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Analysis of Long-Term Biological Data Series: Methodological Problems and Possible Solutions

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Abstract

Long-term (multiyear) series of biological data have some specific features. The analysis of such data is treated as a statistical problem of the extraction of long-term signal from the general variance, which is determined by a complex of factors. An attempt is made to systematize the key points that should be considered in this problem. The main sources of data variability are considered, including the temporal and spatial variability of various scales and methodical and statistical sources. The methodological approaches to the assessment of these effects and the corresponding data correction are briefly discussed. This requires careful and reasoned data preparation and the choice of the appropriate statistical model and analytical methods. Some of these approaches are illustrated by case studies. However, the problem is too complex to have a single, universal solution that is suitable for any situation.

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Notes

  1. The effect was named after an absent-minded entomologist from Jules Verne’s novel Dick Sand, Or, A Captain at Fifteen, who confused Africa with South America and was amazed discovering the tsetse fly.

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ACKNOWLEDGMENTS

The author is grateful to A.D. Naumov and V.O. Mokievsky for their discussion of the study and valuable comments.

Funding

This study was supported by the Russian Science Foundation (grant no. 14-50-00029 for field research and data analysis at the Kara Sea) and the Russian Foundation for Basic Research (project nos. 15-29-02507 and 18-04-00206 for literature analysis and work preparation).

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Authors and Affiliations

  1. Department of Hydrobiology, Biology Faculty, Moscow State University, 119234, Moscow, Russia

    A. I. Azovsky

  2. Shirshov Institute of Oceanology, Russian Academy of Sciences, 117997, Moscow, Russia

    A. I. Azovsky

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  1. A. I. Azovsky
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Correspondence to A. I. Azovsky.

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Translated by L. Solovyova

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Azovsky, A.I. Analysis of Long-Term Biological Data Series: Methodological Problems and Possible Solutions. Biol Bull Rev 9, 373–384 (2019). https://doi.org/10.1134/S2079086419050025

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