MARINE MAMMAL SCIENCE, 20(1):28-47 (January 2004)© 2004 by the Society for Marine Mammalogy

POPULATION STRUCTURE IN AN INSHORECETACEAN REVEALED BY MICROSATELLITE

AND rntDNA ANALYSIS: BOTTLENOSEDOLPHINS (TURSIOPS SP.) IN SHARK BAY,

WESTERN AUSTRALIAMICHAEL KRUTZEN!

WILLIAM B. SHERWIN

School of Biological, Earth and Environmental Sciences,University of New South Wales,Sydney, NSW 2052, Australia

E-mail: michael.kruetzen@unsw.edu.au

PER BERGGREN

Department of Zoology,Stockholm University,

106 91 Stockholm, Sweden

NICK GALES

Applied Marine Mammal Ecology Group,Australian Antarctic Division,Kingston, TAS 7005, Australia

ABSTRACT

We examined population substructure of bottlenose dolphins (Tursiops sp). inShark Bay, Western Australia, using 10 highly polymorphic microsatellite loci, andmitochondrial DNA (mrDNA). For microsatellite analysis, 302 different animalswere sampled from seven localities throughom the bay. Analysis of genetic dif­ferentiation between sampling localities showed a significant correlation betweenthe number of migrants (Nm) calculated from FST' RST' and private alleles, anddistance between localities-a. pattern of isolation-by-distance. For mtDNA, 220individuals from all seven localities were sequenced for a 351 base pair fragment ofthe control region, resulting in eight haplotypes, with two distinct clusters ofhaplotypes. Values of FST and <PST for mtDNA yielded statistically significantdifferences, mostly between localities that were not adjacent to each other, suggest­ing female gene flow over a scale larger than the sampled localities. We also ob­served a significant correlation between the number of female migrants calculatedfrom FST and <PST and the distance of sampling localities. Our results indicate thatdispersal in female dolphins in Shark Bay is more restricted than that of males.

Key words: bottlenose dolphin, control region, microsatellites, populationgenetics, TursiopJ sp.

I Person to whom correspondence should be sent.

28

KRUTZEN ET AL.: POPULATION STRUCTURE OF SHARK BAY DOLPHINS 29

Populations are defined as units of interbreeding organisms with autonomousdynamics and recruitment. Defining the boundaries of a given population is a pre­requisite not only for management purposes such as defining evolutionary sig­nificant units or managing units, but also for investigating the evolution of socialstructure within a population (e.g., paternity and relatedness), since most statisticalprocedures assume that alleles were drawn from a single population. Compared toterrestrial and freshwater environments, where populations are often well definedand genetically different from each other (Avise 2000), investigating populationstructure in marine organisms poses a challenge because of the lack of obviousgeographical boundaries. Furthermore, individuals may be highly mobile and ableto migrate or disperse permanently over vast areas. In the past, genetic markers havebeen used to successfully detect cryptic population structure in freshwater (Allendorfet al. 1976, Carlsson et al. 1999) and marine fish (Nesbo et al. 2000, Hutchinsonet at. 2001). However, many studies have failed to detect significant populationstructuring in the marine environment due to low genetic differentiation, both overlarge (e.g., sperm whale, Physeter macrocephalus; Lyrholm et al. 1999) and small scales(e.g., cod, Gadus morhua; Amason et al. 1992).

The pattern and level of sex-specific dispersal plays an important tole in deter­mining genetic population structure. For example, the most common pattern ob­served in terrestrial mammals is male-biased dispersal with female philopatry(Clutton-Brock 1989), which has also been observed in a number of cetacean species(Brown Gladden et at. 1997, Palsb~ll et al. 1997, Walton 1997, Rosel et al. 1999).However, two highly social odontocete species ~e exceptional: killer whales (Orcinusorca) and pilot whales (Globicephela melas), where both males and females stay wirhinrheir nara1 pod (Amos et al. 1993, Hoelzel et at. 1998) and show little biaseddispersaL

Bottlenose dolphins (Tursiops sp.) inhabit cold temperate to tropical watersworldwide. Some offshore populations seem to undertake seasonal migrations (Wellset at. 1999); for instance, open ocean bottlenose dolphins (Tursiops truncatus) fromCalifornia (Defran and Weller 1999, Defran et at. 1999) and Virginia (Barco et al.1999) show no evidence of site fidelity of individuals throughout the year. Bycontrast, long~term studies suggest bottlenose dolphins inhabiting enclosed bays orrelatively sheltered areas show high site fidelity, as shown in Sarasota Bay, Florida(Wells 1991), Shark Bay, Australia (Connor et al. 1992b, Smo1ker et al. 1992), andthe Moray Firth, Scotland (Wilson et at. 1997). Furthermore, behavioral observa­tions provide evidence for natal philopatry (Connor 2000), which is particularlywell documented for Shark Bay (Smo1ker et al. 1992) and Sarasota Bay (Wells et al.1987, Duffield and Wells 1991, Wells 1991).

In Shark Bay both females and males live in a fission-fusion society (Smolkeret al. 1992). Individual females occupy the study area year after year; females swimtogether in temporary parries when they are not foraging and each femalehas certain associates. Female-female associations are that of a network in which allfemales are connected. Richards (1996) showed that females stay within their natalrange and use home ranges from 5 km2 to 58 km2

. Males also seem to have long­term residency in their natal ranges (Smolker et at. 1992), and usually occupyterritories that are larger than those of females (up to 130 km2

; Connor,unpublished data2

).

2 Personal communication from R. C. Connor, Biology Deparrment, University of Massachusetts­Dartmouth, North Dartmouth, MA 02748, U.S.A., 3 July, 2002.

30 MARINE MAMMAL SCIENCE, VOL. 20, NO.1, 2004

113°EI

25km

113.5°EI

FAU(n ~ 16)

* Geographical location andsubdivision within RCB

\

I, MM3I. ". (,,=45)\ : ..:..: .

'-\

... ':'~.'...vt:~

Monkey ------"" "l.'Mia •

CP

~:. (n/.,/ ~CB~~

'. • . (n ~ 198)

..UIL(n ~36)

DH(n~9)

.'

~':"-. \ A';;::I~

Perth ,

'-/~

26°5-

25.5°8-

Figure 1. (a) Geographical location of bottlenose dolphin samples collected for thisstudy (for abbreviations of sampling localities see text). One point may represent more thanone individuaL

In this study we investigate the population structure of bottlenose dolphins inShark Bay, an inshore population, using methods sensitive to male- and female­mediated gene flow. In particular, we test whether dolphins within Shark Bay arepart of a large, single population, or whether subpopulations might exist within thebay. Other aims of this study were to assess the level of population substructure,estimate levels of gender mediated gene flow, and to determine the relative degreeof philopatry for the different sexes.

