1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Animal Biosciences
  4. Volume 12, 2024
  5. Article

Annual Review of Animal Biosciences

Volume 12, 2024

How Can Genomics Help or Hinder Wildlife Conservation?

Abstract

Genomic data are becoming increasingly affordable and easy to collect, and new tools for their analysis are appearing rapidly. Conservation biologists are interested in using this information to assist in management and planning but are typically limited financially and by the lack of genomic resources available for non-model taxa. It is therefore important to be aware of the pitfalls as well as the benefits of applying genomic approaches. Here, we highlight recent methods aimed at standardizing population assessments of genetic variation, inbreeding, and forms of genetic load and methods that help identify past and ongoing patterns of genetic interchange between populations, including those subjected to recent disturbance. We emphasize challenges in applying some of these methods and the need for adequate bioinformatic support. We also consider the promises and challenges of applying genomic approaches to understand adaptive changes in natural populations to predict their future adaptive capacity.

Keyword(s): adaptation, genetic load, genetic structure, heterozygosity, kinship, management
Loading

Article metrics loading...

/content/journals/10.1146/annurev-animal-021022-051810
2024-02-15
2024-05-03
Loading full text...

Full text loading...

/deliver/fulltext/animal/12/1/annurev-animal-021022-051810.html?itemId=/content/journals/10.1146/annurev-animal-021022-051810&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    Willi Y, Kristensen TN, Sgrò CM, Weeks AR, Ørsted M, Hoffmann AA. 2022. Conservation genetics as a management tool: the five best-supported paradigms to assist the management of threatened species. PNAS 119:e2105076119
     [Google Scholar]
  2. 2.
    Kardos M, Armstrong EE, Fitzpatrick SW, Hauser S, Hedrick PW et al. 2021. The crucial role of genome-wide genetic variation in conservation. PNAS 118:e2104642118
     [Google Scholar]
  3. 3.
    Fuentes-Pardo AP, Ruzzante DE. 2017. Whole-genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations. Mol. Ecol. 26:5369–406
     [Google Scholar]
  4. 4.
    Santure AW, Garant D. 2018. Wild GWAS—association mapping in natural populations. Mol. Ecol. Resourc. 18:729–38
     [Google Scholar]
  5. 5.
    Hoffmann A, Griffin P, Dillon S, Catullo R, Rane R et al. 2015. A framework for incorporating evolutionary genomics into biodiversity conservation and management. Clim. Change Responses 2:1
     [Google Scholar]
  6. 6.
    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. Systemat. 51:245–69
     [Google Scholar]
  7. 7.
    Rellstab C, Dauphin B, Exposito-Alonso M. 2021. Prospects and limitations of genomic offset in conservation management. Evol. Appl. 14:1202–12
     [Google Scholar]
  8. 8.
    Flanagan SP, 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. 11:1035–52
     [Google Scholar]
  9. 9.
    McMahon BJ, Teeling EC, Höglund J. 2014. How and why should we implement genomics into conservation?. Evol. Appl. 7:999–1007
     [Google Scholar]
  10. 10.
    Deng X-L, Frandsen PB, Dikow RB, Favre A, Shah DN et al. 2022. The impact of sequencing depth and relatedness of the reference genome in population genomic studies: a case study with two caddisfly species (Trichoptera, Rhyacophilidae, Himalopsyche). Ecol. Evol. 12:e9583
     [Google Scholar]
  11. 11.
    Nevado B, Ramos-Onsins SE, Perez-Enciso M. 2014. Resequencing studies of non-model organisms using closely related reference genomes: optimal experimental designs and bioinformatics approaches for population genomics. Mol. Ecol. 23:1764–79
     [Google Scholar]
  12. 12.
    Prasad A, Lorenzen ED, Westbury MV. 2022. Evaluating the role of reference-genome phylogenetic distance on evolutionary inference. Mol. Ecol. Resourc. 22:45–55
     [Google Scholar]
  13. 13.
    Slabaugh E, Desai JS, Sartor RC, Lawas LMF, Krishna Jagadish SV, Doherty CJ. 2019. Analysis of differential gene expression and alternative splicing is significantly influenced by choice of reference genome. RNA 25:669–84
     [Google Scholar]
  14. 14.
    Formenti G, Theissinger K, Fernandes C, Bista I, Bombarely A et al. 2022. The era of reference genomes in conservation genomics. Trends Ecol. Evol. 37:197–202
     [Google Scholar]
  15. 15.
