Conservation Genetics

, Volume 19, Issue 1, pp 17–26 | Cite as

Traits-based approaches support the conservation relevance of landscape genetics

  • Meryl C. MimsEmail author
  • Emily E. Hartfield Kirk
  • David A. Lytle
  • Julian D. Olden


Calls for evaluating general principles in landscape genetics reflect a broader recognition that multispecies inference is a promising strategy for supporting conservation actions across wide-ranging taxonomies and geographies. Formal evaluation of frameworks for multispecies inference is critical to identify opportunities for generalization and to avoid misguided extrapolation that results in ineffective conservation and management efforts. Traits-based approaches are now widely recognized as useful in addressing knowledge gaps where species-specific data may not be available or feasible to obtain. Here we present a case for the application of traits-based approaches in landscape genetics to improve conservation application. We discuss the fundamental theoretical framework and growing empirical evidence supporting the utility of traits-based approaches in landscape genetics, and we highlight an example of the implementation of traits to predict landscape genetic relationships for a range of aquatic taxa native to the southwestern United States. Finally, we discuss opportunities, challenges, and future directions of using traits to characterize landscape genetic relationships. Ultimately, traits-based approaches can help address growing calls for the development and testing of general principles in landscape genetics in order to improve application to conservation challenges.


Landscape genetics Conservation genetics Traits-based approaches Multispecies inference Focal traits Trait databases 



This research was funded by the Department of Defense Strategic Environmental Research and Development Program (RC-1724). Additional funding was provided by a National Science Foundation Graduate Research Fellowship (Grant No. DGE-0718124) to MCM and the H. Mason Keeler Endowed Professorship (School of Aquatic and Fishery Sciences, University of Washington) to JDO. We thank Erin Landguth and the reviewers for providing helpful comments that greatly improved this manuscript.


