Hidden Markov random fields and the genetic structure of the scandinavian brown bear population
Journal de la société française de statistique, Volume 148 (2007) no. 1, p. 31-38

Spatial bayesian clustering algorithms can provide correct inference of population genetic structure when applied to populations for which continuous variation of allele frequencies is disrupted by small discontinuities. Here we review works which used bayesian clustering algorithms for studying the Scandinavian brown bears, with particular attention to a recent method based on hidden Markov random field. We provide a summary of current knowledge about the genetic structure of this endangered population potentially useful for its conservation.

Les algorithmes de classification bayésienne spatiale sont utiles afin d'étudier la structure génétique de populations pour lesquelles on observe une variation des fréquences d'allèles généralement continue en espace, mais localement interrompue par de petites discontinuités. Dans cet article, nous présentons une synthèse de travaux récents appliquant ces algorithmes à l'étude de l'ours brun de Scandinavie et nous résumons les connaissances actuelles sur la structure de cette population potentiellement utiles pour sa conservation.

Keywords: population genetic structure, spatial bayesian analysis, clustering analysis, scandinavian brown bear
@article{JSFS_2007__148_1_31_0,
     author = {Ancelet, Sophie and Guillot, Gilles and Fran\c cois, Olivier},
     title = {Hidden Markov random fields and the genetic structure of the scandinavian brown bear population},
     journal = {Journal de la soci\'et\'e fran\c caise de statistique},
     publisher = {Soci\'et\'e fran\c caise de statistique},
     volume = {148},
     number = {1},
     year = {2007},
     pages = {31-38},
     language = {en},
     url = {http://www.numdam.org/item/JSFS_2007__148_1_31_0}
}
Ancelet, Sophie; Guillot, Gilles; François, Olivier. Hidden Markov random fields and the genetic structure of the scandinavian brown bear population. Journal de la société française de statistique, Volume 148 (2007) no. 1, pp. 31-38. http://www.numdam.org/item/JSFS_2007__148_1_31_0/

[1] Blum M., C. Damerval, S. Manel, and O. François (2004). Brownian models and coalescent structures. Theoretical Population Biology 65: 249-261. | Zbl 1109.92027

[2] Corander J., P. Waldmann, and M. Sillanpää (2003). Bayesian analysis of genetic differentiation between populations. Genetics 163: 367-374.

[3] Dawson K. and K. Belkhir (2001). A Bayesian approach to the identification of panmictic populations and the assignment of individuals. Genetical Research 78: 59-77.

[4] Destrempes F., M. Mignotte, and J.-F. Angers (2005). A Stochastic Method for Bayesian Estimation of Hidden Markov Random Field Models With Application to a Color Model. IEEE Transactions on Image Processing 14: 1097-1108. | MR 2171966

[5] François O., S. Ancelet, and G. Guillot (2006). Bayesian clustering using hidden Markov random fields in spatial population genetics. Genetics 174: 805-816.

[6] Green P. and S. Richardson (2002). Hidden Markov models and disease mapping. Journal of the American Statistical Association 97 (460): 1055-1070. | MR 1951259 | Zbl 1046.62117

[7] Kimura N. and G. Weiss (1964). The stepping stone model of population structure and the decrease of genetic correlation with distance. Genetics 49: 561-575.

[8] Malécot G. (1948). Les Mathématiques de l'Hérédité. Paris: Masson. | Zbl 0031.17304

[9] Manel S., E. Bellemain, J. Swenson, and O. François (2004) Assumed and inferred spatial structure of populations: the Scandinavian brown bears revisited. Molecular Ecology 13: 1327-1331.

[10] Manel S., M. Schwartz, G. Luikart, and P. Taberlet (2003) Landscape genetics: combining landscape ecology and population genetics. Trends in Ecology and Evolution 18(4): 189-197.

[11] Preston C. (1974). Gibbs States on Countable Sets. Cambridge: Cambridge University Press. | MR 474556 | Zbl 0297.60054

[12] Pritchard J., M. Stephens, and P. Donnelly (2000). Inference of population structure using multilocus genotype data. Genetics 155: 945-959.

[13] Rogers L. (1987). Effects of food supply and kinship on social behavior, movements, and population dynamics of black bears in northeastern. Minnesota Wildlife 97: 1-72.

[14] Serre D. and S. Pääbo (2004). Evidence for gradients of human genetic diversity within and among continents. Genome Research 14: 16791685.

[15] Swenson J., F. Sandegren, A. Bjarvall, A. Soderberg, M. Wabakken, and M. Franzen (1994). Size, trend, distribution and conservation of the brown bear, Ursus arctos, population in Sweden. Biological Conservation 70: 9-17.

[16] Swenson J. E., F. Sandegren, A. Bjarvall, M. Franzen, and A. Soderberg (1995). The near extinction and recovery of brown bears in Scandinavia in relation to the bear management policies of Norway and Sweden. Wildlife Biology 1: 11-25.

[17] Swenson J. E., F. Sandegren, and A. Soderberg (1998). Geographic expansion of an increasing brown bear population: evidence for presaturation dispersal. Journal of Animal Ecology 67: 819-826.

[18] Taberlet P., J. Swenson, F. Sandegren, and A. Bjarvall (1995). Localization of a contact zone between two highly divergent mitochondrial DNA lineages of the brown bear Ursos arctos in Scandinavia. Conservation Biology 9: 1255-1261.

[19] Waits L., P. Taberlet, J. Swenson, F. Sandegren, and R. Franzen (2000). Nuclear DNA microsatellite analysis of genetic diversity and gene flow in the Scandinavian brown bear Ursus arctos. Molecular Ecology 9: 610-621.

[20] Wright S. (1943). Isolation by distance. Genetics 28: 114-138.