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McGregor, A. (2005). *An Interim Report on the Economic Evaluation of the Hawaii Fruit Fly Area-Wide Pest Management Program.* University of Hawaii Extension Service. McQuate, G.T.; Jones, G.D. & Sylva, C.D. (2003). Assessment of corn pollen as a food source for two tephritid Fruit Fly Species, *Environmental Entomology,* No 32, pp. 141-150. McQuate, G.T. & Vargas, R.I. (2007). Assessment of attractiveness of plants as roosting sites

Nicholls, C.I. & Altieri, M.A. (2004). Agroecological bases of ecological engineering for pest

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*Agronomy for Sustainable Development*, DOI 10.007/s13593-011-0022-4. Ryckewaert, P.; Deguine, J.P.; Brévault, T. & Vayssières, J.-F. (2010). Fruit flies (Diptera:

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sustainable management of crop pests and diseases in agroecosystems: a review,

Tephritidae) on vegetable crops in Reunion Island (Indian Ocean): state of knowledge, control methods and prospects for management, *Fruits*, No 65, pp. 113Bistability is increasingly recognized as a mechanism underlying patterns in a wide variety of ecological time series (reviewed in Scheffer & Carpenter, 2003). One consequence of bistability is that a system can be in either of two stable states for a given set of conditions, each stable state being surrounded by a basin of attraction. Such bistability provides one explanation for pest outbreaks because pest populations can cross a threshold from a low-density, biologically-controlled equilibrium and enter into the basin of attraction of a high-density outbreak equilibrium (see e.g., Ludwig et al., 1978). In such systems, integrated pest management has the dual goal of simultaneously decreasing the magnitude of the outbreak basin of attraction and perturbing the pest population back across the threshold to the biologically controlled state. Decreases in the magnitude of the outbreak basin of attraction arise when management strategies alter basic biological parameters, such as the realized rate of population growth. For example, releasing sterile males decreases fecundity, and hence, birth rates diminish in relation to death rates (Knipling, 1970). Similarly, introduction of predators with larger half-saturation constants changes the landscape of equilibria in favor of biological control (Drury & Lodge, 2008). In the presence of such factors, the required magnitude of direct pest reductions decreases, because the state of the outbreak system moves closer to the threshold between equilibria (e.g., moving leftwards on the top line in Fig. 1). Relatively smaller pest reductions can therefore move the state of the system across the threshold and into the biologically controlled basin of attraction. Here we quantify the effects of such species manipulations in terms of equilibrium resilience to demonstrate how targets can be computed so that pest control schedules can be devised (e.g., Drury, 2007). Our definition of resilience is the distance between an equilibrium and a threshold, which is one component of classical definitions describing the characteristic return time to equilibrium following perturbations (Pimm, 1984).

#### **2. The spruce budworm model**

The Spruce budworm model has a long history in ecological pest management (Ludwig et al., 1978) and has the advantage of being a relatively simply model that nevertheless contains components of integrated pest management while generating bistable dynamics (Strogatz,

The dynamics of Eq. (1) are often studied at equilibrium by first nondimensionalizing (see

Quantifying the Effects of Integrated Pest Management in Terms of Pest Equilibrium Resilience 79

This strategy has the advantage of specifying the left-hand side as a linear function of *x* that when equal to the right-hand side, specifies equilibria. It is thus a simple matter to plot the two sides of the equation on the same axes to see where they intersect (see the solid lines in Fig. 2). Furthermore, the effects of changes to *r* and *k* can be seen by fixing one and varying the other and evaluating the effects on equilibria values. This qualitative approach has obvious intuitive appeal and provides heuristic guidance for pest management. Nevertheless, as composite parameter groupings, *r* and *k* can be difficult to interpret in practice. Additionally, direct pest control measures often require specific objectives, e.g., assessment of pest populations in relation to some economic threshold (Pedgrigo & Zeiss, 1996), which can be difficult to extract from such qualitative exercises. Thus, we revisit Eq. (1) with the goal of developing quantitative methods for assessing the effects of integrated pest management in terms of relative equilibria positions, which in turn allows quantitative predictions of necessary direct

*r* 1 − *x k* = *x*

To analyze Eq. (1), we first recognize that at equilibrium it is a cubic in *X*,

−*X*3 + *KX*2 − *X*

**3.1 Solving for equilibrium pest values: The general case**

Consider the general monic cubic equation (i.e., with leading coefficient of 1),

*RX*

1 − *X K*

Following standard methods for solving cubic equations (Uspensky, 1948), Eq. (4) can be solved yielding three roots, *X*1, *X*2, and *X*3, corresponding to the three possible equilibrium states of the system. As management actions change parameter values, the values of these equilibria change and at bifurcation points even emerge or vanish (see, e.g., Fig. 1-2). Our approach allows assessment of the effects of integrated pest management on pest equilibrium values. Here, we use a simplified version of Cardano's method of solving cubics to evaluate the effects of management activities that alter the intensities of *R* and *B* and the magnitudes of *A* and *K*. We then apply this strategy to the spruce budworm model to solve for the three equilibria. The method is quite general, however, and could be used to solve any cubic model, which are common in pest control, because of the ubiquity of the type III functional response.

 *A*2 + − *BX*2

*f*(*x*) = *x*3 + *ax*2 + *bx* + *c* = 0. (5)

*KB R*

*A* , *r* = *AR*

*B* , *k* = *K*

*A* , and *τ* = *Bt*

1 + *x*2 . (2)

*A*2 + *X*2 = 0 (3)

+ *KA*2 = 0 (4)

*A* as nondimensional

e.g., Murray, 2002). For example, with *x* = *X*

pest control measures (such as spraying insecticides).

parameter groups this yields

**3. Model analysis**

Fig. 1. As the parameter *r* (from Eq. (2)) is increased above 0.3, two new equilibria emerge and the potential for outbreaks is introduced. At large values of *r*, only the outbreak equilibrium exists.

Table 1. Parameters of Eq. (1), their meanings, and representative values used in this study.

1994). Letting *X* represent pest population density, and using the parameters in Table 1, the model is,

$$\frac{dX}{dt} = RX\left(1 - \frac{X}{K}\right) - \frac{BX^2}{A^2 + X^2} \tag{1}$$

where the first term represents logistic growth and the second term represents a Holling type III functional response, which means that predators switch to the pest at some intermediate density, but nevertheless become sated at higher densities thus allowing the pest to escape control.

The dynamics of Eq. (1) are often studied at equilibrium by first nondimensionalizing (see e.g., Murray, 2002). For example, with *x* = *X A* , *r* = *AR B* , *k* = *K A* , and *τ* = *Bt A* as nondimensional parameter groups this yields

$$r\left(1-\frac{\mathbf{x}}{k}\right)=\frac{\mathbf{x}}{1+\mathbf{x}^2}.\tag{2}$$

This strategy has the advantage of specifying the left-hand side as a linear function of *x* that when equal to the right-hand side, specifies equilibria. It is thus a simple matter to plot the two sides of the equation on the same axes to see where they intersect (see the solid lines in Fig. 2). Furthermore, the effects of changes to *r* and *k* can be seen by fixing one and varying the other and evaluating the effects on equilibria values. This qualitative approach has obvious intuitive appeal and provides heuristic guidance for pest management. Nevertheless, as composite parameter groupings, *r* and *k* can be difficult to interpret in practice. Additionally, direct pest control measures often require specific objectives, e.g., assessment of pest populations in relation to some economic threshold (Pedgrigo & Zeiss, 1996), which can be difficult to extract from such qualitative exercises. Thus, we revisit Eq. (1) with the goal of developing quantitative methods for assessing the effects of integrated pest management in terms of relative equilibria positions, which in turn allows quantitative predictions of necessary direct pest control measures (such as spraying insecticides).

#### **3. Model analysis**

2 Will-be-set-by-IN-TECH

Outbreak Equilibrium x\*

Unstable Equilibrium x\*

Refuge Equilibrium x\*

Fig. 1. As the parameter *r* (from Eq. (2)) is increased above 0.3, two new equilibria emerge and the potential for outbreaks is introduced. At large values of *r*, only the outbreak

Parameter Meaning Value in this study *R* population growth rate 0.25 *K* carrying capacity 15 *B* maximal predator attack rate 0.75 *A*2 predator half-saturation constant 1 Table 1. Parameters of Eq. (1), their meanings, and representative values used in this study.

1994). Letting *X* represent pest population density, and using the parameters in Table 1, the

where the first term represents logistic growth and the second term represents a Holling type III functional response, which means that predators switch to the pest at some intermediate density, but nevertheless become sated at higher densities thus allowing the pest to escape

− *BX*2

*A*2 + *X*2 , (1)

 1 − *X K*  3

2

1

Outbreak Basin of Attraction

0.3 0.4 0.5 0.6

r

0

equilibrium exists.

model is,

control.

2

Refuge Basin of Attraction

*dX dt* = *RX*

4

6

x

\*

8

10

12

To analyze Eq. (1), we first recognize that at equilibrium it is a cubic in *X*,

$$RX\left(1-\frac{X}{K}\right)-\frac{BX^2}{A^2+X^2}=0\tag{3}$$

$$-X^3 + KX^2 - X\left(A^2 + \frac{KB}{R}\right) + KA^2 = 0\tag{4}$$

Following standard methods for solving cubic equations (Uspensky, 1948), Eq. (4) can be solved yielding three roots, *X*1, *X*2, and *X*3, corresponding to the three possible equilibrium states of the system. As management actions change parameter values, the values of these equilibria change and at bifurcation points even emerge or vanish (see, e.g., Fig. 1-2). Our approach allows assessment of the effects of integrated pest management on pest equilibrium values. Here, we use a simplified version of Cardano's method of solving cubics to evaluate the effects of management activities that alter the intensities of *R* and *B* and the magnitudes of *A* and *K*. We then apply this strategy to the spruce budworm model to solve for the three equilibria. The method is quite general, however, and could be used to solve any cubic model, which are common in pest control, because of the ubiquity of the type III functional response.

#### **3.1 Solving for equilibrium pest values: The general case**

Consider the general monic cubic equation (i.e., with leading coefficient of 1),

$$f(\mathbf{x}) = \mathbf{x}^3 + a\mathbf{x}^2 + b\mathbf{x} + c = \mathbf{0}.\tag{5}$$

Simplification is now possible because we can choose *k* in Eq. (10) so that,

to the origin and we can substitute it into the remaining equations to arrive at,

*f* � − *a* 3

*f* − *a* 3

*α* = *b* − *a*2

3 . Thus, in the sequel, we consider only Eq. (15).

After substituting *x* = *y* − *a*

where

*x* = *y* − *a*

is to take

or,

(8–11) respectively, Eq. (5) is transformed into,

introducing two new variables *u*, *v* such that,

It then follows from Eq. (18) that,

3*k* + *a* = 0, or *k* = − *a*

Quantifying the Effects of Integrated Pest Management in Terms of Pest Equilibrium Resilience 81

With *k* so defined (i.e., the so-called "Tschirnhaus transformation") our solution is translated

= *b* − *a*2

= *c* − *ba*

3 , *β* = *c* − *ab*

By a simple reverse transformation, any formula for the roots of Eq. (15) can be transformed into a formula for the roots of Eq. (5), i.e., by substituting Eq. (16) into Eq. (15) and using

Cubic equations of the form, Eq. (15), referred to as "depressed cubics", can be solved by

Upon substituting this expression into Eq. (15) we see that *u* and *v* must satisfy the equation,

which has two unknowns. Thus, the problem is indeterminant without another known relation between *u* and *v*, Eq. (17) being one. Another relation that is consistant with Eq. (18)

> *uv* = −*α* 3

> > *uv* = −*α* 3

Solving the cubic Eq. (15) can now be achieved by solving the system of two equations,

3 +

2*a*3

3 +

3 and equating coefficients of like powers of *y* and *k* in Eqs. (7) and

2*a*3

*u*3 + *v*3 + (*α* + 3*uv*)(*u* + *v*) + *β* = 0, (18)

*y*3 + *αy* + *β* = 0, (15)

*y* = *u* + *v*. (17)

*α* + 3*uv* = 0, (19)

*u*3 + *v*3 = −*β*, (21)

*u*3 + *v*3 = −*β*, (22)

. (20)

. (23)

3

. (12)

3 , (13)

27 (14)

27 . (16)

Fig. 2. The left-hand side of Eq. (2) (solid, straight line) crossing the curve described by the right-hand side in three places, correspondig to the three equilibria in Fig. (1), *x*1, *x*2, *x*3. The two dotted straight lines signify the effects of changing *r* on the existence and value of equilibria. The lower dotted line, representing small values of pest intrinsic growth rate, only crosses the curve at a relatively low pest population size; pest outbreaks are not possible. In contrast, the upper dotted line only crosses the curve at a relatively high pest population size; biological control is not possible.

Cardano's technique, dating to the first half of the sixteenth century, involves simplifying Eq, (5) so that it no longer contains the second power of *x*. This is achieved by introducing a new variable *y* and setting,

$$
\mathfrak{x} = \mathfrak{y} + \mathfrak{k},
\tag{6}
$$

where *k* is as yet an arbitrary constant.

