Modeling the spatial distribution of marker species of ground beetles based on GIS technologies

https://doi.org/10.35595/2414-9179-2020-2-26-172-188

View or download the article (Rus)

About the Authors

Tamara A. Avtaeva

Kh.I. Ibragimov Complex Institute of the Russian Academy of Sciences, Department of Biological Research,
Staropromyslovskoe Hwy, 21A, Grozny, Chechen Republic, Russia,
E-mail: avtaeva1971@mail.ru

Andrey V. Skripchinsky

North Caucasus Federal University, Institute of Mathematics and Natural Sciences,
Pushkin str., 1, 355009, Stavropol, Russia,
E-mail: ron1975@list.ru

Dmitriy V. Ivanov

Research Institute for Problems of Ecology and Mineral Wealth Use of Tatarstan Academy of Sciences,
Daurskaya str., Kazan, Russia,
E-mail: water-rf@mail.ru

Raisa A. Sukhodolskaya

Research Institute for Problems of Ecology and Mineral Wealth Use of Tatarstan Academy of Sciences,
Daurskaya str., Kazan, Russia,
E-mail: sukhodolskayaraisa@gmail.com

Abstract

Climate change and related changes in natural ecosystems are the most important international issues of the twenty-first century. In this regard, modeling the dynamics of plant and animal habitats based on the analysis of their relationships with climate parameters and environmental characteristics becomes an urgent task. Modeling the geographical distribution of species is not possible without geoinformation analysis, which allows you to identify both the boundaries of factors that affect the distribution of the species, and the features of its range.

The paper presents the author’s addition to the existing method of ecological and geographical modeling based on GIS technologies that allow to visualize the dynamics of areas in a certain period of time and in connection with changes in bioclimatic parameters.

Modeling the spatial distribution of two marker species of ground beetles makes it possible to extrapolate fragmentary data on specific locations over large territories. The created geoinformation models of the predicted areas revealed their changes for different climate scenarios for 2050 and 2070.

Based on ecological and geoinformation modeling, it was found that the formation of the modern range of Zabrus tenebrioides is significantly influenced by the average daily temperature amplitude for each month, the maximum temperature of the warmest month and the minimum temperature of the coldest month. The distribution of Pterostichus oblongopunctatus is influenced by the average annual temperature, the average daily temperature amplitude for each month, and the average temperature of the driest quarter; the average temperature of the warmest quarter of the year and the amount of precipitation in the driest month of the year. The geoinformation analysis made it possible to identify the dependence of the number of points of species finds and the values of bioclimatic factors. Maps and graphs of the range of species comfort were created. The main trends of changes in the range of Pterostichus oblongopunctatus under changing climate conditions in the “soft” and “hard” scenarios are revealed. Under the influence of climate change, the area of habitats is reduced and their structure is changed.

Keywords

bioclimatic parameters, GIS modeling, spatial distribution, marker species of ground beetles, geoinformation system

