Research Article

Growth Response of Himalayan Spruce to Climate Change in Dry and Moist Temperate Forests of Pakistan

Tanvir Hussain1,2*, Kafeel Ahmad2, Asif Ali1, Nazir Ullah1, Muhammad Inamullah Khan3, Kamal Khan Kakar1 and Said Akhtar Khan1

1Pakistan Forest Institute, Peshawar; 2Centre of Biotechnology & Microbiology, University of Peshawar; 3Department of Environmental Sciences, The University of Haripur.

Received | Jun 13, 2024; Accepted | Aug 19, 2025; Published | January 09, 2026

*Correspondence | Tanvir Hussain, Pakistan Forest Institute, Peshawar; Email: [email protected]

Citation | Hussain, T., K. Ahmad, A. Ali, N. Ullah, M.I. Khan. K.K. Kakar and S.A. Khan. 2026. Growth Response of Himalayan Spruce to Climate Change in Dry and Moist Temperate Forests of Pakistan. Sarhad Journal of Agriculture, 42(1): 01-13.

DOI | https://dx.doi.org/10.17582/journal.sja/2026/42.1.01.13

Keywords | Picea smitiana, Tree rings, Dry and moist forest, Climate response, Drought resilience, Forest management.

Copyright: 2026 by the authors. Licensee ResearchersLinks Ltd, England, UK.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Introduction

Climatic factors like temperature and precipitation plays vital role in the growth and development of trees. They can produce long-term responses to climate changes and extreme events and are recorded as information in the annual ring width (Duchesne et al, 2017). Tree rings have become a key source of studying the impact of climate changes on tree growth and reconstructing past climatic factors (Smerdon et al, 2013) in view of easy sampling, accurate dating, high resolution, strong continuity and wide distribution (Wu, 1990). In the recent past years, growth-climatic relationships have been explored by many researchers in the region e.g., Rauf et al. (2022) studied climate-growth response of Himalayan Fir (Abies pindrow) growing in moist temperate forests of Ayubia, Bara Galli and Kuldana of Khyber Pakhtunkhwa province Ullah et al. (2022) in Spruce (Picea smithiana) and Himalayan Fir (Abies pindrow) growing in Northern areas of Pakistan; Amir et al. (2022) in Himalayan Fir (Abies pindrow), Deodar (Cedrrus deodara), Blue pine (Pinus wallichiana) and Yew (Taxus Baccata) growing in Ayubia National Park; Shah et al. (2019) in Spruce (Picea smithiana) growing in Kalam and Kaghan Valleys; Bukhari et al. (2019) in Blue pine (Pinus wallichiana) growing in Galliat; Asad et al. (2017) in Blue pine (Pinus wallichiana) growing in Korakoram, Pakistan. Many more Himalayan conifers and broad-leaved tree species have been successfully investigated for dendroclimatic purposes but these are limited to eastern Himalaya (Ahmed et al., 2011).

Pakistan ranks 8th most vulnerable country to climate change (Eckstein et al., 2020). Climate data for the previous century (1901-2000) have shown an increase of 0.6 °C in temperature and +25% in precipitation (Sheikh et al., 2015). This rise in temperature closely accompanied with the global rise in temperature (0.6°C) over the previous century (1901-2000). Khyber Pakhtunkhwa is one of the most vulnerable provinces of Pakistan in view of the negative impact of climate change. It is speculated that the moist temperate forests of Himalaya with an elevation of 1525-3660 m (lower Kaghan valley, Galliate and southern part of Kohistan) will experience intense precipitation due to climate change (Pachauri et al., 2014).

Picea smithiana is an important species of coniferous biome in the mountainous areas of Pakistan. It is fairly common at a high elevation from 2500-3300 m in the dry temperate and moist temperate forests of Dir, Chitral, Azad Kashmir, Murree Hills, Hazara, Swat and Kurram agency (Sheik, 1993; Ahmed and Naqvi, 2005). It provides important economic and ecological services as its wood is used in constructional work, railway sleeper, packing cases and in pulp and mulch (Siddiqui and Mahmood, 1996). This species has been declared as endangered or vulnerable in view of climate change and anthropogenic activities in the region (Khan, 2004). Further, little or no work on the growth response of Picea smithiana growing in Barkan Usheri and Bomborate to climate change is reported.

Keeping in view the above scenario, the present study was designed with the objective to i) Develop study sites tree ring width (TRW)) chronology ii) Explore the growth responses of this tree species to the changing climatic conditions in dry and moist temperate forests of Khyber Pakhtunkhwa, Pakkistan over 1965-2015 and iii) Provide a basis for its future protection in the region for better forests management.

