Directory UMM :Data Elmu:jurnal:J-a:Journal of Experimental Marine Biology and Ecology:Vol246.Issue1.MAr2000:

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L

Journal of Experimental Marine Biology and Ecology 246 (2000) 69–83

www.elsevier.nl / locate / jembe

Large scale population structure and gene flow in the

planktonic developing periwinkle, Littorina striata, in

Macaronesia (Mollusca: Gastropoda)

a ,

_*

a a,b

Hans De Wolf

, Ron Verhagen , Thierry Backeljau

a

Ecophysiology and Biochemistry Group and Evolutionary Biology Group, University of Antwerp(RUCA) Groenenborgerlaan 171, B-2020 Antwerp, Belgium

b

Royal Belgian Institute of Natural Sciences(KBIN), Vautierstraat 29, B-1000 Brussels, Belgium

Abstract

Allozymes were used to investigate the genetic structure of 42 populations of the planktonic developing, Macaronesian periwinkle Littorina striata, throughout its entire geographic range (Azores, Madeira, Canary Islands and Cape Verde Islands). This periwinkle is presumed to have a high dispersal and gene flow potential, because it has a planktonic development. It is therefore expected to show little population genetic differentiation. Indeed, based on Wright’s hierarchical

F-statistics, no significant genetic differentiation could be detected among populations, at any of

the specified hierarchical levels (i.e. population, island, and archipelago). Nevertheless, the Cape Verde Islands seemed genetically more diverse (highest mean number of alleles per locus). The number of loci revealing a significant genetic heterogeneity increased with increasing distance between populations, while private alleles based gene flow (Nm) estimates also revealed a tendency towards a geographical pattern. The distribution of rare and private alleles, might account for these observations which suggested some geographical effect. Because of the low frequency at which these alleles occur, their influence on the genetic population structure is negligible, and not picked up by F-statistics.  2000 Elsevier Science B.V. All rights reserved.

Keywords: Allozymes; Gene flow; Littorina striata; Macrogeography; Planktonic development; Macaronesia

1. Introduction

Marine organisms with high dispersal potential often show only limited population

genetic differentiation, because gene flow is usually positively correlated with dispersal

*Corresponding author. Tel.:132-3-218-0347; fax:132-3-218-0497. E-mail address: [email protected] (H. De Wolf,)

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ability (e.g. Crisp, 1978; Palumbi, 1994, 1996). In marine gastropods, for example,

species with planktonic dispersing larvae display higher levels of gene flow and less

population genetic differentiation (e.g., Mitton et al., 1989; Benzie and Williams, 1992;

Brown and Murray, 1995), than nonplanktonic developing species with poor dispersal

capacity (e.g. Johannesson et al., 1993; Johnson and Black, 1995; Rolan-Alvarez et al.,

1995; Trussell, 1996). Nevertheless, even in species with high dispersal abilities there

are several factors that may limit actual dispersal and / or gene flow, thus creating

opportunities for genetic differentiation as well (Palumbi, 1994, 1996). These limitations

include: invisible gene flow barriers, isolation by distance, behavioral limits to dispersal,

selection, and the recent history of a species (Palumbi, 1994, 1996).

For instance, in the Macaronesian (i.e. Azores [AZ], Madeira [MA], Canary Islands

[CA] and Cape Verde Islands [CV]) planktonic developing periwinkle, Littorina striata,

King and Broderip, 1832, no genetic population differentiation is detected at

mi-crogeographical scales (i.e. 5–100 m) (De Wolf et al. 1998a), whereas preliminary

allozyme data tentatively suggest a tendency towards an isolation by distance (IBD)

relationship among more distant populations (i.e. up to 500 km) within an archipelago

(AZ) (Backeljau et al., 1995). In addition, esterase and random amplified polymorphic

DNA (RAPD) patterns reveal a higher degree of genetic variability in the CV than in the

other archipelagos (De Wolf et al. 1998b,c).

In this paper we explore the population genetics of L. striata at macrogeographic

scales by surveying allozyme data from populations covering the entire known

geographic distribution of the species (i.e. up to 2000 km). We particularly aim at

assessing whether the allozyme variation reveals a macrogeographical patterning and if

so, whether this patterning is indeed related to IBD.

2. Materials and methods

2.1. Sampling sites

Forty-two populations of L. striata (n

5 1640) were collected at comparable

wave-exposed sites in 13 Macaronesian Islands, including the archipelagos of the AZ, MA,

˜

CA and CV. Sampled islands included: in AZ, Sao Miguel (AZ1–12), Pico: (AZ13–14),

˜

Santa Maria (AZ15), Faial (AZ16–18), and Sao Jorge (AZ19–20); in MA, Madeira

(MA1–2), Porto Santo (MA3) and Deserta Grande (MA4); in CA, Tenerife (CA1–4)

˜

and Gran Canaria (CA5–6); and finally in CV, Sao Nicolau (CV1–2), Sal (CV3–7), and

˜

Sao Vicente (CV8–12) (Table 1).

