Genetic diversity in Crambe maritima along the English Channel: the ...

ECOGRAPHY 28: 374 /384, 2005

Genetic diversity in Crambe maritima along the English Channel: the role of ocean currents in determining population structure J. M. Bond, R. Daniels and F. Bioret

Bond, J. M., Daniels, R. and Bioret, F. 2005. Genetic diversity in Crambe maritima along the English Channel: the role of ocean currents in determining population structure. / Ecography 28: 374 /384. We utilised genetic markers (ISSR’s) to investigate population structuring in Crambe maritima , a coastal perennial found in isolated populations on either side of the English Channel. Despite the expectation that smaller populations would contain lower levels of genetic diversity, we found no correlation with total population size or the number of flowering plants and genetic diversity. Although populations were genetically differentiated from each other, isolation by distance was not apparent in the whole data set or within the UK samples. Despite being sampled from a wide geographic range, the majority of diversity (71%) was contained within populations, with no unique genotype, or even allele, identifying any population. When genetic distances were plotted using MDS techniques, samples from each population had a tendency to cluster, but the spread of points was wide indicating high levels of gene flow. High levels of gene flow, through the dispersal of seeds, were verified by assignment testing, which showed that 8.7% of individuals were assigned to a population other than the one they were collected from. The pattern of genetic variation can be explained by examining the direction of currents in the English Channel and Bay of Biscay. Gene flow, via seed dispersal, is possible between UK and French populations on either side of the English Channel. In contrast, French populations on the Bay of Biscay coast are effectively isolated by the direction of currents and consequently show a greater degree of genetic differentiation. J. M. Bond ([email protected]) and R. Daniels, CEH Dorset, Winfrith Technology Centre, Dorchester, Dorset, UK DT2 8ZD. / F. Bioret, Lab. Geosyste`mes, IUEM, Univ. de Brest, Place Copernic, F-29280 Plouzane´, France.

Geographic distance between populations is often used as a reliable indicator of gene flow, consequently management decisions are often based on the presumption that geographically close populations are genetically the most similar. However, in many species, actual levels of gene flow between populations are unknown. In plants, the extent to which gene flow is possible depends on the seed dispersal capabilities (Gouyon et al. 1987, Williams 1994) and the extent of pollen transfer in the species involved (Ellstrand 1992). A wide range of species show isolation by distance so that geographically close populations are more similar genetically and show apparently high levels of gene flow,

whilst more distant populations are more genetically distinct, which indicates a lower rate of gene flow. Examples include Olea europaea (Hess et al. 2000), Irvingia gabonensis and I. wombolu (Lowe et al. 2000) and Beta maritima (Raybould et al. 1998). However not all species show isolation by distance, especially where there is some over-riding factor controlling gene flow, for example populations of Alkanna orientalis are isolated by the direction of water flow (Wolff et al. 1997). Other factors that can disrupt isolation by distance include physical barriers to pollen or seed movement, such as mountains or large water bodies (Williams and Arnold 2001), population bottlenecks and historical patterns of

Accepted 3 December 2004 Copyright # ECOGRAPHY 2005 ISSN 0906-7590

374

ECOGRAPHY 28:3 (2005)

