Regeneration of Pinus sibirica Du Tour in the Mountain Tundra of the Northern Urals against the Background of Climate Warming
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Research Subjects
2.3. Sampling Procedures and Data Analysis
2.3.1. Pinus sibirica Natural Regeneration Study
2.3.2. Investigation Relationships between the Natural Regeneration of Pinus sibirica and the Air Temperature and Precipitation
3. Results
3.1. Investigation of the Pinus sibirica Natural Regeneration
3.2. Relationship between Pinus sibirica Natural Regeneration and Air Temperature and Precipitation
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Frelich, L.; Montgomery, R.; Reich, P. Seven Ways a Warming Climate Can Kill the Southern Boreal Forest. Forests 2021, 12, 560. [Google Scholar] [CrossRef]
- Du, E.; Tang, Y. Distinct Climate Effects on Dahurian Larch Growth at an Asian Temperate-Boreal Forest Ecotone and Nearby Boreal Sites. Forests 2022, 13, 27. [Google Scholar] [CrossRef]
- Maiti, R.; Rodriguez, H.G.; Ivanova, N.S. Autoecology and Ecophysiology of Woody Shrubs and Trees: Concepts and Applications; John Wiley & Sons: Oxford, UK, 2016; p. 352. [Google Scholar] [CrossRef]
- Zavyalov, K.; Ivanova, N.; Potapenko, A.; Ayan, S. Influence of soil fertility on the ability of Scots pine (Pinus sylvestris L.) to adapt to technogenic pollution. CERNE 2019, 25, 326–331. [Google Scholar] [CrossRef]
- Juran, S.; Grace, J.; Urban, O. Temporal Changes in Ozone Concentration and Their Impact on Vegetation. Atmosphere 2021, 12, 82. [Google Scholar] [CrossRef]
- Fomin, V.; Ivanova, N.; Mikhailovich, A.; Zolotova, E. Problem of climate-driven dynamics in the genetic forest typology. In Proceedings of the Modern Synthetic Methodologies for Creating Drugs and Functional Materials (mosm2020): AIP Conference Proceedings, Yekaterinburg, Russia, 16–20 November 2020; Volume 2388, p. 030007. [Google Scholar] [CrossRef]
- Afuye, G.A.; Kalumba, A.M.; Ishola, K.A.; Orimoloye, I.R. Long-Term Dynamics and Response to Climate Change of Different Vegetation Types Using GIMMS NDVI3g Data over Amathole District in South Africa. Atmosphere 2022, 13, 620. [Google Scholar] [CrossRef]
- Gao, Y.; Skutsch, M.; Paneque-Gálvez, J.; Ghilardi, A. Remote sensing of forest degradation: A review. Environ. Res. Lett. 2020, 15, 103001. [Google Scholar] [CrossRef]
- Rogers, P.C.; Pinno, B.D.; Landhäusser, S.M.; Šebestaб, J.; Kusbach, A.; Albrectsen, B.R.; Li, G.; Ivanova, N.; Kuuluvainen, T.; Liu, H.; et al. A global view of aspen: Conservation science for widespread keystone systems. Glob. Ecol. Conserv. 2020, 21, e00828. [Google Scholar] [CrossRef]
- Skole, D.L.; Samek, J.H.; Mbow, C.; Chirwa, M.; Ndalowa, D.; Tumeo, T.; Kachamba, D.; Kamoto, J.; Chioza, A.; Kamangadazi, F. Direct Measurement of Forest Degradation Rates in Malawi: Toward a National Forest Monitoring System to Support REDD+. Forests 2021, 12, 426. [Google Scholar] [CrossRef]
- Ivanova, N.; Fomin, V.; Kusbach, A. Experience of Forest Ecological Classification in Assessment of Vegetation Dynamics. Sustainability 2022, 14, 3384. [Google Scholar] [CrossRef]
- Evans, P.; Brown, C. The boreal–temperate forest ecotone response to climate change. Environ. Rev. 2017, 25, 423–431. [Google Scholar] [CrossRef] [Green Version]
