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Summary Pyrogenic savannas with a tree–grassland ‘matrix’ experience frequent fires (i.e. every 1–3 yr). Aboveground responses to frequent fires have been well studied, but responses of fungal litter decomposers, which directly affect fuels, remain poorly known. We hypothesized that each fire reorganizes belowground communities and slows litter decomposition, thereby influencing savanna fuel dynamics.In a pine savanna, we established patches near and away from pines that were either burned or unburned in that year. Within patches, we assessed fungal communities and microbial decomposition of newly deposited litter. Soil variables and plant communities were also assessed as proximate drivers of fungal communities.Fungal communities, but not soil variables or vegetation, differed substantially between burned and unburned patches. Saprotrophic fungi dominated in unburned patches but decreased in richness and relative abundance after fire. Differences in fungal communities with fire were greater in litter than in soils, but unaffected by pine proximity. Litter decomposed more slowly in burned than in unburned patches.Fires drive shifts between fire‐adapted and sensitive fungal taxa in pine savannas. Slower fuel decomposition in accordance with saprotroph declines should enhance fuel accumulation and could impact future fire characteristics. Thus, fire reorganization of fungal communities may enhance persistence of these fire‐adapted ecosystems.
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This content will become publicly available on December 1, 2025
Assessing the effectiveness of vegetation indices in detecting forest disturbances in the southeast Amazon
Amazon forests are becoming increasingly vulnerable to disturbances such as droughts, fires, windstorms, logging, and forest fragmentation, all of which lead to forest degradation. Nevertheless, quantifying the extent and severity of disturbances and their cumulative impact on forest degradation remains a significant challenge. In this study, we combined multispectral data from Landsat sensors with hyperspectral data from the Earth Observing-One (Hyperion/EO-1) sensor to evaluate the efficacy of multiple vegetation indices in detecting forest responses to disturbances in an experimentally burned forest in southeastern Amazonia. Our experimental area was adjacent to an agricultural field and consisted of three 50-ha treatments – an unburned Control, a plot burned every three years, and a plot burned annually from 2004 to 2010. All plots were monitored to assess vegetation recovery after fire disturbance. These areas were also affected by three drought events (2007, 2010, and 2016) over the study period. We evaluated a total of 18 Vegetation Indices (VI), one unique to Landsat, 12 unique to Hyperion/EO-1, and five commons to both satellites (i.e., 6 total from Landsat and 17 from Hyperion). We used linear models (LM) to evaluate how changes in ground observations of forest structure (biomass, leaf area index [LAI], and litter production) associated with fire were captured by the two VIs most sensitive to forest degradation. Our results indicate that the Plant Senescence Reflectance Index (PSRI) derived from Hyperion/EO-1 was the most sensitive to vegetation changes associated with forest fires, increasing by 94% in burned vs. unburned forests. Of the Landsat-derived VIs, we found that the Green-Red Normalized Difference (GRND) were the most sensitive to forest degradation by fire, showing a marked decline (87%) in the burned plots compared with the unburned Control. However, compared to PSRI, the GRND was a better predictor of changes associated with fire, both in the forest interior or forest edge, for the three ground variables: biomass stocks (r2 =0.5–0.8), LAI (r2=0.8–0.9), and litter production (r2=0.4–0.7). This study demonstrate that VIs can detect forest responses to fire and other disturbances over time, highlighting the relative strengths of each VI. In doing so, it shows how the integration of multispectral and hyperspectral data can be useful for monitoring tropical forest degradation and recovery. Moreover, it provides valuable insights into the limitations of existing approaches, which can inform the design of next-generation sensors for global forest monitoring.
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- Award ID(s):
- 2348580
- PAR ID:
- 10571014
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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