Multidimensional Statistical Analysis of the Structure of Plankton and Bottom Communities of Mineralized Rivers of the Lake Elton Basin

The paper presents the results of our comprehensive studies of the saline rivers of the arid Prieltonie region based on hydrobiological surveys of plankton and bottom communities. We have compared two tables of the specific structure according to the results of observations at 13 river sites in 2013 and 2018, which included the numbers of hydrobionts of 94 different taxonomic groups of macrozoobenthos, meiobenthos and zooplankton. Using the method of joint inertia analysis, we have revealed a high statistical consistency of the data matrices, due to objective laws of the spatial distribution of aquatic organisms. A randomization test of the Procrustean correlation coefficient showed the statistical significance (p = 0.00026) of the conjugacy of both specific structures in the space of latent variables. At the same time, a certain trend was noted in the changes in the taxonomic composition of communities at individual stations over time under the conditions of dynamic abiotic factors. We have analyzed the dependence of the taxonomic structure of hydrobionts on a set of 30 abiotic environmental factors obtained during hydrobiological and hydrochemical monitoring of the studied river sections. Using canonical correlation analysis and the projection method on latent structures, a set of ordination diagrams was plotted to allow revealing peculiar “ecological niches” for each group of species with a certain set of characteristics of their biotopes. The plankton and bottom communities were shown to correlate quite well with each other, which indicates a close relationship between them, due to both biotic interactions and a mutually agreed response to changes in aquatic factors.

saline rivers, zooplankton, macrozoobenthos, meiobenthos, community structure, co-inertia analysis, Procrustean analysis, multidimensional ordination

1. Zinchenko T. D., Golovatjuk L. V., Vykhristjuk L. A., Shitikov V. K. Diversity and Structure of Macrozoobenthos Communities in the Highly Mineralized Hara River (near Elton Lake). Povolzhskiy J. of Ecology, 2010, no. 1, pp. 14–30 (in Russian).

2. Zinchenko T. D., Shitikov V. K., Golovatyuk L. V., Gusakov V. A., Lazareva V. I. Multidimensional Block Ordination Analysis of Relations betweenthe Communities of Hydrobionts in Saline Rivers. Biologiia Vnutrennikh Vod, 2019, no. 4, pp. 82–89 (in Russian).

3. Shitikov V. K., Zinchenko T. D. Multivariate Statistical Analysis of Ecological Communities (Review). Theoretical and Applied Ecology, 2019. no. 1, pp. 5–11 (in Russian). DOI: https://doi.org/10.25750/1995-4301-2019-1-005-011

4. Shitikov V. K., Rozenberg G. S. Randomizatsiia i butstrep: statisticheskii analiz v biologii i ekologii s ispol'zovaniem R [Randomization and Bootstrap: Statistical Analysis in Biology and Ecology Using R]. Togliatti, Kassandra Publ., 2014. 314 p. (in Russian).

5. Hervé M. Aide-mémoire de statistique appliquée à la biologie. Construire son étude et analyser les résultats à l’aide du logiciel R. Translation by V. K. Shitikov Togliatti, 2018. 351 p. Available at: https://stok1946.blogspot.com (accessed 24 July 2018) (in Russian).

6. Dolédec S., Chessel D. Co-inertia Analysis: An Alternative Method for Studying Speciesenvironment Relationships. Freshwater Biology, 1994, vol. 31, pp. 277–294.

7. Dray S., Chessel D., Thioulouse J. Procrustean Co-inertia Analysis for the Linking of Multivariate Datasets. Écoscience, 2003, vol. 10, iss. 3, pp. 110–119.

8. González I., Lê Cao K.-A., Davis M. J., Déjean S. Visualising Associations Between Paired “Omics” Data Sets. BioData Minning, 2012, vol. 5, pp. 19. DOI: https://doi.org/10.1186/1756- 0381-5-19

9. Hervé M. R., Nicolè F., Lê Cao K. A. Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology. J. of Chemical Ecology, 2018, vol. 44, iss. 3, pp. 215–234. DOI: https://doi.org/10.1007/s10886-018-0932-6

10. Lê Cao K.-A., Rossouw D., Robert-Granié C., Besse P. A Sparse PLS for Variable Selection when Integrating Omics data. Statistical Applications in Genetics and Molecular Biology, 2008, vol. 7, iss. 1, pp. 35. DOI: https://doi.org/10.2202/1544-6115.1390

11. Singh A., Gautier B., Shannon C. P., Vacher M., Rohart F., Tebutt S. J., Le Cao K.-A. DIABLO-an integrative, multi-omics, multivariate method for multi-group classification. BioRxiv, 2016, no. 067611, pp. 1–50. DOI: https://doi.org/10.1101/067611

12. Wold H. Partial least squares. In: S. Kotz, N. Johnson, eds. Encyclopedia of Statistical Sciences. New York, Wiley, 1985, pp. 581–591.

13. Zinchenko T. D., Shitikov V. K., Golovatyuk L. V., Gusakov V. A., Lazareva V. I. Plankton and Bottom Communities in the Saline Rivers of Lake Elton Basin: Statistical Analysis of Dependences. Arid Ecosystems, 2018, vol. 8, no. 3, pp. 225–230.

14. Zinchenko T. D., Golovatyuk L. V., Abrosimova E. V., Popchenko T. V. Macrozoobenthos in Saline Rivers in the Lake Elton Basin : Spatial and Temporal Dynamics. Inland Water Biology, 2017, vol. 10, no. 4, pp. 384–398.