Insect Resistance, Natural Enemies, and Density-Dependent Processes

CHAPTER 12

Insect Resistance, Natural Enemies, and Density-Dependent Processes David W. Onstad1, Anthony M. Shelton2 and J. Lindsey Flexner3 1

DuPont Pioneer, Wilmington, DE Department of Entomology, Cornell University, Geneva, NY 3 DuPont Pioneer, Wilmington, DE 2

Chapter Outline Natural Enemies: Direct Effects on Selection Natural Enemies: Density-Independent and Density-Dependent Effects Intraspecific, Density-Dependent Factors Conclusions References

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Entomologists usually focus their attention on mortality caused by an insecticide, insecticidal crop, natural enemy, or cultural control applied against a pest. However, ignoring mortality factors that are either naturally occurring or applied less intensively as part of an integrated pest management (IPM) program significantly influences insect resistance management (IRM). These other mortality factors are familiar to entomologists and include: abiotic and biotic processes, biological control, chemical control, and intraspecific competition. In this chapter, we highlight the variety of effects that these mortality factors can have on IRM and the evolution of resistance. Models based only on genotypic and allele frequencies would be adequate for studying the evolution of resistance if all individuals within a population experienced the same environment and selection pressures from the same sources. However, for many insect species, genetic and ecological processes are influenced by insect density. Thus, IRM plans, and the models on which they are based, should account for density-dependent processes. Furthermore, if IPM is the foundation of IRM (Chapter 1) and IPM focuses on insect densities and associated economic losses, then insect densities must be considered in damage calculations as well (Chapter 2). A third reason to consider density Insect Resistance Management DOI: http://dx.doi.org/10.1016/B978-0-12-396955-2.00012-6

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is that stochastic processes, such as genetic drift, may significantly influence system dynamics when populations become small (Caprio and Tabashnik, 1992; Sisterson et al., 2004) In this chapter, we explore three main subjects related to IRM and insect densities. First, we discuss natural enemies that attack one phenotype more than others. This differential mortality directly imposes additional selection pressure due to differences between susceptible and resistant phenotypes. Table 12.1 summarizes many of the empirical studies on natural enemies and their direct influence on resistant and susceptible individuals. Second, we describe how phenotype-neutral mortality factors, including biological control, can influence resistance evolution when the environment is not homogeneous and pest densities are favored in some areas but not in others. In the third section, we focus on common intraspecific processes as an important subset of the phenotype-neutral, mortality factors. We examine the importance of density-dependent survival and carrying capacity when refuges for susceptible individuals are deployed. An ecological process that is density-dependent is one in which the response is entirely or partly determined by the density of one or more species. For example, the attack rate of a parasite (number or proportion of hosts attacked) could depend on either the host’s density or the parasite’s density or both. By density-dependent survival, we mean that the probability of an individual insect surviving is dependent to some extent on that species’ density. Carrying capacity is the maximum arthropod density that a specific environment or habitat can support. Food resources often limit arthropod populations. A refuge is habitat for a pest that does not contain a lethal selective agent, such as a toxin.

NATURAL ENEMIES: DIRECT EFFECTS ON SELECTION Because pests rarely evolve resistance to their natural enemies (but see Chapter 8 for exceptions), the focus of this section is on the influence of natural enemies on the evolution of pest resistance to toxins in insecticides or host plants. With regard to host-plant resistance, Gould et al. (1991) took the lead in this subject when they published their conceptual and mathematical models on tritrophic interactions. The most commonly studied tritrophic system consisted of a plant, a herbivore, and a natural enemy.

