Skip to main content

Landscape genetics goes to sea


A recent study revealing geographical and environmental barriers to gene flow in the harbour porpoise shows the great potential of 'landscape genetics' when applied to marine organisms.

Analysis of the genetic structure of populations using molecular markers is currently undergoing a revolution as a result of the advent of novel conceptual and statistical developments, along with advances in molecular biology and genomics [1]. One of the most promising new avenues consists in combining information on geographical landscape features with analysis of molecular markers in order to understand how environmental factors affect the dispersal of individuals and the size and density of populations. This discipline, termed 'landscape genetics' [2, 3], provides a bridge between landscape ecology and population genetics and has so far concentrated on terrestrial [4] and freshwater [5] organisms. The marine environment may superficially be conceived as coherent and homogenous across large geographical distances. Concordant with this view, several studies have shown significantly lower genetic differentiation among populations of marine fish species as compared to freshwater fishes [6]. Nevertheless, since the late 1990s, studies have increasingly documented genetic differentiation among populations of marine organisms, often coinciding with transitions between different basins [7, 8] and gyres and eddies [9]. Landscape genetics may show particularly strong potential for determining the factors shaping these patterns of genetic structuring in marine organisms.

Barriers to gene flow in the harbour porpoise

In an interesting new study, Fontaine and colleagues [10] applied landscape-genetics methods to analyze the genetic structure of the harbour porpoise (Phocoena phocoena) over a geographical region ranging from the Black Sea to the northernmost parts of the eastern Atlantic. The study was based on analysis of microsatellite DNA variation in a total of 752 individuals. Fontaine et al. first used a well-established program, Structure [11], for determining the number of groups or populations represented by the sampled individuals. The results provided a strong signal for the presence of three genetically distinct groups, corresponding to harbour porpoise from the Black Sea, individuals from the Atlantic Ocean off the Iberian Peninsula, and individuals from a vast region comprising the eastern Atlantic north of the Iberian Peninsula. Application of a new individual-based landscape-genetics statistical method, Geneland [12], which partitions individuals into groups similarly to Structure but simultaneously takes the geographical location of sampled individuals into account, identified the same groups and suggested barriers to gene flow between these three geographical regions. This was further substantiated by a method for estimating real-time dispersal [13], which showed that virtually no gene flow occurs among groups. Finally, the authors demonstrated significant isolation-by-distance (that is, a positive relationship between geographic distance and genetic differentiation) among harbour porpoise from the northern Atlantic range.

What makes the study particularly interesting is the detailed sampling scheme and the integration with oceanographic data, that is, landscape (or seascape) variables, making an explanation of the observed patterns of differentiation possible. It was known beforehand that the Black Sea population is probably reproductively isolated from Atlantic populations; harbour porpoise is absent from the Mediterranean Sea, and the Black Sea population is considered a relict of a more widespread population. However, the barrier to gene flow between the Iberian Peninsula and the northern part of the Atlantic is particularly noteworthy. This discontinuity coincides with a deep trough extending from the deep sea into the continental shelf in the southern Bay of Biscay, which has the effect of creating a zone of warm, oligotrophic (nutrient poor) water. Fontaine et al. [10] suggest that this zone provides an unfavorable habitat for the harbour porpoise, due in particular to its low productivity. In contrast to larger cetaceans, harbour porpoises have a limited capacity for energy storage, do not undertake long feeding migrations and largely depend on the food immediately available [14]. Thus, although the genetic break occurring in the southern Bay of Biscay is concordant with differences in sea-surface temperature, the ultimate cause is productivity, for which sea-surface temperature becomes a proxy. The absence of the species in the Mediterranean Sea coincides with similar environmental conditions, that is, deep water with high surface temperatures and oligotrophic conditions. Despite the difference in geographic scale, the factors isolating Iberian and Black Sea populations are therefore likely to be similar.

Even though strong barriers to gene flow were not observed within the northern Atlantic range, the significant isolation-by-distance suggests differentiation within a continuous population (see also [15]). Thus, the genetic structure of the harbour porpoise appears to reflect two types of factors: geographic distance (as in the northern Atlantic) and distinct discontinuities in the marine environment associated with low productivity.

