Does genotypic diversity of Hydrocotyle vulgaris affect CO2 and CH4 fluxes?

Biodiversity plays important roles in ecosystem functions and genetic diversity is a key component of biodiversity. While effects of genetic diversity on ecosystem functions have been extensively documented, no study has tested how genetic diversity of plants influences greenhouse gas fluxes from plant-soil systems. We assembled experimental populations consisting of 1, 4 or 8 genotypes of the clonal plant Hydrocotyle vulgaris in microcosms, and measured fluxes of CO₂ and CH₄ from the microcosms. The fluxes of CO₂ and CO₂ equivalent from the microcosms with the 1-genotype populations of H. vulgaris were significantly lower than those with the 4- and 8-genotype populations, and such an effect increased significantly with increasing the growth period. The cumulative CO₂ flux was significantly negatively related to the growth of the H. vulgaris populations. However, genotypic diversity did not significantly affect the flux of CH₄. We conclude that genotypic diversity of plant populations can influence CO₂ flux from plant-soil systems. The findings highlight the importance of genetic diversity in regulating greenhouse gas fluxes.

1 Introduction

Greenhouse gas emissions have increased since the pre-industrial era, which is primarily driven by economic development and population increment (Stocker, 2014). Specially, CO₂ is the largest contributor, accounting for 74.4% of the total emissions, and CH₄ is the second largest contributor, accounting for 17.3% (Ritchie et al., 2020). Terrestrial ecosystems are the important carbon sinks of greenhouse gases that are profoundly influenced by plants, soil and other environment factors (Yuan et al., 2015). Specifically, plant species identity and diversity have a direct impact on photosynthetic carbon sequestration and an indirect influence on CO₂ and CH₄ emissions by altering biochemical processes (Fang, 2010; Koelbener et al., 2010).

Plant species diversity can impact ecosystem function, such as primary productivity and nutrient cycling (Cardinale et al., 2012; van der Plas, 2019). As plant species richness in a community increases, the probability of highly productive species arises (i.e., sampling effect), or resources in the community can be used more completely due to niche partition and complementarity among species (i.e., complementarity effect), thereby resulting in an increase e.g., community productivity, invasion resistance and carbon sequestration (Loreau and Hector, 2001; Adomako et al., 2019; Xue et al., 2021). Studies have shown that increasing plant species diversity is potentially able to influence greenhouse gas emissions from ecosystems (Han et al., 2019; Luo et al., 2020; Fan et al., 2021).

Besides species diversity, genetic diversity is another key component of biodiversity (Jiang et al., 2021; Begum et al., 2022; Huang et al., 2022). Similar to the impact of plant species diversity, increasing plant genetic diversity can also affect population performance and ecosystem functions (Hughes et al., 2008; Begum et al., 2022). For instance, genotypic diversity has been shown to affect plant biomass, root morphology and nutrient uptake (Abbott and Stachowicz, 2016; Semchenko et al., 2021; Huang et al., 2022; Begum et al., 2023). So far, it remains unclear whether genotypic diversity of a plant can affect greenhouse gas fluxes from plant-soil systems.

CO₂ flux is determined by photosynthetic carbon input and plant-soil respiration emission, while CH₄ flux is more affected by methanogens and substrate quality (Mo et al., 2015; Gao et al., 2018). The capacity of photosynthetic carbon sequestration and plant-soil respiration can vary greatly among different genotypes of the same plant (Cook-Patton et al., 2011). Additionally, different genotypes of the same plant may be associated with different soil microbial communities that may greatly impact the processes of CH₄ production and soil respiration (Johnson et al., 2010; Latta et al., 2011; Burrill et al., 2023). Hence, we postulated that genotypic identity and genotype diversity of the same plant species may affect greenhouse gas fluxes (e.g., CO₂ and CH₄ fluxes).

We assembled experimental populations consisting of 1, 4 or 8 genotypes of the clonal plant Hydrocotyle vulgaris in microcosms, and measured fluxes of CO₂ and CH₄ from the microcosms. Specifically, we addressed the following two questions: (1) Does genotypic identity of H. vulgaris influence CO₂ and CH₄ fluxes from the microcosms? (2) Does genotypic diversity of H. vulgaris affect CO₂ and CH₄ fluxes from the microcosms?

