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Article

Distribution and Physiology of Juniperus seravschanica Trees in the Genow—The Southernmost and Arid Habitat of Iran

by
Abdolrahman Rahimian Boogar
1,*,
Hassan Salehi
2 and
Esmaeel Seyedabadi
3
1
Department of Horticultural Science and Landscape Engineering, Faculty of Agriculture, University of Zabol, Zabol 98613-35856, Iran
2
Department of Horticultural Science, School of Agriculture, Shiraz University, Shiraz 71441-65186, Iran
3
Department of Agronomy, Faculty of Agriculture, University of Zabol, Zabol 98613-35856, Iran
*
Author to whom correspondence should be addressed.
Water 2022, 14(21), 3508; https://doi.org/10.3390/w14213508
Submission received: 2 August 2022 / Revised: 27 September 2022 / Accepted: 27 October 2022 / Published: 2 November 2022
(This article belongs to the Special Issue Advances in Sustainable Agriculture Progress under Climate Change)
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Abstract

:
Juniperus seravschanica is the southernmost population of Juniperus that has a limited habitat in the world near the equator. In Iran, the lone habitat of this species in the Genow mountains has been endangered with thin foliage, abscissing needles, and dried shoots. The current study investigated the effects of climatic, genetic factors, and physiologic indices on the distribution of J. seravschanica. Distribution was evaluated for 450 ha and physiological indices were evaluated for two groups: (A) trees with dried branches and (B) trees without dried branches. Results showed that the distribution of J. seravschanica in the Genow habitat was influenced by elevation, slope degree, aspect, and distance to stream. Results also indicated that max temperature and precipitation are two effective factors that have the highest effects on falling needles and drying branches of J. seravschanica. Chlorophyll, relative water content (RWC), and relative turgidity (RT) are significantly influenced by max temperature. Endangered trees with dried branches had a lower chlorophyll content, RWC, and RT than trees without dried branches. Vulnerability of J. seravschanica was significantly influenced by its genetic structure. Results of AMOVA showed 83% genetic variability between two groups of J. seravschanica trees.

1. Introduction

Genow is the most important mountainous region, with a unique ecosystem, near the coast of the Persian Gulf in southern Iran. There is high biodiversity of ornamental, medicinal, and animal species in this habitat, which creates a most attractive ecological landscape for tourism [1,2,3]. Climatically, the Hormozgan province has a dry and warm climate, but the climatic factors at the peak of the Genow mountains are lower than the surrounding lowlands [4].
Juniperus seravschanica is an evergreen species of conifers, distributed in Asia in Kazakhstan, Kyrgyzstan, Afghanistan, Pakistan, Iran, and Oman [5,6]. Forests of J. seravschanica have widespread distribution in the south and southeast of Iran, including the natural habitats of Kharmankohe in Fars, and Kohbanan, Khabr, and Rabor in Kerman [7,8]. Therefore, the southernmost populations of Juniperus sp. have an isolated and fragmented habitat far from the habitats of the species located in the Genow mountain [9]. Juniperus sp. has been used by local human as fence plants to protect crop field, and collected their wood for building and fuelwood [10]. Moreover, Juniperus sp. has various usage in traditional medicine according to their activities on anti-inflammatory, antioxidant, antimicrobial, anticholinesterase, and antirheumatic, and they are used to treat or cure, chronic arthritis, gout, and diabetes [11,12]. Also, some species of Juniper with more drooping foliage, and their resistance to drought and water deficit, are suitable for cultivation as an ornamental shrub and tree in urban and ecological landscapes [13,14,15]. In the present decade, drought extension and climate change have led to a recession of J. seravschanica habitats in semi-arid or arid zones [16,17]. Moreover, in southern arid and semi-arid areas in Iran, the max and min temperatures are the most effective factors in the habitat suitability of junipers [18]. Other studies showed that the habitat suitability of Kirengeshoma koreana is influenced by soil profundity, water availability, and light intensity in the floor of the forest in the Mt. Baek-un region, South Korea [19]. Genetic diversity is an important factor in species adaptations against climate change and abiotic stresses [20,21].
Climate changes often along with drought, heat, and salinity stresses have intense effects on plants’ physiological functions [22,23,24]. Relative water content (RWC) is generally influenced by stress and it is a better indicator of water stress for conifers [25]. Furthermore, Juniperus przewalskii has a different pattern of transpiration due to seasons and vapor pressure [26]. Moreover, the study of Picea abies validated that drought stress causes conifer xylem pressure loss [27]. Water stress causes xylem cavitation to occur in the leaves, roots, and stems of plants, and many studies investigated the susceptibility of plant tissue to cavitation and its impact on plant durability and productivity [28,29]. Chlorophyll content is another indicator of stress impacts on plants that is generally used for the investigation of plant behavior under stress conditions [30].
However, in recent decades, the increase in wrongful human activities, quick industrialization, and climate change have led to a threat to plants in natural habitats [31,32]. Hence, conservation of rare species and their habitat is very important for protecting the biodiversity and range of landscapes to improve the life, work, and play environments for human societies [33]. Furthermore, protecting genetic diversity is an essential approach to conserving forest trees in ecological landscapes. In this regard, the gene flow within and among populations has a significant effect on the outlines of genetic variation [21]. Moreover, pollen movement is recognized as a very effective factor for conserving genetic variation within and between populations and their relationship in natural forests [34,35].
Genow is the southernmost habitat of J. seravschanica in Iran, which is influenced by climate change. A previous investigation into the changes in hydro-climate factors of the past 30 years was performed on the southernmost basin of Iran (36), which found that the Genow habitat had an increase in average temperature (0.50–0.75 °C), and decreases in precipitation (75–82 mm/year), runoff (1–20 mm/year), and evapotranspiration (1–25 mm/year), and also a reduced amount of water storage (40–41 mm/year) during the period 1986–2016 [36]. In this study, this habitat was investigated through three major aims: first, to recognize the distribution pattern of endangered trees of J. seravschanica in the Genow habitat, second, to investigate the importance of climatic factors affecting physiological indices of J. seravschanica, and, third, to assess the genetic diversity within and among the population for endangered and healthy trees.

2. Materials and Methods

2.1. Study Area

The growing site of J. seravschanica is located in the Genow area in Hormozgan province, with geographical features at 56°5′58″ E and 27°23′42″ N. The Genow habitat is the lone ecological landscape near Bandar Abbas city, and its biodiversity is influenced by conditioning factors and local human activities. The current study covers approximately 450 ha of the natural habitat of J. seravschanica in Genow. The studied area has a moderate climate, with a long-term annual average temperature of 34–35 °C and rainfall of 50–100 mm (http://www.irimo.ir (accessed on 20 August 2021)). Topographically, the elevation of the investigated site area ranges from 1480 to 2291 m above sea level according to the digital elevation model (DEM) of the study area, while slope degrees range from 0 to 68.82° (Figure 1).