To specify *k*, we begin by using Taylor's formula to approximate *f*(*y* + *k*) by

$$f(y+k) = f(k) + f'(k)y + \frac{f''(k)}{2}y^2 + \frac{f'''(k)}{6}y^3. \tag{7}$$

The terms on the right-hand side of Eq. (7) can be related to the terms of our monic cubic, Eq. (5) yielding,

$$f(k) = k^3 + ak^2 + bk + c,\tag{8}$$

$$f'(k) = 3k^2 + 2ak + b\_\prime \tag{9}$$

$$\frac{1}{2}f''(k) = 3k + a\_\prime\tag{10}$$

$$\frac{1}{6}f^{\prime\prime\prime}(k) = 1.\tag{11}$$

Simplification is now possible because we can choose *k* in Eq. (10) so that,

$$3k + a = 0, \qquad \text{or} \qquad k = -\frac{a}{3}. \tag{12}$$

With *k* so defined (i.e., the so-called "Tschirnhaus transformation") our solution is translated to the origin and we can substitute it into the remaining equations to arrive at,

$$f'\left(-\frac{a}{3}\right) = b - \frac{a^2}{3},\tag{13}$$

$$f\left(-\frac{a}{3}\right) = c - \frac{ba}{3} + \frac{2a^3}{27} \tag{14}$$

After substituting *x* = *y* − *a* 3 and equating coefficients of like powers of *y* and *k* in Eqs. (7) and (8–11) respectively, Eq. (5) is transformed into,

$$
\alpha y^3 + \alpha y + \beta = 0,\tag{15}
$$

where

4 Will-be-set-by-IN-TECH

Fig. 2. The left-hand side of Eq. (2) (solid, straight line) crossing the curve described by the right-hand side in three places, correspondig to the three equilibria in Fig. (1), *x*1, *x*2, *x*3. The two dotted straight lines signify the effects of changing *r* on the existence and value of equilibria. The lower dotted line, representing small values of pest intrinsic growth rate, only crosses the curve at a relatively low pest population size; pest outbreaks are not possible. In contrast, the upper dotted line only crosses the curve at a relatively high pest population

Cardano's technique, dating to the first half of the sixteenth century, involves simplifying Eq, (5) so that it no longer contains the second power of *x*. This is achieved by introducing a

(*k*)*y* + *f* (*k*)

The terms on the right-hand side of Eq. (7) can be related to the terms of our monic cubic,

To specify *k*, we begin by using Taylor's formula to approximate *f*(*y* + *k*) by

*f*

1

1

*f*(*y* + *k*) = *f*(*k*) + *f*

*x* = *y* + *k*, (6)

6 *y*3. (7)

2 *y*2 + *f* (*k*)

*f*(*k*) = *k*3 + *ak*2 + *bk* + *c*, (8)

(*k*) = 3*k*2 + 2*ak* + *b*, (9)

2 *f* (*k*) = 3*k* + *a*, (10)

6 *f* (*k*) = 1. (11)

size; biological control is not possible.

where *k* is as yet an arbitrary constant.

new variable *y* and setting,

Eq. (5) yielding,

$$
\alpha = b - \frac{a^2}{3}, \qquad \beta = c - \frac{ab}{3} + \frac{2a^3}{27}. \tag{16}
$$

By a simple reverse transformation, any formula for the roots of Eq. (15) can be transformed into a formula for the roots of Eq. (5), i.e., by substituting Eq. (16) into Eq. (15) and using *x* = *y* − *a* 3 . Thus, in the sequel, we consider only Eq. (15).

Cubic equations of the form, Eq. (15), referred to as "depressed cubics", can be solved by introducing two new variables *u*, *v* such that,

$$y = \mathfrak{u} + \mathfrak{v}.\tag{17}$$

Upon substituting this expression into Eq. (15) we see that *u* and *v* must satisfy the equation,

$$u^3 + v^3 + (u + 3uv)(u + v) + \beta = 0,\tag{18}$$

which has two unknowns. Thus, the problem is indeterminant without another known relation between *u* and *v*, Eq. (17) being one. Another relation that is consistant with Eq. (18) is to take

$$
\mathfrak{a} + \mathfrak{Z}u\upsilon = 0,\tag{19}
$$

or,

$$
\mu v = -\frac{\alpha}{3}.\tag{20}
$$

It then follows from Eq. (18) that,

$$u^3 + v^3 = -\beta,\tag{21}$$

Solving the cubic Eq. (15) can now be achieved by solving the system of two equations,

$$u^3 + v^3 = -\beta,\tag{22}$$

$$
\mu v = -\frac{\mathfrak{a}}{\mathfrak{3}}.\tag{23}
$$

Because it is convenient to know the sum and product of two unknown quantities, in this case *u*3 and *v*3, we take the cube of the latter equation yielding,

$$
\mu^3 v^3 = -\frac{a^3}{27}.\tag{24}
$$

The final step is to use the relation *xi* = *yi* − *a*

(after multiplying through by −1) or, more simply,

and to simplify the resulting equation, let,

*p*, *q*, and *r* into Eqs. (35–36) yielding,

= 3 *β* 2 −

**3.2 Solving for equilibrium pest values: The spruce budworm model**

*x*3 − *Kx*2 + (*A*2 +

*α* = 1 3

*β* = 1

 *β*2 4 + *α*3 27 + 3

Finally, back transforming the solution into terms of *x* we use the relation,

*xi* = *yi* − *p*

3

possible).

yielding,

where,

yielding,

equilibrium solutions to our original model. Note that at certain values of *α* and *β* a bifurcation occurs and two of the real solutions disappear to be replaced by imaginary solutions. In this study, we have used parameter values that yield three real solutions. In the case of a single real equilibrium the basin of attraction of the outbreak equilibrium either does not exist (i.e., outbreaks are not possible) or consists of the entire space (i.e., biological control is not

Quantifying the Effects of Integrated Pest Management in Terms of Pest Equilibrium Resilience 83

As we have seen, the spruce budworm model, Eq. (1), can be expanded to its cubic form

*q* = *A*2 +

Forming the depressed cubic requires removing the quadratic term from Eq. (38). To do so, let

*x* = *y* − *p* 3

We can now simply substitute our particular model parameters, embodied in *α* and *β*, through

 − *β* 2 +

*KB*

*KB*

3 where *i* ∈ {1, 2, 3} to return the three

*R* )*x* − *KA*2 = 0, (37)

*R* , (40)

, (42)

27 . (47)

*x*3 + *px*2 + *qx* + *r* = 0, (38)

*p* = −*K*, (39)

*r* = −*KA*2. (41)

(3*q* − *p*2), (43)

27 (2*p*3 − 9*pq* + 27*r*), (44)

*y*3 + *αy* + *β* = 0. (45)

, *i* ∈ {1, 2, 3}. (48)

*y* = *u* + *v*, (46)

 *β*2 4 + *α*3

The reason these quantities are convenient is that upon solving one in terms of the other and substituting, a single quadratic equation results. To see this rearrange Eq. (22) as follows,

$$u^3 = -(v^3 + \beta),\tag{25}$$

and substitute this expression for *u*3 into Eq. (24) giving,

$$-(v^3 + \beta)v^3 = \frac{-a^3}{27}.\tag{26}$$

We next expand the left-hand side of Eq. (26) giving,

$$-v^{6} - \beta v^{3} = \frac{-a^{3}}{27} \, , \tag{27}$$

or, substituting *t* = *v*3 and dividing through by (−1),

$$t^2 + \beta t - \frac{\alpha^3}{27} = 0.\tag{28}$$

Thus, apparently, *u*3 and *v*3 can be computed from the roots of Eq. (28). Equating *v*3 to the positive root that results from applying the quadratic formula, we have,

$$v^3 = -\frac{\beta}{2} + \sqrt{\frac{\beta^2}{4} + \frac{\alpha^3}{27}} \tag{29}$$

or

$$v = \sqrt[3]{-\frac{\beta}{2} + \sqrt{\frac{\beta^2}{4} + \frac{\alpha^3}{27}}}.\tag{30}$$

Now we take advantage of the relationship in Eq. (25) to solve for *u* yielding,

$$
\mu^3 = -(v^3 + \beta),
\underbrace{\qquad}\_{\text{---}} \qquad ,. \tag{31}
$$

$$\hat{\rho} = -\left(-\frac{\beta}{2} + \sqrt{\frac{\beta^2}{4} + \frac{\alpha^3}{27}} + \beta\right). \tag{32}$$

$$\hat{\theta} = \frac{\beta}{2} - \sqrt{\frac{\beta^2}{4} + \frac{a^3}{27}} \quad \text{or,} \tag{33}$$

$$
\mu = \sqrt[3]{\frac{\beta}{2} - \sqrt{\frac{\beta^2}{4} + \frac{\alpha^3}{27}}}.\tag{34}
$$

We can now write an expression for *y*, which was set equal to *u* + *v* in Eq. (17) yielding,

$$y = u + v\_{\prime} \tag{35}$$

$$
\hat{\rho} = \sqrt[3]{\frac{\beta}{2} - \sqrt{\frac{\beta^2}{4} + \frac{\alpha^3}{27}}} + \sqrt[3]{-\frac{\beta}{2} + \sqrt{\frac{\beta^2}{4} + \frac{\alpha^3}{27}}}.\tag{36}
$$

The final step is to use the relation *xi* = *yi* − *a* 3 where *i* ∈ {1, 2, 3} to return the three equilibrium solutions to our original model. Note that at certain values of *α* and *β* a bifurcation occurs and two of the real solutions disappear to be replaced by imaginary solutions. In this study, we have used parameter values that yield three real solutions. In the case of a single real equilibrium the basin of attraction of the outbreak equilibrium either does not exist (i.e., outbreaks are not possible) or consists of the entire space (i.e., biological control is not possible).

#### **3.2 Solving for equilibrium pest values: The spruce budworm model**

As we have seen, the spruce budworm model, Eq. (1), can be expanded to its cubic form yielding,

$$(\mathbf{x}^3 - \mathbf{K}\mathbf{x}^2 + (A^2 + \frac{\mathbf{KB}}{R})\mathbf{x} - \mathbf{K}A^2 = \mathbf{0},\tag{37}$$

(after multiplying through by −1) or, more simply,

$$\mathbf{x}^3 + p\mathbf{x}^2 + q\mathbf{x} + r = \mathbf{0},\tag{38}$$

where,

6 Will-be-set-by-IN-TECH

Because it is convenient to know the sum and product of two unknown quantities, in this case

*u*3*v*3 = −*α*3

The reason these quantities are convenient is that upon solving one in terms of the other and substituting, a single quadratic equation results. To see this rearrange Eq. (22) as follows,

− (*v*3 + *β*)*v*3 = −*α*3

− *v*6 − *βv*3 = −*α*3

2 + *βt* − *α*3

2 +

Thus, apparently, *u*3 and *v*3 can be computed from the roots of Eq. (28). Equating *v*3 to the

 *β*2 4 + *α*3

 *β*2 4 + *α*3

 *β*2 4 + *α*3 27 + *β*

*u*3 = −(*v*3 + *β*), (31)

*y* = *u* + *v*, (35)

 *β*2 4 + *α*3

*t*

*v*3 = − *β*

*v* = 3 − *β* 2 +

= − − *β* 2 +

= *β* 2 −

*u* = 3 *β* 2 −

> *β*2 4 + *α*3 27 + 3

= 3 *β* 2 −

Now we take advantage of the relationship in Eq. (25) to solve for *u* yielding,

 *β*2 4 + *α*3

> *β*2 4 + *α*3

We can now write an expression for *y*, which was set equal to *u* + *v* in Eq. (17) yielding,

 − *β* 2 +

positive root that results from applying the quadratic formula, we have,

27 . (24)

27 . (26)

27 , (27)

27 = 0. (28)

27 , (29)

27 . (30)

27 , or, (33)

27 . (34)

, (32)

27 . (36)

*u*3 = −(*v*3 + *β*), (25)

*u*3 and *v*3, we take the cube of the latter equation yielding,

and substitute this expression for *u*3 into Eq. (24) giving,

We next expand the left-hand side of Eq. (26) giving,

or, substituting *t* = *v*3 and dividing through by (−1),

or

$$p = -K\_{\prime} \tag{39}$$

$$q = A^2 + \frac{KB}{R},\tag{40}$$

$$
\sigma = -KA^2.\tag{41}
$$

Forming the depressed cubic requires removing the quadratic term from Eq. (38). To do so, let

$$\mathbf{x} = \mathbf{y} - \frac{\mathbf{p}}{\mathbf{3}}\mathbf{\prime}\tag{42}$$

and to simplify the resulting equation, let,

$$\alpha = \frac{1}{3}(3q - p^2),$$

$$\beta = \frac{1}{27}(2p^3 - 9pq + 27r),\tag{44}$$

yielding,

$$
\mu y^3 + \alpha y + \beta = 0.\tag{45}
$$

We can now simply substitute our particular model parameters, embodied in *α* and *β*, through *p*, *q*, and *r* into Eqs. (35–36) yielding,

$$y = u + v,\tag{46}$$

$$=\sqrt[3]{\frac{\beta}{2}-\sqrt{\frac{\beta^2}{4}+\frac{\alpha^3}{27}}}+\sqrt[3]{-\frac{\beta}{2}+\sqrt{\frac{\beta^2}{4}+\frac{\alpha^3}{27}}}.\tag{47}$$

Finally, back transforming the solution into terms of *x* we use the relation,

$$x\_i = y\_i - \frac{p}{3}, \qquad i \in \{1, 2, 3\}. \tag{48}$$

K : 12 16

Quantifying the Effects of Integrated Pest Management in Terms of Pest Equilibrium Resilience 85

Fig. 4. Shaded contours of the magnitude of the basin of attraction of the outbreak equilibrium as carrying capacity *K* and the square root of half-saturation constant *A* are changed. Scale is normalized from 0 to 1 such that darker colors represent smaller, and

2 4 6 8 10 12 x

Fig. 5. Effect of changing *A* in the spruce budworm model. Increases in *A* generate increases in the magnitude of the biologically controlled basin of attraction by decreasing the value of

Figure 6 shows that increasing *B* lowers the entire *dx*/*dt* curve. In contrast, increasing *R* raises the curve, which also has an intuitive effect, because increased growth rate is expected to favor outbreaks. As Fig. 7 shows, increasing *R* increases the magnitude of the outbreak

The net sum effects of changes in *R* and *B* are depicted in Fig. 8, which shows that lower attack rates and higher pest growth rates both increase the magnitude of the outbreak basin of attraction. In contrast, high attack rates and low growth rates decrease that basin and increase

We have undertaken an analysis of a simple model of pest population dynamics with the goal of understanding the role of specific, commonly encountered model parameters on the

the lower equilibrium and increasing the value of the unstable, middle equilibrium.

basin of attraction at the expense of the biologically controlled basin of attraction.

