FORUM
Roles of Selection Intensity, Major Genes, and Minor Genes in
Evolution of Insecticide Resistance
FRANCIS R. GROETERS1
AND
BRUCE E. TABASHNIK
Department of Entomology, University of Arizona, Tucson, AZ 85721
J. Econ. Entomol. 93(6): 1580Ð1587 (2000)
ABSTRACT A prominent hypothesis about insecticide resistance is that genes of major effect play
a key role in Þeld-evolved resistance because the intensity of selection is extremely high in the Þeld.
A corollary hypothesis is that the lower intensity of selection in laboratory selection experiments
favors polygenic control of insecticide resistance. Contrary to these hypotheses, a literature review
revealed that the intensity of selection for insecticide resistance in the Þeld varies widely and
overlaps broadly with selection intensities in the laboratory. Also contrary to these hypotheses,
results from simulations of population genetic models suggest that selection intensities typical of
laboratory selection experiments favor resistance that is conferred by major genes. Major genes
dominated responses to selection for resistance across a wide range of simulated selection intensities,
with and without Þtness costs and refuges. The simulation results also suggest that the intensity of
selection, rather than the number of loci conferring resistance, is central in determining rates of
resistance evolution and effectiveness of refuges.
KEY WORDS selection, insecticide resistance, monogenic, polygenic, Þtness cost, refuge
EVOLUTION OF RESISTANCE to insecticides is often, but
not always, based on substitution of alleles of major
effect (Roush and McKenzie 1987, Denholm and
Rowland 1992, McKenzie and Batterham 1994, Ashkok
et al. 1998, ffrench-Constant et al. 1999, Heckel et al.
1999). As one component of conceptual models that
include effects of initial resistance allele frequency
and other factors, several authors have suggested that
the extremely high selection intensities presumed to
be associated with exposure to insecticides in the Þeld
favor resistance alleles of major effect (McKenzie et
al. 1992; McKenzie and Batterham 1994; Carrière and
Roff 1995; McKenzie 1996, 2000). A corollary hypothesis is that the lower intensity of selection in laboratory selection experiments favors polygenic control of
resistance. For example, McKenzie and Batterham
(1994) proposed that an insecticide concentration
that kills 90% of the individuals in a susceptible population would be expected to produce polygenically
based resistance, whereas the substantially higher selection intensity occurring in the Þeld would cause a
monogenic response.
Here we review published estimates of the intensity
of selection for insecticide resistance in the Þeld and
describe results from simulations that assess the roles
of major and minor genes in evolution of insecticide
resistance. We used simulations of population genetic
models to assess the relative contributions of major
and minor alleles in evolution of insecticide resistance
across a wide range of selection intensities, with and
without a Þtness cost, and with and without refuges
1
Current address: 172 Lapla Road, Kingston, NY 12401.
from selection. We simulated various six-locus models
that included a small number of major loci, a larger
number of intermediate loci, and many loci of minor
effect. In particular, we tested the hypothesis that
adaptation by major genes is likely only under extremely intense selection. We also compared the effectiveness of refuges for delaying resistance under
monogenic and polygenic inheritance.
Materials and Methods
Empirical Estimates of Selection Intensity in the
Field. We used the estimates of the selection coefÞcient (S) based on data published in 12 articles. The
Þtness of resistant homozygotes exposed to insecticide
was deÞned as 1, and the Þtness of susceptible homozygotes exposed to insecticide was 1-S. Thus, S can
range from 0 to 1, with 1 representing the most intense
selection. S was estimated by measuring mortality after insecticide exposure in all cases except for Culex
pipiens (L.), in which estimates of average S were
based on frequency gradients along a cline (Lenormand et al. 1998).
Basic Simulation Model. We used a stochastic population genetic model with discrete generations that
was implemented on SAS (SAS Institute 1985). We
assumed that resistance is determined by an underlying trait, tolerance, that is normally distributed on a
logarithmic scale. This assumption is supported by
empirical data and is almost universally adopted in
analyses of insecticide resistance (Finney 1971,
Chilcutt and Tabashnik 1995). Tolerance was determined by six unlinked loci, each with two alleles. The
0022-0493/00/1580Ð1587$02.00/0 䉷 2000 Entomological Society of America
December 2000
Table 1.