2.2. Assaying the Effects of Topographical and Climatic Factors on Species Distribution

To create species distribution maps, this study was conducted with three stages: (1) generating a species distribution record map of J. seravschanica in the natural habitat of Genow, (2) preparation of the dataset, (3) multicollinearity analysis of different independent variables [18]. To generate a distribution roster map of the J. seravschanica. At first, the natural habitats of this species were identified in the Genow area. Following that, the location of the presence of 60 trees without dried branches and the location of 70 trees with dried branches of J. seravschanica species within 450 ha of the studied site were recorded using extensive field surveys and the Handy GPS app for Android (version 32.6, https://www.binaryearth.net/HandyGPS/index.php (accessed on 10 October 2021)).
Multicollinearity analysis was carried out on four independent climatic factors, including min/max temperatures, wind speed, and annual mean precipitation. The annual mean of four climatic factors was obtained from the Hormozgan Meteorological Bureau (http://www.hormozganmet.ir (accessed on 20 August 2021)). In collinearity analysis, when the variance inflation factor (VIF) is more than or equal to five and tolerance (T) is less than 0.1, then that means that the investigated conditional variables have collinearity [37]. Furthermore, topographical parameters, including slope, aspect, plan and profile curvatures, stream, distance to stream, and elevation, were extracted from ALOS-DEM with 12.5 m × 12.5 m resolution. The DEM was downloaded from the website of the ALOS PALSAR satellite (https://vertex.daac.asf.alaska.edu/ (accessed on 11 January 2022)).

2.3. Physiological Evaluation of Population

For investigation into the physiological indices and genetic diversity of the endangered population of J. seravschanica, in the natural ecological habitat in Genow, trees were divided into two group: trees with dried branches (A) and trees without dried branches (B) (Figure 2).

2.3.1. Physiological Assessment

For assessments of the physiological indices, fresh needles were collected from ten trees of J. seravschanica, including five trees with dried branches (group A) and five trees without dried branches (group B). Fresh samples were transferred in a polystyrene box to the Laboratory of Physiology, Department of Horticultural Science, Shiraz University, Shiraz, Iran.
The relative water content (RWC) was measured as needle water content according to [38,39,40]. One gram of fresh needles was weighed (FW) and dried in an oven at 70 °C for 24 h, and then its dry weight (DW) was recorded. The RWC was calculated by the following formula: RWC (%) = (FW−DW)/FW × 100. Relative turgidity (RT) of foliage was measured according to Weatherley [41], and 1 g FW of foliage was weighed and floated on distilled water for 24 h. After floating, samples were removed and quickly surface dried with filter paper and fully turgid (FT) weighed; then samples were dried and their DW measured. Finally, relative turgidity was calculated according to field water content/water content of fully turgid tissue with the following formula:
RT   % = Field   water   content   FW − DW water   content   of   fully   turgid   tissue   FT − DW × 100
Foliage water deficit (WD) was assessed according to the protocol described by Barrs [42] and Grzesiak et al. [40], and calculated via the formula: WD (%) = 100 − RT. For electrolyte leakage (EL) assessment, an equal amount of shoot tips sample (1 g) was weighed and placed in 40 × 50 mm culture vessels containing 15 mL distilled water and shaken at 80 rpm on an orbital shaker (Parzan Pajouh Shaker, made in Iran) for 24 h at room temperature. The first conductance of the solutions was measured by a conductivity meter, and then culture vessels autoclaved at 115 °C for 10 min and samples were stored at room temperature for 24 h, and, finally, the second conductance of the solutions was measured by a conductivity meter. EL (%) was calculated as initial measurements/final measurements × 100 [43].
Dimethyl-sulphoxide (DMSO) was used to extract chlorophyll and total chlorophyll assessment [44]. For decolorization of leaf tissue, 5 mL of DMSO was added to a tube having 1 g fresh leaf. Tubes were preheated at 65 °C in a water bath for 24 h, and following were cooled at room temperature for 30 min. Next, extract separation and absorbance at 645 nm, and 663 nm measured.
Additionally, proline content was extracted and analyzed according to the method of Bates [45]. Ten mL of sulfosalicylic acid 3% was added to 200 mg of leaf samples and remained for 48 h at room temperature. Then, 2 mL of the supernatant phase of solution was taken and added to ninhydrin and acetic acid (2 mL of each), and heated at 78 °C in a water bath for 1 h, and following samples were cooled on ice, then toluene (4 mL) was added and vortexed for 20 s. Finally, the supernatant phase containing red complex was used to measure the proline at 520 nm with a spectrophotometer (Shimadzu UV-160A Model, Kyoto, Japan).

2.3.2. Assessment of Genetic Diversity

For genetic diversity assessment, samples were collected from two groups of trees (with and without dried branches). Samples were transferred to the laboratory of Tissue Culture and Biotechnology, Department of Horticultural Science, Shiraz University, in liquid nitrogen and stored in a −25 °C freezer until used. DNA was extracted according to the modified cetyltrimethylammonium bromide (CTAB) method [46]. Moreover, twelve ISSR primers were used to recognize the genetic diversity within and among two groups of trees with and without dried branches.

2.4. Data Analysis

The independent-samples t-test was used to compare means of physiological indices for two groups of investigated trees, and the correlation of climatic factors and factors collinearity were analyzed by linear regression using SPSS v. 26. For investigation of genetic diversity within and among two groups of J. seravschanica, AMOVA analysis was carried out using GeneAleX software, and genetic similarity of evaluating groups assessed with Jaccard’s similarity (J) coefficient using the SIMQUAL program in NTSYSpc v. 2.20.

3. Results

3.1. Topographical Factors’ Effects on Species Distribution

Extracted distribution maps showed that topographical factors have effective roles in the pattern of J. seravschanica distribution in the Genow habitat. Elevation especially influenced the distribution of trees with dried branches (Sample_E) and without dried branches (Sample_H) of J. seravschanica in the ecological landscape of Genow (Figure 3), and the highest density of species in this habitat was observed at 1950–2100 m ASL. Furthermore, slope and aspect maps indicated that the J. seravschanica distribution in the Genow habitat was affected by these two topographic factors. Therefore, this species has greater density on lower slopes, and, in the aspect of northeast, trees were more dispersed (Figure 4). Investigation of streams in the Genow habitat showed that J. seravschanica trees were distributed in the areas closer to streams (Figure 5).

