Труды сотрудников ИЛ им. В.Н. Сукачева СО РАН

: материалы временных коллективов / C. C. Schmullius, C. Thiel, M. A. Korets // Boreal forests in a changing world: challenges and needs for action: Proceedings of the International conference Augus,t 15-21 2011, Krasnoyarsk, Russia: V.N. Sukachev Institute of Forest SB RAS, 2011. - Krasnoyarsk : V.N. Sukachev Institute of forest SB RAS, 2011. - С. 395-400. - Библиогр. в конце ст.
Аннотация: ZAPAS investigates and cross validates methodologies using both Russian and European Earth observation data to develop procedures and products for forest resource assessment and monitoring. Earth observation data include ENVISAT MERIS and ASAR in different acquisition modes, METEOR-M and RESURS-DKI. The methodologies include state-of-the-art optical and radar retrieval algorithms as well as investigation of innovative synergistic approaches. Products include biomass change maps for the years 2007-2008-2009 on a local scale, a biomass and improved land cover map on the regional scale, and a 1 km land cover map as input to carbon accounting model.

Держатели документа:
Институт леса им. В.Н. Сукачева Сибирского отделения Российской академии наук : 660036, Красноярск, Академгородок 50/28

Доп.точки доступа:
Schmullius, C.C.; Шмуллиус С.С.; Thiel, C.; Тил С.; Korets, Mikhail Anatol'yevich; Корец, Михаил Анатольевич

[Text] / M. A. Korets, A. A. Onuchin // Contemp. Probl. Ecol. - 2008. - Vol. 1, Is. 3. - P353-355, DOI 10.1134/S1995425508030107. - Cited References: 7. - The studies were supported by the Foundation of National Science Support and the INTAS-01-0052 project. . - 3. - ISSN 1995-4255РУБ Ecology

Аннотация: Two algorithms are proposed to model ecologically substantiated forest water protection areas using digital elevation method (DEM) and spatial features of a geographic information system (GIS).

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Держатели документа:
[Korets, M. A.
Onuchin, A. A.] RAS, Siberian Branch, Sukachev Inst Forest, Krasnoyarsk 660036, Russia

Доп.точки доступа:
Korets, M.A.; Onuchin, A.A.

[Text] / Y. L. Liu [et al.] // Glob. Planet. Change. - 2013. - Vol. 108. - P85-99, DOI 10.1016/j.gloplacha.2013.06.008. - Cited References: 134. - This research is supported by the NASA Land Use and Land Cover Change program (NASA-NNX09AI26G, NN-H-04-Z-YS-005-N, and NNX09AM55G), the Department of Energy (DE-FG02-08ER64599), the National Science Foundation (NSF-1028291 and NSF-0919331), and the NSF Carbon and Water in the Earth Program (NSF-0630319). The computing is supported by the Rosen Center of High Performance Computing at Purdue University. Special acknowledgment is made here to Prof. Eric Wood of Princeton University for his generous provision of ET dataset in the Vinukollu et al. (2011). Diego Miralles acknowledges the support by the European Space Agency WACMOS-ET project (contract no.4000106711/12/I-NB). . - 15. - ISSN 0921-8181РУБ Geography, Physical + Geosciences, Multidisciplinary

Аннотация: Adequate quantification of evapotranspiration (ET) is crucial to assess how climate change and land cover change (LCC) interact with the hydrological cycle of terrestrial ecosystems. The Mongolian Plateau plays a unique role in the global climate system due to its ecological vulnerability, high sensitivity to climate change and disturbances, and limited water resources. Here, we used a version of the Terrestrial Ecosystem Model that has been modified to use Penman-Monteith (PM) based algorithms to calculate ET. Comparison of site-level ET estimates from the modified model with ET measured at eddy covariance (EC) sites showed better agreement than ET estimates from the MODIS ET product, which overestimates ET during the winter months. The modified model was then used to simulate ET during the 21st century under six climate change scenarios by excluding/including climate-induced LCC. We found that regional annual ET varies from 188 to 286 mm yr(-1) across all scenarios, and that it increases between 0.11 mm yr(-2) and 0.55 mm yr(-2) during the 21st century. A spatial gradient of ET that increases from the southwest to the northeast is consistent in all scenarios. Regional ET in grasslands, boreal forests and semi-desert/deserts ranges from 242 to 374 mm yr(-1), 213 to 278 mm yr(-1) and 100 to 199 mm yr(-1), respectively; and the degree of the ET increase follows the order of grassland, semi-desert/desert, and boreal forest. Across the plateau, climate-induced LCC does not lead to a substantial change (<5%) in ET relative to a static land cover, suggesting that climate change is more important than LCC in determining regional ET. Furthermore, the differences between precipitation and ET suggest that the available water for human use (water availability) on the plateau will not change significantly during the 21st century. However, more water is available and less area is threatened by water shortage in the Business-As-Usual emission scenarios relative to level-one stabilization emission scenarios. (C) 2013 Elsevier B.V. All rights reserved.

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Держатели документа:
[Liu, Yaling
Zhuang, Qianlai
Chen, Min
He, Yujie] Purdue Univ, Dept Earth Atmospher & Planetary Sci, W Lafayette, IN 47907 USA
[Zhuang, Qianlai
Bowling, Laura] Purdue Univ, Dept Agron, W Lafayette, IN 47907 USA
[Pan, Zhihua] China Agr Univ, Coll Resources & Environm Sci, Beijing 100193, Peoples R China
[Pan, Zhihua] Minist Agr, Key Ecol & Environm Expt Stn Field Sci Observat H, Inner Mongolia 011705, Peoples R China
[Tchebakova, Nadja
Parfenova, Elena] Russian Acad Sci, VN Sukachev Inst Forest, Siberian Branch, Krasnoyarsk 660036, Russia
[Sokolov, Andrei] MIT, Dept Earth Atmospher & Planetary Sci, Cambridge, MA 02139 USA
[Kicklighter, David
Melillo, Jerry] Marine Biol Lab, Ctr Ecosyst, Woods Hole, MA 02543 USA
[Sirin, Andrey] Russian Acad Sci, Inst Forest Sci, Lab Peatland Forestry & Ameliorat, Uspenskoye 143030, Moscow Oblast, Russia
[Zhou, Guangsheng] Chinese Acad Sci, State Key Lab Vegetat & Environm Change, Inst Bot, Beijing 100093, Peoples R China
[Chen, Jiquan] Univ Toledo, Dept Environm Sci, Toledo, OH 43606 USA
[Miralles, Diego] Univ Bristol, Sch Geog Sci, Bristol, Avon, England

Доп.точки доступа:
Liu, Y.L.; Zhuang, Q.L.; Chen, M...; Pan, Z.H.; Tchebakova, N...; Sokolov, A...; Kicklighter, D...; Melillo, J...; Sirin, A...; Zhou, G.S.; He, Y.J.; Chen, J.Q.; Bowling, L...; Miralles, D...; Parfenova, E...; NASA [NASA-NNX09AI26G, NN-H-04-Z-YS-005-N, NNX09AM55G]; Department of Energy [DE-FG02-08ER64599]; National Science Foundation [NSF-1028291, NSF-0919331, NSF-0630319]; European Space Agency WACMOS-ET project [4000106711/12/I-NB]

/ M. Korets, A. Onuchin // IAHS-AISH Publication. - 2009. - Vol. 331: Symposium JS.4 at the Joint Convention of the International Association of Hydrological Sciences, IAHS and the International Association of Hydrogeologists, IAH (6 September 2009 through 12 September 2009, Hyderabad) Conference code: 83573. - P510-513 . -
Аннотация: Forest areas can intercept surface runoff from upslope bare areas and transfer it to interflow. Therefore, planting protective forests along the banks of rivers, reservoirs, and lakes preserves natural water sources from pollution. Depending on the particular landscape conditions, the streamside forest shelterbelt (SFS) width is often either wider or narrower than the ecologically substantiated width. As a result, either water quality worsens or the ecologically unjustified prohibition of forest use leads to economic losses. The assessment of SFS width using GIS technologies allows considerable simplification of evaluation procedures and their application in practice. DEM processing is integrated into most modern GIS software packages. For example, the popular ESRI ArcGIS package with its Spatial Analyst module provides extra options for calculating a series of relief-based hydrological features, which include calculation procedures for surface flow direction, length of flow-producing slopes and surface flow accumulations. Two algorithms for GIS-based SFS construction were tested for several rivers of the Yenisei basin and Krasnoyarsk Reservoir, Siberia. The first algorithm is technically simple and based on empirical equations of runoff slope length, slope steepness and soil infiltration. The second one includes a three-dimensional flow accumulation procedure and thus it is more sensitive to real surface structure. Both algorithms are ready to be used in practice. The results obtained indicate that, on average, the SFS width along banks of large rivers might be reduced, while in some cases it should be widened along the banks of small streams. Copyright В© 2009 IAHS Press.