METHODS

Biopsy Sampling

Shark Bay is a large and relatively shallow embayment complex that coversabout 13,000 km2 about 850 km north of Perth on the central Western Australiancoast (approximately 25°30'S, I 13°30'E, Fig. I). Tissue samples of 362 free­ranging bottlenose dolphins were collected between 1994 and 1999 (approximately82% of all samples were obtained in 1997-1999) from various localities acrossShark Bay (Fig. I). Animals were sampled north of Faure Island (FAU), in Red CliffBay (RCB), around Cape Peron (CP), at Dirk Hartog Island (DH), and at UselessInlet and Useless Loop (UIL). We recorded the position of each dolphin darted atthe initial sighting, using a Magellan MX-lO (Magellan) GPS device. We thenfollowed the animals for a maximum of 10 min until a sample was obtained,

KRUTZEN ET AL.: POPULATION STRUCTURE OF SHARK BAY DOLPHINS 31

employing a biopsy system especially designed for small cetaceans, which causesminimal short-term, and apparently no long-term effects on the animals (Kriitzen etal. 2002). Biopsy samples were srored in a sarurated NaClI20% (v/v) dimethyl­sulfoxide solution (Amos and Hoelzel 1991) at room temperature while in the field,and at -20DC upon arrival at the laboratory. The samples were taken from most ofthe known coastal range of bottlenose dolphins on the east side of the eastern gulf ofShark Bay (Preen et al. 1997), as well as mote distant parts within Shark Bay. Weassigned each animal to one of five provisionally defined localities based on thesampling locality (Fig. 1). We based our assignments on the following. Firstly, noneof the animals sampled around FAD and CP could be identified from the exist­ing database with more than 600 photo identifications from RCB, indicating thatanimals from FAD and CP incorporate RCB rarely in their range. Secondly, al­though there are no previous behavioral records for animals from DH and VIL, weassumed that interaction between these two localities is highly limited due to thedistance between them. In order to test for the possibility of isolation by distanceon a finet scale, we subdivided the largest locality (RCB) into three geographicallyadjacent sublocalities (MM I-MM 3, Fig. 1 inset). Although we carinot assumegenetic structure within RCB a priori, the behavioral observations of highly limitedfemale dispersal could lead to substructuring within RCB.

Microsatellite Genotyping and Molecular Sexing

DNA extractions were performed using standard techniques (Davis et at. 1986).Prior to microsatellite analysis, individuals were genetically sexed using primersthat anneal ro the ZFX and ro the SRY gene, resulting in a 44S and a 222 base pairproduct, respectively (Gilson et at. 1998). We used 10 different microsatellite lociin this study: MK3, MKS, MK6, MK8, MK9 (Kriirzen et al. 2001); EVI, EV37(Valsecchi and Amos 1996); KWM12A (Hoelzel et al. 1998); 199/200 (Amos et al.1993); and D22 (Shinohara et al. 1997). The PCR products were run on an ABI377 DNA automated sequencer (Applied Biosystems) according to manufacturer'sinstructions. (JENESCAN, version 3.1 and GENOTYPER, version 1.1.1 (both AppliedBiosystems) software were used to measure the size of the fragments, using theTAMRA SOO standard.

Mitochondrial DNA

A DNA fragment of 3S1 base pairs, comprising rhe proline transfer RNAgene and parts of the hypervariable region I ofthe control region, was amplified for220 different individuals, using the polymerase chain reaction (PCR). We usedprimers dlpl.S (Baker et al. 1993) and dlp3R (developed by the seniot author,S' -GGTTGCTGGTTTCACGC-3'). PCR products were cleaned using purificarioncolumns (QIAquick® 250, Qiagen) according to the manufacturer's instructions.PCR products were then amplified using the dlpl.S primer with the ABI PRISM@BigDye™ Terminaror Cycle Sequencing Ready Reaction kir (Applied Biosystems),according to the manufacturer's specifications, on a GeneAmp® PCR System9600 (Perkin Elmer). Sequencing fragmenrs were detected on an ABI 377Automated Sequencer (Perkin Elmer), and obtained sequences were edited usingSEQUENCINGANALYSIS software, version 3.3 (Applied Biosystems). The relatively shortsize of our peR product deemed sequencing in both directions unnecessary, sincehigh quality readings were obtained over the entire length ofthe product. Alignment

32 MARINE MAMMAL SCIENCE, VOL. 20, NO.1, 2004

of the sequences was carried out by eye using the software SEQApp, version 1.9a169(available from frp://ftp.bio.indiana.edu/molbio/seqapp). We used MACCLADE, ver­sion 3.01 (Sinauer) to filter redundant haplotypes and remove any invariant charactersfrom the data set.

Data Analysis

Prior to data analyses, we checked for animals that might have been sampled morethan once using MSTOOLS, version 2.3 (available from http://acer.gen.tcd.ie/~sdepark/MSmacros.htm).If this was the case, we removed the second entry fromthe data set. Additionally, we removed known first-degree relatives (known fromprevious behavioral observations), because their presence could bias allele frequencies.

A total of 60 individuals was removed from the data set. For the RCB area, oursampling efforts were biased towards known mother-offspring pairs and known orsuspected relatives. We therefore removed 35 animals that had known or suspectedrelatives in the data set. Thirteen dolphins had been sampled twice. For 12 animals,we did not obtain GPS data and therefore did not assign them to any of thelocalities.

Genetic Diversity· within Localities-Microsatellite Data

The software GENEPOP, version 3.3 was used to test for significant deviations fromHardy-Weinberg equilibrium (HWE) at each locality, using the Markov chainmerhod of exact HWE probabiliry (Guo and Thompson 1992, Rousset andRaymond 1995). Sequential Bonferroni adjustment (Rice 1989) for multiple testswas conducted for each population.

To test for the presence of non-amplifying alleles (null alleles), we checked knownmother-offspring pairs for the occurrence of at least one shared allele at each locus.Pedigrees allow direct inference of null alleles, whereas other methods rely on theassumption thar all departure from HWE is due ro null alleles. Additionally,we estimated the frequency of a putative null allele for each locus and locality withthe software CERVUS, version 2.0 (Marshall et al. 1998), which uses an iterativealgorithm based on the difference between observed and expected frequency ofhomozygotes (Summers and Amos 1997). Another indicator for presence of nullalleles is the inbreeding coefficient FIS (Weir and Cockerham 1984), which wecalculated using GENEPOP, version 3.3. Positive FIS values indicate an excess ofbomozygotes, which may be caused by null alleles.

Genetic variation within all seven localities was estimated in three differentways: by calculating the mean number of alleles (:t: SE) per locus, by estimating anunbiased level of heterozygosity within each population (Nei 1978), and observedheterozygosity (Ho) by direct genotype count. The measures were obtained usingthe software BIOSYSL (Swofford and Selander 1981). We also tesred for the presenceof linkage disequilibrium (LD) among the loci by creating contingency tables forall pairs of loci in each population. For each locus pair, an unbiased estimate oK theP-value of the probability test was performed using a Markov chain (Rousset andRaymond 1995). Sequential Bonferroni corrections were applied for all "by locus­pair" simultaneous tests per population and seven "by population" tests per locus-pair.

Genetic Diversity within Localities-Mitochondrial DNA Data

Haplotype diversity within each locality and the pairwise genetic distancebetween haplotypes was calculated using Arlequin, version 2.001 (Schneider et al.

KRUTZEN ET AL.: POPULATION STRUCTURE OF SHARK BAY DOLPHINS 33

Figure 2. Minimum spanning network showing the relationshIps among 220 mtDNAcontrol region sequences. The diameter of each circle is approximately proportional to thenumber of individuals carrying that haplotype. Number of substitutions between eachhaplotype are indicated by hash marks.

