Species distribution models for invasive Eurasian watermilfoil highlight the importance of data quality and limitations of discrimination accuracy metrics

Improving species distribution forecasts by measuring and communicating uncertainty: An invasive species case study

Forecasting invasion risk under future climate conditions is critical for the effective management of invasive species, and species distribution models (SDMs) are key tools for doing so. However, SDM-based forecasts are uncertain, especially when correlative statistical models extrapolate to nonanalog environmental domains, such as future climate conditions. Different assumptions about the functional form of the temperature-suitability relationship can impact predicted habitat suitability under novel conditions. Hence, methods to understand the sources of uncertainty are critical when applying SDMs. Here, we use high-resolution predictions of lake water temperatures to project changes in habitat suitability under future climate conditions for an invasive macrophyte (Myriophyllym spicatum). Future suitability was predicted using five global circulation models and three statistical models that assumed different species-temperature functional responses. The suitability of lakes for M. spicatum was overall predicted to increase under future climate conditions, but the magnitude and direction of change in suitability varied greatly among lakes. Variability was most pronounced for lakes under nonanalog temperature conditions, indicating that predictions for these lakes remained highly uncertain. Integrating predictions from SDMs that differ in their species-environment response function, while explicitly quantifying uncertainty across analog and nonanalog domains, can provide a more robust and useful approach to forecasting invasive species distribution under climate change.

Niche and Range Shifts of the Fall Webworm (Hyphantria cunea Dury) in Europe Imply Its Huge Invasion Potential in the Future

The fall webworm (Hyphantria cunea Dury) has a strong impact on agricultural systems in Europe. However, its invasive potential, which was inherited from its native niche in North America, remains unknown. Here, we investigated the climatic niche and range shifts of the fall webworm in Europe and compared them with those in native North America, then assessed the worms' invasive potential in Europe. Compared with the fall webworm in Europe, those in North America survived in more diverse climatic conditions, which was closely associated with their broader niche and larger potential ranges in Europe. If the fall webworm in Europe could exploit the native niche inherited from those in North America to adapt to climatic conditions in Europe, their potential ranges in Europe could be 5.5-fold those based on the niche as introduced in Europe. The potentially unfilled ranges of the fall webworm in Europe were mainly detected in vast regions of Europe, excluding Norway, Sweden, Finland, North Russia, Hungary, Croatia, Romania, and Ukraine, suggesting that, without strict control, these vast regions might be preferably invaded by the fall webworm in Europe in the future. Therefore, strict control against its invasion is needed. Given that small niche shifts in this invasive insect could result in large range shifts, the niche shifts represent a more sensitive indicator of invasion risk than range shifts.

The Prediction of Distribution of the Invasive Fallopia Taxa in Slovakia

Invasive species are now considered the second biggest threat for biodiversity and have adverse environmental, economic and social impacts. Understanding its spatial distribution and dynamics is crucial for the development of tools for large-scale mapping, monitoring and management. The aim of this study was to predict the distribution of invasive Fallopia taxa in Slovakia and to identify the most important predictors of spreading of these species. We designed models of species distribution for invasive species of Fallopia—Fallopia japonica—Japanese knotweed, Fallopia sachalinensis—Sakhalin knotweed and their hybrid Fallopia × bohemica—Czech knotweed. We designed 12 models—generalized linear model (GLM), generalized additive model (GAM), classification and regression trees (CART), boosted regression trees (BRT), multivariate adaptive regression spline (MARS), random forests (RF), support vector machine (SVM), artificial neural networks (ANN), maximum entropy (Maxent), penalized maximum likelihood GLM (GLMNET), domain, and radial basis function network (RBF). The accuracy of the models was evaluated using occurrence data for the presence and absence of species. The final simplified logistic regression model showed the three most important prediction variables lead by distances from roads and rails, then type of soil and distances from water bodies. The probability of invasive Fallopia species occurrence was evaluated using Pearson’s chi-squared test (χ21). It significantly decreases with increasing distance from transport lines (χ21 = 118.85, p < 0.001) and depends on soil type (χ21 = 49.56, p < 0.001) and the distance from the water, where increasing the distance decrease the probability (χ21 = 8.95, p = 0.003).

Ecological Niche Shifts Affect the Potential Invasive Risk of Rapistrum rugosum (L.) All. in China

Ecological niche is a key concept that links species distributions. Ecological niche shifts are expected to affect the potential invasive risk of alien species. Rapistrum rugosum is an invasive agricultural weed in many countries. Wild populations of R. rugosum have been recorded in China, representing a great threat to the regional crops. Based on distribution records from different regions and relevant environmental variables, the present study predicted the potential distribution and estimated the invasive risk of R. rugosum in China. Ecological niche shifts strongly affected the potential invasive risk of R. rugosum in China. The two most important variables were annual temperature range (Bio7) and mean temperature of the coldest quarter (Bio11). The total suitable habitat for the species covered an area of 287.53 × 10⁴km² and was mainly distributed in Southwest, Southeast, and Central China. Australia, Canada, Brazil, the United States, and Argentina accounted for over 90% of the inspection records of R. rugosum from Chinese entry ports during 2015-2018. The intercepted R. rugosum was frequently mixed in Glycine max (L.) Merr., Hordeum vulgare L., linseed, Triticum aestivum L., and Sorghum bicolor (L.) Moench. Moreover, 80% interceptions were recorded from Tianjin, Guangdong, Nanjing, and Chengdu customs. Climatic conditions do not limit the establishment capability of R. rugosum in China. Our results provide a theoretical reference for the development of monitoring and control measures for this invasive weed.