Search

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

Effect of 24-h-long treatment with hemin (a donor of carbon monoxide, CO, produced with the participation of heme oxygenase) on heat resistance and the processes of ROS formation and neutralization was investigated in wheat (Triticum aestivum L.) seedlings. It was found that treatment of seedlings with hemin in the concentration range from 0.5 to 10 µM induced an elevation of their resistance to damaging heating (45°C, 10 min). The greatest effect was produced by 5 µM hemin. Hemin treatment was associated with a transient elevation in the roots of extracellular peroxidase activity and a rise in the content of hydrogen peroxide. Elevation of H2O2 content was not observed in the presence of its scavenger dimethyl thiourea (DMTU) and upon treatment of seedlings’ roots with CO absorber hemoglobin and inhibitor of extracellular peroxidase sodium azide; however, this pattern was not followed in the presence of NADPH oxidase inhibitor imidazole. Since decomposition of hemin is associated with the production of redox active Fe2+ ions, the specificity of its action as a CO donor was tested by comparing the effects of hemin and 5 µM FeSO4. At the examined concentration, ferrous sulfate did not affect the generation of hydrogen peroxide in the roots and activity of extracellular peroxidase. In 24 h after the beginning of hemin treatment, activities of superoxide dismutase, catalase, and intracellular peroxidase in the roots rose. Elevation in the activity of antioxidant enzymes induced by hemin was not observed in the presence of DMTU, and treatment of seedling roots with FeSO4 did not modify their activity. Exposure to the CO donor after damaging heating also reduced outflux from the seedling roots of compounds absorbed in the ultraviolet spectral region. Positive influence of hemin on heat resistance of the seedlings was eliminated by CO scavenger hemoglobin and hydrogen peroxide absorber DMTU. Treatment of seedlings with FeSO4 did not alter their survival after heating. Hydrogen peroxide is assumed to participate as a signal mediator in realization of the influence of CO donor on the antioxidant system of wheat seedlings and their heat resistance.

Current estimates of stand volume for South Korean forests are mostly derived from expensive field data. Techniques that allow reducing the amount of ground data with reliable accuracy would decrease the cost and time. The fifth National Forest Inventory (NFI) has been conducted annually for all forest areas in South Korea from 2006 to 2010 and using these data we can make a model for estimating the stand volume of forests. The purpose of this study is to test deep learning whether it is available for measurement of stand volume with satellite imageries and geospatial information. The spatial distribution of the stand volume of South Korean forests was predicted with the convolutional neural networks (CNNs) algorithm. NFI data were randomly sampled for training from 90% to 10%, with 10% decrement, and the rest of the area was estimated using satellite imagery and geospatial information. Consequently, we found that the error rate of total stand volume was

The participation of reactive oxygen species (ROS) and nitric oxide (NO), and also enzymatic systems generating them, in the development of heat resistance of wheat (Triticumaestivum L.) seedlings, induced by the hydrogen sulfide (H2S) donor sodium hydrosulfide (NaHS), has been studied. It was found that 24-h pretreatment of seedlings with 0.1–1 mM NaHS increased their survival after the subsequent 10-min damaging heating at 45°C. The content of hydrogen peroxide and nitric oxide increased together with the nitrate reductase (NR) activity in the seedling roots within the first 4 h of their treatment with the H2S donor. The rise in the NO level significantly suppressed by the inhibitor of NR sodium tungstate but not the inhibitor of NO synthase (NG-nitro-L-arginine methyl ester, L-NAME). The hydrogen peroxide scavenger dimethylthiourea (DMTU) and the NADPH oxidase inhibitor imidazole abolished the increase in NR activity and NO content in the roots. However, the nitric oxide scavenger (2-phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide, PTIO) and the inhibitors of the NO-producing enzymes only weakly influenced the increase in H2O2 content caused by the root treatment with sodium hydrosulfide. The NaHS-induced rise in the seedling heat resistance was eliminated by both ROS antagonists (DMTU and imidazole) and NO antagonists (PTIO and tungstate). It is concluded that the boost in the wheat seedling heat resistance, which is caused by exogenous hydrogen sulfide, is mediated by the increased ROS generation, followed by NR activation, and resultant rise in the level of nitric oxide produced by this enzyme.

Performance characteristics of hard-alloy tools are largely depends on the structure. The hard-alloy tool structure can be influenced by various factors. The main factors influencing the structure and properties of hard alloys at heat treatment are ВаCl2 salt bath deoxidation degree, heating temperature, holding time, rate of cooling and cooling medium, temperatures and holding time. Oxidation at temperatures more than 500-600оС and relatively low thermal conductivity of λ = 27.214 W/(m×K) are characteristic for hard alloys under heating.In this regard, it became necessary to study the salt baths deoxidation processes occurred in the course of heat treatment. Insufficient study of hard alloy heat treatment processes is associated with peculiarity of their structure and large assortment, difficulty of setting heat treatment modes.Research of hard alloy sample heating with the subsequent air cooling (normalization) was carried out in a salt baths in thermal area of tool shop.X-ray diffraction analysis was performed by Williamson-Hall method. In our experiments, coherent scattering regions size and WC phase micro-distortions magnitude were defined using MD-10 microdiffractometer. It was found out that phase structure of hard alloys is not change as a result of heat treatment. There are only reflections from carbide phase planes in the diffraction pattern. A quantitative analysis of diffraction reflection broadening using Williamson-Hall method showed that size of the coherent scattering regions for VK8 and T14K8 hard alloys subjected to hardening procedure is more than for sintered alloys.Relevance of the study is due to the fact that heat treatment involved heating in salt baths can be a promising method of improving mechanical and operational properties of hard alloys. This approach ensures necessary strength and performance characteristics of hard alloys without significant economic expenses.The purpose of this paper is to define effect of ВаCl2 salt bath deoxidation degree on physical and mechanical properties of VK8 and T14K8 hard alloys.

