OIKOS 88: 641–651. Copenhagen 2000

A microsatellite analysis of natterjack toad, Bufo calamita,metapopulations

G. Rowe, T. J. C. Beebee and T. Burke

Rowe, G., Beebee, T. J. C. and Burke, T. 2000. A microsatellite analysis of natterjacktoad, Bufo calamita, metapopulations. – Oikos 88: 641–651.

Although it is widely recognised that spatial subdivision of populations is common innature, there is no consensus as to how metapopulation dynamics affect geneticdiversity. We investigated the genetic differentiation of natterjack toads, Bufocalamita, in three regions of Britain where habitat continuity indicated the likelyoccurrence of extensive metapopulations. Our intention was to determine whethergenetic analysis supported the existence of metapopulation structures, if so of whattype, and to identify barriers to migration between subpopulations. Allele frequencieswere determined across eight polymorphic microsatellite loci for a total of 24 toadsubpopulations at three separate sites. Genetic differentiation was assessed using fivemeasures of genetic distance, notably FST, RST, Nei’s standard distance Ds, dm2 andthe Cavalli-Sforza chord distance Dc. B. calamita exhibited small but significant levelsof genetic differentiation between subpopulations in all three study areas, and geneticand geographic distance correlations indicated isolation-by-distance effects in allthree cases. The effects on correlation strengths of compensation for positive (sea,rivers, urban development) and negative (pond clusters) barriers to toad migrationbetween the subpopulations in each area were also determined. Dc, a measure whichassumes that differentiation is caused by drift with negligible mutation effect, yieldedthe most plausible interpretation of metapopulation structures. Overall the patternsof genetic variation suggested the existence of a mixed metapopulation model for thisspecies, with high levels of gene flow compatible with one version of the classicalmodel but often supported by particularly stable subpopulations as in the mainland-island model.

G. Rowe and T. J. C. Beebee (correspondence), School of Biology, Uni6. of Sussex,Falmer, Brighton, UK BN1 9QG (t.j.c.beebee@sussex.ac.uk). – T. Burke, Dept ofAnimal and Plant Sciences, Uni6. of Sheffield, Sheffield, UK S10 2TN.

The study of metapopulation structures and dynamicshas become a central theme in population ecology sincethe realisation that fragmented distributions are morecommon than spatially continuous ones (e.g. Levins1970, Hanski and Gilpin 1991, 1996). However, meta-populations can take many forms which may havedifferent genetic and evolutionary consequences de-pending on how interconnected the demes are, and howfrequently and evenly extinction events occur (e.g. Mc-Cauley 1991, Harrison and Hastings 1996). The classi-cal metapopulation model invokes relatively highsubpopulation turnover and recolonisation rates withequal probabilities across demes, but more realistic

alternatives have been developed in light of increasingevidence from empirical studies. Thus the existence ofsource and sink subpopulations, with the former havinga lower probability of extinction than the latter, formsthe basis of a mainland-island model; and migrationmay be sufficiently frequent that local extinction is rareand what amounts to a single ‘‘patchy’’ populationpersists. These models make various predictions withrespect to genetic differentiation and gene flow betweensubpopulations, and thus may have different evolution-ary implications. In the classical model, differentiationdepends critically on the mode of recolonisation butcan be high if colonists are rare and come only from a

Accepted 5 July 1999

Copyright © OIKOS 2000ISSN 0030-1299Printed in Ireland – all rights reserved

OIKOS 88:3 (2000) 641

few source subpopulations. However, this model is alsocompatible with low differentiation as a result of highgene flow between multiple subpopulations (Wade andMcCauley 1988). In the mainland-island model, genetichomogenisation is likely to dominate because migrationwill mostly be from an unchanging mainland (source)subpopulation, and only weak differentiation is likely(Boorman and Levitt 1973). In this case, however,directional effects of gene flow indicating likely main-land subpopulations may be expected. A single, patchypopulation will of course be panmictic with no signifi-cant genetic differentiation between subpopulations.

