Karyakin I.V., Knizhov K.I. (Russian Raptor Research and Conservation Network; Sibecocenter LLC, Novosibirsk, Russia)<\/em><\/p>\n Many large raptor species are currently rare and most of them are endangered, and thus details of their distribution, abundance, and survival are the most important indicators for planning conservation and restoration measures and assessing the impacts of anthropogenic transformation of the environment and\/or climate change on the populations of these species.<\/p>\n Abundance and spatial distribution of the birds under study are determined during field surveys. At the result, we obtain the distribution density in individuals, pairs, nests per unit area (for example, pairs\/100 km2<\/sup>) \u0438\u043b\u0438 \u0434\u0438\u0441\u0442\u0430\u043d\u0446\u0438\u0438 \u043c\u0435\u0436\u0434\u0443 \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0438\u043c\u0438 \u0438\u043b\u0438 \u0432\u0441\u0435\u043c\u0438 \u0441\u043e\u0441\u0435\u0434\u044f\u043c\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u0436\u043d\u043e \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u0442\u044c \u043a\u0430\u043a \u0432 \u0432\u0438\u0434\u0435 \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 (1-5, \u0432 \u0441\u0440\u0435\u0434\u043d\u0435\u043c 3,5\u00b11,1 \u043a\u043c), \u0442\u0430\u043a \u0438 \u0432 \u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u043e\u043c \u0432\u0438\u0434\u0435 (\u043e\u0442 \u043f\u0440\u043e\u0441\u0442\u044b\u0445 \u043b\u0438\u043d\u0438\u0439, \u0441\u0432\u044f\u0437\u044b\u0432\u0430\u044e\u0449\u0438\u0445 \u0442\u043e\u0447\u043a\u0438 \u043d\u0430\u0431\u043b\u044e\u0434\u0435\u043d\u0438\u0439, \u0434\u043e \u0442\u0440\u0438\u0430\u043d\u0433\u0443\u043b\u044f\u0446\u0438\u0438 \u0414\u0435\u043b\u043e\u043d\u0435 \u0438 \u0441\u0435\u0442\u0438 \u043f\u043e\u043b\u0438\u0433\u043e\u043d\u043e\u0432, \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u043d\u043e\u0439 \u043f\u043e \u0442\u043e\u0447\u043a\u0430\u043c \u043d\u0430\u0431\u043b\u044e\u0434\u0435\u043d\u0438\u0439). \u0414\u0430\u043b\u0435\u0435, \u0434\u043b\u044f \u043f\u043e\u043b\u0443\u0447\u0435\u043d\u0438\u044f \u043e\u0446\u0435\u043d\u043a\u0438 \u0447\u0438\u0441\u043b\u0435\u043d\u043d\u043e\u0441\u0442\u0438, \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u043f\u043e\u043d\u0438\u043c\u0430\u0442\u044c \u043f\u043b\u043e\u0449\u0430\u0434\u044c, \u043d\u0430 \u043a\u043e\u0442\u043e\u0440\u0443\u044e \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e \u044d\u043a\u0441\u0442\u0440\u0430\u043f\u043e\u043b\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u044d\u0442\u0438 \u0434\u0430\u043d\u043d\u044b\u0435. \u0418 \u0441 \u044d\u0442\u0438\u043c \u0443 \u043c\u043d\u043e\u0433\u0438\u0445 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u0435\u0439 \u0432\u043e\u0437\u043d\u0438\u043a\u0430\u044e\u0442 \u043f\u0440\u043e\u0431\u043b\u0435\u043c\u044b — \u043d\u0435\u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u0430\u044f \u043e\u0446\u0435\u043d\u043a\u0430 \u043f\u043b\u043e\u0449\u0430\u0434\u0438 \u043c\u0435\u0441\u0442 \u043e\u0431\u0438\u0442\u0430\u043d\u0438\u044f \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u0435\u043c\u043e\u0433\u043e \u0432\u0438\u0434\u0430 \u043f\u0440\u0438\u0432\u043e\u0434\u0438\u0442 \u043a \u0438\u0441\u043a\u0430\u0436\u0435\u043d\u0438\u044e \u043e\u0446\u0435\u043d\u043a\u0438 \u0447\u0438\u0441\u043b\u0435\u043d\u043d\u043e\u0441\u0442\u0438 \u0438 \u043d\u0438\u0432\u0435\u043b\u0438\u0440\u0443\u0435\u0442 \u0443\u0447\u0451\u0442\u043d\u044b\u0435 \u0443\u0441\u0438\u043b\u0438\u044f. \u041a\u0430\u043a \u043f\u0440\u0430\u0432\u0438\u043b\u044c\u043d\u043e \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u043f\u043b\u043e\u0449\u0430\u0434\u044c, \u043d\u0430 \u043a\u043e\u0442\u043e\u0440\u0443\u044e \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e \u044d\u043a\u0441\u0442\u0440\u0430\u043f\u043e\u043b\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0443\u0447\u0451\u0442\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435? \u041e\u0442\u0432\u0435\u0442 \u043d\u0430 \u044d\u0442\u043e\u0442 \u0432\u043e\u043f\u0440\u043e\u0441 \u043c\u043e\u0436\u0435\u0442 \u0434\u0430\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0432 \u0441\u0440\u0435\u0434\u0435 \u0413\u0418\u0421 \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u0433\u0435\u043e\u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0441\u043b\u043e\u0451\u0432 \u044d\u043a\u043e\u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u043e\u0439 \u0438 \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0439 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438, \u0432 \u0441\u043e\u0432\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u0442\u0435\u0440\u043c\u0438\u043d\u043e\u043b\u043e\u0433\u0438\u0438 — \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u0438\u044f \u0432\u0438\u0434\u043e\u0432 (Species distribution modelling, SDM).<\/p>\n \u0412 \u0445\u043e\u0434\u0435 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0430 SDM, \u0442\u0430\u043a\u0436\u0435 \u0438\u0437\u0432\u0435\u0441\u0442\u043d\u043e\u0433\u043e, \u043a\u0430\u043a \u043c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0440\u0435\u0434\u044b \u043e\u0431\u0438\u0442\u0430\u043d\u0438\u044f \u0438\u043b\u0438 \u0430\u0440\u0435\u0430\u043b\u0430 \u0432\u0438\u0434\u0430, \u0434\u043b\u044f \u0433\u0435\u043e\u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u0438 \u043f\u0440\u0438\u0432\u044f\u0437\u0430\u043d\u043d\u044b\u0445 \u0442\u043e\u0447\u0435\u043a \u043f\u0440\u0438\u0441\u0443\u0442\u0441\u0442\u0432\u0438\u044f \u0432\u0438\u0434\u0430 (\u0437\u0430\u0432\u0438\u0441\u0438\u043c\u0430\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0430\u044f) \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u044f\u044e\u0442\u0441\u044f \u0434\u0430\u043d\u043d\u044b\u0435 \u043e\u0431 \u043e\u043a\u0440\u0443\u0436\u0430\u044e\u0449\u0435\u0439 \u0441\u0440\u0435\u0434\u0435 — \u043a\u043b\u0438\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u0438 \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435, \u0442\u0430\u043a\u0438\u0435 \u043a\u0430\u043a \u0442\u0435\u043c\u043f\u0435\u0440\u0430\u0442\u0443\u0440\u0430, \u0432\u043b\u0430\u0436\u043d\u043e\u0441\u0442\u044c, \u0432\u0435\u0442\u0440\u043e\u0432\u0430\u044f \u043d\u0430\u0433\u0440\u0443\u0437\u043a\u0430, \u0440\u0435\u043b\u044c\u0435\u0444, \u0440\u0430\u0441\u0442\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u043f\u043e\u043a\u0440\u043e\u0432, \u043f\u043e\u0447\u0432\u044b \u0438 \u0442.\u043f. (\u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u044b \u0438\u043b\u0438 \u043d\u0435\u0437\u0430\u0432\u0438\u0441\u0438\u043c\u044b\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435), \u0438 \u043f\u043e\u0441\u0440\u0435\u0434\u0441\u0442\u0432\u043e\u043c \u043a\u043e\u043c\u043f\u044c\u044e\u0442\u0435\u0440\u043d\u044b\u0445 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u043e\u0432 \u0438 \u043c\u0430\u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043c\u0435\u0442\u043e\u0434\u043e\u0432 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u0443\u0435\u0442\u0441\u044f \u0440\u0430\u0441\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0432\u0438\u0434\u0430 \u0432 \u0433\u0435\u043e\u0433\u0440\u0430\u0444\u0438\u0447\u0435\u0441\u043a\u043e\u043c \u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435 \u0438\/\u0438\u043b\u0438 \u0432\u0440\u0435\u043c\u0435\u043d\u0438.<\/p>\n SDM is carried out in six stages: (1) idea conceptualization, (2) data preparation (presence and absence points or background points), (3) method selection (4) model fitting, (5) model evaluation and (6) habitat or area map construction.<\/p>\n 1. Conceptualization.