The Evolution of Landscape Genetics
Accepted Article
K. Petren
Department of Biological Sciences, University of Cincinnati, Cincinnati, OH USA 452210006. [email protected]. 513-556-9719.
ABSTRACT The main objective of this special section is not to review the broad field of landscape genetics, but to provide a glimpse of how the developing landscape genetics perspective has the potential to change the way we study evolution. Evolutionary landscape genetics is the study of how migration and population structure affects evolutionary processes. As a field it dates back to Sewall Wright and the origin of theoretical population genetics, but empirical tests of adaptive processes of evolution in natural landscapes have been rare. Now, with recent developments in technology, methodology and modeling tools, we are poised to trace adaptive genetic variation across space and through time. Not only will we see more empirical tests of classical theory, we can expect to see new phenomena emerging, as we reveal complex interactions among evolutionary processes as they unfold in natural landscapes.
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:10.1111/evo.12278.
This article is protected by copyright. All rights reserved.
1
Special sections often signal the maturation of a topic. This one marks the beginning of one. Landscape genetics was originally viewed as the study of barriers to genetic movement among populations (Manel et al. 2003), but it has since branched out in many directions to include population, landscape and historical ecology, niche modeling, and conservation
Accepted Article
(Storfer et al. 2007, 2010, Sork and Waits 2010). The ecological and environmental perspective remains at its core, as reflected in a recent review (Manel and Holderegger 2013), which stated: “The main objective of modern landscape genetics is to improve our understanding of the effect of global change on genetic patterns”. Evolutionary landscape genetics is the study of how migration and population
structure affect evolutionary processes. Although the topic can be traced back to Sewall Wright (1931), it is still a very young field empirically, as reflected by the three main themes addressed in the eight papers in this special section: (1) Developing methods of analysis that permit evolutionary inferences from genetic landscape data, (2) Connecting landscape genetic and environmental patterns to infer adaptation, and (3) identifying adaptive loci in nature. Most of the papers in this collection touch upon two or even all three of these themes. One might suspect that the driving force behind evolutionary landscape genetics
would be the technical advances of modern genomics. This is part of the story, but a closer look reveals that the development of statistical and modeling approaches is equally responsible. In this section, He et al. (2013) show how spatially explicit demographic modeling can be used to distinguish among alternative historical processes that underlie current population structure. Wang (2013) moves past geographic isolation to explore the concept of ecological isolation: does local adaptation reflect barriers to genetic exchange? The matrix regression approach allows for sophisticated comparisons of many types of data within the same flexible framework. The landscape perspective in general lends comparative power to distinguish general patterns from stochastic events among semi-replicated populations (Petren et al. 2005). The approach can be used to reveal adaptation by simultaneously analyzing trait variation, habitat
This article is protected by copyright. All rights reserved.
2
characteristics and genetic data. For instance, Pavlova et al. (2013) reveal evidence for environmentally driven adaptation of mtDNA by contrasting patterns among loci along a continental-scale ecotone. Hendry et al. (2013) adopt the concept of exchangeability to reveal
Accepted Article
patterns of parallel adaptation to similar habitats. Their approach is adaptable to many kinds of data and allows one to quantitatively explore the exceptions to the general patterns. An intriguing aspect of landscape level studies is the opportunity to directly compare
genetic variation over time by using historical samples (Guinard et al. 2003, Wanderler et al. 2007, Hansen et al. 2009). Landscape-level cross-temporal comparisons of multilocus genetic variation are still uncommon, but they are sure to grow in number, and there is some indication that they may reveal more genetic change over time than is commonly expected (Farrington and Petren 2011). In this issue, Smith et al. (2013) offer a cross-temporal comparison, clinal analysis and a model selection approach to reveal the movement of a hybrid zone over time. Perhaps the most dramatic examples of the comparative power of landscape genetics
will come from the quest to reveal adaptive genetic variation in nature. The power comes when there is some degree of parallel evolution among populations, such that a subsets of populations are affected by a condition, such as an environmental gradient, or an interaction or disturbance caused by other species or humans, while other populations are not similarly affected. Jones et al. (2013) use a landscape genomics approach to reveal adaptive genetic variation along a cline. Their use of simulations to determine the sensitivity and error rates among different methods is important. Simulation offers an opportunity to understand the range of conditions causes that can generate complex patterns. Several simulation tools are now available to model complex evolutionary processes in complex landscapes (Neuenschwander et al. 2008, Hoban et al. 2012, Landguth et al. 2012). Bourret et al. (2013) use a landscape genomics approach and multivariate analysis to identify candidate loci under
This article is protected by copyright. All rights reserved.
