Advanced Approaches to the Visualization of Data Characterizing Distribution Features of Alien Plant Species View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-07

AUTHORS

V. K. Tokhtar

ABSTRACT

Results of application of different approaches to the data visualization during the study of alien plant species have been analyzed and summarized, and the prospects of their use for different purposes have been evaluated. The existing experience in the study of alien plant species shows that traditional methods used to analyze their composition and distribution patterns in different regions are informative only for determining the main tendencies reflecting global processes of a phytobiota synanthropization. At the same time, new state-of-art methods are required to reveal the latent patterns of plant migration and the processes of their naturalization. The most promising approaches to analyzing large volumes of data are multivariate statistical methods. The potential of these methods is determined by their capability to identify relationships between a wide range of floristic and biological data and environmental characteristics, which can be visualized. These methods allow us to present different data in the form of diagrams reflecting interactions between individual species or whole groups of alien plants and climatic or environmental variables. They make it possible to create models of expansion of invasive species. They reflect the current statistical distances and relationships between different objects of study, which makes it possible to identify features of the group strategy of colonization of various natural and/or technogenic habitats by alien species. These strategies depend mainly on the biological characteristics of species, level of anthropogenic transformation of a regional flora, and environmental parameters. More... »

PAGES

263-269

Identifiers

URI

http://scigraph.springernature.com/pub.10.1134/s2075111718030153

DOI

http://dx.doi.org/10.1134/s2075111718030153

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107380696


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