GROETERS AND TABASHNIK: SELECTION INTENSITY AND RESISTANCE
Characteristics of six-locus models simulated
Effect
Initial
frequency
No. of
loci
VPa
5%
thresholdb
A
0.90
0.85
0.80
0.0056
0.0059
0.0063
1
2
3
4.2
2.4
B
1.50
1.00
0.50
0.0033
0.0050
0.0100
1
2
3
5
2.6
C
2.50
0.75
0.33
0.0020
0.0067
0.0152
1
2
3
7.7
3.2
D
4.00
0.33
0.11
0.0013
0.0152
0.0454
1
2
3
16.3
6.6
Model
In model A, effects of each allele are small and nearly equal (polygenic). In model D, the effect of one allele is much larger than the
others (monogenic). Models B and C are intermediate between these
two extremes.
a
Phenotypic variance when allele frequencies at all loci are 0.5 and
heritability is 0.5.
b
The value of tolerance corresponding to an insecticide concentration that would kill 95% of susceptible genotypes (G ⫽ 0). The
criterion for resistance was met when 50% of a population had phenotypic values greater than the 5% threshold.
susceptible allele at each locus had a genotypic effect
0. The genotypic effect of the various resistant alleles
ranged from 0.11 to 4 (Table 1). An individualÕs genotype (G) was obtained by summing genotypic effects across alleles at each locus and across the six loci.
Thus, we assumed no dominance and no epistasis. An
environmental effect sampled from a normal distribution with mean 0 and variance VE was added to each
individualÕs genotype to obtain its phenotype (P). VE
was calculated to provide a heritability (h2) of 0.5
when allele frequencies were equal to 0.5, and genotype frequencies followed HardyÐWeinberg expectations at all loci. Because maximum h2 occurs when
allele frequencies are 0.5 (Falconer 1989), h2 was
almost always ⬍0.5.
Each simulation started with 10,000 individuals. Initial genotype frequencies at each locus followed
HardyÐWeinberg expectations for a given set of initial
allele frequencies. We examined truncation selection
with survival of treated adults at 1, 10, and 50% (equivalent to mortality of 99, 90, and 50%, respectively).
Selection was applied by setting a threshold value for
survival that killed the appropriate percentage of
adults with susceptible genotypes. With the exception
of the variable selection model (see below), the
threshold was constant over time. Thus, selection intensity decreased each generation as tolerance increased in the population. This mimics spraying insecticide at a Þxed concentration during each
generation. Selection was applied separately to males
and females. Survivors mated randomly. The total
number of pairs was equal to the smaller of the number
of survivors of each sex. Thus, each generation a small
number of survivors did not produce offspring because of the absence of a mate. To keep the computing
time required for each simulation reasonable, the
1581
number of offspring per pair was chosen to ensure a
total of ⬇10,000 offspring. The sex of each offspring
was assigned randomly and the expected sex ratio was
1:1. A random sample of 10,000 was chosen to form the
adults of the next generation.
The population was deÞned to be resistant when
50% of the population had tolerance values greater
than a threshold value that would kill 95% of individuals with susceptible genotypes (G ⫽ 0). In other
words, the population had evolved resistance when a
concentration of insecticide that killed 95% of susceptibles killed ⱕ50% of the population. Results presented
are based on the mean of three independent simulations unless speciÞed otherwise.
Four different distributions of allelic effects were
examined (models AÐD, Table 1). In all four cases, the
genotypic range was 0 (homozygous susceptible at all
six loci) to 10 (homozygous resistant at all six loci). In
model A, the effect of each resistance allele was relatively small and nearly equal at each of the six loci.
Model D approached a monogenic model for resistance; one locus accounted for 80% of the potential
resistance. Models B and C were intermediate between these two extremes. The selection threshold
varied among models with different distributions of
effects because for constant heritability and genotypic
range, genetic and environmental variation vary with
the distribution of allelic effects.
The initial frequency for a hypothetical allele of
effect 4 was set to 0.00125 (Table 1). Initial frequencies for alleles of smaller effect were higher in direct
proportion to the magnitude of their effect on resistance. Thus, an allele of effect 1 had an initial frequency of 0.005, and the allele with the smallest effect
(0.11) had the highest initial frequency (0.0454). In
effect, we assumed that, before the population was
exposed to insecticide, alleles with greater effect on
resistance had higher Þtness costs. Before exposure to
insecticide, alleles with greater Þtness costs are expected to have a lower frequency at an equilibrium
between mutation and selection (Falconer 1989).
