Extending our scientific reach in arboreal ecosystems for research and management

The arboreal ecosystem is vitally important to global and local biogeochemical processes, the maintenance of biodiversity in natural systems, and human health in urban environments. The ability to collect samples, observations, and data to conduct meaningful scientific research is similarly vital. The primary methods and modes of access remain limited and difficult. In an online survey, canopy researchers (n = 219) reported a range of challenges in obtaining adequate samples, including ∼10% who found it impossible to procure what they needed. Currently, these samples are collected using a combination of four primary methods: (1) sampling from the ground; (2) tree climbing; (3) constructing fixed infrastructure; and (4) using mobile aerial platforms, primarily rotorcraft drones. An important distinction between instantaneous and continuous sampling was identified, allowing more targeted engineering and development strategies. The combination of methods for sampling the arboreal ecosystem provides a range of possibilities and opportunities, particularly in the context of the rapid development of robotics and other engineering advances. In this study, we aim to identify the strategies that would provide the benefits to a broad range of scientists, arborists, and professional climbers and facilitate basic discovery and applied management. Priorities for advancing these efforts are (1) to expand participation, both geographically and professionally; (2) to define 2–3 common needs across the community; (3) to form and motivate focal teams of biologists, tree professionals, and engineers in the development of solutions to these needs; and (4) to establish multidisciplinary communication platforms to share information about innovations and opportunities for studying arboreal ecosystems

Where and why do particulate organic matter (POM) and mineral-associated organic matter (MAOM) differ among diverse soils?

Soil organic matter (SOM) has often been separated into operational physical fractions, such as particulate organic matter (POM) and mineral-associated organic matter (MAOM), to improve our understanding of SOM persistence. While it is generally assumed that POM and MAOM have distinct biogeochemical characteristics, it remains unresolved where and why POM and MAOM differ in their composition and relationships to total SOM decomposition among heterogenous soils. We analyzed elemental, isotopic, and chemical composition, including diffuse reflectance infrared Fourier transform (DRIFT) spectra, of POM and MAOM in 156 soil samples collected from 20 National Ecological Observatory Network (NEON) sites spanning diverse ecosystems (tundra to tropics) across North America. We used a classic size separation method for POM (53–2000 μm) and MAOM ( 1200 mm annual precipitation (with MAOM C/N > 15). Multiple statistical analyses showed that C quantity and chemical composition of MAOM could as effectively predict soil C decomposition during an 18-month incubation as measures of POM. Thus, POM and MAOM both likely contributed significantly to decomposition over timescales of months, possibly because characteristics of POM and MAOM were often related and/or a large pool size of MAOM could compensate for its lower decomposition rate relative to POM. Further, we found that soil geochemical composition (such as silt and clay, calcium, oxalate-extractable iron and aluminum), along with climate and ecosystem type, could partly predict differences in quantity and composition between POM and MAOM. Overall, relative coupling vs. decoupling between POM and MAOM among soils was predictable based on geochemistry, and these similarities/differences provide insight into variation in the plant-derived sources of MAOM across diverse ecosystems. The importance of MAOM to short-term soil C decomposition has probably been underappreciated.This is a manuscript of an article published as Yu, Wenjuan, Wenjuan Huang, Samantha R. Weintraub-Leff, and Steven J. Hall. "Where and why do particulate organic matter (POM) and mineral-associated organic matter (MAOM) differ among diverse soils?." Soil Biology and Biochemistry 172 (2022): 108756. doi:10.1016/j.soilbio.2022.108756. Posted with permisssion. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License

Standardized Data to Improve Understanding and Modeling of Soil Nitrogen at Continental Scale

Nitrogen (N) is a key limiting nutrient in terrestrial ecosystems, but there remain critical gaps in our ability to predict and model controls on soil N cycling. This may be in part due to lack of standardized sampling across broad spatial–temporal scales. Here, we introduce a continentally distributed, publicly available data set collected by the National Ecological Observatory Network (NEON) that can help fill these gaps. First, we detail the sampling design and methods used to collect and analyze soil inorganic N pool and net flux rate data from 47 terrestrial sites. We address methodological challenges in generating a standardized data set, even for a network using uniform protocols. Then, we evaluate sources of variation within the sampling design and compare measured net N mineralization to simulated fluxes from the Community Earth System Model 2 (CESM2). We observed wide spatiotemporal variation in inorganic N pool sizes and net transformation rates. Site explained the most variation in NEON’s stratified sampling design, followed by plots within sites. Organic horizons had larger pools and net N transformation rates than mineral horizons on a sample weight basis. The majority of sites showed some degree of seasonality in N dynamics, but overall these temporal patterns were not matched by CESM2, leading to poor correspondence between observed and modeled data. Looking forward, these data can reveal new insights into controls on soil N cycling, especially in the context of other environmental data sets provided by NEON, and should be leveraged to improve predictive modeling of the soil N cycle.This article is published as Weintraub-Leff, S. R., Hall, S. J., Craig, M. E., Sihi, D., Wang, Z., & Hart, S. C. (2023). Standardized data to improve understanding and modeling of soil nitrogen at continental scale. Earth's Future, 11, e2022EF003224. https://doi.org/10.1029/2022EF003224. Posted with permission.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited

Resolving the influence of lignin on soil organic matter decomposition with mechanistic models and continental-scale data

