Population Genetic Structure of Codling Moth (Lepidoptera: Tortricidae ...

MOLECULAR ENTOMOLOGY

Population Genetic Structure of Codling Moth (Lepidoptera: Tortricidae) from Apple Orchards in Central Chile EDUARDO FUENTES-CONTRERAS,1,2 JUAN L. ESPINOZA,1 BLAS LAVANDERO,1,3 AND CLAUDIO C. RAMI´REZ3

J. Econ. Entomol. 101(1): 190Ð198 (2008)

ABSTRACT Codling moth, Cydia pomonella (L.) (Lepidoptera: Tortricidae), is the main pest of pome fruits worldwide. Despite its economic importance, little is known about the genetic structure and patterns of dispersal at the local and regional scale, which are important aspects for establishing a control strategy for this pest. An analysis of genetic variability using microsatellites was performed for 11 codling moth populations in the two major apple (Malus domestica Borkh) cropping regions in central Chile. Despite the geographical distances between some populations (⬇185 km), there was low genetic differentiation among populations (FST ⫽ 0.002176), with only slight isolation by distance. Only ⬇0.2% of the genetic variability was found among the populations. Geographically structured genetic variation was independent of apple orchard management (production or abandoned). These results suggest a high genetic exchange of codling moth between orchards, possibly mediated by human activities related to fruit production. KEY WORDS Cydia pomonella, apple pests, microsatellites, insecticide resistance, gene ßow

Population genetic studies of agricultural pests have highlighted the importance of migration and gene ßow in sustainable pest management (Zhou et al. 2000; Han and Caprio 2004; Scott et al. 2005, 2006; Endersby et al. 2006, 2007; Leniaud et al. 2006). Because populations of such pest species are frequently subjected to the application of phytoprotection measures, such as insecticides and transgenic crops, the development of resistance is strongly inßuenced by genetic population structure (Caprio 1998, 2001; Carrie` re et al. 2004). Codling moth, Cydia pomonella (L.) (Lepidoptera: Tortricidae), is the most important pest of pome fruits in temperate areas worldwide. Despite its economic importance, little is known about the genetic structure and patterns of gene ßow at the local and regional scale, which are important aspects for establishing an area wide control strategy (Dorn et al. 1999, Calkins and Faust 2003). Pest management strategies of C. pomonella include regular insecticide treatments, which are known to select for resistance to several insecticide groups (Knight et al. 1994, Sauphanor et al. 1998, Dunley and Welter 2000). Therefore, a deeper understanding of the population genetic structure of this pest could greatly beneÞt management decisions for its control (Timm et al. 2006). Little genetic differentiation between C. pomonella populations from different continents and countries was reported using allozyme markers (Pashley 1983). 1 Departamento de Produccio ´ n Agrõćola, Facultad de Ciencias Agrarias, Universidad de Talca, Casilla 747, Talca, Chile. 2 Corresponding author, e-mail: [email protected]. 3 Instituto de Biologõá Vegetal y Biotecnologõá, Universidad de Talca, Casilla 747, Talca, Chile.

Similarly, low FST (Weir and Cockerham 1984) values were found between different localities and host plants in France and Switzerland (Bu` es and Toubon 1992, Bu` es et al. 1995). Different results were obtained in South Africa using ampliÞed fragment-length polymorphisms (AFLPs), with signiÞcant differentiation between populations at the regional and local scales (Timm et al. 2006). More powerful codominant markers, such as microsatellites, have been developed for C. pomonella during the past few years (Franck et al. 2005, Zhou et al. 2005). These markers have been applied by Franck et al. (2007) to evaluate the structure of C. pomonella populations from France. Low FST values without isolation by distance were observed in this latter study, with no inßuence of the frequency of insecticide sprays on the population structure (Franck et al. 2007). C. pomonella has been traditionally regarded as a rather sedentary pest, likely to develop genetic isolation between geographical regions. However, laboratory evidence suggests that some genotypes have the ability to ßy several kilometers (Keil et al. 2001a, 2001b). Furthermore, other means of movement, such as harvest bin transfer containing the diapausing larvae of C. pomonella between packing facilities and orchards, could represent an important source of dispersal for this species (Higbee et al. 2001). The aim of this study is to analyze the genetic structure of C. pomonella from apple (Malus domestica Borkh) orchards in central Chile, describing the possible mechanism underlying this structure. Indirectly, isolation by distance (IBD) between different populations is estimated and considerations for pest man-

