EPA/600/R-XX/XXX | October 2010 | www.epa.gov/nrmrl



# Risk Assessment of the Evolution of Resistance in Western corn rootworm ( Diabrotica virgifera virgifera LeConte) to Smartstax ® 

# corn
Letter Report

Sustainable Technology Division, Cincinnati, OH 45268 National Risk Management Research Laboratory Office of Research and DevelopmentEPA/600/R-XX/XXX October 2010


# Risk Assessment of the Evolution of Resistance in Western corn rootworm ( Diabrotica vigifera vigifera LeConte) to Smartstax ® 

# corn
Letter Report

October 29, 2010

Michael Caprio 1 

### , John A. Glaser 2

1 Mississippi State University, Department of Entomology and Plant Pathology, Mississippi State, MS 39762-9775 2 US Environmental Protection Agency, Na tional Risk Management Research Laboratory, Sustainable Technology Divis ion, Cincinnati, OH 45268



### Sustainable Environments Branch

Sustainable technology Division National Risk Management Research Laboratory Office of Research and Development Cincinnati, Ohio 45258

U.S. Environmental Protection Agency Office of Research and Development Washington, DC 20460

## Notice

The U.S. Environmental Protection Agency thr ough its Office of Research and Development funded and managed the research described here. It has been subjected to the Agency=s peer and administrative review and has been approv ed for publication as an EPA document. Mention of trade names or commercial products does not constitute endorsement or recommendation for
use

. The information provided in this paper is not intended for determining compliance with state or federal regulations for Plant Iincorporated Pesticide resistance management. For these purposes, information should be obtained from the appropriate local, state and federal regulatory agencies, the Federal Insecticide Fungicide and Rodentic ide Act, the United States Code, the Code of Federal Regulations, the Federal Register and other sources.

## Abstract

Genetically-Modified(GM) crops containing pesticidal traits are valued as environmental
assets since they directly replace broad spec trum chemical pesticides having detrimental
environmental and human health effects. As pa rt of the risk assessment for each new GM
crop registration requires an evaluation of the sustainable lifetime for each new GM crop
variety. Models employing principles of popul ation genetics have been used to simulate field
conditions where the development of insect pest re sistance to the control tr aits of the crop is
encountered. The Monsanto Company’s submission of the Superstax ® corn event for control
of the Western corn rootworm ( Diabrotica vigifera vigifera LeConte) has been evaluated for
the development of insect pest resistance usi ng the EPA developed deterministic two locus
simulation model POPGEN D2L. The design of research is presented with the interpreted

results.

## Foreword

The U. S. Environmental Protection Agency (USEPA) is charged by Congress with protecting the Nation's land, air, and water resources. Under a mandate of national environmental laws, the Agency strives to formulate and implement actions leading to a compatible balance between human activities and the ability of natural systems to support and nurture life. To meet this mandate, USEPA’s re search program is providing data and technical support for solving environmental problems today and building a science knowledge base necessary to manage our ecological resources wisely, understand how pollutants affect our health, and prevent or reduce env ironmental risks in the future.
The National Risk Management Research Laboratory (NRMRL) is the Agency's center for investigation of technological and management approaches for preventing and reducing risks from pollution that threaten human health and the environment. The focus of the Laboratory's research program is on methods and their cost-effectiveness for prevention and control of pollution to air, land, water, and subsurface resource s; protection of water quality in public water systems; remediation of contaminated sites, sedi ments and ground water; prevention and control of indoor air pollution; and restoration of ec osystems. NRMRL collaborates with both public and private sector partners to foster technologies that reduce the cost of compliance and to anticipate emerging problems. NRMRL's research provides solutions to environmental problems by: developing and promoting technologies that protect and improve the environment; advancing scientific and engineering information to support regulatory and policy decisions; and providing the technical support and information transfer to ensure implementation of environmental regulations and strategies at the national, state, and community levels.
This publication has been produced as part of the Laboratory's strategic long-term research plan. It is published and made av ailable by USEPA's Office of Research and Development to assist the user community and to link researchers with their clients.

Sally Gutierrez, Director National Risk Management Research Laboratory

## Contents

Notice.........................................................................................................................

...... Abstract.......................................................................................................................

..... Acknowledgements Foreword.......................................................................................................................

... Contents.......................................................................................................................

..... List of Figures................................................................................................................

.............. List of Tables.................................................................................................................