3.2. Climatic Factors’ Effects on Species Distribution

Results of linear regression models showed that climatic factors investigated in this study had no collinearity (Table 1). According to these results, the lowest T (0.626) and the highest VIF (2.383) were obtained for precipitation (Table 1).
Results of Pearson correlation analysis showed a significant negative correlation between tree groups with max temperature, and there was a significant positive correlation between tree groups with precipitation. Moreover, among the climatic factors, max temperature was negatively correlated with precipitation (Table 2). Correlation analysis of climatic factors and physiological indices showed a positive correlation between max temperature and proline content, WD, and EL of investigated trees. However, the max temperature had a negative correlation with the chlorophyll content, RWC, and RT (Table 2). Also, annual precipitation was negatively correlated with proline content (Table 2). Therefore, an increase in the range of max temperature and reduction of annual precipitation affects the physiological reaction of J. seravschanica and influences this species’ decline in the ecological landscape of Genow.
Moreover, these results showed a significant correlation between investigated physiological indices and two groups of J. seravschanica trees (Table 2). Also, there was a significant positive correlation among chlorophyll content, RWC, and RT in the investigated groups of trees. Furthermore, trees without dried branches (group B) had more chlorophyll content, RWC, and RT, while trees with dried branches (group A) had lower chlorophyll content, RWC, and RT. Therefore, proline content, WD, and EL had a significant negative correlation with groups of trees. Hence, endangered trees with dried branches (group A) had high proline content and more WD and EL than trees without dried branches (group B) (Table 2). Correlation of physiological indices showed that RWC and RT were negatively correlated with WD, EL, and proline content. WD had a positive correlation with EL and proline, but its correlation with chlorophyll content was negative. The EL and proline content had a negative correlation with chlorophyll content, and EL had a positive correlation with the proline content (Table 2).
In addition, an analysis of the relative importance of climatic factors indicated that in the ecological landscape of Genow, max temperature had the strongest effects on J. seravschanica declining than other conditional factors, and precipitation was the second strongest factor (Figure 6).

3.3. Physiological Assessments

Data analysis showed significant differences between two groups of trees for all investigated physiological indices (Table 3). Group statistics showed higher means of proline content, WD, and EL for trees with dried branches than for trees without dried branches (Table 4), while trees with dried branches had a lower amount of chlorophyll content, RWC, and RT than trees without dried branches (Table 4).

3.4. Populations’ Genetic Assessments

Investigation of genetic diversity for two groups of J. seravschanica population existing in the natural habitat of Genow showed significant differences between two groups of investigated trees, with and without dried branches (Table 5). Also, AMOVA analysis showed 83% variation among groups and 17% variation within groups (Table 5). Furthermore, the Jaccard coefficient showed a low similarity between trees with 50% living foliage and trees with 100% living foliage (Figure 7).

4. Discussion

This investigation of J. seravschanica distribution in the ecological landscape of Genow showed that topographical features had effective roles in J. seravschanica distribution. In this regard, the effects of topographic features were confirmed in the restoration of vegetation in a coal mine area [47]. As well, another study investigated the effects of topographic factors on plant distribution and change in several topographic features in the three parallel rivers region of the Tibet plateau, where improvement in the vegetation cover was reported over the period of years from 2000 to 2019 [48]. Therefore, among climatic factors, max temperature had the greatest effects on J. seravschanica declining in the Genow habitat. These results conform to the previous study, which showed that J. seravschanica has more living foliage in cooler and moister conditions [17]. Therefore, temperature is the most important factor that affects the yield and metabolic activities of the plants’ community [49,50]. Moreover, there are interactions between topographical and climatic factors that influence the distribution of plants [51]. Our results showed that J. seravschanica had the lowest distribution in high slope and southern aspects. In high slopes and southern aspects, moisture is usually low and temperature is high. Furthermore, according to the negative correlation between temperature and precipitation in the Genow habitat, the increase of max temperature has occurred alongside the decrease in precipitation, and this change significantly influences the viability of J. seravschanica in this ecological landscape. This result conforms to the results of previous studies on the effects of heat waves on decrease in frequency and amount of rainfall [52]. Therefore, an increase in max temperature can influence biodiversity and ecosystems community by two major impacts: first, it influences biodiversity by the pressure of heat stress, and, second, causes precipitation loss and water limitation, which both influences the declining of J. seravschanica in the ecological landscape of the Genow habitat. These results are similar to prior studies on climate factors and biodiversity [50,53,54,55]. In addition, the studies of the species habitat suitability confirmed that max temperature is the most important factor for habitat suitability for the presence of species [18,56,57].
According to the results of the current study, there were differences in investigated physiological indices of two groups of J. seravschanica trees: trees with dried branches have a lower RWC of needles than trees without dried branches. This result is consistent with the effects of climate change and drought extension on shedding leaves, limiting the growth and reducing the productivity of woody plants [58,59,60]. In the Genow habitat, max temperature and precipitation had the greatest effects on the durability of J. seravschanica. In this regard, previous studies had shown that extension of drought stress and limitation of soil water significantly affected physiological indices and RWC of Douglas-fir [61,62]. Also, many studies have shown the negative outcomes of drought on forest tree mortality or decline across landscapes [63,64,65]. Water deficit (WD) is another important parameter that is generally used to investigate the physiological responses of species under climate change and drought stress [66,67,68]. Hence, in the current study, the trees of J. seravschanica with lower living foliage had the highest WD and EL. In this regard, the findings of Poyatos et al. [69] showed that water-deficit in Pinus sylvestris L. and Quercus pubescens Willd is affected by a combination of two atmospheric and edaphic factors. Therefore, shedding of leaves and growth declining are two main mechanisms occurring in sensitive genotypes of different plants under climate change and drought extension [66,67,70,71]. In the Genow habitat, a decrease of RWC in needles of sensitive genotypes of J. seravschanica leads to defoliation and a decrease in total leaves area; these phenomena were stronger for trees with dried branches. As well, the current study showed that leaves of endangered trees with dried branches had high proline content than trees without dried branches. The current result is aligned with the finding of Diamantoglou and Rhizopoulou [72] regarding proline accumulation in the leaf and bark of Pinus halepensis. In this regard, many studies have shown water stress causes an increase in the concentration of proline in the leaves of plants [73,74,75]. An increase in proline is a powerful mechanism of plant cultivar for tolerance to stress conditions [76,77]. Moreover, the results of the current study showed that trees with dry branches have a lower mean of chlorophyll content. Drought is an important phenomena of climate change and abiotic stress that is affecting chlorophyll content [78]. Under drought stress, the leaf water content will decrease and causes reduction in chlorophyll synthesis rate, while the chlorophyll degradation rate is increased [78,79]. So, in the current study, reduction of chlorophyll content in leaves of trees with dry branches occurred due to this genotype sensitivity to drought and climate change. Max temperature and low precipitation in the Genow habitat can reduce photosynthesis and cause starvation of endangered trees that consequently cause the canopy to wither and shoots to dry, finally resulting in tree death. A previous study reported that an increase in abiotic stresses significantly affected forest degradation [80].
Moreover, the genetic assessment showed variation within the populations of J. seravschanica that exist in the Genow habitat. Two groups of trees with dried branches and without dried branches, assessed in this study, had significant differences genetically. Therefore, in the populations of J. seravschanica existing in the Genow habitat, there are different genotypes, and their vulnerability under climate change depends on genotype characteristics. In this regard, Moran et al. [81] confirmed the relationship between species genetics and their reaction under drought conditions. They reported an effective association between single nucleotide polymorphisms (SNP) and aridity. Other studies reported that the reduction of leaf area by needle abscission and branch death are behaviors of some species of conifers for water loss adjustment under drought and climate change conditions [82,83]. In addition, range shift is the other response of plant species for local adaptation under climate change and can alter the genetic diversity within species in their natural habitat [84,85]. The current study has shown that genotypes without dried branches have higher stability in the face of harmful climate conditions, and that is promising. Plants have different efficiency in adaptation against stress due to genotypes and genetic diversity [73]. Hence, the J. seravschanica trees in the Genow habitat have a logical reaction against climate change according to their genotype, and the genotypes that have no dried branches have high adaptation under max temperature and low precipitation. However, endangered genotypes lose RWC, RT, and chlorophyll content followed by the drying of branches. Consequently, these genotypes may die under intense climate change with high temperatures.

