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Large slow-growing hydrophytes increase wetland carbon storage

Nature Plants volume 12, pages 294–307 (2026)Cite this article

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Abstract

Wetlands, among Earth’s most carbon-dense ecosystems, are vital for climate change mitigation. While plant diversity has been widely shown to increase soil carbon storage in terrestrial ecosystems, its influence in natural wetlands remains unclear. Here, using data from 1,268 natural wetlands surveyed in the US National Wetland Condition Assessment (NWCA), we examined how trait-based plant diversity (functional diversity) and composition (functional identity) affect soil carbon storage. We show that functional diversity had a minimal effect on carbon stocks, and its influence was weakened by elevated soil nutrient availability and non-native plant stress. In contrast, soil carbon storage was generally greater in wetlands dominated by larger, slow-growing and highly hydrophytic plants. Moreover, the benefits of functional identity were contingent on higher water levels and lower human disturbance. These findings suggest that the conservation and restoration of wetlands dominated by large, conservative and hydrophytic species under hydric conditions could help achieve climate change mitigation goals.

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Fig. 1: Results of principal component analysis showing the functional identities (CWMPC1, CWMPC2 and CWMPC3) of wetland communities.
Fig. 2: Fixed effects of functional diversity and functional identity (CWMPC1, CWMPC2 and CWMPC3) on soil carbon stocks.
Fig. 3: Structural equation model showing the effects of climate, water table depth, soil pH, functional diversity and functional identity on soil carbon stocks.
Fig. 4: Environmentally mediated effects of functional diversity and functional identity (CWMPC1, CWMPC2 and CWMPC3) on soil carbon stocks.

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Data availability

The wetland vegetation and soil factor data were downloaded from the NWCA database (https://www.epa.gov/national-aquatic-resource-surveys/nwca). The leaf trait data were downloaded from the TRY database (https://www.try-db.org) and the BIEN 4 database (https://bien.nceas.ucsb.edu/bien/). The Landsat surface reflectance data are available from the Google Earth Engine Data Catalog (https://developers.google.com/earth-engine/datasets/catalog/landsat). The climate data were collected from the CHELSA database (https://chelsa-climate.org). The data that support the findings of this study are available in figshare at https://doi.org/10.6084/m9.figshare.29097845 (ref. 113). Source data are provided with this paper.

Code availability

All codes are archived in figshare at https://doi.org/10.6084/m9.figshare.29097845 (ref. 113).

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Acknowledgements

The National Wetland Condition Assessment 2011 and 2016 data were a result of the collective efforts of dedicated field crews, laboratory staff, data management and quality control staff, analysts and many others from EPA, states, tribes, federal agencies, universities and other organizations. We thank the TRY initiative, the BIEN database, and their contributors and administrators. This work was supported by the National Key Research and Development Program of China (2023YFF1304504 to M.N.), the National Natural Science Foundation of China (32430065 to M.N., 32501442 to H.L., 32522064 and 32471831 to J.L.), the Shanghai Pilot Program for Basic Research–Fudan University 21TQ1400100 (21TQ004 to M.N.), the Shanghai Science and Technology Innovation Action Plan (23015810100 to B.L.), and the Science and Technology Plan Project of Shanghai (23DZ1202700 to J.L.).

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Authors and Affiliations

  1. State Key Laboratory of Wetland Conservation and Restoration, National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, and Institute of Eco-Chongming, School of Life Sciences, Fudan University, Shanghai, China

    Hao Liu, Jinquan Li, Jihua Wu, Bo Li & Ming Nie

  2. State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Ecology, Lanzhou University, Lanzhou, China

    Jihua Wu

  3. State Key Laboratory for Vegetation Structure, Functions and Construction (VegLab), Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China, Institute of Biodiversity, School of Ecology and Environmental Science, and Southwest United Graduate School, Yunnan University, Kunming, China

    Bo Li

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Contributions

M.N. conceived of and designed the study. H.L. performed the analysis and drafted the paper with assistance from M.N. J.L., J.W., B.L. and M.N. contributed to the revisions of the paper.

