Chapter 5
Modeling the Dose Acquisition Process of Bacillus thuringiensis Influence of Feeding Pattern on Survival Downloaded by SWINBURNE UNIV OF TECHNOLOGY on May 18, 2018 | https://pubs.acs.org Publication Date: May 5, 1995 | doi: 10.1021/bk-1995-0595.ch005
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Franklin R. Hall , A. C. Chapple , R. A. J. Taylor , and R. A. Downer
1Laboratory for Pest Control Application Technology, Ohio Agricultural Research and Development Center, Ohio State University, 1680 Madison Avenue, Wooster, OH 44691 Ecogen Europe SRL, 3a Parco Technologico Agro-alimentare dell'Umbria, Frazione Pantalla, 06095 Todi (PG), Italy 3Department of Entomology, Ohio Agricultural Research and Development Center, Ohio State University, 1680 Madison Avenue, Wooster, OH 44691 2
Insecticide formulations and adjuvants are manipulated to optimize the pesticide deposit characteristics on the plant surface. The toxicity, deposit quality and quantity, together with the insect's pattern of feeding, detennine the insecticide's efficacy. A model of the dose-transfer process, The Pesticide Drop Simulator, was used to investigate the effect of feeding and walking parameters of simulated insect foliar feeders on their survival when exposed to a leaf surface treated with a biological insecticide. Survival in the model was found to be most influenced by the speed of walking, the major determinant of the distance apart of feeding holes on the leaf. This result was obtained without explicitly simulating avoidance behavior, but is in agreement with findings where avoidance has been observed. These results serve to remind us how important the feeding, locomotory and searching behavior of defoliators is in the efficacy of pesticides. This conclusion is especially relevant to biological insecticides. The role of modeling in general, and the utility of PDS in particular, in the evaluation of pesticide formulations and additives is also discussed. The efficiency with which pesticides are utilized in agriculture and horticulture is extremely poor (1). In part, this is because fields usually have to be treated as a whole, regardless of tne distribution of the pest within the field (2,5). Even when an infested plant is sprayed with an insecticide, for example, little will be deposited where the pest will encounter it Of the effective deposit, only a fraction will be acouired by the pest (4), and still less will reach the susceptible site within it (i,5, 1% biological efficiency (1). However, this is a best case; a worst case assessment suggests that < 0.001% of the insecticide pennethrin applied against diamondback moth larvae (a worldwide pest of cabbage and other Cruciferae) 0097-6156/95/0595-0068$12.00/0 © 1995 American Chemical Society
Hall and Barry; Biorational Pest Control Agents ACS Symposium Series; American Chemical Society: Washington, DC, 1995.
Downloaded by SWINBURNE UNIV OF TECHNOLOGY on May 18, 2018 | https://pubs.acs.org Publication Date: May 5, 1995 | doi: 10.1021/bk-1995-0595.ch005
5. HALL ET AL.
Modeling the Dose Acquisition Process ofBt
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reaches its intended target (5). Of the > 99% excess pesticide, much of the spray falls from the plant to contaminate the soil or drift from the area. In addition to the environmental cost and increased production cost of excessive pesticide use, insecticide resistance can result from continuous low level exposure (7). The pesticide deposit has two components: deposit quantity (mass per unit area) and deposit quality, (deposit size distribution and the spatial distribution). Deposit quantity is a rough guide to the distribution of the AI through a canopy. However, one 80G7rai diameter droplet of a contact insecticide deposited on a leaf will not give the same biological result as the same volume deposited as 512 100/an randomly or uniformly distributed droplets. Hence, deposit quality is a key component of the application process. Laboratory studies suggest that biological efficiency of insecticides is inversely proportional to drop size: small drops work better for the same amount of AI (8). Thus, it should be possible to optimize the distribution of droplets on the plant to achieve a desired efficacy while reducing the total AI applied by manipulation of the nozzle dynamics and/ or adjuvant characteristics. However, field data do not support a correlation between good deposition of insecticides (1) and biological result In fact, the same amount of AI is usually required with small droplets as with large to get the same control in the field (9-11). One cause of reduced efficacy of small droplets in the field is drift which is also inversely proportional to droplet size. The addition of adjuvants to pesticides remains the standard procedure for improving transfer efficiency by altering impaction characteristics. The effect of formulations and adjuvants on the atomization process has been addressed in some detail at the Laboratory for Pest Control Application Technology (LPCAT) over the last four years (8J2-14) as part of our objective to investigate the enure dose-transfer process. Dose-transier can be defined as the process from atomization to biological effect, including but not limited to atomization, transport to the target, impaction, and retention (application), dose acquisition by the target (transfer), and degradation and non-target fate of AI (attrition). The dose-transfer efficiency is the product of the application and transfer efficiencies, and the biological efficiency is the dose-transfer efficiency adjusted by any loss in potency due to pesticide age or other factors. A synthesis of our knowledge of the dose-transfer process has been compiled in the form of a simulation model of Bt transfer to the diamondback moth on cabbage (Pesticide Drop Simulator, PDS; IS). Our model relates the deposition characteristics (quantity and quality) to the biological effect by simulating the feeding of diamondback larvae on cabbage leaves treated with specified size and spatial distributions of pesticide. Although the specific insecticide modelled is Bt, the model is sufficiently flexible to accommodate other foliar applied insecticides. Equally, the model is not restricted to diamondback larvae. The feeding module can be programmed to simulate a wide range of feeding behaviors. To simulate the fate of a defoliating insect on a pesticide treated leaf, variables incorporated into the simulation include; a statistical description of the relevant behavior of the insect, including feeding and locomotory behavior; a statistical description of the spatial and size distributions of the pesticide droplets on the leaf; a deterministic description of the temporal evolution of the chemical potency, which in turn may depend on the time series of incident solar (usually ultra violet) radiation. Other desiderata include a mathematical (deterministic or stochastic) description of the digestive process as it influences absorption of the toxin and any subsequent moribund behavior, a feedback loop permitting changes in insect behavior in response to toxin intake; inheritance of behaviors from one experimental cycle to the next to simulate selection of pesticide resistance. Most of these latter features have been incorporated in the model, but were not used in these simulations. Hall and Barry; Biorational Pest Control Agents ACS Symposium Series; American Chemical Society: Washington, DC, 1995.
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BIORATIONAL PEST CONTROL AGENTS
The behavioral response of target insects to insecticide deposits can also be an important determinant of insecticide efficacy (16J7). Several responses to the insecticide have been identified: pre-contact response, including odor-mediated response, and post-contact hypersensitive response to the chemical. Head et al (17) found that changes to simple behavioral rules governing locomotory behavior made in response to the presence of insecticide droplets can lead to increased movement, increasing the probability of avoidance. Movement and the aggregative response are key components of the survival of all species (1849). Thus, by extension, searching and avoidance behavior are important determinants of the survival of insects exposed to insecticides. Table L
Examples of feeding damage simulated by the interaction of the distributions of feeding and walking bout and rate
Long Feeding Bout Short Feeding Bout Walking Low High Low High Bout-RateFeeding Rate Feeding Rate Feeding Rate FeedingRate Short-Low 1. many small 2. many small 3. many large 4. many large regular holes irregular holes regular holes irregular holes aggregated aggregated aggregated aggregated Long-Low 5. few small 6. few small 7. few large 8. few large regular holes irregular holes regular holes irregular holes aggregated aggregated aggregated aggregated Short-High 9. many small 10. many small 11. many large 12. many large regular holes irregular holes regular holes irregular holes dispersed dispersed dispersed dispersed Long-High 13. few small 14. few small 15. few large regular holes irregular holes regular holes dispersed dispersed dispersed
16. few large irregular holes dispersed
Chappie et al (20) found in simulations of diamondback larvae that survival and the area eaten grows as the variance of the distribution of drop size increases. The reason is that as the size variance increases, the number mean must decrease because of the cubic relationship between volume and diameter. The smaller number of large droplets are less effective than close cover by many small drops, exactly in accordance with laboratory results. What the laboratory results do not suggest, but the simulation model apparently does, is an interactive effect of droplet distribution (governed by nozzle dynamics and adjuvant characteristics) and feeding/locomotory behavior on survival. Consequently, we ask the question in this paper, do different foraging strategies result m different mortalities for the same spatial and size distributions of droplets on the leaf? Simulation Experiments with PDS The Pesticide Drop Simulator (15) was used to investigate the possible consequences of different feeding patterns on susceptibility of foliar feeding insects to insecticide. Four behavioral parameters govern the feeding pattern of simulated insects in the simulator duration of alternating bouts of feeding and walking, the instantaneous rate of consumption of leaf surface, and the speed of locomotion, called feeding bout length, walking bout length, feeding rate, and walking rate, respectively. The balance of feeding bout and walking bout define how much time is spent on these two basic activities, while the rates
Hall and Barry; Biorational Pest Control Agents ACS Symposium Series; American Chemical Society: Washington, DC, 1995.