............. Terminology.................................................................................................................... CHAPTER 1 Introduction 1.1 Background 1.2 Purpose CHAPTER 2 Study design 2.1 RM Models 2.2 Parameters 2.3 Methods and materials 2.4 Results CHAPTER 3 Discussion and Conclusions References....................................................................................................................... Appendix 1 Resistanc e Management Models Used for this Analysis................................ Appendix 2 Deterministic Parame ter Description................................................... Appendix 2 Sensitiv ity Analysis.......................................................................................

## List of Figures

Figure 1: The distribution of the time taken fo r resistance to evolve in 1000 simulations of a 5% seed-mixture. One val ue of 1000 generations was excluded for the purposes of this graph. Figure 2: The distribution of the time taken fo r resistance to evolve in 1000 simulations of a 5% structured refuge
Figure 3: Direction comparison of the densities of the distributions of the time until resistance evolved for 5% seed-mixtures and 5% structured refuges for the critic al first 200 generations.

## List of Tables

Table 1: Fitness values calculated from the mo st likely values for individual toxins (Table 2) and a dominance of 0.05

Table 2: The PERT-Beta distributions us ed in the corn rootworm risk assessment

Table 3: Quantiles for the distributions of ti me until resistance evolved for 1000 simulations each of the seed-mixture and st ructured refuge models.

# Risk Assessment of the Evolution of Resistance in Western Corn Rootworm( Diabrotica vigifera vigifera LeConte) to Smartstax ® 

# corn
Letter Report

Michael Caprio 1 

### , John A. Glaser 2

1 Misssissippi State University, Department of Entomology and Plant Pathology, Mississippi State, MS 39762-9775 2 US Environmental Protection Agency, Na tional Risk Management Research Laboratory, Sustainable Technology Divis ion, Cincinnati, OH 45268

Keywords: PIP corn variety, PIP crop registration, re sistance management, simulation modeling, POPGEN D2L



### CHAPTER 1 Introduction

The USEPA, under its administrat ion of the Federal Insectic ide Fungicide and Rodenticide
Act (FIFRA), requires the registration of all pe sticides and pesticidal materials. The GM or
Plant Incorporated Protectant (P IP) crops containing pesticidal traits are subject to FIFRA
registration requirements. Concerns relating to future environmental effects and sustainability
of these crops can be investigated only using si mulation models. Model application in this
instance offers an analysis of the useful lifetime for the pestici dal traits. USEPA has clearly
supported the use of these trai ts as replacements of broad sp ectrum pesticides that have
significant environmental footprints.
1.1 Background
Resistance management (RM) simulation models are designed for use as analytical
methodologies to provide realistic assessment of the risk of resistance evolution given theallele frequencies used as initial conditions. New concepts of GM crop deployment such as
refuge-in-the-bag require a deta iled evaluation to ensure the claims associated with each
concept offer the sustainable protection of the crop. Simulation m odels offer a means to
evaluate the threat of crop lo ss through the development of resistance in the near future.

1.2 Purpose
This research effort was designed to evaluat e the relative merit of resistance management
concepts as submitted by Monsanto Company relative to the Superstax ® corn registration.
This research specifically analy zed the relative risks related to the control of western corn

rootworm in seed mixture scenarios



### CHAPTER 2 Study Design

To understand the relative risks related to the evolution of resistance in western corn
rootworm for the Superstax ® corn variety as submitted by Monsanto Company, a two locus
frequency-based deterministic model was developed and evaluated.

2.1 RM Models

POPGEN-D2L

The deterministic model POPGEN-D2L is a modifi cation of a previously developed simple
frequency-based model that incorporates noncom pliance, non-random mating in compliant
fields and larval movement (Caprio & Glaser, 2010a). This dual locus simulation model
designed to evaluate the population dynamics of crop pest insects relating to the development
of resistance to pesticidal controls installed in the crop. The model includes a switch to vary
between a block-refuge design and a seed-mixt ure design. Biological parameters wereprimarily derived from t hat model with the exception of mort ality rates, block size, dispersal
rates and spatial aspects of refuge placement and movement.

