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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 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. 

Despite decades of research documenting the consequences of naturalized and invasive plant species on ecosystem functions, our understanding of the functional underpinnings of these changes remains rudimentary. This is partially due to ineffective scaling of trait differences between native and naturalized species to whole plant communities. Working with data from over 75,000 plots and over 5,500 species from across the United States, we show that changes in the functional composition of communities associated with increasing abundance of naturalized species mirror the differences in traits between native and naturalized plants. We find that communities with greater abundance of naturalized species are more resource acquisitive aboveground and belowground, shorter, more shallowly rooted, and increasingly aligned with an independent strategy for belowground resource acquisition via thin fine roots with high specific root length. We observe shifts toward herbaceous-dominated communities but shifts within both woody and herbaceous functional groups follow community-level patterns for most traits. Patterns are remarkably similar across desert, grassland, and forest ecosystems. Our results demonstrate that the establishment and spread of naturalized species, likely in combination with underlying environmental shifts, leads to predictable and consistent changes in community-level traits that can alter ecosystem functions. 

In order to learn about broad scale ecological patterns, data from large-scale surveys must allow us to either estimate the correlations between the environment and an outcome and/or accurately predict ecological patterns. An important part of data collection is the sampling effort used to collect observations, which we decompose into two quantities: the number of observations or plots ( n ) and the per-observation/plot effort ( E ; e.g., area per plot). If we want to understand the relationships between predictors and a response variable, then lower model parameter uncertainty is desirable. If the goal is to predict a response variable, then lower prediction error is preferable. We aim to learn if and when aggregating data can help attain these goals. We find that a small sample size coupled with large observation effort coupled (few large) can yield better predictions when compared to a large number of observations with low observation effort (many small). We also show that the combination of the two values ( n and E ), rather than one alone, has an impact on parameter uncertainty. In an application to Forest Inventory and Analysis (FIA) data, we model the tree density of selected species at various amounts of aggregation using linear regression in order to compare the findings from simulated data to real data. The application supports the theoretical findings that increasing observational effort through aggregation can lead to improved predictions, conditional on the thoughtful aggregation of the observational plots. In particular, aggregations over extremely large and variable covariate space may lead to poor prediction and high parameter uncertainty. Analyses of large-range data can improve with aggregation, with implications for both model evaluation and sampling design: testing model prediction accuracy without an underlying knowledge of the datasets and the scale at which predictor variables operate can obscure meaningful results. 

Quantifying the resilience of ecological communities to increasingly frequent and severe environmental disturbance, such as natural disasters, requires long-term and continuous observations and a research community that is itself resilient. Investigators must have reliable access to data, a variety of resources to facilitate response to perturbation, and mechanisms for rapid and efficient return to function and/or adaptation to post-disaster conditions. There are always challenges to meeting these requirements, which may be compounded by multiple, co-occurring incidents. For example, travel restrictions resulting from the COVID-19 pandemic hindered preparations for, and responses to, environmental disasters that are the hallmarks of resilient research communities. During its initial years of data collection, a diversity of disturbances—earthquakes, wildfires, droughts, hurricanes and floods—have impacted sites at which the National Ecological Observatory Network (NEON) intends to measure organisms and environment for at least 30 years. These events strain both the natural and human communities associated with the Observatory, and additional stressors like public health crises only add to the burden. Here, we provide a case-study of how NEON has demonstrated not only internal resilience in the face of the public health crisis of COVID-19, but has also enhanced the resilience of ecological research communities associated with the network and provided crucial information for quantifying the impacts of and responses to disturbance events on natural systems—their ecological resilience. The key components discussed are: 1) NEON’s infrastructure and resources to support its core internal community, to adapt to rapidly changing situations, and to quickly resume operations following disruption, thus enabling the recovery of information flow crucial for data continuity; 2) how NEON data, tools, and materials are foundational in supporting the continuation of research programs in the face of challenges like those of COVID-19, thus enhancing the resilience of the greater ecological research community; and 3) the importance of diverse and consistent data for defining baseline and post-disaster conditions that are required to quantify the effects of natural disasters on ecosystem patterns and processes. 

Abstract Biodiversity is a complex, yet essential, concept for undergraduate students in ecology and other natural sciences to grasp. As beginner scientists, students must learn to recognize, describe, and interpret patterns of biodiversity across various spatial scales and understand their relationships with ecological processes and human influences. It is also increasingly important for undergraduate programs in ecology and related disciplines to provide students with experiences working with large ecological datasets to develop students’ data science skills and their ability to consider how ecological processes that operate at broader spatial scales (macroscale) affect local ecosystems. To support the goals of improving student understanding of macroscale ecology and biodiversity at multiple spatial scales, we formed an interdisciplinary team that included grant personnel, scientists, and faculty from ecology and spatial sciences to design a flexible learning activity to teach macroscale biodiversity concepts using large datasets from the National Ecological Observatory Network (NEON). We piloted this learning activity in six courses enrolling a total of 109 students, ranging from midlevel ecology and GIS/remote sensing courses, to upper‐level conservation biology. Using our classroom experiences and a pre/postassessment framework, we evaluated whether our learning activity resulted in increased student understanding of macroscale ecology and biodiversity concepts and increased familiarity with analysis techniques, software programs, and large spatio‐ecological datasets. Overall, results suggest that our learning activity improved student understanding of biological diversity, biodiversity metrics, and patterns of biodiversity across several spatial scales. Participating faculty reflected on what went well and what would benefit from changes, and we offer suggestions for implementation of the learning activity based on this feedback. This learning activity introduced students to macroscale ecology and built student skills in working with big data (i.e., large datasets) and performing basic quantitative analyses, skills that are essential for the next generation of ecologists.