Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests

A large net carbon loss attributed to anthropogenic and natural disturbances in the Amazon Arc of Deforestation

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric measurements suggest that it has been releasing more carbon than it has absorbed because of deforestation and forest degradation. Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes. We used a unique, randomized, repeated, very high-resolution airborne laser scanning survey to provide a direct, detailed, and high-resolution partitioning of aboveground carbon gains and losses in the Brazilian Arc of Deforestation. Our analysis revealed that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7%, respectively. Extrapolating the lidar-based statistics to the study area (544,300 km2), we found that 24.1, 24.2, and 14.5 Tg C y-1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y-1) and other disturbances (50.3 Tg C y-1) were partially counterbalanced by forest growth (44.1 Tg C y-1). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y-1 for the study region attributable to both anthropogenic and natural processes. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system.

Constraining long-term model predictions for woody growth using tropical tree rings

The strength and persistence of the tropical carbon sink hinges on the long-term responses of woody growth to climatic variations and increasing CO2 . However, the sensitivity of tropical woody growth to these environmental changes is poorly understood, leading to large uncertainties in growth predictions. Here, we used tree ring records from a Southeast Asian tropical forest to constrain ED2.2-hydro, a terrestrial biosphere model with explicit vegetation demography. Specifically, we assessed individual-level woody growth responses to historical climate variability and increases in atmospheric CO2 (Ca ). When forced with historical Ca , ED2.2-hydro reproduced the magnitude of increases in intercellular CO2 concentration (a major determinant of photosynthesis) estimated from tree ring carbon isotope records. In contrast, simulated growth trends were considerably larger than those obtained from tree rings, suggesting that woody biomass production efficiency (WBPE = woody biomass production:gross primary productivity) was overestimated by the model. The estimated WBPE decline under increasing Ca based on model-data discrepancy was comparable to or stronger than (depending on tree species and size) the observed WBPE changes from a multi-year mature-forest CO2 fertilization experiment. In addition, we found that ED2.2-hydro generally overestimated climatic sensitivity of woody growth, especially for late-successional plant functional types. The model-data discrepancy in growth sensitivity to climate was likely caused by underestimating WBPE in hot and dry years due to commonly used model assumptions on carbon use efficiency and allocation. To our knowledge, this is the first study to constrain model predictions of individual tree-level growth sensitivity to Ca and climate against tropical tree-ring data. Our results suggest that improving model processes related to WBPE is crucial to obtain better predictions of tropical forest responses to droughts and increasing Ca . More accurate parameterization of WBPE will likely reduce the stimulation of woody growth by Ca rise predicted by biosphere models.

Anthropogenic disturbance exacerbates resilience loss in the Amazon rainforests

Uncovering the mechanisms that lead to Amazon forest resilience variations is crucial to predict the impact of future climatic and anthropogenic disturbances. Here, we apply a previously used empirical resilience metrics, lag-1 month temporal autocorrelation (TAC), to vegetation optical depth data in C-band (a good proxy of the whole canopy water content) in order to explore how forest resilience variations are impacted by human disturbances and environmental drivers in the Brazilian Amazon. We found that human disturbances significantly increase the risk of critical transitions, and that the median TAC value is ~2.4 times higher in human-disturbed forests than that in intact forests, suggesting a much lower resilience in disturbed forests. Additionally, human-disturbed forests are less resilient to land surface heat stress and atmospheric water stress than intact forests. Among human-disturbed forests, forests with a more closed and thicker canopy structure, which is linked to a higher forest cover and a lower disturbance fraction, are comparably more resilient. These results further emphasize the urgent need to limit deforestation and degradation through policy intervention to maintain the resilience of the Amazon rainforests.

Amazon deforestation causes strong regional warming

Tropical deforestation impacts the climate through complex land-atmosphere interactions causing local and regional warming. However, whilst the impacts of deforestation on local temperature are well understood, the regional (nonlocal) response is poorly quantified. Here, we used remote-sensed observations of forest loss and dry season land-surface temperature during the period 2001 to 2020 to demonstrate that deforestation of the Amazon caused strong warming at distances up to 100 km away from the forest loss. We apply a machine learning approach to show nonlocal warming due to forest loss at 2-100 km length scales increases the warming due to deforestation by more than a factor 4, from 0.16 K to 0.71 K for each 10-percentage points of forest loss. We estimate that rapid future deforestation under a strong inequality scenario could cause dry season warming of 0.96 K across Mato Grosso state in southern Brazil over the period 2020 to 2050. Reducing deforestation could reduce future warming caused by forest loss to 0.4 K. Our results demonstrate the contribution of tropical deforestation to regional climate warming and the potential for reduced deforestation to deliver regional climate adaptation and resilience with important implications for sustainable management of the Amazon.

