frequency, especially when the product contains more than one toxin,
affects more than one locus or when cross resistance is not involved.
The 30-year allele frequencies for each locus would provide a third
perspective for assessing risk and aid in our overall understanding of
the process of evolution to transgenic crops.

Given the simplicity of the analysis, this model should have been run
until resistance developed regardless of the number of generations or
years.  If Monsanto wants to demonstrate that resistance never develops
under a particular scenario, then allele frequencies for resistance for
each locus need to be provided showing that there has been no change
over some period of time (either from initial allele frequencies or some
stable equilibrium allele frequency over an extended period of time).

In predicting resistance evolution to Bollgard II, there is not only
uncertainty in the estimation of parameters used in models (as described
in previous sections), but also “model uncertainty,” as is always
the case.  Model uncertainty refers to very different predictions that
can arise from different models.  Unfortunately, while there are
statistical methods to account for parameter uncertainty, it is much
more difficult to formally assess model uncertainty before resistance
evolution occurs.  Of course, once resistance has arisen, it is possible
to validate models against the data.  For example, Livingston et al.
(2002) show that a simple genetic model can characterize the evolution
of pyrethroid resistance in CBW and TBW field populations.  Nonetheless,
in the absence of such empirical data for Bt cotton, and in particular
for a two-toxin product like Bollgard II, model uncertainty can only be
addressed by careful mathematical examination of different models to
understand why they give different results.

	The model which Monsanto used for its analyses (Gustafson and Head
2005) is based on Caprio (1998).  Like all models, this model makes
numerous simplifying assumptions about the biology of TBW and the
processes that affect resistance evolution.  Exploration of this model
(Appendix 2) demonstrates that seemingly subtle differences in the
assumptions not only can have a large impact on predictions about the
rate of resistance evolution, but can also change which of the
parameters have the greatest influence on the rate of resistance
evolution.  This highlights the problem that model uncertainty affects
not only the quantitative predictions about the rate of resistance
evolution, but also the key factors that must be measured to make these
predictions.

	A suite of well-understood models of resistance to a pyramid Bt product
does not yet exist.  Furthermore, key issues like spatial structure,
linkage disequilibrium, and differential movement of males and females
have not been explored in detail.  This makes model uncertainty high.

Parameter Estimates

Parameter uncertainty is a common problem in risk analysis that can
often lead to incorrect analysis and inappropriate conclusions.
However, there are several methodologies for incorporating parameter
uncertainty and exploring the sensitivity of the results to differences
in parameter estimates.  The uncertainties associated with many of the
parameter estimates have been discussed under Charges 1-6.  In general,
these uncertainties can be resolved with the collection of more years of
data and better spatial sampling of the regions.  Even without more
data, a more appropriate statistical analysis should provide a better
understanding of the hosts from which moths developed and the geographic
and temporal distribution of moth abundance in each region.  The most
significant challenge and the greatest uncertainty involves the gossypol
analysis.  All estimates of moth production from non-cotton refuges are
dependent on the validity of this technique (see Panel response to
Charge 3).  Confirmation of the gossypol analysis as a means of
assessing the proportion of moth trap catches originating from cotton
hosts would have the greatest impact on the confidence of risk
assessment regardless of the modeling techniques used.

Both analytical and simulation models have been used in the past to
assess the risk of resistance to plant-incorporated protectant
compounds.  Deterministic and stochastic approaches have been used with
both kinds of models.  Monsanto’s model is a simple analytical model
using perhaps the simplest deterministic approach.  This technique makes
broad assumptions concerning the year-to-year stability of the landscape
characteristics.  Monsanto explicitly defines the proportions of each
product as constant over the 30-year time horizon (see socio-economic
factors below).  Implicitly, this model also assumes year-to-year
stability in the proportions of crop types, the proportions of
continuous and rotated crops, and the spatial distribution of various
hosts throughout the landscape.  Similarly, the biology and behavior of
the insect in response to the landscape is assumed to be stable from
year-to-year.

An alternative to an analytical model is to simulate the system in
detail.  Risk can be assessed using either modeling technique by varying
the parameter estimates across a range of values.  Using a deterministic
approach, inputs can be chosen as fixed values representing the mean (or
median as appropriate) as well as the high and low values representing
some confidence interval (assumed 95% CI).  Monsanto used this basic
approach assuming that the lowest moth trap catches in each region
represents the greatest risk of refuge failure.

Conceptually this is an appropriate methodology but (i) the statistical
analysis of the trap data, (ii) uncertainty about the similarity of male
and female dispersal behavior, (iii) uncertainty about the accuracy of
the gossypol analysis, and (iv) unknown uncertainty in other parameters,
does not permit us to assess the lower bounds of the confidence interval
with any accuracy.  If these issues are resolved, we recommend a
multi-pronged approach involving several modeling strategies, including
simple deterministic models and more-complex simulation models.
Mississippi and East Texas are regions that require particular attention
in these modeling efforts.

Assumptions

	Three principle assumptions of the model were challenged by the Panel,
specifically, widespread dispersal of the pests, socio-economic factors
affecting the market share of the products, and the single locus per
toxin receptor without cross-resistance.  The expected changes to these
assumptions have the potential for resistance to evolve more rapidly
than Monsanto’s results suggest.  Detailed evaluation of these
assumptions follow.

	The Panel also notes that two of the assumptions may be considered
conservative because the literature suggests that resistance to
transgenics involves multiple loci and may be associated with fitness
costs.  The anticipated effects of incorporating less conservative
estimates are described after the challenges.

Spatial and Temporal Variability of Dispersal

	Figures 1 and 2 in Benedict (2005) illustrate the magnitude of
differences in moth trap catches and host origin throughout the season
and between regions.  Confidence in the assumption that TBW is
dispersing widely across the landscape varies throughout the season.  If
there are periods when dispersal is limited and mating moths are derived
from the local landscape, resistance can evolve in a local population as
a "hotspot."  Single-locus spatially explicit models of resistance
evolution (e.g., Peck et al. 1999, Storer et al. 2003, Sisterson et al.
2004) show that when refuges become rare, resistance is much more likely
to occur in hotspots of Bt fields that are relatively isolated from
refuges.  Generations of low dispersal can play a disproportionate role
in the rate of resistance evolution.  The very low FST values reported
in the literature for population differentiation (based on neutral
genetic markers), indicating high gene flow in general for TBW (Han and
Caprio 2004, Benedict 2005), could exist even in the face of the
oscillating temporal dispersal patterns over the season in which a
resistance allele is increasing in frequency.  While low FST values can
reflect the effects of high levels of gene flow at neutral loci caused
by migration over a long period of time, something very different can be
happening to allele frequencies at loci under strong selection pressure.

	The structure of the Monsanto model is non-spatial and therefore
assumes that the distribution of refuge and Bt fields and the dispersal
behavior of the adult insect are similar throughout the landscape.
These are not realistic assumptions.  A model incorporating a spatially
explicit landscape and requiring 2-loci for resistance was developed for
this report by one Panel member (see Appendices 3 and 4).  As found with
single-locus models (Peck et al. 1999, Storer et al. 2003, Sisterson et
al. 2004), in this 2-locus model hotspots can occur when refuges (either
natural or structured) are rare.  When hotspots occur, resistance
evolution can be much more rapid than in the absence of hotspots.  This
poses a challenge for IRM, because strategies must be designed to
eliminate all possible areas where hotspots might arise; a hotspot in a
single county might cause resistance that spreads throughout a region.
The model shows that hotspots can occur even when males are broadly
dispersive provided females have limited dispersal; therefore, direct
information about female movement is needed to assess the risk of
hotspots.  Finally, in the model hotspots seem to overwhelm the effects
of a fitness cost of resistance.  Although fitness costs to resistant
genotypes (i.e., reduced fitness in the absence of selection from Bt)
generally slow resistance evolution (see "Fitness costs" below), this
effect was minimal when the model produced hotspots.

The Panel includes this model to emphasize several points: (1) Model
structure is critically important; Monsanto’s non-spatial model cannot
produce hotspots and therefore might ignore an important component of
resistance evolution.  (2) Simple changes in model assumptions can lead
to dramatically different outcomes. (3) Our ability to determine
appropriate model structure is limited by the lack of basic information
needed to understand resistance evolution, especially in a 2-loci
system.  Because the appropriate model structure cannot be determined,
the Panel cautions against relying on any one model or modeling
technique for evaluating resistance to Bollgard II.

Socio-economic factors

The assumption that the market share of each product would remain stable
over a 30-year time horizon is unrealistic.  If a particular product
fails to control a pest, we should expect growers to change their choice
of control measures.  Monsanto’s product market share scenarios do not
account for this important socio-economic factor.  For example, in
scenario 3-N for Texas (see Table 14 in Gustafson and Head 2005)
resistance to product A (WideStrike) evolves in 19.5 years, resistance
to product B (Bollgard) evolves in 12.8 years, while resistance to
product C (Bollgard II) does not evolve within the 30-year time horizon.
 The efficacy of Bollgard II at 30 years suggests that resistance will
evolve soon after execution of the model was truncated.  It is
unrealistic to assume that growers would continue to use the two failed
products for 17 (Bollgard) and 10 (WideStrike) years.  Continued use of
these products is equivalent to using these areas as refuge for product
C (Bollgard II).

A more realistic assumption is that product market shares will decline
for products A and B and that growers will replace these products with
either an increase in the market share of product C or resort to some
alternative control measure such as pesticides (another alternative
refuge).  However, Monsanto assumes that agricultural producers plant
the maximum allowable proportion of Bt cotton at the state level which,
as time-series data on adoption show, is not likely to be the case over
any 30-year period.  This assumption leads to the simulation result that
Cry1 and Cry2 resistance will evolve more rapidly than is likely to be
the case in reality.  TBW results are unlikely to be affected by
including a grower behavioral response.  However, from a quantitative
perspective, including the grower’s behavioral response (Livingston et
al. 2006) could dramatically change the time to resistance for CBW
results.

Potential for Metabolic Cross-Resistance

One Panel member challenged the assumption that resistance to each toxin
is associated with an independent single locus.

The three-gene model for insect resistance evolution considered by
Monsanto makes assumptions about the action and interaction of
resistance genes that do not fully reflect the resistance mechanisms
reported in the scientific literature.  Thus modifications of the model
to incorporate such resistance mechanisms might yield different
predictions of the durability of Bt cotton.  Three assumptions made by
Monsanto are discussed below:  1) independent toxin action, 2) absence
of minor resistance mechanisms, and 3) absence of a sequestering
mechanism.

1)  Toxins may not act independently.  In the model presented by
Monsanto, survivorship of an insect on a single toxin is represented as
a logistic function of the toxin-receptor complex, and toxins are
assumed to act independently so that the total survivorship is the
product of the single-toxin survivorships.  However, some studies show
that toxins do not act independently, but instead synergistically or
antagonistically (Tabashnik 1992).  An example of antagonistic action,
in which the mortality caused by a combination of toxins is lower than
predicted by the mortality of each singly, is given by Liao et al.
(2002).  In this study, the median lethal concentration (LC50) of Cry1Ac
and Cry2Aa presented together in various ratios was considerably less
than the LC50 predicted by models of independent action of the separate
toxins, for a susceptible strain of Helicoverpa armigera.  This would
imply that the multiplicative aspect of Monsanto's model could be
underestimating the probability of survivorship on Bollgard II cotton of
insects that have resistance to one of the two toxins.

2)  The evolution of "minor" resistance mechanisms may influence
selection on "major" resistance genes.  In the model presented by
Monsanto, the only mutations that affect resistance are in the receptors
for the specific toxins.  This "target-site" resistance has been firmly
established as a major resistance mechanism, and mutations in one of the
receptors (a 12-domain cadherin protein expressed in the midgut
epithelium) have been identified that cause high Cry1Ac resistance in
three lepidopteran species.  (These are TBW (Gahan et al. 2001), pink
bollworm Pectinophora gossypiella (Morin et al. 2003), and the old-world
cotton bollworm Helicoverpa armigera (Xu et al. 2005)).  However,
additional resistance mechanisms are known, such as changes in proteases
that activate the protoxin (Oppert et al. 1997, Li et al. 2004) or
changes in abundances of other binding targets for Cry1Ac such as
alkaline phosphatase (Jurat-Fuentes et al 2005).  These are "minor"
resistance mechanisms that acting alone would not permit survival on
transgenic cotton.  But by reducing the amount of toxin that has access
to the receptor, they may contribute to resistance, especially in
individuals that are heterozygous for the mutation in the receptor
itself.

3)  A novel, recently-proposed resistance mechanism of "toxin
sequestration" could have profound effects on resistance development on
single-gene and pyramided cotton varieties.  Gunning et al. (2005)
claimed that overproduction of esterases in the "silver selected" strain
of Helicoverpa armigera was responsible for Cry1Ac resistance in that
strain.  They proposed that esterases could bind to the toxin and keep
it from interacting with its receptor in the midgut.  Overproduction of
esterases is well-established as a mechanism of resistance to
organophosphorus insecticides in aphids and mosquitoes.  Each esterase
subunit binds a single molecule of the insecticide in its active site,
sequestering the insecticide and keeping it from reaching
acetylcholinesterase, its target in the nervous system.  Gunning et al.
(2005) proposed the same sort of sequestration was operating in the
"silver selected" strain.  Such a mechanism would be expected to be
energetically costly, especially if a 1:1 ratio of sequestering esterase
to Bt toxin were required.  However, if the esterase-toxin complex
aggregates with other toxin molecules, such a ratio might not be
required.  Instead, there might be a general precipitation of toxin,
such as induced by elastase TPP-75 (a digestive protease) produced in
the midgut of the spruce budworm Choristoneura fumiferana, which
precipitates Cry1Aa at a 1% molar ratio (Milne et al. 1998).

The report by Gunning et al. (2005) is highly controversial, and
although it was published under peer review in a respected journal, the
results have not been independently confirmed by other workers to date.
If they are, and if direct evidence is produced for an esterase present
in the midgut lumen in sufficient quantities to prevent Cry1Ac from
binding to its receptor(s) in the midgut, this information would have to
be considered in formulating models of resistance development.  In
addition, if such an esterase could sequester other toxins, such as
Cry1F or Cry2Ab, this single-resistance mechanism could protect the
insect from pyramided varieties of Bt cotton as well as Bollgard.

The consequences of this putative new sequestration mechanism for the
modeling would require addition of a term describing the esterase-toxin
complex.  If this complex could be formed for any Bt toxin, the
three-gene model would probably reduce to a single-gene model where an
increase in the resistance allele frequency would have equal impacts on
the efficacy of Bollgard, WideStrike, and Bollgard II cotton.  Such a
resistance gene, if it existed, would be essentially immune to the
benefits of pyramiding in reducing the threat of resistance.  Moreover,
in this case pre-selection with Bollgard could facilitate
cross-resistance to the pyramided varieties.

