Dataset - Seagrass loss alters sediment carbon trajectories in tropical sediments

This Zenodo record provides the dataset supporting the manuscript “Seagrass loss alters sediment carbon trajectories in tropical sediments.” The dataset integrates newly collected field data from 2025 with harmonized variables derived from previously published studies across nine tropical seagrass meadows in Thailand.

The dataset includes two Excel files:

1. Carbon content.xlsx
This file contains sediment property measurements used in the analysis, including:

• Dry bulk density (DBD)
• Organic matter content (OM)
• Organic carbon content (OC)
• Depth-resolved sediment measurements
• Site, year, and sampling identifiers

These data were used to assess vertical sediment structure and temporal changes in sediment properties across seagrass loss and recovery conditions.

2. Stocks.xlsx
This file contains sediment organic carbon stock estimates, including:

• Whole-core sediment carbon stocks
• Surface sediment carbon stocks (top 20 cm)
• Replicate-level and site-level summaries
• Temporal comparisons across sampling years

These data were used to quantify changes in sediment carbon storage and to derive annualized carbon stock changes and inferred CO₂-equivalent values.

Data sources and integration

The dataset combines:

• Newly collected field data from 2025 (this study)
• Historical datasets compiled from previously published studies

All variables were harmonized to ensure consistency in units, depth intervals, and calculation methods across sites and sampling years. Historical datasets are not redistributed as standalone original files; users should refer to the original publications cited in the associated manuscript for full methodological details and data provenance.

Usage notes

• This dataset is provided to support reproducibility of the analyses presented in the manuscript.
• The repository contains data only; analytical scripts are not included.
• Users should consider site-specific conditions, sampling design, and temporal variability when interpreting the data.
• Historical components of the dataset originate from previously published studies and should be cited accordingly.