0022-0493/08/0190Ð0198$04.00/0 䉷 2008 Entomological Society of America

February 2008

FUENTES-CONTRERAS ET AL.: POPULATION GENETIC STRUCTURE OF C. pomonella

agement, including insecticide resistance management, are discussed. Materials and Methods Sampling. During March 2005 (autumn), diapausing larvae of C. pomonella were collected from cardboard traps placed during the summer on the main trunk of apple trees. The larvae were sexed and preserved in 95% ethanol for laboratory analyses. Figure 1 shows the location of the eleven apple orchards from the OÕHiggins and Maule regions that were included in our study: Graneros 1 (Gra1: 34⬚ 03⬘ 192⬙S, 70⬚ 39⬘ 353⬙ W), Graneros 2 (Gra2: 34⬚ 04⬘ 065⬙ S, 70⬚ 42⬘ 728⬙ W), Gultro 1 (Gul1: 34⬚ 11⬘ 053⬙ S, 70⬚ 46⬘ 033⬙ W), Gultro 2 (Gul2: 34⬚ 12⬘ 536⬙ S, 70⬚ 47⬘ 141⬙ W), San Fernando (SFdo: 34⬚ 34⬘ 542⬙ S, 70⬚ 58⬘ 097⬙ W), Los Niches (LNich: 35⬚ 02⬘ 325⬙ S, 71⬚ 10⬘ 978⬙ W), Molina (Mol: 35⬚ 05⬘ 897⬙ S, 71⬚ 16⬘ 329⬙ W), Pencahue (Penc: 35⬚ 23⬘ 109⬙ S, 71⬚ 48⬘ 360⬙ W), Colõń (Colõń: 35⬚ 27⬘ 899⬙ S, 71⬚ 44⬘ 099⬙ W), San Javier (SJav: 35⬚ 35⬘ 103⬙ S, 71⬚ 44⬘ 475⬙ W), and Linares (Lin: 35⬚ 57⬘ 107⬙ S, 71⬚ 19⬘ 291⬙ W). Gultro 1, Molina, Pencahue and Colõń were abandoned orchards (without insecticide treatments). Los Niches was a certiÞed organic orchard (mating disruption and granulosis virus were used without insecticide treatments); all remaining six orchards were conventional production orchards (regular sprays with organophosphate and pyrethroid insecticides). To obtain a broad scale comparison, larvae from an abandoned orchard from France (Avignon) also were included in the study. Microsatellite Analysis. The following microsatellite markers for C. pomonella were chosen based on their polymorphism level: Cp1.63, Cp2.39, Cp2.P, Cp3.169, Cp1.179, Cp3.56, Cp3.180, and Cp3.K; these sequences and characteristics have been described previously (Franck et al. 2005). DNA extraction was performed using the larvae heads after the “salting out” procedure (Sunnucks and Hales 1996). The tissue was homogenized with pestle inside plastic tubes provided with TNES buffer (50 mM Tris-HCl, pH 7.5, 400 mM NaCl, 20 mM EDTA, and 0.5% sodium dodecyl sulfate). The samples were incubated overnight at 37⬚C with 10 mg/ml proteinase K. Proteins were precipitated with 5 M NaCl, followed by centrifugation at 10,000 rpm. The supernatant was further washed twice with ethanol under cold conditions and subjected to centrifugation. Finally, DNA was dried and suspended in ultrapure distilled water. Polymerase chain reaction (PCR) ampliÞcations were carried out using a Þnal volume of 10 ␮l, containing 10 mM Tris-HCl, pH 9.0, 50 mM KCl, 1.5 mM MgCl2, 50 ␮M each dNTP, 0.4 ␮M each primer, 0.5 U of TaqDNA polymerase (Invitrogen, Sao Paulo, Brazil), and 2 ␮l of DNA template (⬇10 ng/␮l) (Franck et al. 2005). AmpliÞcations were performed with a PTC-200 thermocycler (MJ Research, Watertown, MA) under the following conditions: 2 min at 94⬚C, 30 cycles of 30 s at 94⬚C, 40 s at annealing temperature of 55⬚C for primers Cp3.169 and Cp3.K and 61⬚C for primers Cp1.63, Cp2.39, Cp2.P, Cp1.179, Cp3.56, and Cp3.180, and 40 s at 72⬚C, with a

