Changes in Habitat Conditions of Invaded Forest Communities in Podunajská Nížina and the Impact of Non-Native Species on Biodiversity (SW Slovakia)

Assessment of Parectopa robiniella Clemens (Lepidoptera: Gracillariidae) Effect on Biochemical Parameters of Robinia pseudoacacia Under Conditions of an Industrial City in Steppe Ukraine

Antioxidant enzymatic activity and soluble protein content in Robinia pseudoacacia L. (black locust), invasive tree in Europe, plants were determined under feeding conditions of the caterpillars of Parectopa robiniella Clemens, 1863, invasive miner in Europe, in trees of three (I, II, III) age groups (5–10, 15–25, and 40 years). Biochemical analysis data showed significant differences between reference and experimental leaf samples in different urban ecosystems. There was no association between changes in catalase and peroxidase activity and protein content under the influence of leaf blotch miners depending on the tree age. Significant variability of proteins under P. robiniella influence was noted; the highest of its values were recorded in the leaves of ІІІ-aged trees. In most plantings (and especially in trees of age group III), an increase (1.2 to 2.7 times) in GPx activity or the absence of its changes was registered both in the ecologically favorable zone and in the locations affected by emissions from road transport and industrial enterprises. Plants of age groups I and II in the emission areas of metallurgical, pipe-rolling, and chemical industries experienced a significant decrease in GPx activity (by 20.2–44.6%) in the leaves damaged by the phytophage. In most of the plantings studied, catalase showed an increase/stability of its activity in all age groups of trees under condition of pest attack. The highest increase in САТ activity (2.1 times) was recorded in young R. pseudoacacia plants in an ecologically favorable area of the city.

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).