Post-fire vegetation recovery modulated by burn severity and environmental variables
Global forests are undergoing increasing exposure to wildfires with high frequency and large magnitude under climate change, leading to profound disturbances to ecosystem structure and functionality. However, post-fire vegetation recovery and its linkage to variations of topography, soil and burn severity remains insufficiently understood. In this study, we examined post-fire vegetation recovery and explored their relationships with burn severity and habitat condition based on field surveys and multivariate analyses. It was found that post-fire plant communities were dominated by resprouting woody species and ferns. Species diversity in burned plots was positively related to recovery level but was still lower than in unburned plots (p < 0.01). The biomass allocation was gradually changed from belowground to aboveground components. Belowground biomass (BGB) accounted for 69% of the total biomass in Level I (poor recovery), while it increased to 52% in Level III (good recovery). Burn severity (BS) impaired soil structure and reduced carbon availability, while soil organic carbon (SOC) and available phosphorus (AP) promoted regrowth. Multivariate analyses revealed that aboveground biomass (AGB) was positively associated with SOC (p < 0.01), slope aspect, soil pH, and altitude (p < 0.05), but negatively with BS (p < 0.01). Species diversity was primarily driven by soil bulk density (BD), slope, and AGB (p < 0.05). Additionally, SOC showed positive effects on AGB in surface soils (0–15 cm), while it would inhibit both diversity and BGB in deep soils (15–30 cm). This study highlights the critical role of soil – vegetation feedbacks in shaping recovery trajectories of post-fire forests.
| Item Type | Article |
|---|---|
| Open Access | Not Open Access |
| Keywords | Wildfire disturbance, Vegetation regeneration, Species diversity, Biomass reallocation, Multivariate analysis |
| Teams | Farming Footprints and Adaptations |
| Project | Resilient Farming Futures (WP2): Detecting agroecosystem ‘resilience’ using novel data science methods |
| Date Deposited | 09 Mar 2026 09:44 |
| Last Modified | 09 Mar 2026 09:44 |
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picture_as_pdf - 1-s2.0-S2950509726000298-main.pdf
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subject - Accepted Version
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lock - Restricted to Registered users only
- Creative Commons Attribution
- Available under Creative Commons: Attribution 4.0