3
environmental selection. They obtain impressive statistical power by including 54 populations in their analysis. Finally, Garroway et al. (2013) present an association analysis at the level of individuals to reveal candidate loci associated with habitat features and
Accepted Article
infection risk at surprising small spatial scales. This special section intersects with another focusing on the genetics of speciation
(Nosil and Feder 2013). The landscape approach offers some distinct advantages in detecting associations between genomic regions and traits of interest. Low levels of recombination and the large size of linkage groups is a real barrier to narrowing down associations in pedigree or crossing studies (MacKay et al. 2009). Natural populations offer a longer history of recombination and thus finer-scaled resolution for identifying adaptive trait loci. However, there are also tradeoffs when inferring selection and adaptation using the landscape approach (Manel et al. 2010, Parisod and Holderegger 2012). Smaller linkage groups mean that finerscale genetic maps are required, or adaptive loci may be missed. This problem may be ephemeral, as new technologies are allowing for increasingly dense coverage of the genomes of many individuals (e.g. Elshire et al. 2011). Hybridizing populations offer great promise for identifying gene regions that maintain species differences due to ongoing selection, so-called islands of speciation or divergence (Turner et al. 2005, Ellegren et al. 2012, Nadeau et al. 2012, Renaut et al. 2013). However, there is an increased risk of false positive associations with landscape studies, especially if underlying assumptions, such as high levels of introgression, are not met (Noor and Bennett 2009). Once again, greater power to detect causal associations will be gained as the number of semi-replicated populations surveyed across the natural landscape is increased. Evolutionary landscape genetics will ultimately help to unify ecology and evolution by connecting metapopulation theory with classical theoretical population genetics (Whitlock 2004). Wright’s shifting balance theory has been criticized because there is scant evidence for
This article is protected by copyright. All rights reserved.
4
the process in nature (Coyne et al. 1997). Robust tests would require following adaptive loci across space and over time in natural populations, and this has simply not been possible. But based on these criteria, one could similarly conclude there is scant evidence for Fisherian
Accepted Article
mass selection (Fisher 1930). This lack of direct empirical evidence is sobering, but finally we are at a point where tools are available to thoroughly explore these fundamental processes in natural populations. As we come to understand the complexities of interaction among the processes of migration, selection, drift, extinction and colonization in real populations in natural landscapes, it is safe to say that new phenomena will be revealed that will alter our thinking about evolution. One example is mutation surfing, where a novel mutation can rise to high frequency due to sequential founder effects at the leading edge of an expanding population (Klopfstein et al. 2005, McInerny et al. 2009, Excoffier et al. 2009). So, for those of us who have long pondered the adaptive landscape, and for those who are still forging their research programs, the message is: “Surf’s up!” Catch a ride on the literal adaptive landscape, and you just might discover the next new process of evolution.
This article is protected by copyright. All rights reserved.
5
References
Accepted Article
Bourret, V., Dionne, M., Kent, M., Lien, S., and Bernatchez, L. 2013. Landscape Genomics in Atlantic Salmon (Salmo salar): Searching for gene-environment interactions driving local adaptation. Evolution 67: 0000-0000.
Coyne, J. A., Barton, N. H., and Turelli, M. 1997. A critique of Sewall Wright's shifting balance theory of evolution. Evolution 51: 643-671.
Ellegren, H., Smeds, L., Burri, R., Olason, P. I., Backström, N., Kawakami, T., Künstner, A., Mäkinen, H., Nadachowska-Brzyska, H., Qvarnström, A., Uebbing, S., and Wolf, J. B. W. 2012. The genomic landscape of species divergence in Ficedula flycatchers. Nature 491: 756–760.