2.2 Methods and Materials

The risk assessments utilized a two locus dete rministic model, an exte nsion of the model
used previously (Caprio & Glaser 2010 a&b). This was desig1ned as a two compartment
model, one compartment for hard-core non-compliant fields (continuously non-compliant) and
a second for compliant and unintentionally non-comp liant fields. The latter compartment was
further divided into two compartments, one fo r the Bt-plants and the second for refuge (nonBt) plants. This model extended the model used in an earlier risk assessment to two
independent resistance loci (Caprio & Glaser 2010a& b). The model could be run as either a
seed-mixture model or a structured refuge model. The risk assessment was conducted by
running each version 1000 times, each time resamp ling the parameters listed in Table 2 using
PERT-Beta distributions (Vose 2000). The value of each parameter and the time until

resistance evolved was recorded for each simulati on. Several options exist for sensitivity
analysis of this data, including Bayesi an model analysis, stepwise regression and
standardized regression coefficients. Although the use of standardized regression coefficients
is somewhat controversial, they are appropria te in this case because the variance in each
variable is determined by the PERT-Beta paramete rs and is related to the uncertainty in the
parameter.

The calculation of the fitnesses in this non-additive interaction calculated as follows. The

dominance of locus 1 was h 1 and the dominance at locus 2 was h 2 . Note that when locus 1 is
RR, then the fitness only depends on the locus 2 genotype and vice versa when the locus 2
genotype is RR. The exterior row and column of the table can therefor e be completed from
data submitted as part of the r egistration materials because one of the loci is fully resistantand only one locus is responsible for the mo rtality. The SSSS genotype fitness can be
estimated by the overall survivorship to the dual toxin product (also provided). If we look at
each locus separately, we know the survivorship of an SS is 0.0025, and RR is 1.0. Assuming
a dominance of 0.2, a heterozygote fitness of 0.202 is determined. We also know that the
SSSS fitness is 0.0005, and RRRR is 1.0. With these assumption s, the following table can be
generated:

Locus 1 Locus 2 SS RS RR SS 0.0005 A 0.0025RS B C (RRRRRRSS)*h 2 +RRSSRR 0.0025 (RRRRSSRR)*h 1 +SSRR 1.0000

The remaining difficulty is filling out the cells marked A & B (single heterozygotes) and C (the
double heterozygote class). Making the assumption that the dominance properties would
hold for these genotypes as well, the fitne ss of the SSRS genotype (B) would be calculated

as h 2 *(SSRR-SSSS)+SSSS, wh ile the fitness of the RSSS genotype (A) would be calculated

as h 1 *(RRSS-SSSS) + SSSS. Finally, the double het erozygote fitness was calculated by
using an average dominance: RSRS = (h 1 +h 2 )/2 * (RSRR – RSSS) + RSSS.

Locus 1 Locus 2 SS RS RR SS 0.01800 0. 01900 0.03800 RS 0.02000 0. 02330 0.08610 RR 0.05800 0. 10510 1.00000
Table 1: Fitness values calculated from the mo st likely values for indi vidual toxins (Table 2) and a dominance of 0.05Variable Min Mode Max Comments L1-fit 0.008 0.058 0.1 L2-fit 0.0008 0.038 0.08 Overall Survivorship 0.0003 0.018 0.03This will always be as low as lowest of L1 and L2 Dominance 0.01 0.05 0.2Drawn independently for both loci Initial frequency 0.001 0.005 0.02

These are variables specific to spatial refuges Refuge size 0 0.03 0.06 Always set to 0.05 if seed-mix Non-compliance 0 0.3 0.6Always set to 0 for seed-mix Non-compliance mixing 0.8 0.9 1 Non-random mating 0.2 0.5 0.7Always set to 0 for seed-mix

These are variables specific to seed-mixes Base larval movement 0.05 0.2 0.4Always set to 0 for spatial refuges Days of larval selection 5 10 15 Asymmetrical larval movement 0 0.3 0.5 Always set to 0 for spatial refuges Table 2 : The PERT-Beta distributions used in the c orn rootworm risk assessment2.4 Results

The mean time to resistance for the seed-mixt ure model was 81.9 generat ions, while it was
52.9 generations for the structured refuge simula tions. Histograms of the results from the