    Wold J, Koepfli KP, Galla SJ, Eccles D, Hogg CJ et al. 2021. Expanding the conservation genomics toolbox: incorporating structural variants to enhance genomic studies for species of conservation concern. Mol. Ecol. 30:5949–65
     [Google Scholar]
  16. 16.
    Jiao WB, Schneeberger K. 2020. Chromosome-level assemblies of multiple Arabidopsis genomes reveal hotspots of rearrangements with altered evolutionary dynamics. Nat. Commun. 11:989
     [Google Scholar]
  17. 17.
    Kronenberg ZN, Fiddes IT, Gordon D, Murali S, Cantsilieris S et al. 2018. High-resolution comparative analysis of great ape genomes. Science 360:eaar6343
     [Google Scholar]
  18. 18.
    Tigano A, Jacobs A, Wilder AP, Nand A, Zhan Y et al. 2021. Chromosome-level assembly of the Atlantic silverside genome reveals extreme levels of sequence diversity and structural genetic variation. Genome Biol. Evol. 13:evab098
     [Google Scholar]
  19. 19.
    Benevenuto J, Ferrão LFV, Amadeu RR, Munoz P. 2019. How can a high-quality genome assembly help plant breeders?. GigaScience 8:giz068
     [Google Scholar]
  20. 20.
    Matz MV. 2018. Fantastic beasts and how to sequence them: ecological genomics for obscure model organisms. Trends Genet. 34:121–32
     [Google Scholar]
  21. 21.
    PacBio 2023. PacBio Revio | Long-read sequencing at scale. https://www.pacb.com/revio/
  22. 22.
    Vurture GW, Sedlazeck FJ, Nattestad M, Underwood CJ, Fang H et al. 2017. GenomeScope: fast reference-free genome profiling from short reads. Bioinformatics 33:2202–4
     [Google Scholar]
  23. 23.
    Pflug JM, Holmes VR, Burrus C, Johnston JS, Maddison DR. 2020. Measuring genome sizes using read-depth, k-mers, and flow cytometry: methodological comparisons in beetles (Coleoptera). G3 10:3047–60
     [Google Scholar]
  24. 24.
    Gautier M, Foucaud J, Gharbi K, Cézard T, Galan M et al. 2013. Estimation of population allele frequencies from next-generation sequencing data: pool- versus individual-based genotyping. Mol. Ecol. 22:3766–79
     [Google Scholar]
  25. 25.
    Hivert V, Leblois R, Petit EJ, Gautier M, Vitalis R. 2018. Measuring genetic differentiation from Pool-seq data. Genetics 210:315–30
     [Google Scholar]
  26. 26.
    Lou RN, Jacobs A, Wilder AP, Therkildsen NO. 2021. A beginner's guide to low-coverage whole genome sequencing for population genomics. Mol. Ecol. 30:5966–93
     [Google Scholar]
  27. 27.
    Lou RN, Therkildsen NO. 2022. Batch effects in population genomic studies with low-coverage whole genome sequencing data: causes, detection and mitigation. Mol. Ecol. Resourc. 22:1678–92
     [Google Scholar]
  28. 28.
    Moritz C. 1994. Defining ‘evolutionarily significant units’ for conservation. Trends Ecol. Evol. 9:373–75
     [Google Scholar]
  29. 29.
    Pritchard JK, Stephens M, Donnelly P. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945–59
     [Google Scholar]
  30. 30.
    Jombart T, Devillard S, Balloux F. 2010. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11:94
     [Google Scholar]
  31. 31.
    Malinsky M, Trucchi E, Lawson DJ, Falush D. 2018. RADpainter and fineRADstructure: population inference from RADseq data. Mol. Biol. Evol. 35:1284–90
     [Google Scholar]
  32. 32.
    Lawson DJ, Hellenthal G, Myers S, Falush D. 2012. Inference of population structure using dense haplotype data. PLOS Genet. 8:e1002453
     [Google Scholar]
  33. 33.
    Meisner J, Albrechtsen A. 2022. Haplotype and population structure inference using neural networks in whole-genome sequencing data. Genome Res. 32:1542–52
     [Google Scholar]
  34. 34.
    Battey CJ, Coffing GC, Kern AD. 2021. Visualizing population structure with variational autoencoders. G3 11:jkaa036
     [Google Scholar]
  35. 35.
    Linck E, Battey CJ. 2019. Minor allele frequency thresholds strongly affect population structure inference with genomic data sets. Mol. Ecol. Resourc. 19:639–47
     [Google Scholar]
  36. 36.
