Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Stronger increases but greater variability in global mangrove productivity compared to that of adjacent terrestrial forests

Nature Ecology & Evolution volume 8, pages 239–250 (2024)Cite this article

Subjects

Abstract

Mangrove forests are a highly productive ecosystem with important potential to offset anthropogenic greenhouse gas emissions. Mangroves are expected to respond differently to climate change compared to terrestrial forests owing to their location in the tidal environment and unique ecophysiological characteristics, but the magnitude of difference remains uncertain at the global scale. Here we use satellite observations to examine mean trends and interannual variability in the productivity of global mangrove forests and nearby terrestrial evergreen broadleaf forests from 2001 to 2020. Although both types of ecosystem experienced significant recent increases in productivity, mangroves exhibited a stronger increasing trend and greater interannual variability in productivity than evergreen broadleaf forests on three-quarters of their co-occurring coasts. The difference in productivity trends is attributed to the stronger CO2 fertilization effect on mangrove photosynthesis, while the discrepancy in interannual variability is attributed to the higher sensitivities to variations in precipitation and sea level. Our results indicate that mangroves will have a faster increase in productivity than terrestrial forests in a CO2-rich future but may suffer more from deficits in water availability, highlighting a key difference between terrestrial and tidal ecosystems in their responses to climate change.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Changes in NIRv during 2001–2020 for mangroves and EBFs at the global scale.
Fig. 2: Comparisons in NIRv trends and IAV between mangroves and EBFs.
Fig. 3: Contribution of climatic factors and sea level to NIRv interannual variability.
Fig. 4: Contribution of climatic factors and sea level to NIRv trend.

Similar content being viewed by others

Data availability

All data used in this study are publicly available. The MODIS 250 m spectral reflectance data (MOD13Q1 and MYD13Q1) are available at https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD13Q1 and https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MYD13Q1. Gridded climate data used in this study are available in Supplementary Table 2. Forest cover data can be found at the following websites: Global Mangrove Watch v.3.0 (https://zenodo.org/record/6894273), MCD12Q1 land cover product (https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1) and global forest change map (https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2021_v1_9). ECOSTRESS evapotranspiration data can be accessed at https://www.jpl.nasa.gov/missions/ecosystem-spaceborne-thermal-radiometer-experiment-on-space-station-ecostress. Atmospheric CO2 concentration recorded by the Mauna Loa Observatory can be accessed at https://gml.noaa.gov/ccgg/trends/data.html. The GPP measurements in the three mangrove sites are available in Supplementary Table 1.

Code availability

The code used to analyse these data and generate the results presented in this study can be obtained from https://github.com/GIS-ZhangZhen/MangroveGreenness.

References

  1. Bunting, P. et al. Global mangrove extent change 1996–2020: Global Mangrove Watch version 3.0. Remote Sens. 14, 3657 (2022).

    Article  ADS  Google Scholar 

  2. Lovelock, C. E. & Reef, R. Variable impacts of climate change on blue carbon. One Earth 3, 195–211 (2020).

    Article  ADS  Google Scholar 

  3. Lee, S. Y. et al. Ecological role and services of tropical mangrove ecosystems: a reassessment. Glob. Ecol. Biogeogr. 23, 726–743 (2014).

    Article  Google Scholar 

  4. Taillardat, P., Friess, D. A. & Lupascu, M. Mangrove blue carbon strategies for climate change mitigation are most effective at the national scale. Biol. Lett. 14, 20180251 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Donato, D. C. et al. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 4, 293–297 (2011).

    Article  ADS  CAS  Google Scholar 

  6. Friess, D. A., Adame, M. F., Adams, J. B. & Lovelock, C. E. Mangrove forests under climate change in a 2 °C world. Wiley Interdiscip. Rev. Clim. Change 13, e792 (2022).

    Article  Google Scholar 

  7. Dahdouh-Guebas, F. et al. Cross-cutting research themes for future mangrove forest research. Nat. Plants 8, 1131–1135 (2022).

    Article  PubMed  Google Scholar 

  8. Duke, N. C. et al. Large-scale dieback of mangroves in Australia. Mar. Freshw. Res. 68, 1816 (2017).

    Article  Google Scholar 

  9. Saintilan, N. et al. The lunar nodal cycle controls mangrove canopy cover on the Australian continent. Sci. Adv. 8, eabo6602 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ruehr, S. et al. Evidence and attribution of the enhanced land carbon sink. Nat. Rev. Earth Environ. 4, 518–534 (2023).

