Age dependent growth response of Cedrus deodara to climate change in temperate zone of Western Himalaya

Abstract

The recent warming in the mountain regions affect forest productivity in terms of tree growth, especially in the Himalayan region. However, the effects of climate change on the response of radial growth from different age-class trees in the Himalayan region remains unclear. The sensitivity of different age-class trees can differ from younger to old age-class tree growth which create long-term uncertainty in tree-ring calibration against the climatic parameters. In the present study, we assessed the effect of climate change on the radial growth of Cedrus deodara (cedar) from two different age classes; young (age <100 years) and old (age >100 years) in lower temperate zone of Indian Western Himalaya during the period 1950-2015. We modelled basal area increment (BAI) trends using the Generalized additive models (GAM) which predicted the observed pattern of BAI as a function year and random effect of tree. The trend of old age stand BAI increased significantly by 0.13 cm²/year whereas it significantly declined by -0.27 cm²/year for young Cedar stand. However, since 1990 both age classes showed significant decline (p<0.05) in BAI indicating reduction in tree productivity of cedar species that might be linked to recent accelerated change in temperature and decline in precipitation. Correlation analysis between BAI growth and climate revealed that the BAI from both age-class trees were mainly limited by spring season (March-May) climate, however, the signal was statistically strong for old age deodar stand. The tree age vs DBH relationship of old age stand forest showed significant positive relationship but no relationship was found for young age stand which indicated more environmental stress condition for young age deodar forest stand. Future efforts are required to identify the factors responsible for decline productivity of Cedar in young stand by using wide networks of tree-ring data.

Full text

Trees, Forests and People 8 (2022) 100221
Available online 14 February 2022
2666-7193/© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Age dependent growth response of Cedrus deodara to climate change in
temperate zone of Western Himalaya
☆
Rupesh Dhyani
a
, Rajesh Joshi
b
,
*
, Parminder S. Ranhotra
c
, Mayank Shekhar
c
,
A. Bhattacharyya
c
a
G. B. Pant National Institute of Himalayan Environment, Kosi-Katarmal, Almora-263 643, Uttarakhand, INDIA
b
G. B. Pant National Institute of Himalayan Environment, Sikkim Regional Centre, Pangthang, Gangtok-737 101, Sikkim, INDIA
c
Birbal Sahni Institute of Palaeosciences, University Road, Lucknow (UP), INDIA
ARTICLE INFO
Keywords:
Cedrus deodara
Climate change
Basal Area Increment
Forest productivity
Western Himalaya
Growth response
ABSTRACT
The recent warming in the mountain regions affect forest productivity in terms of tree growth, especially in the
Himalayan region. However, the effects of climate change on the response of radial growth of different age-class
trees in the Himalayan region remains unclear. The sensitivity of different age-class trees can differ from younger
to old age-class tree growth which create uncertainty in tree-ring calibration against the climatic parameters. In
the present study, we assessed the effect of climate change on the radial growth of Cedrus deodara (cedar) from
two different age classes; young (age <100 years) and old (age >100 years) in lower temperate zone of Indian
Western Himalaya for the period 1950-2015 CE. We modelled basal area increment (BAI) using the Generalized
additive model (GAM) which predicted the observed pattern of BAI as a function of year and random effect of
tree. The trend of old age stand BAI increased signicantly by 0.13 cm
2
/year whereas it signicantly declined by
-0.27 cm
2
/year for young deodar stand. However, from 1990 CE both age classes showed signicant decline
(p<0.05) in BAI indicating reduction in tree productivity of cedar species which may be due to recent accelerated
rise in temperature and decline in precipitation. Correlation analysis between BAI growth and climate revealed
that the BAI from both age-class trees were mainly limited by spring season (March-May) climate, moreover, the
signal was statistically strong for old age deodar stand. The tree age vs DBH relationship of old age stand forest
showed signicant positive relationship but no relationship was found for young age stand which indicated more
environmental stress condition for young age deodar forest stand. Future efforts are required to identify the
factors responsible for decline productivity of young deodar stand by using wide networks of tree-ring data.
