Adaptive genetic diversity is a key factor in conservation planning as it relates to the evolutionary potential of populations and their responses to environmental change. Developments in landscape genomics have fostered a proliferation of tests for selection that aim to identify candidate adaptive markers in natural populations. However, these tests rely on different assumptions and may produce contrasting results. Here we applied six tests for selection in a range-wide genomic scan of an Iberian amphibian, Pelobates cultripes, which shows spatially structured genetic variation across its range, encompassing different bioclimatic zones. As a cursory scan for selection, one test identified candidate markers that describe a pattern of putatively adaptive genetic variation, highlighting coastal Atlantic localities as exhibiting putatively non-neutral patterns of genetic variation. While generalized genomic scans for selection in non-model species have limitations, exploratory searches can highlight areas to guide subsequent hypothesis-driven studies and conservation efforts.
This is a preview of subscription content,to check access.
Access this article
Raw data and code for data analysis are available at a permanent release deposited in ZENODO [https://zenodo.org/record/5809117].
Adamack AT, Gruber B (2014) PopGenReport: simplifying basic population genetic analyses in R. Methods Ecol Evol 5:384–387. https://doi.org/10.1111/2041-210X.12158
Ahrens CW, Rymer PD, Stow A, Bragg J, Dillon S, Umbers KDL, Dudaniec RY (2018) The search for loci under selection: trends, biases and progress. Mol Ecol 27:1342–1356. https://doi.org/10.1111/mec.14549
Aitken SN, Yeaman S, Holliday JA, Wang T, Curtis-McLane S (2008) Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol Appl 1:95–111. https://doi.org/10.1111/j.1752-4571.2007.00013.x
Akbari M, Wenzl P, Caig V et al (2006) Diversity arrays technology (DArT) for high-throughput profiling of the hexaploid wheat genome. Theor Appl Genet 113:1409–1420. https://doi.org/10.1007/s00122-006-0365-4
Allendorf FW, Hohenlohe PA, Luikart G (2010) Genomics and the future of conservation genetics. Nat Rev Genet 11:697–709. https://doi.org/10.1038/nrg2844
Baldari CT, Amaldi F (1976) DNA reassociation kinetics in relation to genome size in four amphibian species. Chromosoma 59:13–22
Baloch FS, Alsaleh A, Shahid MQ, Çiftçi V, Sáenz de Miera LE, Aasim M, Nadeem MA, Aktaş H, Özkan K, Hatipoğlu R (2017) A whole genome DArTseq and SNP analysis for genetic diversity assessment in durum wheat from central fertile crescent. PLoS ONE 12:1–18. https://doi.org/10.1371/journal.pone.0167821
Battey CJ, Ralph PL, Kern AD (2020) Space is the place: effects of continuous spatial structure on analysis of population genetic data. Genet 215:193–214. https://doi.org/10.1534/genetics.120.303143
Bay RA, Harrigan RJ, Underwood VL, Gibbs HL, Smith TB, Ruegg K (2018) Genomic signals of selection predict climate-driven population declines in a migratory bird. Science 359:83–86. https://doi.org/10.1126/science.aan4380
Beaumont MA, Balding DJ (2004) Identifying adaptive genetic divergence among populations from genome scans. Mol Ecol 13:969–980. https://doi.org/10.1111/j.1365-294X.2004.02125.x
Beaumont MA, Nichols RA (1996) Evaluating loci for use in the genetic analysis of population structure. Proc Royal Soc B 263:1619–1626. https://doi.org/10.1098/rspb.1996.0237
Beja P, Bosch J, Tejedo M, et al (2009) Pelobates cultripes (errata version published in 2016). The IUCN Red List of Threatened Species 2009: e.T58052A86242868. https://doi.org/10.2305/IUCN.UK.2009.RLTS.T58052A11722636.en
Benestan LM, Ferchaud L, Hohenlohe PA, Garner BA, Naylor GJP, Baums IB, Schwartz MK, Kelley JL, Luikart G (2016) Conservation genomics of natural and managed populations: building a conceptual and practical framework. Mol Ecol 25:2967–2977. https://doi.org/10.1111/mec.13647
