Бюллетень науки и практики, 2020, том 6, № 10

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БЮЛЛЕТЕНЬ НАУКИ И ПРАКТИКИ
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СОДЕРЖАНИЕ

Биологические науки

1.
Алиева Г. Н., Мамедова З. А., Оджаги Д. 
Оценка морфологических признаков и генотипов многомерными статистическими 
методами у некоторых видов дуба ………………………………………………………...
10-19

2.
Ташпулатов Й. Ш., Нурниезов А. А.
Флора и ее анализ. Гидрофильные растения разнотипных водоемов Самаркандской 
области (Узбекистан) ……………………………………………………………………….
20-34

3.
Аббасов Н. К., Фатуллаев П. У., Мамедов И. Б., Кулиев С. Ш.
Экологический анализ видов семейства Fabaceae Lindl. во флоре летних пастбищ 
Нахичеванской автономной республики Азербайджана …………………………………
35-43

4.
Рзаева А. А.
Адаптации листьев Juniperus rufescens Link. в горах Южного Кавказа (Азербайджан) 
44-47

5.
Мустафаева Г. А. 
Наездники (Hymenoptera: Aphelinidae, Aphidiidae) - паразиты тлей (Hemiptera, 
Aphidoidea) Азербайджана …………………………………………………………………
48-60

6.
Талыбов Т. Г., Мамедов И. Б., Фатуллаев П. У., Кулиев С. Ш.
Результаты дистанционного исследования леопарда (Panthera pardus saxicolor, 
Pocock, 1927) в Зангезурском национальном парке им. акад. Г. А. Алиева 
Нахичеванской автономной республики Азербайджана …………………………………
61-72

7.
Волобуев А. Н., Пятин В. Ф., Романчук Н. П., Булгакова С. В., Романов Д. В. 
Анатомо-физиологические и биофизические принципы функционирования мозга в 
состоянии бодрствования и сна ……………………………………………………………
73-94

Науки о Земле

8.
Коржов Ю. В., Лобова Г. А., Стариков А. И., Кузина М. Я. 
О происхождении углеводородов доюрского комплекса Ханты-Мансийского 
месторождения ……………………………………………………………………………...
95-110

9.
Двинин Д. Ю. 
Перспективы снижения негативного антропогенного воздействия на окружающую 
среду в результате развития альтернативной энергетики в Российских регионах ……..
111-117

10.
Асланова Э. Г. 
Распространение выбросов электростанций в атмосфере, их воздействие 
на состояние окружающей среды и человека ……………………………………………..
118-123

Сельскохозяйственные науки

11.
Сарикян К. М., Григорян М. Г., Акобян Э. А. 
Новые сорта томата местной селекции для возделывания в горных регионах Армении 
124-129

12.
Ерофеев С. А., Ветрова С. В., Макаров М. Р. 
Селекция сортов подсолнечника с высокой масличностью ……………………………..
130-134

13.
Гурбанов С. Г. 
Влияние агромелиоративных мероприятий на удельную поверхность почвы …………
135-142

14.
Аббасова Н. Т. 
Влияние внесения неорганических удобрений на показатели урожайности Helianthus
annuus в условиях западной части Азербайджана ……………………………………….
143-148

15.
Асланова Д. Г. 
Влияние схем посадки и неорганических удобрений на вынос элементов питания 
корнеплодов сахарной свеклы ……………………………………………………………..
149-155

16.
Гулиева Е. Н. 
Агропроизводственная  группировка почв зимних пастбищ Ширванской степи ……...
156-163

17.
Гулиева Р. Х. 
Влияние удобрений на прирост соломы озимой пшеницы в Гянджа-Казахском 
массиве ………………………………………………………………………………………
164-168

18.
Ахмедова С. З., Адыгозалов П. М. 
Влияние неорганических удобрений на структурные показатели урожайности озимой 
169-173

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ржи …………………………………………………………………………………………...  