Amphibians are particularly suitable organisms formetapopulation studies because they generally have lowmobility, many species congregate at discrete breedingsites (ponds), and they are usually easy to census (Bee-bee 1996). Anurans and urodeles often exhibit meta-population dynamics, though the genetics of suchmetapopulations have as yet been little studied (e.g. Gill1978, Miaud et al. 1993, Sjogren Gulve 1994). InBritain, the natterjack toad, Bufo calamita, is confinedto specialised habitat types (mostly coastal sand dunesand marshes), and its distribution, population sizes andautecology are well known (Banks et al. 1994, Dentonand Beebee 1994). Several areas exist which containneighbouring clusters of natterjack breeding ponds sep-arated by distances sufficiently small as to fall withinthe movement range of individual toads (i.e. within afew km), and also within semi-continuous zones ofsuitable habitat. However, potential barriers includingrivers, tidal estuaries and urban developments also oc-cur. No amphibians can survive for long in undilutedseawater (Duellman and Trueb 1986) and toads sufferhigh mortality from road traffic in urban areas (Beebee1996). Metapopulation dynamics are likely to occur inthese situations, but can be difficult to study by classi-cal ecological methods when numbers of migrants pergeneration between subpopulations are expected to besmall. We anticipated that genetic analysis should be auseful tool to reveal the nature of metapopulationstructures under these conditions. We therefore set outto test the hypothesis that the patterns of genetic differ-entiation of B. calamita within the study areas wouldindicate the type of metapopulation model most appro-priate for this amphibian, and identify barriers to mi-gration between subpopulations.

We recently identified and characterised eight mi-crosatellite loci in B. calamita (Rowe et al. 1997, 1998)and set out to use them for metapopulation analysis.These loci are all dinucleotide repeats with allele lengthsranging between 100 and 250 base pairs. Microsatellitesare hypervariable and unlike most other molecularmarkers these genes offer good prospects for quantify-ing genetic differentiation over short spatial and tempo-ral scales (e.g. Bruford and Wayne 1993, Jarne andLagoda 1996). Mutations occur at relatively high fre-quency (typically around 10−4 per locus per genera-

tion), mostly by stepwise additions of repeat units butwith occasional large deletions that become more prob-able as an upper size boundary is approached (e.g.Hedrick 1999). Our expectation was that any differenti-ation between neighbouring subpopulations at theseneutral loci would be primarily as a result of driftmodified by local gene flow.

Study sites

We investigated B. calamita populations in three dis-crete areas, all in north-west England and south-westScotland and all of which contained several majorbreeding sites within a few kilometres of each other(Fig. 1). B. calamita has been recorded in all three areasfor more than 100 years, and the various subpopula-tions have been studied in detail for 20–30 years(Banks et al. 1994). No major changes in distribution orpopulation size have been noticed over this period.Toad generation times are about three years (Halley etal. 1996). Subpopulations were postulated on the basisof clusters of breeding ponds within each area. Area Ais on the Merseyside coast between Liverpool and

Fig. 1. The study areas. A, Merseyside coast; B, Solwayestuary; C, Duddon estuary. , Population sampling sites(subpopulations). Lightly shaded areas indicate B. calamitahabitat; heavily shaded areas indicate urban developments;- - - - -, mean low tide line; scale bars show distances in km.

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Southport. It is geographically simple, with five mainbreeding centres in a linear array along the dunecoast. However, potential barriers to toads in theform of urban development (Ainsdale) and a substan-tial river (the Alt) separate the northernmost andsouthernmost breeding centres, respectively. Area B isthe Solway estuary on the border between Englandand Scotland. It is more complex than area A, withseven breeding centres in a two-dimensional array.Potential barriers in this region are mostly in theform of river estuaries. Area C is the Duddon estuaryin southern Cumbria, and is the most complex of thethree. Twelve breeding centres occur around thiscoastline, and apart from the estuary itself potentialbarriers include two small urban developments(Askam and Haverigg) and a railway line. Further-more one breeding site is on an island (North Wal-ney) and another is several kilometres inland in anarea of low mountains (Fells). Most British popula-tions of B. calamita have been subject to regularmonitoring for conservation purposes over the past10–20 years (Banks et al. 1994). The three study ar-eas each supported an average of many hundreds orlow thousands of adult toads over the ten years priorto the present study.

