The Science
Leaf-level gas exchange data inform the mechanistic understanding and model representation of plant fluxes of carbon and water in terrestrial biosphere models where parameters derived from gas exchange data also determine how plants will respond to global environmental change. The high value of leaf-level gas exchange data is exemplified by the many publications that reuse and synthesize gas exchange data. However the previous lack of metadata and data reporting conventions have made full and efficient use of these data difficult. We have proposed a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. The reporting format has been developed for use in ESS-DIVE, but has received strong support from the global plant physiology community.

The Impact
Collection leaf-level gas exchange data requires specialist training, is time consuming, can involve elaborate logistics, and often utilizes techniques adapted to particular experiments, instruments and environments. Thus, resulting data products are low volume, have diverse and heterogeneous content, and are thus not easily shared. Adoption of a common reporting format will make these data more FAIR, that is, Findable, Accessible, Interoperable and Reusable. These characteristics facilitate data synthesis, incorporation into models and scientific discovery. Development of this reporting format has garnered considerable interest beyond the ESS community, with contributions from 80 experts from around the world, including data collectors, modelers, data scientists, and instrument manufacturers.

 Summary
The leaf-level gas exchange reporting format provides recommendations on how to prepare these data for sharing in data repositories. The format comprises defined variable names and definitions, and for a number of the most common measurement types, lists the minimum required data variables. A comprehensive metadata description template has been developed to allow unambiguous interpretation of data by future users. The format strongly encourages archive of the original complete instrument output to allow for novel future use of these valuable data.

 

 

 

 

 

 

Contacts (BER PM): Daniel Stover, SC-23.1, Daniel.Stover@science.doe.gov (301-903-0289) 

PI Contact: Alistair Rogers, Brookhaven National Laboratory, arogers@bnl.gov
Deb Agarwal, Lawrence Berkeley National Laboratory, daagarwal@lbl.gov

Funding
DOE Office of Science BER, Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) community funds. Further support was received from several additional projects funded by the Office of Biological and Environmental Research, including the Next Generation Ecosystem Experiments -Arctic (NGEE-Arctic) and -Tropics (NGEE-Tropics) projects.

Publications
Ely, Kim S., Alistair Rogers, Deborah A. Agarwal, Elizabeth A. Ainsworth, Loren P. Albert, Ashehad Ali, Jeremiah Anderson, et al. (2021). A Reporting Format for Leaf-Level Gas Exchange Data and Metadata. Ecological Informatics 61: 101232. doi: 10.1016/j.ecoinf.2021.101232

Ely, Kim S., Alistair Rogers, Robert Crystal-Ornelas (2020). ESS-DIVE reporting format for leaf-level gas exchange data and metadata. Environmental Systems Science Data Infrastructure for a Virtual Ecosystem. doi:10.15485/1659484

Related Links
https://www.sciencedirect.com/science/article/pii/S1574954121000236?via%3Dihub
https://data.ess-dive.lbl.gov/view/doi:10.15485/1659484
https://github.com/ess-dive-community/essdive-leaf-gas-exchange
https://ess-dive.gitbook.io/leaf-level-gas-exchange/

The Science
Terrestrial biosphere models (TBMs) that are used to project the response of ecosystems to global change need to accurately represent photosynthesis. Photosynthesis, the assimilation of carbon dioxide (CO2) by plants, is the largest carbon flux on the planet and therefore errors in model representation of this key process can have marked impacts on projected ecosystem CO2 exchange with the atmosphere. In TBMs the rate of photosynthesis is determined by three potentially limiting rates; fixation of CO2 by the enzyme rubisco, supply of energy from electron transport, and in some models, use of the products of photosynthesis, triose phosphates. We investigated model representation of this third potentially limiting process – triose phosphate utilization (TPU). We found that TBM representation of TPU was based on uncertain assumptions, failed to capture important responses to temperature, and was associated with an artifact that caused a marked reduction in CO2 uptake and was rarely observed in nature. We advocate for the removal of TPU limitation from TBMs.

