Journal of Forest Research

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Application of a u-w method for the detection of
boreal forest response to environmental changes
in Canada
Amin Niazai, Akira Osawa, Nahoko Kurachi, Tomiyasu Miyaura, Takuya
Kajimoto, Juha M. Metsaranta, Masako Dannoura & Naoki Okada
To cite this article: Amin Niazai, Akira Osawa, Nahoko Kurachi, Tomiyasu Miyaura, Takuya
Kajimoto, Juha M. Metsaranta, Masako Dannoura & Naoki Okada (2021): Application of a u-w
method for the detection of boreal forest response to environmental changes in Canada, Journal ofForest Research, DOI: 10.1080/13416979.2021.1901836
To link to this article: https://doi.org/10.1080/13416979.2021.1901836

Published online: 26 Mar 2021.

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JOURNAL OF FOREST RESEARCH
https://doi.org/10.1080/13416979.2021.1901836

ARTICLE

Application of a u-w method for the detection of boreal forest response toenvironmental changes in Canada
Amin Niazai a, Akira Osawaa, Nahoko Kurachib, Tomiyasu Miyaurac, Takuya Kajimotod, Juha M. Metsaranta
Masako Dannouraa and Naoki Okadaae,aDepartment of Forest and Biomaterials Science, Graduate School of Agriculture, Kyoto University, Ssakyo-ku, Kyoto, Japan; bHiraoka Forest
Institute, Otsu, Japan; cFaculty of Advanced Science and Technology, Ryukoku University, Otsu, Shiga Japan; dTohoku Research Center, Forestry
and Forest Products Research Institute,Shimokuriyagawa, Morioka,Japan; eNatural Resources Canada, Canadian Forest Service, Northern Forestry
Centre, Edmonton, Canada

ABSTRACTARTICLE HISTORY

To better understand the long-term response of boreal forests to increasing environmental changes,
we applied the u-w method to detect growth changes triggered by environmental factors. Three
species (Picea mariana, Picea glauca, and Populus tremuloides) of various sizes and ages were sampled
in a boreal forest in northern Canada. Several stem disks were collected from the base to the crown ofseven or eight trees in each of ten plots and ring width was measured to estimate the annual volume
growth of each tree. Growth shifts, or changes in the phase of volume growth, were observed in every
tree, and some shift years were common to the plots and species, suggesting the same environmental
impact on trees. More frequent growth shifts were observed in the smallest trees in the black spruceplots, but showed no common patterns among the trees of different ages/sizes and species. Common
growth shifts across species and plots were observed after severe drought years associated with fireincidences. We concluded that the u-w method is useful for detecting multi-year climate impacts ontree growth.

Received 21 October 2020
Accepted 8 March 2021

Introduction
Boreal forests are sensitive to global climate change (Serreze
et al. 2000; Intergovernmental Panel on Climate Change 2007)
as the mean global surface temperature has risen by an esti­
mated 0.74°C over the past century (Intergovernmental Panelon Climate Change 2007), altering forest dynamics in numer­
ous ways (Soja et al. 2007). The global warming rate is greaterin the circumpolar region (Food Agriculture Organization2008) and is projected to rise due to the increase in greenhousegases (Wigley et al. 1980; Intergovernmental Panel on Climate
Change 2014). Climate change is expected to influence forest
disturbances such as fire, insect outbreaks, and invasive species
in forest biomes (Dale et al. 2001). The intensity and frequencyof these disturbances are likely to rise (Millar and Stephenson2015) and may cause a change in the forest structure (Taylor
2010).
There is substantial evidence of the impact of climate change
on forest trees. Previous tree ring research on western Canadian
aspen forests showed that drought and insect defoliation have
led to growth decline at a regional scale (Hogg et al. 2005) alongwith increased aspen mortality and biomass declines along thesouthern edge of the Canadian boreal forest (Michaelian et al.
2010). Likewise, satellite image analysis and tree ring analysis
indicated drought-induced productivity decline across Alaska(Beck et al. 2011) and a decline in the radial growth of white
spruce in western Canada (Hogg et al. 2017), and black sprucein most regions across northern Canada (Girardin et al. 2015).
However, it is not easy to predict the influence of climate
on forests. Several studies have reported that trees of differ­ent species and different sites often respond differently to thesame climatic conditions (Martín-Benito et al. ; Way and
CONTACT Amin Niazai

