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...
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ftpubmed:oai:pubmedcentral.nih.gov:9182903 2023-05-15T18:09:14+02:00 The Prediction of Distribution of the Invasive Fallopia Taxa in Slovakia Gašparovičová, Petra Ševčík, Michal David, Stanislav 2022-05-31 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182903/ https://doi.org/10.3390/plants11111484 en eng MDPI http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182903/ http://dx.doi.org/10.3390/plants11111484 © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). CC-BY Plants (Basel) Article Text 2022 ftpubmed https://doi.org/10.3390/plants11111484 2022-06-12T01:11:37Z 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 (χ [Formula: see text]). It significantly decreases with increasing distance from transport lines (χ [Formula: see text] = 118.85, p < 0.001) and depends on soil type (χ [Formula: see text] = 49.56, p < 0.001) and the distance from the water, where increasing the distance decrease the probability (χ [Formula: see text] = 8.95, p = 0.003). Text Sakhalin PubMed Central (PMC) Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) Plants 11 11 1484 |
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Article Gašparovičová, Petra Ševčík, Michal David, Stanislav The Prediction of Distribution of the Invasive Fallopia Taxa in Slovakia |
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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 (χ [Formula: see text]). It significantly decreases with increasing distance from transport lines (χ [Formula: see text] = 118.85, p < 0.001) and depends on soil type (χ [Formula: see text] = 49.56, p < 0.001) and the distance from the water, where increasing the distance decrease the probability (χ [Formula: see text] = 8.95, p = 0.003). |
format |
Text |
author |
Gašparovičová, Petra Ševčík, Michal David, Stanislav |
author_facet |
Gašparovičová, Petra Ševčík, Michal David, Stanislav |
author_sort |
Gašparovičová, Petra |
title |
The Prediction of Distribution of the Invasive Fallopia Taxa in Slovakia |
title_short |
The Prediction of Distribution of the Invasive Fallopia Taxa in Slovakia |
title_full |
The Prediction of Distribution of the Invasive Fallopia Taxa in Slovakia |
title_fullStr |
The Prediction of Distribution of the Invasive Fallopia Taxa in Slovakia |
title_full_unstemmed |
The Prediction of Distribution of the Invasive Fallopia Taxa in Slovakia |
title_sort |
prediction of distribution of the invasive fallopia taxa in slovakia |
publisher |
MDPI |
publishDate |
2022 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182903/ https://doi.org/10.3390/plants11111484 |
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ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
geographic |
Gam |
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Gam |
genre |
Sakhalin |
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Sakhalin |
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Plants (Basel) |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9182903/ http://dx.doi.org/10.3390/plants11111484 |
op_rights |
© 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
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CC-BY |
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https://doi.org/10.3390/plants11111484 |
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Plants |
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11 |
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1484 |
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