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bs_bs_bannerjournal of Zoology Journal of Zoology. Print ISSN ORIGINAL MANUSCRIPT Assessing spatial population structure and heterogeneity in the dronefly L. Francuski & V. Milankov Faculty of
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bs_bs_bannerjournal of Zoology Journal of Zoology. Print ISSN ORIGINAL MANUSCRIPT Assessing spatial population structure and heterogeneity in the dronefly L. Francuski & V. Milankov Faculty of Sciences, Department of Biology and Ecology, University of Novi Sad, Novi Sad, Serbia Keywords gene flow; long-distance dispersal; Bayesian genotypic clustering approaches; population-based statistical methods; pollinators; genetic structure. Correspondence Ljubinka Francuski, Faculty of Sciences, Department of Biology and Ecology, University of Novi Sad, Trg Dositeja Obradovića 2, Novi Sad, Serbia. Tel: ; Fax: Editor: Jean-Nicolas Volff Received 14 October 2014; revised 4 June 2015; accepted 10 June 2015 doi: /jzo Abstract In order to secure pollination services and improve conservation strategies, a better understanding of factors influencing population structure of pollinator species is vital. Here, we aimed to empirically evaluate various individual- and population-based statistical methods for characterization of genetic structure of the widespread dronefly, Eristalis tenax. Thirty-five European populations, comprising 888 individuals, were genotyped at five polymorphic allozyme loci. Three Bayesian genotypic clustering approaches (STRUCTURE, BAPS and Geneland), pairwise F ST estimates, analyses of molecular variance (AMOVA), principle component analysis (PCA) and Mantel tests were applied in a comparative way in attempt to reveal the patterns of gene flow that occurs at various spatial scales in E. tenax. STRUCTURE analysis and PCA results provided no evidence of conspecific differentiation. In contrast, BAPS and Geneland clustering solutions did acknowledge low but significant proportion of among-populations genetic variation revealed by AMOVA. Similarly, pairwise F ST estimates partially argue against genetic homogeneity across Europe. The lack of correlation between genetic distance and both latitude and longitude variation suggests that the flies disperse in multiple directions. Therefore, our results indicate that continued long-range dispersal tend to homogenize populations over time, resulting in little population structure in E. tenax across the European landmass. On the other hand, significant differentiations between geographically proximate populations indicate that dispersal potential may not be realized and that gene flow patterns in E. tenax might be geographically complex. Using information from our genetic approaches will be useful for identifying patterns of migration and population connectivity across continent, which is an important issue for conservation efforts. Since E. tenax is an important pollinator, our results contribute to understanding the potential extent to which this taxon can facilitate gene flow among plant populations across natural and semi-natural habitats, agroecosystems and urban environments. Dronefly-mediated gene flow in plants is likely to occur over large distances and plant dronefly conservation will require large-scale action. Introduction Populations often experience spatial heterogeneity in their environment. Adaptation to such heterogeneous environments can lead to corresponding patterns of population genetic structure (e.g. Blanquart, Gandon & Nuismer, 2012). A reliable identification of population structure is of utmost importance as it reflects past biological processes that can explain the distribution of genetic variation (e.g. Orozco-ter Wengel, Corander & Schlötterer, 2011). For example, high level of population structuring may indicate an adaption of populations to their local environment (Joshi et al., 2001), while a lack of structuring at a large spatial scale may indicate a high tolerance to environmental variability and a large potential of adaptation to novel environmental pressures (Verhoeven et al., 2011). In recent decades, molecular methods have added an additional dimension to the identification of distinct population units and a number of tools are currently available to investigate genetic variation in space and time (e.g. Segelbacher et al., 2010). The extent of gene flow among populations, mediated by the forces of genetic drift and mutation, determines the patterns of variation at selectively neutral genetic loci. Therefore, by characterizing the distribution of genetic variation, population substructuring can be detected and the degree of connectivity among populations estimated (e.g. Journal of Zoology (2015) 2015 The Zoological Society of London 1 Spatial population structure in the dronefly L. Francuski and V. Milankov Ruzzante et al., 2000; Hutchinson, Carvalho & Roger, 2001). From the outset, the genetic structure between populations have traditionally been estimated using population genetic measures such as Wright