2.2. Allozyme electrophoresis

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Table 1

Populations that were analysed with their corresponding abbreviations

Archipelago Island Sampling site Abbreviation

˜

Azores (AZ) Sao Miguel (SM) Mosteiros AZ1–AZ2

Capellas AZ3

Sao Jorge (SJ) F. da St. Cristo AZ14

Urzelina AZ15

Pico (PI) Lajes AZ16

Calhau AZ17

Faial (FA) P. da Almoxariffe AZ18 Capelinhos AZ19 P. da Espalama AZ20 Madeira (MA) Madeira (MA) Canic¸io de Baixo MA1

P. de St. Caterina MA2 Porto Santo (POS) Porto Santo MA3

Deserta Grande (DEG) Doca MA4

Canary Is. (CA) Gran Canaria (GRC) Agoustin CA1–CA4 Tenerife (TEN) Playa da America CA5–CA6

˜

Cape Verde Is. (CV) Sao Nicolau (SAN) Harbour CV1–CV2

Sal (SAL) Santa Maria CV3–CV4

Jose Fonseca CV5–CV6 Pedro de Lumen CV7 ˜

Sao Vicente (SAV) Baia das Gatas CV8–CV9

Mindelo CV10–CV11

Calhau CV12

(MDH, E.C. 1.1.1.37) were resolved in a continuous (gel and tray buffer are identical)

Tris–citric acid buffer at pH 8.0, while hydroxybutyric acid dehydrogenase (HBDH,

E.C. 1.1.1.30) was resolved in a continuous Tris–boric acid / EDTA buffer at pH

5

8.9. Mannose phosphate isomerase (MPI, E.C. 5.3.1.8) was analyzed using a discontinuous

buffer system with a Tris–glycine tray buffer and a Tris–HCl gel buffer, both at pH 9.0.

Enzyme stainings were adapted from Harris and Hopkinson (1976).

2.3. Statistical analysis

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(HW) equilibrium conditions were tested with exact probability tests, implemented by

the GENEPOP software v. 1.1 (Raymond and Rousset, 1995), which applies the Markov

chain method proposed by Guo and Thompson (1992).

Interarchipelago differences in MNA or H

obs

were investigated by means of two

analyses of variance, using the software package STATISTICA v. 5.0 (Statsoft, 1995).

Genetic population differentiation can be expressed by means of hierarchical

F-statistics. When a hierarchical arrangement of populations is assumed, in this case

populations (P) being placed within islands (I), archipelagos (A) and total distribution

area (T), the variance of the observed genetic differentiation among the populations

(var

ST

) can be split up into its variance components. A series of F-statistics can be

obtained (e.g., F , F , F

PI IA PT

and F

AT

), of which the terms in the following equation

(1

2 F )

PT

5 (1

2 F )

PI

?

(1

2 F )

IA

?

(1

2 F

AT

)

represent respectively: total differentiation, differentiation among populations within

islands, differentiation among islands within archipelagos, and differentiation among

archipelagos. These F-values are not additive. Hence, F

IA

reflects only the additional

variance among islands beyond that which exists among populations, and F

AT

reflects

only additional variance among archipelagos beyond that which exists among islands

(Wolf and Campbell, 1995). F-values and corresponding variance components were

calculated with the WRIGHT78 option in BIOSYS (Swofford and Selander, 1989).

Allele frequency heterogeneities among the four archipelagos, were evaluated with

Fisher exact tests applied to R3C contingency tables as implemented by the GENEPOP

v. 1.1 software (Raymond and Rousset, 1995).

The differentiation among islands was further analyzed by means of correspondence

analysis, executed with the NTSYS v. 1.80 software (Rohlf, 1993).

Gene flow (Nm) among archipelagos was estimated using private allele frequencies

(Slatkin, 1985; Slatkin and Barton, 1989). Pairwise Nm values were plotted against

pairwise geographical distances, enabling us to calculate the correlation coefficient and

corresponding regression equation.

A significance level of 5% was used throughout. The sequential Bonferroni technique

was employed to correct for false assignments of significance by chance alone (multiple

test problems) (Rice, 1989).

3. Results

Allele frequencies, H

obs

, H

exp

, and the results of the HW-tests are given in Table 2.