recolonisation (Hewitt 1999) or the persistence of seed banks (Levin 1990). Most studies of terrestrial plants have considered large bodies of water as barriers; consequently little work has been carried out to assess the role ocean currents play in the population structuring of coastal plants over large distances. In coastal plants, species ranges are linear (i.e. stretch along the coastline) and the populations are expected to show isolation by distance. When coastal plants are insect pollinated, the majority of pollen movement is expected to be between neighbours in the same or adjacent patches, with only a small proportion of pollen being transferred greater distances (Waddington et al. 1994, Osborne et al. 1999). For this reason, habitat fragmentation is recognised as a serious threat to the survival of populations (Ellstrand and Elam 1993, Young et al. 1996). As a habitat becomes more fragmented the degree of population isolation increases and levels of gene flow decrease. Increased levels of inbreeding tend to reduce levels of fitness (Simberloff 1988), whilst the loss of genetic variability, as a result of genetic drift, will encourage differentiation between populations (Schaal 1975, Holtsford and Ellstrand 1989). Consequently the amount of genetic variation within a plant population is closely related to the size of a population and its degree of isolation (Frankham 1996, Young et al. 1996). Long distance seed dispersal may alter this linear pattern of gene flow, especially as several coastal species have seeds that are capable of surviving in seawater for days or weeks (Lousley 1994, Welsh and Innes 1999, Quilichini and Debussche 2000). Differences between populations of sea beet Beta maritima associated with cliffs and salt marshes were attributed to differences between gene flow via pollen and seed dispersal (Raybould et al. 1998). On cliffs, where seed dispersal is limited, the main mechanism for gene dispersal is via pollen, whereas in salt marshes, tides move seeds over greater distances along the shoreline. In coastal plants, long distance gene flow may be achieved by seed dispersal and the patterns of dispersal will be largely dependent on current patterns in inshore waters. However, few studies, other than those involving marine organisms (Patarnello et al. 1996, Muss et al. 2001) have considered in detail the role of currents in population structuring (Barker et al. 2002). Studies of coastal plants tend to have concentrated on seed viability studies to prove long distance seed dispersal is possible (Redbo-Torstensson and Telenius 1995, Hamilton 1997, Quilichini and Debussche 2000, He et al. 2004). Genetic markers are ideal for proving that gene flow occurs between populations (Ennos 2001) and have been used to study seed dispersal (Dow and Ashley 1996, Isagi et al. 2000, He et al. 2004). The majority of genetic based studies favour the use of microsatellite markers, which ECOGRAPHY 28:3 (2005)

usually need to be specifically developed for each species (Zhang and Hewitt 2003) and is often seen as a limitation to starting a genetic based study. However, other markers are available and can show patterns of populations structuring and gene flow (Krauss 1999, Clausing et al. 2000, Bond et al. 2002). Here we show that universal markers (i.e. ones that work ubiquitously across all species) can be used to successfully address issues of gene flow. We used the coastal species Crambe maritima and inter-simple sequence repeat (ISSR) markers (Zietkiewicz et al. 1994) to answer the following questions: 1) Does population size influence genetic diversity? 2) Do populations around the coast show isolation by distance? 3) Can any patterns of genetic diversity be explained by the direction of currents in the region?

Materials and methods The study species Crambe maritima is a perennial species of northwest European coasts. Although there are a few isolated populations of C. maritima in northern Spain, it is found mainly in northern France (as far south as southern Brittany), southern and western coasts of Great Britain, and around the Baltic. Populations have shown a decline in abundance during the twentieth century (Scott and Randall 1976, Stewart et al. 1994). Crambe maritima is normally restricted to coastal shingle or a shingle-sand mixture where it grows within the splash zone above the level of all but the highest spring tides. More rarely it is found on scree-like slopes of calcareous sea cliffs (in UK only). Flowers are self or cross-pollinated by a range of flies, bees or beetles (Scott and Randall 1976) and the fruits are buoyant, an adaptation to dispersal in seawater. Even after buoyancy has been lost, some seeds remain viable following four months immersion in seawater (Bond unpubl.). The long survival time of the seeds may have implications for the spread of populations. For germination, the seed appears to require at least some fine material, such as sand, in the substrate (Ge´hu and Ge´hu 1969, Walmsley and Davy 1997). Crambe maritima leaves were collected from three populations in northern Brittany and three populations from the Biscay coast of Brittany, five populations were sampled along the south coast of the UK (Cornwall/ Dorset and Sussex) and three populations in Suffolk (Fig. 1). At each site the total number of plants and the number of flowering plants (Nf) were estimated (Table 1). Sub-populations were sampled at two sites in France and one in England. At Sillon de Talbert, a long shingle spit, the sub-populations (‘‘A’’ 200 plants, ‘‘B’’ /350 plants) were separated by 1 km. On the sand dunes at Brignonan the sub-populations were separated 375

KES ALD LAN BEA & PEV CHE CUC

LA BR

the beach (‘‘E’’ 80 plants). The Kerouiny population, of 50 plants, was on a flattened dune ridge composed of coarse sand. The Gleńan population comprised of /400 plants and formed a fringe around the island of Petit Quignenec among large boulders. The Beachy Head population (/300 plants) was in grassland on ledges and eroded slopes of chalk cliffs. The remaining populations showed no subdivision and were growing on shingle within the splash zone.

