM temperature data were further adjusted for bias to
have the same average monthly mean temperatures as the local
meteorological data where available (see Beedlow et al., 2013; Lee
et al., 2007 for a description of local measurements). We summarized the climate data as seasonal averages of temperature and
precipitation that corresponded to the seasonal climate factors
associated with intra-annual growth. Dew-point deficit (DPD) was
calculated as the difference between the mean air and dew-point
temperature for one or more months.
Precipitation and DPD during the summer months relates to
potential leaf wetness, which is difficult to derive from modeled
climate data (Manter et al., 2005). Daily fog frequency data for the
airports at North Bend, OR (43.41◦ N, 124.24◦ W, 5 m) and Arcata,
CA (40.98◦ N, 124.11◦ W, 64 m) and divisional monthly Palmer
Drought Severity Index (PDSI) data for the period 1895–2012 were
obtained from the National Oceanic and Atmospheric Administration’s National Climate Data Center (available at http://www.ncdc.
noaa.gov).
The PDSI is a measure of soil moisture based on the principles of a
balance between moisture supply and demand. The PDSI during the
summer months is used as a surrogate of volumetric soil moisture
at our sites, as both indices characterize the severity of a wet or
dry summer. The four Coast Range sites are located in the Oregon
Coast division of PDSI, Jackson Place site is located in the Willamette
Valley division, and the other four sites are located in the North
Cascades of Oregon division. Positive PDSI values represent higher
soil moisture, and negative values represent lower soil moisture.
Monthly and seasonal averages of PRISM precipitation and
temperature values correlated well with the corresponding local
climatic variables for the five instrumented sites (Table S1 in
Supplementary Material). DPD correlated well with local vapor
pressure deficit (VPD) measurements, as both indices characterized
(STS) analysis for each site, following Lee et al. (2013) and Beedlow
et al. (2013). STS removes the confounding factors of trend and
past disturbances (i.e., SNC disease) from the EW and LW width
chronologies of Douglas-fir to produce a pure climate signal of tree
growth that can be associated with the seasonal climate factors.
STS is widely used for economics and social data (Harvey, 1989)
and has been applied to ecological data to examine the effects of
multiple climatic and disturbance factors and their interactions on
plant growth (Tingey et al., 2007; Beedlow et al., 2013; Lee et al.,
2013).
The STS approach assumes that the mean response function for
EW and LW ring-widths can be attributed to trend, SNC, and environmental variables. Trend is a change in EW and LW ring-widths
over multiple decades not accounted for by the measured variables
and may be due to temperature and drought stress under climate
change. Trend in the master chronologies of EW and LW ringwidths was modeled using piecewise linear trends with a slope of
zero for the first segment (i.e., no climate trend prior to some join
year) and a non-zero slope for the second segment. The piecewise
linear trends were included in a mean response function with other
components for climate and disease in order to test the hypothesis of a change in growth rates in recent decades and whether the
climate trend can be explained by shifts in temperature and soil
moisture. The join year was determined based on statistical fit and
was used to infer the onset of a change in growth rate due to shifts in
environmental conditions associated with climate change (Fuller,
1976).
The EW and LW ring-width series were combined, chronologically ordered, and treated as a seasonal time series. Let Yt1 and
Yt2 denote the EW and LW chronology time series, respectively,
so that Ytm for m = 1, 2 and t = 1, 2, . . ., N years is a seasonal time
series. Similar to the stochastic dependencies of monthly dendrometer data (Beedlow et al., 2013), the tendency for stem growth of
EW to affect growth of LW and growth of EW and LW next year
was accounted for by stochastic components for regular and seasonal autoregression, AR(p) and SAR(q), respectively, for all sites
and periods where the order of regular autoregression, p, is either
0 or 1, and the order of seasonal autoregression, q, is between 0 and
4. The autocorrelation structure represents the stochastic processes
in which the effect of climate in a given time period is propagated
into future time periods by affecting the food reserves, bud formation, leaf primordia development and tree vigor (Fritts, 1976). The
measured variables that significantly affected EW and LW widths
included maximum daily air temperature (T), precipitation (P), fog
frequency (F), PDSI, and DPD summarized on a monthly or seasonal
period. The basic seasonal time series model with AR(1) × SAR(1)
autoregressive structure and excluding the disturbance component
follows:
m+1
m+1
m+1
m+1
m
m
m
m
m m
m
Ytm − m
t = 1 (Yt−1 − t−1 ) + 2 (Yt−1 − t−1 ) − 1 2 (Yt−2 − t−2 ) + εt
Ytm
− m
t
m
t
ˇ1m
=
=
1m (Ytm−1
+ ˇ2m
m
− m−1
) + 2m (Yt−1
t
179
m−1
− m
) − 1m 2m (Yt−1
t−1
− m−1
) + εm
t
t−1
if m = 1 (Earlywood series)
if m = 2 (Latewood series)
(1)
m
m
m
m
∗ Ct + fm (Ttm , PDSIm
t , DPDt , Pt , Ft ; ␤ )
the atmospheric demand for water. Divisional PDSI consistently
correlated with local volumetric soil moisture readings, most
notably for mid- and high-elevation sites in the North Cascades
Division (Table S1).