s F-statistics or their derivatives, and population genetic distances typified by Nei s D (Hartl & Clark, 1989). In analysis at the population level, statistical estimates are averaged across individuals within populations and a priori assignment of individuals to specific populations is usually based on sampling locations or phenotypes (Rosenberg et al., 2001). With traditional approaches, the use of pre-defined population units may hinder accurate characterization of population structure (e.g. Rueness et al., 2003; Mank & Avise, 2004; Latch & Rhodes, 2006) and these approaches and measures sometimes have received scrutiny and criticism (Pearse & Crandall, 2004). A major contribution to identification of genetically differentiated populations has been the development of individual-based clustering analyses (IBC). With IBC, the operational units are the individual genotypes rather than arbitrarily defined population units, providing both the number of populations and their spatial limits based on the genetic characteristics of each sample (Mank & Avise, 2004). Genotypic clustering techniques are now prominent computational tools as they have proved very useful in the field of population genetics and the range of practical applications is broad (Beaumont & Rannala, 2004; Pearse & Crandall, 2004). However, methods based on clustering individuals (without a priori information about sample locations) might have limited power when gene flow is moderate or high. There are several software programs available for Bayesian clustering analyses, all of which describe a decrease in the ability to detect distinct clusters as levels of genetic differentiation among populations decrease (Pearse & Crandall, 2004; Manel, Gaggiotti & Waples, 2005). In this case, comparative analyses of different methods are needed (Waples & Gaggiotti, 2006). As with any clustering algorithm, however, underlying assumptions are that there are biologically meaningful clusters to be discovered and that structural elements not associated with clustering, such as gradients, are small relative to the elements that contribute to clustering (e.g. Schwartz & McKelvey, 2009). In addition, the possibility of utilizing spatial information from sampled individuals in the inference about genetic clusters has been incorporated into such analyses only recently. Spatial statistical approaches adopted from landscape ecology and other fields allow for more sophisticated tests of correlation between genetic and landscape data. For sparsely informative genetic markers, the geographical information is highly useful (Corander et al., 2008). However, Bayesian genotypic clustering methods do not provide descriptive statistics such as inbreeding coefficients (e.g. F IS) or genetic diversity that are useful to the understanding of local genetic structure for conservation and management actions. Still, moving from representing populations as discrete, non-overlapping patches to gradients of differentially related individuals in complex landscapes should improve our ability to understand population structure and gene flow in complex and dynamical landscapes (Segelbacher et al., 2010). Characterization of the population structure of species is useful in a variety of contexts. The types of questions that can be addressed with both traditional population parameters and individual-based clustering methods include, for example, delineation of population boundaries and detection of cryptic population structure (e.g. Kyle & Strobeck, 2001) or estimation of dispersal rates and patterns (Berry et al., 2005). These questions are central to conservation and management of rare and endangered taxa (e.g. Moritz, 1994), but are also applicable and equally important to widespread and ubiquitous species. Indeed, since many widespread species are often abundant, they are quantitatively important for ecosystem functioning. This is particularly true for insect species that serve as pollinators, herbivores, predators and decomposers as well as food for many vertebrates (e.g. Lange et al., 2012). Furthermore, the decline in the numbers of pollinating insects is continuing and has significant environmental and economic consequences, mainly because of their great agronomic impact (e.g. Biesmeijer et al., 2006). Several studies have explained that this decline is due to multiple factors, including exposure to pesticides, land use changes, decreasing genetic diversity and climate change (Potts et al., 2010; Ratnieks & Carreck, 2010). In light of the above, there is now an urgent need to investigate pollinators. As the extinction of important pollinator species can also impact ecosystem functions, maintaining or establishing functional landscape connectivity is a key component of biodiversity conservation (e.g. Beier et al., 2006; Kettunen et al., 2007). Therefore, this paper is devoted to the study of Eristalis tenax, Linnaeus, 1758 (Diptera, Syrphidae), a widespread hoverfly with potential for long-distance dispersal and migratory behaviour. Since E. tenax appears to