SI

POI

DNA extraction

CRO GLE KER

~80km

Fig. 1. Crambe maritima sampling sites. Populations were designated codes as follows: KER Kerouiny; GLE Gleńan; CRO Crozon; BR Brignonan; SI Sillon de Talbert; POI Pointe de l’Arcouest; LA Lantic Bay; CHE Chesil Beach; BEA Beachy Head; CUC Cuckmere Haven; PEV Pevensey Bay; LAN Languard; ALD Aldeburgh; KES Kessingland.

by 50 m (‘‘A’’ 150 plants, ‘‘B’’ 100 plants). At Lantic Bay a distinction was made between plants growing in a pioneer community close to the seaward edge of the shingle beach (‘‘P’’ 40 plants) and those growing in more established vegetation at the foot of the cliff behind

When the population was small, young leaves were sampled from all plants, otherwise leaves were collected from selected plants evenly distributed throughout the whole population. Each leaf was packed in a separate polythene bag and stored in a cool box. Leaves were posted to the laboratory where they were transferred to a /708C freezer on receipt. From each leaf, a leaf disk of 7 mm diameter, punched by the lid of an Eppendorf tube, was freeze-dried and used for DNA extraction. In total, 358 plants from 14 populations were sampled. DNA extractions were carried out using a Nucleon Phytopure DNA extraction kit (Amersham). The resin within the kit removed polysaccharides from the DNA preparation, which may have inhibited PCR (Demeke and Adams 1992). Samples were resuspended in 100 ml of TE, of which 0.2 ml was used as template DNA in a 10 ml reaction volume.

Table 1. Diversity within populations calculated using the Shannon Diversity Index. For each population, the population code, population size, number of flowering plants (Nf) and number of plants surveyed (N) is indicated. For each primer within population diversity (Hj) was calculated. Population French populations Brittany, Brignonan ‘‘A’’ Brittany, Brignonan ‘‘B’’ Brittany, Sillon de Talbert ‘‘A’’ Brittany, Sillon de Talbert ‘‘B’’ Brittany, Pointe de L’Arcouest Biscay coast, Gleńan Biscay coast, Kerouiny Biscay coast, Crozon English populations Cornwall, Lantic Bay, pioneer ‘‘P’’ Cornwall, Lantic Bay, established ‘‘E’’ Dorset, Chesil beach Sussex, Beachy Head Sussex, Pevensey Bay Sussex, Cuckmere Haven Suffolk, Languard Suffolk, Kessingland Suffolk, Aldeburgh H?sp H?pop % diversity within populations (H?pop/H?sp) % diversity between populations (H?sp-H?pop)/H?sp)