2.5. Structural Time Series model of growth-climate-disease
interactions
To examine the interactions between intra-annual growth and
seasonal climate factors we conducted a Structural Time Series
2
where εm
t ∼Normal Independent Distribution (0, ), Ct = max(0,
Yeart −Year0 ) and fm () is a response surface equation of the
measured variables for m = 1, 2 and t = 1, 2, . . ., N. The unknown
model coefficients are: ˇ1m = seasonality of growth period m, ˇ2m =
trend of growth period m, Year0 = join year, ␤m is a vector of the
response surface coefficients for the measured variables, and 1m
and 2m are the AR(1) and SAR(1) coefficients, respectively, subject
to stationarity constraints.
We summarized monthly climate data to a seasonal period
associated with the seasonal pattern of tree growth. The growing season is typically defined as May 1 through October 31 at
the coast and mid-elevation sites, and June 1 through October 31
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E.H. Lee et al. / Agricultural and Forest Meteorology 221 (2016) 176–188
Fig. 2. Chronologies of earlywood and latewood widths of mature Douglas-fir across a network of sites from the Coast Range to the west side of the Cascade Range of Oregon.
The vertical gray lines denote climatic pointer years of 1918, 1951, 1959, 1984, 1985, and 1986.
at the high elevation sites (Emmingham, 1977). The initiation of
stem growth is largely deterministic in response to a soil temperature growth threshold of ∼5 ◦ C (Bailey and Harrington, 2006;
Emmingham, 1977), whereas the cessation of wood production in
the fall is less deterministic (Denne, 1971).
We used a heterogeneous AR(p) × SAR(q) model with different
autoregressive coefficients for the EW and LW periods based on fit
to account for seasonal changes in autocorrelation. The stochastic
dependencies were identified and modeled based on the autocorrelation and partial autocorrelation functions of the model residuals
and optimization of the Akaike Information Criterion and adjusted
R2 (Beedlow et al., 2013).
Time series intervention analysis (TSIA) (Box and Tiao, 1975)
allowed us to identify the periodic disturbance events associated
with SNC as a series of pulse changes in EW and LW widths, following Lee et al. (2013). The same series of SNC-induced suppression
and release growth pulses of Douglas-fir at the three coast sites
reported in Lee et al. (2013) were used as start values in this analysis. Given the disturbance history of SNC reported in Lee et al.
(2013), we removed the confounding effects of SNC from the EW
and LW width chronologies to provide a pure climate signal of tree
growth. Because the periodic SNC impacts were synchronous at the
coastal study sites, we applied the SNC pulse interventions from the
coast sites to the inland sites as start values.
Maximum Likelihood Estimation (MLE) was used to fit the
STS model with an AR(p) x SAR(q) autoregressive process for
EW and LW width series (Beedlow et al., 2013). MLE of the STS
model parameters was performed using the PROC NLIN procedure in SAS/STAT® software, Version 9.2 of the SAS System, as
described in Fuller (1976). Seasonal time series models for each
site were chosen based on fit and parsimony by optimizing the
adjusted R2 and AIC. The relative contributions of trend, SNC
disturbance, autocorrelation, and the measured variables were
assessed using Kruskal’s (1987) measure of relative importance.
3. Results
The Douglas-fir chronologies were well cross-dated and displayed both high- and low-frequency variations in response to
temperature, soil moisture and SNC at all nine sites (Fig. 2). The
interseries correlations ranged between 0.46 and 0.55 (p < 0.01)
which indicated a high level of coherency in the growth pattern
of individual trees within a site (Table 1). The mean sensitivity
exceeded 0.16 at all sites which indicated tree growth was reasonable sensitive to biotic and abiotic stresses (Table 1). The three
coastal sites (CH, HCTL & HCTU) where SNC impacts on growth
were most severe had the least interseries correlations and the
greatest mean sensitivities because the inter-annual variability was
enhanced by growth anomalies due to the disease. No differences
in interseries correlations and mean sensitivities between inland
sites were evident (Table 1).
3.1. Importance of SNC impact on Douglas-fir growth
Regional synchronous patterns of growth suppression across the
sites, attributable to SNC, appeared in the ring-width chronologies
in 1918, 1951, 1959, and, most notably in 1984–1986 (Fig. 2). At
the coastal sites, the growth suppression patterns displayed ∼30
year periodicities that could not be attributed to climate, but rather,
were attributed to climate-mediated population dynamics of the
fungus P. gaeumannii associated with SNC (Lee et al., 2013). Similar
growth suppression patterns having ∼30 year periodicities were
evident at Falls Creek and other low- to mid-elevation inland sites
that also could not be attributed to climate, but they were coherent
E.H. Lee et al. / Agricultural and Forest Meteorology 221 (2016) 176–188
with the coastal disease cycle and less severe than the coastal sites.
Notably, record low growth at Falls Creek occurred in 1984 when
summer PDSI was near a record high (i.e., abnormally wet summer), indicating a divergence between growth and climate (Figs. 2F
and S6C). The abnormally low growth in 1984–1986 was associated
with record high summer precipitation and warm winter temperatures in preceding years, which were favorable for pathogen
development (Lee et al., 2013). This pattern was not observed at
the two high elevation sites.
The SNC impact on tree growth was modeled as pulse interventions that represent anomalous growth years in which the observed
value diverged significantly (p-value < 0.05) from the climate-based
model prediction of tree growth. Each site had a number of years
with anomalously low or high growth that could not be explained
by the interactions of temperature and soil moisture nor by autocorrelations associated with climatic feedbacks and predisposing
physiological impacts on carbon and water relations. The frequency
of negative pulse interventions associated with SNC between 1895
and 2011 ranged from 10 to 36 years.
