Abstract
Selective logging, fragmentation, and understory fires directly degrade forest structure and composition. However, studies addressing the effects of forest degradation on carbon, water, and energy cycles are scarce. Here, we integrate field observations and high‐resolution remote sensing from airborne lidar to provide realistic initial conditions to the Ecosystem Demography Model (ED‐2.2) and investigate how disturbances from forest degradation affect gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H). We used forest structural information retrieved from airborne lidar samples (13,500 ha) and calibrated with 817 inventory plots (0.25 ha) across precipitation and degradation gradients in the eastern Amazon as initial conditions to ED‐2.2 model. Our results show that the magnitude and seasonality of fluxes were modulated by changes in forest structure caused by degradation. During the dry season and under typical conditions, severely degraded forests (biomass loss %) experienced water stress with declines in ET (up to 34%) and GPP (up to 35%) and increases of H (up to 43%) and daily mean ground temperatures (up to 6.5°C) relative to intact forests. In contrast, the relative impact of forest degradation on energy, water, and carbon cycles markedly diminishes under extreme, multiyear droughts, as a consequence of severe stress experienced by intact forests. Our results highlight that the water and energy cycles in the Amazon are driven by not only climate and deforestation but also the past disturbance and changes of forest structure from degradation, suggesting a much broader influence of human land use activities on the tropical ecosystems.
Highlights
Tropical forests account for 25–40% of total carbon stocks in terrestrial ecosystems (Meister et al, 2012; Sabine et al, 2004), but their maintenance and functioning have been weakened by climate and land use change
For Step 2, we used a collection of 817 forest inventory plots (0.16–0.26 ha) that were surveyed by airborne lidar, which included plots from all study regions as well additional sites available from Sustainable Landscapes Brazil (SLB) and used in a previous study; we developed statistical models based on subset selection of regression (Miller, 1984) and heteroskedastic distribution of residuals (Mascaro et al, 2011) to estimate plot‐level properties from point cloud metrics and field estimates, following the approach by Longo et al (2016)
We focus on the basal area distribution obtained from cross‐validation at disturbance histories within study regions that had at least 20 plots (Figure 3)
Summary
Tropical forests account for 25–40% of total carbon stocks in terrestrial ecosystems (Meister et al, 2012; Sabine et al, 2004), but their maintenance and functioning have been weakened by climate and land use change. Most studies assessing the effects of land use change on tropical forest stocks and fluxes have focused on the effects of deforestation (e.g., Achard et al, 2014; Harris et al, 2012). Disturbed forests (e.g., reduced‐impact logging) store as much carbon as intact forests, while forests impacted by severe or multiple disturbances may lose a significant fraction or most of their original carbon stocks (Alamgir et al, 2016; Berenguer et al, 2014; Ferraz et al, 2018; Longo et al, 2016; Rappaport et al, 2018). Estimates of fluxes from forest degradation and regeneration are more uncertain than emissions from deforestation (Aragão et al, 2014; Bustamante et al, 2016; Morton, 2016), because their impacts on forests are more subtle than deforestation and more difficult to detect and quantify with traditional remote sensing techniques
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