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Brown bear‐caused human injuries and fatalities in Russia are linked to human encroachment

Brown bear‐caused human injuries and fatalities in Russia are linked to human encroachment IntroductionLarge carnivore attacks on people and livestock fuel antipathy toward apex predators (Woodroffe et al. 2005; Penteriani et al. 2016) and challenge conservation management (Dickman & Hazzah, 2016). Even though carnivore‐caused attacks are very rare, compared to incidents caused by other wildlife, attacks have increased worldwide, and bears are among the carnivores that most often attack humans (Can et al. 2014; Bombieri et al. 2019). In North America and Eurasia, the frequency of attacks has been associated with increasing bear populations, bear food conditioning and human disregard of safety rules in bear country (Can et al. 2014; Penteriani et al. 2016; Støen et al. 2018). Environmental and landscape‐related features also explain patterns of large carnivore attacks (Bombieri et al. 2018). Habitat destruction is one of the most serious threats to biodiversity and also fuels human–wildlife conflict. In Africa, for instance, land conversion to agriculture was positively associated with an increase in conflict, including attacks on humans (Mukeka et al. 2019). In turn, retaliatory killing in response to human–wildlife conflicts is an important mortality 201 factor for large carnivores (Treves & Karanth, 2003; Lennox et al. 2018). Food availability has also been associated with annual variation in human–carnivore conflict, such as in the Yellowstone Ecosystem, USA, where brown bears (Ursus arctos) move closer to human habitation and experience higher human‐caused mortality during years of poor crops of whitebark pine (Pinus albicaulis) seeds (Mattson et al. 1992). Thus, carnivore attacks on humans are costly in terms of both human safety and carnivore conservation, highlighting the need for a better understanding of this issue to inform conservation and management (Penteriani et al. 2016; Støen et al. 2018; Bombieri et al. 2019).Russia contains the world’s largest brown bear population (Morrison et al. 2007; McLellan et al. 2017), mostly in Asian Russia (Siberia and the Far East) (Komissarov & Gubar, 2013). However, brown bears have not been studied to the same extent in Russia as elsewhere (Ritchie et al. 2012; Ripple et al. 2014). As of 2019, just 4.3% of the published papers on brown bears worldwide (n = 4820, based on a search in ISI, the Web of Science, on 26 September 2019) originated from Russia, even though it holds ~50% of the world’s population (Kudrenko et al. 2020). Nevertheless, Russia has one of the highest recorded number of brown bear attacks on humans worldwide and recent studies have highlighted the need of further research to understand explanatory factors and inform management (Bombieri et al. 2019; Kudrenko et al. 2020).Forest loss and degradation, due to fire and timber harvesting, has occurred in Russia at large scales for a long time (Hansen et al. 2013), especially in European Russia, but permanent anthropogenic alteration of the forest in the vast Asian Russia is a relatively new phenomenon. For instance, the density of paved roads there has rocketed since 1991 (Federal State Statistics Service (FSSS), 2019) (Fig. 1), favoring human access, disturbance and forest fragmentation, with potential alteration of ecosystem services (Haddad et al. 2015).1FigureChanges in paved road density (km/1000 km2) in Russian administrative units between 2001–2009 (a) and 2010‐2016 (b).In this scenario and to better understand the causes of temporal and spatial patterns of bear‐caused human casualties in Russia, we hypothesized that casualties would be related to both bear factors and to human encroachment of bear habitat (Table 1). In particular, we pose that (1) casualties are positively related to brown bear population size (Penteriani et al. 2016; Bombieri et al. 2019); (2) increasing road density leads to more casualties (Penteriani et al. 2018); (3) casualties occur more frequently within the range of the edible seed‐producing Siberian pine (Pinus sibirica) (Mattson et al. 1992), because within Siberian pine range, seeds of this species provide a crucial autumn food resource for brown bears and other taiga species (Vaisfeld & Chestin, 1993) and are harvested also by humans, causing spatial overlap for a critical, shared resource (Danilov et al. 2016); and (4) trends in bear incidents are influenced by changes in road density, forest cover and degraded forest area, which includes forest loss to diseases, droughts, pests, logging and fires (many of the latter being started by people) (Agency and of Geodetics and Cartography (Roscartography), 2007a; Unified Interdepartmental Information and Statistical System (EMISS), 2018a). In our study, we investigated whether there may be a link between habitat degradation and human–wildlife conflict at a large spatial and temporal scale, that is, across Russia and over two decades. Beyond our specific hypotheses and target species, disentangling links between human disturbance and wildlife is crucial to inform both conservation of multiple species and management of human activities affecting them directly and indirectly (e.g. via habitat degradation).1TableDescription of variables in candidate models of bear incident occurrence in European Russia, Siberia, and the Far East during 2001‐2018VariableCategoryDescriptionBear populationBearEstimated annual brown bear population calculated for each geographic region (European Russia, Siberia and the Far East) based on population numbers in each administrative unit (oblast, krai, okrug) within these geographic regionsHuman populationHumanAnnual human population calculated for each geographic region (European Russia, Siberia and the Far East)Siberian pineEnvironmentalThe presence/absence of Siberian pine stands based on the incident coordinatesForest area degradedEnvironmentalForest area degraded because of fires or other factors (such as diseases, pests, logging) per year calculated for each geographic region based on the data for each administrative unit within these geographic regionsForest coverEnvironmentalPercentage of land covered by forests within each geographic areaPaved road densityEnvironmentalAverage paved road density (km/1000 km2) within each geographic region per annum based on the annual paved road densities for each administrative unit within the geographic regionsMaterials and methodsWe modeled the association between the annual number of bear‐caused casualties (injuries plus fatalities) and paved road density, human population, area of degraded forest, as well as the regional presence/absence of Siberian pine separately for European Russia, Siberia and the Far East. We did not include conflicts without injury, as many/most of them go underreported. Data about casualties included a long‐term dataset compiled by Russian scientists, scientific publications on human–bear conflicts in Russia and Russian media reports