  1. Allendorf FW, Hohenlohe PA, Luikart G (2010) Genomics and the future of conservation genetics. Nat Rev Genet 11:697–709CrossRefPubMedGoogle Scholar
  2. Alofs KM (2016) The influence of variability in species trait data on community-level ecological prediction and inference. Ecol Evol 6:6345–6353CrossRefPubMedPubMedCentralGoogle Scholar
  3. Alp M, Keller I, Westram AM, Robinson CT (2012) How river structure and biological traits influence gene flow: a population genetic study of two stream invertebrates with differing dispersal abilities. Freshw Biol 57:969–981CrossRefGoogle Scholar
  4. Amos JN, Harrisson KA, Radford JQ, White M, Newell G, Mac Nally R, Sunnucks P, Pavlova A (2014) Species- and sex-specific connectivity effects of habitat fragmentation in a suite of woodland birds. Ecology 95:1556–1568CrossRefPubMedGoogle Scholar
  5. Aparicio A, Hampe A, Fernández-Carillo L, Albaladejo G (2012) Fragmentation and comparative genetic structure of four mediterranean woody species: complex interactions between life history traits and the landscape context. Divers Distrib 18:226–235CrossRefGoogle Scholar
  6. Balkenhol N, Gugerli F, Cushman SA, Waits LP, Coulon A, Arntzen JW, Holderegger R, Wagner HH (2009) Identifying future research needs in landscape genetics: where to from here? Landsc Ecol 24:455–463CrossRefGoogle Scholar
  7. Balkenhol N, Cushman SA, Storfer AT, Waits LP (eds) (2016) Landscape genetics: concepts, methods, applications. Wiley, LondonGoogle Scholar
  8. Blanck A, Lamouroux N (2006) Large-scale intraspecific variation in life-history traits of European freshwater fish. J Biogeogr 34:862–875CrossRefGoogle Scholar
  9. Bogan MT, Lytle DA (2011) Severe drought drives novel community trajectories in desert stream pools. Freshw Biol 56:2070–2081CrossRefGoogle Scholar
  10. Bolliger J, Lander T, Balkenhol N (2014) Landscape genetics since 2003: status, challenges and future directions. Landsc Ecol 29:361–366CrossRefGoogle Scholar
  11. Bolnick DI, Amarasekare P, Araújo MS et al (2011) Why intraspecific trait variation matters in community ecology. Trends Ecol Evol 26:183–192CrossRefPubMedPubMedCentralGoogle Scholar
  12. Bowman J, Greenhorn JE, Marrotte RR, McKay MM, Morris KY, Prentice MB, Wehtje M (2016) On applications of landscape genetics. Conserv Genet 17:753–760CrossRefGoogle Scholar
  13. Brennan TC, Holycross AT (2006) A field guide to amphibians and reptiles in Arizona. Arizona Game and Fish Department, PhoenixGoogle Scholar
  14. Cañedo-Argüelles M, Olden JD, Phillipsen I, Schriever TA, Lytle DA (2015) Dispersal strength determines meta-community structure in a dendritic riverine network. J Biogeogr 42:778–790CrossRefGoogle Scholar
  15. Chan LM, Zamudio KR (2009) Population differentiation of temperate amphibians in unpredictable environments. Mol Ecol 18:3185–3200CrossRefPubMedGoogle Scholar
  16. Collins JP, Snyder J (2002) Sonora tiger salamander (Ambystoma tigrinum stebbinsi) recovery plan. U.S. Fish and Wildlife Service, Albuquerque, New MexicoGoogle Scholar
  17. Davies KF, Margules CR, Lawrence JF (2004) A synergistic effect puts rare, specialized species at greater risk of extinction. Ecology 85:265–271CrossRefGoogle Scholar
  18. de Magalhães JP, Costa J (2009) A database of vertebrate longevity records and their relation to other life-history traits. J Evol Biol 22:1770–1774CrossRefPubMedGoogle Scholar
  19. de Morais TC, Ghazoul J, Maycock CR et al (2015) Understanding local patterns of genetic diversity in dipterocarps using a multi-site, multi-species approach: implications for forest management and restoration. For Ecol Manag 356:153–165CrossRefGoogle Scholar
  20. Denver RJ, Mirhadi N, Phillips M (1998) Adaptive plasticity in amphibian metamorphosis: response of Scaphiopus hammondii tadpoles to habitat desiccation. Ecology 79:1859–1872Google Scholar
  21. Epperson BK, McRae BH, Scribner K et al (2010) Utility of computer simulations in landscape genetics. Mol Ecol 19:3549–3564CrossRefPubMedGoogle Scholar
  22. Fluker BL, Kuhajda BR, Harris PM (2014) The effects of riverine impoundment on genetic structure and gene flow in two stream fishes in the Mobile River basin. Freshw Biol 59:526–543CrossRefGoogle Scholar
  23. Foden WB, Butchart SHM, Stuart SN et al (2013) Identifying the world’s most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals. PLoS ONE 8:e65427CrossRefPubMedPubMedCentralGoogle Scholar
  24. Frimpong EA, Angermeier PL (2009) FishTraits: a database of ecological and life-history traits of freshwater fishes of the United States. Fish Res 3:487–495CrossRefGoogle Scholar