1. Introduction
Tree-rings are considered as natural archives that provide signicant
high-resolution annual proxy data for paleo-environmental studies and
climatic reconstructions. A large proportion of tree-ring studies have
been frequently used to derive long term climate histories beyond the
instrumental records (Bhattacharyya and Yadav, 1999; Buckley et al.,
2005; Büntgen et al., 2021; Cook et al., 2004, 1999; D’Arrigo et al.,
2006; Dhyani et al., 2021; Esper et al., 2002; Mann et al., 1999). The
tree-ring based climate reconstructions are generally derived from the
combinations of younger and older replicated tree core samples. These
replicated samples often get mixed by climate signal age effects (Esper
et al., 2008). In such cases, the climate reconstructed from tree-ring data
could hamper tree-ring calibration due to age related noise (Esper et al.,
2008). The tree-ring growth is affected by the age trend, as the rate of
photosynthesis and its physiological traits vary between young (juve-
nile) and mature plants (Yoder et al., 1994). The juvenile effect mainly
exists initially for 50 to 100 years of tree-ring growth (Dorado Li˜
n´
an
et al., 2012). The tree-ring growth is also regulated by the biological
features and genetic factors such as species, age, competition, life forms,
and sensitivity. The noticeable age trend in raw ring width chronology is
usually removed by the mathematical detrending methods assuming
that the climate – tree growth relationship turn into linear over the time
(Cook and Kairiukstis, 2013; Fritts, 2012). The assumption of detrending
☆
This article is part of a special issue entitled: “Ecology of forested ecosystems in mountainous regions: patterns, processes, and management implications”
published at the journal Trees, Forests and People.
* Correspondence author.
E-mail address: dr.rajeshjoshi@gmail.com (R. Joshi).
Contents lists available at ScienceDirect
Trees, Forests and People
journal homepage: www.sciencedirect.com/journal/trees-forests-and-people
https://doi.org/10.1016/j.tfp.2022.100221
Received 1 September 2021; Received in revised form 7 February 2022; Accepted 13 February 2022

Trees, Forests and People 8 (2022) 100221
2
for removing the biological age trend sometime questioned in clima-
te–tree growth relationships due to its age independence (Carrer and
Urbinati, 2004; Rozas et al., 2009; Szeicz and MacDonald, 1995, 1994).
Tree-ring studies have advanced substantially since last few decades,
however, there are still some limitations and drawbacks dropping the
superiority of the tree-ring based climate reconstructions due to age -
climate effect. Recently, studies related to climate - growth relationship
considering different age classes have been waked up and provided age -
climate effect related evidences for various regions of the world (Bond,
2000; Carrer and Urbinati, 2004; Dorado Li˜
n´
an et al., 2012; Esper et al.,
2008; Jiao et al., 2017; K¨
ohl et al., 2017; Rozas et al., 2009; Voelker,
2011). However, despite of the considerable work on tree-ring science,
age-climate effect studies are lacking in the Himalayan region. Hence, a
knowledge gap exists on testing the hypothesis that climate and tree
growth relationships are controlled by tree-age; this calls for further
research on age-effects related studies of climate change in the
Himalaya.
The recent warming has affected the dynamics of forest ecosystems
which has vital implications to future forest management (Dolezal et al.,
2021). It is very challenging to predict the effects of climate change on
vegetation productivity due to disturbances in plant species composition
and age structure over the long time period owing to the different aged
trees responding differently to the environmental changes as well as to
various physiological characteristics (Itter et al., 2017). This can be
addressed by including the age-effect on the radial growth in predicting
the forest productivity.
Cedrus deodara (Roxb. ex D.Don) G.Don, commonly known as Hi-
malayan cedar or deodar, is a source of highly rated timber (Gamble,
1922) and also has sacredness values among some communities in
western Himalaya. It has a wide spatial distribution across the Western
Himalayan region (Champion and Seth, 1968), occurring both in outer
ranges in different exposure to monsoon and in dry outer and inner
valleys. Across the range of its spatial distribution in Himalaya, this
species is the most widely used in tree-ring studies because of its
longevity and climatic sensitivity of tree-ring sequences (Bhattacharyya
et al., 1988). Several studies have reported that the pre-monsoon season
is of critical importance for the growth of Cedrus deodara (Bhattachar-
yya and Shah, 2009; Bhattacharyya and Yadav, 1999; Shah et al., 2019;
Singh et al., 2009, 2006; Yadav, 2011; Yadav et al., 2015, 1999).
However, most of these studies are based on poor sample replication and
ignored age - climate effect on tree radial growth in response to climate.
As far as our knowledge goes, there are no such studies on tree ring
growth response to climate change aspect in the Himalayan region.