Single Locus per Toxin

Here, “single locus per toxin” is defined to mean that it only takes
an allele at a single locus to confer resistance to a single toxin, and
there is no possible cross resistance from such alleles.  Monsanto
modeled resistance to each toxin as a single-locus problem affecting the
independent binding sites.  Therefore, the pyramided products containing
two toxins require resistance to develop at two independent loci.
Excluding the possibility of metabolic cross-resistance presented in the
above scenario, single locus resistance for each toxin (as opposed to
resistance to each toxin requiring multiple resistance alleles at
multiple loci) represents the fastest possible path to resistance
evolution in the TBW where both Cry1Ac and Cry2Ab are considered to each
be at a “high dose” (i.e., if phenotypic resistance is completely
recessive at all loci involved in resistance).  This is not the case
with CBW where even Bollgard II is not a high dose and any resistance
allele with a small effect on tolerance of one or both toxins is
expected to be selected for.  In some cases the forms of resistance that
have been developed in laboratory colonies appear to involve multiple
loci and the aggregation of several minor forms of resistance.  Alves et
al. (2006) assessed the number of loci involved in resistance to the
Cry1Ab for two laboratory-derived resistant strains of Ostrinia
nubilalis, the European corn borer.  One method of assessment suggests
that resistance involves at least 10 different loci while a second
assessment technique suggests there are more than 20 loci involved.  In
contrast, the same techniques imply that resistance in the diamondback
moth, Plutella xylostella, is related to a single locus (which may
explain why this insect exhibits resistance in the field).

We cannot assume that the number of loci involved in Bt resistance in O.
nubilalis is directly translatable to either CBW or TBW.  However, field
level resistance has not evolved in any of these three pests and allele
frequencies for resistance remain at or below the levels observed when
transgenics were first introduced.  Obviously, we cannot assume that
each toxin requires a specific combination of alleles at 10 completely
independent loci.  But, we can assume that resistance to each toxin
involves more than one independent locus.

A two-toxin product with resistance conferred by a single independent
locus for each toxin is probably comparable to a single toxin product
with resistance conferred by two independent loci.  Given this
assumption, the direction of this effect is to delay resistance and the
magnitude of the effect is probably exponentially related to the number
of loci involved.  By modeling resistance as a single locus for each
toxin, the approach is both reasonable and conservative for TBW.  If a
multi-locus resistance were modeled for each toxin the effect would
delay resistance in TBW and the magnitude of the delay would overwhelm
most if not all of the uncertainties mentioned.  This would not be the
case for CBW.

Fitness Costs

The model was run without fitness costs.  This is also a conservative
assumption, perhaps very conservative (except possibly if hotspots
occur; see Appendix 3).  The first paragraph of Gustafson and Head
(2005), Section 4.4 summarizes the literature evidence that Cry1Ac
resistance is associated with fitness costs.

Fitness costs can take many forms, reduced fecundity, delayed
development, and reduced overwintering survival to mention but a few of
the possibilities.  Fitness costs are effectively selection against the
trait of interest once accounting for the direct fitness benefits of the
trait under selection (i.e., factoring out the benefits of resistance to
Bt toxins in Bt fields).  The consequences of including fitness costs in
models of the evolution of resistance dramatically change the possible
outcomes.  If fitness costs are included there are four potential
outcomes:

Resistance could be delayed.

The allele frequency could reach a stable equilibrium.  This type of
system operates as a cline, a gradient of opposing forces that reaches a
balance point with fitness costs selecting against resistance while
toxins select for resistance.  .

The population can become extinct.

The population can persist but the allele is extirpated from the
population.

With some notable exceptions like the diamondback moth (Plutella
xylostella), resistance to the Cry complex of toxins has been developed
in laboratory colonies.  Resistant individuals from these colonies
exhibit developmental delays and a reduction in biomass when compared to
susceptible individuals of comparable age not exposed to the toxin.
These are qualitative descriptions that imply metabolic fitness costs.

Peck et al. (1999) simulated Bt resistance evolution for TBW in North
Carolina.  Developmental delays during the growing season caused a
temporal separation of the adults emerging from refuge and Bt fields.
Although this kind of separation may not fit the typical definition of a
fitness cost, it resulted in positive assortative mating among resistant
individuals and increased the rate of resistance evolution.  However, as
the magnitude of the developmental delays was increased, the time to
resistance evolution became less predictable.  Differences in the
synchrony of the diapausing life-stage with the end of the growing
season often resulted in selection against the individuals that had been
selected for resistance earlier in the growing season.  When these
relationships were combined with allele frequencies below 0.03 the
allele was often extirpated from the population.

Carrière et al. (2001) attempted to estimate the effect of diapause
asynchrony quantitatively in Pectinophora gossypiella.  Three different
laboratory colonies exhibiting different levels of Bt resistance were
reared without exposure to the toxin under photoperiods that should
induce diapause.  Individuals that pupated were not in diapause and
would not survive the winter.  Individuals that did not pupate were
assumed to be in diapause.  Alternative host availability for this pest
in Arizona is so limited that cotton is the only viable host.  So,
individuals that emerge prior to spring planting are unlikely to find
hosts for their offspring.  Homozygous resistant individuals suffered a
71% reduction in spring emergence when compared to homozygous
susceptible individuals.  In addition, the strains with the greatest
level of resistance suffered the greatest overwintering mortality.

Caprio (2006) submitted a public comment to the Panel presenting a
cursory evaluation of the effects of fitness costs for the MidSouth
region (Mississippi).  Figure 7 in that submission shows that 25% of the
model runs led to resistance evolution in less than 15 years in the
absence of structured refuge, while Figure 8 shows that approximately 5%
of the model runs led to resistance evolution with a structured refuge.
Fitness costs in this model were implemented as a reduction in fecundity
ranging from 0.0 to 0.5 with a most likely value of 0.1 but the
mechanism of selection against resistance is irrelevant to the question.
 These fitness costs are still well below the losses reported by
Carrière et al. (2001).  Also submitted to EPA for the Panel was a
second model (Gould et al. 2006) showing that when there is a high dose
of each of two toxins, the resistance alleles to the 2 toxins never
reach a problematic level as long as there is a recessive fitness cost
of 0.05 and a 10% refuge.  Even if selection with a single toxin
cultivar initially raises the frequency of alleles for one toxin above
0.90, after introduction of the two toxin cultivar the frequency of both
alleles reach a very low equilibrium level.  However, the Gould et al.
model is deterministic and must be viewed with caution.

Model runs that result in extinction have been reported since IRM
modeling began (Onstad and Guse 1999, Peck et al. 1999, Sisterson and
Tabashnik 2005).  Peck’s model includes trivial fitness costs as an
offset for the increase in allele frequency due to mutation.  The Onstad
and Guse model includes no explicit fitness costs.  The scenarios in
which the population is driven to extinction result from fitness costs
that are an emergent property of the complex interactions that occur
between the definition of the landscape, the biological and behavioral
characteristics of the target pest and the rarity of the resistance
allele.  Resistance management strategies include an assumption that 1)
extinction is not a probable outcome for chronic pest populations and 2)
resistance will evolve in response to the widespread use of transgenic
crops.  Modelers have therefore focused on the situations and scenarios
that lead to resistance and discounted the conditions that result in
extinction and extirpation of the allele for resistance. Caprio’s
(2006) submission is important because it illustrates the proportion of
model runs that do not conform to these two assumptions about the
outcome.  Nonetheless, Monsanto does not argue that Bollgard II is
likely to cause the extinction of either CBW or TBW, and given this, the
relevance of simulations leading to extinction is uncertain.

Monsanto references several other papers suggesting that fitness costs
are associated with resistance but these three examples illustrate that
fitness costs can delay and perhaps even prohibit the evolution of
resistance to Cry toxins.  There are many potential mechanisms for
fitness costs but regardless of the mechanism, fitness costs represent
one of the best hypotheses for explaining why field populations have not
developed resistance in the past decade.  By not including fitness
costs, Monsanto has taken the most conservative approach to modeling
this system.  If fitness costs were included in the model, the magnitude
of the delay effect could overwhelm the opposing effect of most of the
uncertainties presented.  On the other hand, one Panel member indicated
that the effect of hotspots can easily overwhelm a fitness cost of
resistance and lead to rapid resistance evolution despite high fitness
costs as described in Appendix 3 (see also Table A3-1).

Pre-Selection

Given initial allele frequencies for resistance that are very low, in a
range approximately from 0.001 to 0.01, the time until resistance can be
observed in the field population is an inverse exponential function of
the initial allele frequency.  That is, when the initial allele
frequency is very low, minor increases in the initial allele frequency
result in significantly shorter times to resistance.  Selection for
resistance that may have occurred prior to removal of structured refuge
would result in an increase in the allele frequency for resistance in
the field population.  So, the most conservative answer to the question
is that in this range of allele frequencies any increase shortens the
time to resistance dramatically.

The model uses an initial allele frequency of 0.002 based on Gould et
al. (1997).  In this study, male TBW moths were collected in the
MidSouth region (Mississippi, Louisiana and East Texas) prior to the
introduction of transgenic crops.  The estimated resistance allele
frequency was actually lower than the value used in this model (0.0015
with a 95% CI ranging from 0.0004 to 0.0041).  The study employed a
resistant laboratory colony derived from TBW collected earlier in North
Carolina.  A best approximation of the allele frequency for resistance
in the North Carolina population is 0.001 (Gould et al. 1997).
Uncertainty about this value was strictly qualitative.

Burd et al. (2003) estimated the allele frequency for resistance in CBW
collected in North Carolina in 2000, several years after transgenics
were in use.  Allele frequencies for resistance to the Cry1Ac toxin
averaged 0.00043 with a 95% CI ranging from 0.00001 to 0.00239.  The
frequency of the resistance allele for the Cry2Aa toxin averaged 0.00039
with a 95% CI ranging from 0.00001 to 0.00216.

It must be noted that the rarity of the allele makes it extraordinarily
difficult to detect.  The 95% confidence intervals reflect this
uncertainty.  While the introduction of transgenics has significantly
changed the spatial and temporal properties of the landscape there is
little evidence that any increase in resistance allele frequency has
occurred in field populations in the US over the past decade.  There is
evidence of increasing resistance to Cry1Ac for H. armigera in China
where there are no structured refuges (Kongming Wu, F. Gould, et al.,
unpublished; Table 7-1).  Monsanto’s model was run conservatively at
the mid to high range for allele frequencies for both pests.

Table 7-1.  Relative average development ratings for six-day old larvae
of Helicoverpa armigera F1 and F2 generation female lines in populations
from Anci, Hebei Province and Xiajin, Shandong Province, China.  (From
K. Wu, unpublished.)

	Relative average development rating per line

Means (+ SE) with different letters are significantly different (P <
0.05; LSD test).  Capital letters in same column in same location
indicate difference in different years.  Small letters in same row
indicate differences between generations in same year.

 a LF-R is a Bt-resistant laboratory strain.  SS1 and SS2 are
Bt-susceptible laboratory strains.

Agency Charge

8.  Modeling suggests that the overall durability of Bollgard II cotton
can be enhanced if Bollgard cotton is removed from the marketplace.
This conclusion is supported by other researchers who examined the
benefit of managing resistance evolution to two toxins with dissimilar
modes of action using a pyramided approach (Zhao et al., 2005; Roush,
1998; Livingston et al., 2004; Hurley, 2000; Caprio, 2005).  On the
other hand, the concurrent use of single- and two-gene Bt plants can
offer exposed populations a “stepping stone” to develop resistance
to both proteins.  Bollgard, Widestrike, and Bollgard II cotton exist in
a mosaic in southeastern cotton growing regions, with Bollgard
dominating the total acreage.   In 2004, Bollgard cotton acreage
accounted for >95% of all Bt cotton acreage in the U.S. (see Head et
al., 2005, MRID# 467172-03).  Encouraging the adoption of Bollgard II
will increase the overall durability of all three Bt cotton products.
From an insect management point of view, removal of Bollgard cotton from
the marketplace would benefit the two-gene products, Bollgard II and
WideStrike.

The Panel is asked to address the implications for selection for CBW and
TBW resistance if the mosaic of single gene and dual gene products
remains in the marketplace for a number of years.  How would selection
pressure be reduced if the single gene product is removed from the
marketplace gradually (e.g., >3 years) or rapidly (e.g., ≤3 years)
over a period of years?

Panel Response

	Two important dimensions to the question of mosaics of single and dual
gene products are product market share and temporal variability.
Monsanto addressed the product market share dimension by looking at how
resistance evolved for seven scenarios characterized by different
product market shares.  Temporal variability was not explored by
Monsanto.

 	To explore these two dimensions, it is useful to conduct a controlled
experiment.  In the first experiment, one can hold the temporal
variability of the mosaic constant, while varying product market shares.
 For example, one can assume that the same amount of each product is
planted every year, while allowing the proportion of each product’s
market share to vary from one treatment to the next (Monsanto’s
analysis).  In the second experiment, one can hold the total market
share constant for each product, while allowing the temporal variability
of market shares to increase.  For example, one can assume that the
proportion of cotton acres represented by each product does not change
over a specified period of time (e.g., 200 generations), while allowing
the proportion of market shares for each product in each generation to
vary from one treatment to the next.  In preparation for this SAP
meeting, these two experiments were conducted with the deterministic two
gene/two product simulation model reported in Hurley et al. (2006) using
the parameters reported in Table 8-1.  In the second experiment, four
examples of temporal variation in product market shares were explored,
holding the total product market shares constant at 0.5 over 200
generations.  These examples are reported in Figure 8-1.  These
simulations were designed to demonstrate the general effects of product
market share and temporal variability in product market share.  As such,
they were not calibrated to any specific pests, products, or
agro-ecosystem.

Table 8-1.  Controlled parameter values for simulation model examples.

Proportion of Refuge

0.05

Refuge Survival Rate for all Genotypes

1.0

Recombination Factor

0.50

Proportion of Non-Random Mating

0.0

Shared Toxin (e.g., Cry 1Ac)

                      Susceptible Homozygote Survival Rate

0.0

Heterozygote Survival Rate

0.02

Resistant Homozygote Survival Rate

1.0

Initial Frequency of Resistant Alleles

1.0x10-3

Pyramided Toxin (e.g., Cry 2Ab2)

Susceptible Homozygote Survival Rate

Figure 8-1.  Example of temporally variable transition rates with equal
market shares for single and pyramid toxin products (these transition
rates were used for the analysis reported in Table 8-3).

	In the first experiment, reducing the market share of the single toxin
product slows the evolution of resistance to the pyramided toxin, which
replicates Monsanto’s results (see Table 8-2).  For resistance to the
pyramided toxin, there was not a monotonic relationship between market
share and the evolution of resistance.  Initially, increasing the market
share of the pyramided product speeds the evolution of resistance.  But
once the market share of the pyramided product is high enough, further
increases slow the evolution of resistance.  The explanation of these
results has to do with several countervailing effects.  Increasing the
market share of the pyramided product reduces selection for the shared
toxin, which increases the time to resistance for the shared toxin.
Increasing the time to resistance for shared toxin allows dual modes of
action to operate for a longer period of time in the pyramided product,
which is positive for resistance management.  Increasing the market
share of the pyramided product increases selection for the pyramided
toxin because a greater proportion of the insect population is exposed
to this toxin.  This increased selection pressure reduces the time to
resistance for the pyramided toxin, which is negative for resistance
management.  Increasing the market share of the pyramided product also
means that there will be less refuge for the pyramided toxin once
resistance evolves to the single toxin product because the single toxin
product serves as refuge for the pyramided toxin after resistance to the
shared toxin has evolved.  This effect also reduces the time to
resistance for the pyramided toxin, which is negative for resistance
management.  This simulation illustrates the opportunity cost of
increasing the market share of the pyramided product in order to reduce
selection for the shared toxin.  This opportunity cost is an increase in
selection pressure for the pyramided toxin.

