Artificial neural network modeling of caste odor ... - CiteSeerX

Chemoecology 8:201 –209 (1998) 0937 – 7409/98/040201–09 $1.50 +0.20 © Birkhaüser Verlag, Basel, 1998

Artificial neural network modeling of caste odor discrimination based on cuticular hydrocarbons in termites A.-G. Bagne`res, G. Rivie`re1 and J.-L. Cle´ment C.N.R.S. Laboratoire de Neurobiologie UPR 9024, Communication chimique, 31, chemin Joseph Aiguier, F-13009 Marseille, France, e-mail: [email protected] 1 Present address: C.N.R.S. NBM. UPR. 9011. 31, chemin Joseph Aiguier, F-13009 Marseille, France

Summary. Individuals in an insect colony need to identify one another according to caste. Nothing is known about the sensory process allowing nestmates to discriminate minute variations in the cuticular hydrocarbon mixture. The purpose of this study was to attempt to model caste odors discrimination in four species of Reticulitermes termites for the first time by a non-linear mathematical approach using an ‘‘artificial neural network’’ (ANN). Several rounds of testing were carried out using 1 – the whole hydrocarbon mixtures 2 – mixtures containing the hydrocarbons selected by principal component analysis (PCA) as the most implicated in caste discrimination. Discrimination between worker and soldier castes was tested in all four species. For two species we tested discrimination of four castes (workers, soldiers, nymphs, neotenics). To test cuticular pattern similarity in two sibling species (R. santonensis and R. fla6ipes), we performed two experiments using one species for training and the other for query. Using whole hydrocarbons mixtures, worker/soldier discrimination was always successful in all species. Network performance decreased with the number of hydrocarbons used as inputs. Four-caste discrimination was less successful. In the experiment with the sibling species, the ANN was able to distinguish soldiers but not workers. The results of this study suggest that non-linear mathematical analysis is a good tool for classification of castes based on cuticular hydrocarbon mixture. In addition this study confirms that hydrocarbon mixtures observed are real chemical entities and constitute a true chemical signature or odor. Whole mixtures are not always necessary for discrimination. Key words. Artificial neural network – caste odor – Reticulitermes termites – cuticular hydrocarbons – chemical signature

Introduction Discrimination between individuals is probably essential to maintaining social structure in an insect colony. Olfactory perception of contact pheromones, especially Correspondence to: A.-G. Bagne`res

in termite societies, triggers acceptation or aggression which in turn determines whether a colony is open or closed. Contact between individuals of different castes or functional subcastes also determines specific social behavior including feeding of soldiers, larvae, reproductives by workers (termites) or nurses (ants, bees . . . ), trophallaxy, and proctodeal exchanges (BonavitaCougourdan et al. 1993; LeConte et al. 1995; Soroker et al. 1995). Chemical and behavioral studies have shown that the cuticle of social insects carries a chemical signature, or odor, characteristic of each species, colony and caste (Cle´ment 1982; Howard et al. 1982; Bonavita-Cougourdan et al. 1987; Nowbahari et al. 1990; Bagne`res et al. 1990, 1991a; Howard 1993; Takahashi & Gassa, 1995; Dahbi et al. 1996; Lorenzi et al. 1997; Dahbi & Lenoir 1998; Singer 1998). Odors are determined by minute variations in the mixture of aliphatic compounds, mainly hydrocarbons (Lockey 1988; deRenobales et al. 1989; Nelson & Blomquist 1995). Recent evidence suggests that the cuticle functions as a gland not only maintaining but also modifying the chemical signature (Vander Meer et al. 1989; Provost et al. 1993; Bagne`res et al. 1996; Vauchot et al. 1997, 1998), particularly when necessary to preserve colonial cohesion (Bonavita-Cougourdan et al. 1989, 1996, 1997; Bagne`res et al. 1991, 1996; Vauchot et al. 1996). Social insects are able to identify cuticular compounds of congeners by antennal contact in less than one second (Cle´ment, 1981). This instantaeous identification of the chemical signature has favored social behavior by allowing nestmate recognition and caste discrimination (Cle´ment & Bagne`res 1998). Until now studies comparing different cuticular hydrocarbon patterns have used descriptive linear mathematical techniques (e.g., multivariate analysis). While these techniques allow identification of minute variations in relative proportion, they cannot answer the question of whether these variations can actually be used for diagnostics. In this experimental study using an interesting biological example (different caste odors), we performed tests using a non-linear mathematical approach which is an ‘‘artificial neural network’’ (ANN) (Hinton 1992; Van Camp 1992). Results were compared to methods more widely used by biologists. Like their biological counterparts, ANNs consist of processing elements comparable to ‘‘neurons’’ and con-