\u00a0<\/strong>At this stage, we formulate the main goal of the study and decide on the modeling process design based on our knowledge of the species and the study. Data selection about the species and the environment is an important point at the initial stage. We decide whether to use only our data, or use other available data. Doing so will require some adjustments to the sample design. Next, we need to test the basic assumptions underlying the SDM, such as whether the species is in equilibrium with available environmental variables, whether the data is biased in any way (sampling bias, spatial autocorrelation, etc.), whether there are any environmental changes relative to the time of data collection, etc. Selection of adequate environmental and spatial variables, modeling algorithm, and model complexity should be based on study goals and the hypothesis regarding the relationship between the species under study and the environment in the area selected for study.<\/p>\n 2. Data preparation.<\/strong>\u00a0At this stage, we collect and process factual data about the species (both points of presence and points of absence) and the environment. When preparing data, particular attention should be paid to any inconsistencies in spatial and temporal scaling of dependent and independent variables, i.e. cases where there is a large spatial or temporal difference between species and environmental data, or between environmental data (spatial and climate variables). Also, special attention should be paid to the quality of georeferencing of points of presence and the quality of species identification, which, as a rule, suffers greatly if data is collected by amateurs. In these cases, we need to make decisions about adjusting the data or discarding it. All SDM algorithms require species absence information. If such information is not available, it is replaced by background points or \u201cpseudo-absence\u201d data, which naturally has a negative impact on the quality of the simulation, especially on a large scale. Consideration should be given in advance to how species data will be separated for model training and model testing if the simulation uses all data collected and there are no plans for further testing of the model in the field.<\/p>\n3. Method selection.<\/strong>\u00a0At this stage, we select one or several modeling methods to combine into ensemble models. MaxEnt and Random Forest are integrated into ArcGIS, supported in R, and available online for Google Earth Engine (GEE) users. In recent years, GEE has become increasingly popular as a resource for SDM (Crego\u00a0et al<\/i>., 2022).<\/p>\n 4. Fitting the model.<\/strong>\u00a0This stage is key in SDM. Having received preliminary modeling data, we evaluate the contribution of multicollinearity and decide how to deal with it, determine how many variables can be included in the model without retraining, evaluate spatial or temporal autocorrelation and decide how to deal with it, determine the settings of the model or several models and choose which one provides the result, best or average. At the same stage, we check the plausibility of the selected relationships between species\u2019 points of presence and environmental variables by comparing coefficients and visually inspecting the plotted curves on the graphs.<\/p>\n 5. Model evaluation.<\/strong>\u00a0 \u041d\u0430 \u0434\u0430\u043d\u043d\u043e\u043c \u044d\u0442\u0430\u043f\u0435 \u043c\u044b \u043e\u0446\u0435\u043d\u0438\u0432\u0430\u0435\u043c \u044d\u0444\u0444\u0435\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430 \u0438\u0442\u043e\u0433\u043e\u0432\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043d\u0430\u0431\u043e\u0440\u0430 \u043f\u0440\u043e\u0432\u0435\u0440\u043e\u0447\u043d\u044b\u0445 \u0438\u043b\u0438 \u0442\u0435\u0441\u0442\u043e\u0432\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445: AUC (ROC) (Fielding, Bell, 1997; Fawcett, 2006; Hosmer, Lemeshow, 2013), TSS (Liu et al<\/i>., 2005; Allouche\u00a0et al<\/i>., 2006); R2 and Kappa (Brownlee, 2016; Zhang\u00a0et al<\/i>., 2021). Cross-validation (spatial blocks) is commonly used for this purpose (Roberts\u00a0et al<\/i>., 2017; Valavi\u00a0et al<\/i>., 2019; Crego\u00a0et al<\/i>., 2022). We also select thresholds to binarize predicted probabilities based on cross-validated predictions.