Borrell JS, Zohren J, Nichols RA, Buggs RJA (2020) Genomic assessment of local adaptation in dwarf birch to inform assisted gene flow. Evol Appl 13:161–175. https://doi.org/10.1111/eva.12883
Bragg JG, Supple MA, Andrew RL, Borevitz JO (2015) Genomic variation across landscapes: insights and applications. New Phytol 207:953–967. https://doi.org/10.1111/nph.13410
Camacho-Sanchez M, Velo-Antón G, Hanson J, Verissimo A, Martínez-Solano I, Marques A, Moritz C, Carvalho S (2020) Comparative assessment of range-wide patterns of genetic diversity and structure with SNPs and microsatellites: a case study with Iberian amphibians. Ecol Evol 10:10353–10363. https://doi.org/10.1002/ece3.6670
Capblancq T, Luu K, Blum MGB, Bazin E (2018) Evaluation of redundancy analysis to identify signatures of local adaptation. Mol Ecol Resour 18:1223–1233. https://doi.org/10.1111/1755-0998.12906
Capblancq T, Fitzpatrick MC, Bay RA, Exposito-Alonso M, Keller SR (2020) Genomic prediction of (Mal)adaptation across current and Future climatic landscapes. Annu Rev Ecol Evol S 51:245–269. https://doi.org/10.1146/annurev-ecolsys-020720-042553
Carvalho SB, Brito JC, Crespo EJ, Possingham HP (2010) From climate change predictions to actions—conserving vulnerable animal groups in hotspots at a regional scale. Glob Change Biol 16:3257–3270. https://doi.org/10.1111/j.1365-2486.2010.02212.x
Caye K, Fran O (2018) LFMM 2.0: Latent factor models for confounder adjustment in genome and epigenome-wide association studies. J BioRxiv. https://doi.org/10.1101/255893
Chen H (2018) VennDiagram: generate high-resolution Venn and Euler plots. https://cran.r-project.org/package=VennDiagram
Coop G, Witonsky D, Di Rienzo A, Pritchard JK (2010) Using environmental correlations to identify loci underlying local adaptation. Genetics 185:1411–1423. https://doi.org/10.1534/genetics.110.114819
da Fonseca RR, Albrechtsen A, Themudo GE, Ramos-Madrigal J, Sibbesen JA, Maretty L, Zepeda-Mendoza ML, Campos PF, Heller R, Pereira RJ (2016) Next-generation biology: sequencing and data analysis approaches for non-model organisms. Mar Gen 30:3–13. https://doi.org/10.1016/j.margen.2016.04.012
Dinerstein E, Olson D, Joshi A et al (2017) An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67:534–545. https://doi.org/10.1093/biosci/bix014
Ekblom R, Galindo J (2010) Applications of next generation sequencing in molecular ecology of non-model organisms. Heredity 107:1–15. https://doi.org/10.1038/hdy.2010.152
Ellegren H (2014) Genome sequencing and population genomics in non-model organisms. Trends Ecol Evol 29:51–63. https://doi.org/10.1016/j.tree.2013.09.008
Ellis N, Smith SJ, Pitcher CR (2012) Gradient forests: calculating importance gradients on physical predictors. Ecol 93:156–168. https://doi.org/10.1890/11-0252.1
Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 14:2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x
Falush D, Stephens M, Pritchard JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:1567–1587. https://doi.org/10.1093/genetics/164.4.1567
Fick SE, Hijmans RJ (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37:4302–4315. https://doi.org/10.1002/joc.5086
Fitzpatrick MC, Keller SR (2015) Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol Lett 18:1–16. https://doi.org/10.1111/ele.12376
Fitzpatrick M, Chhatre V, Soolanayakanahally R, Keller S (2021) Experimental support for genomic prediction of climate maladaptation using the machine learning approach gradient forests. Mol Ecol Resour. https://doi.org/10.1111/1755-0998.13374
Flanagan SP, Jones AG (2017) Constraints on the FST—heterozygosity outlier approach. J Hered 108:561–573. https://doi.org/10.1093/jhered/esx048
Flanagan SP, Hoban S, Forester BR, Latch EK, Aitken SN, Hoban S (2018) Guidelines for planning genomic assessment and monitoring of locally adaptive variation to inform species conservation. Evol Appl 11:1035–1052. https://doi.org/10.1111/eva.12569