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A.-G. Bagne`res, G. Rivie`re and J.-L. Cle´ment

CHEMOECOLOGY Fig. 1 Typical representation of an artificial neural network

nections comparable to ‘‘synapses’’. There are three types of processing elements: input, hidden, and output. As their names imply, input and output neurons allow data to enter or exit the network. Hidden neurons allow processing within the network. A typical representation of an artificial neural network is shown in Figure 1. Each connection is associated with a mathematical function, or weight, that simulates the synaptic gap. In operation there are two phases: training (or learning) and query (or recall). Performance of a network depends on the values of the weights associated with all connections in the network.

Materials and methods Animals This study was performed on three European species of Reticulitermes termites (R. (lucifugus) grassei, R. santonensis, R. (l.) banyulensis), and one North American Reticulitermes species (R. fla6ipes). Thirtyfive colonies of R. (l.) grassei and seventeen colonies of R. santonensis were collected in the ‘‘Foreˆt de la Coubre’’ and on the ‘‘Ile d’Oleron’’ in the Charente-Maritime, department of France. Thirty-five colonies of R. (l.) banyulensis were collected near Perpignan in the Pyreńeésorientales department of France. Twenty-five colonies of R. fla6ipes were collected near the University of Georgia in Athens, GA, USA. In addition to the geographical location of each colony, the following informations were also noted: season of collection, species of host tree, date of removal from logs, date of pentane extraction, and date of analysis. Chemical extraction Individual insects were separated, counted, and weighed. Worker extracts were prepared from samples containing a hundred individuals, and soldier extracts from samples containing twenty individuals. The number of individuals in samples used to prepare extracts from other castes depended on how many individuals of the caste were found in each colony. Castes for which less than five extracts could be made were not analyzed. Samples were soaked for 5 min in 2 ml of pentane. The resulting extracts were dried and adjusted to 1 ml of pentane. Five or ten replicates were prepared depending on the number of individuals in the sample. An internal standard (n-heneicosane) was added (800 ng/replicate) (Bagne`res et al. 1990, 1991). A total of 49 extracts from different castes of R. (l.) banyulensis were used including 35 worker and 14 soldier extracts. A total of 49 extracts from different castes and different phenotypes of R. fla6ipes