 

Materials and Methods

Study sites

Figure 1 shows the locations of study sites and table1 represent the geographic and topographic characteristics of the sampling areas. Barkan Usheri a moist temperate forest lies in Dir Forest Division of Khyber Pakhtunkhwa province of Pakistan. The region is dominated by mountains and hills of east Hindukush ranges and has uneven and rough topography (Krishnan et al., 2019). At different locations, temperature variation is prominent within the month and during different months of the year. On upper areas, snow falling generally start at the end of November and moves downward in December and depending of height, it begins to melt in March and continues into April (Banskota, 2000). Bommburate, situated in Chitral Forest Division and exhibited dry temperate climatic conditions. The main natural species found in the valley are Deodar, Kail, Fir, Spruce, and other broadleaved species. It observed hot summers in the south (Arandu-3500 m a.s.1.) to continuously arid and cold in the north (Broghil-12480 m a.s.1.). Generally, the summers are cool and pleasant; the winters are pretty cold. Frost is common and starts in November. The severe frost occurs in December and January. Snow starts falling on high peaks during October. By December, except for a narrow belt along Chitral River, the entire district is covered with 4 to 16 feet deep snow. The snow melts away by the end of May. The elevations above 20,000 feet remain under perpetual snow. The rainfall is scanty and erratic. The bulk of the annual precipitation (about 75%) is received in winter (December-May)(Ghani, 2020).

Climatic data

Due to limited availability of Metereological station data at study sites, climate data i.e., mean temperature, annual precipitation and PDSI from international source Climate Research Unit (CRU) (Osborn and Jones, 2014) was utilized for climate-growth relationship. The grid size of 0.5x0.50 (50 km x 50 km) was used for scaling down study area (Bukhari et al., 2019). The climatic parameters i.e., annual mean temperature and annual total precipitation were used to explore the climate regime and climate change over 1965-2015.

From the climatic data of Barkan Usheri, Upper Dir Forest areas (Figure 2a & Table 2) it was calculated that July was the hottest month with a mean temperature of 20.41 ⁰C and monthly mean rainfall of 76.79 mm. The coldest month was recorded January (-0.84 ⁰C) with precipitation of 62.90 mm. The maximum rainfall of 117.15 mm was measured during March along with the monthly mean temperature of 4.46 ⁰C. A significant increase of 0.76 ⁰C (r=0.43; p<0.002) in annual temperature and a non-significant (r=0.09; p>0.05) increase of 45.2 mm in total annual precipitation was calculated for this studied period. As for as PDSI is concerned, no significant change was recorded for this study area.

It is evident from the climate diagram of Bomborate, Chitral Forest Division (Figure 2b & Table 2) that July was the hottest month with a maximum mean temperature of 17.77 ⁰C along with mean rainfall of 50.72 mm while January was recorded as the coldest month with a minimum mean temperature of -4.58 ⁰C and mean monthly rainfall of 58.55 mm.

 

Table 1: Geographic and topographic features of study sites of khyber pakhtunkhwa, pakistan.

Forest Type	Met. Observatory Stations	Study Sites	Coordinate		Elevation (m)	Slope (%)	Aspect
			Longitude (E)	Latitude (N)	Min-Max	Min-Max
MTF	Dir	Barkan Usheri	71.908905556	35.265986111	1610-2684	17-33	N
DTF	Chitral	Bamborate	71.652538889	35.687111111	2295-2887	36-70	N

 

MTF =Moist Temperate Forests, DTF=Dry Temperate Forests

 

Table 2: Temperature and precipitation regimes over study sites of Khyber Pakhtunkhwa.

Study Sites	Period	Temp. (⁰C) (Min – Max)	Prcp. (mm) (Min – Max)	PDSI	Ince/Dec Temp. (⁰C)	Inc/Dec Precipitation (mm)	Ince/Dec PDSI
BAR	1965-2015	9.16-12.21	523.2-1210.6	-2.97-2.42	0.76*	45.20 NS	0.28 NS
BOR	1965-2015	5.71-7.98	395.6-937.7	-2.74-3.73	0.88*	29.2 NS	0.029NS

 

Min=minimum, Max=maximum, NS=non-significant, *=significant(p<0.05), Incr= Increase, Dec=Decrease

 

The maximum total annual rainfall of 937.70 mm was measured during 1965 with an annual mean temperature of 6.43 ⁰C. An increase of 0.88 ⁰C (r=0.48; p<0.001) and a non-significant increase of 29.2 mm in total annual precipitation were calculated for this studied period. In case of PDSI, no proninent change was calculated for this region.

Sample collection and processing

For sampling, those trees were selected which were old, healthy, free from severe competition with neighbors, and anthropogenic disturbances. 20-30 trees were selected in each study site (Table 1) by following the Simple Random Sampling Method and at DBH (Diameter at breast height) of each tree, cores (wood sample) were extracted with the help of an increment borer. The collected cores were air-dried for two to three days. After fastening with glue on wooden core holders and properly label, the dried cores were then subjected to the sanding process by using sanding paper of different grit (Orvis and Grissino-Mayer, 2002) for producing a smooth surface. The best-surfaced cores free from defects were selected and pointer years were marked under a stereomicroscope using the skeleton plot method as described by Stock and Smily (1968). Each core cross-dated (pattern matching) with other cores of the same site. The tree ring feature i.e., tree ring widths (mm) was measured with WinDendro System (WINDENDRO, 2014a). For this process, all the cores were scanned with calibrated WinDendro Scanner at more than 600 dpi (dot per inch) resolution. The images were saved in Tiff format (Tagged information file format) and the measurement of tree ring features was carried out as described in the manual of WinDendro System software 2014(WINDENDRO, 2014a). In order to check the quality of cross-dating of tree ring series the raw data was subjected to a computer-based program Cofecha (Holmes, 1994; Grissino-Mayer, 2001).