accessed over the Internet. We used the methods of Smith & Herrero (2018) and accepted all collected reports as true, if they included a minimum amount of information, because we were not able to interview involved people or police reports, as in some studies at smaller spatial scales (Smith & Herrero, 2018; Støen et al. 2018).We conducted the analysis at the regional scale. First, we collected the data for each administrative unit (oblast, krai, republic) and then categorized them into three regions (European Russia, Siberia and the Far East) based on climatic, ecological features and human footprint. For each region, we used the annual road density, bear population, area of degraded forest, human population, etc. Annual data on forest areas burned and degraded due to other causes (fires, forest diseases, droughts, pests and logging) were obtained for every administrative unit since 2000 using MODIS satellite active fire and land cover data at 230‐m resolution (Bartalev et al. 2013, 2015, 2016). Then we calculated the cumulative degraded area within each region annually. We omitted percentage of forest area from the final models, because of their insignificant annual variation throughout our study period and because the dataset included ground‐based and not satellite‐based data (Unified Interdepartmental Information and Statistical System (EMISS), 2018b, 2018c). We assigned each attack to whether it occurred within or outside the Siberian pine range.Annual brown bear population estimates were obtained for administrative units for the periods 2001–2007 (Gubar, 2007), 2008–2013 (Komissarov & Gubar, 2013) and 2014–2018 (Matveev, 2018; Table S4 in Kudrenko 2018). Bear‐monitoring methods in these studies included annual surveys on established plots and oat fields, and written surveys completed by hunters. This was the official data from the state authority responsible for wildlife monitoring (‘Centrokhotkontrol’) and other sources that referred to local authorities responsible for the management of hunting species. We could not check the reliability of these estimates. Nevertheless, they are the only available data at the scale of administrative units, and they are likely representative of bear population trends, which were more relevant for our analyses than specific estimates in a given year. We then calculated the approximate annual bear population (by adding annual bear populations in administrative units for each region), human population and road density (density of paved roads in km/1000 km2) for the regions. Human population and road density (density of paved roads in km/1000 km2 within an administrative unit) for the regions were obtained using the same approach. Datasets on unpaved roads for Russia are not comprehensive and complete for the entire study period (Unified Interdepartmental Information and Statistical System (EMISS), 2020). Moreover, the presence and changes in unpaved road network might not always be included in official maps/datasets. Paved roads, however, were constructed by the state and changes in their density were available (Federal State Statistics Service (FSSS), 2019) (Fig. 1). Human population remained stable during most of the study period (Federal State Statistic Service (FSSS), 2020); we included it to check its effect on the incident occurrence.Statistical analysesWe used generalized linear models to test whether changes in human population size, bear population size, paved road density, annual forest area degraded (a proxy of fires, forest diseases, pests and logging) and the presence/absence of Siberian pine were related to the annual number of bear‐caused casualties (i.e. injuries plus fatalities) in Russia, 2001–2018. The response variable was the annual number of casualties per region (European Russia, Siberia, the Far East). Paved road density and estimated brown bear population showed continuous increases and were correlated in all three regions: r = 0.97 (European Russia), r = 0.95 (Siberia), r = 0.7 (Far East) and inclusion of both variables caused multicollinearity in models for European Russia (VIFbears = 17.32, VIFroads = 17.94) and Siberia (VIFbears = 10.18, VIFroads = 10.003). Therefore, we included candidate models with both variables and separate sets of models with either bear population or road density (Table 2). We scaled the variables by dividing each numerical variable by one standard deviation (Zuur et al. 2007).2TableResults from generalized linear models (GLM) using Poisson link function explaining the annual number of people injured/killed by brown bears in European Russia, Siberia, and Far East 2001‐2018. β = parameter estimates, LL=lower limit of the 95% confidence interval, UL=upper limit of the 95% confidence interval, φ= dispersion parameter. The parameters within each model whose 95% confidence interval did not include 0, i.e., the positive or negative direction of effect of those parameters on the response was clear, are highlighted in bold lettersModel structureβLLULAICcDeltaWeighted AICcφEuropean RussiaIncidents ~ forest degraded + Siberian pine + bear population + human population84.30.000.671.39(Intercept)−0.07−1.440.6Parameter: forest degraded−0.23−0.930.18Parameter: Siberian pine−1.61−2.59−0.8Parameter: bear population0.90.451.62Parameter: human population−1.3−4.520.04Incidents ~ forest degraded + Siberian pine + road density + human population87.22.900.158(Intercept)−0.08−1.20.65Parameter: forest degraded−0.23−0.970.21Parameter: Siberian pine−1.6−2.59−0.8Parameter: road density0.780.371.43Parameter: human population−1.01−4.080.04Incidents ~ Siberian pine + bear population + human population88.64.270.079(Intercept)0.04−1.230.64Parameter: Siberian pine−1.64−2.54−0.89Parameter: bear population0.960.541.66Parameter: human population−1.08−4.020.08Incidents ~ Siberian pine + bear population89.14.750.062(Intercept)0.35−0.110.75Parameter: Siberian pine−1.64−2.53−0.89Parameter: bear population0.840.511.2Incidents ~ Siberian pine + road density + human population91.97.590.015(Intercept)0.19−0.870.69Parameter: Siberian pine−1.64−2.54−0.89Parameter: road density0.850.471.44Parameter: human population−0.82−3.310.09Incidents ~ Siberian pine + road density92.27.840.013(Intercept)0.39−0.060.78Parameter: Siberian pine−1.64−2.54−0.89Parameter: road density0.790.461.15Incidents ~ bear population108.123.870.00(Intercept)−0.16−0.610.21Parameter: bear population0.840.511.2Incidents ~ road density111.226.870.00(Intercept)−0.12−0.550.24Parameter: road density0.790.461.15Incidents ~ Siberian pine113.629.260.00(Intercept)0.690.351.00Parameter: Siberian pine−1.64−2.54−0.89Null model132.848.420.00SiberiaIncidents ~ forest degraded + Siberian pine + road density + human population119.70.000.7961.32(Intercept)−0.43−1.050.08Parameter: forest degraded0.01−0.230.22Parameter: Siberian pine1.91.362.53Parameter: road density0.690.450.94Parameter: human population−0.61−1.26−0.48Incidents ~ forest degraded + Siberian pine + bear population + human population122.62.900.187(Intercept)−0.46−1.080.06Parameter: forest degraded0.034−0.20.25Parameter: Siberian pine1.91.362.53Parameter: bear population0.680.440.93Parameter: human population−0.41−1.050.16Incidents ~ bear population + Siberian pine128.89.040.009(Intercept)−0.49−1.080.02Parameter: bear population1.861.332.47Parameter: Siberian pine0.710.490.94Incidents ~ Siberian pine + bear population +human population129.710.000.005(Intercept)−0.48−1.070.02Parameter: Siberian pine1.861.332.47Parameter: bear population0.750.520.98Parameter: human population0.14−0.070.4Incidents ~ Siberian pine + road density132.212.420.002(Intercept)−0.46−1.050.04Parameter: Siberian pine1.861.332.47Parameter: road density0.660.450.89Incidents ~ Siberian pine + road density + human population132.212.500.002(Intercept)−0.47−1.060.03Parameter: Siberian pine1.861.332.47Parameter: road density0.740.510.98Parameter: human population0.17−0.040.4Incidents ~ Siberian pine170.750.950.00(Intercept)−0.25−0.830.23Parameter: Siberian pine1.861.332.47Incidents ~ bear population188.468.650.00(Intercept)0.830.571.05Parameter: bear population0.710.490.94Incidents ~ road density191.872.030.00(Intercept)0.850.61.07Parameter: road density0.660.450.89Far EastIncidents ~ forest degraded + human population235.30.000.4164.93(Intercept)0.730.411.01Parameter: forest degraded−0.32−0.56−0.1Parameter: human population−1.14−1.75−0.59Incidents ~ forest degraded + road density + human population235.50.210.374(Intercept)0.880.521.2Parameter: forest degraded−0.27−0.52−0.04Parameter: road density0.26−0.070.61Parameter: human population−0.53−1.510.38Incidents ~ forest degraded + bear population + road density + human population236.71.370.210(Intercept)0.770.371.13Parameter: forest degraded−0.28−0.54−0.05Parameter: bear population−0.210.540.12Parameter: road density0.27−0.060.6Parameter: human population−0.92−2.010.17Incidents ~ forest degraded250.615.350.00(Intercept)1.070.861.27Parameter: forest degraded−0.32−0.56−0.10Incidents ~ road density254.719.400.00(Intercept)1.050.541.47Parameter: road density0.460.050.9Incidents ~ bear population269.534.170.00(Intercept)1.110.591.54Parameter: bear population0.3−0.160.79Null model276.841.540.00Incidents ~ human population279.043.740.00(Intercept)1.150.961.33Parameter: human population−0.02−0.230.15We selected the most parsimonious model, based on the corrected Akaike’s information criteria (AICc), assuming that models with ∆AICc <2 were equivalent (Burnham & Anderson, 2002), and interpreted the importance of parameters retained in final models using 95% confidence intervals (Zuur et al. 2009); that is, we examined whether 95% confidence intervals overlapped 0 to determine if variables retained in top models were significant and to interpret the direction of their effects on the response variables (annual number of incidents). If it overlapped 0, the direction of a given effect on the response variable was considered unclear. We tested the models for overdispersion and corrected the best models with dispersion parameters >1 using a quasi‐Poisson link function (Zuur et al. 2009) (Table 2, Table S2). The final interpretation of model outcomes was based on the Poisson versions (Table S1). Statistical analyses and data visualization were conducted using RStudio version 3.6.1 (R Core Team, 2019), QGIS (QGIS Development Team, 2019) and the open‐source web tool (Datawrapper, 2018).ResultsIn 2001–2018, brown bears injured at least 178 and killed 132 people in Russia, with most (82%) of the casualties occurring in Asian Russia (n = 264, χ2 = 34.98, P < 0.001), that is, in Siberia and Far East, compared to European Russia (Fig. 2). Most victims were gathering wild resources (22%) and hiking (17%), although bears also injured/killed people in human settlements (16%), or while working outdoors (13%), fishing (10%) and hunting (10%). Affected hunters were unequally distributed between the regions, with 69% of the cases in Siberia (n = 32, χ2 = 18.25, P < 0.001). Bear hunters were rarely injured/killed (n = 2 in European Russia and n = 5 in Asian Russia), yet this might be due to underreported cases related to this particular activity. Casualties were positively associated with the size of the brown bear population and negatively with Siberian pine presence in European Russia, where human population density is very low within the very limited range of Siberian pine and commercial seed gathering is less (Fig. 3, Table 2 and Table S1).2FigureSpatial distribution of brown bear attacks on people during 2001–2018 in Russia, divided into three main geographical regions – European Russia [4 350 626 km2; brown bear population ~ 72 165 (2018)], Siberia [9 917 620 km2; brown bear population ~ 91 700 (2018)], and the Far East [3 112 700 km2; brown bear population ~ 63 000 (2018)].3FigureSpatial distribution of brown bear attacks during 2001‐2018 within (yellow‐green color) and outside the Siberian pine (Pinus sibirica) range in Russia (Malyshev et al. 2008).However, in Siberia, casualties were not related to bear numbers, but were positively associated both with road density and the presence of Siberian pine. In Siberia, every additional kilometer/1000 km2 of road density led to an increase in the casualty occurrence of 0.69 annually (95% CI 0.45, 0.94). During 2010–2018, road density in Siberia and the Far East increased unevenly in the administrative units from less than 1 km to up to 17 km per year. In Siberia, the chance of a casualty was predicted to rise by 1.9 times (95% CI 1.36, 2.53) with the presence of Siberian pine, whereas in European Russia it was predicted to decrease by −1.61 (95% CI −2.59, −0.8). During 2010–2018, the bear population estimates in European Russia varied from 48 190 to 72 165 bears. With a bear population increase of 1000 individuals/year, the risk of a casualty was predicted to rise by 0.9 times (95% CI 0.45, 1.62). Our results were less clear in the Far East, where the annual number of attacks seemed to be related to area of degraded forest (forest burned and lost due to diseases, pests and logging) and changes in the human population size, but the 95% confidence intervals around the estimates of those variables included 0, giving no clear indication of the direction of the effect of those variables on the response (Table 2 and Table S1).DiscussionAt least 310 people were injured (57%) or killed (43%) by bears in 2001–2018 in Russia, with most casualties occurring in Asian Russia (~80%) and affecting people engaged in a variety of outdoor activities (80%). Both bear numbers and human encroachment were involved in the occurrence of casualties, but the importance of these factors varied across Russia. Bear numbers, which have significantly increased in European Russia (Komissarov & Gubar, 2013), were positively related to casualty occurrence in that part of the country. In Siberia, we found a correlation between the presence of Siberian pine, increasing road density and occurrence of casualties, even if Siberia has one the lowest road densities at a global scale (Ibisch et al. 2016; Wang et al. 2018).The negative effects of road construction on habitats and