Combining local, landscape, and regional geographies to assess plant community vulnerability to invasion impact

Invasive species science has focused heavily on the invasive agent. However, management to protect native species also requires a proactive approach focused on resident communities and the features affecting their vulnerability to invasion impacts. Vulnerability is likely the result of factors acting across spatial scales, from local to regional, and it is the combined effects of these factors that will determine the magnitude of vulnerability. Here, we introduce an analytical framework that quantifies the scale-dependent impact of biological invasions on native richness from the shape of the native species-area relationship (SAR). We leveraged newly available, biogeographically extensive vegetation data from the U.S. National Ecological Observatory Network to assess plant community vulnerability to invasion impact as a function of factors acting across scales. We analyzed more than 1000 SARs widely distributed across the USA along environmental gradients and under different levels of non-native plant cover. Decreases in native richness were consistently associated with non-native species cover, but native richness was compromised only at relatively high levels of non-native cover. After accounting for variation in baseline ecosystem diversity, net primary productivity, and human modification, ecoregions that were colder and wetter were most vulnerable to losses of native plant species at the local level, while warmer and wetter areas were most susceptible at the landscape level. We also document how the combined effects of cross-scale factors result in a heterogeneous spatial pattern of vulnerability. This pattern could not be predicted by analyses at any single scale, underscoring the importance of accounting for factors acting across scales. Simultaneously assessing differences in vulnerability between distinct plant communities at local, landscape, and regional scales provided outputs that can be used to inform policy and management aimed at reducing vulnerability to the impact of plant invasions.

Learning from monitoring networks: Few-large vs. many-small plots and multi-scale analysis

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.

SPCIS: Standardized Plant Community with Introduced Status database

The movement of plant species across the globe exposes native communities to new species introductions. While introductions are pervasive, two aspects of variability underlie patterns and processes of biological invasions at macroecological scales. First, only a portion of introduced species become invaders capable of substantially impacting ecosystems. Second, species that do become invasive at one location may not be invasive in others; impacts depend on invader abundance and recipient species and conditions. Accounting for these phenomena is essential to accurately understand the patterns of plant invasion and explain the idiosyncratic results reflected in the literature on biological invasions. The lack of community-level richness and the abundance of data spanning broad scales and environmental conditions have until now hindered our understanding of invasions at a macroecological scale. To address this limitation, we leveraged quantitative surveys of plant communities in the USA and integrated and harmonized nine datasets into the Standardized Plant Community with Introduced Status (SPCIS) database. The database contains 14,056 unique taxa identified within 83,391 sampling units, of which 52.6% have at least one introduced species. The SPCIS database includes comparable information on plant species occurrence, abundance, and native status across the 50 U.S. States and Puerto Rico. SPCIS can be used to answer macro-scale questions about native plant communities and interactions with invasive plants. There are no copyright restrictions on the data, and we ask the users of this dataset to cite this paper, the respective paper(s) corresponding to the dataset sampling design (all references are provided in Data S1: Metadata S1: Class II-B-2), and the references described in Data S1: Metadata S1: Class III-B-4 as applicable to the dataset being utilized.

Modeling the Present and Future Geographic Distribution of the Lone Star Tick, Amblyomma americanum (Ixodida: Ixodidae), in the Continental United States

The Lone star tick (Amblyomma americanum L.) is the primary vector for pathogens of significant public health importance in North America, yet relatively little is known about its current and potential future distribution. Building on a published summary of tick collection records, we used an ensemble modeling approach to predict the present-day and future distribution of climatically suitable habitat for establishment of the Lone star tick within the continental United States. Of the nine climatic predictor variables included in our five present-day models, average vapor pressure in July was by far the most important determinant of suitable habitat. The present-day ensemble model predicted an essentially contiguous distribution of suitable habitat extending to the Atlantic coast east of the 100th western meridian and south of the 40th northern parallel, but excluding a high elevation region associated with the Appalachian Mountains. Future ensemble predictions for 2061-2080 forecasted a stable western range limit, northward expansion of suitable habitat into the Upper Midwest and western Pennsylvania, and range contraction along portions of the Gulf coast and the lower Mississippi river valley. These findings are informative for raising awareness of A. americanum-transmitted pathogens in areas where the Lone Star tick has recently or may become established.

The myth of plant species saturation

Plant species assemblages, communities or regional floras might be termed 'saturated' when additional immigrant species are unsuccessful at establishing due to competitive exclusion or other inter-specific interactions, or when the immigration of species is off-set by extirpation of species. This is clearly not the case for state, regional or national floras in the USA where colonization (i.e. invasion by exotic species) exceeds extirpation by roughly a 24 to 1 margin. We report an alarming temporal trend in plant invasions in the Pacific Northwest over the past 100 years whereby counties highest in native species richness appear increasingly invaded over time. Despite the possibility of some increased awareness and reporting of native and exotic plant species in recent decades, historical records show a significant, consistent long-term increase in exotic species (number and frequency) at county, state and regional scales in the Pacific Northwest. Here, as in other regions of the country, colonization rates by exotic species are high and extirpation rates are negligible. The rates of species accumulation in space in multi-scale vegetation plots may provide some clues to the mechanisms of the invasion process from local to national scales.

The art and science of weed mapping

Land managers need cost-effective and informative tools for non-native plant species management. Many local, state, and federal agencies adopted mapping systems designed to collect comparable data for the early detection and monitoring of non-native species. We compared mapping information to statistically rigorous, plot-based methods to better understand the benefits and compatibility of the two techniques. Mapping non-native species locations provided a species list, associated species distributions, and infested area for subjectively selected survey sites. The value of this information may be compromised by crude estimates of cover and incomplete or biased estimations of species distributions. Incorporating plot-based assessments guided by a stratified-random sample design provided a less biased description of non-native species distributions and increased the comparability of data over time and across regions for the inventory, monitoring, and management of non-native and native plant species.