Methods

Population sampling and microsatellite analysis

Every breeding centre (=subpopulation) at each ofthe three study sites was visited during May or June,larvae were collected and preserved immediately inethanol for transport to the laboratory. Totals of atleast 40 larvae were collected from multiple ponds ateach breeding centre to ensure as representative asampling as possible of the alleles present at each site.At only two sites, one in area B and one in area C,were there too few larvae to meet this target and inthese cases 25 and 38 were collected, respectively.Overall totals of 200 larvae were collected from areaA, 267 from area B and 478 from area C. DNA wasextracted from each larva and used in PCR assayswith primers developed for eight polymorphic mi-crosatellite loci (Bcalm1-Bcalm8) in B. calamita (Roweet al. 1997). [33P]-labelled fragments were elec-trophoresed through 6% polyacrylamide gels, autora-diographed, and alleles scored for each locus againstM13 sequence reference markers (Rowe et al. 1998).

Geographic distance and barrier strengthestimations

Habitat structures and extents as well as landscapefeatures including urban developments, roads and

railway lines were determined using a combinationof large-scale Ordnance Survey maps, post-1985aerial photographs and ‘‘ground truth’’ visits atall three study areas. Breeding ponds situated be-tween the sampling centres were known from pre-vious investigations over three decades (Banks et al.1994).

Unmodified geographical distances were estimatedas the most direct routes between sites consistent withB. calamita habitat requirements, and therefore withlikely migrations of the toads. In area A, for exam-ple, the distance between the northern and southern-most breeding centres was computed along the dunecoast (essentially two sides of a triangle) and notas the direct connecting line, because this specieswill not usually migrate through habitats of thetype found further inland (which are mostly inten-sive agricultural areas). Where estuaries intervened,distances were estimated to include the two nearestpotential crossing points and, particularly in areaB, to take account of mean low tide contours. Itwas assumed that successful crossing of sand flatsat low tide was much more probable than directtraversal of deepwater zones. In area C, as a furtherexample, the total distance computed between thesite west of Haverigg and that north of Askamincluded the terrestrial habitat across to the eastHaverigg site and the estuary distance immediatelyeast of that to the north Askam coast. The distanceto the mountain site in area C was calculated toinclude the length of a connecting stream valleyto the Duddon estuary, based on the known pre-disposition of amphibians to use such corridorsrather than the less suitable surrounding terrain.All the probable routes for toad movements weredecided prior to analysis and not subsequentlychanged.

Modified geographical distances were calculated byvariable subtractions or additions to take account ofputative barriers. These compensations were appliedto every intersite pairwise distance in an area thatwould be affected by the potential barrier.

Microsatellite characterisation

Concordance with Hardy-Weinberg equilibrium wastested for each locus in each subpopulation usingx2 analysis with BIOSYS-1 (Swofford and Selander1981) or an exact test within populations acrossloci (Raymond and Rousset 1995). Differences inlevels of genetic variation across sampling siteswithin each area were assessed by one-way analysisof variance (ANOVA) of arcsine-transformedexpected heterozygosities (He) for all polymorphicloci.

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Genetic distance calculations and statisticalanalysis

All genetic distance estimates were based on pooledmeans across the eight microsatellite loci. FST (Wright1965, Weir and Cockerham 1984) was calculated usingGENEPOP version 2.1 (Raymond and Rousset 1995).Unbiased probability estimates of FST-based differenti-ation were calculated using a Markov chain methodwith a dememorisation number of 1000, 50 batches and1000 iterations per batch. FST assumes an infinite allelemutation model and is a widely used estimator ofgenetic differentiation. Unbiased RST values and theirprobabilities of differing from 0 (based on 1000 permu-tations) and the distance dm2 (Goldstein et al. 1995)were calculated using the program RST CALC version2.2 (Goodman 1997, modified for Nm). RST is theequivalent of FST for loci undergoing stepwise muta-tion, and may therefore be more appropriate for mi-crosatellite data although this expectation has notalways been realised in empirical studies. Nei’s standardgenetic distances Ds (Nei 1987) and Cavalli-Sforza corddistances Dc (Cavalli-Sforza and Edwards 1967) werecalculated using BIOSYS 1 (Swofford and Selander1981). Ds was derived using the infinite alleles model ofmutation, whereas Dc assumes that mutation is in-significant relative to drift and also allows for variationin effective population size. dm2 is one of several relateddistance measures based on stepwise mutation thathave been derived specifically for microsatellites. Thedistance measures dm2, Ds and Dc were computed for allpossible intra-area pairwise subpopulation compari-sons, while the differentiation measures FST and RST

were calculated both as averages across subpopulationswithin each area and as all possible pairwise compari-sons within each area. Nm values as indicators of geneflow were derived from FST and RST using standardformulae (Slatkin and Barton 1989, Slatkin 1995).