2001). We chose the Tamura & Nei distance (Tamura and Nei 1993) with y­correction of 0.5. We inferred the relationships between the haplotypes by con­structing a minimum spanning network, using ARLEQUIN, version 2.001. There weretwo predominant clades with fifteen substitutions between·them (Fig. 2). Thisraised the question whether animals carrying haplotype A or B might forma different sympatric population to animals carrying haplocypes C-H. To test thishypothesis, we partitioned the data set into two groups. In the first group wepooled all the animals carrying a haplotype from one clade (A and B), and inthe other group we pooled the animals carrying haplotypes C-H. Using themicrosatellite allele frequency data, we performed two analyses of molecularvariance (AMOVA; Excoffier et at. 1992) using ARLEQUIN, version 2.001. In the firstanalysis localities were nested within mtDNA clades, whereas in the second analysismtDNA clades were nested within localities. These two analyses respectivelycompare the two hypotheses: firstly, that the major subdivision of microsatellitegenotype frequencies is between members of the two mtDNA groupings, whichmay be sympatric populations with wholly or partially isolated gene pools; andsecondly, that the major subdivision is between localities, with members of the twomtDNA groups exchanging nuclear genes freely.

Genetic Diversity among Localities-Microsatellite Data

To estimate subdivision between pairs of localities, FST and RST statistics wereemployed. The program FSTAT, version 2.93 (Gouder 1995) was used to estimate FSTfrom allele proportions for all population pairs using Weir and Cockerham's e,which assumes an infinite allele model of mutation (Weir and Cockerham 1984).Slatkin's RST' which assumes a stepwise mutation model (Slatkin 1985), was calcu­lated fot all population pairs, employing the software RSTCALC (Goodman 19?7).

Gene flow between pairs of localities (Nm) was estimated from FST' RST' and usingthe ptivate allele method as described in Slatkin (1985). The ptivate allele methoddoes not assume a model ofpopulation structure and its estimator is analogous to FSTin that it is a measure of variance of allele distributions (Slatkin and Barton 1989).

To investigate whether isolation-by-distance occurs between the localities, weassumed that animals are able to move in straight lines using the shortest distancethrough the water between localities. Distances between centers ofeach locality were

34 MARINE MAMMAL SCIENCE, VOL. 20, NO.1, 2004

measured using the GIS software ARCVIEW, version 3.1 (ESRI). We performed Manteltests (Sokal and Rohlf 1995) between matrices of the naturallogarirhm ofgeographicdistance (In D) and In Nm estimates from PST, RST' and private alleles among all sevenprovisional localities, using 5,000 randomizations. The tests were performed usingPOpTOOLS 2.5.5 software (available from http://www.cse.csiro.au/poptools/).

Genetic Diversity among Localities-Mitochondrial DNA Data

We tested the degree of population structure using three approaches. Geneticvariance components were calculated among and within localities using an AMOVAdesign. This hierarchical design investigates what proportion of the genetic varianceis caused by partitioning the data set into localities, and what proportion resultsfrom other factors. For AMOVA, each region contained a single one of the localitieslisted in Table 2, excepr for the samples from ReB (MMI-MM3), which wegrouped within a single region. We also used ARLEQUIN, version 2.001, to calculatepairwise FST values for haplotype frequencies and <f>-statistics, using the Tamuraand Nei (1993) genetic distance with y-correction of 0.5. The null distribution ofpairwise <PST values under the hypothesis of panmixia was obtained by 10,000permutations ofhaplotypes between localities. Using PopTool$ 2.5.5, we performedMantel tests between matrices of In D and In Nm calculated from FST and <f>STamong all localities.

RESULTS

We observed two significant departures from HWE in two localities at differentloci (Table 1). Significant linkage disequilibrium between locus pairs was observedonly within locality MM 1, where five locus pairs showed significant linkage dis­equilibrium after Bonferroni correction. Null allele proportions were, if observed,all below 0.03 for each locus/population pair (data not shown). Furthermore, noneof the loci showed significant positive FIS values across all populations (Table 1),indicating that the presence of null alleles was negligible.

Genetic Diversity within Localities-Microsatellite Data

The 10 microsatellite loci used in this study were highly polymorphic, havingsix to 22 alleles per locus. All loci were polymorphic and the average number ofalleles found ar any locus was 8.53 (SE 0.6). Expected and observed hererozygositieswere high, and varied among localities. The lowest levels of both HE and Ho wereobserved in the localiry with the smallest number of samples (DH).

Genetic Diversity within Localities-Mitochondrial DNA Data

We sequenced 220 different dolphins from all localities. We found eight differenthaplorypes and 18 polymorphic sites (Table 3). A single base pair insertion-deletion(indel) was necessary to align rhe eight haplotypes, resulting in a 351-bp finalfragment (Table 3). The indel, located at site no. 278, differentiated betweenhaplotypes A and B and all others. Ir was present in 72 animals (32.7%) from alllocalities except UIi.

The minimum spanning network shows that two different clusters dominatedthe network (Fig. 2). The average' haplotypic diversity over all localities was

KRUTZEN ET AL: POPULATION STRUCTURE OF SHARK BAY DOLPHINS 35

Table 1. Test results for departures from Hardy-Weinberg equilibrium. P-values and FIS(Weir and Cockerham 1984) for all loci and localities. Significant P-values after sequentialBonferroni corrections (Pcrit. = 0.0071) are marked with an asterisk. FIS values are given initalics.

Locus FAD MM 1 MM2 MM3 CP DH UIL

MK3 0.905 0.335 0.434 0.255 0.088 1.000 0.703-0.126 0.008 0.117 0.011 0.226 -0.032 0.100

MK5 0.753 0.708 0.435 0.370 0.023 1.000 0.633-0.091 -0.032 -0.083 -0.010 -0.178 -0.011 -0.061

MK6 0.572 0.039 0.630 0.400 0.626 0.260 0.2380.019 0.013 -0.090 0.057 -0.063 0.000 0.029

MK8 0.988 0.169 0.125 0.207 0066 1.000 0.629-0.138 -0.092 -0.098 -0.116 -0.170 -0.143 0.083

MK9 0.695 0.850 0.845 0.567 0.716 0.592 0.885-0.333 0.058 0.074 -0.029 0.012 0.020 -0.132

EVI 0.763 0.209 0.231 0.354 0.062 0.183 0.045-0.009 -0.007 -0.035 -0.016 0.016 0.216 -0.018

EV37 0.291 0.513 0.183 0.197 0.341 0.032 0.0250.140 0.026 -0.103 0.032 0.003 0.213 0.126

KWM12A 0.020 0.745 0.003' 0.158 0.473 0.564 0.7670.299 0.025 0.091 0.116 -0.069 0.026 -0.147

199/200 0.545 0.132 0.627 0.145 0.970 1.000 0.4720.130 0.000 -0.096 0.145 -0.033 -0.191 0.088

D22 0.150 0.667 0.893 0.628 0.004' 0.687 0.0100.265 0.045 -0.023 -0.231 0.228 0.028 0.219

66.23%, with the lowest for FAU at 49.17% (Table 2). The relative haplorypefrequencies for each population indicate an association of haplotype frequenciesbetween pairs of groups (Fig. 3). The frequency of haplotype A for exampledecreases with distance from FAD to MM 3, and then increases again towards DH.The ftequency of haplotype E shows the opposite pattern. Two haplotypes areunique to a certain locality: haplotype B is only found in CP, while haplotype F isfound only in UIL.

