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Influence of local and long-distance dispersal on patterns of Bt resistance in cabbage loopers (Trichoplusia… Franklin, Michelle Teresa 2009

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    Influence of local and long-distance dispersal on patterns of Bt resistance in cabbage loopers (Trichoplusia ni)    by  Michelle Teresa Franklin   B.Sc., Simon Fraser University, 2003     A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF    DOCTOR OF PHILOSOPHY  in  The Faculty of Graduate Studies  (Zoology)     The University of British Columbia  (Vancouver)   December 2009    ©Michelle Teresa Franklin, 2009  ii ABSTRACT   Subdivision of populations can significantly impact the maintenance of genetic variation and a population’s response to selection pressures.  Agroecosystems, in which habitats are subdivided and selection pressures from insecticide use vary, provide some of the best opportunities for examining the genetic consequences of local adaptation. The insect pest, Trichoplusia ni has developed resistance to the microbial insecticide, Bacillus thuringiensis (kurstaki) in vegetable greenhouses in British Columbia (BC), Canada. Trichoplusia ni is a subtropical insect and is thought to migrate each spring from California to BC; however it can also persist year-round in BC greenhouses.  Here, I examine the local and regional patterns of Bt resistance and their relation to the population genetic structure of T. ni.  Patterns of Bt resistance suggest that early season dispersal occurs from over- wintered, Bt treated greenhouse populations into neighbouring untreated greenhouse populations in BC.  Field populations surveyed from California to BC remain susceptible to Bt, but have failed to dilute resistance in Bt resistant greenhouse populations.  To examine the influence of the genetic structure on Bt resistance, DNA isolation procedures were developed and primer combinations screened for amplified fragment length polymorphism (AFLP) analysis.  A lack of genetic structure was revealed at the regional scale and indicated that gene flow connects populations from California to BC. At the local scale, a surprising level of spatial and temporal genetic structure was detected.  Greenhouse populations from the local areas of Delta, Langley, and Abbotsford formed genetically distinct groups, whereas the spring and summer field collections showed genetic similarity to long-range migrants.  Temporal collections reveal a genetic  iii shift between spring and summer and autumn collections that potentially could be attributed to the movement of long-range migrants into greenhouse and field populations in the autumn.  Lastly, Bt resistance is positively correlated with genetic differentiation in populations and provides evidence that recurrent Bt sprays likely induce population bottlenecks in greenhouse populations.  Overall, this study provides an example of human-induced changes in which geographically concentrated greenhouses that support year-round populations and strong selection from Bt use significantly alter the genetic structure of T. ni populations in BC.                                iv TABLE OF CONTENTS  Abstract..................................................................................................................... ii  Table of contents...................................................................................................... iv  List of tables ........................................................................................................... vii  List of figures......................................................................................................... viii  Acknowledgements ................................................................................................... x  Co-authorship statement.......................................................................................... xii  CHAPTER 1: General introduction....................................................................... 1     1.1 Populations structure ........................................................................................ 1    1.2 Evolution and Bt resistance............................................................................... 2    1.3 Population structure of T. ni.............................................................................. 5    1.4 Molecular markers............................................................................................ 8    1.5 Thesis objectives and overview......................................................................... 9    1.6 References...................................................................................................... 12  CHAPTER 2: Refuges in reverse: the spread of Bt resistance to unselected greenhouse populations of cabbage loopers Trichoplusia ni................................ 18     2.1 Introduction.................................................................................................... 18    2.2 Methods ......................................................................................................... 20          2.2.1 Greenhouse and field collections............................................................ 20          2.2.2 Bioassay procedures............................................................................... 22          2.2.3 Data analysis.......................................................................................... 23    2.3 Results............................................................................................................ 24          2.3.1 Local patterns of Bt resistance................................................................ 24          2.3.2 Spatial patterns of Bt resistance.............................................................. 26          2.3.3 Temporal patterns of Bt resistance ......................................................... 27          2.3.4 Regional patterns of Bt resistance........................................................... 27    2.4 Discussion ...................................................................................................... 27    2.5 References...................................................................................................... 43  CHAPTER 3: Distinguishing between laboratory-reared and greenhouse- and field collected Trichoplusia ni  (Lepidoptera: Noctuidae) using the amplified fragment length polymorphism method ............................................................................... 48     3.1 Introduction.................................................................................................... 48    3.2 Methods ......................................................................................................... 50          3.2.1 Insect material ....................................................................................... 50  v          3.2.2 DNA extraction...................................................................................... 51          3.2.3 AFLP analysis ....................................................................................... 51          3.2.4 Gel scoring ............................................................................................ 52          3.2.5 Data analysis.......................................................................................... 53    3.3 Results............................................................................................................ 53          3.3.1 Primer combinations and levels of polymorphism detected .................... 53          3.3.2 Genetic variation in laboratory and wild populations.............................. 54    3.4 Discussion ...................................................................................................... 55    3.5 References...................................................................................................... 65  CHAPTER 4: Genetic analysis of cabbage loopers, Trichoplusia ni (Lepidoptera: Noctuidae) along their western North American migratory path ...................... 69     4.1 Introduction.................................................................................................... 69    4.2 Methods ......................................................................................................... 71          4.2.1 Specimen collections ............................................................................. 71          4.2.2 DNA isolation........................................................................................ 72          4.2.3 Mitochondrial sequencing and analysis .................................................. 72          4.2.4 AFLP genotyping and analysis............................................................... 73    4.3 Results............................................................................................................ 77          4.3.1 Specimen collections ............................................................................. 77          4.3.2 Mitochondrial sequencing...................................................................... 77          4.3.3 AFLP genotyping................................................................................... 78    4.4 Discussion ...................................................................................................... 80    4.5 References...................................................................................................... 96  CHAPTER 5: Spatial and temporal changes in genetic structure of greenhouse and field populations of cabbage looper, Trichoplusia ni.......................................... 103     5.1 Introduction.................................................................................................. 103    5.2 Methods ....................................................................................................... 106          5.2.1 Greenhouse and field collections.......................................................... 106          5.2.2 Molecular analysis ............................................................................... 107          5.2.3 Statistical analysis................................................................................ 108    5.3 Results.......................................................................................................... 112          5.3.1 Polymorphism and genetic diversity..................................................... 112          5.2.3 Genetic structure.................................................................................. 113    5.4 Discussion .................................................................................................... 117          5.4.1 Genetic differentiation and dispersal patterns....................................... 117          5.4.2 Temporal variation in genetic differentiation........................................ 120 5.4.3 Genetic structure and Bt resistance....................................................... 121    5.5 References.................................................................................................... 131  CHAPTER 6: General conclusions..................................................................... 137     6.1 General thesis overview................................................................................ 137  vi    6.2 Applications ................................................................................................. 139    6.3 Future directions........................................................................................... 140    6.4 References.................................................................................................... 142  Appendix 1: Summary of sample localities codes, cities in which larvae were collected, collection dates, and latitude and longitude coordinates for each site (chapter 5) ... 146  Appendix 2: Pairwise FST values between Trichoplusia ni collections from greenhouse and field localities throughout the lower mainland of British Columbia, Canada (chapter 5). ......................................................................................................................... 147  Appendix 3: STRUCTURE results obtained when loci that were potentially under selection were trimmed from data analyzed in chapter 5 and reanalyzed................ 155                                   vii  LIST OF TABLES  Table 2.1: Summary of crops, management practices, and Bt applications used in greenhouses and fields prior to sampling, in 2004, and during Trichoplusia ni larval collections, in 2005 and 2006. ................................................................................. 34  Table 2.2: Summary of local and regional collections of Trichoplusia ni ............... 36  Table 3.1: AFLP primer combinations and the number of visually scored fragments for the four laboratory Trichoplusia ni populations used in the initial screening of AFLP primers.................................................................................................................... 59  Table 3.2: Summary of genetic diversity for three AFLP primer sets for laboratory and greenhouse and field Trichoplusia ni populations .................................................... 61  Table 3.3: Pairwise FST values among laboratory and greenhouse and field Trichoplusia ni populations.......................................................................................................... 62  Table 4.1: Summary of collection dates, latitude and longitude coordinates, and the crops that Trichoplusia ni larvae were collected from....................................................... 86  Table 4.2: MtDNA region, primer name and sequence, size of region (bp), the number of individuals sequenced (N), their sampling localities, and corresponding GenBank accession numbers................................................................................................... 87  Table 4.3: AFLP primer combinations and the number of scored fragments for Trichoplusia ni populations surveyed from the west coast of North America in 2006 ................................................................................................................................ 88  Table 4.4: Summary of descriptive statistics for mitochondrial and nuclear markers for Trichoplusia ni collected from 13 sampling localities on the west coast of North America ……………………………………………………………………………………….89  Table 4.5: Pairwise FST values and geographic distances (km) between Trichoplusia ni populations collected from localities along the west coast of North America. .......... 90  Table 5.1: AFLP primer combinations and the number of scored fragments for greenhouse and field Trichoplusia ni populations surveyed in British Columbia.... 123  Table 5.2: Bt resistance levels as measured by the lethal concentration that killed 50% of larvae (LC50) and expected heterozygosity (He) for greenhouse and field collections from Delta, Langley, and Abbotsford, British Columbia and field collections from Santa Maria and Oxnard, California (CA). ................................................................................ 124    viii LIST OF FIGURES  Figure 2.1: Mean proportion dead for Trichoplusia ni progeny assayed for Bt resistance from greenhouses and field populations surveyed in British Columbia .................... 38  Figure 2.2:  LC50 values and fiducial limits for Trichoplusia ni collected from greenhouses and fields in British Columbia............................................................. 39  Figure 2.3: Locations of greenhouses surveyed in 2006.  Circles encompassing greenhouses indicate that Bt resistance levels do not differ among these groups ...... 40  Figure 2.4: Mean proportion dead for greenhouse Trichoplusia ni populations that were assayed for Bt resistance in 2006 in British Columbia.............................................. 41  Figure 2.5: LC50 values and fiducial limits for Trichoplusia ni field populations collected from California, Oregon, and British Columbia in 2006 .......................................... 42  Figure 3.1: AFLP gel showing fingerprints for laboratory, greenhouse, and field Trichoplusia ni individuals using the primer combination E+CGA and M+AGCT .. 63  Figure 3.2: UPGMA tree describing the relationship of wild collected greenhouse and field Trichoplusai ni populations and laboratory populations................................... 64  Figure 4.1: Geographic distribution of sites where Trichoplusia ni were collected along their migration route ............................................................................................... 91  Figure 4.2: Haplotype network of 12 mtDNA haplotypes based on gene regions NAD1 and NAD4 .............................................................................................................. 92  Figure 4.3: !K values calculated according to the method outlined in Evanno et al. (2005) from the clustering results obtained from STRUCTURE for each cluster size (K) from 2 to 10. ........................................................................................................... 93  Figure 4.4: Results of STRUCTURE clustering analysis (K = 3) for 149 Trichoplusia ni collected from British Columbia (BD, BA), Washington (WS), Oregon (OA, OC, OR), and California (CY, CX1, CX2, CS)........................................................................ 94  Figure 4.5: Relationship between Nei’s genetic distance and geographic distance among populations of Trichoplusia ni from California to British Columbia. ....................... 95  Figure 5.1: Geographic distribution of greenhouse and field sites where Trichoplusia ni were collected in British Columbia........................................................................ 126  Figure 5.2: Expected heterozygosity (SE) under Hardy-Weinberg proportions for Trichoplusia ni collections from British Columbia between 2005 and 2007........... 127   ix Figure 5.3: Isolation by distance relationship for greenhouse and field Trichoplusia ni populations............................................................................................................ 128  Figure 5.4: Regression of pairwise FST values on Bt resistance as measured by the mean of the two populations’ LC50 values for 120 pairwise comparisons among 16 greenhouse and field Trichoplusia ni collections from British Columbia.................................. 129  Figure 5.5: Results of STRUCTURE clustering analysis identified three clusters (K = 3) based on 169 polymorphic loci for 426 Trichoplusia ni larvae in 2006 .................. 130                                       x ACKNOWLEDGMENTS   I would like to thank my supervisor, Judy Myers for her mentorship, friendship, support, and enthusiasm for my work over the past five years.  She has provided me with the opportunity to pursue my ideas and questions, while inspiring me with her curiosity and strength.  I would like to thank all members of my committee including, Carol Ritland, Mike Whitlock, David Theilmann, and Wayne Maddison for their valuable insight and guidance.  I also would like to thank Jenny Cory for her advice, feedback on manuscripts, and for being a great rooming partner at conferences.  I thank past and present lab mates, Alida Janmaat, Valerie Caron, Veronica Cervantes, Caroline Jackson, Jackie Shaben, Jerry Ericsson, Amanda Brown, Christal Nieman, Andrea Stephens, Michelle Tseng, and Tom Deane for their thoughts and encouragement.  I owe much thanks to the many assistants that aided in the collection and rearing of T. ni including, Tamara Richardson, Patricia Duffels, Ikkei Shikano, Karmen Scott, Jennifer Yam and a special thanks to my friends Michelle Collette, and Nicole Vander Wal who spent their vacations collecting T. ni in Oregon and Washington.   I also would like to thank Carol Ritland not only for her support as a committee member, but also for her mentorship in the Genetics Data Centre.  I had no experience in a molecular laboratory before starting my PhD and Carol taught me all the skills I required.  I owe much thanks to all the people who spent countless hours helping me complete my genetics laboratory work and for their friendships including, Hesther Yueh, Allyson Miscampbell, Gillian Leung, and Michelle Tang.  I thank Tony Kozak and Kermit Ritland for providing valuable statistical advice and Renaud Vitalis, Xavier Vekemans, Noah Rosenberg, Mark Beaumont, Aurélie Bonin, Olivier Francois, and Stephanie Manel  xi for providing answers over e-mail to my many questions regarding statistical programs for analyzing genetic data.  I am also grateful for all the friendships I have made while at UBC, especially Jackie Ngai (best running partner), Jessica Purcell, and Hesther Yueh.  I owe much thanks to all the greenhouse growers and BC Greenhouse Growers’ Association for allowing me into their greenhouses to collect T. ni.  I would also like to thank Heather Niven from ES Crop Consult, the many Agriculture Extension Advisors, and growers from California, Oregon, and Washington that helped me locate suitable fields for collecting T. ni.  The financial support from the BC Vegetable Greenhouse Growers’ Research Council, BC Investment Agriculture, NSERC Biocontrol Network, and the Entomological Society of Canada made this project possible.  I thank my parents for always believing in me and the love and support they have provided throughout my education.  Lastly, I am grateful to my husband, Kevin Nesbitt for his patience, love and support that he has provided throughout both my undergraduate and graduate career.                    xii  CO-AUTHORSHIP STATEMENT   Chapter 2 was designed by myself and my research supervisor, Judy Myers.  I collected the data, completed the statistical analysis, and wrote the manuscript, while Judy helped edit the manuscript.  Chapters 3, 4, and 5 were designed by myself, Carol Ritland, and Judy Myers.  I carried out the collection of samples and the genetic analysis with teaching and advice from Carol.  I performed the statistical analysis and wrote the manuscript, with editorial comments from Carol and Judy.                             1 CHAPTER 1. GENERAL INTRODUCTION  1.1 POPULATION STRUCTURE  Genetic variation and selection have long been recognized as the primary determinants of evolutionary change (Lande & Shannon 1996; Via 1990).  The subdivision of populations in heterogeneous environments can greatly affect the maintenance of genetic variation and a population’s response to selection pressures (Whitlock 2002).  The structure exhibited by many species can be characterized by the metapopulation concept, whereby spatially isolated local populations are subject to extinction and recolonization and interconnected by migration (Hanski 1998).  Several processes can influence the genetic structure and evolutionary trajectories of species that exist as metapopulations; these include the frequency of extinction-recolonization events, rates of gene flow, the number and source of founders, and differential selection pressures among subpopulations or demes.  Much theoretical work has focused on the genetic consequences of existing within a metapopulation structure (Harrison & Hastings 1996; Pannell & Charlesworth 2000) and have led to some general predictions that can be used to guide research in natural metapopulations.  Wade & McCauley (1988) found that extinction-recolonization events could have a homogenizing effect on population structure if many individuals from a large number of source populations colonize an area.  In contrast, if colonizing individuals arrive from one or a few sources these events can increase genetic drift and subsequent differentiation among subpopulations (Wade & McCauley 1988).  The local equilibrium established among subpopulations after an extinction-recolonization event will depend on the number and source of colonists and subsequent migration among  2 subpopulations (Pannell & Charlesworth 1999).  In general, if a large number of individuals from different demes recolonize a habitat patch, diversity may be higher than can be maintained at equilibrium conditions if migration rates are low (Pannell & Charlesworth 2000).  Thus as the metapopulation equilibrates differentiation is expected to increase within-patch while diversity is reduced.  Alternatively, if only a few individuals from nearby demes recolonize a habitat patch, but subsequent migration rates are high diversity is expected to increase and population differentiation will be reduced overtime (Pannell & Charlesworth 2000).  The subdivision of populations can also impact selection responses in individual demes when selection pressures vary over the landscape (Whitlock 2002).  Models incorporating spatial heterogeneity have found that low migration rates among subpopulations are likely to promote local adaptation (Gillespie 1975; Speith 1979). However, if migration rates are high among subpopulations local selection may be overwhelmed by dispersal and local adaptation will be delayed (Caprio & Tabashnik 1992). 1.2 EVOLUTION AND BT RESISTANCE  Strong directional selection induced by human technologies has accelerated evolutionary change in ecosystems world wide (Palumbi 2001; Smith & Bernatchez 2008).  The arms race between humans and insects in agricultural crops has made insect pests a primary target of these technologies (Palumbi 2001).  Humans have devised numerous methods to control insect pests including: trapping, manual searching, introducing predators, parasitoids, or pathogens, and applying chemical and natural insecticides.  Of these, chemical insecticides has been the most effective method for the  3 short-term control of insect pests.  However, extensive use of chemical insecticides has resulted in the development of resistance in over 500 insect species with some of them resistant to entire insecticide classes (Georghiou & Lagunes-Tejeda 1991).  In recent years, along with the development of new chemical insecticides, natural insecticides with reduced environmental risk have gained in popularity.  In the early 1900s, the insecticidal properties of the bacterium, Bacillus thuringiensis (Bt) was discovered through the death of silkworm caterpillars (Milner 1994).  However, it did not gain in use until the efficacy of Bt formulations were improved enough to make them comparable to that of chemical insecticides.  For over 30 years, the microbial insecticide, Bacillus thuringiensis, subspecies kurstaki has been used to control lepidopteran pests in agricultural crops (Janmaat & Myers 2003).  Some thought that Bt’s complex mode of action and multiple toxin formulations would prevent resistance from evolving. However, laboratory selection experiments revealed that many insect pests have the potential to develop resistance to Bt (Tabashnik 1994) and to date, resistance has evolved in five lepidopteran species in field or greenhouse crops (Ferré & Van Rie 2002; Gassmann et al. 2009; Tabashnik 1994; Tabashnik et al. 2008). Busseola fusca, Helicoverpa zea, and Spodoptera frugiperda have developed resistance to genetically modified crops expressing single Bt toxins (Matten et al. 2008; Tabashnik et al. 2008; Van Rensburg 2007).  Resistance has also been reported in response to spray formulations containing multiple Bt toxins in Plutella xylostella in field populations world wide (Ferré & Van Rie 2002; Tabashnik 1994) and in Trichoplusia ni greenhouse populations in British Columbia (BC), Canada (Janmaat & Myers 2003), where it is the primary Lepidopteran pest (Janmaat 2004).  