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Article

Quantifying Leaf Trait Covariations and Their Relationships with Plant Adaptation Strategies along an Aridity Gradient

by
Yanzheng Yang
1,2,
Le Kang
3,
Jun Zhao
4,
Ning Qi
5,
Ruonan Li
1,
Zhongming Wen
6,
Jalal Kassout
7,
Changhui Peng
8,
Guanghui Lin
2 and
Hua Zheng
1,9,*
1
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
2
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
3
East China Inventory and Planning Institute of the State Administration of Forestry and Grassland, Hangzhou 310019, China
4
China Aero Geophysical Survey & Remote Sensing Center for Natural Resources, Beijing 100083, China
5
School of Information Science & Technology, Beijing Forestry University, Beijing 100083, China
6
Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China
7
Laboratory of Applied Botany, BioAgrodiversity Team, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan 93002, Morocco
8
Department of Biological Sciences, Institute of Environmental Sciences, University of Quebec at Montreal, Montréal, QC H3C 3P8, Canada
9
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Biology 2021, 10(10), 1066; https://doi.org/10.3390/biology10101066
Submission received: 22 September 2021 / Revised: 13 October 2021 / Accepted: 13 October 2021 / Published: 19 October 2021
(This article belongs to the Section Ecology)
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Abstract

:

Simple Summary

Plants usually adopt different strategies to adapt to their surrounding environments. Accurately quantifying plant strategies is of great interest in trait-based ecology, in particular to understand the responses of ecological structures and processes. In the last two decades, these strategies have been described qualitatively; however, the use of quantitative methods is still lacking. In this study, we used a plant functional trait approach to discuss plant strategies along an aridity gradient. We found that eight functional traits divided into four dimensions represent four adaptation strategies: energy balance, resource acquisition, resource investment and water use efficiency. We also concluded that climate and soil together with family (vegetation succession) were the main driving forces of trait covariations. Our study provided a new perspective to understand plant functional responses to aridity gradients, which is helpful for ecological management and vegetation restoration programs in arid regions.

Abstract

A trait-based approach is an effective way to quantify plant adaptation strategies in response to changing environments. Single trait variations have been well depicted before; however, multi-trait covariations and their roles in shaping plant adaptation strategies along aridity gradients remain unclear. The purpose of this study was to reveal multi-trait covariation characteristics, their controls and their relevance to plant adaptation strategies. Using eight relevant plant functional traits and multivariate statistical approaches, we found the following: (1) the eight studied traits show evident covariation characteristics and could be grouped into four functional dimensions linked to plant strategies, namely energy balance, resource acquisition, resource investment and water use efficiency; (2) leaf area (LA) together with traits related to the leaf economic spectrum, including leaf nitrogen content per area (Narea), leaf nitrogen per mass (Nmass) and leaf dry mass per area (LMA), covaried along the aridity gradient (represented by the moisture index, MI) and dominated the trait–environmental change axis; (3) together, climate, soil and family can explain 50.4% of trait covariations; thus, vegetation succession along the aridity gradient cannot be neglected in trait covariations. Our findings provide novel perspectives toward a better understanding of plant adaptations to arid conditions and serve as a reference for vegetation restoration and management programs in arid regions.