19.
Тухтаев А. К. 
Роль сельского хозяйства в экспортном потенциале Узбекистана ………………………
174-178

Медицинские науки

20.
Евсеев А. Б.
Режим питания при гипотиреозе …………………………………………………………..
179-185

21.
Чаулин А. М., Григорьева Ю. В.
Воспаление при атеросклерозе: от теории к практике …………………………………...
186-205

22.
Булгакова С. В., Романчук Н. П. 
Иммунный гомеостаз: новая роль микро- и макроэлементов, здоровой микробиоты …
206-233

Технические науки

23.
Беркетова Л. В., Полковникова В. А. 
К вопросу об эко-, съедобной и быстроразлагающейся упаковке 
в пищевой индустрии ………………………………………………………………………
234-243

24.
Матбабаев М. М. 
Оптоэлектронный датчик относительной влажности воздуха …………………………..
244-252

Экономические науки

25.
Ерлыгина Е. Г., Васильева А. Д. 
Инвестиции в агропромышленный комплекс как фактор устойчивого развития 
государства ………………………………………………………………………………….
253-257

26.
Убайдуллаев К., Алымов А. К.
Перспективы развития промышленности в Республике Каракалпакстан ………………
258-265

27.
Швайба Д. Н. 
Особенности инновационного типа экономического развития ………………………….
266-270

28.
Жиемуратов Т., Женисбаев М. 
Особенности и тенденции развития цифровой экономики Узбекистана ……………….
271-277

Социологические науки

29.
Кузина Н. В. 
Преступность и внешняя миграция в регионах Российской Федерации как индикатор 
для изучения социальной и этно-культурной напряженности, а также риска 
распространения террористической и экстремистской идеологии: уязвимые субъекты 
Федерации (I-е полугодие 2020 г.) …………………………………………………………
278-298

30.
Немцов А. А. 
Осмысление либеральных ценностей в современном российском культурноисторическом контексте ……………………………………………………………………
299-338

31.
Кузина Н. В., Кузина Л. Б. 
Отражение и предотвращение этноконфессиональных конфликтов российского 
мегаполиса в произведениях авторского и массового кинематографа ………………….
339-366

Психологические науки

32.
Герасимова К. Д. 
Культурные и этнические особенности переживания чувашей 
в экстремальных условиях …………………………………………………………………
367-371

Педагогические науки

33.
Отамуродов Г. Р., Матназаров А. Р. Жапаков А. И.
Совершенствование единой информационно-методической системы развития 
управленческой компетенции руководителей высших учебных заведений …………….
372-378

34.
Яковлева Е. В.
Психолого-педагогическая характеристика потребности студентов в самообразовании 
при обучении в вузе ………………………………………………………………………...
379-386

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Исторические науки

35.
Шеркова Т. А. 
Материальные источники додинастического Египта в свете концепции «Культурная 
память» ………………………………………………………………………………………
387-409

36.
Тобакалов Ч. Б. 
Отношения суверенного Кыргызстана в экономической и социальной сферах ………..
410-414

37.
Эшкурбонов С. Б. 
Этно-территориальное расположение населения Сурханского оазиса (на примере 
земледелия) ………………………………………………………………………………….
415-421

38.
Алламуратов Ш. А. 
История амударьинского судостроения …………………………………………………...
422-429

Филологические науки

39.
Турдиева К. Ш. 
Круг детского чтения поэтических произведений А. Арипова ………………………….
430-438

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TABLE OF CONTENTS

Biological Sciences

1.
Aliyeva G., Mammadova Z., Ojagi J. 
Evaluation of Morphological Traits and Genotypes by Multivariate Statistical Methods 
in Some Oak Species ………………………………………………………………………...
10-19

2.
Tashpulatov, Y., Nurniyozov, A. 
Flora and Its Analysis. Hydrophilic Plants of Different Water Bodies of the Samarkand 
Region (Uzbekistan) …………………………………………………………………………
20-34

3.
Abbasov N., Fatullaev P., Mamedov I., Kuliev S. 
Ecological Analysis of Species of Family Fabaceae Lindl. in the Summer Pastures Flora of 
the Nakhchivan Autonomous Republic of Azerbaijan ……………………………………….
35-43

4.
Rzayeva A. 
Adaptations of Juniperus rufescens Link. Leaf’s in South Caucasus Mountains 
(Azerbaijan) ………………………………………………………………………………….
44-47

5.
Mustafayeva G. 
Aphelinids, Aphidiids (Hymenoptera: Aphelinidae, Aphidiidae) - Parasites of Aphids 
(Hemiptera, Aphidoidea) of Azerbaijan ……………………………………………………..
48-60