4  In susceptible Lepidoptera, Bt toxins bind to the midgut brush boarder leading to pore formation and leakage of gut contents into the haemolymph (Ferre & Van Rie 2002).  Changes in the alkalinity of the insect’s haemolymph results in paralysis and death.  The most widespread mode of resistance is associated with reduced or absent binding of Bt toxins to the midgut membrane (Tabashnik et al. 1998).  Wang et al. (2007) demonstrated through Cry1Ab and Cry1Ac binding assays that resistance was associated with a reduction in the binding of Cry toxins to the midgut brush boarder membrane in greenhouse collected T. ni.  Midgut receptors associated with binding of Cry1A toxins include the cadherin protein, aminopeptidase N, alkaline phosphatase, 252 KDa high molecular weight protein, and midgut glycolipids (Wang et al. 2007).  Bioassay using modified Cry1Ab and Cry1Ac toxins that lack helix !-1 and are thought to skip binding to the cadherin receptor protein have been shown to reduce resistance in a greenhouse- selected resistant strains of T. ni (Franklin et al. 2009b).  These results suggest that a lack of binding to the cadherin receptor protein could be contributing to resistance in T. ni greenhouse populations.  Resistance evolved in T. ni greenhouse populations in response to strong selection through repeated sprays of the Bt formulation DiPel (Janmaat & Myers 2003), which is produced by the subspecies kurstaki HD-1 and contains the toxins Cry1Aa, Cry1Ab, Cry1Ac, and Cry2Aa (Liu et al. 1996).  In southern California (CA), transgenic cottons Bollgard and Ingard (Monsanto) expressing the toxin protein gene Cry1Ac, and the new variety, Bollgard II, expressing two toxin genes, Cry1Ac and Cry2Ab or Cry1Ac and Cry1F have provided suitable hosts for T. ni populations (Li et al. 2007).  Thus it is plausible that Bt resistance has also developed in T. ni populations feeding on transgenic  5 cotton in CA.  Trichoplusia ni is a polyphagous insect, feeding on over 160 plant species (Sutherland & Green 1984) and thus if Bt resistance developed in T. ni populations feeding on transgenic cotton it could then spread to populations inhabiting other crop varieties. 1.3 POPULATION STRUCTURE OF T. NI Long-range patterns  Wind currents transport T. ni moths long-distances each spring into areas as far north as southern Canada (Lingren et al. 1979).  Along the west coast of North America it has been proposed that T. ni migrate each spring from areas of southern CA to BC.  Fast wind currents (10-20 m/s) that form at dusk allow insects to travel distances of 100-500 km in a single night, with little energetic cost (Drake & Farrow 1988; Gatehouse 1997). The direction and speed of wind currents determines the location and timing of colonization of wind-borne dispersers (Gatehouse 1997).  The movement of T. ni northward appears to be a seasonal progression, with the date of the first trap collections occurring later in the spring and summer with increasing distance north in the United States (Lingren et al. 1979).  In other wind-borne moths, such as Helicoverpa zea and Spodoptera frugiperda aerial densities decline significantly as migrants move downwind from their source population (one-tenth original density 450 km from source population in Texas) (Wolf et al. 1990).  Therefore, it is quite likely that the number of T. ni moths that colonize areas will decline with increasing distance northward.  Mark recapture studies previously employed to track the movement of T. ni have had dismal success due to low recapture rates.  For example, of over 1.5 million sterile moths released as pupae on St. George Island, Florida only 15 moths were recaptured on  6 the mainland (a distance of 97 km) (Lingren et al. 1979).  Several studies have gained insight into the movement patterns of highly mobile insects by employing molecular methods (Daly & Gregg, 1985; Llewellyn et al. 2003; Mun et al. 1999; Scott et al. 2005). For example, a study that used variation in mitochondrial DNA examined the seasonal movements of the rice planthoppers, Nilaparvata lugens and Sogatella furcifera in Asia, where seasonal populations form in central regions of China, Korea and Japan (Mun et al. 1999).  In populations of N. lugens, the frequency of three mitochondrial (COI) haplotypes suggests that the migrants move from tropical regions into subtropical regions in China, with a secondary migration into Korea and Japan.  In contrast, S. furcifera showed little differentiation between regions despite high levels of sequence diversity, indicating that there is significant gene flow among all regions.  Using microsatellite markers, Scott et al. (2005) were able to identify high levels of migration in Helicoverpa armigera populations in Australia in some years, while in other years migration rates remained low.  Due to the high mobility of T. ni moths and the lack of success using mark-recapture techniques, molecular tools may prove to be the best method for exploring the long-distance movement patterns of T. ni moths. Local patterns  The unique structure of local T. ni populations makes them an ideal study system for investigating the effects of evolutionary forces such as drift, gene flow, and selection in subdivided pest populations.  In BC, T. ni exists in a metapopulation structure among vegetable greenhouses and field crops.  Populations are spatially subdivided into local subpopulations, with episodes of extinction and recolonization occurring annually as field populations die each winter due to cold temperatures (Mitchell & Chalfant 1984) and are  7 thought to be recolonized each spring by long-distance migrants from southern CA. Populations can persist year-round in greenhouses, since the beginning of the growing season starts between December and February and ends in November or December (Janmaat 2004).   At the end of the growing season, greenhouse growers perform a clean- up where plants are removed and the structure is fumigated (Janmaat 2004).  In greenhouses some populations persist into the following growing season if the clean-up is not adequate.  These populations can grow quickly, since each female moth is capable of laying over 1,000 eggs (Mitchell & Chalfant 1984) and due to the warm temperatures in greenhouses (18-25°C) their generation time is as short as one month (Janmaat 2004). Moths are capable of moving in and out of greenhouses through open roof vents. However, moth movements may be constrained early in the growing season because roof vents often remain closed to maintain warm temperatures in greenhouses. Under this scenario, movement between greenhouses would be limited early in the growing season when only a few greenhouse populations remain and migrants have not yet arrived.  Those populations that persisted through the year-end clean-up would have likely experienced a severe population bottleneck due to the clean-up process and would be expected to show significant genetic differentiation from one another.  As greenhouse populations grow in size and long-range migrants arrive in BC, dispersal is likely to occur more frequently among local populations.  In addition, female T. ni typically mate with multiple males (Ward & Landolt 1995), which could further increase the amount of gene flow among populations if females mate before dispersing from their natal habitat.  This being true, we would expect population differentiation to decline and within deme diversity to increase over the summer as migrants and persistent greenhouse  8 populations mix.  The greater the rates of migration among demes, the faster the populations are predicted to approach genetic equilibrium (Whitlock 1992).  Recurrent bottlenecks due to Bt use in greenhouse populations, however, may prevent them from reaching equilibrium.  These populations may experience a significant reduction in genetic variation and become differentiated from other populations through the process of rapid genetic drift (Charlesworth et al. 2003). 1.4 MOLECULAR MARKERS The recent advent of new molecular techniques has substantially increased the number of markers available for studying population genetic structure in natural populations (Mariette et al. 2002).  Amplified fragment length polymorphism (AFLP) is a molecular fingerprinting technique developed by Vos et al. (1995) that allows for the amplification of genomic DNA fragments.  The advantages of the AFLP method include: the large number of polymorphic loci produced genome-wide, the high level of reproducibility, quick start-up time and the relatively low cost (Savelkoul et al. 1999). The major disadvantage of the AFLP technique is that AFLPs are dominant, biallelic marker and provide relatively poor quality information at each locus (Bensch & Akesson 2005; Mariette et al. 2002).  The presence of a band indicates that an individual is either a dominant homozygote (1/1) or heterozygote (1/0) at a specific locus and the absence of a band indicates that an individual is a recessive homozygote (0/0) at that locus.  However, the low quality of information obtained from each locus can be overcome by sampling a large number of loci (at least 10 times more than a co-dominant marker; Mariette et al. 2002). The co-dominant microsatellite marker provides an alternative to AFLP methods for  9 investigating the population genetic structure at the species level.  Although highly informative, microsatellite markers only produce a few loci, require a long start-up period, and can be expensive to develop (Sunnucks 2000).  More importantly, microsatellite development has proved to be particularly difficult in some taxa including those in the order Lepidoptera (Neve & Meglecz 2000).  In lepidopteran species sequences in flanking regions have been found to be repetitive and multiple copies of microsatellite sequences have been found throughout the genome, resulting in few scoreable loci (Zhang 2004).  These findings indicate that AFLPs offer greater promise in identifying fine scale genetic patterns in lepidopteran species such as T. ni. 1.5 THESIS OBJECTIVES AND OVERVIEW  The objectives of this thesis include: 1) to examine patterns of Bt resistance in greenhouses and field populations in BC and field populations along the west coast of North America, 2) to develop DNA isolation procedures and identify suitable AFLP primer combinations for examining the local and regional population structure of T. ni, 3) to use molecular tools to gain inference into the long-range migration patterns of T. ni from CA to BC, and 4) to determine the local population genetic structure of greenhouse and field T. ni populations in BC and how this structure relates to the observed patterns of Bt resistance.  In chapter 2, I use the results from a two-year survey of Bt resistance levels in T. ni greenhouse and field populations in BC and a one-year survey of field populations along the west coast of North America to investigate the spatial and temporal patterns of Bt resistance (Franklin & Myers 2008).  Specifically, I test if seasonal migrants from southern CA are preadapted to Bt resistance and thus facilitate the spread of resistance to  10 greenhouse populations in BC or if over-wintering greenhouse populations in BC act as a source of resistance to surrounding greenhouses and fields.  In chapter 3, I develop DNA isolation procedures and evaluate the utility of AFLPs for distinguishing between laboratory reared and wild collected T. ni populations (Franklin et al. 2009a).  The techniques developed in this chapter are used later to address questions regarding the local and regional population genetic structure of T. ni in chapters 4 and 5.  Chapter 4 examines the migration patterns and genetic structure of T. ni field populations inhabiting regions along the west coast of North America, from CA to BC, using mitochondrial DNA sequence variation and AFLP methods.  I hypothesize that if patterns of seasonal migration agree with the observation that moth aerial densities decrease with increasing distance from their source (Gatehouse 1997), then genetic differentiation will increase with distance traveled.  In chapter 5, I examine the spatial and temporal genetic structure of T. ni greenhouse and field populations in BC and three populations from CA that could serve as a potential source of migrants.  I hypothesize that greenhouse populations in close proximity will show little differentiation, while field populations may show greater similarity to migrant populations.  I also predict that collections from greenhouse populations that persist between growing seasons will be genetically similar.  Finally, I examine the relationship between local patterns of Bt resistance and genetic structure to determine the impact of selection for resistance on patterns of differentiation.  I predict that frequent bottlenecks induced by strong selection for Bt resistance will result in a positive correlation between genetic differentiation and Bt resistance.  In the last chapter,  11 I integrate my findings for local and regional population structure of T. ni and patterns of Bt resistance and discuss them in light of population genetics theory.                                    12 1.6 REFERENCES  Bensch S, Akesson M (2005) Ten years of AFLP in ecology and evolution: why so few animals? Molecular Ecology 14, 2899-2914. Caprio MA, Tabashnik BE (1992) Gene flow accelerates local adaptation among finite populations: simulating the evolution of insecticide resistance. Journal of Economic Entomology 85, 611-620. 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Franklin MT, Myers JH, Ritland CE (2009a) Distinguishing between laboratory-reared and greenhouse- and field-collected Trichoplusia ni (Lepidoptera: Noctuidae) using the amplified fragment length polymorphism method. Annals of the  13 Entomological Society of America 102, 151-157. Franklin MT, Nieman CL, Janmaat AF, Soberon M, Bravo A, Tabashnik BE, Myers JH (2009b) Modified Bacillus thuringiensis toxins and a hybrid B. thuringiensis strain counter greenhouse-selected resistance in Trichoplusia ni. Applied and Environmental Microbiology 75, 5739-5741. Gassmann AJ, Carriere Y, Tabashnik BE (2009) Fitness costs of insect resistance to Bacillus thuringiensis. Annual Review of Entomology 54, 147-163. Gatehouse AG (1997) Behavior and ecological genetics of wind-borne migration by insects. Annual Review of Entomology 42, 475-502. Georghiou GP, Lagunes-Tejeda A (1991) The occurrence of resistance to pesticides in arthropods: an index of cases reported through 1989, FAO, Roma. 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Neve G, Meglecz E (2000) Microsatellite frequencies in different taxa. Trends in Ecology and Evolution 15, 376-377. Palumbi SR (2001) Humans as the world's greatest evolutionary force. Science 293, 1786-1790. Pannell JR, Charlesworth B (1999) Neutral genetic diversity in a metapopulation with recurrent local extinction and recolonization. Evolution 53, 664-676. Pannell JR, Charlesworth B (2000) Effects of metapopulation processes on measures of genetic diversity. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 355, 1851-1864. Savelkoul PH, Aarts HJ, de Haas J, Dijkshoorn L, Duim B, Otsen M, Rademaker JL, Schouls L, Lenstra JA. (1999) Amplified-fragment length polymorphism analysis: the state of an art. Journal of Clinical Microbiology 37, 3083-3091. Scott KD, Lawrence N, Lange CL, Scott LJ, Wilkinson KS, Merritt MA, Miles M, Murray D, Graham GC. (2005) Assessing moth migration and population structuring in Helicoverpa armigera (Lepidoptera: Noctuidae) at the regional scale: example from the Darling Downs, Australia. Journal of Economic Entomology 98, 2210-2219.  16 Smith TB, Bernatchez L (2008) Evolutionary change in human-altered environments. Molecular Ecology 17, 1-8. Speith PT (1979) Environmental heterogeneity: a problem of contradictory selection pressures, gene flow, and local polymorphism. American Naturalist 113, 247-260. Sunnucks P (2000) Efficient genetic markers for population biology. Trends in Ecology and Evolution 15, 199-203. Sutherland DWS, Green GL (1984) Cultivated and wild host plants In: Suppression and management of cabbage looper populations (eds. Lingren PD, Green GL), pp 1- 13.  Technical bulletin/ Agricultureal Research Service, USDA. Tabashnik BE (1994) Evolution of resistance to Bacillus thuringiensis. Annual Review of Entomology 39, 47-79. Tabashnik, B. E., Liu, Y. B., Malvar, T., Heckel, D. G., Masson, L. and J. Ferré (1998) Insect resistance to Bacillus thuringiensis: uniform or diverse? Philosophical Transaction of Royal Society of London. Series B: Biological Sciences 353, 1751- 1756. Tabashnik BE, Gassmann AJ, Crowder DW, Carriere Y (2008) Insect resistance to Bt crops: evidence versus theory. Nature Biotechnology 26, 199-202. Van Rensburg JBJ (2007) First report of field resistance by stem borer, Busseola fusca (Fuller) to Bt-transgenic maize. South African Journal of Plant Soil 24, 147-151. Via S (1990) Ecological genetics and host adaptation in herbivorous insects: the experimental study of evolution in natural and agricultural systems. Annual Review of Entomology 35, 421-446. Vos P, Hogers R, Bleeker M, Reijans M, van de Lee T, Hornes M, Frijters A, Pot J,  17 Peleman J, Kuiper M, Zabeau M (1995) AFLP: a new technique for DNA fingerprinting. Nucleic Acids Research 23, 4407-4414. Wade MJ, McCauley DE (1988) Extinction and colonization: their effects on the genetic differentiation of local populations. Evolution 42, 995-1005. Wang P, Zhao J-Z, Rodrigo-Simon A, Kain W, Janmaat AF, Shelton, AM, Ferré  J, Myers JH. (2007) Mechanism of Resistance to Bacillus thuringiensis Toxin Cry1Ac in a Greenhouse Population of the Cabbage Looper, Trichoplusia ni. Applied and Environmental Microbiology 73, 1199-1207. Ward KE, Landolt PJ (1995) Influence of multiple matings on fecundity and longevity of female cabbage looper moths (Lepidoptera: Noctuidae). Annals of Entomological Society of America 88, 768-772. Whitlock MC (1992) Nonequilibrium population structure in forked fungus beetles: extinction, colonization, and the genetic variance among populations. American Naturalist 139, 952-970. Whitlock MC (2002) Selection, load and inbreeding depression in a large metapopulation. Genetics 160, 1191-1202. Wolf WW, Westbrook JK, Raulston J, Pair SD, Hobbs SE (1990) Recent airborne radar observations of migrant pests in the United States. Philosophical Transactions of the Royal Society of London Series B: Biological Sciences 328, 619-630. Zhang DX (2004) Lepidopteran microsatellite DNA: redundant but promising. Trends in Ecology and Evolution 19, 507-509.     18 CHAPTER 2: REFUGES IN REVERSE: THE SPREAD OF BT RESISTANCE TO UNSELECTED GREENHOUSE POPULATIONS OF CABBAGE LOOPERS TRICHOPLUSIA NI 1  2.1 INTRODUCTION  The high dose refuge strategy has become the primary method to delay resistance evolution in major insect pests of transgenic crops expressing Bacillus thuringiensis (Bt) proteins (Gould 1998).  This approach depends on the persistence of susceptible moths in untreated refuges to act as mates for resistant moths arising from selection by the high doses of Bt toxin in the genetically modified plants.  Many theoretical considerations of moth movement and spatial structure have provided insights into the potential role of refuges in preventing resistance adaptation (Caprio & Tabashnik 1992; Peck et al. 1999; Ives & Andow 2002; Cerda & Wright 2004; Sisterson et al. 2005).  Far less information has been gathered on the actual patterns of movement of resistant moths, and thus the potential spread of Bt resistance from selected to non-selected populations.  In the present study, we demonstrate a situation in which cabbage looper Trichoplusia ni (Hübner), selected for resistance through high use of Bt sprays in some vegetable greenhouses, colonizes untreated greenhouses.  This leads to elevated levels of resistance in unsprayed moth populations in neighbouring greenhouses.  Immigration from susceptible field populations does not apparently counteract this flow of resistance among greenhouse populations and thus, these do not serve as refuges to the greenhouse populations.  1  A version of this chapter has been published. Franklin MT, Myers JH (2008) Refuges in reverse: the spread of Bacillus thuringiensis resistance to unselected greenhouse populations of cabbage loopers Trichoplusia ni. Agricultural and Forest Entomology 10, 119-127.   19 Trichoplusia ni is a sub-tropical insect that over winters in the southern USA (Mitchell & Chalfant 1984) and migrates northwards each summer as far as British Columbia (BC), Canada.  It is a pest on many crop species and is frequently controlled using Bt based microbial agents on field crops in western North America.  In addition, in southern California (CA), first-generation transgenic cottons, Bollgard and Ingard (Monsanto), expressing the single toxin protein Cry1Ac and the latest variety, Bollgard II, expressing two toxin genes, Cry1Ac and Cry2Ab or Cry1Ac and Cry1F, could serve as a host for T. ni populations (Li et al. 2006).  In BC, T. ni are only able to survive in greenhouses if the clean-up at the end of the growing season is not complete, and new field populations are re-established each year from the over-wintering regions of southern CA (Cervantes 2005).  Trichoplusia ni have become resistant in vegetable greenhouses in BC following extensive use of Bt sprays.  This is particularly the case for moth populations that have successfully over-wintered in greenhouses (Janmaat & Myers 2003).  In the present study, we have investigated the spatial and temporal patterns of Bt resistance in T. ni greenhouse and field populations in BC, and regional patterns of Bt resistance in field populations collected from CA, Oregon (OR), and BC.  The aims of our surveys were to determine (1) if seasonal migrants from the southern USA to Canada were preadapted to Bt resistance thus facilitating the development of resistance in greenhouse populations, (2) if resistant populations of T. ni that have successfully over- wintered in greenhouses in BC served as a source of resistance genes to field and ‘unselected’ greenhouse populations, and (3) if moths from susceptible field populations disperse into greenhouses later in the summer and reduce the levels of Bt resistance.  20 2.2 METHODS 2.2.1 Greenhouse and field collections  To examine local patterns of Bt resistance, T. ni larvae were collected from commercial vegetable greenhouses including those growing tomato, cucumber, and pepper crops and cruciferous field crops throughout the lower mainland of BC between March 2005 and September 2006.  All commercial vegetable greenhouses surveyed used integrated pest management practices for the control of insect pests.  Foliar applications for the control of Lepidopteran pests in greenhouses included Bacillus thuringiensis subsp. kurstaki (Berliner) Btk (Dipel and Foray; Valent Biosciences, Libertyville, IL) in all crops, tebufenozide (Confirm; Dow AgroSciences, Calgary, Alberta, Canada) in pepper and tomato crops, and spinosad (Success; Dow AgroSciences) in pepper and cucumber crops.   The majority of cruciferous field crops sampled in BC used organic farming practices and limited foliar applications to Btk (Dipel) and spinosad (Entrust; Dow AgroSciences).  Table 2.1 provides a summary of management practices and Bt applications for all greenhouse and field locations surveyed. Several observers collected larvae by visually searching plants.  Eggs and larvae were removed from the plants and placed in paper or plastic cups with leaves in groups of 30 or less.  The growth stage of collected larvae ranged from first to fifth instar, with third instar larvae representing the median life stage.  Containers were then put in an insulated cooler with ice packs for transport back to the laboratory for sorting.  Larvae were collected from four greenhouses and six fields in 2005 and nine greenhouses and three fields in 2006.  Three of the greenhouses and one of the fields were sampled in both 2005 and 2006.  Low infestations in some greenhouses and crop  21 rotations in the majority of fields inhibited the resurveying of all greenhouses and fields sampled in both 2005 and 2006.  The number of collections from each field and greenhouse varied between one and three per year depending on the abundance and timing of the infestation at each location.  Multiple collections were performed throughout the growing season in three of the greenhouses and one of the field sites in 2005 and five of the greenhouses and one of the field sites surveyed in 2006 (Table 2.1).  To examine patterns of Bt resistance on a larger geographical scale, larvae were collected from a broccoli field in Santa Maria, CA, a cabbage field and mixed cruciferous field in Oxnard, CA, and one broccoli field in Albany, OR during June and July 2006. One collection was performed at each of these sites.  All cruciferous fields surveyed in CA and OR used conventional farming practices and no Bt sprays had been applied prior to our collections during the 2006 growing season.  Management details for these fields are listed in Table 2.1 and collection dates for each site and GPS coordinates are provided in Table 2.2.  Greenhouse and field collected larvae were reared in the laboratory using methods modified from Ignoffo (1963).  Larvae were transferred as groups of five to 30-ml plastic cups or groups of 15 to 175-ml polystyrene cups containing artificial wheat-germ-based diet and were reared at a temperature of 26˚C with a 16:8 (L:D) photoperiod until pupation.  Pupae were removed from cups and placed in a 0.6% bleach solution for five minutes to prevent viral contamination.  When T. ni was the only noctuid species present in greenhouses, pupae were counted and placed directly into a mating cage for emergence and mating. In several greenhouses and all fields however, other similar noctuid larvae were present, such as Autographa californica (Speyer).  Pupae from these greenhouses  22 and fields were placed into individual 30-ml plastic cups and upon adult emergence identified to confirm the species.  Trichoplusia ni moths were counted and put into cages for mating.  Mating cages were housed in a temperature controlled chamber (Conviron, Winnipeg, Canada) at 24˚C with 16:8 (L:D) photoperiod.  Moths were supplied with a 10% sucrose solution and paper towelling was placed around the perimeter of the cage. Moths laid eggs on these paper towels which were changed every two to three days and were stored at 4˚C for a maximum of seven days prior to use in bioassays. 2.2.2 Bioassay procedures  Progeny hatching from eggs laid throughout the laying period were used in all assays to ensure that the results were not biased by differences in development time of resistant and susceptible individuals (Janmaat & Myers 2003).  Susceptibility of larvae to Btk was assayed using Dipel WP (Abbott laboratories, Montréal, Canada).  Dipel is composed of a bacterial spore and five bacterial proteins including Cry1Aa, Cry1Ab, Cry1Ac, Cry2Aa, and Cry2Ab.  Bt solutions, in the concentration range 0.625 to 160 KIU/ml diet, were prepared by serial dilutions with distilled water and mixed with the artificial diet, cooled to below 50˚C, in a 1:10 ratio (Bt solution: artificial diet).  Two ml of Bt treated or control diet were dispensed into 30-ml plastic cups and allowed to cool to room temperature.  Five, five-day-old larvae were placed in each cup and mortality was assessed by gently probing the larvae for movement three days following dosing.  