(Bagne`res et al. 1990) were used including 25 worker and 24 soldier extracts. A total of 57 extracts from different castes of R. (l.) grassei were used including 35 worker, 11 soldier, 6 nymph, and 5 neotenic extracts. A total of 37 extracts from different castes of R. santonensis were used including 17 worker, 7 soldier, 7 nymph, and 6 neotenic extracts. Chemical analysis Extracts were analyzed by gas chromatography (GC) on a Delsi 300 GC equipped with a flame ionization detector (FID), a split-splitless injector (15 sec splitless) and a CPSil5 WCOT capillary column (25 m, 0.25 mm ID, 0.15 mm phase). Data were collected on a Enica 10 integrator. Temperature was programmed from 150°C to 320°C at 5°C a minute, then isothermal for 10 min. The carrier gas was helium (1 bar). Cuticular extracts from R. fla6ipes were made in the United States and analyzed in France in order to use the same analysis equipment. A total of 37 cuticular compounds were quantified in R. (l.) grassei, 47 in R. (l.) banyulensis, 21 in R. fla6ipes, and 21 in R. santonensis. These compounds made up the total mixtures used for multivariate analysis and presented to the ANN. As described elsewhere (Howard et al. 1978, 1980; Bagne`res 1989; Bagne`res et al. 1988, 1990, 1991b), the chemical signatures of these four species are composed mainly of hydrocarbons. Selection of hydrocarbons by multi6ariate analysis Descriptive statistics and multivariate analyses (Principal Component Analysis ‘‘PCA’’, Correlation Analysis, Stepwise Discriminant Analysis) of the relative proportions of the cuticular compounds identified in extracts were performed using Statgraphics software (version 6.0 & Uniware version 2.0). Relative proportions were determined on a Lotus 1-2-3 spreadsheet (version 4.0) after correction with the FID coefficient (Bagne`res 1989; Bagne`res et al. 1990). Percentages used for the final calculation were normalized prior to multivariate analyses. Each vector in PCA analyses was assumed to be the mean of one caste in one colony, since chemical analysis was performed on extracts of several individuals. Separate grids were made for R. (l.) banyulensis and R. (l.) grassei. The same grid was used for R. santonensis and R. fla6ipes which can be considered as sibling species with the same 21 hydrocarbons in different relative proportions (Bagne`res et al. 1990). We performed PCAs for each species with its whole hydrocarbons mixture. The first three axes were always best correlated with separation of the different castes. The most important hydrocarbons involved in the creation of these axes for PCA were selected. New matrices were further refined by progressively removing the hydrocarbons with the increasing correlation index (rA/v), i.e. the correlation coefficient (r) between the discriminating canonical axes (A) and variables (v). This index indicates the importance of each hydrocarbon in construction of the PCA axes.

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Artificial neural network modeling of caste odor discrimination based on cuticular hydrocarbons in termites

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Table 1 List and selection of cuticular hydrocarbons after multivariate analysis. The hydrocarbons were identified by gas-chromatography/massspectrometry in Reticulitermes santonensis (R.s.), Reticulitermes fla6ipes (R.f.), Reticulitermes (lucifugus) grassei (R.(l.)g.), Reticulitermes (lucifugus) banyulensis (R.(l.)b.). Total mixture included hydrocarbons marked with 1 or more plus signs (+, ++, +++, ++++). The most discriminating hydrocarbons for workers and soldiers in PCA were classified according to correlation index (rA/v). For R.s., R.f., R.(l.)g., hydrocarbons with rA/v]0.5 are marked with 2 or more plus signs (++, +++, ++++), hydrocarbons with rA/v]0.6 are marked with 3 or more plus signs (+++, ++++) and hydrocarbons with rA/v]0.7 are marked with 4 plus signs (++++). For R.(l.)b. hydrocarbons with rA/v]0.4 are marked with 2 or more plus signs (++, +++, ++++), hydrocarbons with rA/v\0.5 and 0.6 are marked with 3 or more plus signs (+++, ++++) and hydrocarbons with rA/v] 0.7 are marked with 4 plus signs (++++). The hydrocarbons selected by stepwise discriminant analysis are marked with a black diamond () Hydrocarbons

R.s.

