European Beeor
I Department Barry Lavine of Chemistry Clarkson Potsdam. Dave Carlson University N.Y. 13676 Insects Affecting Man and Animals Research Laboratory USOA. ARS P.O. BOX14565
Gainesville. Fla. 32604 In 1956 the African honeybee, Apis mellifera scutellata, was brought to Brazil for use in a bee-breeding program. The variety of honeybee that resulted from interbreeding the established European bee with the newly imported African type, referred to as the Africanized bee, has since dominated the bee fauna of Brazil and Venezuela. Swarms of Africanized bees have been found in North American ports aboard freighters arriving from South America and Panama. Africanized honeybees have received considerable coverage in the popular press, including stories about hordes of bees stinging victims to death. Many reports have stressed the aggressive behavior of this beeand the inherent danger that Africanized bees pose for both man and domestic animals. The ability of Africanized honeybees to spread throughout much of South and Central America and the likelihood of their spreading into the United States has made them an object of much study. One of the problems in studying Africanized bees is the difficulty of un468A
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5 ecies lrBentifiation Through Chemual Rnalpis The concentration of cUt~~U/ar hydrocarbons Can be us& to inf0rmatiOn the jdenti@ Of a bee specimen equivocally identifying a colony (and particularly an individual bee) as Africanized or European. T h e current method of choice for the identification of Africanized bees is morphometric analysis. The morphometric procedure uses body measurements to identify samples of bees as Africanized or European. Typically, an entomologist measures the length of the forewing or the width of the metatarsus of an adult bee. On the basis of such measurements, the race of the bee often can be readily ascertained. These phenotypic charac59, NO. 6. MARCH 15. 1987
ters, however, are not under simple and direct genetic control, which limits the utility of this approach. We tried a different approach-measuring the concentration of cuticular hydrocarbons-to obtain information about the identity of a bee specimen. The cuticle can he viewed as a sheath that covers the entire body of the adult insect. Previous studies have shown that the high molecular weight hydrocarbons comprising the cuticle are often characteristic of the insect’s species. Therefore, use of these hydrocarbons as taxonomic markers seemed logical. Clearly, with the advent of advanced chemical instrumentation (e.g., computer-controlled capillary gas chromatographs and GC/MS systems), analyses of this type are routine. Furthermore, a chemical approach to taxonomy also offers the added advantage of using characters that are under simpler and more direct genetic control. To evaluate the feasibility of using cuticular hydrocarbons as taxonomic markers for the identification of Africanized honeybees, we obtained a large number of Africanized and European honeybee specimens from Central America, Venezuela, and Florida. The specimens, collected by professional apiculturists, were from three different social castes: nest bees, foragers, and drones. The cuticular hydrocarbons were obtained from the bee specimens by first soaking them in hexane for about 15 minutes. The hydrocarbon fraction was then isolated from the concentrated 0003-2700/87/0359-468A/$O 1.50/0
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1987 American Chemical Society
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Figure 1. A gas-liquid chromatogram of cuticular paraffins from an individual Africanized forager.
Flgure 2. A principal components representation of me pattern space defined by me 10 GC peaks for Venezuelan foragers.
Kovat retention indices (KI) were obtained using mihentic Rparaftin standards. The KI values tw Ihe peaks used in me panern reccgnition study were 2300. 2675. 2675,3075,3100, 3243. 3265.3300, 3443. and 3465.
The first two principal components accoum tor 6 5 % of Ihe total Cumulative variance. The squares represent Atricanired foragers. and the inverted trimg1e5 are European foragers.
between the samples and the amALY71CAL measurements . in the data space A by pictures and graphs. They can provide information about trends A &,-,> present in the data.
hexane washings by a silica gel column. Hexane was used as the eluent. The extracted hydrocarbons (equivalent to 1/*5 of a bee) were analyzed with packed column- , GC. They were co-injected with authentic n-paraffin standards, and Kovat retention indices (KI) were assigned to compounds eluting from the gas chromatographic column. These KI values were used for peak identification. A typical gas chromatographic trace for an Africanized honeybee is shown in Figure 1. Pattern recognition techniques were used in this study to analyze the gas chromatographicdata. For patternrecognition analysis, each chromatogram
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suallv distinmishine the Africanized bee from its European co'usin is virtually impossible.