Elshire R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, E. S., and Mitchell, S. E. 2011. A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species. PLoS ONE 6: e19379.
Excoffier, L., Foll, M., and Petit, R. J. 2009. Genetic consequences of range expansions. Annu. Rev. Ecol. Evol. Syst. 40: 481–501.
Farrington, H., and Petren, K. 2011. A century of genetic change and metapopulation dynamics in the Galápagos warbler finches Certhidea. Evolution 65: 3148–3161.
Fisher, R. A. 1930. The Genetical Theory of Natural Selection. Clarendon, UK. Garroway, C., Radersma, R., Sepil, I., Santure, S., De Cauwer, I., Slate, J., and Sheldon, B. 2013. Fine-scale genetic structure in a wild bird population: the role of limited dispersal and environmentally-based selection as causal factors. Evolution 67: 0000-0000.
This article is protected by copyright. All rights reserved.
6
Guinand, B., Scribner, K. T., Page, K. S., and Burnham-Curtis, M. K. 2003. Genetic variation over space and time: analyses of extinct and remnant lake trout populations in the Upper Great Lakes. Proc. R. Soc. B 270: 425–433.
Accepted Article
Hansen, M. M., Fraser, D. J., Meier, K., and Mensberg, K.-L. D. 2009. Sixty years of anthropogenic pressure: A spatio-temporal genetic analysis of brown trout populations subject to stocking and population declines. Molecular Ecology 18: 2549-2562.
He, Q., Edwards, D. L., and Knowles L. L. 2013. Integrative testing of how environments from the past to the present shape genetic structure across landscapes. Evolution 67: 0000-0000.
Hendry, A. P., Kaeuffer, R., Crispo, E., Peichel, C. L., and Bolnick, D. I. 2013. Evolutionary inferences from the analysis of exchangeability. Evolution 67: 0000-0000.
Hoban, S., Bertorelle, G., and Gaggiotti, O. E. 2012. Computer simulations: tools for population and evolutionary genetics. Nature Rev. Genet. 13: 110-122.
Holderegger, R., and Wagner, H. H. 2008. Landscape Genetics. BioScience 583: 199-207. Jones, F. C., Grabherr, M. G., Chan, Y. F., Russell, P., Mauceli, E., Johnson, J., Swofford, R., Pirun, M., Zody, M. C., White, S., et al. 2012. The genomic basis of adaptive evolution in threespine sticklebacks. Nature 484: 55-61.
Jones, M., Forester, B., Teufel, A., Adams, R., Antstett, D., Goodrich, B., Joost, S., and Manel, S. 2013 Integrating spatially explicit approaches to detect adaptive loci in a landscape genomics context. Evolution 67: 0000-0000.
Klopfstein, K., Currat, M., and Excoffier, L. 2005 The fate of mutations surfing on the wave of a range expansion. Mol. Biol. Evol. 23: 482–490.
Landguth, E. L., Cushman, S. A., and Johnson, N. A. 2012. Simulating natural selection in landscape genetics. Mol. Ecol. Resources 12: 363–368.
This article is protected by copyright. All rights reserved.
7
MacKay, T. F. C., Stone. E. A., and Ayroles, J. F. 2009. The genetics of quantitative traits: challenges and prospects. Nature Rev. Genet. 10:565–577. Manel, S., and Holderegger, R. 2013. Ten years of landscape genetics. Trends Ecol.
Accepted Article
Evol. 10.1016/j.tree.2013.05.012. Manel, S., Joost, S., Epperson, B., Holderegger, R., Storfer, Rosenberg, M., Scribner, K., Bonin, A., and Fortin, M.-J. 2010. Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field. Mol. Ecol. 19: 3760–3772.
Manel, S., Schwartz, M. K., Luikart, G., and Taberlet, P. 2003. Landscape genetics: combining landscape ecology and population genetics. Trends Ecol. Evol. 18:189-197.
McInerny, G. J., Turner, J. R. G., Wong, H. Y., Travis, J. M. J., and Benton, T. G. 2009. How range shifts induced by climate change affect neutral evolution. Proc. R. Soc. B 276: 1527-1534.
