Genetics, Vol. 153, 351-360, September 1999, Copyright © 1999

Bioinvasions of the Medfly Ceratitis capitata: Source Estimation Using DNA Sequences at Multiple Intron Loci

Neil Daviesa,b, Francis X. Villablancaa,c, and George K. Rodericka,b
a Center for Conservation Research and Training, University of Hawaii, Honolulu, Hawaii 96822,
b Department of Environmental Science, Policy and Management, University of California, Berkeley, California 94720-3112
c Department of Biological Science, California Polytechnic State University, San Luis Obispo, California 93407

Corresponding author: Neil Davies, Division of Insect Biology, Department of Environmental Sciences, Policy and Management, 201 Wellman Hall, University of California, Berkeley, CA 94720-3112., ndavies{at}nature.berkeley.edu (E-mail)

Communicating editor: M. SLATKIN


*  ABSTRACT
*TOP
*ABSTRACT
*MATERIALS AND METHODS
*RESULTS
*DISCUSSION
*LITERATURE CITED

The Mediterranean fruit fly, Ceratitis capitata, is a devastating agricultural pest that threatens to become established in vulnerable areas such as California and Florida. Considerable controversy surrounds the status of Californian medfly infestations: Do they represent repeated introductions or the persistence of a resident population? Attempts to resolve this question using traditional population genetic markers and statistical methods are problematic because the most likely source populations in Latin America were themselves only recently colonized and are genetically very similar. Here, significant population structure among several New World medfly populations is demonstrated through the analysis of DNA sequence variation at four intron loci. Surprisingly, in these newly founded populations, estimates of population structure increase when measures of subdivision take into account the relatedness of alleles as well as their frequency. A nonequilibrium, likelihood-based statistical test that utilizes multilocus genotypes suggests that the sole medfly captured in California during 1996 was introduced from Latin America and was less likely to be a remnant of an ancestral Californian population. Many bioinvasions are hierarchical in nature, consisting of several sequential or overlapping invasion events, the totality of which can be termed a metainvasion. Phylogenetic data from multilocus DNA sequences will be vital to understanding the evolutionary and ecological processes that underlie metainvasions and to resolving their constituent levels.


BIOINVASIONS, especially those involving exotic insect pests, have been an undesirable element of U.S. and world agriculture for more than a century (ELTON 1958 Down; FRANK and MCCOY 1991 Down; METCALF 1995B Down). Despite the substantial economic impact of bioinvasions (U.S. CONGRESS OTA 1993), management policy remains largely reactive because it is extremely difficult to predict which species will invade, when they will do so, and what the ecological consequences will be (LAWTON and BROWN 1986 Down; DAEHLER and STRONG 1993 Down; LODGE 1993 Down; NEW 1995 Down; KAREIVA 1996 Down; DAVIES and RODERICK 1999 Down). Nearly all attempts at eradication or control (sterile insect release, biological control, the use of transgenic plants) require the determination of invasion sources and demographic parameters, such as population size, structure, and rate of growth. For example, very different management measures are required when invasions involve a pest species with panmictic population structure compared to those that involve a species with sharply defined and genetically isolated populations (CAREY 1991 Down). Here we address the fundamental issue of how to determine the source of a bioinvasion. Answering this apparently straightforward question has required the development of novel genetic markers and statistical analyses (reviewed by DAVIES et al. 1999 Down). We apply these new techniques to the medfly Ceratitis capitata; in particular, can these approaches distinguish alternative hypotheses concerning the source of medfly infestations in California?

The medfly is arguably the world's most threatening agricultural pest, attacking over 200 different fruits, vegetables, and nuts (CAREY 1991 Down; SHEPPARD et al. 1992 Down; MCPHERON et al. 1994 Down). From their likely origin in Africa, medflies spread rapidly around the world (Figure 1). They were first reported from California in 1975 (for a review see METCALF 1995A Down) and sporadic outbreaks have occurred ever since (Figure 2); it has been suggested that California may have had a resident population for up to 50 yr at a size below the level of detection (CAREY 1991 Down). The economic risk associated with the medfly is considerable: an endemic infestation in California might cost in excess of $1 billion per annum (SIEBERT 1994 Down) and over $350 million has already been spent trying to prevent such an outcome (METCALF 1995B Down).