    Pearman WS, Urban L, Alexander A. 2022. Commonly used Hardy–Weinberg equilibrium filtering schemes impact population structure inferences using RADseq data. Mol. Ecol. Resourc. 22:2599–613
     [Google Scholar]
  37. 37.
    Yi X, Latch EK. 2022. Nonrandom missing data can bias Principal Component Analysis inference of population genetic structure. Mol. Ecol. Resourc. 22:602–11
     [Google Scholar]
  38. 38.
    Browning BL, Browning SR. 2016. Genotype imputation with millions of reference samples. Am. J. Hum. Genet. 98:116–26
     [Google Scholar]
  39. 39.
    Clark LV, Lipka AE, Sacks EJ. 2019. polyRAD: genotype calling with uncertainty from sequencing data in polyploids and diploids. G3 9:663–73
     [Google Scholar]
  40. 40.
    Bradburd GS, Coop GM, Ralph PL. 2018. Inferring continuous and discrete population genetic structure across space. Genetics 210:33–52
     [Google Scholar]
  41. 41.
    Perez MF, Franco FF, Bombonato JR, Bonatelli IAS, Khan G et al. 2018. Assessing population structure in the face of isolation by distance: Are we neglecting the problem?. Divers. Distrib. 24:1883–89
     [Google Scholar]
  42. 42.
    Cullingham CI, Miller JM, Peery RM, Dupuis JR, Malenfant RM et al. 2020. Confidently identifying the correct K value using the ΔK method: When does K = 2?. Mol. Ecol. 29:862–69
     [Google Scholar]
  43. 43.
    Lawson DJ, van Dorp L, Falush D. 2018. A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots. Nat. Commun. 9:3258
     [Google Scholar]
  44. 44.
    Thia JA. 2023. Guidelines for standardizing the application of discriminant analysis of principal components to genotype data. Mol. Ecol. Resourc. 23:523–38
     [Google Scholar]
  45. 45.
    Patterson N, Price AL, Reich D. 2006. Population structure and eigenanalysis. PLOS Genet. 2:e190
     [Google Scholar]
  46. 46.
    Puechmaille SJ. 2016. The program STRUCTURE does not reliably recover the correct population structure when sampling is uneven: subsampling and new estimators alleviate the problem. Mol. Ecol. Resourc. 16:608–27
     [Google Scholar]
  47. 47.
    McVean G. 2009. A genealogical interpretation of principal components analysis. PLOS Genetics 5:e1000686
     [Google Scholar]
  48. 48.
    Li H, Ralph P. 2019. Local PCA shows how the effect of population structure differs along the genome. Genetics 211:289–304
     [Google Scholar]
  49. 49.
    Battey CJ. 2020. Evidence of linked selection on the Z chromosome of hybridizing hummingbirds. Evolution 74:725–39
     [Google Scholar]
  50. 50.
    Martin SA, Lipps GJ Jr, Gibbs HL. 2021. Pedigree-based assessment of recent population connectivity in a threatened rattlesnake. Mol. Ecol. Resourc. 21:1820–32
     [Google Scholar]
  51. 51.
    Martin SA, Peterman WE, Lipps GJ Jr., Gibbs HL. 2022. Inferring population connectivity in eastern massasauga rattlesnakes (Sistrurus catenatus) using landscape genetics. Ecol. Appl. 33:e2793
     [Google Scholar]
  52. 52.
    Weeks AR, Stoklosa J, Hoffmann AA. 2016. Conservation of genetic uniqueness of populations may increase extinction likelihood of endangered species: the case of Australian mammals. Front. Zool. 13:31
     [Google Scholar]
  53. 53.
    Beichman AC, Kalhori P, Kyriazis CC, DeVries AA, Nigenda-Morales S et al. 2023. Genomic analyses reveal range-wide devastation of sea otter populations. Mol. Ecol. 32:281–98
     [Google Scholar]
  54. 54.
    Mathur S, Tomeček JM, Tarango-Arámbula LA, Perez RM, DeWoody JA. 2023. An evolutionary perspective on genetic load in small, isolated populations as informed by whole genome resequencing and forward-time simulations. Evolution 77:690–704
     [Google Scholar]
  55. 55.
    Rousset F 2007. Inferences from spatial population genetics. Handbook of Statistical Genetics DJ Balding, MJ Bishop, C Cannings 945–79. Chichester, UK: John Wiley
     [Google Scholar]
  56. 56.
    Hoffmann AA, Sgrò CM, Kristensen TN. 2017. Revisiting adaptive potential, population size, and conservation. Trends Ecol. Evol. 32:506–17
     [Google Scholar]
  57. 57.
