The Science
Stem respiration is a quantitatively important, but poorly understood component of ecosystem carbon cycling in terrestrial ecosystems. However, a dynamic stem gas exchange system for quantifying real-time stem carbon dioxide (CO₂) efflux (E_s) is not commercially available resulting in limited observations based on the static method where air is recirculated through a stem enclosure. The static method has limited temporal resolution, suffers from condensation issues, requires a leak-free enclosure, which is often difficult to verify in the field, and requires physically removing the chamber or flushing it with ambient air before starting each measurement. Here we present the design of a custom system for real-time off the grid monitoring of stem CO₂ efflux from diverse tropical forests.

The Impact
The system is low cost, lightweight, and waterproof with low power requirements (1.2-2.4 W) for real-time monitoring of stem E_susing a 3D printed dynamic stem chamber and a 12V car battery. Great success was achieved with this system in the Amazon during the rainy season in 2022. This method allows for the use of real-time stem CO₂ efflux measurements to evaluate diurnal patterns of growth and respiration in hyper diverse forests to help resolve some major uncertainties surrounding stem respiration. While temperature is assumed to stimulate growth and its associated respiratory processes, preliminary real-time diurnal data collected with the technique suggest that plant hydraulics are also key, with mid-day water stress in the dry season limiting plant growth and respiratory process. The deployment of the techniques to remote tropical forests in Brazil will allow us to link plant hydraulics and carbon metabolism in ecosystem demographics models like FATES.

Summary
Stem respiration is a quantitatively important, but poorly understood component of ecosystem carbon cycling in terrestrial ecosystems. However, a dynamic stem gas exchange system for quantifying real-time stem carbon dioxide (CO₂) efflux (E_s) is not commercially available resulting in limited observations based on the static method where air is recirculated through a stem enclosure. The static method has limited temporal resolution, suffers from condensation issues, requires a leak-free enclosure, which is often difficult to verify in the field, and requires physically removing the chamber or flushing it with ambient air before starting each measurement.

With the goal of improving our quantitative understanding of biophysical, physiological, biochemical, and environmental factors that influence diurnal E_spatterns, here we present a custom system for quantifying real-time stem E_sin remote tropical forests. The system is low cost, lightweight, and waterproof with low power requirements (1.2-2.4 W) for real-time monitoring of stem E_susing a 3D printed dynamic stem chamber and a 12V car battery. The design offers control over the flow rate through the stem chamber, eliminates the need for a pump to introduce air into the chamber, and water condensation issues by removing water vapor prior to CO₂analysis. Following a simple CO₂infrared gas analyzer (IRGA) calibration and match procedure with a 400-ppm standard, we quantified diurnal E_sobservations over a 24-hours period during the summer growing season from an ash tree (Fraxinus sp.) in Fort Collins, Colorado. The results are consistent with previous laboratory and field studies that show E_s can be suppressed during the day relative to the night.

Contact: Kolby Jardine, Research Scientist, LBNL: Earth and Environmental Sciences Area, Ecology Department (kjjardine@lbl.gov)

Funding
Support for this research was provided as part of the Next Generation Ecosystem Experiments-Tropics (NGEE-Tropics) funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research through contract No. DE-AC02-05CH11231 to LBNL, as part of DOE’s Terrestrial Ecosystem Science Program. We kindly acknowledge Christina Wistrom for her support at the UC Berkeley Oxford Tract Greenhouse where the method was developed and Bryan Taylor at LBNL for support with computer systems and software.

Publication: Jardine, K., Augusto, E., Levine, S. D., Sunder, A., Som, S., & Chambers, J. (2023). Development of a lightweight, portable, waterproof, and low power stem respiration system for trees. MethodsX, 10, 101986. https://doi.org/10.1016/j.mex.2022.101986.

Using high-resolution remote sensing and machine learning, researchers detected forest degradation, and found that forests lose 35% of carbon after fires

The Science
Forest degradation through fires and logging are widespread in the Amazon. Forest degradation changes the forest structure, but it is difficult to detect from space. A team of researchers used commercial satellites with very high resolution, and developed a machine learning system to automatically distinguish intact forests from logged or burned forests. They also used laser sensors on an aircraft to calculate how much carbon forests lose when they are degraded. To get the most precise impact of forest degradation on carbon stocks, the team considered that both their classification and their carbon stocks have uncertainties.

The Impact
The research team found that their machine learning method distinguishes degraded forests from intact forests in 86% of the cases. Sometimes the machine learning approach confuses logged forests with intact forests, but it is very good at identifying burnt areas. The team found that logged forests have almost the same amount of carbon as intact forests. However, forest fires can reduce the amount of carbon by 35%. The team also found that they needed to account for the confusion of the machine learning classification of forest degradation to correctly attribute carbon losses to forest degradation classes.