Most microsatellite variation was not strongly partitioned according tomtDNA clade or locality (Table 4). The AMOVA results for the microsatellitedataset partitioned according ro the mtDNA haplotypes (i.e., haplotypes A, Bvs. C-H) showed that for nesting localities within mtDNA clades, 90.04%of the variation was within sampling localities (P < 0.01). When we nestedmtDNA clades within localities, 94.57% of the variation was within samplinglocalities (P < 0.01). However, a small but significant proportion of the micro­satellite variation was between clades, and, to a lesser extent, between localities(Table 4).

Genetic Diversity Among Loca!ities-Microsatellite Data

Pairwise RST values were significantly different mainly between localities from thetwo d}fferent gulfs in Shark Bay (western gulf= UIL and DH, eastern gulf = FAU,MM 1-3, CP; Table 5). In the eastern gulf, CP was significantly different to all three

'"0,

i':

Table 2. Measures of genetic diversity (± SE) in seven localities of bottlenose dolphins for mitochondrial DNA and microsatellites. l~~

No. of Mean no. No. of No. of Haplotype Nucleotide ~chromosomes of alleles mtDNA mtDNA diversity diversity

~Locality sampled per locus Mean HE Mean Ho samples haplotypes (%) (%)

FAU 0.765 ± 0.048 16 49.17 ± 11.74~

32 7.5 ± 0.9 0.781 ± 0.029 3 1.99 ± 1.1 0MM1 186 10.8 ± 1.5 0.756 ± 0.029 0.753 ± 0.030 63 5 66.51 ± 3.54 2.32 ± 1.21 '"?iMM2 120 9.4 ± 1.4 0.760 ± 0.023 0.774 ± 0.033 43 5 74.64 ± 2.99 2.09 ± 1.11 !"MM3 90 8.8 ± 1.2 0.750 ± 0.033 0.749 ± 0.031 25 3 54.67 ± 9.09 1.64 ± 0.91

~CP 86 9.6 ± 1.3 0.775 ± 0.025 0.779 ± 0.044 34 6 70.23 ± 5.68 2.00 ± 1.07DH 18 5.2 ± 0.7 0.679 ± 0.044 0.657 ± 0.027 9 5 86.11 ± 8.72 2.67 ± 1.54 N

p

UlL 72 8.4 ± 1.0 0.747 ± q.027 0.724 ± 0.035 30 4 62.30 ± 7.47 2.77 ± 2.15 ZP

Total 604 8.53 ± 0.6 0.750 ± 0.01 0.743 ± 0.01 220 8 66.23 ± 11.48 2.21 ± 0.40 :"Naa~

KRUTZEN BT AL.: POPULATION STRUCTURE OF SHARK BAY DOLPHINS 37

Table 3. Polymorphic sites within mtDNA control region sequences for each haplotype.Light strand is reported 5' to Y. The site nwnber is given on top.

1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 3 32 0 0 1 5 0 1 2 4 6 7 7 7 8 8 8 0 2

Haplotype 5 2 4 0 2 8 7 2 9 9 3 6 8 3 6 7 7 9C A C A T G T T T T G C T T C C A GD TE C AF CG AH CA G T C C A C A C C T A C C T GB G T C C A C A C C T A C C T T G

ReB localities. Pairwise values ofFST follow a similar pattern, except that within theeastern gulf, MM 1 and MM 3 were significanrly different from each other.

Pairwise values of Nm varied according to which method was used (Table 5). Nmvalues calculated from FST and RST appeared to have the same order of magnitude,while values calculated from private alleles were generally much smaller, especiallybetween adjacent localities (Table 5). However, there seemed to be a trend with allthe methods: localities geographically closer to each other had higher values of Nm.Irrespective of the method used, all Nm values were greater than 1, and many wereup to two orders of magnitude greater than 1.

The results of Mantel tests for isolation by distance using microsatellites werehighly significant for Nm calculared from FST (r=-0.827, P = 0.008; Fig. 4a), RST(r = -0.829, P = 0.018; Fig. 4b), and private alleles (r = -0.649, P = 0.012; Fig.4c), although the slopes of rhe plors were shallow.

Genetic Diversity among Localitie~-Mitochondria/ DNA Data

Using <PST, about 80% of the total molecular variance was accounted for bydividing the sample into seven localities (<PST = 80.64, P = 0.16; Table 6). Ahierarchical AMOVA atrribured 11.88% of the variation among localities (<pcr =11.88, P < 0.01) and 7.48 % among sublocalities within tegion RCB (<Psc = 7.41,P = 0.08). Values of <PST and FST differ only marginally (Table 7), and are significantfor the same population pairs: UIL is significantly different from all other localities,whereas FAD and MM 1 are different from all other localities except DH. DHis significanrly different only from MM 3. Due to the fact that both <PST and FSTvalues are similar, the resulting values of Nmalso differ only marginally. Weobserved the same trend as for the microsatellites: localities geographically closer toeach othet had higher values of Nm (Table 5). However, overall values of Nm wetean order of magnitude smaller than Nm values calculated from microsatellites. TheNm values for the two localities that are farthest apart are below one (FAU-UIL andMM I-DIL). Both cottelations fot In D and In (Nm ftom FST) and for In D and In(Nm ftom <PST) were significant (r = -0.639, P = 0.041; Fig. 4d).

DISCUSSION

The Shark Bay dolphin population shows genetic differentiation followingWright's isolation-by-distance -mood for both nuclear and mtDNA markers.

38 MARINE MAMMAL SCIENCE, VOL. 20, NO.1, 2004

16 63 43 25 34 9 30

"H[;jG

<IF

IIJE

8D

.C

GiB

DA

UILDHCPMM2MMIFAU MM3

Location

Figure 3. MtDNA haplotype diversity within Shark Bay. Sample size for each locality isgiven on top of each column.

However, while microsatellite markers reveal significant differentiation mainly overlarger distances within the bay, rntDNA markers show significant differentiation ona smaller scale (i.e., between non-adjacent sampling localities within each gulf).These results indicate that female dispersal in Shark Bay dolphins is smaller thanthat for males.

All seven localities showed high levels of HE' ranging from 65% to 79%. Similarlevels of HE were observed for microsatellites in other outbred cetacean species(Valsecchi et at. 1997, Rosel et at. 1999, Fullard et at. 2000). Genotypic dis­equilibrium was evident in locality MM 1 only. This could be due to genetic linkagebetween the loci. However, linkage seems to be unlikely for two reasons: firstly,genotypic equilibrium is present in only one locality; secondly, if locus MK6 waslinked· to three other loci, the other loci should show linkage disequilibrium be­tween them.

Estimates of genetic diversity based on RST and FST calculations from micro­satellite data follow a similar pattern. For 17 out of 21 locality pairs, RST waslarger than PST' If the variance in allele size is small within localities compared with .between localities, then RST should be larger than PST, because the potential forallele convergence with the stepwise mutation model in microsatellites will tend toreduce estimated population differentiation when FST is used. On a historical scale,FST reflects more recent events, since it compares variances in allele frequenciesbetween different populations without taking into account past mutational events.For all locality pairs that did not include DH, significant values of FST were below0.0334, which is considered to be very low genetic differentiation (Hartl and Clark1997). FST values were not significantly different from zero in the four easternmostlocalities (FAD-RCB), except between MM I and MM 3. Although the FST betweenthese two sublocalities is statistically significant, it is relatively small, as indicatedby a Nm value of 17.74. This could not be regarded as isolation, since Nm valuesof 1 are sufficient to prevent differentiation by drift (Crow and Kimura 1970). The

KROTZEN ET AL.: POPULATION STRUcrURE OF SHARK BAy DOLPHINS 39

Table 4. Variance components and permutation probabilities for AMOVAs when datasetwas partitioned according to clades (mtDNA haplotypes groups). df = degrees of freedom,SS = sum of squares, permutation probability is given for the probability that randomizedvalue> observed value.