The Impact
We found that TPU, a key process at the heart of many TBMs, was poorly represented in TBMs and that continued inclusion of TPU in TBM is not supported by current understanding and data. We found that inclusion of TPU limitation in TBMs resulted in unrealistic limitation of photosynthesis (Fig. 1) that in some models could lead to a marked reduction of CO2 uptake and poor representation of the response of photosynthesis to future global change.

Summary
This work brings together several recent lines of evidence and an examination of model representation of TPU that together strongly suggest that TPU should be removed from TBMs. Current formulations of TPU in TBMs are based on assumptions about the relationship between the capacity for carboxylation and the basal rate of TPU that are not based on measured TPU rates and do not account for the independent temperature response of TPU (Kumarathunge et al., 2019). TBM sensitivity analysis demonstrated a limitation of gross primary productivity by TPU at current CO2 concentration but most markedly at high CO2 concentration and at high latitudes (Lombardozzi et al., 2018). However, a synthesis of measurements clearly demonstrated that TPU did not limit CO2 assimilation at current CO2, even at high latitudes (Kumarathunge et al. 2019). In addition, it was recently demonstrated that most TBMs that include TPU also include a quadratic smoothing function of the three potentially limiting processes which introduces an artifactual forth limitation on photosynthesis and results in a marked reduction in modeled CO2 assimilation (Walker et al. 2021).

 

 

 

 

Contact: Alistair Rogers, Brookhaven National Laboratory, arogers@bnl.gov 

Funding
This work was supported by the Next-Generation Ecosystem Experiments (NGEE Arctic and NGEE Tropics) projects that are supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science.

Publications
Kumarathunge DP et al. “No evidence for triose phosphate limitation of light-saturated leaf photosynthesis under current atmospheric CO2 concentration.” Plant, Cell & Environment 42, 3241-3252 (2019). [DOI: 10.1111/pce.13639]

Lombardozzi DL et al. “Triose phosphate limitation in photosynthesis models reduces leaf photosynthesis and global terrestrial carbon storage.” Environmental Research Letters 13, 074025 (2018). [DOI: 10.1088/1748-9326/aacf68]

Rogers A et al  “Triose phosphate utilization limitation: an unnecessary complexity in terrestrial biosphere model representation of photosynthesis.” New Phytologist. 230, 11-22 (2021). [DOI:10.1111/nph.17092]

Walker AP et al. “Multi-hypothesis analysis of Farquhar and Collatz photosynthesis models reveals unexpected influence of empirical assumptions.” Global Change Biology. 27, 804-822 (2020). [DOI: 10.1111/gcb.15366]

The Science
The researchers explore limitations to rapid model-data integration and provide a vision for a new community cyberinfrastructure to reduce the disconnects between empirical research and modeling, including the lags between data collection and model ingest. The team details five key opportunities for community tool development designed to improve the fidelity of the models on which scientists, managers, and policymakers rely; reduce barriers to entry; and increase the speed at which new information is synthesized into a predictive framework.

The Impact
In an era of rapid global change, the capacity to predict the responses of Earth’s natural systems lags behind the ability to monitor and measure changes in the biosphere. A primary bottleneck to improvements in process understanding is the lack of community tools for informing models with observations, which reduces our ability to fully exploit the growing volume and variety of datasets. Addressing this challenge will require new infrastructure investments to provide accessible, scalable, and transparent tools that integrate the expertise of modelers and empiricists to accelerate the pace of discovery.

Summary
Process-based ecosystem models are a primary tool used by scientists, managers, and policymakers to understand and project the impacts of global change on Earth’s natural and managed ecosystems. In recent years, the volume and diversity of observational data have significantly increased, and yet the ability to incorporate this new information into predictive frameworks has lagged behind, slowing the pace of progress in model capacity to forecast natural systems. Furthermore, the insufficient communication between the non-modeling and modeling communities represents an additional bottleneck to improving the representation of underlying processes in models. In addition, the complexity and diversity of process models lead to a technical barrier to entry for new researchers. Given the breadth and depth of these challenges that transcend individual research groups, empirical and modeling communities, and funding agencies, the team argue for the development of a new community-wide cyberinfrastructure: a computational environment facilitating seamless data flows into and out of models to more rapidly simulate natural phenomena, test new hypotheses, perform standardized model evaluation, and more easily interpret results and compare predictions across a range of models. The researchers specifically provide a roadmap for this cyberinfrastructure, including five key opportunities for the development of community tools addressing this need. The team feels this new modeling paradigm is a critical step toward meeting the needs for science and society in the 21st century.