2 Materials and methods

2.1 Species information and preparation

Hydrocotyle vulgaris L. (Araliaceae) is a perennial clonal plant with creeping stems rooted in each node in humid conditions (Dong et al., 2015). Commonly, each stem node has a leaf and a new creeping stem can be formed at the leaf axil (Si et al., 2020). It is native to Europe and the United States, often inhabiting moist habitats such as rivers, ponds, swamps, valleys, and dune grasslands (Xue et al., 2022). This species can reproduce rapidly by clonal growth and show high morphological plasticity (Wang et al., 2022). Different genotypes of H. vulgaris differ in competitive ability (Zhang L. et al., 2022) and clonal integration (Si et al., 2020).

In 2016, 128 ramets of H. vulgaris were collected from 10 different sites in China (Wang et al., 2020; Huang et al., 2022). Total genomic DNA of the ramets were extracted and a total of 20 genotypes were identified by ALFP based on genomic DNA (see Wang et al., 2020 for detail). To meet the experimental requirements and reduce the influence of genotype identity on genotypic diversity (Hughes et al., 2008; Hughes and Stachowicz, 2009), we randomly selected ten genotypes of H. vulgaris to construct populations with different levels of genotypic diversity. Among the ten genotypes, three were from Wenzhou, two were from Chongqing, and one was each of Taizhou, Jiangxi, Hangzhou, Wuhan and Lishui (Wang et al., 2020). Ramets of these ten genotypes were vegetatively propagated in greenhouses of Taizhou University in Taizhou, Zhejiang Province, China. On August 18, 2021, 1920 ramets (a node with a leaf and some adventitious roots) of similar size were selected. The initial height of the ramets was 4.04 ± 0.12 cm (mean ± SE, n =10), and the initial dry weight of the ramets 0.038 ± 0.003 g (mean ± SE, n =10).

2.2 Experimental design

Since 1, 4, and 8 genotypes are commonly used to represent the low, medium, and high genotype diversity (e.g., Hughes et al., 2008; Latta et al., 2011; Xue et al., 2021), we used these three levels of genotypic richness to construct populations. Each population was construed in a pot (16 cm in diameter and 20 cm in height) filled with a 1:1 mixture of river sand and soil collected from a wasteland in Taizhou, Zhejiang Province, China. Each pot was planted with 16 ramets of H. vulgaris. During the experiment, we used strings and labels to distinguish genotypes without disturbing plant growth, and we did not separate the individuals of different genotypes in the mixture plot.

For the 1-genotype populations, each pot was grown with 16 ramets of the same genotype, and all the ten genotypes were used to construct the 1-genotype populations. For the 4-genotype and 8-genotype populations, each pot was grown with 16 ramets of four different genotypes and eight different genotypes, respectively. Each of these two diversity treatments was replicated five times, but the replication was at the diversity level rather than at the genotypic combination level (Appendix Table S1). This approach was commonly used when testing the effect of species or genotypic diversity (Wang et al., 2021; Begum et al., 2022; Huang et al., 2023). We also ensured that the frequency of occurrence of each genotype was the same.

For the 1-genotype treatment, populations of each of the ten genotypes were replicated nine times, resulting a total of 90 pots (populations). For the 4-genotype treatment, each of five 4-genotype populations was replicated three times, making 15 pots. Similarly, for the 8-genotype treatment, each of five 8-genotype populations was replicated three times, resulting in also 15 pots.

The experiment started on 30 August and ended on 30 October 2021, and was conducted in the greenhouse. During the experiment, the daily temperature in the greenhouse was controlled at 25.3°C. All pots were watered every two days, and were randomly repositioned three times during the experiment.

2.3 Measurements

We measured fluxes of CO₂ and CH₄ from each of the microcosms (the pots with the populations of H. vulgaris and the soil) between 09: 00 am and 11: 00 am every 10 days. For the 1-genotype treatment, we randomly selected three microcosms (from the nine microcosms) for each genotype to measure fluxes, and thus 30 microcosms in total. For the 4- and the 8-genoytype treatment, we randomly selected one microcosm (from the three microcosms) for each of the five genotype combinations of each treatment to measure fluxes, and thus ten microcosms in total.

