SDSergey V. Dudov

Sergey V. Dudov

*This is an algorithmically generated author record based on publicly available information.

Current Organizations / Institutes

The organizations/institutes from the scholar's latest article


Sergey V. Dudov has a total of 166 co-authors. Here are the top co-authors by number of shared publications:


Latest Publications

  • Ecosystem Approach in Nature Conservation: Global Experience and Modern Challenges for Russia

    by S. V. Dudov, V. D. Dzizyurova, K. V. Dudova, M. V. Bocharnikov

    Finding solutions against the global biodiversity crisis is a key question in conservation biology. The ecosystem approach in biodiversity conservation is aimed at maintaining the relationships and interactions between elements. The need for such an approach is dictated by the objectives of biodiversity management and natural resource conservation. Maintaining the integrity of communities and ecosystems helps protect species diversity and preserve ecosystem functions. To achieve economic and conservation goals, it is necessary to determine which ecosystems are vulnerable and which are sustainable. Such assessments are rapidly developed in world science, and the principles are considered in the methodology of the International Union for Conservation of Nature (IUCN) Red List of Ecosystems. The methodology is recognized as an international standard in assessing the risk of ecosystem collapse due to a decline in distribution, restricted geographic distribution with continuing declines or threats, abiotic degradation, disruption to biotic processes from the cumulative impact of factors. The use of the IUCN ecosystem vulnerability criteria provides comparable assessments of the state of terrestrial, marine, and freshwater ecosystems. Assessment according to the IUCN criteria is a multidisciplinary scientific problem, for which a variety of materials and analytical tools are used to solve, including remote sensing data and mathematical modeling. The objective of the review is to reveal the principles of the methodology for assessing the vulnerability of ecosystems. The fundamental terms and concepts of the approach are considered, and possible methodological solutions for assessment according to each criteria are discussed using forests as an example. Inclusion of this methodology in nature conservation practice in Russia will make it possible to create a national Red List of ecosystems. This will be the basis for determining regional and national priorities in biodiversity protection and administrative decisions in nature management based on fundamental research.

  • Species-Distribution Modeling: Advantages and Limitations of Its Application. 1. General Approaches

    by A. A. Lissovsky, S. V. Dudov, E. V. Obolenskaya

    For a long time, studies of the distribution of living beings in a geographical space were performed only with empirical methods. A change in the view of a species distribution as a projection of a Hutchinsonian ecological niche led to the formation of the discipline of ecological modeling of the species distribution, which switched faunistics/floristics from data accumulation to a full-fledged scientific industry with experiment planning and result verification. The various methods of species-distribution modeling make it possible to analyze the patterns of the geographical distributional of organisms in the presence of methodological challenges: nonrandomness of the occurrence data, inhomogeneity of the collection efforts, landscape heterogeneity in different scales, etc. The results of species-distribution modeling represent spatially continuous data of habitat suitability and are valuable not only for studies of the habitats themselves but also for a number of disciplines that involve species distributions.


  • Species-Distribution Modeling: Advantages and Limitations of Its Application. 2. MaxEnt

    by A. A. Lissovsky, S. V. Dudov

    The MaxEnt software package is one of the most popular tools for species-distribution modeling. Despite its popularity, researchers usually underestimate the influence of the initial parameters and spatial bias in the occurrence data. The choice of the feature types and regularization multiplier notably affects the spatial “compactness” or “smoothness” of the model. A nonrandom distribution of occurrence points in geographical space requires data correction. The determination of the ecological factors that influence the range formation require the minimization of multicollinearity in the predictor dataset.


  • Open Access

    "Flora of Russia" on iNaturalist: a dataset

    by Alexey Seregin, Dmitriy Bochkov, Julia Shner, Eduard Garin, Igor Pospelov, Vadim Prokhorov, Pavel Golyakov, Sergey Mayorov, Sergey Svirin, Alexander Khimin, et al.

    The "Flora of Russia" project on iNaturalist brought together professional scientists and amateur naturalists from all over the country. Over 10,000 people are involved in the data collection. Within 20 months the participants accumulated over 750,000 photo observations of 6,853 species of the Russian flora. This constitutes the largest dataset of open spatial data on the country’s biodiversity and a leading source of data on the current state of the national flora. About 85% of all project data are available under free licenses (CC0, CC-BY, CC-BY-NC) and can be freely used in scientific, educational and environmental activities.

  • Open Access

    Additions to the flora of Baikal Nature Reserve

    by N.S. Gamova, S.V. Dudov

    Дополнения к флоре Байкальского заповедника


  • Modeling of species distribution with the use of topography and remote sensing data on the example of vascular plants of the Tukuringra Ridge low mountain belt (Zeya State Nature Reserve, Amur Oblast)

    by S. V. Dudov

    Based on the maximum entropy approach released in MaxEnt Software, cartographic models were designed for the spatial distribution of 63 species of vascular plants inhabiting the low mountain belt of the Tukuringra Ridge. The initial data for modeling were the actual localities of species records, data on remote sensing (Landsat multispectral space images), and a digital elevation model. It was found that the structure of factors contributing to the model is related to species ecological amplitude. The distribution of stenotopic species is determined mainly by topography, which is associated with the thermal and humidity conditions of the habitats. Variables based on remote sensing contribute significantly to the models for eurytopic species, and these variables encompassed parameters of the soil and vegetation. In the course of analysis of the obtained models, three principal groups of species were determined. These groups have similar distribution patterns. Species of the first are restricted in their distribution by the slopes of Zeya and Gilyui river gorges. Species of the second are associated with the southern macroslope of the ridge and with the southern slopes of large river valleys. The third group includes species distributed over the entire studied territory.


  • Open Access

    An addition to the flora of Tukuringra Range (Amur Region)

    by Сергей Валерьевич Дудов, Михаил Николаевич Кожин, Ксения Вячеславовна Дудова, Sergey Dudov, Mikhail Kozhin, Ksenia Dudova

    Приводятся сведения о новых местонахождениях 15 видов сосудистых растений на хребте Тукурингра. Cystopteris dickeana R. Sim впервые показан для Приамурья и Амурской области. Aster serpentimontanus Tamamsch, Chamaepericlymenum canadense (L.) Asch. & Graebn, Chamaerhodos erecta (L.) Bunge, Elytrigia jacutorum (Nevski) Nevski., Hippuris vulgaris L., Listera savatieri Maxim. ex Kom., Viola mirabilis L., Cicuta virosa L. и Persicaria lapathifolia (L.) S. F. Gray. являются новыми для Зейского заповедника. Caulinia japonica (Nakai) Nakai, Hydrilla verticillata (L. f.) Royle, Potamogeton octandrus Poir. впервые выявлены в восточной части хребта Тукурингра.

  • New bryophyte records. 7

    by Elena V. Sofronova, Irina V. Czernyadjeva, Sergey V. Dudov, Michael V. Dulin, Vladimir E. Fedosov, Michael S. Ignatov, Elena A. Ignatova, Ksenia G. Klimova, Tatyana I. Koroteeva, Mikhail N. Kozhin, et al.