Landscape genetics in marine environments

The work of Fontaine et al. [10] provides an excellent illustration of the increase in explanatory power that can be gained by integrating molecular data and oceanographic/landscape variables in studies of marine organisms. A handful of other studies have recently used similar landscape-genetics approaches to study both marine invertebrates [16, 17] and marine fishes [1821]. Kenchington et al. [16] and Galindo et al. [17] studied sea scallops (Placopecten magellanicus) and staghorn corals (Acropora cervicornis), respectively, and combined information on the geographic location of barriers to gene flow with predictions of larval dispersal obtained from oceanographic models. Both studies showed that ocean currents influencing the dispersal of juvenile life stages were the most likely factors causing the observed genetic structure. In marine fishes, genetic breaks in Atlantic cod (Gadus morhua) around Iceland have also been related to prevailing ocean currents, suggesting that oceanic fronts may prevent gene flow between locations north and south of the island [20]. These results highlight the importance of ocean currents for shaping genetic structuring in species with pelagic egg and larval stages.

Other studies have related genetic breaks to specific environmental parameters. For instance, barriers to gene flow between geographically proximate Atlantic herring (Clupea harengus) populations in the Baltic Sea and North Sea coincide with marked changes in ambient salinity, suggesting that barriers are maintained through adaptation to local environments [18, 19]. In this way, landscape genetics may provide important new information about the extent of local adaptation in marine environments, and the results can be used to formulate hypotheses that can then be tested using more targeted experimental approaches, for instance using standard or common-garden experiments [22].

Management of marine ecosystems

Landscape genetics is a rapidly evolving discipline, and the specific applications for marine environments are manifold. Management of marine living resources is increasingly shifting towards ecosystem-based management [23]. Using a comparative approach to landscape genetics involving analysis of several species may enable us to delimit geographic management units corresponding to barriers to gene flow shared by several species. The most important barriers to gene flow detected in studies of Atlantic herring and European flounder (Platicthys flesus) [18, 19, 21] are shown on a map of northern Europe (Figure 1). It is evident that the Baltic Sea includes an important genetic transition zone, even though the barriers detected in the two species in this region do not completely overlap. This may be due to patchiness within the spawning areas of herring, whereas the flounder shows a geographically more continuous spawning activity. When results from other species can be superimposed on this map, even more interesting patterns of coincident barriers might be revealed. It should also be noted that the barrier for harbour porpoise in the southern Bay of Biscay detected by Fontaine et al. [10] coincides with a previously established border between biogeographic zones. Thus, the two genetically distinct harbour porpoise populations may in fact represent two at least partially independent ecosystems.

Figure 1
figure 1

Barriers to gene flow detected in Atlantic herring and European flounder, along with the geographical distribution of frequencies of two alleles at the heat-shock protein gene Hsc70 in the flounder. The map shows the main barriers (that is, zones of lowered gene flow) detected by analysis of microsatellite DNA markers in Atlantic herring [18,19] (red dotted lines) and European flounder [21,27] (blue dotted lines). The pie charts denote the frequencies of two alleles (indicated by black and white, respectively) at the Hsc70 locus in the European flounder at the indicated locations [27].

Another very promising use of landscape genetics relates to analysis of selection and local adaptation in marine environments. As described above, so far methods have mostly been used in an exploratory fashion to generate specific hypotheses, but recent developments hold much promise for more direct tests for selection using landscape-based approaches. These methods attempt to include information from specific environmental parameters in addition to the geographic position of the sampled individuals to identify potential selective agents involved in structuring populations and to identify loci subject to selection [24, 25]. Even though the identification of specific environmental parameters as selective agents is challenging (see [21, 26, 27] for discussions), such techniques may prove particularly useful for marine organisms inhabiting regions that already have detailed oceanographic information.

As an example of the potential of a landscape-genetics approach to detecting selection, Hemmer-Hansen et al. [27] analyzed variation in a heat-shock protein gene (Hsc70) in the European flounder. The frequencies of the two observed alleles are shown in Figure 1. Interestingly, there was a pronounced shift in allele frequencies between Baltic Sea and North Sea/Atlantic populations. There was, however, no correspondence between the barriers detected by neutral microsatellite DNA loci and the allele frequencies at Hsc70. In contrast, Hsc70 allele frequencies were very similar among geographically distant samples sharing similar environmental conditions: that is, among all oceanic samples on the one hand and among samples from the Baltic Sea and Lake Pulmanki (a freshwater body connected to the sea) on the other. The latter group of samples is characterized by low salinity and low and fluctuating temperature regimes. Hence, the microsatellite loci suggest the presence of barriers reflecting zones of low dispersal and regions of high dispersal, whereas variation at Hsc70 reflects strong diversifying selection due to differences in environmental conditions, sometimes even in the presence of considerable gene flow.

