Variability in anatomical traits [thickness in μm (
Abstract
Trait variability in response to seasonal variations can be hypothesised as an advantageous strategy for overwintering shrubs. This hypothesis was tested by elucidating patterns of trait variation in an evergreen alpine shrub, Rhododendron anthopogon D. Don. The study site was established at Rohtang (3990 m a.s.l.) in western Himalaya. Its leaves were sampled at 10 time points spanning a period of 1 year (beginning from 22-August-2017 to 14-August-2018) for estimating anatomical traits using light and scanning electron microscopy. The data were analysed using one-way analysis of variance, and the trait-temperature relationships were analysed using linear regression. The results indicated a lower variability in the anatomical traits. A few traits (e.g. cuticle thickness and epidermal scales) were found to be significantly correlated with temperature (p < 0.05). Our analysis revealed increase in cuticle thickness and a decrease in epidermal scales (size) during low-temperature conditions. The lesser variability found in anatomical traits of overwintering shrub could be explained as ‘evolutionary gained adaptive traits’.
Keywords
- acclimatory responses
- broadleaved evergreen shrub
- in situ analysis
- glandular scales
- seasonal variations
- temperature regime
1. Introduction
Temperature, as one of the major abiotic factors in high altitudes limiting plant growth and distribution, varies on seasonal basis to a greater extent [1]. Therefore, the plants growing in such environments must be able to respond to these changes by actively acclimating their biology [2, 3]. Most exposed in this regard are the overwintering woody perennials, which get subjected to substantial variations in temperature in the course of a year, from temperatures reaching as high as up to 30°C the during summers to severe freezing conditions (dropping below −30°C) in the winters [4]. Because the overwintering evergreen woody perennials are exposed to seasonal shifts in temperature (favourable to harsh), they are expected to have evolved better to exhibit transitory acclimatory responses [5, 6]. Therefore, these represent an excellent system to study plant persistence strategies in harsh environments.
At high altitudes, plants must develop their ‘defences’ (during harsh conditions) necessary to survive in a highly variable environment [7]. It has been proposed that persistence in such environments is likely to be facilitated by plasticity in ‘anatomical traits’ of plants [3, 8]. For instance, the changes in leaf anatomical traits (e.g. increased leaf thickness along altitudinal gradient) have been reported to be linked to the plant’s adaptation to changes in environmental conditions (e.g. decrease in temperature, etc.) [9, 10]. However, so far little is known about differential anatomical responses of overwintering evergreen shrubs to seasonal variability. An understanding of the patterns of variation in these traits, while they experience seasonal shifts, will provide insights regarding adaptive strategies in varying environmental conditions (from favourable to harsh).
Studying adaptive responses of species growing in their natural habitat could provide invaluable information about how plants prepare themselves to persist under changing environmental conditions. The common approach to investigate plant responses to environmental variability has been to resample the same plant population(s) and directly make comparison between them [13].
In the present study, the aim was to understand the extent of variations in leaf anatomical traits that enable plant survival in the harsh climate of high elevations. This study will shed light on strategies of plants to persist in high-elevation natural ecosystems. Specifically, it was hypothesised that in response to seasonal variability of high-elevation environments, (i) the evergreen species will show conspicuous changes in anatomical traits (towards optimal values for a given temperature) if their foliage has to last throughout the year including the overwintering phase, (ii) changes in trait values will show reverse trends once the conditions shifts towards growth optima. Specifically, the objectives of the study were: (i) to elucidate the patterns of variability in leaf anatomical traits along the seasonal gradient and (ii) to test for a relationship between traits and temperature.
2. Plant sampling
The study was conducted at a site near Rohtang Pass which lies in the east of Pir Panjal Range of western Himalaya, India. This place is characterised by severe cold and long winters with plenty of snowfall. The study site was established at an elevation of 3990 m a.s.l. (32°37′41″ N latitude and 77°25′65″ E longitude). At the study site, the vegetation remains under snow cover from mid-November to the end of May, and thus, has a short growing season.
