Global patterns and drivers of post-fire vegetation productivity recovery
Journal article, 2024

Wildfires cause critical shifts in ecosystem functions, such as dramatic reductions in vegetation productivity. However, how fast vegetation regains its pre-fire productivity levels and the key influencing factors remain poorly understood on a global scale. Here we present the global estimates of post-fire vegetation productivity recovery from 2004 to 2021 using gross primary productivity observations and related proxies at a spatial resolution of 10 km, employing a random forest model to identify the key factors influencing recovery time. Roughly 87% of burned vegetation regained pre-fire productivity levels within 2 years, with evergreen needleleaf forests and savannas displaying the lengthiest recovery periods. During the recovery phase, post-fire climate conditions, such as soil moisture, vapour pressure deficit and air temperature, had nonlinear impacts on recovery time globally. These climatic factors exhibited a dominant role in regional recovery time in ~89% of the globally assessed area. As climate aridity decreased, the areas where recovery time was dominated by soil moisture and vapour pressure deficit decreased, while the influence of temperature increased. Soil-moisture-dominated regions witnessed reduced proportions of promoting vegetation recovery as aridity decreased, whereas vapour pressure deficit and air-temperature-dominated regions saw an increase in such proportions. Regions with strong human interventions were associated with accelerated vegetation recovery compared with similar ecosystems with smaller human interventions. These findings had important implications for global carbon-cycle assessments and fire-management strategies.

Author

Hongtao Xu

Beijing Normal University

Hans Chen

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Deliang Chen

University of Gothenburg

Yingping Wang

Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Xu Yue

Nanjing University of Information Science and Technology

Bin He

Akesu National Station of Observation and Research for Oasis Agro-ecosystem

Beijing Normal University

Lanlan Guo

Beijing Normal University

Wenping Yuan

Beijing University of Technology

Ziqian Zhong

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Ling Huang

Beijing University of Technology

Fei Zheng

Chinese Academy of Sciences

Tiewei Li

Beijing Normal University

Xiangqi He

Beijing Normal University

Nature Geoscience

1752-0894 (ISSN) 1752-0908 (eISSN)

Vol. 17 9 874-881

Subject Categories

Ecology

DOI

10.1038/s41561-024-01520-3

More information

Latest update

9/21/2024