niazai.amin@gmail.com© 2021 The Japanese Forest Society

KEYWORDS

Climate changes; boreal
forest; tree ring; stem
volume; growth shifts

Oren 2010; Andreu-Hayles et al. 2011), and the response oftrees to climate varies throughout the year (Garrett et al.
2006). Similarly, trees display physiological limitations tocoping with the pace of climate change (Renwick andRocca 2015; Ols et al. 2016), and physiological responses
are highly related to limiting factors (Boisvenue andRunning 2006). Nevertheless, understanding and predicting
the potential consequences of climate change may be one of
the challenges because of inadequate understanding of themechanistic process in forests, and between forest and atmo­
sphere (Ryan 1991; Cannell and Thornley 2003; Boisvenue
and Running 2006). Thus, further investigation of the
impacts of climate on forest structure is imperative (Bonan
2008) for forest management protocols and terrestrial carbon
sequestration in the face of increasing climate change.
Various tree-ring-based methods have been applied to inves­tigate the impact of environmental factors (temperature, pre­cipitation, moisture, and other disturbances) on the response offorest growth at different times and spatial scales. The dendro­
chronological technique is a useful approach for assessing theradial growth response of trees to variability in their environ­ment in short-term change. The key purpose of this method has
been to reconstruct previous climate records rather than to
explicate the growth trends of trees in typical forest systems(D’Arrigo et al. 2005; Carrer 2011; Nehrbass-Ahles et al. 2014;
Jr et al. 2016). Climate signals may be explained reasonably fromradial growth increment (Hc 1983). Alternatively, tree or standvolume is an important tool for the observation of changes intrees and is the basis for forest management planning around theworld (Boisvenue and Running 2006). Furthermore, it has beenreported that growth at the lower and upper parts of the stem

Graduate School of Agriculture, Kyoto University, Ssakyo-ku, Kyoto, Japan.

2

A. NIAZAI ET AL.

section responds differently to specific climatic conditions(Chhin et al. 2010). Despite the difference in stem growth, thedendrochronological technique chiefly focuses on radial growthand does not consider vertical growth; therefore, the response oftrees to environmental changes may not be clearly understood.
Stem analysis, a more detailed approach where trees are sec­tioned at regular intervals so that whole tree growth can be better
quantified directly (Thomson and Van Sickle 1980; LeBlanc
et al. 1987), may improve on the standard approach of quantify­
ing growth at breast height only.
In this study, we applied the u-w method (Hozumi 1985)
to investigate environmental impacts on the stem volume
growth of trees. The change in tree growth appears as
a conspicuous shift of growth phase in the u-w curve. Thisapproach has some advantages, especially for detecting rela­
tively long-term growth changes due to environmental fac­
tors at the individual and stand levels (Hozumi 1987). This
study aimed (1) to detect and examine the effects of droughton the growth of three main species, black spruce (Picea
mariana), white spruce (Picea glauca), and quaking aspen(Populus tremuloides), in Northwest Territories, Canada.
Also, (2) to explore relationships of growth shifts with fire
events, associated with drought. We determined the relation­
ship among growth shifts, drought events, and fire events,using events coincidence analysis (Donges et al. 2016). At thesame time, we examined the species’ sensitivity to environ­mental changes detectable by the u-w method and climate
effects from the perspective of the differences in species, age,and size.

Materials and methods
Site description
The study site was on flat terrain (60°1ʹ14″N, 112°19ʹ43″W,
180 m a.s.l.) situated in the boreal ecozone, along the Salt
River, about 20 km west of the town of Fort Smith,
Northwest Territories, Canada (Figure 1). Regional climate