6.
Talybov T., Mamedov I., Fatullaev P., Kuliev S.
Results of Remote Sensing of a Leopard (Panthera pardus saxicolor, Pocock, 1927) in the 
Aliyev Zangezur National Park of the Nakhchivan Autonomous Republic of Azerbaijan ….
61-72

7.
Volobuev A., Pyatin V., Romanchuk N., Bulgakova S., Romanov D. 
Anatomical-Physiological and Biophysical Principles of Brain Functioning 
in Waking and Sleep …………………………………………………………………………
73-94

Sciences about the Earth

8.
Korzhov Yu., Lobova G., Starikov A., Kuzina M. 
The Hydrocarbon Genesis of pre-Jurassic Complex of Khanty-Mansiyskoe Oil Field ……..
95-110

9.
Dvinin D. 
Prospects for Reducing the Negative Anthropogenic Impact on the Environment as a Result 
of the Development of Renewable Energy in the Russian Regions …………………………
111-117

10.
Aslanova E. 
Distribution of the Waste of the Electric Stations in the Atmosphere, Their Influence on the 
Environment and Human Health …………………………………………………………….
118-123

Agricultural Sciences

11.
Sarikyan K., Grigoryan M., Akobyan E. 
New Tomato Varieties of Local Breeding for Cultivation in the Armenia Mountain Regions 
124-129

12.
Erofeev, S., Vetrova S., Makarov M. 
Sunflower Varieties Selection With High Oil Content ………………………………………
130-134

13.
Gurbanov S. 
Impact of Agromeliorative Measures on the Specific Surface of Soil ………………………
135-142

14.
Abbasova N. 
Inorganic Fertilizers Application Effect on Helianthus annuus Crop Yield Indicators in 
Western Azerbaijan …………………………………………………………………………..
143-148

15.
Aslanova D. 
Effect of Planting Schemes and Inorganic Fertilizers on Removal of the Sugarbeet Root 
Crops Nutrition Elements ……………………………………………………………………
149-155

16.
Guliyeva Ye. 
Agro-industrial Grouping of Winter Pastures of the Shirvan Steppe ………………………..
156-163

17.
Guliyeva R. 
Effect of Fertilizers on the Straw Product Increase of Winter Wheat
in the Ganja-Gazakh Region …………………………………………………………………
164-168

18.
Akhmadоva S. Adigozalov P. 
Effect of Inorganic Fertilizers on Winter Rye Crop Yield Structural Indicators ……………
169-173

19.
Tukhtaev A.
174-178

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The Role of Agriculture in the Export Potential of Uzbekistan ……………………………...

Medical Sciences

20.
Evseev A.
Hypothyroidism Diet Plan …………………………………………………………………...
179-185

21.
Chaulin A., Grigoryeva Ju.
Inflammation in Atherosclerosis: From Theory to Practice …………………………………
186-205

22.
Bulgakova S., Romanchuk N.
Immune Homeostasis: New Role of Micro- and Macroelements, Healthy Microbiota ……..
206-233

Technical Sciences

23.
Berketova L., Polkovnikova V. 
On the Eco-, Edible and Fast-Decomposing Packaging in the Food Industry ………………
234-243

24.
Matbabayev M. 
The Optoelectronic Sensor Relative Humidity ………………………………………………
244-252

Economic Sciences

25.
Erlygina E., Vasilyeva A. 
Investment in the Agroindustrial Sector as a Factor of Sustainable Development 
of the State …………………………………………………………………………………...
253-257

26.
Ubaydullaev K., Alimov A. 
Prospects for Industrial Development in the Republic of Karakalpakstan …………………..
258-265

27.
Shvaiba D. 
Features of the Innovative Type of Economic Development ………………………………...
266-270

28.
Jiemuratov T., Jenisbaev M. 
Features and Trends of Development of the Digital Economy of Uzbekistan ………………
271-277

Sociological Sciences

29.
Kuzina N. 
Crime and External Migration in the Regions of the Russian Federation as an Indicator for 
Studying Social and Ethno-Cultural Tensions, as Well as the Risk of the Spread of Terrorist 
and Extremist Ideology: Defenseless Subjects of the Russian Federation (1st half of 2020) .
278-298