 

 

Contacts
BER Program Manager: Daniel Stover, U.S. Department of Energy Office of Science, Office of Biological and Environmental Research, Earth and Environmental Systems Sciences Division (SC-33.1). Environmental System Science, Daniel.Stover@science.doe.gov
Brian Benscoter, U.S. Department of Energy Office of Science, Office of Biological and Environmental Research, Earth and Environmental Systems Sciences Division (SC-33.1), Environmental System Science, Brian.Benscoter@science.doe.gov
Principal Investigator: Shawn P. Serbin, Scientist, Brookhaven National Laboratory, sserbin@bnl.gov (+1 631-344-3165)

Funding
This review was supported by NASA CMS (grant #80NSSC17K0711), and through the DOE Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Science Focus Area (RUBISCO SFA), which is sponsored by the Earth & Environmental Systems Modeling (EESM) Program in the Climate and Environmental Sciences Division (CESD), and the Next-Generation Ecosystem Experiments (NGEE-Arctic and NGEE- Tropics) supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science, as well as through the United States Department of Energy contract no. DE- SC0012704 to Brookhaven National Laboratory.

Publications
Fer, I., A.K. Gardella, A.N. Shiklomanov, E.E. Campbell, E.M. Cowdery, M.G. De Kauwe, A. Desai, M.J. Duveneck, J.B. Fisher, K.D. Haynes, F.M. Hoffman, M.R. Johnston, R. Kooper, D.S. LeBauer, J. Mantooth, W.J. Parton, B. Poulter, T. Quaife, A. Raiho, K. Schaefer, S.P. Serbin, J. Simkins, K.R. Wilcox, T. Viskari, and M.C. Dietze. “Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data-model integration.” Global Change Biology 27(1), 13–26 (2021). [DOI:10.1111/gcb.15409]

Related Links
Article URL (open-source article): https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.15409
Shiklomanov, A.N. et al. “Enhancing global change experiments through integration of remote-sensing techniques.” Frontiers in Ecology and the Environment 17, 215–224 (2019). [DOI:10.1002/fee.2031] Raczka, B., M.C. Dietze, S.P. Serbin, and K.J. Davis. “What limits predictive certainty of long-term carbon uptake?” Journal of Geophysical Research-Biogeosciences 123(12), 3570–3588 (2018). [DOI:10.1029/2018JG004504] Dietze, M.C., S.P. Serbin, C. Davidson, A.R. Desai, X. Feng, R. Kelly, R. Kooper, D. LeBauer, J. Mantooth, K. McHenry, and D. Wang. “A quantitative assessment of a terrestrial biosphere model’s data needs across North American biomes.” Journal of Geophysical Research-Biogeosciences 119(3), 286–300 (2014). [DOI:10.1002/2013JG002392]

The Science
Remote sensing has become a critically important tool for researchers who study Earth’s ecosystems and minerals. In particular, imaging spectroscopy – or the measurement of many, continuous spectral channels across visible and non-visible wavelengths – and thermal infrared imagery are essential for inferring plant health, ecosystem function, biodiversity, and solid earth research. Reviewing the requirements of the NASA Surface Biology and Geology (SBG) Designated Observable, a proposed global imaging spectroscopy and thermal infrared Earth Observing satellite, over 130 scientists reviewed the current state of imaging spectroscopy algorithms and state-of-the-art methods for remote sensing of surface, terrestrial and aquatic ecosystems.  