376

Pop. code

Pop. size

Nf

N

891 Hj

888 Hj

Average

BR‘‘A’’ BR‘‘B’’ SI‘‘A’’ SI‘‘B’’ POI GLE KER CRO

150 100 200 /350 70 /400 50 10

15 30 140 332 49 160 37 2

15 15 16 15 15 30 30 10

0.15 0.19 0.20 0.16 0.23 0.18 0.22 0.03

0.28 0.23 0.30 0.36 0.37 0.27 0.35 0.07

0.22 0.21 0.25 0.26 0.30 0.23 0.29 0.05

LA‘‘P’’ LA‘‘E’’ CHE BEA PEV CUK LAN KES ALD

40 80 100 /300 /500 /1000 /1000 50 /500

10 3 20 150 350 600 100 35 200

20 20 22 30 30 22 20 28 20

0.20 0.17 0.19 0.18 0.21 0.21 0.24 0.21 0.28 0.22 0.19 85% 15%

0.26 0.26 0.33 0.31 0.37 0.31 0.35 0.41 0.33 0.48 0.30 63% 37%

0.23 0.22 0.26 0.25 0.29 0.26 0.30 0.31 0.31 0.35 0.25 71% 29%


PCR conditions and reproducibility We screened for variation using two universal ISSR primers selected from Univ. of British Columbia’s Primer set #9 (888 and 891). Primer 888 is based on a CA repeat, whilst primer 891 is based on a TG repeat, therefore these primers will not amplify the same bands. ISSR markers amplify numerous sites across the genome generating a series of PCR bands (alleles) that are scored as dominant data. All PCR reactions were carried out in 96 well microtitre plates using 10 ml reaction volumes containing 1.5 mM MgCl2, 0.2 mM dNTP’s, 0.2 mM primer and 1% formamide. The thermal cycling conditions consisted of 30 cycles of 1 min at 948C, 2 min at 558C and 40 s at 728C, followed by a final extension of 5 min at 728C. Each sample was amplified in duplicate, and four negative controls were included on each plate. Banding patterns were visualised by running the samples on 6% acrylamide gels and silver staining using premeasured silver staining reagents (Promega). Bands were scored as present (1) or absent (0). Only bands that amplified strongly were scored, faint bands were considered less reliable and consequently not scored.

Statistical analyses In order to assess the genetic diversity, without making assumptions of Hardy Weinberg equilibrium that could bias interpretation, we used the Shannon diversity index. Shannon diversity index (Hj) was calculated for each population in such a way that values are bounded between 1 and 0 (Lewontin 1972). The species diversity ? ) was calculated in the same way, but using the (Hsp frequency of an allele in the whole sample. The average ? ) was calculated diversity over all populations (Hpop ? for each locus as (1/n)SHj , where n is the number of populations. From these values, the proportion of ? /Hsp ?, diversity within populations was calculated as Hpop and the proportion of diversity among populations as ? /Hpop ? )/Hsp ? . The diversity within each population (Hsp was correlated with both the total population size and the number of plants in flower (Nf). In order to test for a sampling bias the correlation between the number of plants sampled and diversity was tested. The banding patterns were also used to calculate genetic distances between populations using Nei’s genetic distance: D /1 /(2 Nxy/(Nx/Ny)) (Nei and Li 1979); where Nxy is the number of fragments shared between samples x and y; Nx is number of fragments in sample x and Ny the number of fragments in sample y. Nei’s genetic distance was calculated between all pairs of individuals within the TREECON program (Van de Peer 1994). Bootstrapped (500 replicates), genetic distances between each population were calculated within the program RAPDDIST (Armstrong et al. 1994). The position of genetic barriers was then identified using ECOGRAPHY 28:3 (2005)

the BARRIER program, and the significance of any barriers determined by analysis of the bootstrapped datasets (Manni et al. 2004). Genetic distances are commonly used to generate trees, however, trees arrange the individuals in a hierarchical manner, which does not take into account the fact that relationships between individuals in a fertile population are often not hierarchical, but reticulate in nature. Therefore the relationships between individuals were displayed in two dimensions using non-metric multi-dimensional scaling (MDS). The MDS technique takes a distance matrix (in this case genetic distance) and plots all the samples in the configuration that best satisfies all the constraints of the matrix. Therefore, samples with the greatest similarity will be plotted closest together; with the axis of MDS plot representing arbitrary units rather than actual genetic distance. The MDS optimisation procedure places the n points in a two-dimensional plot so as to maximise the agreement between the ranks of actual genetic distances (D) and the ranks of MDS plot distances (Aij). The lack of agreement is measured by a statistic called the STRESS value. A STRESS value B/0.1 correspond to an excellent representation of the data with little chance of a misleading diagram. STRESS values between 0.1 and 0.3 still provide a potentially useful picture, although the detail of the plot cannot be considered truly accurate. Plots with a STRESS value /0.3 should be treated with scepticism especially when B/50 points have been plotted (Krzanowski 1987). MDS plots were drawn using the PRIMER package (Carr 1996). Within the PRIMER package the ANOSIM function was used to statistically test the null hypothesis of ‘‘no differences between populations’’. For the null hypothesis to be true the test statistic (R) will change little when the labels identifying the sites are randomly rearranged. R-values fall between 1 (maximum separation) and 0 (indistinguishable), with R-values /0.75 indicating the groups are well separated, R-values /0.5 indicating overlapping but different groups, and R-values B/0.25 indicating the groups are barely separable (Clarke and Gorley 2001). Significance values, as determined by permutation testing, indicate the reliability of the result. R-values were examined between each population pair to determine which populations showed the greatest genetic separation. An assignment test was used to assess which of the sampled populations a plant was most likely to belong to genetically (Cain et al. 2000, Duchesne and Bernatchez 2002, He et al. 2004). An adult plant assigned to a population other than the one it was sampled from is assumed to be the result of long distance seed dispersal. Long distance pollen dispersal can be ruled out as such plants would have a genetic composition intermediate between two populations and thus remain unassigned with any degree of confidence. Using the ALFPOP 377