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Table 12.1 Experimental Studies of Biological-Control Effects on IRM Effect on Natural evolution to Reference Pest Selecting Agent Enemies crop/toxinT

Helicoverpa armigera Heliothis virescens H. virescens eggs H. virescens larvae H. virescens pupae, adults H. virescens

chickpea

pathogen

tobacco tobacco/ soybean tobacco/ soybean tobacco/ soybean tobacco

predators, parasitoids predators, parasitoids predators, parasitoids predators, parasitoids parasitoid

H. virescens

tobacco

pathogen

Epilachna varivestis Leptinotarsa decemlineata L. decemlineata

bean

predators

potato

predator

potato

predator

Myzus persicae

insecticide

parasitoid

Pectinophora gossypiella P. gossypiella

cotton

nematode

cotton

nematode

Mayetiola destructor Plutella xylostella

wheat

parasitoids

B. thuringiensis

virus

Lawo et al. (2008) accelerate Johnson and Gould (1992) no effect Gould et al. (1991) accelerate Gould et al. (1991) delay/acc. Gould et al. (1991) delay Johnson et al. (1997a) accelerate Johnson et al. (1997a,b) no effect/acc. Gould et al. (1991) delay Arpaia et al. (1997) delay/acc. Mallampalli et al. (2005) delay Foster et al. (2007, 2011) delay Gassmann et al. (2012) delay Hannon et al. (2010) no effect Knutson et al. (2002) delay Raymond et al. (2007) accelerate

T

The natural enemies can delay, accelerate, or have no effect on the evolution of resistance by the pest to the toxin or toxic crop. Note that acc. means accelerate.

Gould et al. (1991) realized at the start of their work that hypotheses derived from the deterministic models would be significantly influenced by a variety of interacting ecological, behavioral, and genetic processes acting over single or multiple generations. Their simplest conclusion was that natural enemies

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that increase differential fitness between susceptible and resistant phenotypes, by attacking more susceptible individuals than resistant individuals, will accelerate the evolution of resistance by the herbivore to the host plant. The opposite effect on resistance evolution is expected when the natural enemy attacks more resistant individuals than susceptible ones. Thus, the early focus of research was on the phenotypic fitness costs imposed on the pest by natural enemies (Gassmann et al., 2009a). To evaluate the hypotheses postulated by Gould et al. (1991), Gould and his colleagues performed a series of experiments using transgenic insecticidal tobacco (Nicotiana tobacum) and potato (Solanum tuberosum), both expressing insecticidal proteins from the bacterium, Bacillus thuringiensis (Bt). Johnson and Gould (1992) conducted field experiments to examine interactions of Heliothis virescens, its natural enemies, and Bt tobacco plants considered partially resistant to H. virescens. They then calibrated a model to study the influence of natural enemies on the evolution of resistance to Bt tobacco. Simulation results indicated that biological control could accelerate evolution to resistant plants. Johnson et al. (1997a,b) carried out controlled studies with a parasitoid species and a pathogenic fungus that attack H. virescens on tobacco. They concluded that the parasitoid would likely delay the evolution of resistance to Bt tobacco, while the pathogen would likely promote the evolution of resistance. Arpaia et al. (1997) investigated predation of Leptinotarsa decemlineata on Bt potato plants in greenhouse and field studies. They included predation rates in a mathematical model to simulate the impact of natural enemies on the evolution of resistance by L. decemlineata to Bt potato. Simulations also included refuges of non-Bt potato plants. Results showed that predation could decrease the rate of evolution. Mallampalli et al. (2005) performed field studies to calibrate a simulation model of L. decemlineata on Bt potatoes to determine the influence of predation on IRM. They discovered that different prey species for a generalist predator that also eats L. decemlineata have different effects on the evolution of resistance to Bt potato: One prey species may delay the evolution of resistance, while the other could potentially accelerate it. Gassmann et al. (2012) performed a meta-analysis on previous experiments, a new experiment, and simulation modeling to explore the interaction of entomopathogenic nematodes and Bt cotton (Gossypium hirsutum) on the evolution of resistance by Pectinophora gossypiella to Bt cotton. Their work extended the findings of Hannon et al. (2010) and demonstrated the effectiveness of entomopathogenic nematodes for reducing the relative