Table 8-2. Example of the relationship between product market share and
time to resistance.

	Single Toxin

Variety

(e.g., Bollgard)	Pyramided

Variety

(e.g., Bollgard II)	Shared

Toxin

(e.g., Cry 1Ac)	Pyramided

Toxin

	Note:  When resistance failed to emerge after 200 generations, the
proportion of resistant alleles is reported in parentheses.

	For the second experiment, high temporal variability speeds the
evolution of resistance for both toxins (Table 8-3).  This experiment
provides one interpretation of what has been called the “stepping
stone” effect.  Holding total market share constant, increased
temporal variability in the adoption of the pyramided product means that
the single toxin product is on the market longer, which speeds the
evolution of resistance to the shared toxin.  This means the pyramided
product will effectively become a single toxin product sooner, which
speeds the evolution of resistance to the pyramided toxin.  This
“stepping stone” effect is negative for resistance management and
will be more pronounced when the market share of the pyramided product
is relatively small.

Table 8-3.  Example of the relationship between the variability of
product market shares (see Figure 8-1) and time to resistance.

Product Market Share Variability	Shared Toxin

(e.g., Cry 1Ac)	Pyramided Toxin

Note:  When resistance failed to emerge after 200 generations, the
proportion of resistant alleles is reported in parentheses.

	While this experiment suggests that a quick transition to pyramided
toxin products may not always be the best strategy due to the
opportunity cost of increasing selection for the pyramided toxin, slow
transition rates can only be supported by relatively heavy selection
pressure on the pyramided toxin.  The Panel agreed that the majority of
papers published over the past decade using a variety of modeling
strategies (from simple deterministic models to complex stochastic and
spatially explicit models) have found that quicker transitions to
pyramided toxin products is preferable in terms of resistance
management.

	The Panel noted that mosaics and slow transitions to pyramided products
could negatively impact minor as well as major resistance mechanisms.
Such mechanisms include alterations in midgut protease activity (Li et
al. 2005) and changes in the abundances of proteins that bind Bt toxins
such as aminopeptidases and alkaline phosphatase (Jurat-Fuentes and
Adang, 2004).  These minor mechanisms cannot by themselves enable
survival of insects on transgenic cotton, but may supplement resistance
levels in heterozygotes for target-site mutations.  Products expressing
a single Bt toxin are more likely to select for these minor mechanisms
than pyramided toxin products because of the higher toxicity of the
latter.  These mechanisms may then boost resistance levels of insects
carrying mutations for the receptor to the pyramided toxin.  This would
accelerate resistance development, relative to the models utilized by
Monsanto that only consider target-site resistance.

	While the majority of the literature that explores the effects of the
transition from single to pyramided toxins finds slow transitions have a
negative impact on resistance management, there are counter examples.  A
stochastic, spatially explicit simulation model (Storer et al. 2003;
Livingston, Storer, Gould, Kennedy, and Van Duyn, unpublished) shows
that quicker transition rates to pyramided toxins slow resistance to the
shared toxin, while increasing resistance to the pyramided toxin.  This
result is an example where there is strong selection for the pyramided
toxin relative to the shared toxin, which is partly attributable to the
resistance fitness costs assumed in the model.  Livingston et al. (2006)
used an extended version of this model that incorporates the behavioral
response of a representative cotton producer and Cry2Ab2 and pyrethroid
resistance evolution, and found that grower behavior can affect
management of Cry1Ac and Cry2Ab2 resistance in CBW.  This result is
driven by two factors: economic and biological.  In the model, the
cotton producer chooses between cotton varieties based on profitability.
 When resistance to the shared toxin (Cry1Ac) evolves in the CBW
metapopulations, the representative farmer switches from planting only
Bollgard (the single toxin product) to planting only Bollgard II (the
pyramided toxin product), which reduces Cry1Ac selection pressure.  A
0.025 fitness cost of carrying the Cry1Ac resistance allele,
conventional insecticide (pyrethroids) use, and the presence of non-Bt
corn and soybean fields, which provide unstructured refuge, subsequently
allows susceptibility in CBW to Cry1Ac to re-evolve.  Furthermore,
profit received by the representative eastern North Carolina producer is
highest when the structured refuge requirement is removed for both
Bollgard and Bollgard II, resistance to Cry1Ac evolves very slowly and
the Cry1Ac resistance allele frequency declines when the producer
switches to Bollgard II, and Cry2Ab2 resistance never evolves.

	The Panel generally agreed that the weight of evidence supports a quick
transition to pyramided products when there is no mechanism for
resistance fitness cost.  One Panel member pointed out that it is for
this reason that the Australian cotton industry developed an agreed
policy of rapid transition from single to pyramided Bt cottons.  To
further protect the future benefits of pyramided products, this policy
imposed a cap on the area of single toxin cotton until pyramided
varieties were ready for release.  However, the Panel also noted that
resistance fitness costs and economic factors that affect grower
behavior could promote the transition from single to pyramided toxin
products.  One Panel member emphasized that it is important to
incorporate grower behavior and what seed producers are doing with
Monsanto’s Cry toxins.  For example, in eastern North Carolina,
cottonseed varieties with the Cry1Ac gene that growers actually plant
are available only when stacked with Monsanto’s Roundup Ready gene;
and cottonseed varieties expressing Cry1Ac and Cry2Ab2 that growers
actually plant are available only when stacked with Monsanto’s Roundup
Ready Flex gene, the latter allowing growers to use Roundup longer
during the growing season and more effectively.  Livingston et al.
(2006) incorporated these important aspects of the eastern North
Carolina situation in their analysis, and the results strongly suggest
that the behavior of cotton growers and seed companies likely will
expedite transition from Bollgard to Bollgard II.

	While Monsanto’s analyses were consistent with the views of the
panelists, Monsanto’s analyses only contributed to the recommendations
of the Panel and did not sway the Panel.  This is important to note,
because it illustrates the importance of having multiple, independent
lines of evidence in making scientific recommendations.

Overall Data/Results Interpretation

Agency Charge

9.  There are three major variables to evaluating structured refuge for
Bt crops:  a) production of a sufficient number of susceptible insects
relative to any resistant survivors of the Bt crop, b) proximity of the
refuge to the transgenic crop to facilitate random mating between
susceptible (from the refuge) and resistant (from the Bt crop) insects,
and c) developmental synchrony of the refuge with the transgenic crop to
promote random mating.

Given Monsanto’s sampling, gossypol analysis, spatial and temporal
analyses, and modeling evaluation, the Agency asks the panel to comment
on whether Monsanto’s analysis scientifically supports the conclusion
that natural refuge will be comparable to the effectiveness of
structured refuge for management of TBW resistance to the Bt proteins
expressed in Bollgard II cotton for each of the four regions:  the
Carolinas, Georgia, Mississippi Delta, and Texas.

Panel Response

General Comments

	Firstly, the Panel would like to compliment Monsanto on the extent and
quality of information provided to support their case that natural
refuge will be sufficient for resistance management for Bollgard II
cotton.  A number of aspects of the research significantly improve
understanding of the ecology of Heliothine pests associated with Bt
cotton systems.

	Nonetheless, there are many uncertainties, caveats, and assumptions
evident throughout the modeling and analyses presented by Monsanto and
revealed by the Panel discussion around the questions posed by EPA.
Given the magnitude of the potential consequences of the decision EPA
must make, the Panel must apply the most rigorous scientific questioning
to all aspects of the proposal, the data, and the conclusions.

The Gossypol Technique

	One particular area of concern relates to the innovative gossypol
analysis developed by Monsanto.  The validity, accuracy and
repeatability of this technique for identifying the non-cotton fraction
of the TBW population is central to Monsanto’s data presentation and
argument for a natural refuge.  Although the Panel received a
description of the analytical technique, the description was incomplete.
 We thus highlight the following concerns:

The proportion of non-cotton moths is determined by the inability of the
analytical assay to detect gossypol in the bodies of field-collected
male moths in pheromone traps.  Since the assay does not provide a
quantitative estimate of the amount of gossypol actually present, it is
potentially liable to significant biases that have not yet been
experimentally evaluated.

For example, if the gossypol technique has a high detection threshold,
it is quite possible that a number of samples would be classified as
being from non-cotton sources when in fact they simply have gossypol
values that are below the detection level of the method.  We thus need
more information about the sensitivity of the method.

If gossypol concentrations decline in male moths from cotton that have
remained dead for a week in a pheromone trap in the field, there could
be a significant underestimation of the fraction of moths originating
from cotton.  This would project into a significant overestimation of
the size of the natural refuge in the models, with a concomitant
underestimate of the time taken to develop resistance in the pests.
This bias factor could equal or exceed the other sources of variation of
concern to the Panel, such as the variability between regions or on
different dates.

	Although Monsanto gave some verbal responses to these concerns during
the public meeting, the Panel recommends a rigorous examination of the
gossypol analysis technique.  This should include at a minimum:

A review by EPA staff with appropriate chemistry expertise;

Publication of the assay method in a peer-reviewed journal;

Performance and publication of experiments to mimic the conditions
experienced by trapped males prior to analysis;

Validation of the methodology by independent laboratories.

	If the innovative gossypol analysis does stand rigorous examination,
the Panel notes that it will be of considerable value for ongoing
research by others working in this field.

	In the remainder of our assessment we have assumed that the gossypol
analysis is accurate and sensitive, and that the estimates of non-cotton
moths are reliable.

Estimates of Natural Refuge and Modeling of Resistance Risk

	Monsanto has clearly incorporated a number of elements of conservatism
throughout their analysis, explicitly utilizing a "worst case" approach
in many parameters in their modeling.  The Panel questions whether
collectively the data and modeling allow a sweeping conclusion that
natural refuge is sufficient to delay resistance to Bollgard II across
the four Cotton Belt regions outlined in the submission (Fig. 9-1) –
North Carolina (NC), Georgia (GA), Mississippi Delta (MS), East Texas
(TX) – at a spatial scale within these regions at which resistance is
most likely to develop.

	Monsanto’s simulations indicate that with current estimates of
natural refuge, development of Bt resistance in TBW to Bollgard II was
unlikely within 30 years in any of the four regions.  Monsanto does not
argue that natural refuge will be strictly comparable to the
effectiveness of structured refuge, except to the extent that it will
delay resistance for at least 30 years.  Because all simulations were
truncated at 30 years, it is not possible for the Panel to judge the
relative effectiveness of resistance management with or without
structured refuge.  That said, a predicted durability of at least 30
years would seem more than adequate for biotechnology-based products.
Monsanto’s decision to use worst-case estimates for input parameters
is conservative and may partially mitigate problems associated with
parameter and model uncertainty.  However, the sensitivity of model
output to parameter values and model assumptions makes this difficult to
assess.

Fig. 9-1.  Modified from Fig. 2 in Gustafson and Head (2005),
incorporating modifications by Reynolds (2006).  Caption in Gustafson
and Head (2005) reads, "Definition of CBW modeling regions, which
include all counties with some Bt. cotton sales (2003-5) and adjacent
counties with at least one of the following crops according to USDA
estimates for 2004: corn, cotton, peanuts, or soybeans."

 	The Panel noted some potentially serious biases in the calculation of
natural refuge for TBW and CBW (see Panel response to Charge 6), which
should be addressed by Monsanto.  Despite the potential biases in
estimates of natural refuge, some Panel members concluded that for
Georgia and North Carolina, Monsanto has demonstrated that there are
significant and reliable non-cotton refuges present as part of the
cropping system and the wider environment, and this should be adequate
to manage Bt resistance in TBW associated with cotton systems involving
Bollgard II cotton.

	Following this FIFRA SAP meeting, a Panel member provided additional
analysis and comments regarding the validity of extrapolating data from
areas sampled by Monsanto in North Carolina and Georgia to areas that
were not sampled.  Such comments were not considered or reviewed by the
Panel during the meeting, and are being provided as an appendix to these
meeting minutes (Appendix 5).

	Other regions are more problematic.  While the simulations suggest
long-term durability against TBW in all regions, the Panel does not
believe that one year of data for East Texas and quite limited and
variable sampling through parts of the MidSouth are sufficient to
establish the reliability of adequate natural refuge for TBW.  Texas
especially must be sampled more thoroughly, both spatially and
temporally.  Areas representing different ecozones in that state must be
sampled.  One year of data does not provide coverage for the somewhat
unpredictable rainfall in that region which may cause high variability
in alternative host availability and phenology.  For some parts of
Texas, we have zero years of data, such as the Lower Rio Grande Valley
and the High Plains.  It was unclear to the Panel whether the Monsanto
amendment covered all of Texas, given that west Texas and the High
plains are currently part of a pink bollworm eradication program
utilizing Bollgard cotton.  Nonetheless, we highlight that the High
Plains are very dry with most cotton being irrigated.  The literature
indicates (Benedict 2005) that there are almost no significant
alternative hosts available to TBW in that area.  Given the wide variety
of agroecozones in Texas, it is not possible to extrapolate data on
natural refuge from East Texas to the Texas High Plains, the Lower Rio
Grande Valley, the Winter Garden Region, the Rolling Plains, etc.

	The variability and uncertainties highlighted above in calculations of
natural refuge and its utility in minimizing the risk of Bt resistance
evolution indicate that Monsanto’s data analysis could be much more
comprehensive.  As noted in its response to Charge 5, the Panel would
prefer a more integrated and comprehensive analysis of the spatial and
temporal variability of refuge estimates and moth trap data to tease out
crucial details regarding the appropriate spatial regions and the
critical temporal periods that pose the most risk.

Worst-case scenarios

	Throughout its submission, Monsanto indicates that it has simulated the
worst-case scenario with regard to the adequacy of natural refuge.  One
Panel member noted that resistance is likely to evolve in one area and
then spread and that by picking counties with the lowest effective
refuge, Monsanto is in fact not selecting the “worst-case” scenario,
but instead the “most-likely” scenario for resistance development.
Little re-assurance can be gained from a risk assessment perspective by
picking the areas with lowest effective refuge.  The true worst-case
scenario involves first selecting the areas with lowest effective refuge
and then using worst-case scenarios for other parameters applied for
these areas.

