Change of Leaf Trait Asymmetry Type in Tilia Cordata Mill. and Betula Pendula Roth Under Air Pollution

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Article Change of Leaf Trait Asymmetry Type in Tilia cordata Mill. and Betula pendula Roth under Air Pollution

Elena A. Erofeeva * and Basil N. Yakimov

Department of Ecology, Lobachevsky State University of Nizhni Novgorod, Nizhni Novgorod 603950, Russia; damselﬂ[email protected] * Correspondence: [email protected] or [email protected]; Tel.: +7-831-462-32-06

Received: 30 March 2020; Accepted: 15 April 2020; Published: 3 May 2020

Abstract: Leaf fluctuating asymmetry (FA) is widely used as an environmental stress index, including pollution. Besides FA, leaf bilateral traits can have directional asymmetry (DA) and antisymmetry (AS), which are considered hereditary. Leaf FA transitioning to DA/AS or mixed asymmetry, under air pollution, has been insufficiently investigated. This study analysed leaf asymmetry types in Tilia cordata Mill. and Betula pendula Roth under traffic air pollution over several years. In addition, the relations of such transitions to pollution, and their effect on FA-integrated index, were studied. The asymmetry types of all studied leaf traits varied with air pollution increase, as well as in control trees in different years. T. cordata most often had FA transition to DA/mixed asymmetry, while B. pendula rarely had a mixed asymmetry and FA transitions to DA/AS were observed with the same frequency. Air pollution impacted FA transitions to other asymmetry types. In most cases their frequency changed non-monotonically that corresponded to hormesis and paradoxical effects. However, FA integrated index in studied trees did not depend on change of leaf asymmetry type. Thus, DA and AS in studied plants were not exclusively hereditary. Hence, the changes of leaf asymmetry type should be considered when using leaf FA in environment assessment.

Keywords: Tilia cordata Mill.; Betula pendula Roth; leaf; ﬂuctuating asymmetry; directional asymmetry; antisymmetry; mixed asymmetry; air pollution; hormesis; paradoxical eﬀect

1. Introduction Developmental stability characterizes the ability of an organism to maintain the trajectory of development within certain bounds [1,2]. Fluctuating asymmetry (FA) is widely used as an index of developmental instability of bilateral morphological structures in plants. Random insignificant deviations from the symmetrical state [3–5], due to the stochastic nature of molecular processes, provide the expression of genes (developmental noise) [6]. FA has a mostly non-hereditary nature, but it is often observed in the background of hereditary types of asymmetry, such as antisymmetry (AS) and directional asymmetry (DA) [2]. FA is characterized by a normal distribution of R - L differences with a mean of zero [1]. DA is the consistent difference between a pair of morphological structures, i.e. the larger structure in the pair occurs consistently on one side [7]. DA reflects a consistent bias of a trait within a species towards greater development on one side of the body than on the other; the coiling and associated anatomical asymmetry of gastropods or the flatfish asymmetry are typical examples. DA has normally distributed R - L differences around a mean that is significantly different from zero [1]. AS is the larger development of any morphological structure in a pair (the left or the right structure) [2]. AS is distinguished by a platykurtic (broad peaked) or bimodal distribution of R - L differences around a mean of zero. In male fiddler crabs, for example, the oversized signalling claw,

Symmetry 2020, 12, 727; doi:10.3390/sym12050727 www.mdpi.com/journal/symmetry Symmetry 2020, 12, 727 2 of 19 which is much larger than the opposing one, occurs with approximately equal frequency on both, the right and left sides in nearly all species [1]. It is known that FA increases under the influence of many environmental stress factors [4,8–11]. Therefore, the FA of the leaves of various plant species is widely used to estimate the level of environmental stress (for bioindication) induced by anthropogenic [12–14], abiotic [10,15–19] and biotic [11,20–22] stressors. Researchers usually determine the bilateral asymmetry type of morphological structure of a plant leaf only once. It is implied that the environment cannot change the asymmetry type as AS and DA have a significant genetic basis and FA is mostly non-hereditary [3]. However, several experimental studies demonstrated a dynamical interrelationship between the three types of asymmetry in different animal species under stress conditions [23–25]. For instance, a high concentration of benzole (10,000 mg / kg) in the nutrient medium caused a transition from FA to DA for the number of bristles in D. melanogaster, and lower concentrations simply increased FA [24]. The insecticide metoxychlor induced the transition from FA to DA in mice for morphological traits of skull bones [25]. Graham et al. [23] reported that environmental stress caused a change of morphogen concentration and a transition from FA to AS in a model of morphogenesis. Similar results were also achieved for natural animal populations. Lens and Van Dongen [26] showed that FA of tarsus traits was mixed with DA in natural bird populations under increasing habitat disturbance. It was hypothesized that increased developmental instability was reflected in a transition from FA to DA and/or AS [26]. These facts allowed us to hypothesize that similar shifts in leaf asymmetry may also occur in plant leaf traits used for environmental quality assessment. Therefore, the study of leaf FA transition to other types of asymmetry under environmental pollution has both, practical implications for bio-indication and theoretical importance for understanding the patterns of leaf asymmetry development under stressful environments. Road transport is a major source of air pollution and soil contamination in most megacities [27,28]. Linden (Tilia cordata Mill.) and birch (Betula pendula Roth) often grow in roadside forest strips in Russian cities. Notably, these species are bioindicators and their leaf FA is often used for environmental quality assessment [29–31]. Additionally, B. pendula, compared to T. cordata, is also more sensitive to certain gaseous air pollutants of urban roadside territories, such as sulphur and nitrogen oxides [32,33], that allows comparison of their responses to the same levels of pollution. This is the reason these species of woody plants have been selected for this study. We previously evaluated leaf FA in these species, over a number of years, which was reported in a publication for B. pendula [34]. At the same time, we did not analyse FA transition to other asymmetry types in T. cordata and B. pendula under different levels of traffic pollution. The application of plant and animal responses for environmental quality assessment is based on the idea that an increasing level of pollution always causes an increase in disturbance of organism parameters, that is, a dose-response relationship is always monotonic (with no extrema) [35,36]. However, it is known that non-monotonic dose-response relations (with maximums and/or minimums) which include hormesis [37–39] and paradoxical effects [40,41] are frequently found for different plant species and parameters, including various pollutant exposures [42–47]. Hence, an increase in the pollution level will not always be accompanied by plant index deteriorations. The hormetic curve has two phases, and is characterized by an improvement of parameter at low doses, and its impairment at high doses compared to the control level [38,48,49]. Paradoxical effects, include both biphasic and multiphase non-monotonic responses, and are characterized by a decrease in the damaging effect with an increase in the toxicant dose [40,41,43,50]. In fact, all non-monotonic dose-response relations, except hormesis, are paradoxical effects. At the same time, pollutant ability to cause hormesis and paradoxical effects in plants for leaf FA transitions to other types of asymmetry remains unexplored. Therefore, this study analysed the change of leaf trait asymmetry type in T. cordata and B. pendula with traffic air pollution in a wide range of values in an urbanized area (with the example of Nizhny Symmetry 2020, 12, 727 3 of 19

Novgorod in Russia) using long-term data. In addition, relations of leaf FA transitions to other types of asymmetry to air pollution levels and their eﬀect on FA integrated index were studied.

