Adaptation to human driven environmental pressures present insightful illustrations of the various facets of contemporary evolution and their implications. The evolution of resistance, the process by which a population of organisms acquires a genetically based decrease of susceptibility to a toxic compound (Tabashnik et al., 2009), may result in spectacularly fast responses to strong selective pressures (e.g., Mallet, 1989). Genetically modified crops, expressing Bacillus thuringiensis (Bt) toxins, are expected to result in elevated and continuous selection whenever Bt crops dominate the landscape (Tabashnik et al., 2013).

Evolution of resistance to Bt toxins expressed by crops is a direct threat to sustained control of pest populations, which may seriously impact agricultural yields. Even if a history of 10−20 years has revealed the first empirical trends (Tabashnik et al., 2013), the questions of how fast resistance to Bt crops toxins is likely to evolve and how quickly it may spread remain challenging. While Bt crops have been deployed for almost two decades, loss of control in pest populations, due to field-evolved resistance, arose in a few pest species only. However, decrease of susceptibility to transgenic Bt toxins (field-evolved resistance), which has not led to control failure, has been observed in many species (Tabashnik et al., 2013). Empirical knowledge about the spatial dynamics of resistance spread is scarce. Extensive gene flow is commonly observed in pest populations (e.g., Daly and Gregg, 1985; Korman et al. 1993; Bourguet et al., 2000; Endersby et al., 2007; Kim et al., 2009) and may promote rapid diffusion of resistance alleles across space (e.g., Peck et al., 1999; Bourguet et al., 2000).

The main option for delaying resistance evolution is the refuge strategy, which amounts to planting non-Bt plants within or surrounding Bt fields, allowing the survival of some susceptible larvae in a Bt-dominated environment. The proportion of refuge crop required to effectively delay resistance evolution is strongly dependent on the recessiveness of the resistance trait and, more generally, on the survival of heterozygote-resistant individuals at the dose of toxin expressed in plant tissues (e.g., see Vacher et al., 2003; Vacher et al., 2004).

The nature and uniformity of the genetic bases of resistance to Bt toxins are thus crucial aspects influencing resistance evolution and management (e.g., Tabashnik et al., 1998). Multiple recessive mutations that cause resistance to Cry1A toxins in Lepidoptera have been reported (see Heckel et al., 2007) in genes encoding Cadherin-like proteins (e.g., Morin et al., 2003) and ABC transporters (e.g., Baxter et al., 2011). In the field, diverse alleles conferring resistance were identified within Chinese populations of Helicoverpa armigera (Zhang et al., 2012), including recessive resistance alleles at the Cadherin locus as well as two non-recessive mutations, one of which was not linked to this locus.

Strong selection acting at a particular resistance locus is expected to affect not only the frequency of the selected mutation but also flanking polymorphisms. Alleles present on the ancestral chromosome on which a favourable mutation arose will tend to increase in frequency as the mutation invades the population, a consequence, which is known as the genetic hitchhiking effect (Kojima and Schaffer, 1967; Maynard-Smith and Haigh, 1974). Selective shifts may therefore give rise to strong linkage disequilibrium, i.e., some non-random allelic associations at the population level, between loci of the genomic region under selection.

Various population genetics approaches have aimed at taking advantage of such phenomena to capture signatures from positive selection (Jensen, 2014), notably in non-model organisms (e.g., de Villemereuil and Gaggiotti, 2015). The size of a hitchhiking effect depends on several factors, including: age and frequency of the mutation under selection; the intensity of selection; and recombination distance to the locus under selection. In particular, the initial frequency of the selected allele in the population before a selective shift, as well as the age of a mutation, has a crucial role (Maynard-Smith and Haigh, 1974; Jensen, 2014). As a consequence, the effectiveness of genome scans is lowered when e.g.: populations have undergone a particular demographic history (e.g., bottleneck); a recessive allele is involved; or selection acts on an ancient and previously neutral allele (Teshima et al., 2006; Jensen, 2014).

In South Africa, the pest Busseola fusca (Lepidoptera, Noctuidae) evolved resistance to the Bt toxin Cry1Ab expressed by transgenic maize within 8 years of the technology being released (Van Rensburg, 2007). Although this pest species is seldom hosted by wild grasses (Le Rü et al., 2006), it is responsible for substantial yield losses in maize, sorghum (Kfir et al., 2002) and more marginally sugarcane crops (Assefa et al., 2015). Being endemic to Sub-Saharan Africa and widely distributed throughout the region (Dupas et al., 2014), B. fusca is considered a pest of economic importance. To date, field resistance in B. fusca remains one of the few cases in the world where Bt crop failure has clearly been documented (see Tabashnik et al., 2013). Resistance evolution in this species was initially ascribed to an insufficient implementation of the refuge strategy (Kruger et al., 2012; Van den Berg et al., 2013). Nonetheless, the existence of a dominant resistance trait, such as found in this pest (Campagne et al., 2013), is expected to have greatly accelerated the spread of resistance and to have drastically reduced refuge efficiency. Furthermore, the presumed low survival of resistant individuals on non-Bt plants is unlikely to have mitigated the fitness benefits conferred by the resistance trait since no fitness cost associated with resistance was found in B. fusca (Kruger et al., 2014).