Six deviations from HW equilibrium were detected, yet none of these remained

significant after Bonferroni correction. Eight alleles were unique to the CV archipelago

(GPI-G, MPI-F, PGD-B, PGD-F, MDH-B, MDH-C, MDH-E and HBDH-F) and two

alleles were unique to the AZ (MDH-A, HBDH-A). Private allele frequencies used for

estimating Nm among archipelagos are given in Table 4 (below).

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Table 2

Allele frequencies, observed (Hobs) and expected (Hexp) heterozygosity levels and exact probabilities (P-ext) for deviation of Hardy–Weinberg equilibria (population abbreviations see Table 1)

Locus AZ1 AZ2 AZ3 AZ4 AZ5 AZ6 AZ7 AZ8 AZ9 AZ10

GPI (N ) 38 37 37 38 38 40 37 34 39 36

A 0.026 0.014 0.014 0.013 0.026 0.013 0.027 0.029 0.000 0.014 B 0.303 0.297 0.284 0.434 0.237 0.325 0.297 0.309 0.244 0.236 C 0.039 0.000 0.000 0.013 0.079 0.000 0.000 0.015 0.077 0.069 D 0.593 0.689 0.688 0.514 0.632 0.612 0.622 0.544 0.653 0.653 E 0.000 0.000 0.000 0.000 0.013 0.000 0.000 0.000 0.000 0.000 F 0.039 0.000 0.014 0.026 0.013 0.050 0.054 0.103 0.026 0.028 G 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Hobs 0.711 0.297 0.405 0.474 0.474 0.475 0.432 0.588 0.333 0.556 Hexp 0.554 0.436 0.444 0.547 0.538 0.517 0.522 0.597 0.507 0.512 P-ext 0.065 0.044 0.729 0.711 0.061 0.743 0.501 0.910 0.001 0.341

MPI (N ) 39 40 40 32 38 40 40 39 37 40

A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 B 0.051 0.138 0.125 0.094 0.053 0.075 0.087 0.115 0.108 0.075 C 0.949 0.862 0.875 0.906 0.947 0.912 0.913 0.885 0.892 0.912 D 0.000 0.000 0.000 0.000 0.000 0.013 0.000 0.000 0.000 0.013 E 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 F 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Hobs 0.103 0.225 0.200 0.188 0.105 0.150 0.175 0.231 0.216 0.175 Hexp 0.097 0.237 0.219 0.170 0.100 0.162 0.160 0.204 0.193 0.162 P-ext 1.000 0.548 0.473 1.000 1.000 0.083 1.000 1.000 1.000 1.000

PGD (N ) 38 38 39 37 37 38 34 35 34 37

A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 B 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C 1.000 0.974 1.000 0.946 0.986 0.974 0.971 0.977 0.956 1.000 D 0.000 0.026 0.000 0.027 0.014 0.000 0.000 0.000 0.029 0.000 E 0.000 0.000 0.000 0.027 0.000 0.026 0.029 0.043 0.015 0.000 F 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Hobs – 0.053 – 0.054 0.027 0.053 0.059 0.086 0.088 – Hexp – 0.051 – 0.102 0.027 0.051 0.057 0.082 0.085 –

P-ext – 1.000 – 0.028 – 1.000 1.000 1.000 1.000 –

MDH (N ) 40 37 39 37 40 39 36 37 37 40

A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.014 0.000 0.000 B 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 D 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.986 1.000 1.000 E 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Hobs – – – – – – – 0.027 – –

Hexp – – – – – – – 0.027 – –

P-ext – – – – – – – – – –

HBDH (N ) 36 40 40 39 37 38 36 35 40 40

A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 B 0.000 0.038 0.025 0.013 0.027 0.013 0.042 0.014 0.025 0.000 C 0.986 0.899 0.899 0.884 0.865 0.961 0.902 0.986 0.962 0.949 D 0.014 0.000 0.013 0.026 0.027 0.000 0.014 0.000 0.000 0.013 E 0.000 0.063 0.063 0.077 0.081 0.026 0.042 0.000 0.013 0.038 F 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Hobs 0.028 0.200 0.200 0.231 0.270 0.079 0.194 0.029 0.075 0.100 Hexp 0.027 0.185 0.185 0.211 0.244 0.077 0.181 0.028 0.073 0.096