9-Tricosene Tricosene n-Tricosane 11-Methyltricosane 4/2-Methyltricosane 9-Tetracosene 3-Methyltricosane n-Tetracosane 11-Methyltetracosane 5-Methyltetracosane 4/2-Methyltetracosane 9-Pentacosene Pentacosene+ Pentacosadiene n-Pentacosane Unknown 13-+ 11-Methylpentacosane 9-Methylpentacosane 7,9-Pentacosadiene ( +5-MeC25) 5-Methylpentacosane 4/2-Methylpentacosane 9,13-Dimethylpentacosane (+3-MeC25) 3-Methylpentacosane 5,17-Dimethylpentacosane n-Hexacosane 13-+ 12-+11-Methylhexacosane 6-Methylhexacosane 4/2-Methylhexacosane 9-Heptacosene n-Heptacosane 13-+ 11-Methylheptacosane 7-Methylheptacosane 5-Methylheptacosane 11,15-Dimethylheptacosane 3-Methylheptacosane 5,17-Dimethylheptacosane n-Octacosane Unknown 14-+ 13-+ 11-Methyloctacosane 6-Methyloctacosane 4-Methyloctacosane 9-Nonacosene 3-Methyloctacosane n-Nonacosane Unknown 15-+13- +11-Methylnonacosane 7-Methylnonacosane 5-Methylnonacosane 5,17-Dimethylnonacosane n-Triacontane 6-Methyltriacontane 5,17-Dimethyltriacontane n-Hentriacontane 15-+13- +11-Hentriacontane 13,17-Dimethylhentriacontane 5-Methylhentriacontane 5,17-Dimethylhentriacontane n-Docotriacontane 12-Methyldocotriacontane n-Tritriacontane 15-+13-Monomethyltritriacontane 13,17-Dimethyltritriacontane Unknown n-Tetratriacontane 13-+11-Methyltetratriacontane n-Pentatriacontane 13-+11-Methylpentatriacontane

+ + + ++++ + ++++ + +++ +++ ++ + +++ + ++++ + +

(e1) (e2) (a3) (m4) (m5) (e6) (m7) (a8) (m9) (m10) (m11) (e12) (e13) (a14) (x15) (m16)

+ ++ + +++ ++ ++++ +++ ++++ ++++ +++ + ++++ ++++ ++++ + +++

+

(mn17)

+

++++

(m18)

+

++++ + +

(m19) (d20) (a21)

++ + +

R.f.

R.(l.)g.

R.(l.)b. + ++ +++

++ ++++ +

++++

+ + +

+++ + ++++ ++++

+++

+++ ++

++ + ++ + ++ ++++ +++ + ++++ + + + + + + + + ++++ + + + + + + +++ ++ + ++ ++++ ++++ + +

+++ ++++ ++++ +++ + ++++ ++++ ++ ++ + + +++ + ++ + ++++ ++ ++++ + ++ + + ++ + + + + + ++++ + + + + + +

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CHEMOECOLOGY

PCA analysis failed to discriminate colonies on the basis of geographic location, host tree species, date of removal from logs, date of pentane extraction, and date of analysis. However it was possible to discriminate R. fla6ipes colonies by season (Bagne`res et al. 1990).

replaced by the n-tricosane (a3) and the unknown (x15). The other eight compounds were the same.

Most discriminating hydrocarbons for workers and soldiers

The ANN package used in this study was NeuralDesk with the Neudesk interface 2.11 (Neural Computer Sciences, Southampton, U.K.). The number of neurons in the input layer of the ANN was equal to the number of columns (hydrocarbons) in the training input grid. The number of output neurons in the output layer was equal to the number of columns (castes) in the training output grid. The number of hidden neurons was equal to the ratio of output and input numbers. For example there were 4 hidden neurons for 9 inputs (hydrocarbons) and 2 outputs (castes). The ANN was trained using the classic Standard Back Propagation Algorithm (SBPA) which calculates the error rate for a given set of weights and then adjusts each weight in the network to minimize error. The slope of the error curve indicates the extent of weight variations: the steeper the slope, the greater the variation. Eighty percent of the test extracts were presented to the network during training (training input). Training was stopped when average error was below 0.1. During query the ANN used fixed weights learned during training to determine output (castes) in function of inputs (hydrocarbons). This approach is like the pattern recognition method applied in electronic noses commonly used to monitor odors in environmental, medical and food industries (Keller et al. 1995). During query we presented the remaining unknown 20% (query input). Query input was chosen at random. Choice of query input did not have great effect on output. For each mixture the ANN indicated a probability value of belonging to a caste on a scale of 0 to 1. If the network is well trained and query input is close to training input, the probability value attributed to the correct caste will be high. However if query input is completely different from training input, the probability value attributed to the correct caste will be lower. In this study we considered output as correct if a probability value greater than 0.80 was attributed to the correct caste. Several rounds of testing were carried out using the whole hydrocarbon mixture or mixtures containing the hydrocarbons selected by either PCA or stepwise discriminant analysis. We tried to discriminate workers and soldiers in all four species. For the two species with a sufficient number of extracts (\5) i.e. R. santonensis and R. (l.) grassei, we tried to discriminate four castes (workers, soldiers, nymphs, neotenics). To test the similarity of the specific cuticular mixtures in the two sibling species (R. santonensis and R. fla6ipes) we performed two experiments using one species for training and the other for query.