Summary
Forest degradation from logging and fires impacts large areas of tropical forests. However, the impact of degradation on carbon stocks remains uncertain because degradation is difficult to detect. This research used high resolution images from PlanetScope and produced a series of metrics that described the forest canopy texture. These metrics were then used to train a machine learning classifier that calculated the probability of forests being intact, burned or logged. The team also used biomass estimates from airborne lidar to calculate the impact of forest degradation on carbon stocks.

The classification approach has an accuracy between 0.69 and 0.93, depending on the site. This study found that changes in carbon stocks due to logging were small, but burned forests store 35% less carbon than intact forests. The team expected and found that uncertainty in carbon losses due to degradation increases when they account for the uncertainty in classification. However, they found that ignoring classification uncertainty could underestimate the impact of degradation on carbon stocks.

Contact: Ekena Rangel Pinagé, College of Forestry, Oregon State University (rangelpe@oregonstate.edu)
Marcos Longo, Lawrence Berkeley National Laboratory, (mlongo@lbl.gov)

Funding
This research was funded by the NASA Land Cover and Land Use Change Program, and by the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. The research carried out at the Jet Propulsion Laboratory, California Institute of Technology, was under a contract with the National Aeronautics and Space Administration.

Publication: Pinagé, E. R., M. Keller, C. P. Peck, M. Longo, P. Duffy, and O. Csillik, 2023: Effects of forest degradation classification on the uncertainty of aboveground carbon estimates in the Amazon. Carbon Balance and Management, 18 (1), 2, doi:10.1186/s13021-023-00221-5.

ForestGEO scientists measured branch, leaf, and stomatal traits from one thousand trees to explore the sources of trait variability and dynamics among and within Amazon tree species.

The Science
Previous work in Amazon forests has shown significant variation in both tree species distribution and drought-induced tree mortality across small ridges and valleys. Forest ecologists measured 18 branch, leaf, and stomatal traits on 1,077 trees of 72 dominant species to identify the underlying functional traits driving such changes across topography while controlling for a highly documented source of trait variability within species—tree size. Researchers found large trait variability across trees within species (i.e., intraspecific) that was related to trees’ topographic location for leaf traits and tree size for branch and stomatal traits.

The Impact
This study demonstrates the importance of accounting for intraspecific trait variation when testing trait-environment relationships and suggests tree size as a critical source of variability to be included in mechanistic models aiming to predict forest dynamics. The next steps include quantifying physiological traits, functional rooting depths, and water table dynamics to comprehensively understand trees’ vulnerability to climatic drivers (e.g., droughts) and their implications for forest composition and ecosystem services.

Summary
Tropical forest responses to variation in water availability, which are critical for understanding and predicting the effects of climate change, depend on trait variation among trees. ForestGEO scientists quantified interspecific (among species) and intraspecific (across trees within species) variation in 18 branch, leaf and stomatal traits for 72 dominant tree species along a local topographic gradient in an aseasonal Amazon terra firme forest. They used these sampling design to test trait relationships with tree size, elevation, and species’ topographic associations as well as trait correlations. Intraspecific trait variation was substantial and exceeded interspecific variation in 10 of 18 traits. For leaf acquisition traits, intraspecific variation was mainly related to tree topographic elevation, while most of the variation in branch, leaf and stomatal traits was related to tree size. Interspecific variation showed no clear relationships with species’ habitat association. Although trait correlations and coordinations were generally maintained among trees and among species, bivariate relationships varied among trees within species, across habitat association classes and across tree size classes. These results demonstrate the magnitude and importance of intraspecific trait variation in tropical trees, especially as related to tree size. Furthermore, these results indicate that previous findings relating interspecific variation with topographic association in seasonal forests do not necessarily generalize to aseasonal forests.

Contact: Daniel Zuleta, Forest Global Earth Observatory, Smithsonian Tropical Research Institute (dfzuleta@gmail.com)

Funding
D. Zuleta was supported by the National Doctoral Scholarship COLCIENCIAS-Colombia (647, 2015-II), the Smithsonian Tropical Research Institute Short-Term Fellowships and the Next Generation Ecosystem Experiments-Tropics, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (https://ngee-tropics.lbl.gov/). Data collection was supported by the Forest Global Earth Observatory (ForestGEO) of the Smithsonian Institution and Universidad Nacional de Colombia.

Publications
Zuleta, D., Muller-Landau, H.C., Duque, A., Caro, N., Cardenas, D., Castaño, N., León-Peláez, J.D., and Feeley, K.J. “Interspecific and intraspecific variation of tree branch, leaf and stomatal traits in relation to topography in an aseasonal Amazon forest.” Functional Ecology 36, 2955– 2968 (2022). [DOI: 10.1111/1365-2435.14199]

Related Links
https://functionalecologists.com/2022/12/08/daniel-zuleta-do-small-scale-changes-in-topography-affect-functional-trait-variability-in-an-aseasonal-amazon-forest/

https://fesummaries.wordpress.com/2022/10/12/from-ridge-to-valleys-do-trees-have-different-characteristics-along-a-short-topographic-gradient-in-an-aseasonal-amazon-forest/

The connection between convective storms and tree mortality in the Amazon projects a large increase in future windthrow events.