exhibits strong seasonal variation with short, moderately
warm summers and long cold winters (Köppen climate clas­
sification Dfb or Dfc), with a mean annual temperature of−2.9 °C and mean annual precipitation of 344 mm from 1985
to 2014, based on records at Fort Smith A (https://climate.
weather.gc.ca). Black spruce, often associated with white
spruce, and quaking aspen are the dominant species in thissite (Figure 1 right). Black spruce and quaking aspen are fireadapted and often regenerate after fire disturbance todevelop an even-aged stands (Jayen et al. 2006).Ten study
plots established at the site were classified into three ageclasses based on the oldest tree in each plot (Table 1):
60 years old (four black spruce and two aspen plots),
160 years old (one black spruce and one mixed spruceplots), and 230 years old (two mixed spruce plots). The
four 60-year spruce plots have developed on a bog, resulting
in their slow growth and the remaining 6 stands establishedon well-drained sites.
Sampling
Eight black spruce plots were sampled, of which three cooccurred with white spruce, and two plots were sampled forquaking aspen (Table 1). At each plot, tree height and stem
diameter at breast height (DBH) and/or stem diameter at
0.2 m height were measured on each sample tree.
Following plot measurements, 7 or 8 trees, spanning therange of tree DBH at each plot were felled for stem analysis.
Sample disks were collected from different heights of a treestem according to the tree height typically as follows; small
trees (less than 2 m): at 0 m (i.e. ground level) and every
0.2 m to the top; medium trees (2 to 8 m): at 0, 0.3, 1.3 andevery 1 m to the top; and large trees (more than 8 m): at 0,0.3, 1.3 and every 2 m to the top. Sample disks were air-dried
and polished with a mechanical sand-belt first and then with
progressively finer grades of sandpaper (80–1200 grit). After
polishing, ring widths were measured along two to four radiiin each disk to an accuracy of 0.01 mm, using the Velmex TA

Figure 1. Location of the study site (Left) and the vegetation map of the site (Right). Left: prepared from Hogg et al. (2013) under the permission of authors. Right:
prepared using Sentinel 2 image, and processed using COPERNICUS/S2 (ImageCollection (‘COPERNICUS/S2ʹ, filter date (2018-3-10 to 2018–12-20)), supervisedclassification and random forest classifier.

JOURNAL OF FOREST RESEARCH

Table 1. Sample plots and trees.
Plot
Q30

Plot size(m2)
593

Density(stem m−2)
0.15

Q31

567

0.17

B38

225

0.73

B40

4.5

26.2

B41

45.9

2.21

B48

48.1

1.83

B42a

359

0.28

B44

232

0.58

B46a

887

0.09

B47a

1127

0.10

SampleHeightTree agetrees DBH (cm)(m)(year)
7
15.2 (9.3, 17.4 (13.1, 66 (66,
21.7)
19.4)
68)
7
14.1 (5.9, 16.0 (10.4, 67 (66,
24.5)
21.1)
68)
7
5.7 (3.6,
4.9 (3.5,
64 (58,
7.7)
6.6)
71)
7
1.0b (0.3, 0.5 (0.2,
46 (36,
2.0)
0.9)
59)
7
5.1b (2.6, 3.2 (1.2,
59 (57,
8.5)
6.2)
63)
7
4.9b (1.8, 3.2 (0.8,
58 (55,
10.2)
6.3)
61)
8
14.6 (3.0, 11.7 (2.8, 156 (141,
26.8)
17.6)
169)
8
9.1(3.4,
8.3 (3.4, 156 (141,
19.3)
14.5)
167)
8
22.1 (9.8, 18.1 (8.2, 190 (161,
35.9)
23.6)
229)
8
20.4(4.7, 16.4 (4.1, 172 (94,
36.3)
25.6)
242)

Values for DBH, height, and age are mean (minimum, maximum) of each plot.
Q: Quaking aspen, B: Blake spruce, a: Black and white spruces mixed. b: Stem
diameter measured at 0.2 m above ground. All plots were sampled in 2010,
but plot B38 is sampled in 2019

system (Velmex Inc, Bloomfield, NY, USA) and the tree ringmeasuring program, MeasureJ2X (VoorTech Consulting,
NH, USA) or WinDendro (Regent Instruments, Quebec,
Canada). For samples with less clear rings, a cross-section(40 μm thick) was prepared from each disk using a sliding
microtome and stained with safranin. Each section was thenmounted on a glass slide, and the ring width was measured.
Finally, the tree ring width series were cross-dated with
COFFECHA (Holmes 1983), to confirm that all true growthrings were measured and assigned the correct calendar year.