    Armstrong EE, Taylor RW, Miller DE, Kaelin CB, Barsh GS et al. 2020. Long live the king: chromosome-level assembly of the lion (Pathera lion) using linked-read, Hi-C, and long-read data. BMC Biol. 18:3
     [Google Scholar]
  58. 58.
    Westbury MV, Petersen B, Garde E, Heide-Jørgensen MP, Lorenzen ED. 2019. Narwhal genome reveals long-term low genetic diversity despite current large abundance size. iScience 15:592–99
     [Google Scholar]
  59. 59.
    Korunes KL, Samuk K. 2021. PIXY: unbiased estimation of nucleotide diversity and divergence in the presence of missing data. Mol. Ecol. Resourc. 21:1359–68
     [Google Scholar]
  60. 60.
    Schmidt TL, Jasper M-E, Weeks AR, Hoffmann AA. 2021. Unbiased population heterozygosity estimates from genome-wide sequence data. Methods Ecol. Evol. 12:1888–98
     [Google Scholar]
  61. 61.
    Andrews KR, Good JM, Miller MR, Luikart G, Hohenlohe PA. 2016. Harnessing the power of RADseq for ecological and evolutionary genomics. Nat. Rev. Genet. 17:81–92
     [Google Scholar]
  62. 62.
    Duchen P, Salamin N. 2021. A cautionary note on the use of genotype callers in phylogenomics. Syst. Biol. 70:844–54
     [Google Scholar]
  63. 63.
    Brauer CJ, Sandoval-Castillo J, Gates K, Hammer MP, Unmack PJ et al. 2023. Natural hybridization reduces vulnerability to climate change. Nat. Clim. Change 13:282–89
     [Google Scholar]
  64. 64.
    van der Valk T, Díez-del-Molino D, Marques-Bonet T, Guschanski K, Dalén L. 2019. Historical genomes reveal the genomic consequences of recent population decline in eastern gorillas. Curr. Biol. 29:165–70.e6
     [Google Scholar]
  65. 65.
    Kardos M, Åkesson M, Fountain T, Flagstad Ø, Liberg O et al. 2018. Genomic consequences of intensive inbreeding in an isolated wolf population. Nat. Ecol. Evol. 2:124–31
     [Google Scholar]
  66. 66.
    Martin CA, Sheppard EC, Illera JC, Suh A, Nadachowska-Brzyska K et al. 2023. Runs of homozygosity reveal past bottlenecks and contemporary inbreeding across diverging populations of an island-colonizing bird. Mol. Ecol. 32:1972–89
     [Google Scholar]
  67. 67.
    Hewett AM, Stoffel MA, Peters L, Johnston SE, Pemberton JM. 2023. Selection, recombination and population history effects on runs of homozygosity (ROH) in wild red deer (Cervus elaphus). Heredity 130:242–50
     [Google Scholar]
  68. 68.
    Stoffel MA, Johnston SE, Pilkington JG, Pemberton JM. 2021. Genetic architecture and lifetime dynamics of inbreeding depression in a wild mammal. Nat. Commun. 12:2972
     [Google Scholar]
  69. 69.
    Narasimhan V, Danecek P, Scally A, Xue Y, Tyler-Smith C, Durbin R. 2016. BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data. Bioinformatics 32:1749–51
     [Google Scholar]
  70. 70.
    Battey CJ, Ralph PL, Kern AD. 2020. Space is the place: effects of continuous spatial structure on analysis of population genetic data. Genetics 215:193–214
     [Google Scholar]
  71. 71.
    Ekblom R, Brechlin B, Persson J, Smeds L, Johansson M et al. 2018. Genome sequencing and conservation genomics in the Scandinavian wolverine population. Conserv. Biol. 32:1301–12
     [Google Scholar]
  72. 72.
    Hasselgren M, Dussex N, von Seth J, Angerbjörn A, Olsen RA et al. 2021. Genomic and fitness consequences of inbreeding in an endangered carnivore. Mol. Ecol. 30:2790–99
     [Google Scholar]
  73. 73.
    Zilko JP, Harley D, Hansen B, Pavlova A, Sunnucks P. 2020. Accounting for cryptic population substructure enhances detection of inbreeding depression with genomic inbreeding coefficients: an example from a critically endangered marsupial. Mol. Ecol. 29:2978–93
     [Google Scholar]
  74. 74.
    Galla SJ, Brown L, Couch-Lewis Y, Cubrinovska I, Eason D et al. 2022. The relevance of pedigrees in the conservation genomics era. Mol. Ecol. 31:41–54
     [Google Scholar]
  75. 75.