    Article  ADS  CAS  Google Scholar 

  11. Chen, C., Riley, W. J., Prentice, I. C. & Keenan, T. F. CO2 fertilization of terrestrial photosynthesis inferred from site to global scales. Proc. Natl Acad. Sci. USA 119, e2115627119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).

    Article  ADS  Google Scholar 

  13. Ball, M. C. in Tropical Forest Plant Ecophysiology (eds Mulkey, S. S. et al.) 461–496 (Springer, 1996).

  14. Lovelock, C. E., Krauss, K. W., Osland, M. J., Reef, R. & Ball, M. C. in Tropical Tree Physiology Vol. 6 (eds Goldstein, G. & Santiago, L.) 149–179 (Springer, 2016).

  15. Cui, X. et al. Stronger ecosystem carbon sequestration potential of mangrove wetlands with respect to terrestrial forests in subtropical China. Agric. For. Meteorol. 249, 71–80 (2018).

    Article  ADS  Google Scholar 

  16. Naskar, S. & Palit, P. K. Anatomical and physiological adaptations of mangroves. Wetl. Ecol. Manag. 23, 357–370 (2015).

    Article  Google Scholar 

  17. Srikanth, S., Lum, S. K. Y. & Chen, Z. Mangrove root: adaptations and ecological importance. Trees 30, 451–465 (2016).

    Article  Google Scholar 

  18. Liang, J. et al. Evapotranspiration characteristics distinct to mangrove ecosystems are revealed by multiple‐site observations and a modified two‐source model. Water Resour. Res. 55, 11250–11273 (2019).

    Article  ADS  Google Scholar 

  19. Sperry, J. S., Tyree, M. T. & Donnelly, J. R. Vulnerability of xylem to embolism in a mangrove vs an inland species of Rhizophoraceae. Physiol. Plant. 74, 276–283 (1988).

    Article  Google Scholar 

  20. Kumar, D. & Scheiter, S. Biome diversity in South Asia—how can we improve vegetation models to understand global change impact at regional level? Sci. Total Environ. 671, 1001–1016 (2019).

    Article  ADS  CAS  Google Scholar 

  21. Ward, N. D. et al. Representing the function and sensitivity of coastal interfaces in Earth system models. Nat. Commun. 11, 2458 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  22. LaFond-Hudson, S. & Sulman, B. Modeling strategies and data needs for representing coastal wetland vegetation in land surface models. New Phytol. 238, 938–951 (2023).

    Article  PubMed  Google Scholar 

  23. Badgley, G., Field, C. B. & Berry, J. A. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv. 3, e1602244 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  24. Zeng, Y. et al. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nat. Rev. Earth Environ. 3, 477–493 (2022).

    Article  ADS  Google Scholar 

  25. Wang, S. et al. Recent global decline of CO2 fertilization effects on vegetation photosynthesis. Science 370, 1295–1300 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  26. Saintilan, N., Wilson, N. C., Rogers, K., Rajkaran, A. & Krauss, K. W. Mangrove expansion and salt marsh decline at mangrove poleward limits. Glob. Change Biol. 20, 147–157 (2014).

    Article  ADS  Google Scholar 

  27. Cavanaugh, K. C. et al. Climate-driven regime shifts in a mangrove–salt marsh ecotone over the past 250 years. Proc. Natl Acad. Sci. USA 116, 21602–21608 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Good, S. P., Moore, G. W. & Miralles, D. G. A mesic maximum in biological water use demarcates biome sensitivity to aridity shifts. Nat. Ecol. Evol. 1, 1883–1888 (2017).

    Article  PubMed  Google Scholar 

  29. Modak, A. & Mauritsen, T. The 2000–2012 global warming hiatus more likely with a low climate sensitivity. Geophys. Res. Lett. 48, e2020GL091779 (2021).

    Article  ADS  Google Scholar 

  30. Ogle, K. et al. Quantifying ecological memory in plant and ecosystem processes. Ecol. Lett. 18, 221–235 (2015).

    Article  PubMed  Google Scholar 

  31. Ballantyne, A. et al. Accelerating net terrestrial carbon uptake during the warming hiatus due to reduced respiration. Nat. Clim. Change 7, 148–152 (2017).