This paper considers the effect of hardening of throwaway cutting inserts made of hard alloy T15K6 using continuous laser impact at various modes. Tests were carried outon inserts made of hard alloy T15K6. The cutting properties of the inserts were determined by cutting on a T612 vertical milling machine. A face mill with a diameter of 100 mm with mechanical mounting of tested inserts was used as a tool.Dry milling was performed using two inserts. At that, cutting mode was of impact natureas the mill diameter was larger than the width of machined workpiece. Number of passes – 5. Cutting modes:v = 197 m/min, h = 1 mm, S = 160 mm/min, b = 90 mm. Machiningwasperformedonworkpieces made of 40X grade steel (GOST 4543-71). Workpiecedimensions– 160x60x90 mm. During the machining, hard alloy inserts moved beyond the workpiece edge and cut into it from the other side. One of the main performance characteristics of hard alloys is material rigidity (modulus of elasticity, Е). Tests were carried out after various types of laser impact at bending and material rigidity was determined by strain gauging. Decreasein the slopeofstraight-lineportionofrelativestrain-versus-stress curve at bending indicates the decrease in hard alloys’ modulus of elasticity after laser processing. Small defect structure is being formed.

Background Assessment of the reasons for the ambiguous influence of forests on the structure of the water balance is the subject of heated debate among forest hydrologists. Influencing the components of total evaporation, forest vegetation makes a significant contribution to the process of runoff formation, but this process has specific features in different geographical zones. The issues of the influence of forest vegetation on river runoff in the zonal aspect have not been sufficiently studied. Results Based on the analysis of the dependence of river runoff on forest cover, using the example of nine catchments located in the forest-tundra, northern and middle taiga of Northern Eurasia, it is shown that the share of forest cover in the total catchment area (percentage of forest cover, FCP) has different effects on runoff formation. Numerical experiments with the developed empirical models have shown that an increase in forest cover in the catchment area in northern latitudes contributes to an increase in runoff, while in the southern direction (in the middle taiga) extensive woody cover of catchments “works” to reduce runoff. The effectiveness of geographical zonality in regards to the influence of forests on runoff is more pronounced in the forest-tundra zone than in the zones of northern and middle taiga. Conclusion The study of this problem allowed us to analyze various aspects of the hydrological role of forests, and to show that forest ecosystems, depending on environmental conditions and the spatial distribution of forest cover, can transform water regimes in different ways. Despite the fact that the process of river runoff formation is controlled by many factors, such as temperature conditions, precipitation regime, geomorphology and the presence of permafrost, the models obtained allow us to reveal general trends in the dependence of the annual river runoff on the percentage of forest cover, at the level of catchments. The results obtained are consistent with the concept of geographic determinism, which explains the contradictions that exist in assessing the hydrological role of forests in various geographical and climatic conditions. The results of the study may serve as the basis for regulation of the forest cover of northern Eurasian river basins in order to obtain the desired hydrological effect depending on environmental and economic conditions.

Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported almost no change in growing stock (+ 1.8%) and biomass (+ 0.6%). Yet remote sensing products indicate increased vegetation productivity, tree cover and above-ground biomass. Here, we challenge these statistics with a combination of recent National Forest Inventory and remote sensing data to provide an alternative estimate of the growing stock of Russian forests and to assess the relative changes in post-Soviet Russia. Our estimate for the year 2014 is 111 ± 1.3 × 109 m3, or 39% higher than the value in the State Forest Register. Using the last Soviet Union report as a reference, Russian forests have accumulated 1163 × 106 m3 yr-1 of growing stock between 1988–2014, which balances the net forest stock losses in tropical countries. Our estimate of the growing stock of managed forests is 94.2 × 109 m3, which corresponds to sequestration of 354 Tg C yr-1 in live biomass over 1988–2014, or 47% higher than reported in the National Greenhouse Gases Inventory.

In this work, we studied the effect of multiple, stepwise hardening with intermediate tempering on the physical, mechanical and operational characteristics of hard alloys. Alloy VK4 was used as the initial material (non-resurfacing 4-sided plates and sticks with a size of 5×5×35 mm). The physical and mechanical properties of the initial hard alloys WC-Co (VK4) were determined. Then, prospecting work was carried out on 11 modes of heat treatment in salt baths. In each mode,10–15 plates were examined. After conducting prospecting studies, the stages and structural changes in the process of double heat treatment of sintered hard alloys were established. The physical properties of the VK4 hard alloy after heat treatment practically did not change (the coercive force increased by 2 times), however, an increase of 10–30% in ultimate strength in compression and hardness was noted. As a result of the tests carried out, it was found that the plates treated by the proposed methods of heat treatment increased their durability by 1.5–2 times.