To have accurate estimation of trait variability, the study necessitated repetitive trait measurements to be conducted on leaves developed in the same year. As observed in the field, the newly developed leaves of
Considering all these facts, the sampling was started in the third week of August, 2017, and continued till November in the same year. Further measurements were not possible as the plants got covered under a thick layer of snow for approximately 240 days. Sampling was again resumed after the snow-melt in mid-June which was continued till second week of August, 2018, to complete a full annual cycle. Sampling was done multiple times in a successive manner with an interval of 2–3 weeks, depending on availability of clear sunny days. Thus, the sampling was accomplished for a total of 10 different time points: 22-August-2017, 12-September-2017, 29-September-2017, 11-October-2017, 23-October-2017, 4-November-2017, 15-June-2018, 28-June-2018, 14-July-2018 and 14-August-2018. At every sampling time point, leaves were cut under deionised water and fixed immediately in FAA [comprising Formaldehyde: Glacial acetic acid: Absolute ethanol in 1:1:18 ratio [15]] to measure the anatomical traits, such as total leaf thickness and thickness of cuticle, epidermis (both adaxial and abaxial), palisade and spongy parenchyma and total mesophyll. Also, sun-exposed, healthy, fully expanded leaves of
Temperatures (for both air and soil) were recorded at the study site during the entire study period, i.e. from August-2017 to August-2018 using temperature data loggers (M-Log5W, GEO Precision, Germany) [16]. Further, to extract the temperature values for each sampling time point, data values of 3 days (sampling day and the 2 days preceding this day) were used as suggested by Lee et al. [17]. The extracted temperature values were used to calculate the mean, minimum and maximum for a particular ‘sampling time-point’ for use in regression analysis.
3. Leaf anatomical measurements
Leaf sample preparations for light microscopy and SEM analysis were performed following the method of Tripp and Fatimah [15]. For light microscopy, rectangular pieces of leaves fixed in FAA were cut transversely avoiding the mid-rib. The samples (transverse sections) were passed through tertiary butyl alcohol (TBA) series (50–100%) and infiltrated with paraffin wax (58–60°C). The sections were embedded in small blocks of paraffin wax, and leaf samples of 12-μm size were obtained with a rotary ultra-microtome (Shandon™ Finesse™ 325, Thermo Scientific). The samples were progressively dehydrated in an ethanol series (30–100%), followed by double staining with 1% aqueous safranin and 0.5% fast green. The sections were permanently mounted on to slides with di-butyl phthalate polystyrene xylene (DPX). Micrographs were taken with camera (Nikon Digital Camera, D5300, Nikon Inc., Japan) mounted on light microscope (Nikon Eclipse E200, Nikon Inc., Tokyo, Japan), focussed at 40×. Total leaf thickness along with thickness of cuticle, epidermis (both adaxial and abaxial surface), palisade and spongy parenchyma and mesophyll tissue were measured (in μm) in randomly selected microscopic fields using ImageJ software. Maximum of three values were taken from three microscopic fields, respectively. These were later averaged for a given trait making one replicate. A total of five such biological replicates were estimated. ImageJ was calibrated with an image of ocular micrometre scale (taken at 40×).
For SEM, five leaves from samples collected at every sampling time point were used to determine the size of epidermal scales. Single leaf tissue was cut into two small rectangular pieces (about 4 × 4 mm) from either side of midrib in order to have representations of both the surfaces (adaxial as well as abaxial) of leaf, making it a replica. Both the surfaces of a leaf were mounted immediately on a single aluminium stub and then coated with a thin film (~30 nm) of gold-palladium for 3 minutes (15 kV, 20 mA) in a sputter coater (Hitachi coating unit E1010). The images were taken using a scanning electron microscope (Hitachi S3400N) at scales of 400 and 200 μm. SEM micrographs taken at 200 μm were used to determine the size of abaxial epidermal scales using image J software. Ten scales per micrograph were selected to calculate the diameter (twice), followed by their area estimation (assuming scale to be circular).