    Patterson TA, Hillary RM, Kyne PM, Pillans RD, Gunasekera RM et al. 2022. Rapid assessment of adult abundance and demographic connectivity from juvenile kin pairs in a critically endangered species. Sci. Adv. 8:eadd1679
     [Google Scholar]
  76. 76.
    Jasper ME, Hoffmann AA, Schmidt TL. 2022. Estimating dispersal using close kin dyads: the KINDISPERSE R package. Mol. Ecol. Resourc. 22:1200–12
     [Google Scholar]
  77. 77.
    Escoda L, Fernández-González Á, Castresana J. 2019. Quantitative analysis of connectivity in populations of a semi-aquatic mammal using kinship categories and network assortativity. Mol. Ecol. Resourc. 19:310–26
     [Google Scholar]
  78. 78.
    Schmidt TL, Elfekih S, Cao L-J, Wei S-J, Al-Fageeh MB et al. 2023. Close kin dyads indicate intergenerational dispersal and barriers. Am. Nat. 201:65–77
     [Google Scholar]
  79. 79.
    Fountain ED, Kang J, Tempel DJ, Palsbøll PJ, Pauli JN, Peery MZ. 2018. Genomics meets applied ecology: characterizing habitat quality for sloths in a tropical agroecosystem. Mol. Ecol. 27:41–53
     [Google Scholar]
  80. 80.
    Schmidt TL, Swan T, Chung J, Karl S, Demok S et al. 2021. Spatial population genomics of a recent mosquito invasion. Mol. Ecol. 30:1174–89
     [Google Scholar]
  81. 81.
    Feutry P, Berry O, Kyne PM, Pillans RD, Hillary RM et al. 2017. Inferring contemporary and historical genetic connectivity from juveniles. Mol. Ecol. 26:444–56
     [Google Scholar]
  82. 82.
    Barfield S, Davies SW, Matz MV. 2023. Evidence of sweepstakes reproductive success in a broadcast-spawning coral and its implications for coral metapopulation persistence. Mol. Ecol. 32:696–702
     [Google Scholar]
  83. 83.
    Bradburd GS, Ralph PL. 2019. Spatial population genetics: It's about time. Annu. Rev. Ecol. Evol. Systemat. 50:427–49
     [Google Scholar]
  84. 84.
    Conomos MP, Reiner AP, Weir BS, Thornton TA. 2016. Model-free estimation of recent genetic relatedness. Am. J. Hum. Genet. 98:127–48
     [Google Scholar]
  85. 85.
    Baetscher DS, Clemento AJ, Ng TC, Anderson EC, Garza JC. 2018. Microhaplotypes provide increased power from short-read DNA sequences for relationship inference. Mol. Ecol. Resourc. 18:296–305
     [Google Scholar]
  86. 86.
    Seidman DN, Shenoy SA, Kim M, Babu R, Woods IG et al. 2020. Rapid, phase-free detection of long identity-by-descent segments enables effective relationship classification. Am. J. Hum. Genet. 106:453–66
     [Google Scholar]
  87. 87.
    Huisman J. 2017. Pedigree reconstruction from SNP data: parentage assignment, sibship clustering and beyond. Mol. Ecol. Resourc. 17:1009–24
     [Google Scholar]
  88. 88.
    Hauser SS, Galla SJ, Putnam AS, Steeves TE, Latch EK. 2022. Comparing genome-based estimates of relatedness for use in pedigree-based conservation management. Mol. Ecol. Resourc. 22:2546–58
     [Google Scholar]
  89. 89.
    Lynch M, Gabriel W. 1990. Mutation load and the survival of small populations. Evolution 44:1725–37
     [Google Scholar]
  90. 90.
    Bertorelle G, Raffini F, Bosse M, Bortoluzzi C, Iannucci A et al. 2022. Genetic load: genomic estimates and applications in non-model animals. Nat. Rev. Genet. 23:492–503
     [Google Scholar]
  91. 91.
    Robinson J, Kyriazis CC, Yuan SC, Lohmueller KE. 2023. Deleterious variation in natural populations and implications for conservation genetics. Annu. Rev. Anim. Biosci. 11:93–114
     [Google Scholar]
  92. 92.
    Keller LF, Waller DM. 2002. Inbreeding effects in wild populations. Trends Ecol. Evol. 17:230–41
     [Google Scholar]
  93. 93.