The R. (l.) grassei matrix was constructed with a total of 46 extracts including 35 worker and 11 soldier extracts. PCA analysis and plotting of the simple correlations showed that axis 1 accounted for 21% of the variance but that axis 2, which accounted for 20.4% of variance, best separated the two castes. Classification of the most discriminating hydrocarbons in PCA according to degree of correlation selected 13 with rA/v]0.5, 8 with rA/v] 0.6, and 5 with rA/v] 0.7 (Table 1). Stepwise discriminant analysis selected 10 hydrocarbons (Table 1) allowing 100% discrimination with no statistical overlap. The R. (l.) banyulensis matrix was constructed with a total of 49 extracts including 35 worker and 14 soldier extracts. PCA analysis and plotting of the simple correlations showed that the two castes were best separated by axis 1 which accounted for 27.3% of variance. Classification of hydrocarbons according to degree of correlation selected 26 with rA/v] 0.4, 17 with rA/v] 0.5 and 0.6, and 11 with rA/v] 0.7 (Table 1). The distance separating the two castes on the plot decreased as the number of variables was reduced (Fig. 2). Stepwise discriminant analysis selected 8 hydrocarbons (Table 1) allowing 100% discrimination with no statistical overlap. The R. fla6ipes matrix was constructed with the CDEF phenotype which was the most homogeneous and abundant in the 19 colonies (Bagne`res et al. 1990). A total of 37 extracts including 19 worker and 18 soldier extracts were used. PCA analysis and plotting of the simple correlations showed that the two castes were best separated by axis 1 (30.7%). Classification of hydrocarbons selected 13 with rA/v]0.5, 10 with rA/v]0.6, and 6 with rA/v]0.7. Stepwise discriminant analysis selected 8 hydrocarbons (Table 1) allowing 100% discrimination with no statistical overlap. The R. santonensis matrix was constructed with a total of 24 extracts including 17 worker and 7 soldier extracts. PCA analysis and plotting of the simple correlations showed that the two castes were best separated by axis 1 which accounted for 26.3% of variance. Classification of hydrocarbons selected 9 with rA/v]0.5, 8 with rA/v]0.6, and 5 with rA/v] 0.7. Stepwise discriminant analysis selected 10 hydrocarbons (Table 1) allowing 100% discrimination with no statistical overlap. Most discriminating hydrocarbons for workers, soldiers, nymphs, and neotenics

Artificial neural network

The most samples for the largest number of castes were obtained in R. santonensis. The matrix was constructed with a total of 37 extracts including 17 worker, 7 soldier, 7 nymph, and 6 neotenic samples. PCA analysis and plotting of the simple correlations showed that axis 1 accounted for 25% of variance and axis 2 for 15.5% and that these two axes permitted acceptable separation between the 4 castes (Fig. 3). Best separation was between soldiers and the other castes on the first axis. The second axis placed neotenics on the positive side and nymphs negatively. Plotting the simple correlations (not shown) selected 9 hydrocarbons with rA/v] 0.5, 7 hydrocarbons with rA/v] 0.6, and 4 hydrocarbons with rA/v]0.7. Stepwise discriminant analysis gave similar results as for worker/soldier discrimination. Only the 11methyltricosane and the 5,17-dimethylpentacosane (see Table 1) were not taken into account for the discrimination between the 4 castes and

Fig. 2 Comparison of PCA plots showing separation of workers (grey area) and soldiers (black area) using a decreasing number of hydrocarbons in R. (l.) banyulensis

Fig. 3 Typical PCA plot showing separation of four castes in R. santonensis by the first two canonical axes. For better visualisation four outlines have been arbitrarily drawn

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Table 2 Results training and query using an ANN to discriminate worker and soldier castes in R. (l.) banyulensis No of hydrocarbons used as inputs

Training (80% of extracts) Workers Probability value (S.D.)