be relatively efficient long-distance pollen disperser, more attention should be drawn to the migratory paths of this hoverfly (Pérez-Bañón et al., 2003) and what is shaping them (Francuski, Ludoški & Milankov, 2013b). Up to date, two studies were brought to bear on questions of E. tenax widerange movement and gave contrasting results. Using genetic (allozymes and mitochondrial DNA sequences of the cytochrome c oxidase subunit I gene) and phenotypic data (wing shape), our previous study suggested that E. tenax populations in the Central-Eastern Mediterranean are largely connected and that there is an absence of geographic structuring across island and coastal populations (Francuski et al., 2014b). On the other hand, application of landscape genetics revealed a subtle but significant subdivision between Balkan populations and populations from Central and Northern Europe of the taxon despite the continuous between-population exchange of individuals across the European landmass (Francuski et al., 2013a). In this paper, we addressed to describe the extent and shape of such substructuring by additional sampling and approaches. We are bearing in mind that for such highly mobile species living in continuous habitats, the definition of population structure is intricate and gene flow patterns of E. tenax might be geographically complex. Indeed, although the high vagility of many species suggests that genetic uniformity should be common in nature, in reality panmixia is rare and most 2 Journal of Zoology (2015) 2015 The Zoological Society of London L. Francuski and V. Milankov Spatial population structure in the dronefly species exhibit some level of differentiation among geographical localities (see Avise, 2004). In order to contribute to our understanding of the E. tenax species, which is important for maintaining pollination services, we addressed the question to what extent the gene flow occurs across the European landmass and its consequence for population structure of the taxon. The objective of this study was to empirically assess various genotypic clustering techniques and population-based statistical methods and approaches in a comparative way in attempt to reveal genetic structure of widespread hoverfly E. tenax. By using Bayesian clustering approaches in addition to conventional population genetic approaches, our goals were (1) to examine whether there was any underlying biologically sensible genetic structure over a large geographic scale and (2) to shed light at the level and patterns of gene flow that occurs at various spatial scales in E. tenax. Material and methods The target taxon Eristalis tenax is a highly suitable organism for research of genetic variation over geographical and temporal scales because of its easy availability, abundance and almost worldwide distribution. Since E. tenax imagines have a lifespan of about 3 months and are active from early spring to late autumn, two or three generations are produced each year (Gilbert, 1986). In Europe, the species is distributed throughout the continent (Speight, 2011) where it exhibits a large-scale migration from the Southern Europe to the Northern Europe possibly as a demonstration of an escape strategy. Namely, field observations indicated that, in spring and summer time, E. tenax starts migrating towards the north, therefore avoiding the hot and dry summer with its risk of drying up the aquatic habitats for larvae (Gatter & Schmid, 1990). A succession of movements through a series of breeding areas probably results in a multiple generations round trip. By late autumn, migrants return to the south to hibernate. The adaptive advantage of migration to the south is an increase of their chance of surviving the winter (Gatter & Schmid, 1990). The immature form of E. tenax is referred to as rat-tailed maggots while the adult form is commonly known as the dronefly and is part of the population of pollinators. Indeed, E. tenax seems to be particularly important pollinator in unfavourable ecological conditions at high altitudes (Zoller, Lenzin & Erhardt, 2002; Zhu & Lou, 2010) and urban environments (e.g. Hennig & Ghazoul, 2012). In addition, the significant role of E. tenax in spreading pollen across distant island plant populations has been highlighted because this hoverfly appears to be an efficient long-distance pollen disperser (Pérez-Bañón et al., 2003; Pérez-Bañón, Petanidou & Marcos-Garcia, 2007). Moreover, the dronefly is economically valuable pollinator of agricultural fields and was classified in the unmanaged insect taxa capable of providing consistent pollination services to mass flowering crops (Jauker et al., 2012; Rader et al., 2012). Sample collection We studied E. tenax from 35 sampling localities across Europe (Fig. 1). The adult flies were collected by hand netting, while Figure 1 Map of population sampling locations from Europe: Finland: (1) Mekrijärvi; (2) Helsinki; (3) The Hague, the Netherlands; (4) Berlin, Germany; (5) Telč, Czech Republic (6) Basel, Switzerland; (7) Klagenfurt, Austria; (8) Bled, Slovenia; Croatia: (9) Gračac; (10) Benkovac; Bosnia and Herzegovina: (11) Prijedor; (12) Banja Luka; (13) Šipovo; (14) Tjentište; (15) Trebinje; Serbia: (16) Ludoš lake; (17) Fruška Gora Mt; (18) Obedska bara; (19) Bratinac; (20) D. Milanovac; (21) Čačak; Montenegro: (22) Orijen Mt; (23) Durmitor Mt; (24) Lovćen Mt; (25) Šasko lake; (26) Negotino, FYR Macedonia; Greece: (27) Litochoro; (28) Volos; (29) Skyros; (30) Chios; (31) Lesvos; (32) Sardinia, Italy; Spain: (33) Alcoy; (34) Agost; (35) Alicante. Journal of Zoology (2015) 2015 The Zoological Society of London 3 Spatial population structure in the dronefly L. Francuski and V. Milankov Table 1 Sampling localities and sample size of Eristalis tenax used for allozyme analysis Country Population Longitude Latitude Date of sampling Allozyme analysis Collectors/published data Finland 1. Mekrijärvi E N 8 July Milankov, V., Rättel, E., Ståhls, G. 2. Helsinki E N 22 August Francuski et al., 2013a The Netherlands 3. The Hague 4 16 E N 30 August Milankov, V. Germany 4. Berlin E N 4 August Francuski et al., 2013a Czech Republic 5. Telč E N 15 July Djurakic, M. Switzerland 6. Basel 7 31 E N 1 August Francuski et al., 2013a Austria 7. Klagenfurt E N 6 August Francuski, Lj., Marčetić, Ð. Slovenia 8. Bled E N 5 August Francuski, Lj., Marčetić, Ð. Croatia 9. Gračac E N 9 August Francuski, Lj., Marčetić, Ð. 10. Benkovac E N 13 June Francuski, Lj., Milankov, V. Bosnia and Herzegovina 11. Prijedor E N 6 October Lukač, M., 12. Banja Luka E N 22 September Milankov, V., Lukač, M. 13. Šipovo E N 25 July Milankov, V. 14. Tjentište E N 28 June June Rättel, E., Milankov, V. Francuski, Lj., Djurakic, M. 15. Trebinje E N 27 June Francuski, Lj., Djurakic, M. Serbia 16. Ludoš lake E N 23 August Francuski et al., 2013a 17. Fruška Gora Mt E N 23 May 20 September Francuski et al., Obedska bara E N 16 August Francuski et al., 2013a 19. Bratinac E N 15 September Francuski et al., 2013a 20. D. Milanovac E N 16 September Francuski et al., 2013a 21. Čačak E N 24 May Francuski, Lj., Djurakic, M. Montenegro 22. Orjen Mt E N 20 June Francuski et al., 2014b 23. Durmitor Mt E N July Francuski et al., 2013b 24. Lovćen Mt E N 22 June Francuski et al., 2014b 25. Šasko lake E N 25 June Francuski Lj, Djurakic M. FYR Macedonia 26. Negotino E N 23 May Francuski Lj, Djurakic M. Greece 27. Litochoro E N 22 May Francuski et al., 2014b 28. Volos E N 20 May Francuski et al., 2014b 29. Skyros E N 18 May Francuski et al., 2014b 30. Chios E N 28 May Francuski et al., 2014b 31. Lesvos E N 23 May Francuski et al., 2014b Italy 32. Sardinia 9 01 E N 28 May Francuski et al., 2014b Spain 33. Alcoy 0 28 E N 29 October Francuski et al., 2014a 34. Agost 0 38 E N 29 October Francuski et al., 2014a 35. Alicante 0 45 E N 23 June Francuski et al., 2014a they were feeding on flowers, or resting on bare ground or vegetation, and frozen live for subsequent genetic analyses. More detailed information on locations and collection dates are summarized in Table 1. We obtained individual genotypes from subsets of individuals used in previous studies: Francuski et al. (2011; 2013a,b; 2014a,b). Sample sizes in populations ranged from 11 to 122 individuals while distance between sampling sites ranged from 17 to 3322 km. Possibly both migrants and locally hibernating individuals contribute to the establishment of the populations across Europe. Unfortunately, data on the migration pattern, life cycles and abundance of the species from the studied area are lacking. Therefore, we cannot analyse the impact of overwintering females and immigrants on the studied samples and results. Remains of specimens are deposited at the Department of Biology and Ecology, University of Novi Sad (Novi Sad, Serbia). Allozyme analysis Allozyme loci have proven to be useful tools for studying population genetics (Milankov et al., 2010) and spatial patterns of genetic diversity of the hoverflies. For instance, application of these markers revealed hidden temporal and spatial diversity within Merodon albifrons Meigen, 1822 (Milankov et al., 2013), as well as intraspecific divergent units and cryptic species within M. avidus Rossi, 1790 species complex (Milankov, Vujić & Ludoški, 2001, Milankov et al., 2009; V. Milankov et al., unpubl. data). Having a certain potential for investigating processes such as gene flow, migration or dispersal (Wagner & Fortin, 2013), allozymes allow us to empirically test the functional relevance of spatial indices such as connectivity used in landscape ecology (Holderegger, Kamm & Gugerli, 2006). 4 Journal of Zoology (2015) 2015 The Zoological Society of London L. Francuski and V. Milankov Spatial population structure in the dronefly A total of 888 specimens (Table 1) were included in the allozyme analysis by vertical polyacrylamide gel electrophoresis. Allozyme polymorphism was studied at five different loci: aldehyde oxidase ( AO; Ao), aspartate amino transferase ( AAT; Aat), esterase (E.C EST; two loci: Est-2, Est-4) and malic enzyme ( ME; Me). A trisborate-
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