relative growth rate of w is (dw/dt)/w. Then, u is defined as
the ratio of the relative growth rate (dw/dt)/w to w, as
equation (1)��
1 1 dw
ð1 Þ
u¼
w w dtThe basic equation of the growth curve of w is presented inequation (2).
� w �m ��
1 dw¼ Aw c 1
ð2Þ
u¼ 2
w dt
W
where W is the asymptote of w, t is the time after theobservation of an initial value of w; A, m, and c are constants(Hozumi 1987). An example of the growth curve, or called
u-w diagram, is illustrated by comparing it with a standard
w-t growth curve in Figure 2. The change in tree growth isobserved as the shift of one curve to the next in Figure 2a.
The growth shifts observed in Figure 2a are more conspic­
uous than the slope changes of the w-t growth curve in
Figure 2b.
Hozumi (1987) demonstrated that growth model consid­
ered in the u-w method is equivalent to Bertalanffy’s growthmodel (Bertalanffy 1949), which was proposed based on thebalance of two physiological processes (anabolism and cata­bolism). Also, Miyaura and Hozumi (1993) estimated experi­mentally the aboveground weight of a tree by the
Bertalanffy’s growth model, indicating that stem volume
growth can be described by using the u-w method.
Detection of growth shift by u-w diagram
In the analysis, u and w are calculated as equations (3) and(4), respectively. w1 and w2 are the stem volume of a tree at t1
and t2, respectively.

Stem volume growth estimates
Stem volume was estimated using the Stem4r program devel­
oped by Ishizuka and modified by Miyaura (http://www.est.
ryukoku.ac.jp/est/miyaura/kenkyu/program/program.html).
In the program, the total volume (V) of a log is calculated
based on Smalian’s formula:V¼

B1 þ B2
�L
2,
where B1 and B2 are the cross-sectional areas inside the barkat the lower and upper end of the log, respectively, and L is
the log length. For the top part of a stem, V is estimated asa cone as follows:
V¼

B1
�L
3

.
The past stem volume of each tree was estimated in the samemanner as above, based on the ring width data of the disks
collected at different heights in the tree.
U-w approach
The u-w method (Hozumi 1985) is a tool for analyzing stem
volume growth of a tree, based on the characteristic of thecurve depicted by the stem volume w and the value u defined
as follows. Given the growth rate of a stem as dw/dt, the3u¼ðlogw2 logw1 Þ2
ðw2 w1 Þðt2 t1 Þ

w¼

w2
logw2

w1
logw1

(3)

(4)

We used the program developed by Aoki (2008, unpublished,
http://www.est.ryukoku.ac.jp/est/miyaura/kenkyu/program/
program.html) to calculate and plot the u-w diagram.
Growth shift detection relies on visual inspection, as shownin Figure 2a, which is composed of four growth phases. The
inception of each curve following the first curve showsa point of growth shift indicated by arrows in a given year,
meaning a certain change in the tree’s growing environment
leading to an increase of the carrying capacity. As a result, thetree’s growth goes into a different phase.
Climate moisture index (CMI) and Fire data
Climate moisture index and fire data relating to the studysite were reconstructed; both variables were used to exam­
ine relationships of growth shifts with drought and fireincidences. Climate data at Fort Smith A was obtained
from the official site of the Government of Canada(https://climate.weather.gc.ca), and subsequently, CMI(cm month−1) values were calculated as precipitation(cm month−1) minus potential evapotranspiration (PET),
based on Eh (1997). PET, estimated in cm month−1, is the

4

A. NIAZAI ET AL.

Figure 2. Growth shift observed in 65-year quaking aspen in two different growth curves. (a) u-w curve; (b) log w-t curve. Each arrow shows a growth shift year.