1000 simulations of each version of the model were similar in shape (Figures 1 & 2), though
bootstrapping revealed significant differences ( p <<0.01, R statistical package, sm.density
function). A comparative density plot for the two di stributions is shown in Fig. 3. In the case
of seed-mixtures, 2.5% of the simulations re sulted in times until resistance evolved of less
than 30 generations, while 17.0% of the structured refuge simulations fell below this limit. To
assist in assessing different levels of risk, di fferent quantiles of the two distributions are show
in Table 3. The quantiles can be interpreted as levels of acceptable risk. If one accepted a
5% chance of not meeting a target as an acc eptable level of risk, the seed-mixture would be
acceptable if the target were 33 generations or less, while a stru ctured refuge would only
meet a target of 22 generations or less. The sensitivity analysis (Append ix 1) indicated that
all variables except non-random mating and non-compliant mixing had statistically significant
impacts on the time it took for resistanc e to evolve. The Bayesian model averaging
suggested that non-compliant mixing should be incl uded in the model. For seed-mixtures, all
variable except base larval movement, asymme trical movement and t he time period over
which larval movement occurred had statistically significant impacts on the time it took for
resistance to evolve. The Bayesian model averaging suggested that the base larval

movement rate should be included as a factor. The difference can be seen in the BMA output.
The best fit model, the one returned by the stepwis e regression, did not include base larval
movement, but the second best fit model did include that variable. In bo th of the BMA runs
only the two top models were used as other models had significantly higher absolute
Bayesian information contents (i.e., they did not fit the data nearly as well).Quantiles 0 5.00% 10.00% 25.00% 50.00% Seed-mixture 23 33 39 51 70 Structured refuge 12 22 26 34 45 Table 3: Quantiles for the distributions of time until resistance evolved for 1000 simulations each of the s eed-mixture and structured refu g e models.

### CHAPTER 3 Discussion

These results provide support for the use of a seed-mixture refuge strategy in a two locus
corn rootworm system. There are several fa ctors included in the model that made the
structured refuge less effective than one might have expected. Corn rootworm movement
and refuge placement, combined with source-sink dynamics, can alter the effective refuge
size. If growers always plant refuges in the same place year after year, it is likely that that
refuge will have more than 5% of t he total egg lay within the 5% of the total area it contains.
Alternatively, if the refuge is mo ved every year, it is possible that very few eggs would be laid
under the new refuge site. In addition, the pr oportion of the growers were assumed to be
non-compliant year after year was increased to a maximum of 60% to account for local areas
where non-compliance might be higher than average. Some non-random mating
(independent of effective refuge size) was also included in structured refuge simulations.
Finally, asymmetrical movement of CRW larv ae off of transgenic plants was included in the
seed-mixture model. This can, at least in some cases, lead to an increased density of larvae
on the non-Bt plants in a seed-mixt ure, somewhat increasing the ef ficacy of that strategy. The
latter two suggestions, however, are not supported by our sensitivity analysis. It should be

noted that several of these fa ctors are dependent of specifics of CRW biology (low vagility,
asymmetrical movement), and caution should be used when extrapolating these results to
other species.

It is relevant to note that these data present more risk for dual gene products than might have
been expected from earlier research (Caprio and Gl aser 2010a & b). This is due primarily to
a change in our perception of the initial resist ance allele frequency for western corn rootwormthat was changed to a minimum of 0.001, a mo st likely value of 0.005 and a maximum value

of 0.02.

References
Caprio, M. A. and D. Sumerford. 2007. Evaluating transgenic plan ts for suitability in pest and resistance management programs, 2nd edition. L. Lacey & H. Kaya [eds]. Field Manual of Techniques for the Application and Evaluation of Entomopathogens.
Caprio, M.A. and J.A. Glaser 2010a, The Use of Simulation Models to Evaluate Pest Resistance Development to Refuge in the B ag Concepts related to Pioneer Submission: Letter Report, January 2010, US EPA Natio nal Risk Management Research Laboratory, Cincinnati, OH.
Caprio, M.A. and J.A. Glaser 2010b, The Use of Simulation Models to Evaluate Pest Resistance Development to Refuge in the B ag Concepts related to Pioneer Submission: Letter Report Addendum, April 2010, US EPA National Risk Management Research Laboratory, Cincinnati, OH.
Hibbard, BE, MI Higdon, DP Duran, YM Schweikert, and MR Ellersieck. 2004. Role of egg density on establishment and pl ant-to-plant m7(3): 871-882
Hibbard, BE, TT Vaughn, IO Oyediran, TL Clark , MR Ellersieck. 2005. Effect of Cry3Bb1 expressing transgenic corn on plant-to-plant mo vement by western corn rootworm larvae (Coleoptera Chrysomeldiae). J. Econ. Entomol. 98(4): 1126-1138
Vose, 2000. Risk Analysis: A quantit ative guide. Wiley, New York.Figure 1: The distribution of the time taken for resistance to evolve in 1000 simulations of a 5% seed-mi xture. One value of 1000 generations was excluded for the purposes of this graph.