5. Conclusions

This investigation of topographical and climatic factors, physiological indices, and population genetic diversity assessment of J. seravschanica declining in the ecological landscape of the Genow habitat showed that the distribution of J. seravschanica can be influenced by slope degree, aspect, elevation, and distance to streams. Higher temperatures and lower precipitation in the Genow habitat are vulnerable conditions for the Juniperus seravschanica. In response to warming temperatures, the genotypes of J. seravschanica with more adaptability have higher durability in the Genow habitat. Moreover, warming continues to be a cause that increases species decline in lower elevations and restricts habitat to higher elevations where conditions are cooler. As well, in response to low precipitation, the J. seravschanica habitat is restricted to closer distances to streams. In addition, aspect and slope degree affect the microclimate of trees. The north aspect with low slope is more suitable for growth of J. seravschanica. Moreover, in the Genow habitat, max temperature negatively influenced trees’ physiological activities and caused the reduction of chlorophyll content, RWC, and RT. Conversely, proline content, WD, and EL increased under max temperature conditions. On the other hand, precipitation is an effective climatic factor that causes the reduction of max temperature and enhances J. seravschanica’s physiological activities. Consequently, future programs for the cultivation and conservation of J. seravschanica should consider the simultaneous effects of slope, aspect, elevation, temperature, and precipitation. Moreover, high genetic variation between two groups of investigated J. seravschanica trees and low within-groups diversity logically is related to whether this species declines or endures. Hence, landscape and conservation managers should use asexual propagation of elite genotypes for forthcoming cultivation. These results can be used in the decision-making process for the conservation of J. seravschanica in the ecological landscape of Genow or other similar habitats where Juniperus are under threat. The results of this study can also be used by resource managers through biodiversity management, in particular when considering the importance of climatic factors for species conservation. Furthermore, the results of genetic diversity are very important for the recognition of elite accessions and decision-making regarding their propagation and future cultivation.

Author Contributions

Conceptualization, A.R.B. and E.S.; Methodology, A.R.B. and E.S., Software, A.R.B. and H.S.; Formal analysis, A.R.B., H.S., and E.S.; investigation, A.R.B., H.S. and E.S.; Writing—original draft preparation, A.R.B. and E.S.; Writing—review and editing, A.R.B. and E.S.; Project administration, A.R.B., H.S. and E.S.; Funding acquisition, A.R.B. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by University of Zabol (IR-UOZ-GR-8952).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