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Держатели документа:
V. N. Sukachev Institute of Forest, Siberian Branch, Russian Academy of Sciences, 50/28, Akademgorodok, 660036, Krasnoyarsk, Russian Federation

Доп.точки доступа:
Korets, M.; Onuchin, A.

/ E. A. Loupian [et al.] // Mitigation and Adaptation Strategies for Global Change. - 2006. - Vol. 11, Is. 1. - P113-145, DOI 10.1007/s11027-006-1013-7 . - ISSN 1381-2386
Аннотация: This paper presents an overview of current satellite-based fire mapping activities at several institutions in Russia that provide operational fire monitoring at federal and regional levels. The current operational systems are based on data from the Advanced Very High Resolution Radiometer (AVHRR) and the TIROS Operational Vertical Sounder (TOVS) on the National Atmospheric and Oceanic Administration (NOAA) operational polar orbiting environmental satellite series. Detailed descriptions of the data acquisition and preprocessing systems, algorithms, and the suite of fire products are provided. Each institution has expertise in addressing a specific aspect of satellite-based fire mapping and monitoring. The methodologies described include proper georegistration of AVHRR data and elimination of false alarms while retaining a high active fire detection rate. Statistical and physical approaches are presented to account for, among other effects, reflection from bright surfaces and clouds, sun-glint, and atmospheric attenuation by smoke and haze. An approach for fire danger estimation is also presented. The fire mapping activities at the various institutions are being organized into a regional network within the international Global Observation of Forest and Landcover Dynamics (GOFC/GOLD) program. Concerted efforts will facilitate the implementation of processing systems for new and improved sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) on the experimental NASA Earth Observing System Terra and Aqua satellites and the Visible/Infrared/ Imager/Radiometer Suite on the next generation National Polar Orbiting Environmental Satellite System (NPOESS). В© Springer 2006.

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Держатели документа:
Space Research Institute (SRI), Russian Academy of Science (RAS), Russian Federation
Center on Forest Ecology and Productivity (CFEP) RAS
Institute of Solar-Terrestrial Physics (ISTP) Siberian Branch RAS
University of Maryland, United States
V. N. Sukachev Institute of Forest Siberian Branch RAS
Krasnoyarsk State University, Russian Federation
Institute of Atmospheric Optics, Siberian Branch RAS, Russian Federation

Доп.точки доступа:
Loupian, E.A.; Mazurov, A.A.; Flitman, E.V.; Ershov, D.V.; Korovin, G.N.; Novik, V.P.; Abushenko, N.A.; Altyntsev, D.A.; Koshelev, V.V.; Tashchilin, S.A.; Tatarnikov, A.V.; Csiszar, I.; Sukhinin, A.I.; Ponomarev, E.I.; Afonin, S.V.; Belov, V.V.; Matvienko, G.G.; Loboda, T.

/ E. N. Valendik [et al.] // Soviet Journal of Remote Sensing (English translation of Issledovanie Zemmli iz Kosmosa). - 1985. - Vol. 2, Is. 5. - P777-788 . -
Аннотация: A method is proposed for estimating an important parameter of the dynamics of forest fires - the rate of their spread - from their successive infrared images. Algorithms are given for processing forest fire images. These algorithms may also prove useful in designing dedicated onboard instrumentation.

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Держатели документа:
Acad of Sciences of the USSR, V. N., Sukachev Inst of Forestry &, Timber, Krasnoyarsk, USSR, Acad of Sciences of the USSR, V. N. Sukachev Inst of Forestry & Timber, Krasnoyarsk, USSR

Доп.точки доступа:
Valendik, E.N.; Dorrer, G.A.; Kalinina, N.A.; Sukhinin, A.I.; Khrebtov, B.A.

/ O. A. Antamoshkin, O. A. Antamoshkina, N. A. Smirnov // IOP Conference Series: Materials Science and Engineering. - 2016. - Vol. 122: 19th International Scientific Conference Reshetnev Readings 2015 (10 November 2015 through 14 November 2015, ) Conference code: 122153, Is. 1, DOI 10.1088/1757-899X/122/1/012003 . -
Аннотация: The paper outlines the general concept of multi-agent approach to develop the automation system for monitoring, forecasting and managing emergency situations and its models and algorithms included. © Published under licence by IOP Publishing Ltd.

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Siberian State Aerospace University, Academician M. F. Reshetnev, Krasnoyarsk, Russian Federation
Siberian Federal University, Krasnoyarsk, Russian Federation
V.N. Sukachev Institute of Forest, SB, RAS, Krasnoyarsk, Russian Federation

Доп.точки доступа:
Antamoshkin, O. A.; Antamoshkina, O. A.; Smirnov, N. A.

/ M. Cailleret [et al.] // Glob. Change Biol. - 2017. - Vol. 23, Is. 4. - P1675-1690, DOI 10.1111/gcb.13535. - Cited References:86. - This study generated from the COST Action STReESS (FP1106) financially supported by the EU Framework Programme for Research and Innovation HORIZON 2020. We are particularly grateful to Professor Dr. Ute Sass-Klaassen from Wageningen University (the Netherlands), chair of the action, for making this metastudy possible. We also thank members of the Laboratory of Plant Ecology from the University of Ghent (Belgium) for their help while compiling the database; Louise Filion for sharing her dataset; Dario Martin-Benito for providing some For-Clim parameters; the ARC-NZ Vegetation Function Network for supporting the compilation of the Xylem Functional Traits dataset; Edurne Martinez del Castillo for the creation of Fig. 1; and two anonymous reviewers and Phillip van Mantgem (USGS) for their suggestions to improve the quality of the manuscript. MC was funded by the Swiss National Science Foundation (Project Number 140968); SJ by the German Research Foundation (JA 2174/3-1); EMRR by the Research Foundation - Flanders (FWO, Belgium), and by the EU HORIZON 2020 Programme through a Marie Sklodowska-Curie IF Fellowship (No. 659191); LDS by a postdoctoral fellowship from the Portuguese Fundacao para a Ciencia e a Tecnologia (FCT) (SFRH/BPD/70632/2010); TA by the Academy of Finland (Project Nos. 252629 and 276255); JAA by the British Columbia Forest Science Program and the Forest Renewal BC (Canada); BB and WO by the Austrian Science Fund (FWF, Hertha Firnberg Programme Project T667-B16 and FWF P25643-B16); VC, PJ, MS, and VT by the Czech Ministry of Education (MSMT, Project COST CZ Nos.; LD13064 and LD14074); JJC, JCLC, and GSB by the Spanish Ministry of Economy (Projects CGL2015-69186-C21-R, CGL2013-48843-C2-2-R, and CGL2012-32965) and the EU (Project FEDER 0087 TRANSHABITAT); MRC by the Natural Sciences and Engineering Research Council of Canada (NSERC) and by the Service de la protection contre les insectes et les maladies du ministere des forets du Quebec (Canada); KC by the Slovenian Research Agency (ARRS) Program P4-0015; AD by the United States Geological Survey (USGS); HD by the French National Research Agency (ANR, DRYADE Project ANR-06VULN-004) and the Metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA); MD by the Israeli Ministry of Agriculture and Rural Development as a chief scientist and by the Jewish National Fund (Israel); GGI by the Spanish Ministry of Economy and Competitiveness (Project AGL2014-61175-JIN); SG by the Bundesministerium fur Bildung und Forschung (BMBF) through the Project REGKLAM (Grant Number: 01 LR 0802) (Germany); LJH by the Arkansas Agricultural Experiment Station (United States of America) and the United States Department of Agriculture - Forest Service; HH by the Natural Sciences and Engineering Research Council of Canada; AMH by the Spanish Ministry of Science and Innovation (Projects CGL2007-60120 and CSD2008-0040) and by the Spanish Ministry of Education via a FPU Scholarship; VIK by the Russian Science Foundation (Grant #14-24-00112); TKi and RV by the Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET Grant PIP 112-201101-00058 and PIP 112-2011010-0809) (Argentina); TKl by the Weizmann Institute of Science (Israel) under supervision of Professor Dan Yakir, by the Keren Kayemeth LeIsrael (KKL) - Jewish National Fund (JNF) (Alberta-Israel Program 90-9-608-08), by the Sussman Center (Israel), by the Cathy Wills and Robert Lewis Program in Environmental Science (United Kingdom), by the France-Israel High Council for Research Scientific and Technological Cooperation (Project 3-6735), and by the Minerva Foundation (Germany); KK by the project 'Resilience of Forests' of the Ministry of Economic Affairs (the Netherlands - WUR Investment theme KB19); TL by the program and research group P4-0107 Forest Ecology, Biology and Technology (Slovenia); RLV by a postdoctoral fellowship from the Portuguese Fundacao para a Ciencia e a Tecnologia (FCT; SFRH/BPD/86938/2012); RLR by the EU FP7 Programme through a Marie Sklodowska-Curie IOF Fellowship (No. 624473); HM by the Academy of Finland (Grant Nos. 257641 and 265504); SM by Sparkling Science of the Federal Ministry of Science, Research and Economy (BMWFW) of Austria; IM by the Hungarian Scientific Research Fund (No. K101552); JMM by the Circumpolar-Boreal Alberta grants program from the Natural Science and Engineering Research Council of Canada; MP by the EU Project LIFE12 ENV/FI/000409; AMP by a Swiss Research Fellowship (Sciex-NMSch, Project 13.; 272 - OAKAGE); JMS by the American National Science Foundation (Grant 0743498); ABS by the British Columbia Ministry of Forests, Lands and Natural Resource Operations (Canada); DS by the Public Enterprise 'Vojvodinasume' (project Improvement of Lowland Forest Management); MLS by the Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET Grant PIP 11420110100080) and by El Fondo para la Investigacion Cientifica y Tecnologica (FONCyT Grant PICT 2012-2009); RT by the Italian Ministry of Education (University and Research 2008, Ciclo del Carbonio ed altri gas serra in ecosistemi forestali, naturali ed artificiali dell'America Latina: analisi preliminare, studio di fattibilita e comparazione con ecosistemi italiani) and by the EU LIFE+ Project MANFOR C.BD. (Environment Policy and Governance 2009, Managing forests for multiple purposes: carbon, biodiversity and socioeconomic wellbeing); ARW by the Natural Sciences and Engineering Council (NSERC) (Canada) through the University of Winnipeg and by Manitoba Conservation (Canada); and JMV by the Spanish Ministry of Economy and Competitiveness (Grant CGL2013-46808-R). Any use of trade names is for descriptive purposes only and does not imply endorsement by the U.S. Government. . - ISSN 1354-1013. - ISSN 1365-2486РУБ Biodiversity Conservation + Ecology + Environmental Sciences