Chronology development

It is not recommended by the tree ring researchers to use raw tree ring series for studying past climatic variations because it reflect small signals. In order to enhance these signals, standardization procedure i.e., cubic spline was adopted using software ARSTAN (Auto-regressive Standardization) which removed age, non-climatic trends and unwanted information like noise from the tree ring series and maximized the required variations as explained by Cook and Briffa (1990). Four chronologies i.e., raw, standard, residual and arstan were obtained by this analysis and residual chronology for this study was utilized.This program also gave various statistics like EPS, SNR and EPS which were used to determine the quality of a chronology (Ahmed and Wahab, 2010). To strengthen the reliability of developed chronologies, pointer and event years of each study site chronology were determined using pointRes program of R packages (Van der et al., 2015).

Climate-growth relationship

To find which chronology best suited for the establishment of the growth-climate relationship, the preliminary correlation was developed between CRU climatic factors and site chronologies. The residual chronology was found more suitable and therefore, it was used for the establishment of growth-climate relationship by Correlation Function Analysis (RFA) using “treeclim” software of R package (Zang and Biondi, 2015; Misi et al., 2019). The Pearson correlation coefficient was determined among tree ring chronology and climatic factors i.e., monthly mean temperature, monthly annual precipitation and PDSI from previous year October to October of the current growth year (Ahmed et al., 2011) as considering the previous year climate may also have an influence on tree growth. The trend analysis of growth for the period 1965-2015 was assessed through Mann-Kendall Test (Bukhari et al., 2019; Misi et al., 2019).

Results

Tree Ring width Chronologies and their Statistics

After cross dating, the final chronologies of Himalayan Spruce for each study site were developed. Table 3 showed the summarized results of COFECHA and

 

Table 3: COFECHA and ARSTAN Statistics of tree ring chronologies from both study sites.

Sites	COFECHA							ARSTAN
	Chronology Span	Years	1Corr with Master	2Mean msmt	3Std Dev	4Auto Corr	5Mean Sens	6Rbar	7SNR	8EPS
BAR	1894-2015	122	0.506	1.48	0.708	0.738	0.235	0.920	23.052	0.958
BOM	1872-2015	144	0.776	1.59	0.742	0.865	0.155	0.349	8.031	0.889

 

1= Correlation with Master Chronology, 2= Mean measurements, 3= Standard Deviation, 4= Auto-correlation, 5= Mean sensitivity, 6= mean inter- series correlation, 7= Signal-to-noise ratio, 8= Expressed population signal.

 

ARSTAN programmes for both chronologies. The chronology developed for Barkan Usheri spanned 122 years (AD 1894-2015) and 144 years (AD 1872-2015) for Bomborate. The correlation with master chronology tells the correlation between two cores, time series, or correlation of one core with master chronology. It shows the common stand-level signal for any site and provides first valuable information about the suitability of any chronology. Its value varies from site to site and species to species and Table 3 showed that both the chronologies possessed acceptable value for this species. The mean ring witdh measurement value indicated the wider or narrow ring patterns of the tree and it also help to find the growth pattern of the species. Himalayan Spruce growing in Bomborate showed a litle bit faster growth than in Barkan Usheri. Mean sensitivity is one of the most important characters which shows the suitability of the core samples (Fritts and Shatz, 1975). It calculate the proportional variation in TRW from one year to the next (Fritts, 1974). It varies from site to site and species to species and ranging from 0 to 1. Cofecha analysis reflected that Usheri Barkan is more sensitive site for Himalayan Spruce than Bomborat. Mean inter-series correlation (Rbar) tells the signal strength of the chronology and explores the common signal strength of the series based upon the sample depth (Cook et al., 2000) and ARSTAN analaysis showed stronger signal stength in Spruce growing in the Usheri Barkan population than Bomborate. A value of 0.85 is consider reasonable for expressed population signal (EPS), which also determines the confidence and strength of common signal in the chronology. However, no minimum value is established to ensure that a chronology is suitable for studying climate growth relationship to reconstruct past climatic factors (Briffa et al., 1988; Ahmed et al., 2010). According to ARSTAN results given in Table 3 both chronologies scored the sophisticated value of EPS for studying climate-growth relationship. Signal-to-noise ratio (SNR) is an expression of the strength of the observed common signal among tree ring indices in the essemble (Wigley et al., 1984) and its high value indicated higher climatic signals in the chronology (Zafar et al., 2010; Fritts, 1976). Table 3 showed that the higher suitable SNR value was found in the chronology of Barkan Usheri (23.052).

Event years are the years with a remarkable increase or decrease in growth whereas the term pointer years refers to years with remarkable growth responses at the stand level (Schweingruber et al., 1990). Following are the pointer years and event years found in the chronologies of both study sites (Figures 4a & b).

 

 

Growth trend

In Barkan Usheri no prominent trend in the growth of Picea smithiana was recorded, however, under the dry temperate climatic conditions of both Bomborate, the upward trend in growth features was recorded (Table 4).

 

Table 4: Pointer years and event years found in the chronologies.