1. Introduction

Aridity acts as a strong environmental filter for plant survival, growth and development and therefore has considerable effects on community structure and ecosystem functions, including primary productivity and nutrient cycling [1,2]. For instance, aridity favors species with small leaves as a part of their adaptation strategy to adapt soil to water deficits and nutrient limitations [3]. Plants undergo environmental adaptation and evolve an optimal phenotype, forming a set of adaptation strategies at both the community and individual levels [4]. To date, researchers have usually ascribed the single axis of a plant’s adaptation strategy to environmental changes, but strategies explained by covariation and a tradeoff in traits related to nutrient acquisition and conservation have been neglected [5,6]. These strategies can be quantified by the covariation of several plant functional traits, which are any morpho-physio-phenological traits that have an impact on plant growth, reproduction and survival [7,8]. From the perspective of trait-based ecology, more information on plant adaptation to aridity gradients is needed.
Functional trait covariation may reflect plant strategies to stressful aridity conditions. In fact, variability in environmental conditions is considered as a filter of traits [9,10]; therefore, variations in plant functional traits reflect shifts in plant adaptation strategies under changing environmental conditions. For example, changes in plant height, leaf mass per area (LMA) and leaf nitrogen content per area (Narea) are directly linked to resource acquisition and drought tolerance, which have been widely reported [8,11,12]. Meanwhile, plants in resource-poor environments prone to arid conditions are expected to favor conservative trait syndromes, such as slower growth rates, lower production and higher root density [13]. Within arid sites, C3 plants show higher values of leaf 13C, which reflect higher water use efficiency [14]. Thus, it has been reported that plants balance their investment in either enzymatic reactions or electronic transport in the process of photosynthesis to avoid wasting resources [15].
Several studies have successfully quantified plant adaptation to arid environments [1,14,16]; nonetheless, only a few of them have yielded results that consider the interpretation difficulty of trait covariation and their controls. Some issues remain to be resolved:
(1)
Traits do not vary independently but show covariation and tradeoff relationships [17]. However, from the perspective of plant functional traits, plant strategies adopted to simultaneously balance conservation and resource acquisition remain unclear.
(2)
Climate and soil are widely recognized to control trait variation [18,19,20]. However, the effects of climate and soil intersections have not been fully quantified, especially under arid conditions.
(3)
Vegetation succession along an aridity gradient resulted in the replacement of larger leaf plants by plants with small and high-efficiency leaves [21]. The contribution of vegetation distribution (family) to trait covariations is still unknown along aridity gradients.
In the past few decades, the Loess Plateau in China has implemented many large-scale ecological protection and restoration projects [22,23]. The observed regional climate changes (especially in precipitation trends) has caused great effects on ecosystem composition and functions [24]. Therefore, quantifying plant adaption to regional climate changes, especially in arid areas, is a matter of urgency. In this study, taking the Loess Plateau as the study area and using multivariate statistics, we aim to (1) quantify the covariation characteristics of eight functional traits and their representative dimensions (adaptation strategies); (2) reveal trait variation along the precipitation and temperature gradients; and (3) explore the controls of eight key traits along the aridity gradient on the Loess Plateau. Hence, the aim of this study is to reveal the adaptation strategies of plants to aridity gradients throughout quantifying the covariation of eight fictional traits.

2. Materials and Methods

2.1. Study Area and Sampling Strategy

The Loess Plateau is a highland area located in the middle of the Yellow River basin in China (Figure 1a) and is prone to serious soil and water loss [25]. With an area of more than 620,000 km2, this region features a temperate continental climate with a mean annual temperature ranging from 4 °C to 14 °C and annual precipitation ranging from approximately 200 mm to 800 mm, forming three distinct zones along an aridity gradient: semihumid (moisture index, MI > 0.67), semiarid (0.55 < MI ≤ 0.67) and arid (MI ≤ 0.55). From southeast to northwest, precipitation decreases on the Loess Plateau (Figure 1b). Thus, from south to north, the temperature (represented by a growing-season monthly mean temperature above 0 °C (mGDD0)) increases and is closely related to changes in elevation. In the past few decades, many ecological projects have been implemented on the Loess Plateau (Figure 1c), and, therefore, the effects of the local climate and plant adaptation to environmental changes have been widely studied.
Eight morphological, chemical and photosynthetic traits were measured and sampled from 105 species and 271 records along 22 sites in August 2016 and 2017 (Figure 1a, Table 1 and Table A1). The 105 species were grouped into 38 families, and the main families are described in Table A2. The sampling procedure allows one to cover all of the vegetation types and climate gradients of the Loess Plateau, which represents an ideal design to reveal plant responses and functional strategies to aridity gradients. Sampling was restricted to dominant species and communities with no clear human disturbance. Thus, along the sampling transect, the vegetation types changed from forestland to shrubland and desert, with noticeable vegetation succession, making it ideal to study changes in ecological structure and functions from the perspective of plant functional traits.