Аннотация: Tree mortality is a key factor influencing forest functions and dynamics, but our understanding of the mechanisms leading to mortality and the associated changes in tree growth rates are still limited. We compiled a new pan-continental tree-ring width database from sites where both dead and living trees were sampled (2970 dead and 4224 living trees from 190 sites, including 36 species), and compared early and recent growth rates between trees that died and those that survived a given mortality event. We observed a decrease in radial growth before death in ca. 84% of the mortality events. The extent and duration of these reductions were highly variable (1-100 years in 96% of events) due to the complex interactions among study species and the source(s) of mortality. Strong and long-lasting declines were found for gymnosperms, shade-and drought-tolerant species, and trees that died from competition. Angiosperms and trees that died due to biotic attacks (especially bark-beetles) typically showed relatively small and short-term growth reductions. Our analysis did not highlight any universal trade-off between early growth and tree longevity within a species, although this result may also reflect high variability in sampling design among sites. The intersite and interspecific variability in growth patterns before mortality provides valuable information on the nature of the mortality process, which is consistent with our understanding of the physiological mechanisms leading to mortality. Abrupt changes in growth immediately before death can be associated with generalized hydraulic failure and/or bark-beetle attack, while long-term decrease in growth may be associated with a gradual decline in hydraulic performance coupled with depletion in carbon reserves. Our results imply that growth-based mortality algorithms may be a powerful tool for predicting gymnosperm mortality induced by chronic stress, but not necessarily so for angiosperms and in case of intense drought or bark-beetle outbreaks.

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ETH, Forest Ecol, Dept Environm Syst Sci, Inst Terr Ecosyst, Univ Str 22, CH-8092 Zurich, Switzerland.
Univ Ulm, Inst Systemat Bot & Ecol, Albert Einstein Allee 11, D-89081 Ulm, Germany.
CREAF, Campus UAB, Cerdanyola Del Valles 08193, Spain.
Vrije Univ Brussel, Lab Plant Biol & Nat Management APNA, Pl Laan 2, B-1050 Brussels, Belgium.
RMCA, Lab Wood Biol & Xylarium, Leuvensesteenweg 13, B-3080 Tervuren, Belgium.
Univ Coimbra, Dept Life Sci, Ctr Funct Ecol, P-3000456 Coimbra, Portugal.
Univ Helsinki, Dept Forest Sci, POB 27 Latokartanonkaari 7, FIN-00014 Helsinki, Finland.
Univ Victoria, Dept Biol, STN CSC, POB 3020, Victoria, BC V8W 3N5, Canada.
Univ Innsbruck, Inst Bot, Sternwartestr 15, A-6020 Innsbruck, Austria.
Univ Milan, Dipartimento Biosci, Via Giovanni Celoria 26, I-20133 Milan, Italy.
Czech Univ Life Sci, Fac Forestry & Wood Sci, Kamycka 961-129, Prague 16521 6, Suchdol, Czech Republic.
CSIC, IPE, Ave Montanana 1005, Zaragoza 50192, Spain.
Swiss Fed Inst Forest Snow & Landscape Res WSL, Zurcherstr 111, CH-8903 Birmensdorf, Switzerland.
Univ Clermont Auvergne, INRA, Unite Mixte Rech UMR PIAF 547, F-63100 Clermont Ferrand, France.
Univ Laval, Dept Sci Bois & Foret, Ctr Forest Res, Fac Foresterie Geog & Geomat, 2405 Rue Terrasse, Quebec City, PQ G1V 0A6, Canada.
Univ Ljubljana, Biotech Fac, Jamnikarjeva 101, Ljubljana 1000, Slovenia.
US Geol Survey, Western Ecol Res Ctr, 47050 Generals Highway, Three Rivers, CA 93271 USA.
INRA, Ecol Forest Mediterraneennes URFM, Site Agroparc, F-84914 Avignon 9, France.
Univ Bordeaux, Unite Mixte Rech UMR BIOGECO 1202, INRA, F-33615 Pessac, France.
Ben Gurion Univ Negev, Dept Geog & Environm Dev, IL-84105 Beer Sheva, Israel.
Inst Nacl Invest & Tecnol Agr & Alimentaria INIA, Ctr Invest Forestal CIFOR, Carretera La Coruna Km 7-5, Madrid 28040, Spain.
Tech Univ Dresden, Inst Forest Bot & Forest Zool, D-01062 Dresden, Germany.
TU Berlin, Fachgebiet Vegetat Tech & Pflanzenverwendung, Inst Landschaftsarchitektur & Umweltplanung, D-10623 Berlin, Germany.
Univ Arkansas, Dept Entomol, Fayetteville, AR 72701 USA.
Univ Kansas, Dept Ecol & Evolutionary Biol, 1450 Jayhawk Blvd, Lawrence, KS 66045 USA.
Max Planck Inst Biogeochem, Hans Knoll Str 10, D-07745 Jena, Germany.
CSIC, Dept Biogeog & Global Change, Natl Museum Nat Hist MNCN, C Serrano 115Bis, Madrid 28006, Spain.
Desert Bot Garden, Dept Res Conservat & Collect, 1201 N Galvin Pkwy, Phoenix, AZ USA.
Humboldt State Univ, Dept Forestry & Wildland Resources, 1 Harpst St, Arcata, CA 95521 USA.
Russian Acad Sci, Siberian Div, Sukachev Inst Forest, Krasnoyarsk 660036, Russia.
Univ Nacl Comahue, Dept Ecol, Quintral S-N, RA-8400 San Carlos De Bariloche, Rio Negro, Argentina.
Consejo Nacl Invest Cient & Tecn, Inst Invest Biodiversidad & Medio Ambiente INIBOM, Quintral 1250, RA-8400 San Carlos De Bariloche, Rio Negro, Argentina.
ARO, Volcani Ctr, Inst Soil Water & Environm Sci, POB 6, IL-50250 Bet Dagan, Israel.
Wageningen Univ, Alterra Green World Res, Droevendaalse Steeg 1, NL-6700 AA Wageningen, Netherlands.
Leiden Univ, Nat Biodivers Ctr, POB 9517, NL-2300 RA Leiden, Netherlands.
Slovenian Forestry Inst, Dept Yield & Silviculture, Vecna Pot 2, Ljubljana 1000, Slovenia.
Pablo de Olavide Univ, Dept Phys Chem & Nat Syst, Carretera Utrera Km 1, Seville 41013, Spain.
Univ Autonoma Barcelona, Cerdanyola Del Valles 08193, Spain.
Univ Lisbon, Forest Res Ctr, Sch Agr, P-1349017 Lisbon, Portugal.
Mediterranean Univ Reggio Calabria, Dept Agr Sci, I-89060 Reggio Di Calabria, Italy.
Tech Univ Madrid, Forest Genet & Physiol Res Grp, Calle Ramiro de Maeztu 7, Madrid 28040, Spain.
Univ Western Sydney, Hawkesbury Inst Environm, Sci Rd, Richmond, NSW 2753, Australia.
Nat Resources Inst Finland Luke, Viikinkaari 4, Helsinki 00790, Finland.
Univ Debrecen, Dept Bot, Fac Sci & Technol, Egyet Ter 1, H-4032 Debrecen, Hungary.
Nat Resources Canada, Northern Forestry Ctr, Canadian Forest Serv, 5320-122nd St, Edmonton, AB T6H 3S5, Canada.
Technol Educ Inst TEI Stereas Elladas, Dept Forestry & Nat Environm Management, Ag Georgiou 1, Karpenissi 36100, Greece.
Nat Resources Inst Finland Luke, POB 18 Jokiniemenkuja 1, Vantaa 01301, Finland.
Natl Inst Res Dev Forestry Marin Dracea, Eroilor 128, Voluntari 077190, Romania.
Open Univ Cyprus, Fac Pure & Appl Sci, CY-2252 Nicosia, Cyprus.
Univ Cyprus, Dept Biol Sci, POB 20537, CY-1678 Nicosia, Cyprus.
Univ Patras, Dept Biol, Div Plant Biol, Patras 26500, Greece.
Univ Colorado, Dept Geog, Boulder, CO 80309 USA.
No Arizona Univ, Dept Geog Planning & Recreat, POB 15016, Flagstaff, AZ 86011 USA.
Wageningen Univ, Forest Ecol & Forest Management Grp, Droevendaalsesteeg 3a, NL-6708 PB Wageningen, Netherlands.
Univ Novi Sad, Inst Lowland Forestry & Environm, Antona Cehova 13,POB 117, Novi Sad 21000, Serbia.
Univ Molise, Dipartimenti Biosci & Terr, I-86090 C Da Fonte Lappone, Pesche, Italy.
Project Ctr Mt Forests MOUNTFOR, EFI, Via E Mach 1, I-38010 San Michele All Adige, Italy.
CCT CONICET Mendoza, Lab Dendrocronol & Hist Ambiental, Inst Argentino Nivol Glaciol & Ciencias Ambiental, Ave Ruiz Leal S-N,Parque Gen San Martin, RA-5500 Mendoza, Argentina.
Estonian Univ Life Sci, Inst Forestry & Rural Engn, Kreutzwaldi 5, EE-51014 Tartu, Estonia.
Univ Alberta, Boreal Avian Modelling Project, Dept Renewable Resources, 751 Gen Serv Bldg, Edmonton, AB T6G 2H1, Canada.
Univ Minnesota, 600 East 4th St, Morris, MN 56267 USA.
Univ Forestry, Kliment Ohridski St 10, Sofia 1756, Bulgaria.