Barkan usheri	Bomborate
Positive pointer year(s): 1910	1879,1893,1894,1906,1913,1950,1975,1986,1996
Negative pointer years: Nil.	1935
Positive event years: 1909	1887,1918,1925,1939,1943,1961,1981
Negative event years: 1903,1904,1911	1966

 

 

 

Growth-climate relationship

Figure 5 represents the growth-climate interaction of Picea smithiana growing under the prevailing moist temperate conditions of BAR and dry temperate of BOM. The results of Correlation Function analysis at BAR showed a positive and significant correlation (r=0.35;p<0.05) between rainfall and the growth characteristic in the month of July of the current growth year (Figure 5a). This revealed that precipitation is acting as limitiing factor for the growth of this species growing in BAR. A significant but negative correlation (r=-0.50; p<0.05) was also observed in April (growth period) which reflected that only availability of water is not suitable for the growth of this species rather optimum temperature is also required that was not available during the

 

growth period. Further, no prominent effect of both temperature as well as precipitation was recorded from the previous year as well as during winter and autum seasons in this area. At the study site of Bomorate, a significant and positive correlation (r=0.24;p<0.05) was observed during the month of July ( current year) as well as during monsoon (r=0.25;p<0.05) and autum season (r=0.32;p<0.05) with precipitation. During growth period (Mar-May) both climatic factors were found to contribute their role but effect was non-significant (Figure 5a).

 

Table 5: Trend analysis of tree growth of himalayan spruce growing in KPK-pakistan.

Name of station	Tree ring feature	Z-Value	p-Value (Upward)	p-Value (Downward)	Trend
BAR	TRW	0.64	0.26	0.73	No
BOM	TRW	2.46	0.00	0.99	UWT

 

BAR= Barkan Usheri, BOM= Bomborate, UWT= Upward Trend.

 

As for as PDSI is concerned, no prominent effects was recorded during previous as well as current growth years for both study sites (Figure 5c).

Discussion

Statistics of the chronology

The climate-growth relationship of a particular site and a specific species is one of the main research subjects in the field of dendroclimatology (Rabbel et al., 2018). Therefore, the selection of a sensitive study site and suitable species which can give maximum climatic signals are pre-requisite to establish a good growth-climate model (Sullivan and Csank, 2016). Conifers are considered more sensitive to climate change than the broad-leaved tree species and Picea smithiana is considered one of the most suitable tree species for studying the past effects of climate in the region (Khan et al., 2008; Ahmed et al., 2009, 2010). The dendroclimatic potential of a tree species

depends on its tree ring characteristics like correlation with master chronology, mean tree ring width, mean sensitivity, standard deviation, 1st order auto-correlation coefficient, high amount of EPS, SNR and Rbar values, sample depth (Fritts, 1975; Ahmed et al., 2010).

Cofecha statistics showed that both the tree ring chronologies scored an acceptable value of correlation with the master chronology and may be used for the study of climate-growth relationship (Wigley et al., 1984). The possible reason is good cross-dating of these tree ring series that generated a higher value of correlation. The highest value of correlation coefficient (0.77) with master chronology was recorded in the dry temperate forest area of Bomborate of Kalash valley and these results are in accordance with the findings of Zafar et al. (2010) and Ahmad et al. (2013) who developed tree ring chronology in Picea smithiana growing in the dry temperate climate of Bagrot, Northern area of Pakistan. The mean ring width parameter is an indication of the pattern of growth of a species i.e., a species producing a wider or narrow growth ring. The values of this parameter found in this study for Himalayan Spruce growing in different climatic areas are in accordance with the findings of other researchers like Ahmad and Naqvi (2005) and Ahmad et al. (2010) reported mean ring width value 1.05, 1.008,1.001,1.009 of Picea smithiana growing in dry temperate and moist temperate areas of Khyber Pakhtunkhwa and Gilgit-Bultistan provinces of Pakistan. Picea smithiana from Barkan Usheri, attained the higher value of mean sensitivity (0.23). According to Speer (2010) a tree ring chronology with a mean sensitivity value of about 0.1 is so complacent that the cross-dating of its tree ring series will be very difficult. On contrary, the chronology with a mean sensitivity value of more than 0.4 is sensitively tricky to cross-date. According to Lamarche, 1974 low mean sensitivity does not necessarily indicate the absence of a strong climatic signal in a chronology. Therefore, the chronology possessing the mean sensitivity value of 0.155 from BAR lie in the acceptable range for studying climate-growth relationship in these areas. A similar mean sensitivity value was recorded by Khan et al. (2008); Ahmad et al. (2010, 2011) and Bargaonkar et al. (2009) and Toledo et al. (2011) in Picea smithia. The statistic of autocorrelation measures the influence of the previous year’s growth on the current year’s growth of a tree species (Fritts, 1976). This feature scored little bit higher values by both studied tree ring chronologies but are within the range of other studies carried out in Pakistan and neighboring country like India and Nepal (Bhattacharyya et al., 1988). This high value represented that the previous year’s growth significantly influencing the current year’s growth of Himalayan Spruce in these areas. For the study of a past event, it is generally considered that the low value of autocorrelation is more useful as compared to higher ones that may create problems during the climate-growth relationship, therefore, it was minimized using the filter technique (Zafar et al., 2010). Climate signals in the tree ring series were examined with Rbar, SNR, and EPS values commonly used by the researchers. The Rbar tells the signal strength of the chronology and explores the common signal strength of the series based upon the sample depth (Cook et al., 2000). The SNR is an expression of the strength of the observed common signal among tree ring indices in the essemble (Wigley et al., 1984) and its high value indicated higher climatic signals in the chronology (Fritts, 1976; Zafar et al., 2010). Chronology confidence and strength of the common signal in the chronology is also determined by EPS and a value of 0.85 is considered reasonable but no minimum value is determined to ensure that a chronology is suitable for studying climate-growth relationship in order to reconstruct past climatic factors like temperature, precipitation etc. (Ahmed et al., 2011). By following these features criterion, both the tree chronologies reflected good values of climate signals in their chronologies and showed suitability and reliability for studying the growth response of Picea smithiana to climate change. A reliable sample depth of a chronology is another prerequisite for studying climate-growth relationship. According to Speer, 2010 about 20 trees (two cores/ tree) per site or stand can remove the individual tree variability and yield a stand-level variability while Fritts (1976) suggested 12 trees (two cores/tree). In view of this scenario, the chronology developed for Bomborate from Chitral Forest Division showed maximum sample depth while the BAR site chronologies showed low sample depth and needed to increase more sample size with longer tree ring series. It is considered that trees experience years with extreme growth conditions in view of climatic or other impact factors (natural or anthropogenic). These years are called event years and can prompt the development of above-average wide or narrow tree rings (Schweingruber et al., 1990). In this study, the presence of positive and negative pointer years, similarly positive and negative event years in both the chronologies showed that this species is perceiving the climatic change signal and incorporating these signals into their annual growth rings.

