Identifying hotspots for plant invasions and forecasting focal points of further spread

Effects of surrounding landscape on the performance of Solidago canadensis L. and plant functional diversity on heavily invaded post-agricultural wastelands

High invasiveness and well-documented negative impact on biodiversity and ecosystem functioning make Solidago canadensis L. a species of global concern. Despite a good understanding of the driving factors of its invasions, it remains unclear how the surrounding landscape may shape invasion success of this species in human-transformed ecosystems. In our study, we investigated the impacts of different landscape features in the proximity of early successional wastelands on S. canadensis biomass allocation patterns. Further, we examined the relationships between the surrounding landscape, S. canadensis cover, and plant functional diversity, used as a supportive approach for the explanation of mechanisms underlying successful S. canadensis invasion. We found that increasing river net length had positive impacts on S. canadensis rhizome, stem, and total above ground biomass, but negative effects on leaf biomass, indicating that vegetative spread may perform the dominant role in shaping the competitiveness of this invader in riverine landscapes. A higher proportion of arable lands positively influenced S. canadensis above ground and flower biomass; thus promoting S. canadensis invasion in agricultural landscapes with the prominent role of habitat filtering in shaping vegetation structure. Concerning an increasing proportion of settlements, flower biomass was higher and leaf biomass was lower, thereby influencing S. canadensis reproductive potential, maximizing the odds for survival, and indicating high adaptability to exist in an urban landscape. We demonstrated high context-dependency of relationships between functional diversity components and surrounding landscape, strongly influenced by S. canadensis cover, while the effects of surrounding landscape composition per se were of lower importance. Investigating the relationships between the surrounding landscape, invasive species performance, and plant functional diversity, may constitute a powerful tool for the monitoring, controlling, and predicting of invasion progress, as well as the assessment of ecosystem invasibility.

Extending coverage and thematic resolution of compositional land cover maps in a hierarchical Bayesian framework

Ecological models are constrained by the availability of high-quality data at biologically appropriate resolutions and extents. Modeling a species' affinity or aversion with a particular land cover class requires data detailing that class across the full study area. Data sets with detailed legends (i.e., high thematic resolution) and/or high accuracy often sacrifice geographic extent, while large-area data sets often compromise on the number of classes and local accuracy. Consequently, ecologists must often restrict their study extent to match that of the more precise data set, or ignore potentially key land cover associations to study a larger area. We introduce a hierarchical Bayesian model to capitalize on the thematic resolution and accuracy of a regional land cover data set, and on the geographic breadth of a large area land cover data set. For the full extent (i.e., beyond the regional data set), the model predicts systematic discrepancies of the large-area data set with the regional data set, and divides an aggregated class into two more specific classes detailed by the regional data set. We illustrate the application of our model for mapping eastern white pine (Pinus strobus) forests, an important timber species that also provides habitat for an invasive shrub in the northeastern United States. We use the National Land Cover Database (NLCD), which covers the full study area but includes only generalized forest classes, and the NH GRANIT land cover data set, which maps White Pine Forest and has high accuracy, but only exists within New Hampshire. We evaluate the model at coarse (20 km² ) and fine (2 km² ) resolutions, with and without spatial random effects. The hierarchical model produced improved maps of compositional land cover for the full extent, reducing inaccuracy relative to NLCD while partitioning a White Pine Forest class out of the Evergreen Forest class. Accuracy was higher with spatial random effects and at the coarse resolution. All models improved upon simply partitioning Evergreen Forest in NLCD based on the predicted distribution of white pine. This flexible statistical method helps ecologists leverage localized mapping efforts to expand models of species distributions, population dynamics, and management strategies beyond the political boundaries that frequently delineate land cover data sets.

