Characterising Soil Eukaryotic Diversity From NEON Metagenomics Datasets

The National Ecological Observatory Network (NEON) is the first-ever continental-scale ecological observatory and is designed to collect and provide open data to the scientific community for the next 30 years. NEON is at a very exciting moment in its history – after almost two decades of design and planning by committed and visionary scientists and engineers, the construction of 47 terrestrial and 34 aquatic field sites is nearly complete. At this moment, nearly 300 field scientists are out in deserts, grasslands, forests, and streams taking measurements that will turn into over 180, freely available, NEON data products. The collection and delivery of these data are designed to enable transformative research on US ecosystems at previously unimaginable spatial and temporal scales. Most ecologists, including myself, entered this field because of a fascination with the causes and complexities of ecological interactions. I began my career studying small mammals, plant reproductive strategies, and bird foraging behavior. I was thrilled to be in the field, recording the patterns of nature near my undergraduate institution in the rolling tallgrass prairies of eastern Kansas. My observations made their way from pencil to paper to high stacks of punch cards sent through large, noisy computers. Fast forward 30 years, and we now have access to large datasets and complex statistical models to synthesize ecological observations in the hope of achieving broad-scale understanding and ecological forecasts. NEON is an NSF-funded large research infrastructure project designed to provide a rich set of resources to scientists. As such, it is vital that members of the scientific community stay engaged throughout the project's lifetime. In the years since NEON was originally imagined, the perception of how closely ecologists have been involved has fluctuated, with some voicing the opinion that NEON was moving forward without sufficient scholarly input. My top priority, as NEON's new Director and Chief Scientist, is to improve and strengthen NEON engagement with the scientific community. To begin, we solicited feedback to inform a strategic plan to guide our engagement activities, not just at headquarters but across all our field sites. Recent feedback demonstrated strong interest in using NEON's data and infrastructure but also some important concerns that need to be addressed. For such a large public investment, I believe that it is essential to show the unique value of the NEON project. For example, rather than being redundant or competitive with other funded programs, NEON provides complementary and comprehensive data from the soils to the atmosphere from an extensive suite of field sites. The data products are publicly available and can be used in conjunction with additional observations or with ongoing or newly proposed experiments at those sites. These resources provide tremendous added value to our research capabilities for addressing novel and societally relevant questions. Feedback from the community has also revealed that many potential users have not searched through our currently available data products: indeed, 38% of respondents to a recent survey said that they did not know if NEON resources are easy to access or whether they are of high quality. In my short time at NEON, I have been impressed with the rigor of the quality assurance and quality control processes in place, starting from when data collection is planned, through implementation of detailed collection protocols, to processing and delivery. All along the path from field to portal, the data are validated and checked to ensure high quality for the user community. In response to feedback, we are actively developing detailed information to make those procedures more transparent to all users. I am excited to be in my new position because I am passionate about ecology, and I believe that NEON is a vast resource for the scientific community. The observatory will facilitate research and education by our community (and beyond) in ways that we have not yet imagined and will propel our knowledge forward. I envision NEON as a hub of innovative research synthesis on ecological responses to environmental change, and as a nucleus of ecological science for the next generation of ecologists. To repeat an often-cited phrase, whatever you thought about NEON, think again. This is a tremendous opportunity for the ecological community, and I invite you to join us at this exciting time. Visit the NEON website (www.neonscience.org) to learn more about ways you can engage and provide your feedback. We want to hear from you and support your exciting work…this is your observatory! SHARON K COLLINGE Chief Scientist/Observatory Director, National Ecological Observatory Network, Boulder, CO