2. Material and Methods

2.1. Study Area and Study Plots We carried out this research in Nizhni Novgorod which is the fifth largest city in Russia with a population of 1,250,619. Nizhni Novgorod is located in European Russia, about 400 km east of Moscow at the confluence of the Oka and the Volga Rivers. The Oka River flows into the Volga and divides the city into two parts. The upland part of Nizhny Novgorod is located on the high eastern bank of the Oka and the lowland part of Nizhny Novgorod occupies the low western bank of the Oka. The main stationary sources of pollution (industrial plants, thermoelectric power station) are located in the lowland part. At the same time, a major source of air pollution in upland part of the city is the road transport, therefore, the study was performed in this area. For the study, we used primary data obtained during 3 years for T. cordata (2010–2011 and 2013) and five years for B. pendula (2007–2008 and 2010–2012). The trees grew in 8–9 model areas (plots) of tree stands planted along roadsides in the upland part of Nizhni Novgorod. It was impossible to study exactly the same set of polluted plots in all years of observation because of the long periods of road repair work. Thus, the set of polluted plots varied slightly in some years. However, we tried, as far as possible, to maintain approximately the same range of traffic intensity in the study sites and plot number. The control plots were far from pollution sources near the village of Kiselikha, 20 km north of Nizhni Novgorod (B. рendula), and in Forest Park Shchelkovsky (T. cordata) situated in the upland part of the city. The studied polluted plots were located at a distance of 1–3 meters from the road and had similar soil conditions, as well as control plots (sod-podzolic soils with mixed upper horizons, normal moisture regime). The soil type is known to be the same in the upland part of the city [51].

2.2. Estimation of Traffic Air Pollution We previously demonstrated that traffic intensity had a high correlation with the content of the main pollutants (oxides of sulphur, nitrogen and carbon as well as benzine, kerosene, benzo[a]pyrene, and formaldehyde) in the air along roads in Nizhni Novgorod (r = 0.8–0.9; p < 0.05). We also revealed the dependences of different plant parameters such as seed production indices, phenological traits, photosynthetic pigment content, leaf FA in B. pendula [34] and lipid peroxidation rate in B. pendula and T. cordata leaf [52] on traffic intensity. These facts confirm that the traffic intensity is an objective indicator of air pollution levels. Therefore, air pollution was estimated by the traffic intensity (vehicles per hour). The traffic intensity was the median of vehicles per hour, counted thrice a weekday: in the morning (from 8 until 10); in the afternoon (from 12 until 15); and in the evening (from 17 until 19). Plot location was chosen so that traffic intensity varied within a wide range, with the minimum and maximum values differing by a factor of several dozens.

2.3. Leaf Trait Selection and Estimation of FA Integrated Index The sun-lit leaves of T. cordata and B. pendula were collected from unshaded areas of the tree crown facing the road. The stages of plant development and leaf growth were also considered during the study, and only parameters of mature reproductive plants (g2) were analyzed. Tree leaves were collected in the second half of July. During this period, most leaves reach the full size but do not yet start to senesce. Therefore, the influence of growth and senescence processes on studied leaf traits can be excluded [34]. Symmetry 2020, 12, 727 4 of 19 Symmetry 2020, 12, x FOR PEER REVIEW 4 of 19 Symmetry 2020, 12, x FOR PEER REVIEW 4 of 19 WeWe collected collected leaf samplessamples atat a a height height of of 1–3 1–3 m. m. Leaves Leaves were were sampled sampled outside outside the branches.the branches. In most In mostcases casesWe we collected we collected leaf leaves leavessamples from from theat a samethe height same trees of trees every1–3 everym. year. Leaves year. We wereWe sometimes sometimes sampled used outsideused the the other the other branches. trees trees on on the In thesamemost same plots,cases plots, we due collected todue tree to cutting leavestree andcuttingfrom pruning. the and same Afterprun treesing. scanningevery After year. thescanning We leaves, sometimes wethe measured leaves, used the we morphological other measured trees on morphologicaltraitsthe same of leaf plots, electronic traits due of imagesleafto electronictree (three cutting repeated images and (t measurements)prunhree ing.repeated After usingmeasurements) scanning a blind the method usingleaves, (thea blindwe operator measured method had (thenomorphological informationoperator had abouttraits no information of the leaf origin electronic ofabout samples) theimages origin [53 (t].hree of samples) repeated [53]. measurements) using a blind method (theWe Weoperator measured measured had nofour four information leaf leaf traits traits inabout in T.T. cordatacordata the origin (Figure(Figure of samples) 1,1, traits traits 1–4) 1–4)[53]. and and five five leaf traits in in B.B. pendula pendula (Figure(FigureWe 2).2). measured In In 2013, 2013, the the four number number leaf traits of of T.T. in cordata cordata T. cordata leafleaf traits(Figure traits was was 1, increasedtraits increased 1–4) to toand seven seven five (Figure (Figureleaf traits 1,1, traits traits in B. 5–7) 5–7)pendula to to assessassess(Figure the the 2). influence influence In 2013, ofthe of the the number trait trait number number of T. cordata on on the the leaf studied studied traits patterns. patterns.was increased to seven (Figure 1, traits 5–7) to assess the influence of the trait number on the studied patterns.

Figure 1. Traits of T. cordata leaf: 1. 1/2 of the width of the leaf in the area of 1/2 of the length of the centralFigureFigure vein; 1.1. TraitsTraits 2. Distance ofof T.T. cordatacordata betweenleaf: leaf: 1.the1. 11/2 /bases2 ofof thethe of widththwidthe first ofof and thethe the leafleaf second inin thethe areaareafrom ofof the 11/2/ 2bottom ofof thethe lengthveinslength of ofof the thethe secondcentralcentral order; vein;vein; 2.3. 2. DistanceAngle Distance between between between the the thecentral bases bases vein of of the th ande first first the and and first the the from second second the fromfrombottom thethe vein bottombottom of the veinsveins second ofof thethe order;secondsecond 4.order; Angleorder; 3. between3. Angle Angle betweenthe between central the the vein central central and vein the vein andseco and thend from firstthe first from the frombottom the bottom the vein bottom veinof the ofvein second the of second the order. second order; 5. Length4.order; Angle of4. betweentheAngle first between from the central the the bottom central vein andvein vein the of andthe second secothe fromsecond order;nd the from bottom 6. Length the veinbottom of of the thevein second second of the from order.second the 5. bottomorder. Length 5. veinofLength the of firstthe of secondthe from first the order; from bottom the7. Distance veinbottom of thevein between second of the th order;secoe endsnd 6. order; Lengthof the 6. first ofLength the and second ofthe the second fromsecond thefrom from bottom the the bottom veinbottom of veinsthevein second of of the the order;second second 7. order. order; Distance 7. betweenDistance thebetween ends ofth thee ends first of and the the first second and fromthe second the bottom from veinsthe bottom of the secondveins of order. the second order.

