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y and Ecology 350 (2007) 234–259www.elsevier.com/locate/jembe

Journal of Experimental Marine Biolog

Contribution of genetics and genomics toseagrass biology and conservation

Gabriele Procaccini a,⁎, Jeanine L. Olsen b, Thorsten B.H. Reusch c

a Laboratory of Benthic Ecology, Stazione Zoologica ‘A. Dohrn’, Punta S. Pietro, 80077 Ischia (Napoli), Italyb Department of Marine Benthic Ecology & Evolution, Centre for Ecological and Evolutionary Studies, University of Groningen,

PO Box 14, 9750 AA Haren, The Netherlandsc Institute for Evolution and Biodiversity, Plant Evolutionary Ecology, University of Münster, Hüfferstr. 1, 48149 Münster, Germany

Received 6 March 2007; received in revised form 17 May 2007; accepted 29 May 2007

Abstract

Genetic diversity is one of three forms of biodiversity recognized by the IUCN as deserving conservation along with speciesand ecosystems. Seagrasses provide all three levels in one. This review addresses the latest advances in our understanding ofseagrass population genetics and genomics within the wider context of ecology and conservation. Case studies are used from themost widely studied, northern hemisphere species Zostera marina, Z. noltii, Posidonia oceanica and Cymodocea nodosa.

We begin with an analysis of the factors that have shaped population structure across a range of spatial and temporal scalesincluding basin-level phylogeography, landscape-scale connectivity studies, and finally, local-scale analyses at the meadow level—including the effects of diversity, clonality and mating system. Genetic diversity and clonal architecture of seagrass meadows differwithin and among species at virtually all scales studied. Recent experimental studies that have manipulated seagrass geneticbiodiversity indicate that genotypic diversity matters in an immediate ecological context, and enhances population growth,resistance and resilience to perturbation, with positive effects on abundance and diversity of the larger community. In terms of thelonger term, evolutionary consequences of genetic/genotypic diversity in seagrass beds, our knowledge remains meagre. It is herethat the new tools of ecogenomics will assist in unravelling the genetic basis for adaptation to both biotic and abiotic change. Geneexpression studies will further assist in the assessment of physiological performance which may provide an early warning systemunder complex disturbance regimes that seagrasses are at or near their tolerance thresholds.

At the most fundamental level, ecological interactions of seagrasses with their environment depends on the genetic architectureand response diversity underlying critical traits. Hence, given the rapid progress in data acquisition and analysis, we predict anincreasing role of genetic and genomic tools for seagrass ecology and conservation.© 2007 Elsevier B.V. All rights reserved.

Keywords: Conservation genetics; Ecogenomics; ESTs; Microsatellites; Molecular ecology; Seagrass

1. Introduction

At the most fundamental level, the response ofseagrasses to their environment depends on their genetic

⁎ Corresponding author. Tel.: +39 081 5833 508; fax: +39 081 984201.E-mail address: [email protected] (G. Procaccini).

0022-0981/$ - see front matter © 2007 Elsevier B.V. All rights reserved.doi:10.1016/j.jembe.2007.05.035

makeup and the interactions of those genes with theenvironment. The effects of gene flow, genetic drift andmutation coupled with life history, demography andisolation determine genetic diversity and its distribution.Simultaneously, selection acts on the various phenotypes,determining ecological adaptation through structural andregulatory changes in the suites of underlying genes. Over

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http://dx.doi.org/10.1016/j.jembe.2007.05.035

235G. Procaccini et al. / Journal of Experimental Marine Biology and Ecology 350 (2007) 234–259

the past 15 years molecular ecologists and populationgeneticists have made great progress in understandingpopulation genetic structure and its dynamics through theapplication of neutral genetic markers. Now, with thedevelopment of non-neutral markers and ecologicalgenomics, possibilities to study the effects of selectionand adaptation are on the horizon. Taken together, theyprovide the catalyst for final integration of ecology withgenetics and allow us to address the central question ofthis review: What can ecologists—focused on the role ofbiodiversity and ecosystem function; and coastal zonemanagers—entrusted with monitoring, mitigation andconservation—gain from a genetic/genomic perspectiveof seagrass biology?

Seagrasses are an ecologically extremely successful,polyphyletic group of marine angiosperms (Waycottet al., 2006) that serve as a foundational species for anextraordinarily productive ecosystem. As such theyrepresent some of the most valuable (Costanza et al.,1997) and, at the same time, vulnerable ecosystems onEarth (Marbà et al., 1996; Short and Wyllie-Echeverria,1996; Green and Short, 2003; Duarte et al., 2005; Orthet al., 2006). Seagrasses are particularly sensitive tolocal environmental degradation such as fluxes in waterclarity, salinity and temperature, all of which are furtheraffected by climate change. In turn, these disturbancescollectively affect ecosystem stability and resiliencecapacity, which are themselves functions of (bio)diversity. Temperate seagrass meadows are typicallydominated by a single species, thus biodiversity isreflected below the unit of species—at the genetic andgenotypic levels. This requires us to recast the

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biodiversity-ecosystem function (BEF) debate belowthe species level, i.e., the population and individuallevels (Reusch et al., 2005; Reusch, 2006). It alsorequires us to include the genetic component in thediscussion about conservation and mitigation (Allendorfand Luikart, 2007; Schwartz et al., 2007).

Literature on seagrass genetics has increased dra-matically over the past 15 years, as a result of thedevelopment of DNA based molecular markers (Fig. 1).Below 100 genetic papers have been published, in-cluding work that has identified direct functional linksof genetic diversity with seagrass ecosystem stability(Procaccini and Piazzi, 2001; Hughes and Stachowicz,2004; Reusch et al., 2005). In the light of these statis-tics, it is unfortunate that several current review articlesor books on seagrass biology and conservation haveignored this impressive body of genetic evidence andcomparative data that continue to accumulate for sea-grasses (e.g. Hemminga and Duarte, 2000; Orth et al.,2006).

The goal of this review is to address the latestadvances in our understanding of temperate, northernhemisphere seagrass population genetics and genomicswithin the wider context of ecology and conservation. Itis not exhaustive; therefore, readers are encouraged toconsult the earlier reviews (for example Waycott, 1998,2000a,b; Reusch, 2000a; Reusch and Hughes, 2006;Waycott et al., 2006) and the original papers that haveemphasized particular aspects in greater detail. We beginwith an overview of standard and new-generationmolecular markers followed by a synthesis of whathas been learned from large-scale phylogeographic

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parate searches made with the Web of Science database (ISI Web ofhed line) and “seagrass# OR individual genus names AND genetic#”tii; Po = Posidonia oceanica; Cn = C. nodosa; other species = all other

236 G. Procaccini et al. / Journal of Experimental Marine Biology and Ecology 350 (2007) 234–259

surveys in an evolutionary timeframe. We then examinepopulation structure and connectivity at the regionalscale, and then we move to local-scale analyses at themeadow level, including effects of diversity, clonalityand the mating system.

From there we examine recent experimental studiesthat have manipulated seagrass genetic biodiversity toassess its role in ecosystem function. We then turn to thecontribution of genetic data in conservation, monitoringand adaptive management of seagrass ecosystems.Finally, we introduce seagrass ecogenomics and theunique opportunities it is beginning to provide towards aquantitative understanding of adaptation in both acontemporary and evolutionary perspective. Case stud-ies will come from the temperate species Zosteramarina, Z. noltii, Posidonia oceanica and Cymodoceanodosa for which the most and more updated data arecurrently available.

2. Molecular markers

The use of molecular markers in ecology andevolution falls within the research fields of molecularecology (e.g. Carvalho, 1998; Avise, 2004; Beebee andRowe, 2004), ecological genetics (Lowe et al., 2004)and now, ecological genomics (Van Straalen andRoelofs, 2006). Much of the progress in this hybriddisciplines are driven by the development of newgenetic markers with varying properties and concomi-tant data analysis methods. In most cases these methodsfacilitate answering long-standing questions that wereformerly intractable for technical reasons. With the birthof (eco)genomics, new classes of markers make itpossible to examine the actual gene(s) that contribute toimportant ecological adaptations—whether the stressesof climate change, resistance to herbivory or disease(Vasemägi and Primmer, 2005).

Predicting species' response to the environment hasbeen identified as a central challenge for biology (USNational Science Board 2000, Lubchenco, 1998) andmolecular approaches are among the central tools.

2.1. The standard toolbox

Different classes of markers provide different levelsof resolution and statistical power, as well as variousadvantageous and disadvantageous properties (e.g.Avise, 2004; Féral, 2002; Procaccini and Maltagliati,2004).

Dominant markers such as RAPDs (random ampli-fied polymorphic DNA, Welsh et al., 1991), AFLPs(amplified fragment length polymorphism, Vos et al.,

1995) and VNTR (variable number of tandem repeats,Nakamura et al., 1987) restrict many standard analysesbecause specific alleles cannot be assigned to loci.RAPDs, in particular, suffer from reproducibilityproblems and have fallen out of favor. AFLPs, however,have re-emerged in the development of loci underselection.

Haplotypes (from haploid genotype) are the multi-locus genotype of a chromosome or gamete (usuallyinferred from mitochondria or chloroplasts). The se-quence of interest is the locus and the variants in thesequences are the alleles, which build the haplotype. Asecond meaning of haplotype refers to a set of singlenucleotide polymorphisms (SNPs) on a single chroma-tid. In this approach, both the diversity of sequencesand their frequency distribution are used to constructhaplotype networks or trees which are the basis forphylogeographic studies. Haplotype data provide adouble framework for both genealogical and frequency-based analyses making it possible to utilize both classicalpopulation genetics (FST) approaches based on allelefrequency data, as well as population-level, phylogeneticand coalescent approaches. The advantage of genealog-ical approaches is that they link spatial history better thanfrequency-based methods and specifically allow for his-torical demographic estimates of past population bottle-necks and expansions based on tree shapes (Hare, 2001).Unfortunately, mitochondrial genomes have evolveddifferently in angiosperms thus depriving plant biologistsof these versatile markers. Likewise, chloroplast se-quences (Olsen et al., 2004), including cp-microsatellites(Provan et al., personal communications) have (so far)been found to have very little variation. For example, ascreen of three chloroplast loci in a test panel of 100individuals of Z. marina representing the entire geo-graphic range, recovered no variation (Provan and Olsen,personal communications) and Provan et al. (personalcommunications) have suggested that a selective sweepthrough the chloroplast genome has reduced diversity inStrangford Lough, N. Ireland. Whether low chloroplastvariation extends to other seagrasses species remains tobe tested, but the outlook does not look promising. Thesolution to this predicament has been the development ofnuclear, co-dominantmarkers—mainlymicrosatellite loci.