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fitness of the Bt-resistant pest on cotton. The nematodes attacked the larvae and reduced the fitness of Bt-resistant moths more than susceptible moths. Fitness was the same without nematodes. Simulation modeling demonstrated that an initial resistance-allele frequency . 0.015 and population bottlenecks can diminish or eliminate the resistance-delaying effects of fitness costs. Hannon et al. (2010) and Gassmann et al. (2012) concluded that some species of nematodes could delay resistance by P. gossypiella to Bt cotton under some conditions. Raymond et al. (2007) studied Plutella xylostella, a microbial insecticide containing Bt, and a pathogenic nucleopolyhedrovirus. They found that the virus increased the fitness costs for Bt-resistant P. xylostella. Raymond et al. (2007) then used a model to investigate how the virus can be used to delay the evolution of resistance to Bt. One option that they advocated is the application of the virus only to refuges not sprayed with Bt. They did not model simultaneous evolution of resistance to both Bt and the virus. Resistant Myzus persicae exhibit a fitness trade-off between resistance to insecticides and avoidance of parasitism through defensive behavior (Foster et al., 2007). Foster et al. (2007) observed a variety of genotypes during periods of exposure to the parasitoid, Diaeretiella rapae, in the presence and absence of measured amounts of alarm pheromone. Wild-type, insecticide-susceptible individuals responded to alarm pheromone in ways that reduced parasitism. Insecticide-resistant M. persicae incurred significantly higher levels of parasitism. Foster et al. (2011) studied the reduced response to alarm pheromone in resistant M. persicae at three spatial scales: a single leaf of Brassica napus var chinensis with a single parasitoid, one plant of B. napus with one parasitoid, and eight plants with five parasitoids. At all scales, fewer insecticide-susceptible individuals became parasitized compared to insecticide-resistant ones. At the largest spatial scale, more susceptible individuals than resistant ones moved from their inoculation leaves to other leaves on the same plant after exposure to parasitoids. Given the fitness cost of insecticide resistance, evolution of resistance would likely be delayed by parasitism. Knutson et al. (2002) evaluated parasitism by several parasitoids of Mayetiola destructor infesting five wheat (Triticum aestivum) cultivars with various levels of host-plant resistance. Parasitism in field cages and in open fields did not vary among wheat cultivars, and parasitism rates were independent of host density. Knutson et al. (2002) concluded that parasitism of M. destructor is compatible with host-plant resistance and may

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extend the usefulness of wheat cultivars by slowing the increase of resistant/virulent pest populations. Lawo et al. (2008) experimentally determined that Helicoverpa armigera larvae resistant to insecticidal chickpea (Cicer arietinum) were more tolerant of infection by the entomopathogenic fungus Metarhizium anisopliae than were susceptible larvae. They concluded that resistance to the insecticidal crop did not influence fitness costs relative to this natural enemy. Lawo et al. (2008) also measured larval movement by the phenotypes on both conventional and insecticidal chickpea plants. Movement did not differ, so neither phenotype would be exposed to the fungus more than the other. Although the authors concluded that biological control would be compatible with this biotechnology, it is possible, as has been previously documented, that resistance to the insecticidal crop could evolve faster if the fungus infects more susceptible individuals than resistant ones. When attack by natural enemies is greater on resistant individuals, this effect can be related to negative cross-resistance (Pittendrigh et al., Chapter 11; Gassmann et al., 2009b).