  1. Najafi Tireh-Shabankareh, K.; Jalili, A.; Khorasani, N.; Jamzad, Z.; Asri, Y. Flora, life forms and chorotypes of plants in the Genu protected area, Hormozgan province (Iran). Pajouhesh Sazandegi 2006, 69, 50–62. (In Persian) [Google Scholar]
  2. Makhdoum, M. National, Forest and Natural Parks; University Publication Center: Tehran, Iran, 2011. (In Persian) [Google Scholar]
  3. Soltanipoor, M.A.; Jonubi, P.; Hesamzadeh Hejazi, S.M.; Mirza, M. Morphometric study of the medicinal and endangered Zhumeria majdae. J. Plant Res. 2018, 31, 1–13. (In Persian) [Google Scholar]
  4. Poorasghariyan, A.; Sisipoor, M.; Ranjbar, S. Evaluation of Bandar-Abbas climate towards the development of tourism. In Proceedings of the 1st National Congress of Health, Environment and Susceptible Development, Islamic Azad University, Bandar-Abas Branch, Bandar Abbas, Iran, 20 February 2011; pp. 1–8. [Google Scholar]
  5. Adams, R.P.; Hojjati, F. Taxonomy of Juniperus in Iran: Insight from DNA sequencing. Phytologia 2012, 94, 219–227. [Google Scholar]
  6. Adams, R.P.; Al-Farsi, A.; Schwarzback, A.E. Confirmation of the southern-most population of Juniperus seravschanica in Oman by DNA sequencing of nrDNA and four cpDNA regions. Phytologia 2014, 96, 218–224. [Google Scholar]
  7. Adams, R.P.; Hojjati, F.; Schwarzbach, A.E. Taxonomy of Juniperus in Iran: DNA sequences of nrDNA plus three cpDNAs reveal Juniperus polycarpos var. turcomanica and J. seravschanica in southern Iran. Phytologia 2014, 96, 19–25. [Google Scholar]
  8. Hojjati, F.; Kazempour-Osaloo, S.; Adams, R.P.; Assadi, M. Molecular phylogeny of Juniperus in Iran with special reference to the J. excelsa complex, focusing on J. seravschanica. Phytotaxa 2018, 375, 135–157. [Google Scholar] [CrossRef]
  9. Jafari, M. Forests Genetic Resources in Iran. The Report on the State of the World’s Forest Genetic Resources. 2012, pp. 1–133. Available online: www.fao.org/documents (accessed on 13 June 2012).
  10. Bhattarai, S.; Chaudhary, R.P.; Taylor, R.S.L. Plants used as fence and fuelwood in manang district, Central Nepal. Sci. World 2007, 5, 107–111. [Google Scholar] [CrossRef]
  11. Miceli, N.; Marino, A.; Köroğlu, A.; Cacciola, F.; Dugo, P.; Mondello, L.; Taviano, M.F. Comparative study of the phenolic profile, antioxidant and antimicrobial activities of leaf extracts of five Juniperus L. (Cupressaceae) taxa growing in Turkey. Nat. Prod. Res. 2018, 34, 1636–1641. [Google Scholar] [CrossRef]
  12. Zhang, Y.; Wu, D.; Kuang, S.; Qing, M.; Ma, Y.; Yang, T.; Wang, T.; Li, D. Chemical composition, antioxidant, antibacterial and cholinesterase inhibitory activities of three Juniperus species. Nat. Prod. Res. 2019, 34, 3531–3535. [Google Scholar] [CrossRef]
  13. Ahani, H.; Jalilvand, H.; Hosseini-Nasr, S.M.; Soltani-Kouhbanani, H.; Ghazi, M.R.; Mohammadzadeh, H. Reproduction of juniper (Juniperus polycarpos) in Khorasan Razavi, Iran. Sci. Pract. 2013, 15, 231–237. [Google Scholar] [CrossRef]
  14. Farjon, A. Juniperus communis. In The IUCN Red List of Threatened Species; IUCN: Cambridge, UK, 2013. [Google Scholar] [CrossRef]
  15. Kentelky, E. The analysis of rooting and growth peculiarities of Juniperus species propagated by cuttings. Bull. UASVM Hortic. 2011, 68, 380–385. [Google Scholar]
  16. El-Juhany, L. Forestland degradation and potential rehabilitation in southwest Saudi Arabia. Aust. J. Basic Appl. Sci. 2009, 3, 2677–2696. [Google Scholar]
  17. MacLaren, C.A. Climate change drives decline of Juniperus seravschanica in Oman. J. Arid. Environ. 2016, 128, 91–100. [Google Scholar] [CrossRef]
  18. Rahimian Boogar, A.; Salehi, H.; Pourghasemi, H.R.; Blaschke, T. Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques. Water 2019, 11, 2049. [Google Scholar] [CrossRef] [Green Version]
  19. Han, H.; Cho, S.; Song, J.; Seol, A.; Chung, H.; Kim, J.; Chung, J. Assessing the potential suitability of forest stands as Kirengeshoma koreana habitat using MaxEnt. Landsc. Ecol. Eng. 2014, 10, 339–348. [Google Scholar] [CrossRef]
  20. Sork, V.L. Gene flow and natural selection shape spatial patterns of genes in tree populations: Implications for evolutionary processes and applications. Evol. Appl. 2016, 9, 291–310. [Google Scholar] [CrossRef]
  21. Oyama, K.; Herrera-Arroyo, M.L.; Rocha-Ramírez, V.; Benítez-Malvido, J.; Ruiz-Sánchez, E.; González-Rodríguez, A. Gene flow interruption in a recently human-modified landscape: The value of isolated trees for the maintenance of genetic diversity in a Mexican endemic red oak. For. Ecol. Manag. 2017, 390, 27–35. [Google Scholar] [CrossRef]
  22. Becklin, K.M.; Anderson, J.T.; Gerhart, L.M.; Wadgymar, S.M.; Wessinger, C.A.; Ward, J.K. Examining Plant Physiological Responses to Climate Change through an Evolutionary Lens. Plant Physiol. 2016, 172, 635–649. [Google Scholar] [CrossRef] [Green Version]
  23. Ashraf, M.A.; Akbar, A.; Askari, S.H.; Iqbal, M.; Rasheed, R.; Hussain, I. Recent Advances in Abiotic Stress Tolerance of Plants Through Chemical Priming: An Overview. In Advances in Seed Priming; Springer: Berlin/Heidelberg, Germany, 2018; pp. 51–79. [Google Scholar]
  24. Benevenuto, R.F.; Agapito-Tenfen, S.Z.; Vilperte, V.; Wikmark, O.-G.; Van Rensburg, P.J.; Nodari, R.O. Molecular responses of genetically modified maize to abiotic stresses as determined through proteomic and metabolomic analyses. PLoS ONE 2017, 12, e0173069. [Google Scholar] [CrossRef] [Green Version]
  25. Jolly, W.M.; Hadlow, A.M.; Huguet, K. De-coupling seasonal changes in water content and dry matter to predict live conifer foliar moisture content. Int. J. Wildland Fire 2014, 23, 480–489. [Google Scholar] [CrossRef] [Green Version]
  26. Wang, F.; Zhang, F.; Gou, X.; Fonti, P.; Xia, J.; Cao, Z.; Liu, J.; Wang, Y.; Zhang, J. Seasonal variations in leaf-level photosynthesis and water use efficiency of three isohydric to anisohydric conifers on the Tibetan Plateau. Agric. For. Meteorol. 2021, 308–309, 108581. [Google Scholar] [CrossRef]
  27. Arend, M.; Link, R.M.; Patthey, R.; Hoch, G.; Schuldt, B.; Kahmen, A. Rapid hydraulic collapse as cause of drought-induced mortality in conifers. Proc. Natl. Acad. Sci. USA 2021, 118, e2025251118. [Google Scholar] [CrossRef]
  28. Brodribb, T.J.; Cochard, H. Hydraulic failure defines the recovery and point of death in water stressed conifers. Plant Physiol. 2009, 149, 575–584. [Google Scholar] [CrossRef] [Green Version]
