Background The Teak defoliator ( Hyblaea puera ) is a pest moth of teak woodlands in India and other tropical regions (e.g. Thailand) and is of major economic significance. This pest is of major concern as it is involved in complete defoliation of trees during the early part of the growing season. Defoliation does not kill teak trees, but it results in huge amount of timber loss. Teak defoliator outbreaks are a regular annual feature in most teak plantations in India and it is extremely difficult to predict the exact time and place of occurrence of these outbreaks. Evidence from the study of the population dynamics of  H. puera  indicated habitual, short range movements of emerging moth populations, suggesting that these populations have spread to larger areas, generation after generation, affecting the entire teak plantations. We were therefore interested in investigating the temporal and spatial relationship among various population groups in Nilambur, Kerala (India) and address the cause of outbreak at the landscape level.
Background
The Teak defoliator ( Hyblaea puera ) is a pest moth of teak woodlands in India and other tropical regions (e.g. Thailand) and is of major economic significance. This pest is of major concern as it is involved in complete defoliation of trees during the early part of the growing season. Defoliation does not kill teak trees, but it results in huge amount of timber loss. Teak defoliator outbreaks are a regular annual feature in most teak plantations in India and it is extremely difficult to predict the exact time and place of occurrence of these outbreaks. Evidence from the study of the population dynamics of  H. puera  indicated habitual, short range movements of emerging moth populations, suggesting that these populations have spread to larger areas, generation after generation, affecting the entire teak plantations. We were therefore interested in investigating the temporal and spatial relationship among various population groups in Nilambur, Kerala (India) and address the cause of outbreak at the landscape level.Hyblaea pueraH. pueraResults The populations were classified into 'endemic', 'epicenter' and 'epidemic' populations based on the time of occurrence and size of infestation. We devised a novel method of screening nuclear and mitochondrial DNA polymorphisms using Randomly Amplified Gene Encoding Primers (RAGEP). We have used this method extensively to evaluate the species specificity, reproducibility and to discriminate among the three different characterised populations of teak defoliator.
Results
The populations were classified into 'endemic', 'epicenter' and 'epidemic' populations based on the time of occurrence and size of infestation. We devised a novel method of screening nuclear and mitochondrial DNA polymorphisms using Randomly Amplified Gene Encoding Primers (RAGEP). We have used this method extensively to evaluate the species specificity, reproducibility and to discriminate among the three different characterised populations of teak defoliator.Conclusions This method also allowed us to comment with some certainty that the endemic teak defoliator,  H. puera  do not play a major role in contributing to large-scale infestations. With respect to the hypotheses put forward regarding the origin of outbreaks of the moth, this study confirms the role of migration in outbreak causation, while negating the belief that endemic populations aggregate to cause an epidemic.
Conclusions
This method also allowed us to comment with some certainty that the endemic teak defoliator,  H. puera  do not play a major role in contributing to large-scale infestations. With respect to the hypotheses put forward regarding the origin of outbreaks of the moth, this study confirms the role of migration in outbreak causation, while negating the belief that endemic populations aggregate to cause an epidemic.H. puera

Background Teak ( Tectona grandis  L.) is a very valuable timber species, and is a member of the moist deciduous and dry deciduous forest types. Teak plantations are threatened by two major pests: the Teak Defoliator ( Hyblaea puera  Cramer) Lepidoptera: Hyblaeidae and the Teak Skeletonizer ( Eutectona machaeralis  (Walker) syn.).  H. puera  is less widely distributed in the tropics: in Oriental and Australian regions (India, Sri Lanka, Burma, Java, Papua New-Guinea, Northern Queensland in Australia, Solomon Islands); in the West Indies; and in South and parts of East Africa [ 1 ]. The Teak defoliator is of major concern since it is involved in complete defoliation of trees during the early part of the growing season. Defoliation does not kill the trees, but does lead to huge timber loss. Recent studies have shown that the defoliation leads to an average loss of 44% of the potential volume increment in four to nine year-old teak plantations. It has been estimated that in the Nilambur teak plantation during the study period, protected trees increased by an annual increment of 6.7 m 3 /ha compared with 3.7 m 3 /ha for unprotected trees, a gain of 3 m 3 /ha per annum [ 2 ]. Teak defoliator outbreaks are a regular annual feature in teak plantations in Kerala, India. It is difficult to predict the exact time and place of these outbreaks. Evidence gathered from the past decade on the population dynamics of  H. puera  indicates habitual, short range movements of emerging moth populations, suggesting that these spread to larger areas, generation after generation, affecting entire teak plantations [ 3 ]. Earlier studies also indicated that the outbreaks begin as small epicenters during the pre-monsoon season [ 4 ]. Populations were classified as 'endemic', 'epicenter' and 'epidemic', based on their time of occurrence and the density of the population as represented by the area it infests. Endemics are insects belonging to the low-density population level; epicenters are patchy, medium density outbreaks that occur during the pre-monsoon season, whilst epidemic represents large area, high-density outbreak populations. An understanding of the origin and spread of the epidemic of this pest, which erupt suddenly following the pre-monsoon rain each year, is an important prerequisite for developing appropriate control strategies. If progenies of the epicenter populations cause the larger epidemics, control of these could prevent major outbreaks. On the other hand, if immigrant moths were involved, it would be difficult to control major outbreaks. Thus, understanding the cause and effect relationship between initial small outbreaks and large outbreaks that occur later in the year is crucial for the control of the pest. Recently, molecular markers have been used to enhance understanding of insect displacements, especially including estimates of movement of particular genotypes and/ or biotypes, reproductive strategy and success. Such approaches have also been used to study founder events [ 5 ], geographical invasions [ 6 ], small and large scale displacements [ 7 , 8 ], including movement of entire population demes [ 9 ], and even altitudinal movements related to habitat patchiness and persistence [ 10 ]. Molecular data can yield valuable information when integrated with information from ethology, field ecology, comparative morphology, systematics and palaeontology [ 11 ]. Use of direct and indirect methods of tracking insects along with description of the role and utility of various molecular markers – protein and DNA – in monitoring insect dispersal, has been extensively reviewed [ 12 ]. Arbitrarily-primed DNA markers, and involving the polymerase chain reaction (PCR), have proved very useful for genetic fingerprinting and for facilitating positional cloning of genes. This class of markers are particularly important for less studied species, for which genome sequence information is generally not known. These technologies include randomly amplified polymorphic DNA (RAPDs) [ 13 , 14 ], DNA amplification fingerprinting (DAF)[ 15 ], and amplified fragment length polymorphisms (AFLPs) [ 16 ]. In this study, we used a variant of the RAPD approach involving various nuclear and mitochondrial gene specific primers to trace the origin of teak defoliator outbreaks. It is expected that the molecular data would provide the necessary information to elucidate the origin of the epidemic population. Such information should prove valuable in planning and implementing measures to control these pests. Therefore, the aim of the present study was to identify the relationship among the three apparent populations – endemic, epicenter and epidemic.