NATURAL ENEMIES: DENSITY-INDEPENDENT AND DENSITY-DEPENDENT EFFECTS Heimpel et al. (2005) were among the first to point out that density-dependent mortality will limit the relative effectiveness of refuges during the early stages of evolution because the refuge population (mostly susceptible individuals) will be limited by the habitat’s carrying capacity or the population’s density-dependent mortality. (Pest populations being extirpated should be considered an entirely different case.) Even before population densities reach high levels, natural survival rates for rare resistant individuals in insecticidal fields are likely to be higher than those in refuges where the populations are more dense. Furthermore, because density-dependent effects may occur in most systems deploying a refuge for IRM, any density-independent mortality factors could potentially mitigate the density-dependent effects in the refuge, as many of the case studies below indicate. Heimpel et al. (2005) modeled various levels and forms of pest egg mortality: density independence, positive density dependence, and inverse density dependence. Heimpel et al. (2005) found that both the magnitude

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and form of egg mortality can influence the rate of resistance evolution to a hypothetical insecticidal crop. They demonstrated that high-densityindependent or density-dependent egg mortality (independent of genotype) delays the evolution of resistance. Furthermore, they concluded that for genotype-independent mortality to influence evolution in a landscape consisting of refuge and toxic habitats, it must be followed by densitydependent mortality in a later life stage. Because densities tend to be higher in refuges and because susceptible individuals have higher densities in refuges, indirect selection can occur by equalizing mortality that otherwise would favor the resistant phenotypes in the insecticidal crop. Thus, Heimpel et al. (2005) demonstrated that natural enemies can influence the evolution of pest resistance even when the attacks on pests are neutral with respect to genotype. Chilcutt and Tabashnik (1999) simulated a model of the interactions of foliar sprays of Bt and a parasitoid in the control of Plutella xylostella. They also modeled the population genetics of P. xylostella and its evolution of resistance to Bt. They concluded that the use of parasitoids could slow the evolution of resistance to Bt by decreasing the number of generations in which insecticide treatments would be required. In a series of experiments at Cornell University’s experiment station in Geneva, New York, USA, Xiaoxia Liu, Mao Chen, and Anthony Shelton performed a multigeneration study of a greenhouse system consisting of Bt broccoli (Brassica olereacea), a population of P. xylostella carrying a low percentage of alleles resistant to the Bt protein in broccoli, foliar insecticides, and one natural enemy of P. xylostella. The natural enemy was either the predator, Coleomegilla maculate, or the parasitoid, Diadegma insulare. Liu et al. (2011, 2012) determined that the predator had no preference for either the resistant or susceptible phenotype of P. xylostella. Liu et al. (2011) found no effects of P. xylostella genotypes on parasitism. Liu et al. (2012) observed one-third as much parasitism on broccoli treated with the insecticide lambda-cyhalothrin compared to nontreated plants, but they observed no effect when broccoli was treated with spinosad. Onstad et al. (2013) used information about the insecticidal broccoli and P. xylostella system to create a model to study the influence of the parasitoid on the long-term pest management and evolution of resistance by P. xylostella. The model included density-dependent mortality of P. xylostella caused by both intraspecific competition and parasitism. Parasitism rate depended on both host and parasitoid density on broccoli. They evaluated the evolution of resistance to Bt broccoli and the two types

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of foliar insecticides. Simulations demonstrated that density-dependent parasitism provided the most reliable, long-term control of P. xylostella populations. Density-dependent parasitism always delays the evolution of resistance to insecticidal broccoli, especially when the refuge size is large. Parasitism also maintains the pest population at the lowest densities over the long run compared to all other treatments, including Bt broccoli by itself. Onstad et al. (2013) also included rainfall, which is an abiotic, pestdensity-independent mortality factor, in the model. Results indicated that resistance evolution is delayed with significant rainfall mortality of pest eggs and neonates. These results with density-independent rainfall mortality support the conclusions about egg mortality and evolution drawn by Heimpel et al. (2005). Onstad et al. (2013) also demonstrated that densitydependent but genotype-independent mortality caused by natural enemies can delay evolution in patchy landscapes in the same way.