What and where? Predicting invasion hotspots in the Arctic marine realm

The risk of aquatic invasions in the Arctic is expected to increase with climate warming, greater shipping activity and resource exploitation in the region. Planktonic and benthic marine aquatic invasive species (AIS) with the greatest potential for invasion and impact in the Canadian Arctic were identified and the 23 riskiest species were modelled to predict their potential spatial distributions at pan-Arctic and global scales. Modelling was conducted under present environmental conditions and two intermediate future (2050 and 2100) global warming scenarios. Invasion hotspots-regions of the Arctic where habitat is predicted to be suitable for a high number of potential AIS-were located in Hudson Bay, Northern Grand Banks/Labrador, Chukchi/Eastern Bering seas and Barents/White seas, suggesting that these regions could be more vulnerable to invasions. Globally, both benthic and planktonic organisms showed a future poleward shift in suitable habitat. At a pan-Arctic scale, all organisms showed suitable habitat gains under future conditions. However, at the global scale, habitat loss was predicted in more tropical regions for some taxa, particularly most planktonic species. Results from the present study can help prioritize management efforts in the face of climate change in the Arctic marine ecosystem. Moreover, this particular approach provides information to identify present and future high-risk areas for AIS in response to global warming.

Characterizing Land Surface Phenology and Exotic Annual Grasses in Dryland Ecosystems Using Landsat and Sentinel-2 Data in Harmony

Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016–2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers.

Invasive Plant Species Establishment and Range Dynamics in Sri Lanka under Climate Change

Plant invasion has been widely recognized as an agent of global change that has the potential to have severe impacts under climate change. The challenges posed by invasive alien plant species (IAPS) on biodiversity and ecosystem stability is growing and not adequately studied, especially in developing countries. Defining climate suitability for multiple invasive plants establishment is important for early and strategic interventions to control and manage plant invasions. We modeled priority IAPS in Sri Lanka to identify the areas of greatest climatic suitability for their establishment and observed how these areas could be altered under projected climate change. We used Maximum Entropy method to model 14 nationally significant IAPS under representative concentration pathways 4.5 and 8.5 for 2050 and 2070. The combined climate suitability map produced by summing up climatic suitability of 14 IAPS was further classified into five classes in ArcMap as very high, high, moderate, low, and very low. South and west parts of Sri Lanka are projected to have potentially higher climatic suitability for a larger number of IAPS. We observed suitable area changes (gains and losses) in all five classes of which two were significant enough to make an overall negative impact i.e., (i) contraction of the very low class and (ii) expansion of the moderate class. Both these changes trigger the potential risk from IAPS in Sri Lanka in the future.

Spatial mismatch analysis among hotspots of alien plant species, road and railway networks in Germany and Austria

Road and railway networks are pervasive elements of all environments, which have expanded intensively over the last century in all European countries. These transportation infrastructures have major impacts on the surrounding landscape, representing a threat to biodiversity. Roadsides and railways may function as corridors for dispersal of alien species in fragmented landscapes. However, only few studies have explored the spread of invasive species in relationship to transport network at large spatial scales. We performed a spatial mismatch analysis, based on a spatially explicit correlation test, to investigate whether alien plant species hotspots in Germany and Austria correspond to areas of high density of roads and railways. We tested this independently of the effects of dominant environments in each spatial unit, in order to focus just on the correlation between occurrence of alien species and density of linear transportation infrastructures. We found a significant spatial association between alien plant species hotspots distribution and roads and railways density in both countries. As expected, anthropogenic landscapes, such as urban areas, harbored more alien plant species, followed by water bodies. However, our findings suggested that the distribution of neobiota is strongest correlated to road/railways density than to land use composition. This study provides new evidence, from a transnational scale, that alien plants can use roadsides and rail networks as colonization corridors. Furthermore, our approach contributes to the understanding on alien plant species distribution at large spatial scale by the combination with spatial modeling procedures.

Climate and Human Pressure Constraints Co-Explain Regional Plant Invasion at Different Spatial Scales