Considering this knowledge gap, the present study aimed at analyzing
the effects of climate change on the radial growth patterns of two
age-classes of cedar trees to detect age-effects of trees on growth-climate
relationships in the western Himalayan region.
2. Material and methods
2.1. Study site and climate
The present study was carried out for the two temperate deodar
stands located in temperate climate at an altitude of ~1700 m amsl in
western Himalaya at Hatkalika and Patalbhuwneswar in Gangolihat,
Pithoragarh district of India (Fig. 1). Both the forests form pure patches
of deodar and situated within the sacred natural sites with least human
disturbances. Since the nearest observed climate data for the site is not
available, therefore, we used gridded Climate Research Unit (CRU TS
V4.03) data for the tree growth and climate analysis for the period 1950-
2019 CE. CRU-TS accurately captures temperature and precipitation
amounts and trends across North Western Himalaya with consistency
Fig. 1. Map of the study area showing sampling sites of old and young forest stand
R. Dhyani et al.

Trees, Forests and People 8 (2022) 100221
3
with observed trend in climate (Negi and Kanda, 2020). The annual
mean temperature for the study site is 19.62 ◦C with July (mean temp.=
26.15 ◦C) being the hottest month and January the coldest month (mean
temp.=10.68 ◦C). The mean annual precipitation is approximately
103.15 mm wherein 78% of the total annual precipitation is accounted
during June to September (Fig. 2).
2.2. Tree ring sampling and age classication
Tree-ring samples in the form of tree cores were collected at the
breast height (~1.3m) from both the stands with two cores collected
from each tree perpendicularly. Total 41 tree core samples from Gang-
olihat and 46 samples from Patalbhuwneswar site were collected. The
collected tree core samples were air-dried, mounted, and polished with
different grit sandpapers so that the annual growth ring boundaries are
clearly visible through the binocular microscope. We considered only
those cores for analysis which include tree bark perpendicular to the
sample pith to avoid any bias in age estimation. The age and ring width
of tree core was measured at 0.001 mm resolution using the LINTAB
measurement system. The quality of measured tree core samples was
tested by quality check COFECHA program (Grissino-Mayer, 2001;
Cook and Holmes, 1996). We also detrend the raw tree-ring width series
using 30 year cubic spline method with a 50% frequency response
function cut-off to remove non-climatic age growth effects (Cook and
Peters, 1981) and to check dendroclimatic potentiality. The quality of
developed tree ring width was evaluated using various statistical pa-
rameters such as mean, standard error, standard deviation, and mean
sensitivity (MS). The MS is a measure of the relative change in ring index
from year to year and is calculated as the absolute difference between
adjacent indices divided by the mean of the two indices (Fritts, 2012).
The strength of common signal among tree-ring width sequences was
measured using signal-to-noise ratio (SNR) (Wigley et al., 1984).
Signal-to-noise ratio is a measure used in tree ring science that compares
the level of a desired signal to the level of background noise. SNR is
dened as the ratio of signal power to the noise power. A ratio higher
than 1:1 indicates more signal than noise. We also calculated
Gleichl¨
augkeitskoefzient (GLK) which is measured in percentage and
expressed as the fraction of annual growth change in two tree-ring width
sequences that occur at the same time (Visser, 2021). GLK or percentage
of parallel variation (%PV) is a non-parametric measure of growth
similarity when comparing tree-ring series. The Hatkalika deodar forest
stand was characterized as old (age>100 years) whereas Patalbhuw-
neswar deodar stand was characterized as young (age<100 years).
Additionally, we measured Diameter at breast height (DBH) of each
sampling tree using the standard method of measurement.
2.3. Analysis of radial growth
The tree growth trend describes the radial growth patterns and
reveal similar or different responses of two age-classes of forest stand in
response to the climate change containing growth trend of ring width
chronologies as well as the basal area increment (BAI). In comparison to
detrended ring width chronology, the BAI is considered as most appro-
priate for estimating the long-term tree growth trend. The BAI depends
on non-standardized raw tree-ring width series, particularly declining
trend in BAI characterizes an actual decline in tree growth under various
environmental stressors (Altman et al., 2016; Rodríguez-Cat´
on et al.,
2015). The BAI is a good indicator of aboveground forest productivity
because it detrends genetic effects from raw tree ring width data and
accurately estimates tree growth throughout the life span of a tree
(Pompa-García and Hadad, 2016). We calculated the annual BAI for
each year using tree ring width data by the standard formula in which
the area of tree ring width of current year is subtracted by preceding
year. The BAI was calculated on the raw tree-ring data series using “bai.
in” function of the dplR package in R programming software (Bunn,
2010). The following formula was used to convert tree-ring width
measurements to BAI (Monserud and Sterba, 1996):
BAIt=
π
(r2
t−r2
t−1)
Where, r
t
is the stem radius at the end of the annual increment, and r
t-
1
is the stem radius at the beginning of the annual tree increment.
