Scholarlink

<p>Accurate global mangrove soil carbon estimates are essential, considering the critical role mangroves play in the coastal carbon cycle. However, current global estimates vary widely, ranging from 2.26 to 10.2 Pg C. Large uncertainties in carbon stocks can be challenging for effective policymaking and inaccurately estimate climate mitigation potential. Here, we identify factors driving spatial and landscape variability by comparing five global mangrove soil carbon stock models. Using the ensemble mean, we generate a global soil carbon stock estimate of 4.13 ± 0.89 Pg C (to 1&#xa0;m depth). Significant (<i>P</i> &lt; 0.001) spatial variability occurred in Africa and Asia, with high standard deviation between models and limited data representation. High standard deviation occurred in specific geomorphic settings, including terrigenous, deltaic, carbonate and open coasts, within microtidal sites and regions of low and high species diversity. Our findings can help guide future research and reduce uncertainties in carbon stock estimates.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatial and landscape-scale variability in global mangrove soil carbon estimates

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月9日

查看全文
  • Lucy Carruthers,
  • Lukas Lamb-Wotton,
  • Jaxine Wolfe,
  • David Lagomasino,
  • Stuart E. Hamilton

摘要

Accurate global mangrove soil carbon estimates are essential, considering the critical role mangroves play in the coastal carbon cycle. However, current global estimates vary widely, ranging from 2.26 to 10.2 Pg C. Large uncertainties in carbon stocks can be challenging for effective policymaking and inaccurately estimate climate mitigation potential. Here, we identify factors driving spatial and landscape variability by comparing five global mangrove soil carbon stock models. Using the ensemble mean, we generate a global soil carbon stock estimate of 4.13 ± 0.89 Pg C (to 1 m depth). Significant (P < 0.001) spatial variability occurred in Africa and Asia, with high standard deviation between models and limited data representation. High standard deviation occurred in specific geomorphic settings, including terrigenous, deltaic, carbonate and open coasts, within microtidal sites and regions of low and high species diversity. Our findings can help guide future research and reduce uncertainties in carbon stock estimates.