Woody plant encroachment (WPE) is transforming grasslands globally, yet accurately mapping this process remains challenging. State-funded, publicly available high-resolution aerial imagery offers a potential solution, including the USDA’s National Agriculture Imagery Program (NAIP) and NSF’s National Ecological Observatory Network (NEON) Aerial Observation Platform (AOP). We evaluated the accuracy of land cover classification using NAIP, NEON, and both sources combined. We compared two machine learning models—support vector machines and random forests—implemented in R using large training and evaluation data sets. Our study site, Konza Prairie Biological Station, is a long-term experiment in which variable fire and grazing have created mosaics of herbaceous plants, shrubs, deciduous trees, and evergreen trees (Juniperus virginiana). All models achieved high overall accuracy (>90%), with NEON slightly outperforming NAIP. NAIP underperformed in detecting evergreen trees (52–78% vs. 83–86% accuracy with NEON). NEON models relied on LiDAR-based canopy height data, whereas NAIP relied on multispectral bands. Combining data from both platforms yielded the best results, with 97.7% overall accuracy. Vegetation indices contributed little to model accuracy, including NDVI (normalized digital vegetation index) and EVI (enhanced vegetation index). Both machine learning methods achieved similar accuracy. Our results demonstrate that free, high-resolution imagery and open-source tools can enable accurate, high-resolution, landscape-scale WPE monitoring. Broader adoption of such approaches could substantially improve the monitoring and management of grassland biodiversity, ecosystem function, ecosystem services, and environmental resilience.

Expanded availability of estimates of ecosystem metabolism and gas exchange from the worlds streams and rivers is rapidly revising estimates of river contributions to global carbon budgets. Here, we present estimates of gross primary production, ecosystem respiration, and gas exchange from 27 streams and rivers across North America, including Puerto Rico, using data from the National Ecological Observatory Network (NEON). Further, we explore how aggregating and processing input data influences model outputs, expanding the methodological knowledge in approaching sensor collection and manipulation for ecosystem-scale modelling. We apply filters to input data to determine how different approaches to quality control of raw data influence the quantity and precision of estimates of ecosystem metabolism. Model estimates are high priority measures of ecosystem function that integrate additional NEON data products that will allow further understanding of stream and river biogeochemistry and ecosystem function across time and space.

AimCanopy structural complexity, which describes the degree of heterogeneity in vegetation density, is strongly tied to a number of ecosystem functions, but the community and structural characteristics that give rise to variation in complexity at site to subcontinental scales are poorly defined. We investigated how woody plant taxonomic and phylogenetic diversity, maximum canopy height, and leaf area index (LAI) relate to canopy rugosity, a measure of canopy structural complexity that is correlated with primary production, light capture, and resource‐use efficiency.LocationOur analysis used 122 plots distributed across 10 ecologically and climatically variable forests spanning a > 1,500 km latitudinal gradient within the National Ecological Observatory Network (NEON) of the USA.Time period2016–2018.Taxa studiedWoody plants.MethodsWe used univariate and multivariate modelling to examine relationships between canopy rugosity, and community and structural characteristics hypothesized to drive site and subcontinental variation in complexity.ResultsSpatial variation in canopy rugosity within sites and across the subcontinent was strongly and positively related to maximum canopy height (r2 = .87 subcontinent‐wide), with the addition of species richness in a multivariate model resolving another 2% of the variation across the subcontinent. Individually, woody plant species richness and phylogenetic diversity (r2 = .17 to .44, respectively) and LAI (r2 = .16) were weakly to moderately correlated with canopy rugosity at the subcontinental scale, and inconsistently explained spatial variation in canopy rugosity within sites.Main conclusionsWe conclude that maximum canopy height is a substantially stronger predictor of complexity than diversity or LAI within and across forests of eastern North America, suggesting that canopy volume places a primary constraint on the development of structural complexity. Management and land‐use practices that encourage and sustain tall temperate forest canopies may support greater complexity and associated increases in ecosystem functioning.

AimRapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).LocationEastern USA.Time period2017–2018.Taxa studiedTrees.MethodsWith a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.ResultsModels using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.Main conclusionsWe found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

Ecosystem coupling: A unifying framework to understand the functioning and recovery of ecosystems

Biogeographic distribution, assembly processes and potential nutrient cycling functions of myxobacteria communities in typical agricultural soils in China

SummaryTo quantify the major environmental drivers of stream bacterial population dynamics, we modelled temporal differences in stream bacterial communities to quantify community shifts, including those relating to cyclical seasonal variation and more sporadic bloom events. We applied Illumina MiSeq 16S rRNA bacterial gene sequencing of 892 stream biofilm samples, collected monthly for 36‐months from six streams. The streams were located a maximum of 118 km apart and drained three different catchment types (forest, urban and rural land uses). We identified repeatable seasonal patterns among bacterial taxa, allowing their separation into three ecological groupings, those following linear, bloom/trough and repeated, seasonal trends. Various physicochemical parameters (light, water and air temperature, pH, dissolved oxygen, nutrients) were linked to temporal community changes. Our models indicate that bloom events and seasonal episodes modify biofilm bacterial populations, suggesting that distinct microbial taxa thrive during these events including non‐cyanobacterial community members. These models could aid in determining how temporal environmental changes affect community assembly and guide the selection of appropriate statistical models to capture future community responses to environmental change.