The work of Fontaine et al. [10], with its convincing correlation between population genetics and physical and ecological features of the marine environment, clearly confirms that landscape genetics has taken successfully to the oceans. We envisage that it will develop into an efficient research vessel with more and more scientists on board.


  1. Luikart G, England PR, Tallmon D, Jordan S, Taberlet P: The power and promise of population genomics: from genotyping to genome typing. Nat Rev Genet. 2003, 4: 981-994. 10.1038/nrg1226.

    Article  CAS  PubMed  Google Scholar 

  2. Manel S, Schwartz MK, Luikart G, Taberlet P: Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol. 2003, 18: 189-197. 10.1016/S0169-5347(03)00008-9.

    Article  Google Scholar 

  3. Storfer A, Murphy MA, Evans JS, Goldberg CS, Robinson S, Spear SF, Dezzani R, Delmelle E, Vierling L, Waits LP: Putting the 'landscape' in landscape genetics. Heredity. 2007, 98: 128-142. 10.1038/sj.hdy.6800917.

    Article  CAS  PubMed  Google Scholar 

  4. Coulon A, Guillot G, Cosson JF, Angibault JMA, Aulagnier S, Cargnelutti B, Galan M, Hewison AJM: Genetic structure is influenced by landscape features: empirical evidence from a roe deer population. Mol Ecol. 2006, 15: 1669-1679. 10.1111/j.1365-294X.2006.02861.x.

    Article  CAS  PubMed  Google Scholar 

  5. Angers B, Magnan P, Plante M, Bernatchez L: Canonical correspondence analysis for estimating spatial and environmental effects on microsatellite gene diversity in brook charr (Salvelinus fontinalis). Mol Ecol. 1999, 8: 1043-1053. 10.1046/j.1365-294x.1999.00669.x.

    Article  Google Scholar 

  6. Ward RD, Woodwark M, Skibinski DOF: A comparison of genetic diversity levels in marine, freshwater and anadromous fishes. J Fish Biol. 1994, 44: 213-232. 10.1111/j.1095-8649.1994.tb01200.x.

    Article  Google Scholar 

  7. Nielsen EE, Hansen MM, Ruzzante DE, Meldrup D, Gronkjær P: Evidence of a hybrid-zone in Atlantic cod (Gadus morhua) in the Baltic and the Danish Belt Sea revealed by individual admixture analysis. Mol Ecol. 2003, 12: 1497-1508. 10.1046/j.1365-294X.2003.01819.x.

    Article  PubMed  Google Scholar 

  8. Johannesson K, Andre C: Life on the margin: genetic isolation and diversity loss in a peripheral marine ecosystem, the Baltic Sea. Mol Ecol. 2006, 15: 2013-2029. 10.1111/j.1365-294X.2006.02919.x.

    Article  CAS  PubMed  Google Scholar 

  9. Ruzzante DE, Taggart CT, Cook D: A nuclear DNA basis for shelf- and bank-scale population structure in northwest Atlantic cod (Gadus morhua): Labrador to Georges Bank. Mol Ecol. 1998, 7: 1663-1680. 10.1046/j.1365-294x.1998.00497.x.

    Article  Google Scholar 

  10. Fontaine MC, Baird SJ, Piry S, Ray N, Tolley KA, Duke S, Birkun A, Ferreira M, Jauniaux T, Llavona A, Oztu Oztürk B, Oztürk A, Ridoux V, Rogan E, Sequeira M, Siebert U, Vikingsson GA, Bouquegneau JM, Michaux JR: Rise of oceanographic barriers in continuous populations of a cetacean: the genetic structure of harbour porpoises in Old World waters. BMC Biology. 5: 30-10.1186/1741-7007-5-30.

  11. Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155: 945-959.

    PubMed Central  CAS  PubMed  Google Scholar 

  12. Guillot G, Estoup A, Mortier F, Cosson JF: A spatial statistical model for landscape genetics. Genetics. 2005, 170: 1261-1280. 10.1534/genetics.104.033803.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Wilson GA, Rannala B: Bayesian inference of recent migration rates using multilocus genotypes. Genetics. 2003, 163: 1177-1191.

    PubMed Central  PubMed  Google Scholar 

  14. Fontaine MC, Tolley KA, Siebert U, Gobert S, Lepoint G, Bouquegneau JM, Das K: Long-term feeding ecology and habitat use in harbour porpoises Phocoena phocoena from Scandinavian waters inferred from trace elements and stable isotopes. BMC Ecol. 2007, 7: 1-10.1186/1472-6785-7-1.