Alien species invasion represents a global threat to biodiversity and ecosystems. Explaining invasion patterns in terms of environmental constraints will help us to assess invasion risks and plan control strategies. We aim to identify plant invasion patterns in the Basque Country (Spain), and to determine the effects of climate and human pressure on that pattern. We modeled the regional distribution of 89 invasive plant species using two approaches. First, distance-based Moran's eigenvector maps were used to partition variation in the invasive species richness, S, into spatial components at broad and fine scales; redundancy analysis was then used to explain those components on the basis of climate and human pressure descriptors. Second, we used generalized additive mixed modeling to fit species-specific responses to the same descriptors. Climate and human pressure descriptors have different effects on S at different spatial scales. Broad-scale spatially structured temperature and precipitation, and fine-scale spatially structured human population density and percentage of natural and semi-natural areas, explained altogether 38.7% of the total variance. The distribution of 84% of the individually tested species was related to either temperature, precipitation or both, and 68% was related to either population density or natural and semi-natural areas, displaying similar responses. The spatial pattern of the invasive species richness is strongly environmentally forced, mainly by climate factors. Since individual species responses were proved to be both similarly constrained in shape and explained variance by the same environmental factors, we conclude that the pattern of invasive species richness results from individual species' environmental preferences.

Climate change and biological invasions: evidence, expectations, and response options

A changing climate may directly or indirectly influence biological invasions by altering the likelihood of introduction or establishment, as well as modifying the geographic range, environmental impacts, economic costs or management of alien species. A comprehensive assessment of empirical and theoretical evidence identified how each of these processes is likely to be shaped by climate change for alien plants, animals and pathogens in terrestrial, freshwater and marine environments of Great Britain. The strongest contemporary evidence for the potential role of climate change in the establishment of new alien species is for terrestrial arthropods, as a result of their ectothermic physiology, often high dispersal rate and their strong association with trade as well as commensal relationships with human environments. By contrast, there is little empirical support for higher temperatures increasing the rate of alien plant establishment due to the stronger effects of residence time and propagule pressure. The magnitude of any direct climate effect on the number of new alien species will be small relative to human-assisted introductions driven by socioeconomic factors. Casual alien species (sleepers) whose population persistence is limited by climate are expected to exhibit greater rates of establishment under climate change assuming that propagule pressure remains at least at current levels. Surveillance and management targeting sleeper pests and diseases may be the most cost-effective option to reduce future impacts under climate change. Most established alien species will increase their distribution range in Great Britain over the next century. However, such range increases are very likely be the result of natural expansion of populations that have yet to reach equilibrium with their environment, rather than a direct consequence of climate change. To assess the potential realised range of alien species will require a spatially explicit approach that not only integrates bioclimatic suitability and population-level demographic rates but also simulation of landscape-level processes (e.g. dispersal, land-use change, host/habitat distribution, non-climatic edaphic constraints). In terms of invasive alien species that have known economic or biodiversity impacts, the taxa that are likely to be the most responsive are plant pathogens and insect pests of agricultural crops. However, the extent to which climate adaptation strategies lead to new crops, altered rotations, and different farming practices (e.g. irrigation, fertilization) will all shape the potential agricultural impacts of alien species. The greatest uncertainty in the effects of climate change on biological invasions exists with identifying the future character of new species introductions and predicting ecosystem impacts. Two complementary strategies may work under these conditions of high uncertainty: (i) prioritise ecosystems in terms of their perceived vulnerability to climate change and prevent ingress or expansion of alien species therein that may exacerbate problems; (ii) target those ecosystem already threatened by alien species and implement management to prevent the situation deteriorating under climate change.

Modeling Hawaiian ecosystem degradation due to invasive plants under current and future climates

Occupation of native ecosystems by invasive plant species alters their structure and/or function. In Hawaii, a subset of introduced plants is regarded as extremely harmful due to competitive ability, ecosystem modification, and biogeochemical habitat degradation. By controlling this subset of highly invasive ecosystem modifiers, conservation managers could significantly reduce native ecosystem degradation. To assess the invasibility of vulnerable native ecosystems, we selected a proxy subset of these invasive plants and developed robust ensemble species distribution models to define their respective potential distributions. The combinations of all species models using both binary and continuous habitat suitability projections resulted in estimates of species richness and diversity that were subsequently used to define an invasibility metric. The invasibility metric was defined from species distribution models with <0.7 niche overlap (Warrens I) and relatively discriminative distributions (Area Under the Curve >0.8; True Skill Statistic >0.75) as evaluated per species. Invasibility was further projected onto a 2100 Hawaii regional climate change scenario to assess the change in potential habitat degradation. The distribution defined by the invasibility metric delineates areas of known and potential invasibility under current climate conditions and, when projected into the future, estimates potential reductions in native ecosystem extent due to climate-driven invasive incursion. We have provided the code used to develop these metrics to facilitate their wider use (Code S1). This work will help determine the vulnerability of native-dominated ecosystems to the combined threats of climate change and invasive species, and thus help prioritize ecosystem and species management actions.