Figure 2. Traits of B. pendula leaf: 1. 1/2 of the width of the leaf in the area of 1/2 of the length of the central vein; 2. Length of the second from the bottom vein of the second order; 3. Distance between Figure 2. Traits of B. pendula leaf: 1. 1/2 of the width of the leaf in the area of 1/2 of the length of the the bases of the ﬁrst and the second from the bottom veins of the second order; 4. Distance between centralFigure vein; 2. Traits 2. Length of B. pendulaof the second leaf: 1. from 1/2 of the the botto widthm veinof the of leaf the insecond the area order; of 1/2 3. Distanceof the length between of the the ends of the ﬁrst and the second from the bottom veins of the second order; 5. Angle between the thecentral bases vein; of the 2. first Length and ofthe the second second from from the the botto bottom mveins vein of of the the second second order; order; 4. 3. Distance Distance between between central vein and the second from the bottom vein of the second order. thethe ends bases of ofthe the first first and and the the seco secondnd from from the the bottom botto mveins veins of ofthe the se condsecond order; order; 5. Angle4. Distance between between the centralManythe ends vein of of studied and the thefirst leafsecond and traits the from seco are thnde widely bottomfrom the used vein bottom of to the estimate veins second of the order. se levelcond of order; leaf FA5. Angle in trees, between including the T. cordatacentral[30,31 vein] and andB. the pendula second[4 from,5]. the bottom vein of the second order. Many of studied leaf traits are widely used to estimate the level of leaf FA in trees, including T. cordataMany [30,31] of and studied B. pendula leaf traits [4,5]. are widely used to estimate the level of leaf FA in trees, including T. cordata [30,31] and B. pendula [4,5].

Symmetry 2020, 12, 727 5 of 19

In our study, the statistical unit was the leaf because different types of asymmetry were observed even in the leaves of the same tree. To estimate leaf asymmetry in T. cordata and B. pendula, ten leaves from each of ten trees growing in the plot (10 leaves 10 trees, n = 100/per plot) were collected. × To assess the influence of sample sizes on identifying different types of asymmetry, the sample size of T. cordata leaves was increased to 200 for each plot in 2013 (20 leaves 10 trees, n = 200/per plot). × We calculated FA integrated index for each leaf sample using the normalized difference algorithm which eliminates the effect of leaf size on the value of this index [4]:

m n 1 X X Lij Rij A = − (1) m n + · i=1 j=1 Lij Rij where Lij and Rij are the values of the jth trait on the right and left sides of the ith leaf, respectively; m—number of studied morphological traits; n—sample size (number of leaves). Regression analysis (Statistica 10.0) was used to evaluate the dependence of FA integrated index on traffic intensity. Shapiro–Wilk’s test (Statistica 10.0) was applied to check data normality. We believed that regression model adequately described the relationship between the test parameters if p-value for the regression equation did not exceed 0.05 and the determination coefficient R2 was greater than 0.5. Points outside the 95% confidence interval for values were excluded from regression analysis [54]. Sample medians were used for graphical data presentation. Kruskal-Wallis and nonparametric Newman–Keuls tests (Primer of Biostatistics 4.03) were applied for multiple comparisons of leaf asymmetry integrated index. The least significant difference was used for multiple comparison at the p < 0.05 level between impact and control medians.

2.4. Analysis of Leaf Trait Asymmetry Types To reveal diﬀerent types of leaf asymmetry, we used the Palmer and Strobeck (1992) criteria (Table1).

Table 1. Criteria for diﬀerent forms of bilateral asymmetry (by Palmer and Strobeck (1992) with our changes).

Diﬀerences between Shape of L-R Form of Asymmetry Median (L-R) the Left (L) and Right Distribution (R) Traits Ideal ﬂuctuating asymmetry (FA) 0 Normal L = R Directional asymmetry (DA) ,0 Normal L , R Platykurtic or bimodal Antisymmetry (AS) 0 L = R (negative kurtosis)

Ideal FA has a normal L-R distribution and an L-R median (or mean) of 0 (L = R) (Table1). To check for significant differences in L-R distribution from the normal curve, we calculated the skewness and kurtosis of the L-R distribution (Statistica 10.0) and compared them to critical values at α = 0.05. We tested whether L-R median = 0 using the Mann-Whitney U test and carried out an analysis of the differences between the left and right value of leaf traits using the Wilcoxon matched pairs test (Statistica 10.0). DA has a normal L-R distribution, the L-R median,0 and the right and left trait values have significant differences (L , R). AS has a platykurtic or bimodal L-R distribution (negative kurtosis) and the L-R median = 0 (R = L) (Table1). We performed a mixture analysis of asymmetry, which allows the representation of an admixture of asymmetry types as a combination of normal distributions with different means and/or variances, and verification of the assumption of normality of their components [24,55]. For model selection we first determined the number of normal components that best described the distribution of the data (L-R). For this purpose, we used a generalized expectation maximization (EM) cluster analysis Symmetry 2020, 12, 727 6 of 19

(Statistica 10.0). After that, we tested if means of the selected components significantly differed from zero (i.e. DA) (Mann–Whitney U test, Statistica 10.0). If means of two directional components had opposite signs and significantly differed from each other (Mann–Whitney U test, Statistica 10.0) we attributed these components to AS. If the cluster had fewer than 10 values (n < 10) it was excluded from the analysis as an artifact. The average proportion of leaves with FA for the studied leaf traits in each of the studied species (T. cordata and B. pendula) was calculated, in order to study the impact of anthropogenic load on FA transition with other types of asymmetry, including identifying the dependence of FA occurrence in the studied leaf traits on traffic intensity. To do this, at each plot in each year of observation, the proportion of leaves with FA for each studied leaf trait was found, and then the proportions were summed and divided by the number of leaf traits. Regression analysis (Statistica 10) was used to determine whether the average FA proportion depended on traffic intensity in each year of observation both in T. cordata and in B. pendula. In addition, regression analysis was applied to determine the effect of FA transition to other types of asymmetry on FA integrated index, i.e. to study the relation of FA integrated index to the average proportion of leaves with FA in T. cordata and B. pendula in each year of observation. The Chi-square test (Statistica 10) with Bonferroni correction in the case of multiple pairwise comparisons was applied to compare the average proportion of leaves with FA in the control and polluted plots.

3. Results

3.1. Analysis of Leaf Trait Asymmetry Type in T. cordata under Air Pollution In 2010, leaf traits 1–4 had FA in most cases, but sometimes their asymmetry type changed under different traffic intensities. Trait 4 had DA in plots 2 and 5 and FA in the other plots. Traits 1–3 also had DA in some plots (plot 5, trait 1; plot 10, trait 2) (Table2). We did not find AS in its pure form, but it existed in one leaf sample together with FA and DA (0.50FA, 0.11DA, 0.39AS), i.e., in this sample, the same trait could have different types of asymmetry in different leaves at the same pollution level (Table2).

Table 2. Asymmetry forms of T. cordata leaf traits in 2010. FA—an ideal ﬂuctuating asymmetry; DA—a directional asymmetry; AS—an antisymmetry. Asymmetry forms which were not FA given in bold.

