Methodology of Leaf Anatomical Structure and Geographic Environmental Dataset of Caryopteris mongholica from Seven Regions in North and Northwest China

Ji, R. X.  Yu, X.  Chang, Y.  Shen, C.  Bai, X. Q.  Xia, X. L.  Yin, W. L.  Liu, C.^*

National Engineering Laboratory of Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China

Abstract: The genetic variation caused by long-term adaptation to the environment significantly affects plant growth and development. Leaves are the most important and sensitive organs in responding to environmental changes. Therefore, an understanding of the adaptive variation in leaf anatomical structures in different environments is the basis for exploring plant adaptation to the environment. Common garden experiments eliminate the influence of environmental gradients and are an effective approach to study the effects of genetic and environmental factors on plant structures. In this study, a common garden experiment was used to analyze the anatomical structures of Caryopteris mongholica leaves from seven different areas in north and northwest China, including Abaga Banner, Alxa Left Banner, Dongwu Banner, Mengxi, and Liangcheng in Nei Mongol (Inner Mongolia), Shenmu in Shaanxi province, and Kangbao in Hebei province. Conventional paraffin sectioning was used to analyze structures, and multiple comparisons, correlation analyses, and a general linear model (GLM) were used to identify the factors driving differences. The genetic variation driven by climate was one of the major factors that caused the differences in leaf anatomical structures from the different areas. The dataset includes the following: (1) geographical location data for the collection sites; (2) C. mongholica leaf cross sections; and (3) analysis of the relations between C. mongholica leaf anatomical structures and the geographic environmental factors that influence them. The dataset is archived in .shp, .kmz, .xls, .jpg, and .doc formats and consists of 14 data files, with a data size of 5.84 MB.

Keywords: Caryopteris mongholica; common garden experiment; leaf anatomical structures

Dataset Availability Statement:

The dataset supporting this paper was published and is accessible through the Digital Journal of Global Change Data Repository at: https://doi.org/10.3974/geodb.2021.01.03.V1.

1 Introduction

Plants are subject to the long-term effects of environmental factors such as temperature, water, and light and in the process of evolution, gradually develop phenotypic and genetic characteristics adapted to the environment. Even within the same species, long-term growth in different geographic environments can produce different degrees of gene differentiation that lead to specific regional characteristics adapted to local geographic environments^[1–4]. The leaf is the plant organ with the largest area exposed to the environment, and its external morphological characteristics and internal anatomical structure can best reflect the adaptive evolution of plants to environmental factors^[5–8]. Therefore, analyzing the anatomical structure of leaves is important in studying the adaptation strategies of plants in extreme environments.

Common garden experiments are used to minimize the effects of site conditions^[9,10] and show that differences in leaf anatomical structures from different regions are due to the genetic variation in plants caused by environmental differences in the original regions. This experiment differs from previous ones on the characteristics of leaf anatomical structure, because the effects of both environmental factors, including climate indices and geographic location, and genetic factors were evaluated. Common garden experiments can provide important insights into local adaptability^[11] and a better understanding of the responses and adaptation mechanisms of plants from different geographical regions to global changes. At present, few studies have used this method to analyze the differences in leaf anatomical structures from different regions and the factors that influence changes in those structures. Therefore, Caryopteris mongholica from seven regions in north and northwest China were planted under the same environmental conditions in a common garden experiment to exclude the effects of environmental factors. To explore the effect of locality on the plant, paraffin sectioning was used to study leaf anatomical structures. With an understanding of adaptive evolution according to regional environments, a theoretical basis is provided for protection of germplasm resources, genetic improvement, and exploration of the mechanisms by which differences in plant genotypes are driven by environmental variation.

2 Metadata of the Dataset

The metadata of Leaf anatomical structure and geographic environment data of Caryopteris Mongholica from 7 provenances^[12] are summarized in Table 1, including the dataset full name, short name, authors, year of the dataset, temporal resolution, data format, data size, data files, data publisher, and data sharing policy, etc.

3 Data Development Methods

3.1 Study Area

The common garden experiment was conducted in the nursery of Beijing Forestry University (40.01??N, 116.34??E), China. The nursery is in the warm temperate zone with a semihumid continental monsoon climate. The mean annual temperature is 11.8 ??C, the annual maximum temperature range is 37.5–42.6 ??C, the annual minimum temperature range is –19.5 to –14.8 ??C, and the mean annual precipitation is 500 to 650 mm^[14]. Most precipitation occurs in the summer, accounting for 70% of the annual total, and severe drought is typical in spring. The research site was covered with a plastic shed overnight and when it rained to prevent the influence of additional water.

