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ROBERT E. PAGE, JR., AND ERIC
H. ERICKSON, JR.
Bee Research Unit, U.S. Department
of Agriculture, Agricultural Research Service, Department of
Entomology, University of Wisconsin, Madison, Wisconsin 53706
FORUM: Ann. Entomol. Soc. Am. 78: 149-158 (1985)
ABSTRACT
We investigate via computer simulation the effectiveness of regulating
the process of Africanization of commercial honey bee populations
in North America following the establishment of an Africanized
feral population. Our results show that regulatory programs designed
with the specific objective of identification and certification
of non-Africanized, commercial colonies within an Africanized
environment are not effective except under a narrow set of unlikely
conditions. Under the conditions of our model, even low levels
of immigration of Africanized drones into non-Africanized areas
results in a high percentage of Africanization and a high probability
of error of classification. We discuss the limitations of such
programs, the kinds of data necessary to assess the likelihood
of their success, and recommend a more effective direction for
future research.
The spread of Africanized honey
bees throughout South and Central America has resulted in much
speculation regarding the bees' potential impact on North American
beekeeping and considerable interest in methods of identification
and control. The United States Department of Agriculture Animal
and Plant Health Inspection Service (APHIS) has recently published
an action plan outlining the procedures to be used for the control
and eradication of Africanized bees in the event of an infestation
or establishment of a feral population in the United States (USDA
1983, Stibick 1984). APHIS intends to regulate queen rearing,
package bee production, hobby beekeeping, and migratory beekeeping
in and out of Africanized areas with the objective of preventing
the spread of undesirable Africanized behavioral characteristics
in the North American honey bee population. The central requirement
of their regulatory plan is the ability to identify Africanized
bees irrespective of the behavioral attributes in question and
certify colonies of honey bees as non-Africanized (including
introgressive hybrids) by making an accurate determination of
the genotypes of queens and workers in colonies.
The current morphometric method used by APHIS to identify Africanized
bees (Daly and Balling 1978) is slow, costly, and probably not
acceptable for a large-scale program (Rinderer and Sylvester
1981). The most commonly proposed alternative method is identification
based on electrophoretic phenotypes of discrete allozymes (Daly
and Balling 1978, Nunamaker and Wilson 1981a, Rinderer and Sylvester
1981, Sylvester 1982). The rationale for using this method is
based on the work of Ayala and Powell (1972) for classifying
individuals belonging to different noninterbreeding sibling species
of Drosophila. However, the effectiveness of this method
for distinguishing between Africanized and non-Africanized colonies
in a common environment has not been considered, is of paramount
importance, and is the subject of this paper.
We present the results of a computer model that simulates the
population genetics of a possible identification and certification
program based on electrophoretic determination of individual
honey bee genotypes. We demonstrate that even low levels of gene
flow from an Africanized feral population into a commercial population
can result in a high percentage of commercial colonies becoming
Africanized and a large error in classification. We then discuss
the efficacy of such a program in the context of a commercial
beekeeping environment and make specific recommendations for
further research.
Methods and Materials
The Model. For this study, we assume (although
it is by no means certain) that some undesirable changes will
occur in North American feral honey bee populations as a consequence
of gene flow from South America via Central America and Mexico,
or by an invasion population or successful point-source introduction.
After this occurs, we assume the feral population is always Africanized
and undesirable. Individual workers are considered Africanized
if they are descendents of any individual from the feral population
after Africanization has occurred. Colonies are Africanized if
they have one or more Africanized subfamilies, the result of
a queen mating with one or more Africanized drones.
The model simulates conditions that might exist in the United
States with migratory beekeeping following Africanization of
areas of the southern United States. A commercial beekeeper moves
his colonies from a northern, non-Africanized location each fall
or spring into a southern, Africanized environment to overwinter,
to sell his pollination services, or to take advantage of early
nectar flows. We assume that all feral colonies in this area
are Africanized and are considered as such even with progressive
gene introgression from the commercial population. While located
in this area, his colonies supersede their queens, or swarm,
and the new virgin queens that are produced mate with drones
in the area, both Africanized (feral) and non-Africanized (commercial).
(We will hence-forth refer to the North American population as
European since it originated primarily from imported European
races.) Before transporting these colonies back to their northern
apiary locations, they are inspected and certified based on a
determination of Africanized and European phenotypes in random
samples of workers from colonies. Exact genotypic determination
is made from electrophoretic phenotypes for one, two, or three
independent gene loci. Queens of Africanized colonies are replaced
(or the whole colony is replaced) by European queens raised in
closed breeding populations or other non-Africanized environments.