Source Variance % Permutationof variation df 55 components variation probability

Localities within mtDNA clades

Among clades 1 64.987 0.314 8.53 <0.01Among localities

within clades 11 54.652 0.052 1.43 <0.01Within localities 421 1395.488 3.315 90.04 <0.01Total 433 1515.127 3.681

MtDNA clades within localities

Among localities 6 37.318 -0.201' -5.73 <0.01Among clades

within localities 6 82.321 0.391 11.16 <0.01Within clades 421 1395.488 3.315 94.57 <0.01Total 433 1515.127 3.315

a Negative variance components, which are rather covariances, can occur in absence ofgenetic structure, because the true value of the parameter being estimated is zero (Schneideret al. 2001). Hence, by chance, slightly positive or slightly negative variance componentscan occur.

achievement of significance in this case is presumably due to very small variancesfor FST, as a result of the large number of samples for each of these localities and thelarge number of loci. The RST values seem to support this theory: within ReB, thefour easternmost localities were not significantly different from each other. Lowgenetic differentiation within the eastern gulf is also reflected by large Nm values,which were l1-P to two orders of magnitude> 1. Values of Nm among all localitiesin the eastern gulf calculated from private alleles were up to one order of magnitude> 1, also indicating a high degree of gene flow. All three In D/ln Nm correlationswere highly significant, indicating that population structure estimated from micro­satellites in Shark Bay can be approximated by the isolation-by-distance model ofWrighr (943).

The minimum spanning nerwork of mrDNA haplorypes (Fig. 2) revealedan interesting aspect that requires further investigation. Haplotypes A and B differby at least fifteen bases from the other lineage containing all other haplotypes.Similar networks were found in Dall's porpoises (Phocoenoides dalli; McMillan andBermingham 1996) and coyores (Canis /atrans; Lehmann and Wayne 1991). Whenthe data set was partitioned according to the mtDNA haplotypes, an AMOVAindicated that there was a small, but significant microsatellite variation betweenanimals from each haplotype cluster (Table 4). However, by far most of the variance(90.04%) was explained by partitioning the data set into the seven localities,suggesting that animals from both haplotype clades interbreed. Due to our extensivenumerical and geographical sampling within Shark Bay, it seems unlikely thatmissing intermediates could be the result of missed mtDNA lineages, which wereneverrheless exranr in Shark Bay. If all rhe haplorypes had evolved in Shark Bay, anyintermediates appear to be extinct now. Alternatively, Shark Bay may have been

""o

Table 5. Pairwise comparisons and Nm values among the seven localities based on microsatellite loci. RST values are shown in the upper matrix andFST values in the lower matrix. Statistically significant results after sequential Bonferroni correction (Pcrit. = 0.0024) are marked with an asterisk.Pairwise Nm values (shown in italics) in the upper matrix were calculated from RST• the values in the lower matrix were obtained using PST- Nm values

I~shown in parentheses in the lower matrix were calculated from private alleles.

ZFAU MM 1 MM2 MM3 CP DH UIL "'

~FAU 0.0022 0.0059 0.0172 0.0502' 0.1121 ' 0.0484' ;:

116.78 42.62 14.31 4.72 1.98 4.91 ~MM 1 0.0017 0.0046 0.0149 0.0227' 0.0899' 0.0368'

~146.81 (6.78) 54.5 16.61 10.78 2.53 6.540.0040 0.0078 0.0288' 0.1023' 0.0240'

zMM2 0.0109 ()

22.69 (4.78) 62.25 (10.32) 31.67 8.42 2.19 10.18!"

'"MM3 0.0290 0.0139' 0.0036 0.0050 0.0733' 0.0210 0,.8.37 (3,32) 17.74 (7.43) 69.19 (8.27) 49.98 3.16 11.62 ~

?CP 0.0248 0.0266' 0.0198' 0.0163' 0.0578 0.0319'

~9.83 (5.36) 9.14 (4.92) 12.37 (4.97) 15.08 (4.67) 4.07 7.58!'"

DH 0.0694 0.0648' 0.0641 ' 0.0612' 0.0326 0.0674 ~

0

3.35 (1.91) 3.60 (3.22) 3.65 (2.12) 3.83 (2.20) 7.41 (4.04) 3.46 0

'"UIL 0.0333' 0.0267' 0.0235' 0.0218' 0.0205' 0.0360

7.26 (3.79) 9.11 (7.45) 10.38 (5.50) 11.21 (3.10) 11.94 (3.55) 6.69 (3.17)

KRUTZEN EY AL.: POPULATION STRUCTURE OF SHARK BAY DOLPHINS 41

(a) 6

r = - 0.827P = 0.008

•••

•'. •· .~. ' ..• •'-

5432o+---~--~-~--~--~

o

'.•••

~~ ."

·.'.'•

•

••

r =·0.829P = 0.018

t; 4eo,

~.5 2

(b) 6

5432o+---~--~--~---~-~

o

(c) 2.5

~ 2

.,;.1.5'C

""I~.E 0.5

r =·0.649P=0.012

• •• •"

•• • ••

••

.... ." •

5432o+---~--~--~---~-~

o

5

o 00o

4

o

o

o

o

o

3

oo

o

o

2

o

r=·0.639P= 0.041

-2 +- ... ~---_,----,._--o=-,

o

6'E'

(d)

In geographic distance through water (kIn)

Figure 4. Plots of the relationship between In D (shortest distance through water in km)between localities and In Nm calculated from microsatellites (a--c) and mtDNA (d). Plotsfor mtDNA In Nm calculated from PST and GlST are almost identical due to very smalldifferences of PST and GlST values (Table 7). Hence, only one graph IS shown.

42 MARINE MAMMAL SCIENCE, VOL. 20, NO.1, 2004

Table 6, Variance components and permutation probabilities for AMOVA usingmtDNA data. df = degrees of freedom, 55 = sums of squares, permutation probability isgiven for P (randomized value> observed value).

Source Variance % Permutationof variation df 55 components variation probability

Among localities 4 112.022 0.510 11.88 <0.01Among sub-localities (MMl-3)

within the locality RCB 2 33.129 0.320 7.48 0.08Within localities 213 736.196 3.456 80.64 0.16

Total 219 881.347 4.286

colonized by two distinct mitochondrial DNA lineages. The most likely explana­tion however seems to be that there is some weak medium-term separation of thematrilines, since most of the animals carrying A haplotypes wete in the eastern gulf.

The results for mitochondrial DNA differ in some aspects from those obtained bymicrosatellites. High levels of genetic variation were observed in the haplotypedistribution over all seven localities. Overall haplotypic diversity was 0.662, sug­gesting that a few haplotypes dominate the data set, which is also supported by theminimum spanning network. The number of haplotypes ranged from three to sixper locality with a total of eight different haplotypes for all localities. These valueswere similar for two other inshore populations of bottlenose dolphins: Moller andBeheregaray (2001) found four different haplorypes in Jervis Bay and Port Srephens(southeast Australia), respectively.

