Combining the effects of surrounding land-use and propagule pressure to predict the distribution of an invasive plant

A Modeling Framework to Frame a Biological Invasion: Impatiens glandulifera in North America

Biological invasions are a major component of global environmental change with severe ecological and economic consequences. Since eradicating biological invaders is costly and even futile in many cases, predicting the areas under risk to take preventive measures is crucial. Impatiens glandulifera is a very aggressive and prolific invasive species and has been expanding its invasive range all across the Northern hemisphere, primarily in Europe. Although it is currently spread in the east and west of North America (in Canada and USA), studies on its fate under climate change are quite limited compared to the vast literature in Europe. Hybrid models, which integrate multiple modeling approaches, are promising tools for making projections to identify the areas under invasion risk. We developed a hybrid and spatially explicit framework by utilizing MaxEnt, one of the most preferred species distribution modeling (SDM) methods, and we developed an agent-based model (ABM) with the statistical language R. We projected the I. glandulifera invasion in North America, for the 2020-2050 period, under the RCP 4.5 scenario. Our results showed a predominant northward progression of the invasive range alongside an aggressive expansion in both currently invaded areas and interior regions. Our projections will provide valuable insights for risk assessment before the potentially irreversible outcomes emerge, considering the severity of the current state of the invasion in Europe.

Mapping the purple menace: spatiotemporal distribution of purple loosestrife (Lythrum salicaria) along roadsides in northern New York State

Purple loosestrife (Lythrum salicaria L.) is an invasive, herbaceous plant, frequently found in wetlands, creating monoculture stands, resulting in intensive management strategies in central New York, Ontario, and Quebec. The goal of this study was to identify the extent of infestations and to investigate factors that promote the spread of purple loosestrife. We attempted to answer several questions regarding level of infestation, connection to mowing, and influence of culverts. During flowering season in July and August, 2017–2019, we mapped infestations along 150 km (93 miles) of state highway between the Adirondack Park and the St. Lawrence River using the ESRI Collector app. The results of our preliminary analysis revealed significant increase in the number of plants (P < 0.001). In addition, a linear correlation analysis demonstrated a higher loosestrife density with an increase in plant species richness and a decrease in the distance to the closest infestation and wetland (P < 0.001 each). We found no statistical evidence that mowing promotes the spread of loosestrife. As expected, there were more individual infestations in highway ditches, but larger and denser infestations in wetlands (P = 0.003 in 2019). Culverts enable purple loosestrife to spread underneath highways and should be managed to prevent spread.

Seasonality and forest edge as drivers of Tradescantia zebrina Hort. ex Bosse invasion in the Atlantic Forest

As a result of biodiversity and ecosystem service losses associated with biological invasions, there has been growing interest in basic and applied research on invasive species aiming to improve management strategies. Tradescantia zebrina is a herbaceous species increasingly reported as invasive in the understory of disturbed forest ecosystems. In this study, we assess the effect of spatial and seasonal variation on biological attributes of this species in the Atlantic Forest. To this end, we measured attributes of T. zebrina associated with plant growth and stress in the four seasons at the forest edge and in the forest interior of invaded sites in the Iguaçu National Park, Southern Brazil. The invasive plant had higher growth at the forest edge than in the forest interior and lower leaf asymmetry and herbivory in the winter than in the summer. Our findings suggest that the forest edge environment favours the growth of T. zebrina. This invasive species is highly competitive in the understory of semi-deciduous seasonal forests all over the year. Our study contributes to the management of T. zebrina by showing that the summer is the best season for controlling this species.

Double-edged effects of climate change on plant invasions: Ecological niche modeling global distributions of two invasive alien plants

The prediction of the potential distribution of invasive alien species is key for the control of their proliferation. This study developed ensemble niche models to explore the distribution patterns of Cecropia peltata and Ulex europaeus under baseline and future conditions, as well as the factors that regulate them. The models were based on occurrence records as well as climate, land-use and topography datasets. Climatic factors played a stronger role than land-use and topographical factors in their distribution patterns. Additionally, temperature seasonality and temperature annual range were the optimal predictor for the global distributions of C. peltata and U. europaeus, respectively. Under the baseline-RCP 8.5 scenario in 2070, significant increases in habitat suitability for C. peltata were generally detected in tropical regions, while for U. europaeus under the same condition, significant increases in habitat suitability were generally observed in west coast of South America and Europe, suggesting the impacts of climate changes on species distribution may be species specific. The contrast changes of suitable habitat areas for U. europaeus under the baseline-2.6 and 8.5 scenarios may suggest that the scenarios of climate changes may modify its distribution patterns and variations in suitable habitats. The double-edged effects of global warming on plant invasions may be a result of the scenario specific climate change and the species-specific responses to changes in climate. Our findings highlight the importance of climate change scenario specific and species-specific research on the impact of climate change on plant invasions.