A minimum of 20 larvae was tested per Bt concentration, and when possible, assays were replicated twice for each population.  The number of parents and progeny tested from each population is listed in Table 2.2.  23 2.2.3 Data analysis  Bioassays with greater than 20% mortality in the control treatment were not included in analyses.  All analyses were performed separately for 2005 and 2006 data.  At sites where multiple collections were performed, linear regression analysis between proportion dead and log-concentration were used to test for parallelism among collection dates (SAS 9.1 2003).  The average proportion dead in each treatment was used for all comparisons because there were no significant interactions between log-concentration and collection date or differences between collection dates at all locations.  For the regional analysis, the average proportion dead for the two fields in Oxnard, CA and two fields in Abbotsford, BC were used to test for differences among regions as there was no evidence of interactions (log-concentration and site) or differences between sites. We obtained 50% lethal concentration (LC50) values and fiducial limits for each location using probit procedures in GENSTAT 5 (1997).  Methods for calculating fiducial limits in GENSTAT 5 follow procedures outlined by Finney (1971).  Abbott’s formula (Abbott 1925) was used to correct for control mortality when the average proportion dead in the control treatment group was greater than 10% for a sampling location. For subsequent analyses, all locations sampled were classified as being field or greenhouse.  Concentration was represented using a scale from 1 to 6, with 1 representing the control treatment and 6 a concentration of 10 KIU/ml diet, respectively. Concentrations ranging from 20 to 160 KIU/ml diet were excluded from these analyses because only a few populations with high levels of resistance were tested at those doses, causing several parameters not to be estimated when included in the model.  Because of the unequal number of observations, a Generalized Linear Model (GLM) procedure in  24 SAS 9.1 was used to test site (field or greenhouse) and concentration as main effects and their interactions for local differences in resistance among greenhouse and field sites in the lower mainland of BC.  We tested for regional differences in moth resistance to Bt between CA, OR, and BC field populations using a PROC GLM in SAS 9.1 with concentration and region as main effects and their interactions. We used analysis of covariance (ANCOVA) with the covariate Bt concentration and main effect ‘location’ to compare Bt resistance among populations from all locations in the lower mainland of BC.  The covariate concentration was transformed to ln- concentration+100 to ensure a linear relationship between concentration and mortality. Bonferroni multiple comparison procedures were used to adjust for the number of meaningful comparisons.  The geographic distances between greenhouses surveyed in 2006 were estimated using spherical distance measures based on the latitude and longitude coordinates for each greenhouse in PASSAGE 1.1 (Rosenberg 2001). Assumptions of normality and homogeneity of variances were met for all analyses conducted.  Reported means and standard errors of the means are based on least square means to adjust for the unequal number of observations and control for the effect of the covariate concentration in covariance analyses. 2.3 RESULTS 2.3.1 Local patterns of Bt resistance  Greenhouse populations of T. ni were more resistant to Bt than were field populations in both 2005 and 2006.  This is most clearly seen by their significantly lower mortality when exposed to Bt for all doses tested in 2005 (F1,8 = 9.77, P = 0.01; Figure 2.1).  Among greenhouse populations, resistance levels varied significantly (F3,23 = 12.43,  25 P < 0.0001); however, one population (G1), a greenhouse in which Bt was used extensively, had a much higher resistance level than the other three in 2005 (G1 vs. G2 t23 = 3.17, P = 0.0042; G1 vs. G3 t23 = 4.14, P = 0.0004; G1 vs. G4 t 23= 5.97, P < 0.0001). No significant differences occurred in resistance levels among local field populations in 2005 (F5,29 = 1.79, P = 0.15). Higher levels of resistance for BC greenhouse populations compared to field populations of T. ni are also shown by the LC50 values.  In 2005 these ranged from 1.29 to 67.5 KIU/ml diet for greenhouse populations and from 0.124 to 1.71 KIU/ml diet for field populations (Figure 2.2).  No significant interaction occurred between site (greenhouse or field) and dose in 2005 (F5,33 = 1.51, P = 0.21). In 2006, LC50 values for BC T. ni greenhouse populations were slightly lower than in 2005 and ranged from 0.72 to 9.66 KIU/ml diet.  For BC field populations LC50 values ranged from 1.16 to 1.75KIU/ml diet (Figure 2.2).  In this year a significant interaction existed between site (greenhouse or field) and dose (F5,50 = 3.30, P = 0.01).  Resistance levels were higher in greenhouse populations when compared to field populations for four of the doses tested  (Dose = 1.25 KIU/ml diet t 50= 4.30, P < 0.0001; Dose = 2.5 KIU/ml diet t50 = 3.38, P = 0.001; Dose = 5.0 KIU/ml diet t50 = 3.01, P = 0.004; Dose = 10 KIU/ml diet t50= 3.14, P = 0.003; Figure 2.1).  Similar to the results from 2005, resistance levels varied significantly among greenhouse populations (F8,51 = 6.83, P < 0.0001), while field populations showed no significant variation in resistance (F2,14 = 1.33, P = 0.30).    26 2.3.2 Spatial patterns of Bt resistance  The survey of nine greenhouse populations in 2006 allowed for a comparison of spatial patterns of Bt resistance among populations.  Spatial patterns of resistance indicate that moths moved between greenhouses in close proximity to one another (Figures 2.3 and 2.4).  One greenhouse population (G11) had persisted through the winter clean-up in 2005 into the 2006 growing season.  This population was exposed to Bt applications during both the 2005 and 2006 growing seasons and probably served as the original source of moths for the greenhouses surveyed that were located 3 to 5 km away (G1, G5, and G6).  Attempts to quantify resistance levels in this population, however, were unsuccessful, as larvae from this greenhouse did not survive in the laboratory. The greenhouse population with the highest level of resistance (G5) was probably colonized by moths from G11 and was subsequently exposed to nine Bt sprays during the 2006 growing season and thus strong selection.  That moths had likely migrated from G5 and G11 to other greenhouses between 1 and 5 km away is indicated by the heightened levels of resistance and later first collection dates in the neighbouring greenhouse populations (G1 and G6) in which T. ni had not persisted through the 2005 winter clean- up and Bt sprays had not been used during 2006 (Figures 2.3 and 2.4).  The ‘unselected’ neighbouring populations, G1 and G6 had similar levels of resistance to each other and to the selected population (G5) (G1 vs. G5 t51 = 1.03, P = 0.31; G1 vs. G6 t51 = 0.29, P = 0.7698; G5 vs. G6 t51 = 1.52, P = 0.13). Two other ‘unselected’ greenhouse populations located less than 4 km apart also had similarly high levels of resistance (G4 vs. G7 t51 = 0.38, P = 0.71).   These greenhouses are located in close proximity to many other greenhouses that may have  27 served as sources of resistant moths. The first sampling date for eight of the nine greenhouse populations surveyed in 2006 occurred prior to when moth larvae were found in field samples.  This indicates that greenhouse populations persisting from the previous year probably colonized other greenhouses before field populations of moths were present. 2.3.3 Temporal patterns of Bt resistance  Resistance levels in both greenhouse and field populations that were sampled multiple times during 2005 and 2006 did not change within growing seasons (P > 0.10 for all comparisons) and showed no trends towards increased susceptibility.  Thus, even though the field populations were much more susceptible to Bt than greenhouse populations, the resistance of greenhouse populations was not apparently reduced through immigration of susceptible field moths. 2.3.4 Regional patterns of Bt resistance  We expected that if southern field populations of T. ni had been exposed to transgenic plants or Bt sprays, they may have had elevated levels of resistance.  For T. ni from fields surveyed in CA and OR however, LC50 values were similar to BC field populations and ranged from 1.51 to 2.35 KIU/ml diet (Figure 2.5). No significant interactions existed between region and dose (F10,41 = 1.83, P = 0.1191) and no significant difference in resistance levels occurred among CA, OR and BC field populations (F2,4 = 0.39, P = 0.7007). 2.4 DISCUSSION  Our results strongly suggest that dispersal of resistant moths can lead to the spread and persistence of Bt resistant genes to greenhouse moth populations that are not  28 treated with Bt.  We observed heightened levels of resistance in two ‘unselected’ greenhouse populations that were located only 5 km from two Bt treated greenhouse populations.  Two other ‘unselected’ greenhouse populations located 4 km apart showed elevated levels of resistance that had likely spread from one or more of the many surrounding greenhouse populations.  The maintenance of resistance in ‘unselected’ populations is unexpected, considering that significant fitness costs are associated with resistance in T. ni populations (Janmaat & Myers 2003).  We discuss a number of key factors that could have contributed to the spread of Bt resistance among greenhouse populations including timing of colonization, dominance of resistance, dispersal, and selection pressures. Greenhouse populations of T. ni that are not eliminated through the winter clean-up process can increase rapidly on the newly planted crops in the spring.  If Bt had been used in the previous year, these populations tend to rapidly develop resistance when sprays are used to reduce the populations the next spring.  For example, the population in greenhouse 1 (G1) persisted from the 2004 growing season and Bt resistance levels reached over 60 KIU/ml diet by March of 2005.  Compared to greenhouse populations, selection through Bt applications was much weaker in BC field populations because cold winter temperatures and short growing seasons help to keep population densities low. Despite the longer growing seasons and warmer temperatures in CA and OR, there was probably no selection for Bt resistance at the field sites sampled, since these fields were farmed conventionally and were not treated with Bt. Moths occurred earlier in most greenhouses than fields.  This implies that persistent greenhouse populations were the source of resistant moths for greenhouses in the  29 surrounding area.  Consistent with our findings, a model developed by Ives & Andow (2002) to evaluate the effectiveness of the high dose refuge strategy found that if a purely susceptible population is not persistent, then resistance could spread rapidly.  This occurs because no susceptible individuals are available for mating and thus, resistant individuals mate with each other and the frequency of the resistant genes rapidly rises.  By the time transitory field populations established in BC, the frequency of resistant genes was likely too high in greenhouse populations for susceptible individuals to have a significant impact on resistance levels.  The lack of reduced resistance in greenhouse populations throughout the growing season supports this interpretation. The high dose refuge strategy developed for Bt crops relies on the key assumption that resistance is functionally recessive (Tabashnik & Croft 1982).  Under this scenario, the mating of susceptible individuals (SS) from the refuge and resistant individuals (RR) from the Bt transgenic crop is expected to delay resistance by producing heterozygous (RS) offspring that are killed by feeding on transgenic plants (Ferré & Van Rie 2002). When resistance is not functionally recessive, models predict that resistance will develop rapidly (Crowder et al. 2005; Cerda et al. 2006).  Genetic determination of Bt resistance in T. ni varies with host plant (Janmaat & Myers 2007).  On pepper plants resistance was completely recessive or potentially underdominant, while on cucumber plants resistance of larvae showed incomplete dominance (Janmaat & Myers 2007).  The two cucumber greenhouses surveyed were the only greenhouses treated with Bt in 2006.  Resistance may have been difficult to delay in these cucumber greenhouses, since the partial dominance of the resistant trait would have favoured the survival of heterozygous (RS) individuals.  All other greenhouses surveyed in 2006 grew peppers and were not treated  30 with Bt. By contrast to our expectations of a rapid decline in resistance in ‘unselected’ populations due to reduced fitness costs, resistance was able to persist in the offspring of several greenhouse populations that were not treated with Bt.  Fitness costs such as reduced pupal and larval weight, progeny size and number, have been identified in laboratory tests of greenhouse collected strains of Bt resistant T. ni (Janmaat & Myers 2003; Janmaat & Myers 2006).  The majority of these populations showed a corresponding decrease in resistance over several generations in the laboratory (Janmaat & Myers 2003). Current resistance levels in BC greenhouse populations have declined considerably since Janmaat & Myers (2003) identified negative pleiotropic effects associated with resistance.  Thus fitness costs that manifest at high levels of resistance may be weak or absent in strains that are only moderately resistant to Bt.  Fitness costs may also be overstated in highly resistant strains if strong selection reduces the effective population size and increases deleterious mutations due to inbreeding (Carriere et al. 2006). Modelling results for Pectinophora gossypiella pink bollworm in Bt cotton fields indicate that resistance can spread when weak to moderate fitness costs are combined with other parameters such as small refuges and incomplete resistance (Tabashnik et al. 2005). Thus in the absence of field populations in the winter in BC that could serve as refuges for susceptible individuals, it is understandable that resistant genes can spread among greenhouse populations despite the possible presence of weak fitness costs. Although T. ni have long seasonal migrations, local patterns of Bt resistance in selected and ‘unselected’ greenhouse populations suggest dispersal distances in the range  31 of 1 to 5 km between greenhouses.  Similarly, mark-recapture estimates for other Lepidopteran insects indicate that a large fraction of moths only disperse very short distances (Mo et al. 2003; Quereshi et al. 2006; Bailey et al. 2007).  For example, greater than 90% of released Diatraea grandiosella and Plutella xylostella were recaptured or expected to stay within 300 m of their release sites (Mo et al. 2003; Quereshi et al. 2006). Short-distance dispersal may increase the frequency of non-random matings and increase the rate of resistance evolution (Bailey et al. 2007).  In Ostrinia nubilalis European corn borer predispersal matings have been found to be common, while matings between resident males and immigrant females occur infrequently (Dalecky et al. 2006).  Indeed, it is quite probable that resistant resident T. ni moths mate before dispersing to greenhouses in the surrounding area.  With the large reproductive potential of T. ni females (up to 1000 eggs laid per female) (Mitchell & Chalfant 1984) postcopulatory dispersal could easily contribute to the spread of resistance among greenhouse populations in BC. Patterns of Bt resistance in BC greenhouse populations suggest that dispersal distances of T. ni moths are sufficiently large to allow matings between resistant individuals from greenhouses and susceptible individuals from fields.  Populations of T. ni are transitory in BC and are not able to over-winter, and this long-range migration can link widely separated populations.  As a secondary pest of Bt cotton (Ehler et al. 1973), we predicted that the use of genetically modified Bt cotton in southern CA might increase the frequency of resistant genes in southern CA populations, which could then spread to populations in northern CA, OR, and BC.  Contrary to our prediction, the results presented here indicate that resistance levels remain low and homogeneous in all the field  32 populations that we surveyed in CA, OR, and BC.  Consistent with these findings, feeding experiments indicate that no T. ni larvae were able to survive when fed Bt cotton for their entire development (Li et al. 2006).  In addition, simulation studies have indicated that resistance is unlikely to develop in T. ni populations inhabiting Bt cotton due to the presence of spatial refuges and temporal refuges (Gutierrez et al. 2006), created by a decline in larval susceptibility with developmental stage, and toxin concentration with plant age (Li et al. 2007).  Variation in susceptibility to Bt among populations of T. ni has, however, recently been reported for populations in Bajio guanajuatense area of Mexico (Tamez-Guerra et al. 2006).  Furthermore, in Arizona, feeding experiments indicated that T. ni was less susceptible to Bt cotton than other Lepidopteran pests, including P. gossypiella and Heliothis virescens Tobacco budworm (Henneberry et al. 2003).  Thus the potential remains for increased resistance to occur in permanent southern populations. Our study provides the first evidence that Bt resistance can spread from selected T. ni populations to ‘unselected’ populations.  Resistance likely develops in greenhouse populations because of strong selection, year-round persistence and the temporal elimination of susceptible field populations in the winter.  Resistance then spreads to other neighbouring greenhouses through local dispersal of resistant moths, prior to the establishment of susceptible field populations.  The rapid evolution of Bt resistance in vegetable greenhouses poses a serious threat to crop production in BC.  Information gathered from studying resistance adaptation in BC populations can aid us in evaluating the effectiveness of the high dose refuge strategy in delaying resistance adaptation in T. ni feeding on Bt crops.  Given the low environmental risk of Bt products and the dramatic  33 rise in their use (Betz et al. 2000), it is imperative that management strategies incorporate knowledge of the insect’s biology and key factors that facilitate the development of Bt resistance.  3 4  Table 2.1 Summary of crops, management practices, and Bt applications used in greenhouses and fields, prior to sampling, in 2004, and during Trichoplusia ni larval collections, in 2005 and 2006.  Farming practices used included integrated pest management (IPM), organic, and conventional methods. Year Site Crop Farming practices Bt application Local collections 2004 G1 Tomato IPM Yes  G2 Tomato IPM No  G3 Pepper IPM Yes  G4 Tomato IPM Yes 2005 G1 Pepper IPM Yes  G2 Tomato IPM Yes  G3 Pepper IPM Yes  G4 Tomato IPM No  F1 Broccoli Organic No  F2 Broccoli Organic Yes  F3 Broccoli Organic Yes  F4 Broccoli Organic Yes  F5 Broccoli Organic Yes  F6 Mixed crucifers Conventional Yes 2006 G1 Pepper IPM No a   G3 Pepper IPM No  G4 Pepper IPM No  G5 Cucumber IPM Yes  G6 Pepper IPM No  G7 Pepper IPM No  G8 Cucumber IPM Yes  G9 Pepper IPM No b   3 5  Year Site Crop Farming practices Bt application  G10 Pepper IPM No  G11 Cucumber IPM Yes  F6 Mixed crucifers Conventional No  F7 Broccoli Organic No  F8 Rutabaga Conventional No Regional collections 2006 Abbotsford 1 BC Mixed crucifers Conventional No  Abbotsford 2 BC Rutabaga Conventional No  Delta BC Broccoli Organic No  Albany OR Broccoli Conventional No  Oxnard 1 CA Mixed crucifers Conventional No  Oxnard 2 CA Cabbage Conventional No  Santa Maria Broccoli Conventional No  a Bt was used in adjoining greenhouse for the control of cutworms.  Trichoplusia ni were not exposed to Bt and were collected 4 months after Bt application when no residue would have remained. b Bt was used in an adjoining cucumber greenhouse that we did not sample.       3 6  Table 2.2 Summary of local and regional collections of Trichoplusia ni performed in 2005 and 2006.  Included is a list of greenhouse and field locations, sampling dates, number of pupae or moths caged (number of parents), number of offspring assayed, and number of assays performed.  Greenhouse and field populations are represented by the letters G and F, respectively. Year Location Sampling date No. of parents No. of offspring assayed No. of assays Latitude (N) Longitude (W) Local collections  2005 G1 18 March 41 224 2 49°02.800´ 122°35.577´  G1 2 May 188 573 2  G2 14 July 41 300 1 49°03.795´ 123°06.573´  G2 14 October 129 670 2  G3 26 April 164 300 1 49°02.615´ 122°26.897´  G4 10 June 50 591 2  G4 6 July 31 274 1 49°04.014´ 123°03.097´  G4 10 August 254 240 1  F1 12 July 11 629 2 49°03.007´ 123°03.343´  F2 2 August 9 276 1 49°07.886´ 123°02.043´  F2 25 August 63 660 2  F3 8 July 17 524 2 49°02.450´ 123°03.858´  F4 8 September 71 232 1 49°03.177´ 123°05.683´  F5 20 July 13 601 2 49°05.036´ 123°08.288´  F6 26 July 17 300 1 49°03.507´ 122°05.667´ 2006 G1 15 September 46 510 2 49°02.852´ 122°35.591´  G3 25 July 75 571 2 49°02.615´ 122°26.897´  G4 25 August 144 480 2 49°04.014´ 123°03.097´  G5 30 June 315 840 3 49°02.726´ 122°38.291´  G5 24 August 70 361 1  G5 30 September 60 265 1  3 7  Year Location Sampling date No. of parents No. of offspring assayed No. of assays Latitude (N) Longitude (W)  G6 10 July 110 595 2 49°02.180´ 122°38.284´  G6 27 September 30 300 1  G7 12 July 21 360 1 49°04.944´ 123°00.394´  G7 21 August 246 661 2  G8 9 June 15 120 1 49°15.123´ 122°41.421´  G8 10 August 115 285 1  G9 9 June 35 300 1 49°15.867´ 122°17.605´  G9 11 August 129 300 1  G10 5 September 212 281 1 49°05.045´ 123°08.290´  G11 19 May 261 0 0 49°02.443´ 122°38.366´  G11 7 August 175 0 0  F6 23 August 23 360 1 49°03.507´ 122°05.667´  F7 8 August 19 300 1 49°06.723´ 123°02.266´  F7 19 September 35 210 1  F8 14 September 40 510 2 49°05.046´ 122°05.805´ Regional collections  2006 Abbostford 1 BC 23 August 23 360 1 49°03.507´ 122°05.667´  Abbotsford 2 BC 14 September 40 510 2 49°05.046´ 122°05.805´  Delta BC 8 August  19 300 1 49°06.723´ 123°02.266´  Delta BC 19 September 35 210 1  Albany OR 27 July 28 695 2 44°43.865´ 123°07.455´  Oxnard 1 CA 29 June 158 780 2 34°12.551´ 119°03.403´  Oxnard 2 CA 29 June 11 421 2 34°19.803´ 119°08.339´  Santa Maria CA 27 June 130 992 2 34°53.550´ 120°30.853´     38  Figure 2.1:  Mean proportion dead (± SE) for Trichoplusia ni progeny assayed for Bt resistance from greenhouse and field populations surveyed in 2005 and 2006 in British Columbia.  Doses ranged from 0 to 10 KIU/ml diet.  Greenhouses and field collections were denoted with —!— and - -"- -, respectively.  Greenhouse populations in 2005 had significantly lower mortality than field populations for all doses (P = 0.01).  In 2006, greenhouse populations had significantly lower mortality for all doses greater than 1.25 KIU/ml diet (P < 0.005 for all comparisons).   39  Figure 2.2: LC50 values and fiducial limits for Trichoplusia ni collected from greenhouses and fields throughout the lower mainland of British Columbia in 2005 (a) and 2006 (b). Greenhouse populations that were treated with Bt are represented by ! and those untreated by # and fields treated with Bt are denoted by $!and those untreated by ".     40  Figure 2.3: Locations of greenhouses surveyed in British Columbia in 2006.  Each greenhouse is represented by latitude and longitude coordinates.  Circles encompassing greenhouses G4 and G7, and G1, G5, G6 indicate that resistance levels do not differ significantly within these greenhouse groups (P > 0.1).  G11 had a population that persisted through the winter and had been exposed to frequent Bt sprays.  This population likely served as the source of resistant individuals for greenhouses G1, G5, and G6.  Attempts were made to test this population for Bt resistance, however collected individuals did not survive in the laboratory.      41  Figure 2.4: Mean proportion dead (± SE) for the nine greenhouse Trichoplusia ni populations that were assayed for Bt resistance in 2006 in British Columbia.  Greenhouse populations with significantly different levels of mortality (P < 0.001) are represented by different letters (a, b, c, d).  Greenhouses treated with Bt are denoted by ! and those that were not treated by ".         42  Figure 2.5: LC50 values and fiducial limits for Trichoplusia ni field populations collected from California (CA), Oregon (OR), and British Columbia (BC) in 2006. The locations surveyed include: BC1=Delta, BC2=Abbotsford, OR=Albany, CA1=Santa Maria, and CA2=Oxnard.          43 2.5 REFERENCES Abbott WS (1925) A method of computing the effectiveness of an insecticide. Journal of  Economic Entomology 18, 265-267. Bailey RI, Bourguet D, Le Pallec A, Ponsard S (2007) Dispersal propensity and settling  preferences of European corn borers in maize field borders. Journal of Applied  Ecology 44, 385-394. Betz FS, Hammond BG, Fuchs RL (2000) Safety and advantages of Bacillus  thuringiensis-protected plants to control insect pests. Regulatory Toxicology and  Pharmacology 32, 156-173. 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Journal of Economic Entomology 99, 937-945.                48 CHAPTER 3: DISTINGUISHING BETWEEN LABORATORY-REARED AND GREENHOUSE- AND FIELD-COLLECTED TRICHOPLUSIA NI (LEPIDOPTERA: NOCTUIDAE) USING THE AMPLIFIED FRAGMENT LENGTH POLYMORPHISM METHOD 2   3.1 INTRODUCTION  The cabbage looper, Trichoplusia ni (Hübner) is an economically important pest in commercial vegetable greenhouses and crucifer field crops in British Columbia (BC), Canada (Janmaat & Myers 2003).  The microbial insecticide, Bacillus thuringiensis kurstaki (Berliner) (Bt) is frequently used to control T. ni in greenhouses.  However, the continued use of Bt has been threatened due to the development of resistance (Janmaat & Myers 2003; Franklin & Myers 2008).  Surveys of Bt resistance in BC greenhouse populations indicate that resistant moths likely move from greenhouse populations strongly selected for Bt resistance following multiple Bt applications (Dipel and Foray; Valent Biosciences, Libertyville, IL) to untreated greenhouse populations in close proximity (Franklin & Myers 2008).  This movement results in elevated levels of Bt resistance in greenhouses that have not been treated with Bt.  These patterns of Bt resistance provide indirect evidence for dispersal of resistant moths between greenhouses. Molecular markers are a potentially valuable tool for quantifying population dispersal. Such markers, with the exception of mitochondrial primer sequences (GenBank, unpublished), have yet to be developed for studying the population structure of T. ni.  The majority of animal studies have used microsatellite markers, which are highly informative at  2 A version of this chapter has been published. Franklin MT, Myers JH, Ritland CE (2009a) Distinguishing between laboratory-reared and greenhouse- and field-collected Trichoplusia ni (Lepidoptera: Noctuidae) using the amplified fragment length polymorphism method. Annals of the Entomological Society of America 102, 151- 157.    49 all loci due to their co-dominant nature (Mariette et al. 2002).  