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Þnal extension at 72⬚C for 3 min. PCR products were separated in 6% polyacrylamide gels using a Sequi-Gen GT electrophoresis cell (Bio-Rad, Hercules, CA). Band sizes were visually estimated using the pGEM3Zf (⫹) Vector (Promega, Madison, WI) as a reference in the same gel. Data Analysis. The number of alleles and allelic richness (a) of the microsatellite loci were calculated for each location as a geographical population with FSTAT version 2.9.3 (Goudet 2001). The frequency of null alleles (Na) for each locus was estimated using FREENA (Chapuis and Estoup 2007). Because high frequency of Na represents an important drawback for population genetic analyses (Pemberton et al. 1995), the three loci with the highest Na frequencies (Cp1.63, Cp2.P, and Cp1.179, mean for locatities above 0.2) were not included in subsequent analyses (Table 1). For the remaining Þve loci (Cp2.39, Cp3.169, Cp3.56, Cp3.180, and Cp3.K), the corrected allele frequencies obtained from FREENA were used. Unbiased heterozygosity estimates, such as HE (NeiÕs gene diversity) and FIS (Þxation index), were calculated for each locus with FSTAT version 2.9.3 (Goudet 2001). To evaluate the effect of phytosanitary management and geographic regions on the genetic variability of C. pomonella, localities with abandoned and organic orchards (Gultro 1, Molina, Los Niches, Pencahue, and Colõń) were grouped and compared with localities with conventional production orchards subjected to insecticide sprays (Graneros 1, Graneros 2, San Fernando, San Javier, and Linares). Similarly, geographic groups for the OÕHiggins region (Graneros 1, Graneros 2, Gultro 1, Gultro 2, and San Fernando) and Maule region (Los Niches, Molina, Pencahue, Colõń, San Javier, and Linares) were analyzed. Mean values of a, HE, and FIS estimates for all loci for each locality were compared according to the management and geographic groups using the MannÐWhitney U test (Sokal and Rohlf 1995). Molecular variance of C. pomonella samples was analyzed partitioning the variance in two different ways. Samples were analyzed either as two groups differing in type of management (abandoned and conventional production apple orchards) or as groups from different geographic regions (OÕHiggins and Maule). In both cases, three-level hierarchical analysis of molecular variance (AMOVA) was performed using the software ARLEQUIN version 2.0 (Schneider et al. 2000) using 1,023 permutations. Hierarchical levels of AMOVA partitioned the genetic variation and provided estimations of FST (Weir and Cockerham 1984). Pairwise FST differentiation tests were performed based on 1,000 permutations of multilocus genotypes also by using ARLEQUIN version 2.0. In addition, this software was used to test for the presence of signiÞcant association between pairs of loci, based on an exact test of linkage disequilibrium including Bonferroni correction. To test for IBD, linearized genetic distance [FST (1 ⫺ FST)⫺1] and geographic distance (log km) were subjected to a linear regression to obtain the PearsonÕs correlation coefÞcient (Sokal and Rohlf 1995). Simi-

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Fig. 1. Map of central Chile (OÕHiggins and Maule regions) indicating the localities from where the eleven C. pomonella samples were obtained. Different symbols indicate the clusters obtained using Bayesian assignment in methods for estimating population genetic structure.