Climate data analysis

Tree growth is influenced by both abiotic and biotic factors of the environment. Among abiotic factors, temperature and precipitation are commonly considered the most effective factors (Toledo et al., 2011). In order to examine the impact of climate change on the flora and fauna of a study site, it is very important to determine the climatic changes that have happened in the area. The statistical analysis of climatic data for the both study sites revealed a prominent increase (0.76 & 0.88 oC respectively) in monthly mean temperature and a non-significant increase in precipitation. This increase in temperature at both study sites BAR & BOM is in accordance with the study of Bukhari et al. (2019) and Bajwa and Satti (2015) who studied the change in climatic conditions and its impact on conifers in the Galliat forest Division.

Growth- climate relationship

Tree growth is affected by several factors, some of which are specific to its location and age, while others are connected to broader environmental variable like temperature, precipitation and PDSI (Yeh and Wensel, 2000; Adams, 2014). It is commonly considered that precipitation played a vital role and act as a limiting factor in the growth of trees under moist conditions (Khan et al., 2022). The temperature along with a suitable supply of water during the growth period are prerequisites for the normal growth of tree species (Pallardy, 2010). Barkan Usheri is situated in the moist temperate forest area of Dir Forest Division, and a positive correlation with precipitation during the month of July (monsoon) followed the findings of Ahmad et al. (2009, 2010). As no prominent response to temperature as well as PDSI was recorded during this period, therefore normal trend in growth was observed. Bomborate belong to dry temperate forest areas and it is generally considered that temperature acts as a limiting factor for the growth of coniferous trees but in this location, again the significant role of precipitation was evident i.e., precipitation during the month of July coupled with high temperature is favoring the growth and an upward trend in growth (Zafar et al., 2010).

Conclusions and Recommendations

Himalayan Spruce growing in the moist and dry temperate forests of Khyber Pakhtunkhwa has great potential for studying growth-climate relationship. The growth feature of this tree found directly affected by precipitation and seasonal precipitation found more influencial than the individual months as in Bomborate. This study serves as a foundation for short-term growth-climate data to create estimates for long-term growth. In order to get more information about the dendroclimatic potential of Himalayan Spruce, it is recommended to correlate the results of this study with other regional studies. Further, the results of this study could be helpful for scientists and researchers to forecast regional and global climate change by reconstructing past climatic factors and to prepare themselves for transformational adaptation, and find solutions through mitigation.

Acknowledgements

We are thankfull to Dr. Fyaz Asad, Assistant Professor, Botany Department, Bacha Khan University, Charsadda-Peshawar, Dr. Sher Shah, Lecturrer, Institute for Agriculture Sciences and Forestry, University of Swat, and Dr. Sarir Ahmad, HoD, Department of Environmental Sciences, The Universith of Agriculture, D.I.Khan for vaalluable comments and discussion. We are also thankful to Pakkistan Forest Institute, Pesshawar for their support during field surveys and Lab. facility for research work.

Novelty Statement

First study of the region that compared growth dynamic of Himalayan Spruce growing under different climatic conditions.

Author,s contribution

Dr.Tanvir Hussain: This is a part of his PhD, planned, executed and drafted the manuscript.

Dr. Kafeel Ahmad: Supervised the PhD research, input valuable comments, corrected manuscript.

Mr.Asif Ali: Helped in manuscript drafting and writing,

Mr. Nazir Ahmad: Sported in data collection, compilation.

Mr.Kamal Khan Kakar: Sported in sample preparation and processing in lab.

Mr. M.Inamullah Khan: Sported in field data, and tree cores sample collection.

Mr. Said Akhtar Khan: Sported in sample preparation and processing in lab.

Generative AI or AI assisted technology statement

The authors declare that no genrative AI was used in the creation of this manuscript.

Conflict of interest

The authors have no conflict of interest.