47 26 17 11

(All) (rA/v] 0.4) (rA/v] 0.6) (rA/v] 0.7)

0.96 0.99 0.69 0.71

(0.05 ) (0 ) (0.22 ) (0.11 )

Queries (20% of extracts) Soldiers

Success rate

Probability value (S.D.)

100.0% 100.0% 13.8% 11.4%

0.99 0.96 0.99 0.31

(0 ) (0 ) (0 ) (0 )

Workers Success rate


100% 100% 100% 0%

0.97 0.97 0.67 0.72

(0.01 ) (0.04 ) (1.18 ) (0.13 )

Soldiers Success rate


100.0% 100.0% 0.0% 16.6%

0.99 0.96 0.99 0.31

(0 ) (0 ) (0 ) (0 )

Success rate 100% 100% 100% 0%

Abbreviations: S.D: standard deviation; rA/v: correlation index

Results Training of the ANN using hydrocarbons selected by stepwise discriminant analysis (Table 1) was always unsuccessful. The average error always exceeded the chosen probability limit. As a result only the total hydrocarbon mixtures and the mixture sets selected by PCA using the rA/v coefficient were used as inputs (Table 1). Discrimination between worker and soldier castes In R. (l.) banyulensis (Table 2), network performance decreased with the number of hydrocarbons used as inputs. Results obtained during training and query were similar (Table 2). With the complete 47-hydrocarbon mixture, the average probability values correctly attributed to the worker and soldier castes were 0.96 and 0.99 respectively during training and 0.97 and 0.99 respectively during query. The success rate was 100% with no misclassification. Using the 26 hydrocarbons with rA/v ]0.4, discrimination was also 100% successful (Table 2). Using the 17 hydrocarbons with rA/v ] 0.5 and 0.6, the average probability value correctly attributed to the worker identification was lower than using the total mixture or 26-hydrocarbon mixture (0.69 vs 0.96 and 0.99 respectively) but discrimination of soldiers was still 100% successful. The success rate for workers was 13.8%. However in 25 of the 29 worker extracts chosen as training inputs, the probability value correctly attributed to the worker caste was higher than 0.80 in 4, over 0.70 i.e. very close to the correct output level in 19, and between 0.31 and 0.25 in 6. For the 6 worker extracts used as query inputs, the correctly attributed probability value was 0.31 in 1, 0.70 in 3, and 0.79 in 2. The average correctly attributed probability during query was 0.67 with a standard deviation (S.D.) of 1.18 and the success rate was 0%. With the 11 hydrocarbons with rA/v ] 0.7, probability values obtained during both training and query were low for soldiers (average 0.31 with a S.D. of 0) and intermediate for workers (average around 0.7 with a S.D. of 0.1). In R. santonensis (Table 3), the average correctly attributed probability value was greater than 0.8 for both workers and soldiers with the total 21 hydrocar-