Climate, SNC, trend, and autocorrelation were important components of the STS model based on Kruskal’s measure of relative
importance (Table 2). The place-based STS regression models fit the
intra-annual ring-width data well and explained 98% of the total
variation in the nine EW and LW width chronologies. Kruskal values for SNC ranged from 0.55 to 0.88 across sites, indicating that
SNC explained the majority of the total variation in radial stem
growth for all low- to mid-elevation study sites. Several trends were
evident for the Kruskal values for SNC. The variations in the intraannual ring-widths, in particular LW widths, were most closely
associated with SNC at the three coastal sites, less so at two inland
sites (WC, FC), and least associated with SNC at drier sites (JP, MM)
and cooler, high Cascade sites (SG, TC) where conditions were less
favorable for the causal fungus (Table 2). SNC-induced reductions
in tree growth were more severe in LW than EW growth regionally.
3.2. Importance of air temperature
Current-year summer temperature and previous-year DPD were
the principal seasonal climatic variables affecting radial stem
growth at all sites based on Kruskal’s measure of relative importance (Table 2). Air temperature affected growth in all seasons,
but the response was seasonal, nonlinear, and varied by elevation
and site conditions. Further, temperature interacted with soil moisture and SNC to influence growth. DPD and soil moisture affected
growth mainly during the annual summer drought period in the
current and previous years. The climate relations are different for
each growth period because EW forms during the cooler, wetter
months (May–July) whereas LW forms during the annual summer drought (July–September). Further, the climate relations are
site-specific because the primary climatic factors influencing physiological processes of carbon and water relations vary by elevation
and site conditions. Clear trends are evident for the place-based climate feedback responses in association with SNC for EW and LW
growth across the region.
3.2.1. Growth response to summer temperature
Summer temperature was an important factor limiting growth
at all sites, particularly at sites where the summer drought was
more pronounced, based on Kruskal’s measure of relative importance (Table 2). We assumed a quadratic response to summer
temperature with a temperature growth optimum (Topt ) that varied
with available soil moisture following Beedlow et al. (2013). To test
the hypothesis that Topt decreases as soil moisture decreases, the
linear coefficient for summer temperature was assumed to change
annually depending upon whether summer PDSI < 0 (dry years) or
PDSI > 0 (wet years). To test the hypothesis that temperature stress
181
Fig. 3. Latewood ringwidth at Cascade Head displays quadratic relationship with
summer temperature.
is moderated by the presence of disease as well as by soil moisture,
the STS model included a separate linear coefficient for summer
temperature for the years with and without disease suppression in
combination with years having PDSI < 0 or PDSI > 0.
Current-year summer temperature in association with soil
moisture had a more pronounced effect on LW than EW growth,
particularly for the drier inland sites at low- to mid-elevations
(Table 2). EW growth response to June–July temperature was
either strictly positive and linear or mostly positive and quadratic
with a growth Topt ranging from 23.2 to 24.8 ◦ C (Table 3). At
sites where summer drought was more pronounced (WC, JP, FC,
MM, TC), growth displayed a quadratic relationship with summer temperature with Topt varying by elevation and site condition,
intra-annually by growth period, and inter-annually by soil moisture availability and disease (Table 3). LW growth response to
July-September temperature was enhanced by low soil moisture
and how often the growth Topt was exceeded during the annual
summer drought. At CH, Topt for LW growth was a constant 21.2 ◦ C
which was seldom exceeded, indicating that soil moisture was not
limiting at this coast site (Fig. 3). At Moose Mountain, Topt ranged
from 23.9 ◦ C to 24.5 ◦ C for EW growth and from 23.6 ◦ C to 25.7 ◦ C
for LW growth depending upon PDSI (Fig. 4A & B and Table 3).
The values of Topt for the other dry sites had similar ranges. For
Falls Creek, LW growth had a linear response to summer temperature with slopes ranging from −0.0082 to −0.0044 and an inferred
Topt < 21 ◦ C which was exceeded in all years. Jackson Place had the
steepest negative LW growth response to summer temperature as
reflected in the shape of a quadratic response with a quadratic coefficient of −0.0018 and Topt < 13.4 ◦ C which was exceeded in all years.
In comparison, Woods Creek and Toad Creek had a relatively flat LW
growth response to summer temperature as indicated by the least
negative quadratic coefficient <−0.0005 (Table 3).
In general, the interactive effects of summer temperature and
PDSI on intra-annual growth were consistent with the secondorder response surface models relating monthly growth rates to
local summer temperature and volumetric soil moisture at these
same sites (Beedlow et al., 2013). The interaction between summer
temperature and PDSI was not statistically significant at the 0.05
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E.H. Lee et al. / Agricultural and Forest Meteorology 221 (2016) 176–188
Table 2
Kruskal value of importance for the Structural Time Series (STS) model. The Kruskal method (Kruskal, 1987) estimates the relative importance of each variable to the fit of
the regression model. Each Kruskal value is the average of the squared partial correlation coefficients over all possible orderings of the predictor variables. Higher Kruskal
values indicate greater relative importance within a site and growth period. Kruskal values ≥0.40 are in bold. Swiss needle cast (SNC) disease impacts are modeled as pulse
interventions calculated as the difference between the observed value and the climate-based prediction of tree growth.