<\/p>\n 6. Constructing a map of habitats or range.<\/strong>\u00a0This is the final stage of SDM, during which we convert our predictive model into a raster and obtain a classified image with the percentage probability of the species occurring in the study area for each pixel. We calculate a probability threshold for the species\u2019 presence on pixels that we include in the final range map, and the size of the buffer built around these pixels to determine the area of habitat. The expediency of using a buffer depends on the scale of the resulting raster; the smaller the scale, the lower the relevance of the buffer. Buffer size is usually determined by the mean nearest neighbor distance (MND) and, depending on the modeling\u2019s goals and objectives, is half, exactly, or twice the MND.<\/p>\n One must always critically evaluate the underlying assumptions in SDM and be aware of the potential limitations associated with a variety of factors: the ability to detect the species, uneven sampling, limitations in the selection of environmental variables, ignorance regarding certain aspects of the species\u2019 biology to identify patterns in its biotopic and territorial preferences, etc. SDM assumes that the species is in equilibrium with its environment, that we know and have carefully selected both the species' point of presence and environmental data, and that we have included all the major factors that determine the species' range limits. It should be understood that these aspects are not stable for several reasons. First, species, especially predators, respond dynamically to changes in the environment, so they will exhibit certain spatial and temporal dynamics and need to be properly taken into account in the modeling. Important factors that determine a species' response to changes in its habitat are its physiology, demography, ability to disperse, degree of tolerance to urbanization, degree of adaptation to changes in environmental factors, and interspecific interactions. All these factors engage seemingly constantly over time, including here and now, and ignoring them can significantly distort modeling results. Therefore, the ideal option for SDM is to check results in the field and adjust them.<\/p>\n Unfortunately, most ornithologists have difficulty using R and desktop GIS, a fact that prevents them from processing the results of their field research in accordance with modern standards. For better implementation of modeling in practice when working with rare species, we have created a software product that allows bird specialists with minimal knowledge of GIS and programming languages, but who have a certain understanding of SDM algorithms and abundance assessment, to solve problems related to modeling distribution and abundance and survival of rare species.\nThis software product is designed for processing various geodata containing observations of species; obtaining data from GEE rasters; classification of biotopes; population estimates, survival rates, etc.<\/p>\n The main interface of the product is a web interface that allows the user to select the process of interest, enter the necessary data, and receive a link to an archive containing processing results1<\/sup>.<\/p>\n For geodata (points, polygons, etc.), it is possible to enter csv, shp, geojson files, as well as manual input using a map. To run algorithms in which it is necessary to add data from GEE rasters, a selection field is provided from the list of available earth remote sensing (ERS) products: NASADEM (NASA JPL, 2020), MOD13A1.061 Terra Vegetation Indices 16-Day Global 500m (Didan, 2021), Geomorpho90m (Amatulli\u00a0et al<\/i>., 2020), Global Habitat Heterogeneity (Tuanmu, Jetz, 2015), Global Wind Atlas (Badger\u00a0et al<\/i>., 2021), World Clim (Fick, Hijmans, 2017), ERA5-Land Monthly Aggregated — ECMWF Climate Reanalysis (Mu\u00f1oz Sabater, 2019), ESA WorldCover 10m v100 (Zanaga\u00a0et al<\/i>., 2021), Dynamic World V1 (Brown\u00a0et al<\/i>, 2022), unclassified satellite data such as surface reflectivity (SR) collection 2 Landsat 8 atmospheric-corrected (blue, red, green, near-infrared and shortwave infrared 1 bands with 30 m spatial resolution) and ALOS-2 PALSAR L-band dual-polarization (HH and HV) SAR data, and NDVI and EVI calculation data from Landsat 8 images using the GEE (normalizedDifference) function. To run algorithms using various thirdparty libraries, data is entered in csv files in the formats required by the corresponding libraries.<\/p>\nAt the current stage, the product includes the following modules: The software product is hosted on the servers of organizations recognized as undesirable in Russia, access to which is blocked by Roskomnadzor. The authors are considering options, including creating a clone on a Russian internet resource.<\/p>\n This work is carried out with financial support from the Critical Ecosystem Partnership Fund (CEPF)2\u00a0<\/sup>\u0432 \u0440\u0430\u043c\u043a\u0430\u0445 \u043f\u0440\u043e\u0435\u043a\u0442\u0430 \u00ab\u0421\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u0438\u0435 \u0443\u0433\u0440\u043e\u0436\u0430\u0435\u043c\u044b\u0445 \u0432\u0438\u0434\u043e\u0432 \u043f\u0435\u0440\u043d\u0430\u0442\u044b\u0445 \u0445\u0438\u0449\u043d\u0438\u043a\u043e\u0432 \u043d\u0430 \u0418\u043d\u0434\u043e-\u041f\u0430\u043b\u0435\u0430\u0440\u043a\u0442\u0438\u0447\u0435\u0441\u043a\u043e\u043c \u043c\u0438\u0433\u0440\u0430\u0446\u0438\u043e\u043d\u043d\u043e\u043c \u043f\u0443\u0442\u0438\u00bb («Endangered Raptors Conservation on the Indo-Palearctic Migration Flyway»).<\/p>\n
\nContact:<\/strong><\/em>
\nIgor Karyakin\u00a0ikar_research@mail.ru<\/a><\/em>
\nKirill Knizhov\u00a0kirillknizhov@gmail.com<\/a><\/em>
\nRecommended citation:<\/strong><\/em> Karyakin I.V., Knizhov K.I.\u00a0<\/i>\u041c\u043e\u0434\u0435\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u0438\u044f, \u0447\u0438\u0441\u043b\u0435\u043d\u043d\u043e\u0441\u0442\u0438 \u0438 \u0432\u044b\u0436\u0438\u0432\u0430\u0435\u043c\u043e\u0441\u0442\u0438 \u0432\u0438\u0434\u043e\u0432: \u043d\u043e\u0432\u044b\u0435 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u0438 \u0438 \u043c\u0435\u0442\u043e\u0434\u044b. — \u041f\u0435\u0440\u043d\u0430\u0442\u044b\u0435 \u0445\u0438\u0449\u043d\u0438\u043a\u0438 \u0438 \u0438\u0445 \u043e\u0445\u0440\u0430\u043d\u0430. 2023. \u0421\u043f\u0435\u0446\u0432\u044b\u043f. 2. \u0421. 347-357. DOI: <\/strong>10.19074\/1814-8654-2023-2-347-357 URL: <\/strong>http:\/\/rrrcn.ru\/en\/archives\/35134<\/a>\n
\n
\nWhile simple factor or cluster analyses integrated into desktop GIS were used in early stages of modeling, today the selection of algorithms has expanded significantly:\nLinear regression methods:<\/em><\/strong>
\n— \u041e\u0431\u043e\u0431\u0449\u0451\u043d\u043d\u0430\u044f \u043b\u0438\u043d\u0435\u0439\u043d\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c (GLM) (Nelder, Wedderburn, 1972),
\n— \u041e\u0431\u043e\u0431\u0449\u0451\u043d\u043d\u0430\u044f \u0430\u0434\u0434\u0438\u0442\u0438\u0432\u043d\u0430\u044f \u043c\u043e\u0434\u0435\u043b\u044c (GAM) (Hastie, Tibshirani, 1990);\nMachine learning methods:<\/em><\/strong>
\n— \u041c\u0435\u0442\u043e\u0434 \u043c\u0430\u043a\u0441\u0438\u043c\u0430\u043b\u044c\u043d\u043e\u0439 \u044d\u043d\u0442\u0440\u043e\u043f\u0438\u0438, \u0440\u0435\u0430\u043b\u0438\u0437\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0432 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0435 MaxEnt (Soberson, Peterson, 2005; Phillips\u00a0et al<\/i>., 2006; Phillips, Dudik, 2008),