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Table 2. Continued

Locus AZ11 AZ12 AZ13 AZ14 AZ15 AZ16 AZ17 AZ18 AZ19 AZ20

GPI (N ) 36 36 35 39 36 39 28 39 40 37

MPI (N ) 39 38 37 39 40 33 38 40 39 38

PGD (N ) 34 37 33 37 40 39 38 39 36 36

A 0.000 0.014 0.000 0.014 0.000 0.000 0.000 0.000 0.000 0.000 B 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C 0.985 0.959 0.955 0.958 0.937 0.987 0.961 0.962 0.988 0.988 D 0.000 0.000 0.000 0.014 0.000 0.013 0.000 0.038 0.014 0.000 E 0.015 0.027 0.045 0.014 0.063 0.000 0.039 0.000 0.028 0.042 F 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Hobs 0.029 0.081 0.091 0.081 0.125 0.026 0.079 0.077 0.083 0.083 Hexp 0.029 0.079 0.087 0.079 0.117 0.025 0.076 0.074 0.081 0.080

P-ext – 1.000 1.000 1.000 1.000 – 1.000 1.000 1.000 1.000

MDH (N ) 40 39 33 39 40 39 38 39 40 38

A 0.000 0.013 0.000 0.013 0.000 0.000 0.013 0.000 0.000 0.000 B 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 D 1.000 0.987 1.000 0.987 1.000 1.000 0.987 1.000 1.000 1.000 E 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Hobs – 0.026 – 0.026 – – 0.026 – – –

Hexp – 0.025 – 0.025 – – 0.026 – – –

P-ext – 1.000 – 1.000 – – 1.000 – – –

HBDH (N ) 40 39 35 40 37 40 35 38 39 38

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Table 2. Continued

Locus MA1 MA2 MA3 MA4 CA1 CA2 CA3 CA4 CA5 CA6

GPI (N ) 34 37 37 36 40 38 40 19 39 38

MPI (N ) 40 40 40 40 39 40 37 20 40 37

PGD (N ) 36 35 34 30 37 34 37 20 38 37

A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 B 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C 0.958 0.957 0.926 0.884 0.986 0.975 0.932 0.950 0.961 0.918 D 0.042 0.029 0.059 0.083 0.000 0.000 0.027 0.025 0.000 0.014 E 0.000 0.014 0.015 0.033 0.014 0.015 0.041 0.025 0.039 0.068 F 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Hobs 0.083 0.086 0.147 0.233 0.027 0.059 0.135 0.100 0.079 0.162 Hexp 0.080 0.083 0.138 0.212 0.027 0.058 0.126 0.096 0.076 0.151 P-ext 1.000 1.000 1.000 1.000 – 1.000 1.000 1.000 1.000 1.000

MDH (N ) 40 38 40 36 35 38 40 20 37 34

A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 B 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 D 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 E 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Hobs – – – – – – – – – –

Hexp – – – – – – – – – –

P-ext – – – – – – – – – –

HBDH (N ) 38 37 39 27 37 36 40 20 38 15

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Table 2. Continued

Locus CV1 CV2 CV3 CV4 CV5 CV6 CV7 CV8 CV9 CV10 CV11 CV12

GPI (N ) 38 38 40 40 40 39 40 39 40 40 40 34

A 0.026 0.039 0.038 0.025 0.013 0.013 0.038 0.026 0.025 0.038 0.025 0.015 B 0.316 0.158 0.273 0.274 0.200 0.244 0.400 0.474 0.325 0.250 0.450 0.397 C 0.026 0.132 0.013 0.000 0.050 0.038 0.000 0.026 0.000 0.000 0.000 0.015 D 0.566 0.579 0.613 0.650 0.652 0.615 0.562 0.397 0.550 0.612 0.474 0.544 E 0.013 0.066 0.025 0.013 0.050 0.064 0.000 0.026 0.025 0.000 0.013 0.000 F 0.053 0.013 0.038 0.038 0.025 0.013 0.000 0.051 0.075 0.075 0.038 0.029 G 0.000 0.013 0.000 0.000 0.013 0.013 0.000 0.000 0.000 0.025 0.000 0.000 Hobs 0.605 0.533 0.475 0.575 0.500 0.462 0.425 0.641 0.550 0.550 0.575 0.353 Hexp 0.576 0.616 0.546 0.500 0.532 0.556 0.522 0.621 0.585 0.555 0.570 0.553 P-ext 0.046 0.311 0.338 0.101 0.156 0.196 0.179 0.106 0.789 0.669 1.000 0.003

MPI (N ) 33 37 36 38 40 39 40 40 38 40 40 40

A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 B 0.076 0.054 0.056 0.053 0.138 0.115 0.038 0.063 0.066 0.125 0.075 0.125 C 0.924 0.946 0.944 0.947 0.862 0.885 0.962 0.925 0.934 0.875 0.913 0.875 D 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.013 0.000 E 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 F 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.013 0.000 0.000 0.000 0.000 Hobs 0.091 0.108 0.111 0.105 0.175 0.231 0.075 0.150 0.132 0.200 0.175 0.250 Hexp 0.140 0.102 0.105 0.100 0.237 0.204 0.072 0.140 0.123 0.219 0.162 0.219 P-ext 0.150 1.000 1.000 1.000 0.134 1.000 1.000 1.000 1.000 0.473 1.000 1.000