bon mixture as well as with the selected sets of 9 hydrocarbons and 8 hydrocarbons. The success rate was 100% for soldiers with the total extract and for workers with the 9- and 8-hydrocarbon sets. One misclassification explains the high S.D. in the other cases. Using only 5 of the 21 hydrocarbons, the average correctly attributed probability value was 0.70 for workers and 0.29 for soldiers, and the success rate 0% for both. In R. fla6ipes using the CDEF phenotype (Table 3), the average correctly attributed probability value using the complete 21-hydrocarbon set was 0.99 for workers and 0.87 for soldiers. The lower average probability for soldiers was due to one low probability value. Results were the same using the selected sets of 13 and 10 hydrocarbons. The correctly attributed probability value was 0.99 in 7 of 18 soldier extracts for a success rate was 39%. The success rate was 0% for workers. With the 6-hydrocarbon set, the average correctly attributed probability value was 0.51 for workers and 0.49 for soldiers. The success rate was 0% for both castes. When the complete 21-hydrocarbon sets of R. santonensis and R. fla6ipes were alternatively used for training and query, correctly attributed probability values were greater than 0.9 for workers but low for soldiers. For workers the success rate was 100% in both cases. However only one soldier was correctly classified when R. fla6ipes was used in training, and none when R. fla6ipes was used in query. The finding that soldier hydrocarbons are more discriminatory is in agreement with previous evidence showing that these two sibling species have very different phenotypes (Bagne`res et al. 1990). In R. (l.) grassei (Table 3) the success rate was similar using the total 37-hydrocarbon mixture or the selected 13- and 8-hydrocarbon sets. Probability values and success rates were always high. Using the 5-hydrocarbon set, the average correctly attributed probability value was 0.75 for workers, but the success rate was 0%. For soldiers the average correctly attributed probability value was 0.24 and the success rate was 0%. Thus the selected set of 8 hydrocarbons appears to be the minimum for correct classification.

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Training input

CHEMOECOLOGY

Query input

Query output

Species

No of hydrocarbons

Species

Workers Probability value (S.D.) Success rate %

Soldiers Probability value (S.D.) Success rate %

R.s. (80% extracts)

All (21) + rA/v] 0.5 (9) ++ rA/v] 0.6 (8) +++ rA/v] 0.7 (5) ++++

R.s. (20% extracts)

0.94 (0.19 ) 94% 0.96 (0 ) 100% 0.90 (0 ) 100% 0.70 (0 ) 0%

0.83 (0 ) 100% 0.94 (0.14 ) 86% 0.87 (0.31 ) 0% 0.29 (0 ) 0%

R.s

All (21)

R.f.

0.99 (0 ) 100%

0.04 (0.2 ) 0%

R.f. (80% extracts)

All (21) + rA/v] 0.5 (13) ++ rA/v] 0.6 (10) +++ rA/v] 0.7 (6) ++++

R.f. (20% extracts)

0.99 (0 ) 100% 0.63 (0 ) 0% 0.64 (0 ) 0% 0.51 (0 ) 0%

0.87 94% 0.61 39% 0.60 33% 0.49 0%

R.f.

All (21)

R.s.

0.90 (0 ) 100%

0.21 (0.29 ) 14%

R.(l.)g. (80% extracts)

All (37) + rA/v] 0.5 (13) ++ rA/v] 0.6 (8) +++ rA/v] 0.7 (5) ++++

R.(l.)g. (20% extracts)

0.90 (0.06 ) 97% 0.99 (0.05 ) 97% 0.98 (0 ) 100% 0.75 (0 ) 0%

0.93 (0 ) 100% 0.76 (0.3 ) 82% 0.81 (0.2 ) 73% 0.24 (0 ) 0%

Table 3 Results training and query using an ANN to discriminate worker and soldier castes in R. santonensis (R.s.), R. fla6ipes (R.f.) and R. (l.) grassei (R.(l.)g.). Results of discrimination using the two sibling species alternatively for training and query are also shown

(0.22 ) (0.3 ) (0.31 ) (0 )

(S.D.): standard deviation; rA/v: correlation index

Discrimination between workers, soldiers, nymphs, and neotenics In R. santonensis (Table 4), discrimination was good for three of the four castes when the network was trained using the total 21-hydrocarbon mixture from the worker, soldier, nymph, and neotenic castes. The highest correctly attributed average probability value was 0.99 for soldiers followed by 0.87 for workers, 0.82 for neotenics, and 0.54 for nymphs. Only one of the 17 worker extracts was misclassified. All soldier extracts were correctly classified. For nymphs the correctly attributed probability values were variable: 0.96 for two extracts, 0.69 in two, and lower than 0.2 in three (classified as workers). Standard deviation was high in nymphs. For neotenics, the correctly attributed probability values were high in five out of six extracts. The ANN did not function using the 9-, 8and 5-hydrocarbon sets. The system was again successful with a mixture containing the 16 major hydrocarbons. Using this 16-hydrocarbon set, we performed different tests to determine the effect of training on query. Three tests using different training input selections were performed. Probability values and success rates varied between test (Table 4). For soldiers the average correctly attributed probability value was 0.91