30.
Nemtsov A. 
Understanding Liberal Values in the Modern Russian Cultural and Historical Context …….
299-338

31.
Kuzina N., Kuzina L. 
Reflection and Prevention of Ethno-Confessional Conflicts Within Russian Megapolis in 
the Works of Author and Popular Cinema …………………………………………………...
339-366

Psychological Sciences

32.
Gerasimova K. 
Cultural and Ethnic Features of Experience of the Chuvash in Extreme Conditions ………..
367-371

Pedagogical Sciences

33.
Otamurodov G., Matnazarov A., Japakov A. 
Improving Methodological System for the Development of Managerial Competence of 
Heads of Higher Educational Institutions ……………………………………………………
372-378

34.
Yakovleva E. 
Psychological and Pedagogical Characteristics of Students’ Demands While Self-studying 
at the Institution of Higher Education ………………………………………………………..
379-386

Historical Sciences

35.
Sherkova T. 
Material Sources of Predynastic Egypt in the Context of the Concept of “Cultural 
memory”………………………………………………………………………………………
387-409

36.
Tobakalov Ch. 
Relations of Sovereign Kyrgyzstan in the Economic and Social Spheres …………………..
410-414

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37.
Eshkurbonov S. 
Ethno-territorial Location of the Population of the Surkhan Oasis (on the Example of 
Farming) ……………………………………………………………………………………...
415-421

38.
Allamuratov Sh.
History of Amu Darya Shipbuilding …………………………………………………………
422-429

Philological Sciences

39.
Turdieva K. 
Poetical Works A. Aripov in Child Reading …………………………………………………
430-438

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БИОЛОГИЧЕСКИЕ НАУКИ / BIOLOGICAL SCIENCES

_______________________________________________________________________________________________

UDC 582.001.4
AGRIS F30

https://doi.org/10.33619/2414-2948/59/01

EVALUATION OF MORPHOLOGICAL TRAITS AND GENOTYPES BY MULTIVARIATE 

STATISTICAL METHODS IN SOME OAK SPECIES

©Aliyeva G., Institute of Dendrology, Azerbaijan National Academy of Sciences,

Baku, Azerbaijan, bio890@mail.ru

©Mammadova Z., Institute of Dendrology, Azerbaijan National Academy of Sciences,

Baku, Azerbaijan, bio890@mail.ru

©Ojagi J., Khazar University, Baku, Azerbaijan, javid_804@yahoo.com

ОЦЕНКА МОРФОЛОГИЧЕСКИХ ПРИЗНАКОВ И ГЕНОТИПОВ МНОГОМЕРНЫМИ 

СТАТИСТИЧЕСКИМИ МЕТОДАМИ У НЕКОТОРЫХ ВИДОВ ДУБА

©Алиева Г. Н., Институт дендрологии НАН Азербайджана,

г. Баку, Азербайджан, bio890@mail.ru

©Мамедова З. А., Институт дендрологии НАН Азербайджана,

г. Баку, Азербайджан, bio890@mail.ru

©Оджаги Д., Хазарский университет, г. Баку, Азербайджан, javid_804@yahoo.com

Abstract. In this study, evaluated some morphological traits and genotypes by multivariate 

statistical methods in some oak species (Q. castaneifolia C. A. Mey, Q. pedunculiflora C. Koch., 
Q. iberica Stev., Q. macranthera Fisch. & C. A. Mey ex Hohen, Q. ilex L.). 910 leaves were 
sampled from 91 trees, 8 population across Azerbaijan, and 6 morphological traits were assessed. 
The indicator traits were analyzed using multidimensional statistical analysis for each species. As 
a result of the component analysis, the three-pointer element (PRIN1, PRIN2, PRIN3) explained 
86.97% of the variance among genotypes. These results provide identification of valuable species 
and patterns in the future selection and application of other genetic programs on the improvement of 
oaks in Caucuses.

Аннотация. Проведена оценка некоторых морфологических признаков и генотипов с 

использованием 
многомерных 
статистических 
методов 
у 
некоторых 
видов 
дуба 

(Q. castaneifolia C. A. Mey, Q. pedunculiflora C. Koch., Q. iberica Stev., Q. macranthera Fisch. & 
C. A. Mey ex Hohen, Q. ilex L.). Было отобрано 910 листьев с 91 деревьев, обследовано 
8 популяций из Азербайджана и оценены 6 морфологических признаков. Индикаторные 
признаки были проанализированы с помощью многомерного статистического анализа для 
каждого вида. В результате компонентного анализа три элемента-указателя (PRIN1, PRIN2, 
PRIN3) охватили 86,97% дисперсии между генотипами. Эти результаты позволяют 
идентифицировать ценные виды и образцы для будущего отбора и применения других 
генетических программ по улучшению дуба на Кавказе.