Background
Teak ( Tectona grandis  L.) is a very valuable timber species, and is a member of the moist deciduous and dry deciduous forest types. Teak plantations are threatened by two major pests: the Teak Defoliator ( Hyblaea puera  Cramer) Lepidoptera: Hyblaeidae and the Teak Skeletonizer ( Eutectona machaeralis  (Walker) syn.).  H. puera  is less widely distributed in the tropics: in Oriental and Australian regions (India, Sri Lanka, Burma, Java, Papua New-Guinea, Northern Queensland in Australia, Solomon Islands); in the West Indies; and in South and parts of East Africa [ 1 ]. The Teak defoliator is of major concern since it is involved in complete defoliation of trees during the early part of the growing season. Defoliation does not kill the trees, but does lead to huge timber loss. Recent studies have shown that the defoliation leads to an average loss of 44% of the potential volume increment in four to nine year-old teak plantations. It has been estimated that in the Nilambur teak plantation during the study period, protected trees increased by an annual increment of 6.7 m 3 /ha compared with 3.7 m 3 /ha for unprotected trees, a gain of 3 m 3 /ha per annum [ 2 ].Tectona grandisHyblaea pueraEutectona machaeralisH. pueraTeak defoliator outbreaks are a regular annual feature in teak plantations in Kerala, India. It is difficult to predict the exact time and place of these outbreaks. Evidence gathered from the past decade on the population dynamics of  H. puera  indicates habitual, short range movements of emerging moth populations, suggesting that these spread to larger areas, generation after generation, affecting entire teak plantations [ 3 ]. Earlier studies also indicated that the outbreaks begin as small epicenters during the pre-monsoon season [ 4 ]. Populations were classified as 'endemic', 'epicenter' and 'epidemic', based on their time of occurrence and the density of the population as represented by the area it infests. Endemics are insects belonging to the low-density population level; epicenters are patchy, medium density outbreaks that occur during the pre-monsoon season, whilst epidemic represents large area, high-density outbreak populations. An understanding of the origin and spread of the epidemic of this pest, which erupt suddenly following the pre-monsoon rain each year, is an important prerequisite for developing appropriate control strategies. If progenies of the epicenter populations cause the larger epidemics, control of these could prevent major outbreaks. On the other hand, if immigrant moths were involved, it would be difficult to control major outbreaks. Thus, understanding the cause and effect relationship between initial small outbreaks and large outbreaks that occur later in the year is crucial for the control of the pest.H. pueraRecently, molecular markers have been used to enhance understanding of insect displacements, especially including estimates of movement of particular genotypes and/ or biotypes, reproductive strategy and success. Such approaches have also been used to study founder events [ 5 ], geographical invasions [ 6 ], small and large scale displacements [ 7 , 8 ], including movement of entire population demes [ 9 ], and even altitudinal movements related to habitat patchiness and persistence [ 10 ]. Molecular data can yield valuable information when integrated with information from ethology, field ecology, comparative morphology, systematics and palaeontology [ 11 ]. Use of direct and indirect methods of tracking insects along with description of the role and utility of various molecular markers – protein and DNA – in monitoring insect dispersal, has been extensively reviewed [ 12 ].Arbitrarily-primed DNA markers, and involving the polymerase chain reaction (PCR), have proved very useful for genetic fingerprinting and for facilitating positional cloning of genes. This class of markers are particularly important for less studied species, for which genome sequence information is generally not known. These technologies include randomly amplified polymorphic DNA (RAPDs) [ 13 , 14 ], DNA amplification fingerprinting (DAF)[ 15 ], and amplified fragment length polymorphisms (AFLPs) [ 16 ]. In this study, we used a variant of the RAPD approach involving various nuclear and mitochondrial gene specific primers to trace the origin of teak defoliator outbreaks. It is expected that the molecular data would provide the necessary information to elucidate the origin of the epidemic population. Such information should prove valuable in planning and implementing measures to control these pests. Therefore, the aim of the present study was to identify the relationship among the three apparent populations – endemic, epicenter and epidemic.Results The nuclear and mitochondrial gene specific primers chosen did not produce any amplification product when used in combination with the corresponding primers as described in the UBC primer set kit [ 17 ]. This resulted in our devising a novel PCR, which we have named RAGEP-PCR. In RAGEP-PCR, we used single nuclear and mitochondrial gene encoding primers at low stringency annealing temperatures. Unlike RAPDs, in RAGEP longer nuclear (21–26 nucleotide) and mitochondrial (19–26 nucleotide) gene encoding primers were utilised, and which we have here extensively employed to evaluate the species taxonomic specificity/reproducibility and to discriminate the endemic, epicenter and epidemic populations of teak defoliator from one another. RAGEP markers were first tested for polymorphisms, species-specificity and repeatability. Similar fingerprinting pattern were observed in subsequent PCRs for the same individual using the same primers (Fig  1 ), which displayed overall robustness and repeatability with RAGEP-PCR. It was also possible to discriminate various moth species based on their species-specific DNA fingerprint pattern (Fig  2 ). The bands scored for each nuclear RAGEP used in the present study were of a size range 200 bp to 1500 bp. With nuclear RAGEP markers, an average of 2–3 monomorphic bands were observed, except for primer CK6-5'. In each marker, the average number of bands scored varied from 7–16. The maximum number of bands was detected using primer cytC-B-3', while the maximum number of monomorphic bands were detected using primer EFS599. Figure 1 Reproducibility of RAGEP fingerprints. mt1-3, nu1-3 depicts reproducibility of RAGEPs using mitochondrial and nuclear primers respectively Figure 2 Species specificity of RAGEP fingerprints. M1-4 depicts variability in Lepidopteran species.  M1-Eutectona machaeralis ,  M2-Sylepta derogata ,  M3-Cnaphalocrocis medinalis ,  M4-Bombyx mori Each individual RAGEP marker gel was screened and a similarity matrix was generated. Subsequently similarity matrixes of all experimental patterns were combined to generate a UPGMA (Unweighted pair-group mathematical average) tree. While evaluating the similarity matrix based on the Dice coefficient for all nuclear specific RAGEP markers and whilst constructing a UPGMA tree, it was observed that the various population groups of  H. puera  fall in two clusters, which are further divided into two major sub clusters. Average similarity between the two major clusters was 20%, while that between the two sub clusters was 34%. In one of the major clusters, we observed all the endemic insects clustering together with some of the populations from the epicenter insects; however, both populations fall in two distinct sub-clusters (Fig  3 ). Similarly in the second major cluster, the remaining populations from the epicenter and entire epidemic insect populations were likewise seen to fall into two distinct sub-clusters. Figure 3 RAGEP fingerprint patterns generated by nuclear gene specific DNA markers in individuals of three populations (Panel B) and UPGMA dendrogram showing clustering of different insect populations of  Hyblaea puera  (Panel A) Using the mitochondrial RAGEP markers, the average numbers of bands scored for each primer ranged from 6–15. All bands scored were of size range 300 bp to 1600 kb. The maximum numbers of bands detected was found using primer SR-J-14233, the minimum numbers using marker N4-N-8924. Among mitochondrial markers, an average of 1–2 monomorphic bands were observed. The maximum number of monomorphic bands was observed using marker CB-N-10920. Two distinct clusters were observed in the UPGMA dendrogram for mitochondrial markers. Similarity between the two clusters was only 20%. One of these clusters comprised the majority of the endemic samples with a few samples from epicenter insects, whilst the other cluster was comparatively larger and had the two major sub clusters. Both these sub-clusters have insects from epicenter and epidemic populations (Fig  4 ). From this dendrogram, it may be deduced that all the seven epidemic population samples tested in the study shared the same gene pool with sets of epicenter populations. In contrast, the endemic populations are genetically distant from the epicenter populations. Figure 4 RAGEP fingerprint patterns generated by mitochondrial gene specific DNA markers in individuals of three populations (Panel B) and UPGMA dendrogram showing clustering of different insect populations of  Hyblaea puera  (Panel A)
Results
The nuclear and mitochondrial gene specific primers chosen did not produce any amplification product when used in combination with the corresponding primers as described in the UBC primer set kit [ 17 ]. This resulted in our devising a novel PCR, which we have named RAGEP-PCR. In RAGEP-PCR, we used single nuclear and mitochondrial gene encoding primers at low stringency annealing temperatures. Unlike RAPDs, in RAGEP longer nuclear (21–26 nucleotide) and mitochondrial (19–26 nucleotide) gene encoding primers were utilised, and which we have here extensively employed to evaluate the species taxonomic specificity/reproducibility and to discriminate the endemic, epicenter and epidemic populations of teak defoliator from one another.RAGEP markers were first tested for polymorphisms, species-specificity and repeatability. Similar fingerprinting pattern were observed in subsequent PCRs for the same individual using the same primers (Fig  1 ), which displayed overall robustness and repeatability with RAGEP-PCR. It was also possible to discriminate various moth species based on their species-specific DNA fingerprint pattern (Fig  2 ). The bands scored for each nuclear RAGEP used in the present study were of a size range 200 bp to 1500 bp. With nuclear RAGEP markers, an average of 2–3 monomorphic bands were observed, except for primer CK6-5'. In each marker, the average number of bands scored varied from 7–16. The maximum number of bands was detected using primer cytC-B-3', while the maximum number of monomorphic bands were detected using primer EFS599.Each individual RAGEP marker gel was screened and a similarity matrix was generated. Subsequently similarity matrixes of all experimental patterns were combined to generate a UPGMA (Unweighted pair-group mathematical average) tree. While evaluating the similarity matrix based on the Dice coefficient for all nuclear specific RAGEP markers and whilst constructing a UPGMA tree, it was observed that the various population groups of  H. puera  fall in two clusters, which are further divided into two major sub clusters. Average similarity between the two major clusters was 20%, while that between the two sub clusters was 34%. In one of the major clusters, we observed all the endemic insects clustering together with some of the populations from the epicenter insects; however, both populations fall in two distinct sub-clusters (Fig  3 ). Similarly in the second major cluster, the remaining populations from the epicenter and entire epidemic insect populations were likewise seen to fall into two distinct sub-clusters.H. pueraUsing the mitochondrial RAGEP markers, the average numbers of bands scored for each primer ranged from 6–15. All bands scored were of size range 300 bp to 1600 kb. The maximum numbers of bands detected was found using primer SR-J-14233, the minimum numbers using marker N4-N-8924. Among mitochondrial markers, an average of 1–2 monomorphic bands were observed. The maximum number of monomorphic bands was observed using marker CB-N-10920.Two distinct clusters were observed in the UPGMA dendrogram for mitochondrial markers. Similarity between the two clusters was only 20%. One of these clusters comprised the majority of the endemic samples with a few samples from epicenter insects, whilst the other cluster was comparatively larger and had the two major sub clusters. Both these sub-clusters have insects from epicenter and epidemic populations (Fig  4 ). From this dendrogram, it may be deduced that all the seven epidemic population samples tested in the study shared the same gene pool with sets of epicenter populations. In contrast, the endemic populations are genetically distant from the epicenter populations.Discussion The first teak plantation in India was started as early as 1842 in Nilambur, Kerala State, India. Preliminary information on the life history of  H. puera  and the nature of its damage was published in 1898 [ 18 ].  