3.2 Plant Materials

One-year-old C. mongholica cuttings, approximately 20-cm stems, with good growth were collected. They were wrapped in damp cloth and immediately brought to Beijing. The cuttings were collected from Abaga Banner, Alxa Left Banner, Dongwu Banner, Mengxi, and Liangcheng in Nei Mongol (Inner Mongolia), Shenmu in Shaanxi province, and Kangbao in Hebei province in late March 2016. The cuttings from the different regions (Table 2) were treated with 0.1% rooting powder ABT1 (Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China) and then planted in flowerpots (450 mm ?? 450 mm). The soil was a sandy loam, and the volume ratio of peat soil, loam soil, and sand was 4:4:3, which is similar to the composition of the field soil in which C. mongholica was collected. All materials were planted in the same soil mix.

Table 1  Metadata summary of the Leaf anatomical structure and geographic environment data of Caryopteris Mongholica from 7 provenances

From three to five cuttings with similar growth from each site, three mature leaves in the middle of each branch were sampled. A 1 cm ?? 1 cm square (approximately) was cut from the middle of each leaf that included the main vein (Figure 1). The pieces were fixed in FAA (formaldehyde:acetic acid:70% ethanol = 1:1:18) and taken to the laboratory for determination of leaf anatomical structures.

Table 2  Locations and climate indices of Caryopteris mongholica collection sites in this study

Note: MAP, mean annual precipitation; MAT, mean annual temperature; GSP, growth season precipitation; GST, growth season temperature; TCM, temperature of coldest month; PE, potential evaporation.

3.3 Methods and Algorithm Principle

Conventional paraffin sectioning was used to analyze the leaf structures^[15]. The leaf squares were fixed for more than 24 h and then dehydrated in a four-step gradient of 70%, 85%, 95%, and 100% alcohol. The covers were sealed to prevent water entering the air space, and xylene was used to increase the transparency of leaf material. The leaf squares were embedded in liquid paraffin (paraffin melting point = 56–57 ??C). After the paraffin solidified, blocks were sliced into sections 8 to 10 µm thick. The sections were stained with safranin-fast green and mounted in neutral gum^[16]. A Leica DM2500 microscope with Leica LAS AF software was used to observe and analyze the sections (Leica, Wezlar, Germany).

Image J software was used to measure the thickness of the cuticle, the upper and lower epidermal cells (UEC, LEC), the upper and lower palisade tissues(UPT, LPT), and the leaf thickness(LT). The tightness of the leaf and the total thickness of the palisade tissue(PT) were calculated^[17]. For each index, the measurement was repeated five times.

Leaf anatomical indices:

Tight = PT/LT ?? 100%

PT = UPT + LPT

Plasticity indices of leaf anatomical structures:

Coefficient of variation = Standard deviation / Arithmetic mean

Plasticity index = (Maximum – Minimum) / Maximum

According to the longitude and latitude of the different C. mongholica collection sites, the monthly average climate data (2007–2016) of each sample site were obtained from the WorldCLIM global high-resolution climate database of ArcGIS 10.2 and used to calculate the climate indices. The climate indices included mean annual precipitation (MAP), growth season precipitation (GSP), annual range of precipitation (ARP), mean annual temperature (MAT), growth season temperature (GST), annual range of temperature (ART), and potential evaporation (PE).

3.4 Data Analysis

A multiple comparisons method was used to compare differences in the anatomical structures of C. mongholica. Pearson correlations were used to analyze the relations between leaf anatomical structures and environmental factors.

Combined with a general linear model (GLM) ANOVA to analyze the effects of collection-site climate on the anatomical structures^[19]. Based on the correlations between collection-site environmental factors and leaf anatomical structures, MAP, MAT, PE, Alt, and Site were selected as environmental factors that were included in the model.

Data analyses were conducted in Excel and R 3.2.2.

3.5 Data Development Flowchart

The following steps were used to produce the dataset (Figure 2): Caryopteris mongholica was collected in seven different sites based on previous data collection and field investigations. The C. mongholica were planted in the nursery of Beijing Forestry University in a common garden experiment. After 18 months, samples were cut from C. mongholica leaves and fixed. Leaf anatomical structures were measured. Correlations were conducted between the anatomical structures themselves and between geographic environmental factors and anatomical structures. The GLM was used to verify the significant factors of influence. Thus, leaf anatomical structure and geographic environmental datasets of C. mongholica collected from seven sites were formed.

Figure 2  Technical flowchart of the analysis of leaf anatomical structures of Caryopteris mongholica and the environmental factors of influence

4 Data Results and Verification

4.1 Dataset composition

The dataset is composed of four files. The data files include geographical location data of the collection sites; 2007 to 2016 climate indices; leaf cross sections and parameters; and GLM analysis of regional climate factors and leaf anatomical structures. The dataset is stored in .shp, .kmz, .xls, .jpg, and .doc formats.

4.2 Data Results

Caryopteris mongholica from the seven collection sites had typical isolateral leaves, and the thickness of leaves was between 192.34 and 270.30 µm (Table 3). The internal structure of leaves was divided into the epidermis, mesophyll, and veins (Figure 3).