The development of microsatellite markers in Lepidopteran insects has proven particularly difficult due to a lack of variable loci and a high number of repetitive sequences in flanking regions (Zhang 2004). Therefore, we have chosen to explore the utility of the molecular fingerprinting technique, Amplified Fragment Length Polymorphism (AFLP) (Vos et al. 1995).  The AFLP technique allows for the amplification of genomic DNA fragments that are separated based on fragment lengths (Vos et al. 1995).  Despite the widespread use of AFLP markers in plant studies, few animal studies and even fewer insect studies have employed AFLP techniques (Bensch & Åkesson 2005).  AFLP markers are dominant and biallelic and thus suffer the disadvantage of producing poor information content at each locus (Mueller & Wolfenbarger 1999).  Sampling a large number of AFLP loci can, however, compensate for the poor information content of each locus (at least 10 times more than a co-dominant marker) (Mariette et al. 2002).  This is easily accomplished because each primer combination can usually generate between 50 and 100 restriction fragments (Vos et al. 1995; Behura 2006).  The purposes of this pilot study were to: (i) develop a DNA isolation procedure for larval T. ni that is suitable for AFLP analysis, (ii) screen AFLP primer sets to determine suitable primer combinations for analysis of T. ni laboratory populations, and (iii) assess the ability of AFLP markers to differentiate laboratory populations from populations collected in a greenhouse and on field crops in BC.      50 3.2 METHODS 3.2.1 Insect material  Larvae were obtained from six laboratory rearing colonies of T. ni that originated from larval collections from vegetable greenhouses in BC in 2001 (GipA, GipB, GlenA, GlenB), a vegetable greenhouse in 2004 (GH), and a laboratory colony (RC) that had been reared in the laboratory for over 15 years.  Wild populations were collected from a tomato (Lycopersicon esculentum) greenhouse (49°05.279´N, 122º59.902´W) on August 10, 2005 and an organic broccoli (Brassica oleracea) field (49°02.450´N, 123°03.858´W) on July 8th, 2005 in Delta, BC.  The initial screening of AFLP primer combinations was performed using four of the laboratory populations (GipB, GlenA, RC, and GH), while three of the laboratory populations (GipA, GlenA, and RC) and the greenhouse and field populations were used to assess the ability of AFLP markers to distinguish between laboratory and wild T. ni populations. Laboratory reared larvae were maintained using methods modified from Ignoffo (1963).  Upon emergence, larvae were transferred as groups of 20 to 175 ml polystyrene cups containing artificial wheat-germ-based diet and were reared at a temperature of 26˚C with a 16:8 (L:D) photoperiod.  Greenhouse- and field-collected larvae were reared on artificial wheat-germ-based diet for a minimum of two days prior to storage at -80ºC to ensure that no plant material was present in their guts. To avoid misidentification of T. ni with the noctuid species, Autographica californica (Speyer), which has a similar larval appearance to T. ni and is present on field crops and in some greenhouses in BC, T. ni larval identification was confirmed by the presence of vestigial prolegs on abdominal segments A3 and A4 and their absence on A. californica   51 larvae (Stehr 1987).  All larvae, selected at the fifth instar, were killed via decapitation and abdominal segments A9 and A10 were removed.  Due to the need for intact DNA molecules, required for AFLP analysis (Reineke et al. 1998), we removed the gut, which contains macromolecule digestive enzymes, by making an incision along the ventral side of the abdomen.  All samples were flash frozen in liquid nitrogen and stored at -80˚C until analysis. 3.2.2 DNA extraction  DNA was isolated from a total of 24 individuals (n=6 for each GipB, GlenA, RC, and GH) to perform the initial screening of AFLP primer combinations.  DNA was extracted from an additional 60 individuals from laboratory populations (n=20 for each GlenA, GipA, and RC) and 12 individuals from both field and greenhouse populations to examine patterns of genetic variation.  DNA isolation procedures followed those outlined in Sambrook et al. (1989).  The concentration and quality of DNA were determined with a spectrophotometer (Ultrospec 3000, Pharmacia Biotech, Piscataway, NJ), and the quality of the DNA viewed by using 0.8% agarose gel electrophoresis in 1x Tris-borate-EDTA.  DNA was diluted to a final concentration of 100 ng/µL. 3.2.3 AFLP analysis  AFLP procedures were preformed using the method outlined in Goodwillie et al. (2006).  Total genomic DNA (2000 ng) was digested with EcoRI and MseI (TRU 9 I) (Hoffmann-La Roche limited, Mississauga, ON Canada) and modified adaptor sequences were ligated to the digest sites with a tailed EcoRI primer site for the pre-amplification step to improve the clarity of the final gel images for scoring.  The tailed primer method was originally designed to create a universal primer based on the M13 phage sequence (LICOR Inc., Lincoln, NE), which helps with lowering of the cost of infrared primers (Goodwillie et   52 al. 2006) used in the final amplification.  The pre-amplification primers were EcoRI+C and MSEI+A.  Amplified fragments were then diluted by 40X and used in the final amplification with the final primer pairs (Table 3.1); before separation on a LiCor automated sequencer (LiCor Inc.). 3.2.4 Gel scoring  Amplified products were denatured and loaded on to a 5.0% polyacrylamide gel and electrophoresis was performed for 3.5 hours on a LI-COR 4200 automated sequencer (LI- COR Inc.).  The number of polymorphic bands was scored visually for primer pairs used in the initial screening.  Repeatability tests using original tissue samples were performed and 12 of the primer sets were visually inspected to ensure that results were reproducible.  Gels from the three primer combinations that were used to examine patterns of genetic variation in laboratory, greenhouse, and field populations were scored using SAGA 2.0 for AFLP bands (LI-COR Inc.).  When scoring AFLP fragments it was assumed that each band corresponded to one locus and co-migrating bands shared the same nucleotide sequence.  Bands were scored as presence or absence data.  Bands that had a strong intensity and were present in greater than 5% of individuals were scored for polymorphism.  Bands that were either faint or present in only a few individuals were excluded from our analyses.  Due to the dominant nature of AFLP markers, the presence of a band indicates that the individual is either homozygous (2 copies of the allele present) or heterozygous (1 copy of the allele present) for that locus and an individual with no band present is considered absent for alleles at that locus (no allele copies present) (Bensch & Åkesson 2005).  The absence of a band at a locus may be due to a point mutation, insertion or deletion, or duplication within the restriction site (Kazachkova et al. 2007).   53  3.2.5 Data analysis  Only loci that were polymorphic (i.e., the band was present in 5 to 95% of individuals) were used to compare the genetic structure among populations and to perform the cluster analysis.  Allele frequencies were estimated in AFLP-SURV 1.0 (Vekemans et al. 2002) with a Bayesian approach assuming Hardy-Weinberg equilibrium and a uniform distribution of allele frequencies to reduce estimation bias from dominant markers (Zhivotovsky 1999).  Expected heterozygosities were calculated for each of the three primer combinations and overall in AFLP-SURV 1.0 using the unbiased estimator of Lynch & Milligan (1994).  Wright’s pairwise FST values were estimated using AFLP-SURV 1.0 for all population comparisons and 1000 random permutations were performed to test for significant genetic differentiation among populations.  Genetic distances were calculated using Nei’s (1972) minimum distance method for pairwise comparisons of populations.  Cluster analysis was performed using the unweighted pair group method with arithmetic mean (UPGMA) and bootstrap support was estimated over 1000 replicate simulations in the software package TFPGA 1.3 (Miller 1997). 3.3 RESULTS 3.3.1 Primer combinations and levels of polymorphism detected  Sixty selective primer combinations were screened among 24 individuals from four T. ni laboratory populations (Table 3.1).  Final amplification with primer combinations of EcoRI +2 selective nucleotides (E+2) and MseI +2 or 3 selective nucleotides (M+2 or 3) resulted in too many fragments to distinguish on gels.  Primer combinations of E+4 and M+3 also resulted in bands that were difficult to visualize.  Many primer combinations of E+3 and M+4   54 and one primer combination of E+2 and M+4 amplified fragments that were easily visualized and were used for the final list of primers chosen for this study.  Twenty-nine of these primer pairs produced gels that could be scored (Table 3.1).  The majority of the primer combinations that could be easily distinguished contained the nucleotide sequence CG at the terminal ends of the primers.  These primer sets produced between 13 and 51 polymorphic fragments with the majority of bands that could be scored ranging in size from 100 to 550 bp. From the 29 primer combinations, three primer sets with the most polymorphic bands that could be reliably scored were selected to examine patterns of genetic variation in three T. ni laboratory populations (Table 3.1).  A total of 139 fragments were scored among the three primer combinations, with 58, 50 and 31 scoreable loci generated from the following primer sets: E+CGA/M+ATGG, E+CGA/M+AGCT, and E+CGG/M+ACAT, respectively.  Of the 139 loci generated, 87, 77, and 93 loci were polymorphic from laboratory populations GipA, GlenA, and RC populations, respectively. 3.3.2 Genetic variation in laboratory and wild populations  Field and greenhouse populations had over 20% more polymorphic loci and higher heterozygosity than the three laboratory populations surveyed (Figure 3.1, Table 3.2).  Mean heterozygosity for laboratory populations was 0.267, while that for greenhouse and field populations was 0.340.  Laboratory populations (GipA and GlenA) that had been maintained in the laboratory for four years did not have higher heterozygosity than population (RC) which had been reared in the laboratory for over 15 years.  These results were consistent with estimates obtained from the analysis of each primer combination separately. Pairwise FST comparisons indicated that all population pairs, with the exception of the greenhouse and field populations, were genetically differentiated (Table 3.3).  High levels of   55 differentiation were observed between laboratory and wild collected greenhouse and field populations (FST = 0.262-0.303; P <0.05), while greenhouse and field populations showed no differentiation (FST = 0.0001; P=0.11).  The cluster analysis also separated greenhouse and field populations from laboratory populations with a high level of bootstrap support (Figure 3.2). 3.4 DISCUSSION Our results demonstrate that AFLP markers are a valuable molecular tool for the identification of population structure in both laboratory and wild T. ni populations. Currently, no other molecular marker has been evaluated to resolve fine scale genetic patterns in T. ni populations.  AFLP markers are particularly useful for discriminating among populations and identifying population level differentiation due to the large number of polymorphic loci that can be generated genome wide and their high level of reproducibility (Savelkoul et al. 1999).  The development of alternative markers, such as co-dominant microsatellites, would be highly informative, but these markers are expensive to develop (Sunnucks 2000), and their development has proven particularly difficult in Lepidopteran species due to a high number of repetitive sequences in flanking regions and multiple copies of microsatellite sequences throughout the genome (Zhang 2004).   Due to the complexity of the T. ni genome, primer combinations of E+3 and M+4 were required to produce loci that were easy to score.  During the screening process, many of the primer combinations that produced banding patterns that could be reliably scored without too many polymorphic bands included nucleotide combinations of CG.  Throughout the genome, nucleotide combinations of CG are less common than many other dinucleotides in eukaryotes (Nussinov 1984; Beutler et al. 1989).  By selecting primers containing CG   56 combinations, the number of AFLP bands is reduced by over two-thirds in insect, fish, and bird species (Bensch & Åkesson 2005).  Hence a possible explanation of the improved resolution of our AFLP gels was the selection of primer combinations containing the CG sequence in either one or both primers. Differences in AFLP fingerprinting patterns can arise from changes in DNA methylation (Donini et al. 1997), which has been found to change with tissue type and age in many eukaryotic organisms (Regev et al. 1998, Bensch & Åkesson 2005, Ruiz-García et al. 2005).  We extracted all DNA from the abdominal tissue of fifth instar T. ni to ensure that AFLP fingerprints were not affected by changes in DNA methylation with larval age and tissue type.  DNA methylation, however, is absent or rare in many invertebrate species (Regev et al. 1998), and thus, may present less of a problem for AFLP analysis in insects. Nevertheless, we took the precaution of using only fifth instar larvae.  We observed 20% more polymorphic loci in greenhouse and field populations compared to laboratory populations.  The lower heterozygosity in laboratory populations is likely due to the effects of genetic drift that occurred over four years in the laboratory populations GipA and GlenA, and over more than 15 years in the RC laboratory population. Our goal in the laboratory is to maintain 200 individuals from each population, but populations declined to as few as 20 to 50 individuals when sporadic periods of viral contamination occurred.  Furthermore, when moths mate in the laboratory it is probable that the effective population size is much smaller than 200 due to the presence of sterile moths, unequal sex ratios, and variation in the number of gametes produced by different individuals (Hedrick 2000).  The large reproductive potential of T. ni females (up to 1000 eggs laid per female) (Mitchell & Chalfant 1984) and their ability to mate repeatedly (Landolt 1995) likely   57 allows even small laboratory populations to maintain a moderate level of genetic diversity. In contrast, greenhouse and field populations do not suffer the effects of drift that laboratory populations do, since gene flow among populations and large population sizes likely maintain high levels of genetic diversity.  Pairwise FST values indicated a moderate to high level of subdivision among laboratory T. ni populations (FST = 0.186 - 0.253).  In agreement with our results, the stored grain pest, Ephestia kuehniella (Zeller) that is subject to similar containment as laboratory T. ni in flour mills reported pairwise FST values ranging from 0.278 to 0.297 (Ryne & Bensch 2008).  Furthermore, immobile species, such as Orchesella cincta (Collembola) that disperse only a few meters each generation also have FST values in close agreement with those reported for T. ni laboratory populations (Timmermans et al. 2005).  In contrast, wild collected greenhouse and field T. ni populations showed no differentiation (FST = 0.0001). For other mobile insects, such as the beetle species, Neochlamisus bebbianae (Brown) and Meligethes aeneus (F.) low to moderate levels of differentiation have been observed, depending on the geographic scale in which they are studied (Kazachkova et al. 2007; Egan et al. 2008).  FST values for populations of N. bebbianae and M. aeneus separated by approximately 300 km were as low as 0.008.  Therefore, it is not surprising that greenhouse and field T. ni populations that are highly mobile and separated by less than 10 km showed no differentiation. Primer sets that were selected based on the screening of laboratory populations produced too many polymorphic bands for easy evaluation of greenhouse and field populations.  Further molecular analysis of greenhouse and field populations would benefit   58 from choosing primer combinations that produced a low number of polymorphic bands during the initial screening of laboratory populations.  Results of our cluster analysis and pairwise FST values indicate that AFLP markers are highly effective for distinguishing between laboratory and field and greenhouse populations of T. ni.  AFLP fragments produced from three primer combinations generated over 65 polymorphic loci in laboratory populations and over 90 polymorphic loci in greenhouse and field populations.  The large number of polymorphic fragments generated through AFLP analysis makes them especially appealing for estimating genetic diversity and studying population structure in wild species where molecular markers are limited (Bensch & Åkesson 2005), as in Lepidopteran insects.  Our work on the development of DNA isolation procedures and the initial screening of AFLP primers for use in T. ni populations provides the foundation for future examination of the population structure of greenhouse and field populations.  Furthermore, given the large number of AFLP loci generated genome-wide, it is possible that AFLP markers may assist in the identification of loci that are under selection for Bt resistance.    59 Table 3.1: AFLP primer combinations and the number of visually scored fragments for the four laboratory Trichoplusia ni populations (GipB, GlenA, RC, and GH) used in the initial screening of AFLP primers.  Standard primer sequences for EcoRI and MseI primers are listed for the first primer combination (5!- 3!).  Subsequent primer combinations only differ by their selective nucleotides.  The EcoRI primer contains a 5! tail that is complementary to the M13 sequence (Goodwillie et al. 2006).  Primer combinations without scored fragments did not produce bands that could be easily visualized due to either too many or too few polymorphic bands. EcoRI primera  MseI primera Number of scored fragments CACGACGTTGTAAAACGACGAC TGCGTACCAATTC+CG GATGAGTCCTGAGTAA+ACCG 20  CT AG -  AAG -  ACA -  ACCG -  ACGT - CAA ACAT -  ACCG -  ACGT - CAC ACAT -  ACCG 39  ACGT 22 CAG ACAT -  ACCG 38  ACGT - CCA ACAT -  ACCG 26  ACGT 27 CCC ACAT -  ACCG 32  ACGT 18 CCG ACAT -  ACCG 15  ACGT - CGA ACC  -  ACAT 24  ACCG 18   60 EcoRI primera  MseI primera Number of scored fragments  ACGT 15  ACTC -  AGCG -  AGCT 45*  ATCC 24  ATCG -  ATGA 30  ATGG 51* CGC ACAT 28  ACCG -  ACGT 31  ACTC -  AGCG -  AGCT 27  ATCC 22  ATCG -  ATGA 27  ATGG 27 CGG ACAT 33*  ACCG 30  ACGT 13  ACTC -  AGCG -  AGCT 16  ATCC 25  ATCG -  ATGA 38  ATGG 23 CGT ACCG -  ACGT - CCCC ACC -  ACG  -  AGG -  a Selective nucleotides only at the 3! prime end of the primer. * Three primer combinations with a large number of polymorphic bands were selected to examine patterns of genetic differentiation among three laboratory populations (GipA, GlenA, and RC) and the greenhouse and field populations.   6 1  Table 3.2: Summary of genetic diversity for three AFLP primer sets for three laboratory (GipA, GlenA, and RC) and greenhouse and field Trichoplusia ni populations. Population No. of individuals      E+CGA/M+AGCT      E+CGG/M+ACAT       E+CGA/M+ATGG            All loci     n H n H n H n H GipA 20 33 0.271 12 0.207 31 0.261 76 0.254 GlenA 20 28 0.273 10 0.213 30 0.280 68 0.264 RC 20 35 0.298 11 0.213 35 0.303 81 0.282 Greenhouse 12 37 0.323 20 0.353 40 0.341 97 0.336 Field 12 34 0.319 21 0.373 38 0.353 93 0.344  n: number of polymorphic loci at the 5% level.  H: expected heterozygosity under Hardy-Weinberg genotype proportions.    62 Table 3.3: Pairwise FST values among laboratory (GipA, GlenA, and RC) and greenhouse and field Trichoplusia ni populations. Populations GipA GlenA RC Greenhouse GipA GlenA 0.253* RC 0.186* 0.212* Greenhouse 0.291* 0.284* 0.262* Field 0.300* 0.303* 0.263* 0.0001  Significance tests are based on the results of 1,000 random permutations. *Indicates that the populations show significant genetic differentiation at the 5% level.    63  Figure 3.1: AFLP gel showing fingerprints for 12 laboratory, 6 greenhouse, and 6 field Trichoplusia ni individuals using the primer combination E+CGA and M+AGCT.  Size markers are indicated on the left side of the gel.  Arrows on the right side of the gel denote two examples of polymorphic loci that were scored for AFLP analysis and asterisks denote two examples of loci that were not scored because they were faint and could represent potential gel artifacts. Fingerprints for field and greenhouse individuals show a higher level of polymorphism than laboratory reared populations.  G and F denote greenhouse and field populations, respectively.    64  Figure 3.2:  UPGMA tree describing the relationship of wild collected greenhouse and field Trichoplusia ni populations and laboratory populations (GipA, GlenA, RC).  The tree was constructed using Nei’s (1972) minimum distance method.  Bootstrap support (percent of similar replicates) over 1000 replicate simulations is indicated above each of the main branches.           65 3.5 REFERENCES Bensch S, Åkesson M (2005) Ten years of AFLP in ecology and evolution: why so few animals? Molecular Ecology 14, 2899-2914. Beutler E, Gelbart T, Han J, Koziol JA, Beutler B (1989) Evolution of the genome and the genetic code: selection at the dinucleotide level by methylation and polyribonucleotide cleavage. Proceedings of National Academy Science USA 86, 192-196. Behura SK (2006) Molecular marker systems in insects: current trends and future avenues. Molecular Ecology 15, 3087-3113. 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Stehr FW (1987) Immature insects, vol. 1. Kendall/Hunt Publishing Company, Dubuque, IA. Sunnucks P (2000) Efficient genetic markers for population biology. Trends in Ecology and Evolution 15, 199-203.   68 Timmermans MJTN, Ellers J, Mariën J, Verhoef SC, Ferwerda EB, Van Straalen MN (2005) Genetic structure in Orchesella cincta (Collembola): strong subdivision of European populations inferred from mtDNA and AFLP markers. Molecular Ecology 14, 2017-2024. Vekemans X, Beauwens T, Lemaire M, Roldan-Ruiz I (2002) Data from amplified fragment length polymorphism (AFLP) markers show indication of size homoplasy and of a relationship between degree of homoplasy and fragment size. Molecular Ecology 11, 139- 151. Vos P, Hogers R, Bleeker M, Reijans M, Lee T, Hornes M, Frijters A, Pot J, Peleman J, Kuiper M, Zabeau M (1995) AFLP: a new technique for DNA fingerprinting. Nucleic Acids Research 23, 4407-4414. Zhang D (2004) Lepidopteran microsatellite DNA: redundant but promising. Trends in Ecology and Evolution 19, 507-509. Zhivotovsky LA (1999) Estimating population structure in diploids with multilocus dominant DNA markers. Molecular Ecology 8, 907-913.           69 CHAPTER 4: GENETIC ANALYSIS OF CABBAGE LOOPERS, TRICHOPLUSIA NI (LEPIDOPTERA: NOCTUIDAE) ALONG THEIR WESTERN NORTH AMERICAN MIGRATORY PATH 3   4.1 INTRODUCTION  Migration has been defined as “movement that takes an organism from a habitat lacking a resource to another, more favorable location” (Cardé 2008).  Many insects migrate in the summer from low latitude, over-wintering areas, to northern environments.  The cabbage looper moths, Trichoplusia ni (Hübner) (Lepidoptera: Noctuidae), are native to subtropical areas of North America, and are among these migrating species. They annually migrate from over- wintering areas in southern California and Mexico to establish breeding populations as far north as Canada.  Cabbage loopers are not able to survive winters north of approximately 400 latitude (Toba et al. 1973).  Trichoplusia ni is a polyphagous insect that feeds on many plants of economic importance, including crucifer crops.  With the development of agriculture in northwestern North America in the last several hundred years, large areas of suitable food plants have become widely available during the summer for northern migrants west of the Cascade Mountains.  This is an example of an insect species that has been influenced by anthropogenic plant range expansions and potentially increased gene flow among populations following agricultural development.  The new opportunity for populations to mix fits the prediction of genetic homogeneity over large distances (Oliver 2006).  Although the nature of the migration is not known, observations from pheromone  3 A version of this chapter has been submitted for publication.  Franklin MT, Ritland CE, Myers JH.  Genetic analysis of cabbage loopers, Trichoplusia ni (Lepidoptera: Noctuidae) along their western North American migratory path.   70 trapping programs along the cabbage looper migration pathway; Central Valley of California (Cahn et al. 2001), Linn County Oregon (McGrath 2008) and Langley British Columbia (Cervantes 2005), all show the first appearance of male moths in early to mid-May.  This pattern suggests that a single, large migration event from over-wintering populations in southern California, Arizona and Mexico could be initiating the northern, seasonal populations at approximately the same time.  If so, reasonably high genetic similarity among populations is predicted.  In Oregon and British Columbia cabbage loopers appear to have two generations a year; one initiated by adult moths flying in early to mid-May and another resulting from moths flying in late July and August.  Few moths were captured after mid-August in Oregon or after September in British Columbia while in some locations in Merced, California, moths were captured in October and even November, although for most locations the last peak of moth flight was in August.  Thus the population pattern is for immigrating moths to establish populations in large areas of newly available crops and increase in numbers over the summer.  Conditions for further reproduction decline as crops are harvested and temperatures cool in the autumn.  While northern migration is easy to recognize by the occurrence of populations in areas that are too cold to support over-wintering populations, the existence of return southern migration is not easy to identify.  Observations of return southern movements of cabbage loopers in the late summer and fall have been suggested from trapping data (Lingren et al. 1979; Lingren et al. 1993; DeBolt et al. 1984) but not proven.  If southward movement of moths does not occur, northern movement is difficult to interpret as an evolutionary process.  If it does occur, gene flow   71 will be enhanced over the species range.  Because northern populations are temporary, little opportunity for selection and differentiation exists. To test the prediction of genetic homogeneity among temporary northern and permanent southern cabbage looper populations, we used mtDNA sequence variation in conjunction with nuclear loci, generated from the amplified fragment length polymorphism (AFLP) method to examine T. ni populations from California to British Columbia.  We hypothesized that the annual migration of moths from California would result in genetic similarity of populations along the 1700 km migration route although random founder effects could create modest heterogeneity among different populations. 4.2 METHODS 4.2.1 Specimen collections  Trichoplusia ni larvae were collected from 13 sites along the west coast of North America, including: Arizona (AZ), California (CA), Oregon (OR), Washington (WA), and British Columbia (BC) (Fig. 4.1, Table 4.1).  Sites were selected through consultation with Agriculture Extension advisors that helped to identify regions where suitable crops were grown and the optimal times for collection of T. ni.  