Four years after being first recorded in the field in 2006, resistance in B. fusca was observed at a distance of ~40 km from the location reported initially (Kruger et al., 2011). By 2011, Bt maize fields infested by B. fusca were observed in various places up to ~400 km distance from the initial location (see van den Berg et al., 2013). The latter sequence of observations raises a number of puzzling questions regarding both the initial distribution and spatial dynamics of resistance traits to Bt-maize in this species. Although gene flow may either swamp local resistance evolution or contribute to the spread of resistance alleles (Bourguet et al., 2000), little is known about the levels of migration and gene flow taking place in populations of B. fusca at sub-regional scales.

On the basis of empirical genetic data collected in the field, controlled crosses, and theoretical considerations, the purpose of this study was to investigate dynamics and diversity of resistance evolution in field populations of B. fusca. Our aims were to: (i) to evaluate the extent of gene flow in this pest within and between sites distributed across the major region of maize cultivation in South Africa; (ii) to identify markers associated with resistance and further investigate spatial distribution of resistance traits; and (iii) to explore the geographic variability of resistance inheritance in B. fusca within the study region.

Materials and methods

Study context and sampling

The first official observation of field resistance in B. fusca was recorded in the Christiana area (Van Rensburg, 2007; Kruger et al., 2011) which is located at the western extremity of the main maize production area (Van den Berg et al., 2013). Available data indicate, between 1998/1999 and 2010/2011, an average proportion of 60% Bt maize in the area where resistance was first reported (Kruger et al., 2012). The landscape is composed of 20% crops (the main one being maize) while most of the matrix consists of semi-natural or natural habitats (source: FAO GeoNetwork, In this area, populations of B. fusca are bivoltine with a diapause stage between cropping seasons. Sampling was performed during the growing season 2010/2011, i.e., ~k≈26 generations after Bt maize was released, across the main region of maize production in South Africa (stretching across more than 600 km East-West).

To carry out population genetics analyses, 20–25 larvae per field were sampled in a total of 28 fields (14 non-Bt fields and 14 Bt fields, Table 1). Sampling was performed within maize fields at advanced stage of development (late vegetative stage or higher), and consisted in five collection points distributed along a line, separated by ~50 m. At each point, we collected five larvae originating from five different plants separated by a few metres from each other. Care was taken to sample larvae in their fourth instar at least, in order to minimise the number of susceptible larvae collected in Bt fields. Larvae originating from different plants were conserved separately in 90% ethanol. Finally, a crude assessment of field damage level was performed in most sampled fields by recording the number of plants with leaf damage in three separate rows chosen at random (at a distance>20 m from each other, depending on an arbitrary number of steps) of 100 plants each (see Table 1). In order to perform controlled crosses, an additional number of larvae was collected in Bt fields at sites where resistance had been reported.

Table 1 General characteristics of the 28 fields sampled: IL is the infestation level (%); n, the sample size; F the inbreeding coefficient in a given field and its 95% estimation interval (IC95%)