Nadeau, N. J., Whibley, A., Jones R. T., Davey, J. W., Dasmahapatra, K. K., Baxter, S. W., Quail, M. A., Joron, M., Ffrench-Constant, R. H., Blaxter, M. L., Mallet, J., and Jiggins, C. D. 2012. Genomic islands of divergence in hybridizing Heliconius butterflies identified by large-scale targeted sequencing. Phil. Trans. R. Soc. B 367: 343–353.
Neuenschwander, S., Hospital, F., Guillaume, F., and Goudet, J. 2008. QuantiNEMO: an individual-based program to simulate quantitative traits with explicit genetic architecture in a dynamic metapopulation Bioinformatics 24: 1552-1553.
Noor, M. A., and Bennett, S. M. 2009. Islands of speciation or mirages in the desert? Examining the role of restricted recombination in maintaining species. Heredity 103: 439–444.
Nosil, P., and Feder, J. L. 2013. Genome evolution and speciation: toward quantitative descriptions of pattern and process. Evolution 67: 2461-2467.
This article is protected by copyright. All rights reserved.
8
Parisod, C., and Holderegger, R. 2012. Adaptive landscape genetics: pitfalls and benefits. Mol. Ecol. 21: 3644–3646. Pavlova, A., Amos, J. N, Joseph, L., Loynes, K., Austin, J. J., Keogh, J. S., Stone, G, N.,
Accepted Article
Nicholls, J. A., and Sunnucks, P. 2013. Perched at the mito-nuclear crossroads: divergent mitochondrial lineages correlate with environment in the face of ongoing nuclear gene flow in an Australian bird. Evolution 67: 0000-0000.
Petren, K., Grant, B. R., Grant, P. R., and Keller, L. F. 2005. Comparative landscape genetics and the adaptive radiation of Darwin’s finches: the role of peripheral isolation. Mol. Ecol. 14: 2943-2957.
Renaut, S., Grassa, C. J., Yeaman, S., Moyers, B. T., Lai, Z., Kane, N. C., Bowers, J. E., Burke, J. M., and Rieseberg, L. H. 2013. Genomic islands of divergence are not affected by geography of speciation in sunflowers. Nature Comm. 4: 1827.
Smith, K., L., Hale, J. M., Gay, L., Kearney, M., Austin, J. J., Parris, K., and Melville, J. 2013. Spatio-temporal changes in the structure of an Australian frog hybrid zone: a 40year perspective. Evolution 67: 0000-0000.
Sork, V. L., and Waits, L. 2010. Contributions of landscape genetics – approaches, insights, and future potential. Mol. Ecol. 19: 3489–3495.
Storfer, A., Murphy, M. A., Evans, J. S., Goldberg, C. S., Robinson, S., Spear, S. F., R Dezzani, R., Delmelle, E., Vierling, L. and Waits, L. P. 2007. Putting the 'landscape' in landscape genetics. Heredity 98: 128–142.
Storfer, A., Murphy, M. A., Spear, S. F., Holderegger, R., and Waits, L. P. 2010. Landscape genetics: where are we now? Mol. Ecol. 19: 3496–3514.
Turner, T. L., Hahn, M. W., and Nuzhdin, S. V. 2005. Genomic islands of speciation in Anopheles gambiae. PLoS Biol. 3: e285
This article is protected by copyright. All rights reserved.
9
Wandeler, P., Hoeck, P. A. E., and Keller, L. F. 2007. Back to the future: museum specimens in population genetics. Trends Ecol. Evol. 22: 634-642. Wang, I. J. 2013. Examining the full effects of landscape heterogeneity on spatial genetic
Accepted Article
variation: a multiple matrix regression approach for quantifying geographic and ecological isolation. Evolution 67: 0000-0000.
Whitlock, M. C. 2004. Selection and drift in metapopulations. Pp. 153-174 in: Ecology, Genetics, and Evolution of Metapopulations I. Hanski and O.E. Gaggiotti, eds. Elsevier, Amsterdam.
Wright, S. 1931. Evolution in Mendelian populations. Genetics 16: 97-157.
This article is protected by copyright. All rights reserved.