H. puera  outbreaks have been reported to begin in small epicenters and later spread to larger areas. It was then suspected that population build-up in the early outbreak epicenters might account for the subsequent widespread epidemic. However, a study using the time lapse (developmental time) between two epidemics to determine whether an earlier epidemic was responsible for causing the subsequent outbreak showed that all subsequent outbreaks could not be attributed to previous outbreaks, thereby indicating the possibility of migrant populations being involved [ 19 ]. Several technical advancements on the DNA fingerprinting methodologies have been established to resolve the taxonomic uncertainties and address the issue on species variability and migration [ 13 - 16 , 20 , 21 ]. The RAGEP-PCR method described here uses gene-specific primers and randomly amplifies the nuclear and mitochondrial-like gene products. Longer mitochondrial (19–26 nucleotide) gene encoding primers are likely to increase the reproducibility and specificity when compared to RAPD technique. This method was found to be efficient, simple and highly reproducible. Here it has been effectively used to discriminate the various population groups of  H. puera  infesting teak plantations in South India. It can also be used to discriminate taxonomically various closely – related moths to the species level. Mitochondrial RAGEP fingerprints are derived from the randomness of RAGEP-PCR. It is difficult to predict with certainty that the bands are diagnostic feature of the mitochondrial genome, but since RAGEP PCR uses gene specific primers, the PCR products could therefore be a result of amplification of homologous genes or pseudogenes which could represent nuclear mitochondrial DNA (NUMTs). Mitochondrial DNA sequences are frequently transferred to the nucleus-giving rise to NUMTs, which are considered to be common in eukaryotes [ 22 ]. Very high rate of horizontal transfer between organellar and nuclear genomes has been reported in the brown mountain grasshopper,  Podisma pedestris  (L.)[ 23 ]. Age groups, sexes, life history variants, etc. and the processes including birth, death, immigration and emigration as different phenotypic classes have been very well defined [ 24 ]. While studying the differentiation process of grain aphid,  Sitobion avenae  (F.) populations across agricultural ecosystems using DNA fingerprinting [(GATA) 4 ] and RAPDs, it was possible to discriminate the micro- and macro geographical heterogeneity [ 25 ]. Highly diagnostic banding patterns in individuals of  S. avenae  on wheat and cocksfoot grass,  Dactylis glomerata  (L.) were observed during the early months of infestation, which declined as the season progressed, largely as a result of genetic drift and local movement between adjacent host species [ 26 ]. Monophyly and a strong biogeographic pattern of each biotype have been reported in whitefly,  Bemisia tabaci  (Gennadius) populations studied throughout the world [ 27 ]. While evaluating the genetic structure in introduced population of the fire ant,  Solenopsis invicta  (Buren) using different classes of markers, it was confirmed that both mitochondrial and nuclear markers display the same hierarchical structure [ 28 ]. Distinct mitochondrial and nuclear DNA sequence divergence patterns for phylogenetic inference has been established among nymphalid butterflies [ 29 ]. The present study using RAGEP-PCR provides a tool for a logical continuation of the earlier work to trace the relationship of endemic, epicenter and epidemic populations of the teak defoliator. The dendrogram produced from nuclear RAGEP clearly indicates that the endemic insects are not involved in causing the epidemic; however, they are apparently involved in the localized spread by building up small epicenter populations. Similarly, while evaluating the observation based on mitochondrial RAGEP's, it is further apparent that endemic populations were not involved in causing the epidemic. This suggests that all the epidemic insects, which are spatially distinct, but temporally co-occurring, share the same gene pool. Randomness of genome amplification methods have been efficiently used in constructing the phylogenetic history in the weevil,  Aubeonymus mariafranciscae  (Roudier), which had diverged recently [ 5 ], whilst the origin of the Argentine stem weevil,  Listronotus bonariensis  (Kuschel) in New Zealand, was traced to the eastern coast of South America [ 30 ]. Use of RAPDs to examine, for example, population subdivision of the saw toothed grain beetle,  Oryzaephilus surinamensis  (L.) [ 31 ], characterization and identification of Asian and North American gypsy moth,  Lymantria dispar  (L.) [ 32 ], host based genotype variation in  S. avenae  [ 33 ], and genotypic variation among different phenotypes of asexual adult winged and wingless of some clones of cereal aphid species [ 34 ], has been well documented. Earlier reports involving molecular DNA markers mention the use of these markers in the detection of sibling species of black flies,  Simulium  spp. [ 35 ], whilst the dynamics of colonization of  Drosophila subobscura  (Collin) [ 36 ] in the west coast of North America and its impact in the sibling species  Drosophila athabasca  Sturtevant and Dobzhansky, and  Drosophila azteca  Sturtevant and Dobzhansky has been extensively studied by allozymes, mitochondrial DNA (mtDNA) and RAPD markers. With the Teak defoliator, earlier studies based on temporal and spatial distribution of the larvae indicated that the epicenters were not constant over the years and did not represent highly favourable local environments [ 3 ]. The present study found little evidence to show that the aggregation of moths belonging to the endemic populations cause the epicenter populations. On the other hand, the findings do suggest the alternate hypothesis, i.e., that immigration of moths from distant teak plantations cause the epidemic, and that there is a continuous inflow of moths during the infestation period. This suggests that under a single demographic structure, two phenotypic classes of  H. puera  coexist during the outbreak season. The degree of variability observed for RAGEPs also argues that this technique could be useful for a variety of questions, including individual identification, strain identification and phylogenetic analysis.
Discussion
The first teak plantation in India was started as early as 1842 in Nilambur, Kerala State, India. Preliminary information on the life history of  H. puera  and the nature of its damage was published in 1898 [ 18 ].  H. puera  outbreaks have been reported to begin in small epicenters and later spread to larger areas. It was then suspected that population build-up in the early outbreak epicenters might account for the subsequent widespread epidemic. However, a study using the time lapse (developmental time) between two epidemics to determine whether an earlier epidemic was responsible for causing the subsequent outbreak showed that all subsequent outbreaks could not be attributed to previous outbreaks, thereby indicating the possibility of migrant populations being involved [ 19 ].H. pueraH. pueraSeveral technical advancements on the DNA fingerprinting methodologies have been established to resolve the taxonomic uncertainties and address the issue on species variability and migration [ 13 - 16 , 20 , 21 ]. The RAGEP-PCR method described here uses gene-specific primers and randomly amplifies the nuclear and mitochondrial-like gene products. Longer mitochondrial (19–26 nucleotide) gene encoding primers are likely to increase the reproducibility and specificity when compared to RAPD technique. This method was found to be efficient, simple and highly reproducible. Here it has been effectively used to discriminate the various population groups of  H. puera  infesting teak plantations in South India. It can also be used to discriminate taxonomically various closely – related moths to the species level. Mitochondrial RAGEP fingerprints are derived from the randomness of RAGEP-PCR. It is difficult to predict with certainty that the bands are diagnostic feature of the mitochondrial genome, but since RAGEP PCR uses gene specific primers, the PCR products could therefore be a result of amplification of homologous genes or pseudogenes which could represent nuclear mitochondrial DNA (NUMTs). Mitochondrial DNA sequences are frequently transferred to the nucleus-giving rise to NUMTs, which are considered to be common in eukaryotes [ 22 ]. Very high rate of horizontal transfer between organellar and nuclear genomes has been reported in the brown mountain grasshopper,  Podisma pedestris  (L.)[ 23 ].H. pueraPodisma pedestrisAge groups, sexes, life history variants, etc. and the processes including birth, death, immigration and emigration as different phenotypic classes have been very well defined [ 24 ]. While studying the differentiation process of grain aphid,  Sitobion avenae  (F.) populations across agricultural ecosystems using DNA fingerprinting [(GATA) 4 ] and RAPDs, it was possible to discriminate the micro- and macro geographical heterogeneity [ 25 ]. Highly diagnostic banding patterns in individuals of  S. avenae  on wheat and cocksfoot grass,  Dactylis glomerata  (L.) were observed during the early months of infestation, which declined as the season progressed, largely as a result of genetic drift and local movement between adjacent host species [ 26 ]. Monophyly and a strong biogeographic pattern of each biotype have been reported in whitefly,  Bemisia tabaci  (Gennadius) populations studied throughout the world [ 27 ]. While evaluating the genetic structure in introduced population of the fire ant,  Solenopsis invicta  (Buren) using different classes of markers, it was confirmed that both mitochondrial and nuclear markers display the same hierarchical structure [ 28 ]. Distinct mitochondrial and nuclear DNA sequence divergence patterns for phylogenetic inference has been established among nymphalid butterflies [ 29 ].Sitobion avenaeS. avenaeDactylis glomerataBemisia tabaciSolenopsis invictaThe present study using RAGEP-PCR provides a tool for a logical continuation of the earlier work to trace the relationship of endemic, epicenter and epidemic populations of the teak defoliator. The dendrogram produced from nuclear RAGEP clearly indicates that the endemic insects are not involved in causing the epidemic; however, they are apparently involved in the localized spread by building up small epicenter populations. Similarly, while evaluating the observation based on mitochondrial RAGEP's, it is further apparent that endemic populations were not involved in causing the epidemic. This suggests that all the epidemic insects, which are spatially distinct, but temporally co-occurring, share the same gene pool.Randomness of genome amplification methods have been efficiently used in constructing the phylogenetic history in the weevil,  Aubeonymus mariafranciscae  (Roudier), which had diverged recently [ 5 ], whilst the origin of the Argentine stem weevil,  Listronotus bonariensis  (Kuschel) in New Zealand, was traced to the eastern coast of South America [ 30 ]. Use of RAPDs to examine, for example, population subdivision of the saw toothed grain beetle,  Oryzaephilus surinamensis  (L.) [ 31 ], characterization and identification of Asian and North American gypsy moth,  Lymantria dispar  (L.) [ 32 ], host based genotype variation in  S. avenae  [ 33 ], and genotypic variation among different phenotypes of asexual adult winged and wingless of some clones of cereal aphid species [ 34 ], has been well documented. Earlier reports involving molecular DNA markers mention the use of these markers in the detection of sibling species of black flies,  Simulium  spp. [ 35 ], whilst the dynamics of colonization of  Drosophila subobscura  (Collin) [ 36 ] in the west coast of North America and its impact in the sibling species  Drosophila athabasca  Sturtevant and Dobzhansky, and  Drosophila azteca  Sturtevant and Dobzhansky has been extensively studied by allozymes, mitochondrial DNA (mtDNA) and RAPD markers.Aubeonymus mariafranciscaeListronotus bonariensisOryzaephilus surinamensisLymantria disparS. avenaeSimuliumDrosophila subobscuraDrosophila athabascaDrosophila aztecaWith the Teak defoliator, earlier studies based on temporal and spatial distribution of the larvae indicated that the epicenters were not constant over the years and did not represent highly favourable local environments [ 3 ]. The present study found little evidence to show that the aggregation of moths belonging to the endemic populations cause the epicenter populations. On the other hand, the findings do suggest the alternate hypothesis, i.e., that immigration of moths from distant teak plantations cause the epidemic, and that there is a continuous inflow of moths during the infestation period. This suggests that under a single demographic structure, two phenotypic classes of  H. puera  coexist during the outbreak season. The degree of variability observed for RAGEPs also argues that this technique could be useful for a variety of questions, including individual identification, strain identification and phylogenetic analysis.H. pueraConclusions The present results appear to validate the hypothesis, that control of  H. puera  epicenter populations would help prevent large-scale outbreaks of the teak defoliator in teak plantations. Therefore, appropriate strategies should be adopted to control the epicenter populations, which occurs in a smaller area. This appears to be a more practical and economical approach for teak defoliator management when compared with management of the pest in the total plantation area covering thousands of hectares. Thus the molecular markers detected using RAGEP-PCR can enhance the understanding of insect population dynamics and aid in tracing the spread and cause of epidemics.