With this approach, initial genetic variance was similar
among models.
Under intense selection and Þnite population size,
allele frequencies can be inßuenced by gametic phase
disequilibrium (also called linkage disequilibrium),
which occurs when alleles at different loci are not in
random association (Falconer 1989). When gametic
phase disequilibrium occurs, the frequency of a gamete carrying any particular combination of alleles
does not equal the product of the frequencies of those
alleles (Hartl 1988). Consider two unlinked loci X and
Y at which alleles X0 and Y0 confer susceptibility while
X1 and Y1 confer resistance. An excess of the genotype
X0Y0/X1Y1 is called positive disequilibrium because
resistance alleles are positively correlated across loci,
and an excess of the genotype X0Y1/X1Y0 is called
negative disequilibrium because resistance alleles are
negatively correlated across loci. In simulations of
model D under intense selection (1% survival), we
checked for gametic phase disequilibrium by calculating the correlation between the number of major
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JOURNAL OF ECONOMIC ENTOMOLOGY
resistance alleles and the number of each of the Þve
minor resistance alleles for each individual (n ⫽ 10,000
individuals per test). The sign of the Þrst statistically
signiÞcant correlation (P ⬍ 0.05) generally denoted
the sign of the association for ensuing generations and
was used to indicate the direction of gametic phase
disequilibrium.
We tested the sensitivity of resistance outcomes to
variation in selection intensity using the basic model
and three variants of the basic model: (1) Þtness cost
model, (2) variable selection model, and (3) refuge
model.
Fitness Cost Model. In the Þtness cost model, we
assumed that in the absence of insecticide, individuals
with higher levels of resistance had higher Þtness
costs. Immatures were not exposed to insecticide, and
all individuals with phenotypic values ⫽ 0 (susceptible) survived to adulthood. We calculated w(P), the
probability of survival to adulthood for an individual
with a phenotypic value of P, according to the following function (Turelli 1984)
再 冎
w共P兲 ⫽ exp ⫺
P2
2␻2
,
where ␻2 is a constant inversely related to the magnitude of selection against resistant individuals in the
absence of insecticide. In light of empirical estimates
of Þtness costs (Roush and McKenzie 1987, Tabashnik
et al. 1994), we set ␻ ⫽ 8 to yield probabilities of
survival to adulthood of ⬇0.50 for individuals homozygous for resistance at all loci, and ⬇0.83 for individuals
heterozygous at all loci (survival probabilities varied
slightly among models according to variation in VE).
To determine if each individual survived to adulthood,
we used a binomial random variable with that individualÕs probability of survival to adulthood [w(P)]
and outcomes of 0 (death) or 1 (survival).
The number of offspring per pair was chosen to
produce ⬇20,000 offspring. This usually provided at
least 10,000 surviving offspring from which a random
sample of 10,000 was chosen to form the adults of the
following generation. For each set of parameters examined, we calculated the resistance delay associated
with a Þtness cost, which is the number of generations
for resistance to evolve with a Þtness cost minus the
number of generations for resistance to evolve without a Þtness cost.
Variable Selection Model. The results of McKenzie
et al. (1980) with resistance to diazinon in the sheep
blowßy Lucilia cuprina (Wiedemann) have been cited
as supporting evidence for the idea that strong selection in the Þeld produces monogenic resistance
whereas weak selection in the laboratory produces
polygenic resistance (McKenzie 2000). Laboratory
strains were started from four Þeld populations that
were nearly Þxed for a major allele. Eight generations
of selection in the laboratory produced a polygenic
response that approximately doubled resistance in
each strain (McKenzie et al. 1980). We examined the
follwing two possibilities: (1) strong selection in the
Þeld (10% survival) followed by weak selection in the
Vol. 93, no. 6
laboratory (50% survival) and (2) weak selection in
the Þeld (50% survival) followed by strong selection
in the laboratory (10% survival). When a population
became resistant (according to the previously described criterion), the threshold for selection was increased to restore survival to its initial percentage.
This corresponds to an increase in the concentration
of insecticide. We allowed 40 generations of selection
to occur in the Þeld, then changed the threshold to
provide the laboratory level of selection for 10 generations. We examined variable selection only for the
allelic effects speciÞed in model C (Table 1) and
included a Þtness cost in the model (␻ ⫽ 8).