We assume that there is no systematic, annual requeening program
for the commercial colonies. We also assume that there is no
progressive Africanization of the commercial population that
serves as the source of replacement queens. Violation of each
of these assumptions changes the expectations we present in an
opposite direction.
The model simulates the conditions of:
(1) haplodiploid population genetics;
(2) multiple, random mating of queens;
(3) differential population size and mating advantage of European
and Africanized drones;
(4) random sampling of workers from commercial colonies;
(5) identification of Africanized colonies within commercial
apiaries based on one, two, or three unlinked loci, two alleles
per locus;
(6) replacement or destruction of queens or colonies identified
as Africanized;
(7) progressive Europeanization of an Africanized, feral population;
(8) progressive Africanization of a regulated commercial population.
The sequence of events for the first generation is:
(1) Select the initial 100 commercial virgin queens from a large
European base population in Hardy-Weinberg equilibrium for one,
two, or three loci used for classification;
(2) mate each of these queens to 10 different males selected
at random from this same large population; each male contributes
an equal number of equally viable spermatozoa; spermatozoa are
randomly mixed within the spermatheca;
(3) sample workers of each colony by random selection of an egg
and a sperm from the spermatheca from each queen and make a determination
whether the colony is Africanized based upon predetermined criteria;
(4) calculate all statistics;
(5) enter genes from the commercial population into the feral
population in predetermined proportions, so newly established
gene frequencies in the feral population are those expected from
the binomial expansion of each locus;
(6) replace all queens heading colonies determined to be Africanized
with new queens selected from the initial base population.
The sequence for subsequent generations is:
(1) Supersede all commercial queens by randomly selecting an
egg and a stored sperm from each queen to represent the genotype
of the new queen;
(2) mate all commercial, supersedure queens to 10 males each,
each male selected at random from an infinitely large pool of
drones composed of drones of commercial and feral colonies in
proportions based on the predetermined levels of gene flow; steps
3 through 5 are identical to the initial generation above.
Each generation, the simulation calculates the following:
(1) The number of commercial colonies that have Africanized workers
present;
(2) the number of commercial colonies determined to be Africanized
by the classification of randomly selected workers;
(3) the number of European colonies classified as Africanized
based on worker samples;
(4) the number of Africanized colonies classified European based
on worker samples;
(5) the number of European commercial colonies correctly classified;
(6) the number of Africanized colonies correctly classified;
(7) the cumulative proportion of Africanized queens within commercial
colonies.
Classification Criteria. We classify the allele at each
locus that is in highest frequency in the feral population as
Africanized. Our starting conditions for all simulations assume
that the most frequent allele at each locus in the Africanized
population occurs at a frequency of at least 0.95 and occurs
in the commercial population at a frequency not greater than
0.05. This results in an initial probability of correct determination
of a single gene of Africanized origin at this locus of at least
0.95, a 0.05 probability of calling an Africanized gene European,
and a 0.05 probability of classifying a European gene in the
commercial population as Africanized. The probability of misclassifying
randomly selected, individual workers in a population where Africanized
and European workers are equally frequent is 0.05 (Ayala and
Powell 1972, Sylvester 1982); however, this error is dependent
on the frequencies of Africanized and European individuals in
the sample population and can range from 0.0025 to 0.0975.
For the case of two classifying loci, the probability of misclassification
of an individual worker selected at random from a population
where Africanized and European workers are equally frequent is
reduced to 0.0025 if the classification of an individual as Africanized
is made on the basis of "any Africanized allele at any locus."
The probability of error is further reduced to 0.000125 for the
three-loci case. These probabilities are gene-frequency-dependent
and change dramatically with gene flow between these populations.
In practice, colonies need to be classified on the basis of analyses
of individual workers sampled from those colonies. Colony samples
involve three separate sampling events: sampling of the drone
population by the queens for mates; sampling of the gametes,
those of the queen that become eggs, and those of the males from
the queen spermathecae; selection of workers for analysis. Each
of these events affect the outcome of the classification procedure.
For these simulations, we eliminate the third sampling event
by selecting worker samples directly from the gametes. This simplifies
the procedure and yields a good approximation of the genotypic
array of workers in large colonies.