10
Accepted Article
K. Petren
Department of Biological Sciences, University of Cincinnati, Cincinnati, OH USA 452210006. [email protected]. 513-556-9719.
ABSTRACT The main objective of this special section is not to review the broad field of landscape genetics, but to provide a glimpse of how the developing landscape genetics perspective has the potential to change the way we study evolution. Evolutionary landscape genetics is the study of how migration and population structure affects evolutionary processes. As a field it dates back to Sewall Wright and the origin of theoretical population genetics, but empirical tests of adaptive processes of evolution in natural landscapes have been rare. Now, with recent developments in technology, methodology and modeling tools, we are poised to trace adaptive genetic variation across space and through time. Not only will we see more empirical tests of classical theory, we can expect to see new phenomena emerging, as we reveal complex interactions among evolutionary processes as they unfold in natural landscapes.
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:10.1111/evo.12278.
This article is protected by copyright. All rights reserved.
1
Special sections often signal the maturation of a topic. This one marks the beginning of one. Landscape genetics was originally viewed as the study of barriers to genetic movement among populations (Manel et al. 2003), but it has since branched out in many directions to include population, landscape and historical ecology, niche modeling, and conservation
Accepted Article
(Storfer et al. 2007, 2010, Sork and Waits 2010). The ecological and environmental perspective remains at its core, as reflected in a recent review (Manel and Holderegger 2013), which stated: “The main objective of modern landscape genetics is to improve our understanding of the effect of global change on genetic patterns”. Evolutionary landscape genetics is the study of how migration and population
structure affect evolutionary processes. Although the topic can be traced back to Sewall Wright (1931), it is still a very young field empirically, as reflected by the three main themes addressed in the eight papers in this special section: (1) Developing methods of analysis that permit evolutionary inferences from genetic landscape data, (2) Connecting landscape genetic and environmental patterns to infer adaptation, and (3) identifying adaptive loci in nature. Most of the papers in this collection touch upon two or even all three of these themes. One might suspect that the driving force behind evolutionary landscape genetics
would be the technical advances of modern genomics. This is part of the story, but a closer look reveals that the development of statistical and modeling approaches is equally responsible. In this section, He et al. (2013) show how spatially explicit demographic modeling can be used to distinguish among alternative historical processes that underlie current population structure. Wang (2013) moves past geographic isolation to explore the concept of ecological isolation: does local adaptation reflect barriers to genetic exchange? The matrix regression approach allows for sophisticated comparisons of many types of data within the same flexible framework. The landscape perspective in general lends comparative power to distinguish general patterns from stochastic events among semi-replicated populations (Petren et al. 2005). The approach can be used to reveal adaptation by simultaneously analyzing trait variation, habitat
This article is protected by copyright. All rights reserved.
2
characteristics and genetic data. For instance, Pavlova et al. (2013) reveal evidence for environmentally driven adaptation of mtDNA by contrasting patterns among loci along a continental-scale ecotone. Hendry et al. (2013) adopt the concept of exchangeability to reveal
Accepted Article
patterns of parallel adaptation to similar habitats. Their approach is adaptable to many kinds of data and allows one to quantitatively explore the exceptions to the general patterns. An intriguing aspect of landscape level studies is the opportunity to directly compare
genetic variation over time by using historical samples (Guinard et al. 2003, Wanderler et al. 2007, Hansen et al. 2009). Landscape-level cross-temporal comparisons of multilocus genetic variation are still uncommon, but they are sure to grow in number, and there is some indication that they may reveal more genetic change over time than is commonly expected (Farrington and Petren 2011). In this issue, Smith et al. (2013) offer a cross-temporal comparison, clinal analysis and a model selection approach to reveal the movement of a hybrid zone over time. Perhaps the most dramatic examples of the comparative power of landscape genetics
will come from the quest to reveal adaptive genetic variation in nature. The power comes when there is some degree of parallel evolution among populations, such that a subsets of populations are affected by a condition, such as an environmental gradient, or an interaction or disturbance caused by other species or humans, while other populations are not similarly affected. Jones et al. (2013) use a landscape genomics approach to reveal adaptive genetic variation along a cline. Their use of simulations to determine the sensitivity and error rates among different methods is important. Simulation offers an opportunity to understand the range of conditions causes that can generate complex patterns. Several simulation tools are now available to model complex evolutionary processes in complex landscapes (Neuenschwander et al. 2008, Hoban et al. 2012, Landguth et al. 2012). Bourret et al. (2013) use a landscape genomics approach and multivariate analysis to identify candidate loci under
This article is protected by copyright. All rights reserved.
