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Abstract Soil moisture is a crucial variable mediating soil‐vegetation‐atmosphere water exchange. As climate and land use change, the increased frequency and intensity of extreme weather events and disturbances will likely alter feedbacks between ecosystem functions and soil moisture. In this study, we evaluated how extreme drought (2015/2016) and postfire vegetation regrowth affected the seasonality of soil water content (0–8 m depth) in a transitional forest in southeastern Amazonia. The experiment included three treatment plots: an unburned Control, an area burned every three years (B3yr), and an area burned annually (B1yr) between 2004 and 2010. We hypothesized that (a) soil moisture at B1yr and B3yr would be higher than the Control in the first years postfire due to lower transpiration rates, but differences between burned plots would decrease as postfire vegetation regrew; (b) during drought years, the soil water deficit in the dry season would be significantly greater in all plots as plants responded to greater evaporative demand; and (c) postfire recovery in the burned plots would cause an increase in evapotranspiration over time, especially in the topsoil. Contrary to the first expectation, the burned plots had lower volumetric water content than the Control plot. However, we found that droughts significantly reduced soil moisture in all plots compared to non‐drought years (15.6%), and this effect was amplified in the burned plots (19%). Our results indicate that, while compounding disturbances such as wildfires and extreme droughts alter forest dynamics, deep soil moisture is an essential water source for vegetation recovery. 

Abstract Forest disturbances associated with edge effects, wildfires, and windthrow events have impacted large swaths of the tropics. Defining the levels of forest disturbance that cause ecologically relevant reductions in fruit and seed (FS) production is key to understanding forest resilience to current and future global changes. Here, we tested the hypotheses that: (1) low‐intensity experimental fires alone would cause minor changes in FS production and diversity in a tropical forest, whereas synergistic disturbance effects resulting from edge effects, wildfires, droughts, and blowdowns would drive long‐term reductions in FS diversity and production; and (2) the functional composition of FS in disturbed forests would shift toward tree species with acquisitive strategies. To test these hypotheses, we quantified FS production between 2005 and 2018 in a large‐scale fire experiment in southeast Amazonia. The experimental treatments consisted of three 50‐ha plots: a Control plot, a plot burned annually (B1yr) and a plot burned every three years (B3yr) between 2004 and 2010. These plots were impacted by edge effects, two droughts (2007 and 2010), and a blowdown event in 2012. Our results show that FS production remained relatively high following low‐intensity fires, but declined where fires were most severe (i.e., forest edge of B3yr). The number of species‐producing FS declined sharply when fires co‐occurred with droughts and a windthrow event, and species composition shifted throughout the experiment. Along the edge of both burned plots, the forest community became dominated by species with faster relative growth, thinner leaves, thinner bark, and lower height. We conclude that compounding disturbances changed FS patterns, with a strong effect on species composition and potentially large effects on the next generation of trees. This is largely due to reductions in the diversity of species‐producing FS where fires are severe, causing a shift toward functional traits typically associated with pioneer and generalist species. 

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.