Selection of model sites of ecotone pine-broadleaf forests for monitoring the climate change impact on them

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This study aimed to justify the selection of model sample plots of ecotone pine-broadleaved communities for monitoring changes of forest composition in the Southern Urals under the influence of climatic changes. Modelling of changes in potential ranges of pine-broadleaved forests of the suballiance Tilio cordatae-Pinenion sylvestris including four associations (Ass. Tilio cordatae-Pinetum sylvestris, Euonymo verrucosae-Pinetum sylvestris, Galio odorati-Pinetum sylvestris, Carici arnellii-Pinetum sylvestris) under scenarios of moderate (RCP4.5) and strong (RCP8.5) climate change was carried out. Current and predicted climatic characteristics, as well as indicators of habitat suitability in 120 known localities of pine-broadleaved forests in the mountain-forest zone of the Republic of Bashkortostan and on the Ufa plateau were calculated. Ten localities of pine-broadleaved forests with the maximum predicted change in habitat suitability were selected for monitoring climatic changes.

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作者简介

N. Fedorov

Ufa Institute of Biology, Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences; Ufa State Petroleum Technological University

编辑信件的主要联系方式.
Email: fedorov@anrb.ru
俄罗斯联邦, 450054 Ufa; 450062 Ufa

S. Zhigunova

Ufa Institute of Biology, Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences; Ufa State Petroleum Technological University

Email: fedorov@anrb.ru
俄罗斯联邦, 450054 Ufa; 450062 Ufa

V. Martynenko

Ufa Institute of Biology, Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences; Academy of Sciences of the Republic of Bashkortostan

Email: fedorov@anrb.ru
俄罗斯联邦, 450054 Ufa; 450008 Ufa

P. Shirokikh

Ufa Institute of Biology, Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences; Ufa State Petroleum Technological University

Email: fedorov@anrb.ru
俄罗斯联邦, 450054 Ufa; 450062 Ufa

O. Mikhaylenko

Ufa State Petroleum Technological University

Email: fedorov@anrb.ru
俄罗斯联邦, 450062 Ufa

I. Bikbayev

Ufa Institute of Biology, Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences; Ufa State Petroleum Technological University