Trait Number Studied Plots, Their Numbers and Traﬃc Intensity 1 2 3 4 1. Forest Park Shchelkovsky (control plot) FA FA FA FA (0 veh/h) 2. Nizhni Novgorod Kremlin FA FA DA DA (63 veh/h) 0.39AS; 3. Nevzorovyh Street FA 0.11DA; FA FA (375 veh/h) 0.50FA 4. Meditsinskaya Street FA FA FA FA (690 veh/h) 5. Timiryazeva Street DA FA FA DA (1302 veh/h) 6. Genkinoy Street FA FA FA FA (1233 veh/h) 7. Belinskogo Street –––– (no data) 8. Gagarina Prospect (Lebedeva Street bus stop) DA FA FA FA (3768 veh/h) 9. Gagarina Prospect (University bus stop) FA DA FA FA (4050 veh/h) 10. Lyadov Square FA DA FA FA (5586 veh/h) Symmetry 2020, 12, 727 7 of 19

In 2011, different deviations from FA were revealed more frequently than in 2010. Besides FA, trait 1–4 had DA, AS or mixed FA and DA/AS (Table3). In 2011, leaf traits 1–3 of trees in the control area had FA. However, trait 4 had DA (Table3). Deviations from FA were observed quite often in polluted plots. At the same time, in addition to DA, AS was detected in one case (trait 3 in plot 10), as well as various types of mixed asymmetry, which were much more common than in 2010 (Table2). In some cases, the type of asymmetry could not be identified, apparently due to the fact that there was a small admixture of leaves in the samples, in which this trait had a type of asymmetry differing from FA (Table3).

Table 3. Asymmetry forms of T. cordata leaf traits in 2011. FA—an ideal ﬂuctuating asymmetry; DA—a directional asymmetry; AS—an antisymmetry. ?—the asymmetry form has not been identiﬁed. Asymmetry forms which were not FA given in bold.

Trait Number Studied Plots, Their Numbers and Traﬃc Intensity 1 2 3 4 1. Forest Park Shchelkovsky (control plot) FA FA FA DA (0 veh/h) 2. Nizhni Novgorod Kremlin FA FA DA DA (60 veh/h) 3. Nevzorovyh Street FA ??? (291 veh/h) 4. Meditsinskaya Street FA FA FA DA (801 veh/h) 5. Timiryazeva Street FA 0.88FA; 0.12DA FA FA (1167 veh/h) 6. Genkinoy Street FA 0.54FA; 0.46DA FA FA (1221 veh/h) 7. Belinskogo Street FA FA FA 0.76FA; 0.24DA (2103 veh/h) 8. Gagarina Prospect (Lebedeva Street bus stop) FA 0.84FA; 0.16DA 0.69FA; 0.28DA DA (3552 veh/h) 9. Gagarina Prospect (University bus stop) DA FA DA FA (4455 veh/h) 10. Lyadova Square DA FA AS DA (5082 veh/h)

In 2013, an increase in the sample size from 100 to 200 leaves and in the trait number from four to seven did not change the situation significantly. Deviations from FA were observed quite frequently (Table4). In the control plot, trees had a deviation from FA for traits 1 (DA) and 2 (0.77 FA; 0.23 AS). In case of road pollution, the transition from FA to DA or to various types of mixed asymmetry was detected for all studied traits in certain plots (Table4). In 2013, we did not find AS in its pure form as in 2010. Thus, for all studied leaf traits of T. cordata FA transitions to other types of asymmetry (mostly to DA) or mixed asymmetry were detected. Such transitions were observed both in the trees of polluted sites and in the control plot. For leaf traits 1–4 (Figure1), studied in all the years of research, it was shown that their asymmetry type in the same trees on both the control and polluted sites varied in different years of observation (Tables2–4). Symmetry 2020, 12, x FOR PEER REVIEW 8 of 19

Studied Plots, Their Numbers and Trait Number Traffic Intensity 1 2 3 4 5 6 7 1. Forest Park Shchelkovsky (control 0.77FA plot) DA FA FA FA FA FA 0.23AS Symmetry 2020(0 veh/h), 12, 727 8 of 19 2. Nizhni Novgorod Kremlin 0.83DA ? FA FA DA DA FA (89 veh/h) 0.17AS Table 4. Asymmetry forms of T. cordata leaf traits in 2013. FA—an ideal fluctuating asymmetry; 3. Nevzorovyh Street DA—a directional asymmetry;FA AS—an antisymmetry.FA ?—the FA asymmetryFA formDA has not beenFA identified. FA (336 veh/h) Asymmetry forms which were not FA given in bold. 4. Meditsinskaya Street FA FA FA DA FA FA FA (750 veh/h) Trait Number Studied Plots, Their Numbers and Traffic Intensity 5. Timiryazeva Street 0.72FA 1 2 3 4 5 6 7 FA FA FA FA FA DA 1. Forest Park Shchelkovsky (control plot) 0.77FA (876 veh/h) 0.28AS DA FA FA FA FA FA (0 veh/h) 0.23AS 6. Genkinoy Street 0.92FA 0.91FA 0.60DA 2. Nizhni Novgorod Kremlin 0.83DA DA ? DAFA FA DA AS DA FA FA (1410 veh/h) (89 veh/h) 0.08DA 0.09DA 0.17AS 0.40AS 3. Nevzorovyh Street 7. Belinskogo Street 0.58FA FA FA FA FA DA 0.90FAFA FA0.60DA (336 veh/h) FA DA DA FA (2145 veh/h)4. Meditsinskaya Street 0.42AS 0.01AS 0.40AS FA FA FA DA FA FA FA 8. Gagarina Prospect(750 veh/h) 5. Timiryazeva Street 0.93FA 0.22DA0.72FA 0.45DA 0.81FA (Lebedeva Street bus stop) FA FA FADA FADA FA FA DA (876 veh/h) 0.07DA 0.78AS0.28AS 0.55AS 0.19AS (3642 veh/h)6. Genkinoy Street 0.92FA 0.91FA 0.60DA DA DA AS FA 9. Gagarina Prospect (University(1410 veh bus/h) 0.08DA 0.09DA 0.40AS 7. Belinskogo Street 0.60FA 0.58FA 0.90FA 0.60DA stop) FA FA FA DADA DA FA FA FA DA (2145 veh/h) 0.40AS 0.42AS 0.01AS 0.40AS 8. Gagarina Prospect (4869 veh/h) 0.93FA 0.22DA 0.45DA 0.81FA (Lebedeva Street bus stop) FA DA DA 10. Lyadov Square 0.07DA 0.78AS 0.55AS 0.19AS (3642 veh/h) ? ? FA FA AS FA FA 9.(3888 Gagarina veh/h) Prospect (University bus stop) 0.60FA FA FA DA FA FA DA (4869 veh/h) 0.40AS 10. Lyadov Square ?? FA FA AS FA FA For leaf traits(3888 1–4 veh (Figure/h) 1), studied in all the years of research, it was shown that their asymmetry type in the same trees on both the control and polluted sites varied in different years of observation (Tables 2–4). Regression analysis revealed the dependence of the average proportion of leaves with FA for Regression analysis revealed the dependence of the average proportion of leaves with FA for the studied leaf traits on traffic intensity in all years of observation (Figure3), that is, the impact of the studied leaf traits on traffic intensity in all years of observation (Figure 3), that is, the impact of anthropogenic load on this index. At the same time, the functional forms of dependence differed anthropogenic load on this index. At the same time, the functional forms of dependence differed in in different years. In 2010, the proportion of FA decreased linearly with increase in pollution level different years. In 2010, the proportion of FA decreased linearly with increase in pollution level (Figure3), that corresponded to data from other authors [ 25]. However, in 2011, this indicator first (Figure 3), that corresponded to data from other authors [25]. However, in 2011, this indicator first increased and then decreased relative to control value. In 2013, on the contrary, the proportion of FA increased and then decreased relative to control value. In 2013, on the contrary, the proportion of FA first decreased, and then increased almost to the control level (Figure3). first decreased, and then increased almost to the control level (Figure 3). Regression analysis showed dependence of FA integrated index on traffic intensity only in 2011 Regression analysis showed dependence of FA integrated index on traffic intensity only in 2011 and 2013. An increase in traffic intensity caused a decrease in this parameter relative to the control and 2013. An increase in traffic intensity caused a decrease in this parameter relative to the control (Figure4) which contradicted the idea of increasing leaf FA with stressful environments [ 4]. At the (Figure 4) which contradicted the idea of increasing leaf FA with stressful environments [4]. At the same time, T. cordata FA integrated index did not depend on average proportion of leaves with FA (R2 same time, T. cordata FA integrated index did not depend on average proportion of leaves with FA < 0.50; p > 0.05) in all years of observation, that is, on FA transitions to other types of asymmetry (data (R2 < 0.50; p > 0.05) in all years of observation, that is, on FA transitions to other types of asymmetry are not shown in Figures). (data are not shown in Figures).

