Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).
Google Scholar
Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).
Google Scholar
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).
Google Scholar
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).
Google Scholar
Wingfield, J. C. et al. Organism-environment interactions in a changing world: a mechanistic approach. J. Ornithol. 152, 279–288 (2011).
Google Scholar
Mendoza-Gonzalez, G., Martinez, M. L., Rojas-Soto, O. R., Vazquez, G. & Gallego-Fernandez, J. B. Ecological niche modeling of coastal dune plants and future potential distribution in response to climate change and sea level rise. Glob. Change Biol. 19, 2524–2535 (2013).
Google Scholar
Saunders, S. P. et al. Community science validates climate suitability projections from ecological niche modeling. Ecol. Appl. 30, 17 (2020).
Google Scholar
Peterson, A. T., Cobos, M. E. & Jimenez-Garcia, D. Major challenges for correlational ecological niche model projections to future climate conditions. Ann. N. Y. Acad. Sci. 1429, 66–77 (2018).
Google Scholar
Mays, H. L. et al. Genomic analysis of demographic history and Ecological niche modeling in the endangered Sumatran Rhinoceros Dicerorhinus sumatrensis. Curr. Biol. 28, 70–76 (2018).
Google Scholar
Malcolm, R. J., Liu, C., Neilson, P. R., Hansen, L. & Hannah, L. A. Global warming and extinctions of endemic species from biodiversity hotspots. Conserv. Biol. 20, 538–548 (2005).
Google Scholar
Fitzpatrick, M. C. & Keller, S. R. Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol. Lett. 18, 1–16 (2015).
Google Scholar
Gotelli, J. N. & Stanton-Geddes, J. Climate change, genetic markers and species distribution modelling. J. Biogeogr. 42, 1577–1585 (2015).
Google Scholar
Ruegg, K. et al. Ecological genomics predicts climate vulnerability in an endangered southwestern songbird. Ecol. Lett. 21, 1085–1096 (2018).
Google Scholar
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).
Google Scholar
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).
Google Scholar
Bay, R. A. et al. Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359, 83–86 (2018).
Google Scholar
Rhone, B. et al. Pearl millet genomic vulnerability to climate change in West Africa highlights the need for regional collaboration. Nat. Commun. 11, 5274 (2020).
Google Scholar
Rahbek, C. et al. Building mountain biodiversity: geological and evolutionary processes. Science 365, 1114–1119 (2019).
Google Scholar
Fjeldså, J., Bowie, R. C. K. & Rahbek, C. The role of mountain ranges in the diversification of birds. Annu. Rev. Ecol. Evol. Syst. 43, 249–265 (2012).
Google Scholar
Freeman, B. G., Scholer, M. N., Ruiz-Gutierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl Acad. Sci. USA 115, 11982–11987 (2018).
Google Scholar
He, J. K., Lin, S. L., Li, J. T., Yu, J. H. & Jiang, H. S. Evolutionary history of zoogeographical regions surrounding the Tibetan Plateau. Commun. Biol. 3, 9 (2020).
Google Scholar
Wu, Y. J. et al. Explaining the species richness of birds along a subtropical elevational gradient in the Hengduan Mountains. J. Biogeogr. 40, 2310–2323 (2013).
Google Scholar
del Hoyo, J., Elliott, A., Sargatal, J. & Christie, D. A. Handbook of the Birds of the World (Lynx Edicions, 2013).
Qu, Y. et al. Lineage diversification and historical demography of a montane bird Garrulax elliotii – implications for the Pleistocene evolutionary history of the eastern Himalayas. BMC Evolut. Biol. 11, 174 (2011).
Google Scholar
Qu, Y. et al. Long-term isolation and stability explain high genetic diversity in the Eastern Himalaya. Mol. Ecol. 23, 705–720 (2014).
Google Scholar
Wang, W. J. et al. Glacial expansion and diversification of an East Asian montane bird, the green-backed tit (Parus monticolus). J. Biogeogr. 40, 1156–1169 (2013).
Google Scholar
Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
Google Scholar
Laine, V. N. et al. Evolutionary signals of selection on cognition from the great tit genome and methylome. Nat. Commun. 7, 9 (2016).
Google Scholar
Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).
Google Scholar
Giorgetta, M. A. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. J. Adv. Model. Earth Syst. 5, 572–597 (2013).
Google Scholar
Gent, P. R. et al. The community climate system model version 4. J. Clim. 24, 4973–4991 (2011).
Google Scholar
Watanabe, M. et al. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335 (2010).
Google Scholar
Voldoire, A. et al. The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dyn. 40, 2091–2121 (2013).
Google Scholar
Frichot, E., Schoville, S. D., Bouchard, G. & Francois, O. Testing for associations between loci and environmental gradients using latent factor mixed models. Mol. Biol. Evol. 30, 1687–1699 (2013).