2.2. Data Descriptions of Traits, Climate and Soil

All plots were located on sunny slopes, and middle-aged plants were sampled. Eight leaf functional traits, namely leaf area (LA), leaf nitrogen content (Nmass), Narea, LMA, leaf dry mass content (LDMC), maximum carboxylation rate standardized to 25 °C (Vcmax25), maximum electron transport rate standardized to 25 °C (Jmax25) and the ratio of intercellular to ambient CO2 concentration (ci:ca, termed χ in this study), which could capture most plant functions, were measured. All traits were measured following standardized protocols, as proposed by Cornelissen et al. [26]. LA is the projected area of a leaf (or leaflet for compound leaves), which is linked to the leaf energy balance [27]. LDMC is the oven-dry mass divided by its fresh mass. LMA is the oven-dry mass divided by fresh LA, which is the inverse of specific leaf area (SLA) and highly related to the resistance of plants to both herbivory and drought [28]. Nmass is the leaf nitrogen per mass, and Narea is the leaf nitrogen per area, which are two indicators of photosynthetic capacity [29]. LDMC, LMA, Nmass and Narea are key traits in the leaf economics spectrum (LES), which reflects a plant’s investment of nutrients along the acquisition–conservation continuum [30,31].
Vcmax and Jmax were calculated using a one-point method from the light-saturated rate of net CO2 fixation at ambient CO2 (Asat) and the light-saturated rate of net CO2 fixation at high CO2 (Amax) [32]. Vcmax and Jmax were standardized to 25 °C following the proposed methods of Niinemets et al. [33]. From the 272 records, 46 records for Vcmax25 and Jmax25 were not measured, and, therefore, we used a multilayer perceptron (MLP) [34], which is a class of feedforward artificial neural networks with high simulation accuracy. In this experiment, the training and validation accuracy for Vcmax25 and Jmax25 reached 0.55. χ was converted from leaf δ13C measurements after eliminating the effects of sampling year and latitude, following the method proposed by Cornwell et al. [35]. χ is a useful indicator in aridity areas because it determines the balance between carbon gain and water loss, which is closely regulated by the stomata and reflects the adaptation of plants in arid conditions.
The climate of each site was defined using three climate variables, namely growing degree days above 0 °C (mGDD0), monthly accumulated photosynthetically active radiation during the thermal growing season (mPAR0) and the ratio of mean annual precipitation to annual equilibrium evapotranspiration (moisture index, MI). MI is a widely recommended indicator of aridity stress, which is the inverse of dry index and can reflect the plant-available water content in the soil. These bioclimatic variables were calculated from observations retrieved from meteorological stations on the Loess Plateau. Two variables representing soil conditions for optimal plant growth were selected, namely soil organic matter (SOM) and soil pH at 0~30 cm depth.

2.3. Multivariate Statistical Analysis

Principal component analysis (PCA) and redundancy analysis (RDA) were used to analyze trait covariation and to reveal the relationships between traits, considering traits as response variables and climate/soil/family variables as predictors [36,37]. PCA and RDA were performed with the vegan package in R language [38]. LA was square-root transformed to create a linear relationship. Calculated using the method of variation partitioning, a Venn diagram was used to show the contributions of the climate, soil and family, and their independent and intersection effects were explicitly considered [39]. The taxonomic rank at the family level was used in forward analysis looking at the shared evolutionary history between species and the assumed similarities in their responses to environmental changes. Thus, for the purpose of our study, it was more suitable to use the family level rather than the genera or species in the statistical analysis. Ordinary linear regression was employed to describe the variation in a single trait response to the climate gradient, and the 95% confidence intervals are shown.

3. Results

3.1. Trait Covariation and Corresponding Adaptation Strategies

The PCA analysis that included all plants and sites confirmed our hypothesis that the eight traits studied are not independent but show covariation patterns (Figure 2, Table 2). The first three axes captured 84.32% of the total variation in the eight traits. The first axis captured 41.13% of variation, which was mainly controlled by LA, Narea, Vcmax25 and Jmax25. The second axis accounted for 32.61% of variation and was dominated by LA, Vcmax25 and Jmax25. The third axis accounted for 10.58% of variation and was dominated by Narea and LMA. Both LA and χ changed independently and did not covary with the other traits (Figure 2). Vcmax25 and Jmax25 closely covaried in the first two PCA axes. Nmass, Narea, LMA and LMDC, four important traits of LES, also presented clear covariation characteristics, especially on the second axis (capturing 32.61% of the total variations), which show similar weights to the first axis.
The correlation matrix (Figure 3) confirmed our results obtained from the PCA analysis that the eight studied traits are organized into four trait dimensions. The first dimension was LA, which had a low correlation with other traits and represented plant behavior during evaporation (energy balance). The second dimension was composed of LDMC, LMA, Narea, and Nmass, which were closely related within this dimension and weakly related to traits in other dimensions. This dimension is linked to a plant’s strategy for resource acquisition. Vcmax25 and Jmax25 constituted the third dimension, with a correlation coefficient reached at 0.77. In this dimension, Vcmax25 and Jmax25 varied tightly because plants should not invest more in either photosynthesis or electronic transport to maintain a lower energy cost. The fourth dimension was χ, which was found to be unrelated to the other traits. This dimension is directly linked with water use efficiency. The clear color differences of points in the histogram (Figure 3) and scatter plots (Figure 2a,b) indicate that trait covariations were not random but presented significant variation along the precipitation gradient.

