Abstract

Gene flow between the two brown bear Ursus arctos populations of Fennoscandia is very weak although bear populations both in Finland and Scandinavia has been increasing substantially during the last 50 years. We examined spatial and temporal pattern in the proportion of adult females in Finland's bear population. First, we expected evidence for decreasing female proportions with increasing distance from the potential core areas in Russia and near Finnish–Russian border. Secondly, we expected increasing proportions of females during our study period of 21 years, i.e. two brown bear generations. We conducted a logistic mixed effects model that would predict a hunter-killed bear's sex based on year, the distance from the source population (Finnish–Russian border), bear management area and bear's age group (adult, sub-adult) as independent variables. Geographic coordinates were used to reduce the apparent spatial autocorrelation. Because female bears form kin clusters, in the final model we used exponential spatial autocorrelation, based on the assumption that the probability of a dead bear being a female increased when the nearest dead bear also was a female. The model demonstrated that the population's population structure did not change spatially during our study period. The only differentiating variables were bear management area and age group, the probability of females being low in reindeer husbandry area, and males predominating among subadults. Because the low proportion of females in Finland's reindeer husbandry area may weaken meta-population viability in Fennoscandia, bear management in northern Finland should save females more efficiently.

Highlights

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  • The only differentiating variables were bear management area and age group, the probability of females being low in reindeer husbandry area, and males predominating among subadults

  • Two-way interaction between year and bear management area (BMA) on sex ratio was not significant (χ2 = 5.38, df = 3, p = 0.146). Both in the first mixed model and the final mixed model taking account spatial autocorrelation, shown in Table 1, the significant fixed effects were age group and BMA, but distance from Russian border was not related to the probability that the nearest dead bear was a female (p = 0.163)

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Summary

Statistical modeling

Dependent variable was Bernoulli distributed variable with the event ‘dead bear is female’. We constructed generalized linear mixed model (GLMM) where the tested independent variables (predictors) were 1) the age class of the bear, 2) bear management area BMA, 3) distance (km) from the Russian border and 4) year. We treated these variables and their two-way interactions as fixed effects. The second model took account spatial autocorrelation and because the range estimate (0.09) and the 95% confidence interval (0.03– 0.28) for the correlation structure indicated that it differed. We computed the model considering a possible spatial autocorrelation using R function glmmPQL in R package MASS (Venables and Ripley 2002). Is a logit-link function and f(.) describes the linear function with arguments Xij (i.e. fixed predictors) and

Dispersion parameter
Results
Number of dead bears
Discussion
Management implications
Full Text
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