Conclusions
The present results appear to validate the hypothesis, that control of  H. puera  epicenter populations would help prevent large-scale outbreaks of the teak defoliator in teak plantations. Therefore, appropriate strategies should be adopted to control the epicenter populations, which occurs in a smaller area. This appears to be a more practical and economical approach for teak defoliator management when compared with management of the pest in the total plantation area covering thousands of hectares. Thus the molecular markers detected using RAGEP-PCR can enhance the understanding of insect population dynamics and aid in tracing the spread and cause of epidemics.H. pueraMethods Sample collection Based on the spatial pattern of infestation in the past, the area was divided into convenient observation units of approximately 50 ha, based on natural boundaries of streams, roads and footpaths. The canopy of teak is continuous within in the observation area. Each area was monitored every 15 days, which was precisely based on the life cycle of  H. puera . Larval samples were collected from the infestation sites. Whenever fifth instar larvae were available, ten larvae were preserved in 70 % alcohol and stored deep frozen at -20°C. If only lower stages were available, i.e., third or fourth instar, they were reared up to 5th instar in the laboratory. Ten 5th instar larvae were preserved for DNA isolation from each sample site, whilst the remaining larvae were reared into the next generation. Each sample was assigned a code number containing the details of Year / Month / Date / Block / Grid / Generation for further reference. Using the duration of each instar (egg – one day; 1 st  and 2 nd  instars – two days each, 3 rd  to 5 th  instars – three days each; pre-pupa – one day and pupa – four days), the temporal data on outbreaks were examined to see whether each subsequent epidemic could be explained on the basis of a previous outbreak. The details of location of pest incidence and extent of infestation were later transferred to the field map in order to understand the spatial pattern of infestation (Fig  5 ). Figure 5 Landscape of Nilambur teak plantation showing distribution of the endemic, epicenter and epidemic populations of  Hyblaea puera Populations were classified as 'endemic', 'epicenter' and 'epidemic', based on their time of occurrence and the density of the population as represented by the area it infests. Five endemic populations, twenty six epicenter populations and seven epidemic populations for the year 2002 were included in the study. Earlier studies had indicated that outbreak begin as small epicenters in February during the pre-monsoon season and end by June. Endemic samples were collected throughout that year based on their stray occurrences in various life stages, whilst epicenter samples from each aggregated patch were collected only from the insects that attained the same stage of its life cycle at the time of collection in that patch. Similarly the epidemic samples were also collected from insects representing the same life stages at the time of collection from each aggressive patch. The temporal relationship between the endemic population and the epicenter populations and that of the epicenter populations with the large-scale epidemics were first worked out. The larval samples that were geographically close and had a difference of one complete life cycle stage between the population groups were subjected to molecular studies to evaluate their relatedness. DNA isolation DNA extraction was performed with a minor modification of isolation and purification protocol as described earlier [ 37 ] being extracted from whole larvae and quantified spectrophotometrically using a spectrophotometer at 260 nm (Shimadzu). The quality of the DNA was checked spectrophotometrically by taking the absorbance ratios of 260/280 nm. Polymerase chain reaction Both nuclear and mitochondrial DNA RAGEP amplifications were performed in a total volume of 30 μl. Each reaction consisted of 1x Taq buffer with 1.5mM MgCl 2 , 1.2 U of  Taq  polymerase (BG), 0.25 mM of dNTPs (Amersham) and 12 pM of primer per reaction. Primers were initially screened for polymorphism and repeatability. Amplifications were performed in similar cycling conditions in a Thermocycler (Biorad) programmed as follows: initial denaturation at 95°C for 5 min., followed by 45 cycles of cycle denaturation at 94°C for 1 min., annealing at 36°C for 1 min., extension at 72°C for 2 min. and final extension at 72°C for 5 mins. The amplification products were separated using 1.2% agarose gel in 0.5 × TBE buffer with ethidium bromide staining to visualize the product separation using a Bio-Rad's Fluor S imager. The molecular weight of each band was estimated by comparing with a co-migrating 100-bp ladder (Amersham). RAGEP fingerprints of each sample from different regions were then interpreted using Fingerprint type module of Bionumerics software (Applied Maths Kortrijk Belgium, ver.2.0). A preliminary screening with 50 nuclear RAGEPs and 37 mitochondrial RAGEP primers were evaluated for polymorphism and repeatability. Only 11 nuclear (Table- 1 ) and mitochondrial RAGEPs (Table- 2 ) from each group was selected for the study, as they showed constant repeatability of highly polymorphic patterns. Species specificity was evaluated by comparing the banding patterns in  H. puera  with those from the Teak skeletonizer,  E. machaeralis  (Walk.), Leaf roller,  Sylepta derogata  (F.), Leaf folder,  Cnaphalocrocis medinalis  (Guenée), and the Silkworm,  Bombyx mori  (L.). Table 1 Insect nuclear gene specific primer sequences used in nuclear RAGEP-PCR S.No Primer name Sequence 1 EFS599 ATC TCC GGA TGG CAC GG(CT) GAC AA 2 22.5drc GAA CCA (AG)TT (AG)AC (AG)TG (AG)AA GAT C 3 LEPWG1 GA(AG)TG(CT)AA(AG)TG(CT)CA(CT)GG(CT)ATGTCT GG 4 CK6-5' GAC CAC CTC CGA GTC ATC TC(CG) ATG 5 CK7-3' CAG GTG CTC GTT CCA CAT GAA 6 CytC-B-3' CAT CTT GGT GCC GGG GAT GTA TTT CTT 7 EF1-5' GAC AAC GTT GGC TTC AAC GTG AAG AAC G 8 Tub3-5' GAT TTG GAG CC(AGCT) GG(AGCT) ACC ATG GA 9 18S-A1984 TCC CTG GTT GAT CCT GCC AGT A 10 S1124 AGC GTA TGG C(AC)T C(AG)A AGAACT G 11 rcM4 ACA GC(CGA) AC(GT) GT(TC) TG(CT) CTC AT(AG) TC Table 2 Insect mitochondrial gene specific primer sequences used in mitochondrial RAGEP-PCR S.No Primer name Sequence 1 C1-J-2183 CAA CAT TTA TTT TGA TTT TTT GG 2 TL2-J-3034 AAT ATG GCA GAT TAG TGC A 3 C2-N-3661 CCA CAA ATT TCT GAA CAT TGA CCA 4 TK-N-3785 GTT TAA GAG ACC AGT ACT TG 5 N4-N-8924 AAA GCT CAT GTT GAA GCT CC 6 CB-J-10612 CCA TCC AAC ATC TCA GCA TGA TGA AA 7 LR-J-12887 CCG GTC TGA ACT CAG ATC ACG T 8 LR-J-13417 ATG TTT TTG TTA AAC AGG CG 9 LR-N-13398 CGC CTG TTT AAC AAA AAC AT 10 SR-J-14233 AAG AGC GAC GGG CGA TGT GT 11 CB-N-10920 CCC TCA GAA TGA TAT TTG TCC TCA Analysis The polymorphic content for nuclear and mitochondrial primers were analyzed using Bionumerics software [ 38 ]. Band search parameters were kept constant as 5% minimum profiling for all the gels. The position tolerance for selection of bands in constructing a dendrogram was kept constant at 1% through out the interpretations. Only bands showing clear and reproducible patterns were included in the final analysis and these were scored. Real-time normalization of gel electrophoresis patterns and band position for all the gels was based on the reference system for the species-specific bands. Normalization helped us to control the brightness and streakiness of bands without altering the lighter bands and also control the inter-gel mobility shifts. Subsequently a data matrix of similarity values was produced for each individual for each marker. The Dice coefficient was used to analyze the similarities of the banding patterns. Consensus similarity matrix and dendrogram based upon individual matrices from different markers were used for pair wise clustering based on unweighted pair group method (UPGMA) with average linkages (11). The UPGMA dendrogram prevails on the assumption that nucleotide substitution rates are same across all branches. It employs a sequential clustering algorithm, in which local topological relationships are identified in order of similarity, and the phylogenetic tree is built in a stepwise manner. All analysis was done using Bionumerics software V-2.