PGD (N ) 39 39 40 37 33 39 40 37 38 39 40 39

A 0.000 0.013 0.013 0.014 0.000 0.000 0.000 0.000 0.000 0.000 0.013 0.000 B 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.026 C 0.923 0.923 0.961 0.904 0.970 1.000 0.925 0.878 0.881 0.911 0.974 0.910 D 0.000 0.000 0.000 0.041 0.015 0.000 0.000 0.027 0.066 0.000 0.000 0.013 E 0.026 0.013 0.013 0.041 0.015 0.000 0.025 0.041 0.000 0.038 0.013 0.051 F 0.051 0.051 0.013 0.000 0.000 0.000 0.050 0.054 0.053 0.051 0.000 0.000 Hobs 0.103 0.154 0.075 0.135 0.061 – 0.150 0.135 0.184 0.179 0.050 0.154 Hexp 0.145 0.145 0.073 0.177 0.059 – 0.141 0.223 0.216 0.167 0.049 0.168 P-ext 0.091 1.000 1.000 0.161 1.000 – 1.000 0.001 0.219 1.000 1.000 0.255

MDH (N ) 40 39 40 40 40 39 40 40 40 40 40 40

A 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 B 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.013 0.000 0.000 0.000 0.000 C 0.000 0.000 0.000 0.013 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 D 1.000 1.000 1.000 0.987 1.000 1.000 1.000 0.987 1.000 1.000 1.000 1.000 E 0.000 0.000 0.000 0.000 0.013 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Hobs – – – 0.025 0.025 – – 0.025 – – – –

Hexp – – – 0.025 0.025 – – 0.025 – – – –

P-ext – – – 1.000 1.000 – – 1.000 – – – –

HBDH (N ) 39 39 39 33 39 39 40 39 40 40 39 39

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Fig. 1. Graphical representation of mean number of alleles per locus at each of the four archipelagos (AZ5Azores; MA5Madeira; CA5Canary Islands; CV5Cape Verde Islands).

This difference was nearly significant (P

5 0.0507) and therefore indicated a

‘‘ten-dency’’. In contrast, H

obs

did not differ significantly among the four archipelagos

(P

5 0.4826). All F

xy

indices were small, indicating no population differentiation at any

of the three analysed hierarchical levels (i.e. F

PI

5 0.005; F

IA

5 0.002; F

AT

,

0.001).

After Bonferroni correction, eight significant allele frequency heterogeneities were

detected when the archipelagos were compared: three (i.e. GPI, PGD, HBDH) among

CV and AZ, two (i.e. GPI, PGD) among CV and MA, one (i.e. PGD) among CV and

CA, one (i.e. PGD) among MA and CA, and one among AZ and MA (i.e. PGD) (Table

3).

The first axis of the correspondence analysis, which explained 33.19% of the

variation, separated the CV from the remaining Macaronesian Islands. This axis mainly

Table 3

a

Exact tests of allele frequency heterogeneities, according to Guo and Thompson (1992)

GPI MPI PGD MDH HBDH

*

AZ–MA 0.05076 0.79000 0.00036 1.00000 0.42472 AZ–CA 0.84836 1.00000 0.27278 0.58522 0.41846

* * *

AZ–CV 0.00001 0.05454 0.00001 0.03950 0.00046

*

MA–CA 0.13578 0.84030 0.00102 – 0.79490

* *

MA–CV 0.00136 0.71848 0.00026 1.00000 0.55006

*

CA–CV 0.02208 0.27440 0.00182 1.00000 0.26526

*

Significant after sequential Bonferroni correction.

a

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Fig. 2. Regression analysis of pairwise Nm estimates against pairwise geographical distances.

expressed effects of PGD-F, GPI-A, GPI-E, and GPI-G. The second factor discriminated

the MA from the other archipelagos, mainly on the basis of PGD-D and GPI-C. This

factor explained only an additional 18.14% of the variation making it less informative.

Both the regression analysis among pairwise Nm values and geographic distances

(Fig. 2) as well as a plot of private alleles based gene flow estimates on a geographic

map of Macaronesia (Fig. 3), were reminiscent of IBD. Hence, the highest Nm value

was observed among MA and CA, the lowest among AZ and CV (Fig. 3, Table 4).