Microsatellites have superseded earlier generationallozyme loci (see Arnaud-Haond et al., 2005; Serraet al., 2007) because of their co-dominant nature, ubiquityand high levels of polymorphism. Typical analyses utilizebetween 8–20 loci, with the number of alleles rangingfrom 4–100 per locus, depending on the specific appli-cation. Microsatellites (also called simple sequencerepeats; SSRs) are iterations of 1–6 bp motifs present in


the genomes of all organisms. Microsatellite regions areextremely polymorphic and most of them vary with a rateof 10−2 to 10−6 mutations/locus/generation (Li et al.,2002). The availability of many loci with many allelesprovides discrimination of individuals (e.g. genet/cloneidentification), power in paternity and assignment tests.The main drawback of microsatellite loci is their poorlyunderstood mutation model (Li et al., 2002), causing bothhomoplasy and underestimations of divergence whichinterfere with some types of analysis. Nevertheless, theyremain the most versatile marker and are relatively easy todevelop in seagrasses (with the possible exception ofEnhalus accoroides). Development time (4–6 months inan experienced lab) and cost are well withinmost researchbudgets. Microsatellites are also commonly found in EST(Expressed Sequence Tag) libraries which can provide analternative to genomics library development if available(see genomics section). Nuclear microsatellite loci arecurrently available for Z. marina (Reusch, 2000b; a), Z.noltii (Coyer et al., 2004a), P. oceanica (Procaccini andWaycott, 1998, Alberto et al., 2003b),C. nodosa (Albertoet al., 2003a; Ruggiero et al., 2004) and Thalassiatestudinum (Van Dijk et al., 2007).

The first microsatellite-based surveys of geneticdiversity in seagrass date back to 1998, when sixpolymorphic loci (Procaccini and Waycott, 1998) wereutilized to address meadow diversity of P. oceanica inthe West Mediterranean basin (Procaccini and Mazzella,1998). Most of the loci were characterized by trinucle-otide repeats (five loci were nuclear and one was fromthe chloroplast). The same loci were later used in abroader scale survey within the Mediterranean Sea(Procaccini et al., 2001, 2002). These studies detectedlow levels of variation within P. oceanica meadows,with average clonal diversity, calculated over 33populations and almost 1000 samples, of 0.32 (G/N,where G is the number of genotypes and N is the numberof samples per population).Many genotypes were sharedamong different populations, resulting in only 109

Table 1Comparison among different sets of microsatellite markers in Posidonia oce

Markers Sites N/site N tot Dg/site

SSR1 4 29–40 146 0.14–0.23SSR2 8 29–50 312 0.31–0.84SSR1 5 22–36 132 0.28–0.50SSR2 5 22–36 132 0.36–0.62SSR1 33 3–47 972 0.00–0.63SSR1/SSR2 34 20–40 1217 0.00–1.00

⁎ Only the five nuclear SSR1 markers were used.SSR1: first set of SSR markers (Procaccini and Waycott, 1998), SSR2: secoDg: G-1/N-1 (Ellstrand and Roose, 1987), L: number of loci, A/site: alleles

genotypes present in total, with an overall G/N valueof 0.11 (Procaccini et al., 2002). Because earliergeneration allozymes, RAPDs and VNTR (e.g. M13among many others) markers had suggested almostcomplete uniclonality in P. oceanica meadows (Capio-mont et al., 1996; Procaccini and Mazzella, 1996) thefinding of variation, albeit low, was very exciting.However, the low variability remained puzzling whencompared with high variability found in the firstmicrosatellite survey of Z. marina (Reusch et al.,1999b). Within a short time, it was decided to developnew dinucleotide loci (Alberto et al., 2003b) which haveindeed revealed much higher levels of diversity (Table 1)that are more similar to those found for Z. marina.Further comparisons of the two sets of Posidonia loci bySerra et al. (2007) have revealed that the markers are nothomogeneous and a complete reanalysis of P. oceanicausing all 13 microsatellites across 34 populations and1200 individuals (Arnaud-Haond et al., 2007, Table 1)has doubled the earlier estimate of genetic/genotypicdiversity, although confirming patterns of geneticsubdivision previously found within the Mediterraneanbasin (Procaccini et al., 2002). In conclusion, ifmicrosatellite diversity is consistently low, then thismay rather be an attribute of the markers chosen than ofthe populations under study, as the two sets ofmicrosatellite markers developed for P. oceanica haveconvincingly shown. As a cautionary note, trinucleotiderepeats and chloroplast derived loci, in particular, may beless polymorphic than dinucleotide microsatellites. Notethat similar problems with microsatellite loci have notbeen encountered for Z. marina, Z. noltii or C. nodosa.

2.2. The newly developing toolbox

The arrival of genomics has important consequencesfor new marker development—in terms of genome-widecoverage, a shift to hundreds or even thousands ofmarkers analysed simultaneously in high throughput

anica

Dg av. L A/site Reference

0.20 5 – Arnaud-Haond et al. (2005)0.60 8 – Arnaud-Haond et al. (2005)0.41 6 11–15 Serra et al. (2007)0.50 7 15–26 Serra et al. (2007)0.29 6 6–13 Procaccini et al. (2002)0.64 12⁎ 15–67 Arnaud-Haond et al. (2007)

nd set of SSR markers (Alberto et al., 2003b), N: number of samples,per site.


platforms; and more favourable properties of themarkers with respect to mutation models, recombinationand neutral/non-neutral status.

Single-nucleotide-polymorphisms (SNPs) are themost abundant loci in the genome with many advanta-geous characteristics for exploring phylogeography,historical demography and population structure withmuch greater refinement and power. Nevertheless, manymore SNP loci are needed as compared with micro-satellites and slower mutation rates may compromisesome applications (Brumfield et al., 2003; Moran et al.,2004). Microsatellite-associated genes (as well as SNP-associated genes) are also being developed, through theavailability of EST libraries (see ‘genomics’ sectionbelow). Because SNPs and microsatellites can now bechosen to be situated in the immediate vicinity ofparticular genes, the potential for some of them to beunder selection through genetic hitchhiking is high andpossible to assess (Luikart et al., 2003; Storz, 2005).Because simple repeat sequences serve as promoterbinding sites, some microsatellite polymorphismsdirectly upstream of genes may have a direct functionalsignificance (Li et al., 2004).

The possibility to type hundreds of gene loci (e.g.mirosatellites or SNPs) at relatively low costs hasspurred the new field of population genomics (Luikartet al., 2003). There are now several excellent exampleswhere microsatellite loci under selection could beidentified, for example by comparing populations incontrasting habitat types (Kelly et al., 2003; Edelist et al.,2006; Jump et al., 2006; Murray and Hare, 2006) Thedevelopment of new “selectively relevant markers” (alsoreferred to as trait associated genes, msat-ESTs, SNP-ESTs or outlier loci) will allow much more meaningfulassessments of genetic diversity in the context ofbiological conservation (van Tienderen et al., 2002).SNPs are also principally suited for such approaches, butdue to their lower mutation rate it is unclear whether ornot they will be useful for detecting rapid adaptiveevolution. In principle, only the typing costs set an upperbound to this class of abundant genetic markers. As amajor advantage over microsatellites, SNP loci willallow the phylogenetic reconstruction of colonizationafter the past ice age because back-mutations can beneglected, at least over shorter (say b10 000 yrs) timescales.

At present, gene linkedmarkers (developed from ESTlibraries) have only been developed for Z. marina (heatstress and recovery; Reusch, pers. comm.) while suchdevelopment is in progress in P. oceanica (Migliaccioet al., 2006; Procaccini, pers. comm.). In Z. marina,microsatellite-associated EST markers have been select-

ed and found to be polymorphic (Oetjen and Reusch, inpress). Average distance for SNPs in Z. marina is 400base pairs based on a preliminary analysis of EST li-braries. For Z. marina, some 1800 SNPs were identifiedfrom approximately 1000 contigs. Of those, a smallsubset of 60 was selected based on the functionality ofthe associated genes and confirmed by re-sequencing,and subsequently, typing primers were developed thatencompass 8–12 loci in multiplex reactions. They arecurrently being tested on the same set of populationsused in the large phylogeographic survey of Olsen et al.(2004) by Ferber and Olsen (personal communications).Some 50 new neutral microsatellite markers have alsobeen developed which will greatly enhance our power touse assignment tests (Reusch, personal communications).In P. oceanica, microsatellite-associated EST mar-kers are also under development (Procaccini, person-al communications).

3. Phylogeographic surveys

Molecular data provide a transcript of historicalpopulation dynamics which in turn provides insight intohow populations have performed in the past and howthey may react in the future. Geographic areas can rangein size from hundreds to thousands of kilometers, i.e.,regional coastlines, ocean basins or entire hemispheres;and time depth can range from thousands to millions ofyears. In particular, paleoclimatic and tektonic eventsthat interrupted gene flow are often still present ashistorical signatures in phylogeographic structure,underlying contemporary shapers of population struc-ture (see next section). Some of the more prominentevents include the closure of the Isthmus of Panama, theopening of the Bering Sea and the effects of theQuaternary Ice ages including the Last Glacial Maxi-mum (LGM, 20,000 yr. BP). Phylogeography thussupplies a deeper spatial-temporal evolutionary windowfrom which to view contemporary population geneticprocesses and consequences (see Avise, 2000 plus twospecial issues of Molecular Ecology 2001 10(3) and2004 13(4)).

In its original form (Avise et al., 1987), phylogeo-graphy was almost exclusively based on mitochondrialhaplotype data (Avise, 2000). As explained earlier inthis review, mt haplotype data are not available forseagrasses owing to the differences in their evolution inangiosperms. While the genealogical component is lost,microsatellite data still provide a powerful way to assessspatial diversity, estimate the degree of geographicsubdivision, gene flow and changes in effectivepopulation size through time. In the most general

http://dx.doi.org/doi:10.1111/j.14712007.01694.x



sense, phylogeographic data provide a signature of howthe past has influenced large-scale distribution patternsin the present.

3.1. The last glacial maximum

The effects of the Last Glacial Maximum (LGM20,000–18,000 years BP) profoundly affected virtuallyall shallow-water, coastal marine habitats in theNorthern Hemisphere. In those areas with widecontinental shelves, notably the European N Atlantic,effects were even more dramatic as sea level dropped by110–150 m (Lambeck and Purcell, 2005) thus exposinglarge areas. Glacial melt, retraction of ice sheets and risein sea level led to concomitant range expansions fromrefugia (mostly in the south) into newly opening areassuch as the Baltic and North Seas in Europe. Althoughthe Atlantic-Mediterranean connection remained open,thermohaline circulation was affected thus partiallycutting off the Mediterranean (Maldonado, 1985; Myerset al., 1998). Within the Mediterranean, coastlines werecertainly exposed in many areas with a strong breakalong the Tunisian-Sicilian line separating the westernand eastern portions of the basin. Against the paleocli-matic backdrop of the LGM we can use geneticinformation to trace locations of former refugia, identifysecondary contact zones, boundary populations anddiversity gradients; as well as estimate historicaldemographic fluctuations—especially bottlenecks.

Given the global scope of their distribution, sea-grasses provide a unique opportunity for comparativephylogeography both within and among species. Atpresent, large-scale studies have been limited to thenorthern hemisphere and only in temperate areas. Theseinclude the Atlantic European coasts (Z. marina, Z.noltii, C. nodosa), the Mediterranean (Z. marina, Z.noltii, P. oceanica, C. nodosa), the west coast of NorthAmerica (Z. marina) and the Japanese Archipelago (Z.marina). To our knowledge, the only extensive phylo-geographic surveys that has been published so far for thesouthern hemisphere regards Posidonia australis andhas been obtained using RAPD and allozyme markers(Waycott et al., 1997). However, the first broad scalesurvey of a tropical seagrass (Thalassia testudnium)using microsatellite data is nearing completion in theCaribbean (Van Dijk et al., personal communications).