Keywords: Quercus, leaf morphology, population, variability, ANOVA, PCA.

Ключевые слова: Quercus, морфология листьев, популяция, изменчивость, ANOVA, 

PCA.

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Introduction

The genus Quercus L. (Fagaceae) is a diversified group of temperate trees with about 

500 species distributed worldwide [1]. Quercus is one of the most important woody genera of 
the Northern hemisphere and considered as one of the main forest tree species in Azerbaijan [2]. 
The oak has a special symbolic, ecological and economical value in Azerbaijan. 

Forest trees, like oaks, rely on high levels of genetic variation to adapt to varying 

environmental conditions. Thus, genetic variation and its distribution are important for the longterm survival and adaptability of oak populations [3–4]. Plant taxonomists believe that the leaves of 
some oak species under environmental change and habitat factors such as elevation change or 
altitudinal gradients show different morphological forms; therefore, several dichotomous keys 
based on morphological characteristics have been developed to describe species and sections within 
Quercus [1, 5].

Differences in phenotypic and physiological responses are associated with the geographical 

locations of populations at local or regional scales. Leaves are organs that are exposed to different 
environmental factors, and it is reasonable to expect that their morphology and structure represent 
the responses of the plants to local conditions, such as water availability or light intensity, as well as 
intra- and interspecific interactions [6–8].

The study of leaf morphology from the aspect of genetic differentiation provides useful 

information on population and intrapopulation variability and can be the basis for the determination 
of species and lower categories as well as intraspecific or interspecific hybrids. The similarity 
between individuals of the same or different populations or between distant and separate 
populations can point to their historical connections and common descent. Morphological 
determination is a good basis for further studies of this kind, and it is often combined with 
chemotaxonomic, cytological and molecular analyses [4, 9–10].

Using the geometric morphometric approach, shape variability is studied as a geometric 

property of leaves without any effect of size, and thus, morphometrics provides a powerful tool for 
exploring shape differences among taxa and for investigating intraspecific variability due to 
genotypic differences and phenotypic plasticity. In fact, new morphometrics are useful for quickly 
generating and managing large amounts of phenotypic data representing many aspects of the 
phenotype [11–12]. The protocol for studying leaf morphology in oaks [13] revealed significant 
differences within and between individuals, populations and species  in particular, when a mean leaf 
shape for each tree was analyzed, the differences between populations and between species were 
highly significant. In fact, the use of PCA represents a useful procedure for extracting new, 
uncorrelated variables for describing the variation in discrete shape traits. The shape variation can 
be visualized and described along the scores of each PC, and its heritability can be tested by 
univariate statistical analyses (i.e. ANOVA), while multi-variate statistical analyses such as 
MANOVA / CVA detect cumulative effects of the shape traits in species differentiation [14].

The previous researches on leaf morphology of Azerbaijan oaks were generally conducted by 

traditional methods [15–19]. In this research, for the first time, the macromorphological properties 
of oak leaves were measured using modern methods and equipment, and the results were analyzed 
by statistical analysis. It is a part of a larger study on the ecological, morphological, and molecular 
characterization of these five species in Azerbaijan. The main goals of the study are: 

1) To collect comparative morphological data of some species of the Quercus genus in 

the country.

2) Assessment of characters and genotypes using multivariate statistical methods. 

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3) Identification of valuable species and patterns in the future selection and application of 

other genetic programs. 

4) Identify descriptor traits for each studying species.

