Assessment of the Influence of environmental factors on carbon stock in forest soils of Bryansk Poles’e

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Abstract

The search informative indicators of variation in soil carbon stock coniferous–broadleaf forest has a high predictive value. This article provides an assessment of the contribution of environmental factors to the variation of carbon stock in the forest soils. The study was carried out on the territory of the Bryansk Forest reserve on 45 sample plots located in different landscapes. Five groups of factors characterizing vegetation, macrofauna, landscape, relief, and history of environmental management were analyzed. Statistical relationships between carbon stock indicators and environmental factors were assessed using machine learning methods. The main factor determining the carbon stock in litter was the quality of litter, formed by the dominants of the trees, herbaceous and moss layers. Position in the landscape and other orographic characteristics were less informative. The highest carbon stock of the OL-subhorizon of the litter was in forests with a high proportion of pine. The most informative indicator for determining the variation in carbon stock FH-subhorizon of the litter was the projective cover of mosses. The results of regression analysis for the carbon stock in the A horizon and in the 0-30 cm layer demonstrated a significant contribution of indicators showing the increased hydromorphism, as well as characteristics connected with functional organization of forest ecosystems, namely the ecological-coenotic structure of plant communities (the proportion of nemoral species in the layer undergrowth and shrubs) and functional diversity of earthworms.

About the authors

A. I. Kuznetsova

Center for Forest Ecology and Productivity of the Russian Academy of Sciences

Author for correspondence.
Email: nasta472288813@yandex.ru
Russian Federation, Moscow, 117997

E. A. Gavrilyuk

Center for Forest Ecology and Productivity of the Russian Academy of Sciences

Email: nasta472288813@yandex.ru
Russian Federation, Moscow, 117997

A. V. Gornov

Center for Forest Ecology and Productivity of the Russian Academy of Sciences

Email: nasta472288813@yandex.ru
Russian Federation, Moscow, 117997

E. V. Ruchinskaya

Center for Forest Ecology and Productivity of the Russian Academy of Sciences

Email: nasta472288813@yandex.ru
Russian Federation, Moscow, 117997

A. P. Geraskina

Center for Forest Ecology and Productivity of the Russian Academy of Sciences

Email: nasta472288813@yandex.ru
Russian Federation, Moscow, 117997

A. D. Nikitina

Center for Forest Ecology and Productivity of the Russian Academy of Sciences

Email: nasta472288813@yandex.ru
Russian Federation, Moscow, 117997

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4. Fig. 1. Study area: 1 – sample plots in nitrophilous-grass black alder forests, 2 – in dwarf shrub-green moss birch forests, 3 – in nemoral-grass birch forests, 4 – in complex pine forests, 5 – in dwarf shrub-green moss pine forests, 6 – in nemoral-grass broadleaf forests, 7 – boundary of the Bryansk Forest Nature Reserve, 8 – moraine-outwash landscape, 9 – outwash landscape, 10 – terrace, 11 – floodplain, 12 – small river floodplains, 13 – other landscape types, 14 – permanent watercourses. Designations of countries and administrative units on the inset map are given according to ISO-3166.

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5. Fig. 2. Scatter plot of normalized carbon stock values (ordination) for (a) landscape types: 1 – moraine-outwash, 2 – outwash, 3 – terrace, 4 – small river floodplain, 5 – Nerussa River floodplain; (b) forest types: 6 – nitrophilous-grass black alder forests, 7 – dwarf shrub-green moss birch forests, 8 – nemoral grass birch forests, 9 – complex pine forests, 10 – dwarf shrub-green moss pine forests, 11 – broadleaf nemoral grass forests.

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6. Fig. 3. Change in the coefficient of determination of the multiple regression model reflecting the effect of adding a variable to the model. CstL – carbon stock of the OL – litter subhorizon; CstFH – carbon stock of the OFH – litter subhorizon; Cst0-10 – carbon stock in the 0–10 cm layer; Cst0-30 – carbon stock in the 0–30 cm layer; CstA – carbon stock of horizon A. The vertical axis of the graphs is the coefficient of determination (R2). The data are signed with the effect of adding a variable on the R2 value. The horizontal axis represents variables, where Base is the basic statistical model. VEG_pine_A – the proportion of pine in the tree layer, %; VEG_emla_A – the total proportion of elm, maple, linden and ash in the tree layer, %; VEG_alnus_A – the proportion of black alder in the tree layer, %; VEG_den_C – the projective cover of the herb layer, %; VEG_den_D – the projective cover of the moss layer, %; VEG_nitr_B – the proportion of nitrophilous species in the undergrowth and shrub layer, %; VEG_nem_B – proportion of nemoral species in the undergrowth and shrub layer, %; SMF_sapr – saprophagous biomass, g/m2; SMF_ngr – number of earthworm groups, LSC_fp – location in river floodplains.

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