Larvae were collected between May and September 2006, with the exception of those collected from AZ.  Cooperators from the University of Arizona made the collections from AZ during November 2006.  Trichoplusia ni were collected as larvae by sampling over a wide area and those used for the AFLP analysis were reared on artificial diet for a minimum of one day to ensure that no plant material was present in the gut. Larvae from AZ were reared in the laboratory for one generation at the University of Arizona, Yuma Agricultural Center prior to their arrival at the University of British Columbia (UBC)   72 (Vancouver, BC Canada).  The gut and its contents were removed from larvae according to procedures outlined in Franklin et al. (2009) to prevent DNA degradation by enzymes found in the gut and larvae were frozen at their 4th or 5th instar of development.  During collections, larvae were transported in a cryogenic vapour shipping tank (MVE Vapor Shipper, Minnesota Valley Engineering Inc, Minnesota, USA) at -130°C until arrival at UBC where the samples are stored long-term at -80°C. 4.2.2 DNA isolation  DNA was isolated from a total of 161 larvae collected from these localities following procedures outlined in Sambrook et al. (1989) for analysis of two regions of the mitochondrial genome and nuclear AFLP markers (Franklin et al. 2009).  DNA was extracted using phenol/chloroform extraction procedures outlined in Sambrook et al. (1989) in protocol 1: isolation of DNA for mammalian cells.  The concentration and quality of DNA was verified with a spectrophotometer (Ultrospec 3000, Pharmacia Biotech, Piscataway, NJ) and by viewing DNA on a 0.8% agarose gel electrophoresis in 1X Tris-borate-EDTA. 4.2.3 Mitochondrial sequencing and analysis  To identify mitochondrial regions with a suitable level of variation for our current study, preliminary analyses were performed to examine the level of variation in seven mitochondrial gene regions (Table 4.2).  Based on preliminary analyses, we chose to focus on a 487 bp region and a 402 bp region of NADH dehydrogenase subunits 1 and 4 (NAD1, NAD4), respectively. Three individuals were sequenced from each collection locality, with the exception of six individuals from the site in Arizona, for a total of 42 individuals (Table 4.1).  Polymerase chain reactions (PCR) for all mitochondrial gene regions were performed with 50 or 100 ng of total   73 genomic DNA, 0.2 mM dNTP (New England Biolabs), (1X) PCR buffer (Stratagene), 1 or 2 units of Paq5000 (Stratagene), 20 pmol each primer (Eurofins MWG Operon).  The PCR conditions used for amplification were as follows: 95°C for 5 min, cycled 35 times at 94°C for 1 min, 55°C for 1 min, 72°C for 1 min, 72°C for 10 min.  PCR products were purified using Wizard® PCR Preps DNA Purification System (Promega) and sequenced with Sequitherm Excel II DNA Sequencing Kit (Interscience) on a PTC 100 Thermal Cycler (MJ Research Inc.) or MultiGene Gradient Thermal Cycler (LabNet) with the following conditions: 95°C for 3 min, cycled 24 times at 95°C for 30 sec, 58°C for 15 sec, 70°C for 30 sec.  Products were loaded on a 5.5% polyacrylamide gel (SequaGel XR, National Diagnostics) and electrophoresis was performed for 10 h on a LI-COR 4200 automated sequencer (LI-COR Inc., Nebraska, USA).  All mitochondrial sequences were submitted to Genbank (see Table 4.2).  Mitochondrial sequences were scored using BaseImagIR Image Analysis Version 04.1 (LI-COR Inc.) and aligned using BioEdit Sequence Alignment Editor (Hall 1999).  Individuals with haplotypes that showed unique variation were sequenced a second time to confirm the sequence variation.  Haplotype diversity (h) (Nei 1987) and nucleotide diversity (!) (Nei 1987) were calculated for gene regions NAD1 and NAD4 and for the concatenated sequence of these two regions using DnaSP (Rozas et al. 2003).  TCS version 1.21 was used to construct a haplotype network based on combined analysis of gene regions NAD1 and NAD4 (Clement et al. 2000). Analysis of molecular variance (AMOVA) was performed to test for genetic structuring among populations from AZ to BC using the "ST statistic (Excoffier et al. 1992).  Significance was assessed under the null hypothesis of no genetic structure using 999 random permutations of the data (GenAlEx6; Peakall & Smouse 2006).   74 4.2.4 AFLP genotyping and analysis  AFLP analysis was performed on 15 individuals from each of nine localities and 14 individuals from a locality near Seattle Washington due to the low number of larvae found at this field locality (Table 4.1).   Other field localities were not included in AFLP analysis because larval densities were too low at these locations.  Procedures followed those outlined in Franklin et al. (2009), with pre-amplification primers of EcoR1+C and MSE+A.  Four primer combinations listed in Table 4.3 were used for the final amplification.  To ensure the repeatability of our results, we took several precautions as outlined in Bonin et al. (2004).  Tissue from late instar larvae (4th and 5th instar) was used because changes in DNA methylation with tissue type and age can lead to differences in AFLP banding patterns (Donini et al. 1997).  Larvae were flash frozen and stored at -80ºC to ensure that DNA molecules remained intact.  The quality of isolated DNA was determined by viewing it on a 0.8% agarose gel electrophoresis and spectophotometer (Ultrospec 3000) and low quality samples were excluded from AFLP analysis.  A pilot study outlined in Franklin et al. (2009) was undertaken to select primer combinations with a suitable level of variation and to ensure the repeatability of the results.  In the pilot study, negative controls were run on all gels to check for contamination and replicate amplification was performed on 11 individuals from independent extractions for six of the screened primer combinations.  Replicate amplifications were run side-by-side with the original amplification and visualized on a LI-COR automated sequencer (LI-COR Inc.).  The repeatability of our pilot study was high (99.99%) and there was no contamination of samples.  In the present study, we discarded low quality samples from scoring, ran negative   75 controls, and performed a repeatability test on nine previously typed individuals.  We used 50- 700 bp IRDye 700 and 800 commercial ladder (LI-COR Inc.) to size our markers for all gels visualized on the LI-COR automated sequencer (LI-COR Inc.).  Scoring procedures followed those recommended in Bonin et al. (2004), with automated scoring using SAGA 2.0 (LI-COR Inc.) in conjunction with the data checked by hand.  The genotype error rate estimated by running previously typed samples side-by-side with newly amplified samples from previously extracted DNA was 2.4%.  The percentage of polymorphic loci (%P) and expected heterozygosity (He) were calculated using AFLP-SURV 1.0 (Vekemans et al. 2002).  Expected heterozygosity was estimated using all loci using a Bayesian approach with non-uniform prior distribution of allele frequencies and assuming Hardy-Weinberg genotypic proportions (Zhivotovsky 1999).  This approach is favoured for dominant markers because it provides reasonable estimates of the null allele frequency at each locus (Bonin et al. 2007).  Pairwise FST values were calculated for all population comparisons using loci that were polymorphic at the 5% level in AFLP-SURV 1.0. Significance was tested by comparing the observed FST value to a distribution of FST values based on 1000 random permutations of individuals among existing populations (Vekemans et al. 2002). The significance level was adjusted using the Bonferroni correction to account for multiple comparisons. A Mantel test of matrix correspondence (Mantel 1967) in GenAlEx version 6.1 (Peakall & Smouse 2006) was used to test for isolation-by-distance following methods of Smouse et al. (1986), by examining the correlation between matrices of Nei’s genetic distance calculated according to methods outlined in Lynch & Milligan (1994) (AFLP-SURV 1.0) and geographic   76 distance.  Geographic distances were calculated from latitude and longitude coordinates based on a modified version of the Haversine Formula implemented in GenAlEx version 6.1.  To test for significance 999 random permutations of the data were performed. The model-based clustering method implemented in STRUCTURE 2.2 (Pritchard et al. 2000; Falush et al. 2007) was used to determine patterns of genetic structure.  Simulations were run under the admixture ancestry model with correlated allele frequencies.  To estimate the number of clusters (K), the following settings were used: burn-in 100,000 steps, run length of 200,000 steps, and 10 replicate simulations of each K value (K =1-10).  The ad hoc statistic !K developed by Evanno et al. (2005) was used to identify the most probable number of clusters. To incorporate spatial information, we used a Bayesian clustering method with a spatially explicit prior implemented in TESS 2.1 (Chen et al. 2007).  AFLP loci were coded according to the method described in Evanno et al. (2005) for dominant markers.  To detect the maximum number of clusters (Kmax) we performed an analysis under the admixture model for K from 1 to 10, using 50 replicate simulations for each K, with a burn-in of 10,000 sweeps, run-length of 50,000 sweeps, and an admixture parameter #=1.  Two values of the spatial interaction parameter were examined (! = 0.7 and 0.9), with higher values giving greater importance to spatial interactions.  The Deviance Information Criterion (DIC) was computed for each run and the mean DIC for the 10 runs with the lowest DIC was computed for each value of K, as an indicator of how well the model fit the data.  Kmax was identified from the inflection point in a plot of the mean DIC against K values from 1 to 7.   Preliminary runs with K > 7 provided a poor fit to the data and were not included in the final analysis.  Results are reported based on the spatial prior ! = 0.7, since results were similar for both values tested.  CLUMPP version 1.1.1   77 (Jakobsson & Rosenberg 2007) was used to correct for label switching and identify the mean cluster membership for individuals based on the 10 replicate simulations in STRUCTURE and TESS for the number of clusters identified by the !K statistic and DIC values, respectively. DISTRUCT (Rosenberg 2004) was used to display the results. 4.3 RESULTS 4.3.1 Specimen collections   Overall, there was a trend for T. ni to be collected later in the summer with increasing latitude, which is indicative of when larvae could be found at these collection sites (Table 4.1). Larval collections from AZ to BC were performed from May to November 2006.  At the southernmost site (AY) larvae were not collected until November because T. ni are at very low densities during the summer in AZ due to the hot, dry conditions. 4.3.2 Mitochondrial sequencing  Preliminary examination showed little to no variation in mitochondrial gene regions COI, COII, CYTB, and NAD5 and therefore they were not used to assess the population structure of T. ni (Table 4.2).  Instead, we sequenced a total of 889 bp of mtDNA from 42 field collected T. ni, of which 487 bp were from the NAD1 gene and 402 bp were from the NAD4 gene.  We identified seven variable nucleotide sites in the NAD1 gene and six variable sites in the NAD4 gene, all of which resulted in synonymous changes.  Nucleotide diversity (!) was highest in populations from CY and WS (Table 4.4).  Nucleotide composition was highly A/T skewed, with mean base pair frequencies for NAD1 and NAD4 of A: 0.46, C: 0.12, G: 0.09, T: 0.33 and A: 0.43, C: 0.15, G: 0.08, T: 0.33, respectively.   78  A total of eight haplotypes were found for the NAD1 region and six for the NAD4 region.  Overall 12 haplotypes were identified from the combined analysis of NAD1 and NAD4 regions, of which eight were private haplotypes (E-L) (Table 4.4).  All populations, with the exception of OA, WM, and WB, had more than one haplotype present.  The most common haplotypes (A and C) were present in 11 of the 13 populations surveyed from BC to AZ. A haplotype network was constructed based on the 12 haplotypes from the combined analysis of NAD1 and NAD4 genes (Figure 4.2).  The haplotype network was comprised of two common haplotypes (A and C) that were found in 29 of the individuals surveyed. No distinct geographical separation of haplotypes appeared in the network.  AMOVA results indicated marginal support for genetic subdivision of the populations, with 13% of the variation attributable to population subdivision ("ST = 0.13, P = 0.053). 4.3.3 AFLP genotyping  A total of 221 AFLP fragments ranging in size from 73 to 531 bp were scored from four primer combinations in 149 individuals.  The number of fragments generated from each primer combination ranged from 37 to 65 (Table 4.3).   Of the 221 fragments scored 15 loci were monomorphic and 39 had a band frequency of less than 5% or greater than 95%.  The percentage of polymorphic bands at or above the 5% level ranged from 67.9 to 86.0% in the populations surveyed, with WS having the lowest number of polymorphic bands and CX1, the most southern population, having the highest (Table 4.4).  The mean expected heterozygosity under Hardy- Weinberg proportions was 0.284, with the lowest observed in the WS population (0.250) and the highest in the OC population (0.302) (Table 4.4).   79  Pairwise FST values between populations ranged from 0.0008 to 0.1121 (Table 4.5).  FST values indicated a lack of differentiation among the populations surveyed from CA where cabbage loopers overwinter, but significant differentiation existed among the majority of CA populations and those north of CA.  Significant differentiation was also observed among most populations surveyed from OR, WA, and BC.  Although the two populations from BC were significantly differentiated, one of the populations was not different from one of the CA populations.  The highest level of differentiation was observed between populations at the localities BA and WS.  Populations at these localities also showed the highest levels of differentiation compared to all other populations surveyed.  AFLP loci that had low polymorphism (i.e. present <5 or >95% of individuals) were excluded from all spatial analysis due to their potential to bias parameter estimates (Lynch & Milligan 1994).  Results from STRUCTURE identified three clusters (K=3) to be the most probable number (Figure 4.3).  The bar plot revealed that the WS population was most distinct from the other populations with high representation in a cluster type that had low representation in all other populations (Figure 4.4).  Populations surveyed from CA showed mixed membership into two groups, with an average membership of 0.446 and 0.444 in the two clusters.  Individuals from populations BA, OR, and OC had moderately high assignment to one of these two clusters and all other populations showed a mixed membership into the two groups.  The most probable number of clusters identified by TESS Bayesian cluster analysis was four (K=4).  When compared to the results of STRUCTURE, a similar pattern of spatial structure was observed, except a portion of individuals from the BA population had membership into a fourth cluster.   80 Consistent with gene flow, a mantel test showed no relationship between geographic and genetic distance for T. ni populations from CA to BC (r = -0.077, P = 0.35, Figure 4.5). 4.4 DISCUSSION  The modest levels of geographic structure in T. ni populations from Arizona to British Columbia, suggest high migratory connectivity for this moth species.  Patterns arising are consistent with significant movement of T. ni from southern, over-wintering populations, with some genetic differentiation occurring among temporary populations in northern regions initiated by annual immigration of moths. We are confident that our results provide an accurate depiction of the population structure of T. ni, since similar conclusions were drawn from both mtDNA and AFLP markers. Our AFLP results from the clustering analyses and pairwise FST values indicate a lack of geographic structure among California populations.  Warm temperatures in southern California allow T. ni populations to persist year-round (Mitchell & Chalfant 1984) and our findings suggest that gene flow connects these populations.  Moths are capable of dispersing at least 500 km in a single night (Drake & Farrow 1988; Gatehouse 1997) and therefore it is not surprising that T. ni populations in California separated by as much as 600 km remain connected. In areas north of California in which field populations are not expected to over-winter, pairwise FST comparisons based on AFLP markers indicate that the majority of populations are weakly, genetically differentiated.  Consistent with these findings, AMOVA results based on mtDNA variation show marginal support for genetic subdivision of populations.  Non-significant pairwise FST comparisons between distant populations however indicate a lack of genetic differentiation in some cases.  For example the California population, CX1 is not differentiated   81 from the British Columbia population, BD more than 1700 km away.  In addition, no significant relationship between isolation and distance along the proposed migration path occurred.  The lack of isolation by distance but statistically significant differentiation among temporary northern populations and California source populations could be due to founder events and random genetic drift during the migration process.  On a local scale, the two populations from BC were genetically differentiated and this might be the result of cabbage loopers that over-winter in some greenhouses there (Franklin & Myers 2008). Excluding comparisons with the distinctive population from Seattle, Washington the highest pair-wise FST value is 0.0605 between the BA and OR populations.  In previous comparisons between three populations maintained in the laboratory for a minimum of four years after collection from vegetable greenhouses, the pair-wise FST values were three to four time higher and ranged from 0.186 to 0.253 (Franklin et al. 2009).  Thus although significant, the populations collected along the proposed migration route are not highly differentiated. Our results are consistent with those from other migratory insects that have observed genetic homogeneity among some populations separated by large geographic distances (Daly & Gregg 1985; Johnson 1987; Peterson & Denno 1998; Mun et al. 1999; Zhou et al. 2000; Llewellyn et al. 2003; Vandewoestijne & Baguette 2004; Scott et al. 2005).  This is in contrast to insects such as Tortrix viridana and Thaumetopoea pityocampa that are widely distributed, but disperse short distances and exhibit strong geographic structure (Salvato et al. 2002; Schroeder & Degen 2008).  In agreement with our findings, Peterson & Denno (1998) found with allozyme data a generally weak relationship between genetic and geographic distance for 16 highly mobile insect species.  Little geographic structure was also observed in the noctuid moth, Helicoverpa   82 armigera in Australia and the eastern Mediterranean (Daly & Gregg 1985; Zhou et al. 2000). However, follow-up analysis of H. armigera migration patterns in Australia revealed high rates of movement and a lack of geographic structure in one year followed by low migration rates and significant geographic structure in the subsequent year (Scott et al. 2005).  The migration patterns of some Lepidopteran species may be affected by significant inter-annual fluctuations in population densities. Low population densities of T. ni were observed along the proposed migration route in 2006 (personal observation) and this could have led to smaller founding populations and genetic differentiation among many of the surveyed populations.  This year contrasts to 2008 in which populations at least in Oregon were exceptionally high (McGrath 2008).  A future study over multiple years would be required to determine if genetic variation was associated with population density variation.  In other studies of migrating moths genetic differentiation and a lack of gene flow in populations helped to identify sources of migrant populations.  For example for S. frugiperda, COI haplotype profiles showed distinct differences between populations from overwintering grounds in southern Texas and Florida (Nagoshi et al. 2008).  These haplotype differences remained as moths migrated and provided evidence that moths migrated north and east from Texas and north from Florida.  Similarly in populations of Nilaparvata lugens COI haplotype differences between the Indochina peninsula and China and Korea provided support that populations from China were the source of migrants for populations in Korea (Mun et al. 1999). Thus despite significant gene flow along their migration pathways, distinct geographic structure can exist between populations using different migration routes.  Our analysis of haplotypes   83 examined from the NAD1 and NAD4 gene regions indicated that the majority of T. ni populations shared common haplotypes and suggests that our survey identified one migration route.  It is possible that distinct migration routes would be identified if future surveys of T. ni populations encompassed regions of eastern North America.  Although our data suggest that gene flow occurs across the survey range, the Seattle, Washington population (WS) was genetically distinct from the other populations.  Two of the three mitochondrial haplotypes were only found in this sample, heterozygosity was the lowest in this population, and pairwise FST values and clustering results of AFLP data indicated that this population was genetically distinct.  It is possible that this population was established by a human associated introduction from another source population (Loxdale & Lushai 1999) such as larvae and pupae being transported with cut cabbage or cruciferous transplants being infested with eggs and larvae (Lingren et al. 1979) or entry through a marine port.  The clustering analysis implemented in STRUCTURE and TESS and pairwise FST values also indicated that the Abbotsford, British Columbia population (BA) was somewhat distinct.  In the last 30 years the development of vegetable greenhouses that potentially provide refuges for over-wintering cabbage looper moths could have contributed to this.  Although a winter clean-up in which greenhouse temperatures are reduced, crops are removed, and houses are fumigated, eliminates most cabbage loopers, pupae can sometimes survive two weeks at 100C and moths still mate after emergence (Caron & Myers 2008).  In this situation populations occur in the greenhouses before they can exist in the fields (Cervantes 2005).  Greenhouse populations are frequently under strong selection from the use of the microbial insecticides based on the toxin produced by Bacillus thurigiensis with resulting increased selection for resistance in some cases   84 (Janmaat & Myers 2003; Franklin & Myers 2008).  Movement of moths from greenhouse populations could influence the genetic structure of surrounding field populations and contribute to greater distinctiveness of the BA population.  Further work on the local genetic structure of greenhouse and field populations in British Columbia suggests that this is likely to be the case (Chapter 5).  Trichoplusia ni larvae were not present in Yuma, Arizona (AY) during the summer when all other populations were collected due to high temperatures and the lack of suitable crops.  Our collaborators collected larvae from Arizona during November when temperatures were cooler and crucifer field crops existed in the area.  We predicted that this population might be genetically distinct from the other populations surveyed due to its location further east and the unsuitable summer temperatures.  However, the two most common mitochondrial haplotypes (A and C) observed in other populations were also present in the Arizona population.  This suggests genetic connectivity between this population and other populations surveyed.  AFLP analysis was not possible with this population.  Because maternal haplotypes remain grouped in migratory species as individuals move together to and from seasonal migration grounds (Loxdale & Lushai 1999), geographic differentiation can remain low among widely separated populations.  In support of this, the migratory monarch butterfly, Danaus plexippus populations in North America show low levels of mitochondrial variation (Brower & Boyce 1991) similar to what we have observed here.  Still unclear with T. ni populations is whether southern migration occurs in the autumn or if northern populations are a genetic dead-end.  Studies such as those of Chapman et al. (2008)   85 using behavioural analysis of preferred flight directions of moths and vertical radar will be necessary to further investigate this.  In future it would be useful to extend our study over multiple years to test for temporal changes in migratory patterns of T. ni in years of high and low population densities. Furthermore, in search of an explanation for the distinct geographic structure of the Washington population, it would be important in the future to examine the migration patterns of T. ni from regions further east.  Understanding migratory connectivity in T. ni will allow us to predict the outcomes of natural verses human induced changes that occur in different habitats at different times of the year.  From an applied perspective, knowledge of the strong migratory connectivity in T. ni may help us predict when populations may be problematic in crops and aid in the management of Bt resistance.        86 Table 4.1: Summary of collection dates, latitude and longitude coordinates, and the crops that Trichoplusia ni larvae were collected from.  Populations in bold are resident populations. MtDNA analysis was done for all populations and AFLP analysis for all population except AZ, WB, and WM. *T. ni larvae were received at the University of British Columbia on Dec 11, 2006.  Larvae were collected from the field approximately one month prior to this date. a  Mixed crucifer crops           Sample locality code City, State/ Province Collection date Latitude (N) Longitude (W) Crop AY Yuma, AZ 10 Nov 2006* 32˚42.750! 114˚42.300! cabbage CX1 CX2 Oxnard field 1, CA Oxnard field 2, CA 29-30 Jun 2006 29-30 Jun 2006 34˚12.561! 34˚19.803! 119˚03.403! 119˚08.339! mixed a  cabbage CS Santa Maria, CA 27-28 Jun 2006 34˚53.550! 120˚30.853! broccoli CY Yuba, CA 22 Jun 2006 39˚08.617! 121˚53.098! tomato OR Roseburg, OR 24 Jul 2006 43˚15.494! 123˚26.415! broccoli OC Corvallis, OR 27 Jul 2006 44˚34.384! 123˚14.209! broccoli OA Albany, OR 27 Jul 2006 44˚43.865! 123˚07.455! broccoli WS Seattle, WA 29 Aug 2006 47˚36.923! 121˚55.206! mixed a  WM Mount Vernon, WA 30 Aug 2006 48˚24.270! 122˚26.306! Broccoli WB Bellingham, WA 30 Aug 2006 48˚43.495! 122˚28.622! mixed a  BD Delta, BC 9 Aug 2006 49˚06.723! 123˚02.266! broccoli BA Abbotsford, BC 14 Sep 2006 49˚05.046! 122˚05.805! rutabaga  8 7  Table 4.2: MtDNA region, primer name and sequence, size of region (bp), the number of individuals sequenced (N), their sampling localities, and corresponding GenBank accession numbers.  