Issue |
BIO Web Conf.
Volume 24, 2020
International Conferences “Plant Diversity: Status, Trends, Conservation Concept” 2020
|
|
---|---|---|
Article Number | 00034 | |
Number of page(s) | 5 | |
DOI | https://doi.org/10.1051/bioconf/20202400034 | |
Published online | 21 September 2020 |
Ecotypification of local populations of rare species Calvatia gigantea (Basidiomycota: Agaricales) in ultracontinental zones of Mongolia and Russia
1 Botanical garden and Research Institute, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia
2 Central Siberian Botanical Garden of the Siberian Branch of the Russian Academy of Sciences (CSBG SB RAS), 630090 Novosibirsk, Russia
3 Novosibirsk State University, 630090 Novosibirsk, Russia
* Corresponding author: kbaikov2018@mail.ru
Study of the levels of climatic comfort in localities of the rare species Calvatia gigantea from Agaricales of Basidiomycota is performed using multimodal ecoinformative approach with maximum entropy method. For numerically exact and correct assessment of the level of climate suitability, we propose recalculate the scale of probalility finding of a species into a new scale of climate suitability, with the next intervals: low suitable (1–3 points), ambivalent (4–6 points), and genuine suitable (7–9 points). Also there are two transit zones between these intervals. It has been astablished that local populations of the species in Altai territorial group differ significantly in levels of climatic comfort (3.2, 5.4, and 6.2 points). Local population near Sharangol in Khentei territorial cluster (Central Mongolia) gets 4.0 points of climate comfort, and local population in Khingan Mountains (Eastern Mongolia) gets 7.3 points, the best result in the set studied. The ecotypification of localities was carried out, according to which all the studied populations of C. gigantea are assigned to different ecotypes, since each studied locality is characterized by the unique climatic spectrum and the specific variable of the first rank.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.