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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. 

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.