For each gene region sequenced, forward primers are listed first and reverse primers are listed second. Region Primer name Primer (5'-3') Size (bp) N No. variable sites Sampling localities 6  GenBank Accession No. CYTB  REVCB2H 1  REVCBJ 1  TGAGGACAAATATCATTTTGAGGW ACTGGTCGAGCTCCAATTCATGT 500 6 1 OR, OA, WS, WB, CS GQ183958- GQ183963 COI COI&IIF 2 COIR GGATTCATTGTTTGAGCTC CATTATATGAATGTTCAGCWGG 549 2 0 OA, WS GQ183955- GQ183956 TRNL2, COII COIF  COI&IIR 2  CCWGCTGAACATTCATATAATG CGCAAATTTCTGAACATTGTCC 546 3 0 CS, OA, WS GQ184054- GQ184056 NAD5 NAD5F 3 NAD5R 3  CTGGAATTGCCGCTAATTATG ATCTCCCTCTAATTACTC 440 5 1 CS, OR, OA, WS, BD GQ184048- GQ184051 GQ184053 NAD1 NAD1F  N1N12595 4  AGGGAGTTCGATTAGTTTCAGC GTAGCATTTTTAACTTTATTAGAACG 487 4 2 7 All GQ183964- GQ184005 NAD4 NAD4F  N4N8727 4  TTAAATATTCTCGAGAAACTCC AAATCTTTRATTGCTTATTCWTC 402 4 2 6 All GQ184006- GQ184047  1 Primers from Simmons & Weller (2001)  2 Primers designed from previously sequenced T. ni from Japan (GenBank Accession No. AB158623) 3 Primers designed from previously sequenced T. ni from Japan (GenBank Accession No. AB158627) 4 Primers from Salvato et al. (2008)     88 Table 4.3: AFLP primer combinations and the number of scored fragments for Trichoplusia ni populations surveyed from the west coast of North America in 2006. Primer combination EcoR1 Mse1 No. of scored fragments 1 Eco+CGG Mse+ATGG 64 2 Eco+CGG Mse+AGCT 37 3 Eco+CCG Mse+ACCG 55 4 Eco+CGA Mse+ACCG 65                         8 9  Table 4.4: Summary of descriptive statistics for mitochondrial and nuclear markers for Trichoplusia ni collected from 13 sampling localities on the west coast of North America.  The number of individuals (N), haplotypes, nucleotide diversity (!), and haplotype diversity (h) are reported for mitochondrial DNA from a 487 bp region of NAD1 and 402 bp region of NAD4 and 889 bp for combined analysis of these two regions.  The % polymorphic loci at the 5% level (%P) and expected heterozygosity under Hardy-Weinberg proportions (He) are reported for AFLP data.  Mitochondrial DNA Nuclear DNA   NAD1 and NAD4  NAD1    NAD4 Locality N Haplotype ! a  h b   Haplotype ! a h b   Haplotype ! a  h b   N %P He AY 6 A(2), C(4) 0.0012 0.53  M 0.0000 0.00  U(2), V(4) 0.0027 0.53  _ _ _ CX1 3 A, B, C 0.0023 1.00  M(2), N 0.0014 0.67  U, V(2) 0.0033 0.67  15 86.0 0.298 CX2 3 A, C, L 0.0023 1.00  M(2), N 0.0014 0.67  U, V, Z 0.0033 1.00  15 77.8 0.280 CS 3 A, C(2) 0.0015 0.67  M 0.0000 0.00  U, V(2) 0.0033 0.67  15 81.9 0.278 CY 3 I, J, K 0.0038 1.00  M, S, T 0.0027 1.00  V, X, Y 0.0050 1.00  15 82.4 0.286 OR 3 A, H(2) 0.0023 0.67  M, S(2) 0.0014 0.67  U, V(2) 0.0033 0.67  15 76.9 0.265 OC 3 A, C, G 0.0023 1.00  M(2), R 0.0014 0.67  U, V(2) 0.0033 0.67  15 83.3 0.302 OA 3 C 0.0000 0.00  M 0.0000 0.00  V 0.0000 0.00  15 83.7 0.301 WS 3 C, E, F 0.0038 1.00  M, P, Q 0.0027 1.00  V, W(2) 0.0050 0.67  14 67.9 0.250 WM 3 A 0.0000 0.00  M 0.0000 0.00  U 0.0000 0.00  _ _ _ WB 3 C 0.0000 0.00  M 0.0000 0.00  V 0.0000 0.00  _ _ _ BD 3 A, B, C 0.0023 1.00  M(2), N 0.0014 0.67  U, V(2) 0.0033 0.67  15 84.2 0.300 BA 3 A, B, D 0.0030 1.00  M, N, O 0.0027 1.00  U, V(2) 0.0033 0.67  15 79.6 0.275 a  nucleotide diversity (Nei 1987) b  haplotype diversity (Nei 1987)  9 0  Table 4.5: Pairwise FST values (below the diagonal) and geographic distances (km) (above the diagonal) between Trichoplusia ni populations collected from localities along the west coast of North America. Population CX1 CX2 CS CY OR OC OA WS BD BA CX1 — 15 153 604 1070 1210 1220 1510 1690 1670 CX2 0.0025 — 140 588 1060 1190 1200 1490 1670 1660 CS 0.0008 0.0138 — 488 964 1100 1120 1420 1590 1580 CY 0.0149 0.0092 0.0139 — 476 614 630 942 1110 1100 OR 0.0282* 0.0381* 0.0398* 0.0337* — 147 166 499 652 656 OC 0.0089 0.0112 0.0208* 0.0156 0.0264* — 20 353 505 509 OA 0.0207* 0.0218* 0.0265* 0.0276* 0.0552* 0.0205* — 334 487 490 WS 0.0687* 0.0696* 0.0830* 0.0665* 0.0696* 0.0671* 0.0964* — 186 164 BD 0.0040 0.0152* 0.0176* 0.0325* 0.0273* 0.0103 0.0236* 0.0560* — 69 BA 0.0316* 0.0332* 0.0492* 0.0514* 0.0605* 0.0365* 0.0453* 0.1121* 0.0282* —  1000 random permutations were used to test for significant genetic differentiation between populations *Indicates that populations were genetically differentiated at a significance level of p<0.001.          91  Figure 4.1: Geographic distribution of sites where Trichoplusia ni were collected along their migration route.  Sampling localities were located in British Columbia (BC), Washington (WA), Oregon (OR), California (CA), and Arizona (AZ).  Locality codes are defined in Table 4.1.     92  Figure 4.2: Haplotype network of 12 mtDNA haplotypes based on gene regions NAD1 and NAD4.  Forty-two specimens were collected from locations in Arizona (AZ), California (CA), Oregon (OR), Washington (WA), and British Columbia (BC).  The size of each circle is proportional to the number of individuals.  Letters within the circles correspond to the haplotype shared by those individuals.  Lines connecting circles represent a single nucleotide change between haplotypes and black dots between circles represent a single unobserved nucleotide change inferred from parsimony analysis.   93   Figure 4.3: !K values calculated according to the method outlined in Evanno et al. (2005) from the clustering results obtained from STRUCTURE for each cluster size (K) from 2 to 10.  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Heredity, 85, 251-256.      103 CHAPTER 5: SPATIAL AND TEMPORAL CHANGES IN GENETIC STRUCTURE OF GREENHOUSE AND FIELD POPULATIONS OF CABBAGE LOOPER, TRICHOPLUSIA NI4  5.1 INTRODUCTION  Human-induced changes have altered ecosystems globally, often accelerating evolutionary change in species through strong selection pressures induced by human technologies (Palumbi 2001; Smith & Bernatchez 2008).  Agricultural pests have been a primary target of these technologies through the extensive use of insecticides that has led to resistance development in over 500 insect species with some being resistant to entire insecticide classes (Georghiou & Lagunes-Tejeda 1991).  Wide spread use of the biopesticide, Bacillus thuringiensis (Bt) in agricultural crops has acted as a strong selective force on insect pest populations world-wide (Palumbi 2001).  In response, resistance to Bt sprays and transgenic crops has rapidly evolved in several insect species (Ferré & Van Rie 2002; Van Rensburg 2007; Tabashnik 1994; Tabashnik et al. 2008; Tabashnik et al. 2009) and threatens the continued success of these products.  To mitigate resistance development, strategies have been developed based on theory to guide the management of insect populations in the hopes of slowing or delaying resistance evolution (Palumbi 2001).  Current models consider gene flow and selection pressures to be key determinants of resistance evolution (Caprio & Tabashnik 1992). Gene flow can promote or retard resistance by transferring susceptible and resistant individuals among treated and untreated habitat patches (Caprio & Tabashnik 1992; Croft & Dunley 1993).  For example, the immigration of susceptible individuals into resistant  4  A version of this chapter has been submitted for publication.  Franklin MT, Ritland CE, Myers JH. Spatial and temporal changes in genetic structure of greenhouse and field populations of cabbage looper, Trichoplusia ni.   104 populations in treated fields can reduce the impact of selective forces and ensuing resistance (Caprio & Tabashnik 1992).  In contrast, the movement of resistant individuals into surrounding untreated field population can spread resistance (Caprio & Tabashnik 1992; Peck et al. 1999), unless resistant alleles are purged in untreated habitats due to fitness costs associated with maintenance of resistance.  Trichoplusia ni (Hübner) (Lepidoptera: Noctuidae), cabbage looper, is a sub- tropical insect that migrates each spring from southern California, where temperatures are warm enough for over-wintering, to seasonably available habitats as far north as Canada (Mitchell & Chalfant 1984).  Previously T. ni were unable to over-winter north of approximately 400 latitude (Toba et al. 1973).  The development of greenhouses in BC has however altered the natural extinction-recolonization dynamics as pupae sometimes survive the winter clean-up at the end of the growing season.  Moths then emerge to mate and lay eggs on newly propagated plants early in the following growing season before migrant moths arrive.  These populations would be able to have four or five generations while two generations a year is the norm for field populations.  The cabbage looper, is a major pest of crucifer field and vegetable greenhouse crops world-wide.  In populations of T. ni in greenhouses in British Columbia (BC), Canada, resistance has developed in response to strong selection through extensive use of Bt sprays (Franklin & Myers 2008; Janmaat & Myers 2003).  Determining the patterns of gene flow among populations of T. ni is a necessary prerequisite for understanding and slowing resistance evolution.  Insight into their movement patterns may be gleaned from the examination of spatial and temporal patterns of Bt resistance (Franklin & Myers 2008).  Trichoplusia ni shows significant variation in the levels of Bt resistance among   105 greenhouse and field populations in BC (Franklin & Myers 2008; Janmaat & Myers 2003).  This variation in resistance may be partially explained by differences in local selection pressures due to variation in spray regimes with location (Roderick 1996), and yet gene flow may also play a major role.  Here we used the putatively neutral genetic markers, amplified fragment length polymorphisms (AFLP), to examine the spatial and temporal genetic structure of greenhouse and field populations of T. ni in BC and three other populations from California.  We correlated patterns of genetic variation with observed patterns of Bt resistance to determine the impact of directional selection on the population genetic structure of T. ni.  Based on patterns of Bt resistance, we predicted that greenhouse populations in close proximity would be genetically similar, while field populations would show greater similarity to California populations that are the potential source of migrants.  Temporally, we expected genetic variation to be low in greenhouse populations in the winter and early spring due to low survival and bottlenecks that could occur during the year-end clean-up.  An overall increase in genetic variation in greenhouse and fields may be anticipated for populations in the summer as migrants from California move into BC. For greenhouses that harbour persistent populations over-winter, we expected genetic similarity between collections from one year to the next.  Finally, we predicted that populations with high levels of resistance would show greater genetic differentiation than susceptible populations, since Bt sprays should both select for resistance and reduce populations causing recurrent bottlenecks.    106 5.2 METHODS 5.2.1 Greenhouse and field collections  To determine the population structure of T. ni we collected larvae from ten commercial vegetable greenhouses growing tomatoes, peppers, and cucumbers and seven cruciferous field crops throughout the lower mainland of BC from March 2005 to March 2007 (Figure 5.1, Appendix 1).  Larvae were collected from seven greenhouses and five fields in 2005, eight greenhouses and three fields in 2006, and two greenhouses in the winter of 2007.  In 2005, four of the greenhouses were sampled three times and two of the fields were sampled twice over the growing season.  Attempts were made to sample multiple times over the growing season in greenhouses and fields in 2006, however due to low population densities our sample size was limited.  To determine if California served as a source of migrants to local BC field and greenhouse populations we also included collections performed in the summer 2006 from one field in Santa Maria, California (CS: 34°53.550!N, 120°30.853!W) and two fields in Oxnard, California (CX1: 34°12.561!N, 119°03.403!W; CX2: 34°19.803!N, 119°08.339!W).  Throughout we define the growing seasons according to four categories including: (1) spring - greenhouse populations are present, but field populations are absent (Apr- Jun); (2) summer - greenhouse and field populations are present (Jul-Aug); (3) fall - greenhouse populations are present, but field populations of T. ni are declining or absent (Sept-Oct); (4) winter - field populations are absent and only a few greenhouse populations that have persisted through the year-end cleanup remain (Nov-Mar). We collected larvae ranging from their first to fourth instars of development by visually searching plants in several rows that spanned the entire range of the field or   107 greenhouse.  In greenhouses a maximum of five larvae were placed into 30 ml plastic cups with leaves.  In fields up to 30 larvae were placed into 473 ml paper cups.  All larvae were kept alive and cool by placing them in an insulated cooler with ice packs until they were later sorted in the laboratory. Greenhouse and field collected T. ni were reared in the laboratory for a minimum of two days.  A maximum of five larvae were placed in 30 ml plastic cups and fed wheat- germ-based diet modified from Ignoffo (1963) at 26ºC, 16 : 8 h (L:D) until they reached their fifth instar.  The abdominal tissue of late instar larvae (4th or 5th) was flash frozen according to procedures outlined in Franklin et al. (2009) and stored in -80ºC for later genetic analysis to ensure that DNA molecules remained intact. 5.2.2 Molecular analysis  DNA isolation procedures and AFLP methods followed those described in Franklin et al. (2009).  Genomic DNA was digested with EcoRI and MseI (TRU 9 I) (Hoffman-La Roche Limited, Mississauga, ON, Canada) enzymes.  Four primer combinations of Eco+3 and Mse+4 were used for the final selective amplification (Table 5.1).  Gel electrophoresis of amplified products was conducted on a LI-COR 4200 automated sequencer for 3.5 h.  AFLP analysis was performed on a total of 1082 individuals from 38 collections from BC, with AFLP profiles generated for between 17 and 30 individuals per collection.  Fifteen individuals from each of the three collection sites in California were also used for AFLP analysis.  Several procedures outlined in Bonin et al. (2004) were followed to evaluate the reproducibility of our AFLP results.  DNA was only isolated from late instar larvae because it is known that changes in DNA methylation with tissue type and age can alter   108 AFLP banding patterns (Donini et al. 1997).  The quality of the isolated DNA was checked by spectophotometer (Ultrospec 3000) and by viewing it on a 0.8% agarose gel electrophoresis.  Samples of low quality DNA were excluded from AFLP procedures. Negative controls were run at each step of the AFLP process to check for contamination and intra- and inter-run replicates were used to ensure samples were repeatable.  A commercial ladder was used to size markers during the scoring process and, as recommended in Bonin et al. (2004), we used automated scoring procedures (SAGA 2.0, LI-COR Inc.) along with checking the data manually.  To estimate the genotype error rate associated with laboratory procedures we ran nine previously typed samples beside newly amplified samples.  We also estimated the error rate associated with laboratory work and scoring procedure by comparing the scoring of the same nine duplicate samples run on separate gels.  After eliminating 16 problematic loci our genotype error rate associated with laboratory procedures was 1.6% and that associated with the combination of laboratory and scoring procedures was 5.4%. 5.2.3 Statistical analysis  Due to the dominant nature of AFLP markers, allele frequencies were estimated in AFLP-SURV (Vekemans et al. 2002) using a Bayesian approach with non-uniform prior and assuming Hardy-Weinberg genotypic proportions (Zhivotovsky 1999). Expected heterozygosity was calculated for all collections in each survey year using the Lynch & Milligan (1994) unbiased estimator.  Due to an unequal number of observations, we used Generalized Linear Model (GLM) procedures in SAS 9.1 (2003) to test for differences in heterozygosity in 2005 between the main effects site (greenhouse or field) and time of collection (spring, summer, and fall) and their interaction.  We   109 excluded the two populations surveyed in 2007 and time of collection in 2006 from the analyses because the limited number of populations that were surveyed multiple times over the growing season in these survey years was small due to low T. ni densities resulting in insufficient power to detect differences between the means.  To account for repeated observations over the growing season we followed split-plot procedures as outlined in Kuehl (1994).  Assumptions of normality and homogeneity of variances were met and reported means and standard errors are based on non-transformed data.  Global and pairwise FST estimates were calculated according to the Lynch & Milligan (1994) method using AFLP-SURV 1.0 (Vekemans et al. 2002).  FST estimates were based on 169 polymorphic loci with band frequencies between 5 and 95%.  The significance level of FST estimates was calculated by performing 100,000 random permutations of individuals among populations.  We tested for isolation by distance (IBD) by regressing FST/(1-FST) estimates for pairwise comparisons among all collections and the 11 field and 27 greenhouse collections separately on the logarithm of geographic distance (Rousset 1997).  A Mantel test of matrix correspondence (Mantel 1967) with 1000 matrix permutations was used to test for significant relationships in IBDWS 3.15 (Jensen et al. 2005).  To test for a significant difference between the IBD slopes of field and greenhouse collections we examined the confidence intervals of the standard linear model estimates.  We also performed 50 permutations based on random datasets of 11 of the greenhouse collections to test if the slope estimates of the greenhouse collections overlapped with that for the 11 field collections.  To examine the influence of Bt resistance on genetic differentiation we regressed FST estimates for pairwise comparisons on the logarithm of the mean Bt concentration   110 killing 50% of the population (LC50) for pairs of collections.  Data from Bt bioassays used in Franklin & Myers (2008) were reanalyzed for each collection separately.  Bt bioassays were performed on five-day-old first generation progeny from greenhouse and field collected larvae.  LC50 estimates were obtained using probit procedures in GENSTAT 11.1 (2008) for 16 of the collections.  LC50 values could not be estimated for other collections due to difficulties collecting T. ni when population densities were low and the resulting shortage of offspring for bioassay tests.  Where multiple collections were performed at the same locality within the same growing season we limited our analysis to one of the collections, due to the lack of independence of LC50 values.  Significance of the relationship was tested using a Mantel test of matrix correspondence (Mantel 1967) in GenAlEx version 6.1 (Peakall & Smouse 2006) with 999 random permutations of the data.  We used the individual-based clustering method implemented in STRUCTURE 2.2 (Falush et al. 2007; Pritchard et al. 2000) and the spatially explicit clustering method implemented in TESS 2.1.1 (Durand et al. 2009) to identify the genetic structure of greenhouse and field populations.  Simulations were performed using 169 polymorphic loci with a band frequency of between 5 and 95%.  We ran simulations including all populations and 2005 and 2006 populations separately.  Populations surveyed multiple times over the growing season in Delta during 2005 were used to examine temporal changes in genetic structure and those from all regions in 2006 were used to identify spatial patterns of genetic structure.  In STRUCTURE, simulations were run under the admixture ancestry model with correlated allele frequencies.  These conditions were chosen, since prior examination of   111 patterns of Bt resistance in these populations suggests that there is a significant amount of gene flow among neighbouring greenhouse populations (Franklin & Myers 2008).  To estimate the number of clusters (K) in STRUCTURE a burn-in of 100,000 steps and run length of 200,000 steps was used to test values of K ranging from 1 to 10, with 10 replicate simulations performed for each value of K.  Values of K greater than ten were tested during preliminary runs, but were not tested further due to the low likelihood values and increased variance.  Due to problems detecting the true number of clusters from the estimated log probability of the data, Pr(X|K) reported by STRUCTURE, we used the ad hoc statistic !K developed by Evanno et al. (2005) and examined the probability of individual assignment for each value of K to identify the most probable number of clusters.  For analysis in TESS 2.1.1, AFLP loci were coded according to methods outlined in Evanno et al. (2005) for dominant markers.  To detect the true number of clusters, we performed admixture analyses for K from 2 to 10 with a conditional autogressive Gaussian model (CAR) and linear trend surface using 100 replicate simulations for each K, with a burn-in of 10,000 sweeps and run-length of 50,000.  The Deviance Information Criterion (DIC) was estimated for each run, as an indicator of how well the model fit the data.  The mean DIC value for the ten runs with the lowest DIC were obtained for each K value and the most probable number of clusters was identified from the inflexion point in a plot of mean DIC vs. K.  For the most probable K, CLUMPP version 1.1.1 (Jakobsson & Rosenberg 2007) with Greedy algorithm and 10,000 repeats was used to correct label switching among replicate simulations and obtain the mean cluster membership for   112 individuals over the 10 replicate simulations in STRUCTURE and TESS.  DISTRUCT (Rosenberg 2004) was used to graphically display these results. 5.3 RESULTS 5.3.1 Polymorphism and genetic diversity  Based on analysis of four AFLP primer combinations, we scored a total of 220 fragments ranging in size from 73 to 531 base pairs (bp) in a total of 1082 individuals from BC.  Due to a high error rate observed for 16 of the loci during repeatability testing, a total of 204 loci were used for analysis (Table 5.1).   Of the 204 scored fragments, five were monomorphic, 30 had a band frequency of less than 5% or greater than 95%, and 169 had band frequencies ranging from 5 to 95%.  We predicted that genetic diversity would be low in greenhouse populations that experienced demographic reductions due to winter clean-up processes or repeated exposure to Bt sprays.  Expected heterozygosity ranged from 0.165 to 0.303 in greenhouse and field collections, with a mean heterozygosity of 0.275 (Table 5.2). Heterozygosity levels did not differ between greenhouse and field populations surveyed in 2005 (F1, 10 = 1.90, P = 0.1983) or 2006 (F1,8 = 3.24, P = 0.1094). The lowest heterozygosity, however was observed in a greenhouse population collected in the spring of 2005 that had greater than six-fold higher Bt resistance than all other populations surveyed in 2005 and had persisted from the previous growing season (G1a; Table 5.2, Figure 5.2).  Low heterozygosity also occurred in two greenhouse populations that persisted from the 2006 growing season into 2007 (G6 and G9; Table 5.2, Figure 5.2). However, exposure to Bt sprays, as indicated by moderate Bt resistance levels, appeared   113 to have little impact on heterozygosity levels in the summer (G4, G5, G7; Table 5.2, Figure 5.2).  We expected heterozygosity levels to increase from spring to summer due to the influx of long-range migrants.  In agreement with our prediction, we observed a marginally significant increase in heterozygosity over the growing season in 2005 (Figure 5.2; F2,7 = 4.59, P = 0.0533) and a similar trend was observed in 2006, although the replication was insufficient to  examine this relationship statistically. 5.3.2 Genetic structure  The global estimate of FST over the 38 BC collections from all years indicated significant population differentiation (FST = 0.061, P < 0.00001).  After using the Bonferroni correction to account for multiple comparisons, 590 of the 703 pairwise comparisons tested were significantly different from zero (Appendix 2).  As expected, all populations were highly differentiated from over-wintering greenhouse populations G1a (0.165 < FST < 0.315, P < 0.00001) and G3 (0.101 < FST < 0.291, P < 0.00001) in 2005. The two populations that persisted in greenhouses from 2006 into the 2007 growing season, also showed moderate differentiation from all other collections (G6, 0.053 < FST < 0.251, P < 0.00001; G9, 0.063 < FST < 0.315, P < 0.00001).  We predicted reduced differentiation between collections from the same greenhouse before and after the winter clean-up.  This held true in G6, where differentiation was the lowest between 2006 and 2007 (FST = 0.053) and in G9, where the pairwise FST estimate comparing the 2006 and winter 2007 was lower than all but one other comparison (FST = 0.082).  During the growing season, differentiation among temporal greenhouse collections was only expected in populations that experienced recurrent bottlenecks from   114 Bt spraying.  We observed no significant differentiation between spring and summer collections from the same greenhouses over the 2005 growing season (0 < FST < 0.008). However, three of the four 2005 spring and summer greenhouse collections differed significantly from fall collections (0.022 < FST < 0.035, P < 0.00001) and in 2006 early summer collections were differentiated from late summer collections in two of the greenhouses (FST = 0.055 and 0.067, P <0.00001).  Due to the high dispersal propensity of moths and lack of dispersal barriers we anticipated that differentiation would be low among field collections.  In agreement in 2005, field collections from the Delta region showed no significant differentiation (0.000 < FST < 0.013) and in 2006 differentiation was significant, but low occurring only between the two fields surveyed in Delta (0.008 < FST < 0.023).  We also predicted that the relationship between distance and differentiation would be weak or negligible in this mobile species.  However, we observed a significant positive relationship between genetic differentiation and geographic distance for all 38 collections from all years (FST/(1 – FST) = 0.057ln(1 + Km) – 0.081, P < 0.001, r =0.344) and when greenhouse and field collections were examined separately (Figure 5.3; greenhouse: FST/(1 – FST) = 0.057ln(1 + Km) – 0.059, P < 0.001, r = 0.314; field: FST/(1 – FST) = 0.011ln(1 + Km) – 0.008, P =0.027, r = 0.710).  The slope for greenhouse collections was greater than that for field collections as expected if the movement of moths is more constrained in greenhouses due to the closing of roof vents.  The 95% confidence intervals for the slopes showed no overlap (greenhouse slope CI = 0.051-0.063; field slope CI = 0.009-0.013) and none of the 50 data subsets of 11 randomly selected greenhouse collections had slopes estimates as low as that for the 11 field collections.  The Mantel’s test also revealed less scatter   115 around the IBD relationship for the 11 field collections than the 27 greenhouse collections.   A positive relationship would be expected between genetic differentiation and Bt resistance, if Bt sprays cause significant demographic reductions and enhanced genetic drift in populations.  In support of this prediction, a strong positive correlation was observed between pairwise FST values and Bt resistance, as measured by the mean of the LC50 values for collection pairs (Figure 5.4; FST = 0.0567ln(LC50) – 0.3843, P = 0.002, r = 0.869).  This relationship remained strong, when G1a with at least six-fold higher Bt resistance than other collections was excluded from the analysis (FST = 0.0385ln(LC50) – 0.2445, P = 0.026, r = 0.572).  