wildlife conservation is a major issue globally (Ibisch et al. 2016; Whittington et al. 2019), and Russia is no exception. Increasing road density allows increased human access to remote areas and causes forest fragmentation, a critical driver of human–wildlife conflicts. For instance, attacks on humans by tigers (Panthera tigris) and Asian elephants (Elephas maximus) were strongly associated with forest fragmentation in Nepal (Acharya et al. 2017). Roads alter brown bear dial activity patterns (Ordiz et al. 2014; Whittington et al. 2019), spatial habitat use (Bischof et al. 2017) and cause attractive sinks, where bears not only find preferred foods but also suffer high mortality (Penteriani et al. 2018; Lamb et al. 2020).The expanding network of paved roads in Russia (Fig. 1), especially during the last decade, has provided greater public access to remote areas within the Siberian pine range (Fig. 3). This increased access may have increased encounters between people and bears seeking the same resources, for example, edible seeds and berries. Seeds provide nutritious food for bears before denning (Vaisfeld & Chestin, 1993) and substantial seasonal income for locals who gather pine seeds commercially for sale and export (Danilov et al. 2016), resulting in more bear‐inflicted injuries and deaths in Siberia. The Siberian pine range also overlaps with highly productive areas for wild berry species (Agency and of Geodetics and Cartography (Roscartography), 2007b), another essential fall food for bears that is also harvested by people (Danilov et al. 2016). This might further explain the high frequency of bear attacks on people gathering wild resources in 2001–2018, whereas in earlier decades, hunting and professional outdoor activities had been the most common activities related to casualties (Kudrenko et al. 2020). The apparent link between the number and location of casualties and road density highlights the importance for wildlife managers to reduce human access into areas with resources for both bears and humans, when possible, by closing or removing appropriate unpaved roads. Managers should also consider promoting the use of bear deterrent spray, which has proved to be effective in North America (Smith et al. 2008), and to initiate public education campaigns on carnivore behavior. For instance, guidelines for human behavior in bear country should recommend not entering the forest alone (Penteriani et al. 2016), avoiding dense vegetation (Ordiz et al. 2013) and keeping dogs on a leash (Penteriani et al. 2016; Støen et al. 2018). These preventive actions should increase the safety of humans exploiting Siberian pine seeds and conducting other outdoor activities, thus favoring carnivore conservation.Bears inhabiting human‐dominated landscapes display multiple behavioral responses and adaptations (Morales‐González et al. 2020). For instance, a variety of human activities trigger bears (and many other species) to be more nocturnal in areas with higher human encroachment than in remoter areas (Gaynor et al. 2018). Bears likely have learned to coexist better with people in highly humanized regions (Komissarov & Gubar, 2013; Zarzo‐Arias et al. 2018), compared to areas with low human density, as Asian Russia. This may be a reason for the higher number of casualties in Asian Russia; at the worldwide scale, bear attacks are more frequent in areas where human density is lower and bear density higher (Bombieri et al. 2019), a pattern supported by our study, where many more attacks occurred in Asian Russia than in European Russia.As pointed out earlier (Kudrenko et al. 2020), the limitations of our research relate to the huge study area and necessarily coarse‐scale environment‐, bear‐ and human‐related variables. Nevertheless, our results demonstrated the link between human‐ and bear‐related variables and the frequency of bear attacks thus reinforcing the findings of previous studies at local (Smith & Herrero, 2018; Støen et al. 2018) and worldwide (Bombieri et al. 2019) scales. Furthermore, our study also revealed a pervasive association between habitat degradation (with increasing road density as its proxy) and injurious encounters between large carnivores and people, reinforcing recent results for other species elsewhere (Acharya et al. 2017). Human transformations of landscapes, in conjunction with climate change, also a threat for bears (Can et al. 2014; Penteriani et al. 2019), precipitated the decline of brown bear populations in the past (Albrecht et al. 2017). Yet, the ultimate cause of carnivore decline, bears included, is human persecution (Morrison et al. 2007; Wolf & Ripple, 2017), which could be continuing to fuel the most dramatic form of human–wildlife conflict nowadays. During our study period (2001–2018), at least 81 bears involved in casualties were killed and 3 wounded (43% of 196 casualties with reported outcome for bears). We did not access data to test any specific hypothesis related to the trends in salmon (Oncorhynchus spp.) numbers in the Russian Far East. Yet, we suggest that investigating the changes in salmon numbers or annual catches of salmonids would contribute to better understanding of the role of salmonids in bear seasonal diet in the coastal Far East, as has been conducted in Japan (Shirane et al. 2021), and how varying salmon abundance may potentially result in more frequent conflicts with people.In European Russia and elsewhere in Western Europe, anthropogenic deforestation and intensive hunting caused megafaunal extinctions and forest habitat loss already by the 19th century (Kaplan et al. 2009; Albrecht et al. 2017). Asian Russia, however, still contains complex large carnivore assemblages, but they are threatened by habitat degradation, for example, by poorly regulated, intensive timber extraction (Food & Agriculture Organization (FAO), 2019), and road construction (FSSS 2019). Therefore, it is crucial to mitigate the ecological influence of roads and other sources of human encroachment that, beyond causing habitat loss and fragmentation, fuel encounters with wildlife and thus potential conflict. This concern applies for the conservation of multiple species and their habitats, but may be especially urgent where extensive human development has not yet occurred.AcknowledgmentsSLB and LB were financially supported by Russian Scientific Foundation, project 19‐18‐00562. FS and SB were financially supported by Russian Scientific Foundation, project 19‐77‐30015.Conflict of interestThe authors declare no competing interests.Data availability statementOur dataset contains sensitive information about bear attacks on people (’human subject data’) and should not be made public easily.ReferencesAcharya, K.P., Paudel, P.K., Jnawali, S.R., Neupane, P.R. & Koehl, M. (2017). Can forest fragmentation and configuration work as indicators of human–wildlife conflict? 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Brown bear‐caused human injuries and fatalities in Russia are linked to human encroachment