expected rate of water vapor loss to the atmosphere,
calculated from mean daily maximum and minimum tem­
perature (°C) for every month. In this study, we useda window of 15-month for CMI calculation, from Juneof preceding year to August of the current year, because
the climate condition of the previous year significantly
influences the tree growth in the current year. Then,
CMI was calculated using the sum of the monthly CMI
values over 15 month periods. Negative CMI values mean
dry conditions, whereas positive ones denote moist
conditions.
In addition, we included the fire incidence data in theanalysis as an indicator of drought which was followed bya fire in the study area. Fire data was downloaded from the
Canadian National Fire Database (CNFDB) provided by theGovernment of Canada (http://cwfis.cfs.nrcan.ca/ha/nfdb).
QGIS software was used (QGIS.org 2019) to extract andcalculate retrospective fire area and fire frequency in thearea including Wood Buffalo National Park adjacent to theresearch site and a circle with a radius of 50 km, where thecenter of the radius (60° 01ʹN, 112°19ʹW) was located in ourstudy area.

The event coincidence rate (ECR) takes values between 0
and 1, where 0 means that A and B never occurred simulta­
neously, and 1 means that A and B always occurred simulta­
neously. For calculating the relationships between two event
series A and B, ECA performs two different test setups toaddress type B events as the precursors and the triggers oftype A events, respectively. Firstly, the precursor coincidence
rate (PCR) is defined as the quotient of the number ofcoincidences in type A and type B events and the numberof type A events, measuring the fraction of type A events thatare preceded by at least one type B event. Secondly, thetrigger coincidence rate (TCR) is described as the numberof event coincidences in type A and type B divided by the
number of type B events, quantifying the fraction of typeB events that are followed by at least one type A event.
Differentiating between PCR and TCR allows quantifying
the strength, directionality, and time lag of statistical inter­
relation between event series (Donges et al. 2016). We exam­
ined the statistical significance of both test setups between
the growth shifts and drought – and fire-events in the period
between 1950 and 2010. Furthermore, we used the lower­
most 10 percentile threshold for drought and the uppermost
90 percentile threshold for growth shifts and fire events forthe coincidence analysis.

Event coincidence analysis (ECA)
To investigate the relationship of growth shifts withdrought events indicated by CMI, and with spatial fire
events as a proxy of severe droughts, we conducted an
event coincidence analysis (Donges et al. 2016), using theR package CoinCalc (Siegmund et al. 2017). ECA calcu­
lates the empirical fraction of co-occurrences in a twoseries of event observations, e.g. event series A and B,
which is referred to as the event coincidence rate (ECR).
The notion of event coincidence is not symmetric, i.e. it
always takes one of the two event series as a reference towhich the second is compared; commonly, in this context
type B events are considered as possibly influencing thetimings of type A events (Siegmund et al. 2017). In thisstudy, events A and B are growth shifts and drought,
respectively.

Results
Growth shifts of trees
Growth shifts were observed in most of the trees in common
periods especially after 1950 (e.g. early 1980s) with someinconsistencies among the trees within each plot andbetween the plots (Figure 3). The bulk of the trees experi­
enced growth shifts in the first three years of the 1950s,
1970s, and 1980s. Also, growth shifts were seen in nearly
half of the trees in 2004, and this is explicitly seen in old treeplots (B42, B44, B46, and B47). However, some visual differ­ences related to the synchronicity of growth shifts in the plots
and between the plots also were observed. For instance,
relatively more synchronous growth shifts observed in quak­ing aspen plots (Q30 and Q31) than those of other plots; by

JOURNAL OF FOREST RESEARCH

5

Figure 3. Growth shifts phases of trees in 10 plots. Q: Quaking aspen plot; B: Black spruce plot. Trees are numbered from the smallest to the largest in height ineach plot. Peaks in each line show growth shifts; + indicate white spruce.