- Grigor’ev, A.A.; Devi, N.M.; Kukarskikh, V.V.; V’yukhin, S.O.; Galimova, A.A.; Moiseev, P.A.; Fomin, V.V. Structure and dynamics of tree stands at the upper timberline in the western part of the Putorana Plateau. Russ. J. Ecol. 2019, 50, 311–322. [Google Scholar] [CrossRef]
- Zhou, W.; Mazepa, V.; Shiyatov, S.; Shalaumova, Y.; Zhang, T.; Liu, D.; Sheshukov, A.; Wang, J.; Sharif, H.E.; Ivanov, V. Spatiotemporal dynamics of encroaching tall vegetation in timberline ecotone of the Polar Urals Region, Russia. Environ. Res. Lett. 2022, 17, 014017. [Google Scholar] [CrossRef]
- Hagedorn, F.; Dawes, M.A.; Bubnov, M.O.; Devi, N.M.; Grigoriev, A.A.; Mazepa, V.S.; Shiyatov, S.G.; Moiseev, P.A.; Nagimov, Z.Y. Latitudinal decline in stand biomass and productivity at the elevational treeline in the Ural Mountains despite a common thermal growth limit. J. Biogeogr. 2020, 47, 1827–1842. [Google Scholar] [CrossRef]
- Fomin, V.; Mikhailovich, A.; Golikov, D.; Agapitov, E. Reconstruction of the Expansion of Siberian Larch into the Mountain Tundra in the Polar Urals in the 20th—Early 21st Centuries. Forests 2022, 13, 419. [Google Scholar] [CrossRef]
- Körner, C.; Poulsen, J. A worldwide study of high altitude treeline temperatures. J. Biogeogr. 2004, 31, 713–732. [Google Scholar] [CrossRef]
- Du, H.; Li, M.-H.; Rixen, C.; Zhang, S.; Stambaugh, M.; Huang, L.; He, H.S.; Wu, Z. Sensitivity of recruitment and growth of alpine treeline birch to elevated temperature. Agric. For. Meteorol. 2021, 304–305, 108403. [Google Scholar] [CrossRef]
- Mihăilă, D.; Bistricean, P.-I.; Horodnic, V.-D. Drivers of Timberline Dynamics in Rodna Montains, Northern Carpathians, Romania, over the Last 131 Years. Sustainability 2021, 13, 2089. [Google Scholar] [CrossRef]
- Holtmeier, F.-K.; Broll, G. Sensitivity and response of northern hemisphere altitudinal and polar treelines to environmental change at landscape and local scales. Glob. Ecol. Biogeogr. 2005, 14, 395–410. [Google Scholar] [CrossRef]
- Hankin, L.E.; Bisbing, S.M. Let it snow? Spring snowpack and microsite characterize the regeneration niche of high-elevation pines. J. Biogeogr. 2021, 48, 2068–2084. [Google Scholar] [CrossRef]
- Maliniemi, T.; Virtanen, R. Anthropogenic disturbance modifies long-term changes of boreal mountain vegetation under contemporary climate warming. Appl. Veg. Sci. 2021, 24, 12587. [Google Scholar] [CrossRef]
- Bader, M.Y.; Llambí, L.D.; Case, B.S.; Buckley, H.L.; Toivonen, J.M.; Camarero, J.; Cairns, D.M.; Brown, C.D.; Wiegand, T.; Resler, L.M. A global framework for linking alpine-treeline ecotone patterns to underlying processes. Ecography 2020, 44, 265–292. [Google Scholar] [CrossRef]
- Shiyatov, S.G.; Mazepa, V.S. Contemporary expansion of siberian larch into the mountain tundra of the Polar Urals. Russ. J. Ecol. 2015, 46, 495–502. [Google Scholar] [CrossRef]
- Lebedev, Y.V. Assessment of Forest Ecosystems in the Economy of Nature Management; Ural branch of the Russian Academy of Sciences: Yekaterinburg, Russia, 2011; p. 574. (In Russian) [Google Scholar]
- Svendsen, J.I.; Krüger, L.C.; Mangerud, J.; Astakhov, V.I.; Paus, A.; Nazarov, D.; Murray, A. Glacial and vegetation history of the Polar Ural Mountains in northern Russia during the Last Ice Age, Marine Isotope Stages 5–2. Quat. Sci. Rev. 2014, 92, 409–428. [Google Scholar] [CrossRef]
- Gorchakovsky, P.L. The Flora of the High-Mountain Urals; Nauka: Moscow, Russia, 1975; p. 281. (In Russian) [Google Scholar]
- Ural Map. Available online: www.welcom-ural.ru/urals/77 (accessed on 10 May 2022).
- Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
- Li, G.; Huang, J.; Guo, H.; Du, S. Projecting species loss and turnover under climate change for 111 Chinese tree species. For. Ecol. Manag. 2020, 477, 118488. [Google Scholar] [CrossRef]
- Zhang, Y.; Liu, G.; Lu, Q.; Xiong, D.; Li, G.; Du, S. Understanding the Limiting Climatic Factors on the Suitable Habitat of Chinese Alfalfa. Forests 2022, 13, 482. [Google Scholar] [CrossRef]
- Zevallos, J.; Lavado-Casimiro, W. Climate Change Impact on Peruvian Biomes. Forests 2022, 13, 238. [Google Scholar] [CrossRef]
- Komarov, V.L. Pine—Pinus (Tourn.) L. In Flora of the USSR; Publishing House of the USSR Academy of Sciences: Moscow-Leningrad, Russia, 1934; pp. 163–164. (In Russian) [Google Scholar]
- Farjon, A. A Handbook of the World’s Conifers; Brill Academic Publishers: Leiden, The Netherlands, 2010. [Google Scholar]
- Kirsanov, V.A. Biological and ecological characteristics of Siberian cedar as the main forester of cedar forests. In Reproduction of cedar forests in the Urals and Western Siberia; Ural Scientific Center of the USSR Academy of Sciences: Sverdlovsk, Russia, 1981; pp. 3–12. (In Russian) [Google Scholar]
- Talantsev, N.K. Siberian Cedar; Forest industry: Moscow, Russia, 1981; p. 93. (In Russian) [Google Scholar]
- Bekh, I.A.; Vorob’yev, V.N. Potential Siberian Stone Pine Forests. Siberian Stone Pine Problems; SB RAS, [Institute of Ecology of Natural Complexes—Branch of the Institute of Forest]: Tomsk, Russia, 1998; p. 123. (In Russian) [Google Scholar]
- Vorob’yov, V.N. Nutcracker and Its Interrelations with Siberian Stone Pine (Experience of Quantitative Analysis); Nauka Publ.: Novosibirsk, Russia, 1982; p. 113. (In Russian) [Google Scholar]
- Ignatenko, M.M. Siberian Cedar; Nauka: Moscow, Russia, 1988; p. 162. (In Russian) [Google Scholar]
- Ivanova, N. Research Methods of Timber-Yielding Plants (in the Example of Boreal Forests). In Biology, Productivity and Bioenergy of Timber-Yielding Plants; Springer: Cham, Switzerland, 2017; pp. 121–137. [Google Scholar] [CrossRef]
- Zhang, X.; Li, G.; Du, S. Simulating the potential distribution of Elaeagnus angustifolia L. based on climatic constraints in China. Ecol. Eng. 2018, 13, 27–34. [Google Scholar] [CrossRef]
- Sun, J.; Feng, L.; Wang, T.; Tian, X.; He, X.; Xia, H.; Wang, W. Predicting the Potential Habitat of Three Endangered Species of Carpinus Genus under Climate Change and Human Activity. Forests 2021, 12, 1216. [Google Scholar] [CrossRef]
- Miranda, J.R.; Silva, R.G.; Juvanhol, R.S. Forest fire action on vegetation from the perspective of trend analysis in future climate change scenarios for a Brazilian savanna region. Ecol. Eng. 2022, 175, 106488. [Google Scholar] [CrossRef]
- Decuyper, M.; Chávez, R.O.; Lohbeck, M.; Lastra, J.A.; Tsendbazar, N.; Hackländer, J.; Herold, M.; Vågen, T.-G. Continuous monitoring of forest change dynamics with satellite time series. Remote Sens. Environ. 2022, 269, 112829. [Google Scholar] [CrossRef]
- Sannikov, S.N.; Tantsyrev, N.V. Survival curves of Siberian stone pine regrowth as the basis for reconstruction dynamics of its number. Russ. J. For. Sci. 2015, 4, 275–281. (In Russian) [Google Scholar]
- Sudachkova, N.E.; Rastorgueva, E.Y.; Kolovskiy, R.A. Physiology of Siberian Stone Pine Undergrowth; Nauka: Moscow, Russia, 1967; p. 123. (In Russian) [Google Scholar]
- Sannikov, S.N.; Tantsyrev, N.V.; Petrova, I.V. Invasion of Siberian Pine Populations in Mountain Tundra in the Northern Urals. Contemp. Probl. Ecol. 2018, 11, 396–405. [Google Scholar] [CrossRef]
- Moiseev, P.A.; Hagedorn, F.; Balakin, D.S.; Bubnov, M.O.; Devi, N.M.; Kukarskih, V.V.; Mazepa, V.S.; Viyukhin, S.O.; Viyukhina, A.A.; Grigoriev, A.A. Stand Biomass at Treeline Ecotone in Russian Subarctic Mountains Is Primarily Related to Species Composition but Its Dynamics Driven by Improvement of Climatic Conditions. Forests 2022, 13, 254. [Google Scholar] [CrossRef]
- Grigoriev, A.A.; Shalaumova, Y.V.; Vyukhin, S.O.; Balakin, D.S.; Kukarskikh, V.V.; Vyukhina, A.A.; Camarero, J.J.; Moiseev, P.A. Upward Treeline Shifts in Two Regions of Subarctic Russia Are Governed by Summer Thermal and Winter Snow Conditions. Forests 2022, 13, 174. [Google Scholar] [CrossRef]
- Bailey, S.N.; Elliott, G.P.; Schliep, E.M. Seasonal temperature–moisture interactions limit seedling establishment at upper treeline in the Southern Rockies. Ecosphere 2020, 12, e03568. [Google Scholar] [CrossRef]
- Schwab, N.; Bürzle, B.; Bobrowski, M.; Böhner, J.; Chaudhary, R.P.; Scholten, T.; Weidinger, J.; Schickhoff, U. Predictors of the Success of Natural Regeneration in a Himalayan Treeline Ecotone. Forests 2022, 13, 454. [Google Scholar] [CrossRef]
- Hoffrén, R.; Miranda, H.; Pizarro, M.; Tejero, P.; García, M.B. Identifying the Factors behind Climate Diversification and Refugial Capacity in Mountain Landscapes: The Key Role of Forests. Remote Sens. 2022, 14, 1708. [Google Scholar] [CrossRef]
- Lu, X.; Liang, E.; Wang, Y.; Babst, F.; Camarero, J.J. Mountain treelines climb slowly despite rapid climate warming. Glob. Ecol. Biogeogr. 2021, 30, 305–315. [Google Scholar] [CrossRef]
- Kharuk, V.I.; Ranson, K.J.; Dvinskaya, S.T.; Dvinskaya, M.L. Response of Pinus sibirica and Larix sibirica to climate change in southern Siberian alpine forest–tundra ecotone. Scand. J. For. Res. 2009, 24, 130–139. [Google Scholar] [CrossRef]
- Bayer, U.; Puschmann, O. Automatic detection of woody vegetation in repeat landscape photographs using a convolutional neural network. Ecol. Inform. 2019, 50, 220–223. [Google Scholar] [CrossRef]
- Dourado-Filho, L.A.; Columby, R.T. An experimental assessment of deep convolutional features for plant species recognition. Ecol. Inform. 2021, 65, 101411. [Google Scholar] [CrossRef]
- Zhou, C.-L.; Ge, L.-M.; Guo, Y.; Zhou, D.-M.; Chun, Y. A comprehensive comparison on current deep learning approaches for plant image classification. J. Phys. Conf. Ser. 2021, 1873, 012002. [Google Scholar] [CrossRef]
Variable | Jan. | Feb. | Mar. | Apr. | May. | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Average temperature (°C) | −16.3 | −13.7 | −5.2 | 1.9 | 8.6 | 14.7 | 17.1 | 13.5 | 8.2 | 0.6 | −8.1 | −13.3 |
Minimum temperature (°C) | −20.3 | −18.3 | −10.2 | −3.2 | 2.8 | 8.7 | 11.4 | 8.5 | 3.8 | −2.6 | −11.5 | −17.0 |
Maximum temperature (°C) | −12.3 | −9.2 | −0.1 | 7.0 | 14.4 | 20.3 | 22.7 | 18.6 | 12.6 | 3.8 | −4.8 | −9.5 |
Precipitation (mm) | 33 | 23 | 24 | 38 | 51 | 67 | 96 | 83 | 64 | 48 | 41 | 35 |
Solar radiation (kJ m−2 day−1) | 1870 | 4489 | 9525 | 14,507 | 19,140 | 21,625 | 19,326 | 13,912 | 8343 | 4160 | 20,581 | 1240 |
Bioclimatic Variables | Description | Value |
---|---|---|
BIO1 | Annual Mean Temperature | 0.67 |
BIO2 | Mean Diurnal Range (Mean of monthly (max temp–min temp)) | 9.31 |
BIO3 | Isothermality (BIO2/BIO7) (×100) | 21.66 |
BIO4 | Temperature Seasonality (standard deviation × 100) | 1189.55 |
BIO5 | Max Temperature of Warmest Month | 22.71 |
BIO6 | Min Temperature of Coldest Month | −20.28 |
BIO7 | Temperature Annual Range (BIO5-BIO6) | 42.99 |
BIO8 | Mean Temperature of Wettest Quarter | 15.12 |
BIO9 | Mean Temperature of Driest Quarter | −11.73 |
BIO10 | Mean Temperature of Warmest Quarter | 15.12 |
BIO11 | Mean Temperature of Coldest Quarter | −14.43 |
BIO12 | Annual Precipitation | 603 |
BIO13 | Precipitation of Wettest Month | 96 |
BIO14 | Precipitation of Driest Month | 23 |
BIO15 | Precipitation Seasonality (Coefficient of Variation) | 44.95 |
BIO16 | Precipitation of Wettest Quarter | 246 |
BIO17 | Precipitation of Driest Quarter | 80 |
BIO18 | Precipitation of Warmest Quarter | 246 |
BIO19 | Precipitation of Coldest Quarter | 91 |
Undergrowth Age, Years Old | 7–10 | 12–20 | 25–35 | 45–56 |
Undergrowth height, cm | 15 ± 0.5 | 32 ± 1.9 | 56 ± 5.1 | 114 ± 8.8 |
Crown diameter, cm | 12 | 24 | 38 | 65 |
Roots length, cm | 25 ± 2 | 50 ± 5 | 95 ± 9 | 125 ± 15 |
Soil nutrition area, m2 | 0.2 | 0.78 | 2.83 | 4.91 |
Jan. | Feb. | Mar. | Apr. | May. | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Current year | ||||||||||||
Minimum temperature | 0.067 | 0.249 | 0.097 | 0.022 | 0.224 | 0.136 | 0.116 | 0.353 * | 0.295 * | 0.199 | 0.014 | 0.091 |
Maximum temperature | 0.001 | 0.227 | 0.019 | −0.089 | 0.281 * | 0.057 | 0.118 | 0.287 * | 0.070 | 0.083 | −0.069 | 0.046 |
Precipitation | 0.108 | 0.024 | 0.282 * | 0.186 | 0.00 | 0.042 | −0.09 | −0.142 | 0.106 | 0.060 | 0.082 | 0.049 |
Previous year | ||||||||||||
Minimum temperature | 0.164 | 0.248 | 0.231 | 0.136 | 0.508 * | 0.462 * | −0.05 | 0.183 | 0.020 | 0.249 | 0.276 * | 0.006 |
Maximum temperature | 0.156 | 0.048 | 0.117 | 0.062 | 0.128 | 0.022 | 0.026 | 0.217 | −0.028 | 0.137 | 0.189 | 0.124 |
Precipitation | −0.054 | −0.138 | 0.281 * | 0.031 | −0.066 | 0.070 | 0.049 | 0.208 | −0.040 | 0.180 | −0.039 | 0.226 |
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Ivanova, N.; Tantsyrev, N.; Li, G. Regeneration of Pinus sibirica Du Tour in the Mountain Tundra of the Northern Urals against the Background of Climate Warming. Atmosphere 2022, 13, 1196. https://doi.org/10.3390/atmos13081196
Ivanova N, Tantsyrev N, Li G. Regeneration of Pinus sibirica Du Tour in the Mountain Tundra of the Northern Urals against the Background of Climate Warming. Atmosphere. 2022; 13(8):1196. https://doi.org/10.3390/atmos13081196
Chicago/Turabian StyleIvanova, Natalya, Nikolai Tantsyrev, and Guoqing Li. 2022. "Regeneration of Pinus sibirica Du Tour in the Mountain Tundra of the Northern Urals against the Background of Climate Warming" Atmosphere 13, no. 8: 1196. https://doi.org/10.3390/atmos13081196
APA StyleIvanova, N., Tantsyrev, N., & Li, G. (2022). Regeneration of Pinus sibirica Du Tour in the Mountain Tundra of the Northern Urals against the Background of Climate Warming. Atmosphere, 13(8), 1196. https://doi.org/10.3390/atmos13081196