INTRASPECIFIC, DENSITY-DEPENDENT FACTORS Comins (1977a,b) and Georghiou and Taylor (1977a,b) were the first to prepare a model for IRM. Their models included densitydependent population growth. However, Tabashnik and Croft (1982) were the first to use a density-dependent survival function for larvae in an IRM model. Alstad and Andow (1995) and Pittendrigh et al. (2004) both used density-dependent survival functions to represent intraspecific competition in their models of resistance evolution in landscapes with refuge and insecticidal crop fields. However, none of these authors discussed the significance of density-dependence in resistance evolution. May and Dobson (1986) described the influence of the densitydependent survival of adults on the evolution of resistance to an insecticide in landscapes without refuges. They classified density-dependent survival as either overcompensating or undercompensating according to the way that the population returns to its long-run equilibrium after a perturbation. May and Dobson (1986) claimed that most insect species exhibit undercompensating density dependence because they recover gradually and monotonically after a disturbance. May and Dobson (1986) concluded that species with undercompensating density dependence evolve resistance more slowly than species with overcompensating density dependence.

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Mitchell and Onstad (2005) used a model of Diabrotica barberi to study the influence of density-dependent larval survival on the evolution of resistance to Bt corn (Zea mays) planted with refuge. They determined that this pest exhibits undercompensating density-dependent survival. Model results indicated that increasing the maximum survival rate (or fecundity) reduced the undercompensating property of the model (the population recovers more quickly from perturbations), and so resistance evolves more quickly. Sisterson et al. (2004) used a stochastic model and discovered that with high adoption of a transgenic insecticidal crop, evolution of resistance occurred faster when carrying capacities for the pest in the crop were reduced. Glaum et al. (2012) used the same density-dependent survival function and parameters as Alstad and Andow (1995). In their Figure 6, they showed that survival of larvae in refuge declines relative to survival in a field of transgenic insecticidal crop as refuge proportion increases. Thus, they concluded that density-dependent survival always produces faster evolution of resistance in a heterogeneous landscape relative to a scenario without density-dependent survival. Density-dependent survival due to intraspecific competition significantly influences the evolution of resistance of Diabrotica virgifera virgifera to Bt corn (Storer, 2003; Onstad et al., 2003; Crowder and Onstad, 2005; Crowder et al., 2005b; Onstad, 2006). In these models, Bt corn expresses a low dose, and oviposition is not uniform across the corn landscape (Pan et al., 2011). Thus, planting the refuge in the same location year after year can delay resistance evolution by permitting susceptible populations to grow in refuges without major disturbance (Pan et al., 2011). Additionally, in the models, as density-dependent, intraspecific competition reduces the number of susceptible beetles in the refuge, it has little impact on the resistant individuals in the insecticidal cornfield and resistance evolves more quickly. For example, omitting density-dependent survival from the model delays evolution of resistance to Bt corn, whereas increasing the maximum survival experienced by the larvae at the lowest densities (typically in Bt cornfields) caused resistance to evolve more quickly (Onstad et al., 2003; Crowder et al., 2005b; Crowder and Onstad, 2005). The type of densitydependent survival was determined by several field studies (Onstad et al., 2001; Onstad et al., 2006,;Hibbard et al., 2010) and was included in the models of Onstad et al. (2001), Crowder and Onstad (2005), Crowder et al., 2005b, Onstad and Meinke (2010), and Pan et al. (2011).