3.2. Zostera marina

Z. marina is the most widely distributed seagrass inthe northern hemisphere and has been the mostintensively studied. Olsen et al. (2004) surveyed some

2000 individuals from 50 locations throughout thespecies’ range using nine microsatellite loci, nuclearrDNA-ITS and cp-matK-intron sequences. In a conven-tional phylogenetic analysis of the ITS and matK-intronagainst a panel of populations, only six nucleotidedifferences were detected and all of these were found inPacific populations, the Atlantic populations remaininginvariant. This signals a very recent sweep of Z. marinainto the Atlantic which was further confirmed using themicrosatellite data in which allelic richness was at leasttwice as high in the Pacific. Further analyses suggestedthat the NWAtlantic is more strongly connected with thePacific than NE Atlantic Europe.

Refugial areas for seaweeds and many shallow-waterinvertebrates have been identified in SW Ireland, theEnglish Channel area extending to the tip of Brittany,and the Iberian Peninsula (Maggs et al., personalcommunications). The signature of refugial areas ishigh diversity which attenuates northward in conjunc-tion with recolonization as new habitat opens up. A falsesignal can arise if there has been secondary contact ofpreviously isolated populations thus creating a highdiversity for a different reason. Fortunately, methods areavailable to determine whether admixture has occurred.Z. marina does not fit the “southern richness, northernpurity” model of Hewitt (2004). Allelic richness isunimodally distributed along the European coasts withthe highest diversity found in the Eastern North Sea/Wadden Sea; an area that ostensibly could not haveserved as a refuge because it was covered by ice until atleast 14,000 years BP. There is also no evidence ofadmixture. At present it is still difficult to explain thisobservation. If a bi-directional recolonization occurredfrom both the south and from the Pacific (a putativeopen connection), then one would expect to haveidentified a contact zone. A second possibility is thatthe area was a refugium after all. Recent paleoclimaticdating of the area suggests that the English Channel andcoastline extending to the modern Danish North Seacoast was ice-free (Ménot et al., 2006). Given thatZostera can live in boreal temperatures and low salinity(McRoy, 1969), this is a feasible explanation. The thirdpossibility is that present day current patterns from thesouthwest and those from the north cause an entrain-ment of seagrasses in the Eastern North Sea/WaddenSea. In any case, concerns about low genetic diversity asan explanation for poor seagrass recovery are unfound-ed. Hydrodynamic changes in currents, sedimentologyand turbidity, combined with increasing periods ofwarm temperatures are more likely causes.

In the Mediterranean and Black Seas, Z. marina isrestricted to isolated lagoons with low allelic diversity.


Some areas are restricted to a few large clones (Venice),while places such as Thau Lagoon (Fr) are highlydiverse. Refugial areas are thought to have been innorthern pockets along the northern edges but allelicrichness comparisons do not support this. Although allpopulations sampled are highly differentiated from oneanother, there is neither isolation-by-distance norobvious connection of Mediterranean populations withAtlantic ones. Recent analysis with more completesampling (Olsen and Procaccini, unpublished) suggeststhat the Mediterranean is a marginal habitat for Z.marina.

Along the North American Pacific coast two majorclades were identified by Olsen et al. (2004): SouthernAlaska-Northern California; and the Channel Islandsacross Point Conception—a well-established biogeo-graphic transition zone. A detailed survey of theCalifornia Channel Islands further indicates the presenceof different biogeographic groupings between coastaland island populations following sea-level changes andisland-mainland connectivity (Coyer et al., personalcommunications). Muniz-Salazar et al. (2005) surveyedthe 1300-km long Baja California peninsula on both thePacific and Gulf of California sides. They found stronginter-regional differentiation but no sub-regional struc-ture, concluding that gene flow was strong and symmet-rical over hundreds of kilometres in conjunction with theregional current regimes and El Niño events. Allelicdiversities were not significantly different between thePacific and Gulf of California populations despite theirbeing perennial and annual, respectively. From thephylogeographic perspective these Z. marina popula-tions are at the southern edge of the species range, whereone might predict stress and low diversity. This is ap-parently not the case, possibly as a result of changingcurrent regimes associated with El Niño events.

A number of as-yet-unpublished surveys are under-way for the California Channel Islands, Alaska andJapan; as well as a more detailed examination of theAtlantic coast between Chesapeake Bay and Newfound-land. Opportunities for meta-analysis involving excel-lent hemisphere-wide sampling are now on the horizon.This mega-data set will be unique and will provide thefirst high- density-sampling, macroecological analysisof a marine primary producer.

3.3. Zostera noltii

Zostera noltii is restricted to the Eastern Atlanticextending from southern Norway to tropical Mauritania,as well as throughout the Mediterranean, Black, Azovand Caspian Seas. Relative to Z. marina, it has a more

southern boundary and was thus hypothesized to fit theclassic refugial model of high southern diversity. Coyeret al. (2004a) surveyed 33 populations from throughoutthe range and found that allelic richness did indeedfollow a south to north attenuation with the principlerefugia probably in Mauritania and the Mediterranean(including the Black and Azov Seas). However, Z. noltiiwas also found to have a very high diversity in theGerman Wadden Sea area. Unlike, Z. marina, however,Z. noltii has up to 25% private alleles in each of themajor geographic groups, yet again, there is no evidencefor admixture. While refugial status and entrainmentmay apply to some extent for Z. noltii, its annual tosemi-perennial mode of reproduction and the role oflocal disturbance by bird and invertebrate grazers havebeen suggested as possible mechanisms driving the highdiversity. Alternatively or in addition to, locally largepopulations may generate a lot of variation thusdecreasing the role of genetic drift relative to isolated,small and highly differentiated populations found insome areas. Distinguishing among these hypotheses willrequire manipulative field experiments.

Mauritania undoubtedly served as a refuge withsubsequent intrusions into the Mediterranean and BlackSeas. Even today there is high diversity in the Black Seaincluding many private alleles. Diekmann et al. (2005)surveyed all Atlantic Portuguese and Spanish popula-tions and found a major split between northern andsouthern populations on either side of the Tagus River/Nazaré Canyon area. Sea surface currents promote up-welling and perpendicular offshore currents thus cre-ating a suitable barrier to this biogeographic transitionzone. Detailed sampling of Z. noltii populations fromthe North African and Spanish Mediterranean coastsis underway (Diekmann, personal communications).So far, however, there is no support for a separateMediterranean/Gibraltar group. Even with better sam-pling (Procaccini, Di Carlo and Olsen, personal commu-nications), the Mediterranean group joins strongly withPortugal and Mauritania. With complete sampling in thecentral Mediterranean around the Tunisian-Sicilianboundary (Procaccini, Di Carlo and Olsen, personalcommunications), it will be interesting to see if additionalsubstructure is detected that differs from the overallphylogeographic pattern of P. oceanica.

3.4. Posidonia oceanica

P. oceanica is endemic to the Mediterranean and, afterZ. marina, is the best studied seagrass species. UnlikeZostera or Cymodocea, P. oceanica has been resident inthe Mediterranean for millions of years and even today,


core samples of thematte suggest that some meadows areseveral thousands of years old (Mateo et al., 1996). Thus,the effects of the LGM were probably relatively minor incomparison to Northern Europe, except for the division atthe basin scale and reduced flow with the Atlantic as aconsequence of sea-level drop. The Adriatic was certainlyaffected as was the current regime within each basin.Initial studies based on tri-nucleotide microsatellites(Procaccini and Waycott, 1998; Procaccini et al., 2001,2002; Ruggiero et al., 2002) revealed a heterogeneousdistribution of extremely low variation, which wasattributed to the predominance of clonal growth resultingin low effective population size (seeMolecular markers inthis review). Also, the finding of large clones and littleapparent sexual reproduction raised concern for how toprotect these “relicts”. With the development of new, di-nucleotide microsatellite loci (Alberto et al., 2003b), itwas found that there was more variation than previouslythought (Arnaud-Haond et al., 2005, 2007). In acompletely new basin-scale analysis, strong differentia-tion between the western and eastern basins across theSiculo–Tunsian Strait has been confirmed (Arnaud-Haond et al., 2007). Allelic diversities are slightly higherin the East as compared to the West with many privatealleles in each basin. However, the highest diversity is inthe central area of the Siculo–Tunesian boundary withevidence for a contact zone.Within the contact zone, thereis no evidence for reproductive isolation or nascentspeciation via linkage disequilibrium (Arnaud-Haondet al., 2007). On going analysis support the existence ofthe contact zone targeting the Eastern Sicily Channel areaas the real boundary between East and West populationgroups and stressing the importance of the Calabrianpeninsula as biogeographic barrier (Serra et al., 2007;unpublished results).

The Adriatic Sea outpost. All meadows analyzednorth of the Gargano Peninsula (41°57′N, Apulia, Italy)have been found to be uni-clonal, with different clonespresent in each different population (Ruggiero et al.,2002, Arnaud-Haond et al., 2007, and unpublishedresults). The northern Adriatic Sea was almost com-pletely exposed during glaciations and post-glacial re-colonisation of this region could have happened bymeans of few genotypes migrating from remnantisolated populations which persisted during glaciationsin one of the Eastern refugia (Ruggiero et al., 2002).

3.5. Cymodocea nodosa

C. nodosa ranges from Mauritania and the CanaryIslands to southern Portugal; and throughout theMediterranean. Two recent studies have investigated

the Canary Archipelago (Alberto et al., 2006; Blanchet al., 2006). Alberto and co-workers (2006) found thatallelic richness was slightly lower than mainlandpopulations but still very high with no significantdifferences across the four islands (10 meadows)investigated. Similar allelic composition observedacross sites suggested a single source founder effectsin which subsequent genetic drift best explained the lackof inter-island structure and isolation-by-distance.Similar results were found by Blanch and co-workers(2006) that also considered Mediterranean samples inthe analysis. The Canarian and Mediterranean popula-tions of C. nodosa were genetically distinct, with theEastern-most Island, Fuerteventura, representing a linkbetween the two areas. The presence of a main East-West direction of gene flow suggests colonization fromthe Mauritanian coast. The larger biogeographic picturedepicting the Canary Islands as a refuge or as a newlypopulated locality (either from Mauritania or theMediterranean) remains uncertain though probable asdemonstrated for the seaweed Cladophoropsis membra-nacea (Van der Strate et al., 2003).

