Methods of the comparative analysis of the insect species composition in different habitats using the software environment R
Abstract
Analysis of publications on the ecology of communities, in focus insects, over the past five years, available in the international database PubMed Central, showed that the most common scheme for assessing the species composition of different habitats are comparison using multivariate analysis of variance (PERMANOVA) and ANOSIM test, visualization of the differences using ordination (NMDS). The scheme is completed by identifying the species that are most responsible for the differences in the samples, using the IndVal and SIMPER procedures. All these methods can be implemented in the R software environment, which is currently a generally recognized standard in scientific publications in the world. The presented article includes a brief overview of the methods of comparative analysis of the insect species composition. The program code for this implementation in R using the results of our own research is also given.
References
2. Сушко ГГ. Методы многомерного анализа данных в синэкологии насекомых. Журнал Белорусского государственного университета. Экология. 2020; 1:38–45.
3. Сушко ГГ. Использование методов анализа данных в энтомологических исследованиях. В: Сборник статей III Международной научно-практической конференции, памяти В. А. Цинкевича. Минск: ГНПО «НПЦ НАН Беларуси по биоресурсам»; 2019. с. 381–383.
4. Шитиков ВК, Зинченко ТД, Розенберг ГС. Макроэкология речных сообществ: концепции, методы, модели. Тольятти: Кассандра; 2012.
5. Шитиков ВК, Розенберг ГС. Рандомизация и бутстреп: статистический анализ в биологии и экологии с использованием R. Тольятти: Кассандра; 2013. 6. Clarke KR. Non-parametric multivariate analysis of changes in community structure. Australian Journal of Ecology. 1993;18:117–143. 7. Legendre P, Legendre L. Numerical Ecology. 2nd edition. Amsterdam: Elsevier; 1998.
8. Кабаков РИ. R в действии. Анализ и визуализация данных в программе R. Москва: ДМК Пресс; 2014.
9. Мастицкий СЭ, Шитиков ВК. Статистический анализ и визуализацияданных с помощью R. Москва: ДМК Пресс; 2015.
10. Джеймс Г, Уиттон Д, Хасти Т, Тибширани Р. Введение в статистическое обучение с примерами на языке R.Москва: ДМК Пресс; 2016.
11. Borcard D, Gillet F, Legendre P. Numerical Ecology with R. Wien: Springer Nature; 2018.
12. Herve M. Aide-mémoire de statistique appliquée à la biologie. Construire son étude et analyser les résultats à l’aide du logiciel R. 2016.
13. PubMed Central (PMC) [Internet]. [Cited 2020 August 15]. Available from: https://www.ncbi.nlm.nih.gov/pmc/
14. R Development Core Team (2010) R: A language and environment for statistical computing. R Foundation for Statistical Com- puting. [Internet]. Vienna: Austria. Available from: https://www.R-project.org/
15. Anderson MJ, Walsh DC. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecological monographs. 2013;83(4):557–574.
16. McCune B, Grace JB. Analysis of ecological communities. Gleneden Beach: MjMSoftware Design; 2002.
17. Ramette A. Multivariate analyses in microbial ecology. FEMS. Microbiological Ecology. 2007; 62(2):142–160.
18. Oksanen J. Multivariate Analysis in Ecology. Lecture Notes. Finland: University of Oulu; 2004.
19. Warton DI, Wright TW, Wang Y. Distance-based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution. 2012;3:89–101.
20. Dufrene M, Legendre P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecolical Monographs. 1997;67:345–366.
21. De Caceres M, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;90(12):66–74.