Figure 2: The distribution of the time taken for resistance to evolve in 1000 simulations of a 5% structured refugeFigure 3: Direction comparison of t he densities of the distributions of the time until resistance evolved for 5% seed-mixtures and 5% structured refu g es for the critical first 200 g enerations.

### Appendix 1 - Resistan ce Management Model Used for this Analysis

The deterministic model is a relatively simp le frequency-based model that incorporates noncompliance, non-random mating in compliant fields and larval movement. It includes a switch
to vary between a block-refuge design and a seed-mixture design. Appendix 2 includes
descriptions of the different parameters. The pr imary difference between t he Bt population in
the non-compliant population an d the Bt-population in the non-random mating population is
that no refuge was planted for the non-compliant fields. Non-compliant populations reduce
both non-random mating and refuge size. (Caprio & Glaser, 2010 a&b).

While we believe that this may overestimate movement of refuges by growers, this was
incorporated into the model by defining the refuge size not as t he physical area devoted to the
refuge, but rather t he proportion of the eggs in a fiel d that start out underneath the new
refuge. This is the effective refuge size to distinguish it from physical refuge size (the
proportion of an area planted to a refuge). These small refuge sizes are independent of noncompliance and result from growers moving their refuge to areas of the field were few eggs
were deposited the previous year. To estima te this important parameter, we used the
stochastic model to estimate egg densities at the st art of a year across our four different field
sizes. In each case, we estimated a best and worst scenario. For the best case scenario, the
grower moved their refuge to t he opposite side of the field, nex t to where the refuge would
have been planted in the neighboring field the previous year (thes e simulations were run with
all growers planting refuges to the same sides of their fields and swapping them every year).
The worst case scenario was based on planting t he refuge in the area of the field where the
egg density was the lowest. Because the egg dens ity distribution across fields has never
been measured, it is unlikely t hat growers could accomplish th is, but it does represent andtheoretical lower bound for effective refuge size. The worst case effective refuge sizes for the

four different field sizes were (from smallest to largest field size): 15%, 11%, 3% and 0.055%.
Few insects were noted to be moving into the center of large fields as reflected by the
effective refuge size of 0.055% for 144 ha fields . Other estimates for dispersal are higher and
may depend on the region of the country. To test the effect of higher dispersal rates we
doubled the vector of daily mo vement (to 29m/day) and repe ated the large field size
simulation In this case the density of eggs in the center of the field was fifty times greater

under the worst case scenario and the effective refuge size was 1%. Most fields in the corn
belt, however, are smaller, in the range of 30-50 ha, and we decided to use a lower limit of 1%
for this parameter.

Asymmetrical movement was included in the model, though the amount movement of
susceptible larvae from transgenic corn is uncer tain (Hibbard et al 2004, Hibbard et al 2005).
Asymmetrical movement was defined as the probability that larvae that have survived
exposure on a Bt-plant will move. When la rvae move, they move onto plant genotypes
according to the ratio of those genotypes in the field. In this case, a larva leaving a transgenic
plant had a 95% probability of moving onto another transgenic plant. As with other rates in the
model, this movement rate is converted into a daily rate (depending on the days of larval
movement parameter).

### Appendix 2 Deterministic Parameter Description

Wss – The fitness of homozygous susceptible genotypes on the transgenic plants. Without
better knowledge of field tests it's difficult to develop a range, but a specific factor over
and under was used to develop our estimate. One question to be asked might be what
is the lowest/highest performance under field conditions. Instead of 98.75% mortality,
could it be 95% (as an example)?

Dominance – The relative fitness of heteroz ygotes. The traditional population genetics
equation was employed:
Wrs = (Wrr – Wss) * h + Wss

where h is the dominance and Wrr = 1.0. Our distribution is derived from an estimate of
h=0.05, but that seems low compared to many estimates based on LC50s. LC50s, however,
are based on a log scale, however this research has used a linear scale. In general, for
products with lower effectiveness, dominance t ends to be higher ( Caprio et al 2007).