    Article  ADS  CAS  Google Scholar 

  32. Peters, W. et al. An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker. Proc. Natl Acad. Sci. USA 104, 18925–18930 (2007).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  33. Adler, R. F., Gu, G., Sapiano, M., Wang, J.-J. & Huffman, G. J. Global precipitation: means, variations and trends during the satellite era (1979–2014). Surv. Geophys. 38, 679–699 (2017).

    Article  ADS  Google Scholar 

  34. Jacotot, A., Marchand, C., Gensous, S. & Allenbach, M. Effects of elevated atmospheric CO2 and increased tidal flooding on leaf gas-exchange parameters of two common mangrove species: Avicennia marina and Rhizophora stylosa. Photosynth. Res. 138, 249–260 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Ruan, L., Yan, M., Zhang, L., Fan, X. & Yang, H. Spatial-temporal NDVI pattern of global mangroves: a growing trend during 2000–2018. Sci. Total Environ. 844, 157075 (2022).

    Article  ADS  CAS  PubMed  Google Scholar 

  36. Chapman, S. K. et al. Mangrove growth response to experimental warming is greatest near the range limit in northeast Florida. Ecology 102, e03320 (2021).

    Article  PubMed  Google Scholar 

  37. Cavanaugh, K. C. et al. Sensitivity of mangrove range limits to climate variability. Glob. Ecol. Biogeogr. 27, 925–935 (2018).

    Article  Google Scholar 

  38. Yao, Q. et al. Mangrove expansion at poleward range limits in North and South America: Late-Holocene climate variability or anthropocene global warming? Catena 216, 106413 (2022).

    Article  Google Scholar 

  39. Saintilan, N. & Rogers, K. Woody plant encroachment of grasslands: a comparison of terrestrial and wetland settings. New Phytol. 205, 1062–1070 (2015).

    Article  PubMed  Google Scholar 

  40. Gu, X. et al. Changes in mangrove blue carbon under elevated atmospheric CO2. Ecosyst. Health Sustain. 9, 0033 (2023).

    Article  Google Scholar 

  41. Ainsworth, E. A. & Rogers, A. The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions. Plant Cell Environ. 30, 258–270 (2007).

    Article  CAS  PubMed  Google Scholar 

  42. Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372 (2005).

    Article  PubMed  Google Scholar 

  43. Piao, S. et al. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Glob. Change Biol. 19, 2117–2132 (2013).

    Article  ADS  Google Scholar 

  44. Terrer, C., Vicca, S., Hungate, B. A., Phillips, R. P. & Prentice, I. C. Mycorrhizal association as a primary control of the CO2 fertilization effect. Science 353, 72–74 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  45. Pan, Y. et al. Contrasting responses of woody and grassland ecosystems to increased CO2 as water supply varies. Nat. Ecol. Evol. 6, 315–323 (2022).

    Article  PubMed  Google Scholar 

  46. Drake, B. G., Gonzàlez-Meler, M. A. & Long, S. P. More efficient plants: a consequence of rising atmospheric CO2? Annu. Rev. Plant Physiol. Plant Mol. Biol. 48, 609–639 (1997).

    Article  CAS  PubMed  Google Scholar 

  47. Krauss, K. W. et al. Mangroves provide blue carbon ecological value at a low freshwater cost. Sci. Rep. 12, 17636 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  48. Maschler, J. et al. Links across ecological scales: plant biomass responses to elevated CO2. Glob. Change Biol. 28, 6115–6134 (2022).

    Article  CAS  Google Scholar 

  49. Hayes, M. A. et al. Foliar water uptake by coastal wetland plants: a novel water acquisition mechanism in arid and humid subtropical mangroves. J. Ecol. 108, 2625–2637 (2020).

    Article  CAS  Google Scholar 

  50. Fisher, J. B. et al. ECOSTRESS: NASA’s next generation mission to measure evapotranspiration from the International Space Station. Water Resour. Res. 56, e2019WR026058 (2020).

    Article  ADS  Google Scholar 

  51. Lagomasino, D. et al. Storm surge and ponding explain mangrove dieback in southwest Florida following Hurricane Irma. Nat. Commun. 12, 4003 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  52. Abhik, S. et al. Influence of the 2015–2016 El Niño on the record-breaking mangrove dieback along northern Australia coast. Sci. Rep. 11, 20411 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yim, M. W. & Tam, N. F. Y. Effects of wastewater-borne heavy metals on mangrove plants and soil microbial activities. Mar. Pollut. Bull. 39, 8 (1999).