1.1 Control y = -5*10-5x + 0.9891 1.0 R2 = 0.75 0.9 p=0.012 0.8 * 0.7 * 0.6 * 0.5

Proportion of leaves with leaves FA of Proportion 0.4 0 1000 2000 3000 4000 5000 6000 Traffic intensity, vehicles per hour

(a)

Figure 3. Cont. Symmetry 2020, 12, x FOR PEER REVIEW 9 of 19 SymmetrySymmetry2020 2020, 12, ,12 727, x FOR PEER REVIEW 9 of9 19 of 19

1.1

* -8 2 1.01.1 y = -7*10 x + 0.0003x + 0.5538 2 * * R-8 2= 0.75 0.91.0 y = -7*10 x + 0.0003x + 0.5538 * p=0.019 * R2 = 0.75 0.80.9 * p=0.019 Control 0.70.8 Control 0.60.7

0.50.6 * 0.40.5 Proportion of leaves with FA * 0.30.4 Proportion of leaves with FA * 0.20.3 0 1000 2000 3000 4000 5000* 6000 0.2 Traffic intensity, vehicles per hour 0 1000 2000 3000 4000 5000 6000 Traffic intensity, vehicles per hour (b) (b)

1.0 y = 7*10-8x2 - 0.0004x + 0.9512 2 0.91.0 -8R 2 = 0.64 y = 7*10 x - 0.0004x + 0.9512 p=0.045 0.80.9 R2 = 0.64 Control 0.70.8 p=0.045 Control * 0.60.7 * 0.50.6 * 0.40.5 * * * Proportion of leaves with FA 0.30.4 * * Proportion of leaves with FA 0.30 1000 2000 3000 4000 5000 6000 Traffic intensity, vehicles per hour 0 1000 2000 3000 4000 5000 6000 Traffic intensity, vehicles per hour (c) (c) Figure 3. Dependence of the average proportion of T. cordata leaves with FA in the studied traits on FiguretrafficFigure intensity 3. 3.Dependence Dependence in 2010 of( aof), the the2011 averageaverage (b) and proportionproportion 2013 (c). *—indicates of of T.T. cordata cordata significant leavesleaves with with differences FA FA in in the the between studied studied treestraits traits in on on tracontroltrafficffic intensity andintensity polluted in in 2010 2010 plots ( a(),a at), 2011 2011p < 0.05. ( b(b)) and and 20132013 ((c). *—indicates *—indicates significant significant differences differences between between trees trees in in controlcontrol and and polluted polluted plots plots at at p p< < 0.05.0.05.

0.065 Control y = -2*10-6x + 0.0595 R2 = 0.55 0.065 Control y = -2*10-6x + 0.0595 0.060 p=0.035 R2 = 0.55 0.060 0.055 p=0.035

0.055 0.050 *

0.050 * * 0.045 FA integrated index * 0.045 0.040 FA integrated index 0 1000 2000 3000 4000 5000 6000 0.040

0 1000 2000 3000Traffic 4000 intensity, vehicles 5000 per hour 6000

Traffic intensity, vehicles per hour

(a) (a)

0.060 y = -6*10-7x + 0.0504 0.058 b 0.060 2 0.056 R = 0.07-7 y = -6*10 x + 0.0504 0.0540.058 p=0.463 b R2 = 0.07 0.0520.056Control p=0.463 0.0500.054 Control 0.0480.052 0.0460.050 FA integrated index 0.0440.048 0.0420.046 FA integrated index 0.0400.044 0.042 0 1000 2000 3000 4000 5000 6000 0.040 Traffic intensity, vehicles per hour 0 1000 2000 3000 4000 5000 6000 Traffic intensity, vehicles per hour (b) (b) Figure 4. Cont.

Symmetry 2020, 12, 727 10 of 19 Symmetry 2020, 12, x FOR PEER REVIEW 10 of 19

0.060 Control y = 2*10-10x2 - 3*10-6x + 0.0564 0.058 R2 = 0.70 0.056 p=0.027 0.054 0.052 0.050 * 0.048 * *

FA integrated index 0.046 0.044 0.042 0 1000 2000 3000 4000 5000 6000 Traffic intensity, vehicles per hour (c)

Figure 4. T. cordata a b Figure 4. DependenceDependence of of T. cordata FA integratedintegrated indexindex on on tra trafficffic intensity intensity in in 2010 2010 ( ),(a 2011), 2011 ( ) ( andb) and 2013 (c). *—indicates significant differences between trees in control and polluted plots at p < 0.05. 2013 (c). *—indicates significant differences between trees in control and polluted plots at p < 0.05. 3.2. Analysis of Leaf Trait Asymmetry Type in B. pendula under Air Pollution 3.2. Analysis of Leaf Trait Asymmetry Type in B. pendula under Air Pollution Similar to T. cordata, B. pendula had FA transitions to other types of asymmetry or to mixed typesSimilar both into theT. cordata control, B. and pendula polluted had plots FA duringtransitions all years to other of observation types of asymmetry for all studied or to leaf mixed traits types(Tables both5– 9in). the In addition, control and asymmetry polluted typesplots during of studied all years leaf traits of observation in the same for trees all studied varied in leaf di traitsfferent (Tablesyears of5–9). observation In addition, (Tables asymmetry5–9). types of studied leaf traits in the same trees varied in different years of observation (Tables 5–9). Table 5. Asymmetry forms of B. pendula leaf traits in 2007. FA—an ideal fluctuating asymmetry; DA—a Tabledirectional 5. Asymmetry asymmetry; forms AS—an of B. antisymmetry. pendula leaf traits Asymmetry in 2007. forms FA—an which ideal were fluctuating not FA given asymmetry; in bold. DA—a directional asymmetry; AS—an antisymmetry. Asymmetry forms which were not FA given Trait Number Studiedin bold. Plots, Their Numbers and Traffic Intensity 1 2 3 4 5 Studied Plots, Their Numbers and Trait Number 1. Kiselikha Village (control) Traffic Intensity FA AS AS AS FA (0 veh/h) 1 2 3 4 5 1. Kiselikha Village (control)2. Krilova Street 0.9FA FA AS DA ASFA FA AS FA FA (0 veh/h) (59 veh/h) 0.1DA 3. Nizhni Novgorod Kremlin 0.71FA 2. Krilova Street FA AS AS FA 0.9FA (108 veh/h) DA FA 0.29DA FA FA (59 veh/h) 0.1DA 4. Lomonosova Street (137 veh/h) DA FA FA FA FA 3. Nizhni Novgorod Kremlin 0.71FA 5. Nesterova Street (303 veh/h)FA FA ASDA FA ASDA DA FA (108 veh/h) 0.29DA 0.73FA 6. Nartova Street (399 veh/h) FA FA FA FA 4. Lomonosova Street (137 veh/h) DA FA FA FA 0.27DA FA 7. Meditsinskaya Street 5. Nesterova Street (303 veh/h) FA DA FAFA FA FADA FA FA DA (973 veh/h) 0.73FA 6. Nartova Street (399 veh/h) FA FA FA0.77FA FA 8. Belinskogo Street (2204 veh/h) FA FA FA FA 0.23AS 0.27DA 7. Meditsinskaya9. Gagarina Street Prospect (Lebedeva bus stop) FA FA FA FA FA FA FA FA DA FA (973 veh/h) (3564 veh/h) 10. Gagarina Prospect (University bus stop)0.77FA 8. Belinskogo Street (2204 veh/h) FA FAFA FA FA FAAS FA FA (3964 veh/h) 0.23AS 9. Gagarina Prospect (Lebedeva bus stop) FA FA FA FA DA (3564 veh/h) 10. Gagarina Prospect (University bus stop) FA FA FA AS FA (3964 veh/h)