  29. Choat, B.; Jansen, S.; Brodribb, T.J.; Cochard, H.; Delzon, S.; Bhaskar, R.; Bucci, S.J.; Feild, T.S.; Gleason, S.M.; Hacke, U.G.; et al. Global convergence in the vulnerability of forests to drought. Nature 2012, 491, 752–755. [Google Scholar] [CrossRef] [Green Version]
  30. Zavafer, A.; Labeeuw, L.; Mancilla, C. Global Trends of Usage of Chlorophyll Fluorescence and Projections for the Next Decade. Plant Phenomics 2020, 2020, 6293145. [Google Scholar] [CrossRef]
  31. Kelleher, C.T.; de Vries, S.M.G.; Baliuckas, V.; Bozzano, M.; Frýdl, J.; Goicoechea, P.G.; Ivankovic, M.; Kandemir, G.; Koskela, J.; Kozioł, C.; et al. Approaches to the Conservation of Forest Genetic Resources in Europe in the Context of Climate Change; European Forest Genetic Resources Programme (EUFORGEN), Bioversity International: Rome, Italy, 2015; pp. i–xiv, 1–46. [Google Scholar]
  32. Kim, E.H.; Shin, J.K.; Jeong, K.S.; Lee, C.S.; Chung, J.M. Genetic variation and structure of Juniperus chinensis L. (Cupressaceae) in Korea. J. Ecol. Environ. 2018, 42, 14. [Google Scholar] [CrossRef]
  33. Nilon, C.H. Urban biodiversity and the importance of management and conservation. Landsc. Ecol. Eng. 2011, 7, 45–52. [Google Scholar] [CrossRef]
  34. Burczyk, J.; Chybicki, I.J. Cautions on direct gene flow estimation in plant populations. Evolution 2004, 58, 956–963. [Google Scholar] [CrossRef]
  35. Hamrick, J.L. Response of forest trees to global environmental changes. For. Ecol. Manag. 2004, 197, 323–335. [Google Scholar] [CrossRef]
  36. Moshir Panahi, D.; Kalantari, Z.; Ghajarnia, N.; Seifollahi-Aghmiuni, S.; Destouni, G. Variability and change in the hydroclimate and water resources of Iran over a recent 30-year period. Sci. Rep. 2020, 10, 7450. [Google Scholar] [CrossRef]
  37. O’Brien, R.M. A Caution Regarding Rules of Thumb for Variance Inflation Factors. Qual. Quant. 2007, 41, 673–690. [Google Scholar] [CrossRef]
  38. AOAC. Official Methods of Analysis; Association of Official Analysis Chemists: Arlington, VA, USA, 2008; pp. 1–15. [Google Scholar]
  39. Kovar, M.; Brestic, M.; Sytar, O.; Barek, V.; Hauptvogel, P.; Zivcak, M. Evaluation of Hyperspectral Reflectance Parameters to Assess the Leaf Water Content in Soybean. Water 2019, 11, 443. [Google Scholar] [CrossRef] [Green Version]
  40. Grzesiak, S.; Hordyńska, N.; Szczyrek, P.; Grzesiak, M.T.; Noga, A.; Szechyńska-Hebda, M. Variation among wheat (Triticum easativum L.) genotypes in response to the drought stress: I—Selection approaches. J. Plant Interact. 2019, 14, 30–44. [Google Scholar] [CrossRef] [Green Version]
  41. Weatherley, P.E. Studies in the water relations of the cotton plant. New Phytol. 1950, 49, 81–97. [Google Scholar] [CrossRef]
  42. Barrs, H.D. Determination of Water Deficit in Plant Tissues. In Water Deficit and Plant Growth; Kozlowski, T.T., Ed.; Academic Press: New York, NY, USA, 1986; pp. 235–368. [Google Scholar]
  43. Bajji, M.; Kinet, J.M.; Lutts, S. The use of the electrolyte leakage method for assessing cell membrane stability as a water stress tolerance test in durum wheat. Plant Growth Regul. 2002, 36, 61–70. [Google Scholar] [CrossRef]
  44. Blanke, M.M. Determination of chlorophyll using DMSO. Vitic. Enol. Sci. 1992, 47, 32–35. [Google Scholar]
  45. Bates, L.S.; Waldren, R.P.; Teare, I.D. Rapid determination of free proline for water-stress studies. Plant Soil 1973, 39, 205–207. [Google Scholar] [CrossRef]
  46. Lodhi, M.A.; Ye, G.N.; Weeden, N.F.; Reisch, B.I. A Simple and Efficient Method for DNA Extraction from Grapevine Cultivars and Vitis Species. Plant Mol. Biol. Rep. 1994, 12, 6–13. [Google Scholar] [CrossRef]
  47. Wang, J.; Wang, H.; Cao, Y.; Bai, Z.; Qin, Q. Effects of soil and topographic factors on vegetation restoration in opencast coal mine dumps located in a loess area. Sci. Rep. 2016, 6, 22058. [Google Scholar] [CrossRef] [Green Version]
  48. Wang, C.; Wang, J.; Naudiyal, N.; Wu, N.; Cui, X.; Wei, Y.; Chen, Q. Multiple Effects of Topographic Factors on Spatio-Temporal Variations of Vegetation Patterns in the Three Parallel Rivers Region, Southeast Qinghai-Tibet Plateau. Remote Sens. 2022, 14, 151. [Google Scholar] [CrossRef]
  49. Brown, J.H.; Gillooly, J.F.; Allen, A.P.; Savage, V.M.; West, G.B. Toward a metabolic theory of ecology. Ecology 2004, 85, 1771–1789. [Google Scholar] [CrossRef]
  50. García, F.C.; Bestion, E.; Warfield, R.; Yvon-Durocher, G. Changes in temperature alter the relationship between biodiversity and ecosystem functioning. Proc. Natl. Acad. Sci. USA 2018, 115, 10989–10994. [Google Scholar] [CrossRef] [Green Version]
  51. Lusk, C.H.; Wiser, S.K.; Laughlin, D.C. Macroclimate and topography interact to influence the abundance of divaricate plants in New Zealand. Front. Plant Sci. 2020, 11, 507. [Google Scholar] [CrossRef]
  52. Fischer, E.M.; Seneviratne, S.I.; Vidale, P.L.; Luthi, D.; Schar, C. Soil moisture—Atmosphere interactions during the 2003 European summer heat wave. J. Clim. 2007, 20, 5081–5099. [Google Scholar] [CrossRef]
  53. IUCN. The IUCN Red List of Threatened Species; IUCN: Gland, Switzerland, 2016. [Google Scholar]
  54. Tirado, R.; Cotter, J. Ecological Farming: Drought-Resistant Agriculture; Greenpeace Research Laboratories, University of Exeter: Exeter, UK, 2010; pp. 1–16. [Google Scholar]
  55. Workie, T.G.; Debella, H.J. Climate Change and Its Effects on Vegetation Phenology across Ecoregions of Ethiopia. Glob. Ecol. Conserv. 2017, 13, e00366. [Google Scholar] [CrossRef]
  56. Wang, C.; Liu, C.; Wan, J.; Zhang, Z. Climate change may threaten habitat suitability of threatened plant species within Chinese nature reserves. PeerJ 2016, 4, e2091. [Google Scholar] [CrossRef] [Green Version]
  57. Guo, Y.; Guo, J.; Shen, X.; Wang, G.; Wang, T. Predicting the Bioclimatic Habitat Suitability of Ginkgo biloba L. in China with Field-Test Validations. Forests 2019, 10, 705. [Google Scholar] [CrossRef]
  58. Bréda, N.; Huc, R.; Granier, A.; Dreyer, E. Temperate forest trees and stands under severe drought: A review of ecophysiological responses, adaptation processes and long-term consequences. Ann. For. Sci. 2006, 63, 625–644. [Google Scholar] [CrossRef] [Green Version]
  59. Wolfe, B.T.; Sperry, J.S.; Kursar, T.A. Does leaf shedding protect stems from cavitation during seasonal droughts? A test of the hydraulic fuse hypothesis. New Phytol. 2016, 212, 1007–1018. [Google Scholar] [CrossRef] [Green Version]