Within the Mediterranean basin, unpublished resultsshow the existence of population clusters whichapproximately correspond to the P. oceanica ones,although a clear differentiation between eastern andwestern Mediterranean populations is not supported inthe distance tree (Ruggiero et al., personal communica-tions). Nevertheless, different levels of genetic diversityboth in terms of clonal diversity and heterozygositywere found between the western and the eastern basins,with eastern populations less diverse than the westernones. The disjunction of North-Tyrrhenian from all otherpopulations is supported by high bootstrap values. Adifferentiation between Northern and Southern Tyrrhe-nian populations has already been observed in P.oceanica (Procaccini et al., 2001) and could be due toseasonal patterns of superficial closed circulation cellsin the north, centre and South-Tyrrhenian Sea. Adriaticpopulations are well distinct and almost completelyuniclonal, suggesting that the hypothesis of post-glacialrecolonization and adaptation of selected genotypes,formulated for P. oceanica (Ruggiero et al., 2002) alsoapplies for C. nodosa.

3.6. Challenges in seagrass phylogeography

Phylogeographic studies have highlighted how pastevents have shapedmodern day distributions providing uswith specific knowledge of major genetic discontinuitiesat the landscape scale, as well as large areas ofconnectivity—typically over scales of many hundreds


to thousands of kilometres. Such studies also highlight therecency of changes in distribution over timeframes of afew thousand rather than millions of years. Newgeneration phylogeographic studies will utilize SNPs,thus permitting a stronger coalescent based framework foranalysis including better estimates of changes in effectivepopulation size and dispersal directionalities linked tocoastal circulation models and GIS (Kidd and Ritchie,2006). Resurveys will also include the use of trait-associated genes thus opening up opportunities to explorethe grand-scale latitudinal, temperature and light gradientsof adaptation. Two other important challenges are: 1) toexamine tropical seagrass phylogeography; and 2) tostudy mixed assemblages of seagrasses (mainly in thetropics). Similar arguments apply to the southernhemisphere whose paleoclimatic and tectonic historiesdiffer considerably from the northern hemisphere.

4. Gene flow and population connectivity

While evolutionary biologists are interested in thehistorical genesis of distribution and genetic structure ofa species, ecologists are primarily interested in contem-porary gene flow among populations—namely, overwhat distance and at what rate individuals or propagulesare exchanged. Aside from a fundamental interest inseagrass dispersal biology, connectivity is highlyrelevant to conservation. Population genetic theorypredicts that local populations become geneticallydepauperate through genetic drift when isolated fromneighbouring populations (Hedrick, 2001; Allendorfand Luikart, 2007), while genetic exchange may providegenetic rescue under changing environmental conditions(Davis and Shaw, 2001).

In almost all plant species, populations are discon-tinuously distributed. Estimating rates of exchangethrough direct tracking of propagules or individuals isnext to impossible over larger (km) distances in plants,and particularly in the marine environment. In manysituations, genetic markers provide the only means toassess genetic exchange (Bohonak, 1999).

The logic in using genetic structure for addressinggene flow is simple. Provided that markers areselectively neutral (see The standard toolbox markersabove), the genetic similarity at polymorphic markerloci reflects genetic exchange among populations. Themore similar allele frequencies are the more gene flowmust occur that prevents populations from diverging.

As a tool for estimating gene flow from allelicfrequency data, Wright introduced the fixation indexFST. Also called standardized allelic variance, FST mea-sures the correlation among alleles within subpopula-

tions with respect to the entire set of populations(Wright, 1969). The product of the effective populationsize (Ne) and the fraction of migrants m (thus theabsolute number of individuals or propagules ex-changed) is inversely proportional to FST, expressed as:

Nem ¼ 1=4⁎1= 1� FSTð Þ

Because this formula relies on several assumptions—such as random mating and constant population size—that are almost never met in nature, its uncritical usagehas been rightly criticized (Hedrick, 1999; Whitlock andMcCauley, 1999). Accordingly, rather than using FST atface value, interpretations should be restricted to relativeanalyses among populations of the same species.Another important consideration is that absolute mag-nitudes of FST values are very sensitive to the hete-rozygosity of the genetic markers employed (Hedrick,1999). In particular, when using highly polymorphicmicrosatellites, FST is always b1 and will only attainvalues well below unity despite complete isolationamong two populations. Therefore, a standardization ofFST has recently been proposed that should be useful inseagrass studies within and among species (Hedrick,2005).

An additional problem in using FST relies in the factthat associated estimates of migration assume migration-drift equilibrium. Hence, measures of migration based onFST will not necessarily reflect the average number ofmigrants per generation, but rather the inherent variancein FST that is recorded as a population experiencesgenetic drift until equilibrium is reached (reviewed inNeigel, 2002). It is here that more assumption freeassignment tests may provide a better alternative foraddressing questions of gene exchange among subpo-pulations (see below).

Global partitions of population differentiation amongsets of populations should be augmented by approachesthat use the spatial information of populations relative toone another. Two such methods are analysis of molecularvariance (AMOVA) and isolation-by-distance (IBD). InAMOVA, the total population data set is divided intosubgroups (e.g., populations within bays), and a formalallocation of total, among-population genetic divergenceinto genetic variance within subgroups, and varianceamong groupings becomes possible. Because the resultsare very sensitive to the a priori chosen scales ofhierarchical nesting of population units, the interpretationof the results remains limited. Moreover, AMOVA doesnot directly estimates migration, but allows to test forsignificant population subdivision at different scales,thereby aiding the identification of breaks in genetic


structure and diversity that are relevant formarine ecologyand biogeography.

A more useful approach is IBD, which depictspairwise population differentiation (as FST) as a functionof spatial distance (Wright, 1943, 1969; Bohonak,1999). The steeper the slope of this function, the greaterthe isolation and the fewer the number of migrantsexchanged. It has been suggested that IBD graphs betransformed to a log-scale when populations are con-nected in a two-dimensional metapopulation structure asopposed to linear stepping stone model along thecoastline (Rousset, 1997). In small-scale populationnetworks (say, 100–500 km), IBD relationships areoften linear without transformation, and we recommenddata transformation only when this significantlyimproves linearity of isolation-by-distance functions.

IBD studies have now been conducted in severalseagrass species, notably P. oceanica, Z. marina andZ. noltii (Reusch et al., 2000; Coyer et al., 2004b; Olsenet al., 2004; Arnaud-Haond et al., 2007). In Europeanwaters Z. marina and Z. noltii populations indicatebreak points of dispersal at a scale of b250 km and 50–100 km, respectively, suggesting that below such adistance, gene flow effectively prevents genetic differ-entiation among populations (Olsen et al., 2004). Giventhe low distances that seeds travel under water (Orthet al., 1994), gene flow by rafting, fruit-bearing shoots ismost likely. This is in line with observations of abundantwrack bearing seeds washed upon the shore (Harwelland Orth, 2002). That arriving rafting-shoots representnovel genotypes coming from a non-local populationhas been demonstrated using genetic markers in twoBaltic eelgrass populations using assignment tests (seebelow) (Reusch, 2002). At the distributional edges ofthe southern and northern European populations ofZ. marina, IBD graphs are steep, indicating that geneticisolation rises quickly with distance. One interpretationsis that predominantly clonal reproduction at range mar-gins as opposed to sexual reproduction leads to lowergene exchange (South-Portugal: Billingham et al., 2003;Finland: Reusch and Boström, personal communica-tions). This also applies, to a lesser extent, to Z. noltii inthe Black Sea/Azov Sea area where IBD relationshipsimmediately rise with geographic distance, in contrast tonorthern Europe (Coyer et al., 2004b). Thus, populationconnectivity as inferred from IBD graphs may differmarkedly among regions, a finding that is of relevancewhen planning the size of marine protected areas.

IBD estimates for P. oceanica are clearly influencedby the break existing at the level of Sicily Channel.Within each of two sub-basins, correlations betweengeographic and genetic distance are weak, suggesting

stochasticity in patterns of gene flow and dispersal atscales of hundreds of kilometres (Arnaud-Haond et al.,2007). Complex circulation patterns in the semi-enclosed basins, life history and potential for episodicalhigh dispersal of floating seeds may account for thispattern. Similarly, in C. nodosa there is no correlationbetween gene flow and geographic distance within theMediterranean basin, suggesting that surface circula-tions drive stochastic patterns of gene flow (Ruggieroet al., unpublished). It seems that the Mediterranean Seais unique in terms of the genetic structure of seagrasspopulations.

Extensions of IBD-approaches have been proposedthat allow for a calculation of the effective populationsize (Ne) (Rousset, 2000), an important quantity thatdetermines the rate of loss of genetic informationthrough drift. Moreover, outlier populations that donot fit into the IBD relationship may serve as startingpoints for further analyses. An IBD analysis alsofacilitates comparisons among studies and species,because it allows comparing the slopes and not absolutemagnitudes of FST (which is related to number andheterozygosity of the loci employed, see above).

Beucase FST based approaches require many untest-able assumptions, a novel class of methods will offersignificant advance for addressing questions of popula-tion connectivity. Assignment tests belong to a novel setof techniques to assess gene flow (Rannala andMountain, 1997; Wilson and Rannala, 2003; Manelet al., 2005). These approaches need a larger set of highlypolymorphic marker loci with many alleles, in order toattain sufficient resolution than traditional, frequencybased equilibrium methods. Individual dispersal eventsare assigned to target populations using multi-locusgenotypes (MLG, see for example Blanch et al., 2006). Aless powerful approach tests the hypothesis that a MLGdoes not originate from a local population, and is thus ofrecent immigrant origin (e.g. Reusch 2002). It is alsopossible to assign a MLG to a set of target populationsbut this requires a dense sampling scheme where allpossible populations have been sampled. Inferences areaided with novel statistical tools (Bayesian inference)that are now possible due to the much increasedprocessor speeds (Beaumont and Rannala, 2004).Coupled with recent advances in marker development,assignment tests provide molecular ecologists withpowerful tools to distinguish historical demographyfrom more recent exchange events.

Additional methods for inferring population geneticstructure from molecular data include Bayesian partition-based approaches, developed by Corander et al. (2007)and implemented in the software BAPS (Corander et al.


2003). Using BAPS, C. nodosa populations from theMediterranean Sea were clustered in panmictic groupswhich were utilized as units for clustering analysis andassignment tests (Ruggiero et al. unpublished, seephylogeography section above).

5. Small-scale population genetic structure

While genetic differentiation among populations isdriven by occasional, rare events (Cain et al 2000),within-population genetic structure is rather determinedby those dispersal, growth and clonal expansion pro-cesses that are most frequent. Genetic variation inwithin-seagrass meadows is organized at two principalhierarchical levels: the genetic/allelic variation presentin the population sample as a result of sexual repro-duction from seeds, and the genotypic/clonal variation asa result of clonal (vegetative) reproduction via spreadingrhizomes. This is illustrated in Box 1. Ramets that belongto the same clone or genet (also called modules) caneither grow intermingled (Ruggiero et al., 2005a) or in adiscrete, phalanx-type pattern (Reusch et al., 1999a,c;Waycott, 1995; Migliaccio et al., 2005). Clones mayattain very large sizes (N50 m in diameter, comprisingthousands of ramets) and presumably, very old ages (afew years to centuries) (Reusch et al., 1999a,c; Ruggieroet al., 2002). The discovery of large clones immediatelyprompted concerns about meadow age, possible in-breeding, stability and resilience to disturbance. Thebasic idea was that meadows dominated by one or a fewlarge clones would be genetically less diverse andpossibly at greater risk due to unavoidable inbreedingand/or less resilient to disturbance (but see Diaz-Almelaet al. (in press) for an example where large clones aremore resilient). Nevertheless, taking into account the twolevels of diversity, it seems that genetic diversity can stillbe high when genotypic diversity is low but of course notso low that there is only one clone.