The Impact
Regular monitoring of the state, functioning and biodiversity of Earth’s terrestrial, freshwater, and coastal aquatic ecosystems is essential for understanding the impacts of severe weather, disturbance, and climate change on natural resources, potential feedbacks to climate and the management of resources, and defining policy. Remote sensing technologies are essential for large-scale monitoring, but current satellite platforms are insufficient to fill this need. The SBG Designated Observable, a novel combination of high spatial resolution spectral and thermal infrared imagery, is uniquely designed to address these challenges and provide key observations for studying hydrological, ecological, weather, climate and solid earth dynamics.  

Summary
Monitoring Earth’s diverse natural resources and managed ecosystems is a significant challenge but essential for balancing the maintenance of health, diversity and resource utilization. Vegetation plays a key role in regulating climate and weather, while the state and health of freshwater and coastal ecosystems impacts global circulation patterns, as well as fisheries and recreation. Scientists and policy makers require tools to provide the information needed to understand how the Earth is changing and to define management strategies for the maintenance of biodiversity. The 2017-2027 National Academy of Sciences Decadal Survey, Thriving on our Changing Planet, identified the critical need for a global imaging spectrometer (IS) combined with a multi-spectral thermal infrared (TIR) imager with a high spatial resolution (~30 meters for the IS and ~60 meters for the TIR) and sub-monthly temporal resolution. The Surface Biology and Geology (SBG) Designated Observable is designed to meet the needs for regular mapping of the state and changes in Earth’s resources. A team of more than 130 scientists synthesized applications and methods for using SBG to provide the observations needed to inform science and management strategies. The team also identified the necessary next steps needed to prepare for an operational SBG-like satellite to monitor Earth.  

 

 

 

 

Contacts
BER Program Manager: Daniel Stover, U.S. Department of Energy Office of Science, Office of Biological and Environmental Research, Earth and Environmental Systems Sciences Division (SC-33.1), Environmental System Science, Daniel.Stover@science.doe.gov
Brian Benscoter, U.S. Department of Energy Office of Science, Office of Biological and Environmental Research, Earth and Environmental Systems Sciences Division (SC-33.1), Environmental System Science, Brian.Benscoter@science.doe.gov
Principal Investigator: Shawn P. Serbin, Scientist, Brookhaven National Laboratory, sserbin@bnl.gov (+1 631-344-3165)

Funding
Support to the lead authors (Cawse-Nicholson and Townsend) was provided by NASA Headquarters and the Jet Propulsion Laboratory, California Institute of Technology. This study was also supported by the Space-based Imaging Spectroscopy and Thermal (SISTER) pathfinder, part of the Surface Biology and Geology (SBG) project, a NASA Earth Science Designated Observable. Adam Erickson’s contribution was supported by an appointment to the NASA Postdoctoral Program at NASA Goddard Space Flight Center, administered by Universities Space Research Association under contract with NASA. Robert Frouin was supported by NASA’s Ocean Biology and Biogeochemistry Program under various grants. The contribution of Michael E. Schaepman is supported by the University of Zurich Research Priority Programme on Global Change and Biodiversity (URPP GCB). Shawn Serbin was supported by the Next-Generation Ecosystem Experiments in the Arctic (NGEE-Arctic) and tropics (NGEE-Tropics) that are supported by the Office of Biological and Environmental Research in the Department of Energy, Office of Science, and through the U.S. DOE contract No. DE-SC0012704 to Brookhaven National Laboratory. The authors thank the other members of the SBG Algorithms Working Group, constituting more than 130 researchers, who participated in telecons and webinars to contribute to the contents of this paper. Two anonymous reviewers provided invaluable insight and recommendations, and we are grateful for their time and suggestions. Part of the research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. © 2021 California Institute of Technology. Government sponsorship is acknowledged.  