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Cailleret, Maxime; Jansen, Steven; Robert, Elisabeth M. R.; Desoto, Lucia; Aakala, Tuomas; Antos, Joseph A.; Beikircher, Barbara; Bigler, Christof; Bugmann, Harald; Caccianiga, Marco; Cada, Vojtech; Camarero, Jesus J.; Cherubini, Paolo; Cochard, Herve; Coyea, Marie R.; Cufar, Katarina; Das, Adrian J.; Davi, Hendrik; Delzon, Sylvain; Dorman, Michael; Gea-Izquierdo, Guillermo; Gillner, Sten; Haavik, Laurel J.; Hartmann, Henrik; Heres, Ana-Maria; Hultine, Kevin R.; Janda, Pavel; Kane, Jeffrey M.; Kharuk, Vyacheslav I.; Kitzberger, Thomas; Klein, Tamir; Kramer, Koen; Lens, Frederic; Levanic, Tom; Calderon, R.; Lloret, Francisco; Lobodo-Vale, Raquel; Lombardi, Fabio; Rodriguez, S.; Makinen, Harri; Mayr, Stefan; Meszaros, Ilona; Metsaranta, Juha M.; Minunno, Francesco; Oberhuber, Walter; Papadopoulos, Andreas; Peltoniemi, Mikko; Petritan, Any M.; Rohner, Brigitte; Sanguesa-Barreda, Gabriel; Sarris, Dimitrios; Smith, Jeremy M.; Stan, Amanda B.; Sterck, Frank; Stojanovic, Dejan B.; Suarez, Maria L.; Svoboda, Miroslav; Tognetti, Roberto; Torres-Ruiz, Jose M.; Trotsiuk, Volodymyr; Villalba, Ricardo; Vodde, Floor; Westwood, Alana R.; Wyckoff, Peter H.; Zafirov, Nikolay; Martinez-Vilalta, Jordi; Torres-Ruiz, Jose Manuel; EU [FP1106, FEDER 0087 TRANSHABITAT, LIFE12 ENV/FI/000409]; Swiss National Science Foundation [140968]; German Research Foundation [JA 2174/3-1]; Research Foundation - Flanders (FWO, Belgium); EU HORIZON Programme through a Marie Sklodowska-Curie IF Fellowship [659191]; Portuguese Fundacao para a Ciencia e a Tecnologia (FCT) [SFRH/BPD/70632/2010, SFRH/BPD/86938/2012]; Academy of Finland [252629, 276255, 257641, 265504]; British Columbia Forest Science Program; Forest Renewal BC (Canada); Austrian Science Fund (FWF) [T667-B16, FWF P25643-B16]; Czech Ministry of Education (MSMT) [LD13064, LD14074]; Spanish Ministry of Economy [CGL2015-69186-C21-R, CGL2013-48843-C2-2-R, CGL2012-32965]; Natural Sciences and Engineering Research Council of Canada (NSERC); Service de la protection contre les insectes et les maladies du ministere des forets du Quebec (Canada); Slovenian Research Agency (ARRS) Program [P4-0015]; United States Geological Survey (USGS); French National Research Agency (ANR) [ANR-06VULN-004]; Metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA); Jewish National Fund (Israel); Spanish Ministry of Economy and Competitiveness [AGL2014-61175-JIN, CGL2013-46808-R]; Bundesministerium fur Bildung und Forschung (BMBF) through the Project REGKLAM (Germany) [01 LR 0802]; Arkansas Agricultural Experiment Station (United States of America); United States Department of Agriculture - Forest Service; Natural Sciences and Engineering Research Council of Canada; Spanish Ministry of Science and Innovation [CGL2007-60120, CSD2008-0040]; Spanish Ministry of Education via a FPU Scholarship; Russian Science Foundation [14-24-00112]; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) (Argentina) [PIP 112-201101-00058, PIP 112-2011010-0809]; Weizmann Institute of Science (Israel); Keren Kayemeth LeIsrael (KKL) - Jewish National Fund (JNF) [90-9-608-08]; Sussman Center (Israel); Cathy Wills and Robert Lewis Program in Environmental Science (United Kingdom); France-Israel High Council for Research Scientific and Technological Cooperation [3-6735]; Minerva Foundation (Germany); Israeli Ministry of Agriculture and Rural Development; project 'Resilience of Forests' of the Ministry of Economic Affairs [KB19]; program and research group Forest Ecology, Biology and Technology (Slovenia) [P4-0107]; EU through a Marie Sklodowska-Curie IOF Fellowship [624473]; Sparkling Science of the Federal Ministry of Science, Research and Economy (BMWFW) of Austria; Hungarian Scientific Research Fund [K101552]; Natural Science and Engineering Research Council of Canada; Swiss Research Fellowship [13.272 - OAKAGE]; American National Science Foundation [0743498]; British Columbia Ministry of Forests, Lands and Natural Resource Operations (Canada); Public Enterprise 'Vojvodinasume'; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) [PIP 11420110100080]; El Fondo para la Investigacion Cientifica y Tecnologica (FONCyT) [PICT 2012-2009]; Italian Ministry of Education (University and Research, Ciclo del Carbonio ed altri gas serra in ecosistemi forestali, naturali ed artificiali dell'America Latina: analisi preliminare, studio di fattibilita e comparazione con ecosistemi italiani); EU LIFE+ Project MANFOR C.BD. (Environment Policy and Governance, Managing forests for multiple purposes: carbon, biodiversity and socioeconomic wellbeing); Natural Sciences and Engineering Council (NSERC) (Canada) through the University of Winnipeg; Manitoba Conservation (Canada)

/ A. V. Lapko [et al.] // Optoelectron. Instrum. Data Proc. - 2019. - Vol. 55, Is. 3. - P230-236, DOI 10.3103/S8756699019030038. - Cited References:18. - This work was supported by the Russian Foundation for Basic Research (Grant No. 18-01-00251). . - ISSN 8756-6990. - ISSN 1934-7944РУБ Physics, Multidisciplinary

Аннотация: A nonparametric algorithm of automatic classification of large arrays of statistical data is considered. Its synthesis is based on decomposition of initial data. The results of decomposition form a set of centers of multidimensional intervals and the corresponding frequencies of occurrence of values of random variables. Based on information obtained, classes corresponding to single-mode fragments of the probability density of features of examined objects are detected. The spatial interpretation of automatic classification results is analyzed. The nonparametric algorithms developed in the study are important tools of processing of data obtained by remote sensing of natural resources.