In Z. marina, small-scale disturbance on clonaldiversity has been experimentally studied in 1×1 mquadrants in the SW Baltic Sea. After 2 years, the localclonal diversity was almost unchanged despite treatmentsthat included up to 75% removal of biomass. However,there was a significant enhancement of new shootrecruitment under intermediate disturbance regimes,indicating that some degree of intermediate physicaldisturbance may lead to maximal clonal diversity bypreventing competitive exclusion of recruitment (Reusch,2006).

Genetic characterization of a meadow or series ofmeadows requires the use of highly polymorphicmolecular markers in order to identify ramets and assign

them to genets. Microsatellite loci provide high powerand allow the calculation of error probabilities associ-ated with clone assignment (Parks and Werth, 1993;Arnaud-Haond and Belkhir, 2007). Although samplingdesigns will vary depending on the level of resolutiondesired, an initial characterization of a meadow in whichnothing is known, involves random samples (preferablywith coordinates) within the meadow (N=50−100ramets) at between 0.5 and 5-m spatial scale, dependingon the size and clonal growth rate of the species. Upongenotyping, identical multi-locus genotypes can beascribed to the same genet or clone if the likelihood ofidentity by chance is below a chosen threshold (Parksand Werth, 1993). Once clonal maps have beenobtained, the resulting meadow structure, includinglevels of clonality and genetic relatedness can be con-veniently described and mapped using spatial autocor-relation (SAC) (Reusch et al., 1999a; Arnaud-Haondand Belkhir, 2007) or network analysis (Hernandez-Garcıa et al., 2006; Rozenfeld et al., in press).

5.1. Clonal structure

Meadow structures in Z. marina encompass the fullspectrum of possibilities—from giant single clonescovering thousands of square meters to completegenotypic uniqueness of virtually every ramet sampled(Reusch et al., 1999b,c; Olsen et al., 2004). Thus,effective population sizes, clonal diversities and geneticconnectivity with the regional population pool varyconsiderably from place to place. Where annual lifehistories predominate (Reusch, 2002; Muniz-Salazaret al., 2005), it is clear that clones will be ephemeraland never larger than a dozen shoots, whereas in perennialmeadows, a range of clonalities are typically presentexcept in very old, very isolated areas which may haveonly a few. In Z. noltii, instead, both genetic and clonaldiversity are high (Coyer et al., 2004b; Diekmann et al.,2005; Ruggiero et al., 2005b) in accordance with bothannual and short-term perennial life-histories. Low clonaldiversity and a marked clumping of clonemates, in whichclones were recognisable as discrete units as expected fora phalanx growing plant (Ruggiero et al., 2005b), werefound in a mixed meadow study with C. nodosa. There issome evidence, in Z. marina, that geographicallymarginal populations have lower clonal diversity (Reuschet al., 1999c; Billingham et al., 2003), in line with thegeneral hypothesis that plant populations lose sexualfertility at their range margins (=geographic partheno-genesis; Bierzychudek, 1985; Eckert, 2002). At thewithin-population scale, high-resolution clone mapsrevealed the partitioning of meadows into areas with

Box 1

Seagrass modularity and consequences for genetic structure.



low and high clonal diversity in Z. marina (Hämmerli andReusch, 2003c; Reusch et al 2005), a feature that was alsofound in the dioecious seagrass species C. nodosa(Reusch et al., 2005). An important finding was that atsome locations, genet intermingling was found (Häm-merli and Reusch, 2003c; Reusch et al., 2005) that had notbeen detected in previous studies conducted at largerscales (Reusch et al., 1999a). This highlights theimportance of sampling scale for detecting genotypepatterns (see also Arnaud-Haond et al., 2005).

P. oceanica presents a very special case forassessing long-term development of meadow structurebecause this species persisted in the Mediterranean Seathroughout the last glaciations. Individual meadowsmay persist for millennia overgrowing the remains ofpast meadows as shown by dating of matte cores(Mateo et al., 1996). The exact age of extant meadowshas not been determined although ancient DNA andfossil material are currently under investigation(Raniello and Procaccini, 2002). In any case, slowrhizomatous growth rates, low rates of sexual repro-duction and seedling establishment (Procaccini et al.,2003; Diaz-Almela et al., 2006) suggest that thesemeadows can preserve their genotypic composition fora very long time.

In the light of these historical considerations, however,P. oceanica meadows displayed a wide range of geneticdiversity, from highly diverse to completely uniclonal(Arnaud-Haond et al., 2007), similar to the faster growingZ. marina. Only populations in the Adriatic sea have verylow genetic and genotypic diversity, suggesting oldpersistent clones (see Challenges in seagrass phylogeo-graphy section above).

Unfortunately, there are very few data available formeadows growing along the North African coasts, sothat overall genetic and clonal diversity for this vaststretch are based exclusively on populations fromTunisia (Procaccini et al., 2002; Arnaud-Haond et al.,2007).

High-resolution mapping at fine scale (Migliaccioet al., 2005) reveals that genetic diversity is not equallydistributed within the meadow, as already found in Z.marina (see above). Areas with higher genetic diversitycorrespond in some cases to areas with higher shootdensity, suggesting a convergence of different genotypescompeting for light or nutrients. Over a ten years timeinterval, the distribution of areas with different levels ofshoot density changed (Zupo et al., 2006). It is interestingto stress that distribution of genetic diversity bettercorrespond to the distribution of shoot density obtained10 years earlier, suggesting that the genetic data keepsmemory of past meadow structure despite changes

occurred in the last 10 years (Migliaccio et al., 2005;Zupo et al., 2006).

C. nodosa is the only dioecious species where finescale genetic structure has been characterized. C.nodosa is characterized by seed basicarpy (i.e. thegermination of seeds when still attached to the base offemale plants) and fast rhizomatous growth. A prioriwe would predict high genetic diversity and high clonalintermingling but also small and well defined neigh-bourhood size, due to low dispersal potential of seeds.Recent empirical data have validated these expectationsonly partly.

Similar to other seagrass species,C. nodosameadowshave very different clonal diversity going from multi-clonal to almost uniclonal (Ruggiero et al., 2005a;Alberto et al., 2006; Blanch et al., 2006; Ruggiero,personal communications). In contrast, within-meadowdistribution of genotypes was consistent with the theo-retical expectations, in a C. nodosa meadow studied offthe Island of Ischia (Gulf on Naples, Italy) (Ruggieroet al., 2005a). Genet distribution was highly intermingledfrom meters to millimetres spatial intervals, resembling a‘guerrilla’ growth strategy, thus allowing the presence ofgametes of the opposite sex in the immediate proximity, arequirement for successful fertilization in a dioeciousspecies (Ruggiero et al., 2005a).

5.2. Spatial autocorrelation

In SAC (spatial autocorrelation), pairwise geneticsimilarities are plotted as a function of distance classes,with genetic similarities higher than expected under arandomly permutated null- distribution showing posi-tive values (=positive autocorrelation). In addition, theactual genotypes can be mapped and connected on amap of the meadow using spatial coordinates. If allsampled ramets are included, positive autocorrelationcan be expected due to genetic identity among membersof the same clone and/or presence of full or half-sibrelatedness. The zero intercept of the correlogramcorresponds to the average clone size (the clonalsubrange, Alberto et al., 2005). If identical ramets areexcluded, the intercept is indicative of the geneticneighborhood size (Wright, 1969).

Analysis of differences between correlograms basedon all sampled ramets, or considering each genet onlyonce can give valuable insights in the occurrence ofsexual reproduction, local scale dispersal of sexualproducts and meadow growth dynamics. Correlogramsof the three species Z. marina, P. oceanica andC. nodosa are shown in Fig. 2, with the geneticsimilarity measured as fij, a quantity that is correlated

fij

2 4 6 8 10 12 14 16

0.16

0.12

0.08

0.04

0.00

Zostera marina

-0.02

-0.01

0.00

0.01

0.02

0.03

0.04

0 5 10 15 20 25 30 35 40 45 50

Cymodocea nodosa

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0 20 40 60 80 100 120 140 160

Posidonia oceanica

meters

meters

meters

fij

fij

Fig. 2. Examples of spatial autocorrelation graphs obtained using thekinship coefficient (fij) in Zostera marina, Posidonia oceanica andC. nodosa. Spatial autocorrelation coefficient has been calculatedincluding all samples (i.e. ramet level = closed circles) and onlycentre points of all clonal fragments (i.e. genet level = open circles)for all three species. 95% confidence-intervals are represented withdotted lines (ramet level) and spotted lines (genet level). Data arefrom Hämmerli et al. 2003c (Z. marina), Migliaccio et al., 2005(P. oceanica) and Ruggiero et al., 2005a,b (C. nodosa).


with the genetic relatedness of genets. Coancestrycoefficient values (fij) are higher for Zostera and Posi-donia in comparison to Cymodocea. Contribution ofclonal growth to autocorrelation is evident in all theexamples. Average size of clones, as inferred by the95% confidence interval intercept is consistently higher

in P. oceanica. It is noteworthy that, althoughdependent from rhizome elongation rate and matingsystem sensu lato, spatial autocorrelation can change indifferent meadows of the same species as consequenceof local conditions, dispersal and incidence of sexualreproduction.

In Z. marina, aggregated clones (3–4 m diameter)have been shown to have significant kinship, whereasseeds from intermediate distances had significantlygreater mass and higher germination rates (Billinghamet al., personal communications). When growing beyonda certain size, genets can be fragmented into severalcontiguous patches, which add a third level of organisa-tion of genotypic/genetic structure to meadows. Theselevels are (1) the modular individual, or ramet (2) acontiguous areas of identical genotypes = clone fragment(3) all members of an identical genotype = clone orgenotype. For eelgrass Z. marina, an inclusive descriptionof all three levels of genetic organization was done byHämmerli and Reusch (2003c). By decomposing thegenetic relatedness (as kinship coefficient fij) amongsample points as 1-m intervals into contributions of allthree organisational levels using spatial autocorrelation,the influence of clonal spread, clone fragmentation andrestricted pollen and seed dispersal for meadow fine-scalestructure could be disentangled. Most importantly, evenwhen each genotype fragment counted only once,significantly positive fij indicated restricted geneticneighbourhood at a scale of 5–7 m (Hämmerli andReusch, 2003c) suggesting low dispersal of pollen andseeds within the meadow.

Low within meadow dispersal of pollen and seedswas also detected in P. oceanica through spatialautocorrelation analysis (Migliaccio et al., 2005;Arnaud-Haond et al., 2007) despite the potential forhigh seed dispersal in this species. Genetic neighbour-hood was higher than in Z. marina or C. nodosa,although still restricted to 40 m, which can be consideredthe average neighbourhood size for Posidonia.

