Modeling a Potential Plant Habitat Using Machine Learning Methods

УДК 519.67, 551.581.1

  • A.V. Vaganov South-Siberian Botanical Garden (Barnaul, Russia); Sakhalin Branch of the Botanical Garden Institute of the Far Eastern Branch of the Russian Academy of Sciences (Yuzhno-Sakhalinsk, Russia) Email: vaganov_vav@mail.ru
  • V.F. Zaikov South-Siberian Botanical Garden (Barnaul, Russia) Email: zaikov@mail.ru
  • O.S. Krotova Altai State University (Barnaul, Russia) Email: kr.olga0910@gmail.com
  • A.I. Musokhranov Altai State University (Barnaul, Russia) Email: mai_22125@mail.ru
  • Z.V. Pokalyakin Altai State University (Barnaul, Russia) Email: zpokalyakin@gmail.com
  • L.A. Khvorova Altai State University (Barnaul, Russia) Email: khvorovala@gmail.com
DOI_disc_logo https://doi.org/10.14258/izvasu(2022)4-13
Keywords: ecological niche, biological species, pseudo-absence points, bioclimatic characteristics, Python programming language, machine learning models, RandomForest

Abstract

The article is devoted to modeling the potential distribution area of the species Pulsatilla turczaninovii Kryl. et Serg. Plant ecological niche modeling is the process of building models using modern computer algorithms and bioclimatic data to predict the distribution range of plant species. The result of the simulation is a model that can be used to map the area of growth or residence of species, predict the range or analyze the impact of the environment on species.

Data are required on both the presence of species and their absence in a particular territory to build effective models for predicting ecological niches of plants. View absence points (or background points) are not registered in databases, but can be generated using different approaches.

This article describes the implementation of three approaches to selecting pseudo-absence points of species in an operationally divided territory and presents the result of modeling the potential distribution area of the species Pulsatilla turczaninovii Kryl. et Serg. using the random forest algorithm — the most popular way to build ensembles of decision trees. The software implementation of the model is carried out in the high-level Python programming language.

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Author Biographies

A.V. Vaganov, South-Siberian Botanical Garden (Barnaul, Russia); Sakhalin Branch of the Botanical Garden Institute of the Far Eastern Branch of the Russian Academy of Sciences (Yuzhno-Sakhalinsk, Russia)

кандидат биологических наук, старший научный сотрудник; старший научный сотрудник

V.F. Zaikov, South-Siberian Botanical Garden (Barnaul, Russia)

младший научный сотрудник

O.S. Krotova, Altai State University (Barnaul, Russia)

преподаватель кафедры теоретической кибернетики и прикладной математики

A.I. Musokhranov, Altai State University (Barnaul, Russia)

студент Института математики и информационных технологий

Z.V. Pokalyakin, Altai State University (Barnaul, Russia)

студент Института математики и информационных технологий

L.A. Khvorova, Altai State University (Barnaul, Russia)

кандидат технических наук, доцент кафедры теоретической кибернетики и прикладной математики

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Published
2022-09-09
How to Cite
Vaganov A., Zaikov V., Krotova O., Musokhranov A., Pokalyakin Z., Khvorova L. Modeling a Potential Plant Habitat Using Machine Learning Methods // Izvestiya of Altai State University, 2022, № 4(126). P. 85-92 DOI: 10.14258/izvasu(2022)4-13. URL: http://izvestiya.asu.ru/article/view/%282022%294-13.
Issue
No 4(126) (2022): Izvestiya of Altai State University
Section
Математика и механика

Copyright (c) 2022 Алексей Владимирович Ваганов , Владимир Федорович Зайков , Ольга Сергеевна Кротова , Андрей Игоревич Мусохранов , Захар Валерьевич Покалякин , Любовь Анатольевна Хворова

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