S. Aitken, S. Yeaman, J. Holliday, T. Wang, and S. Curtis-mclane, Adaptation, migration or extirpation: climate change outcomes for tree populations, Evolutionary Applications, vol.273, issue.1, 2008.
DOI : 10.1111/j.1752-4571.2007.00013.x

J. Albert and S. Chib, Bayesian Analysis of Binary and Polychotomous Response Data, Journal of the American Statistical Association, vol.85, issue.422, 1993.
DOI : 10.1016/0304-4076(84)90007-1

D. Balding, A tutorial on statistical methods for population association studies, Nature Reviews Genetics, vol.5, issue.10, pp.781-792, 2006.
DOI : 10.1038/nrg1155

N. Balkenhol, L. Waits, and R. Dezzani, Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data, Ecography, vol.58, issue.5, 2009.
DOI : 10.1111/j.1600-0587.2009.05807.x

K. Bandeen-roche, D. Miglioretti, S. Zeger, and P. Rathouz, Latent Variable Regression for Multiple Discrete Outcomes, Journal of the American Statistical Association, vol.55, issue.440, pp.1375-1386, 1997.
DOI : 10.1080/01621459.1997.10473658

J. Besag, Statistical Analysis of Non-Lattice Data, The Statistician, vol.24, issue.3, pp.179-195, 1975.
DOI : 10.2307/2987782

C. Chen, E. Durand, F. Forbes, and O. François, Bayesian clustering algorithms ascertaining spatial population structure: a new computer program and a comparison study, Molecular Ecology Notes, vol.101, issue.41, pp.747-756, 2007.
DOI : 10.2307/2408641

URL : https://hal.archives-ouvertes.fr/hal-00370267

H. Chung, B. Flaherty, and J. Schafer, Latent class logistic regression: application to marijuana use and attitudes among high school seniors, Journal of the Royal Statistical Society: Series A (Statistics in Society), vol.22, issue.4, 2006.
DOI : 10.2307/2289457

N. Davies, F. Villablanca, and G. Roderick, Determining the source of individuals: multilocus genotyping in nonequilibrium population genetics, Trends in Ecology & Evolution, vol.14, issue.1, pp.17-21, 1999.
DOI : 10.1016/S0169-5347(98)01530-4

K. Dawson and K. Belkhir, A Bayesian approach to the identification of panmictic populations and the assignment of individuals, Genetics Research, vol.78, issue.01, pp.59-77, 2001.
DOI : 10.1017/S001667230100502X

C. Dayton and G. Macready, Concomitant-Variable Latent-Class Models, Journal of the American Statistical Association, vol.35, issue.401, pp.173-178, 1988.
DOI : 10.1080/01621459.1988.10478584

J. Duminil, S. Fineschi, A. Hampe, P. Jordano, D. Salvini et al., Can Population Genetic Structure Be Predicted from Life-History Traits?, American Naturalist, vol.169, issue.5, pp.662-672, 2007.

E. Durand, F. Jay, O. Gaggiotti, and O. François, Spatial Inference of Admixture Proportions and Secondary Contact Zones, Molecular Biology and Evolution, vol.26, issue.9, pp.1963-1973, 2009.
DOI : 10.1093/molbev/msp106

O. François, S. Ancelet, and G. Guillot, Bayesian Clustering Using Hidden Markov Random Fields in Spatial Population Genetics, Genetics, vol.174, issue.2, pp.805-816, 2006.
DOI : 10.1534/genetics.106.059923

F. Gugerli, T. Englisch, H. Niklfeld, A. Tribsch, Z. Mirek et al., Relationships among levels of biodiversity and the relevance of intraspecific diversity in conservation ??? a project synopsis, Perspectives in Plant Ecology, Evolution and Systematics, vol.10, issue.4, pp.259-281, 2008.
DOI : 10.1016/j.ppees.2008.07.001

URL : https://hal.archives-ouvertes.fr/halsde-00377966

M. Jakobsson and N. Rosenberg, CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure, Bioinformatics, vol.23, issue.14, pp.1801-1806, 2007.
DOI : 10.1093/bioinformatics/btm233