Genetic markers

Total DNA isolation of 20 mg of larvae thoraces was done using the DNeasy Tissue Kit (Qiagen, Hilden, Germany). DNA concentration was determined using a Nanodrop (Thermo Scientific, Wilmington, DE, USA). The amplified fragment length polymorphism (AFLP) reaction was performed as described by Vos et al. (1995) with slight modifications. A quantity of 300 ng of DNA was digested using the following restriction enzymes: EcoRI and PstI. Digestion was made overnight at 37 °C with five units of EcoRI (New England Biolabs, Ipswich, MA, USA), and five units of PstI (New England Biolabs) in a total volume of 25 μl. Ligation of double-stranded EcoRI and PstI adaptors to the ends of the restriction fragments was performed (for 8 h at 15 °C) by adding 10 pmol of EcoRI and PstI adaptors, five units T4 DNA ligase (Promega, Madison, WI, USA). A 5 μl volume of 10 × diluted ligation products were used as a template for pre-amplification, using 10 pmol of EcoRI and MseI primers, 0.4 mm dNTPs, 1.5 mm MgCl2 and 1 unit of GoTaq Flexi DNA Polymerase (Promega) in a final volume of 25 μl. The pre-amplification thermocycle profile was: 94 °C for 5 min, followed by 20 cycles at 94 °C for 30 s, 56 °C for 1 min, 72 °C for 1 min and 72 °C for 5 min. Selective amplification steps were performed using 5 pmol EcoRI (+NN) and PstI (+NN) primers with 2.5 μl of 20 × diluted preamplification product in a final volume of 12.5 μl. Each selective amplification reaction mixture contained 0.4 mm dNTPs, 1.5 mm MgCl2 and 0.5 unit of GoTaq Flexi DNA Polymerase (Promega). The selective amplification thermocycler profile was: 94 °C for 2 min, followed by 12 cycles at 94 °C for 30 s, 65 °C for 30 s, 72 °C for 1 min, followed by 23 cycles at 94 °C for 30 s, 56 °C for 30 s, 72 °C for 1 min and 72 °C for 5 min. The different primers combinations used were: EcoRI+AA/PstI+AG, EcoRI+AG/PstI+AG, EcoRI+GA/PstI+AT, EcoRI+AG/PstI+AT, EcoRI+GA/PstI+AG, EcoRI+CA/PstI+AA, EcoRI+CT/PstI+AT, EcoRI+TG/PstI+GA and EcoRI+GG/PstI+AC. AFLP markers were scored according to the absence/presence of peaks using Genemapper V4.1 (Invitrogen/ThermoScientific, Wilmington, DE, USA). To optimise the reliability of the markers, 65 individuals were processed twice. Loci characterised by more than three discrepancies (5%) were considered prone to genotyping errors and were discarded. A total of 629 polymorphic markers were retained for further statistical analysis. The overall genotyping error rate per locus (see Bonin et al. 2004), calculated as the ratio between observed number of differences and total number of comparisons, was 1.3%.

Data analysis

The inbreeding coefficient FIS was estimated at both population and individual levels, for each sampled field using the software I4A, which is designed to deal with dominant genetic markers (Chybicki et al., 2011). After testing the convergence of different run lengths, data were analysed using 10 000 burn-in and 50 000 sampling iterations considering flat priors for the inbreeding coefficients. Estimates were provided with 95%-credibility intervals.

Rapid resistance evolution is expected to have left specific signatures in the genome of B. fusca as directional selection imposed by Bt toxins may result in increased differentiation between populations at hitchhiking loci. Using Bayescan (see Foll and Gaggiotti, 2008), we scanned the genome of this pest for loci at which genetic differentiation between populations (FST) is significantly higher than the genome baseline (outlier loci). Two sets of markers were defined and analysed separately: non-outlier loci, expected to reflect neutral processes such as genetic drift and gene flow between populations; and outlier loci, further tested for being associated with resistance.

To analyse the set of non-outlier loci, Euclidean genetic distances between individuals were computed (Peakall and Smouse, 2006) and genetic differentiation (ΦST) among fields was estimated with an analysis of molecular variance (Excoffier et al., 1992; Peakall, Smouse 2006). Statistical significance was tested using 999 permutations. A between-field analysis was performed on Principal Coordinate Analysis (PCO) axes (ade4 package, R Core Team, 2012). In addition, pairwise kinship coefficients (Fij) were estimated using the software SPAGeDi (Hardy and Vekemans, 2002). Estimation of kinship coefficients based on dominant markers requires an externally supplied estimate of the inbreeding coefficient. As the vast majority of individuals did not exhibit evidence of inbreeding (see Results section) we used F=0. Average kinship was then calculated within and between fields. The genetic neighbourhood size (Nb*), a measure of the local extent of breeding, was estimated following Hardy's (2003) procedure: Nb*≈(F0–1)/b, where F0 is the average kinship for adjacent individuals (within a field) and b is the slope of the linear decay between kinship and the natural logarithm of distance. Pairs of geographic distances higher than 100 km were excluded (see Hardy, 2003) and a confidence interval was calculated using 999 bootstrap iterations. Nb* is an empirical estimate of the theoretical demographic parameter Nb=4πσ2ρe, where ρe denotes the effective population density (e.g., see Rousset 1997; Hardy, 2003) and σ2 (km2 per generation) is the axial variance of an isotropic Gaussian dispersal distribution (2σ2 being the mean squared parent–offspring-displacement).

Association between field resistance and the genetic structure at outlier loci was then investigated. Analyses of population clustering were performed exclusively on individuals which were sampled in Bt fields. Ward's method for hierarchical clustering based on pairwise genetic distances (Peakall and Smouse, 2006) and the software STRUCTURE 2.3.4 (Pritchard et al., 2000) were used to identify genetic clusters. The latter analysis was executed under the admixture model and correlated allele frequencies, with 10 000 burn-in iterations and 50 000 main iterations. The optimal number of genetic clusters (K) was determined following the procedure described in Evanno et al. (2005), with five replicates for each K-value. For the sake of comparison, the same analysis was performed using the set of non-outlier loci.