February 2008 Table 1. (All) Locus

Cp2.39

Cp3.169

Cp3.56

Cp3.180

Cp3.K

All loci


193

Genetic diversity measurements of C. pomonella per locus for each location separately, and all Chilean localities together

Chile

France

Pop

Gra1

Gra2

Gul1

Gul2

SFdo

LNich

Mol

Penc

Colõń

SJav

Lin

All

Avig

Variable Alleles a Na HE FIS Alleles a Na HE FIS Alleles a Na HE FIS Alleles a Na HE FIS Alleles a Na HE FIS FIS

n ⫽ 27 6 4.11 0.11 0.73 0.09 7 6.61 0.07 0.84 0.03 4 3.79 0 0.51 ⫺0.01 1 1.0 0 0 NA 5 4.96 0 0.69 ⫺0.18 ⫺0.01

n ⫽ 15 6 6.0 0.20 0.79 ⫺0.01 8 8.0 0.11 0.89 0.02 7 7.0 0.01 0.63 0.15 2 2.0 0 0.07 0 5 5.0 0 0.72 0.08 0.05

n ⫽ 38 8 6.53 0.19 0.74 0.04 10 7.85 0.09 0.84 ⫺0.04 9 6.79 0.15 0.72 0.06 3 2.75 0.20 0.40 0.02 7 6.19 0 0.80 ⫺0.06 ⫺0.03

n ⫽ 16 4 4.0 0.45 0.72 ⫺0.04 7 6.93 0.18 0.83 0.02 5 4.87 0.02 0.50 0.13 1 1.0 0 0 NA 7 6.87 0.12 0.86 0.06 0.04

n ⫽ 35 9 6.56 0.22 0.80 0.07 11 8.34 0.17 0.84 ⫺0.02 6 5.69 0.01 0.70 0.14 2 1.95 0 0.13 ⫺0.06 6 5.32 0.05 0.80 0.07 0.06

n ⫽ 39 6 5.42 0.05 0.70 ⫺0.14 12 9.14 0.05 0.86 ⫺0.05 8 6.53 0.11 0.77 0.06 3 2.78 0.23 0.43 0 6 5.33 0 0.77 0.03 ⫺0.02

n ⫽ 39 6 5.23 0.30 0.75 0.08 9 8.27 0.17 0.86 0.05 5 4.86 0.26 0.73 0.02 3 2.92 0.57 0.55 0.11 6 5.69 0.15 0.78 0.01 0.05

n ⫽ 31 5 4.93 0.21 0.79 ⫺0.06 6 5.71 0.09 0.80 ⫺0.01 4 2.97 0.11 0.49 0.08 3 2.93 0.19 0.48 ⫺0.02 5 4.86 0 0.71 ⫺0.01 ⫺0.03

n ⫽ 31 6 5.61 0.05 0.79 ⫺0.11 8 6.93 0.06 0.82 ⫺0.02 6 5.09 0.09 0.54 ⫺0.01 3 2.94 0.51 0.56 0.08 7 6.40 0.30 0.80 0.07 0

n ⫽ 19 6 5.92 0.19 0.80 0.08 6 5.79 0 0.81 ⫺0.04 7 6.29 0.15 0.64 0.09 3 3.0 0.38 0.64 0.01 7 6.78 0.24 0.83 0.11 0.05

n ⫽ 32 8 7.02 0.16 0.74 0.03 10 7.57 0.06 0.83 ⫺0.06 6 5.42 0.09 0.66 ⫺0.04 2 2.0 0 0.27 ⫺0.17 6 5.43 0 0.72 ⫺0.05 ⫺0.04