To qualify CO₂ and CH₄ fluxes from the microcosms, gas concentration was determined with the Ultra-Portable Greenhouse Gas Analyzer (UGGA) (M-GGA-918, Los Gatos Research Corp. USA). During the measurement, we placed the transparent chamber (20 cm in inner diameter and 50 cm in inner height) in the microcosms and fitted it tightly to ensure that the chamber enclosed the plant and soils and sealed. Then we captured the greenhouse gas fluxes from the microcosms. Cumulative fluxes were calculated by multiplying the average of the fluxes of consecutive sampling days by the time interval between them. The positive value of fluxes means that emission is greater than absorption and is a carbon source; when flux is negative, it is a carbon sink.

The carbon dioxide equivalent (CO₂-eq) is a standardized measurement unit that quantifies the effect of greenhouse gases, based on their reference to CO₂ (Stocker, 2014; He et al., 2017). In our experiment, CO₂-eq was defined as (He et al., 2017):

$$C{O}_2-eq=C{O}_2+(GW{P}_M\times M_{CH4})$$

where CO₂ is the carbon dioxide flux (g CO₂ m^-2), M_CH4 is the CH₄ flux (g CH₄ m^-2) and GWP_M is 25 (Stocker, 2014).

At the end of the experiment, we counted ramets of H. vulgaris and then harvested leaves, creeping stems, and root. We also measured leaf area (WinFOLIA Pro 2004a, Regent Instruments, Inc., QC, Canada) and total creeping stem length. All plant materials were sorted, oven-dried at 70°C for 72 h, and weighed.

2.4 Data analysis

We used the additive partitioning method (Loreau and Hector, 2001) to calculate the biodiversity effect of aboveground biomass. The net diversity effect is defined as the difference between the observed yield of H. vulgaris in mixture populations and the expected yield (i.e., the product of the yield of genotypes and the proportion of planted in mixture). It can be partitioned into the selection and complementarity effects (Loreau and Hector, 2001):

$$Selectioneffect=N\ast \text{ cov(}\Delta RY,M)$$

$$Complementarityeffect=N\ast mean(\Delta RY)\ast mean(M)$$

where N is the number of genotypes in the mixture, ΔRY is the change in relative yield for genotypes in the mixture,M is the yield of genotypes in monoculture and cov(ΔRY,M)is covariance of ΔRY and M.

Repeated-measures analysis of variance (ANOVA) was used to test for the effects on fluxes of CO₂, CH₄ and CO₂-eq from the microcosms, with genotypes or genotypic diversity as a between-subject factor and the growth period as a within-subject factor. We used one-way ANOVA to examine the effect of genotype identity or genotypic diversity on plant growth (total biomass, aboveground biomass, belowground biomass, and leaf area) and cumulative fluxes of CO₂, CH₄ and CO₂-eq from the microcosms and Duncan tests for multiple comparisons. Linear regression was used to examine the relationships of cumulative fluxes of CH₄ and CO₂ with total biomass, aboveground biomass, belowground biomass, and leaf area of H. vulgaris. Before analysis, all data were checked for normality and homogeneity of variance. Data of CH₄ flux were logarithmically transformed to satisfy the assumptions of normality and homogeneity of variance. The analyses were conducted with SPSS 18.0 (SPSS Inc., Chicago, IL, U.S.A.). Effects were considered significant if P< 0.05. Partial Least Squares Path Modeling (PLSPM) was used to examine the direct and indirect effects of genotypic diversity of H. vulgaris on CO₂ flux. PLSPM was conducted using the “plspm” R package (Luo et al., 2017).