Methods
Sample collection Based on the spatial pattern of infestation in the past, the area was divided into convenient observation units of approximately 50 ha, based on natural boundaries of streams, roads and footpaths. The canopy of teak is continuous within in the observation area. Each area was monitored every 15 days, which was precisely based on the life cycle of  H. puera . Larval samples were collected from the infestation sites. Whenever fifth instar larvae were available, ten larvae were preserved in 70 % alcohol and stored deep frozen at -20°C. If only lower stages were available, i.e., third or fourth instar, they were reared up to 5th instar in the laboratory. Ten 5th instar larvae were preserved for DNA isolation from each sample site, whilst the remaining larvae were reared into the next generation. Each sample was assigned a code number containing the details of Year / Month / Date / Block / Grid / Generation for further reference. Using the duration of each instar (egg – one day; 1 st  and 2 nd  instars – two days each, 3 rd  to 5 th  instars – three days each; pre-pupa – one day and pupa – four days), the temporal data on outbreaks were examined to see whether each subsequent epidemic could be explained on the basis of a previous outbreak. The details of location of pest incidence and extent of infestation were later transferred to the field map in order to understand the spatial pattern of infestation (Fig  5 ). Figure 5 Landscape of Nilambur teak plantation showing distribution of the endemic, epicenter and epidemic populations of  Hyblaea puera Populations were classified as 'endemic', 'epicenter' and 'epidemic', based on their time of occurrence and the density of the population as represented by the area it infests. Five endemic populations, twenty six epicenter populations and seven epidemic populations for the year 2002 were included in the study. Earlier studies had indicated that outbreak begin as small epicenters in February during the pre-monsoon season and end by June. Endemic samples were collected throughout that year based on their stray occurrences in various life stages, whilst epicenter samples from each aggregated patch were collected only from the insects that attained the same stage of its life cycle at the time of collection in that patch. Similarly the epidemic samples were also collected from insects representing the same life stages at the time of collection from each aggressive patch. The temporal relationship between the endemic population and the epicenter populations and that of the epicenter populations with the large-scale epidemics were first worked out. The larval samples that were geographically close and had a difference of one complete life cycle stage between the population groups were subjected to molecular studies to evaluate their relatedness.
Sample collection
Based on the spatial pattern of infestation in the past, the area was divided into convenient observation units of approximately 50 ha, based on natural boundaries of streams, roads and footpaths. The canopy of teak is continuous within in the observation area. Each area was monitored every 15 days, which was precisely based on the life cycle of  H. puera . Larval samples were collected from the infestation sites. Whenever fifth instar larvae were available, ten larvae were preserved in 70 % alcohol and stored deep frozen at -20°C. If only lower stages were available, i.e., third or fourth instar, they were reared up to 5th instar in the laboratory. Ten 5th instar larvae were preserved for DNA isolation from each sample site, whilst the remaining larvae were reared into the next generation. Each sample was assigned a code number containing the details of Year / Month / Date / Block / Grid / Generation for further reference. Using the duration of each instar (egg – one day; 1 st  and 2 nd  instars – two days each, 3 rd  to 5 th  instars – three days each; pre-pupa – one day and pupa – four days), the temporal data on outbreaks were examined to see whether each subsequent epidemic could be explained on the basis of a previous outbreak. The details of location of pest incidence and extent of infestation were later transferred to the field map in order to understand the spatial pattern of infestation (Fig  5 ).H. pueraPopulations were classified as 'endemic', 'epicenter' and 'epidemic', based on their time of occurrence and the density of the population as represented by the area it infests. Five endemic populations, twenty six epicenter populations and seven epidemic populations for the year 2002 were included in the study. Earlier studies had indicated that outbreak begin as small epicenters in February during the pre-monsoon season and end by June. Endemic samples were collected throughout that year based on their stray occurrences in various life stages, whilst epicenter samples from each aggregated patch were collected only from the insects that attained the same stage of its life cycle at the time of collection in that patch. Similarly the epidemic samples were also collected from insects representing the same life stages at the time of collection from each aggressive patch. The temporal relationship between the endemic population and the epicenter populations and that of the epicenter populations with the large-scale epidemics were first worked out. The larval samples that were geographically close and had a difference of one complete life cycle stage between the population groups were subjected to molecular studies to evaluate their relatedness.DNA isolation DNA extraction was performed with a minor modification of isolation and purification protocol as described earlier [ 37 ] being extracted from whole larvae and quantified spectrophotometrically using a spectrophotometer at 260 nm (Shimadzu). The quality of the DNA was checked spectrophotometrically by taking the absorbance ratios of 260/280 nm.
DNA isolation
DNA extraction was performed with a minor modification of isolation and purification protocol as described earlier [ 37 ] being extracted from whole larvae and quantified spectrophotometrically using a spectrophotometer at 260 nm (Shimadzu). The quality of the DNA was checked spectrophotometrically by taking the absorbance ratios of 260/280 nm.Polymerase chain reaction Both nuclear and mitochondrial DNA RAGEP amplifications were performed in a total volume of 30 μl. Each reaction consisted of 1x Taq buffer with 1.5mM MgCl 2 , 1.2 U of  Taq  polymerase (BG), 0.25 mM of dNTPs (Amersham) and 12 pM of primer per reaction. Primers were initially screened for polymorphism and repeatability. Amplifications were performed in similar cycling conditions in a Thermocycler (Biorad) programmed as follows: initial denaturation at 95°C for 5 min., followed by 45 cycles of cycle denaturation at 94°C for 1 min., annealing at 36°C for 1 min., extension at 72°C for 2 min. and final extension at 72°C for 5 mins. The amplification products were separated using 1.2% agarose gel in 0.5 × TBE buffer with ethidium bromide staining to visualize the product separation using a Bio-Rad's Fluor S imager. The molecular weight of each band was estimated by comparing with a co-migrating 100-bp ladder (Amersham). RAGEP fingerprints of each sample from different regions were then interpreted using Fingerprint type module of Bionumerics software (Applied Maths Kortrijk Belgium, ver.2.0). A preliminary screening with 50 nuclear RAGEPs and 37 mitochondrial RAGEP primers were evaluated for polymorphism and repeatability. Only 11 nuclear (Table- 1 ) and mitochondrial RAGEPs (Table- 2 ) from each group was selected for the study, as they showed constant repeatability of highly polymorphic patterns. Species specificity was evaluated by comparing the banding patterns in  H. puera  with those from the Teak skeletonizer,  E. machaeralis  (Walk.), Leaf roller,  Sylepta derogata  (F.), Leaf folder,  Cnaphalocrocis medinalis  (Guenée), and the Silkworm,  Bombyx mori  (L.). Table 1 Insect nuclear gene specific primer sequences used in nuclear RAGEP-PCR S.No Primer name Sequence 1 EFS599 ATC TCC GGA TGG CAC GG(CT) GAC AA 2 22.5drc GAA CCA (AG)TT (AG)AC (AG)TG (AG)AA GAT C 3 LEPWG1 GA(AG)TG(CT)AA(AG)TG(CT)CA(CT)GG(CT)ATGTCT GG 4 CK6-5' GAC CAC CTC CGA GTC ATC TC(CG) ATG 5 CK7-3' CAG GTG CTC GTT CCA CAT GAA 6 CytC-B-3' CAT CTT GGT GCC GGG GAT GTA TTT CTT 7 EF1-5' GAC AAC GTT GGC TTC AAC GTG AAG AAC G 8 Tub3-5' GAT TTG GAG CC(AGCT) GG(AGCT) ACC ATG GA 9 18S-A1984 TCC CTG GTT GAT CCT GCC AGT A 10 S1124 AGC GTA TGG C(AC)T C(AG)A AGAACT G 11 rcM4 ACA GC(CGA) AC(GT) GT(TC) TG(CT) CTC AT(AG) TC Table 2 Insect mitochondrial gene specific primer sequences used in mitochondrial RAGEP-PCR S.No Primer name Sequence 1 C1-J-2183 CAA CAT TTA TTT TGA TTT TTT GG 2 TL2-J-3034 AAT ATG GCA GAT TAG TGC A 3 C2-N-3661 CCA CAA ATT TCT GAA CAT TGA CCA 4 TK-N-3785 GTT TAA GAG ACC AGT ACT TG 5 N4-N-8924 AAA GCT CAT GTT GAA GCT CC 6 CB-J-10612 CCA TCC AAC ATC TCA GCA TGA TGA AA 7 LR-J-12887 CCG GTC TGA ACT CAG ATC ACG T 8 LR-J-13417 ATG TTT TTG TTA AAC AGG CG 9 LR-N-13398 CGC CTG TTT AAC AAA AAC AT 10 SR-J-14233 AAG AGC GAC GGG CGA TGT GT 11 CB-N-10920 CCC TCA GAA TGA TAT TTG TCC TCA
Polymerase chain reaction
Both nuclear and mitochondrial DNA RAGEP amplifications were performed in a total volume of 30 μl. Each reaction consisted of 1x Taq buffer with 1.5mM MgCl 2 , 1.2 U of  Taq  polymerase (BG), 0.25 mM of dNTPs (Amersham) and 12 pM of primer per reaction. Primers were initially screened for polymorphism and repeatability. Amplifications were performed in similar cycling conditions in a Thermocycler (Biorad) programmed as follows: initial denaturation at 95°C for 5 min., followed by 45 cycles of cycle denaturation at 94°C for 1 min., annealing at 36°C for 1 min., extension at 72°C for 2 min. and final extension at 72°C for 5 mins. The amplification products were separated using 1.2% agarose gel in 0.5 × TBE buffer with ethidium bromide staining to visualize the product separation using a Bio-Rad's Fluor S imager. The molecular weight of each band was estimated by comparing with a co-migrating 100-bp ladder (Amersham). RAGEP fingerprints of each sample from different regions were then interpreted using Fingerprint type module of Bionumerics software (Applied Maths Kortrijk Belgium, ver.2.0).TaqA preliminary screening with 50 nuclear RAGEPs and 37 mitochondrial RAGEP primers were evaluated for polymorphism and repeatability. Only 11 nuclear (Table- 1 ) and mitochondrial RAGEPs (Table- 2 ) from each group was selected for the study, as they showed constant repeatability of highly polymorphic patterns. Species specificity was evaluated by comparing the banding patterns in  H. puera  with those from the Teak skeletonizer,  E. machaeralis  (Walk.), Leaf roller,  Sylepta derogata  (F.), Leaf folder,  Cnaphalocrocis medinalis  (Guenée), and the Silkworm,  Bombyx mori  (L.).H. pueraE. machaeralisSylepta derogataCnaphalocrocis medinalisBombyx moriInsect nuclear gene specific primer sequences used in nuclear RAGEP-PCRInsect mitochondrial gene specific primer sequences used in mitochondrial RAGEP-PCRAnalysis The polymorphic content for nuclear and mitochondrial primers were analyzed using Bionumerics software [ 38 ]. Band search parameters were kept constant as 5% minimum profiling for all the gels. The position tolerance for selection of bands in constructing a dendrogram was kept constant at 1% through out the interpretations. Only bands showing clear and reproducible patterns were included in the final analysis and these were scored. Real-time normalization of gel electrophoresis patterns and band position for all the gels was based on the reference system for the species-specific bands. Normalization helped us to control the brightness and streakiness of bands without altering the lighter bands and also control the inter-gel mobility shifts. Subsequently a data matrix of similarity values was produced for each individual for each marker. The Dice coefficient was used to analyze the similarities of the banding patterns. Consensus similarity matrix and dendrogram based upon individual matrices from different markers were used for pair wise clustering based on unweighted pair group method (UPGMA) with average linkages (11). The UPGMA dendrogram prevails on the assumption that nucleotide substitution rates are same across all branches. It employs a sequential clustering algorithm, in which local topological relationships are identified in order of similarity, and the phylogenetic tree is built in a stepwise manner. All analysis was done using Bionumerics software V-2.
Analysis
The polymorphic content for nuclear and mitochondrial primers were analyzed using Bionumerics software [ 38 ]. Band search parameters were kept constant as 5% minimum profiling for all the gels. The position tolerance for selection of bands in constructing a dendrogram was kept constant at 1% through out the interpretations. Only bands showing clear and reproducible patterns were included in the final analysis and these were scored. Real-time normalization of gel electrophoresis patterns and band position for all the gels was based on the reference system for the species-specific bands. Normalization helped us to control the brightness and streakiness of bands without altering the lighter bands and also control the inter-gel mobility shifts. Subsequently a data matrix of similarity values was produced for each individual for each marker. The Dice coefficient was used to analyze the similarities of the banding patterns. Consensus similarity matrix and dendrogram based upon individual matrices from different markers were used for pair wise clustering based on unweighted pair group method (UPGMA) with average linkages (11). The UPGMA dendrogram prevails on the assumption that nucleotide substitution rates are same across all branches. It employs a sequential clustering algorithm, in which local topological relationships are identified in order of similarity, and the phylogenetic tree is built in a stepwise manner. All analysis was done using Bionumerics software V-2.Authors' contributions CN and MB performed the molecular studies and are responsible for the interpretation of molecular data whilst TV and VVS performed the field data collection and are responsible for spatial and temporal data interpretation. All authors read and approved the final manuscript. MB and VVS conceived and designed the study.
Authors' contributions
CN and MB performed the molecular studies and are responsible for the interpretation of molecular data whilst TV and VVS performed the field data collection and are responsible for spatial and temporal data interpretation. All authors read and approved the final manuscript. MB and VVS conceived and designed the study.


RNA-mediated gene silencing has emerged in recent years as an important mechanism for regulating gene expression. Some of the key discoveries have been made in plants

Although the word ‘revolution’ should not be used lightly in science, there is no other way to describe the recent explosion in our awareness and understanding of RNA-mediated gene silencing pathways. The central player in RNA-mediated gene silencing is a double-stranded RNA (dsRNA) that is chopped into tiny RNAs by the enzyme Dicer. The tiny RNAs associate with various silencing effector complexes and attach to homologous target sequences (RNA or DNA) by basepairing. Depending on the protein composition of the effector complex and the nature of the target sequence, the outcome can be either mRNA degradation, translational repression, or genome modification, all of which silence gene expression ( Figure 1 ). Present in plants, animals, and many fungi, RNA-mediated gene silencing pathways have essential roles in development, chromosome structure, and virus resistance. Although the mechanistic details are still under investigation, RNA-mediated silencing has already provided a powerful tool for studying gene function and spawned a fledgling industry that aims to develop novel RNA-based therapeutics to treat human diseases ( Robinson 2004 ).Many biologists first learned of RNA-mediated gene silencing in 1998 following the discovery, in the nematode worm  Caenorhabditis elegans  ( Fire et al. 1998 ), of a process called RNA interference (RNAi), in which dsRNA triggers sequence-specific mRNA degradation. The roots of RNA-mediated silencing, however, can be traced back 15 years, when a handful of botanical labs stumbled across strange cases of gene silencing in transgenic plants. To highlight the many seminal contributions of plant scientists to the field, we offer here a personal perspective on the origins and history of RNA-mediated gene silencing in plants.Early Silencing Phenomena Starting in the late 1980s, biologists working with transgenic plants found themselves confronted with a ‘bewildering array’ of unanticipated gene silencing phenomena ( Martienssen and Richards 1995 ). Most intriguing were cases in which silencing seemed to be triggered by DNA or RNA sequence interactions, which could occur between two separate transgenes that shared sequence homology or between a transgene and homologous plant gene. Several early examples supplied the prototypes for two types of RNA-mediated gene silencing that are recognized today. In one type, silencing results from a block in mRNA synthesis (transcriptional gene silencing [TGS]); in the second type, silencing results from mRNA degradation (posttranscriptional gene silencing [PTGS]) ( Figure 1 ). TGS was revealed when two different transgene complexes were introduced in sequential steps into the tobacco genome. Each complex encoded different proteins, but contained identical gene regulatory regions (promoters). Unexpectedly, the first transgene complex, which was stably active on its own, often became silenced in the presence of the second ( Figure 2 ). The promoters of the silenced transgenes acquired DNA methylation, a genome modification frequently associated with silencing. Silencing and methylation were reversed when the transgene complexes segregated from each other in progeny, suggesting that interactions between the common promoter regions triggered silencing and methylation ( Matzke et al. 1989 ;  Park et al. 1996 ). Figure 2 Early Examples of Gene Silencing in Transgenic Plants TGS: Normally when two plants harboring separate transgenes encoding resistance to kanamycin (kan) or hygromycin (hyg), respectively, are crossed, 50% of the progeny are resistant to the individual antibiotics and 25% are resistant to a combination of both (top). In cases of silencing, expression of the KAN marker is extinguished in the presence of the HYG marker, as indicated by only 25% kan resistance and no double resistance (middle). PTGS: Transformation of wild-type petunia (bottom left) with a transgene encoding a pigment protein can lead to loss of pigment (white areas) owing to cosuppression of the transgene and homologous endogenous plant gene. (Photos on the left and in the middle were provided by Jan Kooter and on the right were provided by Natalie Doetsch and Rich Jorgensen.) PTGS was discovered in two ways. One involved experiments to evaluate antisense suppression, a promising approach at the time for selectively silencing plant gene expression. In theory, antisense RNA encoded by a transgene should basepair to the complementary mRNA of a plant gene, preventing its translation into protein. Although the control ‘sense’ transgene RNAs are unable to basepair to mRNA and hence should not induce silencing, they often inexplicably did ( Smith et al. 1990 ). In another type of experiment, efforts to enhance floral coloration in petunia by overexpressing a transgene encoding a protein involved in pigment synthesis led paradoxically to partial or complete loss of color ( Figure 2 ). This resulted from coordinate silencing (‘cosuppression’) of both the transgene and the homologous plant gene ( Napoli et al. 1990 ;  Van der Krol et al. 1990 ), later shown to occur at the posttranscriptional level ( De Carvalho et al. 1992 ;  Van Blokland et al. 1994 ) A related phenomenon, called quelling, was observed in the filamentous fungus  Neurospora crassa  ( Romano and Macino 1992 ). Similarly to TGS, PTGS was often associated with DNA methylation of transgene sequences ( Ingelbrecht et al. 1994 ). Two influential papers appeared in the early 1990s. One reported the discovery of RNA-directed DNA methylation in transgenic tobacco plants ( Wassenegger et al. 1994 ). This was the earliest demonstration of RNA-induced modification of DNA, a process that we return to below. A second study showed that plant RNA viruses could be both initiators and targets of PTGS. Plants expressing a transgene encoding a truncated viral coat protein became resistant to the corresponding virus, a state achieved by mutual degradation of viral RNA and transgene mRNA ( Lindbo et al. 1993 ). In addition to forging a link between RNA virus resistance and PTGS, this study included a remarkably prescient model for PTGS that featured an RNA-dependent RNA polymerase (RDR), small RNAs, and dsRNA, all of which were later found to be important for the RNAi. PTGS was subsequently shown in 1997 to protect plants naturally from virus infection ( Covey et al. 1997 ;  Ratcliff et al. 1997 ). Transgene PTGS thus tapped into a preexisting natural mechanism for combating viruses. To recap: by 1998—the year in which RNAi was reported—plant scientists had documented sequence-specific RNA degradation (PTGS), sequence-specific DNA methylation that triggered TGS, and RNA-directed DNA methylation. They had also proposed models for PTGS involving dsRNA ( Lindbo et al. 1993 ;  Metzlaff et al. 1997 ), small RNAs, and RDR ( Lindbo et al. 1993 ).