References

Adams, S.H. 2014. The impact of changing climate on tree growth and wood quality of Sitka spruce. Doctoral dissertation, University of Glasgow, UK.

Ahmed, M. and S.H. Naqvi. 2005. Tree-ring chronologies of Picea smithiana (wall.) Boiss. and its quantitative vegetational description from Himalayan range of Pakistan. Pak. J. Bot., 37(3): 697-707

Ahmed. M., N. Khan and M. Wahab. 2010. Climate response function analysis of Abies pindrow (Royle) Spach. Preliminary results. Pak. J. Bot., 42(1): 165-171

Ahmed. M., J. Palmer, N. Khan, M. Wahab, P. Fenwick, J. Esper and E.D. Cook. 2011. The dendroclimatic potential of conifers from northern Pakistan. Dendrochronol., 29(2): 77-88 https://doi.org/10.1016/j.dendro.2010.08.007

Ahmed. M., M. Wahab and N. Khan. 2009. Dendroclimatic investigation in Pakistan using Picea smithiana (Wall) Boiss. Preliminary results. Pak. J. Bot., 41(5): 2427-2435

Ahmed. M., M. Wahab, N. Khan, J. Palmer, K. Nazim, M.U. Khan, and M.F. Siddiqui.2010. Some preliminary results of climatic studies based on two Pine tree species of Himalayan area of Pakistan. Pak. J. Bot., 42(2): 731-738

Ahmed. M., M. Wahab, N. Khan, M.U. Zafar, and J. Palmer.2010. Tree-ring chronologies from upper Indus basin of Karakorum range, Pakistan. Pak. J. Bot., 42: 295-308

Ahmed. M., M.U. Zafar, A. Hussain, M. Akbar, M. Wahab, and N. Khan. 2013. Dendroclimatic and dendrohydrological response of two tree species from Gilgit valleys. Pak. J. Bot., 45(3): 987-992

Amir. A., S. Muhammad, H. Nawaz, M. Tayyab, U.F. Awan, K. Rasool and K. Zaheer-ud-din. 2022. Growth response of four dominant conifer species in moist temperate region of Pakistan (Ayubia National Park). Notulae Botanicae Hortic. Agrobotan. Cluj-Napoca., 50 (2): 12674-12674 https://doi.org/10.15835/nbha50212674

Anchukaitis, K.J. 2017. Tree rings reveal climate change past, present, and future. Proceedings of the American Philosophical Society. 161(3): 244-263

Asad. F., H. Zhu, E. Liang, M. Ali, M. Hamayun, S.R. Sigdel, M. Khalid and I. Hussain. 2017. Climate signal in tree-ring width chronologies of Pinus wallichiana from the Karakoram mountains in Northern Pakistan. Pak. J. Bot., 49(6): 2466-2473

Bajwa. G.A., M.K. Shahzad and H.K. Satti. 2015. Climate change and its impacts on growth of blue pine (Pinus wallichiana) in Murree Forest division, Pakistan. Sci. Tech. Devel., 34(1): 27-34 https://doi.org/10.3923/std.2015.27.34

Banskota, M. 2000. The Hindu Kush-Himalayas: searching for viable socioeconomic and environmental options. Growth, poverty alleviation, and sustainable resource management in the mountain areas of South Asia. Proceed. Int. Confer. held from 31 January - 4 February 2000 in Kathmandu, Nepal.

Bhattacharyya. A., V.C. LaMarche and F.W. Telewski.1988. Dendrochronological reconnaissance of the conifers of northwest India. Tree-Ring Bulletin 48:21-30

Borgaonkar. H.P., S. Ram and A.B. Sikder. 2009. Assessment of tree-ring analysis of high-elevation Cedrus deodara D. Don from Western Himalaya (India) in relation to climate and glacier fluctuations. Dendrochronol., 27(1): 59-69 https://doi.org/10.1016/j.dendro.2008.09.002

Briffa. K.R., P.D. Jones, J.R. Pilcher and M.K. Hughes.1988. Reconstructing summer temperatures in northern Fennoscandinavia back to AD 1700 using tree-ring data from Scots pine. Arctic Alpine Research., 20(4): 385-394 https://doi.org/10.1080/00040851.1988.12002691

Bukhari. S., G. Bajwa and S. Rehman. 2019. Climate-growth response function of the blue pine (Pinus wallichiana) in Galies Forest Division-Abbottabad, KP, Pakistan. Sar. J. Agric., 35:116-125 https://doi.org/10.17582/journal.sja/2019/35.1.116.125

Cook. E.R., K.R. Briffa, S. Shiyatov and V. Mazepa. 1990. Tree-ring standardization and growth-trend estimation. Methods of dendrochronology: Applications in the environmental sciences. Springer-Science+Business Media, USA.

