IPBES. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, Bonn, 2019).
Eizaguirre, C. & Baltazar-Soares, M. Evolutionary conservation-evaluating the adaptive potential of species. Evol. Appl. 7, 963–967 (2014).
Razgour, O. et al. Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. Proc. Natl. Acad. Sci. USA 116, 10418–10423 (2019).
Frankham, R., Ballou, J. D. & Briscoe, D. A. Introduction to Conservation Genetics (Cambridge University Press, Cambridge, 2010).
Hoffmann, A. A., Sgro, C. M. & Kristensen, T. N. Revisiting adaptive potential, population size, and conservation. Trends Ecol. Evol. 32, 506–517 (2017).
Allendorf, F. W., Luikart, G. H. & Aitken, S. N. Conservation and the Genetics of POPULATIONS (Wiley Blackwell, Malden, 2012).
Caballero, A. & Garcia-Dorado, A. Allelic diversity and its implications for the rate of adaptation. Genetics 195, 1373–1384 (2013).
Lopez-Cortegano, E. et al. Optimal management of genetic diversity in subdivided populations. Front. Genet. 10, 843 (2019).
Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).
Mable, B. K. Conservation of adaptive potential and functional diversity: Integrating old and new approaches. Conserv. Genet. 20, 89–100 (2019).
Holderegger, R., Kamm, U. & Gugerli, F. Adaptive vs. neutral genetic diversity: Implications for landscape genetics. Landsc. Ecol. 21, 797–807 (2006).
Kirk, H. & Freeland, J. R. Applications and implications of neutral versus non-neutral markers in molecular ecology. Int. J. Mol. Sci. 12, 3966–3988 (2011).
Yildirim, Y., Tinnert, J. & Forsman, A. Contrasting patterns of neutral and functional genetic diversity in stable and disturbed environments. Ecol. Evol. 8, 12073–12089 (2018).
Reed, D. H. & Frankham, R. How closely correlated are molecular and quantitative measures of genetic variation? A meta-analysis. Evolution 55, 1095–1103 (2001).
Willi, Y. & Hoffmann, A. A. Demographic factors and genetic variation influence population persistence under environmental change. J. Evol. Biol. 22, 124–133 (2009).
Matuszewski, S., Hermisson, J. & Kopp, M. Catch me if you can: Adaptation from standing genetic variation to a moving phenotypic optimum. Genetics 200, 1255–1272 (2015).
Kirkpatrick, M. & Barton, N. H. Evolution of a species’ range. Am. Nat. 150, 1–23 (1997).
Bridle, J. R., Polechova, J., Kawata, M. & Butlin, R. K. Why is adaptation prevented at ecological margins? New insights from individual-based simulations. Ecol. Lett. 13, 485–494 (2010).
Tigano, A. & Friesen, V. L. Genomics of local adaptation with gene flow. Mol. Ecol. 25, 2144–2164 (2016).
Jacob, S. et al. Gene flow favours local adaptation under habitat choice in ciliate microcosms. Nat. Ecol. Evol. 1, 1407–1410 (2017).
Leffler, E. M. et al. Revisiting an old riddle: What determines genetic diversity levels within species?. PLoS Biol. 10, e1001388 (2012).
Corbett-Detig, R. B., Hartl, D. L. & Sackton, T. B. Natural selection constrains neutral diversity across a wide range of species. PLoS Biol. 13, e1002112 (2015).
Ellegren, H. & Galtier, N. Determinants of genetic diversity. Nat. Rev. Genet. 17, 422–433 (2016).
Zhu, Z. B., Yuan, D. J., Luo, D. H., Lu, X. T. & Huang, S. Enrichment of minor alleles of common SNPs and improved risk prediction for Parkinson’s disease. PLoS ONE 10, e0133421 (2015).
Lei, X. Y., Yuan, D. J., Zhu, Z. B. & Huang, S. Collective effects of common SNPs and risk prediction in lung cancer. Heredity 121, 537–547 (2018).
Huang, S. New thoughts on an old riddle: What determines genetic diversity within and between species?. Genomics 108, 3–10 (2016).
