Representing diurnal shifts in leaf-level water use efficiency may be key to modeling tropical forest gas exchange

The Science
To understand how tropical ecosystems will respond to global change, researchers must correctly represent the relationship between water loss and carbon gain in leaves, known as the water use efficiency (WUE). There are still significant uncertainties associated with the dynamics of WUE over different timescales, from over the day, to the changes experienced over the full lifespan of a leaf. Here we collected data to assess the possible physiological and mechanistic factors which influence WUE dynamics. While WUE does differ between leaves of different phenological stages, the trend was not consistent across species. However, we identified a unidirectional increase in WUE of approximately 2.5 times over the course of the day in five of the six species studied.

The Impact
One of the major roadblocks to accurate representation of transpiration in climate models is an understanding of the physiological factors which most strongly contribute to variation in leaf level WUE. In this study, we demonstrate that including leaf age as a primary driver of WUE did not help to improve or explain variation in modeled transpiration. However, models which accounted for diurnal (within-day) changes in WUE improved the representation of transpiration. These findings provide a roadmap for future investigation into the physiological traits which most strongly influence transpiration over space and time and underscore the need to closely consider the model assumptions, like constant WUE, implicit in many of the models used to project the future of tropical forests.

Summary
A primary source of uncertainty in terrestrial biosphere model projections of ecosystem-scale carbon uptake and water cycling is the relationship between CO₂ assimilation and water loss via stomatal conductance. In models, this relationship is governed by two terms, the stomatal slope (g₁) and intercept (g₀). Accurate mechanistic representation of how the g₁ and g₀ parameters vary over time is crucial, particularly in wet tropical broadleaf forests where trees have a near consistent annual pattern of leaf production and senescence, and precipitation and humidity are strongly seasonal. These stomatal parameters are estimated using leaf-level gas exchange by two alternative methods: (1) a response curve where the environmental conditions are modified for a single leaf, or (2) a survey approach, where repeated measurements are made on multiple leaves over a diurnal range of environmental conditions.

In this study we found that stomatal response curves and survey style measurements produce statistically different estimations of stomatal parameters, which resulted in large (between 26% and 125%) differences in simulated fluxes of water. Furthermore, we found that g₁ varies both diurnally and to a lesser degree with leaf age. Taken together, these results show that models which use stomatal parameters derived from response curves significantly underestimated canopy level transpiration, and that while leaf traits do vary among leaf phenological stage, models, which tend to only include mature vegetation parameterizations, perform similarly to those that explicitly simulate three leaf age stages.

Contact: Kenneth Davidson, Brookhaven National Lab (kdavidson@bnl.gov)

Funding
This work was supported by the Next-Generation Ecosystem Experiments (NGEE) – Tropics project, which is funded by the Biological and Environmental Research (BER) Program within the US Department of Energy’s (DOE) Office of Science and through DOE contract no. DE-SC0012704 to Brookhaven National Laboratory.

Publications
Davidson KJ, Lamour J, Rogers A, Ely KS, et al. “Short-term variation in leaf-level water use efficiency in a tropical forest” New Phytologist, 237, 2069-2087, (2023), [DOI: 10.1111/nph.18684]

The importance of plant water and carbon dynamics to fire behavior and effects and a framework to link remotely sensed estimates to fire models.

The Science
The condition of living woody plants can change fire behavior. Plants can have different levels of dryness throughout the seasons and in different parts of a landscape based water and nutrients in the soil. Lower levels of live fuel moisture in plants can be linked to faster fires and change the way fires burn and how likely plants are to die after a fire. Using live fuel moisture measurements from remote sensing tools, such as airborne systems, and linking these to models of fire behavior and effects we can improve our understanding of how fires may change in the future.

The Impact
Fire behavior models have long used general fuels in broad groups. Now with new types of models and remote sensing measurements of fuels and fires, we can capture more realistic fuels and how they change in both their condition, such as live fuel moisture, and their structure. This information is critically important for fire management in conditions of drought and warming. Linking how living plants change through time and across a landscape to how fires might behave will give us information we need to better support communities in a world with more fire.

Summary
Wildfires have been recognized as a global crisis, but current fire models do not capture how living plants change in response to changing climate. With drought and warming temperatures increasing the importance of living plants as a factor in changing fire behavior, and new capabilities of models, we are able to capture these complex processes and interactions. We provide a renewed focus on capturing live woody plants in fire models. Living plant conditions and properties influence fire combustion and heat transfer and often dictate if a plant will survive. These interactions provide a mechanistic link between living plants and fire behavior and effects that can be included in new models. We include a conceptual framework linking remotely sensed estimates of plant condition to models of fire behavior and effects, which could be a crucial first step toward improving models used for global fire forecasting. This process-based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

Contact: L.T. Dickman, Los Alamos National Laboratory, lee@lanl.gov

Funding
LTD, AJ, RRL and SS were supported by the Los Alamos National Laboratory (LANL) through its Center for Space and Earth Science (CSES). Center for Space and Earth Science is funded by LANL’s Laboratory Directed Research and Development (LDRD) program under project no. 20210528CR. AJ and ZJR received additional funding from LANL LDRD under project no. 20210689ECR. RPF and STM were supported by SERDP project RC18-1346 and an NSERC Discovery Grant. AB acknowledges funding from the Austrian Science Fund (FWF, project P32203) and from the University of Innsbruck (Early-Stage Funding, grant W-171705). MY receives funding from the Australian Research Council, the Australian Research Data Commons, The SmartSat Cooperative Research Centre and Singtel Optus Pty Limited. JKS was supported by the National Center for Atmospheric Research, a major facility sponsored by the National Science Foundation (NSF) under Cooperative Agreement no. 1852977, with additional support from NASA Arctic Boreal Vulnerability Experiment Grant 80NSSC19M0107. JKS, SPS and CX were also supported as part of the Next-Generation Ecosystem Experiments – Tropics, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research. SPS was also partially supported by the NASA Surface Biology and Geology Mission Study (NNG20OB24A) and through the United States Department of Energy contract no. DE-SC0012704 to Brookhaven National Laboratory. CMH acknowledges US Department of Defense (DoD) Strategic Environmental Research and Development Program (SERDP) Project RC19-1119. IA declares support from NSF WIFIRE Commons under grants 2040676 and 2134904. VRD acknowledges funding from MICINN projects RTI2018-094691-B-C31; EU H2020 (grant agreements 101003890). RAP acknowledges support from US Department of Defense Strategic Environmental Research and Development Program’s Closing Gaps Project RC20-1025. USDA Forest Service personnel were supported by annual Forest Service appropriations.

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.

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.

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