n ⫽ 322 13 5.58 0.19 0.77 0.01 17 7.38 0.10 0.85 ⫺0.01 14 5.39 0.09 0.60 0.04 3 2.30 0.19 0.32 0.02 11 5.71 0.08 0.76 ⫺0.01 0.01

n ⫽ 56 11 7.35 0.14 0.84 0.04 14 8.54 0.03 0.76 ⫺0.03 13 8.70 0.15 0.84 ⫺0.02 3 2.22 0.09 0.30 0.11 15 9.35 0.07 0.77 ⫺0.04 0.00

First line indicates no. of alleles, followed by allelic richness (a), frequency of null alleles (Na), NeiÕs gene diversity (HE), and Þxation index (FIS). NA, nonapplicable to monomorphic loci.

larly, matrices of linearized genetic distance and geographic distance were subjected to a Mantel test, based on 1,000 permutations of pairs of localities by using the software XLSTAT version 7.5.2 (Addinsoft 2006). The use of different methods to study the spatial genetic structure of organisms in a sampled region has been strongly recommended in the literature (Storfer et al. 2007). Therefore, the number of genetic clusters was analyzed using the Bayesian method described by Corander et al. (2003). Software BAPS 4.14 (Corander et al. 2003, Corander 2006), which uses stochastic optimization to infer the posterior mode of the genetic structure, was used for this purpose. This analysis also considers a spatial model that uses individual georeferenced multilocus genotypes to assign a biologically relevant nonuniform prior distribution over the space of clustering solutions, thereby increasing the power to detect correctly the underlying population structure. Three types of analyses were carried out; Þrstly an assignment test without any prior information, which determines the level of assignment, based solely on the allele frequencies (admixture analysis). A second test was carried, this was a nonspatial genetic mixture analysis, including “trained clustering,” i.e., individuals were assigned before the analyses to groups based on the locality. The third and last test was a spatial genetic mixture analysis, which considered the georeferenced multilocus genotypes per locality. Thus, the correct genetic structure should be evident

considering these three independent tests, and as the molecular data are very extensive, the spatial and nonspatial clustering models are expected to yield highly similar results (Corander et al. 2008). For all three tests, 10 independent runs for each K ⫽ 2Ð11 were performed. Voronoi tessellation of C. pomonella population genetic structure in space was plotted for visual assessment. Results Allelic Richness and Heterozygote Deficiency. In total, 322 individuals from 11 Chilean localities and 52 individuals from a French locality, were genotyped using Þve microsatellite loci and included in subsequent analyses (Table 1). For all Chilean localities the studied loci were mostly polymorphic, with allelic richness (a) showing a minimum of 1.0 for the locus Cp3.180 in Graneros 1 and Gultro 2, and a maximum of 9.14 for the locus Cp3.169 in Los Niches (Table 1). The French location included in our study showed allelic richness for loci Cp2.39, Cp3.169, and Cp3.180 within the range found for Chilean localities, with a couple of slightly higher values for loci Cp3.56 and Cp3.K (Table 1). Exact tests of genotypic disequilibrium between loci were not signiÞcant after sequential Bonferroni correction, indicating that the loci were independent. No signiÞcant differences were found between management groups of orchards (abandoned versus conventional production) for mean allelic rich-

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Table 2. Results of AMOVA performed for C. pomonella on 11 sampled localities grouped in two different ways, either as management or geographic groups Group Management (abandoned and conventional production apple orchards)

Source of variation df Among groups Among locations within groups Among individuals within locations Within individuals Total

SS

Variance % components variation

Geographical (OÕHiggins and Maule regions)