0

0.25 A

A

1

A: 0.8 1.2

lighter colors larger, basins of attraction.

the biologically controlled basin of attraction.

100

**5. Discussion**

50

50

dx dt

## **4. Results**

Having explicitely solved the cubic equation, we can now evaluate the effects of parameter manipulation on equilibrium resilience. For example, Fig. 3 shows that the magnitude of the outbreak basin of attraction changes relatively rapidly with increasing *K* and *R*. It is not possible to evaluate the relative effects of changes to each using this graph because of the different scales and units. Nevertheless, it is apparent that these two familiar parameters both influence the magnitude of the outbreak basin of attraction.

R : 0.22 0.4

Fig. 3. Shaded contours of the magnitude of the basin of attraction of the outbreak equilibrium as intrinsic growth rate *R* and carrying capacity *K* are changed. Scale is normalized from 0 to 1 such that darker colors represent smaller, and lighter colors larger, basins of attraction. Thus, as expected, large values of *R* and *K* both yield large potential for outbreaks.

When the square root of the predator's half-saturation constant *A* is varied, its effects can be compared to the large changes caused by changes in *K* because they both have units of individuals (Fig. 4). The roughly vertical contours indicate that changes to carrying capacity have a much greater effect than changes to *A*, at least over ranges of each that yield bistability. Note that the slight concave down character of these contours indicates that changes in resilience following changes in *A* are slightly more likely at high values of *K*. The reason for such changes to equilibium resilience can be seen in Fig. (5). Specifically, as the predator half-saturation constant *A*2 increases, the *dx dt* curve is shifted downwards, increasing the magnitude of the biologically controlled basin of attraction at the expense of the outbreak basin.

Finally, changes to the maximal predator attack rate have a large effect on the resilience of the both the biologically controlled equilibrium and the outbreak equilibrium. Specifically, increasing *B*, the maximal predator attack rate *increases* the magnitude of the biologically controlled equilibrium. This matches our intuition, because higher attack rates presumably favor the predator, not the pest. To analyze the effects of changing *B*, we used both a higher value of *B* and a lower value and inspect the effects on the magnitudes of each basin of attraction.

Fig. 4. Shaded contours of the magnitude of the basin of attraction of the outbreak equilibrium as carrying capacity *K* and the square root of half-saturation constant *A* are changed. Scale is normalized from 0 to 1 such that darker colors represent smaller, and lighter colors larger, basins of attraction.

Fig. 5. Effect of changing *A* in the spruce budworm model. Increases in *A* generate increases in the magnitude of the biologically controlled basin of attraction by decreasing the value of the lower equilibrium and increasing the value of the unstable, middle equilibrium.

Figure 6 shows that increasing *B* lowers the entire *dx*/*dt* curve. In contrast, increasing *R* raises the curve, which also has an intuitive effect, because increased growth rate is expected to favor outbreaks. As Fig. 7 shows, increasing *R* increases the magnitude of the outbreak basin of attraction at the expense of the biologically controlled basin of attraction.

The net sum effects of changes in *R* and *B* are depicted in Fig. 8, which shows that lower attack rates and higher pest growth rates both increase the magnitude of the outbreak basin of attraction. In contrast, high attack rates and low growth rates decrease that basin and increase the biologically controlled basin of attraction.

#### **5. Discussion**

8 Will-be-set-by-IN-TECH

Having explicitely solved the cubic equation, we can now evaluate the effects of parameter manipulation on equilibrium resilience. For example, Fig. 3 shows that the magnitude of the outbreak basin of attraction changes relatively rapidly with increasing *K* and *R*. It is not possible to evaluate the relative effects of changes to each using this graph because of the different scales and units. Nevertheless, it is apparent that these two familiar parameters both

R : 0.22 0.4

When the square root of the predator's half-saturation constant *A* is varied, its effects can be compared to the large changes caused by changes in *K* because they both have units of individuals (Fig. 4). The roughly vertical contours indicate that changes to carrying capacity have a much greater effect than changes to *A*, at least over ranges of each that yield bistability. Note that the slight concave down character of these contours indicates that changes in resilience following changes in *A* are slightly more likely at high values of *K*. The reason for such changes to equilibium resilience can be seen in Fig. (5). Specifically, as the predator

magnitude of the biologically controlled basin of attraction at the expense of the outbreak

Finally, changes to the maximal predator attack rate have a large effect on the resilience of the both the biologically controlled equilibrium and the outbreak equilibrium. Specifically, increasing *B*, the maximal predator attack rate *increases* the magnitude of the biologically controlled equilibrium. This matches our intuition, because higher attack rates presumably favor the predator, not the pest. To analyze the effects of changing *B*, we used both a higher value of *B* and a lower value and inspect the effects on the magnitudes of each basin of

Fig. 3. Shaded contours of the magnitude of the basin of attraction of the outbreak equilibrium as intrinsic growth rate *R* and carrying capacity *K* are changed. Scale is normalized from 0 to 1 such that darker colors represent smaller, and lighter colors larger, basins of attraction. Thus, as expected, large values of *R* and *K* both yield large potential for

0

*dt* curve is shifted downwards, increasing the

1

influence the magnitude of the outbreak basin of attraction.

K :14 17

half-saturation constant *A*2 increases, the *dx*

**4. Results**

outbreaks.

basin.

attraction.

We have undertaken an analysis of a simple model of pest population dynamics with the goal of understanding the role of specific, commonly encountered model parameters on the

R : 0.2 0.4

Quantifying the Effects of Integrated Pest Management in Terms of Pest Equilibrium Resilience 87

The effects of pest population growth rate are two-fold. First, increasing pest growth rate increases the outbreak basin of attraction, an intuitive result, because higher pest population growth rates are expected to favor outbreaks. At the same time, increasing *r* decreases the magnitude of the biologically controlled basin of attraction, making escape from predator

The magnitude of the outbreak basin of attraction decreases with increasing half-saturation constant. This constant is comprised of (attack rate × handling time)−1 (Gotelli, 1995). Thus, the effects of *A*2 depend on the relationship between these two biological parameters. In our analysis, for simplicity we used *A* = 1. Increases in the half-saturation constant mean that the predators require higher densities of prey in order to begin being sated. Thus, they can consume more prey before the saturating effects of the functional response take effect.

The maximum feeding rate *B* also has a large and intuitive effect on equilibrium resilience. Because this feeding rate is the inverse of handling time (Gotelli, 1995) introducing natural predators with shorter handling times generates meaningful decreases in the magnitude of the outbreak basin of attraction. Additionally, Kidd & Amarasekare (2011) demonstrated that predators with shorter handling times induce weak transient dynamics of short duration relative to predators with longer handling times. Thus, according to their analysis, when predators with short handling times are introduced (i.e., those with larger maximum feeding rates), large oscillations in the prey population are less likely. According to our analysis, when predators with short handling times and correspondingly higher maximal attack rates are

Using the approach we outline here, schedules for integrated pest management can be developed based on knowledge of specific equilibrium values. For example, solving the model for *x*1, *x*2, and *x*3 allows one to compute the needed change in density to change a system from the outbreak state to the biologically controlled state. Furthermore, our approach permits evaluation of the effects of individual components of integrated pest management on

Fig. 8. Shaded contours of the magnitude of the basin of attraction of the biologically controlled equilibrium as intrinsic growth rate *R* and maximal attack rate *B* are changed. Scale is normalized from 0 to 1 such that darker colors represent smaller, and lighter colors larger, basins of attraction. At large values of *R* and small values of *B* the outbreak basin

dominates, while at high values of *B* and low values of *R*, it goes to zero.

0

1

B : 0.55 0.75

control more feasible.

released, outbreaks become less likely.

Fig. 6. The rate of change in pest population growth rate versus pest population size according to the spruce budworm model. As the maximal predator attack rate *B* increases, the magnitude of the biologically controlled basin of attraction increases, as evidenced by the reduced distance between the two right-most points where *dn*/*dt* = 0. In contrast, when *B* is dereased, the magnitude of the outbreak equilibrium is increased.

Fig. 7. The rate of change in pest population growth rate versus pest population size according to the spruce budworm model. As *R* is increased, the curve, *dx*/*dt* shifts upward bringing the two left-most equilibrium points where *dx*/*dt* = 0 closer together. Thus, the basin of attraction of the biologically controlled equilibrium decreases.

resilience of equilibria. Furthermore, the parameters we have studied are those typically affected by integrated pest management. We defined equilibrium reslience as the distance between the stable equilibria and the unstable equilibrium, which marks the threshold betweem the basins of attraction for the biologically-controlled, and outbreak equilibria.

Our results show that the pest carrying capacity has a large effect on the resilience of the outbreak equilibrium. Specifically, changes in *K* generate large changes in the magnitude of the outbreak basin of attraction. In terms of integrated pest management, this means that pest control strategies that decrease the pest's carrying capacity, such as intercropping to break up monocultures, move the outbreak equilibrium closer to the threshold of the biologically controlled equilibrium. Thus, smaller culling events, or smaller releases of enemies, achieve the desired shift to the biologically controlled equilibrium than otherwise.

10 Will-be-set-by-IN-TECH

1.1 B

2 R

R

B

0.9 B

2 4 6 8 10 12 x

Fig. 6. The rate of change in pest population growth rate versus pest population size according to the spruce budworm model. As the maximal predator attack rate *B* increases, the magnitude of the biologically controlled basin of attraction increases, as evidenced by the reduced distance between the two right-most points where *dn*/*dt* = 0. In contrast, when *B* is

2 4 6 8 10 x

Fig. 7. The rate of change in pest population growth rate versus pest population size according to the spruce budworm model. As *R* is increased, the curve, *dx*/*dt* shifts upward bringing the two left-most equilibrium points where *dx*/*dt* = 0 closer together. Thus, the

resilience of equilibria. Furthermore, the parameters we have studied are those typically affected by integrated pest management. We defined equilibrium reslience as the distance between the stable equilibria and the unstable equilibrium, which marks the threshold betweem the basins of attraction for the biologically-controlled, and outbreak equilibria.

Our results show that the pest carrying capacity has a large effect on the resilience of the outbreak equilibrium. Specifically, changes in *K* generate large changes in the magnitude of the outbreak basin of attraction. In terms of integrated pest management, this means that pest control strategies that decrease the pest's carrying capacity, such as intercropping to break up monocultures, move the outbreak equilibrium closer to the threshold of the biologically controlled equilibrium. Thus, smaller culling events, or smaller releases of enemies, achieve

basin of attraction of the biologically controlled equilibrium decreases.

the desired shift to the biologically controlled equilibrium than otherwise.

dereased, the magnitude of the outbreak equilibrium is increased.

150 100 50

50

50

100

dx dt

50 100

dx dt

Fig. 8. Shaded contours of the magnitude of the basin of attraction of the biologically controlled equilibrium as intrinsic growth rate *R* and maximal attack rate *B* are changed. Scale is normalized from 0 to 1 such that darker colors represent smaller, and lighter colors larger, basins of attraction. At large values of *R* and small values of *B* the outbreak basin dominates, while at high values of *B* and low values of *R*, it goes to zero.

R : 0.2 0.4

The effects of pest population growth rate are two-fold. First, increasing pest growth rate increases the outbreak basin of attraction, an intuitive result, because higher pest population growth rates are expected to favor outbreaks. At the same time, increasing *r* decreases the magnitude of the biologically controlled basin of attraction, making escape from predator control more feasible.

The magnitude of the outbreak basin of attraction decreases with increasing half-saturation constant. This constant is comprised of (attack rate × handling time)−1 (Gotelli, 1995). Thus, the effects of *A*2 depend on the relationship between these two biological parameters. In our analysis, for simplicity we used *A* = 1. Increases in the half-saturation constant mean that the predators require higher densities of prey in order to begin being sated. Thus, they can consume more prey before the saturating effects of the functional response take effect.