Cook. E.R., B.M. Buckley, R.D. D’arrigo and M.J. Peterson. 2000. Warm-season temperatures since 1600 BC reconstructed from Tasmanian tree rings and their relationship to large-scale sea surface temperature anomalies. Clim. Dynam., 16: 79-91 https://doi.org/10.1007/s003820050006

Duchesne. L., L. D’Orangeville, R. Ouimet, D. Houle and D. Kneeshaw. 2017. Extracting coherent tree-ring climatic signals across spatial scales from extensive forest inventory data. PLOS One., 12(12): 1-20 https://doi.org/10.1371/journal.pone.0189444

Eckstein. D., V. Künzel, L. Schäfer and M. Winges. 2021. Global climate risk index 2020. Who suffers most from extreme weather events.Germanwatch. pp. 2000-2019

Fritts, H.C. and D.J. Shatz. 1975. Selecting and characterizing tree ring chronologies for dendroclimatic analysis. Tree-Ring Bullet., 35: 31-40

Fritts, H.C. 1974. Relationships of ring widths in arid-site conifers to variations in monthly temperature and precipitation. Ecol. Monograp., 44(4): 411-440 https://doi.org/10.2307/1942448

Fritts, H.C. 1976. Tree rings and climate Academic Press. New York.

German watch. 2025. Climate Risk Index. Top 10 Most affected countries (https://www.germanwatch.org/en/cri)

Ghani, R. 2020. Analysis of climatic variability and its impact on drought reoccurrences, intensity and trend prediction using multi-indices in Khyber Pakhtunkhwa, Pakistan. PhD thesis. University of Peshawar, Pakistan.

Gauli. A., P.R. Neupane, P. Mundhenk and M. Köhl. 2022. Effect of climate change on the growth of tree species: Dendroclimatological analysis. Forest., 13(4): 1-16 https://doi.org/10.3390/f13040496

Grissino-Mayer, H.D. 2001. FHX2 - Software for analyzing temporal and spatial patterns in fire regimes from tree rings. Tree-Ring Res., 57(1): 115-124

Holmes, R.L.1994. Dendrochronology program library user’s manual. Laboratory of tree-ring research, University of Arizona, Tucson.USA.

Khan. A., P.R. Sheppard and M. Ahmed. 2022. dendroclimatology of a mid-elevation forest in Pakistan. Pak. J. Bot., 54(2): 675-681 https://doi.org/10.30848/PJB2022-2(10)

Khan. N., M. Ahmed and M. Wahab. 2008. Dendroclimatic potential of Picea smithiana (Wall) Boiss, from Afghanistan. Pak. J. Bot., 40(3): 1063-1070.

Khan, S.R. 2004. Status of forest genetic resources conservation and management in Pakistan. In Forest Genetic Resources Conservation and Management: Proceedings of the Asia Pacific Forest Genetic Resources Programme (APFORGEN) Inception Workshop, Kepong, Kuala Lumpur, Malaysia, 15-18 July, 2003. 

Krishnan. R., A.B. Shrestha, G. Ren, R. Rajbhandari, S. Saeed, J. Sanjay, M.A. Syed, R. Vellore, Y. Xu, Q. You, and Y. Ren. 2019. Unravelling climate change in the Hindu Kush Himalaya: rapid warming in the mountains and increasing extremes. The Hindu Kush Himalaya assessment: Mountains, climate change, sustainability and people. https://doi.org/10.1007/978-3-319-92288-1_3

LaMarche Jr, V.C. 1974. Frequency-dependent relationships between tree-ring series along an ecological gradient and some dendroclimatic implications. Tree-ring Bullet.,Vol. 34: 1-20

Malla, R., P.R. Neupane and M. Köhl. 2023. Climate change impacts: Vegetation shift of broad-leaved and coniferous forests. Trees, Forest. People., 14: 100457. https://doi.org/10.1016/j.tfp.2023.100457.

Mazza, G. and D. Sarris. 2021. Identifying the full spectrum of climatic signals controlling a tree species’ growth and adaptation to climate change. Ecolog. Indicat., 130: 108-109 https://doi.org/10.1016/j.ecolind.2021.108109

Misi. D., R. Puchałka, C. Pearson, I. Robertson and M. Koprowski. 2019. Differences in the climate-growth relationship of Scots pine: A case study from Poland and Hungary. Forest., 10(3): 2-12 https://doi.org/10.3390/f10030243

Orvis, K.H. and H.D. Grissino-Mayer. 2002. Standardizing the reporting of abrasive papers used to surface tree-ring samples. Tree-Ring Res., 58(1/2): 47-50

Osborn, T.J. and P.D. Jones. 2014. The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth. Earth Sys. Sci. Data., 6(1): 61-68 https://doi.org/10.5194/essd-6-61-2014

IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

Pachauri. R.K., M.R.Allen, V.R.Barros, J.Broome, W.Cramer, R.Christ,J.A. Church, L.Clarke,Q.Dahe, P. Dasgupta and N.K. Dubash. 2014. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Ipcc. 2014

Pallardy, S.G., 2010. Physiology of woody plants. Academic press. 3rd edn. Missouri, Columbia.

Pavão, D.C.2023. Linking climate, distribution and growth in forest research: can dendrochronology improve species distribution modelling. PhD Thesis, University of the Azores, Portugal.

Pumijumnong. N., P. Songtrirat, S. Buajan, S. Preechamart, U. Chareonwong and C. Muangsong. 2021. Climate control of cambial dynamics and tree-ring width in two tropical pines in Thailand. Agri. For. Met., 303: 108394 https://doi.org/10.1016/j.agrformet.2021.108394

Pumijumnong. N., C. Muangsong, S. Buajan, P. Songtrirat, R. Chatwatthana and U. Chareonwong. 2023. Factors affecting cambial growth periodicity and wood formation in tropical forest trees: a review. Forest., 14(5): 2-28 https://doi.org/10.3390/f14051025

Rabbel. I., B. Neuwirth, H. Bogena, B. Diekkrüger. 2018. Exploring the growth response of Norway spruce (Picea abies) along a small-scale gradient of soil water supply. Dendrochronologia, 52:123-130.