Component/variable (signa )
Cascade Head
A. Sites in Coast Range
R2
AR(p) × SAR(q)
Prior-year Summer/Fall DPDb (−)
Prior-year JJAS PDSIc (+)
Current-year NDJF Tempd (+)
Current-year April Temp (−)
Current-year Summer Temp (±)
Current-year Summer DPD (−)
Current-year Summer PDSI (+)
Current-year Summer Precip (+)
N Bend Fog Frequency (−)
Trend (±)
Pulse interventions for SNC (±)
Component/variable (signa )
b
c
d
Woods Creek
LW
EW
LW
EW
LW
EW
LW
0.98
0.36
0.65
0.99
0.44
0.58
0.99
0.25
0.61
0.99
0.39
0.61
0.99
0.22
0.70
0.98
0.14
0.58
0.06
0.08
0.21 (+)
0.06
0.03
0.06
0.08
0.18 (±)
0.18
0.04
0.07
0.14 (+)
0.06
0.28
0.16
0.18
0.04
0.06
0.25 (+)
0.08
0.20
0.08
0.12
0.05
0.25
0.32 (±)
0.16
0.05
0.07
0.15
0.05
0.25 (±)
0.06
0.16
0.99
0.25
0.52
0.23
0.19
0.15
0.46 (±)
0.13
0.99
0.26
0.47
0.11
0.10
0.05
0.59 (±)
0.10
0.23
0.05
0.83
0.88
0.74
0.11 (−)
0.67
0.16 (−)
0.74
0.76
0.88
Jackson Place
Falls Creek
LW
EW
B. Sites in Willamette Valley and western slope of Cascade Range
0.99
0.99
0.99
R2
0.40
0.32
0.23
AR(p) × SAR(q)
0.47
0.56
0.44
Prior-year Summer/Fall DPDb (−)
Prior-year JJAS PDSIc (+)
0.15
0.02
d
0.04
0.03
0.27
Current-year NDJF Temp (+)
0.25
0.08
Current-year April Temp (−)
0.44 (+)
0.58 (−)
0.44 (±)
Current-year Summer Temp (±)
0.13
Current-year Summer DPD (−)
Current-year Summer PDSI (+)
0.10
0.13
0.14
0.10
0.09
Current-year Summer Precip (+)
Trend (±)
0.35 (−)
0.43 (−)
0.30 (−)
Pulse interventions for SNC (±)
0.70
0.58
0.71
a
Horse Creek Trail Upper
EW
0.09
EW
Horse Creek Trail Lower
0.13
Moose Mtn
0.83
Soapgrass Mtn
Toad Creek
LW
EW
LW
EW
LW
EW
LW
0.99
0.23
0.47
0.98
0.30
0.60
0.08
0.05
0.37 (±)
0.21
0.10
0.15
0.26 (−)
0.61
0.98
0.34
0.69
0.12
0.43
0.16
0.03 (+)
0.99
0.30
0.68
0.18
0.41
0.99
0.29
0.61
0.12
0.24
0.99
0.23
0.70
0.17
0.05
0.60 (−)
0.99
0.29
0.50
0.17
0.10
0.13 (+)
0.23
0.35 (+)
0.31
0.03
0.04
0.16 (+)
0.65
0.19
0.09 (+)
0.70
0.42 (±)
0.15
0.06
0.11
0.05 (+)
0.55
0.29 (−)
0.73
0.55 (±)
0.19
0.10
0.28
0.28 (−)
0.61
0.64
0.33
A positive sign indicates tree growth responds positively to the climate variable and vice versa.
Dewpoint deficit (DPD).
June–September Palmer Drought Severity Index (JJAS PDSI).
November–February mean daily maximum air temperature (NDJF Temp).
level for the more mesic sites (CH, SG) suggesting that soil moisture
was not limiting growth (Table 3).
3.2.2. Growth response to winter temperature
Increasing winter (November–February) temperature increased
growth at all sites (Fig. S2A), and was most important at the highelevation mountain sites where freezing winter temperatures and
snowpack accumulation are pronounced (Table 2). At low- and
mid-elevations, growth responded linearly to winter temperature,
whereas at high-elevations, growth responded nonlinearly. Radial
growth at high-elevations was decreased by freezing winter temperatures in December and January, the coldest months when
minimum temperatures are frequently below 0 ◦ C, as shown for
SG (Fig. 5A and B). The mean winter daily maximum temperature
of 5.1 ◦ C represents a threshold, above which Douglas-fir growth
responds positively and linearly to temperature, and below which
growth responds less positively or negatively to temperature (Fig.
S2A). Cold winters <5.1 ◦ C decreased growth between 1895 and
2010 about 63% of the time for EW and 49% for LW at Toad Creek.
3.2.3. Growth response to spring temperature
Increasing spring temperature, at all sites except Toad Creek
(Fig. S2B), reduced radial stem growth. Warming April temperatures were more limiting for EW than LW growth (Table 2). EW
growth at the warmer low-elevation sites in the Coast Range and
Willamette Valley was more sensitive to April temperatures than
mid- to high-elevation growth in the Cascade Mountains (Fig. S2B).
3.3. Importance of dewpoint deficit
3.3.1. Growth response to current-year dewpoint deficit
Increasing DPD in the current growing season reduced radial
stem growth at all sites (Fig. S3) but growth was less sensitive to
DPD than summer temperature in association with PDSI (Table 2).
No clear trends were evident for growth sensitivity to summer DPD.
HCTU and TC had the steepest negative EW growth response to
summer DPD, and CH and SG had the steepest negative LW growth
response (Fig. S3). Growth was least sensitive to summer DPD at
Falls Creek even though evaporative demand was relatively high
during the growing season.