Neighbourhood size diameter of about 10 m for thegenet levels was found in C. nodosa, suggesting thatmale gametes could also poorly disperse and that geneflow could not be extended enough to avoid biparentalinbreeding. The poor potential for gene dispersalmatches with theoretical expectations due to seedbasicarpy. The relatively large neighbourhood size, i.elarger than in Z. marina (Hämmerli and Reusch, 2003c)and Z. noltii (4–6 m; Zipperle and Olsen, personalcommunications), found in C. nodosa (Alberto et al.,2005; Ruggiero et al., 2005a) highlights that factorsother than mating system itself shape the distribution ofgenetic diversity within seagrass meadows.


Seagrass meadow can grow from the surface toseveral meters depth, encompassing a large range ofwater temperature, light quality and hydrodynamicforces. Genetic subgroups can exist within the samemeadows, as a consequence of adaptation to localenvironmental conditions even in the presence of geneflow. A clear disjunction was recorded in P. oceanicabetween stands growing above and below the summerthermocline, as possible consequence of reproductiveisolation due to time-shifting of the sexual cycle due todifferences in light and temperature (Buia and Mazzella,1991; Procaccini and Mazzella, 1998; Migliaccio et al.,2005). The level at which shoots living at differentdepths are adapted to the differences in environmentalconditions is still an open question (see Box 2).

5.3. Mating system

Seagrasses are mostly dioecious (i.e. 78% of thespecies, Waycott and Les, 1996; Les et al., 1997)compared to only 3% among all angiosperms. Theunderlying fitness advantage accounting for the highfraction of separate sexes are still unclear (Waycottet al., 2006). If this results from a selective advantageof having spatially separated male and female plants,this does not reflect on the genetic structure ofmeadows studies so far (see above). On the otherhand, monoecious species have a putative selectiveadvantage, being able to switch to self-pollinationwhen foreign pollen becomes limiting (=reproductiveassurance). Probably, the evolution of mating systemsis best understood in conjunction with prolificvegetative growth, a decisive characteristic of almostall seagrass species. In particular when clones becomelarge, hundreds of genetically identical flowers mayprovide ample opportunity for selfing. Empiricalevidence comes from Z. marina, a monoecious andself-compatible seagrass species where self-fertiliza-tion occurs under natural conditions despite temporalseparation of male and female flowers. Using highlypolymorphic microsatellite loci, Reusch (2001)reported that the fraction of selfed vs. outcrossedoffspring decreased with local clonal diversity, sug-gesting that seeds were fertilized by their own pollenrather than being discarded once foreign pollenbecame limiting in the vicinity of target female shoots.Because adult plants were in Hardy-Weinberg-equi-librium at most marker loci, strong selection againstselfed offspring must be operating during the plant'searly life-stages (Reusch, 2001). In an analysis ofclone size frequency distribution, Hämmerli andReusch (2003b) discovered that relatively outcrossed

individuals had a greater probability of growth withinthe largest size fraction of N10 m2. In other words,genetically diverse individuals seem most competitive,supporting the adaptive value of mating systemstrategies that promote outcrossing (Hämmerli andReusch, 2003a).

The plasticity of flower development to avoid selfingwere subject of a follow-up study by Hämmerli andReusch (2003c). In large flow-through tanks, the pollenenvironment of female focal plants was manipulated.One treatment group only obtained self pollen from co-flowering males of the same clone, while the othertreatment was spiked with additional small (1%)amounts of outcrossing pollen. Surprisingly, this smallsignal was sufficient to cause a significant advancementof female flowering, while the receptive phase in the self-pollen treatment was delayed. Taken together, this casestudy suggests that mating systems may be more plasticin assuring outcrossing than the simple categorizationinto monoecious/hermaphroditic and dioecious speciesmay suggest. Moreover, the identified fitness costs ofselfing under low clonal diversity (Reusch, 2001)may beone of the selection pressures that ultimately led to theevolution of dioecy (Waycott, 2000a).

In conclusion, assessment of meadow structure isnot an empty experimental exercise. Dissectingpopulation structure reveals that it is the complexresult of: 1) demographic processes—including re-cruitment, mortality, population growth rate andturnover; 2) life history/mating system processes—including monoecy/dioecy and the balance of sexual/asexual reproduction; 3) genetic processes—includinggene flow, genetic drift, selection and mutation; and 4)dispersal potential. Knowing some of these processesis essential for interpreting the distribution of geneticdiversity at a small scale. Vice versa, knowledge ofmeadow genetic structure can represent a fundamentalpre-requisite for understanding basic ecological pro-cesses and for predicting meadow expansion andpersistence. Very instructive are studies of thedistribution of clones, their size and age, and troughthe analysis of small scale dispersal using SpatialAutocorrelation (SAC) approaches. Even if gene flowamong seagrass populations seems to prevent popula-tion differentiation at scales N10 km, it is important toremember that this reflects realized, successful dis-persal events averaged over many generations. Atthe local level of meadows, population structure isdetermined by average dispersal distances of pollenand seeds. Here, the emerging picture is different withthe relevant scales measured in meters rather thankilometres.

Box 2

Genomic tools for seagrass research.An important group of techniques tries to find molecular genetic correlates of metabolic and

physiological functions by measuring the transcription level of genes of interest. As a first step, the in-vitro reverse transcription of mRNA into its complementary DNA (=cDNA) is necessary because theformer is very unstable and not suited as PCR template.

Quantitative, real-time PCR (=QPCR) is a method that can measure the abundance of mRNA ofspecific genes, for example the amount of stress mediating heat shock proteins as a function of watertemperature. To this end, the accumulation of products in a polymerase chain reaction (PCR) aremonitored in ‘real time’ to deduce theamountof initial template, i.e of the target gene's cDNA (Heid et al.,1996).

While QPCR can only handle a fewcandidate genes, there are several techniques that aim at obtaininga more comprehensive picture of gene expression. In subtractive suppression hybridization (SSH)(Diatchenko et al., 1996), the mRNA pool among two experimental conditions, populations or tissues isreciprocally subtracted from each other, leaving only those candidate genes that are expressed more orless under one of the two conditions.

Transcription profiling using microarrays is a medium- to high throughput technique thatsimultaneously measures the expression levels of hundreds to thousands of genes simultaneously(Gibson, 2002). The target cDNAs from the experiment hybridize to complementary probes that arearrayed on filters or glass plates. For detection, target cDNAs are labelled with dyes. Two basic arraymethods are cDNA arrays that use PCR amplicons from the library as probes and a second moresophisticated technique that uses several short oligonucleotides complementary to different stretches allalong a target gene. Microarrays have not yet been used in seagrass research, but their development isunderway for two species (P. oceanica and Z. marina).

In order to develop second generation, molecular markers that measure selectively relevantpolymorphism, an expressed sequence tag (=EST)-library (Bouck and Vision, 2007) is a convenientstarting point. An EST-library is a collection of all genes thatwere transcribed at a specificmoment in timein the chosen genotype and tissue. Because gene sequences are initially not fully characterized, eachcDNAclone is said to be tagged by its sequence. EST libraries are starting points for various data miningapproaches that can identify single nucleotide polymorphism (Morin et al., 2004) and gene-linkedmicrosatellites (Li et al., 2004) as novel groups of genetic markers, as well as candidate genes forexpression analysis using QPCR (see above). Moreover, libraries provide the sequence data fortranscription profiling using microarrays (see above). For SNP- identification, overlapping reads of thesamegene are assembled andSNPs are detectedwithin the resulting contigs as polymorphic nucleotidesin the alignment. Chances of identifying SNPs can be increased by using EST-libraries from differentpopulations. Gene-linked microsatellites are predominantly found in the untranslated sections of geneseither upstream or downstream of the open reading frame.

Genome scans (also called population genomics) use amedium to large number of genetic marker locitomeasure genetic diversity and differentiation among populations. The goal is to detect genetic markersthat reveal more differentiation among contrasting environmental conditions than expected under a nullmodel ofdrift andgene flowalone (Luikart et al., 2003). Thoseoutlier loci point to candidategenes in closevicinity that are under selection. It is currently an open question which class of markers (microsatellites/SNPs) arebest suited fordetecting selection, anhowaspecies' demography,mating systemandmutationrate will influence the choice of markers.


6. The emerging role of genetic diversity for ecosystemfunction

Biological diversity is organized at different levels—from genes to ecosystems—although most studies

utilize species as the working level. Temperate seagrassmeadows are typically dominated by a single (or a few)seagrass species that serves as the structural orfoundational species. Therefore, research on causesand consequences of biological diversity must


emphasize the within-species level, i.e., population andindividual levels (Reusch and Hughes, 2006). Differencesin genetic diversity and clonal architecture of seagrassmeadows abound, thus prompting the question as to whatthe ecological consequences of such variation are.

First of all, “big” probably counts. Diaz-Almela et al.(in press) investigated population resistance and declinein P. oceanica meadows with similar genetic/clonalstructure upstream and downstream from a 10-year oldfish farm. They found a consistent and significantincrease of clonal spread in the impacted areassuggesting a higher mortality of small clones relativeto large ones, possibly due to a higher foraging capacityin large P. oceanica clones through provision of moremicrohabitats in conjunction with clonal integration(Oborny and Kun, 2002).

Beyond size, the consequences of genetic/genotypicdiversity can be broadly subdivided into immediateecological effects and more long-term evolutionaryconsequences. Important ecological effects of biodiver-sity embrace components of stability—including resis-tance to perturbation and resilience, i.e., the return-timeback to the original state. Two recent experiments havehighlighted the functional role of genetic diversity forthe persistence and resilience of mono-dominantseagrass communities in response to disturbance. In Z.marina, experimental manipulation of genotypic diver-sity was associated with larger biomass and fasterrecovery following grazing by brant geese (Hughes andStachowicz, 2004), and in another case following anatural heat wave in the western Baltic (Reusch et al.,2005). These results are in line with earlier findings ofenhanced transplantation success in P. oceanica popula-tions that were more genotypically diverse at micro-satellite loci (Procaccini and Piazzi, 2001). In otherwords, higher genotypic diversity (i.e., positive diversityeffects) enhances resistance and resilience. Using clonalreplicates in monoculture of all tested genotypes in thewestern Baltic experiment, Reusch et al. (2005) werealso able to disentangle the two different components ofpositive diversity effects: the selection effect (i.e., was aparticular genotype better) and the complementarityeffect (i.e., was there a synergism among genotypesenhancing overall performance) (Loreau and Hector,2001). Contrary to expectations, the best performingclone in monoculture was not the one that drove thepositive diversity effect in mixtures, but there was ratheran average positive effect of non-self neighbouringgenotypes that resulted in positive genotype diversityeffects through complementarity. These results areconsistent with a recent terrestrial study in goldenrodthat found cascading effects of genotypic diversity up

the food chain leading to higher arthropod diversity(Crutsinger et al., 2006). Likewise, higher associatedfaunal diversities have been found in positively diverseZ. marina patches (Hughes and Stachowicz, 2004,Reusch et al., 2005). Such community genetics studies(Neuhauser et al., 2003) have thus successfullyintegrated the species and the genotypic level intoecological theory (Vellend and Geber, 2005).