Refuge Model. A percentage of the population (r)
avoided selection. Selection was applied to the remaining (100-r)% of the population. Adults surviving
selection and refuge adults were combined and randomly mated. We employed refuge sizes of 10 and
25%. For each set of parameters examined, we calculated the resistance delay associated with a refuge,
which is the number of generations for resistance to
evolve with a refuge minus the number of generations
for resistance to evolve without a refuge.
Results
Empirical Estimates of Selection Intensity in the
Field. Estimated selection coefÞcients (S) vary from
0 to 1 in the 16 sets of data on nine species of pests that
we examined (Table 2). These estimates suggest that
the intensity of selection in the Þeld is extremely high
in some cases, but it generally varies widely. Variation
in the estimated selection coefÞcients within 15 of the
16 data sets reßects temporal and spatial variation in
insecticide concentration. For example, estimates of S
for Culex quinquefasciatus Say larvae versus chlorpyrifos ranged from 0 Ð1 and those for Leptinotarsa
decemlineata (Say) adults versus permethrin ranged
from 0 to 0.93. The S values for C. pipiens versus
organophosphates are estimated means based on frequency gradients along a cline (Lenormand et al.
1998) and thus do not address spatial and temporal
variation in selection intensity.
Basic Simulation Model. Resistance alleles with major effects dominated responses to selection even under the weakest selection examined (50% of the population killed by insecticide; Table 3). Selection
typical of that used in laboratory experiments or
weaker (10 or 50% survival, respectively) did not produce resistance based solely on changes at loci with
minor effects on resistance. At each of the three selection intensities examined, resistance evolved faster
for models with major gene effects (C and D) than for
models with a more even distribution of allelic effects
across loci (A and B) (Fig. 1).
Intense selection (1% survival) tended to preclude
alleles of minor effect from contributing, especially
when the major allele was responsible for most of the
possible resistance as in models C and D (Table 3).
Examination of the correlations between allele frequencies between the major locus and each of the
minor loci suggests that this was often the result of
December 2000
Table 2.
GROETERS AND TABASHNIK: SELECTION INTENSITY AND RESISTANCE
1583
Selection coefficients for insecticide and acaricide resistance estimated from the field
Species
Life stage
Insecta
Coleoptera
Leptinotarsa decemlineata
Selection
coefÞcient (S)a
Pesticide
Reference
Larval
Adult
Adult
Permethrin
Permethrin
Malathion
0.29Ð1
0Ð0.93
0.45Ð1
Culex pipiens
Adult
Adult
Adult
All
Larval
Larval
0.55Ð1
0.95Ð1
0.57Ð0.88
0.30
0.16
0Ð1
0.3Ð1
0.2Ð1
0Ð0.88
0Ð0.80
Rawlings et al. (1981)
Hodjati and Curtis (1997)
Curtis et al. (1998)
Lenormand et al. (1998)
Culex quinquefasciatus
Lucilia cuprina
HCH
Permethrin
Lambdacyhalothrin
OP-Ace.1b
OP-Esterc
Chlorpyrifos
Diazinon
Dieldrin
Diazinon
Lindane
Egg/larval
Adult
Pyrethroidsd
Cyhalothrin
0.20Ð0.60
0.39Ð0.75
Daly et al. (1988)
Daly and Fisk (1993)
Adult
Dicofol
0.58Ð0.96
Martinson et al. (1991)
Oryzaephilus surinamensis
Diptera
Anopheles culicifacies
Anopheles stephensi
Lepidoptera
Helicoverpa armigera
Acarina
Tetranychus urtichae
Follett et al. (1993)
Muggleton (1986)
Curtis et al. (1984)
McKenzie and Whitten (1982)
McKenzie and Whitten (1984)
a
Fitness of resistant homozygotes ⫽ 1, Þtness of susceptible homozygotes ⫽ 1-S. S was estimated by measuring mortality after insecticide
exposure in all cases except for C. pipiens, in which estimates of average S were based on frequency gradients along a cline (Lenormand et
al. 1998).
b
Resistance to organophosphate insecticides encoded by the acetylcholinesterase locus.
c
Resistance to organophosphate insecticides encoded by the esterase superlocus.
d
Data pooled for cyhalothrin, cypermethrin, deltamethrin, and fenvalerate.
gametic phase disequilibrium (see Materials and Methods). In 10 runs of model D with 1% survival, the 50
associations between the major allele and each of the
Þve minor alleles showed negative disequilibrium in
34 cases and positive disequilibrium in 16 cases.