3
environmental selection. They obtain impressive statistical power by including 54 populations in their analysis. Finally, Garroway et al. (2013) present an association analysis at the level of individuals to reveal candidate loci associated with habitat features and
Accepted Article
infection risk at surprising small spatial scales. This special section intersects with another focusing on the genetics of speciation
(Nosil and Feder 2013). The landscape approach offers some distinct advantages in detecting associations between genomic regions and traits of interest. Low levels of recombination and the large size of linkage groups is a real barrier to narrowing down associations in pedigree or crossing studies (MacKay et al. 2009). Natural populations offer a longer history of recombination and thus finer-scaled resolution for identifying adaptive trait loci. However, there are also tradeoffs when inferring selection and adaptation using the landscape approach (Manel et al. 2010, Parisod and Holderegger 2012). Smaller linkage groups mean that finerscale genetic maps are required, or adaptive loci may be missed. This problem may be ephemeral, as new technologies are allowing for increasingly dense coverage of the genomes of many individuals (e.g. Elshire et al. 2011). Hybridizing populations offer great promise for identifying gene regions that maintain species differences due to ongoing selection, so-called islands of speciation or divergence (Turner et al. 2005, Ellegren et al. 2012, Nadeau et al. 2012, Renaut et al. 2013). However, there is an increased risk of false positive associations with landscape studies, especially if underlying assumptions, such as high levels of introgression, are not met (Noor and Bennett 2009). Once again, greater power to detect causal associations will be gained as the number of semi-replicated populations surveyed across the natural landscape is increased. Evolutionary landscape genetics will ultimately help to unify ecology and evolution by connecting metapopulation theory with classical theoretical population genetics (Whitlock 2004). Wright’s shifting balance theory has been criticized because there is scant evidence for
This article is protected by copyright. All rights reserved.
4
the process in nature (Coyne et al. 1997). Robust tests would require following adaptive loci across space and over time in natural populations, and this has simply not been possible. But based on these criteria, one could similarly conclude there is scant evidence for Fisherian
Accepted Article
mass selection (Fisher 1930). This lack of direct empirical evidence is sobering, but finally we are at a point where tools are available to thoroughly explore these fundamental processes in natural populations. As we come to understand the complexities of interaction among the processes of migration, selection, drift, extinction and colonization in real populations in natural landscapes, it is safe to say that new phenomena will be revealed that will alter our thinking about evolution. One example is mutation surfing, where a novel mutation can rise to high frequency due to sequential founder effects at the leading edge of an expanding population (Klopfstein et al. 2005, McInerny et al. 2009, Excoffier et al. 2009). So, for those of us who have long pondered the adaptive landscape, and for those who are still forging their research programs, the message is: “Surf’s up!” Catch a ride on the literal adaptive landscape, and you just might discover the next new process of evolution.
This article is protected by copyright. All rights reserved.
5
References
Accepted Article
Bourret, V., Dionne, M., Kent, M., Lien, S., and Bernatchez, L. 2013. Landscape Genomics in Atlantic Salmon (Salmo salar): Searching for gene-environment interactions driving local adaptation. Evolution 67: 0000-0000.
Coyne, J. A., Barton, N. H., and Turelli, M. 1997. A critique of Sewall Wright's shifting balance theory of evolution. Evolution 51: 643-671.
Ellegren, H., Smeds, L., Burri, R., Olason, P. I., Backström, N., Kawakami, T., Künstner, A., Mäkinen, H., Nadachowska-Brzyska, H., Qvarnström, A., Uebbing, S., and Wolf, J. B. W. 2012. The genomic landscape of species divergence in Ficedula flycatchers. Nature 491: 756–760.