    Article  Google Scholar 

  54. Passioura, J. B., Ball, M. C. & Knight, J. H. Mangroves may salinize the soil and in so doing limit their transpiration rate. Funct. Ecol. 6, 476 (1992).

    Article  Google Scholar 

  55. Ball, M. C. Ecophysiology of mangroves. Trees 2, 129–142 (1988).

    Article  Google Scholar 

  56. Zhang, Y. et al. Increasing sensitivity of dryland vegetation greenness to precipitation due to rising atmospheric CO2. Nat. Commun. 13, 4875 (2022).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015).

    Article  ADS  PubMed  Google Scholar 

  58. Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  59. Huang, K. & Xia, J. High ecosystem stability of evergreen broadleaf forests under severe droughts. Glob. Change Biol. 25, 3494–3503 (2019).

    Article  ADS  Google Scholar 

  60. Zhu, X., Sun, C. & Qin, Z. Drought‐induced salinity enhancement weakens mangrove greenhouse gas cycling. J. Geophys. Res. Biogeosci. 126, e2021JG006416 (2021).

    Article  ADS  CAS  Google Scholar 

  61. Méndez-Alonzo, R., López-Portillo, J., Moctezuma, C., Bartlett, M. K. & Sack, L. Osmotic and hydraulic adjustment of mangrove saplings to extreme salinity. Tree Physiol. 36, 1562–1572 (2016).

    Article  PubMed  Google Scholar 

  62. Hoppe-Speer, S. C. L., Adams, J. B., Rajkaran, A. & Bailey, D. The response of the red mangrove Rhizophora mucronata Lam. to salinity and inundation in South Africa. Aquat. Bot. 95, 71–76 (2011).

    Article  Google Scholar 

  63. Reef, R., Feller, I. C. & Lovelock, C. E. Nutrition of mangroves. Tree Physiol. 30, 1148–1160 (2010).

    Article  CAS  PubMed  Google Scholar 

  64. Anderegg, W. R. L. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).

    Article  ADS  CAS  PubMed  Google Scholar 

  65. Liu, D., Wang, T., Peñuelas, J. & Piao, S. Drought resistance enhanced by tree species diversity in global forests. Nat. Geosci. 15, 800–804 (2022).

    Article  ADS  CAS  Google Scholar 

  66. Hochard, J. P., Hamilton, S. & Barbier, E. B. Mangroves shelter coastal economic activity from cyclones. Proc. Natl Acad. Sci. USA 116, 12232–12237 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  67. Lovelock, C. E., Feller, I. C., Reef, R., Hickey, S. & Ball, M. C. Mangrove dieback during fluctuating sea levels. Sci. Rep. 7, 1680 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  68. Wang, X. et al. Rebound in China’s coastal wetlands following conservation and restoration. Nat. Sustain. 4, 1076–1083 (2021).

    Article  Google Scholar 

  69. Mengistu, A. G. et al. Sun-induced fluorescence and near-infrared reflectance of vegetation track the seasonal dynamics of gross primary production over Africa. Biogeosciences 18, 2843–2857 (2021).

    Article  ADS  CAS  Google Scholar 

  70. Zhang, J. et al. NIRv and SIF better estimate phenology than NDVI and EVI: effects of spring and autumn phenology on ecosystem production of planted forests. Agric. For. Meteorol. 315, 108819 (2022).

    Article  Google Scholar 

  71. Barr, J. G. et al. Controls on mangrove forest–atmosphere carbon dioxide exchanges in western Everglades National Park. J. Geophys. Res. Biogeosci. 115, G02020 (2010).

    Article  ADS  Google Scholar 

  72. Zhu, X. et al. Potential of sun-induced chlorophyll fluorescence for indicating mangrove canopy photosynthesis. J. Geophys. Res. Biogeosci. 126, e2020JG006159 (2021).

    Article  ADS  CAS  Google Scholar 

  73. Liu, J., Valach, A., Baldocchi, D. & Lai, D. Y. F. Biophysical controls of ecosystem‐scale methane fluxes from a subtropical estuarine mangrove: multiscale, nonlinearity, asynchrony and causality. Glob. Biogeochem. Cycles 36, e2021GB007179 (2022).

    Article  ADS  CAS  Google Scholar 

  74. Feagin, R. A. et al. Tidal wetland gross primary production across the continental United States, 2000–2019. Glob. Biogeochem. Cycles 34, e2019GB006349 (2020).