3.2. Trait Variations along the Aridity Gradient (MI Decreased)

Except LDMC, all the studied traits showed significant variation trends along the aridity gradient (decreasing MI) (Figure 4). LA increased along with an increase in MI, which is consistent with the transition of vegetation types from forests (large leaf areas) to grasslands and deserts (small leaf areas). LDMC was not sensitive to moisture changes. Plants tended to have higher LMA values when facing arid conditions because of its construction cost. Both Nmass and Narea increased significantly along the aridity gradient and were related to leaf construction and photosynthetic capacity. Vcmax25 and Jmax25 were directly linked with photosynthetic capacity, exhibiting an increase along the aridity gradient (decreasing MI), because the growth period was shorter in the arid area than in the humid area, and they required more photosynthesis efficiency to recover construction cost. χ decreased along the aridity gradient, which reflected an adjustment in plant stomata in arid conditions to maintain high water use efficiency.
Trait variation patterns were not the same along the temperature gradient (increasing mGDD0) and the aridity gradients (Figure 5), which were mainly affected by growth temperature and latitude. LA had a pronounced decreasing trend because of the combined effects of vegetation types and latitude, indicating that large areas have a strong ability to regulate temperature. Likewise, LDMC had no significant trend toward temperature changes. Nmass and Narea increased with an increase in temperature; thus, Narea had a higher correlation coefficient than that of Nmass. Vcmax25 increased with an increase in temperature, which is explained by the activity of Rubisco regulating by temperature. Jmax25 reflects photosynthetic electron transport ability, which was higher under warm conditions. χ decreased with an increase in temperature as a result of the tendency of plants to close their stomata to avoid water loss in arid areas.

3.3. Trait Covariations and Adaptation Strategies Related to Climate and Soil Variables

The highlighted four dimensions composed from the eight studied traits remained independent when filtered by climate and soil variables (Figure 6). In fact, covariations in the eight traits could be explained by the effects of climate and soil variables reaching 27.6% (Table A1), presenting distinct environmental adaptation of plants. The most important information was the covariations in LA along the MI and soil pH gradients, which dominated the first RDA axis (Figure 6a). Three traits in LES (Narea, Nmass and LMA) also changed with MI changes, but LDMC was not sensitive to the first two RDA axes. The second RDA axis (RDA2) was mainly dominated by the covariations in Vcmax25 and Jmax25 along the temperature (mGDD0) and soil pH gradient, which accounted for 5.2%. The third axis accounted for only 0.5% of trait covariations, which was related to χ variation along with MI, mGDD0 and mPAR0 (Figure 6b). Along with the climate and soil variables, the traits also showed covariation trends within four trait dimensions. For example, three traits in LES covaried along RDA1, and two photosynthetic traits (Vcmax25 and Jmax25) covaried along RDA2.

3.4. Controls of Trait Covariations along the Aridity Gradient

In total, 50.19% of the covariations in traits can be explained by the combination of climate, soil and family (phylogeny/vegetation succession) (Figure 7). The largest contribution comes from family, which accounted for 38.59% (Table 3). The contribution of climate to the eight trait covariations reached 20.41%, and the contribution of soil accounted for 18.23%. For LA, both climate and soil contributed considerably to its variation; however, the independent control of climate was small, and notable information was found at the intersections of climate, soil and family. For LDMC, the contribution of climate and soil was small, and family was the main determining factor of its variation. LMA was also controlled by family, and the contribution of climate and soil was small. Compared with Nmass, Narea was largely controlled by climate and was less affected by family. Vcmax25 and Jmax25 were mainly controlled by family, less than 40% originated from the three factors combined. For χ, the contributions of climate, soil and family ranged from 13.8% to 18.0%.
The eight studied traits had different responses to environmental changes. The intersection of climate and soil contributed 10.39% to their covariations, while family had a strong intersection with climate/soil variables (16.65%) (Figure 7a), indicating that species evolved to adapt to their changing environments along the aridity gradient. Only LA, LMA, Narea and χ were sensitive to aridity changes, while the contributions of other traits were less than 10%, and the most notable feature was the joint effects of family that intersected with climate/soil variables. After the effects of climate and soil were removed, variations in LDMC, LMA, Nmass, Narea and Vcmax25 were largely controlled by family. For LDMC, LMA, Vcmax25, Jmax25 and χ, more than 50% of the variation was unexplained by the combination of climate, soil and family, which may suggest an effect of local microclimate conditions, trait–trait relationships and intrinsic factors.