4. Discussion

As in other planktonic developing species, population differentiation measured by

F-statistics was negligible. With F

xy

values ranging from 0.005 to

,

0.001, they fell well

within the range of F

xy

values reported in other planktonic developers (e.g. Mitton et al.,

1989; Benzie and Williams, 1992; Johannesson, 1992; Stiven, 1992; Ford and Mitton,

1993; Karakousis et al., 1993; Saavedra et al., 1993). This confirms the expectation, (e.g.

Crisp, 1978; Mitton et al., 1989), that the planktonic development of L. striata decreases

the likelihood of genetic population differentiation.

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Fig. 3. Graphical representation of gene flow intensities (bold lines) between the different Macaronesian archipelagos, based on private allele frequencies. Dotted arrows indicate major oceanic sea currents (AZ5 Azores; MA5Madeira; CA5Canary Islands; CV5Cape Verde Islands; NAC5North Atlantic Sea Current; LC5Labrador Sea Current).

increasing with increasing geographic distance (from one among CV and CA to three

among CV and AZ); (3) the separate ordination of CV in the correspondence analysis;

and (4) the suggestive relationship among the private allele based Nm values and the

geographical distance, which needed to be explained.

These observations, none of which of is related to heterozygosity levels, might be

Table 4

Gene flow (Nm) estimates based on the frequency of private alleles, P(1), according to Slatkin (1985) (I) and

a

Slatkin and Barton (1989) (II), for each of the possible archipelago comparisons Nsam P(1) Nm I Nm II Pairwise geographic

distance (km)

AZ–MA 450.4 0.0018 120.5 37.8 960

AZ–CA 478.9 0.0020 92.0 29.9 1200

AZ–CV 610.2 0.0039 19.2 7.8 2400

MA–CA 175.4 0.0020 251.2 81.7 432

MA–CV 316.8 0.0051 21.8 9.7 1440

CA–CV 306.6 0.0057 18.1 8.4 1200

a

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explained by the distribution of rare alleles (i.e. alleles occurring at low frequencies) and

private alleles (i.e. alleles that occur in a single population). In contrast to the

F-statistics, all other statistics used in this study (i.e. MNA, genetic heterogeneity

analysis, correspondence analysis, P(1) based Nm estimation) might seriously be

affected by the presence / absence of alleles occurring at very low frequencies, which all

private alleles in this study do. Effects of private alleles are not so much picked up by

F-statistics, because with this approach, the variance in allele frequency among

populations at a given locus is standardized by its mean allele frequency. Effects of a

few alleles occurring in a single or a few populations at small frequencies are overriden

by the effects of alleles, occurring in all populations at consistently high frequencies.

Private alleles were found at the CV archipelago (i.e. eight) and at the AZ (i.e. two), and

accounted for respectively 26.6% and 6.6% of all alleles found at both island groups.

Hence, in contrast to the F-statistics, these alleles do seem to suggest a geographical

pattern. Obviously this suggestive distribution of private alleles remains to be explained,

particularly as a parallel study of esterase variation suggested an increase in mean

number of esterase bands for the CV populations (De Wolf et al., 1998b), while a study

of RAPD loci revealed yet again a higher degree of genetic variability at the CV

populations when compared to the other Macaronesian populations (De Wolf et al.

1998c).

Strict IBD might explain the nonrandom allelic distribution, though seems unlikely

given that only private alleles were nonrandomly distributed. If IBD would affect the

distribution of alleles, one would expect it to affect all alleles, which is clearly not the

case.

In the absence of any detailed information on the larval behaviour of L. striata, we

assume that the distribution of private alleles in L. striata is affected by invisible barriers

to gene flow (i.e. direction of the sea currents) and the recent history of the species.

As the present day clockwise oceanic circulation (Fig. 3) has not changed much since

the opening of the North Atlantic, during the Jurassic (Berggren, 1980), it would seem

that larval dispersal patterns in Macaronesia may have been quite constant too. This

seems somewhat problematic since: (1) the oldest fossil of L. striata is known from

tertiary deposits in the CV (Reid, 1996); (2) the CV, together with Fuerteventura and

Gomera (i.e. CA) are with their alleged Jurassic or Cretaceous origin the oldest

´

Macaronesian Islands (Mitchell-Thome, 1976); (3) L. striata might possibly have

occupied the Eocene and Oligocene Macaronesian shores (i.e. CV

1 Fuerteventura

1 Gomera) (see Fig. 120 in Reid, 1996), long before the AZ emerged above sea level; and

(4) the CV populations of L. striata are both morphologically and genetically more

diverse than elsewhere (De Wolf et al., 1998b,c,d). All these observations suggest that L.

striata colonized the AZ from the CV.