Publications
Cawse-Nicholson, K., P. A. Townsend, D. Schimel, A. M. Assiri, P. L. Blake, M. F. Buongiorno, P. Campbell, N. Carmon, K. A. Casey, R. E. Correa-Pabón, K. M. Dahlin, H. Dashti, P. E. Dennison, H. Dierssen, A. Erickson, J. B. Fisher, R. Frouin, C. K. Gatebe, H. Gholizadeh, M. Gierach, N. F. Glenn, J. A. Goodman, D. M. Griffith, L. Guild, C. R. Hakkenberg, E. J. Hochberg, T. R. H. Holmes, C. Hu, G. Hulley, K. F. Huemmrich, R. M. Kudela, R. F. Kokaly, C. M. Lee, R. Martin, C. E. Miller, W. J. Moses, F. E. Muller-Karger, J. D. Ortiz, D. B. Otis, N. Pahlevan, T. H. Painter, R. Pavlick, B. Poulter, Y. Qi, V. J. Realmuto, D. Roberts, M. E. Schaepman, F. D. Schneider, F. M. Schwandner, S. P. Serbin, A. N. Shiklomanov, E. N. Stavros, D. R. Thompson, J. L. Torres-Perez, K. R. Turpie, M. Tzortziou, S. Ustin, Q. Yu, Y. Yusup, and Q. Zhang. 2021. “NASA’s surface biology and geology designated observable: A perspective on surface imaging algorithms”. Remote Sensing of Environment 257, 112349.[DOI: https://doi.org/10.1016/j.rse.2021.112349]

Related Links
Article URL (open-source article): https://www.sciencedirect.com/science/article/pii/S0034425721000675?via%3Dihub
Article DOI: https://doi.org/10.1016/j.rse.2021.112349
NASA Surface Biology and Geology Designated Observable: https://sbg.jpl.nasa.gov/
Remote Sensing of Plant Biodiversity: https://link.springer.com/book/10.1007%2F978-3-030-33157-3
Serbin S.P., Townsend P.A. (2020) Scaling Functional Traits from Leaves to Canopies. In: Cavender-Bares J., Gamon J.A., Townsend P.A. (eds) Remote Sensing of Plant Biodiversity. Springer, Cham. https://doi.org/10.1007/978-3-030-33157-3_3

The Science
Scientific hypotheses describe how processes might work in the natural world. Computer models are built from mathematical descriptions of these hypotheses. Alternative hypotheses are common, especially in environmental sciences, yet most computer models cannot easily switch among these alternative hypotheses. DOE scientists have developed a new model that can switch between hypotheses and prioritize which process to study further, a “multi-hypothesis model.” Using the model to study common leaf photosynthesis models, scientists found the surprising importance of a process that has previously received little attention. New data were then collected to discriminate among the alternative hypotheses finding support for the more traditional approach.

The Impact
Leaf photosynthesis models simulate the rhythms of CO2 transfer from the atmosphere to plants. This study highlights a key shortfall in photosynthesis modeling and in our general approach to developing and using predictive models of the terrestrial biosphere. Models can reach the same end point in multiple ways. This can lead to models “getting it right for the wrong reasons.” The multi-hypothesis approach will help to identify key processes causing model differences and evaluate alternative hypotheses to describe those processes. Ultimately leading to more robust predictions of terrestrial ecosystems.

Summary
Leaf photosynthesis models are the beating heart of global carbon cycle models. These photosynthesis models simulate the rhythms of CO2 transfer from the atmosphere into plants and terrestrial ecosystems. The reigning king of photosynthesis models is the Farquhar model published in 1980. However, despite its almost ubiquitous use, there are a number of variations in how the mechanics of various component processes are mathematically described, i.e. there are various mathematical hypotheses that describe some of the sub-processes within the overarching Farquhar model. The consequences of these alternative choices have never been formally investigated. In part this is because methods to formally investigate model sensitivity to variation in how processes are represented have only recently been developed. Novel multi-hypothesis modeling methods were applied to investigate the influence of 14 parameters and four processes with alternative representations in photosynthesis models, finding the surprising dominance of a process that has not been extensively evaluated with data. Running the alternatives of this dominant process in global models resulted in a difference in photosynthesis equivalent to annual human CO2 emissions. This multi-hypothesis model evaluation identified as important two alternative hypotheses for photosynthetic limiting rate selection. Novel, high-resolution photosynthesis measurements were designed and undertaken to discriminate among these hypotheses. General support for the original Farquhar implementation was found and is recommended for use to reduce uncertainty in global photosynthesis simulations.