4. Discussion

4.1. Importance and Significance of Studying Trait Covariations in Arid Areas

The present study illustrates the power of using large trait datasets from multiple co-existing species, spanning a broad range of climates, to reveal general patterns of trait covariations. It also shows the utility of multivariate analysis in summarizing these patterns of covariations and the research interest in variance partitioning as a means of attributing trait variability to different (and sometimes intersecting) causes of variation. However, there is a need for further extensive trait data collection, including root-related traits and isotopic measurements, thus covering an extended range of worldwide climate types.
By analyzing the covariations of eight traits along the aridity gradient, we confirmed the existence of four main dimensions of variation, which is in accordance with previous findings [40,41]. Within these dimensions, trait–trait relationships were close, but few correlations were found between them, which could mean fewer traits representing adaptation strategies, especially in the field of ecological modeling. RDA analysis presented similar dimensionality, indicating that the trait dimensions remained consistent and stable along the arid gradient. These findings have implications toward the understanding of diverse plant strategies and species diversity. LA is the first dimension and represents a plant’s strategy to maintain temperature. The second dimension is composed of LES-related traits in LES, which is an efficient indicator of quick growth and resource use. The third dimension is linked with photosynthesis, which balances construction investment and cost. The fourth dimension is χ, which is an indicator of water use efficiency.
We observed that the family contribution to trait covariation, which was characterized by phylogenetic replacement along the aridity gradient, was the most important determiner of trait variation. The family contribution to the eight trait covariations reached more than 38%. For LDMC, climate and soil explain only a small fraction of the trait variation; however, large variation was caused by the family factor. Therefore, predicting plant distribution changes and vegetation sensitivity under climate conditions is crucial to community and functional ecology. If the family dynamics and trait variations in each family were quantified, the family’s role and the associated mechanisms could be accurately captured [42]. This result implies that vegetation succession cannot be neglected in trait-based ecological modeling.
Apart from the family contribution to trait covariations, the considerable role of phenotypic plasticity, originating from both a genetic and environmental basis, cannot be neglected. Phenotypic plasticity is the ability of an organism or a single genotype to exhibit different phenotypes, including behavior, morphology and physiology, in different environments [43]. For instance, in this present study, the most widely distributed plant, Lespedeza davurica, had clear differences in leaf area along the aridity gradient, which was explained by its capacity to maintain more water in its leaves and to adapt to low temperatures. Phenotypic variability is defined as the tendency or the potential of an organism to exhibit different phenotypes under a wide range of environmental conditions, which offer an important adaptation capacity in response to environmental changes [44]. Additionally, during field sampling, efforts need to be oriented to the effects of plant age and microclimate, which could also have a significant effect on the expression of trait covariations.

4.2. Mechanism of Trait Covariations along the Aridity Gradient

Trait covariations occurred along the aridity gradient, and the underlying mechanisms were behind the explanation of the different plant adaptation strategies. LA is linked with the theory of energy balance [27], which means that warm and humid conditions favor plants with large leaves; in contrast, plants with small leaves are limited by the lower temperatures. Thus, variations in Nmass, Narea, LMA and LDMC confirm the universal nature of LES, which reflects the optimal method of balancing the construction cost and payback time of leaves [45,46]. As expected from the theory of coordination hypothesis, close covariations were seen in Vcmax25 and Jmax25, which shows that photosynthesis depends on both carbonylation and electron transport, as leaves seek a balance when investing in these two processes to avoid energy waste [15,47]. χ is an indicator of CO2 drawdown from air to leaf, and it is described in Farquhar’s model [48] and applied in most ecological models. Hence, variation in χ is linked with the least-cost hypothesis, which states that leaves minimize the sum of the cost of transpiration and carboxylation [49].

4.3. Challenges and Future Directions

In this study, we quantified covariations in eight important traits that captured the most information on ecosystem functions and plant adaptations, and, thus, we explored their controls. However, a more in-depth analysis is needed regarding three main aspects. First, we sampled leaf functional traits, but some traits, such as root traits (specific root length, fine root density etc.) [50] and Huber values (the ratio of the sapwood cross-section and the supporting leaf area) [51] were equally important to studying plant adaptations. Second, family was listed as the most important control, but the driving mechanism and its intersection effects with climate and soil are not fully understood. Consequently, understanding the relationships between trait covariations and family would be helpful for regional ecological restoration and management programs. Third, vegetation-based models explore the incorporation of trait variations [52,53]; however, the extent and magnitude of trait variation and, thus, their covariation characteristics have not been fully quantified. In this sense, we suggest strengthening the observation of trait covariations in the field and building consistent theoretical equations for vegetation models.
We strongly suggest considering ‘functional diversity’ in ecological modeling. In fact, trait-based models can represent the co-existence of multiple trait combinations, considering the fact that this diversity provides increased community resilience when facing environmental changes [54]. The challenge is to find a generally applicable method to specify the range of proper trait combinations that is consistent with the observed patterns of trait variation within sites. Ultimately, vegetation models should be able to predict, for example, and experimentally determine the relationships between species diversity and ecosystem function [55], but this potential has yet to be realized.

5. Conclusions

By analyzing the covariations of eight traits, we found that plants within arid regions adopted at least four strategies related to energy balance, resource acquisition, resource investment and water use efficiency, which reflect plant adaptations to environmental changes. We also confirmed that climate, soil and family contributed to trait covariations, but the control of family in trait covariations could not be neglected. Our findings linked trait covariations with multiple adaptation strategies and provide a reference for regional ecological management and species selection in arid areas.