Table 3  Leaf anatomical indices of Caryopteris mongholica

Note: UEC, upper epidermal cell; LEC, lower epidermal cell; UPT, upper palisade tissue; LPT, lower palisade tissue; PT, palisade tissue; LT, leaf thickness.

Figure 3  Leaf cross sections of Caryopteris mongholica (A. Mesophyll structure; B. Main vein structure; C. Mesophyll and main vein structure)

(Note: Co, collenchyma; LEC, lower epidermal cell; LPT, lower palisade tissue; MVP, main vein phloem; MVX, main vein xylem; St, stomatal chamber; UEC, upper epidermal cell; UPT, upper palisade tissue)

Most of the leaf anatomical structures were correlated with other structures, with UPT, LPT, PT, LT, and Tight highly significantly positively correlated with other structures (P < 0.01, Table 4).

4.3 Dataset Verification

Leaf anatomical structures were significantly correlated with one or more of geographical location (latitude, longitude), temperature, and precipitation (Table 5).

Longitude and latitude were highly significantly positively correlated with UEC but were significantly negatively correlated with UPT, LPT, PT, LT, and Tight (P < 0.05). On the geographical gradient, UEC gradually increased from west to east and south to north, whereas UPT, LPT, PT, LT, and Tight gradually decreased.

Table 4  Pearson coefficients of correlation between leaf anatomical structures of Caryopteris mongholica

*P < 0.05; **P < 0.01.

Note: UEC, upper epidermal cell; LEC, lower epidermal cell; UPT, upper palisade tissue; LPT, lower palisade tissue; PT, palisade tissue; LT, leaf thickness.

Leaf UPT, LPT, PT, LT, and Tight were all significantly positively correlated with MAT (P < 0.05), whereas UEC was significantly negatively correlated with MAT and GST (P < 0.05) and significantly positively correlated with ART (P < 0.05). As temperature increased, UPT, LPT, PT, LT, and Tight increased and UEC decreased.

The precipitation indices MAP, GSP, and ARP were only significantly negatively correlated with UPT (P < 0.01), LT (P < 0.05), and PT (P < 0.05).

Table 5  Pearson coefficients of correlation between environmental factors at collection sites and leaf anatomical structures of Caryopteris mongholica

*P < 0.05; **P < 0.01.

According to the ANOVA GLM analysis (Table 6), the genetic variation driven by the climate (temperature, precipitation, evapotranspiration) of the original collection sites had significant effects on leaf anatomical structures of C. mongholica, with between 34.09% and 81.43% of the variation explained. The variation explained for LEC was the smallest (34.09%), and that for Tight was the largest (81.43%). Among the climate indices of the collection sites, MAT had a significant effect on all eight leaf anatomical indices, with very highly significant effects on UEC, LT, UPT, LPT, PT, and Tight (P < 0.001) and between 21.69% and 71.89% of the variation explained. The PE significantly affected UEC, LT, UPT, LPT, and PT (P < 0.05), with between 5.48% and 14.34% of the variation explained. The MAP significantly affected LT, UPT, LPT, PT, and Tight, with between 4.78% and 45.93% of the variation explained. The MAP had very highly significant effects on LT, UPT, and PT (P < 0.001). The Alt had significant effects on LT, UPT, LPT, and PT, with between 8.33% and 16.76% of the variation explained.

Table 6  Summary of general linear models for the effects of climate variations (MAP, MAT, PE, Alt, Site) at different collection sites on leaf anatomical structures of Caryopteris mongholica

Note: * P < 0.05; * P < 0.01; *** P < 0.001.

5 Discussion and Conclusion

Leaf anatomical structures of C. mongholica from seven collection sites from 2006 to 2017 in north and northwest China in daily were measured, and the significant environmental factors that caused differences in the structures were determined. A common garden experiment was used to minimize the influence of environmental gradients of the seven collection sites. Leaf anatomical structures were subject to multiple comparisons and correlation analyses, and a general linear model was used to identify the significant climate indices driving changes in leaf structure. The genetic variation driven by climate was one of the major factors that caused the differences in leaf anatomical structures from the different collection sites. This dataset shows local adaptation in leaf structures and therefore will help to understand the adaptation mechanisms of plants in different regions in responding to global change. In addition, the dataset can provide basic data for research on global change and germplasm resource protection.

Author Contributions

Liu, C., Xia, X. L., Yin, W. L., and Ji, R. X. were responsible for the overall design and development of the dataset. Yu, X., Chang, Y., Shen, C., Bai, X. Q., and Ji, R. X. collected plant materials. Ji, R. X. processed leaf-related data and performed data validation. Liu, C. designed the model and algorithm. Ji, R. X. wrote the data paper.

Conflicts of Interest

The authors declare no conflicts of interest.

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