Email: fedorov@anrb.ru
俄罗斯联邦, 450054 Ufa; 450062 Ufa

参考

  1. Rustad L., Campbell J., Dukes J.S. et al. Changing climate, changing forests: the impacts of climate change on forests of the Northeastern United States and Eastern Canada // Newtown Square, Pennsylvania, USA: US Department of Agriculture, Forest Service, Northern Research Station. 2012. 48 p. https://doi.org/10.2737/NRS-GTR-99
  2. Лескинен П., Линднер М., Веркерк П.Й. и др. Леса России и изменение климата. Что нам может сказать наука // Европейский институт леса. 2020. 142 с. https://doi.org/10.36333/wsctu11
  3. Matias L., Linares J.C., Sanchez-Miranda A. et al. Contrasting growth forecasts across the geographical range of Scots pine due to altitudinal and latitudinal differences in climatic sensitivity // Global Change Biology. 2017. V. 23. № 10. P. 4106–4116. https://doi.org/10.1111/gcb.13627
  4. Pirovani D.B., Pezzopane J.E.M., Xavier A.C. et al. Climate change impacts on the aptitude area of forest species // Ecological Indicators. 2018. V. 95. P. 405–416. https://doi.org/10.1016/j.ecolind.2018.08.002
  5. Kasper J., Leuschner C., Walentowski H. et al. Winners and losers of climate warming: Declining growth in Fagus and Tilia vs. stable growth in three Quercus species in the natural beech–oak forest ecotone (western Romania) // Forest Ecology and Management. 2022. V. 506. Art. 119892. https://doi.org/10.1016/j.foreco.2021.119892
  6. Allen C.D., Macalady A.K., Chenchouni H. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests // Forest Ecology and Management. 2010. V. 259. № 4. P. 660–684. https://doi.org/10.1016/j.foreco.2009.09.001
  7. McDowell N.G., Allen C.D., Anderson-Teixeira K. et al. Pervasive shifts in forest dynamics in a changing world // Science. 2020. V. 368. № 6494. Art. eaaz9463. https://doi.org/10.1126/science.aaz9463
  8. Zald H.S.J., Spies T.A., Huso M. et al. Climatic, landform, microtopographic, and overstory canopy controls of tree invasion in a subalpine meadow landscape, Oregon Cascades, USA // Landscape Ecology. 2012. V. 27. P. 1197–1212. https://doi.org/10.1007/s10980-012-9774-8
  9. Peterson D.W., Kerns B.K., Dodson E.K. Climate change effects on vegetation in the pacific northwest: a review and synthesis of the scientific literature and simulation model projections; General technical report PNWGTR-900 // US Forest Service: Portland, OR, USA. 2014. P. 183. https://doi.org/10.2737/PNW-GTR-900
  10. Muffler L., Beierkuhnlein C., Aas G. et al. Distribution ranges and spring phenology explain late frost sensitivity in 170 woody plants from the Northern Hemisphere // Global Ecology and Biogeography. 2016. V. 25. № 9. P. 1061–1071. https://doi.org/10.1111/geb.12466
  11. Bascietto M., Bajocco S., Mazzenga F. et al. Assessing spring frost effects on beech forests in Central Apennines from remotely-sensed data // Agricultural and Forest Meteorology. 2018. V. 248. P. 240–250. https://doi.org/10.1016/j.agrformet.2017.10.007
  12. Федеральная служба по гидрометеорологии и мониторингу окружающей среды (Росгидромет) // Третий оценочный доклад об изменениях климата и их последствиях на территории Российской Федерации. Общее резюме / Под ред. Шумакова И.А. СПб.: Наукоемкие технологии, 2022. 124 с.
  13. Барталев С.А., Жижин М.Н., Лупян Е.А. и др. Возможности исследований влияния изменений климата на состояние растительного покрова: концепция проекта CLIVT // Современные проблемы дистанционного зондирования Земли из космоса. 2008. V. 5. № 2. P. 272–278.
  14. Им С.Т., Харук В.И. Климатически индуцированные изменения в экотоне альпийской лесотундры плато Путорана // Исследование Земли из космоса. 2013. № 5. P. 32–44. https://doi.org/10.7868/S0205961413040052
  15. Moiseev P.A., Gaisin I.K., Bubnov M.O. et al. Dynamics of tree vegetation in steppificated areas on the slopes of the Southern Kraka Massif during the past 80 years // Russ. Journal of Ecology. 2018. V. 49. № 2. P. 190–195. https://doi.org/10.1134/S1067413618020108
  16. Fedorov N.I., Martynenko V.B., Zhigunova S.N. et al. Changes in the distribution of broadleaf tree species in the central part of the Southern Urals since the 1970s // Russ. Journal of Ecology. 2021. V. 52. P. 118–125. https://doi.org/10.1134/S1067413621020053
  17. Fedorov N.I., Zhigunova S.N., Martynenko V.B. et al. The influence of climate and relief on the distribution of forest communities in different botanical and geographical districts of the Southern Urals // Russ. Journal of Ecology. 2022. V. 53. № 6. P. 427–436. https://doi.org/10.1134/S1067413622060042