Data were tested for normality and transformed(usually as log10 or square root) where necessary beforeuse in correlation or regression analysis using theMINITAB computer package. Mantel tests of correla-tion significance (exact probabilities) were performedwith 1000 permutations using GENEPOP.

Results

Characterisation of microsatellite loci

The eight microsatellite loci used in this study exhibiteda total of 21 alleles in the three study areas, and meanexpected heterozygosities ranged between 0.242 and0.376 (Rowe et al. 1998). Before attempting to analysemetapopulation structures by genetic methods it is im-portant to determine whether the marker loci fulfil thenecessary criteria. We have already established that the

eight B. calamita microsatellites are in linkage equi-librium (Rowe et al. 1999). Out of the 124 possible testsfor Hardy-Weinberg equilibrium across the eight loci inthe 24 subpopulations (excluding 68 instances whereloci were monomorphic), 13 deviated from expectationsat the 5% level. However, these non-equilibrium caseswere distributed evenly among the eight loci (rangingbetween 9 and 13% of cases where the loci were poly-morphic) and no population was significantly out ofequilibrium at the 5% level when tested across thecombined eight loci. Virtually all discrepancies fromHardy-Weinberg equilibrium were caused by het-erozygote excesses. We therefore concluded that null-al-leles, an occasional problem with microsatellite studies(Pemberton et al. 1995), were unlikely to be significantin our analysis.

A possible problem with genetic distance measuresusing microsatellites is that many are subject to bias ifpopulations vary substantially in overall genetic diver-sity. Specifically, a tendency has been found for dis-tance measures to correlate inversely with the meanexpected heterozygosity (He) of population pairs(Paetkau et al. 1997). We therefore compared unbiasedHe estimates across loci at all subpopulations withineach area of study using one-way ANOVAs of arcsine-transformed data. In no cases were significant differ-ences detectable within study areas. Thus for area A,F=0.57, df=4, P=0.687; for area B, F=0.92, df=6,P=0.498; and for area C, F=0.06, df=11, P=1. Ittherefore seems unlikely that genetic distance measure-ments between sites within the study areas were con-founded by differences in genetic diversity.

Metapopulations in the study areas

Subpopulations in the three study areas might be partsof single panmictic populations, sets of completely iso-lated demes, or components of partially interconnectedmetapopulations. Each of these situations has differentgenetic expectations. In the first, data from the pooledsubpopulations should be in Hardy-Weinberg (HW)equilibrium with no evidence of significant differentia-tion. In the second, each subpopulation will be in HWequilibrium, but the pooled data will not, and differen-tiation between subpopulations will be high. Metapop-ulations will lie inbetween these extremes. Tests forconcordance with HW equilibria were carried outacross loci for the pooled subpopulations of areas A, Band C. Only area A was not significantly out of equi-librium (x2=15.8, df=10, P=0.1053). For area B,x2=108.4, df=12, PB0.0001; and for area C, x2=�,df=16, PB0.00001. Summary statistics of populationdifferentiation within all three study areas are sum-marised in Table 1. FST and RST values were strikinglysimilar and significantly different from 0 in all cases,indicating that subpopulations in each area were, tovarying degrees, genetically distinct from one another.

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Table 1. Population differentiation within the study areas. Study areas and sampling sites (subpopulations) were as shown inFig. 1. Adult population sizes were estimates from surveys over 10–20 years (Banks et al. 1994). Mean distances and ranges arebetween adjacent sites. FST and RST (unbiased) were pooled across all loci and all sampling sites. (P), Probability of not beingsignificantly different from 0.

Study area No. sampling FST (P)Mean distance in km betweenApproximate RST (P)sites (Range)population sizesites

A 5 2500–3000 2.8 (2.0–3.6) 0.060 (B0.0001) 0.057 (B0.0001)B 7 3000–3500 0.241 (B0.0001)7.1 (2.0–16.0) 0.224 (B0.0001)C 12 3500–4000 3.3 (0.5–9.0) 0.111 (B0.0001) 0.108 (B0.0001)

These observations collectively indicated that metapop-ulations probably existed in all three study areas, butthat structuring was highest in area B and lowest inarea A.

