Spatially Varying Relationships between Alien Plant Distributions and Environmental Factors in South Korea

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...

Full description

Bibliographic Details
Main Authors: Jeong-Soo Park, Hyohyemi Lee, Donghui Choi, Youngha Kim
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Plants
Subjects:
Online Access:https://www.mdpi.com/2223-7747/10/7/1377
id doaj-d3f1ea3ba47d4d53a6dea0cef9f647c6
record_format Article
spelling doaj-d3f1ea3ba47d4d53a6dea0cef9f647c62021-07-23T14:01:45ZengMDPI AGPlants2223-77472021-07-01101377137710.3390/plants10071377Spatially Varying Relationships between Alien Plant Distributions and Environmental Factors in South KoreaJeong-Soo Park0Hyohyemi Lee1Donghui Choi2Youngha Kim3Division of Ecological Safety, National Institute of Ecology, Seocheon 33657, KoreaDivision of Ecological Safety, National Institute of Ecology, Seocheon 33657, KoreaDivision of Ecological Safety, National Institute of Ecology, Seocheon 33657, KoreaDivision of Ecological Safety, National Institute of Ecology, Seocheon 33657, KoreaInvasive 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, <i>Lactuca scariola</i> L. and <i>Aster pilosus</i> 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.https://www.mdpi.com/2223-7747/10/7/1377invasive alien plantgeographically weighted regressionpartial least squares regressionanthropogenic effect<i>Lactuca scariola</i><i>Aster pilosus</i>
collection DOAJ
language English
format Article
sources DOAJ
author Jeong-Soo Park
Hyohyemi Lee
Donghui Choi
Youngha Kim
spellingShingle Jeong-Soo Park
Hyohyemi Lee
Donghui Choi
Youngha Kim
Spatially Varying Relationships between Alien Plant Distributions and Environmental Factors in South Korea
Plants
invasive alien plant
geographically weighted regression
partial least squares regression
anthropogenic effect
<i>Lactuca scariola</i>
<i>Aster pilosus</i>
author_facet Jeong-Soo Park
Hyohyemi Lee
Donghui Choi
Youngha Kim
author_sort Jeong-Soo Park
title Spatially Varying Relationships between Alien Plant Distributions and Environmental Factors in South Korea
title_short Spatially Varying Relationships between Alien Plant Distributions and Environmental Factors in South Korea
title_full Spatially Varying Relationships between Alien Plant Distributions and Environmental Factors in South Korea
title_fullStr Spatially Varying Relationships between Alien Plant Distributions and Environmental Factors in South Korea
title_full_unstemmed Spatially Varying Relationships between Alien Plant Distributions and Environmental Factors in South Korea
title_sort spatially varying relationships between alien plant distributions and environmental factors in south korea
publisher MDPI AG
series Plants
issn 2223-7747
publishDate 2021-07-01
description 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, <i>Lactuca scariola</i> L. and <i>Aster pilosus</i> 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.
topic invasive alien plant
geographically weighted regression
partial least squares regression
anthropogenic effect
<i>Lactuca scariola</i>
<i>Aster pilosus</i>
url https://www.mdpi.com/2223-7747/10/7/1377
work_keys_str_mv AT jeongsoopark spatiallyvaryingrelationshipsbetweenalienplantdistributionsandenvironmentalfactorsinsouthkorea
AT hyohyemilee spatiallyvaryingrelationshipsbetweenalienplantdistributionsandenvironmentalfactorsinsouthkorea
AT donghuichoi spatiallyvaryingrelationshipsbetweenalienplantdistributionsandenvironmentalfactorsinsouthkorea
AT younghakim spatiallyvaryingrelationshipsbetweenalienplantdistributionsandenvironmentalfactorsinsouthkorea
_version_ 1721286367508430848