Fitness Cost Model. A Þtness cost typical of that
associated with insecticide resistance (see Materials
and Methods) had little effect on the genetic basis of
resistance. Strong or intense selection (survival ⫽ 10
or 1%) overcame the Þtness cost and caused resistance
to evolve with only slight delays compared with models with no cost (Fig. 2). Even with 50% survival, the
delay in evolution of resistance was only approximately Þve or six generations for models AÐC. Thus,
the total time for resistance to evolve was only ⬇1.3
times longer with a Þtness cost than without a Þtness
cost. For model D and 50% survival, however, tolerance increased in the population but allele frequencies reached an equilibrium and the resistance criterion was not met (Table 4). Importantly, there was no
indication that intermediate or minor loci contributed
more to resistance than they did without a Þtness cost
(Fig. 3; Table 4; compare with Table 3), or that resistance could be achieved through increases in the frequencies of resistance alleles at intermediate or minor
loci when the major allele ceased to increase in frequency (Fig. 3).
Variable Selection Model. With 10% survival in the
Þeld over 40 generations and variable selection using
Table 3. Allele frequencies when resistance had evolved in the
basic model
Survival, %
Model
Allelic
effect
50
10
1
A
0.90
0.85
0.80
0.29
0.24
0.22
0.29
0.22
0.23
0.38
0.28
0.29
B
1.50
1.00
0.50
0.46
0.24
0.09
0.67
0.19
0.08
0.68
0.25
0.06
C
2.50
0.75
0.33
0.60
0.07
0.05
0.62
0.05
0.04
0.77
0.04
0.04
D
4.00
0.33
0.11
0.60
0.04
0.07
0.66
0.02
0.07
0.69
0.02
0.05
Fig. 1. Number of generations required for resistance to
evolve in the basic model, where resistance is polygenic in
model A, monogenic in D, and intermediate in model B and
C (see Table 1).
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JOURNAL OF ECONOMIC ENTOMOLOGY
Fig. 2. Additional number of generations required for
resistance to evolve in the Þtness cost model relative to the
basic model. Resistance failed to evolve for model D and 50%
survival; thus, no data point is shown.
model C (see Materials and Methods), resistance alleles with major or intermediate effects increased to
Þxation and minor alleles also increased substantially
(Fig. 4a). In each of six simulations, three bouts of Þeld
selection occurred. After each bout, the selection
threshold was increased, which corresponds to an
increase in insecticide concentration (see Materials
and Methods). In the Þrst bout, resistance evolved by
generation 6. At the end of the Þrst bout, average allele
frequencies for major, intermediate, and minor loci
were 0.71, 0.06, and 0.03, respectively. In the second
bout, resistance occurred between generations 20 and
26. After the second bout, average allele frequencies
for major, intermediate, and minor loci were 1, 0.57,
and 0.10, respectively. With the third bout of selection,
terminated at generation 40, the average allele frequencies for intermediate and minor alleles were
⬎0.99 and 0.52, respectively (Fig. 4a). Ten subsequent
generations of laboratory selection at 50% survival
boosted the frequency of resistance alleles at minor
loci to 0.66 (Fig. 4a).
Table 4. Allele frequencies when resistance had evolved in the
fitness cost model
Model
Allelic
effect
A
0.90
0.85
0.80
B
Survival, %
50
10
1
0.32
0.25
0.24
0.29
0.30
0.24
0.32
0.30
0.26
1.50
1.00
0.50
0.51
0.23
0.09
0.64
0.20
0.07
0.64
0.18
0.07
C
2.50
0.75
0.33
0.61
0.09
0.06
0.70
0.06
0.03
0.75
0.04
0.07
D
4.00
0.33
0.11
0.56a
0.04a
0.06a
0.70
0.02
0.05
0.85
0.01
0.05
a
After 40 generations of selection, allele frequencies were at equilibrium and the criterion for resistance was not met (see Fig. 3).
Vol. 93, no. 6
Fig. 3. Allele frequency changes for model D (most resistance caused by a single locus) with a Þtness cost and 50%
survival. Results from a typical run are illustrated.