Author Contributions

Conceptualization, Y.Y., G.L. and C.P.; methodology, L.K. and J.Z.; writing—original draft preparation, Y.Y.; visualization, N.Q. and R.L.; review and editing, Y.Y. and J.K.; supervision, Z.W., H.Z., G.L. and C.P.; project administration, H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (41925005, 41701051 and 41801181).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available on request due to restrictions, e.g., privacy or ethical. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the strict management of various data and technical resources within the research teams.

Acknowledgments

Many thanks to Boheng Wang, Lanjun Zhao, Xiaofeng Su, Jiang Bian and Rui Yang for field assistance.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Biology 10 01066 g0a1 550
Figure A1. Distribution histogram of the eight studied plant functional traits, including (a) LA: leaf area, (b) LDMC: leaf dry mass content, (c) LMA: leaf mass per area, (d) Nmass: leaf nitrogen content, (e) Narea: leaf nitrogen per area, (f) Vcmax25: maximum carboxylation rate standardized to 25 °C, (g) Jmax25: maximum electron transport rate standardized to 25 °C and (h) χ: ratio of intercellular to ambient CO2 concentration.
Figure A1. Distribution histogram of the eight studied plant functional traits, including (a) LA: leaf area, (b) LDMC: leaf dry mass content, (c) LMA: leaf mass per area, (d) Nmass: leaf nitrogen content, (e) Narea: leaf nitrogen per area, (f) Vcmax25: maximum carboxylation rate standardized to 25 °C, (g) Jmax25: maximum electron transport rate standardized to 25 °C and (h) χ: ratio of intercellular to ambient CO2 concentration.
Biology 10 01066 g0a1
Table
Table A1. Trait loading, eigenvalues and percentage of trait variation explained by redundancy analysis (RDA) axes constrained by climate and soil variables. Values greater than 0.4 are labeled in bold.
Table A1. Trait loading, eigenvalues and percentage of trait variation explained by redundancy analysis (RDA) axes constrained by climate and soil variables. Values greater than 0.4 are labeled in bold.
TraitRDA1RDA2RDA3RDA4RDA5PC1
log√LA2.040.260.050.000.00−0.07
log Nmass−0.100.000.08−0.170.05−0.25
log Narea−0.510.020.06−0.080.04−0.50
log LMA−0.410.02−0.030.08−0.01−0.25
log LDMC−0.04−0.01−0.10−0.06−0.18−0.03
Log Vcmax25−0.330.740.00−0.17−0.03−1.42
Log Jmax25−0.400.760.020.170.02−2.19
logit χ0.230.09−0.30−0.040.080.09
Eigenvalue0.470.110.010.010.000.68
Proportion explained (%)21.305.220.480.440.1931.09
Cumulative proportion (%)21.3026.5227.0127.4527.6458.74
Table
Table A2. Description of families with a record number greater than 8.
Table A2. Description of families with a record number greater than 8.
FamilySpecies
AceraceaeArtemisia gmelinii, Artemisia capillaries, Artemisia mongolica, Lespedeza davurica, Artemisia giraldii, Heteropappus altaicus, Cirsium setosum, Echinops sphaerocephalus, Artemisia desertorum, Artemisia frigida, Artemisia sieversiana, Scorzonera austriaca, Artemisia argyi, Lespedeza cuneata, Carpesium cernuum, Aster tataricus
GramineaePhragmites australis, Bothriochloa ischaemum, Stipa grandis, Stipa bungeana, Cleistogenes caespitosa, Setaria viridis, Cleistogenes hancei, Cleistogenes chinensis, Leymus secalinus, Bromus inermis, Elymus dahuricus, Triarrhena sacchariflora
RosaceaePotentilla tanacetifolia, Fragaria vesca, Crataegus cuneata, Armeniaca vulgaris, Pyrus betulaefolia, Prunus salicina, Rosa xanthina, Spiraea Salicifolia, Cotoneaster multiflorus, Amygdalus davidiana, Rubus parvifolius, Pyrus betulaefolia, Amygdalus triloba
LeguminosaeSophora davidii, Astragalus melilotoides, Thermopsis lanceolata, Glycyrrhiza uralensis, Sophora davidii, Oxytropis racemosa, Astragalus adsurgens, Caragana Korshinskii, Astragalus membranaceus, Lespedeza bicolor, Indigofera bungeana,
CaprifoliaceaeLonicera japonica, Viburnum schensianum, Lonicera maackii
ElaeagnaceaeHippophae rhamnoides, Elaeagnus umbellata, Elaeagnus pungens

