Tagirova O., Kulagin A., Zaitsev G. Seasonal dynamics of changes in the morphological parameters of silver birch (Betula pendula Roth) leaves in the conditions of industrial impact (Ufa, Republic of Bashkortostan) // Principy èkologii. 2019. № 2. P. 110‒118. DOI: 10.15393/j1.art.2019.8742

Issue № 2

Original research

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Seasonal dynamics of changes in the morphological parameters of silver birch (Betula pendula Roth) leaves in the conditions of industrial impact (Ufa, Republic of Bashkortostan)

Introduction

Within the boundaries of large industrial centers there are violations of the formation of structural components of the ecosystem. In technogenic conditions woody plants experience inhibition of growth and development (Кулагин, 1974, 1980). It was shown that woody plants growing in conditions of predominant hydrocarbon pollution of the environment, are characterized by adaptive reactions such as an increase in the seasonal duration of growth, the formation of an increased mass of assimilation organs, changes in the architecture of the root system and root mass (Кулагин, Зайцев, 2008).

The object of the research is plantations of silver birch (Betula pendula Roth).

The purpose of the research is identification of seasonal dynamics of morphological parameters of leaves of the silver birch on quantitative characteristics of closeness of correlation communication.

Materials

The research was carried out in the territory of Ufa industrial center in 2016. In the forest plantations of Ufa permanent sampling plots (SP) are laid in contrasting forest conditions.

The plot SPI (polluted zone) is laid close to oil refineries on the territory of Ordzhonikidze district in forest cultures. The formula of the stand is 10 B. The average diameter is 24 cm, the average height is 21m, the density is 0.8, the average age is 43 years. Undergrowth: Populus balsamifera L., Quercus robur L., Fraxinus excelsior L., Tilia cordata Mill. Crown density is 0.4. Shrub layer: Sorbus aucuparia L., Padus avium Mill., Euonymus verrucosus SCOP, Corylus avellana L., Acer platanoides L. Projective cover is 30 %. Grass cover: Plantago major L., Arctium lappa L., Aegopodium podagraria L., Polygonatum odoratum (Mill.). Druce (Polygonatum officinale All.), Poa angustifolia L., Geum urbanum L., Paris quadrifolia L., Galium odioratum (L.) Scop., Artemisia glauca Pall., Arctium nemorales Lej., Urtica dioica L., Calamagrostis epigeios (L.) Roth., Chelidonium majus L., Poa supine Schrad. Total projective cover is 35 %.

 

 

Fig. 1. Placing trial plots in Ufa

 

The plot SPII (zone of relative control – without the impact of industrial emissions) was laid in the Volna Park on the territory of the Leninsky district of Ufa in forest cultures. The formula of the stand is 10 B. The average diameter is 25 cm, the average height is 23 m, the density is 0.8, the average age is 46 years. Undergrowth: Populus balsamifera L., Tilia cordata Mill., Acer platanoides L., Ulmus glabra Huds. Crown density is 0.4. Shrub cover: Sorbus aucuparia L., Euonymus verrucosus SCOP. Projective cover is 30 %. Grass cover: Cirsium vulgare (Savi) Ten., Cynoglossum officinalis L., Achillea millefolium L., Geum urbanum L., Galium odioratum (L.) Scop., Aegopodium podagraria L., Artemisia vulgaris L., Agrimonia pilosa Lebed., Dryopteris lilix-mas (L.) Schott, Asarum europaeum L., Urtica dioica L., Arctium nemorales Lej., Plantago major L., Campanula trachelium L., Taraxacum oridinalis Wigg., Chelidonium majus L., Linaria vulgaris Mill. Total projective cover is 70 %.

Methods

Work on the characteristics of the species composition and the state of woody vegetation was carried out according to standard methods, at that the following methods were used:

1. The methods of studying forest communities (Методы изучения…, 2002).
2. Research methods of studying the morphological parameters of leaves (Bradshaw et al., 2007; Tech et al., 2018) with the help of the program AreaS.
3. Statistical processing (Плохинский, 1970; Зайцев, 1984; Mathematics…, 20017) of the results of investigation was conducted using the programs STATISTICA, Excel и GraphPad Prism (Ивантер, Коросов, 2014).

According to the growing conditions, the leaves should be collected from plants that are in the same environmental conditions (level of illumination, moisture, etc.). We selected plants that grow in open areas that have reached a generative age state. The leaves were collected from the same part of the crown from different sides (North, South, West, East), with the maximum number of available branches relatively evenly around the tree (100 leaves were selected monthly from each sample area). Leaves from shortened shoots were used. The size of the leaves was similar, average for this plant (Cornelissen et al., 2003).

