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Plant litter decomposition is a primary control on carbon fluxes in terrestrial ecosystems around the world. Individually, the key mediators of decomposition rates—litter traits, temperature, and moisture—are relatively well understood. However, our understanding of how combined drivers influence decomposition remains limited. To test how multiple, interactive climate change factors directly alter decomposition rates and indirectly influence leaf litter decomposition rates by altering substrate chemistry, we conducted two decomposition experiments within the Boreal Forest Warming at an Ecotone in Danger (B4WarmED) study in Minnesota, USA. Our first experiment decomposed ambient-grown leaf litter from eight common tree species under a factorial combination of warming and rainfall reduction treatments. We found that the direct effects of combined warming and rainfall reduction increased litter half-life by 42% ± 11% in comparison to ambient plots with no warming or rainfall reduction. In contrast, only rainfall reduction influenced litter mean residence time, which increased by 37% ± 18% in comparison to ambient rainfall plots. Our second experiment decomposed ambient- and warm-grown leaf litter from the same eight species under ambient and warmed conditions. We found that warming slowed decomposition of both litter types, but warm-grown litter had a 22% ± 6.5% shorter half-life than ambient-grown leaf tissue under ambient environmental conditions. Warm grown litter half-life then increased by 36% ± 11% with warmed environmental conditions. Our results highlight that climate change could slow carbon and nutrient cycling in systems where moisture becomes a limiting factor. In addition, our study demonstrates that there may be an overlooked relationship between the growth conditions of plants and the temperature of decomposition. This nuanced understanding of decomposition can then support carbon cycling models and more effective nature-based climate mitigation efforts. 

Abstract The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly‐generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations. 

The communities of Puerto Rico are highly vulnerable to climate change as the archipelago has experienced a multitude of compounding crises and extreme weather events in recent years. To address these issues, the research, analysis, and design of grand challenge solutions for disaster-prone regions like Puerto Rico can utilize collaborative transdisciplinary efforts. Local non-governmental and community-based organizations have a pivotal role in the reconstruction processes and the building of community and environmental resilience in underserved communities. This paper contributes an empirical case study of an online transdisciplinary collaboration between a group of academics and a Puerto Rican non-governmental organization, Caras con Causa. From participant observation, it includes a document analysis of meeting notes with cohort members who were involved in a collaborative National Science Foundation Project, The INFEWS-ER: A Virtual Resource Center Enabling Graduate Innovations at the Nexus of Food, Energy, and Water Systems, with Caras con Causa between October 2020 and April 2021. Caras con Causa focuses on uplifting Puerto Ricans by creating and administering environmental, educational, economic, and community programs, highlighting disaster relief and resilience to help Puerto Rican food, energy, and water systems. Eight key discussion themes emerged from the document analysis: team organization, collaboration with Caras con Causa, deliverables, team contributions, context understanding, participation outcomes, technology setup, and lessons learned. We analyze each of the emerging themes to explain how academics may use transdisciplinary skill sets in addition to standard disciplinary-based approaches or techniques to enhance the institutional capacity of a non-governmental organization doing community resilience work to benefit local food, energy, and water systems. While the learned lessons in this non-governmental organization-academic collaboration may be context-specific, we provide insights that may be generalizable to collaborations in comparable transdisciplinary settings. 

Disturbances fundamentally alter ecosystem functions, yet predicting their impacts remains a key scientific challenge. While the study of disturbances is ubiquitous across many ecological disciplines, there is no agreed-upon, cross-disciplinary foundation for discussing or quantifying the complexity of disturbances, and no consistent terminology or methodologies exist. This inconsistency presents an increasingly urgent challenge due to accelerating global change and the threat of interacting disturbances that can destabilize ecosystem responses. By harvesting the expertise of an interdisciplinary cohort of contributors spanning 42 institutions across 15 countries, we identified an essential limitation in disturbance ecology: the word ‘disturbance’ is used interchangeably to refer to both the events that cause, and the consequences of, ecological change, despite fundamental distinctions between the two meanings. In response, we developed a generalizable framework of ecosystem disturbances, providing a well-defined lexicon for understanding disturbances across perspectives and scales. The framework results from ideas that resonate across multiple scientific disciplines and provides a baseline standard to compare disturbances across fields. This framework can be supplemented by discipline-specific variables to provide maximum benefit to both inter- and intra-disciplinary research. To support future syntheses and meta-analyses of disturbance research, we also encourage researchers to be explicit in how they define disturbance drivers and impacts, and we recommend minimum reporting standards that are applicable regardless of scale. Finally, we discuss the primary factors we considered when developing a baseline framework and propose four future directions to advance our interdisciplinary understanding of disturbances and their social-ecological impacts: integrating across ecological scales, understanding disturbance interactions, establishing baselines and trajectories, and developing process-based models and ecological forecasting initiatives. Our experience through this process motivates us to encourage the wider scientific community to continue to explore new approaches for leveraging Open Science principles in generating creative and multidisciplinary ideas.