The hierarchical AMOVA design revealed significant. population subdivision,and a significant amount of variation was explained by the variation betweensampling localities. Pairwise comparisons of <PST and FST values revealed that fromFAD to CP, values of <PST and FST for most of the non-adjacent localities weresignificanr (except MM 2 and CPl. These results indicate weak female philopatrywith leakage.of female mtDNA into adjoining localities at least for the eastern gulf.The small sample size for DH probably contributes to the fact that this locality isonly diffetent from MM 3 and VIL. Compared to all other localiries, values of FST

and <PST for VIi were generally much higher. VIi is significantly different from allother localities. This is mostly due to the relatively high frequency of haplotype F,which is unique to this locality, and the lack of haplotype A, indicating very lowfemale movement at least between the most distant parts of the eastern and thewestern gulf of Shark Bay.

Values of FST calculated from mtDNA are in most cases at least one order ofmagnitude larger than those calculated from microsatellites, especially between moredistant localities. Hence, Nm values calculated from microsatellite FST are in mostcases (12 out of 15 when DH is omitted due to small sample size) more than four timeslarger than those calculated from mtDNA FST' with six cases being more than an orderof magnitude larger. Taking into account that mtDNA genomes have the effectivepopulation size ofonly one-fourth ofautosomal nuclear genes, which leads to a higherrate of local differentiation by random genetic drift (Avise et al. 1984), comparisons ofboth FST and Nm values derived from microsatellites and mtDNA suggest limitedfemale gene flow compared to that of males. Among non-adjacent localities withinEast Shark Bay, the number of female migrants was too small to prevent dif­ferentiation due to genetic drift, indicating some female philopatry over a scale

KROTzEN EY AL: POPULATION STRUCTURE OF SHARK BAY DOLPHINS 43

Table 7. Genetic differentiation of mitochondrial DNA (d-Ioop). Pairwise comparisonsamong the seven localities and Nm values. <PST values are shown in the upper matrix and FSTvalues in the lower matrix. Statistically significant results after Bonferroni correction (Pcdt, =0.0024) are marked with an asterisk. Pairwise Nm values (shown in italics) in the uppermatrix were calculated from <PST, the values in the lower matrix were obtained using FST-

FAD MM 1 MM2 MM3 CP DH DIL

FAD 0.0745 0.2486* 0.3939* 0.2752* -0.0408 0.7186*6.21 1.51 0.77 1.32 Inl 0.20

MM 1 0.0743 0.0464 0.1482* 0.0918* -0.0270 0.3585*6.23 10.28 2.87 4.95 Inl 0.89

MM2 0.2462* 0.0448 0.0069 0.0140 0.0881 0.2041 *1.53 10.65 72.43 35.33 5.18 1.95

MM3 0.3898* 0.1444* 0.0062 0.0199 0.2203* 0.1560*0.78 2.96 79.66 24.65 1.77 2.71

CP 0.2719* 0.0904* 0.0152 0.0208 0.1045 0.2578*1.33 5.03 32.45 23.54 4.28 1.44

DH -0.0399 -0.0258 0.0864 0.2166* 0.1015 0.6367'Inl Inl 5.28 1.81 4.43 0.29

UIL 0.7152* 0.3551* 0.2060* 0.1636* 0.2617' 0.6350*0.20 0.91 1.93 2.56 1.41 0.29

larger than the sampling localities. As for microsatellites, female mediated gene flowseemed to follow the isolation-by-distance model ofWrighr (1943).

Various studies on both terrestrial (e.g., Britten and Glasford 2002, Masta et at,2003) and marine organisms (e.g., Daemen et al. 2001, Pogson et at. 2001) haveshown that dispersal of adults is usually strongly dependent on distance, and canlead to a gradual shift of genetic population structure with distance, Our resultssupport behavioral data suggesting natal philopatry for Shark Bay dolphins (Connoret al. 1992b, Smolker et al. 1992, Richards 1996), wirh males incorporaring rheirnatal area intO larger adult ranges. Natal philopatry seems to be a pattern at twoscales: within East Shark Bay, as well as throughout the entire surveyed embay­ment complex. Furthermore, our genetic data support the findings by Smolkeret at, (1992), who suggested that female-female associations are that of a networkin which all females are connected. For such a scenario one would expect adjacentlocalities to be genetically similar for mtDNA, with a gradual shift in haplotypefrequencies with increasing distance, which is the case in Shark Bay.

In two other delphinid species, the extent of philopatry appears to be strongercompared to Shark Bay. In pilot whales neither sex disperses from the natal pod(Amos et at, 1993), nor do males mate within their natal pod, an unusual situationfor mammals. Hoelzel et al. (998) also showed female philopatry for resident andtransient killer whales and suggested that dispersal of males would be limited towithin local populations_

The population structure in male bottlenose dolphins in Shark Bay may bea factor in the evolution of mating strategies. Connor et at, (1992a, b) showed thatsexually mature males form alliances to compete over access for females that are inestrus. Evolutionary theory predicts males cooperating in this way may gaininclusive fitness benefits if they were related to each other (Hamilton 1964). Long­range dispersal of males to other areas would minimize the chance of allying with

44 MARINE MAMMAL SCIENCE, VOL. 20, NO.1, 2004

a related partner. By incorporating their natal range in their home range, sexuallymature dolphins would increase the chance of allying with a related partner, whichhas recently been shown for male dolphins from Shark Bay that fotm small andlong-lasting alliances (Kriitzen et at. 2003).

ACKNOWLEDGMENTS

Special thanks to Lynne Barre, Hugh Finn, Michael Heithaus, Doro Heimeier, KerstinBilgmann, Leah Page, Richard Connor, Colleen Simms, Helen McLachlan-Berggren, JanetMann, and Rachel Smolker who helped in collecting samples and information in the field.Darren Crayn, Matthew Taylor, Kelly Waples, and Joe Zuccarello helped in obtaining the d­loop sequences. The Monkey Mia resort gave valuable support during our field studies. JohnBlesing from Shark Bay Resources allowed us to conduct research from their facilities inUseless Loop and also gave valuable logistical support. Kieran Wardle supported us while weconducted research off Dirk Hartog Island. Patty Rosel, Anna Lindholm, and one anonymousreviewer gave helpful comments on earlier drafts of this manuscript. The research wascarried out under the permit #SF002958 issued by Conservation and Land Management(CALM) to MK and WBS. Ethics approval was given from the University of New SouthWales (#99/52). The study was partly funded by the Australian Research Council (ARC),the WV Scott Foundation, and the Monkey Mia Dolphin Research and Education Trust.

LITERATURE CITED

AllENDORF, F. w., N. RYMAN AND G. STAHL. 1976. Genetic variation in Scandinavian browntrout (Salmo trutta 1.): Evidence of distinct sympatric populations. Hereditas 83:73-82.

AMOS, B., AND A. R. HOELZEL. 1991. Long-term preservation of whale skin for DNA anal­ysis. Pages 99-103 in A. R. Hoelzel and G. P. Donovan, eds. Genetic ecology of whalesand dolphins. International Whaling Commission, Cambridge, UK.

AMOS, B., C. SCHLOTTERER AND D. TAUTZ. 1993. Social structure of pilot whales revealed byanalytical DNA profiling. Science 260:670-672.

ARNASON, E., S. PALSON AND A. ARASON. 1992. Gene flow and lack of population differ­entiation in Atlantic cod, Gadus morhua 1., from Iceland, and comparison of cod fromNorway and Newfoundland. Journal of Fish Biology 40:751~770.