    Article  ADS  CAS  Google Scholar 

  75. Rienecker, M. M. et al. MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Clim. 24, 3624–3648 (2011).

    Article  ADS  Google Scholar 

  76. Du, J. et al. Global satellite retrievals of the near-surface atmospheric vapor pressure deficit from AMSR-E and AMSR2. Remote Sens. 10, 1175 (2018).

    Article  ADS  Google Scholar 

  77. Huffman, G. J. et al. The Global Precipitation Climatology Project (GPCP) combined precipitation dataset. Bull. Am. Meteorol. Soc. 78, 5–20 (1997).

    Article  ADS  Google Scholar 

  78. Osland, M. J. et al. Climatic controls on the global distribution, abundance, and species richness of mangrove forests. Ecol. Monogr. 87, 341–359 (2017).

    Article  Google Scholar 

  79. Rovai, A. S. et al. Macroecological patterns of forest structure and allometric scaling in mangrove forests. Glob. Ecol. Biogeogr. 30, 1000–1013 (2021).

    Article  Google Scholar 

  80. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article  ADS  Google Scholar 

  81. Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  83. Keeling, C. D. et al. Atmospheric carbon dioxide variations at Mauna Loa Observatory, Hawaii. Tellus 28, 538–551 (1976).

    Article  ADS  CAS  Google Scholar 

  84. Chen, Y. & Kirwan, M. L. Climate-driven decoupling of wetland and upland biomass trends on the mid-Atlantic coast. Nat. Geosci. 15, 913–918 (2022).

    Article  ADS  CAS  Google Scholar 

  85. Zhou, L. et al. Widespread decline of Congo rainforest greenness in the past decade. Nature 509, 86–90 (2014).

    Article  ADS  CAS  PubMed  Google Scholar 

  86. Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P. & Friedl, M. A. Hierarchical mapping of annual global land cover 2001 to present: the MODIS Collection 6 Land Cover product. Remote Sens. Environ. 222, 183–194 (2019).

    Article  ADS  Google Scholar 

  87. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article  ADS  CAS  PubMed  Google Scholar 

  88. Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11, 4621 (2020).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  89. Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Winkler, A. J. et al. Slowdown of the greening trend in natural vegetation with further rise in atmospheric CO2. Biogeosciences 18, 4985–5010 (2021).

    Article  ADS  CAS  Google Scholar 

  91. Cortés, J. et al. Where are global vegetation greening and browning trends significant? Geophys. Res. Lett. 48, e2020GL091496 (2021).

    Article  ADS  Google Scholar 

  92. Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  93. Kirwan, M. L. & Gedan, K. B. Sea-level driven land conversion and the formation of ghost forests. Nat. Clim. Change 9, 450–457 (2019).

    Article  ADS  Google Scholar 

  94. Medhaug, I., Stolpe, M. B., Fischer, E. M. & Knutti, R. Reconciling controversies about the ‘global warming hiatus’. Nature 545, 41–47 (2017).

    Article  ADS  CAS  PubMed  Google Scholar 

  95. Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).

    Article  ADS  CAS  PubMed  Google Scholar 

  96. Perri, S., Katul, G. G. & Molini, A. Xylem–phloem hydraulic coupling explains multiple osmoregulatory responses to salt stress. New Phytol. 224, 644–662 (2019).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Yangfan Li is the main corresponding author of the study. Yangfan Li and Z.Z. acknowledge support from the National Natural Science Foundation of China (Grant No. 42276232), the Internal Program of State Key Laboratory of Marine Environmental Science (Grant No. MELRI2205) and the China Scholarship Council (Grant No. 202106310079). X.L. acknowledges support from the Singapore Ministry of Education (Grant No. A-0003625-00-00) and the Singapore Energy Center core project (Grant No. A-8000179-00-00). D.A.F. thanks Michael and Mathilda Cochran for endowing the Cochran Family Professorship in Earth and Environmental Sciences at Tulane University. We thank N. Xu at Hohai University for his feedback on an earlier version of this work.