Confidence in model estimates of soil CO2 flux depends on assumptions regarding fundamental mechanisms that control the decomposition of litter and soil organic carbon (SOC). Multiple hypotheses have been proposed to explain the role of lignin, an abundant and complex biopolymer that may limit decomposition. We tested competing mechanisms using data-model fusion with modified versions of the CN-SIM model and a 571-day laboratory incubation dataset where decomposition of litter, lignin, and SOC was measured across 80 soil samples from the National Ecological Observatory Network. We found that lignin decomposition consistently decreased over time in 65 samples, whereas in the other 15 samples, lignin decomposition subsequently increased. These “lagged-peak” samples can be predicted by low soil pH, high extractable Mn, and fungal community composition as measured by ITS PC2 (the second principal component of an ordination of fungal ITS amplicon sequences). The highest-performing model incorporated soil biogeochemical factors and daily dynamics of substrate availability (labile bulk litter:lignin) that jointly represented two hypotheses (C substrate limitation and co-metabolism) previously thought to influence lignin decomposition. In contrast, models representing either hypothesis alone were biased and underestimated cumulative decomposition. Our findings reconcile competing hypotheses of lignin decomposition and suggest the need to precisely represent the role of lignin and consider soil metal and fungal characteristics to accurately estimate decomposition in Earth-system models.This article is published as Yi, B., Lu, C., Huang, W., Yu, W., Yang, J., Howe, A., Weintraub-Leff, S. R., & Hall, S. J. (2023). Resolving the influence of lignin on soil organic matter decomposition with mechanistic models and continental-scale data. Global Change Biology, 00, 1–13. https://doi.org/10.1111/gcb.16875. Posted with permission.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made

Do we need to understand microbial communities to predict ecosystem function? A comparison of statistical models of nitrogen cycling processes

International audienceDespite the central role of microorganisms in biogeochemistry, process models rarely explicitly account for variation in communities. Here, we use statistical models to address a fundamental question in ecosystem ecology: do we need to better understand microbial communities to accurately predict ecosystem function? Nitrogen (N) cycle process rates and associated gene abundances were measured in tropical rainforest soil samples collected in May (early wet season) and October (late wet season). We used stepwise linear regressions to examine the explanatory power of edaphic factors and functional gene relative abundances alone and in combination for N-cycle processes, using both our full dataset and seasonal subsets of the data. In our full dataset, no models using gene abundance data explained more variation in process rates than models based on edaphic factors alone, and models that contained both edaphic factors and community data did not explain significantly more variation in process rates than edaphic factor models. However, when seasonal datasets were examined separately, microbial predictors enhanced the explanatory power of edaphic predictors on dissimilatory nitrate reduction to ammonium and N2O efflux rates during October. Because there was little variation in the explanatory power of microbial predictors alone between seasonal datasets, our results suggest that environmental factors we did not measure may be more important in structuring communities and regulating processes in October than in May. Thus, temporal dynamics are key to understanding the relationships between edaphic factors, microbial communities and ecosystem function in this system. The simple statistical method presented here can accommodate a variety of data types and should help prioritize what forms of data may be most useful in ecosystem model development

People, infrastructure, and data: A pathway to an inclusive and diverse ecological network of networks

Abstract Macrosystem‐scale research is supported by many ecological networks of people, infrastructure, and data. However, no network is sufficient to address all macrosystems ecology research questions, and there is much to be gained by conducting research and sharing resources across multiple networks. Unfortunately, conducting macrosystem research across networks is challenging due to the diversity of expertise and skills required, as well as issues related to data discoverability, veracity, and interoperability. The ecological and environmental science community could substantially benefit from networking existing networks to leverage past research investments and spur new collaborations. Here, we describe the need for a “network of networks” (NoN) approach to macrosystems ecological research and articulate both the challenges and potential benefits associated with such an effort. We describe the challenges brought by rapid increases in the volume, velocity, and variety of “big data” ecology and highlight how a NoN could build on the successes and creativity within component networks, while also recognizing and improving upon past failures. We argue that a NoN approach requires careful planning to ensure that it is accessible and inclusive, incorporates multimodal communications and ways to interact, supports the creation, testing, and promulgation of community standards, and ensures individuals and groups receive appropriate credit for their contributions. Additionally, a NoN must recognize important trade‐offs in network architecture, including how the degree of centralization of people, infrastructure, and data influence network scalability and creativity. If implemented carefully and thoughtfully, a NoN has the potential to substantially advance our understanding of ecological processes, characteristics, and trajectories across broad spatial and temporal scales in an efficient, inclusive, and equitable manner

Design, development, and implementation of IsoBank: A centralized repository for isotopic data.

Acknowledgements: We thank all community members who assisted with the design, implementation, and testing of the IsoBank repository. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Stable isotope data have made pivotal contributions to nearly every discipline of the physical and natural sciences. As the generation and application of stable isotope data continues to grow exponentially, so does the need for a unifying data repository to improve accessibility and promote collaborative engagement. This paper provides an overview of the design, development, and implementation of IsoBank (www.isobank.org), a community-driven initiative to create an open-access repository for stable isotope data implemented online in 2021. A central goal of IsoBank is to provide a web-accessible database supporting interdisciplinary stable isotope research and educational opportunities. To achieve this goal, we convened a multi-disciplinary group of over 40 analytical experts, stable isotope researchers, database managers, and web developers to collaboratively design the database. This paper outlines the main features of IsoBank and provides a focused description of the core metadata structure. We present plans for future database and tool development and engagement across the scientific community. These efforts will help facilitate interdisciplinary collaboration among the many users of stable isotopic data while also offering useful data resources and standardization of metadata reporting across eco-geoinformatics landscapes