Effective frameworks for assessing soil health are a priority, yet the value and adoption of soil biological indicators remain debated. Biological indicators provide essential information on ecosystem functions such as nutrient cycling, population regulation, and organic matter decomposition. Nematode-based indices (NBIs) have long been used by soil ecologists to study biodiversity, the soil food web and ecosystem functions. DNA metabarcoding of soil nematode communities is being increasingly used as a more cost-efficient and high-throughput approach than traditional morphology-based identification. Molecular-based NBIs have great potential as soil health bioindicators if method uncertainties can be addressed. We critically review recent nematode metabarcoding methods for soils and highlight the advantages and limitations associated with different workflow steps including the nematode elutriation method, DNA extraction method, barcode and primer choices, and database selection. Based on an analysis of 95 studies, we propose an updated standardised metabarcoding workflow that includes (a) nematode elutriation from large soil quantities, (b) bulk nematode DNA extraction, (c) the use of the 18S rRNA primers NF1/18Sr2b and (d) curated reference databases for sequence identification. While challenges in concordance between molecular and morphological NBIs remain, improved standardisation of DNA metabarcoding protocols may enable more detailed investigations of nematode distribution across diverse environments and testing of molecular-based NBIs in relation to soil disturbances, management practices, temporal scales, soil functional processes, and soil physicochemical properties. These efforts could strengthen the inclusion of NBIs in models and frameworks supporting soil health management strategies. • Soil eDNA is increasingly being used in metabarcoding protocols compared to bulk nematode extracted DNA; • The 18S rRNA barcode and NF1-18Sr2b primers provide optimal coverage and taxonomic resolution; • New nematode-specific databases show potential for improved identification and nematode-based index (NBI) calculation; • Similar ecological patterns and NBIs shown for metabarcoding and morphological data; • Calibration of molecular-based NBIs by nematode abundance counts needs further investigation.

Regulation of soil micro-foodwebs to root secondary metabolites in cultivated and wild licorice plants

Mapping ecosystem services (ESs) is an important tool for providing the quantitative information necessary for the optimal use and protection of ecosystems and biodiversity. A common mapping approach is to apply established empirical relationships to ecosystem property maps. Often, ecosystem properties that provide services to humanity are strongly related to the land use and land cover, where the spatial allocation of the land cover in the landscape is especially important. Land use and land cover maps are, therefore, essential for ES mapping. However, insight into the uncertainties in land cover maps and how these propagate into ES maps is lacking. To analyze the effects of these uncertainties, we mapped pollination efficiency as an example of an ecosystem function, using two continental-scale land cover maps and two global-scale land cover maps. We compared the outputs with maps based on a detailed national-scale map. The ecosystem properties and functions could be mapped using the GLOBCOVER map with a reasonable to good accuracy. In homogeneous landscapes, an even coarser resolution map would suffice. For mapping ESs that depend on the spatial allocation of land cover in the landscape, a classification of satellite images using fractional land cover or mosaic classes is an asset.

Impacts of prescribed fire and mechanical shredding of aboveground vegetation for fire prevention on ecosystem properties, structure, functions and overall multi-functionality of a semi-arid pine forest