Assurance of Natural Refuge

	Current requirements for the registration of Bollgard and Bollgard II
include monitoring for Bt resistance evolution (currently done by USDA
Stoneville for Bt cotton).  Resistance will be easier to prevent than
remediate after it has arisen.  By removing structured refuge, Monsanto
will remove farmer control of effective refuges and transfer that
“responsibility” to other farmers and the natural environment.  How
can Monsanto guarantee that natural refuge will always be there and be
adequate?  Who would be responsible for that assurance?  The Panel
suggests that if the structured refuge requirement were removed, a
comprehensive monitoring effort would have to extend beyond resistance
monitoring per se to include monitoring levels of effective refuge
through estimates of area, moth productivity, or ongoing gossypol
analyses of moths trapped in cotton-growing areas.

	This leaves open the residual issue of how areas of non-cotton host
crops for TBW (tobacco/peanuts/soybean) or CBW (largely maize/soybean)
might change in the future if refuge requirements are dropped for
Bollgard II cotton.  Will more of the maize/peanut/soybean acreage be
switched to cotton with the result that natural refuge will decrease?
The modeling analysis of Gustafson et al. (2005) includes simulations of
significantly reduced soybean area, and the authors conclude that it
would have little impact on effective refuge for CBW.  However, they do
not include the assumption that the area taken out of soybeans is
replaced with Bollgard II cotton.  Furthermore, no consideration is
given to the possibility that the percent acreage of corn planted to Bt
cultivars will not increase as stacked transgenic corn cultivars become
more prevalent.

Specific Comments on the three key components required for an effective
refuge

a) Production of a sufficient number of susceptible insects relative to
any resistant survivors of the Bt crop.

	A number of different modeling approaches suggest that pyramided plants
like Bollgard II, including two highly efficacious proteins, require
smaller effective refuges than a single gene product and those quite
small refuges can delay resistance for considerable periods.  Based on
the use of the gossypol technique and carbon isotope ratios, and the
various calculations of areas of potential refuge, Monsanto has clearly
demonstrated that a significant proportion of moths across all regions
are generated from non-cotton sources.  This point is more fully
discussed in the Panel's response to Charge 2.  Setting aside for the
moment the inherent uncertainties in all simple models and the lingering
uncertainties about temporal and spatial variability of natural refuge,
Monsanto’s modeling of the durability of Bollgard II against TBW
utilized worst case estimates of natural refuge from the data set and
indicated resistance could be delayed for at least 30 years by reliance
on natural refuge.  Nonetheless the refuge data also demonstrate that
the currently structured refuge crops of non-Bt cotton are contributing
a significant proportion of both TBW and CBW moths to populations across
the Cotton Belt and must thus be contributing to the delay of resistance
over and above that provided by natural refuge.  What is a sufficient
amount of refuge?  EPA's having already accepted a 5% structured refuge
as adequate for resistance management for Bollgard cotton could suggest
in simplistic terms that any credible estimates for natural refuge above
this level would represent a comparable or at least acceptable
protection against resistance.

	Because resistance will likely evolve in areas with little effective
refuge, these areas are of particular concern.  When the estimated
proportion of effective refuge for CBW and TBW is low (< 10%), higher
levels of uncertainty attach to a number of assumptions and calculations
for the proportion of effective refuge:

1) The estimates of effective refuge become less certain as the
estimates become smaller, because in areas with high Bt cotton usage
(small refuges), few insects are collected (see Panel response to Charge
3).  Thus, when estimates of effective refuge are low, they are also
less precise.  This might be addressed to some extent by additional,
more-intensive sampling as suggested under Charges 2 and 3.

2) When the proportion of effective refuge is low, models of resistance
evolution become more sensitive to these values.  For example, the
difference in time to resistance between the cases of 25% and 20% refuge
is often small compared to the difference between the cases of 10% and
5% refuge.

3) Model uncertainty (different predictions produced by structurally
different models) increases when the amount of refuge is small.

4) Resistance can evolve in “hotspots” where the proportion of
refuge area is small.  This is shown by numerous models of resistance
evolution that include spatially explicit landscapes (unlike the
Monsanto model presented).

5) Small amounts of effective refuge likely will coincide with greater
variability in the amount of refuge through the growing season.
Therefore, depending on weather conditions, there could be generations
that effectively experience no refuge.

6) If there is little effective refuge, the amount of refuge could be
highly variable due to future changes in cropping patterns,
non-agricultural land use, application of insecticides or other insect
control measures in effective refuge, and environmental change as
discussed above.

	While any one of these areas of uncertainty might not in fact prove
dangerous to IRM, as a group they represent unacceptably high levels of
uncertainty.  All of these areas of uncertainty increase and compound
each other when the area of effective refuge is below 5-10%.

	Spatially explicit modeling (Peck et al. 1999, Storer et al. 2003, and
Sisterson et al. 2004, 2005) demonstrates that, with certain assumptions
about pest mobility, resistance development can commence as hotspots in
the landscape.  In general the evidence for wide-scale mobility of TBW
and CBW might mitigate that effect; however, we do not know the
geographic scale of such hotspots for TBW.  It might be the county
scale, but possibly is smaller than that based on what we know from the
literature about dispersal and gene flow during the cotton-host season
(e.g., Korman et al. 1993, Sparks et al. 1993, Leonard et al. 1995,
Schneider 1999, Han and Caprio 2002, 2004).  The more comprehensive data
analysis suggested under Charges 4 and 5 will assist in identifying the
appropriate spatial and temporal scale for pooling of data and for
identifying gaping holes in the provision of natural refuge that might
serve to generate resistance hotspots.  These “gaping holes” might
reflect inadequate spatial sampling or temporal patterns driven by
climate whereby natural refuge appears adequate in a region one year,
but inadequate the next due to differences in the timing or intensity of
rainfall.  This might well be an issue for the MidSouth and Texas region
where natural refuge is lower and climatic extremes may lead to
significant change in the adequacy of natural refuge.  For these reasons
we argue that further data are necessary to characterize the level of
variability to be expected, so that an informed judgment can be made
regarding the suitability of natural refuge.  Although weather is also
variable in North Carolina and Georgia, the contribution of alternative
hosts is so great that this region may be well-buffered in the provision
of natural refuge.

b) Proximity of the refuge to the transgenic crop to facilitate random
mating between susceptible (from the refuge) and resistant (from the Bt
crop) insects.

	With structured refuge it is possible to specify and regulate the
proximity of refuges to Bt crops.  It is not possible to manage or to
assure proximity when relying on natural refuge, which might derive from
patches of alternative crops and wild hosts which are unlikely to be
distributed uniformly.  Spatial structure of the crop and refuge
environment will interact with mobility of the pest.  Both CBW and TBW
are potentially highly mobile insects (Benedict 2005, Schneider et al.
1989) relative to the spatial patterning of hosts and non-hosts in the
cropping regions where they occur.  This does not necessarily mean that
all moths move extensively every generation and in fact there is good
evidence that they do not (Hayes 1991, Korman et al. 1993, Sparks et al.
1993, Leonard et al. 1995, Schneider 1999, 2003, Bagwell et al. 2000,
Han and Caprio 2002, 2004).  In some parts of the Cotton Belt there may
be the potential for hotspots of resistance to develop where TBW
populations are regularly associated with Bt cotton and lack localized
refuge.  Monsanto’s trapping of male moths in pheromone traps adjacent
to cotton crops indicates at least that those moths which had been
generated on non-cotton hosts have moved into the vicinity of a cotton
crop.

	Monsanto has done a sound job in seeking to quantify land-use patterns
in the counties where data were collected, and in most areas sampled the
combined analyses clearly indicate that non-cotton hosts do contribute a
proportion of moths to local populations.  However, the counties sampled
are not always representative of all counties within a given state.
Also, with only 1 or 2 years of data it is difficult to assess the
reliability of natural refuge in proximity to Bt cotton in some parts of
the western end of the Cotton Belt.

c) Developmental synchrony of the refuge with the transgenic crop to
promote random mating

	There have always been questions about the synchrony of heliothine
populations on Bt crops and refuges.  For CBW and H. armigera in
Australia and elsewhere, it appears that survivors on Bollgard cotton
develop more slowly than those on conventional refuge crops.
Development rates, and therefore adult emergence times, of both CBW and
TBW can differ substantially depending on the host plant species
(Lukefahr and Martin 1964, Nadguada and Pitre 1983, Hayes 1988).  This
immediately raises the specter of lack of synchrony of emergent
refuge-generated moths with those from Bt crops.  TBW displays quite
distinct generational patterns across seasons in the Cotton Belt.  Even
so, generational peaks span 2-3 weeks and get progressively broader
through the season due to partially overlapping generations.  The
temporal scale used in Monsanto’s analysis (monthly) is probably
appropriate for these species.  The more comprehensive generalized
linear modeling of the refuge estimates suggested by this Panel would
provide a more acceptable assessment of the spatial and temporal
variability in refuge adequacy.

Conclusions

	The Panel understands that both Monsanto and cotton industry
representatives would prefer a single management strategy to accompany
Bollgard II cotton across the Cotton Belt, as is the case with Bollgard
and Widestrike cotton.  However, based on the data provided, the Panel
does not believe a single conclusion regarding adequacy of natural
refuge can be applied across the whole Cotton Belt “from Texas to
North Carolina.”

	With the caveat that the accuracy of the gossypol technique must be
validated, some Panel members support removal of the structured refuge
requirement for Bollgard II cotton in North Carolina and Georgia.  The
refuge requirement should not be removed in Alabama and parts of Texas
because these areas were not sampled.  The refuge requirement should not
be removed in Tennessee and East Texas because of insufficient data.
The refuge requirement should not be removed in Mississippi, Arkansas,
and Louisiana because the data presented suggest that the natural refuge
may be inadequate.  A reassessment of these recommendations, based on
additional data from the excluded areas/states, could be warranted.

REFERENCES

Alves, A. P., T. A. Spencer, B. E. Tabashnik, and B. D. Siegfried. 2006.
Inheritance of resistance to the Cry1Ab Bacillus thuringiensis toxin in
Ostrinia nubilalis (Lepidoptera: Crambidae).  J. Econ. Entomol. 99:
494-501.

Bagwell, R., D. Cook, J. Adamczyk, B. Leonard, and S. Micinski. 2000.
Status of insecticide resistance in budworm and bollworm in Louisiana
during 1999, pp. 914-918. In: Proceedings of the Beltwide Cotton
Conferences, National Cotton Council, Memphis, TN.

Benedict, J. H. 2005. Biology and dispersal of the cotton bollworm and
tobacco budworm in North America, pp. 51-119.  In: G. Head and D.
Gustafson, Production of tobacco budworm from alternative host plants
and the role of these host plants as natural refuge for Bollgard II®
cotton.  EPA-HQ-OPP-2006-0217-0010.

BPPD. 2004.  Technical review of Monsanto's submission:  "Impact of
Effective refuge size and typical insecticide use practices on model
predictions of years to resistance of tobacco budworm and cotton
bollworm to Bollgard cotton."  S. Matten memorandum to L. Cole dated
April 22, 2004.

BPPD. 2006.  EPA review of data and support materials submitted by
Monsanto to amend the insect resistance management requirements for
Bollgard II Bt cotton.  A. Reynolds, and S. Matten memorandum to L.
Cole, May 16, 2006.  EPA-HQ-OPP-2006-0217-0007.

Burd, A. D., F. Gould, J. R. Bradley, J. W. Van Duyn, and W. J. Moar.
2003. Estimated frequency of nonrecessive Bt resistance genes in
bollworm, Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae) in eastern
North Carolina.  J. Econ. Entomol. 96: 137-142.

Caprio, M. A. 1998. Two-compartment model for insect resistance.
http://www.msstate.edu/Entomology/PGjava/ILSImodel.html

Caprio, M. A. 2006. An independent assessment of the risk of the
evolution of resistance in Helicoverpa zea to Bollgard II cotton with
natural and structures refuges in the MidSouth.
EPA-HQ-OPP-2006-0217-0018.2.

Carrière, Y., C. Ellers-Kirk, A. L. Patin, M. A. Sims, S. Meyer, Y.-B.
Liu, T. J. Dennehy, and B. E. Tabashnik. 2001. Overwintering cost
associated with resistance to transgenic cotton in the pink bollworm
(Lepidoptera: Gelechiidae).  J. Econ. Entomol. 94: 935-941.

Dobson, A. J. 2002. An Introduction to Generalized Linear Models, Second
Edition. Chapman & Hall/CRC Press LLC, Boca Raton, FL.

Farrow, R. A., and J. C. Daly. 1987. Long-range movements as an adaptive
strategy in the Genus Heliothis (Lepidoptera: Noctuidae): a review of
its occurrence and detection in four pest species.  Aust. J. Zool. 35:
1-24.

Fitt, G. P. 1989. The ecology of Heliothis  species in relation to
agro-ecosystems.  Annu. Rev. Entomol. 34: 17-52.

Fitt, G. P. 1991. Host selection in Heliothinae. Chapter 7, in
"Reproductive Behaviour in Insects - Individuals and Populations", J.
Ridsdill-Smith and W. Bailey (eds.), Chapman and Hall.

Fitt, G. P., M. P. Zalucki, and P. H. Twine. 1989. Temporal and spatial
patterns in pheromone-trap catches of Helicoverpa spp. (Lepidoptera:
Noctuida) in northern New South Wales, Australia: implications for pest
management.  J. Econ. Entomol. 83: 1827-1836.

Fitt, G. P., M. L. Dillon, and J. G. Hamilton. 1995. Spatial dynamics of
Helicoverpa populations in Australia: simulation modelling and empirical
studies of adult movement.  Computers Electronics Agric. 13: 177-192.

Gahan, L. J., F. Gould, and D. G. Heckel 2001. Identification of a gene
associated with Bt resistance in Heliothis virescens.  Science 293:
857-860.

Gould, F., A. Anderson, A. Jones, D. Sumerford, D. G. Heckel, J. Lopez,
S. Micinski, R. Leonard, and M. Laster. 1997. Initial frequency of
alleles for resistance to Bacillus thuringiensis toxins in field
populations of Heliothis virescens.  Proc. Natl. Acad. Sci. USA 94:
3519-3523.

Gould, F., N. Blair, M. Reid, T. L. Rennie, J. Lopez, and S. Micinski.
2002. Bacillus thuringiensis-toxin resistance management: stable isotope
assessment of alternate host use by Helicoverpa zea. Proc. Natl. Acad.
Sci. 99: 16581-16586.

Gould, F., M. B. Cohen, J. S. Bentur, G. G. Kennedy, J. Van Duyn. 2006.
Impact of small fitness costs on pest adaptation to crop varieties with
multiple toxins: a heuristic model.  J. Econ. Entomol. (in press).

Gunning, R.V., H. T. Dang, F. C. Kemp, I. C. Nicholson, and G. D.
Moores. 2005. New resistance mechanism in Helicoverpa armigera threatens
transgenic crops expressing Bacillus thuringiensis Cry1Ac toxin.  Appl.
Environ. Microbiol. 71: 2558-63.

Gustafson, D. and G. Head. 2004. Impact of updated alternative host and
pyrethroid effectivness data on model predictions of cotton bollworm
resistance development to Bollgard® cotton. Monsanto Company, Report
number MSL-19326. St. Louis, Missiouri.