F statistics

df

0.57 5.17

⫺0.00003 0.00112

⫺0.01 0.22

FCT ⫽ ⫺0.0001 FSC ⫽ 0.0023**

311 158.57

0.01271

2.55

322 156.00 643 320.31

0.48447 0.49827

97.23

1 9

Variance % components variation

SS

F statistics

0.79 5.03

0.00047 0.00085

0.09 0.17

FCT ⫽ 0.0010* FSC ⫽ 0.0017*

FIS ⫽ 0.0256**

311 158.57

0.01271

2.55

FIS ⫽ 0.0256**

FIT ⫽ 0.0277**

322 156.00 643 320.31

0.48447 0.49850

97.19

FIT ⫽ 0.0281**

1 9

*, P ⬍ 0.05; **, P ⬍ 0.001.

ness (U ⫽ 9, P ⬎ 0.05) and mean NeiÕs gene diversity index (U ⫽ 10, P ⬎ 0.05) for all loci. Similarly, no signiÞcant differences were detected between geographic regions (OÕHiggins and Maule) for mean allelic richness (U ⫽ 13, P ⬎ 0.05) for all loci, but mean NeiÕs gene diversity index was signiÞcantly higher for Maule than for OÕHiggins region (U ⫽ 3, P ⫽ 0.03), which is accounted for by locus Cp3.180 (U ⫽ 1, P ⫽ 0.01). Based on the observed heterozygosities and HE, the FIS was estimated for all loci and localities (Table 1). Little variation in FIS values was observed, without signiÞcant departure from HardyÐWeinberg equilibrium in all loci and locations evaluated (P ⬎ 0.05) (Table 1). The French locality included in our study showed the HE and FIS for all loci similar to the range found for Chilean locations (Table 1). Similarly, no signiÞcant differences were found between management-type groups of locations (abandoned versus conventional production) or geographic regions (OÕHiggins and Maule) for multilocus FIS estimates (U ⫽ 13.5 and U ⫽ 10.5, respectively; both P ⬎ 0.05) or mean FIS estimates (U ⫽ 15 and U ⫽ 12, respectively; both P ⬎ 0.05) for all loci. Genetic Differentiation and Isolation by Distance. The three-level hierarchical AMOVA of locations grouped by type of management (Table 2) revealed no signiÞcant variance in genetic structure between management-type groups (percentage of variation ⫺0.01%, FCT ⫽ ⫺0.00007, P ⬎ 0.05). Contrastingly, Table 3.

analysis of variation among localities within groups revealed small, yet highly signiÞcant, variance among localities within groups (percentage of variation 0.22%, FSC ⫽ 0.00225 P ⬍ 0.001). Similarly, variation of individuals within locations was also signiÞcant (percentage of variation 2.55%, FIS ⫽ 0.02556 P ⬍ 0.001. However, within individual variation showed the highest percentage variation (percentage of variation 97.23%, FIT ⫽ 0.02768, P ⬍ 0.001). Genetic differentiation estimated reached an FST ⫽ 0.002176, suggesting low genetic differentiation. AMOVA of localities grouped by geographic regions (Table 2) revealed a small but signiÞcant variance in genetic structure between OÕHiggins and Maule (percentage of variation 0.09%, FCT ⫽ 0.00095, P ⬎ 0.05). All other levels were signiÞcant (Tables 2), with a FST value of 0.026477. Pairwise FST revealed only 10 signiÞcant values out of 55 comparisons, also suggesting low genetic differentiation between localities for C. pomonella from Chile (Table 3). The highest FST values were observed between Graneros 1 and Colõń, San Javier, Pencahue and Linares; all these comparisons involved northern and southern limits of the sampled localities (Table 3). Colõń showed Þve of 10 signiÞcant pairwise FST comparisons with other locations. A pattern of IBD was found as a signiÞcant positive regression between linearized FST and geographic distance (r ⫽ 0.33; F ⫽ 6.24; df ⫽ 1, 53; P ⫽ 0. 016) (Fig. 1). Consistently, Mantel tests also showed a signiÞcant association be-