    Mathur S, DeWoody JA. 2021. Genetic load has potential in large populations but is realized in small inbred populations. Evol. Appl. 14:1540–57
     [Google Scholar]
  94. 94.
    Dussex N, Van Der Valk T, Morales HE, Wheat CW, Díez-del-Molino D et al. 2021. Population genomics of the critically endangered kākāpō. Cell Genom. 1:100002
     [Google Scholar]
  95. 95.
    Leroy G, Carroll EL, Bruford MW, DeWoody JA, Strand A et al. 2018. Next-generation metrics for monitoring genetic erosion within populations of conservation concern. Evol. Appl. 11:1066–83
     [Google Scholar]
  96. 96.
    Smeds L, Ellegren H. 2022. From high masked to high realized genetic load in inbred Scandinavian wolves. . Mol. Ecol. 32:1567–80
     [Google Scholar]
  97. 97.
    Wang Q, Lan T, Li H, Sahu SK, Shi M et al. 2022. Whole-genome resequencing of Chinese pangolins reveals a population structure and provides insights into their conservation. Commun. Biol. 5:821
     [Google Scholar]
  98. 98.
    Díez-del-Molino D, Sánchez-Barreiro F, Barnes I, Gilbert MTP, Dalén L. 2018. Quantifying temporal genomic erosion in endangered species. Trends Ecol. Evol. 33:176–85
     [Google Scholar]
  99. 99.
    Szulkin M, Sheldon BC. 2007. The environmental dependence of inbreeding depression in a wild bird population. PLOS ONE 2:e1027
     [Google Scholar]
  100. 100.
    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
     [Google Scholar]
  101. 101.
    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
     [Google Scholar]
  102. 102.
    Phelps MP, Seeb LW, Seeb JE. 2020. Transforming ecology and conservation biology through genome editing. Conserv. Biol. 34:54–65
     [Google Scholar]
  103. 103.
    O'Leary SJ, Puritz JB, Willis SC, Hollenbeck CM, Portnoy DS. 2018. These aren't the loci you're looking for: principles of effective SNP filtering for molecular ecologists. Mol. Ecol. 27:3193–206
     [Google Scholar]
  104. 104.
    Dauphin B, Rellstab C, Wüest RO, Karger DN, Holderegger R et al. 2023. Re-thinking the environment in landscape genomics. Trends Ecol. Evol. 38:261–74
     [Google Scholar]
  105. 105.
    Pallares LF, Lea AJ, Han C, Filippova EV, Andolfatto P, Ayroles JF. 2023. Dietary stress remodels the genetic architecture of lifespan variation in outbred Drosophila. . Nat. Genet. 55:123–29
     [Google Scholar]
  106. 106.
    Tanaka K, Sasaki K, Matsumura K, Yajima S, Miyatake T. 2021. Genomic characterization between strains selected for death-feigning duration for avoiding attack of a beetle. Sci. Rep. 11:21816
     [Google Scholar]
  107. 107.
    Quigley KM, Bay LK, van Oppen MJH. 2020. Genome-wide SNP analysis reveals an increase in adaptive genetic variation through selective breeding of coral. Mol. Ecol. 29:2176–88
     [Google Scholar]
  108. 108.
    Csilléry K, Rodríguez-Verdugo A, Rellstab C, Guillaume F. 2018. Detecting the genomic signal of polygenic adaptation and the role of epistasis in evolution. Mol. Ecol. 27:606–12
     [Google Scholar]
  109. 109.
    Fuller ZL, Mocellin VJ, Morris LA, Cantin N, Shepherd J et al. 2020. Population genetics of the coral Acropora millepora: toward genomic prediction of bleaching. Science 369:eaba4674
     [Google Scholar]
  110. 110.
    Stern DB, Anderson NW, Diaz JA, Lee CE. 2022. Genome-wide signatures of synergistic epistasis during parallel adaptation in a Baltic Sea copepod. . Nat. Commun. 13:4024
     [Google Scholar]
  111. 111.
    Fitzpatrick MC, Chhatre VE, Soolanayakanahally RY, Keller SR. 2021. Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests. Mol. Ecol. Resourc. 21:2749–65
     [Google Scholar]
  112. 112.
    Gienapp P. 2020. Opinion: Is gene mapping in wild populations useful for understanding and predicting adaptation to global change?. Glob. Change Biol. 26:2737–49
     [Google Scholar]
  113. 113.
    Bush A, Mokany K, Catullo R, Hoffmann A, Kellermann V et al. 2016. Incorporating evolutionary adaptation in species distribution modelling reduces projected vulnerability to climate change. Ecol. Lett. 19:1468–78
     [Google Scholar]
  114. 114.
