Preprints
https://doi.org/10.5194/bg-2021-255
https://doi.org/10.5194/bg-2021-255

  14 Oct 2021

14 Oct 2021

Review status: this preprint is currently under review for the journal BG.

Predicting mangrove forest dynamics across a soil salinity gradient using an individual-based vegetation model linked with plant hydraulics

Masaya Yoshikai1, Takashi Nakamura1, Rempei Suwa2, Sahadev Sharma3, Rene Rollon4, Jun Yasuoka5, Ryohei Egawa5, and Kazuo Nadaoka1 Masaya Yoshikai et al.
  • 1School of Environment and Society, Tokyo Institute of Technology, Tokyo, 152-8552, Japan
  • 2Forestry Division, Japan International Research Center for Agricultural Sciences (JIRCAS), Ibaraki, 305-8686, Japan
  • 3Institute of Ocean and Earth Sciences, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
  • 4Institute of Environmental Science & Meteorology, College of Science, University of the Philippines, Diliman, Quezon City, 1001, Philippines
  • 5Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo, 152-8552, Japan

Abstract. In mangrove forests, soil salinity is one of the most significant environmental factors determining mangrove forest distribution and productivity as it limits plant water uptake and carbon gain. However, salinity control on mangrove productivity through plant hydraulics has not been investigated by existing mangrove models. Thus, we present a new individual-based model linked with plant hydraulics to incorporate physiological characterization of mangrove growth under salt stress. Plant hydraulics was associated with mangroves nutrient uptake and biomass allocation apart from water flux and carbon gain. The developed model was performed for two-coexisting species of Rhizophora stylosa and Bruguiera gymnorrhiza in a subtropical mangrove forest in Japan. The model predicted that the productivity of both species was affected by soil salinity through downregulation of stomatal conductance, while B. gymnorrhiza trees grow faster and suppress the growth of R. stylosa trees by shading that resulted in a B. gymnorrhiza-dominated forest under low soil salinity conditions (< 28 ‰). Alternatively, the increase in soil salinity significantly reduced the productivity of B. gymnorrhiza compared to R. stylosa, leading to an increase in biomass of R. stylosa despite the enhanced salt stress (> 30 ‰). These predicted patterns in forest structures across soil salinity gradient remarkably agreed with field data, highlighting the control of salinity on productivity and tree competition as factors that shape the mangrove forest structures. The model reproducibility of forest structures was also supported by the predicted self-thinning processes, which likewise agreed with field data. In addition, the mangroves morphological adjustment to increasing soil salinity – by decreasing transpiration and increasing hydraulic conductance – was reasonably predicted. Aside from the soil salinity, seasonal dynamics in atmospheric variables (solar radiation and temperature) was highlighted as factors influencing mangrove productivity in a subtropical region. The physiological principle-based improved model has the potential to be extended to other mangrove forests in various environmental settings, thus contributing to a better understanding of mangrove dynamics under future global climate change.

Masaya Yoshikai et al.

Status: open (until 25 Nov 2021)

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Masaya Yoshikai et al.

Masaya Yoshikai et al.

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Short summary
This study presents a new individual-based vegetation model to investigate salinity control on mangrove productivity. The model incorporates plant hydraulics and tree competition and predicted unique and complex patterns of mangrove forest structures that varies across soil salinity gradient. The presented model does not hold an empirical expression of salinity influence on productivity, and thus may provide a better understanding of mangrove forest dynamics in future climate change.
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