3.3.2. Growth response to previous-year dewpoint deficit
Prior-year DPD (summer/fall) was the primary climatic factor
limiting growth at the coast (CH, HCTU, HCTL) and high elevation sites (SG, TC) and an important climatic factor at other sites
with Kruskal values ranging from 0.44 to 0.70 (Table 2). Growth
at all sites was reduced by increasing DPD in the previous summer/fall when evaporative demand was high and after LW growth
had peaked (Fig. S4). Prior-year DPD was roughly equal in importance to current-year summer temperature at sites where summer
drought was more pronounced (WC, JP, FC, MM). Increasing DPD
E.H. Lee et al. / Agricultural and Forest Meteorology 221 (2016) 176–188
183
Table 3
Temperature importance during the growing season. Growth response to summer temperature varies with available soil moisture and severity of Swiss Needle Cast disease.
The slope coefficients represent the influence of temperature on earlywood (EW) and latewood (LW) width. Values are regression slopes with standard errors in parentheses.
Current-year summer temperature corresponds to June-July for EW and July-September for LW. Slope values in bold are significantly different from zero at the 0.05 level of
significance.
Site
EW or LW
Linear
A. Sites in Coast Range
EW
Cascade Head
LW
Horse Creek
Trail Lower
EW
LW
Horse Creek
Trail Upper
EW
LW
Woods Creek
EW
LW
Site
EW or LW
Years with PDSI > 0
Years with Average Palmer
Drought Severity Index (PDSI)
0.0288
(0.0020)
0.2822
(0.0284)
0.0011
(0.0017)
0.0515
(0.0333)
0.2001
(0.0313)
0.1859
(0.0268)
0.1008
(0.0138)
0.0258
(0.0240)
Quadratic
Topt (◦ C)
−0.0066
(0.0007)
21.2
−0.0008
(0.0007)
−0.0041
(0.0007)
−0.0038
(0.0006)
−0.0021
(0.0003)
−0.0005
(0.0005)
32.2
24.3
24.6
24.5
28.0
Quadratic
0.0007
(0.0002)
0.0009
(0.0002)
0.0012
(0.0001)
0.0012
(0.0002)
0.0009
(0.0001)
0.0019
(0.0001)
Topt (◦ C)
Topt (◦ C)
B. Sites in Willamette Valley and west slopes of Cascade Mountains
0.0173
EW
Jackson Place
(0.0012)
0.0489
−0.0018
13.3
LW
(0.0461)
(0.0009)
0.0980
−0.0021
23.7
EW
Falls Creek
(0.0129)
(0.0003)
−0.0069
LW
(0.0006)
0.1221
−0.0025
24.3
EW
Moose
(0.0148)
(0.0004)
Mountain
LW
0.0692
−0.0014
24.7
(0.0291)
(0.0006)
0.0011
EW
Soapgrass
(0.0001)
Mountain
0.0121
LW
(0.0010)
0.0217
EW
Toad Creek
(0.0009)
0.0242
−0.0005
24.2
LW
(0.0096)
(0.0003)
in the prior-year summer/fall reduced growth more in years when
SNC was present than in years when SNC was absent at all sites
(Fig. S4). The hypothesis of a stationary slope coefficient for prioryear DPD was rejected at the 0.05 level of significance for all sites.
SNC was estimated to reduce growth between 1895 and 2010 about
36% of the time for EW and 56% for LW at CH, the most diseased
site (Fig. S5). The difference in the DPD slope between years with
and without disease was the greatest for CH (Fig. 6) and least for
the mountain sites and reflects the relative severity of SNC between
coastal and inland areas (Fig. S4). No distinct patterns were evident
in growth sensitivity to DPD although LW growth for a drier site, JP,
and a severely diseased site, CH, showed increased DPD sensitivity
when SNC impacted growth. At CH, decreased growth sensitivity
to DPD occurred about 25% of the time for EW and 21% for LW
when growth was released from SNC following a disease suppression event (Fig. 6). The cyclical patterns of growth suppression, no
suppression, and release associated with SNC were consistent with
Change in
linear
coefficient
0.0028
(0.0001)
0.0005
(0.0002)
0.0018
(0.0001)
0.0025
(0.0001)
0.0010
(0.0002)
0.0027
(0.0002)
0.0009
(0.0001)
0.0008
(0.0001)
Change in
linear
coefficient
Topt (◦ C)
−0.0014
(0.0001)
−0.0007
(0.0001)
24.1
Years with PDSI < 0 & less
disease suppression
Change in
linear
coefficient
Topt (◦ C)
−0.0022
(0.0001)
25.7
34.4
24.8
24.8
24.7
30.2
Years with PDSI > 0
Years with Average Palmer
Drought Severity Index (PDSI)
Linear
Change in
linear
coefficient
Years with PDSI < 0 and
disease suppression
Topt (◦ C)
13.4
24.1
24.5
25.7
27.3
Years with PDSI < 0 &
disease suppression
Change in
linear
coefficient
−0.0022
(0.0001)
−0.0009
(0.0002)
−0.0018
(0.0001)
−0.0013
(0.0001)
−0.0021
(0.0001)
−0.0005
(0.0002)
Years with PDSI < 0 & less
disease suppression
Topt (◦ C)
Change in
linear
coefficient
Topt (◦ C)
13.1
−0.0015
(0.0002)
12.9
23.2
−0.0035
(0.0001)
23.9
24.5
−0.0011
(0.0001)
25.0
−0.0031
(0.0003)
−0.0025
(0.0001)
−0.0018
(0.0001)
23.6
22.4
the tree-ring reconstructions of SNC history at these same coastal
sites. (Lee et al., 2013).
3.4. Climate trend in recent decades
In the PNW, the climate since 1975 has been exceptionally
warm and dry compared with earlier decades. We hypothesized
that the warm and dry period has altered the growth rate of trees.
Growth declines began ∼1980 at low-elevation valley sites (WC,
JP) and ∼1990 at mid-elevation mountain sites (FC, MM) where the
annual summer drought was less pronounced. For example, growth
declines at MM occurred after several decades of unprecedented
warming and when non-climatic growth trends no longer existed
(Figures S1 and S6). In contrast, growth began to increase ∼1990 at
the two high-elevation sites due to warming temperatures resulting in increasing winter photosynthesis, earlier snowmelt, and
a longer growing season (Fig. 7). The three coast sites had no
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E.H. Lee et al. / Agricultural and Forest Meteorology 221 (2016) 176–188
Fig. 6. Growth response to previous-year dewpoint deficit (DPD) at Cascade Head
varies depending upon the severity of Swiss needle cast disease (SNC). Growth is
more sensitive to DPD in years with high disease severity and less sensitive in years
with low disease severity. The change in slope is a reliable indicator of SNC disease
impact.
Fig. 4. Response of (A) earlywood and (B) latewood width of Douglas-fir at Moose
Mountain to summer temperature varies with available soil moisture and disease
severity. Temperature optimum decreases in dry years (PDSI < 0) and increases in
wet years (PDSI > 0). Latewood sensitivity to temperature increases with decreasing
soil moisture and disease severity.
Fig. 7. Climatic trends in earlywood (EW) and latewood (LW) since ∼1980 for
sites in the Coast Range and Willamette Valley and ∼1990 for sites in the Cascade
Mountains of Oregon. No trend was observed at coast sites. All slope values are
significantly different from zero at the 0.05 level of significance with one exception (NS = not significant). CH = Cascade Head; HCTL & HCTU= Horse Creek Trail
Lower & Upper, WC = Woods Creek, JP = Jackson Place, FC = Falls Creek, MM = Moose
Mountain, SG = Soapgrass, TC = Toad Creek.
significant trend. Also, no significant trend in growth rate was
detected prior to the join year, indicating the absence of both agerelated and climatic trends in earlier decades.
4. Discussion
Douglas-fir in western Oregon experience an array of climate
and SNC regimes depending on geography, elevation, and site conditions. Our analysis indicates that air temperature and DPD are the
most important climate factors affecting Douglas-fir growth in the
region, and that soil moisture and SNC modify the growth response
to these climate factors. Surprisingly, the ubiquitous SNC was as
important as the climate factors to Douglas-fir growth across the
region and can affect growth more than climate at some sites.
4.1. Interactions of climatic factors and SNC
Fig. 5. Adjusted (A) earlywood and (B) latewood width of Douglas-fir is less sensitive
to winter temperature below ∼5.1 ◦ C at Soapgrass Mountain, a mesic high elevation
site above the snowline on west slope of the Cascade Mountains of Oregon.
The severity of SNC impact on annual radial stem growth is
most significant in years when climate conditions are favorable
E.H. Lee et al. / Agricultural and Forest Meteorology 221 (2016) 176–188
for the causal fungus, P. gaeumannii (Black et al., 2010; Lee et al.,
2013). SNC impacts tree growth directly and indirectly as indicated
by an interaction with prior-year DPD and current-year summer
temperature. The interacting effects of prior-year DPD and SNC
disease on tree growth is consistent with an isotope study that
showed a strong negative correlation of tree-ring 13 C with prioryear June-September relative humidity for diseased Douglas-fir but
not for less diseased Douglas-fir treated with a fungicide (Saffell
et al., 2014). The one-year lag between the physical blockage of
stomata by P. gaeumannii and tree-ring 13 C and growth is caused
by reduced assimilation and storage associated with impaired stomatal conductance and transpiration (Farquhar et al., 1982, 1989;
Evans et al., 1986). The interaction of DPD and SNC had the most
effect in June-September when evaporative demand is high and
pseudothecia of P. gaeumannii mature to a size to impair gas
exchange of infected needles, resulting in stomatal occlusion and
reduced photosynthetic capacity (Manter et al., 2003). In severe
cases of SNC, pseudothecia reduced stomatal conductance by about
83% and photosynthetic rate by about 72% (Manter et al., 2000).
High evaporative demand, water stress, and SNC during the
summer drought in the prior year drastically reduces a tree’s ability to photosynthesize (Helms, 1963; Salo, 1974; Emmingham and
Waring, 1977) resulting in less food reserves (Lassoie, 1982) and
below-average tree growth (Fritts, 1966; Zaerr, 1971).
4.2. Temperature stress is exacerbated by low soil moisture
Although temperature is an important climate factor, it may not
correlate well with annual ring-width growth for several reasons:
(1) the response to summer temperature is curvilinear with a shifting temperature optimum depending upon growth period and soil
moisture availability (Beedlow et al., 2013); (2) EW and LW widths
have opposing relationships with summer temperature, and total
annual ring-width, calculated as the sum of EW and LW, is a mixture of the positive and negative temperature condition; and (3)
the effect of summer temperature on tree growth is not static, and
instead, the temperature slope varies from year-to-year depending
on soil moisture and SNC.