The classification of colonies based on evaluation of worker
samples can be complex. We sampled 2, 6, or 10 workers from each
colony for identification. Classification of a colony as Africanized
was determined by one of three methods that varied among simulations:
(1) any worker sampled has an Africanized allele at any locus;
(2) a sample of workers from a colony has an Africanized allele
represented at each locus, but not necessarily in a single sampled
worker; (3) any one of the workers sampled has an Africanized
allele at all loci considered. We used methods 1 and 3 for most
simulations because they represent the extreme classification
criteria.
We chose six workers per colony as our sample size for most of
our simulations because it results in a >0.95 probability
of sampling both genes at a given locus of the queen. An equal
probability of sampling each of the mates of each queen requires
much larger sample sizes and would be too costly in application.
We tested different models by varying the number of loci used
for determination, the gene frequencies at these loci, the number
of workers sampled from each colony for classification, the method
of classification, and the amount of gene flow from the Africanized
(feral) population into the commercial population and from the
commercial population into the feral population. We simulated
10 iterations of 20 generations for each model. We calculated
directly from the results the mean and standard deviation for
each characteristic measured.(1)
Results and Discussion
Migration. Low levels of gene migration from commercial
populations to feral populations are necessary to maintain the
gene frequency dispersion between populations that is needed
to classify individual members. As European genes increase in
frequency in the feral population, the probability of classifying
an Africanized worker as European increases, resulting in an
increase in frequency of Africanized colonies and queens in the
commerical apiary (Tables 1-4; all tables are located in the
Appendix). It should be noted, however, that increased migration
of the more desirable European genes into the feral population
should result in a more desirable, less Africanized feral gene
pool.
High success rates of Africanized drones mating with commercial
supersedure queens enhance colony classification because they
increase the proportion of Africanized worker genotypes to be
sampled and thereby increase the chance that one of them will
be sampled. Even low levels of immigration result in high levels
of Africanization because of the multiple mating of queens. Immigration
rates of 0.50 result in virtually 100% Africanization of colonies
headed by queens that mate in that environment.
A particularly notable deficiency in all of the classification
methods we tested is their inefficiency in detecting Africanization
under low levels of gene flow from the feral to the commercial
populations. Rinderer and Sylvester (1981) state that detection
of Africanized bees in the field is not at all difficult on the
basis of colony behavior. If this is the case, we need a method
for detecting low levels of gene introgression. In order to do
this, large sample sizes are needed from each colony, a condition
that greatly increases the misclassification of European colonies.
Based on these simulations, Africanization will be widespread
before levels of gene flow are sufficient for efficient classification
and control.
These results point out the need to define clearly acceptable
levels of Africanization in commercial and feral environments.
A single subfamily of workers having an Africanized father may
alter colony behavior sufficiently to merit rejection of the
whole colony. There is also the associated probability that one
or more of the members of this subfamily may become queens. We,
by necessity, assume that all feral populations are Africanized,
undesirable, and always remain so. In reality the composition
of the feral population is expected to change with gene flow
from the commercial populations. The effect of this gene flow
upon the behavioral attributes of the feral population will determine
the necessity to continue an identification and certification
program. If behavioral attributes remain adverse even with significant
change in the genetic composition of the population, then the
result will be a diminished ability to detect Africanization
and a progressively undesirable commercial population. If, however,
the attributes of the feral population become less adverse with
gene flow from the commercial population, then the ability to
detect Africanization will still diminish but with less significant
consequences.
(1) Authors will provide copies of the
simulation program on request.
Classification Criteria. The method of classification
affects both the probability of correctly classifying Africanized
colonies and the probability of misclassifying those that are
not. Method 1 is most efficient for detecting Africanized colonies,
but it results in a large percentage of misclassification of
European colonies when there is an overlap of allelic frequencies
at classifying loci (Tables 1 and 2). Method 1 also results in
the slowest Africanized gene introgression into the commercial
population as well as the slowest accumulation of Africanized
queens.
Method 3 results in fewer misclassifications of European colonies
but is less effective for determining Africanized colonies (Tables
3 and 4).
Method 3 works very well under conditions of high mating activity
or numerical advantage of Africanized drones but poorly under
conditions of high rates of introgression from commercial to
feral populations. Method 2 yields results intermediate between
those of methods 1 and 3 and is not presented separately.