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Pacific after an ancestral species migrated from the eastern Atlantic to the west coast of

North America via the Panama Strait (Reid, 1996).

The subsequent further widening of the Atlantic and the decrease in surface current

intensity may have gradually prevented continued gene flow from CV to AZ, so that CV

no longer acted as source for gene flow to the north. This could explain: (1) the higher

number of private alleles in CV; and (2) the elevated genetic and morphological

diversity in CV.

A possible test for this hypothesis should include an estimation of the duration of the

larval dispersal stage and a survey of plankton following the westward currents from the

CV. In addition, if this scenario would be correct, we would expect that the two AZ

private alleles are relatively recent and have not yet spread towards the south. The

proposed migration scenario is highly tentative and for example does not consider the

possibility of human transport, which cannot be a priori excluded given the intense naval

traffic in Macaronesia (Grunning, 1967).

In conclusion, this migration scenario only accounts for the distribution of the private

alleles. It does not suggest a hierarchical population genetic structuring, for such a

structuring was not apparent. This was previously also reported by De Wolf et al.

(1998b), who found no differentiation among radular myoglobin patterns among L.

striata from the four archipelagos.

Finally the lack of genetic structuring does not necessarily mean that there is no

structure, especially as only five polymorphic enzyme loci were involved in this

analysis, of which one was relatively invariable (i.e. MDH). Nevertheless similar results

(i.e lack of genetic structuring and elevated genetic variability in CV) were obtained in

two other population genetic surveys, involving the analysis of esterase and RAPD

profiles (De Wolf et al. 1998b,c), suggesting that the present allozyme data is sufficiently

informative, and that IBD is indeed not affecting the population genetic structure of L.

striata.

Acknowledgements

This research was supported by the MAST 3 programme of the European Commission

under contract number MAS3-CT95-0042 (AMBIOS) and PRAXIS XXI 2 / 2.1 / BIA /

169 / 94 (Portugal). Travel expenses were partly covered by STRIDE, Portugal. The

authors would like to thank D. Verbergt (Hogere Zeevaartschool, Antwerpen), P.G.B.

Jones (Hydrographic office of the UK) and J. Geys (RUCA, Antwerpen) for providing

us with background information and literature, while H. Van Paeschen (KBIN) kindly

made the drawings. H. De Wolf is a Postdoctoral Fellow of the Fund for Scientific

Research — Flanders (Belgium) (F.W.O.). [SS]

References

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Backeljau, T., Warmoes, T., 1992. The phylogenetic relationships of ten Atlantic littorinids assessed by allozyme electrophoresis. In: Grahame, J., Mill, P., Reid, D. (Eds.), Proceedings of the Third International Symposium on Littorinid Biology, The Malacological Society of London, London, pp. 9–24.

Backeljau, T., Breugelmans, K., Brito, C., De Bruyn, L., De Wolf, H., Timmermans, J., 1995. Macrogeog-raphic genetic homogeneity in Littorina striata from the Azores (Mollusca: Prosobranchia). In: Ac¸oreana, Suppl., pp. 159–171.

Benzie, J.A.H., Williams, S.T., 1992. No genetic differentiation of giant clam (Tridacna gigas) populations in the Great Barrier Reef, Australia. Mar. Biol. 113, 373–377.

Berggren, W.A., 1980. Les courants de l’Atlantique. La Recherche 113, 786–795.

Brown, L.D., Murray, N.D., 1995. Population genetics, gene flow, and stock structure in Haliotis rubra and Haliotis laevigata. In: Shepperd, S.A., Tegner, M.J., Guzman del Proo, S.A. (Eds.), Proceedings of the first International Symposium on Abalone, Abalone of the World — Biology, Fisheries and Culture, Fishing News Books, pp. 24–33.

Crisp, D.J., 1978. Genetic consequences of different reproductive strategies in marine invertebrates. In: Battaglia, B., Beardmore, J.A. (Eds.), Marine Organisms. Ecology and Evolution, Plenum, New York. De Wolf, H., Backeljau, T., Verhagen, R., 1998a. Spatio–temporal genetic structure and gene flow between two

distinct shell morphs of the planktonic developing periwinkle, Littorina striata (Mollusca: Prosobranchia). Mar. Ecol. Prog. Ser. 163, 155–163.

De Wolf, H., Backeljau, T., Verhagen, R., 1998b. Lack of significant esterase and myoglobin differentiation in the periwinkle, Littorina striata (Gastropoda, Prosobranchia). Hydrobiologia 378, 27–32.

De Wolf, H., Backeljau, T., Verhagen, R., 1998c. Congruence between allozyme and RAPD data in assessing macrogeographical genetic variation in the periwinkle Littorina striata (Mollusca: Gastropoda). Heredity 81, 486–492.