2.4. Statistical analysis
We used Generalized additive model (GAM) using the R package
‘mgcv’ to model BAI. GAM allows to model the inherent complexity and
variability of wood formation dynamics (Cuny et al., 2013). GAM esti-
mate nonlinear outcomes to response variable using thin plate spline
(TPS) approaches (Wood, 2017). To balance goodness of t and spline
perturbation, spline parameters such as knots and rigidity are adjusted
Fig. 2. Ombrothermic diagram showing mean monthly temperature and precipitation average over the period 1950-2015 CE.
R. Dhyani et al.

Trees, Forests and People 8 (2022) 100221
4
to the data (Wood, 2017). GAM was tted to the BAI for each year on
each individual tree using the R mgcv package (Wood, 2006). Initially,
GAM was tted where the response variable is predicted by the sum of a
non-linear function of the year and a random effect of the tree ID. The
mathematical form of the model is as follows:
BAI ∼gam(year +s(ID,bs =re))
Where, year is the effect of the year and ID is the random effect of
individual tree.
We also performed regression analysis between DBH and age for both
young and old stands. To identify the important climate variables
limiting the radial growth of both age-class stand trees, we performed
Pearson correlation analysis between the BAI and climate variables
mean annual temperature and precipitation considering the biological
growth period from the prior October to the current September (Fritts,
2012).
3. Results
3.1. Chronology statistics
The statistical characteristics of the developed tree-ring chronologies
for two age-classes are presented in Table 1. The chronology length of
old age stand was 412 years (1604-2015 CE) whereas it was 92 years
(1924-2015 CE) for young age stand. The mean ring width of younger
tree (3.99 m) was greater than older tree age stand (1.35 m). The sta-
tistical parameters Mean Sensitivity (MS) and Standard Deviation (SD)
showed higher values in old age tree in comparison to younger stand
chronology indicating higher inter annual variation in old trees than
younger trees. The SNR value was also higher in old stand chronology
(16.20) than young stand chronology (11.46) indicating old age chro-
nology has more climate signal than younger age. Similarly, the
Expressed population signal (EPS) value was higher in old age chro-
nology than younger and both satisfy the EPS threshold value >0.85.
The summary statistics of age, DBH and BAI is provided in the Table 2.
3.2. Trend in BAI in young vs old age classes
The similarity index GLK showed that tree-ring chronologies from
young and old age classes exhibit good similarity value (64%, n=66)
indicating that the radial growth trend provided common annual vari-
ation which further suggested that the age-class for both stands was
affected by the similar climatic conditions. The GAM predicted the
observed pattern of BAI as a function of year and random effect of tree
with smoothing term for both young and mature age stand classes
(Fig. 3). The mean BAI for younger stand was greater (53.81cm
2
) in
comparison to older age stand (24.97cm
2
). The overall annual trend of
old forest BAI showed signicant increase by 0.13cm
2
/year (Fig. 3)
whereas there was a signicant decline by -0.27 cm
2
/year (Fig. 3) for
young cedar stand during the period 1950-2015 CE. Interestingly, we
observed that since 1990 CE annual variation in BAI showed similar
growth patterns in the two age-classes of trees, with downward trends
from 1990 to 2015 CE. However, the declining pattern was more pro-
nounced in young trees than old trees in the recent three decades. We
also analyzed relation between DBH and age using elds measurements.
The relationship between DBH and age for old stand showed signicant
positive relation (R
2
=0.59, p<0.01) whereas no signicant relationship
was found for DBH and young age stand (Fig. 4).