IRF – The initial resistance allele frequency. My impression from deal ing with CRW folks is
that this value has tended to be higher in empirical estimates than we have seen for other

insects.

Block refuge model parameterization:

Values cited above are shared wi th the larval movement model and changes in would effect

both the block and larval movement models to a similar degree.Refuge proportion – Initially set this to 20%. However, it can be viewed as the proportion of
eggs in a compliant field that end up being in a refuge (perhaps after being moved), and the
non-random oviposition patterns can be addressed usi ng this parameter. If more individuals
survive in refuges, and they tend to lay eggs near that area, then the number of eggs in the
soil to start the new growing s eason will be greatest near the refuge from the previous year. If
the refuge is moved, it stands to reason that the new refuge, even if it covers 20% of the area,
will receive less than 20% of the eggs ovipos ited in the field. Based on this modeling
research, this fact and the likelihood that refuges are moved are identified as key
assumptions of previous modeling efforts. Unfo rtunately, there is little empirical data on the
non-random oviposition patterns of CRW.

Non-compliance – This is the proportion of fields that are planted without a refuge.
Individuals emerging from this ar ea mate as a separate population. The most likely value is a
literature value (CSPI). This parameter should be based on a field by field basis and
incorporate some estimate of amount of non-comp liance. A person planting a 3% refuge in a
field is not the same as someone not planting any re fuge. Alternatively, it shouldn't measure if
a field is compliant (as yes./no answer), but how compliant a non-compliant field is. General

comments have been encountered which indicated t hat 30% non-compliance was too low. It
is not obvious how non-compliance was esti mated. This parameter may be important

because it determines how much Bt-c orn is planted without any refuge.

Non-compliance mixing – This determines how much movement there is between compliant
and non-compliant fields. If non-co mpliant growers are accidentally non-compliant (i.e. they
never got around to planting the refuge because of weather), then they will generally be
compliant in the next year, and this parameter s hould be close to 1.0. On the other hand, ifnon-compliant growers are grower s who purposefully don't plant refuges and continue this
practice year-by-year, then this paramete r should be low. How low would depend on how
much movement naturally occurs between non-comp liant and compliant fiel ds. Inevitably this

leads to discussion of the cluste ring of non-compliant fields.

Non-random mating – This parameter addresses the proportion of the population in the Btportion of compliant fields that is far enough away from the refuges that they do not mate at
random. These differ from non-compliant fields in that refuge is plant ed, but it was too far
away. The non-random oviposition that result s from this non-random mating (and limited
dispersal capability) is covered by the reductions in refuge size discussed above.

Larval movement model parameters

The refuge proportion is fixed at 0.05 in the larval movement model.

Base larval movement – This is the amount of movement that might occur in a non-transgenic
field with populations low enough so that significant density-dependent dispersal is not a

factor. In the stochastic m odels, population decline is found to be a greater problem than
density-dependent factors such as mortality or dispersal. The model does include
asymmetrical movement, with the movement of individuals on Bt plants increased by an
amount related to their daily mortality rate s. Susceptibles ther efore move more than
heterozygotes, and resistant homozygotes would only move at the base larval movement
rate. Of course, an individual leaving a transgenic plant only has a 5% change of moving
onto a non-Bt plant. Various estimates of la rval dispersal rates have been encountered but
mostly on the low side. Generally, this research assumes that populations will be low in seedmix fields and there will be little to no density- dependent larval dispersal. This could be an

area of concern since the selectio n of alternate values is relatively unimportant compared to
other parameters and a precise estimate would be a misapplication of resources.

Days of larval movement – Movement and larval development are assumed to occur over this
period of days. Movement occurs once per day . Daily survivorship is constant and is

determined as the rate that raised to the days power results in the overall fitness listed for the
genotype in the original table. In a previous ca se, that was 0.0125 for susceptible larvae. If
the days of larval movement is 10, then the daily survivorship rate is 0.645. This means that
on day 1, 36.5% of the SS larvae on Bt die (assu me conservatively that mortality occurs
before dispersal) , and of t he remaining 64.5%, 36.5% will di sperse (see above). Of these
dispersants, only 5% will move onto non-Bt plants, increasing the refuge size from 5% to ca.

6%. If the base larval movement is low (remem ber it is spread over days as well), then this
process can lead to an increase in the refuge population and actually de crease the rate of

resistance evolution. This featur e was installed in the model just to see what effect it might
have. The value was varied between 5, 10 and 15 and it had very little effect on the results.