Effects of forest degradation classification on the uncertainty of aboveground carbon estimates in the Amazon

BACKGROUND

Tropical forests are critical for the global carbon budget, yet they have been threatened by deforestation and forest degradation by fire, selective logging, and fragmentation. Existing uncertainties on land cover classification and in biomass estimates hinder accurate attribution of carbon emissions to specific forest classes. In this study, we used textural metrics derived from PlanetScope images to implement a probabilistic classification framework to identify intact, logged and burned forests in three Amazonian sites. We also estimated biomass for these forest classes using airborne lidar and compared biomass uncertainties using the lidar-derived estimates only to biomass uncertainties considering the forest degradation classification as well.

RESULTS

Our classification approach reached overall accuracy of 0.86, with accuracy at individual sites varying from 0.69 to 0.93. Logged forests showed variable biomass changes, while burned forests showed an average carbon loss of 35%. We found that including uncertainty in forest degradation classification significantly increased uncertainty and decreased estimates of mean carbon density in two of the three test sites.

CONCLUSIONS

Our findings indicate that the attribution of biomass changes to forest degradation classes needs to account for the uncertainty in forest degradation classification. By combining very high-resolution images with lidar data, we could attribute carbon stock changes to specific pathways of forest degradation. This approach also allows quantifying uncertainties of carbon emissions associated with forest degradation through logging and fire. Both the attribution and uncertainty quantification provide critical information for national greenhouse gas inventories.

The drivers and impacts of Amazon forest degradation

Approximately 2.5 × 10⁶ square kilometers of the Amazon forest are currently degraded by fire, edge effects, timber extraction, and/or extreme drought, representing 38% of all remaining forests in the region. Carbon emissions from this degradation total up to 0.2 petagrams of carbon per year (Pg C year^-1), which is equivalent to, if not greater than, the emissions from Amazon deforestation (0.06 to 0.21 Pg C year^-1). Amazon forest degradation can reduce dry-season evapotranspiration by up to 34% and cause as much biodiversity loss as deforestation in human-modified landscapes, generating uneven socioeconomic burdens, mainly to forest dwellers. Projections indicate that degradation will remain a dominant source of carbon emissions independent of deforestation rates. Policies to tackle degradation should be integrated with efforts to curb deforestation and complemented with innovative measures addressing the disturbances that degrade the Amazon forest.

Tropical forests post-logging are a persistent net carbon source to the atmosphere

Logged and structurally degraded tropical forests are fast becoming one of the most prevalent land-use types throughout the tropics and are routinely assumed to be a net carbon sink because they experience rapid rates of tree regrowth. Yet this assumption is based on forest biomass inventories that record carbon stock recovery but fail to account for the simultaneous losses of carbon from soil and necromass. Here, we used forest plots and an eddy covariance tower to quantify and partition net ecosystem CO₂ exchange in Malaysian Borneo, a region that is a hot spot for deforestation and forest degradation. Our data represent the complete carbon budget for tropical forests measured throughout a logging event and subsequent recovery and found that they constitute a substantial and persistent net carbon source. Consistent with existing literature, our study showed a significantly greater woody biomass gain across moderately and heavily logged forests compared with unlogged forests, but this was counteracted by much larger carbon losses from soil organic matter and deadwood in logged forests. We estimate an average carbon source of 1.75 ± 0.94 Mg C ha^-1 yr^-1 within moderately logged plots and 5.23 ± 1.23 Mg C ha^-1 yr^-1 in unsustainably logged and severely degraded plots, with emissions continuing at these rates for at least one-decade post-logging. Our data directly contradict the default assumption that recovering logged and degraded tropical forests are net carbon sinks, implying the amount of carbon being sequestered across the world's tropical forests may be considerably lower than currently estimated.