Hoffmann, A. A. & Willi, Y. Detecting genetic responses to environmental change. Nat. Rev. Genet. 9, 421–432 (2008).
Sgro, C. M., Lowe, A. J. & Hoffmann, A. A. Building evolutionary resilience for conserving biodiversity under climate change. Evol. Appl. 4, 326–337 (2011).
Luikart, G., England, P. R., Tallmon, D., Jordan, S. & Taberlet, P. The power and promise of population genomics: From genotyping to genome typing. Nat. Rev. Genet. 4, 981–994 (2003).
Pfeiffer, V. W. et al. Partitioning genetic and species diversity refines our understanding of species-genetic diversity relationships. Ecol. Evol. 8, 12351–12364 (2018).
Forester, B. R., Lasky, J. R., Wagner, H. H. & Urban, D. L. Comparing methods for detecting multilocus adaptation with multivariate genotype-environment associations. Mol. Ecol. 27, 2215–2233 (2018).
Dolédec, S. & Chessel, D. Co-Inertia analysis—An alternative method for studying species environment relationships. Freshw. Biol. 31, 277–294 (1994).
Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, Amsterdam, 2012).
Mackintosh, A. et al. The determinants of genetic diversity in butterflies. Nat. Commun. 10, 3466 (2019).
Franks, S. J. & Hoffmann, A. A. Genetics of climate change adaptation. Ann. Rev. Genet. 46, 185–208 (2012).
Scheben, A., Yuan, Y. X. & Edwards, D. Advances in genomics for adapting crops to climate change. Curr. Plant Biol. 6, 2–10 (2016).
Manel, S. et al. Genomic resources and their influence on the detection of the signal of positive selection in genome scans. Mol. Ecol. 25, 170–184 (2016).
Gomulkiewicz, R. & Houle, D. Demographic and genetic constraints on evolution. Am. Nat. 174, E218–E229 (2009).
Willi, Y., Van Buskirk, J. & Hoffmann, A. A. Limits to the adaptive potential of small populations. Annu. Rev. Ecol. Evol. S. 37, 433–458 (2006).
Lehnert, S. J. et al. Genomic signatures and correlates of widespread population declines in salmon. Nat. Commun. 10, 2996 (2019).
Vellend, M. et al. Drawing ecological inferences from coincident patterns of population- and community-level biodiversity. Mol. Ecol. 23, 2890–2901 (2014).
Bertin, A. et al. Genetic variation of loci potentially under selection confounds species-genetic diversity correlations in a fragmented habitat. Mol. Ecol. 26, 431–443 (2017).
Elshire, R. J. et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6, e19379 (2011).
Troncoso, A. J., Bertin, A., Osorio, R., Arancio, G. & Gouin, N. Comparative population genetics of two dominant plant species of high Andean wetlands reveals complex evolutionary histories and conservation perspectives in Chile’s Norte Chico. Conserv. Genet. 18, 1047–1060 (2017).
Vigneau, E. & Qannari, E. M. Clustering of variables around latent components. Commun. Stat-Simul. C. 32, 1131–1150 (2003).
Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Vigneau, E., Chen, M. K. & Qannari, E. M. ClustVarLV: An R package for the clustering of variables around latent variables. R J. 7, 134–148 (2015).
Oksanen, J. et al.Vegan: Community Ecology Packagehttps://cran.r-project.org/web/packages/vegan/index.html (2018).
Dray, S. & Dufour, A. B. The ade4 package: Implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).
Dyer, R. Gstudio: An R Package for the Spatial Analysis of Population Genetic Datahttps://github.com/dyerlab/gstudio/ (2017).
Lumley, T. & Miller, A. Leaps: Regression Subset Selectionhttps://cran.r-project.org/web/packages/leaps/index.html (2009).
Mazerolle, M. J. AICcmodavg: Model Selection and Multimodel Inference Based on (Q)AIC(c)https://cran.r-project.org/web/packages/AICcmodavg/index.html (2020).
Source: Ecology - nature.com
