Modelling of the spatial distribution of Vaccinium myrtillus (Ericaceae) in the mountains of the Kabardino-Balkarian Republic (Central Caucasus)

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

Based on 41 occurrence points, the models of bilberry (Vaccinium myrtillus L.) spatial distribution in the mountains of the Kabardino-Balkarian Republic (Central Caucasus) were developed. The models predicted the total potential distribution of the species and the distribution of forest and grassland populations separately. Maxent (Maxent software for species habitat modelling) was used as the main modelling method due to its efficiency in constructing distribution models based on presence points of biological objects. The main abiotic predictors of bilberry spatial distribution were precipitation seasonality (less than 30 %) and the amount of precipitation in the summer quarter (300–370 mm), which characterized the optimal habitats of the species as areas with moderate humidity. The average minimum temperature of the coldest month in optimal habitats of V. myrtillus was at least –13°C, which is probably related to the species sensitivity to soil freezing. Terrain was of the least importance for the distribution of bilberry in the mountains. The most suitable habitats of the species were predicted both on gentle (e.g. river terraces covered with pine forests) and steep slopes with average angles up to 40°С (up to 30°С for forest populations, and up to 50°С for meadow populations). Priority protection areas, where V. myrtillus is most likely to be found (80–100 %) are located on wooded river terraces and mountain slopes in the subalpine and alpine belts of the Baksan, Chegem, Cherek, Sukan and Khaznidon gorges of the Kabardino-Balkarian Republic.

About the authors

I. E. Emuzov

Kabardino-Balkarian State Agrarian University named after V.M. Kokov

Author for correspondence.
Email: igor.emuzov@mail.ru
Russian Federation, Nalchik