While new evidence does suggest that genotypicdiversity matters in an immediate ecological context(Hughes and Stachowicz, 2004, Reusch et al., 2005),our knowledge about the evolutionary consequences ofgenetic/genotypic diversity in seagrass beds are meagreat best. Heritable genetic variation is a prerequisite foradaptive evolution, and continual adaptation to changesin the biotic/abiotic environment is required for allpopulations, last but not least in the light of globalchange (Endler, 1986). Unfortunately, the diversitydisplayed by neutral genetic markers (The standardtoolbox) is only weakly correlated with quantitativegenetic variation that makes up the phenotype andmatters for selection (Lynch, 1996). At the level ofquantitative traits, seagrasses offer a number of uniqueadvantages because they replicate identical modules ofthe same genet by natural ‘cloning’. This means thattranslocation experiments utilizing replicated modulesof a clone can control for environmental influenceswhile allowing for an assessment of broad senseheritabilities, i.e. a separation of ‘gene×environment’-interactions. We know of only one study where suchdata are available and that study revealed a 4-folddifference in shoot number between the weakest cloneand the best clone during the 2003-European heat wave(Reusch et al., 2005). Such response diversity toextreme events may soon become critical for manycoastal organisms, with potential ecosystem conse-quences in the case of seagrasses or corals. Secondly,rapid adaptive evolution is increasingly identified as onemechanism that will allow population persistence in theface of global change (Davis et al., 2005, Bradshaw andHolzapfel, 2006). It is clear that evolutionary responseto, e.g., alterations in sea surface temperatures, novelpathogens or increasing turbidity, is only possible givenadditive genetic variation in seagrass populations(Lynch, 1996; Jump and Penuelas, 2005).

Important open questions in the genetic biodiversity-ecosystem function debate for seagrasses include: 1) therelationship between genotypic (clonal) and genetic(allelic) diversity in multi-species seagrass beds (Vel-lend and Geber, 2005; see Ruggiero et al., 2005b as anexample); 2) whether the two levels of diversity havesimilar or divergent roles (Vellend and Geber, 2005);


and 3) whether a correlation exists between theassociated species richness/diversity on one hand, andgenetic diversity of the foundational seagrass species ofthe particular meadow type on the other.

7. Adding the genetic component to conservationand restoration

Seagrasses are among the most vulnerable ecosys-tems on earth (Short and Wyllie-Echeverria, 1996) andtheir worldwide decline is due to threats from habitatdestruction through fishing and dredging, pollution,invasive species and, last but not least, climate change(Orth et al., 2006). Conservation biology is problem-solving oriented and dedicated to the preservation ofbiodiversity (Allendorf and Luikart, 2007). Untilrecently, ecological and genetic processes have beenconsidered separately, mainly because it has beenwidely believed that demographic and ecologicalfactors would have more immediate impacts forconservation than putatively slower genetic processes.In a comprehensive meta-analysis of 170 species andindependent computer simulations, Spielman et al.(2004) showed that most species are not driven toextinction before genetic factors impact them and thus,ecological, demographic and genetic processes mustbe considered together. We fully agree and here iswhy for seagrasses.

Although medium-range dispersal events occuroccasionally, seagrasses exhibit low effective popula-tion sizes and small genetic neighbourhoods, asdemonstrated through spatial genetic structure withpositive genetic autocorrelation at the m-scale. More-over, frequent clonal spread leads to low genotypicaldiversity at local and population-wide scales. Thesedemographic and population genetic attributes implythat seagrasses are particularly vulnerable to geneticerosion under environmental challenges. Thus, there isevery reason to believe that negative genetic feedbackswith demographic processes represent a considerableadditional threat to seagrasses, as is the case in manyother plant species (Hedrick, 2001). Likewise, but on amore positive note, it is important to remember thatgenetically driven positive feedbacks also exist. Asdiscussed in the previous section, genetic and clonaldiversity create more resilient and disturbance-tolerantseagrass communities including higher diversity ofassociated fauna and flora leading to greater “function”(Duffy, 2006; Reusch and Hughes, 2006).

Although proximal, human mediated factors relatedto habitat quality can be ameliorated (at least in theory),climatic conditions are now changing rapidly, posing the

question as to whether seagrasses possess sufficientadditive genetic variation to respond via rapid contem-porary evolution to changing environments (Davis et al.,2005; Jump and Penuelas, 2005; Bradshaw andHolzapfel, 2006). The answer to this question has boththeoretical and practical significance. At the theoreticallevel, understanding the genetic basis for adaptationremains a fundamental question for evolutionarybiologists and geneticists through understanding thegenotype–phenotype divide. On the applied level,information on important feedback loops betweenecology and population genetics provides insight onprojected range shifts, guidance for selecting the geneticsource and diversity of donor material for restoration,and in understanding interactions of genetic isolationand demographic uncertainty of remaining habitatpatches in an increasingly fragmented landscape.

8. Genetics and restoration

A management strategy whose goal is to increase thepopulation size through transplantation must take intoconsideration the genetic variance since increasing thenumber of ramets may actually lead to lowered geneticvariation by relying on the reproductive success of a fewclonal individuals. If restored populations have a highnumber of genotypes, outcrossing rates and fitness willincrease and the possibility for populations to adapt toenvironmental changes will be enhanced. On the darkside, however, introduction of genotypes that are dis-tinct from native populations may lead to higher mor-tality (Williams, 2001) if not locally adapted; or theintroduced genotypes may mate with local genotypesproducing offspring that are less fit, a phenomenoncalled outbreeding depression (refs: Fischer andMatthies,1997; Waser et al., 2000). Although not documented inseagrasses so far, it is probable that such events haveoccurred in Z. marina.

A number of seagrass management plans includerestoration. Unfortunately, worldwide success of sea-grass transplantation and restoration is only around 30%(Fonseca et al., 1998) and virtually none that we areaware of have used genetically guided criteria in theselection of donor material. A few pilot experiments,however, show that such an approach is important andmay explain some of the restoration measures thatfailed. In P. oceanica, the genetic polymorphism ofdonor populations was positively correlated withsurvival rate, increase in rhizome length and ramifica-tion number of transplanted shoots (Procaccini andPiazzi, 2001). This has also been shown with Z. marina,where genetically diverse donor stocks performed better


in the early transplantation success. Nevertheless,transplanted beds showed significant reduction ingenetic diversity in comparison to untransplantedpopulations (Williams and Davis, 1996; Williams andOrth, 1998; Williams, 2001). In both pilot studies,geographic location of donor beds did not seem to play arole in later transplantation success. In the case of P.oceanica, we now know that the donor material camefrom within the same phylogeographic clade (Procacciniet al., 2002; Arnaud-Haond et al., 2007). Differences intransplantation performances due to local adaptation ofgenotypes would be more likely to occur betweenmeadows located in different phylogeographic clades,e.g. western and eastern Mediterranean ones in thisexample. In Z. marina, a study from the Baltic Searevealed marked home site advantage in a reciprocaltransplant experiment among sites 50 km distant,suggesting that pilot studies should be performed tosingle out the best performing donor populations(Hämmerli and Reusch, 2002).

To sum up, the added values of genetic data formitigation and management are several. First, data onstanding diversity at the landscape scale is crucial inguiding decisions about appropriate source material forrestoration as illustrated above. Second, seagrasses existas metapopulations and are thus very sensitive tofragmentation. Connectivity among patches may allowfor symmetrical replenishment or for a source-sinkmodel. In the later case, loss of the source patch willcause the loss of the dependent patch. Thus, location-specific estimates of patch size and connectivity areessential for determining the size of the panmictic/demographic unit (also a management unit). Manypopulation structure analyses on seagrasses showmarked population exchange based on gene flow (FST)at scales ranging from as little as 2 km to N100 km.However, actual genetic neighboorhods based ondispersal of pollen and seeds are far smaller. Assessmentof these nested scale dynamics is now computationallypossible given many new markers and powerfulassignment tests (Palsbøll et al., 2007). There is alsothe possibility that local processes interact withconnectivity. Populations with a high contribution ofclonality to population reproductive rate will produceand exchange fewer propagules. (Billingham et al.,personal communications). Third, identification of in-troduced cryptic species and/or biogeographic popula-tions of the same seagrass species can only be achievedwith genetic data because morphological identificationis not always reliable. For example, a recently complet-ed survey of Z. marina in the California Channel Islands(Coyer and Olsen et al., personal communications)

indicates taxonomic misidentification of Z. marina,Z. pacifica and Z. asiatica. Evidence for hybrids wasalso found. Finally, the ability to effectively manageseagrass ecosystems, of course, goes beyond an assess-ment of correct transplantation procedures and descrip-tion of the genetic population structure. Managers alsowant to know “how the patient is doing”. This meansthat there must be some sort of temporal monitoring,including data that provide information on how thepopulations are coping with stress, new selective re-gimes and, ultimately, adaptation. New generation geno-mic data used in tandem with standard genetic data willopen this door.

9. Genetics in monitoring

The main objectives of any monitoring programmeare to provide information about imminent and longerrange changes in a system (that are usually negative) inorder to guide management. Given that biodiversity is—at least in general terms—positively related to ecosys-tem function, most countries monitor biodiversity pri-marily at the species level. In seagrasses, the level isshifted to the intra-specific level of biodiversity keepingin mind that most temperate systems involve only oneor a few seagrass species. Ideally, the monitored pa-rameters should provide an early warning signal that thesystem is in distress, long before conditions becomeirreversible and possibly un-restorable. We argue that agenetic monitoring program can provide informationrelevant to both ecological and evolutionary time frames,while costing less and beingmore sensitive than standardprograms (Schwartz et al., 2007).

Seagrasses have been legally recognized in theEuropean Union (EU) Water Framework Directive(WFD, Directive 2000/60/EC) as key coastal ecosys-tems. A primary requirement of the directive is themandatory use of organisms as bioindicators for theassessment and evaluation of ecological status. Inverte-brates, phytoplankton, macroalgae and seagrasses havebeen identified as Biological Quality Elements (BQEs).Seagrasses are, therefore, bioindicators and part of theBQEs. Classification systems for EU coastal waters arecurrently under study. In the case of P. oceanica in theMediterranean, this includes a set of descriptors that canbe statistically analyzed and utilized to diagnose“meadow/area health” as: high, good, moderate, pooror bad according to the WFD. What actually constitutesthe cut-off values among these five categories is, as yet,undefined. Common descriptors include: shoot density,lower limit depths, cover, rhizome growth type, leafproduction and rhizome elongation; while additional


suggested descriptors include P, N, non-structuralcarbohydrate content and various trace metals (Casazzaet al., 2006). Issues related to sampling design, samplingfrequency, data analysis and translation into the WFDcategories is currently being tested by Mediterraneanrim member states (Pergent-Martini et al., 2005). In therest of Atlantic Europe the main seagrass is Z. marina(and secondarily Z. noltii). Descriptors being monitoredby the UK Technical Advisory Group (2006) include:shoot density and spatial extent, and presence ofdisturbance-sensitive taxa. These are used as indicesfor assignment to one of the five WFD categories.Denmark is taking advantage of a unique historical dataset on Z. marina to assess shifts from reference levelsagainst current meadow depths and change in salinitythat can be modelled (Krause-Jensen et al., 2005). At theglobal scale, SeagrassNet (Short et al., 2005, www.SeagrassNet.org) uses a standardized protocol formonitoring based on distribution, species compositionand shoot density, canopy height, biomass, andcontinuously measured temperature and salinity. Resultsfrom this program generally indicate that seagrasses aredeclining in virtually all areas impacted by humans orwhere global change stress is high. In temperate sites,climate change, increased grazing by migratory birdsand eutrophication are considered the main causes(Short et al., 2006). While all of these programmesprovide valuable qualitative information on “ecosystemhealth” and quantitative documentation of decline, theydo not, indeed cannot, provide an early warning systemfor pre-emptive, adaptive management. For that, the“Genetic health” descriptors must be included in theequation. These include maintenance of evolutionarypotential as well as resilience and resistance capacityunder various forms of stress.

