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Abstract Increasing fine root carbon (FRC) inputs into soils has been proposed as a solution to increasing soil organic carbon (SOC). However, FRC inputs can also enhance SOC loss through priming. Here, we tested the broad-scale relationships between SOC and FRC at 43 sites across the US National Ecological Observatory Network. We found that SOC and FRC stocks were positively related with an across-ecosystem slope of 7 ± 3 kg SOC m⁻²per kg FRC m⁻², but this relationship was driven by grasslands. Grasslands had double the across-ecosystem slope while forest FRC and SOC were unrelated. Furthermore, deep grassland soils primarily showed net SOC accrual relative to FRC input. Conversely, forests had high variability in whether FRC inputs were related to net SOC priming or accrual. We conclude that while FRC increases could lead to increased SOC in grasslands, especially at depth, the FRC-SOC relationship remains difficult to characterize in forests. 

ABSTRACT AimNon‐native plants have the potential to harm ecosystems. Harm is classically related to their distribution and abundance, but this geographical information is often unknown. Here, we assess geographical commonness as a potential indicator of invasive status for non‐native flora in the United States. Geographical commonness could inform invasion risk assessments across species and ecoregions. LocationConterminous United States. Time PeriodThrough 2022. Major Taxa StudiedPlants. MethodsWe compiled and standardised occurrence and abundance data from 14 spatial datasets and used this information to categorise non‐native species as uncommon or common based on three dimensions of commonness: area of occupancy, habitat breadth and local abundance. To assess consistency in existing categorizations, we compared commonness to invasive status in the United States. We identified species with higher‐than‐expected abundance relative to their occupancy, habitat breadth or residence time. We calculated non‐native plant richness within United States ecoregions and estimated unreported species based on rarefaction/extrapolation curves. ResultsThis comprehensive database identified 1874 non‐native plant species recorded in 4,844,963 locations. Of these, 1221 species were locally abundant (> 10% cover) in 797,759 unique locations. One thousand one hundred one non‐native species (59%) achieved at least one dimension of commonness, including 565 species that achieved all three. Species with longer residence times tended to meet more dimensions of commonness. We identified 132 species with higher‐than‐expected abundance. Ecoregions in the central United States have the largest estimated numbers of unreported, abundant non‐native plants. Main ConclusionsA high proportion of non‐native species have become common in the United States. However, existing categorizations of invasive species are not always consistent with species' abundance and distribution, even after considering residence time. Considering geographical commonness and higher‐than‐expected abundance revealed in this new dataset could support more consistent and proactive identification of invasive plants and lead to more efficient management practices. 

ABSTRACT AimBeta diversity quantifies the similarity of ecological assemblages. Its increase, known as biotic homogenisation, can be a consequence of biological invasions. However, species occurrence (presence/absence) and abundance‐based analyses can produce contradictory assessments of the magnitude and direction of changes in beta diversity. Previous work indicates these contradictions should be less frequent in nature than in theory, but a growing number of empirical studies report discrepancies between occurrence‐ and abundance‐based approaches. Understanding if these discrepancies represent a few isolated cases or are systematic across a diversity of ecosystems would allow us to better understand the general patterns, mechanisms and impacts of biotic homogenisation. LocationUnited States. Time Period1963–2020. Major Taxa StudiedVascular plants. MethodsWe used a dataset of more than 70,000 vegetation survey plots to assess differences in biotic homogenisation with and without invasion using both occurrence‐ and abundance‐based metrics of beta diversity. We estimated taxonomic biotic homogenisation by comparing beta diversity of invaded and uninvaded plots with both classes of metrics and investigated the characteristics of the non‐native species pool that influenced the likelihood that these metrics disagree. ResultsIn 78% of plot comparisons, occurrence‐ and abundance‐based calculations agreed in direction, and the two metrics were generally well correlated. Our empirical results are consistent with previous theory. Discrepancies between the metrics were more likely when the same non‐native species was at high cover at both plots compared for beta diversity, and when these plots were spatially distant. Main ConclusionsIn about 20% of cases, our calculations revealed differences in direction (homogenisation vs. differentiation) when comparing occurrence‐ and abundance‐based metrics, indicating that the metrics are not interchangeable, especially when distances between plots are high and invader diversity is low. When data permit, combining the two approaches can offer insights into the role of invasions and extirpations in driving biotic homogenisation/differentiation. 

