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Inferring the population structure of Myzus persicae in diverse agroecosystems using microsatellite markers

Published online by Cambridge University Press:  01 March 2013

Juan Antonio Sanchez*
Affiliation:
Departamento de Biotecnología y Protección de Cultivos, Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA), C/Mayor, 1, 30150 La Alberca (Murcia), Spain
Michelangelo La-Spina
Affiliation:
Departamento de Biotecnología y Protección de Cultivos, Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA), C/Mayor, 1, 30150 La Alberca (Murcia), Spain
Pedro Guirao
Affiliation:
Departamento de Producción Vegetal y Microbiología, Escuela Politécnica Superior de Orihuela, Universidad Miguel Hernández, Ctra. de Beniel, km 3,2, 03312 Orihuela, Alicante
Fernando Cánovas
Affiliation:
Departamento de Biotecnología y Protección de Cultivos, Instituto Murciano de Investigación y Desarrollo Agrario y Alimentario (IMIDA), C/Mayor, 1, 30150 La Alberca (Murcia), Spain CCMAR, CIMAR-Laboratório Associado, University of Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
*
*Author for correspondence: Juan Antonio Sanchez Phone: +34 968 362787 Fax: +34 968 366792 E-mail: cagitan2@hotmail.com; juana.sanchez23@carm.es
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Abstract

Diverse agroecosystems offer phytophagous insects a wide choice of host plants. Myzus persicae is a polyphagous aphid common in moderate climates. During its life cycle it alternates between primary and secondary hosts. A spatial genetic population structure may arise due to environmental factors and reproduction modes. The aim of this work was to determine the spatial and temporal genetic population structure of M. persicae in relation to host plants and climatic conditions. For this, 923 individuals of M. persicae collected from six plant families between 2005 and 2008 in south-eastern Spain were genotyped for eight microsatellite loci. The population structure was inferred by neighbour-joining, analysis of molecular variance (AMOVA) and Bayesian analyses. Moderate polymorphism was observed for the eight loci in almost all the samples. No differences in the number of alleles were observed between primary and secondary hosts or between geographical areas. The proportion of unique genotypes found in the primary host was similar in the north (0.961 ± 0.036) and the south (0.987 ± 0.013), while in the secondary host it was higher in the north (0.801 ± 0.159) than in the south (0.318 ± 0.063). Heterozygosity excess and linkage disequilibrium suggest a high representation of obligate parthenogens in areas with warmer climate and in the secondary hosts. The FST-values pointed to no genetic differentiation of M. persicae on the different plant families. FST-values, AMOVA and Bayesian model-based cluster analyses pointed to a significant population structure that was related to primary and secondary hosts. Differences between primary and secondary hosts could be due to the overrepresentation of parthenogens on herbaceous plants.

Information

Type
Research Paper
Copyright
Copyright © Cambridge University Press 2013 
Figure 0

Fig. 1. Location of the sampling areas (top) and maps with the isoclines of the average (bottom left) and minimum temperatures in January (bottom right).

Figure 1

Table 1. Localities, area, year and month of sampling, hosts and number of individuals genotyped (N) in M. persicae samples.

Figure 2

Table 2. Population code: 1 = AGHER05, 2 = CAPEP05, 3 = CAPEP06, 4 = CAPEP08, 5 = CAHER05, 6 = CAHER06, 7 = CAHER07, 8 = CAHER08, 9 = CAPRU05, 10 = CAPRU08, 11 = JUPRU08, 12 = MUPRU08, 13 = NOHER05, 14 = NOHER08, 15 = NOPRU05, and 16 = NOPRU08.

Figure 3

Table 3. Pairwise comparison for genetic differentiation based on FST-values for M. persicae samples on different plant families. URT, urticaceae; SOL, solanaceae; PLA, plantaginaceae; MAL, malvaceae; BRA, brassicaeae; CON, convolvulaceae; CHE, chenopodiaceae; ROS, Rosaceae (Prunus spp.). NS = non-significant, *P < 0.05, **P < 0.01, ***P < 0.001. Significance after the correction of P by Bonferroni. Number of individuals genotyped (excluding duplicates) in brackets.

Figure 4

Table 4. Pairwise comparison for genetic differentiation based on FST-values for M. persicae samples (see Table 1 for sample codes). P-values obtained after 136,000 permutations. NS = non-significant, *P < 0.05, **P < 0.01, ***P < 0.001; P significance after Bonferroni correction.

Figure 5

Fig. 2. Population dendrogram for samples of M. persicae collected from 2005 to 2008 on different host plants (see table 1 for sample codes). Numbers on nodes show bootstrap support (10,000 pseudo replications).

Figure 6

Fig. 3. STRUCTURE analyses for K = 2 and 3 clusters. M. persicae population structure based on microsatellite scores. Each line represents a single individual and is fragmented in different colours according to the coefficient of ancestry of each individual.

Figure 7

Table 5. Hierarchical analysis of molecular variance (AMOVA) for samples of M. persicae collected from 2005 to 2008 grouped by host category (crop, secondary and primary hosts). Degrees of freedom (df), sums of squared deviations (SSD), the percentage of the total variance because of each level, and the probability test calculated after 1023 permutations.

Figure 8

Table 6. Hierarchical AMOVA for samples of M. persicae collected from 2005 and 2008 on different host categories grouped by year. Degrees of freedom (df), sums of squared deviations (SSD), the percentage of the total variance because of each level, and the probability test calculated after 1023 permutations.

Figure 9

Table 7. Results of Bayesian analyses using STRUCTURE. Percentage of individuals in the M. persicae samples assigned to each cluster according to their respective probabilities of ancestry for K = 2 and K = 3 in samples.