F. Jay, O. François, and M. Blum, Predictions of Native American Population Structure Using Linguistic Covariates in a Hidden Regression Framework, PLoS ONE, vol.10, issue.1, 2011.
DOI : 10.1371/journal.pone.0016227.s006

URL : https://hal.archives-ouvertes.fr/hal-00655073

F. Jay, S. Manel, N. Alvarez, E. Durand, W. Thuiller et al., Forecasting changes in population genetic structure of alpine plants in response to global warming, Molecular Ecology, vol.459, issue.10, 2012.
DOI : 10.1111/j.1365-294X.2012.05541.x

URL : https://hal.archives-ouvertes.fr/hal-00775883

C. Lee and T. Mitchell-olds, Quantifying effects of environmental and geographical factors on patterns of genetic differentiation, Molecular Ecology, vol.18, issue.22, pp.4631-4642, 2011.
DOI : 10.1111/j.1365-294X.2011.05310.x

J. Lichstein, T. Simons, S. Shriner, and K. Franzreb, SPATIAL AUTOCORRELATION AND AUTOREGRESSIVE MODELS IN ECOLOGY, Ecological Monographs, vol.72, issue.3, pp.445-463, 2002.
DOI : 10.2307/1939174

D. Linzer and J. Lewis, poLCA: An R Package for Polytomous Variable Latent Class Analysis, Journal of Statistical Software, vol.42, issue.10, 2011.

S. Manel, M. Schwartz, G. Luikart, and P. Taberlet, Landscape genetics: combining landscape ecology and population genetics, Trends in Ecology & Evolution, vol.18, issue.4, pp.189-197, 2003.
DOI : 10.1016/S0169-5347(03)00008-9

URL : https://hal.archives-ouvertes.fr/halsde-00279786

D. Nychka, R. Furrer, and S. Sain, fields: Tools for Spatial Data. R package version 8.2-1, URL http, 2015.

C. Parmesan and G. Yohe, A globally coherent fingerprint of climate change impacts across natural systems, Nature, vol.4, issue.6918, pp.37-42, 2003.
DOI : 10.1038/34073

J. Pritchard, M. Stephens, and P. Donnelly, Inference of Population Structure Using Multilocus Genotype Data, Genetics, vol.155, issue.2, pp.945-959, 2000.

Q. Project, Qt: Cross-Platform Application Framework. Version 5, 2015.

R. Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2015.

B. Ripley, Spatial Statistics, pp.10-1002, 1981.
DOI : 10.1002/0471725218

G. Segelbacher, S. Cushman, B. Epperson, M. Fortin, O. François et al., Applications of landscape genetics in conservation biology: concepts and challenges, Conservation Genetics, vol.28, issue.2, pp.375-385, 2010.
DOI : 10.1007/s10592-009-0044-5

V. Sork, F. Davis, R. Westfall, A. Flint, M. Ikegami et al., Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata N??e) in the face of climate change, Molecular Ecology, vol.1, issue.17, pp.3806-3823, 2010.
DOI : 10.1111/j.1365-294X.2010.04726.x

S. Spiegelhalter, N. Best, B. Carlin, and A. Linde, Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.93, issue.4, pp.583-639, 2002.
DOI : 10.1002/1097-0258(20000915/30)19:17/18<2265::AID-SIM568>3.0.CO;2-6

M. Stephens, Dealing with label switching in mixture models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.62, issue.4, pp.795-809, 2000.
DOI : 10.1111/1467-9868.00265

A. Storfer, M. Murphy, J. Evans, C. Goldberg, S. Robinson et al., Putting the ???landscape??? in landscape genetics, Heredity, vol.163, issue.4, pp.128-142, 2006.
DOI : 10.1641/0006-3568(2004)054[0511:HSRRSD]2.0.CO;2

J. Vermunt and J. Magidson, Latent class models for classification, Computational Statistics & Data Analysis, vol.41, issue.3-4, pp.531-53710, 2003.
DOI : 10.1016/S0167-9473(02)00179-2

P. Vounatsou, T. Smith, and A. Gelfand, Spatial modelling of multinomial data with latent structure: an application to geographical mapping of human gene and haplotype frequencies, Biostatistics, vol.1, issue.2, pp.177-189, 2000.
DOI : 10.1093/biostatistics/1.2.177