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Copyright © 2022 The Zoological Society of London
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Abstract

IntroductionLarge carnivore attacks on people and livestock fuel antipathy toward apex predators (Woodroffe et al. 2005; Penteriani et al. 2016) and challenge conservation management (Dickman & Hazzah, 2016). Even though carnivore‐caused attacks are very rare, compared to incidents caused by other wildlife, attacks have increased worldwide, and bears are among the carnivores that most often attack humans (Can et al. 2014; Bombieri et al. 2019). In North America and Eurasia, the frequency of attacks has been associated with increasing bear populations, bear food conditioning and human disregard of safety rules in bear country (Can et al. 2014; Penteriani et al. 2016; Støen et al. 2018). Environmental and landscape‐related features also explain patterns of large carnivore attacks (Bombieri et al. 2018). Habitat destruction is one of the most serious threats to biodiversity and also fuels human–wildlife conflict. In Africa, for instance, land conversion to agriculture was positively associated with an increase in conflict, including attacks on humans (Mukeka et al. 2019). In turn, retaliatory killing in response to human–wildlife conflicts is an important mortality 201 factor for large carnivores (Treves & Karanth, 2003; Lennox et al. 2018). Food availability has also been associated with annual variation in human–carnivore conflict, such as in the Yellowstone Ecosystem, USA, where brown bears (Ursus arctos) move closer to human habitation and experience higher human‐caused mortality during years of poor crops of whitebark pine (Pinus albicaulis) seeds (Mattson et al. 1992). Thus, carnivore attacks on humans are costly in terms of both human safety and carnivore conservation, highlighting the need for a better understanding of this issue to inform conservation and management (Penteriani et al. 2016; Støen et al. 2018; Bombieri et al. 2019).Russia contains the world’s largest brown bear population (Morrison et al. 2007; McLellan et al. 2017), mostly in Asian Russia (Siberia and the Far East) (Komissarov & Gubar, 2013). However, brown bears have not been studied to the same extent in Russia as elsewhere (Ritchie et al. 2012; Ripple et al. 2014). As of 2019, just 4.3% of the published papers on brown bears worldwide (n = 4820, based on a search in ISI, the Web of Science, on 26 September 2019) originated from Russia, even though it holds ~50% of the world’s population (Kudrenko et al. 2020). Nevertheless, Russia has one of the highest recorded number of brown bear attacks on humans worldwide and recent studies have highlighted the need of further research to understand explanatory factors and inform management (Bombieri et al. 2019; Kudrenko et al. 2020).Forest loss and degradation, due to fire and timber harvesting, has occurred in Russia at large scales for a long time (Hansen et al. 2013), especially in European Russia, but permanent anthropogenic alteration of the forest in the vast Asian Russia is a relatively new phenomenon. For instance, the density of paved roads there has rocketed since 1991 (Federal State Statistics Service (FSSS), 2019) (Fig. 1), favoring human access, disturbance and forest fragmentation, with potential alteration of ecosystem services (Haddad et al. 2015).1FigureChanges in paved road density (km/1000 km2) in Russian administrative units between 2001–2009 (a) and 2010‐2016 (b).In this scenario and to better understand the causes of temporal and spatial patterns of bear‐caused human casualties in Russia, we hypothesized that casualties would be related to both bear factors and to human encroachment of bear habitat (Table 1). In particular, we pose that (1) casualties are positively related to brown bear population size (Penteriani et al. 2016; Bombieri et al. 2019); (2) increasing road density leads to more casualties (Penteriani et al. 2018); (3) casualties occur more frequently within the range of the edible seed‐producing Siberian pine (Pinus sibirica) (Mattson et al. 1992), because within Siberian pine range, seeds of this species provide a crucial autumn food resource for brown bears and other taiga species (Vaisfeld & Chestin, 1993) and are harvested also by humans, causing spatial overlap for a critical, shared resource (Danilov et al. 2016); and (4) trends in bear incidents are influenced by changes in road density, forest cover and degraded forest area, which includes forest loss to diseases, droughts, pests, logging and fires (many of the latter being started by people) (Agency and of Geodetics and Cartography (Roscartography), 2007a; Unified Interdepartmental Information and Statistical System (EMISS), 2018a). In our study, we investigated whether there may be a link between habitat degradation and human–wildlife conflict at a large spatial and temporal scale, that is, across Russia and over two decades. Beyond our specific hypotheses and target species, disentangling links between human disturbance and wildlife is crucial to inform both conservation of multiple species and management of human activities affecting them directly and indirectly (e.g. via habitat degradation).1TableDescription of variables in candidate models of bear incident occurrence in European Russia, Siberia, and the Far East during 2001‐2018VariableCategoryDescriptionBear populationBearEstimated annual brown bear population calculated for each geographic region (European Russia, Siberia and the Far East) based on population numbers in each administrative unit (oblast, krai, okrug) within these geographic regionsHuman populationHumanAnnual human population calculated for each geographic region (European Russia, Siberia and the Far East)Siberian pineEnvironmentalThe presence/absence of Siberian pine stands based on the incident coordinatesForest area degradedEnvironmentalForest area degraded because of fires or other factors (such as diseases, pests, logging) per year calculated for each geographic region based on the data for each administrative unit within these geographic regionsForest coverEnvironmentalPercentage of land covered by forests within each geographic areaPaved road densityEnvironmentalAverage paved road density (km/1000 km2) within each geographic region per annum based on the annual paved road densities for each administrative unit within the geographic regionsMaterials and methodsWe modeled the association between the annual number of bear‐caused casualties (injuries plus fatalities) and paved road density, human population, area of degraded forest, as well as the regional presence/absence of Siberian pine separately for European Russia, Siberia and the Far East. We did not include conflicts without injury, as many/most of them go underreported. Data about casualties included a long‐term dataset compiled by Russian scientists, scientific publications on human–bear conflicts in Russia and Russian media reports accessed over the Internet. We used the methods of Smith & Herrero (2018) and accepted all collected reports as true, if