4. Statistical analysis
The mean ± standard deviation was calculated from five independent replicates for all the variables considered for the samples collected at each sampling time point. Linear model assumptions of normality and homoscedasticity were tested using Shapiro-Wilk test and modified Levene’s test, respectively. After the data met basic requirements of analysis of variance, one-way ANOVA was performed, and the means were compared to understand the variations in anatomical leaf traits across the 10 time points. This was followed by Tukey’s
5. Anatomical trait variability and temperature relationships
The overall range of leaf anatomical traits of
Time-point | Total thickness | Cuticle thickness | Adaxial Ep. thickness | Abaxial Ep. thickness | Mesophyll thickness | Palisade thickness | Spongy thickness | Scales size (mm2) |
---|---|---|---|---|---|---|---|---|
22-August-2017 | 26.44 ± 1.058 | 0.712 ± 0.051 | 0.927 ± 0.043 | 1.215 ± 0.107 | 23.50 ± 1.430 | 13.12 ± 1.308 | 9.989 ± 1.198 | 0.025 ± 0.001 |
12-September-2017 | 25.53 ± 2.216 | 0.750 ± 0.016 | 1.028 ± 0.150 | 1.172 ± 0.089 | 22.19 ± 2.256 | 13.08 ± 1.272 | 8.560 ± 1.121 | 0.023 ± 0.007 |
29-September-2017 | 24.45 ± 1.686 | 0.743 ± 0.059 | 0.927 ± 0.136 | 1.146 ± 0.208 | 21.14 ± 2.215 | 13.29 ± 1.525 | 8.727 ± 1.118 | 0.023 ± 0.003 |
11-October-2017 | 25.39 ± 0.728 | 0.765 ± 0.042 | 1.075 ± 0.130 | 1.161 ± 0.070 | 21.22 ± 1.430 | 12.74 ± 1.379 | 9.066 ± 0.731 | 0.022 ± 0.003 |
23-October-2017 | 26.32 ± 2.139 | 0.774 ± 0.057 | 0.941 ± 0.047 | 1.233 ± 0.076 | 23.44 ± 2.543 | 13.92 ± 2.906 | 8.966 ± 1.540 | 0.018 ± 0.005 |
04-November-2017 | 26.98 ± 3.391 | 0.838 ± 0.075 | 0.977 ± 0.174 | 1.234 ± 0.117 | 23.63 ± 3.775 | 14.21 ± 2.468 | 8.891 ± 1.693 | 0.017 ± 0.002 |
15-June-2018 | 29.99 ± 5.396 | 0.841 ± 0.041 | 1.045 ± 0.098 | 1.244 ± 0.168 | 27.08 ± 6.036 | 14.82 ± 1.514 | 10.18 ± 2.268 | 0.015 ± 0.003 |
28-June-2018 | 28.87 ± 1.689 | 0.789 ± 0.069 | 1.043 ± 0.154 | 1.263 ± 0.246 | 25.47 ± 1.484 | 14.77 ± 0.426 | 10.16 ± 1.611 | 0.015 ± 0.002 |
14-July-2018 | 30.21 ± 4.268 | 0.778 ± 0.090 | 1.000 ± 0.088 | 1.146 ± 0.072 | 27.45 ± 5.181 | 15.90 ± 2.215 | 11.03 ± 2.527 | 0.017 ± 0.003 |
14-August-2018 | 30.36 ± 3.295 | 0.695 ± 0.050 | 1.159 ± 0.105 | 1.122 ± 0.099 | 27.20 ± 3.583 | 16.17 ± 1.828 | 10.69 ± 1.827 | 0.021 ± 0.004 |
Further, the change in mean values of some of the leaf anatomical traits of
Parameter | Df | TSSq | MSSq | ||
---|---|---|---|---|---|
Total thickness (μm) | 9 | 220.0 | 24.45 | 2.825 | 0.011 |
Cuticle thickness (μm) | 9 | 0.101 | 0.011 | 3.332 | <0.001 |
Adaxial epidermis thickness (μm) | 9 | 0.243 | 0.027 | 1.869 | 0.085 |
Abaxial epidermis thickness (μm) | 9 | 0.111 | 0.012 | 0.645 | 0.752 |
Mesophyll thickness (μm) | 9 | 265.7 | 29.52 | 2.610 | 0.018 |
Palisade parenchyma thickness (μm) | 9 | 64.72 | 7.190 | 2.190 | 0.040 |
Spongy parenchyma thickness (μm) | 9 | 35.26 | 3.917 | 1.442 | 0.204 |
Abaxial Epidermal scales (mm2) | 9 | 0.006 | 0.0007 | 27.18 | <0.001 |
The adaxial and abaxial surfaces of leaf showed the presence of glandular scales (Figures 3 and 5). These were typically distributed throughout the abaxial surface of leaves during whole of the study period. However, these were mainly observed on the adaxial surface during August (22-August-2017 and 14-August-2018). The size of these glandular scales was estimated to be in the range from 0.015 to 0.025 mm2. Variations in the size of abaxial epidermal scales due to changes in temperature regime were significantly more pronounced in comparison to other studied anatomical traits. Their size decreased during the early winter time points (i.e. 23-October-2017 and 4-November-2017) (Figure 6).