The difference in marker frequency at outlier loci between Bt and non-Bt fields was tested using generalised linear mixed models (Venables and Ripley, 2002) with spatially correlated random effects, assuming a binomial distribution. Crop type (i.e., Bt vs. non-Bt) was treated as a fixed effect and the geographic coordinates of the fields were considered as random components of the analysis. Correlations between marker frequencies at outlier loci were evaluated with Pearson's correlation coefficient (R Core Team, 2012). Linkage disequilibrium between outlier loci was tested following Hill's procedure (Hill, 1974), which provides a parametrically explicit maximum-likelihood computation method for dominant markers. Both a standardised estimate of linkage disequilibrium (r) and its associated log10-likelihood ratio (LR) were calculated.

Controlled crosses

Heritability of resistance traits provides insights into the genetic bases of resistance. To explore the geographic variations in larvae survival on Bt maize, we performed single-pair crosses between resistant parents and a strain of a priori susceptible larvae originating from Tanzania (Tz), where no Bt crops are grown. Three sites, where resistance had been reported, were selected: Valhaarts (Va), an irrigation scheme close (~50 km) to the location, where resistance was first reported and in which resistance has been previously studied (Kruger et al., 2011); Lichtenburg (Li) at 180–200 km distance from the initial location; and Bethleem (Be), at ~300 km from the initial location (see also Figure 1). Controlled crosses were performed between adults originating from Bt fields and susceptible individuals: Va × Tz; Li × Tz; and Be × Tz. Progeny survival was assessed in a greenhouse on Bt and non-Bt maize planted in pots. Maize plants were inoculated with 20–30 neonates, which were gently transferred with a paintbrush within the whorl of young maize plants (i.e., from the 5th leaf collar stage). Plants were contained in fine-meshed nets to avoid larvae escaping. The number of individuals surviving on a plant was recorded after a period of 21 days. Preliminary observations indicate that survival after 2 weeks on Bt maize of individuals originating from Tanzania, is typically 2%. A fraction of the offspring were reared on non-Bt maize as a control. About 1700 larvae were used in the experiment. Survival of the progeny on maize was compared using Fisher’s exact tests (R Core Team, 2012). Contingent upon emergence of F1 adults and various experimental constraints, sib-crosses were undertaken in order to test whether homozygosity could reinforce resistance in F2 offspring of selected parents. One sib-cross was successfully performed among individuals originating from the Li × Tz cross and which survived on Bt maize.

Figure 1
figure 1

Map of the South African localities where larvae of Busseola fusca were collected. Empty dots represent Bt fields; dark-grey dots, non-Bt field; square dots, locations sampled for the controlled crosses (Va, Be and Li). Location where resistance was initially reported is represented by a cross. Numbers are the field IDs as displayed in Table 1.

Theoretical resistance spread

One approach for understanding the geographic distribution of resistance is to model the spatial spread of resistance alleles. In our survey, larvae were found to survive on Bt maize at various locations which also correspond to sites where resistance was reported (Van den Berg et al., 2014), translating into a vast expanse of approximately 200 km × 300 km. Assuming relatively simple genetic bases of resistance, the most elementary scenarios consistent with these observations could include: (i) a rapid propagation of a unique mutation conferring resistance from an initial location; or (ii) ancient and widespread resistance mutation(s), before the selective shift. We explored the feasibility of a dominant mutation spreading across space, from an initially confined distribution, under the selective pressure of Bt maize. Resistance was considered to involve a single locus, with a resistance allele (R) of frequency q, and a susceptible allele (S) of frequency 1 – q. For modelling purposes, the spatial distribution of Bt and non-Bt plants is considered continuous and random. The proportion of Bt crop in the landscape (ω) is assumed to determine the survival probability (e.g., see Tabashnik et al., 2008; Jin et al., 2015) of the three genotypes (RR, RS and SS): WSS=1 – ω, WRS=(1 – ω)+h × ω and WRR=1, where h denotes the dominance of the resistance allele. Spatial spread of a resistance allele under selection was considered under a demogenetic system of deterministic reaction-diffusion equations proposed by Tyutyunov et al. (2008) and slightly modified to our purpose (see Supplementary Appendix S1). The reaction term of the model accounts for local increase of resistance allele frequency, in relation to ω, h, WSS, WRR, q and the birth rate β per individual and per generation. The diffusion term captures the effect of individual dispersal under diffusive Brownian motion (diffusion constant D), scaling to 2σ2=4D after one generation.