Research was carried out on herbarium material. From each batch of leaves, some leaves were selected randomly (Cornelissen et al., 2003), in which the following parameters was measured: leaf area (cm²), leaf mass (g). The weight of the leaf blade was determined in the air-dry state on electronic laboratory scales ВЛТЭ-150 (Госметр, Russia). The area of the leaf was measured using the program for determining the area of complex shapes "AreaS" 2.1, which is based on scanning two figures, the area of one of them is known (template), followed by their comparison and calculation of the area of the other figure. The error in determining the area does not exceed 0.001 %. To determine the area of figures using the "AreaS" program, we used hardware and software: PC Aquarius Pro P30 S42, a scanner (Canon LaserBase MF6560PL), a graphic editor with the ability to scan images (IrfanView).

Weather characteristics of the research year are given for the Ufa-Dema meteorological station (latitude-54°43¢, longitude-55°50¢) according to the Institute of hydrometeorological information – world data center (RIHMI-WDC) (WMO index – 28722) and the national climate data center of the National oceanic and atmospheric administration (NCDC NOAA) (GHCND:RSM00028722).

Results

It was previously established (Кулагин, Тагирова, 2015) that the relative vital state of birch stands exposed to emissions from oil refineries is characterized as "weakened". Birch trees on PSI have a poorly formed openwork crown (crown density – 55-65%), trunks are poorly cleared of dead branches (the proportion of dead branches – 20-45 %). There are damages of the trunks by entomosis (egg laying, stem settlement), phytopathological damage (the formation of fungal bodies on the trunk) and dryness of treetops. The relative vital state of plantings in the zone of relative control (PSII) is characterized as "healthy". The density of the crown is 85-95 %. The presence of dead branches on the trunk-from 1 to 15 %. The degree of damage to the leaves by toxicants and insects is 0-10 %. Dryness of treetops is not expressed, phytopathological damage is absent, damage of the trunks by entomosis (egg laying, stem settlement) is insignificant.

As a result of the studies conducted in the pollution zone (PSI) and in the relative control zone (PSII), the average values of the area and mass of leaves during the growing season were obtained based on calculations (table. 1). 

 

Table 1. Seasonal changes in the area and mass of leaves of the silver birch (Betula pendula Roth) in 2016 in different growing conditions (Ufa industrial center)

 

Date	PSI (pollution zone)		PSII (relative control zone)
	Leaf area, cm2	Leaf mass, g	Leaf area, cm2	Leaf mass, g
June	12.46	0.07	14.09	0.08
July	13.89	0.10	13.58	0.09
August	14.03	0.10	15.34	0.11
September	-	-	12.85	0.10
October	15.88	0.12	15.14	0.11

 

It was found that in 2016, against the background of the average monthly temperature and precipitation values (table. 2) in the pollution zone (PSI), there is a uniform increase in the area and mass of birch leaves from June to October. At the same time, in the zone of relative control (PSII), such dynamics was detected only in the mass of leaves. The obtained values for the area of birch leaves in the zone of relative control during the growing season differ. The maximum leaf area values were found in August, and the minimum values were found in September.

 

Table 2. Brief description of the weather conditions of the year of research on the weather station Ufa-Dema

 

Month	Temperature, °С			Humidity, %		Sum	Amount (max.)	Number of days
	ср.	мин.	макс.	ср.	мин.	precipitation	precipitation, mm	with precipitation
I	-12	-29.9 (02.01.2016)	+0.8 (09.01.2016)	74	33 (23.01.2016)	50	9.0 in 12 h. (20.01.2016)	22
II	-4.6	-18.8 (12.02.2016)	+6.3 (25.02.2016)	80	46 (21.02.2016)	42	5.0 in 12 h. (17.02.2016)	16
III	-1.2	-17.2 (21.03.2016)	+11.0 (26.03.2016)	73	32 (16.03.2016)	30	5.0 in 12 h. (17.02.2016)	19
IV	9.1	-3.4 (09.04.2016)	+24.1 (16.04.2016)	66	21 (25.04.2016)	44	10.0 in 12 h. (03.04.2016)	18
V	14.3	-1.3 (09.05.2016)	+30.3 (27.05.2016)	54	16 (05.05.2016)	26	8.0 in 12 h. (13.05.2016)	12
VI	17.8	+2.3 (02.06.2016)	+30.3 (21.06.2016)	61	23 (02.06.2016)	56	21.0 in 12 h. (21.06.2016)	10
VII	21.1	+10.0 (07.07.2016)	+32.5 (31.07.2016)	62	24 (28.07.2016)	18	6.0 in 12 h. (10.07.2016)	8
VIII	23.2	+1.9 (30.08.2016)	+35.4 (17.08.2016)	55	14 (31.08.2016)	19	14.0 in 12 h. (12.08.2016)	9
IX	12.2	+3.3 (08.09.2016)	+24.8 (22.09.2016)	75	15 (01.09.2016)	61	11.0 in 12 h. (13.09.2016)	23
In a year	5.1	-35.6 (21.12.2016)	+35.4 (17.08.2016)	69	14 (31.08.2016)	507	21.0 in 12 h. (21.06.2016)	210

 

On PSI (table. 3) there is a higher coefficient of variation (above 30 %), which indicates a high variability of leaves.