3.3. BAI - Climate relationship
The BAI growth-climate relationships analyzed by Pearson correla-
tion analysis revealed that the BAI from both age-class trees was mainly
determined by the spring season (March-May) precipitation (Fig. 5). The
BAI of old age deodar stand was correlated signicantly positive with
the mean temperature in November (r =0.26, p<0.05) and December (r
=0.22, p<0.05) months of the previous year and negatively correlated
with March (r =-0.24, p<0.05), April (r =-0.29, p<0.05) and May (r =
-0.31, p<0.05) of the current year (Fig. 5a). The BAI for young age class
was only signicantly negatively correlated with the mean temperature
in November of the previous year (r =-0.33, p<0.05) and current year of
August (r =-0.39, p<0.05) and September (r =-0.35, p<0.05) (Fig. 5a).
Similarly, the BAI for old forest stand was signicantly positively
correlated with mean precipitation in March (r =0.29, p<0.05), April (r
=0.40, p<0.05) and May (r =0.36, p<0.05) of the current year
(Fig. 5b). The BAI for younger age stand showed positive correlation
with mean precipitation in March (r =0.26, p<0.05) and August (r =
0.29, p<0.05) of the current year (Fig. 5b).
4. Discussion
In general, the radial growth of a tree is mainly determined by
climate and juvenile trend, thus it is difcult to identify climate signal
alone (Fritts, 2012). However, recent studies based on dendrochro-
nology demonstrated that the tree-ring chronologies still preserve the
age effect due to altered physiological processes of old and young trees
(Bond, 2000; Carrer and Urbinati, 2004; Jiao et al., 2017). Cedrus deo-
dara is evergreen and shade-tolerant tree and appears to reduce water
loss by maintaining lower stomatal conductance. Other studies from the
western Himalayan region also indicate that warm and dry springs exert
a strong negative inuence on growth of Himalayan cedar in open
exposed sites (Bhattacharyya and Yadav, 1999). This could reect
accentuated less moisture in the initial growing season because of the
increased soil moisture loss when springs are warm and dry.
The nding of our study showed that the young stand tree growth
was two fold greater than old stand growth rate under similar climatic
condition as indicated by mean raw ring width chronology. This is
because of the fact that the tree radial growth is determined by the ge-
netic factor, and gradually shows thinner ring width and slower growth
rate with increasing age (Fritts, 2012). The sensitivity (MS) of Cedrus
deodara increased with age with the higher value of MS (0.219) in old
trees as compared to younger trees (Table 1). The difference in MS
values might be due to physiological mechanisms and environmental
stresses (Carrer and Urbinati, 2004; Fritts, 2012). Other studies also
discussed the higher climatic sensitivity in older age trees due to decline
in the water transport efciency triggered by improved hydraulic
resistance (Carrer and Urbinati, 2004; Ettl and Peterson, 1995; Jiao
et al., 2017; Ryan et al., 1997; Yu et al., 2008). Also, while considering
Table 1
Summary statistics of tree ring width chronology of old and young deodar forest
stands
Parameters Old Young
Sample size (Cores/Trees) 40/20 40/20
Chronology length (years) 412 (1604-2015
CE)
92 (1924-2015
CE)
Mean ring width (mm) 1.35 3.99
Mean Sensitivity 0.24 0.18
Standard deviation 0.20 0.17
AC1 (First-Order serial
autocorrelation)
0.17 0.11
Expressed population signal 0.92 0.89
SNR 16.20 11.46
Table 2
Summary statistics of Age, DBH and BAI of old and young deodar forest stands
Statistics Age (Years) DBH (m) BAI (Cm2)
Young Old Young Old Young Old
Mean 77.70 171.64 0.82 1.04 53.81 24.97
Standard Error 2.50 15.60 0.03 0.04 1.20 0.57
Standard Deviation 12.28 110.32 0.16 0.29 9.81 4.64
R. Dhyani et al.

Trees, Forests and People 8 (2022) 100221
5
physiological mechanisms, the old trees are more vulnerable to the
climate change impacts and show greater effects of water transportation
and complexity with height and girth increase (Ryan and Yoder, 1997).
Further, the water stress constraint the stomatal conductance as well as
gas exchange due to prior stomata closure, which in turn results the tree
radial growth to be further sensitive to climate change (Kolb and Stone,
2000). Younger trees, with less capable root systems, are unable to tap
the existing water and become water-limited, whereas older trees with
extensive root systems may tap deeper water sources, allowing them to
have higher rates of transpiration and photosynthesis (Ryan et al.,
2000).
Between two age classes, the oldest stand BAI showed a signicant
negative relationship with the spring temperature whereas the correla-
tion become low in younger age stand in spring season temperature.