    Article  PubMed Central  PubMed  Google Scholar 

  15. Andersen LW, Ruzzante DE, Walton M, Berggren P, Bjørge A, Lockyer C: Conservation genetics of harbour porpoises, Phocoena phocoena, in eastern and central North Atlantic. Conserv Genet. 2001, 2: 309-324. 10.1023/A:1012534212853.

    Article  Google Scholar 

  16. Kenchington EL, Patwary MU, Zouros E, Bird CJ: Genetic differentiation in relation to marine landscape in a broadcast-spawning bivalve mollusc (Placopecten magellanicus). Mol Ecol. 2006, 15: 1781-1796. 10.1111/j.1365-294X.2006.02915.x.

    Article  CAS  PubMed  Google Scholar 

  17. Galindo HM, Olson DB, Palumbi SR: Seascape genetics: A coupled oceanographic-genetic model predicts population structure of Caribbean corals. Curr Biol. 2006, 16: 1622-1626. 10.1016/j.cub.2006.06.052.

    Article  CAS  PubMed  Google Scholar 

  18. Jørgensen HBH, Hansen MM, Bekkevold D, Ruzzante DE, Loeschcke V: Marine landscapes and population genetic structure of herring (Clupea harengus L.) in the Baltic Sea. Mol Ecol. 2005, 14: 3219-3234. 10.1111/j.1365-294X.2005.02658.x.

    Article  PubMed  Google Scholar 

  19. Bekkevold D, Andre C, Dahlgren TG, Clausen LA, Torstensen E, Mosegaard H, Carvalho GR, Christensen TB, Norlinder E, Ruzzante DE: Environmental correlates of population differentiation in Atlantic herring. Evolution Int J Org Evolution. 2005, 59: 2656-2668.

    Article  Google Scholar 

  20. Pampoulie C, Ruzzante DE, Chosson V, Jorundsdottir TD, Taylor L, Thorsteinsson V, Danielsdottir AK, Marteinsdottir G: The genetic structure of Atlantic cod (Gadus morhua) around Iceland: insight from microsatellites, the Pan I locus, and tagging experiments. Can J Fish Aquat Sci. 2006, 63: 2660-2674. 10.1139/F06-150.

    Article  CAS  Google Scholar 

  21. Hemmer-Hansen J, Nielsen EE, Grønkjær P, Loeschcke V: Evolutionary mechanisms shaping the genetic population structure of marine fishes; lessons from the European flounder (Platichthys flesus L.). Mol Ecol. 2007, 16: 3104-3118. 10.1111/j.1365-294X.2007.03367.x.

    Article  CAS  PubMed  Google Scholar 

  22. Kawecki TJ, Ebert D: Conceptual issues in local adaptation. Ecol Lett. 2004, 7: 1225-1241. 10.1111/j.1461-0248.2004.00684.x.

    Article  Google Scholar 

  23. Pikitch EK, Santora C, Babcock EA, Bakun A, Bonfil R, Conover DO, Dayton P, Doukakis P, Fluharty D, Heneman B, et al: Ecosystem-based fishery management. Science. 2004, 305: 346-347. 10.1126/science.1098222.

    Article  CAS  PubMed  Google Scholar 

  24. Foll M, Gaggiotti O: Identifying the environmental factors that determine the genetic structure of populations. Genetics. 2006, 174: 875-891. 10.1534/genetics.106.059451.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Joost S, Bonin A, Bruford MW, Després L, Conord C, Erhardt G, Taberlet P: A spatial analysis method (SAM) to detect candidate loci for selection: towards a landscape genomics approach to adaptation. Mol Ecol. 2007, 18: 3955-3969. 10.1111/j.1365-294X.2007.03442.x.

    Article  Google Scholar 

  26. Sarup P, Sorensen JG, Dimitrov K, Barker JSF, Loeschcke V: Climatic adaptation of Drosophila buzzatii populations in southeast Australia. Heredity. 2006, 96: 479-486. 10.1038/sj.hdy.6800828.

    Article  CAS  PubMed  Google Scholar 

  27. Hemmer-Hansen J, Nielsen EE, Frydenberg J, Loeschcke V: Adaptive divergence in a high gene flow environment: Hsc70 variation in the European flounder (Platicthys flesusL.). Heredity.

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Michael Møller Hansen.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Hansen, M.M., Hemmer-Hansen, J. Landscape genetics goes to sea. J Biol 6, 6 (2007).

Download citation

  • Published:

  • DOI:


  • Gene Flow
  • Landscape Genetic
  • Harbour Porpoise
  • Marine Living Resource
  • Placopecten Magellanicus