Google Scholar
Forester, B. R., Jones, M. R., Joost, S., Landguth, E. L. & Lasky, J. R. Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol. Ecololgy 25, 104–120 (2016).
Google Scholar
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).
Google Scholar
Li, C. et al. Two Antarctic penguin genomes reveal insights into their evolutionary history and molecular changes related to the Antarctic environment. Gigascience 3, 27 (2014).
Google Scholar
Pirri, F. et al. Selection-driven adaptation to the extreme Antarctic environment in Emperor penguin. Preprint at bioRxiv https://doi.org/10.1101/2021.12.14.471946 (2021).
Wang, L. C. et al. Involvement of the Arabidopsis HIT1/AtVPS53 tethering protein homologuein the acclimation of the plasma membrane to heat stess.J. Exp. Bot. 62, 3609–3620 (2011).
Google Scholar
Piñol, R. A. et al. Preoptic BRS3 neurons increase body temperature and heart rate via multiple pathways. Cell Metab. 33, 1389–1403 (2021).
Google Scholar
Guilherme, A. et al. Neuronal modulation of brown adipose activity through perturbation of white adipocyte lipogenesis. Mol. Metab. 16, 116–125 (2018).
Google Scholar
Zhang, Y., Guo, W., zhang, Y., Zhang, H. & Wu, C. Insights into hypoxic adaptation in Tibetan chicken embryos from comparative proteomics. Comp. Biochem. Physiol. Part D. 31, 100602 (2019).
Google Scholar
Pizzagalli, M. D., Bensimon, A. & Superti-Furga, G. A guide to plasma membrane solute carrier proteins. FEBS J. 288, 2784–2835 (2020).
Google Scholar
Qu, Y. et al. Rapid phenotypic evolution with shallow genomic differentiation during early stages of high elevation adaptation in Eurasian Tree Sparrows. Natl Sci. Rev. 7, 113–127 (2020).
Google Scholar
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).
Google Scholar
Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity Distrib. 13, 252–264 (2007).
Google Scholar
Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD – a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).
Google Scholar
Chen, Y. et al. Large-scale genome-wide reveals climate adaptive variability in a cosmopolitan pest. Nat. Commun. 12, 7206 (2021).
Google Scholar
Clarke, R. T., Rothery, P. & Raybould, A. F. Confidence limits for regression relationships between distance matrices: Estimating gene flow with distance. J. Agric. Biol. Environ. Stat. 7, 361–372 (2002).
Google Scholar
Excoffier, L., Dupanloup, I., Huerta-Sanchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).
Google Scholar
Foden, W. B. et al. Climate change vulnerability assessment of species. WIREs Clim. Change 10, e551 (2019).
Google Scholar
Smith, T. B. et al. Genomic vulnerablity and soci-economic threats under climate change in an African rainforest bird. Evolut. Appl. 14, 1239–1247 (2021).
Google Scholar
Liu, B., Liang, E. Y., Liu, K. & Camarero, J. J. Species- and elevation-dependent growth responses to climate warming of mountain forests in the Qinling Mountains, central China. Forests 9, 11 (2018).
Dang, H. S., Zhang, Y. J., Zhang, K. R., Jiang, M. X. & Zhang, Q. F. Climate-growth relationships of subalpine fir (Abies fargesii) across the altitudinal range in the Shennongjia Mountains, central China. Clim. Change 117, 903–917 (2013).
Google Scholar
Lingua, E., Cherubini, P., Motta, R. & Nola, P. Spatial structure along an altitudinal gradient in the Italian central Alps suggests competition and facilitation among coniferous species. J. Veg. Sci. 19, 425–436 (2008).
Google Scholar
Zhang, D. C., Zhang, Y. H., Boufford, D. E. & Sun, H. Elevational patterns of species richness and endemism for some important taxa in the Hengduan Mountains, southwestern China. Biodivers. Conserv. 18, 699–716 (2009).
Google Scholar
Zhang, R. Z., Zheng, D., Yang, Q. Y. & Liu, Y. H. Physical Geography of Hengduan Mountains (Science Press, 1997).
Liu, Y. et al. Sino-Himalayan mountains act as cradles of diversity and immigration centres in the diversification of parrotbills (Paradoxornithidae). J. Biogeogr. 43, 1488–1501 (2016).
Bush, A. et al. Incorporating evolutionary adaptation in species distribution modeling reduces projected vulnerability to climate change. Ecol. Lett. 17, 1468–148 (2016).
Google Scholar
Sparks, M. M., Westley, A. A. H., Falke, J. A. & Quinn, T. P. Thermal adaptation and phenotypic plasticity in a warming world: insights from common garden experiments on Alaskan sockeye salmon. Glob. Change Biol. 23, 5203–5217 (2017).