Hot spots and anomalies of CO2 over eastern Amazonia, Brazil: A time series from 2015 to 2018

The easternmost Amazon, located in the Maranhão State, in Brazil, has suffered massive deforestation in recent years, which has devastated almost 80% of the original vegetation. We aim to characterize hot spots, hot moments, atmospheric carbon dioxide anomalies (Xco₂, ppm), and their interactions with climate and vegetation indices in eastern Amazon, using data from NASA's Orbiting Carbon Observatory-2 (OCO-2). The study covered the period from January 2015 to December 2018. The data were subjected to regression, correlation, and temporal analysis, identifying the spatial distribution of hot/cold moments and hot/cold spots. In addition, anomalies were calculated to identify potential CO₂ sources and sinks. Temporal changes indicate atmospheric Xco₂ in the range from 362.2 to 403.4 ppm. Higher Xco₂ values (hot moments) were concentrated between May and September, with some peaks in December. The lowest values (cold moments) were concentrated from November to April. SIF 771 W m^-2 sr^-1 μm^-1 explained the temporal changes of Xco₂ in 58% (R² adj = 0.58; p < 0.001) and precipitation in 27% (R² adj = 0.27; p ≤ 0.001). Spatial hot spots with 90% confidence were more representative in 2016. The maximum and minimum Xco₂ (ppm) anomalies were 6.19 ppm (source) and -6.29 ppm (sink), respectively. We conclude that the hot moments of Xco₂ in the eastern Amazon rainforest are concentrated in the dry season of the year. Xco₂ spatial hot spots and anomalies are concentrated in the southern region and close to protected areas of the Amazon rainforest.

Climate and land management accelerate the Brazilian water cycle

Increasing floods and droughts are raising concerns of an accelerating water cycle, however, the relative contributions to streamflow changes from climate and land management have not been assessed at the continental scale. We analyze streamflow data in major South American tropical river basins and show that water use and deforestation have amplified climate change effects on streamflow extremes over the past four decades. Drying (fewer floods and more droughts) is aligned with decreasing rainfall and increasing water use in agricultural zones and occurs in 42% of the study area. Acceleration (both more severe floods and droughts) is related to more extreme rainfall and deforestation and occurs in 29% of the study area, including southern Amazonia. The regionally accelerating water cycle may have adverse global impacts on carbon sequestration and food security.

Increasing floods and droughts are raising concerns of an accelerating water cycle. A new study shows that the terrestrial water cycle in Brazil has been mostly drying or accelerating, aligned with changes in rainfall, water use, and forest cover.

Feedback in tropical forests of the Anthropocene

Tropical forests are complex systems containing myriad interactions and feedbacks with their biotic and abiotic environments, but as the world changes fast, the future of these ecosystems becomes increasingly uncertain. In particular, global stressors may unbalance the feedbacks that stabilize tropical forests, allowing other feedbacks to propel undesired changes in the whole ecosystem. Here, we review the scientific literature across various fields, compiling known interactions of tropical forests with their environment, including the global climate, rainfall, aerosols, fire, soils, fauna, and human activities. We identify 170 individual interactions among 32 elements that we present as a global tropical forest network, including countless feedback loops that may emerge from different combinations of interactions. We illustrate our findings with three cases involving urgent sustainability issues: (1) wildfires in wetlands of South America; (2) forest encroachment in African savanna landscapes; and (3) synergistic threats to the peatland forests of Borneo. Our findings reveal an unexplored world of feedbacks that shape the dynamics of tropical forests. The interactions and feedbacks identified here can guide future qualitative and quantitative research on the complexities of tropical forests, allowing societies to manage the nonlinear responses of these ecosystems in the Anthropocene.

Impact of Ice-Storms and Subsequent Salvage Logging on the Productivity of Cunninghamia lanceolata (Chinese Fir) Forests

