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Logging and forest conversion are occurring at alarming rates in tropical forests. These disturbances alter soil microbial community structure and functions. While direct links between changes in soil properties, such as pH and microbial community structure are well established, the indirect effects of logging and forest conversion on soil microbial community structure and functions are poorly understood. We used a space-for-time substitution to investigate the changes in soil microbial diversity and functions across a forest recovery gradient in the tropical montane forests of northern Borneo. We used surface (top 5 cm) soil to assess soil physicochemical and microbial (next-generation DNA sequencing) properties, and standardized litterbags (Tea Bag Index) to assess litter decomposition and stabilization. Our results show that bacterial and fungal diversity increases with recovery time and reaches pre-disturbance levels between 60- and 80-years post-disturbance. Litter decomposition rate constants increased linearly with increasing bacterial and fungal diversity. Litter stabilization also increased linearly with fungal diversity, but was highest at intermediate levels of bacterial diversity. Our results provide insights on the effects of forest logging and conversion on soils and highlight the tight coupling between soil microbial diversity and soil functions in tropical montane forests. 

The dramatic increase in greenhouse gas (GHG) emissions by humans over the past century and a half has created an urgency for monitoring, reporting, and verifying GHG emissions as a first step toward mitigating the effects of climate change. Fifteen percent of global GHG emissions come from agriculture, and companies in the food and beverage industry are starting to set climate goals. We examined the GHG emissions reporting practices and climate goals of the top 100 global food and beverage companies (as ranked by Food Engineering) and determined whether their goals are aligned with the science of keeping climate warming well below a 2°C increase. Using publicly disclosed data in CDP Climate reports and company sustainability reports, we found that about two thirds of the top 100 global food and beverage companies disclose at least part of their total company emissions and set some sort of climate goal that includes scope 1 and 2 emissions. However, only about half have measured, disclosed, and set goals for scope 3 emissions, which often encompass about 88% of a company's emissions across the entire value chain on average. We also determined that companies, despite setting scope 1, 2, and 3 emission goals, may be missing the mark on whether their goals are significantly reducing global emissions. Our results present the current disclosure and emission goals of the top 100 global food and beverage companies and highlight an urgent need to begin and continue to set truly ambitious, science-aligned climate goals. 

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.

 

Soil microbial communities play critical roles in various ecosystem processes, but studies at a large spatial and temporal scale have been challenging due to the difficulty in finding the relevant samples in available data sets as well as the lack of standardization in sample collection and processing. The National Ecological Observatory Network (NEON) has been collecting soil microbial community data multiple times per year for 47 terrestrial sites in 20 eco‐climatic domains, producing one of the most extensive standardized sampling efforts for soil microbial biodiversity to date. Here, we introduce the neonMicrobe R package—a suite of downloading, preprocessing, data set assembly, and sensitivity analysis tools for NEON’s newly published 16S and ITS amplicon sequencing data products which characterize soil bacterial and fungal communities, respectively. neonMicrobe is designed to make these data more accessible to ecologists without assuming prior experience with bioinformatic pipelines. We describe quality control steps used to remove quality‐flagged samples, report on sensitivity analyses used to determine appropriate quality filtering parameters for the DADA2 workflow, and demonstrate the immediate usability of the output data by conducting standard analyses of soil microbial diversity. The sequence abundance tables produced byneonMicrobecan be linked to NEON’s other data products (e.g., soil physical and chemical properties, plant community composition) and soil subsamples archived in the NEON Biorepository. We provide recommendations for incorporatingneonMicrobeinto reproducible scientific workflows, discuss technical considerations for large‐scale amplicon sequence analysis, and outline future directions for NEON‐enabled microbial ecology. In particular, we believe that NEON marker gene sequence data will allow researchers to answer outstanding questions about the spatial and temporal dynamics of soil microbial communities while explicitly accounting for scale dependence. We expect that the data produced by NEON and theneonMicrobeR package will act as a valuable ecological baseline to inform and contextualize future experimental and modeling endeavors.

 

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.