 

 

Table 3. Seasonal changes in the area (S, cm²) and mass (M, g) of the leaves of the silver birch (Betula pendula Roth) in 2016 in different growing conditions (Ufa industrial center)

 

Character	June		July		August		September		October
	S, сm2	M, г	S, сm2	М, г	S, сm2	М, г	S, сm2	М, г	S, сm2	М, г
PSI (n = 10)
Minimum	9.53	0.05	7.76	0.05	7.99	0.05	-	-	12.00	0.09
Maximum	15.93	0.09	19.21	0.18	16.99	0.16	-	-	20.44	0.18
Average	12.46	0.07	13.90	0.10	14.04	0.10	-	-	15.89	0.12
Standard deviation	2.28	0.02	4.31	0.04	3.353	0.03	-	-	2.48	0.03
Standard error	0.72	0.01	1.36	0.01	1.06	0.01	-	-	0.79	0.01
Coefficient of variation, %	18.25	22.24	31.04	36.55	23.88	30.07	-	-	15.62	21.93
Sum	124.6	0.68	139	0.96	140.4	1.02	-	-	158.9	1.24
PSII (n = 10)
Minimum	9.13	0.04	8.66	0.06	10.07	0.08	8.60	0.06	9.66	0.06
Maximum	19.18	0.12	18.06	0.12	19.92	0.14	16.42	0.13	20.42	0.16
Average	14.09	0.08	13.58	0.09	15.34	0.11	12.86	0.10	15.15	0.11
Standard deviation	3.52	0.02	2.87	0.02	3.32	0.02	2.79	0.03	3.55	0.03
Standard error	1.11	0.01	0.91	0.01	1.05	0.01	0.93	0.01	1.12	0.01
Coefficient of variation, %	24.97	25.30	21.12	19.96	21.65	21.93	21.69	24.60	23.45	28.70
Sum	141	0.82	135.8	0.92	153.4	1.06	115.7	0.91	151.5	1.11

 

The phenomenon of uneven growth of birch leaves in June 2016 (the period of active leaf growth) under conditions of relative pollution was revealed, which is consistent with the opinion of S.A. Mamaev (1973) about the exceptional variability of the leaves of the silver birch and does not contradict the information about the intra-leaf differentiation of leaves (Ермолова и др., 2014). To characterize the features of the formation of birch leaves, a correlation analysis was performed and the depth of the correlation between the area and weight of the leaves of the silver birch was estimated (table. 4).

 

Table 4. Correlation (R2) between the studied characteristics of the weight and area of the leaves of the silver birch in different growing conditions on the territory of the Ufa industrial center

Date	PSI (pollution zone)	PSII (relative control zone)
June	0.47	0.73
July	0.71	0.71
August	0.52	0.75
September	-	0.96
October	0.61	0.94

 

In the pollution zone (PSI), a moderate relationship between area and mass was found in June and August. In July and October, there is a linear relationship between the parameters under study. In the relative control zone (PSII), the correlation analysis in June, July, and August revealed a linear relationship between the area and the mass of leaves. In September, the relationship between the parameters is very strong, almost linear. In October, a correlation analysis revealed a strong relationship between area and mass. The study revealed a direct relationship between the area and mass of leaves in both the relative pollution zone and the relative control zone (Ивантер, Коросов, 2014).

Conclusions

Under the conditions of pollution, there was a uniform increase in the area and mass of birch leaves from June to October, while in the zone of relative control, such dynamics was detected only by the mass of leaves.

It is shown that the tightness of the correlation between the leaf area and the leaf mass in the pollution zone and the nature of the relationship in June is classified as "weak", in July – "strong", in August and October – "moderate". According to the quantitative characteristics of the tightness of the relationship between the leaf area and the leaf mass in the zone of relative control, the nature of the correlation relationship is classified as "strong".

Numerical values of the leaf mass and leaf area of the silver birch indicate that in the conditions of the industrial zone, there is a uniform development of the leaf during the growing season of 2016.

The correlation analysis allowed to establish an ambiguous relationship between the leaf mass and the leaf area, which can be explained by adaptive reactions of the silver birch, which are manifested during seasonal leaf development, taking into account the dynamics of climatic parameters.

The formation of the assimilation apparatus of the silver birch in conditions of prevailing petrochemical pollution of the environment occurs without significant deviations, which is a specific reaction to the hydrocarbon type of pollution.

References