Foll M, Gaggiotti O (2008) A genome-scan method to identify selected loci appropriate for both dominant and codominant markers: a Bayesian perspective. Genetics 180:977–993. https://doi.org/10.1534/genetics.108.092221
Forester BR, Lasky JR, Wagner HH, Urban DL (2018) Comparing methods for detecting multilocus adaptation with multivariate genotype—environment associations. Mol Ecol 27:2215–2233. https://doi.org/10.1111/mec.14584
Fox J, Weisber S (2011) An R companion to applied regression, 2nd edn. Sage, Thousand Oaks CA
François O, Martins H, Caye K, Schoville S (2016) A tutorial on controlling false discoveries in genome scans for selection. Mol Ecol 25:454–469. https://doi.org/10.1111/mec.13513
Frichot E, François O (2015) LEA: an R package for landscape and ecological association studies. Method Ecol Evol 6:925–929. https://doi.org/10.1111/2041-210X.12382
Frichot E, Schoville SD, Bouchard G, François O (2013) Testing for associations between loci and environmental gradients using latent factor mixed models. Mol BiolEvol 30:1687–1699. https://doi.org/10.1093/molbev/mst063
Funk WC, Zamudio KR, Crawford AJ (2018) Advancing understanding of amphibian evolution, ecology, behavior, and conservation with massively parallel sequencing. In: Population genomics. Springer, Cham.
Funk WC, Forester BR, Converse SJ, Darst C, Morey S (2019) Improving conservation policy with genomics: a guide to integrating adaptive potential into U.S. Endangered Species Act decisions for conservation practitioners and geneticists. Conserv Genet 20:115–134. https://doi.org/10.1007/s10592-018-1096-1
Galán P, Cabana M, Ferreiro R (2010) Estado de conservación de Pelobates cultripes en Galicia. B Asoc Herpetol Espan 21:90–99
Gouin N, Bertin A, Espinosa MI, Snow DD, Ali JM, Kolok AS (2019) Pesticide contamination drives adaptive genetic variation in the endemic mayfly Andesiops torrens within a semi-arid agricultural watershed of Chile. Environ Pollut 255:113099. https://doi.org/10.1016/j.envpol.2019.113099
Guedes, P. C. G. (2019). Spatial patterns of genetic diversity in Hyla molleri. (M.Sc. dissertation) Universidade de Lisboa. http://hdl.handle.net/10451/38315
Guo B, Lu D, Liao WB, Merilä J (2016) Genomewide scan for adaptive differentiation along altitudinal gradient in the Andrew’s toad Bufo andrewsi. Mol Ecol 25:3884–3900. https://doi.org/10.1111/mec.13722
Gutiérrez-Rodríguez J, Barbosa AM, Martínez-Solano Í (2017) Present and past climatic effects on the current distribution and genetic diversity of the Iberian spadefoot toad (Pelobates cultripes): an integrative approach. J Biogeog 44:245–258. https://doi.org/10.1111/jbi.12791
Hanson JO, Rhodes JR, Riginos C, Fuller RA (2017) Environmental and geographic variables are effective surrogates for genetic variation in conservation planning. PNAS 114:12755–12760. https://doi.org/10.1073/pnas.1711009114
Hanson JO, Veríssimo A, Velo-Antón G, Marques A, Camacho-Sanchez M, Martínez-Solano Í, Gonçalves H, Sequeira F, Possingham HP, Carvalho SB (2021) Evaluating surrogates of genetic diversity for conservation planning. Conserv Biol 35:634–642. https://doi.org/10.1111/cobi.13602
Hermisson J, Pennings PS (2005) Soft sweeps: molecular population genetics of adaptation from standing genetic variation. Genetics 169:2335–2352. https://doi.org/10.1534/genetics.104.036947
Hoban S, Kelley JL, Lotterhos KE, Antolin MF, Bradburd G, Lowry DB, Poss ML, Reed LK, Storfer A, Whitlock MC (2016) Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions. Am Nat 188:379–397. https://doi.org/10.1086/688018
Hohenlohe PA, Catchen J, Cresko WA (2012) Population genomic analysis of model and nonmodel organisms using sequenced RAD tags. In: Pompanon F, Bonin A (eds) Data production and analysis in population genomics. Methods in molecular biology (methods and protocols), 888th edn. Humana Press, Totowa, NJ