The maximum feeding rate *B* also has a large and intuitive effect on equilibrium resilience. Because this feeding rate is the inverse of handling time (Gotelli, 1995) introducing natural predators with shorter handling times generates meaningful decreases in the magnitude of the outbreak basin of attraction. Additionally, Kidd & Amarasekare (2011) demonstrated that predators with shorter handling times induce weak transient dynamics of short duration relative to predators with longer handling times. Thus, according to their analysis, when predators with short handling times are introduced (i.e., those with larger maximum feeding rates), large oscillations in the prey population are less likely. According to our analysis, when predators with short handling times and correspondingly higher maximal attack rates are released, outbreaks become less likely.

Using the approach we outline here, schedules for integrated pest management can be developed based on knowledge of specific equilibrium values. For example, solving the model for *x*1, *x*2, and *x*3 allows one to compute the needed change in density to change a system from the outbreak state to the biologically controlled state. Furthermore, our approach permits evaluation of the effects of individual components of integrated pest management on

**5**

*USA*

**Manipulation of Natural Enemies in**

**for Sustainable Insect Pest Control**

Cesar Rodriguez-Saona1, Brett R. Blaauw2 and Rufus Isaacs2 *1Department of Entomology, Rutgers University, New Brunswick 2Department of Entomology, Michigan State University, East Lansing*

**Agroecosystems: Habitat and Semiochemicals**

Plants are not capable of running away from their enemies, i.e., the herbivores that may eat them. However, under certain circumstances, plants can rely on the natural enemies of insect herbivores for protection. These natural enemies include other insects that are predators and parasitoids. To help protect plants from damage caused by insect herbivores, practical methods have been developed and evaluated to conserve and augment natural enemies of several agricultural pests. These strategies include improving the suitability of the crop landscape for natural enemies by manipulating the resources available for these insects, and the use of semiochemicals to attract predators and parasitoids. This chapter will review recent studies exploring the potential for manipulating the behavior of natural enemies through vegetational diversification of crop habitats and the use of semiochemicals to enhance biological control in agroecosystems, and we will discuss how these might be

Increasing the diversity within crops is predicted to provide a greater number of opportunities for natural enemies to survive in agricultural systems (Fig. 1). Thus, pest outbreaks tend to be less common in polycultures than in monocultures (Root, 1973; Andow, 1991). Crop diversification tends to increase natural enemy abundance and diversity, providing a system more resilient to pest population increase. Overall farming diversity within the agroecosystem may also affect biological control by natural enemies, due in part to a wider range of flowering plants that provide nectar (carbohydrate) and pollen (protein) resources to insects during more times of the growing season. Vegetational diversity can also provide support for insect biological control at the local and landscape levels (Thies et al., 2003; Roschewitz et al., 2005; Bianchi et al., 2006; Gardiner et al., 2009). Farmers can make some simple changes to their crop systems to manipulate vegetational diversity, through addition of plants that provide specific functions (Landis et al., 2000; Gurr et al. 2003; Isaacs et al., 2009). Below, we provide an overview of those methods and describe

**1. Introduction**

combined to improve crop protection.

situations where such changes have reduced pest infestation.

**2. Vegetational diversity**

distance to the threshold between equilibria. Thus, the effects of strategies such as release of sterile males and enhancement of natural predators can be understood in terms of decreased culling requirements.

Acknowledgements: Professor Adam Hammett kindly read the manuscript and made useful comments that improved clarity, especially in the final steps of the solution of the cubic.

#### **6. References**

## **Manipulation of Natural Enemies in Agroecosystems: Habitat and Semiochemicals for Sustainable Insect Pest Control**

Cesar Rodriguez-Saona1, Brett R. Blaauw2 and Rufus Isaacs2 *1Department of Entomology, Rutgers University, New Brunswick 2Department of Entomology, Michigan State University, East Lansing USA*

## **1. Introduction**

12 Will-be-set-by-IN-TECH

88 Integrated Pest Management and Pest Control – Current and Future Tactics

distance to the threshold between equilibria. Thus, the effects of strategies such as release of sterile males and enhancement of natural predators can be understood in terms of decreased

Acknowledgements: Professor Adam Hammett kindly read the manuscript and made useful comments that improved clarity, especially in the final steps of the solution of the cubic.

Abramowitz, M. & Stegun, I. A. (1964). *Handbook of Mathematical Functions*, National Bureau

Drury, K. L. S. (2007). Shot noise perturbations and mean first passage times between stable

Drury, K. L. S. & Lodge, D. M. (2008). Using mean first passage times to quantify equilibrium resilience in perturbed intraguild predation systems, *Theoretical Ecology* 2(1): 41–51.

Kidd, D., P. Amarasekare. (2011). The role of transient dynamics in biological pest control: insights from a host-parasitoid community, *Journal of Animal Ecology* (1365-2656). Knipling, E. (1970). Suppression of pest lepidoptera by releasing partially sterile males, a

Ludwig, D., Jones, D. D. & Holling, C. S. (1978). Qualitative analysis of insect outbreak

Pedgrigo, L. & Zeiss, M. (1996). *Analyses in Insect Ecology and Management*, Iowa State

Scheffer, M. & Carpenter, S. (2003)Catastrophic regime shifts in ecosystems: linking theory to

Strogatz, S. H. (1994). *Nonlinear Dynamics and Chaos with Applications to Physics, Biology, Chemistry, and Engineering*, Addison-Wesley Publishing Co., Reading, MA.

systems: the spruce budworm and forest, *J. Anim. Ecol.* 47: 315–332.

Pimm, S. L. (1984). The complexity and stability of ecosystems, *Nature* 307: 321–326.

Gotelli, N. (1995). *A primer of ecology*, Sinauer Associates, Inc, Sunderland, MA.

states, *Theoretical Population Biology* 72: 153–166.

theoretical approach, *BioScience* 20(8): 465–470.

observation, *Trends Ecol. Evol.* 18(12): 648–656.

Uspensky, J. (1948). *Theory of Equations*, McGraw Hill.

Murray, J. D. (2002). *Mathematical Biology*, Springer-Verlag.

University Press, Ames.

culling requirements.

of Standards.

**6. References**

Plants are not capable of running away from their enemies, i.e., the herbivores that may eat them. However, under certain circumstances, plants can rely on the natural enemies of insect herbivores for protection. These natural enemies include other insects that are predators and parasitoids. To help protect plants from damage caused by insect herbivores, practical methods have been developed and evaluated to conserve and augment natural enemies of several agricultural pests. These strategies include improving the suitability of the crop landscape for natural enemies by manipulating the resources available for these insects, and the use of semiochemicals to attract predators and parasitoids. This chapter will review recent studies exploring the potential for manipulating the behavior of natural enemies through vegetational diversification of crop habitats and the use of semiochemicals to enhance biological control in agroecosystems, and we will discuss how these might be combined to improve crop protection.

## **2. Vegetational diversity**

Increasing the diversity within crops is predicted to provide a greater number of opportunities for natural enemies to survive in agricultural systems (Fig. 1). Thus, pest outbreaks tend to be less common in polycultures than in monocultures (Root, 1973; Andow, 1991). Crop diversification tends to increase natural enemy abundance and diversity, providing a system more resilient to pest population increase. Overall farming diversity within the agroecosystem may also affect biological control by natural enemies, due in part to a wider range of flowering plants that provide nectar (carbohydrate) and pollen (protein) resources to insects during more times of the growing season. Vegetational diversity can also provide support for insect biological control at the local and landscape levels (Thies et al., 2003; Roschewitz et al., 2005; Bianchi et al., 2006; Gardiner et al., 2009). Farmers can make some simple changes to their crop systems to manipulate vegetational diversity, through addition of plants that provide specific functions (Landis et al., 2000; Gurr et al. 2003; Isaacs et al., 2009). Below, we provide an overview of those methods and describe situations where such changes have reduced pest infestation.

Manipulation of Natural Enemies in Agroecosystems:

**2.1.1 Push-pull strategy**

Pickett, 2004).

Habitat and Semiochemicals for Sustainable Insect Pest Control 91

Intercropping, which is the cultivation of two or more species within the same field, is a common method to increase beneficial insect diversity within agroecosystems (Fig. 1B) (Vandermeer, 1989; Theunissen, 1994). Intercropping crop plants with flowering species such as clovers can also provide a favorable habitat for a variety of beneficial insects that may not otherwise survive in a single crop environment, and hence intercropping may provide natural pest management by increasing the abundance and diversity of insect natural enemies in the agroecosystem (Theunissen, 1994). Diverse systems encourage complex food webs that involve more interactions among vegetation, pests, and natural enemies, providing resources for a diverse group of organisms and allowing for alternative resources and food sources. Thus, polycultures and natural ecosystems with higher diversity tend to be more stable and less subject to fluctuations in pest and disease populations (Altieri & Nicholls, 2004). As an example of this, Beizhou et al. (2011) recently reported that intercropping pear orchards with aromatic plants significantly reduced pest abundance and increased the ratio of natural enemies to pests when compared to orchards with only natural grass or clean tillage. They also found higher abundance of natural enemies and reduced numbers of major pests in intercropped orchards. Hence, intercropping the pear orchard with aromatic plants led to improved insect pest

management by enhancing the activity of the insect natural enemy community.

The 'push-pull' strategy uses a combination of stimuli to manipulate the behavior of insect pests and/or natural enemies and to alter their distribution and abundance in agroecosystems (Miller & Cowles, 1990; Khan et al., 1997). The push-pull approach works by repelling or deterring the pest insects (push) away from the main crop by using deterring chemical stimuli. Simultaneously, highly appealing stimuli are used to attract the pests (pull) from the main crop to other areas such as trap crops where the pests aggregate and are easier to control (Khan et al., 1997). While on their own each individual component (the push and the pull) of the approach may not be effective at reducing pest numbers below economic thresholds, combining the push and pull components increases the efficacy of such a strategy. Also, since the push and pull components are commonly non-toxic this strategy is compatible with supporting insect natural enemies and biological control (Khan & Pickett, 2004). A suitable push-pull strategy will be unique for each system it is used for, and hence the development of effective push-pull approaches requires an understanding of the targeted pest's biology and interactions with its hosts and natural enemies (Khan &

Push-pull strategies are not a new idea, but one of the most successful examples was developed recently in Africa for controlling stem borers on cereal crops (Khan et al., 2001).  The adult stem borer moths are cryptic and nocturnal while the larvae feed within the crop stem making both adults and larvae difficult to see and to control. Chemical pesticides are a common method of control for stem borers, but this is not an economical or safe approach for many resource-poor, small-scale farms where these pests are common. Hence, a push-pull strategy was developed using technologies appropriate and economical for such farmers (Khan & Pickett, 2004). This strategy involves a combined use of intercropping and trap crops, and uses plants that are also locally-appropriate and that can be used in their agricultural system. While some push-pull

Fig. 1. Strategies for manipulating natural enemies in agroecosystems for enhanced insect pest control. A.) Conventional method with no manipulation of habitat leads to high pest numbers and few natural enemies entering the crop from the surrounding landscapes. Yellow circles represent natural enemy populations and green circles represent pest abundance. B.) The addition of inter-cropping, cover crops, or supplemental food sources to an agroecosystem may lead to an increase in natural enemy abundance and a potential decrease in insect pest abundance within field settings, but relies on the presence of insect pests to attract natural enemies into the crop. C.) The addition of semiochemical-based lures, such as herbivore-induced plant volatiles (HIPVs) may attract natural enemies into the crop to enhance biological control, but does not provide resources to directly enhance natural enemy abundance. HIPV lures are represented by pink circles. D.) Combining habitat manipulation and HIPV strategies in an agroecosystem may increase natural enemy abundance within field settings as well as directly attract natural enemies into the crop to enhance biological control.

## **2.1 Inter-cropping, monocultures, polycultures**

The response of beneficial insect populations to habitat manipulation depends upon their ability to use or exploit one or more of the plant components of the agroecosystem (Altieri & Nicholls, 2004). Crop systems that are dominated by a single plant species only provide resources to those select organisms that can exploit that single plant species. Hence, monocultures are an example of agroecosystems with low diversity and may be more susceptible to pest or disease outbreaks (Theunissen, 1994; Altieri & Nicholls, 2004). Because of this increased susceptibility, management and external inputs are essential to support low diversity agroecosystems. On the other hand, reliance on diverse plantings, a range of natural enemies that are supported by these plants, and associated crop management strategies can in some cases help maintain pest populations below economic thresholds (Altieri & Nicholls, 2004).

Intercropping, which is the cultivation of two or more species within the same field, is a common method to increase beneficial insect diversity within agroecosystems (Fig. 1B) (Vandermeer, 1989; Theunissen, 1994). Intercropping crop plants with flowering species such as clovers can also provide a favorable habitat for a variety of beneficial insects that may not otherwise survive in a single crop environment, and hence intercropping may provide natural pest management by increasing the abundance and diversity of insect natural enemies in the agroecosystem (Theunissen, 1994). Diverse systems encourage complex food webs that involve more interactions among vegetation, pests, and natural enemies, providing resources for a diverse group of organisms and allowing for alternative resources and food sources. Thus, polycultures and natural ecosystems with higher diversity tend to be more stable and less subject to fluctuations in pest and disease populations (Altieri & Nicholls, 2004). As an example of this, Beizhou et al. (2011) recently reported that intercropping pear orchards with aromatic plants significantly reduced pest abundance and increased the ratio of natural enemies to pests when compared to orchards with only natural grass or clean tillage. They also found higher abundance of natural enemies and reduced numbers of major pests in intercropped orchards. Hence, intercropping the pear orchard with aromatic plants led to improved insect pest management by enhancing the activity of the insect natural enemy community.