AVISE,]. C. 2000. Phylogeography. Harvard University Press, Cambridge, MA.AVISE,). C,]. E. NEIGEL AND]' ARNOLD. 1984. Demographic influences on mitochondrial

DNA lineage survivorship in animals populations. Journal of Molecular Evolution 20:99-105.

BAKER, C S., A. PERRY,]. 1. BANNISTER, M. T. WEINRICH, R. B. ABERNETHY,). CALAMBOKIDIS,J. LIEN, R. H. LAMBERTSEN,]. U. RAMIREZ AND O. VASQUEZ. 1993. Abundant mito­chondrial DNA variation and world-wide population structure in humpback whales.Proceedings of the National Academy of Sciences, USA 90:8239-8243.

BARCO, S. G., W. M. SWINGLE, W. A. MCLEu.AN, R. N. HARRIS AND D. A. PABST. 1999.Local abundance and distribution of bottlenose dolphins (Tursiops truncatus) in thenearshore waters of Virginia Beach, Virginia. Marine Mammal Science 15:394-408.

BRITTEN, H. B., AND]' W. GLASFORD. 2002. Genetic population structure of the Dakotaskipper (Lepidoptera: Hesperia dacotae): A North American native prairie obllgate.Conservation Genetics 3:363-374.

BROWN GLADDEN, J. G., M. M. FERGUSON AND]. W CLAYTON. 1997. Matriarchal geneticpopulation structure of North American beluga whales Delphinapterus leucas (Cetacea:Monodontidae). Molecular Ecology 6:1033-1046.

CARLSSON,)., K. H. OLsEN,). NILSSON, O. OVERLI AND O. B. STABELL. 1999. Microsatellitesreveal fine-scale genetic structure in stream-living brown tcout. Journal ofFish Biology55:1290-1303.

KRUTZEN ET AL: POPULATION STRUCTURE OF SHARK BAY DOLPHINS 45

CLUTION-BROCK, T. H. 1989. Mammalian mating systems. Proceedings of the Royal Societyof London, Series B: Biological Sciences 236:339-372.

CONNOR, R. C 2000. Group living in whales and dolphins. Pages 199-218 in J. Mann,R. C Connor, P. 1. Tyack and H. Whitehead, eds. Cetacean societies. Chicago UniversityPress, Chicago, 11.

CONNOR, R. C, R. A. SMOLKER AND A. F. RICHARDS. 1992a. Dolphin alliances andcoalitions. Pages 415-443 in A. H. Harcourt and F. B. M. de Waal, eds. Coalitions andalliances in humans and other animals. Oxford University Press, Oxford, UK.

CONNOR, R. C, R. A SMOLKER AND A. F. RICHARDS. 1992b. Two levels of alliance formationamong male bottlenose dolphins (Tursiops sp.). Proceedings of the National Academy ofSciences, USA 89:987-990.

CROW,J. F., AND M. KIMURA. 1970. An introduction to population genetic theory. Harper &

Row, New York, NYDAEMEN, E., T. CROSS, F. OllEVIER AND F. A. M. VOLCKAERT. 2001. Analysis of the genetic

structure of European eel (Anguilla anguilla) using microsatellite DNA and mtDNAmarkers. Marine Biology 139:755-764.

DAVIS,1. G., M. D. DIBNER AND J. F. BATTEY. 1986. Basic methods in molecular biology.Elsevier Science Publishing, New York, NY

DEFRAN, R. H., AND D. W. WEllER. 1999. Occurrence, distribution, site fidelity, and schoolsize of bottlenose dolphins (Tursiops truncatus) off San Diego, California. MarineMammal Science 15:366-380.

DEFRAN, R. H., D. W. WELLER, D. 1. KEllY AND M. A. ESPINOSA. 1999. Range character­istics of pacific coast bottlenose dolphins (Tursiops truncatus) in the southern CaliforniaBight. Marine Mammal Science 15:381-393.

DUFFIELD, D. A, AND R. S. WELLS. 1991. The combined application of chromosome, proteinand molecular data for the investigation of social unit structure and dynamics inTursiops truncatus. Pages 155-169 in A R. Hoelzel and G. P. Donovan, eds. Geneticecology of whales and dolphins. International Whaling Commission, Cambridge, UK.

EXCOFFIER, 1., P. E. SMOUSE AND J. M. QUATTRO. 1992. Analysis of molecular varianceinferred from metric distances among DNA haplotypes: Application to human mito­chondrial DNA restriction data. Genetics 131:479-491.

FUllARD, K. J., G. EARLY, J. HEIDE-JORGENSEN, D. BLOCH, A. ROSING-ASVID AND W. AMos.2000. P,?pulation structure of long-finned pilot whales in the north Atlantic: A cor­relation with sea surface temperature? Molecular Ecology 9:949-958.

GILSON, A., M. SYVANEN, K. LEVINE AND J. D. BANKS. 1998. Deer gender determination bypolymerase chain reaction: Validation study and application to tissues, bloodstains, andhair forensic samples from California. California Fish and Game 84:159-169.

GooDMAN, S. J. 1997. Rst calc: A collection of computer programs for calculating estimatesof genetic differentiation from microsatellite data and determining their significance.Molecular Ecology 6:881-885.

GOUDET, J. 1995. Fstat (version 1.2): A computer program to calculate F-statistics. Journalof Herediry 86:485-486.

Guo, S. W., AND E. A. THOMPSON. 1992. Performing the exact test of Hardy-Weinbergproportion for multiple alleles. Biometrics 48:361-372.

HAMILTON, W. D. 1964. The genetical evolution of social behaviour, I, II. Journal ofTheoretical Biology 7:1-52.

HARTL, D. 1., AND A. G. CLARK. 1997. Principles of population genetics. Sinauer,Sunderland, MA.

HOELZEL, A. R., M. DAHLHEIM AND S. J. STERN. 1998. Low genetic variation among killerwhales (Orcinus orca) in the eastern north Pacific and genetic differentiation betweenforaging specialists. Heredity 89:121-128.

HUTCHINSON, W F., G. R. CARVALHO AND S. I. ROO-ERS. 2001. Marked genetic structur­ing in localised spawning populations of cod Gadus morhua in the North Sea and

46 MARINE MAMMAL SCIENCE, VOL. 20, NO. 1, 2004

adjoining waters, as revealed by microsatellites. Marine Ecology Progress Series223:251-260.

KRUTZEN, M., E. VALSECCHI, R. C. CONNOR AND W. B. SHERWIN. 2001. Characterisation ofmicrosatellites in Tursiops aduncus. Molecular Ecology Notes 1:170-172.

KRi'rTzEN, M., 1. M. BARRE, L. M. MOllER, M. R. HEITHAUS, C. SIMMS AND W B. SHERWIN.

2002. A biopsy system for small cetaceans: Darting success and wound healing inTursiops spp. Marine Mammal Science 18:863-878.

KROTZEN, M., W. B. SHERWIN, R. C. CONNOR, 1. M. BARRE, T. VANDE CASTEELE,J. MANNAND R. BROOKS. 2003. Contrasting relatedness patterns in bottlenose dolphins (Tursiopssp.) with different alliance strategies. Proceedings of the Royal Society London, SeriesB: Biological Science 270:497-502.

