Abstract

Invasive alien plants can severely threaten biodiversity and cause economic losses in the agricultural industry; therefore, identifying the critical environmental factors related to the distribution of alien plants plays a crucial role in ecosystem management. In this study, we applied partial least squares regression (PLSR) and geographically weighted regression (GWR) to estimate the important environmental factors affecting the spread of two invasive and expansive plants, Lactuca scariola L. and Aster pilosus Willd., across South Korea. GWR provides more accurate predictions than ordinary least squares regression, and the local coefficients of GWR allow for the determination of the spatial relationships between alien plant distributions and environmental variables. Based on the model’s results, the distributions of these alien species were significantly associated with anthropogenic effects, such as human population density, residential area, and road density. Furthermore, the two alien species can establish themselves in habitats where native plants cannot thrive, owing to their broad tolerance to temperature and drought conditions. This study suggests that urban development and expansion can facilitate the invasion of these species in metropolitan cities.

Highlights

  • IntroductionIncreasing global trade is one of the main reasons for biological invasions worldwide

  • Increasing global trade is one of the main reasons for biological invasions worldwide.Such invasions have become a matter of great concern because of their potential negative impacts on ecosystems and the agricultural industry [1]

  • A. pilosus was identified at 1700 geographical points throughout South Korea and L. scariola at 1253 points

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Summary

Introduction

Increasing global trade is one of the main reasons for biological invasions worldwide. Such invasions have become a matter of great concern because of their potential negative impacts on ecosystems and the agricultural industry [1]. Numerous studies have been conducted on the relationships between alien plant distributions and environmental variables [6,7,8]. We applied geographically weighted regression (GWR), which can incorporate spatial non-stationarity and estimate local coefficients. Ordinary least squares (OLS) regression and the stationary coefficient model assume that the relationships between variables are the same across the entire space and compute the parameters as average values across all locations [9]. GWR is a tool that is more frequently used in healthcare and demographic research rather than ecological studies [11,12,13]

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