Author information

Authors and Affiliations

  1. State Key Laboratory of Marine Environmental Science, Key Laboratory of Coastal and Wetland Ecosystems (Ministry of Education), College of the Environment and Ecology, Xiamen University, Xiamen, China

    Zhen Zhang, Yi Li & Yangfan Li

  2. Department of Geography, National University of Singapore, Singapore, Singapore

    Zhen Zhang & Xiangzhong Luo

  3. Center for Nature-Based Climate Solutions, National University of Singapore, Singapore, Singapore

    Xiangzhong Luo

  4. Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA, USA

    Daniel A. Friess

  5. Jiangsu Collaborative Innovation Center for Modern Crop Production/Key Laboratory of Crop Physiology and Ecology in Southern China, Nanjing Agricultural University, Nanjing, China

    Songhan Wang

Authors
  1. Zhen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiangzhong Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daniel A. Friess
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Songhan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yangfan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z.Z. conceptualized the study. Z.Z., X.L. and Yangfan Li designed the research. Z.Z. performed the analysis and drafted the initial manuscript. X.L. substantially revised the paper. D.A.F., S.W., Yi Li and Yangfan Li contributed to result interpretation and made substantial contributions to manuscript refinement.

Corresponding authors

Correspondence to Xiangzhong Luo or Yangfan Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Lola Fatoyinbo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 The correlation between NIRv and GPP at three mangrove flux sites.

The red lines give the fitted mean linear relationship between NIRv and GPP. Shading indicates the 95% confidence intervals estimated by bootstrapping (n = 1000). P values were determined through two-sided Pearson’s correlation significance test.

Extended Data Fig. 2 Temporal variations in NIRv of mangroves and EBFs and environmental factors in coastal grids.

a-e, Time variations in annual NIRv for mangroves and EBFs over the globe, America, Africa, Asia, and Oceania, respectively. NIRv are normalized by the long-term average. The dashed lines give the overall linear trend. The trend rates in the legend were computed from the Theil-Sen slope estimator. P values were determined through two-sided Mann-Kendall trend test. Shading indicates the 95% confidence intervals estimated by bootstrapping (n = 1000). f-j, Interannual fluctuances in annual detrended NIRv for mangroves and EBFs over the globe, America, Africa, Asia, and Oceania, respectively. The numbers in the legend indicate the coefficient of variation of each NIRv time series to reflect the interannual variability. k-o, Time variations in annual temperature, precipitation and sea-level anomaly for the coastal grids over the globe, America, Africa, Asia, and Oceania, respectively.

Extended Data Fig. 3 Time series of annual NIRv (a) and environmental factors (air temperature, precipitation, and sea-level anomaly) (b) in the Gulf of Carpentaria, Australia.

Temperature, precipitation and sea-level anomaly is from MERRA2, GPCP, and CMEMS datasets, respectively. Shaded areas show ±1 standard deviation of the mean.

Extended Data Fig. 4 The comparisons between mangroves and EBFs in their ecohydrological properties.

Differences in marginal biological water use fraction (∂Tc/∂P) (a) and marginal water use efficiency (MWUE) (b). All comparisons were performed under controlled geographical conditions using the two-sided paired t-test to eliminate spatial mismatch. Error bars show 95% confidence intervals estimated by bootstrapping (n = 1000), and the dots represent the average values.

Extended Data Fig. 5 eCO2-induced NIRv trends calculated using factorial simulation.

The right panels depict the latitudinal pattern of trends averaged per 1° latitude band.

Extended Data Fig. 6 Simulated NIRv trends for mangroves and EBFs, respectively.

a, Temperature-contributed NIRv trends. b, VPD-contributed NIRv trends. The right panels depict the latitudinal pattern of trends averaged per 1° latitude band.

Extended Data Fig. 7 Model performance in simulating observed difference in NIRv IAV and trend between mangroves and EBFs.

a,c, Comparison of ΔIAV between observed and simulated from climate forcing data 1 (a) and climate forcing data 2 (c). b,d, Comparison of Δtrend between observed and simulated from climate forcing data 1 (b) and climate forcing data 2 (d). Climate forcing data 1 represents factors from MERRA2, GPCP, and NOAA CarbonTracker CT2022 datasets. Climate forcing data 2 represents factors from ERA5, CHIRPS, TerraClimate and Mauna Loa observatory. Scatter refers to each paired coastal grid cell (n = 1475). The red shaded areas show 95% confidence intervals for the regression fits. P values were determined through one-sided F-test.

Supplementary information

Supplementary Information

Supplementary Figs. 1–12 and Tables 1–3.

Reporting Summary

Peer Review File

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., Luo, X., Friess, D.A. et al. Stronger increases but greater variability in global mangrove productivity compared to that of adjacent terrestrial forests. Nat Ecol Evol 8, 239–250 (2024). https://doi.org/10.1038/s41559-023-02264-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-023-02264-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing