- PII
- S30345359S0024114825020013-1
- DOI
- 10.7868/S3034535925020013
- Publication type
- Article
- Status
- Published
- Authors
- Volume/ Edition
- Volume / Issue number 2
- Pages
- 153-170
- Abstract
- Identification of the leading factors determining forest cover differentiation is a still understudied topic in ecology and biogeography. This study’s objective is to assess the contribution of natural and anthropogenic factors to the formation of modern forest cover diversity on the example of the Moscow region. As a result of classification of 1032 field relevés, 13 vegetation community types were identified based on the dominant forest-forming tree species and phytocenotic spectra of plants in subordinate layers. Using statistical methods, the heterogeneity of the identified community types’ floristic composition and the accuracy of their classification were assessed, and the ordination of communities in the ecological framework was done. The relationship of community types with biotopic local factors was analysed using Ellenberg scales. Most pairs of identified community types differed significantly by the results of Duncan’s test (p < 0.05) for all biotope properties. A list of indicator species for the identified community types (IndVal) was compiled. It was also shown that the most significant local factors determining the identified community types were soil acidity, nutrient abundance and moisture. At the upper spatial level, community variability was studied in relation to external environmental factors based on global spatial databases, and the relationship with individual forest cover fragmentation indicators was assessed. Among the most significant factors were the climatic ones (average annual temperatures and precipitation). Terrain (elevation above sea level) also significantly affected the composition of communities. Anthropogenic factors (distance from settlements, forest cover fragmentation) had a smaller impact on the differentiation of community types compared to natural ones.
- Keywords
- фитоценотическое разнообразие лесной покров природные и антропогенные факторы шкалы Элленберга глобальные пространственные базы данных Московский регион
- Date of publication
- 15.11.2024
- Year of publication
- 2024
- Number of purchasers
- 0
- Views
- 12
References
- 1. Анненская Г.Н., Жучкова В.К., Калинина В.Р., Мамай И.И., Низовцев В.А., Хрусталева М.А., Цесельчук Ю.Н. Ландшафты Московской области и их современное состояние. Смоленск: СГУ, 1997. 296 с.
- 2. Грибова С.А., Исаченко Т.И., Лавренко Е.М. Растительность Европейской части СССР. Л.: Наука, 1980. 429 с.
- 3. Игнатов М.С., Игнатова Е.А. Флора мхов средней части Европейской России. M.: KMK, 2003. Т. 1-2. 960 с.
- 4. Котлов И.П. Пространственная структура лесного покрова Московской области (оценка на основе количественных метрик фрагментации): автореф. дис… кандидата биологических наук: 1.5.15. М.: ИПЭЭ РАН, 2023. 165 с.
- 5. Лесной план Московской области на 2019-2028 годы. Книга 1 и 2. 2023. https://klh.mosreg.ru/dokumenty/napravleniya-deyatelnosti/lesnoe-planirovanie/proekty-dokumentov-lesnogo-planirovaniya/26-09-2023-12-19-27-lesnoy-plan-moskovskoy-oblasti-na 2019-2028-gody-k?utm_referrer=https%3a%2f%2fwww.google.com%2f
- 6. Литвиненко Л.Н., Калинина А.А. Распределение осадков на территории Московской области при наличии и отсутствии крупного антропогенного образования // Экология урбанизированных территорий. 2018. № 2. С. 66-71.
- 7. Осипов В.В., Гаврилова Н.К. Аграрное освоение и динамика лесистости Нечерноземной зоны РСФСР. М.: Наука, 1983.
- 8. Пшегусов Р.Х., Темботова Ф.А., Саблирова Ю.М. Основные закономерности пространственной локализации различных типов хвойных и хвойно-широколиственных лесов северного макросклона Западного Кавказа по материалам дистанционного зондирования Земли // Вопросы лесной науки. 2019. Т. 2. № 3. С. 1-11.
- 9. Тишков А.А. Актуальная биогеография как методологическая основа сохранения биоразнообразия // Вопросы географии. 2012. № 134. С. 15-57.
- 10. Черепанов С.К. Сосудистые растения России и сопредельных государств. СПб., 1995. 990 с.
- 11. Черненькова Т.В., Суслова Е.Г., Морозова О.В., Беляева Н.Г., Котлов И.П. Биоразнообразие лесов Московского региона // Экосистемы: экология и динамика. 2020. Т. 4. № 3. С. 61-144.
- 12. Черненькова Т.В., Котлов И.П., Беляева Н.Г., Суслова Е.Г., Морозова О.В. Оценка и картографирование ценотического разнообразия лесов Московского региона // Лесоведение. 2022. № 6. С. 617-630.
- 13. Ahmed O.S., Wulder M.A., White J.C., Hermosilla T., Coops N.C., Franklin S.E. Classification of annual non-stand replacing boreal forest change in Canada using Landsat time series: a case study in northern Ontario // Remote Sensing Letters. 2017. V. 8. № 1. P. 29-37.
- 14. Akinyemi F.O., Tlhalerwa L.T., Eze P.N. Land degradation assessment in an African dryland context based on the Composite Land Degradation Index and mapping method // Geocarto International. 2021. V. 36. № 16. P. 1838-1854.
- 15. Balmford A., Bond W. Trends in the state of nature and their implications for human well-being // Ecology Letters. 2005. V. 8. № 11. P. 1218-1234.
- 16. Batjes N.H., Ribeiro E., van Oostrum A. Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019) // Earth System Science Data. 2020. V. 12. № 1. P. 299-320.
- 17. Chernenkova T.V., Morozova O.V. Classification and Mapping of Coenotic Diversity of Forests // Contemporary Problems of Ecology. 2017. V. 10. № 7. P. 738-747.
- 18. Cushman S.A., McGarigal K., McKelvey K., Vojta C.D., Reagan C.M. Analysis for Habitat Monitoring // USFS Wildlife Habitat Technical Guide, 2013.
- 19. Dufrêne M., Legendre P. Species Assemblages and Indicator Species: the Need for a Flexible Asymmetrical Approach // Ecological Monographs. 1997. V. 67. № 3. P. 345-366.
- 20. Ellenberg H. Vegetation Ecology of Central Europe. Cambridge: Cambridge University Press, 1988.
- 21. Ellenberg H., Weber H.E., Düll R., Wirth V., Werner W., Paulissen D. Zeigerwerte von Pflanzen in Mitteleuropa // Scripta Geobotanica. 1991. V. 18. P. 1-248.
- 22. Fick S.E., Hijmans R.J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas // International Journal of Climatology. 2017. V. 37. № 12. P. 4302-4315.
- 23. Forman R.T.T., Godron M. Landscape Ecology. New York: John Wiley and Sons Ltd., 1986. P. 620.
- 24. Hansen M.C., Potapov P.V., Moore R., Hancher M., Turubanova S.A., Tyukavina A., Thau D., Stehman, S.V., Goetz S.J., Loveland T.R. High-Resolution Global Maps of 21st-Century Forest Cover Change // Science. 2013. V. 342. P. 850-853.
- 25. Hengl T. Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. 2018.
- 26. Hengl T., Gupta S. Soil water content (volumetric%) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. 2019.
- 27. Hengl T., Wheeler I. Soil organic carbon content in x 5 g / kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. 2018.
- 28. Hutchinson G.E. Concluding Remarks // Cold Spring Harbor Symposia on Quantitative Biology. 1957. V. 22. P. 415-427.
- 29. Kotlov I., Chernenkova T., Belyaeva N. Urban forests of Moscow: typological diversity, succession status, and fragmentation assessment // Landscape Ecology. 2023. V. 38. № 12. P. 3767-3789.
- 30. Loreau M., Hector A. Partitioning selection and complementarity in biodiversity experiments // Nature. 2001. V. 412. № 6842. P. 72-76.
- 31. McBratney A.B., Mendonça Santos M.L., Minasny B. On digital soil mapping // Geoderma. 2003. V. 117. № 1. P. 3-52.
- 32. Mücher C.A., Klijn J.A., Wascher D.M., Schaminée J.H.J. A new European Landscape Classification (LANMAP): A transparent, flexible and user-oriented methodology to distinguish landscapes // Ecological Indicators. 2010. V. 10. № 1. P. 87-103.
- 33. Potapov P., Hansen M.C., Pickens A., Hernandez-Serna A., Tyukavina A., Turubanova S., Zalles V., Li X., Khan A., Stolle F., Harris N., Song X.-P., Baggett A., Kommareddy I., Kommareddy A. The Global 2000-2020 Land Cover and Land Use Change Dataset Derived From the Landsat Archive: First Results // Frontiers in Remote Sensing. 2022. V. 3.
- 34. Potere D., Schneider A., Angel Sh., Civco D.L. Mapping urban areas on a global scale: which of the eight maps now available is more accurate? // International Journal of Remote Sensing. 2009. V. 30. № 24. P. 6531-6558.
- 35. Rocchini D., Lenoir J. Remote sensing at the interface between ecology and climate sciences // Meteorological Applications. 2021. V. 28. № 5. P. 1-6.
- 36. Spiecker H. Silvicultural management in maintaining biodiversity and resistance of forests in Europe - temperate zone // Journal of environmental Management. 2003. V. 67. № 1. P. 55-65.
- 37. Tichý L. JUICE, software for vegetation classification // Journal of Vegetation Science. 2002. V. 13. № 3. P. 451-453.
- 38. Tronin A.A., Gornyy V.I., Kritsuk S.G., Latypov I. Sh. Nighttime lights as a quantitative indicator of anthropogenic load on ecosystems // Current Problems in Remote Sensing of the Earth from Space. 2014. V. 11. № 1. P. 237-244.
- 39. Wang Z., Shrestha R., Yao T., Kalb V. Black Marble User Guide (Version 1.2). 2021.
- 40. https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/viirs/VIIRS_Black_Marble_UG_v1.2_April_2021.pdf
- 41. Zhang X., Liu L., Chen X., Xie Sh., Gao Y. Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach // Remote Sensing. 2019. V. 11. № 9. P. 1056.
- 42. SRTM 90m Digital Elevation Database [Электронный ресурс] // CGIAR Platform for Big Data in Agriculture. URL: https://bigdata.cgiar.org/srtm90m-digital-elevation-database/
- 43. Top 10 Lists // World Resources Institute Research. 2024. URL: https://research.wri.org/gfr/top-ten-lists (дата обращения: 01.09.2024).
- 44. R Core Team // European Environment Agency. 2020. URL: https://www.eea.europa.eu/data-and-maps/indicators/oxygen-consuming-substances-in-rivers/r-development-core-team 2006 (дата обращения: 19.04.2024).