Gustafson, D. I., and G. P. Head. 2005. Modeling the impact of natural
refuge on the evolution of tobacco budworm and cotton bollworm
resistance to Bollgard II® cotton.  EPA-HQ-OPP-2006-0217-0011.

Gustafson, D. I., G. P. Head, and M. A. Caprio. 2005. Modeling the
impact of alternative hosts and pyrethroid sprays on Helicoverpa zea
(Boddie) adaptation to Bollgard® cotton.  J. Econ. Entomol.
(submitted).  Included in supporting materials from Monsanto to EPA,
HYPERLINK
"http://www.regulations.gov/fdmspublic/custom/jsp/search/searchresult/do
cumentSearchResult.jsp" \l "#" \o "EPA-HQ-OPP-2006-0217-0013"
EPA-HQ-OPP-2006-0217-0013 .

Han, Q., and M. A. Caprio. 2002. Temporal and spatial patterns of
allelic frequencies in cotton bollworm (Lepidoptera: Noctuidae).
Environ. Entomol. 31: 462-468.

Han, Q., and M. A. Caprio. 2004. Evidence from genetic markers suggests
seasonal variation in dispersal in Heliothis virescens (Lepidoptera:
Noctuidae).  Environ. Entomol. 33: 1223-1231.

Hartstack, A. W., J. D. Lopez, R. A. Mueller, W. L. Sterling, E. G.
King, J. A. Witz, and A. C. Eversull. 1982. Evidence of long range
migration of Heliothis zea (Boddie) into Texas and Arkansas. Southwest.
Entomol. 7: 188-201.

Hayes, J. L. 1988. A comparative study of adult emergence phenologies of
Heliothis virescens (F.) and H. zea (Boddie) (Lepidoptera: Noctuidae) on
various hosts in field cages.  Environ. Entomol. 17: 344-349.

Hayes, J. L. 1991. Dynamics of nocturnal activity of moths in the
Heliothis complex (Lepidoptera: Noctuidae) in cotton.  J. Econ. Entomol.
84: 855-865.

Head, G., and D. Gustafson. 2005. Production of tobacco budworm from
alternative host plants and the role of these host plants as natural
refuge for Bollgard II® cotton.  EPA-HQ-OPP-2006-0217-0010.

Head, G., and R. Voth. 2004. A final report on studies to assess
production of Helicoverpa zea from alternate host plants and from the
external unsprayed non-Bt cotton refuge for Bollgard® cotton. Monsanto
Company, Report number MSL-19238, MRID 46222401. St. Louis, Missouri.

Head, G., M. C. McCann, and J. W. Mullins. 2005. Scientific and economic
justification for not requiring structured cotton refuges for Bollgard
II® cotton in the U.S. Cotton Belt from Texas to the East Coast.
EPA-HQ-OPP-2006-0217-0012.

Helsel, D. R. 2005. Nondetects and Data Analysis, Statistics for
Censored Environmental Data. Wiley and Sons, Inc. New Jersey.

Hendrix, W. H., T. H. Mueller, J. R. Phillips, and O. K. Davis. 1987.
Pollen as an indicator of long-distance movement of Heliothis zea
(Lepidoptera: Noctuidae).  Environ. Entomol. 61: 1148-1151.

Hurley, T. M.  2000.  Research report:  A Bioeconomic evaluation of the
gradual introduction of multiple toxin Bt corn.  Prepared for Monsanto
Company.  February 2000.

Hurley, T.M., S. Secchi, and R. Hellmich. 2006. Managing European Corn
Borer Resistance to Bt Corn with Dynamic Refuges.  In: Agricultural
Biotechnology: Economics and Policy (R.E. Just, J.M. Alston, and D.
Zilberman, eds.), New York, Springer Publishers. (In press)

Ives, A. R., and D. A. Andow. 2002. Evolution of resistance to Bt crops:
directional

selection in structured environments.  Ecol. Lett. 5: 792-801.

Jackson, R. E., J. R. Bradley, J. Van Duyn, B. R. Leonard, K. C. Allen,
R. Luttrell, J. Ruberson, J. Adamcyzk, J. Gore, D. D. Hardee, R. Voth,
S. Sivasupramaniam, J. W. Mullins, and G. Head. 2006. Regional
assessment of Helicoverpa zea (Lepidoptera: Noctuidae) populations on
cotton and non-cotton crop hosts.  Environ. Entomol. (submitted).
(EPA-HQ-OPP-2006-0217-0013)

Jurat-Fuentes, J. L., and M. J. Adang. 2004. Characterization of a
Cry1Ac-receptor alkaline phosphatase in susceptible and resistant
Heliothis virescens larvae.  Eur. J. Biochem. 271: 3127-35.

Kennedy, G. G., and N. P. Storer. 2000. Life systems of polyphagous
arthropod pests in temporally unstable cropping systems.  Annu. Rev.
Entomol. 45: 467-493.

Korman, A. K., J. Mallet, J. L. Goodenough, J. B. Graves, J. L. Hayes,
D. E. Hendricks, R. Luttrell, S. D. Pair, and M. Wall. 1993. Population
structure in Heliothis virescens (Lepidoptera: Noctuidae): an estimate
of gene flow.  Ann. Entomol. Soc. Am. 86: 182-188.

Lee, Y., J. A. Nelder, and Y. Pawitan. 2006. Generalized Linear Models
with Random Effects, Unified Analysis via H-likelihood.  Monographs on
Statistics and Applied Probability 106, Chapman & Hall/CRC LLC, Boca
Raton, FL.

Leonard, B. R., J. B. Graves, G. E. Burris, A. M. Pavloff, and G.
Church. 1989. Heliothis sp. (Lepidoptera: Noctuidae) captures in
pheromone traps: species composition and relationship to oviposition in
cotton.  J. Econ. Entomol. 82: 574-579.

Leonard, B. R., C. A. White, and J. B. Graves.  1995.  Insecticide
resistance frequencies in overwintering and field-collected tobacco
budworms, pp. 967-971. In 1995 Proceedings Beltwide Cotton Conf.,
National Cotton Council, Memphis, TN.

Li, H., B. Oppert, R. A. Higgins, F. Huang, K. Y. Zhu, and L. L.
Buschman. 2004. Comparative analysis of proteinase activities of
Bacillus thuringiensis-resistant and -susceptible Ostrinia nubilalis
(Lepidoptera: Crambidae).  Insect Biochem. Mol. Biol. 34: 753–762.

Li, H., B. Oppert, R. A. Higgins, F. Huang, L. L. Buschman, J. R. Gao,
and K. Y. Zhu. 2005. Characterization of cDNAs encoding three
trypsin-like proteinases and mRNA quantitative analysis in Bt-resistant
and -susceptible strains of Ostrinia nubilalis.  Insect Biochem. Mol.
Biol. 35: 847-60.

Liao, C., D. G. Heckel, and R. Akhurst 2002. Toxicity of Bt insecticidal
proteins for Helicoverpa armigera and Helicoverpa punctigera
(Lepidoptera:  Noctuidae), major pests of cotton.  J. Invert. Pathol.
80:55-63.

Livingston, M. J., G. A. Carlson, and P. L. Fackler. 2002. Use of
mathematical models to estimate characteristics of pyrethroid resistance
in tobacco budworm and bollworm (Lepidoptera: Noctuidae) field
populations.  J. Econ. Entomol. 95: 1008-1017.

Livingston, M. J., G. A. Carlson, and P. L. Fackler. 2004.  Managing
resistance evolution in two pests to two toxins with refugia.  Amer. J.
Agric. Econ. 86: 1-13.

Livingston, M. J., N. P. Storer, J. W. Van Duyn, and G. G. Kennedy.
2006. A structured refuge requirement may not be needed to promote
economic efficiency in eastern North Carolina.  J. Agric. Appl. Econ.
(in press).

Louis, T. A.  1981.  Confidence intervals for a binomial parameter after
observing no successes.  Amer. Statistician 35: 154.

Lukefahr, M. J., and D. F. Martin. 1964. The effects of various larval
and adult diets on fecundity and longevity of the bollworm, tobacco
budworm, and cotton leafworm.  J. Econ. Entomol.  57: 233-235.

Luttrell, R. G., K. Knighten, W. F. Kitten, G. L. Andrews, F. A. Harris,
and J. Reed. 1991. Monitoring pyrethroid resistance in the tobacco
budworm in Mississippi: implications for resistance management.
Southwest. Entomol. Supplement 15: 5-26.

McCullagh, P., and J. A. Nelder. 1989. Generalized Linear Models, Second
Edition. Chapman & Hall/CRC Press LLC, Boca Raton, FL.

Milne, R., T. Wright, H. Kaplan, and D. Dean. 1998. Spruce budworm
elastase precipitates Bacillus thuringiensis delta-endotoxin by
specifically recognizing the C-terminal region.  Insect Biochem. Mol.
Biol. 28:1013-1023.

Morin, S., R. W. Biggs, M. S. Sisterson, L. Shriver, C. Ellers-Kirk, D.
Higginson, D. Holley, L. J. Gahan, D. G. Heckel, Y. Carrière, T. J.
Dennehy, J. K. Brown, and B. E. Tabashnik. 2003.  Three cadherin alleles
associated with resistance to Bacillus thuringiensis in pink bollworm.
Proc. Natl. Acad. Sci. USA 100: 5004-5009.

Mueller, T. F., and J. R. Phillips. 1983. Population dynamics of
Heliothis-spp in spring weed hosts in southeastern Arkansas:
survivorship and stage-specific parasitism.  Environ. Entomol. 12:
1846-1850.

Mueller, T. F., V. E. Harris, and J. R. Phillips, Jr. 1984. Theory of
Heliothis (Lepidoptera: Noctuidae) management through reduction of the
1st spring generation – a critique.  Environ. Entomol. 13: 625-634.

Nadguada, D., and H. Pitre. 1983. Development, fecundity, and longevity
of the tobacco budworm (Lep.: Noctuidae) fed soybean, cotton, and
artificial diet at three temperatures.  Environ. Entomol. 12: 582-586.

Neunzig, H. H. 1969. The biology of the tobacco budworm and the corn
earworm in North Carolina.  North Carolina Agr. Expt. Sta. Tech. Bull.
196.

Norris, R. F., and M. Kogan. 2005. Ecology of interactions between weeds
and arthropods.  Annu. Rev. Entomol. 50: 479-503.

Onstad, D. W., and C. A. Guse. 1999. Economic analysis of transgenic
maize and nontransgenic refuges for managing European corn borer
(Lepidoptera: Pyralidae).  J. Econ. Entomol. 92: 1256-1265.

Oppert, B., K. J. Kramer, R. W. Beeman, D. Johnson, and W. H. McGaughey.
1997. Proteinase-mediated insect resistance to Bacillus thuringiensis
toxins.  J. Biol. Chem. 272: 23473-23476.

Ott, R. L., and M. Longnecker. 2001. An Introduction to Statistical
Methods and Data Analysis, Fifth Edition. Duxbury, Pacific Grove, CA.

Peck, S. L., F. Gould, and S. P. Ellner. 1999. Spread of resistance in
spatially extended regions of transgenic cotton: implications for
management of Heliothis virescens (Lepidoptera: Noctuidae).  J. Econ.
Entomol. 92: 1-16.

Reynolds, A. 2006. National refuge proposal for Bollgard II Bt cotton:
EPA overview and IRM considerations.  EPA-HQ-OPP-2006-0217-0039.

Roush, R. T. 1998. Two-toxin strategies for management of insecticidal
transgenic crops:  can pyramiding succeed where pesticide mixtures have
not?  Phil. Trans. Roy. Soc. 353: 1777-1786.

SAP. 2004. Meeting Minutes of the FIFRA Scientific Advisory Panel
Meeting, June 8-10, 2004.  A Set of Scientific Issues Being Considered
by the U.S. Environmental Protection Agency Regarding:  Product
Characterization, Human Health Risk, Ecological Risk, and Insect
Resistance Management for Bacillus thuringiensis (Bt) Cotton Products.
United States Environmental Protection Agency, Washington, D. C.
HYPERLINK "http://www.epa.gov/scipoly/sap/2004/june/final1a.pdf"
http://www.epa.gov/scipoly/sap/2004/june/final1a.pdf .

SAS Institute, Inc. 2006. Littell, R. C., G. A. Milliken, W. W. Stroup,
R. D. Wolfinger, and O. Schabenberger.  SAS for Mixed Models, Second
Edition. SAS Press, Cary, NC, US.

Schneider, J. C. 1999. Dispersal of a highly vagile insect in a
heterogeneous environment. Ecology 80: 2740-2749.

Schneider, J. C. 2003. Overwintering of Heliothis virescens (F.) and
Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae) in cotton fields of
northeast Mississippi.  J. Econ. Entomol. 96: 1433-1447.

Schneider, J. C., R. T. Roush, W. F. Kitten, and M. L. Laster. 1989.
Movement of Heliothis virescens (Lepidoptera: Noctuidae) in Mississippi
in the spring: Implications for area-wide management.  Environ. Entomol.
18: 438-446.

Sisterson, M. S., and B. E. Tabashnik. 2005. Simulated effects of
transgenic Bt crops on specialist parasitoids of target pests.  Environ.
Entomol. 34: 733-742.

Sisterson, M. S., L. Antilla, Y. Carrière, C. Ellers-Kirk, and B. E.
Tabashnik. 2004. Effects of insect population size on evolution of
resistance to transgenic crops.  J. Econ. Entomol. 97: 1413-1424.

Sisterson, M. S., Y. Carrière, T. J. Dennehy, and B. E. Tabashnik.
2005. Evolution of resistance to transgenic crops: interactions between
insect movement and field distribution.  J. Econ. Entomol. 98:
1751-1762.

Sparks, A. N., R. D. Jackson, and C. L. Allen. 1975. Corn earworms:
capture of adults in light traps on unmanned oil platforms in the Gulf
of Mexico.  J. Econ. Entomol. 68: 431-432.

Sparks, T. C., J. B. Graves, and B. R. Leonard. 1993. Insecticide
resistance and the tobacco

budworm: past, present and future.  Rev. Pestic. Toxicol. 2: 149-183.

Stadelbacher, E. A. 1981. Role of early-season wild and naturalized host
plants in the buildup of the F1 generation of Heliothis zea and H.
virescens in the Delta of Mississippi.  Environ. Entomol. 10: 766-770.

Storer, N. P., S. L. Peck, F. Gould, J. W. Van Duyn, and G. G. Kennedy.
2003. Spatial processes in the evolution of resistance in Helicoverpa
zea (Lepidoptera: Noctuidae) to Bt transgenic corn and cotton in a mixed
agroecosystem: A biology-rich stochastic simulation model.  J. Econ.
Entomol. 96: 156-172.

Tabashnik, B. E. 1992. Evaluation of synergism among Bacillus
thuringiensis toxins.  Appl. Environ. Microbiol. 58: 3343-3346.

USDA, NASS. 2006. Agricultural Statistics Data Base: State and county
data. (http://www.nass.usda.gov/).