The Science
A leading cause of tree mortality in the Amazon is windthrow, i.e., tree broken or uprooted by high winds and heavy rainfall in an extreme storm. Here we built a linkage between extreme storms in the atmosphere and forest mortality on the land surface. Global warming makes extreme storms more intense, and using this linkage, the projected storms are likely to make tree mortality by windthrow commonplace over about 50% more of the Amazon by the end of the century.

The Impact
Amazon forests play important roles in regulating global carbon cycle, but variable natural disturbances increase the uncertainty of the carbon capacity. Extreme storms are important drivers of tree mortality in the Amazon region. In this study, we provide a framework for representing the coupling between forest mortality on the land surface and extreme storms in the atmosphere. This analysis highlights the potential for predicting the rate of future storm-driven tree mortality, a driver of tree mortality that currently is not included in global models and emphasizes the need to improve land-atmosphere relationship in models.

Summary
Forest mortality caused by convective storms (windthrow) is a major disturbance in the Amazon. However, the linkage between windthrows at the surface and convective storms in the atmosphere remains unclear. In addition, the current Earth system models (ESMs) lack mechanistic links between convective wind events and tree mortality. In this study, we manually map 1012 large windthrow events encompassing 30 years from 1990-2019 and generate hourly convective available potential energy (CAPE, which represents the environment to produce storms) from ERA5 reanalysis data. Here we find an empirical relationship that maps CAPE, which is well simulated by ESMs, to the spatial pattern of large windthrow events. This relationship builds connections between strong convective storms and forest dynamics in the Amazon. Based on the relationship, our model projects a 51% ± 20% increase in the area favorable to extreme storms, and a 43 ± 17% increase in windthrow density within the Amazon by the end of this century under the high-emission scenario (SSP 585). These results indicate significant changes in tropical forest composition and carbon-cycle dynamics under climate change.

Contact
Yanlei Feng, University of California, Berkeley, ylfeng@berkeley.edu
Jeffrey Chambers, Lawrence Berkeley National Lab, jchambers@lbl.gov

Funding
This research was supported as part of the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract number DE-AC02-05CH11231. We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.

Publications
Feng, Y., et al., “Amazon windthrow disturbances are likely to increase with storm frequency under global warming”. Nature Communications, 14(1), pp.1-8, (2023), [DOI: 10.1038/s41467-022-35570-1]

Related Links
Climate Change Likely to Uproot More Amazon Trees, News from Berkeley Lab
Storms are a major source of forest mortality in the Amazon, Earth.com
Thunderstorms Caused by Climate Change Will Most Likely Increase the Number of Large Windthrow Events in the Amazon, Nature World News

Amazon windthrows characteristics driven by environmental variables

The Science
Windthrows (trees uprooted and broken by winds) are common across the Amazon and affect forest dynamics, composition, structure, and carbon balance. Yet, the current understanding of the spatial variability of windthrows is limited. We present the first study to examine the occurrence, area, and direction of windthrows and the control that environmental variables exert on them across the whole Amazon.

The Impact
We found that windthrows are more frequent and larger in the northwestern and the central Amazon. The predominant direction of windthrows is westward. Rainfall, surface elevation, and soil characteristics explain the variability of windthrows but their effects vary regionally. A better understanding of the spatial dynamics of windthrows will improve understanding of the functioning of Amazon forests.

Summary
A total of 392 Landsat 8 images covering the entire Amazon were used and 1116 windthrows were identified based on their shape and spectral characteristics over a 30-year period. Windthrows were integrated with environmental data in a geospatial analysis framework.

Contact
Robinson Negron-Juarez, Lawrence Berkeley National Lab, Staff Scientist (robinson.inj@lbl.gov)

Funding
This study was supported by the Office of Science, Office of Biological and Environmental Research of the US Department of Energy, Agreement grant. DE-AC02-05CH11231, Next Generation Ecosystem Experiments-Tropics, and the Office of Science’s Regional and Global Model Analysis of the US Department of Energy, Agreement grant. DE-AC02-05CH11231, Reducing Uncertainties in Biogeochemical Interactions through Synthesis Computation Scientific Focus Area (RUBISCO SFA).

Publications
Negron-Juarez, R.I., Marra, D., Feng, Y., Urquiza-Muñoz, J.D., Riley, W.J., Chambers, J.Q. Windthrows characteristics and their regional association with rainfall, soil and surface elevation in the Amazon (2022) Environ. Res. Lett. https://doi.org/10.1088/1748-9326/acaf10