3 Results

3.1 Genotypic differences in greenhouse gas fluxes

Genotype of H. vulgaris had a significant effect on fluxes of CO₂ and CO₂-eq (Table 1). The values of the CO₂ flux and CO₂-eq flux from the microcosms were the smallest when they were planted with the genotype of TZ-9 and the genotype of WZ-2, largest when they were planted with the genotype of CQ-2, and intermediate when they were planted with any other genotypes (Figures 1A, C). The growth of the H. vulgaris population was the largest when the population consisted of the genotype of TZ-9 or the genotype of LS-3, smallest when it consisted of the genotype of CQ-2, and intermediate when it consisted of any of the other genotypes (Appendix Figure S1). Irrespective of the genotypes planted, the values of the CO₂ flux and CO₂-eq flux from the microcosms all decreased sharply during the experiment, but the differences between genotypes became larger with time (Figures 1A, C). However, genotype had no effect on the CH₄ (Table 1; Figure 1B).

TABLE 1

Table 1 ANOVAs of effects of genotype of Hydrocotyle vulgaris, growth period and their interaction on fluxes of CO₂, CH₄ and CO₂ equivalent (CO₂-eq) from the microcosms.

FIGURE 1

Figure 1 Fluxes of (A) CO₂, (B) CH₄ and (C) CO₂ equivalent (CO₂-eq) from the microcosms with populations consisting each of the ten genotypes of H. vulgaris. Vertical bars are standard errors.

The cumulative CO₂ flux varied significantly among the ten genotypes (F_{9, 20} = 2.77, P = 0.028). The cumulative flux of CO₂ from the microcosms were the lowest when they were planted with the genotype of TZ-9 (-134.87 ± 18.83 g m^-2, mean ± SE) and the genotype of WZ-2 (-134.41 ± 14.98 g m^-2), the highest when they were planted with the genotype CQ-2 (-46.51 ± 1.62 g m^-2), and intermediated when they were planted with other genotypes (Figure 2A). However, the cumulative flux of CH₄ did not differ significantly among different genotypes (Figure 2B, F_{9, 20} = 1.14, P = 0.383).

FIGURE 2

Figure 2 Cumulative fluxes of (A) CO₂ and (B) CH₄ from the microcosms with populations consisting each of the ten genotypes of H. vulgaris. Bars and vertical lines show means and SE. Bars sharing the same letter are statistically not different at P = 0.05. Different letters (a–c) at the end of bars indicate significant difference in each panel.

3.2 Effects of genotypic diversity on greenhouse gas fluxes

Genotypic diversity of H. vulgaris had a significant effect on fluxes of CO₂ and CO₂-eq (Table 2). The CO₂ flux and CO₂-eq from the microcosms with the 1-genotype populations of H. vulgaris were significantly higher (i.e., values were more negative) than those with the 4- and 8-genotype populations, and such an effect increased significantly with increasing the growth period (significant interaction effect in Table 2; Figures 3A, C). Consequently, cumulative fluxes of CO₂ (F_{2, 37} = 5.07, P = 0.011; Figure 4A) and CO₂-eq (F_{2, 37} = 5.07, P = 0.011; Figure 4C) from the microcosms with the 1-genotype populations were significantly higher than those from the microcosms with the 4-and 8-genotype populations. While the CH₄ flux increased significantly with increasing the growth period, it was not significantly influenced by genotypic diversity of H. vulgaris (Table 2, Figure 3B). Consequently, genotypic diversity did not significantly affect the cumulative flux of CH₄ (F_{2, 37}  =1.45, P = 0.247; Figure 4B).

TABLE 2

Table 2 ANOVAs of effects of genotypic diversity of Hydrocotyle vulgaris, growth period and their interaction on fluxes of CO₂, CH₄ and CO₂ equivalent (CO₂-eq) from the microcosms.

FIGURE 3

Figure 3 Effects of genotypic diversity of H. vulgaris and growth period on fluxes of (A) CO₂, (B) CH₄ and (C) CO₂ equivalent (CO₂-eq) from the microcosms. Vertical bars are standard errors.

FIGURE 4

Figure 4 Effects of genotypic diversity of H. vulgaris on cumulative fluxes of (A) CO₂, (B) CH₄ and (C) CO₂-eq from the microcosms. Bars and vertical lines show means and SE. F, P and degrees of freedom of one-way ANOVAs are given. Different letters (a–b) at the end of bars indicate significant difference in each panel.