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Figure 1. The biogeography of the medfly invasion.



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Figure 2. Chronology of recent medfly infestations in California. Shaded regions are counties in which medflies have been trapped, prompting control programs. For more information see http://www.cdfa.ca.gov/pests/medfly/mediterranean_fly.html.

The history of medfly invasions in California has been reviewed recently by several authors (MCPHERON et al. 1995 Down; RODERICK 1996B Down; GASPARICH et al. 1997 Down; HAYMER et al. 1997 Down) but the origin(s) of these infestations remain uncertain and controversial. Early work focused on mtDNA, but the many advantages of mtDNA over nuclear loci (MOORE 1995 Down; RODERICK 1996A Down) may not apply to medfly invasions (RODERICK et al. 1998 Down). First, medflies exhibit lekking behavior, an extreme form of mating bias that reduces the effective population size of nuclear loci to levels similar to those of mtDNA (HOELZER 1997 Down). Second, restriction fragment length polymorphism (RFLP) analysis of mtDNA reveals low levels of genetic variation in New World populations, and medflies caught in Southern California prior to 1992 share a single mtDNA haplotype that is common throughout Central America and some other parts of the world (SHEPPARD et al. 1992 Down; MCPHERON et al. 1994 Down, MCPHERON et al. 1995 Down; STECK et al. 1996 Down; GASPARICH et al. 1997 Down). Clearly, the precise identification of origins is impossible on the basis of these mtDNA data alone, and the single vs. multiple origin of Californian medflies remains unresolved (GASPARICH et al. 1997 Down).

Allozymes are variable in Mediterranean and African populations, but less so in populations outside of Africa (GASPERI et al. 1991 Down; MALACRIDA et al. 1992 Down; BARUFFI et al. 1995 Down). Fortunately, it is now possible to assess DNA variation directly, and high levels of diversity have been documented among invading medfly populations using several DNA-based nuclear markers, including microsatellites (B. MCPHERON and D. PRASHER, personal communication), randomly amplified polymorphic DNA (RAPDs; HAYMER et al. 1997 Down), and introns (GOMULSKI et al. 1998 Down; VILLABLANCA et al. 1998 Down). VILLABLANCA et al. 1998 Down demonstrated that significantly more diversity is retained in invading populations at intron loci than is retained at mitochondrial or allozyme loci. The substantial amount of variation found with nuclear DNA markers offers the possibility that potential sources might be resolved on a relatively fine geographic scale. Here we examine variation at the four intron loci sequenced by VILLABLANCA et al. 1998 Down to investigate whether these markers can distinguish invading medfly populations in Central America and eastern South America, i.e., those that mtDNA studies indicate as the most likely source of Californian medflies (GASPARICH et al. 1997 Down).

Three important questions are addressed by the current study: First, does sequence variation at intron loci reveal significant population structure? Second, how do frequency and distance-based approaches compare in their estimation of population subdivision? Finally, can we utilize genetic population structure to determine the origin of a recent medfly outbreak in California? For the last question, we focus on the single fly (B-96) captured at Burbank, California, in 1996. Using a statistical test that assigns multilocus genotypes to potential source populations based on the frequency of alleles in those sources (RANNALA and MOUNTAIN 1997 Down), we ask whether this fly was recently introduced to California, or whether it represents a remnant of a previous infestation. The results have direct consequences for medfly management strategy: either more effort must be directed toward eradication or interception policies should be focused on the most common source, if it can be identified.