References

  1. Abdurahmanov G.M., Magomedova M.Z. Features of the geographical distribution of endemic species of ground beetles of the genus Carabus in the Caucasus. South of Russia: ecology, development, 2008. No 3. P. 45–52 (in Russian).
  2. Bolotov I.I., Frolov A.A. Range modeling and analysis of the contribution of factors to the climate niche of Parnassius mnemosyne L. 1758 (Lepidoptera: Papilionidae). Vestnik of Northern (Arctic) Federal University. Natural Sciences, 2015. No 1. P. 56–67 (in Russian).
  3. Bousquet Y. Tribe Pterostichini. Catalogue of Palearctic Coleoptera. V. 1. Archostemata — Myxophaga — Adephaga. Stenstrup, Denmark: Apollo Books, 2003. P. 462–521.
  4. Brygadyrenko V.V. Evaluation of ecological niches of abundant species of Poecilus and Pterostichus (Coleoptera: Carabidae) in forests of steppe zone of Ukraine. Entomologica Fennica, 2016. No 27 (2). Р. 81–100.
  5. Dicken P. Global shift: mapping the changing contours of the world economy. 7th edition. London: Guilford Press, 2015. 618 p.
  6. Douma J.C., Witte J.-Ph.M., Aerts R., Bartholomeus R.P., Ordonez J.C., Venterink H.O., Wassen M.J., Van Bodegom P.M. Towards a functional basis for predicting vegetation patterns; incorporating plant traits in habitat distribution models. Ecography, 2012. V. 35. P. 294–305.
  7. Dudov S.V. Modeling of species distribution based on terrain and remote sensing data on the example of vascular plants of the Lower mountain zone of the Tukuringra ridge (Zeysky reserve, Amur region). Zhurnal obshchej biologii (Journal of General Biology), 2016. No 1. V. 77. P. 16–28 (in Russian).
  8. Guisan A., Thuiller W. Predicting species distribution: offering more than simple habitat models. Ecological Letters, 2005. V. 8. P. 993–1009.
  9. Guisan A., Zimmermann N.E. Predictive habitat distribution models in ecology. Ecological Modelling, 2000. V. 135. P. 147–186.
  10. Hurka K. Carabidae of the Czech and Slovak Republics. Zlin, Czech Republic: Kabourek, 1996. 565 р.
  11. Huyong Y., Lei F., Yufei Z., Li F., Di W., Chaoping Z. Prediction of the spatial distribution of Alternanthera philoxeroides in China based on ArcGIS and MaxEnt. Global Ecology and Conservation. Elsevier BV, March 2020, e00856. V. 21. 15 p. DOI: 10.1016/j.gecco.2019.e00856.
  12. von Kreckwitz H. Sind Nahrungsmenge und Korpergewich von Bedeutung fur die Gonadenreifung des Carabiden Pterostichus nigrita Payk. in verschiedenen Photoperioden? Zoologischer Anzeiger. Jena, Germany: Elsevier, 1980. Bd. 204. H. 3/4. P. 157–164 (in German).
  13. Kryzhanovskij O.L., Belousov I.A., Kabak I.I., Kataev B.M., Makarov K.V., Shilenkov V.G. A checklist of the ground-beetles of Russia and adjacent lands (Insecta, Coleoptera, Carabidae). Sofia, Bulgaria: Pensoft Publishers, 1995.
  14. Phillips S.J., Dudic M. Modelling of species distribution with Maxent: new extentions and a comprehensive evaluation. Ecography, 2008. V. 31. P. 161–175.
  15. Pithan F., Mauritsen T. Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nature Geoscience, 2014. V. 7. P. 181–184. DOI: 10.1038/NGEO2071.
  16. Portilla Cabrera C.V., Selvaraj J.J. Geographic shifts in the bioclimatic suitability for Aedes aegypti under climate change scenarios in Colombia. Heliyon, January 2020. V. 6. Iss. 1. P. e03203. DOI: 10.1016/j.heliyon.2019.e03101.
  17. Qin A., Liu B., Guo Q., Bussmann R.W., Ma F., Jian Z., Xu G., Pei Sh. Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Global Ecology and Conservation, April 2017. Amsterdam: Elsevier. V. 10. P. 139–146. DOI: 10.1016/j.gecco.2017.02.004.
  18. Rakhmatullina I.R., Rakhmatullin Z.Z., Latypov E.R. Modeling of growth conditions and analysis of the contribution of factors to the formation of high-priority stands of common pine (Pinus sylvestris L.) in the MaxEnt program (on the example of the Bugulminsko-Bebeleyevskaya upland within the Republic of Bashkortostan). Forestry, 2017. No 3. Р. 104–111 (in Russian).
  19. Sandanov D.S. Modern approaches to modeling the diversity and spatial distribution of plant species: prospects for their use in Russia. Tomsk State University Journal, 2019. No 46. P. 82–114. DOI: 10.17223/19988591/46/5 (in Russian).
  20. Thiele H.-U., Einflusse der Photoperiode auf die Diapause von Carabiden. Zeitschrift für Angewandte Entomologie, 1966. Bd. 58. P. 143–149 (in German).
  21. Thiele H.-U., Konen H. Interaction between photoperiodism and temperature with respect to the control of dormancy in the adult stage of Pterostichus oblongopunctatus F. (Coleoptera, Carabidae). II. The development of the reproductionpotential during the winter months in the field. Oecologia (Berlin), 1975. V. 19. P. 339–343.
  22. Tufte E.R. Beautiful еvidence. Cheshire, CT: Graphics Press, 2006. 213 p.
  23. Yang X.Q., Kushwaha S.P.S., Saran S., Xu J., Roy P.S. Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecological Engineering, 2013. No 51. P. 83–87. DOI: https://doi.org/10.1016/j.ecoleng.2012.12.004.
  24. Yuanjun Z., Wei W., Hao L., Baizhu W., Xiaohui Y., Yanshu L. Modelling the potential distribution and shifts of three varieties of Stipa tianschanica in the eastern Eurasian Steppe under multiple climate change scenarios. Global Ecology and Conservation, October 2018. Elsevier. V. 16. e00501. DOI: 10.1016/j.gecco.2018.e00501.
  25. Zhang L., Jing Z., Li Z., Liu Y., Fang S. Predictive modeling of suitable habitats for Cinnamomum Camphora (L.) Presl using maxent model under climate change in China. International Journal of Environmental Research and Public Health, 31 Aug 2019. V. 16. Iss. 17. P. 3185. DOI: 10.3390/ijerph16173185.

For citation: Avtaeva T.A., Skripchinsky A.V., Ivanov D.V., Sukhodolskaya R.A. Modeling the spatial distribution of marker species of ground beetles based on GIS technologies InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: Moscow University Press, 2020. V. 26. Part 2. P. 172–188. DOI: 10.35595/2414-9179-2020-2-26-172-188 (In Russian)