ArticleGlobal change biology2024
Recovery Following Recurrent Fires Across Mediterranean Ecosystems.
Article in Global change biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Enhanced autosuccession after wildfire in a transitional sub-Mediterranean forest ecosystem: evidence from the Kras Plateau (Slovenia).Frontiers in plant science · 2026Article
- Long-term recovery of canopy 3D structural diversity following wildfires in the world's largest temperate woodland.Proceedings. Biological sciences · 2025Article
- Hydroclimatic Rebound Drives Extreme Fire in California's Non-Forested Ecosystems.Global change biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
In fire-prone regions such as the Mediterranean biome, fire seasons are becoming longer, and fires are becoming more frequent and severe. Post-fire recovery dynamics is a key component of ecosystem resilience and stability. Even though Mediterranean ecosystems can tolerate high exposure to extreme temperatures and recover from fire, changes in climate conditions and fire intensity or frequency might contribute to loss of ecosystem resilience and increase the potential for irreversible changes in vegetation communities. In this study, we assess the recovery rates of burned vegetation after recurrent fires across Mediterranean regions globally, based on remotely sensed Enhanced Vegetation Index (EVI) data, a proxy for vegetation status, from 2001 to 2022. Recovery rates are quantified through a statistical model of EVI time-series. This approach allows resolving recovery dynamics in time and space, overcoming the limitations of space-for-time approaches typically used to study recovery dynamics through remote sensing. We focus on pixels burning repeatedly over the study period and evaluate how fire severity, pre-fire vegetation greenness, and post-fire climate conditions modulate vegetation recovery rates of different vegetation types. We detect large contrasts between recovery rates, mostly explained by regional differences in vegetation type. Particularly, needle-leaved forests tend to recover faster following the second event, contrasting with shrublands that tend to recover faster from the first event. Our results also show that fire severity can promote a faster recovery across forested ecosystems. An important modulating role of pre-fire fuel conditions on fire severity is also detected, with pixels with higher EVI before the fire resulting in stronger relative greenness loss. In addition, post-fire climate conditions, particularly air temperature and precipitation, were found to modulate recovery speed across all regions, highlighting how direct impacts of fire can compound with impacts from climate anomalies in time and likely destabilise ecosystems under changing climate conditions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.