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This paper presents the results of estimating the population density and abundance of Ursus arctos (hereinafter – brown bear) in the Southern Forestry of the Central Forest State Nature Biosphere Reserve (CFNR), West of European Russia, in 2021 based on the Random Encounter Model (REM) based upon data obtained from camera traps. Methods for obtaining parameters necessary for building a model are demonstrated. A total of 7970 camera trap nights were worked out at 46 stations, and 502 independent trap events were obtained. The average relative abundance index (RAI) was 6.28 ± 1.59. The total average brown bear population density was 0.086 ± 0.034 individuals per 1 km2. The approximate estimated abundance was 18.98 ± 7.54 individuals. The coefficient of variation was 38%. Population density estimates had a pronounced seasonal dynamics. The minimum value was recorded for the period from 24 June to 23 July (individuals feeding on meadows and ants outside the CFNR core area), and the maximum for the period from 24 July to 22 August (brown bears feeding by berries in the CFNR core area). We found a strong significant correlation between brown bear population density and its relative abundance index (r = 0.81, p < 0.05). It was found that with an increase in the sampling period duration, the estimate of the population density noticeably decreases (r = -0.53, p < 0.05). Parameters of the average travel speed and activity level are a subject to the greatest variability, which determines the significant variability of the day range. In general, the method of population density estimation using REM is highly promising to carry out the brown bear population size estimation in forests and mountain forests, where visual estimations are difficult or impossible.

Estimating the population density of mammals has long been one of the problematic tasks of both fundamental population ecology and practical programmes for their conservation and management. The majority of methods for population density estimation using camera traps are focused on individually marked species. This review paper presents the theoretical and practical foundations of a method, Random Encounter Model (REM), used for estimating the population density of unmarked mammal species using camera traps. Based on an extensive analysis of the literature and our personal practical experience, we discussed the theory and practice for the application of this method, as well as its strengths and weaknesses. In this method, if we know parameters of the effective detection zone of a camera trap (radius and angle), and the length of the day range, it is possible to correct the trapping rate (i.e. the number of independent trap events per total number of camera traps-nights) in order to calculate the population density of species. The effective detection zone of a camera trap is determined through modelling using computer vision algorithms. The mammal day range is calculated based on its activity level and travel speed, taking into account behavioural patterns based on machine learning models. For REM, a random or systematic design for the camera trap placements should be used. If camera traps are installed against trails or roads, appropriate correction factors must be applied. The effectiveness and reliability of REM has been confirmed by many independent population density estimates, including capture-recapture analyses, visual transect counts, and scat counts. To date, the implementation of REM and its extensions is presented in the R programming environment. It has been established that the main difficulties in the use of the REM are technical imperfections of the camera traps themselves, the relatively large required number of their stations (at least 50 or more), as well as long calibration work. For all these difficulties, possible solutions are proposed. In conclusion, practical recommendations are provided for the use of REM in studies in Protected Areas.

The journal «Nature Conservation Research» (hereinafter – journal NCR) was founded in 2016. It is published by the Fund for Support and Development of Protected Areas «Bear Land». This review paper is aimed to characterise the first five years of the publishing activities of the journal NCR. We present the actual values of scientometric indicators of the journal NCR, and the main characteristics of the journal, interesting for authors (e.g. peer-review time, rejection rate). To characterise citation parameters of the journal NCR, we have used articles published in 2016–2020. For other characteristics, we have considered a period of 2016–2021. In addition, we have considered some aspects of the recognition of the journal NCR by the authors who have submitted manuscripts from 22.09.2020 to 14.04.2022. As a result of the journal NCR's development, during 2016–2021, it was included in the databases of the Russian Index of Science Citation (RISC) (in 2016), Directory of Open Access Journals (in 2016), Emerging Sources Citation Index within Web of Science Core Collection (WoS CC) (in 2017), Scopus (in 2018), as well as in the list of peer-reviewed journals accepted by the Russian Higher Attestation Commission (in 2018). To date, the journal NCR is classified to the 2nd quartile (Q2) according to values of CiteScore2021 = 2.9 (Scopus) and SJR2021 = 0.362 (Scopus or Scimago Journal Ranking), and to the 3rd quartile (Q3) according to the value of JCI2021 = 0.36 in Journal Citation Report (Clarivate Analytics). The Impact factor RISC2021 is 2.969. In late 2021, the mean peer-review time, i.e. the time from submission to the acceptance decision, was 20.9 ± 1.5 (min–max: 5.4–51.7) weeks, and the rejection rate was 57.8%, where the main part of manuscripts has been rejected without peer-review starting (desk rejection). The main reasons for desk rejection were predominantly the inconsistency of the manuscript with the research scope or the main requirements of the journal NCR. The highest number of articles published in 2016–2020, is devoted to mammals, vascular plants, and insects. However, the highest number of citations was received by articles devoted to the IUCN Red List assessment, wildfires, and insects. Researchers from 53 organisations, managing Protected Areas in Russia, Hungary, Philippines, and Morocco, have published articles in the journal NCR in 2016–2020. In the papers published in 2016–2020, 691 Protected Areas served as the study area. Authors have found the journal NCR predominantly based on recommendations by their colleagues, but the role of databases Scopus, WoS CC, and the research network ResearchGate is high, too. For the authors, the indexation of a journal in international databases (Scopus, WoS CC), free publication, and open access to the content play a paramount role for submitting a manuscript to the journal NCR. The majority of the authors read previously published articles before preparing and submitting a manuscript to the journal NCR. About 25% of the authors permanently follow the content of papers published in the journal NCR. The full main text in English is available in the Appendix at the end of this review paper.