*  MATERIALS AND METHODS
*TOP
*ABSTRACT
*MATERIALS AND METHODS
*RESULTS
*DISCUSSION
*LITERATURE CITED

Collections and laboratory analysis:
Figure 1 details the recent spread of medflies from their ancestral range in sub-Saharan Africa (HARDY and DELFINADO 1980 Down; WHITE and ELSON-HARRIS 1992 Down; MCPHERON et al. 1995 Down; METCALF 1995A Down; FOOTE 1997 Down). Medfly samples were obtained from populations in California, Costa Rica, Guatemala, Mexico, Brazil, Peru, Greece, Hawaii, and Africa (Table 1). The Californian medflies were all collected in southern California between 1992 and 1996 (Figure 2). No medflies were found in 1995 and only a single individual (B-96) was captured during 1996. We treat all these Californian medflies as a single population for the purposes of statistical analysis. Under the null hypothesis that there is a resident medfly population in California, we assume that these flies, captured in the same geographic area, represent a single biological population (CAREY 1991 Down).


 
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Table 1. Collection data

Extraction protocols and some of the sequences (from California, Brazil, Malawi, Hawaii, and Greece) have been published previously (see VILLABLANCA et al. 1998 Down). Briefly, DNA was isolated using a phenol-chloroform extraction and ethanol precipitation. Exon-primed, intron crossing (EPIC) primers (PALUMBI and BAKER 1994 Down; PALUMBI 1996 Down) were constructed to amplify introns from within four different nuclear loci: the first intron of Cu/Zn superoxide dismutase (Sod), muscle-specific actin (Actin), vitellogenin 1 gamma intron 2 (Vg), and first chorion intron s36 (Chorion). Amplification and cloning were as in VILLABLANCA et al. 1998 Down with the following modifications. PCR conditions, previously optimized for the Perkin-Elmer (PE; Norwalk, CT) 480 thermocycler, were adjusted for use with the PE 9600 and 9700. Reactions consisted of 30 µl water, 5 µl 8 mM dNTPs, 5 µl 10x PE Taq buffer, 2.4 µl of each 10 mM primer, 5 µl MgCl2 (25 mM), 0.12 µl PE Taq polymerase (5 units/µl), and 1 µl of genomic DNA. Thermocycler parameters were 94° for 30 sec, 55° for 30 sec, and 72° for 40 sec, repeated for 40 cycles. Parameters for Sod were 94° for 30 sec, 60° for 1 min, and 72° for 1.5 min for 40 cycles.

As the PCR products in any reaction may include two alleles from the target locus, amplifications were cloned to isolate a single allele for sequencing. Following cloning, double-stranded DNA was prepared for sequencing using QuantumPrep (Bio-Rad Laboratories, Hercules, CA) following the manufacturer's guidelines. Sequences were obtained using an ABI 377 automated sequencer. Because of the large amount of sequencing that must be performed to recognize heterozygotes, we sequenced a single allele for most individuals. Cloned PCR products may contain errors incorporated by DNA polymerases (PAABO and WILSON 1988 Down), and thus different clones from the same PCR reaction are expected to differ by ~1:1000 bases due to Taq polymerase copying errors. We sought to reduce this source of error by ignoring singletons, which are nucleotide positions that vary in only one allele (sensu VILLABLANCA et al. 1998 Down).

Phylogenetic analysis:
Phylogeography is a powerful means of investigating the history of populations (AVISE 1994 Down); however, phylogenetic analysis can be complicated for nuclear genes because of recombination (BEGUN and AQUADRO 1992 Down; HUDSON 1994 Down) and the persistence of ancestral alleles (TEMPLETON and SING 1993 Down). TEMPLETON et al. 1992 Down and TEMPLETON and SING 1993 Down provided a means for recognizing and dealing with alleles produced by recombination. In effect, the method focuses phylogenetic reconstruction on the most recent substitutions before attempting to resolve deeper nodes. Assuming that more recent changes are less likely to include homoplasy or recombination, sequences separated by one substitution are linked first, and the resulting groups form the basis for linking alleles separated by two substitutions, and so on. Details of the method are provided by TEMPLETON 1998 Down and TEMPLETON and SING 1993 Down.