contrast, young black spruce trees (B38, B40, B41, and B48)
appeared to show more or less inconsistent and asynchro­nous growth shifts. On the other hand, growth shifts in old
black spruce trees (B42, B44, B46, and B47) were not syn­chronized in the former 80 years (1870–1950), and thereafter
started to show synchronous growth shifts in the later
60 years (1950–2010). Overall, the synchronous growth shifts
among and between plots suggests that certain common
regional factors may have triggered these growth shifts.
Common temporal patterns of growth shift occurrenceswere observed between plots despite the difference of species
and age (Figure 4). Besides, the patterns of growth shifts
appeared to be more similar between the plots of the same
species and similar age than those of different species or age.
For example, the growth shifts of quaking aspen plots (Q30
and Q31), young black spruce plots (B38, B40, B41, and B48),
and old black spruce plots (B42, B44, B46, and B47) show
a similar pattern in each group. On the other hand, someapparent differences were seen in their growth shift patterns
when comparing young black spruce plots with those of old
black spruce or comparing quaking aspen plots. Growthshifts in the young black spruce plots (B38, B40, B41, andB48) were more frequent than in other plots but less syn­chronized within each plot. Despite the differences in age
and species, common growth shift patterns were observed
across the plots especially in the early 1950s (old spruce andquaking aspen plots), 1970s (young spruce and quaking
aspen plots), 1980s (across all plots) and 2000s (old spruces
plots).
Additionally, the size-related trend of growth shifts wasobserved for spruce trees (Figure 5). A significant differencein growth shifts frequency was seen in old black spruce plots(p = 0.01) where trees less than 5 m displayed higher occur­
rence of growth shifts than those of larger trees, but nosignificant differences were observed among the other fourheight classes greater than 5 m (Figure 5c). Trees in young
black spruce plots showed no significant differences ingrowth shift frequency among three size classes althougha decreasing trend of growth shift frequency was observedwith increasing height (Figure 5b). The mean frequency of
the growth shifts in each of three height classes of young
spruce plots was greater than those of height classes over 5 m
in old spruce plots (Figure 5b and 5c). Thus, as observed
above, the smaller spruce trees with heights less than 5 m

6A. NIAZAI ET AL.

Figure 4. Percentage of trees exhibiting growth shifts in each plot.

tended to show more frequent growth shifts than the larger
spruce trees. On the other hand, size-related differences were
not observed in quaking aspen trees.

Growth shifts coincidence with climate effects
The occurrence of growth shifts visually showed synchrony
with events of drought, fire frequency, and fire area in com­mon periods (the early 1950s, 1970s, 1980s, and the 2000s in
Figure 6). ECA indicated that the years of growth shifts werelargely related to drought and to both fire variables, althoughsome differences were observed in their PCR and TCR values(Figure 7). PCR test shows significant (p< 0.05) relationships
between fire events and growth shifts where 5 out of 7 growthshifts corresponding with both fire variables (PCR = 0.714,
Figure 7b, and 7c). However, the PCR test indicated nosignificant relationships of growth shifts (p= 0.1) withdrought events despite 4 out 7 growth shifts corresponding
with episodes of droughts especially in the first three years of
the 1950s and 1970s (Figure 7a). Additionally, similar PCRs
(0.714) were observed in both fire variables with growthshifts. It shows that the number of corresponded growthshifts is equal with each of the fire variables. The statisticsof the PCR test suggests that both fire variables were related
as a precursor to growth shift and show correspondence

interrelationships between them. Also, in the TCR test, insig­
nificant relationships (p= 0.1) were seen between growthshifts and drought episodes and fire frequency, while show­
ing significant relationships with fire areas (p = 0.04) . It
means that the number of events of fire areas corresponding
with growth shifts is greater than those of drought episodeand fire frequency (Figure 7d, 7e, and 7f). TCR test implies
a possible causal influence of both fire and drought events. It
means that, in most cases, occurrences of drought episodes
and fire events were followed by one or two growth shifts in
an interval of about 2 or 3 successive years, and it suggests
a possible lag effect on growth shifts. Overall, Coincidence
rates in both tests are comparable; it implicitly suggests thatdrought followed by fire led to correspondence relationships
with growth shifts.

Discussion
Factors causing growth shifts
Our study shows drought causes growth shifts in boreal
forest trees, which is indicated in the correspondence ofgrowth shifts (e.g. the early 1950s, 1970s, 1980s, and 2004)
with drought episodes and fire events. The tree species andtree individuals sensitive to drought stress die because of
severe drought episodes or prolonged dry periods, which

JOURNAL OF FOREST RESEARCH

7

surviving trees following drought-related mortality episodes
would lead to growth shifts in each tree and stand.
The mechanism of trees’ growth reduction by drought iswater shortage leading to low stomatal conductance and sub­sequently to low photosynthesis, but forest fires associatedwith drought also may affect tree growth as indicated by the
relationships between growth shifts and fire data (Figure 7).
Fire smoke and combustion particles may limit stomatalfunctions, resulting in the growth reduction of trees. Welack experimental evidence to show if growth shifts occurred
due partially to fire smoke although coincidence tests weresignificant (Figure 7). Effects of fire smoke remained unan­swered and may warrant further investigations in the future.