  18. Phillips S.J., Anderson R.P., Schapire R.E. Maximum entropy modeling of species geographic distributions // Ecological Modelling. 2006. V. 190. № 3-4. P. 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  19. IPCC. Climate Change 2021: The Physical Science Basis; IPCC Sixth Assessment Report. [Электронный ресурс]. 2021. URL: https://www.ipcc.ch/report/ar6/wg1/ (дата обращения 22.10.2024).
  20. Moss R.H., Edmonds J.A., Hibbard K.A. et al. The next generation of scenarios for climate change research and assessment // Nature. 2010. V. 463. № 7282. P. 747–756. https://doi.org/10.1038/nature08823
  21. McSweeney C.F., Jones R.G., Lee R.W. et al. Selecting CMIP5 GCMs for downscaling over multiple regions // Climate Dynamics. 2015. V. 44. P. 3237–3260. https://doi.org/10.1007/s00382-014-2418-8
  22. Gent P.R., Danabasoglu G., Donner L.J. et al. The community climate system model version 4 // Journal of Climate. 2011. V. 24. № 19. P. 4973–4991. https://doi.org/10.1175/2011JCLI4083.1
  23. Bentsen M., Bethke I., Debernard J.B. et al. The norwegian earth system model, NorESM1-M—Part 1: Description and basic evaluation of the physical climate // Geoscientific Model Development. 2013. V. 6. № 3. P. 687–720. https://doi.org/10.5194/gmd-6-687-2013
  24. Watanabe S., Hajima T., Sudo K. et al. MIROC-ESM 2010: Model description and basic results of CMIP5-20c3m experiments // Geoscientific Model Development. 2011. V. 4. № 4. P. 845–872. https://doi.org/10.5194/gmd-4-845-2011
  25. Volodin E.M., Dianskii N.A., Gusev A.V. Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations // Izvestiya, Atmospheric and Oceanic Physics. 2010. V. 46. P. 414–431. https://doi.org/10.1134/S000143381004002X
  26. Karger D.N., Conrad O., Bohner J. et al. Climatologies at high resolution for the earth’s land surface areas // Scientific Data. 2017. V. 4. № 1. P. 1–20. https://doi.org/10.1038/sdata.2017.122
  27. Poggio L., de Sousa L.M., Batjes N.H. et al. SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty // Soil. 2021. V. 7. № 1. P. 217–240. https://doi.org/10.5194/soil-7-217-2021
  28. Dormann C.F., Elith J., Bacher S. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance // Ecography. 2013. V. 36. № 1. P. 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
  29. Fedorov N., Zhigunova S., Shirokikh P. et al. Analysis of the potential range of pine-broadleaf ecotone forests Tilio-Pinenion and its changes under moderate and strong climate change in the 21st century // Plants. 2023. V. 12. Art. 3698. https://doi.org/10.3390/plants12213698
  30. Swets J.A. Measuring the accuracy of diagnostic systems // Science. 1988. V. 240. № 4857. P. 1285–1293. https://doi.org/10.1126/science.3287615
  31. Hart J.L., Buchanan M.L., Clark S.L., Torreano S.J. Canopy accession strategies and climate-growth relationships in Acer rubrum // Forest Ecology and Management. 2012. V. 282. P. 124–132. https://doi.org/10.1016/j.foreco.2012.06.033
  32. Шиятов С.Г. Динамика древесной и кустарниковой растительности в горах Полярного Урала под влиянием современных изменений климата / Екатеринбург: Изд-во УрО РАН, 2009. 216 с.
  33. Кравцова В.И., Лошкарева А.Р. Динамика растительности экотона тундра–тайга на Кольском полуострове в связи с климатическими колебаниями // Экология. 2013. № 4. С. 275–283. https://doi.org/10.7868/S0367059713040082
  34. Семеняк Н.С., Соломина О.Н., Долгова Е.А., Мацковский В.В. Климатический сигнал в различных параметрах годичных колец сосны обыкновенной на Соловецком архипелаге // Геосферные исследования. 2022. № 4. С. 149–164. https://doi.org/10.17223/25421379/25/10
  35. Fedorov N., Muldashev A., Mikhaylenko O. et al. Forecast the habitat sustainability of Schoenus ferrugineus L.(Cyperaceae) in the Southern Urals under climate change // Plants. 2024. V.13. № 11. Art. 1563. https://doi.org/10.330/9plants13111563

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2. Fig. 1. Areas of distribution of pine-broadleaf forests of the Tilio-Pinenion subunion in the Republic of Bashkortostan (white dots indicate locations of geobotanical descriptions).

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3. Fig. 2. Types of change in the suitability of habitat conditions under possible moderate (RCP4.5) and strong (RCP8.5) climate changes: 1 – in the mountain-forest zone under the RCP4.5 scenario; 2 – in the mountain-forest zone under the RCP8.5 scenario; 3 – on the Ufa Plateau under the RCP4.5 scenario; 4 – on the Ufa Plateau under the RCP8.5 scenario.

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4. Fig. 3. Localization points of pine-broadleaf forests of the Tilio-Pinenion subunion: a – all localization points of pine-broadleaf forests used for modeling; b – localization points of pine-broadleaf forests selected for monitoring the impact of climate change.

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