Population analysis and assignment test:
Population structure was determined through the analysis of molecular variation (AMOVA) method of EXCOFFIER et al. 1992 Down as implemented by the computer program ARLEQUIN 1.1 (SCHNEIDER et al. 1997 Down). Indices of population subdivision analogous to standard F-statistics (WRIGHT 1951 Down, WRIGHT 1965 Down) are calculated for each locus and their significance assessed using permutation procedures that randomly redistribute individuals among populations. For each locus, this general approach was used to estimate two F-statistics, {theta} and {Phi}. {theta} is based on allele frequencies only (WEIR and COCKERHAM 1984 Down; see EXCOFFIER et al. 1992 Down), while {Phi} also takes into account the genetic distance among alleles (EXCOFFIER et al. 1992 Down), in this case the average Kimura 2-parameter distance.

The computer program TFPGA (MILLER 1998 Down) was used to calculate the average {theta} (WEIR and COCKERHAM 1984 Down) across all loci on the basis of allele frequencies. The average {Phi} across loci, {Phi}AMOVA, was the mean of {Phi} values for the four loci, and its significance was assessed following Fisher's method for combining probabilities (see SOKAL and ROHLF 1995 Down). Population differentiation averaged over loci was also examined using the exact test (RAYMOND and ROUSSET 1995 Down) as implemented by TFPGA (MILLER 1998 Down). Again, both frequency and distance-based approaches were used. Exact tests were performed on the basis of the distribution among populations of (i) alleles and (ii) phylogenetically derived allele groups, or clades. In the latter case, alleles in the same one-step clade of the Templeton networks were pooled. TFPGA was then used to calculate the average {theta}Templeton (WEIR and COCKERHAM 1984 Down) across all loci on the basis of allele frequencies and then across all loci on the basis of the one-step allele groups. Significance of both statistics was assessed through 10,000 bootstrap replications over loci.

To determine the origin of the B-96 medfly, RANNALA and MOUNTAIN's (1997) assignment test (see DAVIES et al. 1999 Down) was employed using their computer program IMMANC (http://allele.bio.sunysb.edu/). Assuming that loci are independent, the likelihoods of drawing a test multilocus genotype from a resident and from a potential source population are calculated. The difference in likelihood (ln {lambda}) is negative if an individual is more likely to be an immigrant than a resident. Monte Carlo simulations are used to assess significance: random genotypes are generated from allele frequency distributions conditioned on the observed allele frequencies (the variance of these distributions decreases with increasing sample size). The probability of obtaining the observed value is assessed by comparison with the distribution of random values. Because the B-96 genotype was included in the Californian population (and not the potential source) when estimating the "resident" allele frequencies, the test is conservative with respect to the null hypothesis that B-96 is a resident—in this case of California (DAVIES et al. 1999 Down). Currently, IMMANC accepts only diploid data and we therefore analyzed our data (which included only one of the two alleles possessed by each individual at each locus) by assuming that all (unobserved) genotypes were homozygotes. Although this modification does not alter the observed allele frequencies, it does artificially reduce their variance in the simulation analysis, making the interpretation of significance values problematic. The genotype probabilities calculated in this way are only approximate and the approximation will work best when the frequencies of the sampled alleles are high (i.e., when genotypes have a high probability of being homozygous).


*  RESULTS
*TOP
*ABSTRACT
*MATERIALS AND METHODS
*RESULTS
*DISCUSSION
*LITERATURE CITED

A total of 237 medfly sequences were obtained for four intron loci in 74 individuals (Table 1). Not every individual was sequenced for all four loci due to problems in obtaining PCR amplification, which were probably due to the degraded state of some specimens. After a correction for errors associated with cloning PCR products (see above), 68 Actin sequences consisting of 293 bp revealed 19 alleles distinguished by 12 polymorphic sites. Fifty-seven Chorion sequences consisting of 394 bp revealed 18 alleles distinguished by 14 polymorphic sites. Sixty Sod sequences consisting of 812 bp revealed 38 alleles distinguished by 56 polymorphic sites including four insertion or deletion events. Fifty-two Vg sequences consisting of 409 bp revealed 32 alleles distinguished by 24 polymorphic sites including eight indels. The phylogenetic relationships of the alleles are shown for each locus in Figure 3. No striking phylogeographic structure was evident from visual inspection of the networks, suggesting that the distribution of alleles represents the random sorting of ancestral polymorphism.