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Onstad (2006) included two kinds of density-dependent survival functions in a model of D. virgifera virgifera. One was for intraspecific competition (described above), and the other represented a decrease in mortality caused by the Bt toxin in plant tissues as larval density increases. Onstad postulated that the wounds made by the initial feeding larvae permit other larvae to access less toxic root tissue. Results were sensitive to the intraspecific competition with resistance evolution delayed when densitydependence was eliminated. Onstad (2006) found little difference in the results produced by the density-dependent and typical densityindependent functions for toxin mortality. After Onstad (1988) developed a density-dependent survival function for O. nubilalis larvae, most of Onstad’s subsequent papers related to IRM for O. nubilalis used that function or a carrying capacity of 22 larvae/plant (Chapter 14). Kang et al. (2012) created a different density-dependent survival function for O. nubilalis larvae based on an unpublished dataset. Because high-dose, insecticidal corn can extirpate the populations when 20% or less of the corn landscape consists of refuge (Onstad and Guse, 1999; Bell et al., 2012; Hutchison et al., 2010), model results indicated that the intraspecific competition in this species is not important for IRM. In a valuable demonstration of modeling based on alternative representations of nature, Wilhoit (1991) used two hypothetical models to demonstrate how seed mixtures of toxic and nontoxic plants in combination with biological control could delay or prevent the evolution of resistance by aphids. Wilhoit (1991) created a simple deterministic model and a complex stochastic model. The deterministic nonlinear model simulated intraspecific competition between two asexual phenotypes in a field of resistant and susceptible plants. It included mating, sexual reproduction during only one generation at the end of the season. The stochastic simulation model included many nonlinear equations for plant growth, aphid behavior, and predation. The model simulated plant-to-plant movement by both predators and aphids. In both models, the rate of immigration into the field from overwintering sites could differ between the two aphid phenotypes. Wilhoit (1991) determined that the seed mixture reduced the probability of the resistant (superior) phenotype dominating the aphid population. He also discovered that the resistant aphid could be excluded by the susceptible phenotype because of delayed arrival time by the former into the field. Wilhoit stated that this late-arrival disadvantage is likely to happen when aphids reach the field by random immigration and when resistant aphids are initially less numerous. The effect also depends on mortality

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due to predation increasing as density increases. In both models, the end-of-season sexual activity and genetics had little effect on the results compared to the competition during the season between asexual aphids. Cannibalism is an extreme form of intraspecific competition that has been included in IRM models for two species of insects. For both Diatraea grandiosella (Guse et al., 2002; Onstad et al., 2002) and Helicoverpa zea (Storer et al., 2003a,b) at most one larva typically survives on a corn plant per insect generation. Storer et al. (2003a,b) assumed that resistant larvae would mature faster and therefore more likely win cannibalistic encounters with susceptible larvae. Cannibalism therefore acts to increase the fitness differential between resistant and susceptible phenotypes on insecticidal corn. Storer et al. (2003a) stated that the role played by H. zea cannibalism in resistance evolution is both density dependent and resistance-allele frequency dependent. Model results indicated that as cannibalism in H. zea populations intensified, the rate of evolution of resistance to insecticidal corn increased (Storer et al., 2003b). Horner and Dively (2003) found that H. zea feeding on sublethal levels of insecticidal corn reduced the frequency of cannibalistic behaviors when old and young larvae were paired together. Exposure to the insecticidal corn had no significant effect on the timing or the level of mortality due to cannibalism. However, Horner and Dively (2003) postulated that cannibalistic encounters could result in partially resistant larvae feeding on nontoxic younger larvae, thus temporarily providing an escape from exposure to the plant toxin and increase the selective differential between susceptible and resistant individuals. Chilcutt (2006) observed slightly more cannibalism by H. zea larvae reared on insecticidal corn than on conventional corn. He concluded that the negative effects of insecticidal corn on larvae were compensated by increased cannibalism but higher survival of winners on Bt corn in comparison with larvae reared on conventional corn. However, Chilcutt et al. (2007) drew different conclusions when more younger larvae and fewer older larvae were observed on Bt corn.

CONCLUSIONS For IPM, we typically try to maintain pests far below the carrying capacity of the environment. For example, Peck and Ellner (1997) modeled a system in which the pest is maintained below an economic threshold for an insecticide to which the pest evolves resistance. In this case, greater population growth and higher pest densities cause more frequent