If genetic differentiation between subpopulations isor recently has been influenced by occasional intersitemigration (as expected in metapopulation models), theextent of differentiation is expected to correlate withgeographical distance between subpopulations and iso-lation-by-distance should be apparent (Slatkin 1993).Preliminary correlations of five genetic distance mea-sures with putative ‘‘migration route’’ geographical dis-tances (unmodified for potential barriers) betweensubpopulations, within each of the three study areas,are shown in Table 2. In all cases the correlations werein the expected direction and isolation-by-distance wasindeed suggested. Because intersite distances are notindependent, these correlations were only indicative oftrends and probabilities of significance (which requireMantel tests) were not determined at this stage. Pooledacross all three areas, Dc produced the highest averagecorrelation. The derivation of this genetic distance mea-sure, unlike the others, assumes that drift rather thanmutation is the dominant factor in genetic differentia-tion between demes. FST- and RST-based correlationswere broadly similar, but lower than those based on theclassical genetic distance measures. The same order ofcorrelation strengths was also seen if direct geographi-cal distances, making no assumptions about toad move-ment routes, were employed though all correlationswere weaker than those for the unmodified ‘‘migrationroute’’ distances (data not shown). As expected, geneticdistances between sampling sites were generally small.Ds, for example, ranged between 0.016 and 0.070 inarea A where average FST and RST were lowest andbetween 0.021 and 0.166 in area B where average FST

and RST were highest.

Area A as a simple caseBecause area A was relatively simple geographicallyand had only five putative subpopulations, we used it asa model to investigate the effects of potential barrierson genetic and geographical distance correlations, andthus on metapopulation structure. In the first instancewe hypothesised that ponds between subpopulationsshould act as negative barriers, promoting gene flow by

acting as intermediate breeding sites and thus reducinggenetic differentiation. In Fig. 2 we show the effect onthe five distance correlations of subtracting 0.25–1 kmper intervening B. calamita breeding site from truegeographic distances. Correlations improved up toabout 0.5–0.75 km/pond for all measures.

We then investigated potential positive barrier ef-fects, as well as random controls, on the correlationbetween geographical and Cavalli-Sforza cord (Dc) dis-tances. Dc was selected on account of its strong perfor-mance in comparison with the other genetic distancemeasures tested during the preliminary analysis (Table2). Fig. 3 shows the effects of extending the truegeographical distances by increasing amounts to takeaccount of the urban development at Ainsdale and ofthe river Alt near the south end of the area. In bothcases the correlations improved, plateauing when 1.5–2km extra distances were added to compensate for bar-rier effects. As controls, we added extra geographicdistances at two other intersite localities, arbitrarilychosen, where no barriers were actually apparent. Asalso shown in Fig. 3, inserting extra distances in eitherof these places reduced the strengths of the correlationssubstantially.

Fig. 4 shows the effects of taking account of negativeand positive barriers on distance correlations in a se-quential, hierarchical order depending upon their ap-parent strengths. Thus intersite breeding ponds(negative barriers) had the greatest effect on r, andurban development had more impact than the river Altwhen superimposed on the pond effects. In the final

Table 2. Initial correlations of genetic and geographical dis-tances within study areas. Figures in parentheses are thenumbers of intersite comparisons at each study area. Rankorder is the overall correlation of each genetic distance mea-sure averaged across the three study areas, with 1=highestand 5= lowest.

Distance Correlations at study areas Rank ordermeasure

A (10) B (21) C (66)

FST 0.508 0.561 0.290 540.2480.7260.503RST

Ds 0.458 0.739 0.313 32dm2 0.3190.7990.52710.4180.6620.629Dc

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Fig. 2. Pond compensation effects on distance correlations inarea A. , FST ; , RST ; , Ds ; �, dm2; �, Dc.