Integrated assessment of biological invasions

As the main witnesses of the ecological and economic impacts of invasions on ecosystems around the world, ecologists seek to provide the relevant science that informs managers about the potential for invasion of specific organisms in their region(s) of interest. Yet, the assorted literature that could inform such forecasts is rarely integrated to do so, and further, the diverse nature of the data available complicates synthesis and quantitative prediction. Here we present a set of analytical tools for synthesizing different levels of distributional and/or demographic data to produce meaningful assessments of invasion potential that can guide management at multiple phases of ongoing invasions, from dispersal to colonization to proliferation. We illustrate the utility of data-synthesis and data-model assimilation approaches with case studies of three well-known invasive species--a vine, a marine mussel, and a freshwater crayfish--under current and projected future climatic conditions. Results from the integrated assessments reflect the complexity of the invasion process and show that the most relevant climatic variables can have contrasting effects or operate at different intensities across habitat types. As a consequence, for two of the study species climate trends will increase the likelihood of invasion in some habitats and decrease it in others. Our results identified and quantified both bottlenecks and windows of opportunity for invasion, mainly related to the role of human uses of the landscape or to disruption of the flow of resources. The approach we describe has a high potential to enhance model realism, explanatory insight, and predictive capability, generating information that can inform management decisions and optimize phase-specific prevention and control efforts for a wide range of biological invasions.

The new flora of northeastern USA: quantifying introduced plant species occupancy in forest ecosystems

Introduced plant species have significant negative impacts in many ecosystems and are found in many forests around the world. Some factors linked to the distribution of introduced species include fragmentation and disturbance, native species richness, and climatic and physical conditions of the landscape. However, there are few data sources that enable the assessment of introduced species occupancy in native plant communities over broad regions. Vegetation data from 1,302 forest inventory plots across 24 states in northeastern and mid-western USA were used to examine and compare the distribution of introduced species in relation to forest fragmentation across ecological provinces and forest types, and to examine correlations between native and introduced species richness. There were 305 introduced species recorded, and 66 % of all forested plots had at least one introduced species. Forest edge plots had higher constancy and occupancy of introduced species than intact forest plots, but the differences varied significantly among ecological provinces and, to a lesser degree, forest types. Weak but significant positive correlations between native and introduced species richness were observed most often in intact forests. Rosa multiflora was the most common introduced species recorded across the region, but Hieracium aurantiacum and Epipactus helleborine were dominant in some ecological provinces. Identifying regions and forest types with high and low constancies and occupation by introduced species can help target forest stands where management actions will be the most effective. Identifying seemingly benign introduced species that are more prevalent than realized will help focus attention on newly emerging invasives.

TEASIng apart alien species risk assessments: a framework for best practices

Some alien species cause substantial impacts, yet most are innocuous. Given limited resources, forecasting risks from alien species will help prioritise management. Given that risk assessment (RA) approaches vary widely, a synthesis is timely to highlight best practices. We reviewed quantitative and scoring RAs, integrating > 300 publications into arguably the most rigorous quantitative RA framework currently existing, and mapping each study onto our framework, which combines Transport, Establishment, Abundance, Spread and Impact (TEASI). Quantitative models generally measured single risk components (78% of studies), often focusing on Establishment alone (79%). Although dominant in academia, quantitative RAs are underused in policy, and should be made more accessible. Accommodating heterogeneous limited data, combining across risk components, and developing generalised RAs across species, space and time without requiring new models for each species may increase attractiveness for policy applications. Comparatively, scoring approaches covered more risk components (50% examined > 3 components), with Impact being the most common component (87%), and have been widely applied in policy (> 57%), but primarily employed expert opinion. Our framework provides guidance for questions asked, combining scores and other improvements. Our risk framework need not be completely parameterised to be informative, but instead identifies opportunities for improvement in alien species RA.