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Держатели документа:
Russian Acad Sci, Siberian Branch, Inst Computat Modeling, Akademgorodok 50,Bldg 44, Krasnoyarsk 660036, Russia.
Russian Acad Sci, Siberian Branch, Sukachev Inst Forest, Akademgorodok 50,Bldg 28, Krasnoyarsk 660036, Russia.
Reshetnev Siberian Univ Sci & Technol, Pr Im Gazety Krasnoyarskii Rabochii 31, Krasnoyarsk 660037, Russia.

Доп.точки доступа:
Lapko, A. V.; Lapko, V. A.; Im, S. T.; Tuboltsev, V. P.; Avdeenok, V. A.; Russian Foundation for Basic Research [18-01-00251]

/ E. V. Fedotova, Yu. A. Maglinets, R. V. Brezhnev, A. G. Vyrvinskiy // Sovrem. Probl. Distancionnogo Zondirovania Zemli kosm. - 2020. - Vol. 17, Is. 4. - С. 195-203, DOI 10.21046/2070-7401-2020-17-4-195-203 . - ISSN 2070-7401
Аннотация: Evaluation of vegetation bio-productivity, yield prediction, is effectively carried out using simulation models of plant growth. To calculate the value of the aboveground biomass in these models, the leaf area index (LAI) is used. In the agromonitoring service of the Institute of Space and Information Technologies, a productivity forecasting component is being developed using available field map systems showing crops and remote sensing data in the public domain. In this paper, we propose an approach to solving the problem of obtaining the LAI time series during the growing season for agricultural objects. Landsat-8 OLI and Sentinel-2 medium resolution data are used. These data have time resolution restrictions. The use of daily MODIS data is not possible due to their low spatial resolution, taking into account the typical size of agricultural fields of Krasnoyarsk region central part. Algorithms for data fusion with low and medium spatial resolutions are considered to obtain NDVI with the necessary frequency in the absence of medium-resolution data. The construction of the NDVI using data from different systems for LAI estimation required the introduction of additive coefficients for time series alignment using the VEGA Pro service as the base values. The model of calculating LAI from NDVI in linear exponential form is used. The developed approach allows the LAI assessment with the frequency necessary for the work of the predictive model for yield estimating. © 2020 Space Research Institute of the Russian Academy of Sciences. All rights reserved.

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Держатели документа:
Siberian Federal University, Krasnoyarsk, 660074, Russian Federation
Sukachev Institute of Forest SB RAS, Krasnoyarsk Scientific Center SB RAS, Krasnoyarsk, 660036, Russian Federation

Доп.точки доступа:
Fedotova, E. V.; Maglinets, Yu. A.; Brezhnev, R. V.; Vyrvinskiy, A. G.

/ I. V. Zenkov, A. V. Lapko, V. A. Lapko [и др.] // Comput. Opt. - 2021. - Vol. 45, Is. 2. - С. 253-+, DOI 10.18287/2412-6179-CO-801. - Cited References:13. - The research was funded by RFBR, Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science, project number 20-41-240001. . - ISSN 0134-2452. - ISSN 2412-6179РУБ Optics

Аннотация: A nonparametric algorithm for automatic classification of large statistical data sets is proposed. The algorithm is based on a procedure for optimal discretization of the range of values of a random variable. A class is a compact group of observations of a random variable corresponding to a unimodal fragment of the probability density. The considered algorithm of automatic classification is based on the "compression" of the initial information based on the decomposition of a multidimensional space of attributes. As a result, a large statistical sample is transformed into a data array composed of the centers of multidimensional sampling intervals and the corresponding frequencies of random variables. To substantiate the optimal discretization procedure, we use the results of a study of the asymptotic properties of a kernel-type regression estimate of the probability density. An optimal number of sampling intervals for the range of values of one- and two-dimensional random variables is determined from the condition of the minimum root-mean square deviation of the regression probability density estimate. The results obtained are generalized to the discretization of the range of values of a multidimensional random variable. The optimal discretization formula contains a component that is characterized by a nonlinear functional of the probability density. An analytical dependence of the detected component on the antikurtosis coefficient of a one-dimensional random variable is established. For independent components of a multidimensional random variable, a methodology is developed for calculating estimates of the optimal number of sampling intervals for random variables and their lengths. On this basis, a nonparametric algorithm for the automatic classification is developed. It is based on a sequential procedure for checking the proximity of the centers of multidimensional sampling intervals and relationships between frequencies of the membership of the random variables from the original sample of these intervals. To further increase the computational efficiency of the proposed automatic classification algorithm, a multithreaded method of its software implementation is used. The practical significance of the developed algorithms is confirmed by the results of their application in processing remote sensing data.

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Держатели документа:
Siberian Fed Univ, Svobodny Av 79, Krasnoyarsk 660041, Russia.
Inst Computat Modelling SB RAS, Akademgorodok 50, Krasnoyarsk 660036, Russia.
Sukachev Inst Forest SB RAS, Akademgorodok 50, Krasnoyarsk 660036, Russia.
Reshetnev Siberian State Univ Sci & Technol, Krasnoyarsky Rabochy Av 31, Krasnoyarsk 660037, Russia.
Fed Res Ctr Informat & Computat Technol, Krasnoyarsk Branch, Mira Av 53, Krasnoyarsk 660049, Russia.

Доп.точки доступа:
Zenkov, I., V; Lapko, A., V; Lapko, V. A.; Im, S. T.; Tuboltsev, V. P.; Avdeenok, V. L.; RFBRRussian Foundation for Basic Research (RFBR); Krasnoyarsk Regional Fund of Science [20-41-240001]; Krasnoyarsk Territory

/ I. V. Zenkov, A. V. Lapko, V. А. Lapko [и др.] // Comput. Opt. - 2021. - Vol. 45, Is. 2. - С. 253-260, DOI 10.18287/2412-6179-CO-801 . - ISSN 0134-2452
Аннотация: A nonparametric algorithm for automatic classification of large statistical data sets is proposed. The algorithm is based on a procedure for optimal discretization of the range of values of a random variable. A class is a compact group of observations of a random variable corresponding to a unimodal fragment of the probability density. The considered algorithm of automatic classification is based on the «compression» of the initial information based on the decomposition of a multidimensional space of attributes. As a result, a large statistical sample is transformed into a data array composed of the centers of multidimensional sampling intervals and the corresponding frequencies of random variables. To substantiate the optimal discretization procedure, we use the results of a study of the asymptotic properties of a kernel-type regression estimate of the probability density. An optimal number of sampling intervals for the range of values of one-and twodimensional random variables is determined from the condition of the minimum root-mean square deviation of the regression probability density estimate. The results obtained are generalized to the discretization of the range of values of a multidimensional random variable. The optimal discretization formula contains a component that is characterized by a nonlinear functional of the probability density. An analytical dependence of the detected component on the antikurtosis coefficient of a one-dimensional random variable is established. For independent components of a multidimensional random variable, a methodology is developed for calculating estimates of the optimal number of sampling intervals for random variables and their lengths. On this basis, a nonparametric algorithm for the automatic classification is developed. It is based on a sequential procedure for checking the proximity of the centers of multidimensional sampling intervals and relationships between frequencies of the membership of the random variables from the original sample of these intervals. To further increase the computational efficiency of the proposed automatic classification algorithm, a multithreaded method of its software implementation is used. The practical significance of the developed algorithms is confirmed by the results of their application in processing remote sensing data. © 2021, Institution of Russian Academy of Sciences. All rights reserved.