  60. Polle, A.; Chen, S.L.; Eckert, C.; Harfouche, A. Engineering Drought Resistance in Forest Trees. Front. Plant Sci. 2019, 9, 1875. [Google Scholar] [CrossRef] [Green Version]
  61. Du, B.; Jansen, K.; Kleiber, A.; Eiblmeier, M.; Kammerer, B.; Ensminger, I.; Gessler, A.; Rennenberg, H.; Kreuzwieser, J. A coastal and an interior Douglas-fir provenance exhibit different metabolic strategies to deal with drought stress. Tree Physiol. 2016, 36, 148–163. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Kleiber, A.; Duan, Q.; Jansen, K.; Junker, L.V.; Kammerer, B.; Rennenberg, H.; Ensminger, I.; Gessler, A.; Kreuzwieser, J. Drought effects on root and needle terpenoid content of a coastal and an interior Douglas-Fir provenance. Tree Physiol. 2017, 37, 1648–1658. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Allen, C.D.; Breshears, D.D.; McDowell, N.G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 2015, 6, 155. [Google Scholar] [CrossRef]
  64. McCauley, L.A.; Bradford, J.B.; Robles, M.D.; Shriver, R.K.; Woolley, T.J.; Andrews, C.A. Landscape-scale forest restoration decreases vulnerability to drought mortality under climate change in southwest USA ponderosa forest. For. Ecol. Manag. 2022, 509, 120088. [Google Scholar] [CrossRef]
  65. Wlostowski, A.N.; Jennings, K.S.; Bash, R.E.; Burkhardt, J.; Wobus, C.W.; Aggett, G. Dry landscapes and parched economies: A review of howdrought impacts nonagricultural socioeconomic sectorsin the US Intermountain West. WIREs Water 2022, 9, e1571. [Google Scholar] [CrossRef]
  66. Medeiros, D.B.; da Silva, E.C.; Santos, H.R.B.; Pacheco, C.M.; Musser, R.S.; Nogueira, R.J.M.C. Physiological and biochemical responses to drought stress in Barbados cherry. Braz. J. Plant Physiol. 2012, 24, 181–192. [Google Scholar] [CrossRef] [Green Version]
  67. Du, P.; Arndt, S.K.; Farrell, C. Relationships between plant drought response, traits, and climate of origin for green roof plant selection. Ecol. Appl. 2018, 28, 1752–1761. [Google Scholar] [CrossRef]
  68. Hussein, H.A.A.; Alshammari, S.O.; Kenawy, S.K.M.; Elkady, F.M.; Badawy, A.A. Grain-Priming with L-Arginine Improves the Growth Performance of Wheat (Triticum aestivum L.) Plants under Drought Stress. Plants 2022, 11, 1219. [Google Scholar] [CrossRef]
  69. Poyatos, R.; Llorens, P.; Piñol, J.; Rubio, C. Response of Scots pine (Pinus sylvestris L.) and pubescent oak (Quercus pubescens Willd.) to soil and atmospheric water deficits under Mediterranean mountain climate. Ann. For. Sci. 2008, 65, 306. [Google Scholar] [CrossRef] [Green Version]
  70. Couso, L.L.; Fernández, R.J. Phenotypic plasticity as an index of drought tolerance in three Patagonian steppe grasses. Ann. Bot. 2012, 110, 849–857. [Google Scholar] [CrossRef] [Green Version]
  71. Luo, Z.; Guan, H.; Zhang, X.; Zhang, C.; Liu, N.; Li, G. Responses of plant water use to a severe summer drought for two subtropical tree species in the central southern China. J. Hydrol. 2016, 8, 1–9. [Google Scholar] [CrossRef] [Green Version]
  72. Diamantoglou, S.; Rhizopoulou, S. Free Proline Accumulation in Sapwood, Bark and Leaves of Three Evergreen Sclerophylls and a Comparison with an Evergreen Conifer. J. Plant Plrysiol. 1992, 140, 361–365. [Google Scholar] [CrossRef]
  73. Shinde, S.S.; Kachare, D.P.; Satbhai, R.D.; Naik, R.M. Water stress induced proline accumulation and antioxidative enzymes in groundnut (Arachis hypogaea L.). Legum. Res. 2018, 41, 67–72. [Google Scholar] [CrossRef] [Green Version]
  74. Liang, X.; Zhang, L.; Natarajan, S.K.; Becker, D.F. Proline Mechanisms of Stress Survival. Antioxid. Redox Signal. 2013, 19, 998–1011. [Google Scholar] [CrossRef] [Green Version]
  75. Molinari, H.C.; Marura, C.J.; Darosb, E.; Freitas de Camposa, M.K.; Portela de Carvalhoa, J.F.R.; Filhob, J.B.; Pereirac, L.P.; Vieira, L.E. Evaluation of the stress inducible production of proline in transgenic sugarcane (Saccharum spp.) osmotic adjustment, chlorophyll fluorescence and oxidative stress. Physiol. Planta 2007, 130, 218–229. [Google Scholar] [CrossRef]
  76. Alhasnawi, A.N. Role of proline in plant stress tolerance: A mini review. Res. Crops 2019, 20, 223–229. [Google Scholar]
  77. Vendruscolo, E.C.G.; Schuster, I.; Pileggi, M.; Scapim, C.A.; Molinari, H.B.C.; Marur, C.J.; Vieira, L.G.E. Stress-induced synthesis of proline confers tolerance to water deficit in transgenic wheat. J. Plant Physiol. 2007, 164, 1367–1376. [Google Scholar] [CrossRef]
  78. Tezcan, A.; Aslan, G.E. Kaman, H. Evaluation of drought stress on the chlorophyll content of the plants: A review on the solanaceae family. Fresenius Environ. Bull. 2019, 28, 4636–4641. [Google Scholar]
  79. Shin, Y.K.; Bhandari, S.R.; Jo, J.S.; Song, J.W.; Lee, J.G. Effect of Drought Stress on Chlorophyll Fluorescence Parameters, Phytochemical Contents, and Antioxidant Activities in Lettuce Seedlings. Horticulturae 2021, 7, 238. [Google Scholar] [CrossRef]
  80. Teshome, D.T.; Zharare, G.E.; Naidoo, S. The Threat of the Combined Effect of Biotic and Abiotic Stress Factors in Forestry Under a Changing Climate. Front. Plant Sci. 2020, 11, 601009. [Google Scholar] [CrossRef]
  81. Moran, E.; Lauder, J.; Musser, C.; Stathos, A.; Shu, M. The genetics of drought tolerance in conifers. New Phytol 2017, 216, 1034–1048. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  82. Koepke, D.F.; Kolb, T.E.; Adams, H.D. Variation in woody plant mortality and dieback from severe drought among soils, plant groups, and species within a northern Arizona ecotone. Oecologia 2010, 163, 1079–1090. [Google Scholar] [CrossRef]
  83. Eldhuset, T.D.; Nagy, N.E.; Volařík, D.; Børja, I.; Gebauer, R.; Yakovlev, I.A.; Krokene, P. Drought affects tracheid structure, dehydrin expression, and above- and belowground growth in 5-year-old Norway spruce. Plant Soil 2013, 366, 305–320. [Google Scholar] [CrossRef]
  84. Lenoir, J.; Gégout, J.C.; Marquet, P.A.; de Ruffray, P.; Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 2008, 320, 1768–1771. [Google Scholar] [CrossRef] [PubMed]
  85. McInerny, G.J.; Turner, J.R.G.; Wong, H.Y.; Travis, J.M.J.; Benton, T.G. How range shifts induced by climate change affect neutral evolution. Proc. R. Soc. B 2009, 276, 1527–1534. [Google Scholar] [CrossRef] [PubMed]
Water 14 03508 g001 550
Figure 1. Habitat basin and DEM (A), and slope degree (B).
Figure 1. Habitat basin and DEM (A), and slope degree (B).
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Water 14 03508 g002 550