Hohenlohe PA, Funk WC, Rajora OP (2021) Population genomics for wildlife conservation and management. Mol Ecol 30:62–82. https://doi.org/10.1111/mec.15720
Hu J, Huang Y, Jiang J, Guisan A (2019) Genetic diversity in frogs linked to past and future climate changes on the roof of the world. J Appl Ecol 88:953–963. https://doi.org/10.1111/1365-2656.12974
Jaccoud D, Peng K, Feinstein D, Kilian A (2001) Diversity Arrays: a solid state technology for sequence information independent genotyping. Nucleic Acids Res 29:e25. https://doi.org/10.1093/nar/29.4.e25
Jackson JM, Pimsler ML, Oyen KJ, Strange JP, Dillon ME, Lozier JD (2020) Local adaptation across a complex bioclimatic landscape in two montane bumble bee species. Mol Ecol 2020:920–939. https://doi.org/10.1111/mec.15376
Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23:1801–1806. https://doi.org/10.1093/bioinformatics/btm233
Jiang B, Wu T, Wong WH (2018) Approximate Bayesian computation with Kullback-Leibler divergence as data discrepancy. In: Proceedings of the 21st international conference on artificial intelligence and statistics (AISTATS) 2018, vol 84. Lanzarote, Spain, p 11
Jombart T (2015) A tutorial for the spatial analysis of principal components (sPCA) using adegenet 2.0.0.
Joost S, Kalbermatten M, Bonin A (2008) Spatial analysis method (SAM): a software tool combining molecular and environmental data to identify candidate loci for selection. Mol Ecol Resour 8:957–960. https://doi.org/10.1111/j.1755-0998.2008.02162.x
Legendre P, Legendre L (1998) Numerical ecology, 2nd edn. Elsevier, Amsterdam
Lewontin RC, Krakauer J (1973) Distribution of gene frequency as a test of the theory of the selective neutrality of polymorphisms. Genet 74:175–195
Liedtke HC, Garrido JG, Codina AE, Gut M, Alioto T, Gomez-Mestre I (2019) De novo assembly and annotation of the larval transcriptome of two spadefoot toads widely divergent in developmental rate. G3: Genes Genom Geneti 9:2647–2655. https://doi.org/10.1534/g3.119.400389
Lischer HEL, Excoffier L (2012) PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28:298–299. https://doi.org/10.1093/bioinformatics/btr642
Lotterhos KE, Whitlock MC (2014) Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests. Mol Ecol 23:2178–2192. https://doi.org/10.1111/mec.12725
Lotterhos KE, Whitlock MC (2015) The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol Ecol 24:1031–1046. https://doi.org/10.1111/mec.13100
Lotterhos KE, Card DC, Schaal SM, Wang L, Collins C, Verity B (2017) Composite measures of selection can improve the signal-to-noise ratio in genome scans. Methods Ecol Evol 8:717–727. https://doi.org/10.1111/2041-210X.12774
Lowry DB, Hoban S, Kelley JL, Lotterhos KE, Reed LK, Antolin MF, Storfer A (2017) Breaking RAD: an evaluation of the utility of restriction site-associated DNA sequencing for genome scans of adaptation. Mol Ecol Resour 17:142–152. https://doi.org/10.1111/1755-0998.12635
Luikart G, England PR, Tallmon D, Jordan S, Taberlet P (2003) The power and promise of population genomics: from genotyping to genome typing. Nat Rev Genet 4:981–994. https://doi.org/10.1038/nrg1226
Luu K, Bazin E, Blum MG (2017) pcadapt: an R package to perform genome scans for selection based on principal component analysis. Mol Ecol Resour 17:67–77. https://doi.org/10.1111/1755-0998.12592
Mable BK (2018) Conservation of adaptive potential and functional diversity: integrating old and new approaches. Conserv Genet 21:89–100. https://doi.org/10.1007/s10592-018-1129-9
MacArthur R (1957) On the relative abundance of bird species. P Natl A Sci 43:293–295. https://doi.org/10.1073/pnas.43.3.293
Marangoni F, Tejedo M, Gomez-Mestre I (2008) Extreme reduction in body size and reproductive output associated with sandy substrates in two anuran species. Amphibia-Reptilia 29:541–553. https://doi.org/10.1163/156853808786230370
Oksanen, J., Blanchet, F. G., Friendly, M. et al. (2019). vegan: Community Ecology Package. R package version 2.5–6.