they included a minimum amount of information, because we were not able to interview involved people or police reports, as in some studies at smaller spatial scales (Smith & Herrero, 2018; Støen et al. 2018).We conducted the analysis at the regional scale. First, we collected the data for each administrative unit (oblast, krai, republic) and then categorized them into three regions (European Russia, Siberia and the Far East) based on climatic, ecological features and human footprint. For each region, we used the annual road density, bear population, area of degraded forest, human population, etc. Annual data on forest areas burned and degraded due to other causes (fires, forest diseases, droughts, pests and logging) were obtained for every administrative unit since 2000 using MODIS satellite active fire and land cover data at 230‐m resolution (Bartalev et al. 2013, 2015, 2016). Then we calculated the cumulative degraded area within each region annually. We omitted percentage of forest area from the final models, because of their insignificant annual variation throughout our study period and because the dataset included ground‐based and not satellite‐based data (Unified Interdepartmental Information and Statistical System (EMISS), 2018b, 2018c). We assigned each attack to whether it occurred within or outside the Siberian pine range.Annual brown bear population estimates were obtained for administrative units for the periods 2001–2007 (Gubar, 2007), 2008–2013 (Komissarov & Gubar, 2013) and 2014–2018 (Matveev, 2018; Table S4 in Kudrenko 2018). Bear‐monitoring methods in these studies included annual surveys on established plots and oat fields, and written surveys completed by hunters. This was the official data from the state authority responsible for wildlife monitoring (‘Centrokhotkontrol’) and other sources that referred to local authorities responsible for the management of hunting species. We could not check the reliability of these estimates. Nevertheless, they are the only available data at the scale of administrative units, and they are likely representative of bear population trends, which were more relevant for our analyses than specific estimates in a given year. We then calculated the approximate annual bear population (by adding annual bear populations in administrative units for each region), human population and road density (density of paved roads in km/1000 km2) for the regions. Human population and road density (density of paved roads in km/1000 km2 within an administrative unit) for the regions were obtained using the same approach. Datasets on unpaved roads for Russia are not comprehensive and complete for the entire study period (Unified Interdepartmental Information and Statistical System (EMISS), 2020). Moreover, the presence and changes in unpaved road network might not always be included in official maps/datasets. Paved roads, however, were constructed by the state and changes in their density were available (Federal State Statistics Service (FSSS), 2019) (Fig. 1). Human population remained stable during most of the study period (Federal State Statistic Service (FSSS), 2020); we included it to check its effect on the incident occurrence.Statistical analysesWe used generalized linear models to test whether changes in human population size, bear population size, paved road density, annual forest area degraded (a proxy of fires, forest diseases, pests and logging) and the presence/absence of Siberian pine were related to the annual number of bear‐caused casualties (i.e. injuries plus fatalities) in Russia, 2001–2018. The response variable was the annual number of casualties per region (European Russia, Siberia, the Far East). Paved road density and estimated brown bear population showed continuous increases and were correlated in all three regions: r = 0.97 (European Russia), r = 0.95 (Siberia), r = 0.7 (Far East) and inclusion of both variables caused multicollinearity in models for European Russia (VIFbears = 17.32, VIFroads = 17.94) and Siberia (VIFbears = 10.18, VIFroads = 10.003). Therefore, we included candidate models with both variables and separate sets of models with either bear population or road density (Table 2). We scaled the variables by dividing each numerical variable by one standard deviation (Zuur et al. 2007).2TableResults from generalized linear models (GLM) using Poisson link function explaining the annual number of people injured/killed by brown bears in European Russia, Siberia, and Far East 2001‐2018. β = parameter estimates, LL=lower limit of the 95% confidence interval, UL=upper limit of the 95% confidence interval, φ= dispersion parameter. The parameters within each model whose 95% confidence interval did not include 0, i.e., the positive or negative direction of effect of those parameters on the response was clear, are highlighted in bold lettersModel structureβLLULAICcDeltaWeighted AICcφEuropean RussiaIncidents ~ forest degraded + Siberian pine + bear population + human population84.30.000.671.39(Intercept)−0.07−1.440.6Parameter: forest degraded−0.23−0.930.18Parameter: Siberian pine−1.61−2.59−0.8Parameter: bear population0.90.451.62Parameter: human population−1.3−4.520.04Incidents ~ forest degraded + Siberian pine + road density + human population87.22.900.158(Intercept)−0.08−1.20.65Parameter: forest degraded−0.23−0.970.21Parameter: Siberian pine−1.6−2.59−0.8Parameter: road density0.780.371.43Parameter: human population−1.01−4.080.04Incidents ~ Siberian pine + bear population + human population88.64.270.079(Intercept)0.04−1.230.64Parameter: Siberian pine−1.64−2.54−0.89Parameter: bear population0.960.541.66Parameter: human population−1.08−4.020.08Incidents ~ Siberian pine + bear population89.14.750.062(Intercept)0.35−0.110.75Parameter: Siberian pine−1.64−2.53−0.89Parameter: bear population0.840.511.2Incidents ~ Siberian pine + road density + human population91.97.590.015(Intercept)0.19−0.870.69Parameter: Siberian pine−1.64−2.54−0.89Parameter: road density0.850.471.44Parameter: human population−0.82−3.310.09Incidents ~ Siberian pine + road density92.27.840.013(Intercept)0.39−0.060.78Parameter: Siberian pine−1.64−2.54−0.89Parameter: road density0.790.461.15Incidents ~ bear population108.123.870.00(Intercept)−0.16−0.610.21Parameter: bear population0.840.511.2Incidents ~ road density111.226.870.00(Intercept)−0.12−0.550.24Parameter: road density0.790.461.15Incidents ~ Siberian pine113.629.260.00(Intercept)0.690.351.00Parameter: Siberian pine−1.64−2.54−0.89Null model132.848.420.00SiberiaIncidents ~ forest degraded + Siberian pine + road density + human population119.70.000.7961.32(Intercept)−0.43−1.050.08Parameter: forest degraded0.01−0.230.22Parameter: Siberian pine1.91.362.53Parameter: road density0.690.450.94Parameter: human population−0.61−1.26−0.48Incidents ~ forest degraded + Siberian pine + bear population + human population122.62.900.187(Intercept)−0.46−1.080.06Parameter: forest degraded0.034−0.20.25Parameter: Siberian pine1.91.362.53Parameter: bear population0.680.440.93Parameter: human population−0.41−1.050.16Incidents ~ bear population + Siberian pine128.89.040.009(Intercept)−0.49−1.080.02Parameter: bear population1.861.332.47Parameter: Siberian pine0.710.490.94Incidents ~ Siberian pine + bear population +human population129.710.000.005(Intercept)−0.48−1.070.02Parameter: Siberian pine1.861.332.47Parameter: bear population0.750.520.98Parameter: human population0.14−0.070.4Incidents ~ Siberian pine + road density132.212.420.002(Intercept)−0.46−1.050.04Parameter: Siberian pine1.861.332.47Parameter: road density0.660.450.89Incidents ~ Siberian pine + road density + human population132.212.500.002(Intercept)−0.47−1.060.03Parameter: Siberian pine1.861.332.47Parameter: road density0.740.510.98Parameter: human population0.17−0.040.4Incidents ~ Siberian pine170.750.950.00(Intercept)−0.25−0.830.23Parameter: Siberian pine1.861.332.47Incidents ~ bear population188.468.650.00(Intercept)0.830.571.05Parameter: bear population0.710.490.94Incidents ~ road density191.872.030.00(Intercept)0.850.61.07Parameter: road density0.660.450.89Far EastIncidents ~ forest degraded + human population235.30.000.4164.93(Intercept)0.730.411.01Parameter: forest degraded−0.32−0.56−0.1Parameter: human population−1.14−1.75−0.59Incidents ~ forest degraded + road density + human population235.50.210.374(Intercept)0.880.521.2Parameter: forest degraded−0.27−0.52−0.04Parameter: road density0.26−0.070.61Parameter: human population−0.53−1.510.38Incidents ~ forest degraded + bear population + road density + human population236.71.370.210(Intercept)0.770.371.13Parameter: forest degraded−0.28−0.54−0.05Parameter: bear population−0.210.540.12Parameter: road density0.27−0.060.6Parameter: human population−0.92−2.010.17Incidents ~ forest degraded250.615.350.00(Intercept)1.070.861.27Parameter: forest degraded−0.32−0.56−0.10Incidents ~ road density254.719.400.00(Intercept)1.050.541.47Parameter: road density0.460.050.9Incidents ~ bear population269.534.170.00(Intercept)1.110.591.54Parameter: bear population0.3−0.160.79Null model276.841.540.00Incidents ~ human population279.043.740.00(Intercept)1.150.961.33Parameter: human population−0.02−0.230.15We selected the most parsimonious model, based on the corrected Akaike’s information criteria (AICc), assuming that models with ∆AICc <2 were equivalent (Burnham & Anderson, 2002), and interpreted the importance of parameters retained in final models using 95% confidence intervals (Zuur et al. 2009); that is, we examined whether 95% confidence intervals overlapped 0 to determine if variables retained in top models were significant and to interpret the direction of their effects on the response variables (annual number of incidents). If it overlapped 0, the direction of a given effect on the response variable was considered unclear. We tested the models for overdispersion and corrected the best models with dispersion parameters >1 using a quasi‐Poisson link function (Zuur et al. 2009) (Table 2, Table S2). The final interpretation of model outcomes was based on the Poisson versions (Table S1). Statistical analyses and data visualization were conducted using RStudio version 3.6.1 (R Core Team, 2019), QGIS (QGIS Development Team, 2019) and the open‐source web tool (Datawrapper, 2018).ResultsIn 2001–2018, brown bears injured at least 178 and killed 132 people in Russia, with most (82%) of the casualties occurring in Asian Russia (n = 264, χ2 = 34.98, P < 0.001), that is, in Siberia and Far East, compared to European Russia (Fig. 2). Most victims were gathering wild resources (22%) and hiking (17%), although bears also injured/killed people in human settlements (16%), or while working outdoors (13%), fishing (10%) and hunting (10%). Affected hunters were unequally distributed between the regions, with 69% of the cases in Siberia (n = 32, χ2 = 18.25, P < 0.001). Bear hunters were rarely injured/killed (n = 2 in European Russia and n = 5 in Asian Russia), yet this might be due to underreported cases related to this particular activity. Casualties were positively associated with the size of the brown bear population and negatively with Siberian pine presence in European Russia, where human population density is very low within the very limited range of Siberian pine and commercial seed gathering is less (Fig. 3, Table 2 and Table S1).2FigureSpatial distribution of brown bear attacks on people during 2001–2018 in Russia, divided into three main geographical regions – European Russia [4 350 626 km2; brown bear population ~ 72 165 (2018)], Siberia [9 917 620 km2; brown bear population ~ 91 700 (2018)], and the Far East [3 112 700 km2; brown bear population ~ 63 000 (2018)].3FigureSpatial distribution of brown bear attacks during 2001‐2018 within (yellow‐green color) and outside the Siberian pine (Pinus sibirica) range in Russia (Malyshev et al. 2008).However, in Siberia, casualties were not related to bear numbers, but were positively associated both with road density and the presence of Siberian pine. In Siberia, every additional kilometer/1000 km2 of road density led to an increase in the casualty occurrence of 0.69 annually (95% CI 0.45, 0.94). During 2010–2018, road density in Siberia and the Far East increased unevenly in the administrative units from less than 1 km to up to 17 km per year. In Siberia, the chance of a casualty was predicted to rise by 1.9 times (95% CI 1.36, 2.53) with the presence of Siberian pine, whereas in European Russia it was predicted to decrease by −1.61 (95% CI −2.59, −0.8). During 2010–2018, the bear population estimates in European Russia varied from 48 190 to 72 165 bears. With a bear population increase of 1000 individuals/year, the risk of a casualty was predicted to rise by 0.9 times (95% CI 0.45, 1.62). Our results were less clear in the Far East, where the annual number of attacks seemed to be related to area of degraded forest (forest burned and lost due to diseases, pests and logging) and changes in the human population size, but the 95% confidence intervals around the estimates of those variables included 0, giving no clear indication of the direction of the effect of those variables on the response (Table 2 and Table S1).DiscussionAt least 310 people were injured (57%) or killed (43%) by bears in 2001–2018 in Russia, with most casualties occurring in Asian Russia (~80%) and affecting people engaged in a variety of outdoor activities (80%). Both bear numbers and human encroachment were involved in the occurrence of casualties, but the importance of these factors varied across Russia. Bear numbers, which have significantly increased in European Russia (Komissarov & Gubar, 2013), were positively related to casualty occurrence in that part of the country. In Siberia, we found a correlation between the presence of Siberian pine, increasing road density and occurrence of casualties, even if Siberia has one the lowest road densities at a global scale (Ibisch et al. 2016; Wang et al. 2018).The negative effects of road construction on habitats and wildlife conservation is a major issue globally (Ibisch et al. 2016; Whittington et al. 2019), and Russia is no