Genetic structure at non-outlier loci

The five primer combinations yielded 629 polymorphic and reproducible fragments in B. fusca, for the entire set of 693 individuals. All individuals had different AFLP profiles, differentiated by at least eight markers. Estimates of population inbreeding coefficient were low, ranging between 0.007 and 0.058 in the 28 sampled fields (Table 1), indicating no strong heterozygote deficiency. Similarly, estimates of median individual inbreeding were <0.01 in ~97% of individuals. The outlier analysis carried out with all 28 fields detected 26 loci characterised by FST>0.07 (logarithm of the posterior odds, log10 PO>1) while the non-outliers were characterised by a lower differentiation FST=0.027 (s.d.=0.008). On this basis, loci were split into two groups: ‘outlier’ and ‘non-outlier’ loci and were further analyzed separately.

Evidence for extensive gene flow between populations was found when investigating the genetic structure of non-outlier loci. Overall genetic differentiation among fields was low but non-null (ΦST=0.03, P<0.001). Even at long distances (>400 km), average pairwise genetic differentiation between sampled fields was weak (ΦST≈0.04). In spite of a lack of genetic differentiation at broad spatial scales, a smooth pattern of isolation by distance was found. Firstly, pairwise ΦST were associated with geographic distances (R=0.47; P<0.001). Similarly, kinship coefficients were significantly correlated with log-transformed distances (R=0.52; P<0.001; Figure 2a). Parameters of the latter regression yielded the estimate for the neighbourhood size, Nb*≈(F0–1)/b=706 with a 95% bootstrap interval (526, 1010), which translates into ρe.σ2≈55 (i.e., with Nb*≈4πσ2ρe), which is well above the theoretical threshold of ρe.σ2>1 at which a population distributed in a continuous space behaves nearly as a panmictic population (see Maruyama, 1972). Secondly, a between-class (i.e., fields) PCO (Figure 2b) revealed congruent mapping between B-PCO axes and the geographical origin of the field-collected populations (see Figure 1 for the locations); e.g., clusters of fields (5, 6, 7), (2, 3, 4) and (54, 64, 68, 71, 75). The small amount of variation captured by the analysis (6.7% of the total inertia; 1.7% onto axes 1 and 2) consistently reflected low levels of genetic structure.

Figure 2
figure 2

Isolation by distance in B. fusca populations (non-outlier loci). (a) Pairwise kinship coefficients (Fij) as a function of distance (km). The curve corresponds to the regression of kinship as a function of log-transformed distance; dotted lines represent the 95% confidence interval of the regression slope. (b) Between-field analysis performed on Principal Coordinate (PCO) axes. Barycentre of each field sampled is denoted by its ID (number); individuals collected in the different fields correspond to grey dots; lines relate each individual to its field of origin.

Outlier loci and resistance

The genetic structure of Bt fields at outlier loci revealed two spatially segregated groups of Bt fields, henceforth A1 and A2 (Figure 3). The optimal number of clusters obtained using a Bayesian clustering method (STRUCTURE) was K=2 in both sets of loci; higher values of K did not result in better fits of the model (see Supplementary Appendix S2). Although essentially no genetic structure appeared in non-outliers, a clustering based on outlier loci brought to light a clearer pattern which was further supported by the Ward's clustering method (Figure 3). One cluster was composed of five Bt fields (3, 5, 7, 43 and 65) which were spatially aggregated (area A1, Figure 1) in the surroundings of the location where resistance was first reported. While the extent of A1 could be sketched as an area of ~100 km radius, A2 was defined as an outgroup which included the remaining nine Bt fields.

Figure 3
figure 3

Comparison of population structures between both the outliers and non-outlier loci, for individuals collected in Bt fields. The dendrogram (left) was obtained based on Euclidean genetic distances obtained with the data set of outlier loci. Two genetic groups are distinguished: A1 and A2. Comparative STRUCTURE plots (right) of the 14 Bt fields using outlier loci and non-outlier loci. The number of clusters in each analysis was set at K=2. The length of individual bars corresponds to the probability of membership to a cluster. Numbers identify the Bt field sampled.

A second round of outlier analysis (using Bayescan) was performed within each group A1 and A2, including the four closest non-Bt fields (1, 2, 4 and 6) in A1 and the remaining ones in A2. These additional analyses did not yield outliers which were not detected previously (Table 2). However, some outliers returned in the first round were not detected as significant (log10 PO<1) in the second round, presumably due to a reduction in statistical power.

Table 2 Overview of the outlier scan.

The association of marker frequency at outlier loci with Bt and non-Bt fields was then examined. Mixed generalised linear model with spatially correlated random effects showed significant differences in frequency at four outlier markers (L192, L204, L299 and L369) in A1 (P<0.005, Table 2) whereas no significant association was found in A2.