The article presents the results of ecological niche and habitat suitability modelling of Ursus arctos, performed in the Central Forest State Nature Reserve and its buffer zone (Tver region, Russia), using the GNESFA factor analysis method. Briefly discussed are the basic rules for performing such a study, approaches and modelling techniques. The ecological niche was modelled using FANTER, ENFA and MADIFA. The habitat suitability was modelled using Mahalanobis distances analysis. As ecogeographical variables, vegetation indices, morphometric characteristics of the relief, proximity rasters and types of landscape cover were used. The presence points of the species were recorded on permanent routes using a GPS navigator during 2008–2018. As a final result, six models with a different combination of input data (presence points and environmental variables) were selected. As a set of independent test data, we used U. arctos' presence points recorded by the forest rangers and research staff. The scenarios of the influence of utilisation weights on presence points and multicollinearity on variables are separately modelled. The global marginality of the best model was 1.17, while the tolerance was 0.69. The distance to anthropogenic food sources, the NDVI and the projective cover of grasslands made the highest contribution along the marginality axis. In the first three axes of specialisation, coniferous forests, Sphagnum pine (Pinus sylvestris) forests, and the distance to the rivers made the largest contribution. AUC values ranged from 0.66 to 0.73. Values of the continuous Boyce index were from 0.85 to 0.93. The area of the partially violated buffer zone turned out to be more suitable for U. arctos than the inner area of the Central Forest State Nature Reserve. Utilisation weights and the number of ecogeographical variables had a significant impact on the results of all analyses.

On 30 September – 4 October 2019, the First school-seminar on camera traps in European Russia was held in the «Bryansk Forest» State Nature Reserve (Russia). Thirty-five specialists from Protected Areas, institutes of RAS, universities, NGOs, and commercial firms participated. During the lectures, workshops, field lessons and discussions, various issues of using camera traps for wildlife conservation were discussed. They included different topics, from choosing the correct camera traps to statistical analysis of the data obtained. This workshop served as a starting point to create a Russian-speaking organised community of camera trap users for wildlife conservation.

The article presents results of the brown bear habitat suitability and distribution modelling, conducted in the Central Forest Nature Reserve and its buffer zone (West-European Russia, Tver region) using the MaxEnt approach. The basic rules for performing such a study, approaches and modelling techniques were reviewed briefly. Vegetation indices, morphometric characteristics of the relief, proximity rasters and land cover types were used as predictors. The occurrence points of the studied species were recorded on permanent routes using a GPS navigator during 2008–2018. Eight models with different combinations of input data (occurrence points and environment parametres) were chosen as final set. We used two main modelling approaches. In the first approach, for modelling we used only occurrence points reflecting the relationships of the species with the habitat (feeding places mainly). On the basis of them, habitat suitability models were built. In the second approach, we used all occurrence points of the studied species. On the basis of them, distribution models were built. The set of brown bear occurrence points registered by forest rangers and researchers was used as an independent test data set. The scenarios of the influence of anthropogenic food sources (abandoned apple orchards and oat fields) were modelled separately. The obtained AUCtest values ranged from 0.61 to 0.73. The maximum TSStest was 0.50. The continuous Boyce index ranged from 0.63 to 0.99. The models correctly recognised from 68% to 82% of independent points. The predictor of anthropogenic food sources largely contributed to all models, where it was presented, and highly distorted the overall picture of suitability and distribution. In other cases, grasslands, NDVI, and young deciduous forests had the highest contribution. In the study area, brown bears preferred grasslands, concentrated on moraine-kama ridges, which provided them with food throughout the wakeful period, as well as forest glades, scarce forests, young deciduous and mixed forests with dense undergrowth and nemoral spruce forests. The study area of the partially disturbed buffer zone was more suitable for brown bears than the intact areas of the Protected Area (66–67% and 51% of suitable habitats, respectively). We identified main omissions in the applied method of occurrence point's registration. This could lead to incorrect estimates of the contribution of some predictors (underestimations of boreal spruce forests, raised bogs and floodplain meadows).