Pairwise genetic differentiation (FST) between studied localities for C. pomonella

Pop

Gra1

Gra2

Gul1

Gul2

SFdo

LNich

Mol

Penc

Colõń

SJav

Gra1 Gra2 Gul1 Gul2 SFdo LNich Mol Penc Coln SJav Lin

0.002 0.001 0.001 0.001 0.003 0.004 0.004* 0.006* 0.005* 0.004*

⫺0.001 0.000 ⫺0.001 0.002 0.001 0.002* 0.003* 0.002 0.003

0.001 0.000 0.001 0.000 0.003 0.003* 0.002 0.002

⫺0.001 0.001 0.000 0.000 0.001 0.001 0.002

0.000 0.000 0.001 0.001 0.003 0.001

⫺0.001 0.001 0.002* 0.001 0.001

0.001 0.001 0.000 0.000

0.003* 0.003 0.002

0.004* 0.002

0.002

* P ⬍ 0.05 with FisherÕs method.

February 2008


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Fig. 2. Geographic distance versus linearized genetic differentiation between localities sampled for C. pomonella indicating signiÞcant isolation by distance.

tween FST and geographic distance (r ⫽ 0.36, P ⫽ 0.02). Population Structure. The analysis with BAPS resulted in four clusters: (Graneros 1-Pencahue), (Graneros 2-Gultro 1-San Fernando-San Javier-Linares), (Gultro 2), and (Colõń-Los Niches-Molina) [log P (XK⫺1) ⫽ ⫺4310.8 and P (K) ⬇1] (Fig. 2). All three tests carried out merged to the same output, which is visually explained in the Voronoi tessellations obtained with BAPS, producing geographically coherent clusters, with the exception of Graneros 1-Pencahue, which were geographically disjunct (Fig. 3). Discussion C. pomonella populations from the main apple producing regions in Chile showed a low genetic structuration, but a signiÞcant pattern of IBD and detectable levels of geographical clustering was noted. This geographically structured genetic variation was independent of apple orchard management (production or abandoned), but it also was observed between geographic regions (OÕHiggins and Maule). Observed FST values are similar to those reported in Europe for France and Switzerland (Bu` es et al. 1995) and for France and Italy (Franck et al. 2007). An important difference with those studies was the signiÞcant IBD found in the Chilean populations, albeit on a smaller spatial scale than our study (⬇5Ð185 km) in comparison with those from Europe (⬇1Ð1,100 km) (Franck et al. 2007). Apple-growing areas in Chile are located almost continuously along the central valley ßanked by the coastal and Andes ranges (Gwynne 1999). Furthermore, domestic culturing of apple, pear (Pyrus communis L.), quince (Cydonia oblonga Mill.), and walnut (Juglans regia L.) is a widespread practice in rural Chilean landscape (Gwynne 1999); those trees

are usually maintained without insecticide treatments and may act as “stepping stones” for dispersal of C. pomonella. A different situation was reported for apple-growing areas in South Africa, where signiÞcant differentiation between populations occurred even at small geographic distances (⬇1 km) (Timm et al. 2006). In this case, the relative isolation of fruit production areas in the Western Cape, and the absence of wild hosts for C. pomonella, have been suggested as possible explanations for this result (Timm et al. 2006). Another explanation for differences found in genetic structuration could be differences in the molecular markers used between studies (i.e., AFLP in South Africa, allozymes in Europe, and microsatellites in Europe and Chile). However, low levels of genetic structuration in populations from France and Chile also have been observed with microsatellites, which are powerful tools to detect genetic variation compared with allozymes. The recent spread of modern fruticulture in Chile (past 50 yr for export-oriented production) (Gwynne 1999) and the introduction of C. pomonella to the southern cone of America at the beginning of the 20th century (Artigas 1994), could provide an alternative explanation for this low genetic structuration. Such a result could be associated with the common and rather recent origin of C. pomonella populations from Chile, maintained even with restricted current gene ßow. Bayesian spatial analysis represent an independent evaluation of geographic structuration of the populations studied and was partially congruent with the IBD relationship, because cluster 2 included populations distributed in both OÕHiggins and Maule regions subjected to conventional production management, which are likely to be connected by bin transfer between packing facilities and orchards. Wood bins are known to frequently carry diapausing larvae of C.