Factors affecting growth shift

Figure 5. Comparison of growth shift frequency among different tree-heightclasses. (a) Quaking aspen (Plots Q30 and Q31); (b) Young black spruce (Plots
B38, B40. B41, and, B48); (c) Old black spruces (Plots B42, B44, B46, and B47); (n)number of trees in each height class; vertical bar shows standard error; *Significantly different (Tukey’s test, p < 0.05).

enables the survived trees to utilize more resources leading totheir temporal active growth, i.e. growth shift. This interpre­
tation is exhibited in Figure 2a; a growth shift always accom­
panies an increase of w, meaning that the carrying capacity
for survived trees temporally increases. The active tree
growth often shows a one or two-year lag as indicated in
Figures 6 and 7. It is noteworthy that even though thedrought is not very severe, continuous dry years can cause
trees’ death and survivors’ growth shift as indicated in theearly 1980s and 2000s, during which four or five dry years
continued (Figure 6a and 6d).
Drought effects in Canadian boreal forests have been
widely reported as, the massive mortality of aspen following
severe drought along the southern edge of the Canadianboreal forest (Michaelian et al. 2010), a decline in droughtrelated radial growth of black spruce (Deslauriers et al. 2014),
and climatic drying-induced reduction of white spruce inwestern Canada (Hogg et al. 2017). Based on our results,
drought-induced growth reductions and growth release inSome factors are affecting the mechanism through which
growth shifts are induced. Tree size appears to be one of
the factors affecting the frequency of growth shifts. Relatively
more growth shifts were observed in shorter trees than intaller trees when comparing short and tall trees within andacross plots (Figure 5b and 5c). Short trees are expected to be
more sensitive to slight changes in the local environment(e.g. shading and competition for space and nutrients),
which do not have a significant influence on tall trees. This
interpretation is supported by the smaller difference in thegrowth shift frequency among trees in Q30 and Q31 (Figure5a), where all the aspen trees were taller than 10 meters,
which is greater than the 5-meter threshold for high versus
low growth shift frequency in spruce (Figure 5c)
Trees at younger ages seem to have experienced growthshifts asynchronously within and across the plots, as seen in
the old plots B42, B44, B46, and B47 (Figure 3). Growth shifts
seemed less synchronized across the trees before 1950 and thenthey tended to be more synchronous. It is thought that withinstand factors (i.e. competition, nutrients, and shading) affect thegrowth in the early to mid-growth stages (Huang et al. 2013),
and exogenous factors (i.e. temperature and precipitation) are
more likely to affect trees as they age. Also, age-related physio­
logical changes occur in trees that may accrue their sensitivity toexternal factors. For instance, a shift in carbon allocation tovarious parts of the tree (Ryan et al. 1997), decreasing photo­
synthetic efficiency and leaf size, and gas exchange attributeschange over time with increasing age (Kull and Koppel 1987).
Therefore, the susceptibility and vulnerability of trees to envir­
onmentally driven factors are expected to increase as they age.
Thus, synchronous growth shifts after 1950 can be ascribed
partly to age-induced sensitivity changes to environmental fac­
tors and the severity of drought episodes especially after 1950.
Species-specific sensitivity to environmental factors also
causes a difference in the growth shift synchronicity amongplots. The growth shift synchronicity in two quaking aspen
plots appeared more consistent than in the spruce plots(Figure 3). One of the potential causes that led to synchro­
nicity can be the severity of drought as aspen is one of themost drought-intolerant species (Chen et al. 2017). Similarly,
Hogg et al. (2002) also observed synchronous biomass
decline among aspen trees in northwestern Alberta, Canada
and implied that drought and insect defoliation may havebeen the factors that led to a synchronous decline over vast
areas of aspen.

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A. NIAZAI ET AL.