Summary Are non‐native plants abundant because they are non‐native, and have advantages over native plants, or because they possess ‘fast’ resource strategies, and have advantages in disturbed environments? This question is central to invasion biology but remains unanswered.We quantified the relative importance of resource strategy and biogeographic origin in 69 441 plots across the conterminous United States containing 11 280 plant species.Non‐native species had faster economic traits than native species in most plant communities (77%, 86% and 82% of plots for leaf nitrogen concentration, specific leaf area, and leaf dry matter content). Non‐native species also had distinct patterns of abundance, but these were not explained by their fast traits. Compared with functionally similar native species, non‐native species were (1) more abundant in plains and deserts, indicating the importance of biogeographic origin, and less abundant in forested ecoregions, (2) were more abundant where co‐occurring species had fast traits, for example due to disturbance, and (3) showed weaker signals of local environmental filtering.These results clarify the nature of plant invasion: Although non‐native plants have consistently fast economic traits, other novel characteristics and processes likely explain their abundance and, therefore, impacts. 

Abstract Here we present aboveground biomass (AGB) estimates from individual tree diameters scaled to whole‐tree biomass estimates using generalized allometric equations for 35 National Ecological Observatory Network (NEON) sites within the United States and Puerto Rico. These data are in both a standalone data file made publicly available via Figshare and as an R data package (NEONForestAGB) that allows for direct import of data into the R statistical computing environment. AGB is an Essential Climate Variable (ECV), yet biomass estimation from large forest inventory data can be cumbersome. Here we seek to provide a useful data set for community use from NEON data. The data set includes 92,281 unique individuals of 478 different species from 1,216 terrestrial observation plots for 360,570 biomass estimates between the years 2014 and 2023. 

Abstract Throughfall is the dominant input of water to most terrestrial ecosystems and is primarily driven by precipitation quantity, although the relationship varies among sites. A wide range of meteorological and site‐based properties also influence throughfall and may explain this variability, but their importance for accurately predicting throughfall quantities across differing sites remains unknown. Here I develop models to predict daily throughfall quantities at ∼1 m²resolution based on up to 19 environmental parameters using multi‐year data from sites throughout the US. Three random forest models of varying complexity were trained to predict throughfall: a simple model (RF‐1) driven solely by precipitation quantity, and more complex models that incorporated an additional eight (RF‐9) and eighteen (RF‐19) variables. RF‐1 was able to predict throughfall quantities (±28%) and accuracy was modestly improved by including additional model parameters (±24–26%). Improvements in model performance were most apparent for smaller precipitation events (<10 mm), which are less likely to fully saturate the canopy (22% improvement in prediction accuracy for the RF‐19 model). Precipitation quantity, maximum intensity, and duration were consistently identified as the most important drivers of throughfall, whereas variables relating to evaporative potential and canopy water storage capacity were identified as moderately important. These models allow the impacts of environmental changes (e.g., forest regrowth after clearcutting or increased precipitation intensity) to be evaluated, as well as inform decisions about which parameters to include in field‐ and model‐based studies of throughfall and its converse, interception, when resources are limited. 

Abstract Gas transfer velocity () controls gas fluxes between aquatic ecosystems and the atmosphere. In streams, is controlled by turbulence and, thus, local hydrology and geomorphology. Resultantly, variability in can be large and modeling from physical parameters can have large uncertainty. Here, we leverage a large dataset of estimates derived from tracer‐gas experiments in 22 US streams across a range of discharges. Our analysis shows that was highly variable both spatially across and temporally within streams, with estimates of spanning three orders of magnitude. Overall, scaled with discharge in steep streams due to relatively high stream power, but not in low‐slope streams, where stream power was relatively low even at high flows. Understanding how responds to stream discharge in a wide variety of streams is key to creating temporally and spatially resolved estimates of biogeochemical processes in streams. 