Peres-Neto PR, Jackson DA (2001) How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia 129:169–178. https://doi.org/10.1007/s004420100720
Pérez-Cruz, F. (2008). Kullback-Leibler Divergence Estimation of Continuous Distributions. In IEEE International Symposium (pp. 1666–1670). Toronto, Canada. https://doi.org/10.1109/ISIT.2008.4595271
Pabijan M, Palomar G, Antunes B, Antoł W, Zieliński P, Babik W (2020) Evolutionary principles guiding amphibian conservation. Evol Appl 13:857–878. https://doi.org/10.1111/eva.12940
Primmer CR, Papakostas S, Leder EH, Davis MJ, Ragan MA (2013) Annotated genes and nonannotated genomes: cross-species use of gene ontology in ecology and evolution research. Mol Ecol 22:3216–3241. https://doi.org/10.1111/mec.12309
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959. https://doi.org/10.1093/genetics/155.2.945
Raiche G (2010) nFactors: an R package for parallel analysis and non graphical solutions to the Cattell scree test. R Package Version 2(3):3
Razgour O, Forester B, Taggart JB et al (2019) Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections. PNAS 116:6–11. https://doi.org/10.1073/pnas.1820663116
Recuero E (2014) Sapo de espuelas - Pelobates cultripes. In: Salvador A, Martínez-Solano I (eds) Enciclopedia virtual de los vertebrados Españoles. Museo Nacional de Ciencias Naturales, Madrid. http://www.vertebradosibericos.org/. Accessed 21 June 2020
Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R (2015) A practical guide to environmental association analysis in landscape genomics. Mol Ecol 24:4348–4370. https://doi.org/10.1111/mec.13322
Rellstab C, Dauphin B, Exposito-Alonso M (2021) Prospects and limitations of genomic offset in conservation management. Evol Appl 14:1202–1212. https://doi.org/10.1111/eva.13205
Revelle W (2018) psych: Procedures for personality and psychological research. Northwestern University, Evanston, Illanois, USA
Sánchez-Montes G, Wang J, Ariño AH, Martínez-Solano Í (2018) Mountains as barriers to gene flow in amphibians: quantifying the differential effect of a major mountain ridge on the genetic structure of four sympatric species with different life history traits. J Biogeog 45:318–331. https://doi.org/10.1111/jbi.13132
Savolainen O, Lascoux M, Merilä J (2013) Ecological genomics of local adaptation. Nat Rev Genet 14:807–820. https://doi.org/10.1038/nrg3522
Scrucca L, Fop M, Murphy TB, Raferty AE (2016) mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. R J 8:289–317
Sgrò CM, Lowe AJ, Hoffmann AA (2011) Building evolutionary resilience for conserving biodiversity under climate change. Evol Appl 4:326–337. https://doi.org/10.1111/j.1752-4571.2010.00157.x
Shafer ABA, Wolf JBW, Alves PC et al (2015) Genomics and the challenging translation into conservation practice. Trends Ecol Evol 30:78–87. https://doi.org/10.1016/j.tree.2014.11.009
Stephan W (2016) Signatures of positive selection: from selective sweeps at individual loci to subtle allele frequency changes in polygenic adaptation. Mol Ecol 25:79–88. https://doi.org/10.1111/mec.13288
Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. PNAS 100:9440–9445. https://doi.org/10.1073/pnas.1530509100
Storey JD, Bass AJ, Dabney A, Robinson D (2018) qvalue: Q-value estimation for false discovery rate control. R package version 2.16.0
Stucki S, Orozco-terWengel P, Forester BR et al (2017) High performance computation of landscape genomic models including local indicators of spatial association. Mol Ecol Resour 17:1072–1089. https://doi.org/10.1111/1755-0998.12629