Figure 6. Correspondence of growth shifts with drought and fire events. (a) Percentage of all sampled trees showing growth shifts, (b) Fire area, (c) fire frequency,(d) CMI during the years 1950–2010. (d) Dash horizontal line marks the 10th and 90th percentile thresholds used for event coincidence analysis of growth shift, firearea, and fire frequency and drought.

Application of the u-w method to environmental
research
The application of the u-w method demonstrated several
advantages related to examining the impacts of climatechange on forests compared to other conventional methods
as dendrochronology, which examines the relationshipbetween climate variables and ring width. These advantages
include (1) detecting long-term effects of environmental
impacts on stem volume growth, (2) examining the areal
forest dynamics by extending from individual tree toa stand, and (3) applicable to various sizes of trees andspecies. On the other hand, the u-w method has some limita­
tions. For instance, it is (1) less suitable for detecting succes­
sive growth shifts within a decade, (2) more time consuming
for sample preparation and tree-ring measurement (3) andless suited for applying to stands with widely uneven tree
ages.
Sampling is vital for the assessment of the retrospective
environmental stresses on forest growth using the
u-w method. To explain the responses of tree populations toenvironmental changes, it is crucial to choose representative
tree species including various sizes. However, this study sug­gests that extremely small or large trees in a stand exhibit
different growth behaviors from those of other trees. A certain
methodological improvement is necessary for dealing withextremely small or large trees for further application of the
u-w method. Furthermore, it is recommended to avoid thejuvenile growth stage in examining the environmental effectof a regional scale because local factors (e.g. competition forspace, light, and nutrients) may substantially influence growthduring this period.

JOURNAL OF FOREST RESEARCH

9

Figure 7. Event coincidence analysis of growth shift and relating factors in the period of 1950 to 2010. CC.plot function of the R package CoinCalc was used to show
the coincidences for the growth shifts, CMI, fire area, and fire frequency. (a), (b) and (c) are the precursor coincidence rates (PCR) between growth shifts (red) andrelating factors (blue); (d), (e) and (f) are the trigger coincidence rates (TCR) between growth shifts (red) and relating factors (blue). Climate moisture index (CMI) is
an indication of drought. Thick blue and red bars mark the coincided events; light color shows events without coincidence. In (a), the PCR of growth shift and CMI iscalculated as the ratio of the number of growth shifts preceded by CMI extremes (four red color events) and total growth shifts (four thick red and three light redcolor events). In (d), the TCR of growth shift and CMI is calculated as the ratio of the number of CMI extremes followed by growth shifts (four blue color events) andtotal CMI extremes (four thick blue and three light blue color events).

In this study, we confirmed that the u-w method has
a significant potential for detecting the environmental
impact on forests, especially on boreal forests which areoften composed of trees of even ages due to regeneration
after stand-replacing fires. The method needs further
improvement for application to wider types of forests but
shows potential as a new alternative tool for analyzing forest
dynamics in the face of increasing global warming.

Author contribution statement
Study conception and design: AO. Acquisition of data: AN, AO, NK,
TK, and JM. Analysis and interpretation of data: AN, NK, TM, MD, andNO. Drafting of the manuscript: AN and NO. All authors have given
consent to the publication of the final version of the draft.

Disclosure statement
The authors declare no conflict of interest.

Acknowledgments
We thank Prof. Akira Kato for his technical advice on analyzing the firedata of the study site, and Dr. Yojiro Matsuura for providing useful
information on the soil properties of the study site. Also, we thankMr. Soichiro Fujii for allowing us to use his tree ring data in our study,
and Aurora Research Institute for issuing a research license and forhelping organize the field activity. We also extend our gratitude to RuthAnn Gal, David Hahn, Keiji Kushida, Mayuko Jomura, and Kai
Moriguchi for their cooperation in the field during sample collection.
This study was financially supported by a grant from the Japan Society
for Promotion of Science to AO (Grant No. 21405008).

Funding
This work was supported by the Japan Society for the Promotion of
Science [21405008].

ORCID
Amin Niazai
http://orcid.org/0000-0001-8334-0569
Juha M. Metsaranta
http://orcid.org/0000-0003-0450-3783

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A. NIAZAI ET AL.

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