## **2.1.1 Push-pull strategy**

90 Integrated Pest Management and Pest Control – Current and Future Tactics

Fig. 1. Strategies for manipulating natural enemies in agroecosystems for enhanced insect pest control. A.) Conventional method with no manipulation of habitat leads to high pest numbers and few natural enemies entering the crop from the surrounding landscapes. Yellow circles represent natural enemy populations and green circles represent pest

abundance. B.) The addition of inter-cropping, cover crops, or supplemental food sources to an agroecosystem may lead to an increase in natural enemy abundance and a potential decrease in insect pest abundance within field settings, but relies on the presence of insect pests to attract natural enemies into the crop. C.) The addition of semiochemical-based lures, such as herbivore-induced plant volatiles (HIPVs) may attract natural enemies into the crop to enhance biological control, but does not provide resources to directly enhance natural enemy abundance. HIPV lures are represented by pink circles. D.) Combining habitat manipulation and HIPV strategies in an agroecosystem may increase natural enemy abundance within field settings as well as directly attract natural enemies into the crop to

The response of beneficial insect populations to habitat manipulation depends upon their ability to use or exploit one or more of the plant components of the agroecosystem (Altieri & Nicholls, 2004). Crop systems that are dominated by a single plant species only provide resources to those select organisms that can exploit that single plant species. Hence, monocultures are an example of agroecosystems with low diversity and may be more susceptible to pest or disease outbreaks (Theunissen, 1994; Altieri & Nicholls, 2004). Because of this increased susceptibility, management and external inputs are essential to support low diversity agroecosystems. On the other hand, reliance on diverse plantings, a range of natural enemies that are supported by these plants, and associated crop management strategies can in some cases help maintain pest populations below economic thresholds

enhance biological control.

(Altieri & Nicholls, 2004).

**2.1 Inter-cropping, monocultures, polycultures**

The 'push-pull' strategy uses a combination of stimuli to manipulate the behavior of insect pests and/or natural enemies and to alter their distribution and abundance in agroecosystems (Miller & Cowles, 1990; Khan et al., 1997). The push-pull approach works by repelling or deterring the pest insects (push) away from the main crop by using deterring chemical stimuli. Simultaneously, highly appealing stimuli are used to attract the pests (pull) from the main crop to other areas such as trap crops where the pests aggregate and are easier to control (Khan et al., 1997). While on their own each individual component (the push and the pull) of the approach may not be effective at reducing pest numbers below economic thresholds, combining the push and pull components increases the efficacy of such a strategy. Also, since the push and pull components are commonly non-toxic this strategy is compatible with supporting insect natural enemies and biological control (Khan & Pickett, 2004). A suitable push-pull strategy will be unique for each system it is used for, and hence the development of effective push-pull approaches requires an understanding of the targeted pest's biology and interactions with its hosts and natural enemies (Khan & Pickett, 2004).

Push-pull strategies are not a new idea, but one of the most successful examples was developed recently in Africa for controlling stem borers on cereal crops (Khan et al., 2001).  The adult stem borer moths are cryptic and nocturnal while the larvae feed within the crop stem making both adults and larvae difficult to see and to control. Chemical pesticides are a common method of control for stem borers, but this is not an economical or safe approach for many resource-poor, small-scale farms where these pests are common. Hence, a push-pull strategy was developed using technologies appropriate and economical for such farmers (Khan & Pickett, 2004). This strategy involves a combined use of intercropping and trap crops, and uses plants that are also locally-appropriate and that can be used in their agricultural system. While some push-pull

Manipulation of Natural Enemies in Agroecosystems:

flowering borders (Blaauw & Isaacs, unpublished data).

**2.3 Selection of supplemental food sources**

**2.3.1 Flowering plants**

al., 2009; Hogg et al., 2011).

Habitat and Semiochemicals for Sustainable Insect Pest Control 93

enabled natural enemy populations inside the crop to rebound more rapidly after insecticide applications (Walton & Isaacs, 2011). An increase in natural enemies, particularly parasitoids, has been observed in apple orchards adjacent to flowering borders compared to orchards without flowering borders. This increase in parasitoid numbers also coincided with a doubling of parasitism of spotted tentiform leafminer (*Phyllonorycter blancardella* (Fabr.)) in apple trees adjacent to flowering borders, compared with orchards without

Flowering field margins adjacent to crop fields can provide necessary resources for natural enemies of crop pests during periods when crop flowers are not present, thus maintaining high populations of insect predators and parasitoids, which are supported by a provision of nutrients throughout the season (Sotherton, 1984; Ahern & Brewer, 2002; Büchi, 2002; Sanchez et al., 2003; Wanner et al., 2006a, 2006b). Such natural areas were once common in most agricultural landscapes, particularly between plantings, along roadsides, or as part of woody hedgerows, but as the production of crops has increased and intensified, these non-cropped areas are becoming less common (Sotherton, 1998). Current crop production techniques shape the physical structure of agricultural landscapes (Robinson & Sutherland, 2002), and with increased reliance on mechanization and pesticides, vegetative diversity in farmlands has

Flowering plant strips adjacent to fields help support beneficial insect biodiversity in agricultural landscapes (Baggen & Gurr, 1998; Dufour, 2000; Carreck & Williams, 2002; Fiedler & Landis, 2007a, 2007b; Tuell et al., 2008)**.** Much of the testing of flowering plants has been done with non-native annual or biennial flowering species, although these often bloom in one growing season requiring annual sowing. This makes these resource plants costly to successfully maintain , whereas native perennial flowering plants are sown once, adapted to the local environment, less likely to become invasive, and may increase native beneficial insect diversity in agricultural landscapes (Stephens et al., 2006, Fiedler & Landis, 2007a, 2007b). A well-designed flowering border adjacent to a crop field will provide necessary resources and alternative food source for natural enemies during periods when crop pest and crop flower numbers are low, thus maintaining high populations of natural enemies supported by the provision of nutrients throughout the season (Landis et al., 2000; Isaacs et

Sufficient flower abundance and appropriate vegetation structure are required to support diverse populations of insects (Zurbrügg & Frank, 2006), and therefore manipulation of structurally resource-poor habitats through the addition of flowering plants and grasses can increase beneficial insect populations in agricultural landscapes (Long et al., 1998; Kells et al., 2001; Rebek et al., 2005). Many beneficial insects, including natural enemies, require access to alternate hosts, overwintering habitats, a constant food supply, and appropriate microclimates in order to survive (Johnson & Triplehorn, 2005). The majority of predators and parasitoids are omnivores and require non-prey food, such as pollen and nectar, as part

decreased causing negative impacts on natural enemies (Ryszkowski et al., 1993).

**2.3.2 Nutritional requirements: Pollen and nectar resources**

strategies use chemical deterrents and attractants to create the push-pull effect (see Section 3), this strategy does not use any chemical deterrents or toxins, but instead uses plant species that repel the pest away from the main crop while also attracting insect natural enemies to the fields. The repelled stem borers are attracted to nearby trap plants where the moths lay their eggs, but their larvae are unable to develop, thus reducing the number of trapped pest insects (Khan and Pickett, 2004). The farms that implemented the push-pull strategy in Africa have experienced lower pest abundance, and also an overall enhancement of beneficial insect abundance (Khan et al., 2001).

While the work of Khan et al. in Africa is primarily in cereal-based farming systems, the push-pull approach may be applicable to a much wider range of agricultural pest problems in a variety of crops, if the appropriate components can be developed and implemented.

## **2.2 Cover crops**

A major means of conserving beneficial insects and stabilizing their populations is to meet the ecological requirements of these insects within or near the cropping environment (Landis et al., 2000). To be effective, many natural enemies and pollinators need access to alternate hosts, overwintering habitats, constant food supply, and appropriate microclimates. These requirements can be fulfilled through the inclusion of a diverse assemblage of flowering plants within agricultural landscapes.

Cover crops are planted in crop fields, either in rotation with annual crops or within perennial crops. These plants have been widely used to reduce soil erosion, add or retain soil nutrients, produce organic matter, reduce soil compaction, and also aid in pest control (Bugg, 1991; Bugg & Waddington, 1994). They are used in sustainable and organic agricultural systems to enhance soil health and crop nutrition.

Flowering species such as buckwheat (*Fagopyrum esculentum* Moench) or clovers (*Trifolium* spp.) have been promoted as cover crops to provide flowering resources for insects when the crop is not in bloom. Increasingly, the use of flowering cover crops is seen as one component of an overall 'farmscaping' approach that aims to make farmland suitable for a range of beneficial organisms throughout the growing season. Ideally, cover crops will provide shelter and resources for natural enemies, enhancing their populations and hence biological control of insect pests (Bugg & Waddington, 1994). Plants should be screened for their attractiveness to not only the target biological control agent, but also to other potential competitors for floral resources (Hogg et al., 2011) (see below).

The deployment of cover crops within the row middles of perennial crops may create a conflict with insecticide-based pest management activities that would kill the natural enemies active within the field. This will depend on the type of insecticide used (since many of the more recently-developed insecticides are quite selective), on the tolerance of the natural enemies to the insecticide, and on their ability to re-colonize fields from the perimeter. The potential for killing beneficial insects inside crop fields is one reason that strip plantings of flowering plants within field margins or perimeter plantings are considered. Placement of flowering resources adjacent to crop fields may minimize exposure to insecticides while also providing the resources for natural enemies that can then re-invade the crop fields. Indeed, flowering borders adjacent to blueberry fields have enabled natural enemy populations inside the crop to rebound more rapidly after insecticide applications (Walton & Isaacs, 2011). An increase in natural enemies, particularly parasitoids, has been observed in apple orchards adjacent to flowering borders compared to orchards without flowering borders. This increase in parasitoid numbers also coincided with a doubling of parasitism of spotted tentiform leafminer (*Phyllonorycter blancardella* (Fabr.)) in apple trees adjacent to flowering borders, compared with orchards without flowering borders (Blaauw & Isaacs, unpublished data).

## **2.3 Selection of supplemental food sources**

## **2.3.1 Flowering plants**

92 Integrated Pest Management and Pest Control – Current and Future Tactics

strategies use chemical deterrents and attractants to create the push-pull effect (see Section 3), this strategy does not use any chemical deterrents or toxins, but instead uses plant species that repel the pest away from the main crop while also attracting insect natural enemies to the fields. The repelled stem borers are attracted to nearby trap plants where the moths lay their eggs, but their larvae are unable to develop, thus reducing the number of trapped pest insects (Khan and Pickett, 2004). The farms that implemented the push-pull strategy in Africa have experienced lower pest abundance, and also an overall enhancement of beneficial insect

While the work of Khan et al. in Africa is primarily in cereal-based farming systems, the push-pull approach may be applicable to a much wider range of agricultural pest problems in a variety of crops, if the appropriate components can be developed and

A major means of conserving beneficial insects and stabilizing their populations is to meet the ecological requirements of these insects within or near the cropping environment (Landis et al., 2000). To be effective, many natural enemies and pollinators need access to alternate hosts, overwintering habitats, constant food supply, and appropriate microclimates. These requirements can be fulfilled through the inclusion of a diverse

Cover crops are planted in crop fields, either in rotation with annual crops or within perennial crops. These plants have been widely used to reduce soil erosion, add or retain soil nutrients, produce organic matter, reduce soil compaction, and also aid in pest control (Bugg, 1991; Bugg & Waddington, 1994). They are used in sustainable and organic

Flowering species such as buckwheat (*Fagopyrum esculentum* Moench) or clovers (*Trifolium* spp.) have been promoted as cover crops to provide flowering resources for insects when the crop is not in bloom. Increasingly, the use of flowering cover crops is seen as one component of an overall 'farmscaping' approach that aims to make farmland suitable for a range of beneficial organisms throughout the growing season. Ideally, cover crops will provide shelter and resources for natural enemies, enhancing their populations and hence biological control of insect pests (Bugg & Waddington, 1994). Plants should be screened for their attractiveness to not only the target biological control agent, but also to other potential

The deployment of cover crops within the row middles of perennial crops may create a conflict with insecticide-based pest management activities that would kill the natural enemies active within the field. This will depend on the type of insecticide used (since many of the more recently-developed insecticides are quite selective), on the tolerance of the natural enemies to the insecticide, and on their ability to re-colonize fields from the perimeter. The potential for killing beneficial insects inside crop fields is one reason that strip plantings of flowering plants within field margins or perimeter plantings are considered. Placement of flowering resources adjacent to crop fields may minimize exposure to insecticides while also providing the resources for natural enemies that can then re-invade the crop fields. Indeed, flowering borders adjacent to blueberry fields have

assemblage of flowering plants within agricultural landscapes.

agricultural systems to enhance soil health and crop nutrition.

competitors for floral resources (Hogg et al., 2011) (see below).

abundance (Khan et al., 2001).

implemented.

**2.2 Cover crops**

Flowering field margins adjacent to crop fields can provide necessary resources for natural enemies of crop pests during periods when crop flowers are not present, thus maintaining high populations of insect predators and parasitoids, which are supported by a provision of nutrients throughout the season (Sotherton, 1984; Ahern & Brewer, 2002; Büchi, 2002; Sanchez et al., 2003; Wanner et al., 2006a, 2006b). Such natural areas were once common in most agricultural landscapes, particularly between plantings, along roadsides, or as part of woody hedgerows, but as the production of crops has increased and intensified, these non-cropped areas are becoming less common (Sotherton, 1998). Current crop production techniques shape the physical structure of agricultural landscapes (Robinson & Sutherland, 2002), and with increased reliance on mechanization and pesticides, vegetative diversity in farmlands has decreased causing negative impacts on natural enemies (Ryszkowski et al., 1993).