Table 6. Asymmetry forms of B. pendula leaf traits in 2008. FA—an ideal fluctuating asymmetry; DA—a directional asymmetry; AS—an antisymmetry. ?—the asymmetry form has not been identified. Asymmetry forms which were not FA given in bold.

Studied Plots, Their Numbers and Trait Number Traffic Intensity 1 2 3 4 5 1. Kiselikha Village (control) DA FA FA FA DA (0 veh/h)

Symmetry 2020, 12, 727 11 of 19

Table 6. Asymmetry forms of B. pendula leaf traits in 2008. FA—an ideal ﬂuctuating asymmetry; DA—a directional asymmetry; AS—an antisymmetry. ?—the asymmetry form has not been identiﬁed. Asymmetry forms which were not FA given in bold.

Trait Number Studied Plots, Their Numbers and Traﬃc Intensity 1 2 3 4 5 1. Kiselikha Village (control) DA FA FA FA DA (0 veh/h) 2. Melnikova-Pecherskogo Street FA FA FA ? FA (4 veh/h) 3. Nizhni Novgorod Kremlin FA FA FA FA DA (72 veh/h) 4. Lomonosova Street (162 veh/h) FA FA FA FA DA 5. Hevzorovyh Street (293 veh/h) DA DA FA FA DA 6. Nartova Street (477 veh/h) FA FA DA FA DA 7. Meditsinskaya Street FA FA DA DA DA (989 veh/h) 8. Timiryazeva Street ? DA FA FA DA (1434 veh/h) 8. Belinskogo Street (2333 veh/h) FA FA ? FA FA 9. Gagarina Prospect (Lebedeva bus stop) FA FA FA FA DA (3135 veh/h) 10. Gagarina Prospect (University bus stop) DA DA FA FA DA (3784 veh/h)

Table 7. Asymmetry forms of B. pendula leaf traits in 2010. FA—an ideal ﬂuctuating asymmetry; DA—a directional asymmetry; AS—an antisymmetry. Asymmetry forms which were not FA given in bold.

Trait Number Studied Plots, Their Numbers and Traﬃc Intensity 1 2 3 4 5 1. Kiselikha Village (control) FA FA AS AS FA (0 veh/h) 2. Melnikova-Pecherskogo Street FA FA AS AS FA (51 veh/h) 3. Nizhni Novgorod Kremlin DA AS FA FA DA (63 veh/h) 4. Lomonosova Street AS DA AS FA FA (153 veh/h) 0.33 FA 5. Nevzorovih Street (282 veh/h) FA FA FA FA 0.67 DA 6. Nartova Street (573 veh/h) φA AC AC AC φA 7. Meditsinskaya Street DA FA FA DA DA (618 veh/h) 8. Timiryazeva Street AS FA FA DA DA (1239 veh/h) 8. Belinskogo Street (2706 veh/h) FA FA FA FA AS 9. Gagarina Prospect (Lebedeva bus stop) (3291 veh/h) AS FA AS FA AS 10. Gagarina Prospect (University bus stop) (3990 veh/h) FA FA FA DA FA Symmetry 2020, 12, 727 12 of 19

Table 8. Asymmetry forms of B. pendula leaf traits in 2011. FA—an ideal ﬂuctuating asymmetry; DA—a directional asymmetry; AS—an antisymmetry. Asymmetry forms which were not FA given in bold.

Trait Number Studied Plots, Their Numbers and Traﬃc Intensity 1 2 3 4 5 1. Kiselikha Village (control) (0 veh/h) FA FA AS AS FA 2. Nizhni Novgorod Kremlin (60 veh/h) FA DA FA AS FA 3. Melnikova-Pecherskogo Street (84 veh/h) FA FA FA DA FA 4. Lomonosova Street (120 veh/h) FA DA DA DA AS 5. Nevzorovih Street (186 veh/h) FA DA DA DA AS 6. Nartova Street (591 veh/h) DA FA FA FA AS 7. Meditsinskaya Street (915 veh/h) DA AS AS AS FA 8. Tmiryazeva Street (1167 veh/h) DA FA FA FA DA 0.81 FA; 0.80 FA; 8. Belinskogo Street (2103 veh/h) FA FA FA 0.19 DA 0.20 DA 9. Gagarina Prospect (Lebedeva bus stop) (3552 veh/h) FA DA FA FA AS 10. Gagarina Prospect (University bus stop) (4455 veh/h) FA FA DA FA FA

Table 9. Asymmetry forms of B. pendula leaf traits in 2012. FA—an ideal ﬂuctuating asymmetry; DA—a directional asymmetry; AS—an antisymmetry. ?—the asymmetry form has not been identiﬁed. Asymmetry forms which were not FA given in bold.