Corresponding author

Correspondence to Ming Nie.

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Nature Plants thanks Jay Sah and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Spatial distribution and sampling design of NWCA wetland sites.

a Spatial distribution of 1,268 wetland sites surveyed during 2011 and 2016 by the NWCA, showing long-term averages of mean annual temperature (MAT) and monthly climate moisture index (CMI). b Sampling design, including a 40 m radius standard assessment area (AA) with five vegetation plots and a surrounding buffer area. Normalized difference vegetation index (NDVI) derived from nine Landsat pixels (3×3 grid) overlapping the AA.

Source Data

Extended Data Fig. 2 A priori structural equation model used to assess the direct and indirect effects of climate, soil factors, functional diversity, and functional identity on wetland soil organic carbon stocks.

The variables considered in the models included functional diversity, functional identity, mean annual temperature (MAT), climate moisture index (CMI), water table depth (WTD), soil pH, productivity, root area density, and soil carbon to nitrogen (C/N) ratio. Functional identity was represented by the three principal components (PCs) from the PCA of the community-weighted means of trait values: CWMPC1 (representing plant resource economics), CWMPC2 (representing hydrophytic status), and CWMPC3 (representing plant size). Functional diversity (FD) was represented by functional dispersion. Proposed interpretations of the pathways (indexed by pathway number) are provided in Supplementary Table 3.

Extended Data Fig. 3 Structural equation models showing the effects of climate, water table depth, soil pH, functional diversity, and functional identity on the soil carbon stocks of the topsoil and subsoil layers.

a,c Path diagrams of factors influencing soil carbon stocks in the topsoil (a, n = 1,163) and subsoil (b, n = 784) layers. The final model with significant (P < 0.05, two-sided t-based Wald tests) pathways is shown, with black arrows representing positive relationships and red arrows representing negative relationships. The numbers on the arrows are standardized path coefficients, with arrow thickness proportional to their magnitude. b,d Standardized direct and indirect effects of each variable on soil carbon stocks in the topsoil (b) and subsoil (d) layers, ranked by the total effects. The goodness-of-fit statistics for the models for the topsoil and subsoil are as follows: Fisher’s C = 54.58, P = 0.132, and df = 44 and Fisher’s C = 47.13, P = 0.346, and df = 44, respectively, indicating a close model–data fit. MAT, mean annual temperature; CMI, climate moisture index; WTD, water table depth; Soil C/N, soil carbon to nitrogen ratio; and FD, functional diversity.

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Extended Data Fig. 4 Environmental context-dependent responses of soil carbon stocks to functional diversity and functional identity (CWMPC1, CWMPC2, and CWMPC3).

Partial regression plots illustrating the significant interactive effects on soil carbon stocks across the entire soil profile (0–100 cm, a–c), topsoil layer (0–30 cm, d–i), and subsoil layer (30–100 cm, j–m), with bootstrapped 95% confidence intervals shaded in the corresponding colors, as fitted by the most parsimonious linear mixed-effects models (Supplementary Tables 5–7). F values, denominator degrees of freedom (df), and significance (two-sided P value) were obtained using Type III Wald F-tests with Satterthwaite’s method. For scaled MAT, CMI, WTD, soil Olsen P, and soil pH, the categories are defined as follows: low (one standard deviation (SD) below the mean, -1), moderate (mean value, 0), and high (one SD above the mean, 1). Sample sizes (n) are 1,221 (topsoil) and 814 (subsoil and entire) for MAT, CMI, WTD, and soil pH; and 1,213 (topsoil) and 810 (subsoil and entire) for soil Olsen P. For anthropogenic stressors, the colors represent low, moderate, and high levels of stress intensity. Sample sizes (n = low/moderate/high) are as follows: 629/280/305 (topsoil) and 394/197/221 (subsoil and entire) for STR_NNP; 354/465/395 (topsoil) and 226/315/271 (subsoil and entire) for STR_PALT; and 1,081/68/65 (topsoil) and 722/48/42 (subsoil and entire) for STR_HM. WTD, water table depth; Soil Olsen P, soil Olsen phosphorus; STR_NNP, non-native plants stress; STR_PALT, physical alteration stress; STR_HM, heavy metal stress; and FD, functional diversity.