Gene Frequencies. Misclassification of European colonies
is completely eliminated when the commercial base population
does not have any Africanized alleles at the loci considered
(Tables 2 and 4). However, when this is the case, the ability
to identify correctly Africanized colonies in the commercial
population decreases with classification method 1 under conditions
of gene flow from commercial populations into Africanized feral
populations. This decrease is a consequence of the reduced immigration
of European alleles of the Africanized type into the Africanized
population. The presence of alleles of Africanized type in the
commercial population reduces the detectable rate of change of
allele frequencies in the feral population with continuing introgression,
and results in a higher rate of commercial colony rejection with
queen supersedure. It is the progressive increase of European
alleles in the Africanized, feral population that reduces the
efficiency of the classification system. Initial gene frequencies
over the range we tested have little effect on classification
efficiency for classification method 3 and when migration only
occurs from the feral into the commercial population.
Sample Size. Classification of Africanized colonies improves
with increasing worker sample sizes (Table 5). Costs of analyses
also increase because more workers must be processed and because
more European colonies are rejected without cause. Economic analyses
incorporating risk assessment are needed to determine how much
misclassification is tolerable.
Numher of Loci. An increase in numbers of loci used
for classification results in an increasing probability of correctly
classifying individual workers (Table 6). However, the effect
of increasing numbers of loci on the overall identification and
certification program is complex.
Increasing numbers of loci result in lower classification precision
under the conditions of classification method 3. This counterintuitive
result is a consequence of gene introgression and increasing
frequencies of Africanized workers of overlap genotypes that
are not classified Africanized. When method 1 is used for classifying
colonies, increasing numbers of loci do result in increasing
precision for identifying Africanized colonies because overlap
genotypes are classified as Africanized. However, as a consequence,
this method results in greater misclassification of European
commercial colonies under the initial conditions of allele frequency
overlap.
After numerous efforts directed toward finding electrophoretic
variability in and among honey bee populations, there are only
five potentially usable marker loci available for this type of
program. Three of these show possible gene frequency differentiation
between South American and North American bee populations; two
of these are expressed in adult honey bees (Mestriner 1969, Mestriner
and Contel 1972, Bruckner 1974, Sylvester 1976, Contel et al.
1977, Martins et al. 1977, Pamilo et al. 1978, Gartside 1980,
Nunamaker and Wilson 1981a,b, 1982, Sheppard and Berlocher 1984).
Even if more usable markers are detected, they will not necessarily
result in more precision in classification and can in fact lead
to less precision within cross mating populations.
Conclusions
The results of these simulations
clearly demonstrate that regulatory programs directed toward
the identification and certification of Africanized colonies
in a commercial context are effective only under a strict set
of conditions: extreme numerical or mating advantage of Africanized
drones; low levels of gene flow from the commercial populations
into the feral populations; and extreme initial gene frequency
differences between commercial and feral populations at the loci
used for classification.
It is unlikely that the restrictive conditions necessary for
the success of an identification and certification program as
outlined by APHIS will be realized in commercial beekeeping areas
of the United States for the following reasons: (1) Cross matings
between European drones and Africanized queens do occur with
apparently little constraint (Kerr and Bueno 1970). There is
no published evidence for sufficient mating advantage of Africanized
drones to meet the stated conditions (see also Kerr et al. 1980).
(2) Extensive hybridization appears to have occurred between
African and European populations throughout Brazil. This is suggested
by the large number of sex alleles segregating in some of these
populations (Adams et al. 1977). Africanized bees show considerable
variability in behavior and appearance in Venezuelan populations,
suggesting a broad genetic background (R. Page, personal observations).
(3) Estimated frequencies of proposed allozyme markers in North
and South America show considerably more overlap than those we
assume for our simulations (Sylvester 1976, 1982).
New APHIS research is currently directed toward finding and identifying
specific hydrocarbons of taxonomic significance (Carlson and
Bolton 1984, McDaniel et al. 1984). This method, as well as morphometric
analysis, has the same inherent difficulty as electrophoretic
methods: cross breeding between commercial and Africanized populations
will change the expected genotypic and phenotypic distributions
of both populations toward those intermediate between the two,
resulting in more phenotypic overlap and the inability to classify
the intermediate populations. Introgressive breeding throughout
Central America and Mexico, even if minimal, can greatly alter
the reliability of the classification system.
It may still be possible to construct usable programs for the
purpose of slowing the spread of undesirable effects of gene
flow from South America, but these programs must be based on
information regarding the genetic mechanisms behind the behavioral
attributes, gene frequency distributions for marker loci, population
densities, reproductive dynamics, mating behavior, and the costs
of analyses. We believe that for the present time the most promising
area of research directed toward the goal of certification of
European bees is the development of closed-population breeding
programs and faster, more efficient methods of queen marking
and finding.