3.3 Relationships between gas fluxes and plant growth

The cumulative CO₂ flux was significantly negatively related to total biomass (R² = 0.41, P< 0.01), aboveground biomass (R² = 0.41, P< 0.01), belowground biomass (R² = 0.38, P< 0.01), and total leaf area (R² = 0.34, P< 0.01) of the H. vulgaris populations (Figure 5). However, the cumulative CH₄ flux was not significantly related to total biomass (R²< 0.01, P = 0.980), aboveground biomass (R²< 0.01, P = 0.695), belowground biomass (R²< 0.01, P = 0.878) or leaf area (R² = 0.01, P = 0.520) of the H. vulgaris populations. The cumulative CO₂ flux was not significantly related to any of the soil properties (Appendix Figure S2).

FIGURE 5

Figure 5 Relationships of the cumulative CO₂ flux with (A) total biomass, (B) abovegroud biomass, (C) belowground biomass and (D) leaf area of the H. vulgaris populations. F-, R2-, and p-values obtained from linear regressions are also presented.

4 Discussion

We found that both genotypic identity and genotypic diversity of H. vulgaris influenced CO₂ flux and CO₂-eq from the plant-soil systems, suggesting that intraspecific variations of the same species can influence greenhouse gas fluxes. However, values of CO₂ flux and CO₂-eq from the microcosms were all negative, regardless of genotypic identity and diversity of H. vulgaris. This result suggests that photosynthesis was greater than respiration and CH₄ emission (Fabre et al., 2020; Augusto and Boča, 2022).

CO₂ flux varies among plant species (Priault et al., 2009). Our study shows that CO₂ flux differs among genotypes, although the variation is less pronounced compared with plant species (Hughes et al., 2008; Crawford and Rudgers, 2012). Previous research has demonstrated the different traits of genotypic identity including growth rate (Poorter et al., 2005), respiration (Bulut et al., 2023), photosynthesis (Blackman et al., 2016) and related leaf traits (leaf nitrogen, leaf life, specific leaf area, etc.) (Grady et al., 2013). Our study similarly demonstrated significant differences of multiple traits among different genotypes, such as aboveground and belowground biomass, total biomass, and leaf area (Appendix Figure S1). The genotypes (TZ-9 from Taizhou and WZ-2 from Wenzhou) with significant carbon sequestration capacity had better population performance (Appendix Figure S1), indicating that these variations might account for the observed differences in CO₂ flux. Moreover, previous studies have shown that genotypic identity may result in changes of root exudates production (Mönchgesang et al., 2016; Semchenko et al., 2018; Sun et al., 2021), and the microbial communities (Eck et al., 2019; Fernández et al., 2020; Begum et al., 2022). These alterations not only affect soil organic matter input, but also influence soil microbial activity (Raaijmakers et al., 2009). However, we did not find the effect of soil properties on CO₂ flux, which may be due to the fact that we measured only a limited number of soil properties (i.e., soil moistures, soil total nitrogen, soil ammonium and nitrate nitrogen) and also due to the short experimental period (60 days), so that the effect of the plant on the soil was not enough to affect the CO₂ flux (Appendix Figure S2). As a result, genotypic identity may impact plant photosynthesis and respiration rates, ultimately resulting in differences in CO₂ flux (Mo et al., 2015; Gao et al., 2018; de Vries et al., 2019).

Surprisingly, the microcosms with the 1-genotype populations of H. vulgaris, had stronger carbon absorption capacity than those with the 4- and 8-genotype populations, suggesting that genotypic diversity of the same species can influence greenhouse gas flux. This might be because genotypic diversity has a negative effect on primary productivity and morphological traits of our plant communities (Adler et al., 2011). Previous studies have shown that genotypic diversity can increase population biomass (Hughes and Stachowicz, 2009), or has no effect or a negative effect on primary productivity (Fridley and Grime, 2010; Crawford and Rudgers, 2012). Such an inconstancy might be due to differences in environments, genotypes competition and resource availability (Fridley, 2002; Wang et al., 2019). In this experiment, the stolon of H. vulgaris, as stoloniferous plant, was restricted by the space of the experimental container during clonal reproduction, and the competition among genotypes increased. On the other hand, the more similar the phylogeny and the more overlapping the functional traits, the more inclined to increase competition among species and genotypes through selection effects (Venail and Vives, 2013; Huang et al., 2020). We hypothesized that although there were differences among genotypes, with the increase of genotypes, the probability of niche overlap increased, and the limited complementarity was more due to competition among H. vulgaris (Appendix Figure S3). Intense competition and resource constraints resulted in less biomass and leaf area, which was related to CO₂ flux. We did not list all possible genotypic combinations, but there may be differences between genotypic combinations, and further studies can be designed to explore the underlying mechanisms.