This article is a continuation of the review of software for storing, processing and analysing data from camera traps. The review includes the latest software for processing data from camera traps that have appeared in recent years, as well as some already outdated solutions. For the first time, the topics of work of such software with video files and GIS are highlighted in Russian. We provide detailed descriptions of such thematic software as SpeedyMouse, TrophyRoom, ViXeN, camtrapR, CPW Photo Warehouse, Wild.ID, Camelot and ZSL CTAP. We briefly reviewed online services for camera trap data processing and web applications: eMammal, Snapshot Serengeti, Agouti, Western Shield, TRAPPER, as well as the global data repository for camera traps data – Wildlife Insights. The possibilities of working with camera traps in GIS are separately considered: e.g. plugins for QGIS (Geotag and Import Photos and eVis), as well as the GeoTagged Photos to Points tool for ArcGIS. Many advantages and disadvantages of the presented solutions, as well as some methods of working with them, are analysed. On the example of own studies in the Central Forest Nature Biosphere Reserve (Russia) a comparative analysis of various programs and approaches in the use of camera traps as a method of zoological studies was conducted. In addition, general recommendations were given on working with them for other Protected Areas. We identified the key characteristics of the programs for working with camera traps that are worth paying attention to when choosing a particular solution. For all of these software packets, there are hyperlinks for their free download, as well as hyperlinks to user manuals and tutorials. The purpose of the article was not only to identify the most optimal (from author's viewpoint) tools for working with the data of camera traps, but also to acquaint readers with the diversity of such means in world practice.

Here are presented the results of the analysis of daily activity patterns obtained from the data of camera traps for five large mammals (elk Alces alces, wild boar Sus scrofa, brown bear Ursus arctos, grey wolf Canis lupus, Eurasian lynx Lynx lynx) and three medium ones (European badger Meles meles, raccoon dog Nyctereutes procyonoides, mountain hare Lepus timidus) for the territory of the Central Forest Nature Reserve, Valdai Upland, Russia. Data were collected in the period 2010–2017 and the trap effort was 30 158 camera days from 21 locations. Most of the mammals surveyed showed activity at night and twilight hours (71% of the pictures). The hare was most active among all and dominant at night. In many respects it is similar to the activity of a raccoon dog, which type can be defined as nocturnal too. Unlike a hare, a raccoon dog has a weak peak in the daytime and less activity in the night. Badgers movements are confined to the twilight and nighttime. The share of nocturnal activity of large ungulates such as elk and wild boar was approximately the same and amounted to about 45% of all registrations. The wild boar is slightly more active during the day and in the evening and is not active at all in the morning. The elk is active in the morning, and in the daytime and to a lesser extent in the evening. The lynx and the bear have similar cathemeral activity patterns: almost half of all their meetings occurred at daylight hours and only slightly – less than 40% – at night. The brown bear had the maximum number of registrations in the daytime among all the studied species. Despite the fact that the main object of lynx feeding in the reserve is the hare, there was no high degree of overlap between them (γˆ = 0.75). In the group of large carnivores, the wolf was noticeably distinguished, more than half of its registrations were at night, and a third – on daytime. Daily activities of the wolf and its main prey elk showed a large overlap (γˆ = 0.89). The seasonal variations of daily activity of all species were also shown. According to the results of factor analysis, each of the studied species was divided into one of three separate groups. The first group included species with a tendency to nocturnal activity (wolf, elk, hare, badger, and raccoon dog), the second group – cathemeral animals (bear and lynx). In the third group was only the wild boar, whose activity was associated with the evening hours. This is the first long-term continuous camera trap survey in Russia and it provides detailed daily activity patterns for multiple large and medium-sized sympatric mammals.

An overview of the main problems in collection, storage, processing and analysis of data obtained by camera traps is conducted. Basing on reviewed national papers related to this theme, the conclusion is drawn that there is need for a detailed description of methodological approaches and tools for data processing in national studies. The most common software among foreign researchers for camera trap data processing in biodiversity studies and conservation projects is presented. An overview of thematic software such as Photospread, Aardwolf, CameraTrapManager, MapView, CameraBase and DeskTEAM is provided. Advantages and disadvantages, as well as working methods are detailed in the study. The principles of image metadata storage, methods of extraction and processing are considered. Comparative analysis of the different software and approaches to use camera traps as a method of zoological research are provided. General recommendations regarding their use in protected areas are provided, illustrated by studies conducted in the Central Forest Reserve. The key characteristics of software for camera traps that should be considered when choosing a specific solution are identified. Primary characteristics are the ability to import all image metadata, quick and easy tagging, export to the majority of formats. Wishes to improve and discuss methodological approaches among national researchers are expressed. Despite a large variety of available technical means, the need to develop national software for working with camera trap data is separately noted. All listed software items are provided with download hyperlinks, and user's manual hyperlinks.