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Figure 1. Distributions of the sampling sites across the aridity gradient on the Loess Plateau. (a) Vegetation distribution—the numbers represent the site IDs in Table 1. (b) Distribution map for moisture index (MI). (c) Distribution map for growing-season monthly mean temperature above 0 °C (mGDD0).
Figure 1. Distributions of the sampling sites across the aridity gradient on the Loess Plateau. (a) Vegetation distribution—the numbers represent the site IDs in Table 1. (b) Distribution map for moisture index (MI). (c) Distribution map for growing-season monthly mean temperature above 0 °C (mGDD0).
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Figure 2. Principal component (PC) patterns of eight plant functional traits. (a) PC1 versus PC2. (b) PC2 versus PC3. (c) PC3 versus PC4. LA: leaf area; Nmass: leaf nitrogen content; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration.
Figure 2. Principal component (PC) patterns of eight plant functional traits. (a) PC1 versus PC2. (b) PC2 versus PC3. (c) PC3 versus PC4. LA: leaf area; Nmass: leaf nitrogen content; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration.
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Figure 3. Correlations among eight plant functional traits. (A–D) Four trait dimensions of eight traits. LA: leaf area; Nmass: leaf nitrogen content per mass; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration. “*” indicates p < 0.05. “**” indicates p < 0.01. “***” indicates p < 0.001.
Figure 3. Correlations among eight plant functional traits. (A–D) Four trait dimensions of eight traits. LA: leaf area; Nmass: leaf nitrogen content per mass; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration. “*” indicates p < 0.05. “**” indicates p < 0.01. “***” indicates p < 0.001.
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Figure 4. Trait variations along the aridity gradient (decreasing moisture index, MI). (a) LA versus MI. (b) LDMC versus MI. (c) LMA versus MI. (d) Nmass versus MI. (e) Narea versus MI. (f) Vcmax25 versus MI. (g) Jmax25 versus MI. (h) χ versus MI. The bottom right numbers are the correlation coefficients and their significance, with “**” indicating p < 0.01. LA: leaf area; Nmass: leaf nitrogen content per mass; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration.
Figure 4. Trait variations along the aridity gradient (decreasing moisture index, MI). (a) LA versus MI. (b) LDMC versus MI. (c) LMA versus MI. (d) Nmass versus MI. (e) Narea versus MI. (f) Vcmax25 versus MI. (g) Jmax25 versus MI. (h) χ versus MI. The bottom right numbers are the correlation coefficients and their significance, with “**” indicating p < 0.01. LA: leaf area; Nmass: leaf nitrogen content per mass; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration.
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Figure 5. Trait variations along the temperature gradient (growing-season monthly mean temperature above 0°C, mGDD0). (a) LA versus mGDD0. (b) LDMC versus mGDD0. (c) LMA versus mGDD0. (d) Nmass versus mGDD0. (e) Narea versus mGDD0. (f) Vcmax25 versus mGDD0. (g) Jmax25 versus mGDD0. (h) χ versus mGDD0. The bottom right numbers are the correlation coefficients and their significance, with “**” indicating p < 0.01. LA: leaf area; Nmass: leaf nitrogen content per mass; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration.
Figure 5. Trait variations along the temperature gradient (growing-season monthly mean temperature above 0°C, mGDD0). (a) LA versus mGDD0. (b) LDMC versus mGDD0. (c) LMA versus mGDD0. (d) Nmass versus mGDD0. (e) Narea versus mGDD0. (f) Vcmax25 versus mGDD0. (g) Jmax25 versus mGDD0. (h) χ versus mGDD0. The bottom right numbers are the correlation coefficients and their significance, with “**” indicating p < 0.01. LA: leaf area; Nmass: leaf nitrogen content per mass; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration.
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Figure 6. Redundancy analysis (RDA) for eight traits and climate/soil variables. (a) RDA1 versus RDA2. (b) RDA2 versus RDA3. LA: leaf area; Nmass: leaf nitrogen content per mass; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration. MI: moisture index; mGDD0; monthly growth temperature above 0 °C; mPAR0: monthly photosynthetically active radiation above 0 °C; pH: soil pH; SOM: soil organic matter content.