Supple MA, Shapiro B (2018) Conservation of biodiversity in the genomics era. Genom Biol 19:131. https://doi.org/10.1186/s13059-018-1520-3
Verity R, Collins C, Card DC, Schaal SM, Wang L, Lotterhos KE (2017) minotaur: A platform for the analysis and visualization of multivariate results from genome scans with R Shiny. Mol Ecol R 17:33–43. https://doi.org/10.1111/1755-0998.12579
Vitalis R, Gautier M, Dawson KJ, Beaumont MA (2014) Detecting and measuring selection from gene frequency data. Genetics 196:799–817. https://doi.org/10.1534/genetics.113.152991
Walters SJ, Robinson TP, Byrne M, Wardell-Johnson GW, Nevill P (2020) Contrasting patterns of local adaptation along climatic gradients between a sympatric parasitic and autotrophic tree species. Mol Ecol 29:3022–3037. https://doi.org/10.1111/mec.15537
Wang IJ, Bradburd GS (2014) Isolation by environment. Mol Ecol 23:5649–5662. https://doi.org/10.1111/mec.12938
Wickham H (2011) The split-apply-combine strategy for data analysis. J Stat Soft 40:1–29
Yurchenko AA, Daetwyler HD, Yudin N, Schnabel RD, Vander Jagt CJ, Soloshenko V, Lhasaranov B, Popov R, Taylor JF, Larkin DM (2018) Scans for signatures of selection in Russian cattle breed genomes reveal new candidate genes for environmental adaptation and acclimation. Sci Rep 8:12984. https://doi.org/10.1038/s41598-018-31304-w
Zuur AF, Leno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Method Ecol Evol 1:3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x
We thank Martiño Cabana for providing locality data. We also thank Iria Pazos for her help during field work. Beatriz Alvarez and Isabel Rey provided access to tissue samples in the DNA and Tissue collection at Museo Nacional de Ciencias Naturales (Consejo Superior de Investigaciones Científicas-Spain).
This work was developed under the project PTDC/BIA-BIC/3545/2014, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). AJDM, JOH, MCS, SBC, PT, GVA and AV were funded through national funds from Fundação para a Ciência e Tecnologia (CEECIND/01464/2017, ICETA/EEC2018/16, DL57/2016/CP1440/CT0008, CEECIND/00937/2018, and DL57/2016, respectively).
Conflict of interest
The authors declare no conflict of interest.
Consent to Participate
No human or animal participants were used in the development of this manuscript.
Consent to Publish
All authors have consented to the submission of this manuscript for publication.
Clinical Trials Registration
No clinical trials were conducted in the development of this manuscript.
Research involving Animal Rights
Fieldwork for obtaining tissue samples was done with the corresponding permits from the Portuguese administration (Instituto da Conservação da Natureza e das Florestas: nº 355–356/2018/CAPT) and regional administrations in Spain (Xunta de Galicia: EB-016/2018; Junta de Castilla y León: EP/CyL/726/2015; Gobierno de Cantabria: 1230/2018; Gobierno del Principado de Asturias: 002115/2018).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Below is the link to the electronic supplementary material.
About this article
Cite this article
Marques, A.J.D., Hanson, J.O., Camacho-Sanchez, M. et al. Range-wide genomic scans and tests for selection identify non-neutral spatial patterns of genetic variation in a non-model amphibian species (Pelobates cultripes). Conserv Genet 23, 387–400 (2022). https://doi.org/10.1007/s10592-021-01425-3