  25. Galpern P, Manseau M, Wilson P (2012) Grains of connectivity: analysis at multiple spatial scales in landscape genetics. Mol Ecol 21:3996–4009CrossRefPubMedGoogle Scholar
  26. Garcia RA, Araújo MB, Burgess ND, Foden WB, Gutsche A, Rahbek C, Cabeza M (2014) Matching species traits to projected threats and opportunites from climate change. J Biogeogr 41:724–735CrossRefPubMedPubMedCentralGoogle Scholar
  27. Gubili C, Mariani S, Weckworth BV, Galpern P, McDevitt AD, Hebblewhite M, Nickel B, Musiani M (2017) Environmental and anthropogenic drivers of connectivity patterns: a basis for prioritizing conservation efforts for threatened populations. Evol Appl 10:199–211CrossRefPubMedGoogle Scholar
  28. Hedrick PW (2005) A standardized genetic differentiation measure. Evol Int J Org Evol 59:1633–1638CrossRefGoogle Scholar
  29. Hintze C, Heydel F, Hoppe C et al (2013) D3: the dispersal and diaspore database—baseline data and statistical dispersal. Perspect Plant Ecol Evol Syst 15:180–192CrossRefGoogle Scholar
  30. Hoban SM, Hauffe HC, Pérez-Espona S et al (2013) Bringing genetic diversity to the forefront of conservation policy and management. Conserv Genet Resour 5:593–598CrossRefGoogle Scholar
  31. Hughes JM, Huey JA, Schmidt DJ (2013) Is realised connectivity among populations of aquatic fauna predictable from potential connectivity? Freshw Biol 58:951–966CrossRefGoogle Scholar
  32. Jones KE, Bielby J, Cardillo M et al (2009) PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90:2648CrossRefGoogle Scholar
  33. Kattge J, Diaz S, Lavorel S et al (2011) TRY—a global database of traits. Glob Change Biol 17:2905–2935CrossRefGoogle Scholar
  34. Keller D, Holderegger R, van Strien MJ, Bolliger J (2015) How to make landscape genetics beneficial for conservation management? Conserv Genet 16:503–512CrossRefGoogle Scholar
  35. Kelly RP, Palumbi SR (2010) Genetic structure among 50 species of the northeastern Pacific rocky intertidal community. PLoS ONE 5:e8594CrossRefPubMedPubMedCentralGoogle Scholar
  36. Kleyer M, Bekker RM, Knevel IC et al (2008) The LEDA Traitbase: a database of life-history traits of the Northwest European flora. J Ecol 96:1266–1274CrossRefGoogle Scholar
  37. Lancaster J, Downes BJ (2017) Dispersal traits may reflect dispersal distances, but dispersers may not connect populations demographically. Oecologia 184:171–182CrossRefPubMedGoogle Scholar
  38. Landguth EL, Cushman SA, Schwartz MK, McKelvey KS, Murphy M, Luikart G (2010) Quantifying the lag time to detect barriers in landscape genetics. Mol Ecol 19:4179–4191CrossRefPubMedGoogle Scholar
  39. Laughlin DC, Joshi C, van Bodegom PM, Bastow ZA, Fulé PZ (2012) A predictive model of community assembly that incorporates intraspecific trait variation. Ecol Lett 15:1291–1299CrossRefPubMedGoogle Scholar
  40. Manel S, Holderegger R (2013) Ten years of landscape genetics. Trends Ecol Evol 28:614–621CrossRefPubMedGoogle Scholar
  41. Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197CrossRefGoogle Scholar
  42. McGill BJ, Enquist BJ, Weiher E, Westoby M (2006) Rebuilding community ecology from functional traits. Trends Ecol Evol 21:178–185CrossRefPubMedGoogle Scholar
  43. McRae BH (2006) Isolation by resistance. Evol Int J Org Evol 60:1551–1561CrossRefGoogle Scholar
  44. Mims MC, Phillipsen IC, Lytle DA, Hartfield Kirk EE, Olden JD (2015) Ecological strategies predict associations between aquatic and genetic connectivity for dryland amphibians. Ecology 96:1371–1382CrossRefPubMedGoogle Scholar
  45. Mims MC, Hauser L, Goldberg CS, Olden JD (2016) Genetic differentiation, isolation-by-distance, and metapopulation dynamics of the Arizona treefrog (Hyla wrightorum) in an isolated portion of its range. PLoS ONE 11:e0160655CrossRefPubMedPubMedCentralGoogle Scholar
  46. Myhrvold NP, Baldridge E, Chan B et al (2015) An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96:3109CrossRefGoogle Scholar
  47. Oliveira BF, São-Pedro VA, Santos-Barrera G, Penone C et al (2017) AmphiBIO, a global database for amphibian ecological traits. Sci Data 4:170123CrossRefPubMedPubMedCentralGoogle Scholar
  48. Pacifici AM, Foden WB, Visconti P et al (2015) Assessing species vulnerability to climate change. Nat Clim Chang 5:215–225CrossRefGoogle Scholar
  49. Parr CS, Wilson N, Leary P et al (2014) The encyclopedia of life v2: providing global access to knowledge about life on earth. Biodivers Data J 2:e1079CrossRefGoogle Scholar
  50. Peterman WE, Connette GM, Semlitsch RD, Eggert LS (2014) Ecological resistance surfaces predict fine-scale genetic differentiation in a terrestrial woodland salamander. Mol Ecol 23:2402–2413CrossRefPubMedGoogle Scholar