Trait Number Studied Plots, Their Numbers and Traﬃc Intensity 1 2 3 4 5 1. Kiselikha Village (control) DA DA ? FA DA (0 veh/h) 2. Campus of Lobachevski University DA AS FA DA DA (6 veh/h) 0.60 FA 3. Nizhni Novgorod Kremlin (102 veh/h) AS FA FA DA 0.40 DA 4. Nevzorovih Street (285 veh/h) FA DA FA FA DA 5. Nartova Street (663 veh/h) FA AS FA FA FA 6. Meditsinskaya Street DA DA FA FA DA (915 veh/h) 7. Tmiryazeva Street (1308 veh/h) FA FA FA FA DA 0.71 FA 8. Belinskogo Street (2487 veh/h) FA FA FA DA 0.29 DA 9. Gagarina Prospect (Lebedeva bus stop) 0.80 FA ? FA DA FA (3762 veh/h) 0.20 DA 10. Gagarina Prospect (University bus stop) 0.90 FA FA DA DA FA (4245 veh/h) 0.10 DA

We compared a frequency of diﬀerent asymmetry types in T. cordata and B. pendula for the same leaf traits using data obtained in the same years (2010–2011, total data for all plots). Proportions of FA and DA in these species were the same (Table 10). However, AS was more often observed in B. pendula, and mixed asymmetry was detected more often in T. cordata (Table 10). Regression analysis showed the dependence of the average proportion of leaves with FA for the studied leaf traits on traﬃc intensity in 2007, 2008 and 2012 (Figure5), that is, the impact of anthropogenic load on the frequency of FA transitions to other types of asymmetry. All revealed dependencies were non-monotonic (Figure5). In 2010–2011, dependencies were not observed ( R2 < 0.50; p > 0.05) (data not shown in Figures). Symmetry 2020, 12, 727 13 of 19

Table 10. Frequency of different leaf asymmetry types in B. pendula and T. cordata in the same years of observation (2010–2011). FA—an ideal fluctuating asymmetry; DA—a directional asymmetry; AS—an antisymmetry. For each type of asymmetry, the frequency is calculated using the total data of all plots and traits in 2010–2011. * Indicates significant differences between T. cordata and B. pendula at p < 0.05.

Asymmetry Type Plant Species, Any Types of Any Sample Size FA DA AS Mixed Deviations Symmetry 2020, 12, x FOR PEER REVIEW Asymmetry from14 of FA20 B. pendula (n = 65) 0.56 0.06 0.17 0.05 0.25 0.05 0.03 0.02 0.45 0.06 ± ± ± ± ± T. cordata (n = 56) 0.68 0.06 0.20 0.05 0 * 0.13 * 0.05 0.32 0.06 ± ± ± ±

1.3

1.1 * * * 0.9 *# *# *# *# 0.7

0.5 -11 3 -7 2 y = 6*10 x - 5*10 x + 0.0009x + 0.574 Control 2 0.3 R = 0.68 p=0.005 Proportion leaves of with FA

0.1 0 1000 2000 3000 4000 5000 Traffic intensity, vehicles per hour

(a)

1.1 1.0 * * 0.9 0.8 * 0.7 * 0.6 Control 0.5 0.4 ** * 0.3 y = -2*10-10x3 + 8*10-7x2 - 0.0011x + 0.829 2 Proportiion of leavesProportiion with of FA 0.2 R = 0.68 p=0.038 0.1 0 500 1000 1500 2000 2500 3000 3500 4000 Traffic intensity, vehicles per hour

(b)

1.0 0.9 * 0.8 * * 0.7 * 0.6 * *# 0.5 *# 0.4 y = -1*10-7x2 + 0.0005x + 0.3631 2 0.3 R = 0.72 p=0.023 Control

Proportion leaves of with FA 0.2 0.1 0 1000 2000 3000 4000 5000

Traffic intensity, vehicles per hour

(c)

FigureFigure 5. Dependence 5. Dependence of the of the average average proportion proportion of B.B. pendula pendula leavesleaves with with FA in FA the in studied the studied traits on traits on traffic intensitytraffic intensity in 2007 in 2007 (a), ( 2008a), 2008 (b )(b and) and 2012 2012( (cc).). *—indicates significant significant differences differences between between trees in trees in controlcontrol and polluted and polluted plots plots at p at< p0.05; < 0.05; #—indicates #—indicates significant significant differences differences in in relation relation to the to themaximum maximum of the dependence.of the dependence.

Symmetry 2020, 12, 727 14 of 19 Symmetry 2020, 12, x FOR PEER REVIEW 14 of 19

Symmetry 2020, 12, x FOR PEER REVIEW 14 of 19 Regression analysis revealed a dependence of(c)B. pendula FA-integrated index on traﬃc intensity in all observation years. Unlike T. cordata, this parameter(c) for B. pendula increased with air pollution in mostFigure cases 5. Dependence (Figures6 and of the7) thataverage corresponded proportion of to B. thependula idea leaves of increasing with FA in leaf the FAstudied under traits stressful on environmentstrafficFigure intensity 5. Dependence [4]. in Similar 2007 of(a to),the 2008T. average cordata (b) and proportion, B. 2012 pendula (c). of *—indicates B.FA pendula integrated leaves significant with index FA differences didin the not studied dependbetween traits trees on on average in proportioncontroltraffic ofintensityand leaves polluted in with 2007 plots FA(a), at (R2008 p2 < ( 0.05;b0.50;) and #—indicates p2012> 0.05) (c). *—indicates in significant all years significant differences of observation, differences in relation that between is,to the on treesmaximum FAtransitions in to otherofcontrol the forms dependence. and of polluted asymmetry plots at (data p < 0.05; not #—indicates shown in Figures). significant differences in relation to the maximum of the dependence.

0.060

0.060 0.058 y = 1*10-12x3 - 9*10-9x2 + 2*10-5x + 0.0499 2 0.058 y = 1*10-12x3 - 9*10-9Rx2 += 2*10 0.77-5x + 0.0499 2 p=0.023 0.056 R = 0.77 0.056 p=0.023 0.054 0.054 0.052 0.052 FA integrated index 0.050 FA integrated index 0.050 Control 0.048 Control 0.048

0.046 0.046 0 500 1000 1500 2000 2500 3000 3500 4000 4500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 Traffic intensity, vehivles per hour Traffic intensity, vehivles per hour (a()a )

-6 1) 1)y =y 3*10 = 3*10-6x +x 0.052 + 0.052 2 R2 R= 0.42 = 0.42 0.070 p=0.030p=0.030 (with(with control); control); 2) 2)y=4*10 y=4*10-6x+0.049-6x+0.049 0.065 ControlControl R2=0.78R2=0.78 p=0.001p=0.001 0.060 (without(without control) control)

0.055 **

FA integrated index *

FA integrated index * * * * * * 0.050 * 0.050 * * * * 0.045 0.045

0.040 0.040 0 500 1000 1500 2000 2500 3000 3500 4000 0 500 1000 1500 2000 2500 3000 3500 4000 Traffic intensity, vehicles per hour Traffic intensity, vehicles per hour (b) (b)

Figure 6. Dependence of B. pendula FA integrated index on traffic intensity in 2007 (a) and in 2008 (b) Figure 6. Dependence of of B.B. pendula pendula FAFA integrated index on on traffic traffic intensity intensity in in 2007 2007 ( (aa)) and and in in 2008 2008 ( (bb)) ([([34],34], with with changes). changes). *—indicates*—indicates significantsignificant differ differencesences between between trees trees in in control control and and polluted polluted plots plots at ([34],at p

0.075 y = -4*10-9x2 + 2*10-5x + 0.032 0.075 R2 = 0.71 y = -4*10-9x2 + 2*10-5x + 0.032 * p=0.007 0.065 2 * R = 0.71 * p=0.007 0.065 * 0.055 * 0.055 * * * * 0.045 * * * * * * * 0.045 * FA integrated index 0.035 *

FA integrated index 0.035 0.025

Control 0.025 0.015 0Control 500 1000 1500 2000 2500 3000 3500 4000 4500

0.015 Traffic intensity, vehicles per hour 0 500 1000 1500 2000 2500 3000 3500 4000 4500

Traffic intensity, vehicles per hour (a)

Figure(a 7.) Cont.