Weak selection (50% survival) in the Þeld also
caused the resistance allele at the major locus to reach
Þxation, but alleles at intermediate and minor loci
remained at low frequencies (Fig. 4b). With weak
selection in the Þeld, only two bouts of selection occurred in the Þrst 40 generations. In the Þrst bout of
selection, resistance evolved between generations 19
and 21, with average allele frequencies for major,
intermediate and minor loci of 0.62, 0.07, and 0.05,
respectively. The second bout of selection (terminated at generation 40) resulted in Þxation of the
major allele, but average frequencies at intermediate
and minor loci were only 0.12 and 0.07, respectively.
Laboratory selection at 10% survival for 10 subsequent
generations caused resistance alleles at intermediate
and minor loci to increase rapidly to average frequencies of 0.82 and 0.21, respectively.
Refuge Model. Refuges of 10 or 25% delayed the
evolution of resistance (Fig. 5). The magnitude of the
delay depended strongly on selection intensity, but
was not inßuenced greatly by the distribution of allelic
effects among major and minor loci. Refuges were
most effective when selection for resistance was intense. With survival at 10 or 50%, the delays in evolution of resistance (relative to simulations with no
refuge) were only approximately two to four generations with a 10% refuge and six to eight generations
with a 25% refuge. With intense selection (1% survival), however, a 10% refuge doubled or tripled the
number of generations for resistance to evolve and a
25% refuge increased the time to resistance by Þve- to
sixfold. For example, with 1% survival, resistance
evolved in three generations for model C with no
refuge and in approximately 18 generations with a 25%
refuge.
Discussion
Published empirical estimates reviewed here (Table 2) show that the intensity of selection for insecticide resistance in the Þeld varies widely and overlaps
broadly with selection intensities in the laboratory.
Further, the simulation results described here show
December 2000
GROETERS AND TABASHNIK: SELECTION INTENSITY AND RESISTANCE
1585
Fig. 4. Allele frequency changes for the variable selection model with (a) strong Þeld selection (10% survival) for 40
generations followed by weak laboratory selection (50% survival) for 10 generations and (b) weak Þeld selection (50%
survival) for 40 generations followed by strong laboratory selection (10% survival) for 10 generations. When the population
achieved the resistance criterion, the selection threshold was increased to restore the initial survival (see Materials and
Methods). This change in threshold corresponds to an increase in insecticide concentration and represents the beginning of
a new bout of selection. In each case, a typical run is illustrated.
that intense selection for resistance is not required to
produce resistance that is conferred primarily by alleles with major effects. Indeed, the simulations show
that if major genes for resistance are present, even at
lower initial frequencies than minor genes, the major
genes will predominate responses to selection across
a wide range of selection intensities, with and without
Þtness costs or refuges. Based on this information, we
propose a simple explanation for why so many cases of
insecticide resistance are conferred by major rather
than minor genes: If major genes for resistance are
present, they will increase in frequency more rapidly
than minor genes under a wide variety of conditions.
Rather than a new paradigm, our proposed explanation represents a reÞnement of previously published conceptual models. As aptly noted in previous
articles, major alleles cannot contribute to resistance
if they are absent from a population (Roush and
McKenzie 1987, McKenzie and Batterham 1994). If
major alleles are rare, a laboratory strain initiated from
a small to moderate number of Þeld-collected individuals may fail to evolve monogenic resistance
because no major alleles are present (Roush and
McKenzie 1987). Conversely, consistent with our simulation results, recent experiments with laboratory
strains of L. cuprina show that once a major allele was
established, selection with low concentrations of dieldrin produced a monogenic response (Scott et al.
2000).
The results of our simulations depend on assumptions
about the frequency of major and minor resistance alleles before exposure to insecticide. We assumed that
before a population is exposed to insecticide, major resistance alleles are less common than minor resistance
alleles, with a range in initial frequency of 0.00125Ð0.0454
for all alleles examined (Table 1). However, there is little
empirical information in this area, even for alleles of
major effect (Roush and McKenzie 1987, Tabashnik
1994, Gould 1998). Lande (1983) showed that polygenic
responses are likely when initial frequencies of major
alleles are much lower than those for minor alleles, selection is weak, and Þtness costs associated with major
alleles are high.
The information reported here suggests that the
pattern of monogenically based Þeld resistance followed by the appearance of polygenic variation after
selection in the laboratory is not caused by intense
Þeld selection followed by weak laboratory selection.