References Cited
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W. E. Kerr, and Z. L. Paulino. 1977. Estimation of the number of sex alleles and
queen matings from diploid male frequencies in a population of
Apis mellifera. Genetics 86: 583-596.
Ayala, F. J., and J. R. Powell. 1972. Allozymes as diagnostic
characters of sibling species of Drosophila. Proc. Natl.
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Bruckner, D. 1974. Reduction of biochemical polymorphism
in honeybees (Apis mellifica). Experientia 30: 618-619.
Carlson, D. A., and A. B. Bolton. 1984. Identification
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Contel, L. P. B., M. A. Mestriner, and E. Martins. 1977.
Genetic control and Developmental expression of malate dehydrogenase
in Apis mellifera. Biochem. Genetics 15: 859-876.
Daly, H. V., and S. S. Balling. 1978. Identification
of Africanized honeybees in the Western Hemisphere by discriminant
analysis. J. Kans. Entomol. Soc. 51: 857-869.
Gartside, D. F. 1980. Similar allozyme polymorphism in
honeybees (Apis mellifera) from different continents.
Experientia 36: 649-650.
Kerr, W. E., and D. Bueno. 1970. Natural crossing between
Apis mellifera adansonii and Apis mellifera ligustica.
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Genetics 15: 357-366.
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Collins. 1984. Hydrocarbons of the cuticle, sting apparatus,
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Received for publication 18 July 1984; accepted 26 November
1984.
Appendix
This appendix contains the
tabulated results of the computer simulations discussed in the
text. For each table, migration rates are presented for feral
to commercial populations (F -> C) and commercial to feral
(C -> D). Means and standard deviations are presented based
on 10 iterations of each simulation. Row values are as follows:
| I. |
The number of
colonies in the commercial population (based on 100 colonies)
that have Africanized workers; |
| II. |
the number of
Africanized colonies correctly identified based on the worker
samples; |
| III. |
the number of
colonies classified Africanized based on worker samples; |
| IV. |
the number of
colonies with Africanized queens; |
| V. |
the number of
colonies having Africanized queens that are correctly identified
based upon the workers sampled. |
Row III for generation 1 shows the continual error associated
with misclassification of European queens mated outside of the
Africanized area. The difference between I and II shows the efficiency
for determination of Africanized colonies based on worker samples.
The difference between II and III shows the number of European
supersedure queens incorrectly classified. The number of European
colonies correctly classified is given by the formula 100 - row
I - (row III - row II). The difference between rows IV and V
demonstrates the efficiency of detecting Africanized queens.
The relevant simulation parameters for individual tables are:
Table 1. Results
of computer simulations for the following conditions: (1) two
classifying loci; (2) initial frequency of the designated Africanized
allele is 0.95 for each locus in the feral population;
(3) initial frequency of the Africanized allele is 0.05 for each
locus in the commercial population; (4) six workers are randomly
selected from each colony for identification; (5) method 1 is
used for classification of Africanization.
Table 2. The initial
conditions for the models generating the data presented here
are identical to those for Table 1 except for the following:
(1) the frequency of the Africanized allele is 1.00 at both determining
loci in the feral population; (2) the frequency of the Africanized
allele is 0.00 at both loci in the commercial populations.
Table 3. The initial
conditions for the models generating the data presented here
are identical to those for Table 1 except that method 3 was used
for classifying worker genotypes.
Table 4. The initial
conditions for the models generating the data presented here
are identical to those for Table 1 except for the following:
(1) the frequency of the Africanized allele is 1.00 at both determining
loci in the feral population; (2) the frequency of the Africanized
allele is 0.00 at both loci in the commercial population; and
(3) method 3 was used for classifying worker genotypes.
Table 5. Initial
conditions for the models generating these results are as those
presented for Tables 1 and 3 except for differences in the number
of workers sampled from each colony, each generation, as indicated.
Table 6. These
data show the effects of increasing numbers of loci used for
classifying workers. Sets A, B, and C assume maximum gene frequency
dispersion between feral and commercial populations with one,
two, and three loci, respectively, using classification method
3 and samples of six workers per colony. Set D has starting conditions
identical to Table 3 except three loci are used. Set E has starting
conditions identical to Table 1 except three loci are used.
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