 

Contact: Anthony Walker, Senior Scientist, Oak Ridge National Lab, walkerap@ornl.gov

Funding
U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research: Next Generation Ecosystem Experiments-Tropics, ORNL Terrestrial Ecosystem Science SFA, university grant DE-SC0019438. National Science Foundation support for National Center for Atmospheric Research.

Publications
Walker, A. P. et al. “Multi-hypothesis comparison of Farquhar and Collatz photosynthesis models reveals the unexpected influence of empirical assumptions at leaf and global scales.” Global Change Biology 27, 804–822 (2021). [DOI: 10.1111/gcb.15366]

Related Links
Huntingford, C. & Oliver, R. J. “Converging towards a common representation of large-scale photosynthesis.” Global Change Biology 27, 716-718 (2020).

The Science
Tall trees are key drivers of ecosystem processes in tropical forests, but what controls the distribution of the very tallest trees remains poorly understood. The recent discovery of giant trees over 80 meters tall in the Amazon forest requires a reevaluation of current thinking.

The Impact
We found that changes in wind and light availability drive giant tree distribution as much as precipitation and temperature, together shaping the forest structure of the Brazilian Amazon. The location of giant trees should be carefully considered by policymakers when identifying important hot spots for the conservation of biodiversity.

Summary
In this study, we employed the largest airborne lidar data collection in the Amazon to contribute to the understanding of (a) how resources and disturbances shape the maximum height distribution across the Brazilian Amazon, and (b) what drives the occurrence of giant trees (taller than 70 m). We conducted an extensive analysis relating environmental variables to the maximum height recorded in the lidar transects (Figure 1). Common drivers of height development are fundamentally different from those influencing the occurrence of giant trees. We found that changes in wind and light availability drive giant tree distribution as much as precipitation and temperature, together shaping the forest structure of the Brazilian Amazon. Ultimately, the association between environmental conditions and mechanisms of natural selection are key to understanding the complexity of this process in a changing climate.

 

 

 

 

Contacts (BER PM): Daniel Stover, SC-23.1, Daniel.Stover@science.doe.gov (301-903-0289)

PI Contact: Eric Bastos Gorgens, Universidade Federal dos Vales do Jequitinhonha e Mucuri – UFVJM – Brazil, eric.gorgens@ufvjm.edu.br

Funding
Funding was provided by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Brasil (CAPES; Finance Code 001); Conselho Nacional de Desenvolvimento Científico e Tecnológico (Processes 403297/2016‐8 and 301661/2019‐7); Amazon Fund (grant 14.2.0929.1); National Academy of Sciences and US Agency for International Development (grant AID‐OAA‐A‐11–00012); Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM); and Instituto Nacional de Pesquisas Espaciais (INPE). D. Almeida was supported by the São Paulo Research Foundation (#2018/21338‐3 and #2019/14697‐0). B. Gimenez, G. Spanner, and N. Higuchi were supported by INCT‐Madeiras da Amazônia and Next Generation Ecosystem Experiments‐Tropics (NGEE‐Tropics), as part of DOE’s Terrestrial Ecosystem Science Program – Contract No. DE‐AC02‐05CH11231. T. Jackson and D. Coomes were supported by the UK Natural Environment Research Council grant NE/S010750/1. M. Nunes was supported by the Academy of Finland (decision number 319905). J. Rosette was supported by the Royal Society University Research Fellowship (URF\R\191014).

Publications
Gorgens, E. B., Nunes, M. H., Jackson, T., Coomes, D., Keller, M., Reis, C. R., Valbuena, R., Rosette, J., Almeida, D. R. A., Gimenez, B., Cantinho, R., Motta, A. Z., Assis, M., Pereira, F. R. S., Spanner, G., Higuchi, N., Ometto, J. P. (2021). Resource availability and disturbance shape maximum tree height across the Amazon. Global Change Biology, 27(1), 177-189. https://doi.org/10.1111/gcb.15423