These four loci were therefore considered strong candidates associated with resistance to Bt toxins. Spatial distribution of these putative hitchhikers within A1 was highly consistent (Figure 4): marker frequency in the Bt fields was either systematically lower (L192, L204 and 299) or systematically higher (L369) than in the non-Bt fields. It is worth noticing that these contrasts were stronger in the two most Southern Bt fields of A1 (5 and 7), suggesting a higher resistance allele frequency in these fields. Overall, allelic frequencies at the four outlier markers (in the 28 fields) were significantly inter-correlated (see also Supplementary Appendix S3). Pearson's correlation coefficients were: 0.6<R<0.7 (P<0.001) in the pairs (L192-L204), (L192-299) and (L204-299); R=-0.51 (P=0.005) in the pair (L204-L369); R=-0.44 (P=0.017) in the pair (L192-L369); and not significant in one pair of loci only, R=−0.30 (P=0.115).

Figure 4
figure 4

Marker frequency at four outliers loci for the different locations sampled. Black-and-white pies represent Bt fields; shaded-grey pies, non-Bt fields. The darker fraction of the pies corresponds to the absence of marker (i.e., homozygote for the null allele). The shaded area delineates the region A1, while by default A2 is composed of any fields outside this region.

In addition, linkage disequilibrium was detected among putative hitchhikers within the smaller and more homogeneous area A1. Highly significant pairwise linkage disequilibrium values 0.42<| r |<0.47 (log10-likelihood ratios LR3) were obtained for the pairs of loci (L192-L369), (L204-L369) and (L204-L299), while the remaining three pairs were characterised by r-values>0.25: r=0.38 for the pair (L192-L369) (LR=2.4), 0.25<| r |<0.32 (LR>1.13) for (L299-L369) and (L192-L299). Consistently, genetic differentiation at these four outlier loci was elevated between Bt and non-Bt fields of A1 (ΦST=0.24; P<0.001) as well as between Bt fields of A1 and A2 (ΦST=0.21; P<0.001). By contrast, it was low (ΦST<0.05) within either type of field (Bt or non-Bt) and within each area (A1 and A2).

Controlled crosses

The site (Va) was located in the area A1 in which genetic contrasts were observed, while (Li) and (Be) were located farther apart in A2 (see Figure 1). Overall survival was 0.67 when offspring was reared on non-Bt maize, significantly higher (P<0.001) than in offspring reared on Bt maize (Figure 5). When reared on Bt maize, survival of Va × Tz offspring (0.32) was clearly higher than both Li × Tz and Be × Tz offspring (P<0.001). Given substantial heterogeneity within the two latter crosses, the highest survival in each type of offspring (0.11, 0.13) was compared with that of Va × Tz offspring reared on Bt; it was unambiguously confirmed that progeny obtained with individuals from Valhaarts outperformed progeny of the other crosses on Bt maize (P<0.001; Figure 5). Although survival in a Li × Tz progeny reared on Bt maize was particularly low (6/383), a F2 sib-cross between two out of the six survivors resulted in significantly increased survival on Bt maize (51/251, P<0.001), suggesting the existence of a recessive resistance trait in (Li).

Figure 5
figure 5

Survival of progeny on Bt maize after 21 days. Crosses were performed between individuals originating from a susceptible Tanzanian strain (Tz) and individuals originating from Bt fields in three different locations: Lichtenburg (Li × Tz), Bethleem (Be × Tz) and Valhaarts (Va × Tz), the latter being in the area A1. The control consisted of the three same types of cross for which progeny was reared on non-Bt maize. M/F indicate whether the resistant parent was a male (M) or a female (F); error bars represent standard errors of the different proportions.

Theoretical resistance spread

On the basis of a demogenetic system of deterministic reaction-diffusion equations, we show that the lower propagation speed (V) of resistance is a constant which takes the form (Supplementary Appendix S1, see also Tyutyunov et al., 2008):

The time required for a concentric wave of resistance to travel a distance d (km) may simply be approximated by Td / V generations.

From controlled crosses performed in the laboratory we estimated that a female of B. fusca produces on average ~250 viable neonates (see also Khadioli et al., 2014). In laboratory experiments, survival (egg to adult stage) of B. fusca has been estimated to be, respectively, of order 40% and 15–20% in optimal and in sub-optimal temperature conditions (Khadioli et al., 2014). In a different species of Noctuidae (Helicoverpa zea) the estimate of survival to adulthood in the field was close to 5% (Vargas and Nishida, 1980). Assuming a 1:1 sex ratio, we determined a conservative estimate of β=0.05 × 250/2≈6.