Genetic monitoring offers the best opportunity toquantitatively track populations in both spatial andtemporal timeframes. This starts with a basic character-ization of an area—getting to know the meadows inyour local and regional area—with respect to levels ofgenetic and clonal diversity, clone size, geneticneighbourhood size, effective population size andmetapopulation connectivity. The level of detail can beadjusted to the specific long term objectives and theresources available. Even at the coarsest level, suchinformation provides direct insights about the demo-graphic structure and history of the area in question.With periodic resampling, changes in population geneticmetrics such as allelic richness, allele frequencydistribution shifts, heterozygosity, effective populationsize and population interconnectivity can be followedand used to model probable effects. Network models are

particularly promising in this regard (Rozenfeld et al., inpress). These metrics are especially important in areaswhere fragmentation and isolation are prevalent. Giventhe fact that: 1) appropriate neutral loci exist for themajor species; 2) considerable baseline data is alreadyavailable for Zostera and Posidonia; and 3) well-worked out sampling and genotyping protocols areavailable, the implementation of such a “genetic health”program is perfectly feasible. Such programs are nowbecoming widely applied for conservation and manage-ment of not only rare and endangered species, but also incommercial fisheries and wildlife management (manyexamples in Allendorf and Luikart, 2007; see also VanOppen and Gates, 2006 for coral reefs) Given theimportance of seagrass ecosystem services and generalincrease in seagrass monitoring programs (19 programsencompassing 30 species, 44 countries and N2000 sites;reviewed in Orth et al., 2006), it is high time to im-plement a genetic component to complement the majoradvances that are being made in, for example, waterquality standards and the creation of marine protectedareas.

10. Ecogenomics for seagrasses

Given that seagrass beds are already impacted byglobal change, associated stress (Williams, 2001; Greveet al., 2003; Reusch et al., 2005), one major issue will bethe potential of seagrass beds to adapt to globalenvironmental change. The majority of data and resultspresented in this review collected over the past 10 yearswere obtained by neutral genetic markers. Their value isindisputable, because high-resolution baseline data onneutral population genetic processes driven by geneticexchange and drift are critically important for correctinferences on selectively relevant variation. Only whendata under a population genetic null-model are con-trasted with putative selective signals are correctconclusions possible. A collection of molecular tools,dubbed ecological genomics or ecogenomics (Ouborgand Vriezen, 2007) utilizes techniques that wereoriginally developed for genetic models such as Arabi-dopsis, to seek the molecular genetic basis of traits suchas stress resistance, pathogen defense, herbivore deter-rence and life-history traits in plants (Vasemägi andPrimmer, 2005). Fortunately, such techniques are be-coming increasingly applicable to non-model organismsincluding seagrasses.

While the goal for any genomic study ultimatelyincludes the full characterization of the genome, thereare several less expensive approaches immediatelyapplicable to the seagrass research community (see

http://www.SeagrassNet.org

http://www.SeagrassNet.org

http://dx.doi.org/doi:10.1098/rsif.2007.0230

http://dx.doi.org/doi:10.1098/rsif.2007.0230


Hofmann et al., 2005 for other marine examples). Onesuch technique, the construction of complementaryDNA (=cDNA) or expressed sequence tag (=EST)libraries represents sequence collections of all mRNA(converted into complementary or cDNA) that aretranscribed at a given point in time in a specified tissue(Bouck and Vision, 2007). The primary technicaladvantage is that sequencing efforts are focussing onthe relatively small amount of coding genetic variationfound in eukaryotes, while the majority of non-transcribed genome is ignored. EST libraries representthe starting point for a number of approaches relevant tothe ecological genetics of natural populations (Bouckand Vision, 2007). First, they serve as starting point forthe development of second-generation molecular mar-kers that may reflect selection (and not only neutralgenetic processes), such as EST linked microsatellites(Li et al., 2002) or single nucleotide polymorphism(SNP, Brumfield et al., 2003) (see Molecular markerssection). Second, they provide necessary data fortranscription profiling, the comparison of the transcrip-tome among experimental conditions or populations forkey gene discovery. This may involve real time PCR orthe development of microarrays to query gene expres-sion as it occurs in nature.

EST libraries have been constructed and are currentlybeing analyzed for two important seagrass species,Z. marina and P. oceanica. In Z. marina, five EST-libraries with 1000–3000 reads each were constructedunder different temperature stress, and under control (=nostress) conditions (Reusch, Rheinhardt et al., personalcommunications). A preliminary analysis revealed thatthere are numerous EST-linked microsatellites in theimmediate vicinity of the coding regions, either in the 3′-or 5′ untranslated region of the genes (Li et al., 2002). Afirst collection encompassing 13 novel, EST –linkedmicrosatellite marker proved to be more polymorphicthan the relatively short repeat region would suggest(Oetjen and Reusch, in press). Research on detectingmolecular adaptation through genome scans (Luikartet al., 2003) is underway among subtidal vs. intertidalpopulation pairs in the German Bight that experiencedrastically different environmental conditions (Oetjen andReusch, personal communications). The overall goal is tofind marker gene differentiation that exceeds the dif-ferentiation among populations displayed under a neutralgenetic scenario. If successful, marker types that reflectselectively relevant polymorphism in, for example, desic-cation resistance or osmoregulation, will be identified. InP. oceanica, 5000 sequences have been obtained from asingle EST library constructed by pooling RNAs fromplants living at different depths (Migliaccio et al., 2006).

These were chosen because previous comparisons usingneutral microsatellite loci have also found depth-specificgenotypes (Migliaccio et al., 2005). This library providesthe basis for the understanding of selective traits for lightand temperature and is being further analyzed usinghybridization with specific-condition-RNAs and throughthe production of subtractive libraries. Here too, micro-satellite-ESTs are being screened for selection (Procac-cini, personal communications).

Microarrays consist of multiple individual cDNAclones derived from EST libraries arranged on a slide orchip. Arrays can accommodate from hundreds to tens-of-thousands of spots on an area the size of yourthumbnail (Thomas and Klaper, 2004). Labelled collec-tions of mRNAs obtained from individuals exposed tothe stress of interest are hybridized to the array and thequantitative expressions of genes that are present both onthe array and in the sample are measured. For stressevaluation in seagrasses, this approach will allow us toidentify species or genotypes that are stress generalistsand specialists for one or a suite of stressors simulta-neously. Another highly valuable approach will be tocompare different ecotypes from within the same speciesto indentify the molecular genetic correlates of environ-mental adaptation (Whitehead and Crawford, 2006).

Looking just a little ahead, it is possible to envisageecogenomic tools that have the potential to revealexactly which stressors seagrasses are exposed to andfor how long. This type of information is invaluable forscientists as well as coastal managers.

Development of first generation microarrays is alreadyunderway in P. oceanica, where genes involved in themetabolism of heavy metals and in water transport havebeen isolated and characterized (Maestrini et al., 2004;Cozza et al., 2006). Heat shock proteins encoding genesthat confer metabolic protection and refolding ofdenatured proteins under temperature stress (Bostonet al., 1996) are also under investigation in Z. marina.As at step towards quantification of stress geneexpression, housekeeping genes need to be identified asbaselines for the measurements using real-time, quanti-tative PCR (Ransbotyn and Reusch, 2006).

Naturally, complete sequencing of a few seagrassgenomes is highly desirable. Z. marina is the firstcandidate and, as sequencing costs come down andbioinformatics improve, it is not unrealistic to expectdata from additional genomes within the coming fiveyears. Seagrasses are a polyphyletic group thatreturned to the sea four times independently. Thus,comparative genomic and transcriptomic approachesare promising to study contingency and the repeat-ability of evolution at the phenotypic and genotypic



level. For a start, it would be very interesting to compareEST collections among both genera Posidonia andZostera to compare similarities and differences in theirmolecular adaptation to the marine environment. Usingtests of molecular adaptation after successful alignmentof homologous sequences could be used to identifygenes that are under strong stabilizing selection,indicating critical essential functions in the marineworld (Wright and Gaut, 2005). Secondly, those genesthat reveal more divergence, apparent through anelevated ratio of replacement substitutions, wouldindicate metabolic functions that differ among bothgenera. Taken together, we predict rapid progress in theapplication of ecogenomic tools in seagrass genetics andconservation within the coming five years.

11. Concluding remarks

In the public eye, seagrasses do not have the iconicstatus of coral reefs yet rank as high or higher in terms ofecosystem services (Costanza et al., 1997). Conserva-tion programs, governmental directives and internation-al biodiversity organizations all recognize theirimportance and many monitoring programs are activeworldwide (Green and Short, 2003). Unfortunately,conventional monitoring is unable to forecast the likelycumulative effects of known and emerging stressors ofseagrass communities and ecosystems. It is here thatgenetic and genomic data have much to offer.

There is no question that strong environmental factorswill continue to have strong effects on seagrass ecosystemfunction.Nevertheless, seagrass ecosystemswill adapt andreshape themselves through their reservoir of geneticdiversity. Neutral-marker-based phylogeographic andpopulation genetic studies over a range of geographicscales provide useful indicators of natural history,contemporary changes and offer new projections underclimate change; while gene expression studies in nature—the field of ecogenomics—will allow us to learn moreabout stress responses at the molecular level for a widevariety of stressors. We fully expect the development ofnew generation diagnostic and monitoring tools within thecoming 5 years that will provide an early warning system.The ultimate ability to effectively manage seagrassecosystems will depend upon a better understanding ofstressors—increased temperature, light reduction, pres-ence of pathogens, effects of invasive species—and theirselective role in adaptation. It is here that ecogenomicswillplay an important role.

We hope that this review will stimulate an activemovement towards the integration of genetic andgenomic data in to seagrass conservation and ecology.

In their recent review of the global crisis for seagrassecosystems, Orth et al. (2006) concluded that aquantitative analysis of seagrass trajectories couldform the foundation for incorporation of seagrassesinto a global science policy for the world's oceans. Weagree, just don't forget to include the genes.

Acknowledgements

Motivation for this review was inspired by discus-sions with colleagues from the two EU FP6 Network ofExcellence, Marine-Genomics-Europe (GOCE-CT-2004–505403) and Marine Biodiversity and EcosystemFunction (MarBEF - GOCE-CT-2003–505446) and inparticular with members of a workshop held in Münsterin 2007 sponsored by Marine-Genomics-Europe andwith the members of the response mode project GBIRM(MarBEF). [SS]

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