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Figure 3. The haplotype networks as estimated by the method described by TEMPLETON 1998 Down for a, Sod; b, Actin; c, Vg; and d, Chorion. Haplotype identities are noted within circles by a number and distances between haplotypes are proportional to the number of base pair differences between haplotypes. The size of the circles is proportional to the number of individuals sharing a particular haplotype. Haplotypes marked Ox are inferred haplotypes identified by parsimony. Dashed lines denote single-step clades (see text).

Statistical analysis revealed substantial population structure among American medfly populations (Latin America plus California) and among Latin American populations only (excluding California). For American populations, both frequency and distance-based F-statistics were significant when combined across loci with the distance-based estimates, {Phi}AMOVA and {theta}Templeton, being considerably larger than the frequency-based {theta} (Table 2). For Latin American populations, only {theta} and {theta}Templeton were significant, and again {theta}Templeton was twice as large as {theta}. AMOVA utilizes the same statistical framework and we present a comparison of locus by locus {theta} and {Phi}AMOVA in Table 3. Consistent with the across-loci results, {Phi}AMOVA is always >{theta} for all American populations but there is no clear pattern when only Latin America populations are considered. In all cases, Chorion and Vg revealed the most population differentiation.


 
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Table 2. F-statistics and exact tests for population differentiation over all loci


 
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Table 3. Results of AMOVA for each locus

The likelihood of drawing the B-96 fly from the Californian population was compared to the likelihood that it was an immigrant from a Latin American population (Table 4). Whether B-96 was more like an immigrant than one would expect by chance was determined through comparison of the observed ln {lambda} values with the ln {lambda} of 1000 multilocus genotypes randomly generated from the Californian population. Despite the conservative nature of the test, Table 4 shows that the B-96 genotype was less likely ({alpha} < 0.05) to be a resident of California than an immigrant from no less than four potential sources: Costa Rica, Guatemala, Mexico, or Peru. While the need to assume homozygosity in our data makes the interpretation of significance values difficult, the ancestral Californian population we sampled seems unlikely, according to this analysis, to be the source of the B-96 medfly. To assess which of the four Latin American populations was the most likely source, we conducted further assignment tests. B-96 was assumed to be a resident of Costa Rica, Guatemala, Mexico, and Peru, in turn. This null hypothesis was only rejected in one case: B-96 is more likely to be an immigrant from Costa Rica than a resident of Peru ({alpha} < 0.05). However, a Bonferroni correction for multiple comparisons would render this result insignificant.


 
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Table 4. Results of assignment test (see text)


*  DISCUSSION
*TOP
*ABSTRACT
*MATERIALS AND METHODS
*RESULTS
*DISCUSSION
*LITERATURE CITED

Contemporary bioinvasions, such as those of the medfly, are often characterized by a unique set of demographic and genetic features that result from a small number of colonizing individuals and the rapid growth and spread of new populations. Together these characteristics make it difficult to determine the source of bioinvasions, partly because it is hard to distinguish potential source populations that were only recently established themselves (DAVIES et al. 1999 Down). In this study, we investigated sequence variation at nuclear intron loci to see if these markers could reveal population structure, and whether they could be used to determine the origin of a recent medfly "outbreak." Genetic analysis of DNA sequence variation at four intron loci, Sod, Vg, Actin, and Chorion, revealed significant population structure among previously indistinguishable medfly populations (GASPARICH et al. 1997 Down) in California, Central America, and eastern South America. Much of the subdivision was due to the Californian population, although significant structure was also evident when the Latin American populations were treated alone. The ability to discern differences among populations is a prerequisite for attempts to determine the origin of invading individuals, and intron variation appears to provide enough resolution to distinguish the source of very recent bioinvasions. An assignment test (RANNALA and MOUNTAIN 1997 Down) was applied to the single medfly captured in California (at Burbank, Los Angeles County) during 1996. The results suggested that the multilocus genotype of this fly was unlikely to have arisen from the ancestral Californian population that we sampled; it was more likely introduced from Latin America. There was less ability to distinguish sources within Latin America, although the demonstration of significant population structure indicates that additional intron data is likely to further reduce the number of potential sources.