LEHMANN, N., AND R. K. WAYNE. 1991. Analysis of coyote mitochondrial DNA genotypefrequencies: Estimation of effective number of alleles. Genetics 128:405-416.

LYRHOLM, T., O. LEIMAR, B. JOHANNESON AND U. GYLLENSTEN. 1999. Sex-biased dispersalin sperm whales: Contrasting mitochondrial and nuclear genetic structure of globalpopulations. Proceedings of the Royal Society London, Series B: Biological Science266:347-354.

MARSHAll, T. C, ]. SLATE, 1. E. B. KRUUK AND J. M. PEMBERTON. 1998. Statisticalconfidence for likelihood based paternity inference in natural populations. MolecularEcology 7:639-655.

MASTA, S. E., N. M. LAURENT AND E. J. ROUTMAN. 2003. Population genetic structure of thetoad Bufo woodhousii: An empirical assessment of the effects of haplotype extinction onnested cladistic analysis. Molecular Ecology 12:1541-1554.

McMILLAN, W.O., AND E. BERMINGHAM. 1996. The phylogeographic pattern of mito­chondrial DNA variation in the Dall's porpoise Phocoenoides dalli. Molecular Ecology5:47-61.

MOLLER, 1. M., AND 1. B. BEHEREGARAY. 2001. Coastal bottlenose dolphins from south­eastern Australia are Tursiops aduncus according to sequences of the mitochondrial DNAcontrol region. Marine Mammal Science 17:249-263.

NEI, M. 1978. Estimation of average heterozygosity and genetic distance from a smallnumber of individuals. Genetics 89:583-590.

NESBO, C 1., E. K. RUENESS, S. A. IVERSEN, D. W. SKAGEN AND K. S. JAKOBSEN. 2000.Phylogeography and population history of Atlantic mackerel (Scomber scombrus 1.);a genealogical approach reveals genetic structuring among the eastern Atlanticstocks. Proceedings of the Royal Society London, Series B: Biological Science 267:281-292.

PALSB011, P. J., M. P. HEIDE-J0RGENSEN AND R. DIETZ. 1997. Population structure andseasonal movements of narwhals, Monodon monoceros, determined from mtDNA analysis.Heredity 78:284-292.

POGSON, G. H., K. A. MESA AND R. G. BOUTILIER. 2001. Isolation by distance in the Atlanticcod, Gadus morhua, at large and small geographic scales. Evolution 55:131-146.

PREEN, A. R., H. MARSH, 1. R. LAWLER, R. I. T. PRINCE AND R. SHEPHERD. 1997.Distribution and abundance of dugongs, turtles, dolphins and other megafauna inShark Bay, Ningaloo Reef and Exmouth Gulf, Western Australia. Wildlife Research24:185-208.

RICE, W. B. 1989. Analyzing tables of statistical tests. Evolution 43:223-225.RICHARDS, A. E 1996. Life history and behavior of female dolphins (Tursiops sp.) in Shark

Bay, Western Australia. Ph.D. disseration. The University of Michigan, Ann Arbor,MI. 226 pp.

ROSEL, P. E., S. C FRANCE, J. Y. WANG AND T. D. KOCHER. 1999. Genetic structure ofharbour porpoise Phocoena phocoena populations in the northwest Atlantic based onmitochondrial and nuclear markers. Molecular Ecology 8:S41-554.

ROUSSET, E, AND M. RAYMOND. 1995. Testing heterozygote excess and deficiency. Genetics140:1413-1419.

KRUTZEN ET AL.: POPULATION STRUCTURE OF SHARK BAY DOLPHINS 47

SCHNEIDER, S., D. ROESSELI AND 1. EXCOFFIER. 2001. Arlequin ver. 2.001: A software forpopulation genetics data analysis. Generics and Biometry Laboratory, University ofGeneva, Geneva, Switzerland. Available from http://anthro.unige.ch/arlequin.

SHINOHARA, M., X. DOMINGO-RoURA AND O. TAKENAKA. 1997. Microsatellites in thebottlenose dolphin Tursiops truncatus. Molecular Ecology 6:695-696.

SLATKIN, M. 1985. Rare alleles as indicators of gene How. Evolution 39:53-65.SLATKIN, M., AND N. H. BARTON. 1989. Methods for estimating gene How. Evolution

43:1349-1368.SMOLKER, R. A., A. F. RICHARDS, R. C. CONNOR AND J. W. PEPPER. 1992. Sex-differences in

patterns of association among Indian Ocean bottle-nosed dolphins. Behaviour 123:38-69.

SoKAL, R. R., AND F. J. ROHLF. 1995. Biometry: The principles and practice of statistics inbiological research. W. H. Freeman and Company, New York, NY.

SUMMERS, K., AND W. AMOS. 1997. Behavioural, ecological and molecular genetic analyses ofreproductive strategies in the Amazonian datt-poison frog, Dendrobates ventrimaculatus.Behavioral Ecology 8:260-267.

SWOFFORD, D. 1., AND R. B. SELANDER. 1981. Biosys-l: A Fortran program for thecomprehensive analysis of electrophoretic data in population genetics and systematics.Journal of Heredity 72:281-283.

TAMURA K., AND M. NEr. 1993. Estimation of the number of nucleotide substitutions in thecomrol region of mitochondrial DNA in humans and chimpanzees. Molecular Biologyand Evolution 10:512-526.

VALSECCHI, E., AND W. AMOS. 1996. Microsatellite markers for the study of cetaceanpopulations. Molecular Ecology 5:151-156.

VALSECCHI, E., P. PALSB0LL, P. HALE, D. GLOCKNER-FERRARI, M. FERRARI, P. CLAPHAM,E lARSEN, D. MATTILA, R. SEARS, ]. SIGURJONSSON, M. BROWN, P. CORKERON ANDB. AMos. 1997. Mierosatellite genetic distances between oceanic populations of thehumpback whale (Megaptera novaeangliae). Molecular Biology & Evolution 14:355-362.

WALTON, M. J. 1997. Population structure of harbour porpoises Phocoena phocoena in the seasaround the UK and adjacent waters. Proceedings of the Royal Society of London, SeriesB: Biological Sciences 264:89-94.

WEIR, B. S., AND C. C. COCKERHAM. 1984. Estimating F-statistics for the analysis ofpopulation structure. Evolution 38:1358-1370.

WELLS, R. S.· 1991. The role of long-term study in understanding the social structure ofa bottlenose dolphin community. Pages 198-225 in K. Pryor and K. S. Norris, eds.Dolphin societies: Discoveries and puzzles. University of California Press, Berkeley, CA.

WEILS, R. S., H. 1. RHINEHART, P. CUNNINGHAM, J. WHALEY, M. BARAN, C. KOBERNA ANDD. P. COSTA. 1999. Long distance offshore movements of bottlenose dolphins. MarineMammal Science 15:1098-1114.

WELLS, R. S., M. D. SCOTT AND A. B. IRVINE. 1987. The social structure of free-rangingbottlenose dolphins. Pages 247-305 in H. Genoways, ed. Current mammalogy.Plenum Press, New York, NY.

WIISON, B., P. M. THOMPSON AND P. S. HAMMOND. 1997. Habitat use by bottlenosedolphins-seasonal distribution and stratified movement patterns in the Moray Firth,Scotland. Journal of Applied Ecology 34:1365-1374.

WRIGHT, S. 1943. Isolation by distance. Genetics 28:114-138.

Received: 24 July 2002Accepted: 5 August 2003