The upper most level of genetic structure revealed by the analysis of all collections in STRUCTURE separated populations into two groups (K = 2).  In support of persistent populations in greenhouses, as indicated by early collection dates prior to the arrival of migrants in some greenhouses, this model showed greenhouse populations from the Langley area (G1, G5, G6, G11) were genetically distinct from all other populations independent of their year of collection.  Membership into the two clusters was mixed for G5 in 2005 (cluster 1: 0.45, cluster 2: 0.55) and for G6 in 2006 (cluster 1: 0.70, cluster 2: 0.30), however as these populations likely experienced a population bottleneck during the winter clean-up and persisted into the following growing seasons, membership into cluster 2 increased to near 100%.  The early season collection (May 2nd) from G1a in 2005 had 99% membership into cluster 2 and could have served as one of the main sources of migrants to the other Langley greenhouses in 2005, while G1b, collected in   116 2006 showed mixed membership into the two clusters (cluster 1: 0.42, cluster 2: 0.58) and could have been influenced by migrants from other areas.  When collections performed in 2005 were examined independently of the other years, the STRUCTURE and TESS models with four clusters (K = 4) provided the best fit to the data.  In agreement with FST results, Langley and Abbotsford greenhouse populations, G1a and G3 from 2005, where early collection dates provided support for their persistence, were found to be genetically distinct from all other collections in this analysis, as well as distinct from one another.  The majority of individuals from Delta were assigned to one of two clusters in 2005.  Also in accord with FST measurements, temporal collections in Delta revealed a shift in cluster membership between individuals collected in the spring and summer and those collected in the fall.  The mean population membership into cluster one declined on average 28% from spring and summer collections when compared to that observed in the fall, while membership into cluster two increased by 24% in the fall.  The results of STRUCTURE and TESS revealed three clusters (K = 3) when BC and California populations surveyed in 2006 were analyzed (Figure 5.5).  The California populations clustered with both Abbotsford collections and the Delta field collections and Langley greenhouse collections that showed genetic similarity to Abbotsford when only local populations were examined.  This provided some evidence that California populations likely serve as a source of migrants for some of the greenhouse and field localities in BC.  However, spring and summer greenhouse collections from Delta and Langley remained distinct and are suggestive of populations persisting in greenhouses year-round.  One of the field collections from Delta in September (F7) grouped with   117 Delta greenhouses and may indicate that moths emigrate from greenhouse populations into neighbouring fields late in the growing season when roof vents are more likely to remain open.  When the two greenhouse collections performed in 2007 were included in the analysis with the 2006 collections, two clusters were identified based on the "K estimated from the Evanno method (Evanno et al. 2005).   However, the mean individual assignment probability (q-hat) for K = 2 was lower (0.80) than that for K = 3 (0.88) indicating that three clusters provided a more robust model.  In agreement, TESS simulations based on the inflexion point for the mean DIC found K = 3 to be the most likely solution.  Under the K = 3 solution, the 2007 collections clustered with those from the same location surveyed in 2006, providing further evidence that these populations persisted through the 2006 winter clean-up into the following growing season in these greenhouses (G6, G9). 5.4 DISCUSSION 5.4.1 Genetic differentiation and dispersal patterns  Like many other migratory moths, T. ni is capable of utilizing wind currents to travel vast distances (Mitchell & Chalfant 1984).  Despite this, we identified significant temporal and spatial population genetic structure in T. ni greenhouse and field populations on the local scale, where populations were separated by as little as 30 km. This is surprising, considering the lack of population differentiation that was observed in T. ni populations surveyed along the west coast of North America (chapter 4) and the high levels of gene flow that have been reported at both the local and regional scale in   118 other highly mobile moth species (Zhou et al. 2000; Han & Caprio 2002; Endersby et al. 2006; Endersby et al. 2007).  Human-induced changes such as the concentration of greenhouses that are capable of supporting year-round populations and the ensuing strong bottlenecking effects due to periodic clean-ups and Bt application are likely responsible for significantly altering the genetic structure of T. ni populations in BC.  Significant spatial variation in the genetic structure of T. ni populations in 2006 separated populations from Delta, Langley, and Abbotsford into three genetically distinct groups (Figure 5.5).  The inclusion of California populations into this analysis provided evidence that these southern populations were a source of migrants to some greenhouse and field populations in BC, although the Delta and Langley greenhouse populations remained genetically distinct from migrants.  Genetic structuring on a local scale has also been reported in the migratory moth, Helicoverpa armigera in Australia, where it is thought that drought conditions impacted its migratory capacity and resulting genetic structure (Scott et al. 2005).  The dispersal patterns of T. ni were also captured by examining the isolation by distance (IBD) relationship for local field and greenhouse populations.  Highly mobile insects are typically characterized by weak or no IBD relationship (Peterson & Denno 1998).  This held true, when no IBD pattern was observed for T. ni populations separated by as much as 1700 km along their migration route from CA to BC (chapter 4).  It was therefore surprising to find a significant IBD relationship among local T. ni populations that were separated by at most 75 km (Figure 5.3).  When greenhouse and field collections were considered separately the IBD slope was more than five times greater for   119 greenhouse than field collections.  Greater differentiation is likely observed for greenhouse collections because dispersal may be limited between greenhouses because roof vents are frequently closed during the winter and spring to regulate temperatures. Furthermore, the effects of drift and inbreeding will be accentuated in greenhouse populations where warm temperatures and longer growing seasons allow for a greater number of generations per growing season than in field populations.  We also observed greater scatter around the IBD relationship for greenhouses than fields, which is likely explained by frequent population bottlenecks.  These bottlenecks result in non- equilibrium conditions that are known to lead to increased drift and scatter in IBD patterns (Peterson & Denno 1998).  Our results support the evidence based on patterns of Bt resistance (Franklin & Myers 2008) that populations frequently persist through the winter clean-up in greenhouses into the following growing season.  Genetic analysis of greenhouse populations from all years identified a distinct cluster of five populations from the Langley area in STRUCTURE and TESS, independent of the survey year.  In this region suitable field habitat is absent and it is likely that a few greenhouse populations persist through the winter to later colonize surrounding greenhouses.  In addition, the winter collections from the two greenhouses (G6 and G9) that were surveyed in 2007 clustered with collections from those greenhouses in the summer and fall 2006.  Differentiation was low between the 2006 and 2007 collections when compared with collections from other populations.  Lending further support to the persistence of greenhouse population in BC, the first collection date for greenhouse populations primarily occurred prior to the expected summer arrival of field populations (Table 5.2).   120 5.4.2 Temporal variation in genetic differentiation  In persistent greenhouse populations, slightly lower heterozygosity levels were observed in the spring when compared to the summer and fall (Figure 5.2).  This pattern also held when examining the overall seasonal change in heterozygosity for all populations surveyed in 2005 and although data were insufficient to test for significance, a similar pattern also held in 2006.  In 2005 and 2006 all spring collections were from greenhouse populations, since field populations were not available this early in the growing season.  These populations would have gone through population bottlenecks during the winter clean-up, and fit predictions of population genetic theory for reduced heterozygosity levels and increased differentiation (Nei et al. 1975; Harrison & Hastings 1996).  Heterozygosity levels rebounded in these populations in collections made later in the growing season as expected following the rapid population growth that occurred (Nei et al. 1975).  In addition, immigration of only a few individuals from surrounding populations or migrants could also have replenished genetic diversity in those populations.  A temporal shift in the genetic structure was observed in Delta populations in 2005, when survey efforts were concentrated on conducting multiple collections over the growing season.  Specifically, a genetic shift in cluster membership between the spring and summer collections and fall collections was identified in STRUCTURE and TESS and significant differentiation, as measured by pairwise FST values, was observed between spring/summer collections and fall collections.  Despite multiple attempts to replicate temporal collections in 2006, it was not possible due to the low population densities of T. ni in greenhouses and fields.  Our findings are consistent with Scott et al. (2006) who   121 observed a genetic shift late in the season in Helicoverpa armigera populations based on changes in microsatellite allele frequencies in the Murrumbidgee Valley of Australia. It is unlikely that selection for Bt resistance caused the shift in genetic structure because resistance is likely to only involve one or a small subset of genes rather than the whole genome (Tabashnik et al. 1997).  Results from STRUCTURE using a data set that excluded influential loci by trimming the 10% of loci with the highest FST values and the 10% of loci with the lowest FST values, showed little change in the temporal structure of T. ni populations (Appendix 3).  After trimming, a large number of loci still remained (135 loci) for analysis and this suggests that temporal patterns of genetic structure are likely not linked to a few highly influential loci. 5.4.3 Genetic structure and Bt resistance  Intense selection through extensive Bt use in some greenhouses significantly impacted the genetic structure of local T. ni populations and resulted in a strong positive correlation between Bt resistance, as measured by LC50 values, and genetic differentiation (Figure 5.4).  A similar relationship was found in French codling moth, Cydia pomonella populations, where the mean number of insecticide sprays was positively correlated with levels of genetic differentiation (Franck et al. 2007).  Taken together, these results suggest that insecticide applications cause population bottlenecks that enhance genetic drift and subsequent differentiation.  Genetic diversity may also have been impacted in populations that were exposed to repeated Bt sprays.  Diversity was lowest in a greenhouse population (G1a) that had likely persisted through the winter clean-up and had high levels of resistance, inferring the cause is due to frequent Bt applications.  Given these conditions, it is not possible to   122 separate the effects of population bottlenecks that were caused by repeated Bt exposure from those that resulted from winter clean-up procedures.  Local patterns of Bt resistance suggest that resistance spreads to neighbouring greenhouses through the dispersal of resistant moths early in the growing season, prior to the arrival of migrants (Franklin & Myers 2008).  Models indicate that to delay resistance, high levels of gene flow are needed between treated and untreated habitat patches, however to spread resistance requires only moderate levels of dispersal (Caprio & Tabashnik 1992).  The genetic structure and patterns of heterozygosity described by AFLP markers support our predictions and indicate that in areas with a high concentration of greenhouses, a small number of T. ni persists through the year-end clean-up and dispersal occurs on a very local scale, with little input from long-range migrants.  These conditions are favourable to the spread of resistance in T. ni greenhouse populations, but do little to retard its development.  In contrast, most field populations show greater genetic similarity to long-range migrants.  Under this scenario, any selection for genes conferring Bt resistance or movement of resistant individuals from greenhouse populations into fields would be diluted by the immigration of long-range migrants that have been found to be susceptible (Franklin & Myers 2008).  Human alterations to the environment have had profound impact on the evolutionary trajectories of many species and at great economic cost (Palumbi 2001).  To slow the evolution of Bt resistance current greenhouse management practices need to be altered.  Greenhouse T. ni populations can no longer be treated as discrete entities, rather greenhouse growers must implement coordinated management plans that consider the Bt usage of the greenhouses that surround them.   123 Table 5.1: AFLP primer combinations and the number of scored fragments for greenhouse and field Tricohplusia ni populations surveyed from 2005 to 2007 in British Columbia, Canada. Primer combination EcoR1 (3'-NNN) Mse1 (3'-NNN) No. scored fragments* 1 CGG ATGG 56 2 CGG AGCT 36 3 CCG ACCG 51 4 CGA ACCG 61  *Loci that were identified as problematic based on genotype error rate analysis were excluded.                 124 Table 5.2: Bt resistance levels as measured by the lethal concentration that killed 50% of larvae (LC50) and expected heterozygosity (He) for greenhouse and field collections from Delta, Langley, and Abbotsford, British Columbia and field collections from Santa Maria and Oxnard, California (CA).  LC50 values are missing for some collections due to the limited success collecting T. ni larvae when population densities were low. Sample locality code City Collection date LC50 (KIU/ml diet)a He (±SE) G2 Delta May 24, 05 - 0.290 (0.011)   Jul 14, 05 2.86 0.290 (0.010)   Oct 14, 05 6.93 0.277 (0.011) G4  Jun 10, 05 2.50 0.297 (0.010)   Aug 11, 05 0.74 0.289 (0.010)   Oct 5, 05 - 0.284 (0.011)   Jul 18, 06 - 0.280 (0.012)   Aug 25, 06 4.47 0.278 (0.011) G7  May 19, 05 - 0.292 (0.011)   Aug 3, 05 - 0.299 (0.010)   Oct 21, 05 - 0.270 (0.011)   Jul 12, 06 4.69 0.266 (0.011)   Aug 21, 06 5.80 0.277 (0.012) G12  May 17, 05 - 0.290 (0.011)   Aug 10, 05 - 0.290 (0.010)   Sep 23, 05 - 0.285 (0.011) F1  Jul 12, 05 1.71 0.295 (0.010) F2  Aug 2, 05 0.77 0.294 (0.011)   Aug 25, 05 1.27 0.294 (0.011) F3  Jul 8, 05 1.08 0.282 (0.011) F4  Aug 8, 05 - 0.294 (0.010)   Sep 19, 05 - 0.288 (0.011) F5  Jul 21, 05 0.81 0.304 (0.010)   Aug 17, 06 - 0.288 (0.011) F7  Aug 9, 06 2.28 0.288 (0.011)   Sep 19, 06 2.19 0.282 (0.011) G1a* Langley May 2, 05 69.4 0.165 (0.013) G1b*  Sep 15, 06 5.31 0.270 (0.011) G5  Jun 16, 05 - 0.273 (0.011)   Jun 30, 06 11.4 0.240 (0.013) G6  Sep 28, 06 - 0.273 (0.011)   Jan 31, 07 - 0.242 (0.012) G11  May 12, 06 - 0.255 (0.012)    125 Sample locality code City Collection date LC50 (KIU/ml diet)a He (±SE) G3  Abbotsford Apr 26, 05 2.42 0.232 (0.013)   Jul 20, 06 2.00 0.269 (0.011) G9  Aug 11, 06 - 0.262 (0.011)   Mar 22, 07 - 0.244 (0.013) CS Santa Maria, CA Jun 27-28, 06 1.50 0.278 (0.010) CX1 Oxnard field 1, CA Jun 29-30, 06 2.33 0.298 (0.011) CX2 Oxnard field 2, CA Jun 29-30, 06 2.70 0.280 (0.011) *G1a and G1b represent two separate greenhouses that are located only 11m from one another. a LC50 value for a susceptible laboratory reference population was 2.18 KIU/ml diet.   126  Figure 5.1: Geographic distribution of greenhouse and field sites where Trichoplusia ni were collected in British Columbia, Canada.  Collection sites were located in the cities of Delta, Langley, and Abbotsford.  The red and blue stars on the inset map denote the general location of collection sites in British Columbia, Canada and California, USA, respectively.  1 2 7    Figure 5.2: Expected heterozygosity (SE) under Hardy-Weinberg proportions for Trichoplusia ni collections from British Columbia between 2005 and 2007.  G and F denote greenhouse and field collections, respectively.  Bar shading denotes Bt resistance levels (white: not assessed, light grey: LC50< 4 KIU/ml diet, dark grey: LC50= 4-12 KIU/ml diet, black: LC50= 12 -70 KIU/ml diet.   128   Figure 5.3: Isolation by distance relationship for greenhouse and field Trichoplusia ni populations.  Grey circles represent comparisons between greenhouse collections and black squares denote comparisons between field collections.  Greenhouse: FST/(1 – FST) = 0.057ln(1 + Km) – 0.059 (r = 0.314, P < 0.001); field: FST/(1 – FST) = 0.011ln(1 + Km) – 0.008 (r = 0.710, P =0.027).   129     Figure 5.4: Regression of pairwise FST values on Bt resistance as measured by the mean of the two populations’ LC50 values for 120 pairwise comparisons among 16 greenhouse and field Trichoplusia ni collections from British Columbia.  FST = 0.0567ln(LC50) – 0.3843 (P = 0.002, r = 0.869).  1 3 0    Figure 5.5: Results of STRUCTURE clustering analysis identified three clusters (K = 3) based on 169 polymorphic loci from 426 Trichoplusia ni larvae in 2006.   Labels above represent the regions in which larvae were collected including: Delta, Langley, Abbotsford, and California.  Labels below refer to the month of collection and the locality codes for the greenhouses and fields as defined in Figure 5.1.  Each vertical line represents an individual that is divided into K coloured segments depicting their membership into each of K clusters.   131 5.5 REFERENCES Bonin A, Bellemain E, Eidesen PB, Pompanon F, Brochmann C, Taberlet P. (2004) How to track and assess genotyping errors in population genetics studies. Molecular Ecology 13, 3261-3273. Caprio MA, Tabashnik BE (1992) Gene flow accelerates local adaptation among finite populations: simulating the evolution of insecticide resistance. Journal of Economic Entomology 85, 611-620. Croft BA, Dunley JE (1993) Habitat patterns and pesticide resistance Jon Wiley & Sons Inc., New York. Donini P, Elias ML, Bougourd SM, Koebner RMD (1997) AFLP fingerprinting reveals pattern differences between template DNA extracted from different plant organs. Genome 40, 521-526. Durand E, Jay F, Gaggiotti OE, Francois O (2009) Spatial inference of admixture proportions and secondary contact zones. Molecular Biology and Evolution 26, 1963-1973. Endersby NM, Hoffmann AA, McKechnie SW, Weeks AR (2007) Is there genetic structure in populations of Helicoverpa armigera from Australia? Entomologia Experimentalis et Applicata 122, 253-263. Endersby NM, McKechnie SW, Ridland PM, Weeks AR (2006) Microsatellites reveal a lack of structure in Australian populations of the diamondback moth, Plutella xylostella (L.). Molecular Ecology 15, 107-118. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14,   132 2611-2620. Falush D, Stephens M, Pritchard JK (2007) Inference of population structure using multilocus genotype data: dominant markers and null alleles. Molecular Ecology Notes 7, 574-578. Ferré J, Van Rie J (2002) Biochemistry and genetics of insect resistance to Bacillus thuringiensis. Annual Review of Entomology 47, 501-533. Franck P, Reyes PFM, Olivares J, Sauphanor B (2007) Genetic architecture in codling moth populations: comparison between microsatellite and insecticide resistance markers. Molecular Ecology 16, 3554-3564. Franklin MT, Myers JH (2008) Refuges in reverse: the spread of Bacillus thuringiensis resistance to unselected greenhouse populations of cabbage loopers Trichoplusia ni. Agricultural and Forest Entomology 10, 119-127. Franklin MT, Myers JH, Ritland CE (2009) Distinguishing between laboratory-reared and greenhouse- and field-collected Trichoplusia ni (Lepidoptera: Noctuidae) using the amplified fragment length polymorphism method. Annals of the Entomological Society of America 102, 151-157. Genstat version 11.1 (2008) Rothamsted Experimental Station, Lawes Agricultural Trust, Harpenden, UK. Georghiou GP, Lagunes-Tejeda A (1991) The occurrence of resistance to pesticides in arthropods: an index of cases reported through 1989, FAO, Roma. Han QF, Caprio MA (2002) Temporal and spatial patterns of allelic frequencies in cotton bollworm (Lepidoptera : Noctuidae). Environmental Entomology 31, 462-468. Harrison S, Hastings A (1996) Genetic and evolutionary consequences of metapopulation   133 structure. Trends in Ecology and Evolution 11, 180-183. Ignoffo CM (1963) A successful technique for mass-rearing cabbage loopers on a semisynthetic diet. Annals of Entomological Society of America 56, 178-182. Jakobsson M, Rosenberg NA (2007) CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801-1806. Janmaat AF, Myers J (2003) Rapid evolution and the cost of resistance to Bacillus thuringiensis in greenhouse populations of cabbage loopers, Trichoplusia ni. Proceedings of the Royal Society B: Biological Sciences 270, 2263-2270. Jensen JL, Bohonak AJ, Kelley ST (2005) Isolation by distance, web service. BMC Genetics 6, 6. Kuehl RO (1994) Statistical principles of research design and analysis Duxbury Press, Belmont California. Lynch M, Milligan BG (1994) Analysis of population genetic-structure with RAPD markers. Molecular Ecology 3, 91-99. Mantel N (1967) Detection of disease clustering and a generalized regression approach. Cancer Research 27, 209-220. Mitchell ER, Chalfant RB (1984) Biology, behaviour and dispersal of adults In: Supression and management of cabbage looper populations (eds. Lingren PD, Green GL), pp. 14-18. Technical bulletin/ Agricultural Research Service, USDA. Nei M, Maruyama T, Chakraborty R (1975) The bottleneck effect and genetic variability in populations. Evolution 29, 1-10. Palumbi SR (2001) Humans as the world's greatest evolutionary force. Science 293,   134 1786-1790. Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6, 288-295. Peck SL, Gould F, Ellner SP (1999) Spread of resistance in spatially extended regions of transgenic cotton: implications for management of Heliothis virescens (Lepidoptera: Noctuidae). Journal of Economic Entomology 92, 1-16. Peterson MA, Denno RF (1998) The influence of dispersal and diet breadth on patterns of genetic isolation by distance in phytophagous insects. American Naturalist 152, 428-446. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155, 945-959. Roderick GK (1996) Geographic structure of insect populations: gene flow, phylogeography, and their uses. Annual Review of Entomology 41, 325-352. Rosenberg NA (2004) DISTRUCT: a program for the graphical display of population structure. Molecular Ecology Notes 4, 137-138. Rousset F (1997) Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219-1228. SAS Institute Inc. (2003) sas 9.1. The SAS institute Inc., Cary, NC. Scott KD, Lawrence N, Lange CL, Scott LJ, Wilkinson KS, Merritt MA, Miles M, Murray D, Graham GC (2005) Assessing moth migration and population structuring in Helicoverpa armigera (Lepidoptera: Noctuidae) at the regional scale: example from the Darling Downs, Australia. Journal of Economic Entomology 98, 2210-2219.   135 Scott LJ, Lawrence N, Lange CL, Graham GC, Hardwick S, Rossiter, L, Dillon ML, Scott KD (2006) Population dynamics and gene flow of Helicoverpa armigera (Lepidoptera: Noctuidae) on cotton and grain crops in the Murrumbidgee Valley, Australia. Journal of Economic Entomology 99, 155-163. Smith TB, Bernatchez L (2008) Evolutionary change in human-altered environments. Molecular Ecology 17, 1-8. Tabashnik BE (1994) Evolution of resistance to Bacillus thuringiensis. Annual Review of Entomology 39, 47-79. Tabashnik BE, Liu Y-B, Finson N, Masson L, Heckel DG (1997) One gene in diamondback moth confers resistance to four Bacillus thuringiensis!toxins. Proceedings of the National Academy of Sciences of the United States of America 94, 1640-1644. Tabashnik BE, Gassmann AJ, Crowder DW, Carriere Y (2008) Insect resistance to Bt crops: evidence versus theory. Nature Biotechnology 26, 199-202. Tabashnik BE, Van Rensburg JBJ, Carriere Y (2009) Field-Evolved Insect Resistance to Bt Crops: Definition, Theory, and Data. Journal of Economic Entomology 102, 2011-2025. Van Rensburg JBJ (2007) First report of field resistance by stem borer, Busseola fusca (Fuller) to Bt-transgenic maize. South African Journal of Plant Soil 24, 147-151. Vekemans X, Beauwens T, Lemaire M, Roldan-Ruiz I (2002) Data from amplified fragment length polymorphism (AFLP) markers show indication of size homoplasy and of a relationship between degree of homoplasy and fragment size. Molecular Ecology 11, 139-151.   136 Zhivotovsky LA (1999) Estimating population structure in diploids with multilocus dominant DNA markers. Molecular Ecology 8, 907-913. Zhou X, Faktor O, Applebaum SW, Coll M (2000) Population structure of the pestiferous moth Helicoverpa armigera in the eastern Mediterranean using RAPD analysis. Heredity 85, 251-256.                     137 CHAPTER 6: GENERAL CONCLUSIONS 6.1 GENERAL THESIS OVERVIEW  Much theoretical research has focused on the evolutionary consequences of population structure (Pannell & Charlesworth 1999; Slatkin 1987; Wade & McCauley 1988; Whitlock & McCauley 1990).  However, a great need for empirical research remains.  Human-altered ecosystems can provide some of the most useful simplified, model systems for examining the impact of genetic structure on evolutionary adaptation. The manuscripts in this thesis explore the evolution of Bt resistance in migratory and transient T. ni populations that are spatially and temporally structured in greenhouse and field crops.  Many theoretical models built on population genetic theory, have been developed to explore strategies to prevent resistance adaptation of insects in response to transgenic crops expressing Bt proteins (Caprio & Tabashnik 1992; Peck et al. 1999; Sisterson et al. 2005).  A key component of these models is the dispersal of susceptible insects between untreated refuges and Bt treated fields (Gould 1993).  In chapter 2 (Franklin & Myers, 2008), I demonstrated the opposite pattern, whereby Bt resistance spreads from greenhouse populations of T. ni that have been selected for resistance to Bt into neighbouring untreated greenhouses in British Columbia (BC).  My prediction that long- range migrants inhabiting areas in southern California (CA), where Bt cotton is grown could be the source of resistance to BC populations was not supported.  Based on these results, I developed the hypothesis that the spread of resistance was due to resistant populations persisting in greenhouses through the winter and colonizing neighbouring populations prior to the arrival of susceptible migrants the next summer.   138  To further investigate the influence of the potential persistence of greenhouse populations and dispersal of resistant moths among greenhouse populations, molecular markers were required to examine the local and long-distance population structure of T. ni.  