    Wilson AJ, Réale D, Clements MN, Morrissey MM, Postma E et al. 2010. An ecologist's guide to the animal model. J. Anim. Ecol. 79:13–26
     [Google Scholar]
  115. 115.
    Gagnaire PA, Gaggiotti OE. 2016. Detecting polygenic selection in marine populations by combining population genomics and quantitative genetics approaches. Curr. Zool. 62:603–16
     [Google Scholar]
  116. 116.
    Thomson CE, Winney IS, Salles OC, Pujol B. 2018. A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance. PLOS ONE 13:e0197720
     [Google Scholar]
  117. 117.
    Bérénos C, Ellis PA, Pilkington JG, Pemberton JM. 2014. Estimating quantitative genetic parameters in wild populations: a comparison of pedigree and genomic approaches. Mol. Ecol. 23:3434–51
     [Google Scholar]
  118. 118.
    Gervais L, Perrier C, Bernard M, Merlet J, Pemberton JM et al. 2019. RAD-sequencing for estimating genomic relatedness matrix-based heritability in the wild: a case study in roe deer. Mol. Ecol. Resourc. 19:1205–17
     [Google Scholar]
  119. 119.
    Perrier C, Delahaie B, Charmantier A. 2018. Heritability estimates from genomewide relatedness matrices in wild populations: application to a passerine, using a small sample size. Mol. Ecol. Resourc. 18:838–53
     [Google Scholar]
  120. 120.
    Urban L, Miller AK, Eason D, Vercoe D, Shaffer M et al. 2022. Non-invasive real-time genomic monitoring of the critically endangered kākāpō. eLife 12:RP84553
     [Google Scholar]
  121. 121.
    Liddell E, Sunnucks P, Cook CN. 2021. To mix or not to mix gene pools for threatened species management? Few studies use genetic data to examine the risks of both actions, but failing to do so leads disproportionately to recommendations for separate management. Biol. Conserv. 256:109072
     [Google Scholar]
  122. 122.
    Hoffmann AA, White VL, Jasper M, Yagui H, Sinclair SJ, Kearney MR. 2021. An endangered flightless grasshopper with strong genetic structure maintains population genetic variation despite extensive habitat loss. Ecol. Evol. 11:5364–80
     [Google Scholar]
  123. 123.
    Turbek SP, Funk WC, Ruegg KC. 2023. Where to draw the line? Expanding the delineation of conservation units to highly mobile taxa. . J. Hered. 114:300–11
     [Google Scholar]
  124. 124.
    Haig SM. 1998. Molecular contributions to conservation. Ecology 79:413–25
     [Google Scholar]
  125. 125.
    Frankham R, Ballou JD, Briscoe DA 2002. Introduction to Conservation Genetics Cambridge, UK: Cambridge Univ. Press
  126. 126.
    Allendorf FW. 2017. Genetics and the conservation of natural populations: allozymes to genomes. Mol. Ecol. 26:420–30
     [Google Scholar]
  127. 127.
    Forsdick NJ, Martini D, Brown L, Cross HB, Maloney RF et al. 2021. Genomic sequencing confirms absence of introgression despite past hybridisation between a critically endangered bird and its common congener. Glob. Ecol. Conserv. 28:e01681
     [Google Scholar]
  128. 128.
    Hoffmann A, Jasper M, White V, Yagui H, Kearney M. 2022. Long-term biogeographic processes dominate patterns of genetic diversity in a wingless grasshopper despite substantial recent habitat fragmentation. Mol. Ecol. 32:3150–64
     [Google Scholar]
  129. 129.
    Pazmiño DA, Maes GE, Simpfendorfer CA, Salinas-de-León P, van Herwerden L. 2017. Genome-wide SNPs reveal low effective population size within confined management units of the highly vagile Galapagos shark (Carcharhinus galapagensis). Conserv. Genet. 18:1151–63
     [Google Scholar]
  130. 130.
    Quek ZBR, Ng JY, Jain SS, Long JS, Lim SC et al. 2022. Low genetic diversity and predation threaten a rediscovered marine sponge. Sci. Rep. 12:22499
     [Google Scholar]
  131. 131.
    von Takach B, Ranjard L, Burridge CP, Cameron SF, Cremona T et al. 2022. Population genomics of a predatory mammal reveals patterns of decline and impacts of exposure to toxic toads. Mol. Ecol. 31:5468–86
     [Google Scholar]
  132. 132.