### Appendix 3 – Sensitivity Analysis **Structured Refuges**

Call: lm(formula = time ~ dominance + IRF + l2dom + l2wss + Non.compliance + non.compliance.mixing + non.random.mating + Overall + Refuge + wss, data = DatasetStruct)

Residuals: Min 1Q Median 3Q Max -33.035 -8.763 -3.390 4.458 229.222

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 141.953 16.615 8.544 < 2e-16 *** dominance -115.819 18.192 -6.366 2.96e-10 *** IRF -3417.503 217.135 -15.739 < 2e-16 *** l2dom -386.607 18.362 -21.054 < 2e-16 *** l2wss -564.326 44.426 -12.703 < 2e-16 *** Non.compliance -28.121 5.747 -4.893 1.16e-06 *** non.compliance.mixing -27.456 17.147 -1.601 0.110 non.random.mating 6.533 7.237 0.903 0.367 Overall 2504.917 119.961 20.881 < 2e-16 *** Refuge 825.109 60.190 13.708 < 2e-16 *** wss -905.127 38.116 -23.746 < 2e-16 ***

--- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 18.27 on 989 degrees of freedom Multiple R-squared: 0.6627, Adjusted R-squared: 0.6593 F-statistic: 194.3 on 10 and 989 DF, p-value: < 2.2e-16

Stepwise regression: Df Sum of Sq RSS AIC <none> 331361 5865.4 + non.compliance.mixing 1 885 330476 5869.6 + non.random.mating 1 301 331060 5871.4 - Non.compliance 1 7979 339340 5882.3 - dominance 1 13531 344892 5898.5 - l2wss 1 53272 384633 6007.6 - Refuge 1 62456 393817 6031.1 - IRF 1 83047 414408 6082.1 - Overall 1 145166 476527 6221.8 - l2dom 1 147904 479265 6227.5 - wss 1 188771 520132 6309.3

Call: lm(formula = time ~ dominance + IRF + l2dom + l2wss + Non.compliance + Overall + Refuge + wss, data = DatasetStruct)Coefficients: (Intercept) dominance IRF l2dom l2wss 120.38 -115.78 -3423.40 -386.44 - 559.39 Non.compliance Overall Refuge wss -28.09 2500.98 823.09 -905.43

Standardized regression coefficients: > lm.beta(RegModel.2) dominance IRF l2dom -0.11778716 -0.29144635 -0.39016175

l2wss Non.compliance non.compliance.mixing -0.23694429 -0.09057749 -0.02969303

non.random.mating Overall Refuge 0.01670632 0.38795030 0.25349491

wss -0.44004402

iterative Bayesian Model Averaging summary: Call:

iBMA.bicreg.matrix(x = as.matrix(DatasetStructnrs[, 1:10]), Y = DatasetStructnrs$time, sorted = TRUE, nbest = 10)

completed 1 iteration

number of iterations in last call = 100

variables selected = 1 2 3 4 5 6 7 8 9

statistics for variables examined so far

variable status max Probne0 n times in model

1 wss selected 100 1

2 dominance selected 100 1

3 l2wss selected 100 1

4 l2dom selected 100 1

5 Overall selected 100 16 IRF selected 100 1

7 Refuge selected 100 1

8 Non.compliance selected 100 1

9 Non.compliance.mixing selected 10.8 1

10 Non.random.mating dropped 0 1

Results of running BMA on final set of selected variables:

Call:

bicreg(x = currentX, y = cs$Y, wt = cs$wt, maxCol = cs$maxNvar + 1, nbest = 10)

2 models were selected

Best 2 models (cumulative posterior probability = 1 ):

p!=0 EV SD model 1 model 2

Intercept 100.0 123.102 10.44 120.38 145.52

wss 100.0 -905.259 38.11 -905.43 -903.86

dominance 100.0 -115.823 18.20 -115.78 -116.15

l2wss 100.0 -559.995 44.36 -559.39 -565.01

l2dom 100.0 -386.431 18.37 -386.44 -386.37

Overall 100.0 2501.336 120.03 2500.98 2504.25

IRF 100.0 -3423.297 217.21 -3423.40 -3422.49

Refuge 100.0 823.258 60.22 823.09 824.67

Non.compliance 100.0 -28.085 5.75 -28.09 -28.03Non.compliance.mixing 10.8 -3.023 10.35 . -27.91

nVar 8 9

r2 0.661 0.662

BIC -1027.91 -1023.69

post prob 0.892 0.108**Seed-Mixtures**

Call: lm(formula = time ~ AsymmMove + BaseLarvalMovement + dominance + IRF + Overall + SelectionDays + w2dominance + w2ss + wss, data = DatasetLM)