Similarly, older stand BAI signicantly correlated with spring precipi-
tation positively, however, correlation become lower for young stand.
These varied tree growth - climate response are linked to different
physiological mechanisms associated with the tree age. Tree at young
stage of growth operate overspend strategies to conrm the growth
potential essential for the tree establishment (Carrer and Urbinati,
2004). The overspend capacity may be decided by the previous year’s
tree growth and nutrition, whereas a decrease in photosynthesis and tree
growth would be likely due to high temperature (Meinzer et al., 2011).
We also observed negative correlation of BAI with warmer spring
and summer climates and positively linked with precipitation signifying
the warming induced water stress. Apart from warming induced water
stress, growth decline is also attributable to various additional stressors
(Silva et al., 2010). This is also conrmed by the age and DBH rela-
tionship with strong association between older age (>100 years) vs DBH
and poor relationship between younger age (<100 years) vs DBH. The
BAI of both age stand showed increasing trend from 1950 till 1990 CE,
and thereafter reduction in BAI, however the decline was more pro-
nounced in younger age stand (Fig. 3). The decline in BAI at younger age
might be associated with short term changes in climate due to which tree
may limit the physiological activity crucial for the growth (Tainter et al.,
1990). Trees that survive to older age tend to display slow early growth
rate than young trees (Colangelo et al., 2021). Furthermore, when tree
growth is negatively correlated with temperature and positively with
Fig. 3. Trend of BAI as predicted by GAM for both old vs young deodar forest stands during the period 1950-2015 CE. The shaded plot indicate 95% con-
dence interval.
Fig. 4. The scatter plots showing the relationship between age vs DBH for old and young deodar forest stands.
R. Dhyani et al.

Trees, Forests and People 8 (2022) 100221
6
precipitation, decline in old stand BAI is caused by long-term increase in
tree water use efciency which leads to water stress (Silva et al., 2009,
2010). The mean annual temperature and precipitation showed an
increasing and decreasing trend respectively, which coincides with the
period of growth decline in BAI (Fig. 5a & b). Hence, the simultaneous
analysis of variations in mean temperature and precipitation for both the
sites, the young trees seem to be more vulnerable to long-term drought
effects.
Similar ndings have been reported for different conifer trees across
the mountain regions over the globe (Baral et al., 2019; Benito et al.,
2013; Colangelo et al., 2021; Jiao et al., 2017; Pompa-García and
Hadad, 2016; Van Den Brakel and Visser, 1996). Overall, the old stand
trees seems to be more resilient to long-term spring season moisture
stress than young stand of trees in the study area due to the capacity of
old stand trees to absorb more water with a good root system.
Conclusion
The present study showed the evidence of age effect on tree growth -
climate relationship based on tree-ring data of old and young stand in
western Himalayan region. The older deodar stand tree exhibited strong
sensitivity to spring season climate in comparison to the younger age
stand, which suggested that age is a important factor that derive the
growth - climate relationship in case of Cedrus deodara. The older age
group (>100 years) considered reliable for deriving growth - climate
relationship suggesting sampling of comparatively older age stand trees
for the study of tree-ring based climate reconstructions. The declining
tree productivity especially in younger age stand trees in recent past
calls for the proper forest management considering the age classes of
Himalayan cedar. However, more research is required to justify age
effect climate growth relation by carrying out tree-ring studies on
different species in the Himalayan region. Further, it is paramount to put
dedicated research efforts towards analyzing age - climate effect on
radial growth of trees in response to changing climate to enhance the
precision of climate reconstructions using tree-ring data. The appro-
priate studies on different age stand are also recommended to analyze
forest productivity in the fate of climate change. Based on such studies,
suitable management interventions can be devised using different stand
age classes, which could be very important for the commercial thinning
and wood production.
Declaration on Conict of Interest
None
Acknowledgements
Authors are thankful to the former Director of GBPNIHE, Late Dr. R S
Rawal for providing necessary facilities for conducting the research.
Financial support received under National Mission on Sustaining the
Himalayan Ecosystem (NMSHE): Task Force-3 for conducting the study
is gratefully acknowledged. We also thank Department of Forest, gov-
ernment of Uttarakhand for support in implementation of the study. This
study is a collaborative work between GBPNIHE and BSIP. The authors
extend gratitude to the Director BSIP for providing necessary support.
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