Results Primers 888 and 891 revealed a total of 76 scorable bands, of which 44 were from primer 888 and 32 from primer 891. Levels of polymorphism were high, with 97.7% of scorable bands being polymorphic at locus 888 and 96.8% at locus 891 (data not shown) Although levels of polymorphism were high, private alleles (bands unique to one population) were identified only from 6 plants at Pevensey Bay (PEV), Sussex, and only 1 band was involved. Diversity was high within all but one of the populations (Crozon Peninsula, CRO). The distribution of diversity among and within populations showed that a high proportion, averaging 71%, of diversity resided within populations (Table 1). There was no discernible relationship (p /0.178) between genetic diversity and total population size (Fig. 2) or between genetic diversity and number of flowering plants (p /0.263). However, the very small population of eight vegetative and two fruiting plants at Crozon shows a very low level of genetic diversity compared with all other populations. There was also no relationship between the number of plants screened in each population and genetic diversity (p /0.238). Therefore our results are not an artefact of sampling, and in Crambe maritima there appears to be no correlation between population size and levels of genetic diversity. The MDS plot (Fig. 3) shows the genetic relationships between all individual plants (N /358) screened in the study. The STRESS value for the MDS plot was 0.27, reflecting the large number of data points, therefore individual points may be misleading but the overall pattern is still regarded as informative. The R-values 378

Average diversity

0.3

0.2

0.1

0

500

1000

Population size Fig. 2. Crambe maritima total population size plotted against population genetic diversity as measured by the Shannon Index.

provide a more accurate picture of the extent of separation between each population pair (Table 2). The only non-significant comparisons were between populations SI‘‘A’’/KES and POI/LAN, all other comparisons were statistically significant indicating that the populations are genetically separated. On the MDS plot, the clearest distinction is between samples from the Bay of Biscay (KER, GLE and CRO) and all other samples. This greater difference between the Biscay populations is reflected in the ANOSIM results with R-values approaching 1 (Table 2). On the MDS plot there is a tendency for the British populations to lie along a west to east gradient. The Suffolk populations (Languard, Kessingland and Aldeburgh) lie to the bottom left of the MDS plot and the Cornwall, Dorset and Sussex populations to the right. However, this broad trend is not geographically straightforward. In particular, the clifftop population at Beachy Head shows a greater genetic similarity to the distant Chesil Beach population than with adjacent Cuckmere Haven population. Mantel testing of the relationship between geographical and genetic distance, using only the British samples, was non-significant 2

1

MDSAxis2

program (Duchesne and Bernatchez 2002) the likelihood a plant’s genotype belonged to each population was calculated, the individual was then assigned to the population with the highest likelihood. We used the criterion that a plant could only be allocated to a population when it was ten times more likely to belong to that population than any other. By using such a stringent setting we can be confident that assignment to a population other than the one the plant was collected from is likely to represent long distance seed dispersal. We also used the p values (obtained from simulation within the ALFPOP program) to assess if an individual represented gene flow from an unsampled population (Duchesne and Bernatchez 2002). When the p value for an individual plant was B/0.001 for all sampled populations, this plant was taken as representing an unsampled source population. The significance of any correlation between genetic distances and the geographic distance (measured as the shortest distance across water) between each population was tested using a Mantel test.

0

–1

–2 –2

–1

0

MDSAxis1

1

KES ALD LAN PEV CUC BEA CHE POI CRO GLE KER BR "A" BR "B" SI "A" SI "B" LA "E" LA "P"

Fig. 3. MDS plot showing spatial structuring of all Crambe maritima plants. Stress /0.27. ECOGRAPHY 28:3 (2005)

0.71 0.48 0.78 0.58 0.78 0.56 0.78 0.9 0.89 0.21 0.45

0.34 0.89 0.86 0.85 0.81 0.93 0.82 0.81 0.68 0.79 0.82 0.95 0.93 0.93 0.93 0.74 0.65 0.84 0.69