Residuals: Min 1Q Median 3Q Max -63.358 -16.039 -6.685 7.804 810.420

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 244.0960 12.3854 19.708 < 2e-16 *** AsymmMove 17.0510 14.1070 1.209 0.2271 BaseLarvalMovement -46.9418 20.5009 -2.290 0.0222 * dominance -226.3551 36.2211 -6.249 6.12e-10 *** IRF -4976.0100 438.0047 -11.361 < 2e-16 *** Overall 3576.2111 246.6545 14.499 < 2e-16 *** SelectionDays -0.6829 0.7174 -0.952 0.3414 w2dominance -605.4791 37.3586 -16.207 < 2e-16 *** w2ss -1081.5755 90.2482 -11.984 < 2e-16 *** wss -1504.3279 76.6823 -19.618 < 2e-16 ***

--- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 37.16 on 990 degrees of freedom Multiple R-squared: 0.5517, Adjusted R-squared: 0.5476 F-statistic: 135.3 on 9 and 990 DF, p-value: < 2.2e-16

Stepwise regression: Df Sum of Sq RSS AIC <none> 1377719 7276.5 + BaseLarvalMovement 1 7262 1370457 7278.2 + AsymmMove 1 2337 1375381 7281.7 + SelectionDays 1 1148 1376570 7282.6 - dominance 1 57428 1435147 7310.5 - IRF 1 179238 1556957 7391.9 - w2ss 1 199628 1577347 7404.9 - Overall 1 286797 1664515 7458.7 - w2dominance 1 369432 1747151 7507.2 - wss 1 538532 1916251 7599.6

Call: lm(formula = time ~ dominance + IRF + Overall + w2dominance + w2ss + wss, data = DatasetLM)

Coefficients: (Intercept) dominance IRF Overall w2dominance w2ss 234.4 -232.7 -4988.5 3543.8 -608.7 - 1084.9wss -1511.4

Standardized regression coefficients > lm.beta(RegModel.1) AsymmMove BaseLarvalMovement dominance IRF 0.02590690 -0.04890295 -0.13375587 -0.24243465

Overall SelectionDays w2dominance w2ss 0.31162764 -0.02038417 -0.34873674 -0.25629715

wss -0.42115295

iterative Bayesian Model Averaging summary: Call:

iBMA.bicreg.matrix(x = as.matrix(DatasetLMnrs[, 1:9]), Y = DatasetLMnrs$time, sorted = TRUE, nbest = 10)

completed 1 iteration

number of iterations in last call = 100

variables selected = 1 2 3 4 5 6 7

statistics for variables examined so far

variable status max Probne0 n times in model

1 wss selected 100 1

2 dominance selected 100 1

3 w2ss selected 100 1

4 w2dominance selected 100 1

5 Overall selected 100 1

6 IRF selected 100 1

7 BaseLarvalMovement selected 29.2 1

8 SelectionDays dropped 0 1

9 AsymmMove dropped 4.9 1Results of running BMA on final set of selected variables:

Call:

bicreg(x = currentX, y = cs$Y, wt = cs$wt, maxCol = cs$maxNvar + 1, nbest = 10)

2 models were selected

Best 2 models (cumulative posterior probability = 1 ):

p!=0 EV SD model 1 model 2

Intercept 100.0 236.96 8.92 234.35 242.85

wss 100.0 -1509.29 76.75 -1511.40 -1504.51

dominance 100.0 -231.47 36.20 -232.67 -228.77

w2ss 100.0 -1084.60 90.39 -1084.93 -1083.85

w2dominance 100.0 -607.94 37.30 -608.69 -606.27

Overall 100.0 3546.71 246.37 3543.80 3553.29

IRF 100.0 -4989.24 438.61 -4988.47 -4990.98

BaseLarvalMovement 30.7 -14.43 24.46 . -46.97

nVar 6 7

r2 0.548 0.551

BIC -753.00 -751.38

post prob 0.693 0.307