    Cassin-Sackett L, Campana MG, McInerney NR, Lim HC, Przelomska NA et al. 2021. Genetic structure and population history in two critically endangered Kaua′i honeycreepers. Conserv. Genet. 22:601–14
     [Google Scholar]
  133. 133.
    Marshall IR, Brauer CJ, Wedderburn SD, Whiterod NS, Hammer MP et al. 2022. Longitudinal monitoring of neutral and adaptive genomic diversity in a reintroduction. Conserv. Biol. 36:e13889
     [Google Scholar]
  134. 134.
    Colpitts J, McLoughlin PD, Poissant J 2022. Runs of homozygosity in Sable Island feral horses reveal the genomic consequences of inbreeding and divergence from domestic breeds. BMC Genom. 23:501
     [Google Scholar]
  135. 135.
    Riaño G, Fontsere C, de Manuel M, Talavera A, Burriel-Carranza B et al. 2022. Genomics reveals introgression and purging of deleterious mutations in the Arabian leopard (Pathera pardus nimr). bioRxiv. https://doi.org/10.1101/2022.11.08.515636
  136. 136.
    Harrisson KA, Magrath MJL, Yen JDL, Pavlova A, Murray N et al. 2019. Lifetime fitness costs of inbreeding and being inbred in a critically endangered bird. Curr. Biol. 29:2711–17.e4
     [Google Scholar]
  137. 137.
    Ottewell KM, Bickerton DC, Byrne M, Lowe AJ. 2016. Bridging the gap: a genetic assessment framework for population-level threatened plant conservation prioritization and decision-making. Divers. Distrib. 22:174–88
     [Google Scholar]
  138. 138.
    DeWoody JA, Harder AM, Mathur S, Willoughby JR. 2021. The long-standing significance of genetic diversity in conservation. Mol. Ecol. 30:4147–54
     [Google Scholar]
  139. 139.
    Ørsted M, Hoffmann AA, Sverrisdóttir E, Nielsen KL, Kristensen TN. 2019. Genomic variation predicts adaptive evolutionary responses better than population bottleneck history. PLOS Genet. 15:e1008205
     [Google Scholar]
  140. 140.
    Kosch T, Silva C, Brannelly L, Roberts A, Lau Q et al. 2019. Genetic potential for disease resistance in critically endangered amphibians decimated by chytridiomycosis. Anim. Conserv. 22:238–50
     [Google Scholar]
  141. 141.
    Teixeira JC, Huber CD. 2021. The inflated significance of neutral genetic diversity in conservation genetics. PNAS 118:e2015096118
     [Google Scholar]
  142. 142.
    Stahlke AR, Epstein B, Barbosa S, Margres MJ, Patton AH et al. 2021. Contemporary and historical selection in Tasmanian devils (Sarcophilus harrisii) support novel, polygenic response to transmissible cancer. Proc. R. Soc. B 288:20210577
     [Google Scholar]
  143. 143.
    Lachish S, McCallum H, Jones M. 2009. Demography, disease and the devil: life-history changes in a disease-affected population of Tasmanian devils (Sarcophilus harrisii). J. Anim. Ecol. 78:427–36
     [Google Scholar]
  144. 144.
    Hoffmann AA, Miller AD, Weeks AR. 2021. Genetic mixing for population management: from genetic rescue to provenancing. Evol. Appl. 14:634–52
     [Google Scholar]
  145. 145.
    Shaw RE, Spencer PB, Gibson LA, Dunlop JA, Kinloch JE et al. 2023. Linking life history to landscape for threatened species conservation in a multiuse region. Conserv. Biol. 37:e13989
     [Google Scholar]
  146. 146.
    Bonin A, Nicole F, Pompanon F, Miaud C, Taberlet P. 2007. Population adaptive index: a new method to help measure intraspecific genetic diversity and prioritize populations for conservation. Conserv. Biol. 21:697–708
     [Google Scholar]
  147. 147.
    Fitzpatrick MC, Keller SR, Lotterhos KE. 2018. Response to Comment on “Genomic signals of selection predict climate-driven population declines in a migratory bird. .” Science 361:eaat7279
     [Google Scholar]
/content/journals/10.1146/annurev-animal-021022-051810
Loading
How Can Genomics Help or Hinder Wildlife Conservation?
Annual Review of Animal Biosciences 12, 45 (2024); https://doi.org/10.1146/annurev-animal-021022-051810
/content/journals/10.1146/annurev-animal-021022-051810
/content/journals/10.1146/annurev-animal-021022-051810
Loading

Data & Media loading...

Supplemental Material

Supplementary Data

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error