Figure 2. Endangered trees in the ecological landscape of Genow; (A) tree with dried branches and (B) tree without dried branches.
Figure 2. Endangered trees in the ecological landscape of Genow; (A) tree with dried branches and (B) tree without dried branches.
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Figure 3. Topographical maps of the study area elevation and samples distribution pattern (Sample_H, trees without dried branches; Sample_E, trees with dried branches).
Figure 3. Topographical maps of the study area elevation and samples distribution pattern (Sample_H, trees without dried branches; Sample_E, trees with dried branches).
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Water 14 03508 g004 550
Figure 4. Topographical maps of slope (A) and aspect (B) of the study area and samples distribution pattern (Sample_H, trees without dried branches; Sample_E, trees with dried branches).
Figure 4. Topographical maps of slope (A) and aspect (B) of the study area and samples distribution pattern (Sample_H, trees without dried branches; Sample_E, trees with dried branches).
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Figure 5. Topographical maps of streams in the study area (A) and distance (B) of investigated samples to stream (Sample_H, trees without dried branches; Sample_E, trees with dried branches).
Figure 5. Topographical maps of streams in the study area (A) and distance (B) of investigated samples to stream (Sample_H, trees without dried branches; Sample_E, trees with dried branches).
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Figure 6. Analysis of the relative importance of climatic factors on declining J. seravschanica.
Figure 6. Analysis of the relative importance of climatic factors on declining J. seravschanica.
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Figure 7. Result of the genetic similarity of two groups of J. seravschanica (group 1; trees with dried branches), (group 2; trees without dried branches).
Figure 7. Result of the genetic similarity of two groups of J. seravschanica (group 1; trees with dried branches), (group 2; trees without dried branches).
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Table
Table 1. Coefficients † of multicollinearity of climatic factors with two groups of J. seravschanica.
Table 1. Coefficients † of multicollinearity of climatic factors with two groups of J. seravschanica.
ModelUnstandardized CoefficientsStandardized CoefficientstSig.Collinearity Statistics
BStd. ErrorBetaToleranceVIF
11
(Constant)		−14.6444.226 −3.466.018
Min-Temperature 0.3610.4990.1340.7230.5020.7831.278
Max-Temperature 0.4190.0961.0074.3890.0070.5101.962
Wind speed0.0810.0370.4102.2090.0780.7791.284
Precipitation 0.0030.0050.1580.6260.5590.4202.383
Note(s): † Dependent Variable: Trees with dried branches (group A), trees without dried branches (group B).
Table
Table 2. Pearson correlation between climatic factors and physiological indices of two groups of J. seravschanica existing in the Genow habitat.
Table 2. Pearson correlation between climatic factors and physiological indices of two groups of J. seravschanica existing in the Genow habitat.
Variables Tree GroupsMin-Temp.Max-Temp.Wind SpeedAnnual Precip.ChlorophyllProlineRWCRTWDEL
Tree groups1
Min-temp. 0.1081
Max-temp.−0.833 **−0.3411
Wind speed−0.3170.090−0.0591
Annual Precip. 0.559 *0.413−0.652*−0.2831
Chlorophyll0.786 **−0.180−0.676 *−0.3470.4181
Proline−0.837 **−0.1400.918 **0.182−0.640 *−0.778 **1
RWC0.947 **0.220−0.798 **−0.2900.4130.668 *−0.742 *1
RT0.935 **−0.003−0.703 *−0.4210.4850.872 **−0.702 *0.887 **1
WD−0.935 **0.0030.703 *0.421−0.485−0.872 **0.702 *−0.887 **−1.000 **1
EL−0.884 **−0.0810.709 *0.393−0.465−0.5490.786 **−0.865 **−0.720 *0.720 *1
Note(s): ** Correlation is significant at 0.01 level (2-tailed). * Correlation is significant at 0.05 level (2-tailed).
Table
Table 3. Independent samples t-test analysis for physiological indices.
Table 3. Independent samples t-test analysis for physiological indices.
Independent Samples Test
Levene’s Test for Equality of Variancest-Test for Equality of Means
tdfSig. (2-Tailed)Mean Difference95% Confidence Interval of the Difference
LowerUpper
Chlorophyll−3.59780.007 **0.26246−1.54924−0.33876
Proline4.33080.003 **0.276220.559041.83296
RWC−8.30380.000 **1.26949−13.46744−7.61256
RT−7.42880.000 **0.63008−6.13297−3.22703
WD7.42880.000 **0.630083.227036.13297
EL5.34780.001 **1.021893.107527.82048
Note(s): ** Two investigated groups of J. seravschanica have significant difference at 0.01 level.
Table
Table 4. Group statistics for investigated trees of J. seravschanica.
Table 4. Group statistics for investigated trees of J. seravschanica.
Tree SamplesNMeanStd. DeviationStd. Error Mean
Chlorophyllwith dried branches51.95600.499430.22335
without dried branches52.90000.308220.13784
Prolinewith dried branches52.39000.602370.26939
without dried branches51.19400.136490.06104
RWCwith dried branches538.00002.236071.00000
without dried branches548.54001.748710.78205
RTwith dried branches593.18001.213260.54259
without dried branches597.86000.716240.32031
WDwith dried branches56.82001.213260.54259
without dried branches52.14000.716240.32031
ELwith dried branches514.14001.451620.64918
without dried branches58.67601.764680.78919
Table
Table 5. AMOVA analysis of two tree groups’ genetic diversity.
Table 5. AMOVA analysis of two tree groups’ genetic diversity.
SourcedfSSMSEst. Var.%PhiPTSstat
Valuep (Rand ≥ Data)
Among groups184.50084.50016.23083%0.8290.007
Within groups826.8003.3503.35017%
Total9111.300 19.580100%
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Rahimian Boogar, A.; Salehi, H.; Seyedabadi, E. Distribution and Physiology of Juniperus seravschanica Trees in the Genow—The Southernmost and Arid Habitat of Iran. Water 2022, 14, 3508. https://doi.org/10.3390/w14213508

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Rahimian Boogar A, Salehi H, Seyedabadi E. Distribution and Physiology of Juniperus seravschanica Trees in the Genow—The Southernmost and Arid Habitat of Iran. Water. 2022; 14(21):3508. https://doi.org/10.3390/w14213508

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Rahimian Boogar, Abdolrahman, Hassan Salehi, and Esmaeel Seyedabadi. 2022. "Distribution and Physiology of Juniperus seravschanica Trees in the Genow—The Southernmost and Arid Habitat of Iran" Water 14, no. 21: 3508. https://doi.org/10.3390/w14213508

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