It was found that the air and soil temperatures were found to be positively correlated with each other. Moreover, similar correlation was observed between majority of the studied traits and temperature (both air and soil) (
6. Anatomical traits can be explained more as ‘evolutionary gained adaptive traits’
Plants often exhibit considerable variations in their anatomical traits enabling them to adapt to changing environments [20]. Therefore, the analysis of anatomical traits is crucial for understanding of plant functioning and survival at high elevations. It has been suggested that leaf anatomical structures are associated with physiological functionality in
The peculiar anatomical structures such as epidermal appendages, act as evolutionary adaptive traits and help plants in protection against harsh environmental conditions [26]. In the present study, the presence of unique overlapping glandular scales on abaxial side of leaves (covering the entire surface) was observed (Figures 3–5). Scales of similar shape but larger in size could also be recognised on the adaxial surface of leaves which, however, were evident only during August. On this side of leaf, these scales were unnoticeable during rest of the time period, which could be due to deposition of epi-cuticular wax on the adaxial surface of leaves. The presence of such leaf scales has also been reported in other
Overall, our results suggested that the leaf anatomy is relatively less sensitive to seasonal variations, as depicted by low variability observed in majority of the anatomical traits. The less variability could be attributed to the fact that the plant may not invest considerably in structural adjustments to counter seasonal variations. This can be explained by the fact that this evergreen shrub, occupying the highest elevations in the Himalaya, survives the harsh environmental conditions throughout the year, which is likely to be achieved via consistency in leaf anatomical traits. Thus, the results reinforce the idea that structural traits ‘in general’ are less variable and are ‘evolutionary gained adaptive traits’ [30, 31]. The low investment in structural adjustments may be due to their higher construction cost leading to diminishing returns [32, 33].
7. Conclusions
The findings presented here contribute to the understanding of intra-annual plant trait variability (the type of response and its magnitude) in an overwintering evergreen shrub. The evidences outlined above indicate that the leaf anatomy is less sensitive to seasonal variations. Low variability in leaf anatomical traits of
Acknowledgments
Authors are thankful to the Director, IHBT, Palampur, for providing the necessary facilities. N.R. and N.E.H. were the recipients of senior research fellowship and junior research fellowship, respectively, from CSIR, India, during the study. Dr. Avnesh Kumari and Dr. Sita Kumari are acknowledged for their help during SEM analysis. Also, Rahul Kumar Rana, Lakhbeer Singh, Manish Kumar Sharma, Nandita Mehta and Om Prakash are acknowledged for their help with field survey.
Author’s contributions
N.R. carried out the plant sampling, anatomical trait measurements, SEM analysis, statistical analysis and drafting of the manuscript. D.T. performed the plant sampling, SEM analysis and statistical analysis. N.E.H. performed the plant sampling and anatomical trait measurements. A.C. conceived the study, designed the experiment, provided statistical guidance, edited and finalised the manuscript.
References
- 1.
Niinemets Ü. Does the touch of cold make evergreen leaves tougher? Tree Physiology. 2016; 36 :267-272. DOI: 10.1093/treephys/tpw007 - 2.
Enquist BJ, Bentley LP, Shenkin A, Maitner B, Savage V, Michaletz S, et al. Assessing trait-based scaling theory in tropical forests spanning a broad temperature gradient. Global Ecology and Biogeography. 2017; 26 :1357-1373. DOI: 10.1111/geb.12645 - 3.