Multilocus genotyping is clearly a powerful technique and many different markers, including allozymes, mtDNA, and microsatellites can be analyzed simultaneously following the statistical methodology outlined here (see also DAVIES et al. 1999 Down). In most cases additional markers will increase the power of these tests, although some markers may be incompatible with this approach. RAPDs, for example, are very useful in providing high levels of genetic variation and their main advantage over introns and microsatellites is that they can be applied with very little prior genetic knowledge of a species (WILLIAMS et al. 1990 Down). Unfortunately, it is not clear how RAPD data can be incorporated into the same statistical framework as introns and other markers, where genotypes can be identified at each locus.

RANNALA and MOUNTAIN's (1997) assignment test applies statistical rigor to source estimation; however, further modifications of the test are desirable. First, laboratory scoring mistakes must be taken into account. For example, scoring errors occur in allozyme studies with a frequency of ~1% (LATHROP et al. 1983 Down). One can attempt to correct for scoring mistakes prior to analysis, as we did for the errors associated with sequencing cloned PCR products. Alternatively, an error rate factor can be incorporated into the analysis; such an approach was used by MARSHALL et al. 1998 Down in their multilocus paternity test. A second source of error that should be considered is the implications of not sampling all the potential sources. Again with a focus on paternity testing, MARSHALL et al. 1998 Down presented a simulation method to assess the likelihood that a more probable source remains unsampled. Finally, assignment tests focus on the origin of single multilocus genotypes, and we have applied such a test to a bioinvasion consisting of a solitary fly. More often, however, bioinvasions consist of many invading individuals, each of which will have its own associated likelihood of being from one source or another. With such multiple assignments, one will be able to plot a distribution of likelihood statistics for the invading population as a whole, adding a new level of complexity to source estimation. For example, a bimodal distribution would imply that the invading population had two sources, but how can one assess the significance of such a conclusion, and how should one correct for multiple comparisons? We are currently examining these issues with respect to some large medfly infestations in California that have occurred since 1996.

The final purpose of our investigation was to explore and compare the utility of frequency- and distance-based approaches in the study of invading populations. Phylogeographic structure (AVISE 1989 Down, AVISE 1994 Down; RODERICK and VILLABLANCA 1996 Down; RODERICK and GILLESPIE 1998 Down) is not expected in recently founded populations because there has been little time for mutations to occur, and the relationships among alleles reflect evolutionary events in the ancestral range of the species rather than their history in newly occupied areas (VILLABLANCA et al. 1998 Down; DAVIES et al. 1999 Down). For example, MCGUIGAN et al. 1998 Down reported significant differences in haplotype frequencies over a fine geographic scale among Australian frog populations; however, a smaller and insignificant F-statistic was obtained when the genetic distance among alleles was considered as well. MCGUIGAN et al. 1998 Down suggested that the barriers to gene flow had not been in place long enough to influence the distribution of genetic relatedness. We expected American medflies to show a similar pattern; however, the opposite was true: distance estimates of population subdivision not only revealed significant population structure among American medflies, but yielded larger F-statistics than those based on allele frequencies alone. Various approaches have been developed that utilize phylogenetic data to detect gene flow (SLATKIN 1994 Down), yet current assignment tests do not consider the relatedness of alleles. Commonly used multilocus markers such as microsatellites (GLENN 1998 Down) do not easily permit the phylogenetic analysis of alleles; however, when sequence data are available, our data suggest that frequency-based assignment tests might waste useful information. A new test should assess the multilocus likelihood of sampling a given set of alleles from a potential source population on the basis of the distance between alleles as well as their frequencies.

Interestingly, the inclusion of California increased distance- as well as frequency-based estimates of population subdivision. Californian medfly populations are the most recent, so founder events have not eroded phylogeographic signal; indeed they appear to have enhanced it. The average relatedness of alleles in derived populations might be increased simply by sampling from a single, phylogenetically heterogeneous population. Diversity within a species could be converted into divergence between populations when the species becomes fragmented because founder events sample only a small proportion of the ancestral variation and tend to include alleles of the dominant phylogenetic clade. Phylogeographic structure among recently founded populations might also occur if each derived population was founded from a different population of a phylogeographically structured source.