A resistance allele being propagated under constant selective pressure typically results in fast rapid spread. On the basis of equation (1), we explored the scale at which a dominant resistance allele may spread within ~26 generations (i.e., 13 years, and assuming h=1, ω=0.6, β=6) from an initially local distribution. Selection due to a moderate fraction of Bt crop in the landscape (ω≈0.6) and operating on a fully dominant resistance allele (h=1) yields speeds of several kilometres per year, e.g., 4.6 km per year<V<17 km per year for 1<σ2<10. Over 26 generations, the spread of resistance would attain a radius of several tens of kilometres (i.e., ~60–220 km) (Figure 6). Under the same parameter values, a high axial dispersal variance (σ2≈25 km2 per generation) would be required for the resistance wave to travel from the location where resistance was first reported to the farthest Bt fields in which individuals were found to survive (Figure 6).

Figure 6
figure 6

Expected resistance spread due to selection operated by Bt toxins (ω=0.6) after k=26 generations. Resistance was assumed initially confined at the location where it was first reported (centre of the circles). Dotted, dashed and solid circles correspond to distances obtained with increased axial dispersal variance (σ2=0.75, 3 and 6 km2 per generation). External border of the hatched ring represents the dispersal parameter required σ2≈25 km2 to encompass all resistant populations collected in this study. Empty dots correspond to sampled Bt fields; dark-grey dots, non-Bt field; square dots, locations sampled for the controlled crosses (Va, Be and Li). Numbers are the field IDs as displayed in Table 1.


Being one of the few instances of field-evolved resistance leading to Bt crop failure (Tabashnik et al., 2009; Tabashnik et al., 2013), Bt-resistance in B. fusca may be considered an insightful study case in the context of Bt crop deployment worldwide. Our results confirmed that a sharp selective shift, as indicated by the extensive uptake of Bt maize (Kruger et al., 2012), resulted in rapid evolution of resistance. Beyond the detection of outlier loci, our results provide further understanding of both gene flow and field resistance in this pest, notably in terms of spatial distribution, dynamics and diversity of resistance in the field.

Gene flow and resistance spread

In line with what was shown in several other moth species (e.g., Korman et al., 1993; Nibouche et al., 1998; Bourguet et al., 2000; Han and Caprio, 2002), our empirical results reflect extensive gene flow and low levels of population inbreeding in B. fusca. To a first approximation, the whole population seems close to panmixia at coarse scales (700 km × 400 km). It has been suggested that B. fusca could be characterised by limited dispersal capacities (Sezonlin et al., 2006). However, consistent with Dupas et al. (2014), our study did not reveal strong barriers to gene flow. Estimate of neighbourhood size in B. fusca (Nb*≈700 individuals) had the same order of magnitude as in the European corn borer Ostrinia nubilalis (Nb*≈500 on maize, see Martel et al., 2003). Classical methods for estimating gene flow (see Rousset, 1997; Hardy, 2003) cannot yield a separate estimation of the parameters σ2 and ρe, such that the product of these has to be considered, i.e., ρe.σ2≈55 in our study. Assuming an effective density of 14<ρe<55 individuals per km2 therefore amounts to assuming a dispersal parameter 1<σ2<4 km2. Similar effective population densities, i.e., 5<ρe<50 individuals per km2, have been proposed in other species of wild moths (Saccheri et al., 2008) and moth pests (Martel et al., 2003). Mean flight distance of several kilometres was observed in males of the moth pest Spodoptera litura, using a release-recapture experiment. In B. fusca, observations of dispersing females crossing an inter-field of 1.6 km have been reported (see Harris and Nwanze, 1992). Although dispersal distances and timing may differ between males and females, these observations are roughly compatible with a dispersal parameter σ2 scaling to a few km2 per generation.

Extensive gene flow in pest populations may affect the pace of resistance evolution by mitigating local increase of resistance with susceptible alleles originating from non-Bt fields, or by contributing to an accelerated spatial spread of resistance pockets. Besides gene dispersal (σ), the speed of resistance propagation crucially depends on the inheritance of resistance (i.e., h, the dominance parameter in our model) and the population ecology of a pest, notably its birth rate (β). These parameters would clearly deserve further consideration in order to better comprehend the spatial dynamics of resistance. Nonetheless, realistic numerical applications suggested a travelling speed of a few kilometres per generations. It seems plausible that a resistance pocket, initially small, may have quickly expanded into a bigger area of radius several tens of kilometres or more. Within 13 years (k=26 generations), one might expect the radius of such a pocket to increase by ~100 km (provided ω≈0.6, h=1, β=6, σ2≈2.1 km2, ρe≈26.8), an order of distance which is commensurate with the size of A1. However, our model suggested that only high dispersal variance could result in a propagation radius that would encompass all apparently resistant populations, which might lie at the margin of what can be reasonably assumed.