Both our dendrometer and tree ring-width data support a ∼20 ◦ C
temperature optimum for growth that varies depending upon site
conditions, intra-annually depending upon growth period, and
inter-annually depending upon summer soil moisture (Beedlow
et al., 2013). Earlywood forms in May, June, and July before soil
moisture is minimal and air temperature is at or below the growth
optimum. Latewood forms in mid-July, August, and September,
during the summer drought when air temperature is at or above the
optimum and soil moisture is minimal. Consequently, EW growth
responds positively to June-July temperature while LW growth
responds negatively to July-September temperature, more so in
warm and dry environments. In dry environments, growth is more
sensitive to summer temperature in years with low soil moisture
(PDSI < 0) than in years with high soil moisture (PDSI > 0).
Our findings are consistent with plant metabolism studies that
show a strong and immediate response of CO2 assimilation and
dark respiration to temperature with a temperature optimum ranging from 16 ◦ C to 26 ◦ C (Larcher, 2003). The temperature optimum
for net photosynthesis varies with light intensity. Longer-term
exposure to a new temperature regime leads to adjustments of
metabolism, causing a shift in the temperature optimum within
a growing season and between years. Growth response to temperature is not static but interacts with nutrient and water availability
and developmental stage (Körner, 2008).
Water stress limits growth of Douglas-fir across the region (Case
and Peterson, 2005; Pederson et al., 2006; Littell et al., 2008; Waring
and Franklin, 1979; Watson and Luckman, 2002; Zhang and Hebda,
2004; Chen et al., 2010). Our findings are consistent with these
185
correlation-based studies that showed the importance of temperature and water in the prior- and current-year on Douglas-fir growth.
However, the interactive effects of temperature and soil moisture
were not inferred by correlation because this statistical measure is
limited to examining climate effects that are linear, additive, and
invariant in the absence of growth anomalies associated with forest
disturbance agents (e.g., SNC) and other latent non-climatic factors.
Further, we used master chronologies of EW and LW width which
allowed the timing of growth to be dated and associated with the
seasonal climate factors.
4.3. Importance of antecedent conditions
Climatic conditions during the 12 months prior to spring onset
of cambial growth were consistently more important than climatic
conditions during the current growing season. Prior-year summer/fall DPD was a key limiting factor of growth at all study sites
whereas winter temperature was a key limiting factor at high elevations. These lagged responses reflect the potential importance of
physiological and environmental effects on the buildup of carbon
storage reserves through photosynthesis during periods with low
cambial activity. Physiologically controlled, lagged responses and
autocorrelation are common in conifer species (Fritts, 1974; Ohse
et al., 2012; Lloyd et al., 2013).
Warmer winter temperatures enhance growth, particularly at
higher elevations. Net photosynthesis is substantial during the dormant season for trees in the PNW (Helms, 1963; Salo, 1974; Waring
and Franklin, 1979) and up to 50% of predicted annual net carbon assimilation by Douglas-fir occurs between October and May
(Emmingham and Waring, 1977). Our data show that ring-width is
least sensitive to winter temperatures below ∼5 ◦ C (seasonal mean
daily maximum temperature), indicating that little to no photosynthesis occurs below this threshold. A typical leaf achieves less than
10% of maximum net photosynthesis at 0 ◦ C (Larcher, 2003). The
importance of winter temperature limiting Douglas-fir growth has
also been inferred on Vancouver Island, British Columbia based on
a growth simulation model, 3-PG, under comparable winter conditions to our high-elevation sites (Coops et al., 2007).
Our findings support the hypothesis that winter photosynthesis
is a key advantage that allows conifers to outgrow broadleaves and
be prevalent in the PNW (Waring and Franklin, 1979; Brubaker,
1980). According to that hypothesis, tree growth in the PNW is
impacted by decreased carbohydrate reserves caused by low winter
photosynthesis associated with suboptimal temperatures, increasingly so at higher elevations where winter precipitation falls mainly
as snow. Short growing seasons, frost, freezing winds, low soil temperatures, and deep snowpack limit high-elevation tree growth
(Körner, 1998, Körner, 2003). Moreover, if evaporative demand is
high in the previous summer-fall, followed by a cold winter, then
photosynthate production and storage are low, and root growth is
reduced (Salo, 1974; Lassoie and Salo, 1981). The reduction in root
growth and low photosynthate production are not fully expressed
until the current year of growth.