Xu, X. J., L. Y. Yu, and Y. D. Wu. 2005. Disruption of a cadherin gene
associated with resistance to Cry1Ac delta-endotoxin of Bacillus
thuringiensis in Helicoverpa armigera.  Appl. Environ. Microbiol. 71:
948-954.

Zalucki, M. P., and M. J. Furlong. 2005. Forecasting Helicoverpa
populations in Australia: a comparison of regression based models and a
bioclimatic based modelling approach.  Insect Sci.  12: 45-56.

Zar, J. H. 1996. Biostatistical Analysis, Third Edition. Prentice-Hall
Inc., Upper Saddle River, NJ.

Zhao, J.-Z., J. Cao, H. L. Collins, S. L. Bates, R. T. Roush, E. D.
Earle, and A. M. Shelton. 2005. Concurrent use of transgenic plants
expressing a single and two Bacillus thuringiensis genes speeds insect
adaptation to pyramided plants.  Proc. Natl. Acad. Sci. USA 102:
8426-8430.

APPENDIX 1

Correct Derivation of Equation 7

To understand the mistake that Monsanto made in their derivation of
Equation 7, it is useful to reconstruct Monsanto’s equations from a
slightly different perspective.  Consider a landscape of area (.  This
area is divided into five distinct compartments: Bt cotton ((B1), Bt
corn ((B2), non-Bt cotton ((R1), non-Bt corn ((R2), and non-cotton C3
((R3) crops such that ( = (B1 + (B2 + (R1 + (R2 + (R3.  Let moth
production per unit area for each of these crops prior to Bt selection
be (ij for i = B, R and j = 1, 2, 3.  Total moth production for this
landscape can then be written as m = (B1(B1 + (B2(B2 +(R1(R1 + (R2(R2 +
(R3(R3.  Average moth production per unit area is M = m/(.

Effective refuge (Reff) is defined as the proportion of moths produced
by non-Bt and non-cotton C3 crops (i.e., R1, R2, and R3 crops),

 , which is exactly the same as Monsanto’s Equation (5).

Natural refuge (Rnat) consists of the proportion of moths produced on
non-cotton, non-Bt hosts (i.e., R2 and R3 crops) such that

 .

  Rnat, such that RnatCBW > Rnat.

Figure A1-1 illustrates the compartments used by Monsanto to calculate
the effective refuge.  When Monsanto calculates natural refuge using
Equation (7), it effectively eliminates the non-Bt cotton compartment
(Figure A1-2).  By doing this Monsanto effectively assumes a reduction
in the area of interest: (’ = (B1 + (B2 + (R2 + (R3, instead of (.
This assumption can create a bias because Mij is calculated per unit
area based on Aij= (ij/( in Figure A1-1, not (ij/(’ in Figure A1-2.
How much bias can this assumption produce?  Based on the data reported
in

Figure A1-1.  Crop acreage compartments for Monsanto’s effective
refuge calculation.

Figure A1-2.  Crop acreage compartments for Monsanto’s natural refuge
calculation.

Tables 2 and 5 (Gustafson and Head 2005) assuming LBB1 = LBB2 = 1 prior
to Bt selection, Monsanto’s estimate of the effective refuge (RnatCBW)
will be 37, 12, 6, and 44% higher than the natural refuge estimate given
by Rnat for Georgia, Mississippi, North Carolina, and Texas,
respectively.

For the current effective refuge, Reff, simulation, Monsanto only needs
their correct estimate of the effective refuge, so their result should
not be biased.  This is not true for natural refuge simulations.  For
natural refuge, Rnat, simulations, Monsanto keeps the area of cotton
fixed, while deleting structured refuge for Bollgard II for the
simulations reported in Table 14 and all structured refuge for
simulations reported in Figure 4.  This effectively reduces non-Bt
cotton acreage, while replacing it with Bt cotton acreage.  If ( is the
proportional reduction in non-Bt cotton acreage, effective and natural
refuge become

and

 ,

 Rnat such that RnatCBW > Rnat for ( ( 0.  This result suggests the
upward bias in Monsanto’s natural refuge estimate will be either
systematically increasing or decreasing in the market share of Bollgard
II depending on the relative moth productivity of Bt and non-Bt cotton
prior to Bt selection.  The data in Tables 2 and 5 suggest EB1 LBB1 LSB1
> ER1 LBR1 LSR1 for all regions such that increasing Bollgard II market
share, increases the upward bias in Monsanto’s natural refuge
calculation.

APPENDIX 2

Model Uncertainty

The Caprio model (Caprio 1998) used by Monsanto does not explicitly
incorporate population densities.  Modifying the model to include
population densities and density-dependent population growth rates
creates a model that violates some reasonable assumptions about the
biology and ecology of insects like TBW.  This illustrates a difficulty
in assessing models that, even though simple in structure, nonetheless
have properties that are not transparent.  This illustrates the problem
of model uncertainty.

A general “2-patch” model of resistance evolution

To explore this issue, we produced a model (code attached below) for
single-locus resistance based on the following assumptions:

1. A fraction Q of the suitable habitat (in which females lay eggs) is
refuge, and a fraction 1–Q is Bt.

2. Following emergence, males and females disperse randomly, so that the
proportion of males and females in refuge and Bt fields are Q and 1 –
Q.

3. Mating is random, and the sex ratio is 1:1.

4. Bt is high dose.  Therefore, survival from Bt of homozygous resistant
larvae is sRR = 1, while 1 >> sRS ≥ sSS.  Genotype does not affect
survival of larvae in refuge.

5. The net reproductive rate of larvae in refuge (i.e., the survival of
larvae times the number of female offspring produced per female larva
that survives to adulthood) is given by a function fR(xR), where xR is
the density of larvae.

6. The net reproductive rate of larvae in Bt fields that survive the Bt
toxin is given by a function fBt(xBt), where xBt is the density of
larvae that survive Bt toxin.  fBt(xBt) is a continuous function (but
not necessarily monotonically decreasing).  For notational convenience,
let fBt(xBt) = FBt in the limit as xBt approaches zero.  Thus, FBt is
the net reproductive rate of larvae that survive Bt toxins when the
density of surviving larvae in Bt fields is very low.  FBt is assumed to
be a constant.

7. In the absence of a resistance allele (i.e., for a purely susceptible
population), the mean density of insects approaches some fixed value X.
Because the model does not include temporal environmental variability,
this condition will be satisfied whenever a purely susceptible
population is persistent (i.e., does not go extinct and does not
increase in density to infinity).

	Under these assumptions, the following mathematical result holds (see
Ives and Andow 2002 for methods).  Let p(t) denote the frequency of the
resistance allele in generation t.  Then the asymptotic rate of increase
of the frequency of the resistance allele in the limit as p(t)
approaches zero is given by

Here, “asymptotic rate of increase” means the rate of increase after
transient fluctuations in the relative frequencies of the resistance
allele in Bt and refuge fields have dampened out.  Although this
approximation strictly holds only in the limit as p(t) approaches zero,
numerical studies show it generally to be accurate for p(t) < 0.1.  For
p(t) > 0.1, resistance normally occurs in only a few generations for
most reasonable parameter values, so the breakdown in the approximation
when p(t) > 0.1 does not greatly affect conclusions that can be drawn.

	The interesting consequence of this result is that the asymptotic rate
of resistance evolution does not depend on the function fR(xR), the
survival of larvae in the refuge and the fecundity of the surviving
females.  It also does not depend on the functional form of fBt(xBt),
only on fBt(xBt -> 0) = FBt.  Therefore, the theorem holds for a fairly
broad class of models.

	A key conclusion for models that conform to assumptions 1-7 is the
importance of FBt, the net reproductive rate of larvae that survive Bt
toxins when the density of surviving larvae in Bt fields is very low.
The higher FBt, the more rapid resistance evolution.  Unfortunately, it
is very difficult to estimate FBt in the field.

Comparison with the Caprio model (1998).

	The Caprio model (1998) forms the basis of the model Monsanto uses to
assess the durability of Bollgard II.  The Caprio model considers a case
such as Bollgard, in which there is a single resistance locus and only
two types of habitat, Bt crop and refuge.  For simplicity, here we will
address this model, rather than the multi-patch model presented by
Monsanto, although the general comments should apply to both.  Also, we
focus on the high-dose case corresponding to TBW.

	To compare to the Caprio model, we derived a model that explicitly
accounts for densities and conforms to assumptions 1-7 above.  For
population growth in Bt fields, we make the simple assumption that if
larvae survive Bt, they have density-independent population growth; in
other words, fBt(xBt) = FBt for all densities xBt.  For population
growth in the refuge fields, we make the simple assumption that the
population densities of larvae surviving in the refuge have a fixed
value xrefuge.  Thus, density-dependent population growth in the refuge
is strong enough to bring populations up to carrying capacity in the
refuge every generation.  This carrying capacity can be affected by
insecticide spraying in the refuge, however, such that if the
insecticide leaves only a fraction k of insects alive, the carrying
capacity is reduced to k xrefuge.  We selected these assumptions about
fBt and fR only because they are simple; as discussed above, other
assumptions give essentially the same results.  We will refer to this as
the FBt-fixed model, since it assumes a constant value of FBt.

	Figure A2-1A compares times to resistance computed from the Caprio
model (red dashed line) to those predicted by the FBt-fixed model just
described (black line).  In the Caprio model, the time to resistance is
much more sensitive to the amount of refuge Q than in the FBt-fixed
model.

	To understand why these models differ, we constructed a modified
version of the Caprio model that explicitly incorporates population
densities and satisfies assumptions 1-5 and 7 above.  It turns out to be
impossible to incorporate density into the Caprio model in a way that
satisfies all assumptions 1-7.  In the modified Caprio model, the
reproductive rate of larvae in Bt fields that survive Bt is 1/(kQ).  In
other words, to formulate the Caprio model to include densities, the
value of FBt must be inversely proportional to the survival of larvae
from spraying in the refuge, k, and the proportion of refuge fields, Q.
The fact that this modified Caprio model gives the same predicted rates
of resistance evolution is shown in Figure A2-1A by the correspondence
between the lines for the modified Caprio model (green line) and the
original Caprio model (red line).  The dependence of FBt on kQ in the
modified Caprio model is shown in Figure A2-1B.

Fig. A2-1. (A) Generations to resistance (from resistance allele
frequency of 0.002 to 0.5) and (B) corresponding values of FBt for
resistant insects in Bt crops versus the proportion refuge Q.  The red
dashed line is the Caprio model (1998).  The dotted green line is the
model with fixed size of the insect population in the refuge, xrefuge =
Q, and FBt of resistant insects in Bt crops the same as in the refuge
(FBt = 1/Q).  The solid black line has a constant FBt of 50.  Other
parameter values are: sRR = 1, sRS = 0.001, sSS = 0.001, k (survival
from insecticide) = 1, and R (proportion of insects leaving natal
habitat) = 1.

The models make different qualitative predictions about factors
affecting resistance evolution

	The FBt-fixed model makes different predictions from the Caprio model
about the consequences of spraying insecticides in refuges.  For the
FBt-fixed model, the rate of resistance evolution is not sensitive to
insecticide spraying in the refuge (Fig. A2-2).  This seemingly
unintuitive pattern is a direct mathematical consequences of the
theoretical result about the asymptotic rate of resistance evolution
described above, in which the rate of resistance evolution does not
depend on the reproduction rate of insects in refuges.  This contrast
between models illustrates how implicit assumptions in models can change
qualitative conclusions drawn from the models.

Fig. A2-2.  (A) Generations to resistance (from resistance allele
frequency of 0.002 to 0.5) and (B) corresponding values of FBt for
resistant insects in Bt crops versus the survival of insects from
insecticide in the refuge, k.  The red dashed line is the Caprio model
(1998).  The dotted green line is the model with fixed size of the
insect population in the refuge, xrefuge = kQ, and FBt of resistant
insects in Bt crops the same as in the refuge (FBt = 1/kQ).  The solid
black line has a constant FBt of 50.  Other parameter values are: Q =
0.1, sRR = 1, sRS = 0.001, sSS = 0.001, and R (proportion of insects
leaving natal habitat) = 1.

	Although the lack of effect of insecticide spraying may seem
unintuitive, it has a simple explanation (Ives and Andow 2002) using the
following reasoning:

(i) The rate of resistance evolution depends on the proportion of the
susceptible population that is killed by Bt.

(ii) In the high-dose case when the frequency of a resistance allele is
low, essentially the entire population is in the refuge.

(iii) The proportion of the susceptible population killed by Bt equals
the proportion of females that leaves the refuge and oviposits in Bt
fields.

(iv) Reducing the population size in the refuge does not change the
proportion of females leaving the refuge.

(v) Therefore, reducing the size of the population in the refuge through
spraying will not change the rate of resistance evolution.

While this argument begins to break down mathematically when the
frequency of the resistance allele reaches 0.1, by this time there will
be only a very few more generations before the resistance allele reaches
0.5 and control fails.