0.54

0.52 0.12 (n.s.) 0.56 0.42 0.5

LAN

0.68 0.26 (p B/0.05) 0.62 ALD KES UK Suffolk

UK South coast

Biscay

0.93 0.82

0.13 (n.s.) 0.81 0.81 0.49 0.84 0.57 0.6 0.82 0.56 0.69 0.56 0.87 0.24 0.5 0.71 0.53 0.82 0.84 0.88 0.73 0.97 0.77 0.97 0.51 0.58 0.8 0.76 0.82 0.8 0.93 0.75 0.91 0.86 0.92 0.76 0.89 0.85 0.92 0.95 0.92 0.94 0.81 0.91 0.72 0.92 0.63 0.77 0.33 0.95 0.96 0.99 0.83 0.88 0.58 0.83 0.51 0.7 0.91 0.69 0.97 0.96 0.99 0.95 0.97 0.95 0.98 0.89 0.95 0.76 0.88 0.78 0.92 0.96 0.96 0.88 0.93 0.85 0.95 0.69 0.78 0.61 0.64 0.95 0.75 0.99 0.98 0.97 0.98 0.98 0.95 0.95 0.75 0.82 BR‘‘A’’ BR‘‘B’’ SI‘‘A’’ SI‘‘B’’ POI GLE KER CRO LA‘‘E’’ LA‘‘P’’ CHE BEA PEV CUC N. France

0.51

0.42

KES ALD CUC PEV BEA CHE LA‘‘P’’ LA‘‘E’’ CRO KER GLE POI SI‘‘B’’ SI‘‘A’’ BR‘‘B’’ BR‘‘A’’

Table 2. Pairwise population R-values. Unless otherwise indicated values were significant at the 0.01/p/0.001 level or above.


(p /0.19). Mantel testing of the relationship between geographical and genetic distance using the whole dataset also showed no significant relationship (p / 0.77). The position of inferred barriers to gene flow is shown in Fig. 4, with the thickness of the barriers (shown by black lines) representing the proportion of bootstrapped datasets that supported the barrier. All barriers are shown, but only barriers that were supported by /75% of the datasets are considered truly accurate; in these cases the actual bootstrap value is shown. Significant barriers to gene flow are concentrated in the region of the plot corresponding to the Biscay samples, whereas barriers to gene flow between the UK samples are poorly supported. Assignment testing proved to be highly successful with 90.2% of plants allocated to a particular population, with 81.3% of plants being allocated to the population they were sampled from (Table 3). In contrast, 8.9% of plants were assigned to a population other than the one they were collected from, thus implying long distance seed dispersal. When the subpopulations are examined there is an obvious effect of separation distance: the populations at Lantic Bay which are separated by less than ten meters have numerous examples of plants allocated to the other population and as the distance increases (BR populations isolated by 50 m, SI populations separated by 1 km) the number of plants assigned to the other subpopulation declines (Table 3). A total of 9.8% of plants were not assigned to any population under the strict criterion of being 10 times more likely to belong to one population than another, but of these unassigned plants only 5 were unambiguously considered to come from an unsampled source population. The remaining ambiguously assigned plants were either most likely (using the likelihood of allocation) to belong the sampled population (16 plants) or a geographically adjacent population (12 plants, of which 6 are from the Lantic Bay populations) with only 3 plants being assigned to a geographically distant population. The patterns from the assignment data fitted the predicted positions of barriers to gene flow.

Discussion Molecular markers, in particular microsatellites, have shown that long distance dispersal of seeds is possible (Dow and Ashley 1996, Isagi et al. 2000, He et al. 2004). Here we show that universal, dominant markers can also reveal information about the long distance dispersal of seeds. More interestingly, the patterns of seed dispersal we identify correspond broadly with those of currents within the English Channel and Bay of Biscay. 379

KES ALD LAN PEV BEA CUC

Fig. 4. Barriers to gene flow. The dashed lines represent hypothetical population boundaries, populations are labelled by their population codes. Barriers to gene flow are indicated by solid black lines, with the thickness of the line indicating the degree of support. Only barriers supported at the / 75% level are considered accurate, in these cases the % support for the barrier is indicated.

CHE LA

100

SI 75

BR

POI

100 75

100 98

CRO 87

KER

population boundaries barriers to geneflow supported by >75% barriers to geneflow supported by 21–40% barriers to geneflow supported by 10–20% barriers to geneflow supported by