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selection for resistance. Because of the traditional emphasis on maintaining pests at very low densities, it is not surprising that the densitydependent survival of many important pests has not been measured. However, entomologists often investigate this phenomenon because of an interest in intraspecific competition or biological control. Given the cases and conclusions summarized in this chapter, it seems prudent for densitydependent survival to be measured and included in future models, particularly because the use of refuge is so prevalent in IRM for transgenic insecticidal crops. Furthermore, all IRM models should include all major mortality factors so that credible and accurate predictions can be made. This recommendation matches the main theme of the book: that IRM must be considered part of IPM and that IRM will be most effective with good IPM. Of course, all of these recommendations become harder to implement when a pest infests multiple crops or when the pest inhabits crops and wild host plants in the landscape. Too often we have little knowledge of the pest’s biology and ecology on its alternative hosts in an agroecosystem, although behavior and demography in the entire landscape can have significant consequences for resistance evolution (see Onstad and Carrie`re, Chapter 10). It is not easy to measure density-dependent mortality or even the carrying capacity for a pest population in a given environment. It is likely more difficult to measure density-dependent fecundity and dispersal. Nevertheless, almost all IRM models depend on simplifying assumptions about fecundity, larval movement, and adult dispersal, and these processes could determine the effectiveness of a refuge as much as densitydependent larval survival. In addition, any density-independent processes that cause individuals emerging in a refuge to remain and mate and lay most of their eggs in the refuge will promote population growth closer to the higher densities and limits. Onstad and Meinke (2010) found that reductions in density-independent fecundity had the same effect on simulated resistance evolution as increased egg mortality modeled by Heimpel et al. (2005) and described above. Onstad and Meinke (2010) also demonstrated that even genotype-specific fecundity reductions that seem to favor resistant phenotypes can actually delay resistance evolution because of density-dependent survival in the refuge. In this chapter, we have demonstrated that biological control can significantly affect IRM (Riddick et al., 2000; Lundgren et al., 2009). Interactions of natural enemies with other control tactics imposing selection pressure on pests can complicate IPM or lead to simple solutions. For

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example, two modeling studies have demonstrated how natural enemies and geographically distributed pest populations interact over time (but neither accounted for the evolution of resistance) in landscapes with conventional and insecticidal crops. Bell et al. (2012) demonstrated how a microsporidian pathogen and Bt corn interacted to drive populations of O. nubilalis to historically low levels in the central United States. Sisterson and Tabashnik (2005) modeled a pest and its parasitoid in a 9000-ha region with 900 fields, each planted with either an insecticidal crop or a refuge. They concluded that risk of regional parasitoid loss can be assessed from its life-history traits and reduced by increasing the percentage of refuge fields, fixing refuge locations, and reducing insecticide applications in refuges. With the introduction of Bt plants into agricultural systems, it has now become increasingly possible to dramatically reduce the use of broad-spectrum insecticides while still effectively controlling key target pests (Qaim et al., 2008; Romeis et al., 2008; Shelton, 2012). There is well-documented evidence that the currently deployed Bt plants, with their narrow spectrum of activity, have no direct negative effect on natural enemies (Romeis et al., 2006; Naranjo, 2009; Tian et al., 2012). This is in stark contrast to the use of traditional, broader spectrum insecticides that decrease natural enemy abundance and the biological control function they exert (Wolfenbarger et al., 2008; Naranjo 2009; Chen et al., 2008). Furthermore, studies have shown that the pest insects can rapidly evolve resistance to traditional broad-spectrum insecticides, while important natural enemies that could regulate the pest populations do not have such an ability (Xu et al., 2001). This phenomenon often results in rapid pest outbreaks and crop losses. The evidence is clear that Bt plants can contribute to natural enemy conservation and help maintain pest populations at lower levels. It is becoming increasingly evident that the combination of Bt plants and biological control agents can also delay the evolution of resistance by the pest species to Bt plants (Onstad et al., 2013) and be a useful combination for IPM. As stakeholders attempt to improve IPM by taking advantage of both biological control and host-plant resistance, interactions between natural enemies and pests targeted by insecticidal crops will become even more important in the future (Onstad et al., 2011). The extensive and interesting history of the genetic modification and use of toxin-resistant natural enemies could not be incorporated into this chapter. Good reviews can be found in publications by Hoy (1990, 2003).

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