| Personal injury and property loss caused by wildlife often deteriorates the relationship between humans and animals,prompting retaliatory killings that threaten species survival.Tibetan brown bear(Ursus arctos pruinosus),an umbrella species on the Qinghai-Tibet Plateau,is of great significance for maintaining the health and stability of local ecosystems.In recent years,conflicts between herders and brown bears in the Sanjiangyuan region have increased dramatically,seriously affecting community enthusiasm for brown bears and the conservation of other species and encouraging retaliatory killings.Understanding the current status of these conflicts,herders'knowledge and attitudes and identifying the risk areas and conflict driving mechanisms are crucial to formulate and implement effective mitigation and bear conservation measures.We conducted field research in Zhiduo county of Sanjiangyuan region from 2017 to 2019,and carried out research on the characteristics of human-bear conflicts(HBC),herders'knowledge and attitudes towards bears,HBC risk areas,drivers of HBC and mitigation measures.First,a semi-structured questionnaire was designed to investigate the types,spatial distribution and seasonal variation of HBC,as well as herders'tolerance towards bears.Second,the risk areas and diffusion paths of HBC were assessed based on occurrence points of HBC using the maximum entropy(Max Ent)model with geoenvironmental variables,anthropogenic disturbance variables and bear ecology variables considered,followed by the use of the Circuit Theory model.Relationships between the environmental variables and spatial distribution of HBC were determined,and spatially differentiated mitigation measures based on risk areas were discussed.Third,we proposed the hypothetical drivers leading to increased HBC,including changes in herder practices and brown bear foraging behavior,insufficient natural food and recent recovery of the existing bear population.The hypothetical drivers of HBC were verified through household interviews,marmot density surveys,bear faeces collection,and diet analysis based on metagenomic sequencing,and the main drivers of HBC were identified.Fourth,we evaluated the effectiveness of existing HBC mitigation measures,and gained understanding of the herder knowledge surrounding potential HBC mitigation measures.Main research results and conclusions:(1)HBC characteristics and herders'knowledge and attitudes.High-incidence areas of HBC in Qinghai province are distributed within or close to Sanjiangyuan National Park,including Zhiduo,Nangqian and Qumalai counties.A total of 92.31%(n=288)of respondents reported that they suffered bear damage.More respondents inside of SNP suffered bear damage than those outside.The representative type of HBC is house break-ins,with the main property lost being doors,windows,furniture,and daily supplies.Brown bears cause a smaller number of livestock loss than wolves and snow leopards,and bears mainly prey on sheep.House break-ins and human attacks primarily occurred in summer,and livestock predation primarily occurred in autumn.Respondents'age(?~2=10.08,P=0.001),house break-in experience(?~2=12.596,P<0.001)and compensation satisfaction(?~2=14.521,P<0.001)were the key factors found to affect respondents'tolerance towards brown bears.Respondents who were younger and experienced house break-ins had more negative attitudes towards brown bears.Most respondents were able to tolerate brown bears preying on free-range livestock,but house break-ins and human attacks are more difficult to accept.(2)Identifying the risk regions of HBC.The Max Ent model identified the total risk area of HBC was 11577.91 km~2,of which the high,medium and low risk area was 1133.24 km~2,4811.66 km~2 and 5633.01 km~2,respectively.According to Jackknife test results,the five environmental variables with the highest contribution rate to the risk area were LUCC(34.1%),human population density(31.4%),NDVI(10.4%),distance to rivers(10.1%),and distance to lakes(4.8%).A total of 50.43%of risk areas were distributed at an altitude of 4600-4800 m,while 58.31%of the risk areas were distributed in the Yangtze River Zone of Sanjiangyuan National Park.The high-risk areas were mainly distributed in Duocai,accounting for 45.26%of the total high-risk area.Circuitscape Theory simulated that risk diffusion paths extended southeast to northwest,connecting the inside of Sanjiangyuan National Park to its outside border.The risk diffusion paths with strong current were mainly distributed in eastern Suojia and southeastern Duocai.The risk diffusion paths with strong current were mainly distributed in the alpine meadow habitat of high and medium risk areas.(3)Driving factors of HBC.The driving factors of HBC were associated with herder settlement method,bear foraging behavior and population recovery.Since the establishment of winter homes,herders have been accustomed to storing food in them,and brown bears have gradually learned to utilize the food in unattended homes.A total of 88.78%(n=277)of respondents moved seasonally,and most winter homes(n=262,83.97%)were left unattended in summer,increasing the probability of brown bears intruding the homesteads.Although91.35%(n=285)of the respondents no longer store food in unattended homes,brown bears still frequently wandered around the winter homes for food as herders did not properly dispose of dead livestock and household garbage.Among the diet composition of brown bears(n=21scats),the total frequency ratio of species were wild animals(51.11%),livestock(21.11%),vegetation(20%),and grain from settlements(7.78%).The top five most frequent occurrences of individual species among the 21 scats were Pantholops hodgsonii(n=17,80.95%),Marmota himalayana(n=13,61.09%),Allium mongolicum Regel(n=8,38.1%),Ovis aries(n=7,33.33%),and Canis lupus(n=7,33.33%);the top 5 with the maximum abundance of individual species were Marmota himalayana(a=0.60),Ovis aries(a=0.49),Bos grunniens(a=0.44),Hordeum vulgare(a=0.41),and Ochotona curzoniae(a=0.25),the frequency and abundance of Marmota himalayana and Ovis aries were both high.The number of Marmota himalayana are increasing year by year,but the density of the species had no significant effect on brown bears utilizing human food(P=0.329).Distance to rocky outcrops(P=0.022)and distance to winter homes(P=0.040)were the key factors affecting brown bears utilizing human food,indicating brown bears tend to look for human food near winter homes closer to rocky outcrops.In the past 10 years,the number of brown bears has increased(n=217,69.55%),and the increase in HBC is likely linked to this population recovery.(4)Evaluation of the effectiveness of HBC mitigation measures.It has been recorded that there are 16 mitigation measures for HBC,and 96.47%(n=301)of the respondents have used multiple measures simultaneously.According to respondents,the effective measure to protect winter supplies was to remove them from winter homes(n=249),the effective measure to protect winter homes was to ask relatives to keep watch(n=13),and the effective measure to protect free-range livestock was to use traditional grazing methods instead of semi-traditional grazing methods(n=30)and strap a solar soundbox to a yak(n=8).Solar street lights had a deterrent effect on brown bears,and it was effective to protect livestock,winter homes and herders(n=28).The newer technology available in the form of electric fences did not work well because of the unstable voltage of photovoltaic cells and the improper installation of ground wires(n=4).Most respondents held high expectations to introduce steel bins(n=297,95.19%),build concrete walls(n=295,94.55%)and improve electric fence technology(n=257,82.37%)to protect property,and 58.33%(n=182)held high expectations to introduce bear spray to protect personal safety.Up to 71.79%(n=224)of the respondents were not satisfied with the current compensation for damages as complicated process of evidence acquisition,insufficient compensation and delayed payment;most incidents of house break-ins caused by brown bears were not financially resolved.Compensation methods that respondents expected was cash(n=246,78.85%),commercial insurance(n=39,12.5%),grain(n=17,5.45%),and other(n=10,3.21%). |
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