Materials and methods 

Study populations. Study species were Q. castaneifolia C. A. Mey., Q. pedunculiflora 

C. Koch., Q. iberica Stev., Q. macranthera Fisch. & C. A. Mey. ex Hohen. Plants were collected in 
diverse forest types between −28 and 2200 m in elevation in Azerbaijan. The geography and 
ecology of these areas are given in Table 1. 91 tree specimens (Q. castaneifolia C. A. Mey.,
Q. pedunculiflora C. Koch., Q. iberica Stev., Q. macranthera Fisch.  & C. A. Mey. ex Hohen, 
Q. ilex L.) were chosen [6] from 8 inhabitants of Quercus trees around Azerbaijan (Table 1) in 2017. 
Chestnut-leaved oak (Q. castaneifolia) leaf samples were collected from Hirkan National Park 
(HNP) — Astara, Lankaran plain (LP) and Mardakan arboretum (MA) (56–63). Georgian oak 
(Q. iberica Stev.) (11–20) leaf samples were collected from Ismailli. The study areas of pedunculate 
oak (Q. pedunculiflora) were Baku (Botanical garden) (87–91), Absheron (Mardakan arboretum) 
and Ganja. Caucasian oak (Q. macranthera) leaf samples belong to Goygol National Park. And 
finally, holm oak (Q. ilex l.) leaf samples were gathered from Baku (Botanical garden and Officers’ 
Park and Absheron (Mardakan arboretum). The  same sampling design and methods were applied 
for each population. 10 mature trees of small area (0.5–1.0 ha) of homogeneous open oak forest 
were selected. 8–10 m tall trees were chosen and four outermost branches (light subsample) and 
four innermost branches (shade subsample) of each tree crowns were randomly selected. To avoid 
seasonal and positional variations, samples were collected from different branches at approximately 
the same height and location, where leaf growth had stopped. Branches were collected from the four 
cardinal compass directions. The leaves’ ages were practically the same, although there is a small 
variation in budburst among trees and within trees. In experimental design, only branch position 
considered [20–21]. The most important factor within-plant variation is inner vs outer position of 
branch regardless of compass direction or height.

Table 1. 

GEOGRAPHIC LOCATION AND CLIMATE CONDITIONS OF THE SAMPLED OAK POPULATIONS

[1, 22]

Pa — annual precipitation (millimeters); T — mean annual temperature, °C).

Locality
Geographic
coordinates

Altitude,

m

Pa, mm
T, °C

Baku
40°23ʹN
49°51ʹE

−28
990–1200
14.2

Absheron
40°33ʹN
49°30ʹE

8
180–300
14–15

Ismailli
40°35ʹN
47°45ʹE

500–800
500–1000
14.0–14.5

Gabala
41°25ʹN
47°23ʹE

900
800–850
10–12

Ganja
40°40ʹN
46°21ʹE

400–450
200–300
13.1

Goygol
40°37ʹN
46°34ʹE

1000–2200
500–900
13.5

Lankaran
plain

39°24ʹN
48°58ʹE

−28–200
1280
14.1

Hirkan National Park
38°47ʹN
48°69ʹE

534
1200–1750
11–13

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Morphometric analysis. The morphological study of the oak leaf included 10 leaf samples per 

tree, on 91 trees in 7 populations, which makes a total of 910 leaves (10 trees per population) [6, 13, 
23]. Generally, 6 morphometric parameters were analyzed. The morphological characters utilized in 
this study:

LA (cm2) — leaf area.
LL (cm) — leaf length.
LW (cm) — leaf width.
LP (cm) — leaf perimeter.
R — Ratio (R=LL/LW).
F — Leaf shape Factor. 

Morphological traits were measured by CI-202 LESER AREA METER (USA) on ten leaves 

stripped of the petiole for each subsample. For each character, mean values of each population were 
calculated.

Statistical analysis. Two statistical tests namely KMO (Kaiser–Meyer–Olkin) and Bartlett 

were used for correctly performance of PCA. The most important data on population and individual 
variability were described by results of descriptive statistics. Species was treated, as a fixed 
variable; trees were considered as a random factor nested within species because trees were 
representative of each population. Statistical significance of different sources of variation, with 
the population as a fixed and the trees as a random factor, was determined by using the analysis of 
variance (ANOVA, SPSS 16, PAST and MSTATC). This analysis used only the characteristics that 
showed statistical significance as determined by the results of ANOVA.

Results and discussion

It is becoming increasingly clear that not only trait means, and genetic structure can vary 

within a species, but also phenotypic plasticity in those traits. Moreover, the mean value and 
the plasticity of a trait may interact [24]. From the perspective of assessing the contribution of 
plasticity to persistence and distributional shifts under climate change, it is the adaptive component 
that is of interest, i.e., plasticity that allows a genotype to maintain high fitness across 
environmental gradients [22, 25]. There is ample evidence, though, that populations within 
a species experiencing different environmental conditions often differ in phenotypic characters and 
genetic structure [26].