Soil aggregates have been identified as a critical biogeochemical indicator of soil health, playing a pivotal role in addressing numerous environmental challenges and maintaining ecological equilibrium within soil environments. However, there is a paucity of scientific literature that have provided a comprehensive understanding of the role of soil aggregates in the environmental and ecosystem functions of soils. The objective of this review article therefore is to provide a comprehensive overview of the environmental and ecosystem functions of soil aggregates. Subsequently, the effects of land use and/or changes in land use in the delivery of these functions were examined. It is established that soil aggregates play a pivotal role in five environmental and ecosystem functions within the soil, including: (i) the provision of habitat for soil microorganisms by regulating niche formation and predation; (ii) the long-term sequestration of soil organic carbon (SOC) within microaggregates, preserved in macroaggregates; (iii) the regulation of nutrient exchange at the soil‒plant‒water‒atmosphere interface; (iv) the immobilisation of pollutants such heavy metal; and (v) the regulation of water movement in the soil. Land use has a major influence on the ability of soil aggregates to deliver these functions. The restoration of natural ecosystems (forests, grasslands, wetlands) has an overall positive effect, while farming, on the other hand, has a negative effect. Nevertheless, adopting sustainable management practices such as agroforestry, the use of organic soil amendments and reduced or no tillage can significantly reduce the adverse effects observed. Future research should look into how soil aggregates help capture carbon dioxide in dry areas through the inorganic carbon pathway and work on creating large-scale models to observe how these aggregates change and their effects on the environment and ecology.

Identifying generic proxies for assessing ecosystem properties and conservation status of semi-natural grasslands

竞争-耐胁迫-杂草型植物（CSR）对策理论主要研究植物对环境胁迫和干扰的适应性特化。从功能生态学角度讲，CSR理论是通过解释各种功能性状协同变异的主导维度来反映植物的生态对策。而物种所采取的生态对策将决定其在群落中的生态位，并最终会影响生态系统过程与功能。自1974年Grime首次提出CSR对策的概念以来，CSR对策思想就受到了生态学家很大的关注，经过近半个世纪的发展，目前已经形成了比较完整的理论和研究方法体系。试图在回顾CSR理论及其研究方法发展的基础上，阐述其基本内涵，并重点从功能性状变异空间、植物适应性、群落过程和生态系统特性等4个方面概述CSR理论及其分析模型的验证及应用，梳理其最新研究进展。CSR理论已经得到了很多研究的广泛验证并应用于多种尺度上的植物生态学研究，但仍有一些方面值得进一步发展完善，如第四区缺失问题、功能性状变异主轴以外的变异、CSR对策与功能性状种内变异和谱系关系的联系等方面需要深入研究。;Competitor, stress-tolerator, ruderal (CSR) theory, regarded as one of the best developed plant ecological strategy schemes, mainly studies the adaptive specialisation that plants evolve to achieve fitness in response to natural selection pressures, resulting in a triangular distribution of species optima along axes of habitat productivity and disturbance. From the perspective of functional ecology, the CSR theory reflects plant ecological strategies by explaining the principal functional spectra in the multidimensional trait space, that is, the world-wide economics spectrum and the size spectrum as fundamental gradients of plant evolution are major components of the CSR strategy variation. The ecological strategies adopted by plants then determine their ecological niche in the community and ultimately affect ecosystem processes and functions. Thus the CSR theory could not only be used as a framework for functionally classifying plants and predicting impacts of environmental change on vegetation, but also serves as a conceptual framework for investigating and interpreting community processes and ecosystem functioning. Since the concept of CSR strategies was first raised by Grime in 1974, the idea of CSR strategies has attracted much attention from ecologists, and the CSR theory has formed a relatively complete system of theories and research methods over the last half century. In this paper, we introduce the development and characteristics of the CSR theory and its methods to expound the core philosophy of the theory, review the verification and application of the CSR theory and its models by addressing the relevance of CSR strategies to functional trait variations, species adaptation, community processes and ecosystem properties, and summarize the advances in this theory. The CSR theory has been extensively validated by many studies and widely applied as a very useful analysis tool to plant ecology research on a variety of scales, but it needs further research, such as the missing fourth corner in the Grime's C-S-R triangular model, the rest functional variations beyond the principally functional trait spectra that are not explained by the CSR scheme, and the relationships between CSR strategies and the intraspecific trait variation and phylogenetic signal. Especially for the rest functional variations, the CSR theory does not preclude the existence of additional axes of variability subordinate to the principal functional spectra, nor that these may be certainly key to fitness and survival in specific cases. The CSR theory could be seen as a general adaptive strategy, since it is consistent with economics and size tradeoffs evident throughout the tree of life, hinting at overarching natural selection filters operating universally within the biosphere.