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Fig. 3. Voronoi tessellation of population structure in space of C. pomonella, estimated using BAPS. Location numbers are as follow: (1) Graneros 1, (2) Graneros 2, (3) Gultro 1, (4) Gultro 2, (5) San Fernando, (6) Los Niches, (7) Molina, (8) Pencahue, (9) Colõń, (10) San Javier, and (11) Linares.

pomonella (Higbee et al. 2001), possibly facilitating dispersal over great distances. On the contrary, cluster four included only abandoned-organic orchards in the Maule region, which do not exchange bins with conventional orchards; therefore C. pomonella dispersal was probably more limited to adult ßight capabilities. Although laboratory evidence suggests that some individuals of C. pomonella have the ability to ßy several kilometers (Keil et al. 2001a, 2001b), this species has been regarded as a rather poor disperser with significant Þtness-costs associated with mobility (Gu et al. 2006). Clusters 1 and 3 are geographically disjunct and differentiate signiÞcantly from 2 and 4. Although variability between orchards was relatively low, a significant genetic structure based on the high allelic diversity at each orchard was evident, probably modulated by the spatial structure. Our indirect estimation of gene ßow suggests that dispersal of C. pomonella, probably mediated by human intervention in the fruit production agroecosystem of central Chile, is an important element to explain the low genetic structuration. However, direct evaluations of dispersal under Þeld conditions are necessary to support this hypothesis (Bossart and Prowell 1998). This could be obtained in the future with the complementary use of available immunomarking techniques (Jones et al. 2006).

Insecticide resistance of C. pomonella has been documented for main apple production areas worldwide (Knight et al. 1994, Sauphanor et al. 1998, Pasquier and Charmillot 2003). The use of neutral microsatellites, representing mainly mutation and genetic drift processes, also has been studied together with insecticide resistance markers, representing the selection process (Franck et al. 2007). A high differentiation at insecticide resistance markers was associated with insecticide selection pressure in contrast with the low differentiation observed at microsatellite neutral markers (Franck et al. 2005). Until now there is little evidence for major development of insecticide resistance in central Chile, despite the high spray pressure that C. pomonella is subjected to each season (Fuentes-Contreras et al. 2007). The practice of sharing bins between orchards that export with the same company, as well as adult ßight from untreated trees surrounding conventional production orchards, could produce an immigration of susceptible individuals from “source” populations. Those immigrants could delay the development of insecticide resistance in the “sink” populations in conventional production orchards which are frequently depleted by insecticide treatments (Caprio 2001, Carrie` re et al. 2004). A critical test of this hypothetical metapopulation dynamic could be the study of individual migrants (from bin piles and untreated domes-

February 2008


tic trees), and how these inßuence the insecticide resistance proÞle and genetic structure C. pomonella populations in production orchards.

Acknowledgments Samples from France and methodological protocols for microsatellites were kindly provided by Pierre Franck and Benoit Sauphanor (Institut National de la Recherche Agronomique-Avignon, France). The technical assistance of Cecilia Navia and Wilson Barros also is acknowledged. This work was funded by Fondo de Desarrollo Cientõ´Þco y Tecnolo´ gico (FONDECYT) grants 1040673 and 7040042 to E.F.C. Partial funding was also obtained from Anillo ACT 38 from Programa Bicentenario de Ciencia y Tecnologõá (PBCT) to C.C.R. and E.F.C. Postdoctoral Þnancial support to B.L. from PBCT is acknowledged.

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