In this study, the relative importance of 6 morphological traits of leaves for each genotype 

were analyzed. It was demonstrated that environmental factors are associated with morphological 
variation in different oak species that occurs in different types of forests. It has been documented 
that the leaf morphological variability of species along elevational gradients is related to 
environmental factors [19].

The principal component method was used to investigate the importance of various traits in 

genotypes. Two statistical tests — KMO and Bartlett tests are used for the correct performance of 
principal component analysis statistically (Table 2). According to the results of these two tests, 
the KMO test value (0.58) and the statistical significance of the Bartlett test indicate that the 
“principal component” analysis was correctly implemented. As a result of the component analysis, 
three pointer elements explained 86.97% of the variance among genotypes (Table 3.). In the studied 
oak populations, each element effectively explained the interpopulation variations up to threepointer elements (Figure). However, this variation began to decline sharply after three pointer 

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elements. As a result, all analyses were performed on the basis of three selected pointer elements 
(PRIN1, PRIN2, and PRIN3). Table 3 shows the values of the pointer elements obtained based on 
the morphological characters. Table 4 reflects the values of indicator elements based on genotypes. 
It is possible to select effective genotypes based on one or more traits through the values of these 
elements. The results provided a clear description of the typical leaf shape of each species, and 
the differences between the species were evident when the mean contours were visualized and 
compared.

Table 2. 

RESULTS OF KMO AND BARTLETT TESTS

0.58
Kaiser-Meyer-Olkin (Measure of sampling adequacy)

265.27

15

0.000

Bartlett’s experiment                  Xi square
The degree of exemption
Significance

Table 3. 

RESULTS OF THE ANALYSIS OF COMPONENTS FOR EACH STUDIED TRAITS

PRIN3
PRIN2
PRIN1
Morphological characters

0.27
0.60
0.13
Leaf area

0.54
−0.05
0.29
Leaf length

0.35
0.50
−0.01
Leaf width

0.57
−0.201
0.09
Perimeter

−0.27
−0.01
0.94
Ratio

−0.33
0.58
−0.06
Factor

15.19
32.49
39.29
Variation percentage

86.97
71.78
39.29
Total variation

components

Figure. Scree plot based on analysis of components.

PRIN1 is significant because it explains 39.29% of the total variations (Table 3). R and LL 

were evaluated at maximum value in the current PRIN. Selection of valuable genotypes on the basis 
of the first indicator elements (it is clear that genotypes 11, 12, 20, 61, 67, 68, 72, 75, 78, 79, 80, 81, 
82, 84, 85, 86 and 87 are highly valued for PC1 (Table 4.) will bring about the development of traits 
such as R (LL/LW) and LL in these genotypes. The second indicator element (PRIN2) was 

Eigenvalue

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explained 32.49% of the total variation (Table 4). Significant traits in this PRIN were LA, LW, and 
F. The most valuable genotypes for the second indicator element (PRIN2) were genotypes 12, 42, 
44, 45, 47, 49, 61, 70, and 73. The third indicator element (PRIN3) contains 15.9% of the total 
variation. LL, LW, and P traits were the most important traits in these PRIN. The most valuable are 
genotypes 39 and 52 in the current PRIN. This creates ample opportunities for the use of current 
materials as appropriate parental forms in future breeding and other genetic programs.

We found that leaf length and ratio are the most discriminating leaf descriptor between 

Q. macranthera, Q. iberica and Q. pedunculiflora. Leaf area, leaf width and factor are significative 
morphological traits for Q. castaneifolia. Leaves of holm oak are smaller than other studied species. 
Ratio (leaf length / leaf width) may be descriptor for holm oaks (Q. ilex).

Multivariate analysis of variance provides an important tool for visualizing the morphological 

traits that characterize this species complex and play a notable role in the identification and 
systematics of this plant species. These results provide identification of valuable species and 
patterns in the future selection and application of other genetic programs on the improvement of 
oaks in Caucuses. 

Table 4. 