Milla R, Giménez-Benavides L, Montserrat-Martí G. Replacement of species along altitude gradients: The role of branch architecture. Annals of Botany. 2008; 102 :953-966. DOI: 10.1093/aob/mcn187 - 4.
Strimbeck GR, Schaberg PG, Fossdal CG, Schröder WP, Kjellsen TD. Extreme low temperature tolerance in woody plants. Frontiers in Plant Science. 2015; 6 :1-15. DOI: 10.3389/fpls.2015.00884 - 5.
Stushnoff C, Cox SE. Temperature-related shifts in soluble carbohydrate content during dormancy and cold acclimation in Populus tremuloides . Canadian Journal of Forest Research. 2001;31 :730-737. DOI: 10.1139/cjfr-31-4-730 - 6.
Wang X, Arora R, Horner HT, Krebs SL. Structural adaptations in overwintering leaves of thermonastic and nonthermonastic Rhododendron species. Journal of the American Society for Horticultural Science. 2008;133 :768-776. DOI: 10.21273/JASHS.133.6.768 - 7.
Coldren GA. The multiple stress gradient hypothesis: Expansion of the revised stress gradient hypothesis using a mangrove and salt marsh study system [Dissertation]. Florida Atlantic University; 2013. p. 157 - 8.
Henn JJ, Buzzard V, Enquist BJ, Halbritter AH, Klanderud K, Maitner BS, et al. Intraspecific trait variation and phenotypic plasticity mediate alpine plant species response to climate change. Frontiers in Plant Science. 2018; 9 :1548. DOI: 10.3389/fpls.2018.01548 - 9.
Kofidis G, Bosabalidis AM, Moustakas M. Combined effects of altitude and season on leaf characteristics of Clinopodium vulgare L. (Labiatae). Environmental and Experimental Botany. 2007;60 :69-76. DOI: 10.1016/j.envexpbot.2006.06.007 - 10.
Lianopoulou V, Bosabalidis AM, Patakas A, Lazari D, Panteris E. Effects of chilling stress on leaf morphology, anatomy, ultrastructure, gas exchange, and essential oils in the seasonally dimorphic plant Teucrium polium (Lamiaceae). Acta Physiologiae Plantarum. 2014;36 :2271-2281. DOI: 10.1007/s11738-014-1605-x - 11.
Liang E, Dawadi B, Pederson N, Eckstein D. Is the growth of birch at the upper timberline in the Himalayas limited by moisture or by temperature? Ecology. 2014; 95 :2453-2465. DOI: 10.1890/13-1904.1 - 12.
Thakur D, Chawla A. Functional diversity along elevational gradients in the high altitude vegetation of the western Himalaya. Biodiversity and Conservation. 2019; 28 :1977-1996. DOI: 10.1007/s10531-019-01728-5 - 13.
Felde VA, Kapfer J, Grytnes J-A. Upward shift in elevational plant species ranges in Sikkilsdalen, Central Norway. Ecography (Cop.). 2012; 35 :922-932. DOI: 10.1111/j.1600-0587.2011.07057.x - 14.
Marian CO, Krebs SL, Arora R. Dehydrin variability among Rhododendron species: A 25-kDa dehydrin is conserved and associated with cold acclimation across diverse species. The New Phytologist. 2004;161 :773-780. DOI: 10.1111/j.1469-8137.2003.01001.x - 15.
Tripp EA, Fatimah S. Comparative anatomy, morphology, and molecular phylogenetics of the African genus satanocrater (acanthaceae). American Journal of Botany. 2012; 99 :967-982. DOI: 10.3732/ajb.1100354 - 16.
Rathore N, Thakur D, Kumar D, Chawla A, Kumar S. Time-series eco-metabolomics reveals extensive reshuffling in metabolome during transition from cold acclimation to de-acclimation in an alpine shrub. Physiologia Plantarum. 2021; 173 :1-17. DOI: 10.1111/ppl.13524 - 17.
Lee TD, Reich PB, Bolstad PV. Acclimation of leaf respiration to temperature is rapid and related to specific leaf area, soluble sugars and leaf nitrogen across three temperate deciduous tree species. Functional Ecology. 2005; 1 :640-647. DOI: 10.1111/j.1365-2435.2005.01023.x - 18.