was represented by a data vector X = ( x I , x ~ , x J , x ~.,. . . . x n ) , wherecomponent XJ is the area of the j t h peak. Such a vector also can be considered as a point in an n-dimensional Euclidean space. A set of chromatograms, therefore, is represented by a set of points in an n-dimensional space. The expectation is that points representing the chromatograms of Africanized honeybees will cluster in a limited region of the space distant from the points corresponding to the European honeybees. Pattern recognition can be summartzed as a collection of techniques that can help the scientist understand the structure of the data. (The data structure is the overall relation of each Sample to every other sample in the data set.) In this study, only 10 of the chromatographic hands were considered for pattern recognition analysis (see Figure 1). Compounds comprising these bands have been found in the comb wax produced by nest bees, and the concentration pattern of the wax constituents is believed toconvey information about the genetic ancestry of the colony. The first step was to use mapping and disDlav techniaues to examine the structu;edithedata.Thesr techniques attempt t u illustrate relationships
We used a technique called principal components analysis to visualize the relative position of the data points (chromatograms) in the high-dimensional space. This technique can be summarized as a method for transforming the original variables (GC peak areas in our case) into new, uncorrelated variables. The new variables are called principal components. Each principal component is a linear combination of the original variables. The most informative principal component is the first, and the least informative is the last. Typically, the first two principal components are used to generate a plot representing the relative position of the data points in the high-dimensional space. In Figure 2, the results of a principal components mapping experiment are shown for 60 forager bee specimens from Venezuela. Half of the specimens are Africanized bees: the other half are European. The Africanized bees are well separated from the European bees in the two-dimensional map. These bees were collected from managed colonies that were maintained in the same apiary for more than four months. Therefore, differences between the hydrocarbon profiles of the two groups cannot he attributed to nutritional or en\,irmmentai factor>and must he related t o the identity of the bees.
ANALYTICAL CHEMISTRY, VOL. 5 9 , NO. 6, MARCH 15, 1987
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Mapping experiments of this nature were also performed for a set of drone bees from Argentina and a set of nest bees from Panama and Costa Rica. In both experiments the Africanized bees were well separated from the European bees in the principal component space. Having studied the structure of the data with mapping and display techniques, we then developed a classification rule. Nonparametric linear discriminant functions were used for these studies. They can be visualized as decision surfaces dividing a data space into different regions. For a binary classifier, the data space is divided into two regions. Chromatograms representing Africanized honeybees will lie on one side of the decision surface; chromatograms characteristic of European honeybees will lie on the other side. The test data consisted of 109 chromatograms of cuticular hydrocarbon extracts obtained from Africanized and European bee specimens (49 Africanized, 60 European). Foragers and nest bees were included in the training set. A discriminant function was developed from the 10 GC peaks for the purpose of separating Africanized bees from European honeybees. When the linear learning machine method was applied to the 10 GC peaks, it could correctly classify every sample in the training set. To further test the predictive abilit y of these descriptors and the linear discriminant associated with them, a prediction set of 55 chromatograms (15 Africanized, 40 European) was employed. The classification rule developed from our training set (109 chromatograms) correctly classified every chromatogram in the prediction set. These results demonstrate that information derived solely from the cuticular hydrocarbons could correctly categorize the bees by race (Africanized or European), which implies a direct relationship between the concentration pattern of these compounds and the race of the bees. The results of this study clearly demonstrate that the concentration pattern of cuticular hydrocarbons for honeybees conveys important taxonomic information. Thus an entomologist can correctly identify the race of a given bee specimen simply by measuring the concentration of only a few hydrocarbons. This approach to taxonomy provides the biologist with a powerful new tool for investigating complex entomological systems. Furthermore, species identification can now be placed firmly on a chemical basis.
Acknowledgments This research was supported by the Molecular Design Limited Corporation. The authors thank Douglas Henry for critically reviewing the manuscript.