\n— \u0421\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0439 \u043b\u0435\u0441 (Random Forest, RF) — \u043c\u0435\u0442\u043e\u0434 \u0430\u043d\u0441\u0430\u043c\u0431\u043b\u0435\u0432\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u043f\u0443\u0442\u0451\u043c \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u044f \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0430 \u0434\u0435\u0440\u0435\u0432\u044c\u0435\u0432 \u0440\u0435\u0448\u0435\u043d\u0438\u0439 \u0432\u043e \u0432\u0440\u0435\u043c\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f (Breiman, 2001),
\n— \u0423\u0441\u0438\u043b\u0435\u043d\u043d\u044b\u0435 \u0434\u0435\u0440\u0435\u0432\u044c\u044f \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 (BRT),
\n— \u0421\u0432\u0451\u0440\u0442\u043e\u0447\u043d\u044b\u0435 \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u044b\u0435 \u0441\u0435\u0442\u0438 (CNN) (LeCun\u00a0et al<\/i>., 1989),
\n— \u0413\u0435\u043d\u0435\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u043d\u0430\u0431\u043e\u0440\u0430 \u043f\u0440\u0430\u0432\u0438\u043b (GARP) (Stockwell, 1999; Stockwell, Peters, 1999),
\n— \u041c\u0430\u0448\u0438\u043d\u043d\u043e\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435, \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u044e\u0449\u0435\u0435 \u0432\u0435\u043a\u0442\u043e\u0440\u043d\u044b\u0435 \u0441\u0435\u0442\u0438 (Support VectorMachines, SVM) (Cortes, Vapnik, 1995; Vapnik\u00a0et al<\/i>., 1997)
\n— XGBoost (eXtreme Gradient Boosting, XGB) (Chen, Guestrin, 2016).\n
\n1) Obtaining data from GEE rasters for given points (result presented in a table with data selected for points from rasters included in the GEE collection);
\n2) Obtaining a classified raster for a given area and a set of points of presence and absence of a view (training points) using the RF and MaxEnt classifiers based on GEE (both classifiers allow, for a given area of interest, a set of training points and selected remote sensing products from GEE, to obtain a classified one with using appropriate GEE raster methods of the area of interest. It is possible to cross-validate the selected models and evaluate their predictive effectiveness);
\n3) Three different methods to stimulate population size:
\n3.1) \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044f \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0445 \u0442\u043e\u0447\u0435\u043a \u0432 \u0440\u0435\u0433\u0443\u043b\u044f\u0440\u043d\u043e\u0439 \u0441\u0435\u0442\u0438 — \u044d\u0432\u0440\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0439 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 \u043e \u0442\u043e\u0447\u043a\u0430\u0445 \u043f\u0440\u0438\u0441\u0443\u0442\u0441\u0442\u0432\u0438\u044f \u0432\u0438\u0434\u0430 \u0438 \u043e\u0431 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u043e\u0431\u043b\u0430\u0441\u0442\u044f\u0445 \u0433\u0435\u043d\u0435\u0440\u0438\u0440\u0443\u0435\u0442 \u0441\u043b\u0443\u0447\u0430\u0439\u043d\u044b\u0435 \u0442\u043e\u0447\u043a\u0438, \u0438\u043c\u0438\u0442\u0438\u0440\u0443\u044f \u0440\u0430\u0441\u0441\u0435\u043b\u0435\u043d\u0438\u0435 \u0432\u0438\u0434\u0430 \u043f\u043e \u043e\u0431\u0449\u0435\u0439 \u043e\u0431\u043b\u0430\u0441\u0442\u0438 \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u0430;
\n3.2) Distance — \u043c\u0435\u0442\u043e\u0434, \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u043d\u0430 \u043c\u043e\u0434\u0435\u043b\u0438 Distance Sampling (Thomas\u00a0et al<\/i>., 2010; Buckland\u00a0et al<\/i>., 2015; Miller\u00a0et al<\/i>., 2019), that accepts input of a file with the necessary variables for points and areas and displays detailed statistics as a result;
\n3.3) Simple site surveys using calculation of a weighted average indicator for species distribution density (Karyakin, 2004) with an calculation of asymmetric confidence interval (Ravkin, Chelintsev, 1990);
\n4) Estimation of nest survival based on the RMARK library (Laake, 2013). The survival calculation module includes processing of nest survival data using the nest method of the RMARK library, which can account for various variables in remote sensing data and infers the importance of variables for nest survival.\n
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