Flowering plant strips adjacent to fields help support beneficial insect biodiversity in agricultural landscapes (Baggen & Gurr, 1998; Dufour, 2000; Carreck & Williams, 2002; Fiedler & Landis, 2007a, 2007b; Tuell et al., 2008)**.** Much of the testing of flowering plants has been done with non-native annual or biennial flowering species, although these often bloom in one growing season requiring annual sowing. This makes these resource plants costly to successfully maintain , whereas native perennial flowering plants are sown once, adapted to the local environment, less likely to become invasive, and may increase native beneficial insect diversity in agricultural landscapes (Stephens et al., 2006, Fiedler & Landis, 2007a, 2007b). A well-designed flowering border adjacent to a crop field will provide necessary resources and alternative food source for natural enemies during periods when crop pest and crop flower numbers are low, thus maintaining high populations of natural enemies supported by the provision of nutrients throughout the season (Landis et al., 2000; Isaacs et al., 2009; Hogg et al., 2011).

## **2.3.2 Nutritional requirements: Pollen and nectar resources**

Sufficient flower abundance and appropriate vegetation structure are required to support diverse populations of insects (Zurbrügg & Frank, 2006), and therefore manipulation of structurally resource-poor habitats through the addition of flowering plants and grasses can increase beneficial insect populations in agricultural landscapes (Long et al., 1998; Kells et al., 2001; Rebek et al., 2005). Many beneficial insects, including natural enemies, require access to alternate hosts, overwintering habitats, a constant food supply, and appropriate microclimates in order to survive (Johnson & Triplehorn, 2005). The majority of predators and parasitoids are omnivores and require non-prey food, such as pollen and nectar, as part

Manipulation of Natural Enemies in Agroecosystems:

overwintering, or nesting of natural enemies.

lacewings in the agroecosystem (McEwen & Sengonca, 2001).

mites (English-Loeb et al., 2002).

**2.5 Landscape influences on natural enemies**

suppression of crop pests.

**2.4 Shelters**

Habitat and Semiochemicals for Sustainable Insect Pest Control 95

plants can also conserve released natural enemies to provide sustainable, long-term

Natural enemies of insect pests require shelter from environmental hazards, and a lack of shelter during periods of heat, cold, rain, or pesticide application may be highly detrimental to their survival. Availability of appropriate habitats may promote foraging, resting,

Physical environmental conditions profoundly affect natural enemy activity during the growing season. For example, excessive wind is thought to limit foraging by adult hoverflies (Beane & Bugg, 1998). Hedgerows, windbreaks, or shelter-belts can protect croplands in windy areas, and provide some protection to the windward as well as to the leeward side. Shelter can reduce soil erosion, and improve photosynthetic and water-use efficiency by crop plants, and can lead to locally elevated temperatures in the sheltered areas. Because hedgerows and windbreaks often contain flowering plants used by many natural enemies, the effects of shelter and of flowers may be difficult to separate (Beane & Bugg, 1998).

Overwintering and resting aggregations of various natural enemies are often observed in crop fields. Typical sites for such aggregations vary among species, and include herbaceous and woody plants as well as human-made structures (Beane & Bugg, 1998). Houses for lacewings have even been built and tested to provide shelter during harsh weather conditions (McEwen & Sengonca, 2001). These lacewing houses have been successfully used as a tool for augmenting biological control in crop fields by increasing the number of

At the small scale at which mite biological control operates, shelters are important for the survival of predatory species. Some plants have naturally-occurring shelters, called domatia, that predatory mites use as a protected location. They can then forage from these sites on leaves to reach pest mites, and leaves with greater domatia structures tend to have higher populations of predatory mites (Karban et al., 1995; Loughner et al., 2008). Leaves with domatia also protect predatory mites from other natural enemies (intraguild predation) (Norton et al., 2001), and lead to lower densities of leaf-feeding mites and foliar fungal pathogens (Norton et al., 2000; English-Loeb et al., 2005). These findings help explain why certain grape cultivars that possess domatia are less susceptible to mite and mildew outbreaks. Such information could be used in breeding programs to develop crop cultivars that are more likely to have lower pest mite populations, due to their harboring of predatory

Research into landscape-level effects on biological control of insect pests has developed rapidly over the past 20 years, in concert with the expansion of the field of landscape ecology (Turner et al., 2001). New techniques and tools have become available for detailed analysis of aerial imagery or remotely sensed data of the landscapes surrounding crop fields, and combining this with measurements of pest-natural enemy interactions in crop fields has provided new insights, as reviewed by Bianchi et al. (2006). In general, increased

of their diet. Natural enemies from a broad range of orders including Hymenoptera, Diptera, Coleoptera, Heteroptera, Neuroptera, Araneae, and Acari have been observed to require and/or benefit from access to flowering resources (Wäckers et al., 2005). Access to pollen and nectar sources can significantly increase the activity, longevity, and fecundity of these predators and parasitoids (Wäckers et al., 2008; Hogg et al., 2011), and thus, the availability of flowering resources can be essential to natural enemy efficacy in biological control of pest insects (van Rijn & Sabelis, 2005). These non-prey requirements can be fulfilled with a diverse assemblage of flowering plants, which will provide necessary resources that support populations of predators and parasitoids throughout the season.

Simple addition of flowering plants to farms may not be sufficient to gain the expected increase in biological control, however, and in some cases it may be counter-productive due to supplying resources for pest insects. In recent years there has been a much greater understanding of the need to tailor resource plants for the specific natural enemies that can provide pest suppression, but further investigation is needed to tailor this to specific crop systems (Jonsson et al., 2008). In one line of investigation, the nutritional quality of plant resources has been investigated in detail, revealing the range of suitability of different plants as food for parasitoids and predators (reviewed by Wäckers, 2005). Additionally, the need to select plants that are beneficial to the natural enemies without providing resources to pest insects has driven the careful evaluation of pest and natural enemy life history traits on candidate floral resources. Baggen et al. (2003) evaluated a range of potential resource plants and found that only lacy phacelia (*Phacelia tanacetifolia* Benth.) and Nasturtium (*Tropaeolum majus* L.) provided resources for natural enemies without also enhancing moth pest performance, as the other tested plants did. In field trials of this selective planting approach, Begum et al. (2006) found higher parasitism of light brown apple moth (*Epiphyas postvittana* (Walker)) eggs in vineyard plots where three types of flowering resource plants were sown under the vines. In more recent studies, this rewarding plant strategy has been combined with attraction of natural enemies (see Fig. 1) in an attract-and-reward approach. Initial reports from combining these two tactics indicate that while this approach has potential for synergy, further work is required to realize the full potential (Simpson et al., 2011a, 2011b).

#### **2.3.3 Alternative prey**

As mentioned above, most natural enemies benefit from having access to alternate hosts/prey. Taking advantage of this knowledge, banker plant systems (or open rearing systems) can be used to augment populations of natural enemies in greenhouse and field settings on ornamental and food crops (Van Driesche et al., 2008). Although many natural enemies can be purchased and released to augment biological control, they often leave or die once the targeted pest has been controlled. By combining aspects of augmentative and conservation biological control, banker plant systems attempt to alleviate these factors (Frank, 2010). Banker plant systems generally consist of a non-crop plant that is deliberately infested with a non-pest herbivore. The non-pest herbivores serve as alternative hosts/prey for a desired parasitoid or predator of the target crop pest. A banker plant system is typically based on the use of alternative host/prey in the form of non-pest herbivores, but it can also be based around the use of surrogate food, such as pollen for generalist natural enemies (Huang et al., 2011). As a form of conservation biological control, banker plant systems provide alternative food or hosts for natural enemies so they can survive and reproduce for long periods even when no pests are present in the crop (Frank, 2010). Banker plants can also conserve released natural enemies to provide sustainable, long-term suppression of crop pests.

## **2.4 Shelters**

94 Integrated Pest Management and Pest Control – Current and Future Tactics

of their diet. Natural enemies from a broad range of orders including Hymenoptera, Diptera, Coleoptera, Heteroptera, Neuroptera, Araneae, and Acari have been observed to require and/or benefit from access to flowering resources (Wäckers et al., 2005). Access to pollen and nectar sources can significantly increase the activity, longevity, and fecundity of these predators and parasitoids (Wäckers et al., 2008; Hogg et al., 2011), and thus, the availability of flowering resources can be essential to natural enemy efficacy in biological control of pest insects (van Rijn & Sabelis, 2005). These non-prey requirements can be fulfilled with a diverse assemblage of flowering plants, which will provide necessary resources that support populations of predators and parasitoids throughout the season.

Simple addition of flowering plants to farms may not be sufficient to gain the expected increase in biological control, however, and in some cases it may be counter-productive due to supplying resources for pest insects. In recent years there has been a much greater understanding of the need to tailor resource plants for the specific natural enemies that can provide pest suppression, but further investigation is needed to tailor this to specific crop systems (Jonsson et al., 2008). In one line of investigation, the nutritional quality of plant resources has been investigated in detail, revealing the range of suitability of different plants as food for parasitoids and predators (reviewed by Wäckers, 2005). Additionally, the need to select plants that are beneficial to the natural enemies without providing resources to pest insects has driven the careful evaluation of pest and natural enemy life history traits on candidate floral resources. Baggen et al. (2003) evaluated a range of potential resource plants and found that only lacy phacelia (*Phacelia tanacetifolia* Benth.) and Nasturtium (*Tropaeolum majus* L.) provided resources for natural enemies without also enhancing moth pest performance, as the other tested plants did. In field trials of this selective planting approach, Begum et al. (2006) found higher parasitism of light brown apple moth (*Epiphyas postvittana* (Walker)) eggs in vineyard plots where three types of flowering resource plants were sown under the vines. In more recent studies, this rewarding plant strategy has been combined with attraction of natural enemies (see Fig. 1) in an attract-and-reward approach. Initial reports from combining these two tactics indicate that while this approach has potential for synergy,

further work is required to realize the full potential (Simpson et al., 2011a, 2011b).

As mentioned above, most natural enemies benefit from having access to alternate hosts/prey. Taking advantage of this knowledge, banker plant systems (or open rearing systems) can be used to augment populations of natural enemies in greenhouse and field settings on ornamental and food crops (Van Driesche et al., 2008). Although many natural enemies can be purchased and released to augment biological control, they often leave or die once the targeted pest has been controlled. By combining aspects of augmentative and conservation biological control, banker plant systems attempt to alleviate these factors (Frank, 2010). Banker plant systems generally consist of a non-crop plant that is deliberately infested with a non-pest herbivore. The non-pest herbivores serve as alternative hosts/prey for a desired parasitoid or predator of the target crop pest. A banker plant system is typically based on the use of alternative host/prey in the form of non-pest herbivores, but it can also be based around the use of surrogate food, such as pollen for generalist natural enemies (Huang et al., 2011). As a form of conservation biological control, banker plant systems provide alternative food or hosts for natural enemies so they can survive and reproduce for long periods even when no pests are present in the crop (Frank, 2010). Banker

**2.3.3 Alternative prey**

Natural enemies of insect pests require shelter from environmental hazards, and a lack of shelter during periods of heat, cold, rain, or pesticide application may be highly detrimental to their survival. Availability of appropriate habitats may promote foraging, resting, overwintering, or nesting of natural enemies.

Physical environmental conditions profoundly affect natural enemy activity during the growing season. For example, excessive wind is thought to limit foraging by adult hoverflies (Beane & Bugg, 1998). Hedgerows, windbreaks, or shelter-belts can protect croplands in windy areas, and provide some protection to the windward as well as to the leeward side. Shelter can reduce soil erosion, and improve photosynthetic and water-use efficiency by crop plants, and can lead to locally elevated temperatures in the sheltered areas. Because hedgerows and windbreaks often contain flowering plants used by many natural enemies, the effects of shelter and of flowers may be difficult to separate (Beane & Bugg, 1998).

Overwintering and resting aggregations of various natural enemies are often observed in crop fields. Typical sites for such aggregations vary among species, and include herbaceous and woody plants as well as human-made structures (Beane & Bugg, 1998). Houses for lacewings have even been built and tested to provide shelter during harsh weather conditions (McEwen & Sengonca, 2001). These lacewing houses have been successfully used as a tool for augmenting biological control in crop fields by increasing the number of lacewings in the agroecosystem (McEwen & Sengonca, 2001).

At the small scale at which mite biological control operates, shelters are important for the survival of predatory species. Some plants have naturally-occurring shelters, called domatia, that predatory mites use as a protected location. They can then forage from these sites on leaves to reach pest mites, and leaves with greater domatia structures tend to have higher populations of predatory mites (Karban et al., 1995; Loughner et al., 2008). Leaves with domatia also protect predatory mites from other natural enemies (intraguild predation) (Norton et al., 2001), and lead to lower densities of leaf-feeding mites and foliar fungal pathogens (Norton et al., 2000; English-Loeb et al., 2005). These findings help explain why certain grape cultivars that possess domatia are less susceptible to mite and mildew outbreaks. Such information could be used in breeding programs to develop crop cultivars that are more likely to have lower pest mite populations, due to their harboring of predatory mites (English-Loeb et al., 2002).