5. HALL ET AL.
Modeling the Dose Acquisition Process ofBt
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respectively determine how much is eaten and how far apart are the feeding spots. These four parameters can be made deterministic, or be described by stochastic functions, in addition, probability density functions can be defined for the turning rate and turning Dias. Given the large number of stochastic distributions that can be used to describe these parameters, the number of iant&ativelydiSerent feeding patterns that can be aefined is effectively infinite, owever, lo^aZiaTive^different feeding/locomotory patterns can be selected using PDS to simulate different feeding strategies (Table I). Table I summarizes the feeding damage resulting from qualitatively different combinations of simulated behavior. For example, the choice of short feeding bout and feeding rate relative to walking results in a large number of small pock marks on the leaf, simulating mite damage. A longer feeding bout combined with higher feeding rate result in feeding damage comprising large regular shaped holes such as is produced by many lepidopterous larvae. Simulating with high feeding rates results in feeding holes with irregular outlines. High rates of feeding combined with short bouts result in long narrow feeding holes, similar to the damage created by leafminers. The shape of the distribution also affects the pattern of damage on the leaf. High coefficient of variation results in iiTegularly-shaped feeding holes.
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The Influence of Insect Feeding Behavior on Efficacy To investigate the possibility that the pattern of feeding might influence the efficacy of a pesticiae application, we chose 16 patterns of feeding and walking corresponding to the qualitative patterns defined in Table I. To simplify matters, the uniform distribution was chosen as the distribution for the behavioral components. We recognize that this is an improbable distribution in nature, however we justify it by its mathematical tractability. The uniform distribution has mean and variance defined in terms of its parameters in a particularly simple way: Mean
=
VariancQ
M(X) =
= V(X)
=
a +- - ( 6 - a )
i ^
,
(
6
~
a
)
(la)
2
^
where a and b are the lower and upper bounds respectively. The amount eaten per unit time and shape of feeding hole are the important variables of feeding pattern. The total amount eaten is derived from the sums, products and ratios of the feeding and locomotory parameters. Thus, the simplicity of the uniform distribution makes it comparatively easy to define the expected distribution of amount eaten in terms of the feeding and locomotory parameters. The sum, Z, of two random variables, X and Y, have mean and variance: M(Z)
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M(X)+M(X)
(2a)
K(Z)
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V(X)
(2b)
+ V(Y)
The mean and variance of the product, Z = XY are: M(Z) K(Z)
=
M(X)-M(Y) M
+
(3a) 2
• ^\i^ i^^ --ikzr)
Hall and Barry; Biorational Pest Control Agents ACS Symposium Series; American Chemical Society: Washington, DC, 1995.
(3b)
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BIORATIONAL PEST CONTROL AGENTS
Hie mean and variance of the ratio, Z = X/Yarz: M(Z) K(Z)
-
M(AT)/iW(r) M ( Z ) . | ^ ^
(4a)
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^
2-
M(Z) /
4b
< >
Equations 3b and 4b differ only in the sign of the covariance term, Cov(X,Y) which in this case is assumed to be zero. Downloaded by SWINBURNE UNIV OF TECHNOLOGY on May 18, 2018 | https://pubs.acs.org Publication Date: May 5, 1995 | doi: 10.1021/bk-1995-0595.ch005
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Table EL Parameter values of uniformly distributed behavioral parameters used in simulations to examine the effect of insect feeding pattern on susceptibility to insecticides
Behavior Pattern
Level lower (a)upper(b) Mean
Feeding bout (min)
short long
0 5
5 10
25 75
1.443 1.443
low high high
0 5 10 15
10 15 20 25
5 10 15 20
2.887 2.887 2.887 2.887
short short short short long long long long
5 173 30 425 25 625 100 1375
15 275 40 525 35 725 110 1475
10 225 35 475 30 675 105 1425
2.887 2.887 2.887 2.887 2.887 2.887 2.887 2.887
0 5
10 15
5 10
2.887 2.887
Feeding rate (pixels/min) low
Walking bout (min)
Stdev
Walking rate (pixels/win) low high
Table II shows the parameter values used for feeding and locomotion. They were chosen such that the mean consumption rate was 60 pixels per hour. Distances and rates are measured in pixels for convenience: transforming to conventional units gives inconvenient numbers. The simulations were run for 48 hours, so the expected amount eaten in the absence of pesticide was 2880 pixels. Table IIIA shows the expected means and standard deviations of the distributions of feeding and walking, and of the derived parameters. Note that although the variances of the primary distributions are the same for all simulations, the resulting variances of amount eaten are not all the same, despite the constant expected amount eaten. It is the effect of this variation in resulting behavior we are principally concerned with, for it is this variation which is the raw material of selection and evolution (21). The other model variables werefixedas follows: 1. 100insectsweresimulatedfor48hrforeachofthe 16 feeding patterns; 2. feeding occurred throughout the 48 hr period without diurnal periodicity; 3. the pesticide simulated was Bt, with attrition defined by 5
P(0 = exp{-.0.0001 • F(0°* }
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02). However, the total amount eaten by the two groups were significantly different (surviving, 2094±.7.4; fatal, 93.4±.4.1; r=27.9, cr< 0.001). Within each group, analysis of variance of consumption rate with Run as the treatment resulted in significant differences between the treatments for both surviving simulations (^5^304=134.9, a