Early Silencing Phenomena
Starting in the late 1980s, biologists working with transgenic plants found themselves confronted with a ‘bewildering array’ of unanticipated gene silencing phenomena ( Martienssen and Richards 1995 ). Most intriguing were cases in which silencing seemed to be triggered by DNA or RNA sequence interactions, which could occur between two separate transgenes that shared sequence homology or between a transgene and homologous plant gene. Several early examples supplied the prototypes for two types of RNA-mediated gene silencing that are recognized today. In one type, silencing results from a block in mRNA synthesis (transcriptional gene silencing [TGS]); in the second type, silencing results from mRNA degradation (posttranscriptional gene silencing [PTGS]) ( Figure 1 ).TGS was revealed when two different transgene complexes were introduced in sequential steps into the tobacco genome. Each complex encoded different proteins, but contained identical gene regulatory regions (promoters). Unexpectedly, the first transgene complex, which was stably active on its own, often became silenced in the presence of the second ( Figure 2 ). The promoters of the silenced transgenes acquired DNA methylation, a genome modification frequently associated with silencing. Silencing and methylation were reversed when the transgene complexes segregated from each other in progeny, suggesting that interactions between the common promoter regions triggered silencing and methylation ( Matzke et al. 1989 ;  Park et al. 1996 ).PTGS was discovered in two ways. One involved experiments to evaluate antisense suppression, a promising approach at the time for selectively silencing plant gene expression. In theory, antisense RNA encoded by a transgene should basepair to the complementary mRNA of a plant gene, preventing its translation into protein. Although the control ‘sense’ transgene RNAs are unable to basepair to mRNA and hence should not induce silencing, they often inexplicably did ( Smith et al. 1990 ). In another type of experiment, efforts to enhance floral coloration in petunia by overexpressing a transgene encoding a protein involved in pigment synthesis led paradoxically to partial or complete loss of color ( Figure 2 ). This resulted from coordinate silencing (‘cosuppression’) of both the transgene and the homologous plant gene ( Napoli et al. 1990 ;  Van der Krol et al. 1990 ), later shown to occur at the posttranscriptional level ( De Carvalho et al. 1992 ;  Van Blokland et al. 1994 ) A related phenomenon, called quelling, was observed in the filamentous fungus  Neurospora crassa  ( Romano and Macino 1992 ). Similarly to TGS, PTGS was often associated with DNA methylation of transgene sequences ( Ingelbrecht et al. 1994 ).Two influential papers appeared in the early 1990s. One reported the discovery of RNA-directed DNA methylation in transgenic tobacco plants ( Wassenegger et al. 1994 ). This was the earliest demonstration of RNA-induced modification of DNA, a process that we return to below. A second study showed that plant RNA viruses could be both initiators and targets of PTGS. Plants expressing a transgene encoding a truncated viral coat protein became resistant to the corresponding virus, a state achieved by mutual degradation of viral RNA and transgene mRNA ( Lindbo et al. 1993 ). In addition to forging a link between RNA virus resistance and PTGS, this study included a remarkably prescient model for PTGS that featured an RNA-dependent RNA polymerase (RDR), small RNAs, and dsRNA, all of which were later found to be important for the RNAi. PTGS was subsequently shown in 1997 to protect plants naturally from virus infection ( Covey et al. 1997 ;  Ratcliff et al. 1997 ). Transgene PTGS thus tapped into a preexisting natural mechanism for combating viruses.To recap: by 1998—the year in which RNAi was reported—plant scientists had documented sequence-specific RNA degradation (PTGS), sequence-specific DNA methylation that triggered TGS, and RNA-directed DNA methylation. They had also proposed models for PTGS involving dsRNA ( Lindbo et al. 1993 ;  Metzlaff et al. 1997 ), small RNAs, and RDR ( Lindbo et al. 1993 ).RNAi RNAi was discovered in experiments designed to compare the silencing activity of single-stranded RNAs (ssRNAs) (antisense or sense) with their dsRNA hybrid. While only marginal silencing of a target gene was achieved after injecting worms with the individual strands, injection of a sense–antisense mixture resulted in potent and specific silencing ( Fire et al. 1998 ). This unequivocally fingered dsRNA as the trigger of silencing. Shortly thereafter, dsRNA was shown to provoke gene silencing in other organisms, including plants ( Waterhouse et al. 1998 ). Indeed, the relatedness of RNAi, PTGS, and quelling was confirmed when genetic analyses in worms, plants, and  Neurospora  identified common components in the respective silencing pathways ( Denli and Hannon 2003 ). This included the aforementioned RDR, which can synthesize dsRNA from ssRNA templates (see  Figure 1 ). PTGS is now accepted as the plant equivalent of RNAi. The discovery of RNAi established a requirement for dsRNA in silencing, but details of the mechanism remained unclear. In 1999, plant scientists studying PTGS provided a crucial clue when they detected small (approximately 25 nucleotide-long) RNAs corresponding to silenced target genes in transgenic plants ( Hamilton and Baulcombe 1999 ). They proposed that the small RNAs provided the all-important specificity determinant for silencing. Consistent with this, a rapid succession of studies in  Drosophila  systems demonstrated that 21–23 nucleotide ‘short interfering'RNAs (siRNAs), derived from cutting longer dsRNA, can guide mRNA cleavage ( Zamore et al. 2000 ;  Elbashir et al. 2001 ); identified RISC (RNA-induced silencing complex), a nuclease that associates with small RNAs and executes target mRNA cleavage ( Hammond et al. 2000 ); and identified Dicer, the enzyme that chops dsRNA into short RNAs ( Bernstein et al. 2001 ) (see  Figure 1 ). RNAi/PTGS was detected originally in experiments involving transgenes, injected RNAs, or viruses. Did the RNAi machinery also generate small RNAs for host gene regulation? Strikingly, the newly discovered siRNAs were the same size as several ‘small temporal’ RNAs, first identified in 1993 as important regulators of developmental timing in worms ( Lee et al. 1993 ;  Reinhart et al. 2000 ). Everything came together in 2001 when heroic cloning efforts unearthed dozens of natural small RNAs 21–25 nucleotides in length, first from worms and flies and later from plants and mammals ( Lai 2003 ;  Bartel 2004 ). Similar to siRNAs, the natural small RNAs, dubbed microRNAs (miRNAs), arise from Dicer processing of dsRNA precursors and are incorporated into RISC ( Denli and Hannon 2003 ). In many cases, miRNAs effect silencing by basepairing to the 3′ ends of target mRNAs and repressing translation (see  Figure 1 ). miRNAs are now recognized as key regulators of plant and animal development. Identifying their target genes and full range of action are areas of intense research ( Lai 2003 ;  Bartel 2004 ). Up until 2002, RNAi/PTGS and miRNAs were the most avidly studied aspects of RNA-mediated gene silencing. The next major advance, however, abruptly turned attention back to RNA-guided modifications of the genome. By 2001, plant scientists working on RNA-directed DNA methylation and TGS had demonstrated a requirement for dsRNAs that are processed to short RNAs, reinforcing a mechanistic link to PTGS ( Mette et al. 2000 ;  Sijen et al. 2001 ). This established the principle of RNA-guided genome modifications, but the generality of this process was uncertain because not all organisms methylate their DNA. Widespread acceptance came with the discovery in 2002 of RNAimediated heterchromatin assembly in fission yeast ( Hall et al. 2002 ;  Volpe et al. 2002 ). This silencing pathway uses short RNAs produced by Dicer and other RNAi components to direct methylation of DNA-associated proteins (histones), thus generating condensed, transcriptionally silent chromosome regions (heterochromatin) (see  Figure 1 ). Targets of this pathway include centromeres, which are essential for normal chromosome segregation. The RNAi-dependent heterochromatin pathway has been found in plants ( Zilberman et al. 2003 ) and  Drosophila  ( Pal-Bhadra et al. 2004 ) and likely represents a general means for creating condensed, silent chromosome domains.