VALUES OF INDICATOR ELEMENTS IN ACCORDANCE WITH STUDIED GENOTYPES

Genotypes
PC 1
PC 2
PC 3
Genotypes
PC 1
PC 2
PC 3

1
0.01
−1.59
−0.17
49
−1.73
2.65
−0.64

2
0.02
−1.52
0.10
50
−1.11
0.64
−0.28

3
0.03
−0.46
−0.10
51
−2.46
0.21
−0.60

4
−0.28
−1.49
0.01
52
−4.45
1.17
5.32

5
−0.06
−1.52
0.07
53
−1.17
0.44
−0.36

6
1.26
−0.85
0.39
54
−0.98
1.36
−0.37

7
0.52
−1.17
0.17
55
−1.46
1.62
−0.45

8
0.15
−1.60
0.16
56
0.31
0.62
−0.17

9
0.52
−1.09
0.04
57
−0.74
−0.18
−0.39

10
−0.39
−1.72
−0.06
58
−0.68
−0.49
−0.45

11
1.40
−0.33
0.60
59
0.73
1.13
−0.07

12
2.11
1.83
0.46
60
−0.59
−0.61
−0.21

13
1.40
0.46
0.37
61
1.47
1.65
0.31

14
−0.67
0.25
−0.21
62
0.01
0.74
−0.18

15
0.96
0.27
0.17
63
−0.81
−0.01
−0.37

16
0.15
−0.41
−0.10
64
−0.41
−0.51
−0.31

17
0.04
−0.40
0.15
65
−0.16
−0.26
−0.54

18
0.27
0.32
−0.02
66
0.33
−0.22
−0.46

19
0.34
−1.14
0.20
67
2.44
1.38
0.48

20
1.52
0.50
0.30
68
1.85
0.10
0.39

21
−1.28
−2.59
−0.14
69
0.57
−0.11
−0.03

22
−1.18
−1.32
−0.58
70
−0.73
2.18
−0.58

23
−1.05
−1.86
−0.28
71
0.81
0.471
−0.25

24
0.25
−3.25
0.18
72
2.44
1.38
0.48

25
0.48
−1.72
−0.15
73
1.10
2.37
−0.26

26
0.31
−2.06
−0.14
74
−0.23
0.66
−0.66

27
0.10
−2.44
−0.03
75
1.93
1.34
0.10

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Genotypes
PC 1
PC 2
PC 3
Genotypes
PC 1
PC 2
PC 3

28
0.36
−2.024
0.04
76
−1.23
1.32
−1.16

29
0.17
−2.05
−0.04
77
1.08
0.24
0.30

30
0.41
−1.48
−0.07
78
1.63
0.84
0.39

31
0.35
−1.22
−0.12
79
1.71
0.74
0.34

32
0.15
−2.02
−0.09
80
2.38
0.48
0.61

33
−0.23
−2.70
−0.09
81
1.62
1.08
0.27

34
0.58
−1.50
0.09
82
2.81
1.48
0.56

35
−0.31
−2.33
−0.15
83
0.26
−0.92
0.04

36
−1.85
−0.59
−0.19
84
1.64
−0.37
0.38

37
−2.32
1.27
−0.74
85
1.99
−0.04
0.36

38
−2.32
−0.06
−0.64
86
3.32
2.96
0.62

39
−4.02
0.72
5.88
87
2.04
2.67
−0.28

40
−2.00
0.02
−0.71
88
1.36
0.18
−0.06

41
−3.42
0.93
−1.46
89
0.67
0.11
−0.26

42
−4.12
1.97
−1.46
90
0.85
0.66
−0.16

43
−2.97
0.59
−1.28
91
0.11
−0.62
−0.31

44
−1.87
2.50
−1.10

45
−2.39
2.48
−1.02

46
0.33
−0.11
0.33

47
−0.29
2.07
−0.03

48
0.33
−0.11
0.33

1–10 Q. ilex (Absheron), 11–20 Q. iberica (Ismayilli), 21–25 Q. ilex (Baku 1), 26–35 Q. ilex (Baku 2), 

36–45 Q. castaneifolia (Hirkan ), 46–55 Q. castaneifolia (Lankaran), 56–63 Q. castaneifolia (Absheron), 
64–66 Q. pedunculiflora (Absheron), 67–76 Q. pedunculiflora (Ganja), 77–86 Q. macranthera (Goygol), 
87–91 Q. pedunculiflora (Baku).

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