Symmetry 2020, 12, 727 15 of 19 Symmetry 2020, 12, x FOR PEER REVIEW 15 of 19

0.080

0.070 y = 1*10-5x + 0.024 R2 = 0.79 * 0.060 p=0.001 *

* 0.050 *

FA integrated index 0.040 *

0.030 ** *

0.020 Control

0.010 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Traffic intensity, vehicles per hour

(b)

-13 3 -9 2 -6 0.065 y = -5*10 x + 5*10 x - 9*10 x + 0.050 2 R = 0.86 * p=0.006 0.060

0.055

FA integrated index 0.050 Control

0.045

0.040 0 500 1000 1500 2000 2500 3000 3500 4000 4500

Traffic intensity, vehicles per hour

(c)

Figure 7. Dependence of B. pendula FA integrated index on traffic traffic intensity in 2010 ( a), 2011 (b) and 2012 (c) ([[34,34 ],with with changes]. changes). *—indicates *—indicates significant significant di differencesfferences between between trees trees in in control control and polluted plots at p < 0.05.0.05. 4. Discussion 4. Discussion In the present work, we showed that all the studied leaf traits of T. cordata and B. pendula changed In the present work, we showed that all the studied leaf traits of T. cordata and B. pendula the asymmetry type both in trees that grew in polluted plots and control ones, although previously FA changed the asymmetry type both in trees that grew in polluted plots and control ones, although for these leaf traits was reported by other authors who determined the leaf asymmetry once [4,30,31]. previously FA for these leaf traits was reported by other authors who determined the leaf Our results indicate that leaf asymmetry type needs to be evaluated multiple times. Apparently, asymmetry once [4,30,31]. Our results indicate that leaf asymmetry type needs to be evaluated FA transition to other asymmetry types was due, not only anthropogenic load, but also to weather multiple times. Apparently, FA transition to other asymmetry types was due, not only variation in the control plots in different years. It was observed that adverse weather conditions anthropogenic load, but also to weather variation in the control plots in different years. It was impacted the leaf bilateral asymmetry [17,18]. This fact was also considered in detail in our previous observed that adverse weather conditions impacted the leaf bilateral asymmetry [17,18]. This fact studies [34,56]. was also considered in detail in our previous studies [34,56]. Moreover, FA transitions to other asymmetry types were species specific, despite the fact that Moreover, FA transitions to other asymmetry types were species specific, despite the fact that the total percentage of deviations from FA was the same for two species. T. cordata most often had an the total percentage of deviations from FA was the same for two species. T. cordata most often had an equally likely transition from FA to DA and various types of mixed asymmetry, which corresponded to equally likely transition from FA to DA and various types of mixed asymmetry, which corresponded results of other studies for animals [23–25] and plants [26]. B. pendula, in contrast, rarely had a mixed to results of other studies for animals [23–25] and plants [26]. B. pendula, in contrast, rarely had a asymmetry, and FA transitions to DA and AS were observed with the same frequency. mixed asymmetry, and FA transitions to DA and AS were observed with the same frequency. For the most years of observations, the high possibility of traffic air pollution impact on FA For the most years of observations, the high possibility of traffic air pollution impact on FA transitions to other asymmetry types in T. cordata and B. pendula was established. Despite this, an transitions to other asymmetry types in T. cordata and B. pendula was established. Despite this, an increase in anthropogenic load did not always lead to an increase in the probability of FA switching to increase in anthropogenic load did not always lead to an increase in the probability of FA switching DA and/or AS, which is believed to be observed under the exposure to environmental stressors [25]. to DA and/or AS, which is believed to be observed under the exposure to environmental stressors T. cordata had both a linear decrease in FA proportion in the studied leaf traits with an increase in [25]. T. cordata had both a linear decrease in FA proportion in the studied leaf traits with an increase traffic intensity (in 2010), and a non-monotonic changes of this parameter (in 2011 and 2013). In B. in traffic intensity (in 2010), and a non-monotonic changes of this parameter (in 2011 and 2013). In B. pendula, similar dependencies were only non-monotonic. Some of the non-monotonic responses of T.

Symmetry 2020, 12, 727 16 of 19 pendula, similar dependencies were only non-monotonic. Some of the non-monotonic responses of T. cordata (in 2011) and B. pendula (in 2007 and 2012) corresponded to hormesis, whereby low pollution levels caused an increase in FA proportion, while high pollution reduced it relative to the control level. Other dependencies were typical two-phase or multi-phase paradoxical effects (T. cordata in 2013; B. pendula in 2008). Apparently, only strong external stressors caused a linear decrease in the proportion of leaves with FA. It was reported that significant stress led to a switch from FA to DA and/or AS [25]. For instance, in 2010, similar change of this T. cordata parameter was induced by combination of traffic air pollution and drought during the leaf growth period (in May-June of this year) [34]. B. pendula was less demanding of soil moisture compared to T. cordata, therefore, linear relationships were not observed even under drought conditions. The high frequency of non-monotonic dose-effect relationships was revealed for various plant indices under stressful environments [37–39], including the effects of chemical pollutants on physiological and biochemical parameters [43,44,57], as well as on leaf FA [44,58]. In this study, we first found non-monotonic responses in development of leaf bilateral asymmetry type, that is, for leaf morphogenesis. Our results indicated that DA and AS of T. cordata and B. pendula leaf traits were not exclusively hereditary and their development depended on environmental conditions. Apparently, the morphogenesis of leaf bilateral asymmetry is more plastic than it is considered, which may be due to the plasticity of epigenetic regulation of leaf structure development. It is currently known that epigenetic regulation, such as DNA methylation, histone modification, histone variants, chromatin remodeling, and small RNAs play crucial role as linkers between the environment and the genome in plants. They are involved in the regulation of leaf development [59–61] and its responses to abiotic stresses [62,63], including the development of leaf bilateral asymmetry [61]. Therefore, we assume that epigenetic mechanisms of leaf morphogenesis can be very plastic and the bilateral asymmetry type of the same leaf trait can change rather quickly under different environmental conditions. We showed that the FA-integrated index of two plant species reacted differently to the increase in pollution level. In B. pendula, which was more sensitive to exhaust pollutants [32,33], there was a typical stressful increase in this parameter relative to the control over all years of observations, which indicated a disruption of leaf developmental stability. In T. cordata, which was more resistant to air pollution, by contrast, this index did not change (in 2010), and even decreased relative to control in 2011 and 2013, i.e., traffic pollution increased leaf development stability. Apparently, this was due to the stimulating effect in the first phase of hormesis. At the same time, FA integrated index of T. cordata and B. pendula leaf did not depend on the frequency of FA transitions to other asymmetry types. Apparently, this was caused by insignificant leaf asymmetry under DA and AS. Perhaps, a significant asymmetry disrupted normal leaf functioning, and such leaves were eliminated by natural selection. It was reported that woody plants, including B. pendula and T. codata, lost some amount of leaves in summer [64–67]. Therefore, in our study, we demonstrated the need for a long-term assessment of leaf trait asymmetry type in plants used in bioindication. We also emphasize the need to study the non-monotonic response role in the development of leaf asymmetry with pollution impact, and the identification of molecular mechanisms underlying FA transition to other types of asymmetry.

Author Contributions: E.A.E. and B.N.Y. performed research, statistical analysis, and wrote the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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