Outlier loci and resistance

While larvae surviving in Bt fields were observed across a vast area, a genetic signature associated with field-evolved resistance could be attributed to a genetic hitchhiking process within a smaller region only. The existence of such a pattern is also suggestive of a recent geographical spread of resistance. Indeed, due to genetic recombination, associations between resistance allele and alleles at linked loci are not expected to persist in time, unless recombination distance is very low (see Maynard-Smith and Haigh, 1974). Correlated allele frequencies and linkage disequilibrium are not necessarily indicative of physical linkage as they may also result from various stochastic processes at population level. Nevertheless, given an observed lack of neutral genetic structure, even at coarse spatial scale, we expect neither population differentiation, demographic history nor genetic drift to strongly affect linkage disequilibrium patterns in the wild.

In line with genetic hitchhiking arising due to resistance evolution, consistent differences in outlier marker frequencies were observed between Bt and non-Bt fields. The size of these differences could reflect an intermediate frequency of resistance alleles in A1 (see also Supplementary Appendix S4). On the one hand, a review study (Tabashnik et al., 2013) evaluated the proportion of resistant B. fusca individuals to be≈q2+2hq(1 − q)50%, in the core resistance region, which translates into a resistance allele frequency q>0.25 assuming a strictly dominant (h=1) monogenic trait. On the other hand further assuming a resistance allele exclusively associated with null alleles (a0) at either outlier loci L192, L204 or L299, one would expect the frequency of homozygotes a0/a0 (corresponding to an absence of marker) to be close to P(a0/a0)=q2/(q2+2hpq) in Bt fields, i.e., leading to q=2 × P(a0/a0)/[1+P(a0/a0)]. Given P(a0/a0)≈0.3 at these loci in Bt fields (see Table 2), we attain a crude estimate q≈0.46. Finally, relaxing the stringent assumptions of full linkage and strict dominance, this exercise suggests a resistance allele frequency 0.25<q<0.5 (see also Supplementary Appendix S4).

Note that some outlier loci could not be associated with resistance, which suggests that these loci may not be related to resistance at all, or reflect a lack of power in some analyses, notably due to the use of dominant genetic markers. However, it cannot be excluded that a fraction of these outliers, loosely linked to the resistance trait, underwent quick hitchhiking but did not shift significantly during the time for which associations persisted, contributing to some levels of genetic differentiation between areas (see also Supplementary Appendix S5).

Uniformity of resistance

Since the first observations of field resistance in B. fusca were reported, in the Western part of the maize cutivation region, most conspicuous failures to control populations of B. fusca in Bt fields (e.g., see Van Rensburg, 2007; Kruger et al., 2011) have been observed in the surroundings of this location. Moreover, the resistance trait associated with these failures appeared to be dominant (Campagne et al., 2013). Our results showing differential progeny survival on Bt plants and genetic contrasts (between A1 and A2) support the idea of a dominant resistance trait being confined within A1. Furthermore, it is coherent with the chronology of resistance development in South Africa (Van den Berg et al., 2013) and with the levels of infestation in Bt fields perceived by farmers at different locations: perceived field infestations >10% mostly occurred within 100−150 km from the site where resistance was initially reported (Kruger et al., 2012; Van den Berg and Campagne, 2014).

By contrast, information about resistance found within A2 is rather scarce (Van den Berg et al., 2013). Even if survival of some susceptible individuals on plant tissues expressing a low dose of toxins might partly explain the development of larvae on Bt fields within A2, our results would advocate for independent evolution of resistance. Indeed, increased survival on Bt maize obtained in a F2 sib-cross clearly suggested the existence of at least one recessive resistance trait in A2. Similarly to what has already been reported in another pest (e.g., Zhang et al., 2012; Jin et al., 2013), diverse resistance mutations characterised by different levels of recessiveness might therefore be responsible for the observed pattern of widespread field resistance to Bt maize in South Africa.

Finally, our results draw a valuable picture of resistance distribution and dynamics for one of the few instances of resistance evolution leading to control failure in the field. Evidence suggests resistance being a nonuniform trait across the extended region of maize production. The distribution of the main dominant resistance trait seems confined to a smaller region (100 km radius). The scale at which the distribution of this trait takes place could correspond to a recent geographical spread as confirmed by theoretical considerations showing a fast propagation of a dominant resistance trait across space. This study illustrates both potential challenges and insights of a strong evolutionary response scaling, in space and time, with the selective pressure of drastic environmental change.

Data Archiving

AFLP data are available from Dryad doi:10.5061/dryad.n3r35.