Fig. 4. Cumulative compensation effects on distance correla-tions in area A. , Pond effects alone; �, 0.5 km/pond withadded river Alt effects; , 0.5 km/pond with added Ainsdaledevelopment effects; , 0.5 km/pond, 2 km/Ainsdale develop-ment with added river Alt effects. Genetic distances are Dc inall cases.

order, ponds were therefore compensated first, thenurban development and finally the river. Optimisedconditions were the subtraction of 0.5 km/interveningpond, addition of 2 km to take account of urbandevelopment and 1.5 km for the river. Application ofthe final modified geographical distances to correlationswith all five genetic distance measures are shown inTable 3. All correlations were stronger after barriercompensation than before it, with Dc still producing thestrongest (P [Mantel]B0.001).

Area B and C relationshipsIt was important to determine whether conclusionsdrawn from the geographically simple Area A aboutbarrier effects on metapopulation structures were alsovalid in more complex situations. In Area B the mostobvious features to test for effects on metapopulationstructure were once again ponds, as negative barriers,but also the effects of intervening estuary as positive

ones. There were no substantive urban developmentsbetween the sampling sites in this area. As shown inFig. 5, on the basis of Dc ponds again acted as strongnegative barriers (with an optimal correlation improve-ment on subtraction of 0.75–1 km per pond) butestuary was an equally strong positive barrier, requiringaddition of \2 km/km real sea distance to attainmaximal compensation. Inserting a hypothetical posi-tive barrier as a control at a site where no barrier wasrecognised again led to a decrease (this time slight) in r.A second set of tests with dm2, which in area B showeda high initial correlation with geographical distance, isalso shown in Fig. 5. It responded in a similar way toDc. Cumulative effects on r for both genetic distancemeasures, compensating first for estuary and then for

Fig. 3. Barrier compensation effects on distancecorrelations in area A. Genetic distances were allCavalli-Sforza Chord (Dc). , Ainsdale development;, River Alt; , Control 1; �, Control 2.

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Table 3. Adjusted correlations of genetic and geographical distances at study area A. Final correlations were made afteralterations to geographical distances (GD): −0.5 km/intervening pond, +2 km/Ainsdale, +1.5 km/river Alt.

Distance measure Initial correlation with GD Final correlation with GD % change (final relative to initial)

FST 0.508 0.690 +35.8RST 0.503 +32.80.668

+45.2Ds 0.458 0.665dm2 0.527 +15.60.609Dc 0.629 0.876 +39.3

ponds, improved overall correlations for both distancemeasures but only very slightly above the optimum foreach positive or negative barrier alone (data notshown).

The effects of a similar exercise using data from areaC, by far the most geographically complex of the study,are summarised in Table 4. Correlations in this case,while always in the expected direction, were lowerthroughout than in the other two sites. There were fewor no breeding ponds between the various samplingsites in this area, and negative barrier effects weretherefore not testable here. The two small towns had nodetectable barrier effects, while both sea and a railwayline near the north end of the area were consistent withweak barriers. Dc again performed better than the othermeasures in terms of yielding the strongest correlationwith modified or unmodified geographical distances.

A summary of optimised regressions between Dc andmodified geographical distances for all three sites isgiven in Table 5. All correlations were highly significantafter Mantel permutation tests to compensate for non-independence of intersite pairwise comparisons.

Discussion

Occasional migration between subpopulations is a re-quired characteristic of metapopulations, but can bedifficult to demonstrate when the frequency of move-ment is low, or migrations are highly episodic. Geneticanalyses which allow migration rates to be inferredrather than directly observed offer a potentially valu-able alternative approach in these situations. Natterjacktoads exhibited low but significant levels of geneticdifferentiation, as well as interdemic isolation-by-dis-tance, within all three study areas. These results indi-cate the existence of metapopulation structures, andconvergence of FST and RST estimates together with therelatively high performance of Dc as a geographicaldistance correlate suggest that the genetic differentia-tion is dominated by drift with relatively high levels ofgene flow (Slatkin 1995, Hitchings and Beebee 1997).However, despite theoretical expectations to the con-trary (Slatkin 1993), direct migration rate estimatorsbased on FST and RST correlated less well with geo-graphic distances than did classical genetic distancemeasures (especially Dc). This was equally true if, for

example, a stepping stone model was applied in area Awhere an essentially one-dimensional distribution ofsubpopulations occurred. The Cavalli-Sforza chord dis-tance consistently outperformed the others we tested, asjudged by both initial correlations with geographicaldistance and by response to compensation for negativeand positive barriers to gene flow. It was also notable,however, that the relative performance of dm2 was bestin area B where gene flow was lowest among the threeregions and where mutation effects might therefore beof greater significance than in area A or C.