Elshire R. J., Glaubitz, J. C., Sun, Q., Poland, J. A., Kawamoto, K., Buckler, E. S., and Mitchell, S. E. 2011. A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species. PLoS ONE 6: e19379.
Excoffier, L., Foll, M., and Petit, R. J. 2009. Genetic consequences of range expansions. Annu. Rev. Ecol. Evol. Syst. 40: 481–501.
Farrington, H., and Petren, K. 2011. A century of genetic change and metapopulation dynamics in the Galápagos warbler finches Certhidea. Evolution 65: 3148–3161.
Fisher, R. A. 1930. The Genetical Theory of Natural Selection. Clarendon, UK. Garroway, C., Radersma, R., Sepil, I., Santure, S., De Cauwer, I., Slate, J., and Sheldon, B. 2013. Fine-scale genetic structure in a wild bird population: the role of limited dispersal and environmentally-based selection as causal factors. Evolution 67: 0000-0000.
This article is protected by copyright. All rights reserved.
6
Guinand, B., Scribner, K. T., Page, K. S., and Burnham-Curtis, M. K. 2003. Genetic variation over space and time: analyses of extinct and remnant lake trout populations in the Upper Great Lakes. Proc. R. Soc. B 270: 425–433.
Accepted Article
Hansen, M. M., Fraser, D. J., Meier, K., and Mensberg, K.-L. D. 2009. Sixty years of anthropogenic pressure: A spatio-temporal genetic analysis of brown trout populations subject to stocking and population declines. Molecular Ecology 18: 2549-2562.
He, Q., Edwards, D. L., and Knowles L. L. 2013. Integrative testing of how environments from the past to the present shape genetic structure across landscapes. Evolution 67: 0000-0000.
Hendry, A. P., Kaeuffer, R., Crispo, E., Peichel, C. L., and Bolnick, D. I. 2013. Evolutionary inferences from the analysis of exchangeability. Evolution 67: 0000-0000.
Hoban, S., Bertorelle, G., and Gaggiotti, O. E. 2012. Computer simulations: tools for population and evolutionary genetics. Nature Rev. Genet. 13: 110-122.
Holderegger, R., and Wagner, H. H. 2008. Landscape Genetics. BioScience 583: 199-207. Jones, F. C., Grabherr, M. G., Chan, Y. F., Russell, P., Mauceli, E., Johnson, J., Swofford, R., Pirun, M., Zody, M. C., White, S., et al. 2012. The genomic basis of adaptive evolution in threespine sticklebacks. Nature 484: 55-61.
Jones, M., Forester, B., Teufel, A., Adams, R., Antstett, D., Goodrich, B., Joost, S., and Manel, S. 2013 Integrating spatially explicit approaches to detect adaptive loci in a landscape genomics context. Evolution 67: 0000-0000.
Klopfstein, K., Currat, M., and Excoffier, L. 2005 The fate of mutations surfing on the wave of a range expansion. Mol. Biol. Evol. 23: 482–490.
Landguth, E. L., Cushman, S. A., and Johnson, N. A. 2012. Simulating natural selection in landscape genetics. Mol. Ecol. Resources 12: 363–368.
This article is protected by copyright. All rights reserved.
7
MacKay, T. F. C., Stone. E. A., and Ayroles, J. F. 2009. The genetics of quantitative traits: challenges and prospects. Nature Rev. Genet. 10:565–577. Manel, S., and Holderegger, R. 2013. Ten years of landscape genetics. Trends Ecol.
Accepted Article
Evol. 10.1016/j.tree.2013.05.012. Manel, S., Joost, S., Epperson, B., Holderegger, R., Storfer, Rosenberg, M., Scribner, K., Bonin, A., and Fortin, M.-J. 2010. Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field. Mol. Ecol. 19: 3760–3772.
Manel, S., Schwartz, M. K., Luikart, G., and Taberlet, P. 2003. Landscape genetics: combining landscape ecology and population genetics. Trends Ecol. Evol. 18:189-197.
McInerny, G. J., Turner, J. R. G., Wong, H. Y., Travis, J. M. J., and Benton, T. G. 2009. How range shifts induced by climate change affect neutral evolution. Proc. R. Soc. B 276: 1527-1534.