In chapter 3, I described the development of DNA isolation procedures and outline suitable AFLP primer combinations for the examination of laboratory and field collected T. ni (Franklin et al. 2009).  With the exception of previously developed mitochondrial primer sequences (GenBank, unpublished), AFLP markers were the first molecular technique to be developed to study the population structure of T. ni.  Quick start-up time, high reproducibility, and the large number of loci generated genome wide (Savelkoul et al. 1999), have made them a particularly useful marker for examining the population structure of T. ni.  In chapter 2, I proposed, based on earlier work of Lingren et al. (1979), that T. ni migrate from southern CA to areas as far north as southern BC.  Based on the observed susceptibility of field populations, I conclude that susceptible T. ni migrate into BC each summer, but do not dilute Bt resistance in greenhouse populations (chapter 2).  To lend support to this conclusion, I examine the population structure and migratory connectivity of T. ni from Arizona (AZ) to BC using mitochondrial sequence variation and AFLP markers (chapter 4).  The results support the prediction that there is migratory connectivity between populations from AZ and BC.  In agreement with our study, several others studies have failed to identify genetic structure in migratory insects over large spatial scales (Daly & Gregg 1985; Johnson 1987; Llewellyn et al. 2003; Scott et al. 2005).  In chapter 5, I examined the local genetic structure of T. ni populations and its   139 relation to the patterns of Bt resistance examined in chapter 2.  Given the lack of genetic structure observed along their migration route (chapter 4), it was surprising that genetic structuring was observed on a local scale.  Spatially, populations separated into three distinct clusters that roughly corresponded to their geographic locations of Delta, Langley, and Abbotsford, BC.  When CA source populations described in chapter 4 were reanalyzed with local populations surveyed in 2006, they appeared to be genetically differentiated from to Delta greenhouse populations and early season Langley greenhouse populations.  This supports my prediction from chapter 2 that susceptible long-range migrants do not move into greenhouses sufficiently early in the growing season to influence Bt resistance.  In addition, temporal patterns indicated that collections from greenhouses where populations had likely persisted from one year to the next were genetically similar.  Taken together, these results supported my interpretation that greenhouse populations persisting between growing seasons harbour moths with resistant genes that then spread to neighbouring greenhouse populations early in the growing season.  Finally, in chapter 5, I observed a positive correlation between Bt resistance and genetic differentiation.  Populations that had persisted through winter clean-ups in greenhouses had lower heterozygosity and greater geographical differentiation.  These results supported predictions based on population genetic theory that show bottlenecks can reduce heterozygosity and increased differentiation among populations (Harrison & Hastings 1996; Nei et al. 1975). 6.2 APPLICATIONS  One of the goals of this research was to help develop strategies in conjunction with the greenhouse growers in BC that would reduce Bt resistance in T. ni greenhouse   140 populations.  Based on the finding from my thesis, I have recommended that greenhouse growers implement coordinated management plans that consider the Bt usage among greenhouses in the surrounding area.  Of primary concern to greenhouse growers should be the elimination of persistent populations during winter clean-ups.  However, when a population does persist, efforts should be made among neighbouring greenhouse growers to discuss the use of sprays or schedules.  If Bt sprays were frequently used to control the population the previous growing season it was recommended that greenhouse growers use an alternative product such as Confirm or Spinosad for control.  Lastly, no insecticide is immune to resistance development.  Therefore it is important for managers to alternate between insecticide products that differ in their mode of action. 6.3 FUTURE DIRECTIONS  The research presented in this thesis is the first to investigate the population genetic structure of T. ni and will hopefully provide a valuable basis for future research. For instance, in chapter 2 the examination of patterns of Bt resistance provides insight into the movement patterns and selection response of T. ni, but does not allow for the impacts of these processes to be disentangled.  Future identification of the genes conferring Bt resistance would allow selective forces to be quantified and separated from the effects of other evolutionary processes such as drift and migration.  In chapter 4, I examine the long-range migration patterns of T. ni and find that the one population in Washington was distinct from the other populations surveyed.  I hypothesized that this may reflect the genetic makeup of migrants from regions further east that were not included in this survey.  To test this would require the extension of the survey range to areas further east.  An alternative explanation is that this population arose   141 from an independent introduction from some other source population, possibly associated with introduced plants.  Lastly, in chapter 5 an interesting temporal pattern of genetic structure was observed in 2005, whereby collections from the spring and summer were genetically distinct from fall collections in greenhouse and field populations in Delta, BC.  Since long-range migrants were not surveyed in 2005, it was impossible to rule out that this was a genetic signature due to the influx of long-range migrants from the south.  A more extensive survey of temporal greenhouse and field collections over multiple years would be required to confirm that this pattern was not an anomaly observed in only 2005. Collections from migrant source populations in CA would also be needed to test if genetic change was the result of the influx of migrants.  An alternate explanation for this pattern could be that different genotypes are selected for in the spring and summer than in the fall.  Genome scans using AFLP markers have been used in other studies to detect loci under selection (Bonin et al. 2006; Vitalis et al. 2001).  However suitable quantitative methods for detecting loci under selection from temporal samples using dominant AFLP markers do not exist.  In collaboration with theoretical population genetics I am interested in developing quantitative techniques to examine loci subject to selection.        142 6.4 REFERENCES  Bonin A, Taberlet P, Miaud C, Pompanon F (2006) Explorative genome scan to detect candidate loci for adaptation along a gradient of altitude in the common frog (Rana temporaria). Molecular Biology and Evolution 23, 773-783. Caprio MA, Tabashnik BE (1992) Gene flow accelerates local adaptation among finite populations: simulating the evolution of insecticide resistance. Journal of Economic Entomology 85, 611-620. Daly JC, Gregg P (1985) Genetic variation in Heliothis in Australia: species identification and gene flow in two pest species H. armigera (Hubner) and H. punctigera Wallengren (Lepidoptera: Noctuidae). Bulletin of Entomological Research 75, 169-184. Franklin MT, Myers JH (2008) Refuges in reverse: the spread of Bacillus thuringiensis resistance to unselected greenhouse populations of cabbage loopers Trichoplusia ni. Agricultural and Forest Entomology 10, 119-127. Franklin MT, Myers JH, Ritland CE (2009) Distinguishing between laboratory-reared and greenhouse- and field-collected Trichoplusia ni (Lepidoptera: Noctuidae) using the amplified fragment length polymorphism method. Annals of the Entomological Society of America 102, 151-157. Gatehouse AG (1997) Behavior and ecological genetics of wind-borne migration by insects. Annual Review of Entomology 42, 475-502. Gould F (1993) Sustainability of transgenic insecticidal culttivars: integrating pest genetics and ecology. Annual Review of Entomology 43, 701-726. Harrison S, Hastings A (1996) Genetic and evolutionary consequences of metapopulation   143 structure. Trends in Ecology and Evolution 11, 180-183. Johnson SJ (1987) Migration and the life history strategy of the fall armyworm, Spodoptera frugiperda in the western hemisphere. Insect Science and its Application 8, 543-549. Lingren PD, Henneberry TJ, Sparks AN (1979) Current knowledge and research on movement of the cabbage looper and related looper species Movement of highly mobile insects: concepts and methodology in research, 394-405. Llewellyn KS, Loxdale HD, Harrington R, Brookes CP, Clark  SJ, Sunnucks P. (2003) Migration and genetic structure of the grain aphid (Sitobion avenae) in Britain related to climate and clonal fluctuation as revealed using microsatellites. Molecular Ecology 12, 21-34. Mun JH, Song YH, Heong KL, Roderick GK (1999) Genetic variation among Asian populations of rice planthoppers, Nilaparvata lugens and Sogatella furcifera (Hemiptera: Delphacidae): mitochondrial DNA sequences. Bulletin of Entomological Research 89, 245-253. Nagoshi RN, Meagher RL, Flanders K, Gore J, Jackson R, Lopez J, Armstrong JS, Buntin GD, Sansone C, Leonard BR. (2008) Using haplotypes to monitor the migration of fall armyworm (Lepidoptera: Noctuidae) corn-strain populations from Texas and Florida. Journal of Economic Entomology 101, 742-749. Nei M, Maruyama T, Chakraborty R (1975) The bottleneck effect and genetic variability in populations. Evolution 29, 1-10. Pannell JR, Charlesworth B (1999) Neutral genetic diversity in a metapopulation with recurrent local extinction and recolonization. Evolution 53, 664-676.   144 Pannell JR, Charlesworth B (2000) Effects of metapopulation processes on measures of genetic diversity. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 355, 1851-1864. Peck SL, Gould F, Ellner SP (1999) Spread of resistance in spatially extended regions of transgenic cotton: implications for management of Heliothis virescens (Lepidoptera: Noctuidae). Journal of Economic Entomology 92, 1-16. Savelkoul PH, Aarts HJ, de Haas J, Dijkshoorn L, Duim B, Otsen M, Rademaker JL, Scholuls L, Lenstra JA. (1999) Amplified-fragment length polymorphism analysis: the state of an art. Journal of Clinical Microbiology 37, 3083-3091. Scott KD, Lawrence N, Lange CL, Scott LJ, Wilkinson KS, Merritt MA, Miles M, Murray D, Graham GC (2005) Assessing moth migration and population structuring in Helicoverpa armigera (Lepidoptera: Noctuidae) at the regional scale: example from the Darling Downs, Australia. Journal of Economic Entomology 98, 2210-2219. Sisterson MS, Carriere Y, Dennehy TJ, Tabashnik BE (2005) Evolution of resistance to transgenic crops: interactions between insect movement and field distribution. Journal of Economic Entomology 98, 1751-1762. Slatkin M (1987) Gene flow and the geographic structure of natural populations. Science 236, 787-792. Vitalis R, Dawson K, Boursot P (2001) Interpretation of variation across marker loci as evidence of selection. Genetics 158, 1811-1823. Wade MJ, McCauley DE (1988) Extinction and colonization: their effects on the genetic differentiation of local populations. Evolution 42, 995-1005.   145 Whitlock MC (2002) Selection, load and inbreeding depression in a large metapopullation. Genetics 160, 1191-1202. Whitlock MC, McCauley DE (1990) Some population genetic consequences of colony formation and extinction: genetic correlations within founding groups. Evolution 44, 1717-1724.                  1 4 6  Appendix 1: Summary of sample localities codes, cities in which larvae were collected, collection dates, and latitude and longitude coordinates for each site.  Locality codes beginning with ‘G’ denote greenhouse collections and those beginning with ‘F’ denote field collections.  CS, CX1, and CX2 represent California localities that are coded according to chapter 4.                           *G1a and G1b represent two separate greenhouses that are located only 11m from one another. a Two collections were performed at this location in Aug 2005.  The dates of the two collections were Aug 2nd and 25th. Sample locality code City, Province/State Collection date Crop Latitude (N) Longitude (W) G2 Delta, BC May 05, Jul 05, Oct 05 tomato 49˚03.795´ 123˚06.573´ G4  Jun 05, Aug 05, Oct 05, Jul 06, Aug 06 tomato 49˚04.014´  123˚03.097´  G7  May 05, Aug 05, Oct 05, July 06, Aug 06 pepper 49˚04.944´  123˚00.394´  G12  May 05, Aug 05, Sep 05 tomato 49˚03.795´ 123˚06.573´ F1  Jul 05 broccoli 49˚03.007´ 123˚03.343´ F2  Aug 05a broccoli 49˚07.886´ 123˚02.043´ F3  Jul 05 broccoli 49˚02.450´ 123˚03.858´ F4  Aug 05, Sep 05 broccoli 49˚03.177´ 123˚05.683´ F5  Jul 05, Aug 06 broccoli 49˚05.036´ 123˚08.288´ F7  Aug 06, Sep 06 broccoli 49˚06.723´ 123˚02.266´ G1a* G1b* Langley, BC May 05, Sep 06 tomato pepper 49˚02.814´ 49˚02.822´ 122˚35.586´ 122˚35.586´ G5  Jun 05, Jun 06 cucumber 49˚02.726´ 122˚38.291´ G6  Sep 06, Jan 07 pepper 49˚02.180´ 122.38.284´ G11  May 06 cucumber 49˚02.443´ 122˚38.366´ G3 Abbotsford, BC Apr 05, Jul 06 pepper 49˚02.615´ 122˚26.897´ G9  Aug 06, Mar 07 pepper 49˚05.867´ 122˚17.605´ F8  Sep 06 rutabaga 49˚05.046´ 122˚05.805´ CX1 CX2 Oxnard, CA Jun 06 mixed crucifers cabbage 34˚12.561´ 34˚19.803´ 119˚03.403´ 119˚08.339´ CS Santa Maria, CA Jun 06 broccoli 34˚53.550´ 120˚30.853´   147 Appendix 2: Pairwise FST values between Trichoplusia ni collections from greenhouse and field localities throughout the lower mainland of British Columbia, Canada.  F4 Aug 8, 05 F4 Sep 9, 05 F7 Aug 9, 06 F7 Sep 19, 06 G4 Jun 10, 05 F4 Aug 8, 05 F4 Sep 9, 05 0.0077 F7 Aug 9, 06 0.0075 0.0057 F7 Sep 19, 06 0.0189 0.0248 0.0079 G4 Jun 10, 05 0.0138 0.0307 0.0249 0.0104 G4 Aug 11, 05 0.0130 0.0225 0.0150 0.0146 0.0083 G4 Oct 5, 05 0.0150 0.0141 0.0258 0.0318 0.0290 G4 Jul 18, 06 0.0427 0.0307 0.0424 0.0374 0.0468 G4 Aug 25, 06 0.0554 0.0595 0.0419 0.0396 0.0409 F8 Sep 14, 06 0.0391 0.0349 0.0344 0.0442 0.0532 G7 May 19, 05 0.0077 0.0064 0.0111 0.0271 0.0242 G7 Aug 3, 05 0.0106 0.0147 0.0172 0.0234 0.0166 G7 Oct 21, 05 0.0161 0.0241 0.0254 0.0174 0.0184 G7 Jul 12, 06 0.0541 0.0569 0.0541 0.0193 0.0438 G7 Aug 21, 06 0.0789 0.0893 0.0686 0.0487 0.0625 F3 Jul 8, 05 0.0018 0.0067 0.0108 0.0229 0.0233 G2 May 24, 05 0.0141 0.0353 0.0281 0.0120 0.0124 G2 Jul 14, 05 0.0044 0.0202 0.0141 0.0112 0.0095 G2 Oct 14, 05 0.0174 0.0224 0.0205 0.0149 0.0210 G5 Jun 16, 05 0.0190 0.0193 0.0277 0.0455 0.0417 G5 Jun 30, 06 0.0911 0.0998 0.0907 0.0773 0.0790 G11 May 12, 06 0.0705 0.0671 0.0697 0.0728 0.0723 F1 Jul 12, 05 0.0056 0.0092 0.0174 0.0221 0.0180 G6 Sep 28, 06 0.0480 0.0446 0.0396 0.0415 0.0397 G6 Jan 31, 07 0.0998 0.0927 0.1091 0.1171 0.1104 G12 May 17, 05 0.0126 0.0127 0.0100 0.0206 0.0171 G12 Aug 10, 05 0.0108 0.0148 0.0080 0.0186 0.0114 G12 Sep 23, 05 0.0204 0.0297 0.0221 0.0246 0.0226 G3 Apr 26, 05 0.1105 0.1199 0.1100 0.1006 0.1210 G3 Jul 20, 06 0.0257 0.0303 0.0211 0.0373 0.0389 F2 Aug 2, 05 0.0084 0.0127 0.0158 0.0274 0.0174 F2 Aug 25, 05 0.0085 0.0000 0.0158 0.0268 0.0280 G9 Aug 11, 06 0.0318 0.0316 0.0320 0.0332 0.0356 G9 Mar 22, 07 0.0957 0.0837 0.0818 0.1004 0.0988 G1 May 2, 05 0.2095 0.2193 0.2045 0.1967 0.1961 G1 Sep 15, 06 0.0368 0.0333 0.0405 0.0414 0.0454 F5 Jul 21, 05 0.0071 0.0062 0.0122 0.0281 0.0206 F5 Aug 17, 06 0.0126 0.0064 0.0142 0.0228 0.0283    148     G4 Aug 11, 05 G4 Oct 5, 05 G4 Jul 18, 06 G4 Aug 25, 06 F8 Sep 14, 06 F4 Aug 8, 05 F4 Sep 9, 05 F7 Aug 9, 06 F7 Sep 19, 06 G4 Jun 10, 05 G4 Aug 11, 05 G4 Oct 5, 05 0.0349 G4 Jul 18, 06 0.0495 0.0242 G4 Aug 25, 06 0.0455 0.0685 0.0675 F8 Sep 14, 06 0.0499 0.0603 0.0688 0.0866 G7 May 19, 05 0.0171 0.0269 0.0406 0.0523 0.0378 G7 Aug 3, 05 0.0017 0.0331 0.0501 0.044 0.0406 G7 Oct 21, 05 0.0253 0.0291 0.0443 0.0557 0.0528 G7 Jul 12, 06 0.0598 0.0463 0.0612 0.0649 0.0921 G7 Aug 21, 06 0.0781 0.0885 0.1037 0.0289 0.1115 F3 Jul 8, 05 0.0143 0.0264 0.0499 0.0529 0.0382 G2 May 24, 05 0.0082 0.0330 0.0543 0.0461 0.0549 G2 Jul 14, 05 0.0032 0.0277 0.0520 0.0422 0.0440 G2 Oct 14, 05 0.0269 0.0145 0.0363 0.0571 0.0519 G5 Jun 16, 05 0.0437 0.0365 0.0536 0.0794 0.0492 G5 Jun 30, 06 0.0871 0.1111 0.1198 0.1364 0.0963 G11 May 12, 06 0.0731 0.0711 0.1034 0.1289 0.0836 F1 Jul 12, 05 0.0094 0.0193 0.0460 0.0500 0.0386 G6 Sep 28, 06 0.0503 0.0536 0.0648 0.0920 0.0459 G6 Jan 31, 07 0.1189 0.1036 0.1114 0.1426 0.1114 G12 May 17, 05 0.0042 0.0407 0.0528 0.0519 0.0397 G12 Aug 10, 05 0.0086 0.0267 0.0435 0.0549 0.0471 G12 Sep 23, 05 0.0176 0.0427 0.0530 0.0550 0.0474 G3 Apr 26, 05 0.1252 0.1331 0.1281 0.1549 0.1168 G3 Jul 20, 06 0.0367 0.0555 0.0631 0.0615 0.0245 F2 Aug 2, 05 0.0109 0.0325 0.0544 0.0482 0.0430 F2 Aug 25, 05 0.0240 0.0106 0.0324 0.0630 0.0369 G9 Aug 11, 06 0.0420 0.0561 0.0586 0.0813 0.0280 G9 Mar 22, 07 0.0978 0.1143 0.1118 0.1297 0.0854 G1 May 2, 05 0.2056 0.2258 0.2394 0.2480 0.2274 G1 Sep 15, 06 0.0499 0.0452 0.0639 0.0904 0.0239 F5 Jul 21, 05 0.0107 0.0257 0.0433 0.0515 0.0378 F5 Aug 17, 06 0.0305 0.0174 0.0332 0.0627 0.0412    149     G7 May 19, 05 G7 Aug 3, 05 G7 Oct 21, 05 G7 Jul 12, 06 G7 Aug 21, 06 F4 Aug 8, 05 F4 Sep 9, 05 F7 Aug 9, 06 F7 Sep 19, 06 G4 Jun 10, 05 G4 Aug 11, 05 G4 Oct 5, 05 G4 Jul 18, 06 G4 Aug 25, 06 F8 Sep 14, 06 G7 May 19, 05 G7 Aug 3, 05 0.0050 G7 Oct 21, 05 0.0259 0.0270 G7 Jul 12, 06 0.0573 0.0599 0.0417 G7 Aug 21, 06 0.0830 0.0736 0.0746 0.0547 F3 Jul 8, 05 0.0078 0.0094 0.0254 0.0611 0.0813 G2 May 24, 05 0.0232 0.0122 0.0225 0.0451 0.0676 G2 Jul 14, 05 0.0091 0.0000 0.0199 0.0528 0.0580 G2 Oct 14, 05 0.0319 0.0355 0.0144 0.0410 0.0718 G5 Jun 16, 05 0.0251 0.0366 0.0326 0.0742 0.1068 G5 Jun 30, 06 0.0959 0.0961 0.0894 0.1206 0.1490 G11 May 12, 06 0.0789 0.0792 0.0776 0.1095 0.1460 F1 Jul 12, 05 0.0106 0.0032 0.0245 0.0546 0.0813 G6 Sep 28, 06 0.0451 0.0479 0.0486 0.0761 0.1127 G6 Jan 31, 07 0.1024 0.1013 0.1218 0.1319 0.1645 G12 May 17, 05 0.0054 0.0000 0.0266 0.0642 0.0801 G12 Aug 10, 05 0.0051 0.0060 0.0246 0.0575 0.0818 G12 Sep 23, 05 0.0167 0.0081 0.0324 0.0582 0.0822 G3 Apr 26, 05 0.1305 0.1235 0.1152 0.1526 0.1732 G3 Jul 20, 06 0.0224 0.0244 0.0465 0.0842 0.0907 F2 Aug 2, 05 0.0042 0.0000 0.0227 0.0672 0.0762 F2 Aug 25, 05 0.0080 0.0115 0.0230 0.0549 0.0903 G9 Aug 11, 06 0.0328 0.0379 0.0462 0.0739 0.0983 G9 Mar 22, 07 0.0850 0.0844 0.1077 0.1396 0.1563 G1 May 2, 05 0.2270 0.2070 0.2146 0.2503 0.2529 G1 Sep 15, 06 0.0471 0.0404 0.0474 0.0758 0.1008 F5 Jul 21, 05 0.0021 0.0011 0.0297 0.0618 0.0775 F5 Aug 17, 06 0.0092 0.0185 0.0265 0.0492 0.0878   150   F3 Jul 8, 05 G2 May 24, 05 G2 Jul 14, 05 G2 Oct 14, 05 G5 Jun 16, 05 F4 Aug 8, 05 F4 Sep 9, 05 F7 Aug 9, 06 F7 Sep 19, 06 G4 Jun 10, 05 G4 Aug 11, 05 G4 Oct 5, 05 G4 Jul 18, 06 G4 Aug 25, 06 F8 Sep 14, 06 G7 May 19, 05 G7 Aug 3, 05 G7 Oct 21, 05 G7 Jul 12, 06 G7 Aug 21, 06 F3 Jul 8, 05 G2 May 24, 05 0.0249 G2 Jul 14, 05 0.0119 0.0000 G2 Oct 14, 05 0.0357 0.0233 0.0217 G5 Jun 16, 05 0.0290 0.0542 0.0375 0.0285 G5 Jun 30, 06 0.1010 0.0948 0.0794 0.0986 0.0971 G11 May 12, 06 0.0746 0.0835 0.0715 0.0650 0.0579 F1 Jul 12, 05 0.0032 0.0173 0.0065 0.0290 0.0262 G6 Sep 28, 06 0.0472 0.0494 0.0384 0.0527 0.0552 G6 Jan 31, 07 0.1057 0.1207 0.1108 0.1120 0.1062 G12 May 17, 05 0.0130 0.0171 0.0040 0.0349 0.0366 G12 Aug 10, 05 0.0110 0.0141 0.0029 0.0264 0.0383 G12 Sep 23, 05 0.0208 0.0139 0.0096 0.0408 0.0458 G3 Apr 26, 05 0.1212 0.1231 0.1126 0.1251 0.1183 G3 Jul 20, 06 0.0263 0.0482 0.0275 0.0475 0.0376 F2 Aug 2, 05 0.0060 0.0221 0.0032 0.0324 0.0338 F2 Aug 25, 05 0.0100 0.0329 0.0155 0.0225 0.0161 G9 Aug 11, 06 0.0358 0.0476 0.0359 0.0416 0.0412 G9 Mar 22, 07 0.0995 0.1244 0.0947 0.1044 0.1037 G1 May 2, 05 0.2246 0.1904 0.1891 0.2070 0.2062 G1 Sep 15, 06 0.0365 0.0483 0.0399 0.0461 0.0367 F5 Jul 21, 05 0.0041 0.0252 0.0107 0.0332 0.0229 F5 Aug 17, 06 0.0144 0.0386 0.0239 0.0219 0.0188      151   G5 Jun 30, 06 G11 May 12, 06 F1 Jul 12, 05 G6 Sep 28, 06 G6 Jan 31, 07 F4 Aug 8, 05 F4 Sep 9, 05 F7 Aug 9, 06 F7 Sep 19, 06 G4 Jun 10, 05 G4 Aug 11, 05 G4 Oct 5, 05 G4 Jul 18, 06 G4 Aug 25, 06 F8 Sep 14, 06 G7 May 19, 05 G7 Aug 3, 05 G7 Oct 21, 05 G7 Jul 12, 06 G7 Aug 21, 06 F3 Jul 8, 05 G2 May 24, 05 G2 Jul 14, 05 G2 Oct 14, 05 G5 Jun 16, 05 G5 Jun 30, 06 G11 May 12, 06 0.0429 F1 Jul 12, 05 0.0913 0.0664 G6 Sep 28, 06 0.0401 0.0218 0.0456 G6 Jan 31, 07 0.1187 0.0786 0.1023 0.0527 G12 May 17, 05 0.0904 0.0788 0.0068 0.0494 0.1150 G12 Aug 10, 05 0.0881 0.0706 0.0082 0.0321 0.1035 G12 Sep 23, 05 0.0895 0.0783 0.0202 0.0406 0.1038 G3 Apr 26, 05 0.1535 0.1596 0.1193 0.1281 0.2163 G3 Jul 20, 06 0.0962 0.0882 0.0304 0.054 0.1129 F2 Aug 2, 05 0.1039 0.0851 0.0037 0.0529 0.1131 F2 Aug 25, 05 0.0987 0.0625 0.0075 0.0409 0.0879 G9 Aug 11, 06 0.0952 0.0756 0.0389 0.0398 0.1044 G9 Mar 22, 07 0.1645 0.1442 0.0968 0.1105 0.1889 G1 May 2, 05 0.1787 0.1650 0.2051 0.1734 0.2515 G1 Sep 15, 06 0.0806 0.0490 0.0299 0.0285 0.0808 F5 Jul 21, 05 0.0985 0.0746 0.0023 0.0468 0.0959 F5 Aug 17, 06 0.1013 0.0629 0.0147 0.0443 0.0930      152   G12 May 17, 05 G12 Aug 10, 05 G12 Sep 23, 05 G3 Apr 26, 05 G3  Jul 20, 06 F4 Aug 8, 05 F4 Sep 9, 05 F7 Aug 9, 06 F7 Sep 19, 06 G4 Jun 10, 05 G4 Aug 11, 05 G4 Oct 5, 05 G4 Jul 18, 06 G4 Aug 25, 06 F8 Sep 14, 06 G7 May 19, 05 G7 Aug 3, 05 G7 Oct 21, 05 G7 Jul 12, 06 G7 Aug 21, 06 F3 Jul 8, 05 G2 May 24, 05 G2 Jul 14, 05 G2 Oct 14, 05 G5 Jun 16, 05 G5 Jun 30, 06 G11 May 12, 06 F1 Jul 12, 05 G6 Sep 28, 06 G6 Jan 31, 07 G12 May 17, 05 G12 Aug 10, 05 0.0034 G12 Sep 23, 05 0.0125 0.0104 G3 Apr 26, 05 0.1089 0.1224 0.1198 G3 Jul 20, 06 0.0270 0.0361 0.0286 0.1052 F2 Aug 2, 05 0.0021 0.0087 0.0190 0.1148 0.0299 F2 Aug 25, 05 0.0151 0.0147 0.0277 0.1096 0.0253 G9 Aug 11, 06 0.0356 0.0411 0.0465 0.1279 0.0251 G9 Mar 22, 07 0.0886 0.0980 0.0922 0.1770 0.0630 G1 May 2, 05 0.2072 0.1934 0.1903 0.2906 0.2298 G1 Sep 15, 06 0.0432 0.0424 0.0443 0.1041 0.0365 F5 Jul 21, 05 0.0049 0.0088 0.0212 0.1154 0.0259 F5 Aug 17, 06 0.0221 0.0184 0.0377 0.1171 0.0368     153   F2 Aug 2, 05 F2 Aug 25, 05 G9 Aug 11, 06 G9 Mar 22, 07 G1 May 2, 05 F4 Aug 8, 05 F4 Sep 9, 05 F7 Aug 9, 06 F7 Sep 19, 06 G4 Jun 10, 05 G4 Aug 11, 05 G4 Oct 5, 05 G4 Jul 18, 06 G4 Aug 25, 06 F8 Sep 14, 06 G7 May 19, 05 G7 Aug 3, 05 G7 Oct 21, 05 G7 Jul 12, 06 G7 Aug 21, 06 F3 Jul 8, 05 G2 May 24, 05 G2 Jul 14, 05 G2 Oct 14, 05 G5 Jun 16, 05 G5 Jun 30, 06 G11 May 12, 06 F1 Jul 12, 05 G6 Sep 28, 06 G6 Jan 31, 07 G12 May 17, 05 G12 Aug 10, 05 G12 Sep 23, 05 G3 Apr 26, 05 G3 Jul 20, 06 F2 Aug 2, 05 F2 Aug 25, 05 0.0111 G9 Aug 11, 06 0.0385 0.0339 G9 Mar 22, 07 0.0921 0.0931 0.0817 G1 May 2, 05 0.2289 0.2205 0.2151 0.3155 G1 Sep 15, 06 0.0441 0.0291 0.0355 0.1154 0.1942 F5 Jul 21, 05 0.0019 0.0085 0.0301 0.0902 0.2170 F5 Aug 17, 06 0.0153 0.0042 0.0373 0.0888 0.2304      154   G1 Sep 15, 06 F5 Jul 21, 05 F4 Aug 8, 05 F4 Sep 9, 05 F7 Aug 9, 06 F7 Sep 19, 06 G4 Jun 10, 05 G4 Aug 11, 05 G4 Oct 5, 05 G4 Jul 18, 06 G4 Aug 25, 06 F8 Sep 14, 06 G7 May 19, 05 G7 Aug 3, 05 G7 Oct 21, 05 G7 Jul 12, 06 G7 Aug 21, 06 F3 Jul 8, 05 G2 May 24, 05 G2 Jul 14, 05 G2 Oct 14, 05 G5 Jun 16, 05 G5 Jun 30, 06 G11 May 12, 06 F1 Jul 12, 05 G6 Sep 28, 06 G6 Jan 31, 07 G12 May 17, 05 G12 Aug 10, 05 G12 Sep 23, 05 G3 Apr 26, 05 G3 Jul 20, 06 F2 Aug 2, 05 F2 Aug 25, 05 G9 Aug 11, 06 G9 Mar 22, 07 G1 May 2, 05 G1 Sep 15, 06 F5 Jul 21, 05 0.0341 F5 Aug 17, 06 0.0372 0.0088     155 Appendix 3: STRUCTURE results obtained when loci that were potentially under selection were trimmed from data analyzed in chapter 5 and reanalyzed.   It was suspected that a small subset of loci could be under selection due to the temporal shift in cluster membership observed in the STRUCTURE results between spring and summer collections and fall collections in 2005 (chapter 5).  To examine the influence of loci that were potentially under selection on the results presented in chapter 5, we excluded 10% of the loci with the highest and 10% of loci with the lowest Fst values based on loci specific FST estimates obtained from the 2005 Delta populations where the temporal pattern was observed.  The remaining 135 loci were used to run STRUCTURE simulations using the same model specifications as outlined in the method section of chapter 5. Results  Based on the Evanno method (Evanno et al., 2005) of detecting the true number of clusters, three clusters were identified to be the best fit model for the full analysis of all collections in STRUCTURE.  However, two of the ten runs performed for the K = 2 model had reached a different solution with a lower likelihood value than the other eight runs.  When these runs were excluded from the Evanno method of detection, K = 2 had four times the value of the ad hoc statistic, !K and was identified as the most probable solution.  The results of the STRUCTURE analysis for K = 2 produced similar results to that observed when all loci were included and grouped Langley populations from all survey years as distinct from all other populations (see chapter 5 for detailed results).  When collections from 2005 were examined independent of the other survey years the results based on trimmed loci closely matched that from the analysis of all polymorphic loci and found four clusters (K = 4) to be the most probable (Figure 1).   In   156 Delta, a temporal shift in cluster membership was observed from the spring and summer collections to the fall collections.  In addition, Langley and Abbotsford collections from G1a and G3 that had likely persisted from the previous growing season were found to be distinct from all other collections.  In 2006, the Evanno method, based on !K, detected K = 2 to be the most probable number of clusters (Figure 2a).  However, the mean individual assignment probability (q-hat) for K = 2 was lower (0.76) than that for K = 3 (0.84) and thus K = 3 provided a more robust grouping for the 2006 collections (Figure 2b).  The three clusters correspond to the same groupings found for the analysis of 2006 collections for all polymorphic loci (chapter 5) and roughly corresponded to the geographic locations of the collections in the cities of Delta, Langley, and Abbotsford.              1 5 7    Appendix 3, Figure 1: Results of STRUCTURE clustering analysis identified four clusters (K = 4) based on the 135 loci that remained after the data was trimmed for 654 individuals surveyed in 2005 from 22 collections.  Labels above represent the cities (Delta, Langley, Abbotsford) in which larvae were collected.  Labels below refer to the month of collection and locality codes for greenhouses and fields as defined in Table 1, chapter 5.  Each vertical line corresponds to an individual that is divided into K coloured segments depicting the individual’s membership into each of the K clusters.     1 5 8  (a)  (b)  1 5 9  Appendix 3, Figure 2: Results of STRUCTURE clustering analysis based on the 135 loci that remained after the data was trimmed for 381 individuals surveyed in 2006 from 14 collections (a) two clusters (K = 2) (b) three clusters (K = 3).  Although the highest !K, based on the Evanno method, was detected at K = 2 the individual assignment probabilities were highest for K = 3 indicating that this was a more robust grouping.  Labels above represent the cities (Delta, Langley, Abbotsford) in which larvae were collected.  Labels below refer to the month of collection and locality codes for greenhouses and fields as defined in Table 1, chapter 5.  Each vertical line corresponds to an individual that is divided into K coloured segments depicting the individual’s membership into each of the K clusters.    

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