What, then, do the genetic data infer about metapop-ulation structures in B. calamita? All three study areasexhibited significant differentiation, the extent of whichwas related to mean distances between subpopulations.Virtually all the adjacent subpopulations in areas A andC were separated by distances that individual toads caneasily traverse within a year providing intervening habi-tat is suitable (Denton and Beebee 1994), whereas bothgeographical distances and levels of genetic differentia-tion in area B were rather larger. The generally lowlevels of differentiation observed do not support aclassical metapopulation model with high rates ofturnover and recolonisation by small numbers of toadsfrom a small subset of surviving subpopulations (Levins1970). Nor are they readily compatible with a single‘‘patchy’’ but panmictic population structure. More

Fig. 5. Pond and barrier effects on distance correlations inarea B. , Dc and pond effects; �, Dc and estuary effects (kmadded per km estuary); �, Dc control; , dm2 and pondeffects; , dm2 and estuary effects; �, dm2 control.

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Table 4. Adjusted correlations of genetic and geographical distances at study area C.

Distance Correlations after adjustments to geographical distancemeasure

+2 km/km sea andNo adjustment +2 km/km sea +1 km/urban area +0.5 km/railway line0.5 km/railway

0.306FST 0.290 0.306 0.278 0.293RST 0.248 0.225 0.2300.169 0.179Ds 0.313 0.3280.329 0.302 0.316dm2 0.319 0.253 0.2510.236 0.227

0.438Dc 0.418 0.435 0.396 0.423

probable is either a classical model with high levels ofgene flow between multiple subpopulations, or a mainland-island model in which at least one subpopulationis more stable over time than the rest. In the formercase, a simple linear negative correlation between themean Nm for each subpopulation and the mean dis-tance between the subpopulation and all others is ex-pected. With a mainland-island model, however,asymmetric patterns of gene flow with ‘‘mainland’’ sitesexhibiting unusually high mean Nm and genetic diver-sity are predicted to occur. As shown in Fig. 6, thesimple relationship was realised most clearly in area B(with r= −0.924, df=5, PB0.01). In area A thenegative relationship was also evident, though withonly five subpopulations the pattern was less convinc-ing. One subpopulation in area A apparently had anunexpectedly high mean Nm. This location (Ainsdale)supports by far the largest density of breeding ponds inarea A (Smith and Payne 1980) and had the secondhighest level of genetic diversity, as measured by mean(unbiased) expected heterozygosity. In the case of areaC, the negative relationship was weak (r= −0.378)and distorted by three sites with unusually large meanNm values. These included two with the largest num-bers of breeding ponds and the highest levels of geneticdiversity in the area. It may well be, therefore, that amainland-island situation has existed at least in areas Aand C in the recent past. The occurrence of asymmetri-cal patterns of gene flow in these cases may also explainwhy using FST and RST to measure isolation-by-dis-tance gave less clear results than standard genetic dis-tance measures.

There are of course important caveats in the interpre-tation of these data in the context of metapopulationstructures and dynamics. We have no way of knowing,particularly in areas B and C with their complex two-dimensional sample site relations, how accurate our

speculations were concerning the most probable toadmovement routes. It is quite probably for this reasonthat correlations were generally low in area C where thepossible permutations for migration routes were partic-ularly high. Secondly, we cannot be sure that ourhypotheses about what constitutes barriers to thesetoads are true. However, the autecology of this specieshas been very extensively investigated (e.g. Banks andBeebee 1988, Denton and Beebee 1994) and it would besurprising if these hypotheses were markedly at fault.Perhaps most importantly, the question arises as towhether correlations between genetic and geographicaldistances are in principle a useful way of assessingmetapopulation structures. We believe our results sug-gest they are, for three reasons. Firstly, there werecorrelations in the expected directions between the dis-tance measures even at the outset where geographicaldistances were uncorrected for potential barriers. Sec-ondly, the genetic distance measures responded simi-larly, and in the expected ways, to compensations forprospective negative or positive barrier effects. More-over, controls in which hypothetical non-existent barri-ers were simulated arbitrarily between sampling sitesalways yielded a reduction in correlation strength. Fi-nally, there was broad conformance between the threestudy areas with respect to correlation effects and dis-tance measure performances.