Rauf. Z., A. Khan, S. Siddiqui, S. Saleem, T. Iqbal, S.A. Shah, N. Zarif and W. Iqbal. 2022. Radial growth, present status and future prospects of west Himalayan fir (Abies pindrow Royle) growing in the moist temperate forest of Himalayan mountains of Pakistan. J. For. Sci., 68(9): 344-356 https://doi.org/10.17221/3/2022-JFS

Rajput. K.S., K.K. Kapadane, D.G. Ramoliya, K.D. Thacker and A.D. Gondaliya. 2022. Inter-and intraxylary phloem in vascular plants: a review of subtypes, occurrences, and development. Forest., 13(12): 2174 https://doi.org/10.3390/f13122174

Schweingruber. F.H., D. Eckstein, F. Serre-Bachet and O.U. Bräker. 1990. Identification, presentation and interpretation of event years and pointer years in dendrochronology. Dendrochronol., 8: 9-38

Shah. H., N. Jehan, S.S. Rehman, and S.S.B. Bukhari. 2019. Comparative study of climate change and its impact on ring-widths of spruce (Picea smithiana) at Kalam and Kaghan Forest Divisions, Khyber Pakhtunkhwa, Pakistan. Sar. J. Agric., 35(3): 788-797 https://doi.org/10.17582/journal.sja/2019/35.3.788.797

Sheikh, M.I. 1993.Trees of Pakistan(Vol. 110). Islamabad Pictorial Printers.

Sheikh. M.M., N. Manzoor, N. Rehman, M. Adnan and A.M. Khan. 2015. Past and Projected Impacts of Climate Change on Forests of Pakistan. GCISC- National Centre for Physics Complex-Islamabad.

Siddiqui, K.M., M. Ayaz and M. Iqbal.1996. Properties and Uses of Pakistani Timbers. Forest Products Research Division. Pakistan Forest Institute, Peshawar.

Smerdon, J. E., D.S. Kaufman, A. Asrat. 2013. Continental-scale temperature variability during the past two millennia. Nature Geosci., 10: 1038

Sohar. K., J. Altman, E. Lehečková and J. Doležal. 2017. Growth–climate relationships of Himalayan conifers along elevational and latitudinal gradients. Int. J. Clim., 37(5): 2593-2605 https://doi.org/10.1002/joc.4867

Speer, J.H., 2010. Fundamentals of tree-ring research. 1st edn. University of Arizona Press, USA.

Stokes, M.A. and T.L. Smiley. 1968. An Introduction to Tree-Ring Dating. University of Arizona Press, USA.

Sullivan, P.F. and A.Z. Csank.2016 . Contrasting sampling designs among archived datasets: implications for synthesis efforts. Tree Physiol. 36(9):1057-1059

Toledo. M., L. Poorter, M. Peña-Claros, A. Alarcón, J. Balcázar, C. Leaño, J.C. Licona, O. Llanque, V. Vroomans, P. Zuidema and F. Bongers. 2011. Climate is a stronger driver of tree and forest growth rates than soil and disturbance. J. Ecol., 99(1): 254-264 https://doi.org/10.1111/j.1365-2745.2010.01741.x

Ullah, H., X. Wang, Q. Hussain, A. Khan, N. Ahmad, N. Ali, M.W. Riaz and I. Hussain. 2022. The climate-growth relationship between Picea smithiana (Wall.) Boiss. and Abies pindrow (Royle ex D. Don) Royle along the latitudinal gradient in Northern Pakistan. Forest. 13(8): 1315 https://doi.org/10.3390/f13081315

Van der Maaten-Theunissen, M., E. Van der Maaten and O. Bouriaud. 2015. pointRes: an R package to analyze pointer years and components of resilience. Dendrochronol., 35: 34-38 https://doi.org/10.1016/j.dendro.2015.05.006

Wigley, T.M., K.R. Briffa and P.D. Jones. 1984. On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. J. Appl. Met. Clim. 23(2): 201-213 https://doi.org/10.1175/1520-0450(1984)023<0201:OTAVOC>2.0.CO;2

WINDENDRO, 2014a. WinDendro Reference Manual, Version 2014a, December. 2014. Regent Instruments, Quebec. Canada.

Wu, X. D.1990. Tree rings and climate change. China Meteorological Press, Beijing, China.

Yeh, H.Y. and L.C. Wensel. 2000. The relationship between tree diameter growth and climate for coniferous species in northern California. Canad. J. Forest Res., 30 (9): 1463-1471 https://doi.org/10.1139/x00-074

Zafar. M.U., M. Ahmed, M.A. Farooq, M. Akbar and A. Hussain. 2010. Standardized tree ring chronologies of Picea smithiana from two new sites of Northern area Pakistan. World Appl. Sci. J., 11(12):1531-1536

Zang, C. and F. Biondi. 2015. treeclim: an R package for the numerical calibration of proxy-climate relationships. Echograp., 38(4): 431-436 https://doi.org/10.1111/ecog.01335