Iterative Models for Early Detection of Invasive Species across Spread Pathways

Species distribution models can be used to direct early detection of invasive species, if they include proxies for invasion pathways. Due to the dynamic nature of invasion, these models violate assumptions of stationarity across space and time. To compensate for issues of stationarity, we iteratively update regionalized species distribution models annually for European gypsy moth (Lymantria dispar dispar) to target early detection surveys for the USDA APHIS gypsy moth program. We defined regions based on the distances from the invasion spread front where shifts in variable importance occurred and included models for the non-quarantine portion of the state of Maine, a short-range region, an intermediate region, and a long-range region. We considered variables that represented potential gypsy moth movement pathways within each region, including transportation networks, recreational activities, urban characteristics, and household movement data originating from gypsy moth infested areas (U.S. Postal Service address forwarding data). We updated the models annually, linked the models to an early detection survey design, and validated the models for the following year using predicted risk at new positive detection locations. Human-assisted pathways data, such as address forwarding, became increasingly important predictors of gypsy moth detection in the intermediate-range geographic model as more predictor data accumulated over time (relative importance = 5.9%, 17.36%, and 35.76% for 2015, 2016, and 2018, respectively). Receiver operating curves showed increasing performance for iterative annual models (area under the curve (AUC) = 0.63, 0.76, and 0.84 for 2014, 2015, and 2016 models, respectively), and boxplots of predicted risk each year showed increasing accuracy and precision of following year positive detection locations. The inclusion of human-assisted pathway predictors combined with the strategy of iterative modeling brings significant advantages to targeting early detection of invasive species. We present the first published example of iterative species distribution modeling for invasive species in an operational context.

Employing a spatio-temporal contingency table for the analysis of cork oak cover change in the Sa Serra region of Sardinia

Land cover change analyses are common and, especially in the absence of explanatory variables, they are mainly carried out by employing qualitative methods such as transition matrices or raster operations. These methods do not provide any estimation of the statistical significance of the changes, or the uncertainty of the model and data, and are usually limited in supporting explicit biological/ecological interpretation of the processes determining the changes. Here we show how the original nearest-neighbour contingency table, proposed by Dixon to evaluate spatial segregation, has been extended to the temporal domain to map the intensity, statistical significance and uncertainty of land cover changes. This index was then employed to quantify the changes in cork oak forest cover between 1998 and 2016 in the Sa Serra region of Sardinia (Italy). The method showed that most statistically significant cork oak losses were concentrated in the centre of Sa Serra and characterised by high intensity. A spatial binomial-logit generalised linear model estimated the probability of changes occurring in the area but not the type of change. We show how the spatio-temporal Dixon’s index can be an attractive alternative to other land cover change analysis methods, since it provides a robust statistical framework and facilitates direct biological/ecological interpretation.

Setting Priorities for Monitoring and Managing Non-native Plants: Toward a Practical Approach

Land managers face the challenge to set priorities in monitoring and managing non-native plant species, as resources are limited and not all non-natives become invasive. Existing frameworks that have been proposed to rank non-native species require extensive information on their distribution, abundance, and impact. This information is difficult to obtain and often not available for many species and regions. National watch or priority lists are helpful, but it is questionable whether they provide sufficient information for environmental management on a regional scale. We therefore propose a decision tree that ranks species based on more simple albeit robust information, but still provides reliable management recommendations. To test the decision tree, we collected and evaluated distribution data from non-native plants in highland grasslands of Southern Brazil. We compared the results with a national list from the Brazilian Invasive Species Database for the state to discuss advantages and disadvantages of the different approaches on a regional scale. Out of 38 non-native species found, only four were also present on the national list. If management would solely rely on this list, many species that were identified as spreading based on the decision tree would go unnoticed. With the suggested scheme, it is possible to assign species to active management, to monitoring, or further evaluation. While national lists are certainly important, management on a regional scale should employ additional tools that adequately consider the actual risk of non-natives to become invasive.