Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods

Szilassi, Péter; Szatmári, Gábor; Pásztor, László; Árvai, Mátyás; Szatmári, József; Szitár, Katalin; Papp, Levente

doi:10.3390/plants8120593

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Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods

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Department of Physical Geography and Geoinformatics, University of Szeged, Egyetem utca 2, 6722 Szeged, Hungary

Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Herman Ottó út 15, 1022 Budapest, Hungary

Institute of Ecology and Botany, Centre for Ecological Research, Alkotmány u. 2-4, 2163 Vácrátót, Hungary

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Department of Geoinformatics, University of Salzburg, Schillerstraße 30, 5020 Salzburg, Austria

Author to whom correspondence should be addressed.

Plants 2019, 8(12), 593; https://doi.org/10.3390/plants8120593

Received: 4 November 2019 / Revised: 9 December 2019 / Accepted: 10 December 2019 / Published: 12 December 2019

(This article belongs to the Special Issue Invasive Plants)

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Abstract

For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (Asclepias syriaca L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as well as an EU-scale land cover databases (CORINE) for the analyses. For the abundance data of A. syriaca, we applied the field survey photos from the Land Use and Coverage Area Frame Survey (LUCAS) Land Cover database for the European Union. With machine learning algorithm methods, we quantified the relative weight of the environmental variables on the abundance of common milkweed. According to our findings, soil texture and soil type (sandy soils) were the most important variables determining the occurrence of this species. We could exactly identify the actual land cover types and the recent land cover changes that have a significant role in the occurrence the common milkweed in Europe. We could also show the role of climatic conditions of the study area in the occurrence of this species, and we could prepare the potential distribution map of common milkweed for the study area. View Full-Text

Keywords: CORINE; Common milkweed; land cover change; soil texture; aridification; potential infected area; environmental driving forces; biological invasions

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MDPI and ACS Style

Szilassi, P.; Szatmári, G.; Pásztor, L.; Árvai, M.; Szatmári, J.; Szitár, K.; Papp, L. Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods. Plants 2019, 8, 593. https://doi.org/10.3390/plants8120593

AMA Style

Szilassi P, Szatmári G, Pásztor L, Árvai M, Szatmári J, Szitár K, Papp L. Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods. Plants. 2019; 8(12):593. https://doi.org/10.3390/plants8120593

Chicago/Turabian Style

Szilassi, Péter, Gábor Szatmári, László Pásztor, Mátyás Árvai, József Szatmári, Katalin Szitár, and Levente Papp. 2019. "Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods" Plants 8, no. 12: 593. https://doi.org/10.3390/plants8120593

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Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods

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