## **2.5 Landscape influences on natural enemies**

Research into landscape-level effects on biological control of insect pests has developed rapidly over the past 20 years, in concert with the expansion of the field of landscape ecology (Turner et al., 2001). New techniques and tools have become available for detailed analysis of aerial imagery or remotely sensed data of the landscapes surrounding crop fields, and combining this with measurements of pest-natural enemy interactions in crop fields has provided new insights, as reviewed by Bianchi et al. (2006). In general, increased

Manipulation of Natural Enemies in Agroecosystems:

**3. Semiochemical-based manipulation**

Habitat and Semiochemicals for Sustainable Insect Pest Control 97

The term "semiochemical" (semeion = sign or signal in Greek) is used to describe a chemical or mixture of chemicals that can act as messengers in interactions among organisms (Nordlund & Lewis, 1976; Dicke & Sabelis, 1988; Vet & Dicke, 1992). It includes chemicals that mediate interactions among individuals within the same species, i.e., intraspecific communication (= *pheromones*), and those that mediate interactions among individuals belonging to different species, i.e., interspecific communication (= *allelochemicals*). Among allelochemicals, compounds can be classified as *allomones* if their production benefits the emitter, *kairomones* if their production benefits the receiver or *synomones* if their production

An approach to using semiochemicals for pest control is to exploit ways to chemically augment, conserve, or enhance the efficacy of natural enemies in cropping systems. Here we provide a review of semiochemical-natural enemy interactions and describe ways in which these compounds, particularly those emitted by plants, can be employed to enhance natural

Plant volatiles play a critical role as signals in tri-trophic level interactions. These are interactions involving three trophic levels; for example, plants (1st trophic level), herbivores (2nd trophic level), and the natural enemies of herbivores (predators and parasitoids) (3rd trophic level). Peter Price and collaborators (Price et al., 1980) were the first to specifically emphasize the importance of including the third trophic level when considering plantherbivore interactions. Their seminal contribution was of particular importance because interactions among organisms from more than two trophic levels are known to be common

Plants can influence the natural enemies of herbivores by emitting behavior-modifying volatile organic compounds. Specifically, plants damaged by herbivores often produce a blend of volatiles (Paré & Tumlinson, 1999), commonly referred to as herbivore-induced plant volatiles (HIPVs) (Mumm & Dicke, 2010). These HIPVs consist of a mixture of the so-called green-leaf volatiles (C6 aldehydes, alcohols, and acetates), terpenes (monoterpenes, sesquiterpenes, homoterpenes), and aromatic compounds, among others (Pichersky et al., 2006). The release of HIPVs may signal the presence of potential prey or hosts and, therefore, can be exploited by natural enemies to locate the prey organism (Sabelis et al., 1999; Verkerk, 2004). In the last 10 years there has been an increased interest in using these compounds to manipulate natural enemy behaviors for insect pest control

Vinson (1976) described five steps parasitoids and other natural enemies need to follow during host searching and selection; these are: 1) host habitat location; 2) host location; 3) host acceptance; 4) host suitability; and 5) host regulation. It is clear now that plant chemical cues are particularly important in aiding parasitoids during the first step. In order to locate the host habitat, natural enemies use long-distance cues from plants. These cues (e.g. HIPVs)

benefits both the emitter and receiver (Dicke & Sabelis, 1988; Vet & Dicke, 1992).

enemy attraction and ultimately reduce pest populations.

**3.1 Herbivore-induced plant volatiles (HIPVs)**

in nature (Hunter & Price, 1992; Ohgushi, 2005).

in agricultural crops.

**3.1.1 Brief overview of HIPVs**

habitat fragmentation, isolation and decreased landscape structural complexity destabilize the biotic interactions that regulate pest populations (Robinson et al., 1992; Landis et al., 2000; Tscharntke et al., 2007). In practical terms, this means that farms in more intensively managed landscapes can rely less on naturally-occurring biological control than those that are in more diverse landscapes. In more diverse landscapes that contain multiple crop types, natural habitat, perennial wooded land, and a greater availability of flowering resources, natural enemies are more likely to have their ecological requirements met near to the crop field and are less likely to disperse. This then translates into a greater abundance and diversity of natural enemy insects available during periods of pest population growth to limit the trajectory of that growth and limit pest populations (Marino & Landis, 1996; Bommarco, 1998; Thies et al., 2003; Schmidt & Tscharntke, 2005, Tscharntke et al., 2005).

Having high landscape diversity, including flowering plants, near crop fields can also interact with the management approach taken on farms to affect the natural enemy population available to control pests in crop fields. Thus, Ostman et al. (2001) found that aphid population growth was slower in organic than conventional farms, and fields set in landscapes with greater proportion of perennial crops and with more field margins received more biological control. Spider populations are also sensitive to the landscape, with spider species richness increasing with the proportion of non-crop habitat in the landscape (Schmidt et al., 2005), irrespective of whether farms were managed using organic or conventional tactics. Density of spiders was 62% higher in organically managed fields, and within the conventional fields there was a positive correlation between the proportion of non-crop habitat in the surrounding landscape and the spider density. These trends indicate that field management as well as what landscape they are set in will influence the availability of natural enemies to provide biological control services to crop fields.

The economic implications of how crop management and landscape composition affect the services that natural enemies provide have only recently been addressed. Landis et al. (2008) examined the value of biological control being provided to limit populations of soybean aphid, *Glycines max* (L.)*,* in the context of increasing corn production for ethanol. Their analyses found a \$33/ha value of biological pest control for producers who employed integrated pest management, with this value provided largely from the surrounding landscapes. Increased planting of corn, and the associated reduction in landscape diversity led to a \$58 million/yr cost to farmers caused by reduced biological control of this insect, and this translated into lower yields and higher pesticide costs. In a more recent study, Meehan et al. (2011) have analyzed broad-scale landscape, pest, and pesticide use data across the Midwestern United States. Landscape simplification was correlated with higher pest pressure and greater dependence on pesticides, with multi-million dollar costs to farmers that can be attributed in part to the changes in availability of natural pest regulation supplied by surrounding landscapes.

This section has highlighted the aspects of agricultural habitats that can be manipulated to provide resources for natural enemies. But, as we have just seen, not all landscapes have high abundance of natural enemies. It may therefore be important to focus populations of natural enemies at crop plants where their pest-controlling services are needed, by harnessing the chemical interactions among organisms. This is an active and exciting area of research that can exploit the power of chemical signaling to manipulate natural enemies for the benefit of agriculture.

## **3. Semiochemical-based manipulation**

96 Integrated Pest Management and Pest Control – Current and Future Tactics

habitat fragmentation, isolation and decreased landscape structural complexity destabilize the biotic interactions that regulate pest populations (Robinson et al., 1992; Landis et al., 2000; Tscharntke et al., 2007). In practical terms, this means that farms in more intensively managed landscapes can rely less on naturally-occurring biological control than those that are in more diverse landscapes. In more diverse landscapes that contain multiple crop types, natural habitat, perennial wooded land, and a greater availability of flowering resources, natural enemies are more likely to have their ecological requirements met near to the crop field and are less likely to disperse. This then translates into a greater abundance and diversity of natural enemy insects available during periods of pest population growth to limit the trajectory of that growth and limit pest populations (Marino & Landis, 1996; Bommarco, 1998; Thies et al., 2003; Schmidt & Tscharntke, 2005, Tscharntke et al., 2005).

Having high landscape diversity, including flowering plants, near crop fields can also interact with the management approach taken on farms to affect the natural enemy population available to control pests in crop fields. Thus, Ostman et al. (2001) found that aphid population growth was slower in organic than conventional farms, and fields set in landscapes with greater proportion of perennial crops and with more field margins received more biological control. Spider populations are also sensitive to the landscape, with spider species richness increasing with the proportion of non-crop habitat in the landscape (Schmidt et al., 2005), irrespective of whether farms were managed using organic or conventional tactics. Density of spiders was 62% higher in organically managed fields, and within the conventional fields there was a positive correlation between the proportion of non-crop habitat in the surrounding landscape and the spider density. These trends indicate that field management as well as what landscape they are set in will influence the

availability of natural enemies to provide biological control services to crop fields.

supplied by surrounding landscapes.

the benefit of agriculture.

The economic implications of how crop management and landscape composition affect the services that natural enemies provide have only recently been addressed. Landis et al. (2008) examined the value of biological control being provided to limit populations of soybean aphid, *Glycines max* (L.)*,* in the context of increasing corn production for ethanol. Their analyses found a \$33/ha value of biological pest control for producers who employed integrated pest management, with this value provided largely from the surrounding landscapes. Increased planting of corn, and the associated reduction in landscape diversity led to a \$58 million/yr cost to farmers caused by reduced biological control of this insect, and this translated into lower yields and higher pesticide costs. In a more recent study, Meehan et al. (2011) have analyzed broad-scale landscape, pest, and pesticide use data across the Midwestern United States. Landscape simplification was correlated with higher pest pressure and greater dependence on pesticides, with multi-million dollar costs to farmers that can be attributed in part to the changes in availability of natural pest regulation

This section has highlighted the aspects of agricultural habitats that can be manipulated to provide resources for natural enemies. But, as we have just seen, not all landscapes have high abundance of natural enemies. It may therefore be important to focus populations of natural enemies at crop plants where their pest-controlling services are needed, by harnessing the chemical interactions among organisms. This is an active and exciting area of research that can exploit the power of chemical signaling to manipulate natural enemies for The term "semiochemical" (semeion = sign or signal in Greek) is used to describe a chemical or mixture of chemicals that can act as messengers in interactions among organisms (Nordlund & Lewis, 1976; Dicke & Sabelis, 1988; Vet & Dicke, 1992). It includes chemicals that mediate interactions among individuals within the same species, i.e., intraspecific communication (= *pheromones*), and those that mediate interactions among individuals belonging to different species, i.e., interspecific communication (= *allelochemicals*). Among allelochemicals, compounds can be classified as *allomones* if their production benefits the emitter, *kairomones* if their production benefits the receiver or *synomones* if their production benefits both the emitter and receiver (Dicke & Sabelis, 1988; Vet & Dicke, 1992).

An approach to using semiochemicals for pest control is to exploit ways to chemically augment, conserve, or enhance the efficacy of natural enemies in cropping systems. Here we provide a review of semiochemical-natural enemy interactions and describe ways in which these compounds, particularly those emitted by plants, can be employed to enhance natural enemy attraction and ultimately reduce pest populations.

## **3.1 Herbivore-induced plant volatiles (HIPVs)**

Plant volatiles play a critical role as signals in tri-trophic level interactions. These are interactions involving three trophic levels; for example, plants (1st trophic level), herbivores (2nd trophic level), and the natural enemies of herbivores (predators and parasitoids) (3rd trophic level). Peter Price and collaborators (Price et al., 1980) were the first to specifically emphasize the importance of including the third trophic level when considering plantherbivore interactions. Their seminal contribution was of particular importance because interactions among organisms from more than two trophic levels are known to be common in nature (Hunter & Price, 1992; Ohgushi, 2005).

Plants can influence the natural enemies of herbivores by emitting behavior-modifying volatile organic compounds. Specifically, plants damaged by herbivores often produce a blend of volatiles (Paré & Tumlinson, 1999), commonly referred to as herbivore-induced plant volatiles (HIPVs) (Mumm & Dicke, 2010). These HIPVs consist of a mixture of the so-called green-leaf volatiles (C6 aldehydes, alcohols, and acetates), terpenes (monoterpenes, sesquiterpenes, homoterpenes), and aromatic compounds, among others (Pichersky et al., 2006). The release of HIPVs may signal the presence of potential prey or hosts and, therefore, can be exploited by natural enemies to locate the prey organism (Sabelis et al., 1999; Verkerk, 2004). In the last 10 years there has been an increased interest in using these compounds to manipulate natural enemy behaviors for insect pest control in agricultural crops.

## **3.1.1 Brief overview of HIPVs**

Vinson (1976) described five steps parasitoids and other natural enemies need to follow during host searching and selection; these are: 1) host habitat location; 2) host location; 3) host acceptance; 4) host suitability; and 5) host regulation. It is clear now that plant chemical cues are particularly important in aiding parasitoids during the first step. In order to locate the host habitat, natural enemies use long-distance cues from plants. These cues (e.g. HIPVs)

Manipulation of Natural Enemies in Agroecosystems:

**3.1.3 Belowground tri-trophic interactions**

herbivore levels (Rasmann & Turlings, 2008).

**3.1.4 Plant elicitors of HIPVs**

because the latter should have an innate response to HIPVs.