Matlab code for the modified Caprio (1998) model

% Caprio.m

% written by Anthony R. Ives, 9 June 2006

% produces a model similar to Caprio 1998, but with explicit insect

% densities and a two patch environment (removing the third "patch" of

% dispersing insects in Caprio 1998

clear

clf

% baseline values

R=1;        % proportion of insects leaving natal habitat before mating

m=1;        % proportion of insects leaving mating habitat after mating

Q=.05;      % proportion of refuge

k=1;        % survival from insecticde in refuge

sRR=1;      % survival of RR genotypes in Bt crop

sSS=.001;

sRS=.001;

L=1;        % survival of all genotypes in refuge (excluding
insecticide)

F1=50;      % fecundity of females in Bt crops

x0=k;       % population size of insects in refuge

% iterate over three cases:

% 1 - Caprio assumption of population growth

% 2 - fixed fecundity in Bt crops

% 3 - fecundity in Bt crops = Q*k

for flag=1:3

    output=[];

    for z=.02:.01:.2

        Q=z;

        % this allows males and females to have different proportions

        % leaving Bt and refuge fields

        R1m=R;

        R1f=R;

        R2m=R;

        R2f=R;

        m1m=m;

        m1f=m;

        m2m=m;

        m2f=m;

        % survivals

        ORR=[sRR 0;0 k*L];

        ORS=[sRS 0;0 k*L];

        OSS=[sSS 0;0 k*L];

        % initial densities X and frequencies P

        X=k*[0;Q];

        P=.002*[1;1];

        t=1;

        while (P'*X)/sum(X) < 0.5

            t=t+1;

            % set population size in refuge equal to k*Q

            x0=k*Q;

            % pre-mating movement of alleles in males

            z1s=((1-R1m)+(1-Q)*R1m)*X(1);

            z1d=(1-Q)*R2m*X(2);

            z2s=((1-R2m)+Q*R2m)*X(2);

            z2d=Q*R1m*X(1);

            Mm=[z1s/(z1s+z1d) z1d/(z1s+z1d);z2d/(z2s+z2d)
z2s/(z2s+z2d)];

            % pre-mating movement of alleles in females

            z1s=((1-R1f)+(1-Q)*R1f)*X(1);

            z1d=(1-Q)*R2f*X(2);

            z2s=((1-R2f)+Q*R2f)*X(2);

            z2d=Q*R1f*X(1);

            Mf=[z1s/(z1s+z1d) z1d/(z1s+z1d);z2d/(z2s+z2d)
z2s/(z2s+z2d)];

            % movement of alleles and mating to give genotypic
frequences

            WRR=(Mf*P).*(Mm*P);

            WRS=(1-Mf*P).*(Mm*P)+(Mf*P).*(1-Mm*P);

            WSS=(1-Mf*P).*(1-Mm*P);

            % redistribution of densities during premating dispersal

            d11=((1-R1f)+(1-Q)*R1f);

            d12=(1-Q)*R2f;

            d21=Q*R1f;

            d22=((1-R2f)+Q*R2f);

            D=[d11 d12;d21 d22];

            X=D*X;

            % genotypic densities

            XRR=WRR.*X;

            XRS=WRS.*X;

            XSS=WSS.*X;

            % post-mating movement of females

            d11=((1-m1f)+(1-Q)*m1f);

            d12=(1-Q)*m2f;

            d21=Q*m1f;

            d22=((1-m2f)+Q*m2f);

            % postmating movement and selction

            D=[d11 d12;d21 d22];

            XRR=ORR*D*XRR;

            XRS=ORS*D*XRS;

            XSS=OSS*D*XSS;

            % calculation of new frequencies and densities

            P=(XRR+XRS./2)./(XRR+XRS+XSS);

            X=XRR+XRS+XSS;

            % 1 - Caprio assumption of population growth

            if flag==1

                X=X./sum(X);

            end

            % 2 - fixed fecundity in Bt crops

            if flag==2

                X(1)=F1*X(1);

                X(2)=x0;

            end

            % 3 - fecundity in Bt crops = Q*k

            if flag==3

                F1=1/(Q*k);

                X(1)=F1*X(1);

                X(2)=x0;

            end

        end

        output=[output;z t F1];

    end;

    c='rkg';

    figure(1)

    subplot(2,1,1)

    plot(output(:,1),output(:,2),[c(flag),'-'])

    xlabel('Proportion refuge')

    ylabel('Gens To Failure')

    hold on

    if flag==2 | flag==3

        subplot(2,1,2)

        plot(output(:,1),output(:,3),[c(flag),'-'])

        xlabel('Proportion refuge')

        ylabel('R0')

        axis([0 .2 0 60])

        hold on

    end

end

hold off

APPENDIX 3

Evolution of Resistance Hotspots

	The hotspot phenomenon can be evaluated only in a model that includes
explicit spatial descriptions of the landscape.  A spatially explicit
2-loci model (described in more detail in Appendix 4) demonstrates that
hotspots and rapid evolution are possible for Bollgard II.  The model
used by Monsanto is spatially implicit, since insects are assumed to
disperse uniformly over the region modeled.  Even though CBW and TBW are
highly mobile, it is unlikely that TBW are well-mixed over the regions
considered during the cotton growing season (see Panel response to
Charge 6).

The spatially explicit model is designed to illustrate some key factors
that affect the rate of resistance evolution.  It is not designed to
make detailed predictions or incorporate all of the complexity likely to
affect resistance evolution.  Therefore, we did not incorporate many
elements of realism included in the spatially implicit Monsanto model.
In particular, we consider only Bt and refuge fields, and assume that
the proportion of Bt and refuge fields in the environment does not
change through time.  Monsanto assumes that both CBW and TBW only use
cotton in half of their 6 annual generations.  Rather than having
generations with and without selection, here we simply assume that there
are 3 selected generations per year.  We assume refuge fields are
distributed randomly over space; in reality, refuges will likely be
clustered, leaving relatively larger areas of contiguous Bt fields where
hotspots of resistance are likely to occur.  Finally, we assume that the
resistance alleles at both resistance loci have initial frequencies of
0.002, and that one of the toxins (Cry 2Ab2) is much more effective than
the other (Cry 1Ac), as is the case for CBW.  See Appendix 4 for more
details.

Two different versions of the model were produced corresponding to the
Caprio 1-locus model and the FBt-fixed model described in Appendix 2.
The only difference between these models is the assumption about
density-dependence.  In the Caprio version of the spatial model, the
reproductive rate of larvae in Bt fields that survive Bt is 1/(kQ).  In
the FBt-fixed model, FBt = 50.

	Figure A3-1 shows the spatial distribution of allele frequencies
produced in a simulation of the Caprio version of the model when there
is 5% refuge and both males and females have a dispersal radius of 6
fields.  The simulation shows a “hotspot” of resistance evolution in
which the allele frequency of both resistance alleles reach a peak.
This type of hotspot occurs, loosely speaking, because there is a
limited number of insects dispersing from the refuge into a region of
contiguous Bt fields.  Although in this model hotspots are generated by
limited dispersal, hotspots can be generated in other ways.  For
example, hotspots will occur even when refuges are within dispersal
range of Bt fields if the numbers of susceptible insects from the refuge
entering the Bt fields is low.  This possibility is apparent from the
information provided by Monsanto showing high variation in the number of
insects captured in different traps.  Therefore, hotspots can occur even
when the dispersal distance is much greater than 6 fields, as used in
this model.

Figure A3-1: Years to crop failure due to resistance evolution in the
spatially explicit 2-loci Caprio model (Appendix 4) in which the
effective refuge area is 5%, and males and females have a dispersal
radius of 6 fields. There is no cost of resistance.  In this simulation,
resistance failure occurred in 4 years.  Other parameter values are: saa
= 0, sAa = 0.001, saa = 1, sBB = 0.1, sBb = 0.2, and sbb = 1.

	 Table A3-1 gives the number of years required for resistance evolution
for several models and assumptions about dispersal.  The first model is
the spatially explicit version of the model based on Caprio (1998).
When there is limited dispersal of both males and females (labeled
“limited dispersal”) or global dispersal of males and limited
dispersal of females (labeled “limited female dispersal”), hotspots
occur when the effective refuge size, Reff, is 5% but not 10%.  This
illustrates that the likelihood of hotspots can be very sensitive to the
effective refuge size.  Note that the hotspots occur even when there is
global dispersal of males.  While there is considerable information
showing male dispersal is high, female dispersal is less well understood
(see Charge 1).  In the model, limited female dispersal is sufficient to
cause hotspots even when males show global dispersal.

The FBt-fixed version of the model in which the maximum reproduction
rate of resistant females in Bt fields is FBt = 50 similarly shows
hotspots when there is 5% refuge.  We considered a further case in which
all male and female insects leave their natal fields if they emerge in
refuge (Rrefuge = 1), but 20% of males and female remain in the Bt
fields if they successfully emerge there (RBt = 0.8).  This corresponds
to the case in which males and females remain in suitable habitat.  This
case similarly shows hotspots.

Table A3-1. Years to crop failure due to resistance evolution in the
spatially explicit 2-loci model (Appendix 4).  Values corresponding to
evolution in “hotspots” are given in bold.  “Limited dispersal”
corresponds to a maximum dispersal distance of 6 fields, and “limited
female dispersal” corresponds to global male dispersal and a maximum
dispersal distance of 6 fields for females.  Parameter values common to
all simulations are: saa = 0, sAa = 0.001, saa = 1, sBB = 0.1, sBb =
0.2, and sbb = 1.

Caprio 2-loci

FBt = 50

FBt = 50

RBt = 0.8

FBt = 50

RBt = 0.8

Rrefuge = 1

† Values are averages of 10 simulations, with the minimum and maximum
years to failure given in brackets.

*1 In this set of simulations, the median number of years to resistance
was 4, and 1 of the 10 was > 334 years.

*2 In this set of simulations, the median number of years to resistance
was 14, and 2 of the 10 were > 334 years.

	As a final case, we included a cost of resistance in which the survival
of larvae homozygous and heterozygous for the resistant allele to the
most effective toxin (Cry 2Ab2) have reduced survivals of 0.8 in Bt and
refuge fields compared to the survivals of 1 for susceptible larvae in
refuge fields.  This represents a strong cost of resistance that
increases Bt crop durability to over 1300 years when insects are
completely mixed (as assumed by Monsanto).  However, when there is
limited dispersal of females only, the median durability of Bt crop is
14 years.  This illustrates that hotspots can overwhelm a cost of
resistance, because within the hotspot (unlike the refuge) there is
little competition between resistant and susceptible insects.
Therefore, the cost of resistance has little effect to slow resistance
evolution.

	Note that the times to resistance produced by this model are not
inconsistent with the results from a detailed, spatially explicit model
presented by Caprio (2006) for the Panel.  This model assumes that the
effective, unstructured refuge is approximately 10% and there is a cost
of resistance.  Caprio reports that 25% of simulations without a
structured refuge led to resistance in less than 25 years.  In Table
A3-1 the case with a cost of resistance and Reff = 0.10 gives an average
time to resistance of 33 years.  In general, the Caprio model provided
to the Panel (Caprio 2006) seemed to predict shorter times to resistance
than the model presented by Monsanto (Gustafson and Head 2005), although
the reason for this difference is unclear.  The models differ in so many
ways, lack of strong concordance might be expected.

	We use this modeling exercise only to make three points: (1) hotspots
can occur for a two-toxin product (Bollgard II) , as they do for a
one-toxin product (Peck et al. 1999, Sisterson et al. 2004), and lead to
rapid resistance evolution, (2) hotspots can occur even when males are
broadly dispersive when females have limited dispersal, and (3) hotspots
may overwhelm the effects of a cost of resistance.  However, this model
suffers from model uncertainty just like the model used by Monsanto.
Rather than a completed model that can give quantitative predictions,
instead we view it as only a starting point to develop a greater
understanding of resistance evolution to a two-toxin product.
Unfortunately, our current level of understanding resistance evolution
for two-toxin products is too limited to make quantitative predictions.

	The Panel realizes that the EPA would like a model that is rigorously
validated and capable of making predictions about the rate of resistance
evolution.  Unfortunately, for the case of Bollgard II, such a model
does not exist.  Therefore, the Panel feels that it is best to
acknowledge our ignorance rather than hide it.  While it might be
tempting to use the complexity of predicting the rate of resistance
evolution as an excuse to rely on simple models and simple analyses,
this runs the risk of ignoring processes that might lead to rapid
resistance.

	A particular limitation of the 2-loci model presented here is that it
is an “infinite population model” in which population densities are
modeled rather than individual insects.  In effect, infinite population
models include fractional individuals, and model the mean frequency of
resistance and the mean density of insects.  For example, if there are
two resistance loci with resistance alleles at frequency 0.001, then the
probability of a doubly homozygous resistant individual (assuming
completely random mating) is 10-12.  Thus, the population size would
have to be roughly 1012 before there was a reasonable chance of finding
a doubly resistant individual.  Preliminary simulations show that
infinite population models likely overestimate the time to resistance
rather than underestimate it relative to models that explicitly
following integer individuals and individual alleles.  Therefore,
hotspots would be more likely in more-realistic simulation models.
Nonetheless, this is an issue for further research.

APPENDIX 4

A spatially explicit 2-loci model of resistance evolution

This Appendix derives a spatially explicit version of Monsanto’s base
model.  Monsanto does not provide equations necessary to reconstruct
their model, so several specific assumptions about their model had to be
made.

Two versions of the spatially explicit model were derived.  The first is
based on Caprio (1998).  We also used a modified version was also
derived in which both the population size in the refuge, xrefuge, and
the per capita population growth rate of resistant females in Bt fields,
FBt, were fixed.  To simplify the presentation, we assume that there are
only Bt and refuge fields, rather than distinguishing the multiple types
of refuges.  Furthermore, we assume that the proportion of refuge in the
environment does not change through time.  Because only half the
generations of either CBW or TBW are likely to be subjected to selection
for Bt resistance, we assume that 50% of the generations are unselected.
 This is approximated by assuming there are 3 generations per year, all
of which are selected.  We do not present a formal analysis of the
model, and it has not been independently proofed.  Nonetheless, code is
provided at the end of the Appendix.

Below we first describe a spatially implicit model that should be
comparable to Monsanto’s model (although without incorporating
multiple habitat types and variation in habitat types through time).
This spatially implicit model could be modified and used to verify
Monsanto’s calculations.  Here, it is used to verify the computer code
for the spatially explicit model, since both models give the same output
when the spatially explicit model includes “global” dispersal of
insects over the entire region.

1.  Spatially implicit model

	The spatially implicit model assumes that if males and females disperse
from their natal fields, they disperse evenly throughout a region and
settle in Bt and refuge fields in proportion to the area of Bt and
refuge fields.  In Monsanto’s model, all males and females are assumed
to disperse from their natal field, although here we generalize by
including the assumption of Caprio (1998) that a proportion R of insects
disperse while 1–R remain in their natal fields.  After dispersal,
mating is random and females oviposit in the fields in which they mate.
Because the Monsanto model assumes all insects disperse, post-mating
dispersal makes no difference to the model, so for simplicity we have
not included it.

	Selection occurs on the larvae within fields.  Rather than use the
formula provided by Monsanto to calculate the survivals of the 9
possible genotypes, here for simplicity we assume that the survival of
2-loci genotypes is equal to the product of survivals for each locus
separately.  Thus, if the survival of genotype Aa to one toxin (e.g.,
Cry 1Ac) is sAa and the survival of genotype Bb to a second toxin (e.g.,
Cry 2Ab2) is sBb, then the survival of the genotype AaBb to both toxins
is sAaBb = sAasBb.  In the model, genotypes are state variables rather
than gene frequencies, because strong selection will generate linkage
disequilibrium even when alleles of different loci segregate
independently.

2.  Spatially explicit model

	The spatially explicit model includes the same processes modeled within
Bt and refuge fields as in the spatially implicit model.  These
processes, however, are mapped onto space.  Space consists of a 50 x 50
grid of cells with “wrap-around” boundaries (i.e., on a torus).
Cells are assigned randomly as either Bt or refuge, with the probability
that a cell is refuge being Q.  When insects disperse from cells, they
are distributed among neighboring cells.  In particular, up to a maximum
distance of n (selected as a parameter in the model), the dispersing
population is distributed by a geometric distribution in all directions
from their natal field.

	Note that the way in which refuges are dispersed in the spatially
explicit model is conservative, in that refuges are spread uniformly
throughout space.  If refuges were clustered, there would be larger
areas of Bt fields without nearby refuges, and hotspots would be more
likely.

	To include a cost of resistance, we assume that survivals of resistant
genotypes are reduced in both Bt and refuge fields.  To compute the cost
for different genotypes, we assume survivals are multiplicative in the
same manner as survivals from Bt.