Figure 6. Redundancy analysis (RDA) for eight traits and climate/soil variables. (a) RDA1 versus RDA2. (b) RDA2 versus RDA3. LA: leaf area; Nmass: leaf nitrogen content per mass; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration. MI: moisture index; mGDD0; monthly growth temperature above 0 °C; mPAR0: monthly photosynthetically active radiation above 0 °C; pH: soil pH; SOM: soil organic matter content.
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Figure 7. Contributions (%) of climate, soil and family to trait variations. The negative values can be treated as zero because all the values are adjusted correlation coefficients. LA: leaf area; Nmass: leaf nitrogen content; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration.
Figure 7. Contributions (%) of climate, soil and family to trait variations. The negative values can be treated as zero because all the values are adjusted correlation coefficients. LA: leaf area; Nmass: leaf nitrogen content; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration.
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Table
Table 1. Characteristics of the studied sites from the China Loess Plateau transect.
Table 1. Characteristics of the studied sites from the China Loess Plateau transect.
SchemeSite NameLatitude (Degree)Longitude (Degree)Elevation (m)Vegetation TypesNo. of Species SamplesMoisture Index (MI)
01Jiumen35.64110.58440.3grassland100.54
02Wangfeng35.72110.44721grassland120.61
03Qiaozigou35.81110.271219.9forestland240.69
04Caijiachuan135.75109.891603.2forestland260.82
05Caijiachuan235.82109.911244.92forestland200.75
06Laohugou35.97110.161061.32forestland210.69
07Zhonglousi36.21109.921087.42forestland320.68
08Lushanmiao36.67109.481220grassland140.63
09Houjiacun36.77109.421200grassland110.62
10Jiugou136.8109.361071grassland100.61
11Jiugou236.8109.361067grassland80.61
12Caozhuang36.86109.31154grassland100.61
13Liuping36.91109.271204grassland70.61
14Caohe36.97109.161311grassland150.6
15Fengcigeda37.09109.051434grassland70.59
16Liandaowan37.19108.971476grassland100.56
17Tianciwan37.32108.911592grassland80.56
18Lugouqu37.44108.911560grassland80.53
19Xiasandun37.5108.871584grassland70.54
20Shuanghaize37.69108.871347desert40.46
21Guojiazhuang37.94108.881153desert40.4
22Batuwan37.99108.741155desert30.38
Table
Table 2. Trait loading, eigenvalues and percentage of trait variation explained by the first three principal components (PCs). LA: leaf area; Nmass: leaf nitrogen content; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration.
Table 2. Trait loading, eigenvalues and percentage of trait variation explained by the first three principal components (PCs). LA: leaf area; Nmass: leaf nitrogen content; Narea: leaf nitrogen per area; LMA: leaf mass per area; LDMC: leaf dry mass content; Vcmax25: maximum carboxylation rate standardized to 25 °C; Jmax25: maximum electron transport rate standardized to 25 °C; χ: ratio of intercellular to ambient CO2 concentration.
TraitsPC1PC2PC3
log√LA−1.97 a1.980.42
log Nmass0.310.000.14
log Narea0.80−0.101.05
log LMA0.49−0.100.90
log LDMC0.14−0.120.36
log Vcmax251.300.98−0.00
log Jmax251.711.62−0.35
logit χ−0.170.05−0.29
Eigenvalue0.900.710.23
Proportion explained (%)41.1332.6110.58
Cumulative proportion (%)41.1373.7384.32
a Values greater than 0.5 are in bold.
Table
Table 3. Total contributions (%) of climate, soil and family to trait variations.
Table 3. Total contributions (%) of climate, soil and family to trait variations.
TraitsClimate (%)Soil (%)Family (%)
All traits20.4118.2338.57
log√LA45.0542.5054.79
log LDMC5.580.2440.92
log LMA 10.538.9928.10
log Nmass3.923.7353.63
log Narea 12.9810.3841.44
log Vcmax259.403.9331.27
log Jmax253.903.6922.30
logit χ13.8714.5517.92
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Yang, Y.; Kang, L.; Zhao, J.; Qi, N.; Li, R.; Wen, Z.; Kassout, J.; Peng, C.; Lin, G.; Zheng, H. Quantifying Leaf Trait Covariations and Their Relationships with Plant Adaptation Strategies along an Aridity Gradient. Biology 2021, 10, 1066. https://doi.org/10.3390/biology10101066

AMA Style

Yang Y, Kang L, Zhao J, Qi N, Li R, Wen Z, Kassout J, Peng C, Lin G, Zheng H. Quantifying Leaf Trait Covariations and Their Relationships with Plant Adaptation Strategies along an Aridity Gradient. Biology. 2021; 10(10):1066. https://doi.org/10.3390/biology10101066

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Yang, Yanzheng, Le Kang, Jun Zhao, Ning Qi, Ruonan Li, Zhongming Wen, Jalal Kassout, Changhui Peng, Guanghui Lin, and Hua Zheng. 2021. "Quantifying Leaf Trait Covariations and Their Relationships with Plant Adaptation Strategies along an Aridity Gradient" Biology 10, no. 10: 1066. https://doi.org/10.3390/biology10101066

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