Scopus

Держатели документа:
Siberian Federal University, Svobodny Av. 79, Krasnoyarsk, 660041, Russian Federation
Institute of Computational Modelling SB RAS, Akademgorodok 50, Krasnoyarsk, 660036, Russian Federation
Sukachev Institute of Forest SB RAS, Akademgorodok 50, Krasnoyarsk, 660036, Russian Federation
Reshetnev Siberian State University of Science and Technology, Krasnoyarsky Rabochy Av. 31, Krasnoyarsk, 660037, Russian Federation
Krasnoyarsk Branch of the Federal Research Center for Information and Computational Technologies, Mira Av. 53, Krasnoyarsk, 660049, Russian Federation

Доп.точки доступа:
Zenkov, I. V.; Lapko, A. V.; Lapko, V. А.; Im, S. T.; Tuboltsev, V. P.; Аvdeenok, V. L.

/ P. W. Crous, L. Lombard, M. Sandoval-Denis [et al.] // Stud. Mycol. - 2021. - Vol. 98. - Ст. 100116, DOI 10.1016/j.simyco.2021.100116. - Cited By :2 . - ISSN 0166-0616
Аннотация: Recent publications have argued that there are potentially serious consequences for researchers in recognising distinct genera in the terminal fusarioid clade of the family Nectriaceae. Thus, an alternate hypothesis, namely a very broad concept of the genus Fusarium was proposed. In doing so, however, a significant body of data that supports distinct genera in Nectriaceae based on morphology, biology, and phylogeny is disregarded. A DNA phylogeny based on 19 orthologous protein-coding genes was presented to support a very broad concept of Fusarium at the F1 node in Nectriaceae. Here, we demonstrate that re-analyses of this dataset show that all 19 genes support the F3 node that represents Fusarium sensu stricto as defined by F. sambucinum (sexual morph synonym Gibberella pulicaris). The backbone of the phylogeny is resolved by the concatenated alignment, but only six of the 19 genes fully support the F1 node, representing the broad circumscription of Fusarium. Furthermore, a re-analysis of the concatenated dataset revealed alternate topologies in different phylogenetic algorithms, highlighting the deep divergence and unresolved placement of various Nectriaceae lineages proposed as members of Fusarium. Species of Fusarium s. str. are characterised by Gibberella sexual morphs, asexual morphs with thin- or thick-walled macroconidia that have variously shaped apical and basal cells, and trichothecene mycotoxin production, which separates them from other fusarioid genera. Here we show that the Wollenweber concept of Fusarium presently accounts for 20 segregate genera with clear-cut synapomorphic traits, and that fusarioid macroconidia represent a character that has been gained or lost multiple times throughout Nectriaceae. Thus, the very broad circumscription of Fusarium is blurry and without apparent synapomorphies, and does not include all genera with fusarium-like macroconidia, which are spread throughout Nectriaceae (e.g., Cosmosporella, Macroconia, Microcera). In this study four new genera are introduced, along with 18 new species and 16 new combinations. These names convey information about relationships, morphology, and ecological preference that would otherwise be lost in a broader definition of Fusarium. To assist users to correctly identify fusarioid genera and species, we introduce a new online identification database, Fusarioid-ID, accessible at www.fusarium.org. The database comprises partial sequences from multiple genes commonly used to identify fusarioid taxa (act1, CaM, his3, rpb1, rpb2, tef1, tub2, ITS, and LSU). In this paper, we also present a nomenclator of names that have been introduced in Fusarium up to January 2021 as well as their current status, types, and diagnostic DNA barcode data. In this study, researchers from 46 countries, representing taxonomists, plant pathologists, medical mycologists, quarantine officials, regulatory agencies, and students, strongly support the application and use of a more precisely delimited Fusarium (= Gibberella) concept to accommodate taxa from the robust monophyletic node F3 on the basis of a well-defined and unique combination of morphological and biochemical features. This F3 node includes, among others, species of the F. fujikuroi, F. incarnatum-equiseti, F. oxysporum, and F. sambucinum species complexes, but not species of Bisifusarium [F. dimerum species complex (SC)], Cyanonectria (F. buxicola SC), Geejayessia (F. staphyleae SC), Neocosmospora (F. solani SC) or Rectifusarium (F. ventricosum SC). The present study represents the first step to generating a new online monograph of Fusarium and allied fusarioid genera (www.fusarium.org). © 2021 Westerdijk Fungal Biodiversity Institute