Matlab code for the spatially implicit model

% Caprio2loci.m

% Tony Ives, 16 June 2006

% This is for EPA use only and is copyrighted by Tony Ives

clear

%size of grid

N=50;

% flag2=1 for fixed R0, flag2=2 for Caprio model

flag2=2;

% reproductive rate in Bt fields (under flag2=1)

F1=50;

%dispersal of males (rm) and females (rf) from Bt (1) and refuge (2)
fields

R1m=1;

R1f=1;

R2m=1;

R2f=1;

% proportion of refuge

Q=.05;

x0=Q;

% spraying survival in refuge

k=1;

% initial gene frequencies

p1=.002;

p2=.002;

s1=[0 .001 1];

s2=[.1 .2 1];

Z=s1'*s2;

Z=Z(:);

sAABB=Z(1);

sAaBB=Z(2);

saaBB=Z(3);

sAABb=Z(4);

sAaBb=Z(5);

saaBb=Z(6);

sAAbb=Z(7);

sAabb=Z(8);

saabb=Z(9);

s=[sAABB sAaBB saaBB sAABb sAaBb saaBb sAAbb sAabb saabb];

% calculate V for mating of genotypes

VV(:,:,1)=[1 .5 0;.5 .25 0;0 0 0];

VV(:,:,2)=[0 .5 1;.5 .5 .5;1 .5 0];

VV(:,:,3)=[0 0 0; 0 .25 .5;0 .5 1];

for i2=1:3

    for i1=1:3

        V(:,:,3*(i2-1)+i1)=kron(VV(:,:,i2),VV(:,:,i1));

    end

end

% add cost of resistance

s1=[1 .8 .8];

s2=[1 1 1];

Z=s1'*s2;

k=Z(:);

O1=diag(k.*s);

O2=diag(k);

% initially assume genotypes at H-W and unlinked

Wi=[(1-p1)^2 2*p1*(1-p1) p1^2];

Wj=[(1-p2)^2 2*p2*(1-p2) p2^2];

W=Wi'*Wj;

W=W(:);

W1=W;

W2=W;

X=[10^-4;x0];

P1=p1;

P2=p2;

t=0;

output=[0 P1 P2 0];

while (P1 < 0.5 | P2 < 0.5) && X(1) < x0/2 && t < 2000

    t=t+1;

    % pre-mating movement of males

    z1s=((1-R1m)+(1-Q)*R1m)*X(1);

    z1d=(1-Q)*R2m*X(2);

    z2s=((1-R2m)+Q*R2m)*X(2);

    z2d=Q*R1m*X(1);

    Mm=[z1s/(z1s+z1d) z1d/(z1s+z1d);z2d/(z2s+z2d) z2s/(z2s+z2d)];

    Mm=kron(Mm,eye(9));

    % pre-mating movement of females

    z1s=((1-R1f)+(1-Q)*R1f)*X(1);

    z1d=(1-Q)*R2f*X(2);

    z2s=((1-R2f)+Q*R2f)*X(2);

    z2d=Q*R1f*X(1);

    Mf=[z1s/(z1s+z1d) z1d/(z1s+z1d);z2d/(z2s+z2d) z2s/(z2s+z2d)];

    Mf=kron(Mf,eye(9));

    Wm=Mm*[W1;W2];

    Wf=Mf*[W1;W2];

    % mating

    WW=[];

    for i=1:2

        Wmi=Wm(9*(i-1)+1:9*i);

        Wfi=Wf(9*(i-1)+1:9*i);

        for j=1:9

            WW=[WW;sum(sum((Wfi*Wmi').*V(:,:,j)))];

        end

    end

    W1=WW(1:9);

    W2=WW(10:18);

    % redistribution of densities during premating dispersal

    d11=((1-R1f)+(1-Q)*R1f);

    d12=(1-Q)*R2f;

    d21=Q*R1f;

    d22=((1-R2f)+Q*R2f);

    D=[d11 d12;d21 d22];

    X=D*X;

    % selection

    Wn1=O1*W1;

    Wn2=O2*W2;

    W1=Wn1./sum(Wn1);

    W2=Wn2./sum(Wn2);

    % density-dependent population growth

    if flag2==1

        BtXincrease=F1*sum(Wn1);

    else

        BtXincrease=(sum(Wn1)/sum(W1))*x0/(X(2)*sum(Wn2)/sum(W2));

    end

    X(1)=BtXincrease*X(1);

    X(2)=x0;

    % compute frequencies

    P1=(X(1)*(sum(W1([3 6 9]))+.5*sum(W1([2 5 8]))) + ...

        X(2)*(sum(W2([3 6 9]))+.5*sum(W2([2 5 8]))))/(X(1)+X(2));

    P2=(X(1)*(sum(W1([7 8 9]))+.5*sum(W1([4 5 6]))) + ...

        X(2)*(sum(W2([7 8 9]))+.5*sum(W2([4 5 6]))))/(X(1)+X(2));

    output=[output;t P1 P2];

end

GensToFailure=t

YearsToFailure=ceil(t/3)

figure(1)

semilogy(output(:,1),output(:,2),'k',output(:,1),output(:,3),'b')

xlabel('Time (generations)')

ylabel('Allele frequencies')

hold on

Matlab code for the spatially explicit model

% Caprio2lociSpatial.m

% Tony Ives, 16 June 2006

% Modified from a single-locus model written by Nic Lehmann-Ziebarth and

% Tony Ives

% This is for EPA use only and is copyrighted by Tony Ives

clear

%size of grid

N=50;

% flag2=1 for fixed R0, flag2=2 for Caprio model

flag2=2;

% reproductive rate in Bt fields (under flag2=1)

F1=50;

%dispersal of males (rm) and females (rf) from Bt and refuge fields

rm1=1;

rm2=1;

rf1=1;

rf2=1;

rm=[rm1 rm2];

rf=[rf1 rf2];

% proportion of refuge

Q=.05;

x0=Q;

% spraying survival in refuge

k=1;

% male dispersal: (1) horizontal/vertical; (2) diamond; (3) global
dispersal

disperseflagm=2;

% female dispersal: (1) horizontal/vertical; (2) diamond; (3) global
dispersal

disperseflagf=2;

%nearest n neighbor movement

n=6;

% initial gene frequencies

p1=.002;

p2=.002;

s1=[0 .001 1];

s2=[.1 .2 1];

%s2=[1 1 1];

Z=s1'*s2;

Z=Z(:);

sAABB=Z(1);

sAaBB=Z(2);

saaBB=Z(3);

sAABb=Z(4);

sAaBb=Z(5);

saaBb=Z(6);

sAAbb=Z(7);

sAabb=Z(8);

saabb=Z(9);

s=[sAABB sAaBB saaBB sAABb sAaBb saaBb sAAbb sAabb saabb]';

% calculate V for mating of genotypes

VV(:,:,1)=[1 .5 0;.5 .25 0;0 0 0];

VV(:,:,2)=[0 .5 1;.5 .5 .5;1 .5 0];

VV(:,:,3)=[0 0 0; 0 .25 .5;0 .5 1];

for i2=1:3

    for i1=1:3

        V(:,:,3*(i2-1)+i1)=kron(VV(:,:,i2),VV(:,:,i1));

    end

end

% add cost of resistance

%s1=[1 .8 .8];

s1=[1 1 1];

s2=[1 1 1];

Z=s1'*s2;

C=Z(:);

% set up survivals

O1=k.*C.*s;

O2=k.*C;

O=[O1 O2];

AABB=O(1,:);

AaBB=O(2,:);

aaBB=O(3,:);

AABb=O(4,:);

AaBb=O(5,:);

aaBb=O(6,:);

AAbb=O(7,:);

Aabb=O(8,:);

aabb=O(9,:);

% set up dispersal matrix Mm

if disperseflagm==1,

    %xx is movement rate to 4 nearest squares.

    %Distribution over squares is pseudo geometric (scale by 1/2)

    M=zeros(N,N);

    xx=(8*(1-.5^n))^(-1);

    for w=1:n

        xw=xx*.5^(w-1);

        M=M+xw*(diag(ones(N-w,1),w)+diag(ones(w,1), N-w));

    end

    Mm=M+M';

end

% set up dispersal matrix Mf

if disperseflagf==1,

    %xx is movement rate to 4 nearest squares.

    %Distribution over squares is pseudo geometric (scale by 1/2)

    M=zeros(N,N);

    xx=(8*(1-.5^n))^(-1);

    for w=1:n

        xw=xx*.5^(w-1);

        M=M+xw*(diag(ones(N-w,1),w)+diag(ones(w,1), N-w));

    end

    Mf=M+M';

end

if disperseflagm==2,

    switch n,

        case 2

        load 'M50_n2'

    case 3,

        load 'M50_n3'

    case 4,

        load 'M50_n4'

    case 5

        load 'M50_n5'

    case 6

        load 'M50_n6'

    end

    Mm=M;

end

if disperseflagf==2,

    switch n,

        case 2

        load 'M50_n2'

    case 3,

        load 'M50_n3'

    case 4,

        load 'M50_n4'

    case 5

        load 'M50_n5'

    case 6

        load 'M50_n6'

    end

    Mf=M;

end

%This creates random distribution of Bt sites over the field

Btgrid=1+(rand(N,N)<Q);

O=zeros(N,N,9);

O(:,:,1)=AABB(Btgrid);

O(:,:,2)=AaBB(Btgrid);

O(:,:,3)=aaBB(Btgrid);

O(:,:,4)=AABb(Btgrid);

O(:,:,5)=AaBb(Btgrid);

O(:,:,6)=aaBb(Btgrid);

O(:,:,7)=AAbb(Btgrid);

O(:,:,8)=Aabb(Btgrid);

O(:,:,9)=aabb(Btgrid);

%grid of male and female movement proportions

RM=rm(Btgrid);

RF=rf(Btgrid);

% grid of initial densities

X=10^-4*ones(N,N);

X(Btgrid==2)=x0;

% initially assume genotypes at H-W and unlinked

Wi=[(1-p1)^2 2*p1*(1-p1) p1^2];

Wj=[(1-p2)^2 2*p2*(1-p2) p2^2];

W=Wi'*Wj;

W=W(:);

for i1=1:N

    for i2=1:N

        WW(i1,i2,:)=W;

    end

end

W=WW;

t=0;

Tmax=10^3;

Plist=[];

while t < Tmax && (p1 < .5 | p2 < .5)

    t=t+1;

    % dispersal of male alleles

    for i=1:9

        Xs=(1-RM).*W(:,:,i).*X;

        Xd=RM.*W(:,:,i).*X;

        if disperseflagm==1,

            %disperse males and alleles to 4 nearest cells

            Xd=(Mm*Xd+Xd*Mm);

        elseif disperseflagm==2,

            %This is diamond dispersal

            Xd=reshape(Mm*Xd(:),N,N);

        else

            %global dispersal

            Xd=mean(mean(Xd))*ones(N,N);

        end

        Xm(:,:,i)=Xs+Xd;

    end

    Wm=Xm./repmat(sum(Xm,3),[1 1 9]);

    % dispersal of female alleles and females

    for i=1:9

        Xs=(1-RF).*W(:,:,i).*X;

        Xd=RF.*W(:,:,i).*X;

        if disperseflagf==1,

            %disperse males and alleles to 4 nearest cells

            Xd=(Mf*Xd+Xd*Mf);

        elseif disperseflagf==2,

            %This is diamond dispersal

            Xd=reshape(Mf*Xd(:),N,N);

        else

            %global dispersal

            Xd=mean(mean(Xd))*ones(N,N);

        end

        Xf(:,:,i)=Xs+Xd;

    end

    Wf=Xf./repmat(sum(Xf,3),[1 1 9]);

    Xf=sum(Xf,3);

    %mating

    for i1=1:N

        for i2=1:N

            for j=1:9

                WWm=reshape(Wm(i1,i2,1:9),9,1);

                WWf=reshape(Wf(i1,i2,1:9),9,1);

                W(i1,i2,j)=sum(sum((WWf*WWm').*V(:,:,j)));

            end

        end

    end

    % pre-selection allele frequencies

    Wo=W;

    %selection

    for i=1:9

        Wn(:,:,i)=W(:,:,i).*O(:,:,i);

    end

    surv=sum(Wn,3)./sum(W,3);

    W=Wn./repmat(sum(Wn,3),[1 1 9]);

    % compute new densities

    X=surv.*Xf;

    if flag2==1

        X(Btgrid==1)=F1*X(Btgrid==1);

    else

        X(Btgrid==1)=(1/(k*Q))*X(Btgrid==1);

    end

    X(Btgrid==2)=x0;

    % compute gene frequencies

    p1=0;

    p2=0;

    for i1=1:N

        for i2=1:N

            p1=p1+X(i1,i2)*(sum(W(i1,i2,[3 6 9]))+.5*sum(W(i1,i2,[2 5
8])));

            p2=p2+X(i1,i2)*(sum(W(i1,i2,[7 8 9]))+.5*sum(W(i1,i2,[4 5
6])));

            P1(i1,i2)=sum(Wo(i1,i2,[3 6 9]))+.5*sum(Wo(i1,i2,[2 5 8]));

            P2(i1,i2)=sum(Wo(i1,i2,[7 8 9]))+.5*sum(Wo(i1,i2,[4 5 6]));

        end

    end

    p1=p1/sum(X(:));

    p2=p2/sum(X(:));

    Plist=[Plist; t p1 p2 mean(mean(X))];

end

% printed output

GensToFailure=t

YearsToFailure=ceil(t/3)

meanX=mean(X(:))

if 1

    % graph of gene frequencies through time

    figure(1)

    semilogy(Plist(:,[2 3]))

    xlabel('time')

    ylabel('allele frequency')

    % graph 3D picture of gene frequencies

    figure(100)

    subplot(2,1,1)

    surf(P1)

    view(2)

    axis([1 N 1 N 0 1])

    subplot(2,1,2)

    surf(P2)

    view(2)

    axis([1 N 1 N 0 1])

end

Appendix 5

Post meeting comments from one Panel member regarding:

 County-level Variation in Non-cotton Cultivated Hosts of the Tobacco
Budworm

	In response to the charge to assess the adequacy of natural refuges by
region (Charge 9), one Panel member provided the following additional
analyses and comments concerning the adequacy of Monsanto's spatial
sampling scheme for extrapolating to areas that were not sampled.  Such
comments were provided by the Panel member after the meeting and were
not considered for review by the Panel, thus they do not reflect a
consensus Panel position.

Monsanto's assertion that non-cotton hosts constitute an adequate
natural refuge to delay counteradaptation by TBW to Bollgard II cotton
in the East region (i.e., Monsanto's "North Carolina" and "Georgia"
regions; see Fig. 9-1 under Charge 9, or Fig. 2 in Gustafson and Head
2005) depends in large part, at least for the states north of Georgia,
on the data Monsanto provided on host use by TBW from five counties in
North Carolina (three counties for two years and two counties for one
year).  Cropping data from the United States Department of Agriculture,
National Agricultural Statistics Service (USDA, NASS 2006) show that
these sampled counties are not as representative of the entire South
Carolina, North Carolina, and Virginia area as one could desire (Fig.
A5-1).  In South Carolina and North Carolina, there exist multi-county
areas containing tens of thousands to a hundred thousand acres of cotton
in which (tobacco + peanut) acreage constitutes < 5% of the (cotton +