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Dataset
Data for: Are ghost forests a substantial source of methane from reservoirs?
Mendeley Data
14/11/2025
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Abstract
Methane (CH4) is a potent greenhouse gas that is increasing in the atmosphere, being a major driver of climate change. Tree stem CH4 emissions are a rapidly advancing research field however emissions from dead trees remain poorly studied. This is of particular concern in reservoir "ghost forests", where large areas of standing dead trees can form, and remain submerged in CH4-enriched waters, providing a potential CH4-flux pathway along the soil-tree-atmosphere continuum, for many decades. This study quantified the drivers of seasonal CH4 and carbon dioxide (CO2) emissions from a ghost forest within a subtropical reservoir alongside diffusive and ebullition fluxes, across two seasons. We compared the influence of sediment organic carbon, water level and temperature fluctuations on ghost forest stem CH4 emissions, to the diffusive and ebullition flux pathways, at three within reservoir sites (North, Mid and South). The highest ghost forest CH4 fluxes occurred near the reservoir inflow site (South) during summer (1172 ± 338 µmol m-2 stem d-1). At the same location the average CH4 fluxes from ghost forest stems and ebullition were significantly higher, 5.8 and 2.7 respectfully, during summer, compared to winter. Ghost forest CH4 fluxes contributed an additional ~15% to the overall reservoir greenhouse gas budget, beyond conventional methods which generally only consider ebullition and diffusive flux pathways. Our findings reveal the need to recognise ghost forest CH4 emissions from reservoirs and encourage management strategies to balance CH4 mitigation with other ecological benefits of standing ghost forests. This data comprises of all the soil, aquatic, ebullition and tree stem methane fluxes recorded over two seasons.
Details
- Title
- Data for: Are ghost forests a substantial source of methane from reservoirs?
- Creators
- Johannes Dittmann - Southern Cross University
- Publisher
- Mendeley Data
- Identifiers
- 991013390052302368
- Copyright
- All content on this site: Copyright © 2026 Elsevier inc, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training and similar technologies. For all open access content, the Creative Commons licensing terms apply.
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Dataset