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Держатели документа:
Westerdijk Fungal Biodiversity Institute, Utrecht, 3508 AD, Netherlands
Wageningen University and Research Centre (WUR), Laboratory of Phytopathology, Droevendaalsesteeg 1, Wageningen, 6708 PB, Netherlands
Netherlands Institute of Ecology (NIOO-KNAW), Department of Microbial Ecology, Droevendaalsesteeg 10, Wageningen, 6708 PB, Netherlands
Department of Biology, Carleton University, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
Plant Protection Department, Agricultural Institute of Slovenia, Hacquetova ulica 17, Ljubljana, 1000, Slovenia
Department of Plant Science and Landscape Architecture, University of Maryland, College Park, MD, United States
Escuela de Biologia and Centro de Investigaciones en Productos Naturales, Universidad de Costa Rica, San Pedro, Costa Rica
Unitat de Micologia, Facultat de Medicina i Ciencies de la Salut i Institut d'Investigacio Sanitaria Pere Virgili (IISPV), Universitat Rovira i Virgili, Reus, 43201, Spain
Department of Clinical Plant Science, Faculty of Bioscience, Hosei University, 3-7-2 Kajino-cho, Koganei, Tokyo, 184-8584, Japan
ARC-Plant Health and Protection, Private Bag X5017, Stellenbosch, Western Cape 7599, South Africa
State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China
University of Chinese Academy of Sciences, Beijing, 100049, China
Department of Botany & Plant Pathology, Oregon State University, Corvallis, OR 97330, United States
Department of Microbial Drugs, Helmholtz Centre for Infection Research GmbH (HZI), Inhoffenstrasse 7, Braunschweig, 38124, Germany
Sporometrics, Toronto, ON, Canada
Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
Plant and Microbial Biology, 111 Koshland Hall, University of California, Berkeley, CA 94720-3102, United States
Senckenberg Biodiversity and Climate Research Center, Senckenberganlage 25, Frankfurt am Main, D-60325, Germany
Department of Biochemistry, Genetics and Microbiology, Forestry and Agricultural Biotechnology Institute (FABI), Faculty of Natural and Agricultural Sciences, University of Pretoria, P. Bag X20, Hatfield, Pretoria, 0028, South Africa
Department of Biotechnology and Biomedicine, DTU-Bioengineering, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
Systematic Mycology Lab., Botany and Microbiology Department, Faculty of Science, Suez Canal University, Ismailia, 41522, Egypt
Department of Plant Protection, Faculty of Agriculture, University of Kurdistan, P.O. Box 416, Sanandaj, Iran
Department of Medical Microbiology, King's College Hospital, London, UK, United Kingdom
Department of Infectious Diseases, Imperial College London, London, UK, United Kingdom
Department of Mycology and Plant Resistance, V. N. Karazin Kharkiv National University, Maidan Svobody 4, Kharkiv, 61022, Ukraine
Department of Food Science and Technology, Cape Peninsula University of Technology, P.O. Box 1906, Bellville, 7535, South Africa
School of Forest Resources and Conservation, University of Florida, Gainesville, FL, United States
Department of Plant Pathology and Microbiology, College of Bio-Resources and Agriculture, National Taiwan University, No.1, Sec.4, Roosevelt Road, Taipei, 106, Taiwan
Iranian Research Institute of Plant Protection, Agricultural Research, Education and Extension Organization (AREEO), P.O. Box 19395-1454, Tehran, Iran
Natural History Museum, University of Oslo, Norway
Department of Natural History, NTNU University Museum, Trondheim, Norway
Setor de Micologia/Departamento de Biociencias e Tecnologia, Instituto de Patologia Tropical e Saude Publica, Universidade Federal de Goias/Federal University of Goias, Rua 235 - s/n – Setor Universitario - CEP: 74605-050, Goiania, Brazil
Departamento de Agronomia, Universidade Federal Rural de Pernambuco, Recife, PE 52171-900, Brazil
Departamento de Parasitologia y Micologia, Instituto de Higiene, Facultad de Medicina – Universidad de la Republica, Av. A. Navarro 3051, Montevideo, Uruguay
Department of Pharmaceutical Science, University of Perugia, Via Borgo 20 Giugno, Perugia, 74, Italy
Instituto de Investigaciones Fundamentales en Agricultura Tropical Alejandro de Humboldt (INIFAT), Academico Titular de la Academia de Ciencias de, Cuba
Grupo de Investigacion Celular y Molecular de Microorganismos Patogenos (CeMoP), Departamento de Ciencias Biologicas, Universidad de Los Andes, Bogota, 111711, Colombia
Mycology Laboratory, New York State Department of Health Wadsworth Center, Albany, NY, United States
Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchatel, Neuchatel, CH-2000, Switzerland
Senckenberg Museum of Natural History Gorlitz, PF 300 154, Gorlitz, 02806, Germany
Mycotheque de l'Universite catholique de Louvain (MUCL, BCCMTM), Earth and Life Institute – ELIM – Mycology, Universite catholique de Louvain, Croix du Sud 2 bte L7.05.06, Louvain-la-Neuve, B-1348, Belgium
Department of Microbiology, Babcock University, Ilishan Remo, Ogun State, Nigeria
The Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing, 100083, China
Laboratorio de Micologia Clinica, Hospital de Clinicas, Universidad de Buenos Aires, Buenos Aires, Argentina
Facultad de Farmacia y Bioquimica, Universidad de Buenos Aires, Buenos Aires, Argentina
Royal Botanic Gardens, Kew, Richmond, Surrey TW9 3DS, United Kingdom
Laboratorio de Salud de Bosques y Ecosistemas, Instituto de Conservacion, Biodiversidad y Territorio, Facultad de Ciencias Forestales y Recursos Naturales, Universidad Austral de Chile, casilla 567, Valdivia, Chile
Institute of Grapevine and Wine Sciences (ICVV), Spanish National Research Council (CSIC)-University of La Rioja-Government of La Rioja, Logrono, 26007, Spain
Institut fur Biologie, Karl-Franzens-Universitat Graz, Holteigasse 6, Graz, 8010, Austria
Applied genomics research group, Universidad de los Andes, Cr 1 # 18 a 12, Bogota, Colombia
Center for Safe and Improved Food, Scotland's Rural College (SRUC), Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, United Kingdom
Biorefining and Advanced Materials Research Center, Scotland's Rural College (SRUC), Kings Buildings, West Mains Road, Edinburgh, EH9 3JG, United Kingdom
Department of Agricultural, Forestry and Food Sciences (DISAFA), University of Torino, Largo P. Braccini 2, Grugliasco, TO 10095, Italy
BioAware, Hannut, Belgium
Research Group Mycology, Department of Biology, Ghent University, 35 K.L. Ledeganckstraat, Ghent, 9000, Belgium
Faculty of Science, University of South Bohemia, Branisovska 31, Ceske Budejovice, 370 05, Czech Republic
Department of Botany, Swedish Museum of Natural History, P.O. Box 50007, Stockholm, SE-104 05, Sweden
Microbe Division/Japan Collection of Microorganisms RIKEN BioResource Research Center, 3-1-1 Koyadai, Tsukuba, Ibaraki, 305-0074, Japan
Department of Botany, Charles University in Prague, Prague, Czech Republic
Center of Excellence in Fungal Research, Mae Fah Luang University, Chaing Rai, 57100, Thailand
Cornell University, 334 Plant Science Building, Ithaca, NY 14850, United States
Department of Health Sciences, Faculty of Medicine and Health Sciences, University of Mauritius, Reduit, Mauritius
Manaaki Whenua Landcare Research, Private Bag 92170, Auckland, 1142, New Zealand
EMSL Analytical, Inc., 200 Route 130 North, Cinnaminson, NJ 08077, United States
Department of Nutrition and Dietetics, Faculty of Health Sciences, Yeditepe University, Turkey
Department of Plant and Soil Sciences, University of Pretoria, P. Bag X20 Hatfield, Pretoria, 0002, South Africa
Institute of Environmental Biology, Ecology and Biodiversity, Utrecht University, Utrecht, 3584 CH, Netherlands
Laboratory for Biological Diversity, Ruder Boskovic Institute, Bijenicka cesta 54, Zagreb, HR-10000, Croatia
University of Veterinary Medicine, Vienna (VetMed), Institute of Food Safety, Food Technology and Veterinary Public Health, Veterinaerplatz 1, 1210 Vienna and BiMM – Bioactive Microbial Metabolites group, Tulln a.d. Donau, 3430, Austria
University of California, Davis, One Shields Ave., Davis, CA 95616, United States
Department of Agricultural Biological Chemistry, College of Agriculture & Life Sciences, Chonnam National University, Yongbong-Dong 300, Buk-Gu, Gwangju, 61186, South Korea
Ascofrance, 64 route de Chize, Villiers-en-Bois, 79360, France
The Key Laboratory of Molecular Biology of Crop Pathogens and Insects of Ministry of Agriculture, The Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, Institute of Biotechnology, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310058, China
V.N. Sukachev Institute of Forest SB RAS, Laboratory of Reforestation, Mycology and Plant Pathology, Krasnoyarsk, 660036, Russian Federation
Reshetnev Siberian State University of Science and Technology, Department of Chemical Technology of Wood and Biotechnology, Krasnoyarsk, 660037, Russian Federation
School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Ecosciences Precinct, G.P.O. Box 267, Brisbane, 4001, Australia
Department of Botany, Faculty of Science, Palacky University Olomouc, Slechtitelu 27, Olomouc, CZ-783 71, Czech Republic
Department of Agricultural, Food, Environmental and Forestry Science and Technology (DAGRI), Plant Pathology and Entomology section, University of Florence, P.le delle Cascine 28, Firenze, 50144, Italy
Graduate school of Bioresources, Mie University, Kurima-machiya 1577, Tsu, Mie 514-8507, Japan
Gothenburg Global Biodiversity Center at the Department of Biological and Environmental Sciences, University of Gothenburg, Box 461, Gothenburg, 405 30, Sweden
Department of Microbiology and Biochemistry, Faculty of Natural and Life Sciences, University of Batna 2, Batna, 05000, Algeria
Laboratorio de Micodiversidad y Micoprospeccion, PROIMI-CONICET, Av. Belgrano y Pje. Caseros, Argentina
Universidade de Lisboa, Faculdade de Ciencias, Biosystems and Integrative Sciences Institute (BioISI), Campo Grande, Lisbon, 1749-016, Portugal
Microbial Screening Technologies, 28 Percival Rd, Smithfield, NSW 2164, Australia
Dipartimento di Agricoltura, Alimentazione e Ambiente, sez. Patologia vegetale, University of Catania, Via S. Sofia 100, Catania, 95123, Italy
Phytopathology, Van Zanten Breeding B.V., Lavendelweg 15, Rijsenhout, 1435 EW, Netherlands
National Fungal Culture Collection of India (NFCCI), Biodiversity and Palaeobiology (Fungi) Group, Agharkar Research Institute, Pune, Maharashtra 411 004, India
Laboratory of Mycology and Phytopathology – (LAMFU), Department of Chemical and Food Engineering, Universidad de los Andes, Cr 1 # 18 a 12, Bogota, Colombia
Plant Pathology and Population Genetics, Laboratory of Microorganisms, National Gene Bank, Tunisia
Laboratory of Emerging Fungal Pathogens, Department of Microbiology, Immunology, and Parasitology, Discipline of Cellular Biology, Federal University of Sao Paulo (UNIFESP), Sao Paulo, 04023062, Brazil
USDA-ARS Mycology & Nematology Genetic Diversity & Biology Laboratory, Bldg. 010A, Rm. 212, BARC-West, 10300 Baltimore Ave, Beltsville, MD 20705, United States
Departamento de Micologia Prof. Chaves Batista, Universidade Federal de Pernambuco, Centro de Biociencias, Cidade Universitaria, Av. Prof. Moraes Rego, s/n, Recife, PE CEP: 50670-901, Brazil
Centre for Crop Health, University of Southern Queensland, Toowoomba, Queensland 4350, Australia
College of Plant Protection, Northwest A&F University, Yangling, Shaanxi, China
Faculty of Natural and Agricultural Sciences, Department of Plant Sciences, University of the Free State, P.O. Box 339, Bloemfontein, 9300, South Africa
Queensland Plant Pathology Herbarium, Department of Agriculture and Fisheries, Dutton Park, Queensland 4102, Australia
Royal Botanic Garden Edinburgh, 20A Inverleith Row, Edinburgh, EH3 5LR, United Kingdom
Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
Food and Wine Research Institute, Eszterhazy Karoly University, 6 Leanyka Street, Eger, H-3300, Hungary
Department of Life Sciences and Systems Biology, University of Torino and Institute for Sustainable Plant Protection (IPSP-SS Turin), C.N.R, Viale P.A. Mattioli, 25, Torino, I-10125, Italy
Center for Yunnan Plateau Biological Resources Protection and Utilization, College of Biological Resource and Food Engineering, Qujing Normal University, Qujing, Yunnan 655011, China
Shandong Provincial Key Laboratory for Biology of Vegetable Diseases and Insect Pests, College of Plant Protection, Shandong Agricultural University, Taian, 271018, China
Fitosanidad, Colegio de Postgraduados-Campus Montecillo, Montecillo-Texcoco, Edo. de Mexico 56230, Mexico
Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures GmbH, Inhoffenstrasse 7 B, Braunschweig, 38124, Germany
Museum of Evolution, Uppsala University, Norbyvagen 16, Uppsala, SE-752 36, Sweden
Ministry of Agriculture Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Biotechnology, College of Agriculture and Biotechnology, Zhejiang University, No. 866 Yuhangtang Road, Hangzhou, 310058, China
Goethe-University Frankfurt am Main, Department of Biological Sciences, Institute of Ecology, Evolution and Diversity, Max-von-Laue Str. 13, Frankfurt am Main, D-60438, Germany
LOEWE Centre for Translational Biodiversity Genomics, Georg-Voigt-Str. 14-16, Frankfurt am Main, D-60325, Germany