More work is clearly needed to explore the phylogenetic consequences of invasions and a better understanding of invasion genetic patterns will provide a deeper insight into the ecological and evolutionary processes that underlie bioinvasions. It is important to consider in such studies that invasions often involve a hierarchy of events, the totality of which might be termed a metainvasion. The metainvasion begins with a primary invasion, when a species first colonizes a new area from its ancestral source. Subsequently, secondary and tertiary invasions arise as the newly established populations themselves seed new areas. The genetic changes that result from these events are complex and phylogenetic analyses may be informative at some levels but not others. A primary invasion of the medfly occurred from Africa to the Mediterranean. The invasion of Latin America may be another primary invasion, direct from Africa, or a secondary invasion from the Mediterranean. Californian medfly infestations thus represent secondary or tertiary events in the global medfly metainvasion. Indeed, California may be subject to repeat invasions that could superimpose one another. The phylogeny of alleles as well as their frequencies in each population may need to be considered to unravel the history of the global medfly metainvasion and to distinguish its component levels. Phylogeographic structure in newly colonized regions may arise as a second-hand signal resulting from multiple introductions or be due to the combination of different levels of the metainvasion—as here we combined California with Latin America. Whether the source (most probably in Africa) or sources of Latin American medflies is phylogeographically structured or simply a phylogenetically heterogeneous population will be determined by future genetic analysis of intron variation already under way in our laboratory. We are also addressing these questions theoretically through simulation studies.

To conclude, powerful genetic tools are now available to investigate the source of contemporary bioinvasions, and the statistical methods for analyzing these data continue to become more sophisticated. In the future, the challenge will be to integrate such genetic results with ecological and interception data to fully elucidate the process of range expansion exemplified by the medfly and other invasive species. It seems likely that the origin of Californian medfly infestations can be determined through the use of multilocus genotyping techniques such as microsatellites and methods such as single-strand conformation polymorphism (SSCP; LESSA 1992 Down; ORTI et al. 1997 Down) that rapidly screen intron variation. In the case of Californian medflies, source populations should be monitored for a suite of markers, and the database of allele frequencies regularly updated as these dynamic populations evolve due to drift or the introduction of new alleles through gene flow. (At the moment little is known about how the genetic make-up of source populations varies with time.) With adequate baseline data from source populations, new medfly infestations in California or elsewhere could be rapidly genotyped and assigned to a source. Due to its economic significance, the medfly infestation has become a model system for the study of contemporary bioinvasions and has several important lessons for other cases where limited funding is likely to restrict the amount of research effort. The genetic analysis of new bioinvasions should begin with mtDNA and allozymes; however, highly variable nuclear regions, such as introns, should also be considered. Multilocus genotyping provides a rapid method of determining the origin of invasions, whether using nonsequencing methods of screening intron variation and/or other types of markers. While the routine screening of new infestations may not require the expense of sequencing, for a full understanding of the bioinvasion process, DNA sequences at multiple loci are an invaluable and versatile tool.


*  ACKNOWLEDGMENTS

We thank M. Soedarjo and B. Thorsby for their invaluable work in the lab. We are grateful to B. Rannala and J. Mountain for providing us with their computer program and for discussing its application to our study. We also thank S. R. Palumbi and A. Bohonak for comments on previous drafts of this article. Financial support was provided by the California Deparartment of Food and Agriculture, U.S. Department of Agriculture National Research Initiative and Agricultural Research Service, and National Science Foundation awards 9510822 (to F.X.V.), 9726573, 9813418, and 9816384.

Manuscript received February 17, 1999; Accepted for publication June 1, 1999.


*  LITERATURE CITED
*TOP
*ABSTRACT
*MATERIALS AND METHODS
*RESULTS
*DISCUSSION
*LITERATURE CITED

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