R Development Core Team. R: A language and environment for statistical computing. R Found. Stat. Comput. 2019; 1 :409. DOI: 10.1007/978-3-540-74686-7 - 19.
Kassambara A. ggpubr:“ggplot2” based publication ready plots. R Package Version 0.1 6. 2017 - 20.
Peguero-Pina JJ, Sisó S, Sancho-Knapik D, Díaz-Espejo A, Flexas J, Galmés J, et al. Leaf morphological and physiological adaptations of a deciduous oak ( Quercus faginea Lam.) to the Mediterranean climate: A comparison with a closely related temperate species (Quercus robur L.). Tree Physiology. 2016;36 :287-299. DOI: 10.1093/treephys/tpv107 - 21.
Cai Y-F, Li S-F, Li S-F, Xie W-J, Song J. How do leaf anatomies and photosynthesis of three Rhododendron species relate to their natural environments? Botanical Studies. 2014;55 :36. DOI: 10.1186/1999-3110-55-36 - 22.
Oliveira MT, Souza GM, Pereira S, Oliveira DAS, Figueiredo-Lima KV, Arruda E, et al. Seasonal variability in physiological and anatomical traits contributes to invasion success of Prosopis juliflora in tropical dry forest. Tree Physiology. 2017;37 :326-337. DOI: 10.1093/treephys/tpw123 - 23.
Chabot BF, Chabot JF. Effects of light and temperature on leaf anatomy and photosynthesis in Fragaria vesca . Oecologia. 1977;26 :363-377. DOI: 10.1007/BF00345535 - 24.
Prozherina N, Freiwald V, Rousi M, Oksanen E. Interactive effect of springtime frost and elevated ozone on early growth, foliar injuries and leaf structure of birch ( Betula pendula ). The New Phytologist. 2003;159 :623-636. DOI: 10.1046/j.1469-8137.2003.00828.x - 25.
He N, Liu C, Tian M, Li M, Yang H, Yu G, et al. Variation in leaf anatomical traits from tropical to cold-temperate forests and linkage to ecosystem functions. Functional Ecology. 2018; 32 :10-19. DOI: 10.1111/1365-2435.12934 - 26.
Bosabalidis AM, Kofidis G. Comparative effects of drought stress on leaf anatomy of two olive cultivars. Plant Science. 2002; 163 :375-379. DOI: 10.1016/S0168-9452(02)00135-8 - 27.
Nilsen ET, Webb DW, Bao Z. The function of foliar scales in water conservation: An evaluation using tropical-mountain, evergreen shrubs of the species Rhododendron in section Schistanthe (Ericaceae). Australian Journal of Botany. 2014;62 :403-416. DOI: 10.1071/BT14072 - 28.
Wang X, Mao Z, Choi K, Park K. Significance of the leaf epidermis fingerprint for taxonomy of Genus Rhododendron . Journal of Forest Research. 2006;17 :171-176. DOI: 10.1007/s11676-006-0041-1 - 29.
Sheperd T, Griffiths DW. The effects of stress on plant cuticular waxes. The New Phytologist. 2006; 171 :469-499 - 30.
Aroca R. Plant Responses to Drought Stress: From Morphological to Molecular Features. Berlin, Heidelberg: Springer; 2013. DOI: 10.1007/978-3-642-32653-0 - 31.
Matesanz S, Valladares F. Ecological and evolutionary responses of Mediterranean plants to global change. Environmental and Experimental Botany. 2014; 103 :53-67. DOI: 10.1016/j.envexpbot.2013.09.004 - 32.
Niklas KJ, Cobb ED, Niinemets Ü, Reich PB, Sellin A, Shipley B, et al. “Diminishing returns” in the scaling of functional leaf traits across and within species groups. Proceedings of the National Academy of Sciences of the United States of America. 2007; 104 :8891-8896. DOI: 10.1073/pnas.0701135104 - 33.
Thakur D, Rathore N, Chawla A. Increase in light interception cost and metabolic mass component of leaves are coupled for efficient resource use in the high altitude vegetation. Oikos. 2019;128:254-263. DOI: 10.1111/oik.05538