The impacts of ice-storms on forests have received growing attention in recent years. Although there is a wide agreement that ice-storms significantly affect forest structure and functions, how frequent ice-storms and subsequent salvage logging impact productivity of subtropical coniferous forests in the future still remains poorly understood. In this study, we used the Ecosystem Demography model, Version 2.2 (ED-2.2), to project the impact of salvage logging of ice-storm-damaged trees on the productivity of Cunninghamia lanceolata-dominated coniferous forest and C. lanceolata-dominated mixed coniferous and broadleaved forests. The results show that forest productivity recovery is delayed in coniferous forests when there is no shade-tolerant broadleaved species invasion after ice-storms, and C. lanceolata could continue to dominate the canopy in the mixed coniferous and broadleaved forests under high-frequency ice-storms and subsequent salvage logging. The resistance and resilience of the mixed coniferous and broadleaved forests to high-frequency ice-storms and subsequent salvage logging were stronger compared to coniferous forests. Although conifers could continue to dominate the canopy under shade-tolerant broadleaved species invasion, we could not rule out the possibility of a future forest community dominated by shade-tolerant broadleaf trees because there were few coniferous saplings and shade-tolerant broadleaf species dominated the understory. Our results highlight that post-disaster forest management should be continued after high-frequency ice-storms and subsequent salvage logging in C. lanceolata forests to prevent possible shade-tolerant, late successional broadleaf trees from dominating the canopy in the future.

JA, Sanchez‐Azofeifa GA, De Deurwaerder HPT, Krishna Moorthy SM, Schnitzer SA, Marvin DC, Longo M, Liu C, Broadbent EN, Almeyda Zambrano AM, Muller‐Landau HC, Detto M, Verbeeck H. Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

Lianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated trees. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition, and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (-30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana-specific traits were also responsible for a significant reduction of tree (-19%) and ecosystem (-7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of lianas on forest functioning, and paves the way for the evaluation of the large-scale impacts of lianas on forest biogeochemical cycles.

Qualifying the Information Detected from Airborne Laser Scanning to Support Tropical Forest Management Operational Planning

(1) Background: Forests throughout the world are managed to fulfil a range of commercial and ecosystem services. The same applies to managed areas of the Amazon forest. We explore a method of sustainable forest management (SFM) which anticipates the result of processes of natural mortality of large, mature trees that could fall and damage their neighbors. Collecting all the information required for planning logging in the Brazilian Amazon is, currently, a hard, time-consuming and expensive task. (2) Methods: This information can be obtained more quickly, accurately and objectively by including airborne laser scanning (ALS) products in the operational plan. We used ALS point clouds to isolate emergent crowns from the canopy height model. Then, we performed field work to validate the existence of these trees, and to understand how many commercial trees (tree diameter ≥ 50 cm) we identified by orienting the trees search through the emergent canopy model. (3) Results: We were able to detect 184 (54.4%) trees from 338 field-recorded individuals in 20 plots (totaling 8 ha). Of the detected trees, 66 individuals were classified as having potential for commerce. Furthermore, 58 individuals presented the best stem quality for logging, which represents more than seven high quality commercial trees per hectare. The logistic regression showed that the effects that positively influence the emergent crown formation are strongly presented in the commercial species. (4) Conclusions: Using airborne laser scanning can improve the SFM planning in a structurally complex, dense and mixed composition tropical forest by reducing field work in the initial stages of management. Therefore, we propose that ALS operational planning can be used to more efficiently direct field surveys without the need for a full census.

Model-Based Estimation of Amazonian Forests Recovery Time after Drought and Fire Events

In recent decades, droughts, deforestation and wildfires have become recurring phenomena that have heavily affected both human activities and natural ecosystems in Amazonia. The time needed for an ecosystem to recover from carbon losses is a crucial metric to evaluate disturbance impacts on forests. However, little is known about the impacts of these disturbances, alone and synergistically, on forest recovery time and the resulting spatiotemporal patterns at the regional scale. In this study, we combined the 3-PG forest growth model, remote sensing and field derived equations, to map the Amazonia-wide (3 km of spatial resolution) impact and recovery time of aboveground biomass (AGB) after drought, fire and a combination of logging and fire. Our results indicate that AGB decreases by 4%, 19% and 46% in forests affected by drought, fire and logging + fire, respectively, with an average AGB recovery time of 27 years for drought, 44 years for burned and 63 years for logged + burned areas and with maximum values reaching 184 years in areas of high fire intensity. Our findings provide two major insights in the spatial and temporal patterns of drought and wildfire in the Amazon: (1) the recovery time of the forests takes longer in the southeastern part of the basin, and, (2) as droughts and wildfires become more frequent—since the intervals between the disturbances are getting shorter than the rate of forest regeneration—the long lasting damage they cause potentially results in a permanent and increasing carbon losses from these fragile ecosystems.