Habitat and Semiochemicals for Sustainable Insect Pest Control 99

instar larvae (Takabayashi et al., 1995). To cope with this variability, natural enemies can learn to associate HIPVs with the presence of their prey or host (Lewis & Tumlinson, 1988; Vet & Dicke, 1992; Vet et al., 1995; Allison & Hare, 2009). This learning capacity is thought to be more critical for generalist natural enemies than specialists (Steidle & van Loon, 2003),

Another important characteristic of HIPVs is their specificity (Turlings & Wäckers, 2004). The specificity of certain tri-trophic systems allows natural enemies to differentiate plant volatile blends associated with their prey from those of non-prey (Dicke, 1994; Du et al., 1996; Dicke, 1999). De Moraes et al. (1998) first demonstrated the specificity of the volatile response to herbivory in plants and the effects on natural enemies. The authors found that tobacco, maize, and cotton plants produce distinct volatile blends in response to damage by larvae of two related lepidopteran herbivores: *Heliothis virescens* (Fabricius) and *Helicoverpa zea* (Boddie). The parasitoid *Cardiochiles nigriceps* Viereck exploits these differences during host location by being attracted only to HIPVs released from its host *H. virescens* (De Moraes et al., 1998). This specificity has also been reported in the tri-trophic system involving pea plants, pea aphids (*A. pisum*), and the parasitoid *A. ervi* (Du et al., 1998; Powell et al., 1998). Other studies, however, have reported a lack of specificity. For example, McCall et al. (1993) found that the parasitoid *Microplitis croceipes* (Cresson) is equally attracted to volatiles induced by its host *H. zea* and its non-host *S. exigua*. Similarly, the tri-trophic system of cabbage-caterpillars-*Cotesia* sp. lacked specificity at the herbivore level, but not at the plant level where differences in attractiveness to parasitoids were found (Geervliet et al., 1996).

There is now an abundant literature showing that tri-trophic level interactions occur aboveground (as described above). However, relatively little is known about the way organisms from different trophic levels interact belowground. This is particularly true for the roles of HIPVs in attraction of soil-inhabiting natural enemies to root volatiles. For example, Boff et al. (2001) found that the entomopathogenic nematode *Heterorhabditis megidis* Poinar, Jackson & Klein is attracted to the roots of *Thuja occidentalis* L. damaged by the weevil *Otiorhynchus sulcatus* Germar. However, the volatile responsible for this attraction was not identified. Recently, Rasmann et al. (2005) found that larvae of the corn rootworm, *Diabrotica virgifera virgifera* LeConte, feeding on corn roots induce the emission of (E)-β-caryophyllene, which in turn attracts entomopathogenic nematodes. Similar to aboveground interactions, interactions belowground can be specific at both the plant and

Jasmonic acid (JA) and its volatile derivative methyl jasmonate (MeJA) are phytohormones involved in plant defenses against herbivores (Karban & Baldwin, 1997), that can also play a key role in the production and emission of HIPVs (Hilker et al., 2002; Kessler et al., 2004). Plants treated topically with JA or MeJA increase their volatile emissions (Hopke et al., 1994; Gols et al., 1999; Ament et al., 2004; Hare, 2007); however, the volatiles produced often differ from those induced by herbivore damage (Dicke et al., 1999; Rodriguez-Saona et al., 2001). Other phytohormones involved in the emission of HIPVs include salicylic acid (SA) and

originate mainly from plants damaged by the natural enemies' host or prey (e.g. pest insects). In most instances HIPVs provide natural enemies with a highly detectable and reliable signal. HIPVs are classified as synomones because they can benefit both the emitting plant as well as the responding natural enemy (Vet et al., 1991). Once the host habitat is located, natural enemies utilize compounds produced by the host or prey (kairomones), such as volatiles emitted from body scales, honeydew, or the herbivore's frass. Compared with HIPVs, kairomones are more reliable in providing information to natural enemies about the location of their host or prey; however, they are not as detectable (Vet & Dicke, 1992).

Dicke & Sabelis (1988) provided the first evidence that lima bean plants (*Phaseolus lunatus* L.) damaged by the two-spotted spider mite *Tetranychus urticae* Koch emit a blend of volatiles that attract the predatory mite *Phytoseiulus persimilis* Athias-Henriot. In these early studies, this phenomenon was referred to as a 'cry for help', because of the potential fitness benefits to the injured plants from attracting the natural enemies of herbivores (Dicke et al., 1990a; Dicke & Sabelis, 1992). A second tri-trophic system extensively studied in the early 1990s involved corn (*Zea mays* L.), the chewing herbivore *Spodoptera exigua* (Hübner), and its parasitoid *Cotesia marginiventris* (Cresson). Turlings et al. (1991, 1993) showed that *C. marginiventris* utilizes compounds emitted from corn seedlings damaged by the lepidopteran herbivore to locate its host. HIPVs can also mediate plants-aphids (sucking herbivores)-natural enemy interactions. For example, Du et al. (1998) showed that the parasitoid *Aphidius ervi* Haliday is attracted to beans, *Vicia faba* L., infested by the pea aphid *Acyrthosiphon pisum* (Harris). More recent evidence shows that egg deposition by herbivores can also induce a volatile response in plants and consequently attract egg parasitoids (Meiners & Hilker, 1997, 2000; Hilker & Meiners, 2002; Colazza et al., 2004). For example, Meiners & Hilker (1997) found that oviposition by the elm leaf beetle *Xanthogaleruca luteola*  (Müller) induces volatile emissions from its host plant *Ulmus minor* Mill., that attract the egg parasitoid *Oomyzus gallerucae* (Fonscolombe).

This plant volatile response to herbivore damage often differs from artificial damage (Dicke et al., 1990a; Turlings et al., 1990; De Moraes et al., 1998), indicating that the caterpillar or other pest insect induces production of specific HIPVs in the plant. These can be induced locally, i.e. at the site of damage, as well as systemically, i.e. from distal undamaged parts of a damaged plant (Turlings & Tumlinson, 1992; Dicke et al., 1993; Röse et al., 1996).

## **3.1.2 Characteristics of HIPVs**

The emission of HIPVs is common in plants (Dicke & Vet, 1999; Dicke & van Loon, 2000); however, the induced volatile blends are highly variable (Dicke, 2000; Turlings & Wäckers, 2004). The volatile blend often varies depending on plant cultivar (e.g. Loughrin et al., 1995; Gouinguené et al., 2001), plant age (Takabayashi et al., 1994; Turlings et al., 2002), plant part (Turlings et al., 1993), and abiotic factors (Gouinguené & Turlings, 2002). Emissions of HIPVs also vary depending on the species and age of the herbivore (Takabayashi et al., 1995; Gouinguené et al., 2003). For example, Takabayashi et al. (1995) found that corn plants emit greater quantities of volatiles when damaged by 1st and 2nd instar larvae of *Pseudaletia separata* Walker than when damaged by 5th instars. The parasitoid *Cotesia kariyai* (Watanabe) attacks young *P. separata* and is attracted to volatiles emitted by corn damaged by early

originate mainly from plants damaged by the natural enemies' host or prey (e.g. pest insects). In most instances HIPVs provide natural enemies with a highly detectable and reliable signal. HIPVs are classified as synomones because they can benefit both the emitting plant as well as the responding natural enemy (Vet et al., 1991). Once the host habitat is located, natural enemies utilize compounds produced by the host or prey (kairomones), such as volatiles emitted from body scales, honeydew, or the herbivore's frass. Compared with HIPVs, kairomones are more reliable in providing information to natural enemies about the location of their host or prey; however, they are not as

Dicke & Sabelis (1988) provided the first evidence that lima bean plants (*Phaseolus lunatus* L.) damaged by the two-spotted spider mite *Tetranychus urticae* Koch emit a blend of volatiles that attract the predatory mite *Phytoseiulus persimilis* Athias-Henriot. In these early studies, this phenomenon was referred to as a 'cry for help', because of the potential fitness benefits to the injured plants from attracting the natural enemies of herbivores (Dicke et al., 1990a; Dicke & Sabelis, 1992). A second tri-trophic system extensively studied in the early 1990s involved corn (*Zea mays* L.), the chewing herbivore *Spodoptera exigua* (Hübner), and its parasitoid *Cotesia marginiventris* (Cresson). Turlings et al. (1991, 1993) showed that *C. marginiventris* utilizes compounds emitted from corn seedlings damaged by the lepidopteran herbivore to locate its host. HIPVs can also mediate plants-aphids (sucking herbivores)-natural enemy interactions. For example, Du et al. (1998) showed that the parasitoid *Aphidius ervi* Haliday is attracted to beans, *Vicia faba* L., infested by the pea aphid *Acyrthosiphon pisum* (Harris). More recent evidence shows that egg deposition by herbivores can also induce a volatile response in plants and consequently attract egg parasitoids (Meiners & Hilker, 1997, 2000; Hilker & Meiners, 2002; Colazza et al., 2004). For example, Meiners & Hilker (1997) found that oviposition by the elm leaf beetle *Xanthogaleruca luteola*  (Müller) induces volatile emissions from its host plant *Ulmus minor* Mill., that attract the egg

This plant volatile response to herbivore damage often differs from artificial damage (Dicke et al., 1990a; Turlings et al., 1990; De Moraes et al., 1998), indicating that the caterpillar or other pest insect induces production of specific HIPVs in the plant. These can be induced locally, i.e. at the site of damage, as well as systemically, i.e. from distal undamaged parts of

The emission of HIPVs is common in plants (Dicke & Vet, 1999; Dicke & van Loon, 2000); however, the induced volatile blends are highly variable (Dicke, 2000; Turlings & Wäckers, 2004). The volatile blend often varies depending on plant cultivar (e.g. Loughrin et al., 1995; Gouinguené et al., 2001), plant age (Takabayashi et al., 1994; Turlings et al., 2002), plant part (Turlings et al., 1993), and abiotic factors (Gouinguené & Turlings, 2002). Emissions of HIPVs also vary depending on the species and age of the herbivore (Takabayashi et al., 1995; Gouinguené et al., 2003). For example, Takabayashi et al. (1995) found that corn plants emit greater quantities of volatiles when damaged by 1st and 2nd instar larvae of *Pseudaletia separata* Walker than when damaged by 5th instars. The parasitoid *Cotesia kariyai* (Watanabe) attacks young *P. separata* and is attracted to volatiles emitted by corn damaged by early

a damaged plant (Turlings & Tumlinson, 1992; Dicke et al., 1993; Röse et al., 1996).

detectable (Vet & Dicke, 1992).

parasitoid *Oomyzus gallerucae* (Fonscolombe).

**3.1.2 Characteristics of HIPVs**

instar larvae (Takabayashi et al., 1995). To cope with this variability, natural enemies can learn to associate HIPVs with the presence of their prey or host (Lewis & Tumlinson, 1988; Vet & Dicke, 1992; Vet et al., 1995; Allison & Hare, 2009). This learning capacity is thought to be more critical for generalist natural enemies than specialists (Steidle & van Loon, 2003), because the latter should have an innate response to HIPVs.

Another important characteristic of HIPVs is their specificity (Turlings & Wäckers, 2004). The specificity of certain tri-trophic systems allows natural enemies to differentiate plant volatile blends associated with their prey from those of non-prey (Dicke, 1994; Du et al., 1996; Dicke, 1999). De Moraes et al. (1998) first demonstrated the specificity of the volatile response to herbivory in plants and the effects on natural enemies. The authors found that tobacco, maize, and cotton plants produce distinct volatile blends in response to damage by larvae of two related lepidopteran herbivores: *Heliothis virescens* (Fabricius) and *Helicoverpa zea* (Boddie). The parasitoid *Cardiochiles nigriceps* Viereck exploits these differences during host location by being attracted only to HIPVs released from its host *H. virescens* (De Moraes et al., 1998). This specificity has also been reported in the tri-trophic system involving pea plants, pea aphids (*A. pisum*), and the parasitoid *A. ervi* (Du et al., 1998; Powell et al., 1998). Other studies, however, have reported a lack of specificity. For example, McCall et al. (1993) found that the parasitoid *Microplitis croceipes* (Cresson) is equally attracted to volatiles induced by its host *H. zea* and its non-host *S. exigua*. Similarly, the tri-trophic system of cabbage-caterpillars-*Cotesia* sp. lacked specificity at the herbivore level, but not at the plant level where differences in attractiveness to parasitoids were found (Geervliet et al., 1996).

## **3.1.3 Belowground tri-trophic interactions**

There is now an abundant literature showing that tri-trophic level interactions occur aboveground (as described above). However, relatively little is known about the way organisms from different trophic levels interact belowground. This is particularly true for the roles of HIPVs in attraction of soil-inhabiting natural enemies to root volatiles. For example, Boff et al. (2001) found that the entomopathogenic nematode *Heterorhabditis megidis* Poinar, Jackson & Klein is attracted to the roots of *Thuja occidentalis* L. damaged by the weevil *Otiorhynchus sulcatus* Germar. However, the volatile responsible for this attraction was not identified. Recently, Rasmann et al. (2005) found that larvae of the corn rootworm, *Diabrotica virgifera virgifera* LeConte, feeding on corn roots induce the emission of (E)-β-caryophyllene, which in turn attracts entomopathogenic nematodes. Similar to aboveground interactions, interactions belowground can be specific at both the plant and herbivore levels (Rasmann & Turlings, 2008).

## **3.1.4 Plant elicitors of HIPVs**

Jasmonic acid (JA) and its volatile derivative methyl jasmonate (MeJA) are phytohormones involved in plant defenses against herbivores (Karban & Baldwin, 1997), that can also play a key role in the production and emission of HIPVs (Hilker et al., 2002; Kessler et al., 2004). Plants treated topically with JA or MeJA increase their volatile emissions (Hopke et al., 1994; Gols et al., 1999; Ament et al., 2004; Hare, 2007); however, the volatiles produced often differ from those induced by herbivore damage (Dicke et al., 1999; Rodriguez-Saona et al., 2001). Other phytohormones involved in the emission of HIPVs include salicylic acid (SA) and

Manipulation of Natural Enemies in Agroecosystems:

Medium-long lasting. Slowrelease devices (4 weeks or more)

Relatively easy to apply and adopt