Abstract Automated processing of environmental data is hindered by the wide array of unit representations provided in the metadata of digital datasets. For example, gm/m2, g/m2, gm-2, g/m^2, g.m-2 and gramPerMeterSquared are all representations of a single complex unit that might be human-readable but are not machine-interpretable. Connectingad hocunits to a single unit concept in an ontology permits the identification of datasets sharing units and provides additional information regarding labels, definitions, dimensions and transformations provided in the ontology. Here we use successive string transformations to linkad hocunit representations to units in the QUDT ontology (e.g., unit: GM-PER-M2). Although only 896 of 7,110 distinct units in a corpus of ecological metadata from DataONE, the Environmental Data Initiative and the U.S. National Ecological Observatory Network were matched, 324,811 unit uses (instances) out of 355,057 of total unit uses were successfully mapped to QUDT units (91%). The resulting lookup table was used to enable a web service and R functions for adding annotation elements to Ecological Metadata Language documents. 

Summary Phenological response to global climate change can impact ecosystem functions. There are various data sources from which spatiotemporal and taxonomic phenological data may be obtained: mobilized herbaria, community science initiatives, observatory networks, and remote sensing. However, analyses conducted to date have generally relied on single sources of these data. Siloed treatment of data in analyses may be due to the lack of harmonization across different data sources that offer partially nonoverlapping information and are often complementary. Such treatment precludes a deeper understanding of phenological responses at varying macroecological scales. Here, we describe a detailed vision for the harmonization of phenological data, including the direct integration of disparate sources of phenological data using a common schema. Specifically, we highlight existing methods for data harmonization that can be applied to phenological data: data design patterns, metadata standards, and ontologies. We describe how harmonized data from multiple sources can be integrated into analyses using existing methods and discuss the use of automated extraction techniques. Data harmonization is not a new concept in ecology, but the harmonization of phenological data is overdue. We aim to highlight the need for better data harmonization, providing a roadmap for how harmonized phenological data may fill gaps while simultaneously being integrated into analyses. 

Abstract QuestionsGrasslands provide important provisioning services worldwide and their management has consequences for these services. Management intensification is a widespread land‐use change and has accelerated across North America to meet rising demands on productivity, yet its impact on the relationship between plant diversity and productivity is still unclear. Here, we investigated the relationship between plant diversity and grassland productivity across nine ecoclimatic domains of the continental United States. We also tested the effect of management intensification on diversity and productivity in four case studies. MethodsWe acquired remotely sensed gross primary productivity data (GPP, 1986–2018) and plant diversity data measured at different spatial scales (1, 10, 100, 400 m²), as well as climate variables including the Palmer drought index from two ecological networks. We used general linear mixed models to relate GPP to plant diversity across sites. For the case study analysis, we used linear mixed models to relate plant diversity to management intensity, and tested if the management intensity influenced the relationship between GPP (mean and temporal variation) and drought. ResultsAcross all sites, we observed positive relationships among species richness, productivity, and the temporal stability of mean annual biomass production. These relationships were not affected by the scale at which species richness was observed. In three out of the four case studies, we observed that management effects on species richness were only significant at broader scales (i.e., ≥10 m²) with no clear effect found at the commonly used 1‐m²quadrat scale. In one case study, species‐poor, intensively managed pastures presented the highest productivity but were more sensitive to dry conditions than less intensified pastures. However, in other case studies, we did not observe significant effects of management intensity on the magnitude or stability of productivity. ConclusionsGeneralization across studies may be difficult and require the development of intensification indices general enough to be applied across diverse management strategies in grazilands. Understanding how management intensification affects grassland productivity will inform the development of sustainable intensification strategies.