Nadeau, N. J., Whibley, A., Jones R. T., Davey, J. W., Dasmahapatra, K. K., Baxter, S. W., Quail, M. A., Joron, M., Ffrench-Constant, R. H., Blaxter, M. L., Mallet, J., and Jiggins, C. D. 2012. Genomic islands of divergence in hybridizing Heliconius butterflies identified by large-scale targeted sequencing. Phil. Trans. R. Soc. B 367: 343–353.
Neuenschwander, S., Hospital, F., Guillaume, F., and Goudet, J. 2008. QuantiNEMO: an individual-based program to simulate quantitative traits with explicit genetic architecture in a dynamic metapopulation Bioinformatics 24: 1552-1553.
Noor, M. A., and Bennett, S. M. 2009. Islands of speciation or mirages in the desert? Examining the role of restricted recombination in maintaining species. Heredity 103: 439–444.
Nosil, P., and Feder, J. L. 2013. Genome evolution and speciation: toward quantitative descriptions of pattern and process. Evolution 67: 2461-2467.
This article is protected by copyright. All rights reserved.
8
Parisod, C., and Holderegger, R. 2012. Adaptive landscape genetics: pitfalls and benefits. Mol. Ecol. 21: 3644–3646. Pavlova, A., Amos, J. N, Joseph, L., Loynes, K., Austin, J. J., Keogh, J. S., Stone, G, N.,
Accepted Article
Nicholls, J. A., and Sunnucks, P. 2013. Perched at the mito-nuclear crossroads: divergent mitochondrial lineages correlate with environment in the face of ongoing nuclear gene flow in an Australian bird. Evolution 67: 0000-0000.
Petren, K., Grant, B. R., Grant, P. R., and Keller, L. F. 2005. Comparative landscape genetics and the adaptive radiation of Darwin’s finches: the role of peripheral isolation. Mol. Ecol. 14: 2943-2957.
Renaut, S., Grassa, C. J., Yeaman, S., Moyers, B. T., Lai, Z., Kane, N. C., Bowers, J. E., Burke, J. M., and Rieseberg, L. H. 2013. Genomic islands of divergence are not affected by geography of speciation in sunflowers. Nature Comm. 4: 1827.
Smith, K., L., Hale, J. M., Gay, L., Kearney, M., Austin, J. J., Parris, K., and Melville, J. 2013. Spatio-temporal changes in the structure of an Australian frog hybrid zone: a 40year perspective. Evolution 67: 0000-0000.
Sork, V. L., and Waits, L. 2010. Contributions of landscape genetics – approaches, insights, and future potential. Mol. Ecol. 19: 3489–3495.
Storfer, A., Murphy, M. A., Evans, J. S., Goldberg, C. S., Robinson, S., Spear, S. F., R Dezzani, R., Delmelle, E., Vierling, L. and Waits, L. P. 2007. Putting the 'landscape' in landscape genetics. Heredity 98: 128–142.
Storfer, A., Murphy, M. A., Spear, S. F., Holderegger, R., and Waits, L. P. 2010. Landscape genetics: where are we now? Mol. Ecol. 19: 3496–3514.
Turner, T. L., Hahn, M. W., and Nuzhdin, S. V. 2005. Genomic islands of speciation in Anopheles gambiae. PLoS Biol. 3: e285
This article is protected by copyright. All rights reserved.
9
Wandeler, P., Hoeck, P. A. E., and Keller, L. F. 2007. Back to the future: museum specimens in population genetics. Trends Ecol. Evol. 22: 634-642. Wang, I. J. 2013. Examining the full effects of landscape heterogeneity on spatial genetic
Accepted Article
variation: a multiple matrix regression approach for quantifying geographic and ecological isolation. Evolution 67: 0000-0000.
Whitlock, M. C. 2004. Selection and drift in metapopulations. Pp. 153-174 in: Ecology, Genetics, and Evolution of Metapopulations I. Hanski and O.E. Gaggiotti, eds. Elsevier, Amsterdam.
Wright, S. 1931. Evolution in Mendelian populations. Genetics 16: 97-157.
This article is protected by copyright. All rights reserved.
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