RNAi
RNAi was discovered in experiments designed to compare the silencing activity of single-stranded RNAs (ssRNAs) (antisense or sense) with their dsRNA hybrid. While only marginal silencing of a target gene was achieved after injecting worms with the individual strands, injection of a sense–antisense mixture resulted in potent and specific silencing ( Fire et al. 1998 ). This unequivocally fingered dsRNA as the trigger of silencing. Shortly thereafter, dsRNA was shown to provoke gene silencing in other organisms, including plants ( Waterhouse et al. 1998 ). Indeed, the relatedness of RNAi, PTGS, and quelling was confirmed when genetic analyses in worms, plants, and  Neurospora  identified common components in the respective silencing pathways ( Denli and Hannon 2003 ). This included the aforementioned RDR, which can synthesize dsRNA from ssRNA templates (see  Figure 1 ). PTGS is now accepted as the plant equivalent of RNAi.The discovery of RNAi established a requirement for dsRNA in silencing, but details of the mechanism remained unclear. In 1999, plant scientists studying PTGS provided a crucial clue when they detected small (approximately 25 nucleotide-long) RNAs corresponding to silenced target genes in transgenic plants ( Hamilton and Baulcombe 1999 ). They proposed that the small RNAs provided the all-important specificity determinant for silencing. Consistent with this, a rapid succession of studies in  Drosophila  systems demonstrated that 21–23 nucleotide ‘short interfering'RNAs (siRNAs), derived from cutting longer dsRNA, can guide mRNA cleavage ( Zamore et al. 2000 ;  Elbashir et al. 2001 ); identified RISC (RNA-induced silencing complex), a nuclease that associates with small RNAs and executes target mRNA cleavage ( Hammond et al. 2000 ); and identified Dicer, the enzyme that chops dsRNA into short RNAs ( Bernstein et al. 2001 ) (see  Figure 1 ).RNAi/PTGS was detected originally in experiments involving transgenes, injected RNAs, or viruses. Did the RNAi machinery also generate small RNAs for host gene regulation? Strikingly, the newly discovered siRNAs were the same size as several ‘small temporal’ RNAs, first identified in 1993 as important regulators of developmental timing in worms ( Lee et al. 1993 ;  Reinhart et al. 2000 ). Everything came together in 2001 when heroic cloning efforts unearthed dozens of natural small RNAs 21–25 nucleotides in length, first from worms and flies and later from plants and mammals ( Lai 2003 ;  Bartel 2004 ). Similar to siRNAs, the natural small RNAs, dubbed microRNAs (miRNAs), arise from Dicer processing of dsRNA precursors and are incorporated into RISC ( Denli and Hannon 2003 ). In many cases, miRNAs effect silencing by basepairing to the 3′ ends of target mRNAs and repressing translation (see  Figure 1 ). miRNAs are now recognized as key regulators of plant and animal development. Identifying their target genes and full range of action are areas of intense research ( Lai 2003 ;  Bartel 2004 ).Up until 2002, RNAi/PTGS and miRNAs were the most avidly studied aspects of RNA-mediated gene silencing. The next major advance, however, abruptly turned attention back to RNA-guided modifications of the genome. By 2001, plant scientists working on RNA-directed DNA methylation and TGS had demonstrated a requirement for dsRNAs that are processed to short RNAs, reinforcing a mechanistic link to PTGS ( Mette et al. 2000 ;  Sijen et al. 2001 ). This established the principle of RNA-guided genome modifications, but the generality of this process was uncertain because not all organisms methylate their DNA. Widespread acceptance came with the discovery in 2002 of RNAimediated heterchromatin assembly in fission yeast ( Hall et al. 2002 ;  Volpe et al. 2002 ). This silencing pathway uses short RNAs produced by Dicer and other RNAi components to direct methylation of DNA-associated proteins (histones), thus generating condensed, transcriptionally silent chromosome regions (heterochromatin) (see  Figure 1 ). Targets of this pathway include centromeres, which are essential for normal chromosome segregation. The RNAi-dependent heterochromatin pathway has been found in plants ( Zilberman et al. 2003 ) and  Drosophila  ( Pal-Bhadra et al. 2004 ) and likely represents a general means for creating condensed, silent chromosome domains.More Lessons from Plants Plant scientists can chalk up other ‘firsts’ in RNA-mediated gene silencing. Systemic silencing, in which a silencing signal (short RNA or dsRNA) moves from cell to cell and through the vascular system to induce silencing at distant sites, was initially detected in plants in 1997 ( Palauqui et al. 1997 ;  Voinnet and Baulcombe 1997 ) and later in worms ( Fire et al. 1998 ), although not yet in  Drosophila  or mammals. Viral proteins that suppress silencing by disarming the PTGS-based antiviral defense mechanism were discovered by plant virologists in 1998 ( Anandalakshmi et al. 1998 ;  Béclin et al. 1998 ;  Brigneti et al. 1998 ;  Kasschau and Carrington 1998 ). One of these, the p19 protein of tombusviruses, acts as a size-selective caliper to sequester short RNAs from the silencing machinery ( Vargason et al. 2003 ). A recent study suggests that animal viruses encode suppressors of RNA-mediated silencing ( Li et al. 2004 ). Although RNA-mediated gene silencing pathways are evolutionarily conserved, there are various elaborations in different organisms. For example, the plant  Arabidopsis  has four Dicer-like (DCL) proteins, in contrast to mammals and worms, whose genomes encode only one Dicer protein ( Schauer et al. 2002 ). The RDR family has also expanded in  Arabidopsis  to include at least three active members. An important goal has been to determine the functions of individual family members. Previous studies in  Arabidopsis  have shown that DCL1 is needed for processing miRNA precursors important for plant development ( Park et al. 2002 ;  Reinhart et al. 2002 ), but not for siRNAs active in RNAi ( Finnegan et al. 2003 ). The paper by  Xie et al. (2004)  in this issue of  PLoS Biology  delineates distinct functions for DCL2, DCL3, and RDR2. Nuclear-localized DCL3 acts with RDR2 to generate short RNAs that elicit DNA and histone modifications; DCL2 produces short RNAs active in antiviral defense in the cytoplasm of cells. This study illustrates nicely how RNA silencing components have diversified in plants to carry out specialized functions. By identifying small RNAs as agents of gene silencing that act at multiple levels throughout the cell, molecular biologists have created a new paradigm for eukaryotic gene regulation. Plant scientists have figured prominently in RNA-mediated silencing research. Instrumental to their success was the early ability to produce large numbers of transgenic plants, which displayed a rich variety of gene silencing phenomena that were amenable to analysis. The agricultural biotechnology industry provided incentives to find ways to stabilize transgene expression and use transgenic approaches to modulate plant gene expression and to genetically engineer virus resistance. As exemplified by the petunia cosuppression experiments, nonessential plant pigments provide conspicuous visual markers that vividly reveal gene silencing. The history of gene silencing research shows once again that plants offer outstanding experimental systems for elucidating general biological principles.
More Lessons from Plants
Plant scientists can chalk up other ‘firsts’ in RNA-mediated gene silencing. Systemic silencing, in which a silencing signal (short RNA or dsRNA) moves from cell to cell and through the vascular system to induce silencing at distant sites, was initially detected in plants in 1997 ( Palauqui et al. 1997 ;  Voinnet and Baulcombe 1997 ) and later in worms ( Fire et al. 1998 ), although not yet in  Drosophila  or mammals. Viral proteins that suppress silencing by disarming the PTGS-based antiviral defense mechanism were discovered by plant virologists in 1998 ( Anandalakshmi et al. 1998 ;  Béclin et al. 1998 ;  Brigneti et al. 1998 ;  Kasschau and Carrington 1998 ). One of these, the p19 protein of tombusviruses, acts as a size-selective caliper to sequester short RNAs from the silencing machinery ( Vargason et al. 2003 ). A recent study suggests that animal viruses encode suppressors of RNA-mediated silencing ( Li et al. 2004 ).Although RNA-mediated gene silencing pathways are evolutionarily conserved, there are various elaborations in different organisms. For example, the plant  Arabidopsis  has four Dicer-like (DCL) proteins, in contrast to mammals and worms, whose genomes encode only one Dicer protein ( Schauer et al. 2002 ). The RDR family has also expanded in  Arabidopsis  to include at least three active members. An important goal has been to determine the functions of individual family members. Previous studies in  Arabidopsis  have shown that DCL1 is needed for processing miRNA precursors important for plant development ( Park et al. 2002 ;  Reinhart et al. 2002 ), but not for siRNAs active in RNAi ( Finnegan et al. 2003 ). The paper by  Xie et al. (2004)  in this issue of  PLoS Biology  delineates distinct functions for DCL2, DCL3, and RDR2. Nuclear-localized DCL3 acts with RDR2 to generate short RNAs that elicit DNA and histone modifications; DCL2 produces short RNAs active in antiviral defense in the cytoplasm of cells. This study illustrates nicely how RNA silencing components have diversified in plants to carry out specialized functions.PLoS BiologyBy identifying small RNAs as agents of gene silencing that act at multiple levels throughout the cell, molecular biologists have created a new paradigm for eukaryotic gene regulation. Plant scientists have figured prominently in RNA-mediated silencing research. Instrumental to their success was the early ability to produce large numbers of transgenic plants, which displayed a rich variety of gene silencing phenomena that were amenable to analysis. The agricultural biotechnology industry provided incentives to find ways to stabilize transgene expression and use transgenic approaches to modulate plant gene expression and to genetically engineer virus resistance. As exemplified by the petunia cosuppression experiments, nonessential plant pigments provide conspicuous visual markers that vividly reveal gene silencing. The history of gene silencing research shows once again that plants offer outstanding experimental systems for elucidating general biological principles.