4.4. Growth trends associated with climate change
In the 20th century, the PNW has experienced high climatic
variability including episodes of persistent (>10 years) droughts
and wet regimes, and a strong warm phase of the Pacific Decadal
Oscillation (PDO) (1925–1946), followed by a strong cold phase
(1947–1978). The period from 1917 to 1940 was exceptionally
warm and dry as indicated by record low summer PDSI < 0, and
was followed by several wet periods (PDSI > 0) from 1941 to 1955
and from 1968 to 1984. Annual temperatures in the PNW have
increased about 0.7 ◦ C between 1895 and 2011 (Kunkel et al.,
2013). Most of the warming occurred since the 1970s, with all
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E.H. Lee et al. / Agricultural and Forest Meteorology 221 (2016) 176–188
but two years since 1998 above the 20th century average. At lowto mid-elevation inland sites where the annual summer drought
is pronounced, Douglas-fir growth declined beginning ∼1980 on
the east slopes of the Coast Range and in the Willamette Valley
and ∼1990 on the west slopes of the Cascade Mountains of Oregon, indicative of greater temperature and drought stress caused
by a warming trend. Multi-decadal growth declines in the PNW
first began ∼1950 for white spruce (Picea glauca (Moench) Voss)
in Alaska where temperatures have risen at twice the global rate
(Barber et al., 2000; D’Arrigo et al., 2004). In cool environments
at high elevations in the Cascade Mountains, growth increased
beginning ∼1990 due to a warming trend increasing winter photosynthesis, advancing growth initiation and extending the growing
season. The multi-decadal trends in intra-annual ring-width data
are an extension of the climate-induced decadal trends in basal
area increment first reported for monthly dendrometer data for
the years 1998–2009 at these same sites (Beedlow et al., 2013). The
recent growth decline at the two mid-elevation sites (FC, MM) corresponds with decreasing volumetric soil moisture and increasing
soil temperature in July during the years 1998–2012, whereas no
soil moisture trends were observed at the other field sites (Beedlow
et al., 2013). At the high elevation site, TC, the EW series increased at
a slightly faster rate than the LW series, indicating that an increase
in temperature and drought stress in late summer under future
warming may eventually offset the benefit of a longer growing
season. While warmer winter temperatures under future climate
change will likely enhance high-elevation growth, the variability
in annual growth will likely increase due to a projected increase
in SNC impacts as climatic conditions become more favorable for
fungal development in cooler environments (Lee et al., 2013).
longer as reliable a predictor of local climatic and soil conditions
and tree growth at drier sites during its cool phases.
Douglas-fir forests will respond differently to climate change
depending upon location and site condition, as temperatures continue to rise and precipitation patterns shift toward wetter winters
and drier summers. Because Douglas-fir growth is limited by temperature, soil moisture and DPD, a warmer climate due to climate
change may allow the species to grow faster at wet and high elevations, but will decrease growth in warm and dry environments.
The impacts of climate change will be most apparent and substantial in warm and dry environments at low- to mid-elevations east of
the Coast Range. Higher summer and fall temperatures and lower
summer precipitation and soil moisture are expected to increase
temperature and water stress, more so at drier sites. Because the
greatest warming due to climate change is predicted to occur in
the winter, high-elevation growth will likely continue to benefit
from higher rates of winter photosynthesis and a longer growing season, but decreased snowpack and earlier snowmelt may
be detrimental. Moreover, high evaporative demand in the previous summer and fall, followed by a warm, dry summer may offset
the benefit of a warmer winter and an extended growing season at drier, temperature-sensitive high elevations in the Cascade
Mountains. Further, SNC will be especially important to Douglas-fir
growth regionally because warmer winters will increase the severity and frequency of SNC and expand its range northward and to
higher elevations where environmental conditions were previously
unsuitable for the causal fungus P. gaeumannii.
4.5. Pacific Decadal Oscillation
Acknowledgments
In the 20th century, dendroecological studies show a correlation
of PNW Douglas-fir growth with annual indices of PDO is significant and negative at high elevations (Gedalof et al., 2004; Case and
Peterson, 2005). Further, a growth model simulation study shows
Douglas-fir and ponderosa pine are most vulnerable at lower elevations to increasing temperature associated with warm phases
of PDO and the Pacific North American (PNA) circulation patterns
since 1975 (Coops and Waring, 2011). We also found an inverse
relationship with PDO in the form of a decrease in the growth
temperature optimum (i.e., greater temperature stress) during the
warm PDO phases and an increase in Topt during the cool PDO
phases, particularly at lower elevations inland where the annual
summer drought is more pronounced. The changes in temperature
effects on Douglas-fir growth were most evident in the warm PDO
phase between 1925 and 1946 when PDSI values were at record
lows. The drought of the 1930s is the second most severe drought
of the last 250 years (Gedalof et al., 2004). At several of our dry sites
in the Willamette Valley and the Cascade Mountains, Topt shifted
lower with decreased soil moisture (PDSI < 0) in 68% of the years
1925–1946 (warm PDO) and 31% of the years 1947–1978 (cool
PDO). A similar shift in temperature and water sensitivity due to
periodic shifts in the PDO has also been observed for white spruce
at Alaskan treelines (Ohse et al., 2012).
The strong linkage between cool PDO phases and increased tree
growth has since weakened as evidenced by the persistence of
decreasing tree growth and increasing temperature in the PNW
during a mostly cool PDO phase (1998–2014). The PDO shifted from
a warm phase to a cool phase following the exceptionally strong
El Niño of 1997–1998, but is weakening and shifting to a higher
frequency under climate change (Fang et al., 2014). While the sea
surface temperature variability exhibited by the PDO affects global
and regional climate (Christensen et al., 2013), the PDO index is no
The authors thank Dr. René Alfaro for his thoughtful review
and helpful suggestions and Dr. Donald Phillips, Katie Steele and
Erin Corrigan for their valuable assistance in the collection and
processing of tree core samples. The authors thank Jonathan
Halama for GIS assistance in mapping the study sites. The research
described in this article has been funded wholly by the U.S. Environmental Protection Agency. It has been subjected to review by the
National Health and Environmental Effects Research Laboratory’s
Western Ecology Division and approved for publication. Approval
does not signify that the contents reflect the views of the Agency,
nor does mention of trade names or commercial products constitute endorsement or recommendation for use.
5. Conclusions
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.agrformet.2016.
02.009.
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