H. M. Nazranov

Kabardino-Balkarian State Agrarian University named after V.M. Kokov

Email: igor.emuzov@mail.ru
Russian Federation, Nalchik

M. I. Malkandueva

Kabardino-Balkarian State Agrarian University named after V.M. Kokov

Email: igor.emuzov@mail.ru
Russian Federation, Nalchik

A. A. Gadieva

Kabardino-Balkarian State Agrarian University named after V.M. Kokov

Email: igor.emuzov@mail.ru
Russian Federation, Nalchik

References

  1. Galushko A. I. 1980. [Flora of the North Caucasus: Determinant. Vol. 3]. Rostov-on-Don. 328 p. (In Russian)
  2. Staritsyn V. V., Polyakova E. V. 2022. The content of ascorbic acid in blueberry fruits in the Kholmogorsky tectonic knot of the Arkhangelsk region. — Advances in Current Natural Sciences. 6: 77–82. https://doi.org/10.17513/use.37844 (In Russian)
  3. Määttä-Riihinen K. R., Kähkönen M. P., Törrönen A. R., Heinonen I. M. 2005. Catechins and procyanidins in berries of Vaccinium species and their antioxidant activity — J. Agricult. Food Chem. 53(22): 8485–8491. https://doi.org/10.1021/jf050408l
  4. Tsepkova N. L., Gadiyeva A. A., Gadiyev A. R. 2015. The objects of secondary forest exploitation in the national park ''Prielbrusye'' (the Central Caucasus). — The Agrarian Scientific Journal. 11: 26–29. https://elibrary.ru/vctzvl (In Russian)
  5. [Red Book of the Kabardino-Balkarian Republic]. 2018. Nalchik. 496 p. https://zapovednik-kbr.ru/krasnaya-kniga/ (In Russian)
  6. Chadaeva V. A., Mollayeva M. Z., Sablyrova Yu. M. 2018. Vaccinium vitis-idaea (Ericaceae) production and Pinus sylvestris ssp. kochiana (Pinaceae) renewal in pine forests of national park «Prilebrusye». — Rastitelnye Resursy. 54(2): 190–200. https://elibrary.ru/tiijel (In Russian)
  7. Tembotova F. A., Pshegusov R. Kh., Tlupova Yu. M. 2012. [Forests of the northern macroslope of Central Caucasus (Elbrus and Tersk zone variants)]. — In: [Biological diversity of forest ecosystems. Vol. 1.]. Moscow. P. 242–259. (In Russian)
  8. Elith J., Franklin J. 2013. Species distribution modeling. — In: Encyclopedia of Biodiversity (Second Edition). Oxford. P. 692–705. https://doi.org/10.1016/B978-0-12-384719-5.00318-X
  9. Duarte A., Whitlock S. L., Peterson J. T. 2018. Species distribution modeling. — In: Encyclopedia of Ecology (Second edition). Oxford. P. 189–198. https://doi.org/10.1016/B978-0-12-409548-9.10572-X
  10. Pshegusov R. Kh. 2023. From spatial distribution to ecological niche: modeling issues within the correlation approach. — Izvestiya Rossiyskoy Akademii Nauk. Seriya Biologicheskaya. 8: 16–24. https://doi.org/10.31857/S1026347023600802 (In Russian)
  11. Pshegusov R., Tembotova F., Chadaeva V., Sablirova Y., Mollaeva M., Akhomgotov A. 2022. Ecological niche modeling of the main forest-forming species in the Caucasus. — Forest ecosystems. 9: 100019. https://doi.org/10.1016/j.fecs.2022.100019
  12. Chadaeva V., Pshegusov R. 2022. Identification of degradation factors in mountain semiarid rangelands using spatial distribution modelling and ecological niche theory. — Geocarto International. 37(27): 15235–15251. https://doi.org/10.1080/10106049.2022.2096701
  13. Pshegusov R., Chadaeva V. 2023. Modelling the nesting-habitat of threatened vulture species in the Caucasus: an ecosystem approach to formalising environmental factors in species distribution models. — Avian Research. 14: 100131. https://doi.org/10.1016/j.avrs.2023.100131
  14. Osorio-Olvera L., Lira-Noriega A., Soberón J., Peterson A.T., Falconi M., Contreras-Díaz R.G., Martínez-Meyer E., Barve V., Barve N. 2020. ntbox: An r package with graphical user interface for modeling and evaluating multidimensional ecological niches. — Methods Ecol. Evol. 11(10): 1199–1206. https://doi.org/10.1111/2041-210X.13452
  15. WorldClim2: WorldClim Climate Data base. 2024. https://worldclim.org/version2 (Accessed 12.01.24).
  16. SRTM: Shuttle Radar Topography Mission. 2024. https://srtm.csi.cgiar.org/ (Accessed 9.01.24).
  17. Dormann C., Elith J., Bacher S., Buchmann C., Carl G., Carré G., García Márquez J. R., Gruber B., Lafourcade B., Leitão P., Münkemüller T., McClean C., Osborne P. E., Reineking B., Schröder B., Skidmore A. K., Zurell D., Lautenbach S. 2013. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. — Ecography. 36(1): 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
  18. Naimi B., Hamm N., Groen T. A., Skidmore A. K., Toxopeus A. G. 2014. Where is positional uncertainty a problem for species distribution modelling. — Ecography. 37(2): 191–203. https://doi.org/10.1111/j.1600-0587.2013.00205.x
  19. Phillips S. J., Dudík M. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. — Ecography. 31(2): 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
  20. Hijmans R. J., Phillips S. J., Leathwick J., Elith J. 2017. dismo: Species Distribution Modeling. R Package Version 1.3-3. https://CRAN.R-project.org/package=dismo
  21. Iverson L. R., Rebbeck J., Peters M. P., Hutchinson T., Fox T. 2019. Predicting Ailanthus altissima presence across a managed forest landscape in southeast Ohio. — For. Ecosyst. 6: 41. https://doi.org/10.1186/s40663-019-0198-7
  22. Muscarella R., Galante P. J., Soley-Guardia M., Boria R. A., Kass J.M., Uriarte M., Anderson R. P. 2014. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for MaxEnt ecological niche models. — Methods Ecol. Evol. 5(11): 1198–1205. https://doi.org/10.1111/2041-210X.12261
  23. Akaike H. A. 1974. New look at the statistical model identification. — IEEE Trans. Automat. Contr. 19(6): 716–723. https://doi.org/10.1109/TAC.1974.1100705
  24. Fielding A. H., Bell J. F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. — Environmental Conservation. 24(1): 38–49. https://doi.org/10.1017/S0376892997000088
  25. Unidata: NetCDF User's Guide (NUG): version 1.1. Boulder, CO: UCAR/Unidata, 2024. https://doi.org/10.26024/nw73-vm64 (Accessed 12.01.24).
  26. Tennekes M. 2018. tmap: Thematic maps in R. — J. Stat. Softw. 84(6): 1–39. https://doi.org/10.18637/jss.v084.i06
  27. Ramensky I. A., Tsatsenkin I. A., Chizhikov O. N., Antipin N. A. 1956. [Ecological assessment of fodder lands by vegetation cover]. Moscow. 472 p. (In Russian)
  28. Kislitsyna A. V., Egoshina T. L. 2016. Key resource and population parameters of Vaccinium myrtillus L. in south taiga forest ecosystem of the Kirov Region. — Vestnik of Volga State University of Technology. Series: Forest. Ecology. Nature Management. 3(31): 77–86. https://doi.org/10.15350/2306-2827.2016.3.77; https://elibrary.ru/wvovnn (In Russian)
  29. Luzan A. A. 2014. Features of Vaccinium myrtillus L. growing and fruiting in upper stream of the Iya River (Tulun District of Irkutsk Region). — Vestnik IrGSKHA. 64: 42–49. https://elibrary.ru/teswyh (In Russian)
  30. Popov S. Yu. 2019. Biotopic and ecological preferences of blueberry, cow berry and bilberry in Pinega Nature Reserve. — Lesovedeniye. 3: 215–227. https://doi.org/10.1134/S0024114819030070 (In Russian)
  31. Egorova N. Yu., Egoshina T. L., Yaroslavtsev A. V. 2021. Vaccinium myrtillus L. in Kirov region (southern taiga subzone): phytocoenotic confinement and ecological preferences. — Tomsk state University Journal of Biology. 53: 68–88. https://doi.org/10.17223/19988591/53/4 (In Russian)
  32. Grom I. I. 1967. [Yield of wild berries in the northern districts of the Komi ASSR]. — Rastitelnye Resursy. 3(2):193–198. (In Russian)
  33. Timoshok E. E. 2000. The ecology of bilberry (Vaccinium myrtillus L.) and cowberry (Vaccinium vitis-idaea L.) in Western Siberia. — Rus. J. Ecol. 31(1): 8–13. https://doi.org/10.1007/BF02799719
  34. [Atlas of natural ranges and resources of medicinal plants of the USSR]. 1983. Moscow. 340 p. (In Russian)

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Russian Academy of Sciences