Our data have implications for the understanding ofB. calamita population dynamics and for those of othertaxa with similar life histories. Although bufonids oftenexhibit a high degree of philopatry (Reading et al.1991), B. calamita is well known as a pioneering specieswhich rapidly colonises new, early-successional habitats(Boomsma and Arntzen 1985). As expected, therefore,individual B. calamita seem able to move freely insuitable habitats (dunes or saltmarshes) where theseextend uninterrupted along coastlines. Although genetic

Table 5. Summary statistics of Cavalli-Sforza chord and modified geographic distance correlations.

Study area A Study area B Study area C

0.202+0.0064 Dg0.128+0.0062 Dg0.136+0.0063 DgRegression: Dc=91.2%r2 (adjusted)= 17.9%73.4%

P (Mantel)= 0.01300.0081

Dg : Modified geographic distance.

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Fig. 6. Gene flow and inter-site distances. Mean FST-based Nm values and geographic distances modified to optimisecorrelations with Dc (see text) were computed between each subpopulation and all others within each area. A: Area A; B: AreaB; C: Area C. , Outlier with unexpectedly high Nm ; — , regression including all subpopulations; - - -, regression excludinghigh-Nm subpopulations.

differentiation was detectable in such circumstances, ithas evidently been too weak to permit substantive drifteffects to operate. The existence of breeding pondsintermittently along coastal habitat patches substan-tially reduced genetic differentiation and any conse-quent risk of isolation and inbreeding depression.Urban areas, however, probably act as substantive bar-riers to natterjack toads where they impact heavily onhabitat continuity as was the case at Ainsdale in areaA. B. calamita travels extensively along beaches andcan probably circumvent small coastal developments,such as Haverigg and Askam in area C, that use beachareas only with low intensity. Sea water evidently con-stitutes an impediment to movement between sites, butin zones with large intertidal mud or sand flats (such asarea C and some parts of area B) was by no means acomplete barrier. Gene flow in all three areas was highwith most Nm estimates substantially greater than 1

(Table 6), the minimum level required to prevent sub-stantive differentiation. Thus in area C, only 5 out of 66pairwise Nm values derived from unbiased FSTs weresignificantly B1 and 3 of these involved North Wal-ney, the only completely isolated island population. Inarea B as many as 12 out of 21 FST-based Nm valueswere B1, but even here there were values \1 betweenthe nearest sites north and south of the Solway estuary.It seems quite likely that individual toads occasionally

Table 6. Mean and median intrasite pairwise Nm estimates.

Pairwise Nm estimates: mean (median)Study areaRST-derivedFST-derived

13.470 (4.740)A 5.096 (4.330)1.915 (0.966)1.547 (0.726)B

C 3.601 (2.515) 3.927 (2.481)

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cross these areas at low tide when extensive sand flatsare exposed.

The increasing popularity of microsatellite markers inmolecular ecology and evolution highlights a generalneed to determine the most appropriate genetic distancemeasures for use with these loci. A recent simulationstudy with microsatellite data indicated that althoughall such measures were sensitive to both loci and samplenumbers, overall Ds and dm2 performed best as time-re-lated estimators whereas Dc generated the most accu-rate tree branching topographies (Takezaki and Nei1996). An alternative to simulation is to determine howdistance measures perform in the context of real popu-lations, and several studies have already reported suchcomparisons. It is becoming increasingly clear that allmicrosatellite-based genetic distances only show linearrelationships with time for rather short periods, essen-tially because there is usually an upper reflectingboundary on allele size and the high mutation rates atthese loci result in relatively rapid saturation with allpossible alleles (e.g. Paetkau et al. 1997). However,while this limits the usefulness of microsatellites forlong-term evolutionary studies it makes them ideal andsensitive markers for the levels of differentiation to beexpected in metapopulations over much shorter peri-ods. Microsatellites clearly offer the prospect of quan-tifying genetic differentiation at the level of scaleappropriate to metapopulation analysis, and the possi-bility to test hypotheses about barriers to individualmovement. These properties may prove applicable tometapopulation studies in a broad range of organisms.

Acknowledgements – We thank the Biotechnology and Biolog-ical Sciences Research Council for financial support.

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