De Wolf, H., Backeljau, T., Van Dongen, S., Verhagen, R., 1998d. Large scale patterns of shell variation in Littorina striata, a planktonic developing periwinkle from Macaronesia (Mollusca: Prosobranchia). Mar. Biol. 131, 309–317.

Ford, M.J., Mitton, J.B., 1993. Population structure of the pink barnacle Tetraclita squamosa rubescens, along the California coast. Mol. Mar. Biol. Biotech. 2, 147–153.

Grunning, J.F., 1967. Africa Pilot, Vol. 1, London.

Guo, S.W., Thompson, E.A., 1992. Performing the exact test of Hardy–Weinberg proportion for multiple alleles. Biometrics 48, 361–372.

Harris, H., Hopkinson, D.A., 1976. Handbook of Enzyme Electrophoresis in Human Genetics, Elsevier, Amsterdam.

Johannesson, K., 1992. Genetic variability and large scale differentiation in two species of Littorinid gastropods with planktotrophic development, Littorina littorea (L.) and Melarhaphe (Littorina) neritoides (L.) (Prosobranchia: Littorinacea), with notes on a mass occurrence of M. neritoides in Sweden. Biol. J. Linn. Soc. 47, 285–299.

Johannesson, K., Johannesson, B., Rolan-Alvarez, E., 1993. Morphological differentiation and genetic cohesiveness over a microenvironmental gradient in the marine snail Littorina saxatilis. Evolution 47, 1770–1778.

Johnson, M.S., Black, R., 1995. Neighbourhood size and the importance of barriers to gene flow in an intertidal snail. Heredity 75, 142–154.

Karakousis, Y., Spandou, E., Sophronidis, K., Triantaphyllidis, C., 1993. Morphological and allozymic variation in populations of Mytilus galloprovincialis from the Aegean Sea. J. Moll. Stud. 59, 165–173.

´

Mitchell-Thome, R.C., 1976. Geology of the Middle Atlantic Islands, Gebruder Borntraeger, Berlin, Stuttgart. Mitton, J.B., Berg Jr, C.J., Orr, K.S., 1989. Population structure, larval dispersal, and gene flow in the queen

conch, Strombus gigas, of the Caribbean. Biol. Bull. 177, 356–362.

Palumbi, S.R., 1994. Genetic divergence, reproductive isolation, and marine speciation. Annu. Rev. Ecol. Syst. 25, 547–572.

Palumbi, S.R., 1996. Macrospatial genetic structure and speciation in marine taxa with high dispersal abilities. In: Ferraris, D., Palumbi, S.R. (Eds.), Molecular Zoology Advances, Strategies and Protocols, Wiley-Liss, New York.

(15)

Reid, D.G., 1996. Systematics and Evolution of Littorina, The Ray Society, London. Rice, W.R., 1989. Analyzing tables of statistical tests. Evolution 43, 223–225.

Rohlf, F.J., 1993. NTSYS-pc: Numerical Taxonomy and Multivariate Analysis System, version 1.80, Applied Biostatistics, Setauket, New York.

Rolan-Alvarez, E., Zapata, C., Alvarez, G., 1995. Distinct genetic subdivision in sympatric and sibling species of the genus Littorina (Gastropoda: Littorinidae). Heredity 74, 1–9.

Saavedra, C., Zapata, C., Guerra, A., Alvarez, G., 1993. Allozyme variation in European populations of the oyster Ostrea edulis. Mar. Biol. 115, 85–95.

Scheltema, R.S., 1995. The relevance of passive dispersal for the biogeography of Caribbean molluscs. Am. Malacol. Bull. 11, 99–115.

Slatkin, M., 1985. Rare alleles as indicators of gene flow. Evolution 39, 53–65.

Slatkin, M., Barton, N.H., 1989. A comparison of three indirect methods for estimating average levels of gene flow. Evolution 43, 1349–1368.

Statsoft, 1995. STATISTICA for Windows (computer manual). Statsoft Inc., Tulsa, Oklahoma.

Stiven, A.E., 1992. Genetic structure in a population of the ribbed mussel Geukensia demissa (Dillwyn) in a North Carolina salt marsh tidal gradient. J. Exp. Mar. Biol. Ecol. 164, 31–44.

Swofford, D.L., Selander, R.B., 1989. BIOSYS. A Computer Program for the Analysis of Allelic Variation in Population Genetics and Biochemical Systematics. Release 1.7, University of Illinois, Urbana.

Trussell, G.C., 1996. Phenotypic plasticity in an intertidal snail: The role of a common crab predator. Evolution 50, 448–454.