Доп.точки доступа:
Crous, P. W.; Lombard, L.; Sandoval-Denis, M.; Seifert, K. A.; Schroers, H. -J.; Chaverri, P.; Gene, J.; Guarro, J.; Hirooka, Y.; Bensch, K.; Kema, G. H.J.; Lamprecht, S. C.; Cai, L.; Rossman, A. Y.; Stadler, M.; Summerbell, R. C.; Taylor, J. W.; Ploch, S.; Visagie, C. M.; Yilmaz, N.; Frisvad, J. C.; Abdel-Azeem, A. M.; Abdollahzadeh, J.; Abdolrasouli, A.; Akulov, A.; Alberts, J. F.; Araujo, J. P.M.; Ariyawansa, H. A.; Bakhshi, M.; Bendiksby, M.; Ben Hadj Amor, A.; Bezerra, J. D.P.; Boekhout, T.; Camara, M. P.S.; Carbia, M.; Cardinali, G.; Castaneda-Ruiz, R. F.; Celis, A.; Chaturvedi, V.; Collemare, J.; Croll, D.; Damm, U.; Decock, C. A.; de Vries, R. P.; Ezekiel, C. N.; Fan, X. L.; Fernandez, N. B.; Gaya, E.; Gonzalez, C. D.; Gramaje, D.; Groenewald, J. Z.; Grube, M.; Guevara-Suarez, M.; Gupta, V. K.; Guarnaccia, V.; Haddaji, A.; Hagen, F.; Haelewaters, D.; Hansen, K.; Hashimoto, A.; Hernandez-Restrepo, M.; Houbraken, J.; Hubka, V.; Hyde, K. D.; Iturriaga, T.; Jeewon, R.; Johnston, P. R.; Jurjevic, Z.; Karalti, I.; Korsten, L.; Kuramae, E. E.; Kusan, I.; Labuda, R.; Lawrence, D. P.; Lee, H. B.; Lechat, C.; Li, H. Y.; Litovka, Y. A.; Maharachchikumbura, S. S.N.; Marin-Felix, Y.; Matio Kemkuignou, B.; Matocec, N.; McTaggart, A. R.; Mlcoch, P.; Mugnai, L.; Nakashima, C.; Nilsson, R. H.; Noumeur, S. R.; Pavlov, I. N.; Peralta, M. P.; Phillips, A. J.L.; Pitt, J. I.; Polizzi, G.; Quaedvlieg, W.; Rajeshkumar, K. C.; Restrepo, S.; Rhaiem, A.; Robert, J.; Robert, V.; Rodrigues, A. M.; Salgado-Salazar, C.; Samson, R. A.; Santos, A. C.S.; Shivas, R. G.; Souza-Motta, C. M.; Sun, G. Y.; Swart, W. J.; Szoke, S.; Tan, Y. P.; Taylor, J. E.; Taylor, P. W.J.; Tiago, P. V.; Vaczy, K. Z.; van de Wiele, N.; van der Merwe, N. A.; Verkley, G. J.M.; Vieira, W. A.S.; Vizzini, A.; Weir, B. S.; Wijayawardene, N. N.; Xia, J. W.; Yanez-Morales, M. J.; Yurkov, A.; Zamora, J. C.; Zare, R.; Zhang, C. L.; Thines, M.

/ L. S. Maergoiz // J. Appl. Ind. Math. - 2021. - Vol. 15, Is. 2. - P302-306, DOI 10.1134/S1990478921020113 . - ISSN 1990-4789
Аннотация: Abstract: In connection with the topical problem of creating comfortable atmosphere in urbanenvironment, we present a mathematical algorithm for allocating quotas of harmful emissionsbetween its sources in a megacity. Our construction is based on some recently developed methodof optimal distribution of limited resources between differentiated groups of people. © 2021, Pleiades Publishing, Ltd.

Scopus

Держатели документа:
Federal Research Center “Krasnoyarsk Scientific Center of the Siberian Branch of theRussian Academy of Sciences,” Sukachev Institute of Forest, Krasnoyarsk, 660036, Russian Federation

Доп.точки доступа:
Maergoiz, L. S.

[Текст] / В. Г. Суховольский // Сибирский лесной журнал. - 2021. - № 5. - С. 26-36, DOI 10.15372/SJFS20210504 . - ISSN 2311-1410

ГРНТИ

34.33.19

Аннотация: Исследованы модели динамики численности ряда видов лесных насекомых на основе представлений о вспышке массового размножения как фазового перехода первого рода. В качестве объектов изучения рассмотрены популяции сибирского шелкопряда ( Dendrolimus sibiricus Tschetv.) в Сибири и на Дальнем Востоке, сосновой пяденицы ( Bupalus piniaria L.) в Европе, непарного шелкопряда ( Lymantria dispar (L.)) на Урале, серой лиственничной листовертки ( Zeiraphera griseana (Hübner)) в Альпах. Для ряда видов лесных чешуекрылых (Lepidoptera) построены модели, позволяющие оценить критические плотности популяций и в связи с этим предложить алгоритмы, на основе которых возможно принимать решения о проведении защитных мероприятий. Динамика численности популяций при вспышках массового размножения описана по модели динамики численности как аналога фазового перехода в физических системах. Для снижения уровня ошибок в ходе учетов численности популяций вредителей временных рядов популяционной динамики рассмотренных видов предложен алгоритм их трансформации. В качестве характеристики популяционной динамики предложена функция состояния, вычисляемая как обратная величина вероятности нахождения популяции в состоянии с заданной плотностью. Для функций состояния популяций с режимами вспышек массового размножения установлено наличие двух локальных минимумов и одного локального максимума - потенциального барьера. Предложен метод расчета функций состояния популяций на основе данных временных рядов динамики численности, описаны их характеристики, такие как локальные устойчивые, критическая и полукритическая плотности, восприимчивость к изменению плотности популяции. Введены показатели - индикаторы риска возникновения вспышек массового размножения. Для изученных видов насекомых-филлофагов даны оценки рисков вспышек массового размножения
Models of the population dynamics of forest insects are considered based on the concept of an outbreak as a first order phase transition of the (this sentence is not complete) As objects of the studies, the population of the Siberian silkmoth in Siberia and the Far East, the population of the pine moth in Europe, the population of the gypsy moth in the Urals, and the population of the gray larch leaf worm in the Alps are considered. In this work, models fo same species of forest insects are considered, that make it possible to estimate the critical population densities and, in this regard, to propose algorithms, on the basis of which it is possible to make decisions on the implementation of protective measures. A model of the population dynamics is considered as an analog of a phase transition in physical systems to describe the dynamics of the population. An algorithm for transforming of population dynamics time series is proposed to reduce the level of errors in the course of density counting of pest populations. A state function is proposed as a characteristic of population dynamics, calculated as the reciprocal of the probability of finding a population in a state with a given population density. The functions of the state of populations with modes of outbreaks are characterized by the presence of two local minima and one local maximum - a potential barrier. A method is proposed for calculating the functions of state of populations based on data from time series of population dynamics, characteristics of state functions are described, such as local stable densities, critical and semi-critical density, susceptibility of the state function to changes in population density, and the half-width of the potential barrier. Indicators are introduced - indicators of the risk of outbreaks. Assessments of the risks of outbreaks are given for the studied species of phyllophagous insects

РИНЦ

Держатели документа:
ИЛ СО РАН : 660036, Красноярск, Академгородок, 50, стр. 28

Доп.точки доступа:
Soukhovolsky Vladislav Grigor'yevich