Instead, the empirical estimates of selection intensity
and simulation results imply that this pattern may be
produced by the opposite sequence, weak Þeld selection followed by strong laboratory selection.
A series of bouts of strong selection (10% survival)
over 40 generations caused Þxation of resistance alleles with major and intermediate effects as well as
increases in the frequency of minor alleles to ⬎0.50
(Fig. 4a). If a population was sampled after the 40
generations of Þeld selection and the genetic basis of
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JOURNAL OF ECONOMIC ENTOMOLOGY
Fig. 5. Additional number of generations required for
resistance to evolve in the refuge model relative to the basic
model. Refuge sizes are (a) 10% and (b) 25%.
resistance was analyzed rigorously, resistance would
appear to be controlled by several loci including one
with a major effect. A similar analysis after the 10
generations of laboratory selection would be expected
to reveal the same pattern. In contrast, with 40 generations of weak Þeld selection (50% survival), the
resistance allele at the major locus was virtually Þxed,
but resistance alleles at intermediate and minor loci
showed only small increases in frequency (Fig. 4b).
An analysis of the genetic basis of resistance could
easily overlook the contribution of these relatively
rare alleles of intermediate and minor effects, leading
to the conclusion that resistance is monongenically
based. Further selection at levels typical of laboratory
selection experiments (10% survival) would then substantially boost the frequency of intermediate and
minor resistance alleles and give rise to a sudden
appearance of polygenic variation for resistance. Thus,
a populationÕs selection history can affect the relative
frequency of major and minor alleles for resistance.
The analysis reported here has some immediate
implications for resistance research and management.
The idea that intense selection should be employed in
laboratory selection experiments to mimic Þeld evolution appears to be invalid. Moreover, use of intense
selection in the laboratory may be counterproductive
because it could increase the chances of losing rare
resistance alleles. Thus, it is best to use moderate to
strong selection and to initiate selection experiments
with large samples of insects that have genetic diver-
Vol. 93, no. 6
sity representative of Þeld populations (Tabashnik
1992). In some cases, mutagenesis may be useful for
generating genetic variation (McKenzie et al. 1992,
McKenzie and Batterham 1998).
The most important practical implication of this
study comes from simulation results showing that the
ability of refuges to delay evolution of resistance is not
inßuenced greatly by the relative contributions of
major and minor genes for resistance (Fig. 5). Instead,
under the assumptions of each of the four models of
inheritance examined (i. e., models AÐD), refuges
were more effective when selection was intense (1%
survival) than when selection was weaker (10 or 50%
survival) (Fig. 5). Thus, for predicting the success of
refuges, understanding of selection intensity is critical,
but knowledge of the number of loci and their relative
contributions to resistance is not.
Our results with refuges conÞrm and extend previous results from one-locus models showing that refuges are most effective when resistance is functionally
recessive; i. e., the mortality of heterozygotes is similar
to that of susceptible homozygotes (Georghiou and
Taylor 1977, Tabashnik and Croft 1982). We assumed
that the effects of resistance alleles were additive.
Thus, when selection was intense in our simulations,
the mortality of heterozygotes was high and refuges
were most effective in delaying resistance.
Entomologists have long debated whether resistance is usually caused by a single gene or many. To
dispel the polarization that has sometimes characterized this debate, we suggest that resistance is affected
by many genes, but that the distribution of effects
across loci is not uniform. One or a few loci may often
account for most of the resistance. In these cases, if we
can locate and understand the major loci we can elucidate the mechanism of resistance and improve the
ability to track and delay the evolution of resistance.
However, one should not assume that a polygenic basis
for resistance is not possible in the Þeld. Arguments to
this effect based on the presumption that selection
intensity in the Þeld is extremely high do not appear
to be valid. Finally, our results suggest that the distribution of allelic effects among major and minor loci
has a relatively small impact on the rates of evolution
of resistance and effects of refuges, whereas the intensity of selection is crucial.
Acknowledgments
We thank Yves Carrière, Fred Gould, John McKenzie, and
Rick Roush for their thoughtful comments. This work was
supported by award 99-35302-8300 from the National Research Initiative Competitive Grants Program of the USDA
and by the University of Arizona.
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Received for publication 29 February 2000; accepted 8 July
2000.