  51. Phillipsen IC, Kirk EEH, Bogan MT, Mims MC, Olden JD, Lytle DA (2015) Dispersal ability and habitat requirements determine landscape-level genetic patterns in desert aquatic insects. Mol Ecol 96:1371–1382CrossRefGoogle Scholar
  52. Radinger J, Wolter C (2014) Patterns and predictors of fish dispersal in rivers. Fish Fish 15:456–473CrossRefGoogle Scholar
  53. Richardson JL (2012) Divergent landscape effects on population connectivity in two co-occurring amphibian species. Mol Ecol 21:4437–4451CrossRefPubMedGoogle Scholar
  54. Richardson JL, Brady SP, Wang IJ, Spear SF (2016) Navigating the pitfalls and promise of landscape genetics. Mol Ecol 25:849–863CrossRefPubMedGoogle Scholar
  55. Romiguier J, Gayral P, Ballenghien M et al (2014) Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature 515:261–263CrossRefPubMedGoogle Scholar
  56. Sabo JL, Finlay JC, Kennedy T, Post DM (2010) The role of discharge variation in scaling of drainage area and food chain length in rivers. Science 330:965–967CrossRefPubMedGoogle Scholar
  57. Schriever TA, Bogan MT, Boersma KS, Cañedo-Argüelles M, Jaeger KL, Olden JD, Lytle DA (2015) Hydrology shapes taxonomic and functional structure of desert stream invertebrate communities. Freshw Sci 34:399–409CrossRefGoogle Scholar
  58. Segelbacher G, Cushman SA, Epperson BK, Fortin M-J, Francois O, Hardy OJ, Holderegger R, Taberlet P, Waits LP, Manel S (2010) Applications of landscape genetics in conservation biology: concepts and challenges. Conserv Genet 11:375–385CrossRefGoogle Scholar
  59. Stewart DR, Underwood ZE, Rahel FJ, Walters AW (2017) The effectiveness of surrogate taxa to conserve freshwater biodiversity. Conserv Biol. Google Scholar
  60. Storfer A, Murphy MA, Spear SF et al (2010) Landscape genetics: where are we now? Mol Ecol 19:3496–3514CrossRefPubMedGoogle Scholar
  61. Storfer A, Mech SG, Reudink MW, Lew K (2014) Inbreeding and strong population subdivision in an endangered salamander. Conserv Genet 15:137–151CrossRefGoogle Scholar
  62. Taylor HR, Dussex N, van Heezik Y (2017) Bridging the conservation genetics gap by identifying barriers to implementation for conservation practitioners. Glob Ecol Conserv 10:231–242CrossRefGoogle Scholar
  63. Trumbo DR, Spear SF, Baumsteiger J, Storfer A (2013) Rangewide landscape genetics of an endemic Pacific northwestern salamander. Mol Ecol 22:1250–1266CrossRefPubMedGoogle Scholar
  64. Van Strein MJ, Keller D, Holderegger R (2012) A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models. Mol Ecol 21:4010–4023CrossRefGoogle Scholar
  65. Vellend M, Lajoie G, Bourret A, Múrria C, Kembel SW, Garant D (2014) Drawing ecological inferences from coincident patterns of population and community-level biodiversity. Mol Ecol 23:2890–2901CrossRefPubMedGoogle Scholar
  66. Vieira NKM, Poff NL, Carlisle DM, Moulton II SR, Koski MK, Kondratieff BC (2006) A database of lotic invertebrate traits for North America. U.S. Geological Survey, Data Series 187Google Scholar
  67. Wagner HH, Fortin M-J (2013) A conceptual framework for the spatial analysis of landscape genetic data. Conserv Genet 14:253–261CrossRefGoogle Scholar
  68. Wells KD (2007) The ecology and behavior of amphibians. The University of Chicago Press, ChicagoCrossRefGoogle Scholar
  69. Whiteley AR, McGarigal K, Schwartz MK (2014) Pronounced differences in genetic structure despite overall ecological similarity for two Ambystoma salamanders in the same landscape. Conserv Genet 15:573–591CrossRefGoogle Scholar
  70. Whitlock MC, McCauley DE (1999) Indirect measures of gene flow and migration: FST not equal to 1/(4 N m + 1). Heredity 82:117–125CrossRefPubMedGoogle Scholar
  71. Williams SE, Shoo LP, Isaac JL et al (2008) Towards an integrated framework for assessing the vulnerability of species to climate change. PLoS Biol 6:2621–2626CrossRefPubMedGoogle Scholar
  72. Winemiller KO, Fitzgerald DB, Bower LM, Pianka ER (2015) Functional traits, convergent evolution, and periodic tables of niches. Ecol Lett 18:737–751CrossRefPubMedPubMedCentralGoogle Scholar
  73. Wright S (1943) Isolation by distance. Genetics 28:114–138PubMedPubMedCentralGoogle Scholar

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© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Biological SciencesVirginia TechBlacksburgUSA
  2. 2.Department of Integrative BiologyOregon State UniversityCorvallisUSA
  3. 3.School of Aquatic and Fishery SciencesUniversity of WashingtonSeattleUSA

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