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Extended Data Fig. 5 Fixed effects of functional diversity and functional identity (CWMPC1, CWMPC2, and CWMPC3) on soil carbon stocks in the surface soil layer (0–10 cm).

The green dots represent the values predicted by partial regression with each explanatory variable (a), the black lines represent the mean values, and the shaded areas represent the 95% confidence intervals fitted by the linear mixed-effects model (full model, n = 1,257). F values, denominator degrees of freedom (df), and significance (two-sided P values) were obtained using Type III Wald F-tests with Satterthwaite’s method. The bar plots (b) are standardized estimates (mean and 95% confidence intervals) of predictor variable effects on soil carbon stocks derived from the most parsimonious model using two-sided t-based Wald tests. Marginal and conditional R2 values are given. MAT, mean annual temperature; CMI, climate moisture index; WTD, water table depth; Plant cover, total plant cover; and FD, functional diversity.

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Extended Data Fig. 6 Correlations between species richness and functional diversity and functional identity (CWMPC1, CWMPC2, and CWMPC3).

The solid blue lines indicate ordinary least squares (OLS) linear regression fits (n = 1,268). The shaded bands represent the 95% confidence intervals of the regression predictions. R2 values and significance levels (P values, two-sided t-tests) are given. FD, functional diversity.

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Extended Data Fig. 7 Environmentally mediated effects of functional diversity and functional identity (CWMPC1, CWMPC2, and CWMPC3) on productivity, root area density, and the soil C/N ratio.

Standardized coefficients from linear mixed-effects models showing the interactive effects of functional characteristics and different environmental factors on productivity, root area density, and the soil C/N ratio. The colors of the dots indicate different levels of environmental factors. For scaled MAT, CMI, WTD, soil Olsen P, and soil pH, the categories are defined as follows: low (one standard deviation (SD) below the mean, -1), moderate (mean value, 0), and high (one SD above the mean, 1). Sample sizes (n) are 1,257 (productivity), 1,171 (root area density), and 1,246 (soil C/N ratio) for MAT, CMI, WTD, and soil pH; and 1,249 (productivity), 1,163 (root area density), and 1,238 (soil C/N ratio) for soil Olsen P. For anthropogenic stressors, the colors represent low, moderate, and high levels of stress intensity. Sample sizes (n = low/moderate/high) are as follows: 655/285/310 (productivity), 597/275/294 (root area density), and 650/283/306 (soil C/N ratio) for STR_NNP; 364/480/406 (productivity), 333/447/386 (root area density), and 363/477/399 (soil C/N ratio) for STR_PALT; and 1,109/70/71 (productivity), 1,037/66/63 (root area density), and 1,098/70/71 (soil C/N ratio) for STR_HM. The mean values and 95% confidence intervals of the parameter estimate for each predictor are shown. Filled and empty symbols indicate significant (P < 0.05) and nonsignificant interactive effects between environmental factors and functional characteristics, respectively, based on two-sided Type III Wald F-tests with Satterthwaite’s method. MAT, mean annual temperature; CMI, climate moisture index; WTD, water table depth. Soil Olsen P, soil Olsen phosphorus; Soil C/N, soil carbon to nitrogen ratio; STR_NNP, non-native plant stress; STR_PALT, physical alteration stress; STR_HM, heavy metal stress; and FD, functional diversity.

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Liu, H., Li, J., Wu, J. et al. Large slow-growing hydrophytes increase wetland carbon storage. Nat. Plants 12, 294–307 (2026). https://doi.org/10.1038/s41477-026-02221-y

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