Google Scholar
Merow, C., Wilson, A. M. & Jetz, W. Integrating occurrence data and expert maps for improved species range predictions. Glob. Ecol. Biogeogr. 26, 243–258 (2017).
Google Scholar
Weisenfeld, N. I., Kumar, V., Shah, P., Church, D. M. & Jaffe, D. B. Direct determination of diploid genome sequences. Genome Res. 27, 757–767 (2017).
Google Scholar
Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1, 18 (2012).
Google Scholar
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).
Google Scholar
She, R., Chu, J. S. C., Wang, K., Pei, J. & Chen, N. GenBlastA: enabling BLAST to identify homologous gene sequences. Genome Res. 19, 143–149 (2009).
Google Scholar
Birney, E., Clamp, M. & Durbin, R. GeneWise and genomewise. Genome Res. 14, 988–995 (2004).
Google Scholar
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
Google Scholar
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Google Scholar
McKenna, A. et al. The genome analysis toolkit: a mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Google Scholar
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Google Scholar
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
Google Scholar
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar
Robinson, J. D., Bunnefeld, L., Hearn, J., Stone, G. N. & Hickerson, M. J. ABC inference of multi-population divergence with admixture from unphased population genomic data. Mol. Ecol. 23, 4458–4471 (2014).
Google Scholar
Nazareno, A. G., Bemmels, J. B., Dick, C. W. & Lohmann, L. G. Minimum sample sizes for population genomics: an empirical study from an Amazonian plant species. Mol. Ecol. Resour. 17, 1136–1147 (2017).
Google Scholar
Willing, E. M., Dreyer, C. & van Oosterhout, C. Estimates of genetic differentiation measured by FST do not necessary require large sample size when using many SNP markers. PLoS One 7, e2649 (2012).
Google Scholar
Keenan, K., Mcginnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. diveRsity: an Rpackage for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).
Google Scholar
Rellstab, C., Gugerli, F., Eckert, I. A., Hancock, M. A. & Holderegger, R. A practical guide to environmental assocaition analysis in landscape genomics. Mol. Ecol. 24, 4348–4370 (2015).
Google Scholar
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).
Google Scholar
Xie, C. et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 39, W316–W322 (2011).
Google Scholar
Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).
Google Scholar
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).
Google Scholar
Boria, R. A., Olson, L. E., Goodman, S. M. & Anderson, R. P. Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol. Model. 275, 73–77 (2014).
Google Scholar
Anderson, R. P. & Raza, A. The effect of the extent of the study region on GISmodels of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. J. Biogeogr. 37, 1378–1393 (2010).
Google Scholar
Pearson, R. G., Raxworthy, C., Nakamura, M. & Peterson, A. T. Predicting species distributions from small numbers of occurrence records: a test case using crypticgeckos in Madagascar. J. Biogeogr. 34, 102–117 (2007).
Google Scholar
Heming, N. M., Dambros, C. & Gutiérrez, E. E. ENMwizard: advanced techniques for Ecological Niche Modeling made easy. https://github.com/HemingNM/ENMwizard (2018).
Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling. Ecography 37, 191–203 (2014).
Google Scholar
Muscarella, R. et al. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for MAXENT ecological niche models. Methods Ecol. Evol. 5, 1198–1205 (2014).
Google Scholar
Owens, H. L. et al. Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecol. Model. 263, 10–18 (2013).
Google Scholar
Akaike, H. New look at statistical-model identification. IEEE Trans. Autom. Control AC19, 716–723 (1974).
Google Scholar
Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).
Google Scholar
Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol. Evol. 3, 327–338 (2012).
Google Scholar
Bellard, C. et al. Will climate change promote future invasions? Glob. Change Biol. 19, 3740–3748 (2013).
Google Scholar
Elith, J., Kearney, M. & Phillips, S. The art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342 (2010).
Google Scholar
Anantharaman, R., Hall, K., Shah, V. B. & Edelman, A. Circuitscape in Julia: high performance connectivity modelling to support conservation decisions. Proc. JuliaCon Conf. 1, 58 (2020).
Google Scholar
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar
Anderson, D. R. & Burnham, K. P. Avoiding pitfalls when using information-theoretic methods. J. Wildl. Manag. 66, 912–918 (2002).
Google Scholar
Van Strien, M. J., Keller, D. & Holderegger, R. A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models. Mol. Ecol. 21, 4010–4023 (2012).
Google Scholar
Bartoń, K. MuMIn: multi-model inference, R package version 1.9.13 (2013).
Zhang, G. et al. Comparative genomics reveal insights into avian genome evolution and adaptation. Science 346, 1311–1320 (2014).
Google Scholar
Roesti, M., Kueng, B., Moser, D. & Berner, D. The genomics of ecological vicariance in threespine stickleback fish. Nat. Commun. 6, 8767 (2015).
Google Scholar
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