Our findings revealed a negative correlation between CO₂ flux and both biomass and leaf area of H. vulgaris, which is consistent with previous studies (Moore et al., 2002; Larmola et al., 2003). This relationship can be largely explained by the fact that CO₂ assimilation is highly dependent on a plant’s photosynthetic potential and aboveground biomass (Wohlfahrt et al., 2008; Han et al., 2013), while leaf area is indicative of its photosynthetic capacity (Kaitaniemi, 2007; Lusk et al., 2019). Although studies have shown that biomass is also a predictor of CO₂ flux (Emery and Fulweiler, 2014), in our experiment, we observed a higher rate of photosynthesis than respiration. As aboveground biomass and leaf area increased, net photosynthesis strengthened, leading to a decline in CO₂ flux that ultimately became increasingly negative.

We found no significant relationship between CH₄ flux and biomass, which is consistent with previous studies (Koelbener et al., 2010; Mo et al., 2015; Gao et al., 2018). As CH₄ is produced by methanogens and then emitted through plants or soil (Zhang Y. et al., 2022), it is likely that biomass is not the sole predictor of CH₄ production (Gong et al., 2021). Furthermore, we observed no significant differences in CH₄ flux across genotypes in H. vulgaris. One possible explanation is that variation in genotypic identity of H. vulgaris may not strongly influence methanogen abundance or activity similarly to how specific identities do (Berg and Smalla, 2009; Edwards et al., 2015; Liechty et al., 2020). Additionally, CH₄ flux may be mediated by processes related to transport from the soil to atmosphere (Ding et al., 2005; Bhullar et al., 2013; Andresen et al., 2017). However, the development of aerenchyma tissues and lacunae proportions in shoots and roots may not differ significantly among different genotypes of H. vulgaris (Ding et al., 2005). Consequently, limited trait variability across genotypes may also contribute to the lack of difference in CH₄ flux of genotypic diversity observed in our experiment (Sutton Grier and Megonigal, 2011).

In conclusion, our results provide evidence that genotypic identity and diversity of the same species can influence greenhouse gas fluxes from plant-soil systems. These findings highlight the importance of intraspecific variation in mediating greenhouse gas fluxes and suggest that intraspecific variation should be considered when modelling regional and global patterns of greenhouse gas fluxes. One caveat is that this study was conducted in microcosms so that its reality is limited. Future studies could be designed to test how genotypic diversity can mediate greenhouse gas in field conditions.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

J-TZ: Writing – original draft. WX: Investigation, Writing – review & editing. J-QG: Writing – review & editing, Conceptualization. Q-WL: Investigation, Writing – review & editing. W-HY: Investigation, Writing – review & editing. F-HY: Conceptualization, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (grant 42271107) and New Area Science and Technology Innovation Project (2022XACX1000).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2023.1272313/full#supplementary-material

Supplementary Figure 1 | The total biomass, aboveground biomass, belowground biomass and leaf area of the ten genotypes of H. vulgaris. Bars and vertical lines show means and SE. Bars sharing the same letter are statistically not different at P = 0.05.

Supplementary Figure 2 | Structure equation model showing the direct and indirect effects of genotypic diversity of H. vulgaris on CO₂ flux. Red and blue arrows reflect positive and negative path ways respectively. Numbers along the arrows, as well as the width of the arrows, indicate standardized path coefficients. Significance levels of each predictor: *P< 0.05, **P< 0.01, ***P< 0.01.

Supplementary Figure 3 | Net diversity effect, complementarity effect, selection effect on the aboveground biomass for 4- and 8-genotype populations of H. vulgaris. Bars and vertical lines show means and SE. Difference of values between 4- and 8-genotype populations were tested by independent samples t-test. * indicates the significance at P< 0.05.

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