exception. Increasing road density allows increased human access to remote areas and causes forest fragmentation, a critical driver of human–wildlife conflicts. For instance, attacks on humans by tigers (Panthera tigris) and Asian elephants (Elephas maximus) were strongly associated with forest fragmentation in Nepal (Acharya et al. 2017). Roads alter brown bear dial activity patterns (Ordiz et al. 2014; Whittington et al. 2019), spatial habitat use (Bischof et al. 2017) and cause attractive sinks, where bears not only find preferred foods but also suffer high mortality (Penteriani et al. 2018; Lamb et al. 2020).The expanding network of paved roads in Russia (Fig. 1), especially during the last decade, has provided greater public access to remote areas within the Siberian pine range (Fig. 3). This increased access may have increased encounters between people and bears seeking the same resources, for example, edible seeds and berries. Seeds provide nutritious food for bears before denning (Vaisfeld & Chestin, 1993) and substantial seasonal income for locals who gather pine seeds commercially for sale and export (Danilov et al. 2016), resulting in more bear‐inflicted injuries and deaths in Siberia. The Siberian pine range also overlaps with highly productive areas for wild berry species (Agency and of Geodetics and Cartography (Roscartography), 2007b), another essential fall food for bears that is also harvested by people (Danilov et al. 2016). This might further explain the high frequency of bear attacks on people gathering wild resources in 2001–2018, whereas in earlier decades, hunting and professional outdoor activities had been the most common activities related to casualties (Kudrenko et al. 2020). The apparent link between the number and location of casualties and road density highlights the importance for wildlife managers to reduce human access into areas with resources for both bears and humans, when possible, by closing or removing appropriate unpaved roads. Managers should also consider promoting the use of bear deterrent spray, which has proved to be effective in North America (Smith et al. 2008), and to initiate public education campaigns on carnivore behavior. For instance, guidelines for human behavior in bear country should recommend not entering the forest alone (Penteriani et al. 2016), avoiding dense vegetation (Ordiz et al. 2013) and keeping dogs on a leash (Penteriani et al. 2016; Støen et al. 2018). These preventive actions should increase the safety of humans exploiting Siberian pine seeds and conducting other outdoor activities, thus favoring carnivore conservation.Bears inhabiting human‐dominated landscapes display multiple behavioral responses and adaptations (Morales‐González et al. 2020). For instance, a variety of human activities trigger bears (and many other species) to be more nocturnal in areas with higher human encroachment than in remoter areas (Gaynor et al. 2018). Bears likely have learned to coexist better with people in highly humanized regions (Komissarov & Gubar, 2013; Zarzo‐Arias et al. 2018), compared to areas with low human density, as Asian Russia. This may be a reason for the higher number of casualties in Asian Russia; at the worldwide scale, bear attacks are more frequent in areas where human density is lower and bear density higher (Bombieri et al. 2019), a pattern supported by our study, where many more attacks occurred in Asian Russia than in European Russia.As pointed out earlier (Kudrenko et al. 2020), the limitations of our research relate to the huge study area and necessarily coarse‐scale environment‐, bear‐ and human‐related variables. Nevertheless, our results demonstrated the link between human‐ and bear‐related variables and the frequency of bear attacks thus reinforcing the findings of previous studies at local (Smith & Herrero, 2018; Støen et al. 2018) and worldwide (Bombieri et al. 2019) scales. Furthermore, our study also revealed a pervasive association between habitat degradation (with increasing road density as its proxy) and injurious encounters between large carnivores and people, reinforcing recent results for other species elsewhere (Acharya et al. 2017). Human transformations of landscapes, in conjunction with climate change, also a threat for bears (Can et al. 2014; Penteriani et al. 2019), precipitated the decline of brown bear populations in the past (Albrecht et al. 2017). Yet, the ultimate cause of carnivore decline, bears included, is human persecution (Morrison et al. 2007; Wolf & Ripple, 2017), which could be continuing to fuel the most dramatic form of human–wildlife conflict nowadays. During our study period (2001–2018), at least 81 bears involved in casualties were killed and 3 wounded (43% of 196 casualties with reported outcome for bears). We did not access data to test any specific hypothesis related to the trends in salmon (Oncorhynchus spp.) numbers in the Russian Far East. Yet, we suggest that investigating the changes in salmon numbers or annual catches of salmonids would contribute to better understanding of the role of salmonids in bear seasonal diet in the coastal Far East, as has been conducted in Japan (Shirane et al. 2021), and how varying salmon abundance may potentially result in more frequent conflicts with people.In European Russia and elsewhere in Western Europe, anthropogenic deforestation and intensive hunting caused megafaunal extinctions and forest habitat loss already by the 19th century (Kaplan et al. 2009; Albrecht et al. 2017). Asian Russia, however, still contains complex large carnivore assemblages, but they are threatened by habitat degradation, for example, by poorly regulated, intensive timber extraction (Food & Agriculture Organization (FAO), 2019), and road construction (FSSS 2019). Therefore, it is crucial to mitigate the ecological influence of roads and other sources of human encroachment that, beyond causing habitat loss and fragmentation, fuel encounters with wildlife and thus potential conflict. This concern applies for the conservation of multiple species and their habitats, but may be especially urgent where extensive human development has not yet occurred.AcknowledgmentsSLB and LB were financially supported by Russian Scientific Foundation, project 19‐18‐00562. FS and SB were financially supported by Russian Scientific Foundation, project 19‐77‐30015.Conflict of interestThe authors declare no competing interests.Data availability statementOur dataset contains sensitive information about bear attacks on people (’human subject data’) and should not be made public easily.ReferencesAcharya, K.P., Paudel, P.K., Jnawali, S.R., Neupane, P.R. & Koehl, M. (2017). Can forest fragmentation and configuration work as indicators of human–wildlife conflict? 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Journal

Animal Conservation – Wiley

Published: Jun 1, 2022

Keywords: habitat degradation; human–bear conflict; Pinus sibirica; road density; Ursus arctos; Russia; human–wildlife conflict; carnivores

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