and the success rate was 86%. Corresponding data for the other castes was variable with success rates ranging from 0% to 100%. Contrary to PCA, the ANN allowed excellent discrimination between workers and neotenics. In R. (l.) grassei (Table 4) the lowest error value achieved using the total 37-hydrocarbon set during training was 0.35 instead of 0.1, the normal end point. As for R. santonensis the lowest correctly attributed probability values were obtained for nymphs (0.09).

Discussion Differences in the proportion of cuticular and glandular compounds have been noted between castes (and subcastes) of termites, ants, and honeybees (Howard et al. 1978; Watson et al. 1989; Roisin et al. 1990; Gassa & Takahashi 1995; Brown et al. 1996; Plettner et al. 1996, 1997; Haverty et al. 1996), and some of these differences have been implicated in caste discrimination (Bonavita-Cougourdan et al. 1993). However neurophysiological and behavioral studies have only began to elucidate the mechanisms of inter- and intra-specific recognition in insects.

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Training input

Query input

Query output

Species

No of hydrocarbons

Species

No of tests

Workers Probability value (S.D.) Success rate %

Soldiers Probability value (S.D.) Success rate %

Nymphs Probability value (S.D.) Success rate %

Neotenics Probability value (S.D.) Success rate %

R.s.

21

R.s.

1 test

0.87 (0 ) 94%

0.99 (0 ) 100%

0.54 (0.35 ) 29%

0.82 (0.36 ) 83%

R.s.

16

R.s.

Test a

0.66 0% 0.84 76% 0.81 88% 0.77 55%

0.99 (0 ) 100% 0.82 (0.33 ) 86% 0.91 (0.10 ) 71% 0.91 (0.21 ) 86%

0.36 0% 0.78 71% 0.48 43% 0.54 38%

(0.19 )

0.89 (0.03 ) 100% 0.79 (0 ) 0% 0.97 (0.02 ) 100% 0.88 (0.08 ) 67%

0.20 (0 ) 0%

0.09 (0 ) 0%

Test b Test c 3 Tests R.(l.)g.

37

R.(l.)g.

1 Test

(0.17 ) (0.24 ) (0.22 ) (0.23 )

0.63 (0 ) 0%

(0.31 ) (0.30 ) (0.38 )

207

Table 4 Results training and query using an ANN to discriminate between workers, soldiers, nymphs, and neotenics in R. santonensis (R.s.) and R. (l.) grassei (R.(l.)g.).

0.21 (0.34 ) 17%

(S.D.): standard deviation

An Artificial Neuron Network configured as for an electronic nose to mimic olfactory perception was used to classify termite extracts by caste as a function of relative proportions of cuticular hydrocarbons in three European species of Reticulitermes termites (R. (l.) grassei, R. santonensis, R. (l.) banyulensis), and one North American species (R. fla6ipes). We used an ANN configured in the feed-forward mode with one hidden layer and performed training by minimizing the error rate. This type of system is considered as a universally consistent classifier (Devroye et al. 1997). All other conditions necessary for good generalization (Sarle 1997) were also met including pertinence of input (hydrocarbons) to the target (caste) and training with a large, representative subset of the complete set of cases (80% of extracts). As a result we can assume that our ANN was a good tool for classification of natural pheromones used in chemical communication and, unlike PCA analyses, it could allow diagnostic between castes. To our knowledge this is the first time an ANN has been used for this novel application. Our results corroborate several general hypothesis about social insects. First concerning the chemical signature, this study confirms that hydrocarbon mixtures observed on the cuticle of termites and other social insects are real chemical entities and that they constitute a true chemical signature or odor. The PCA plot of the four castes of R. santonensis indicates that each caste in Reticulitermes spp. has a relatively distinctive signature. The low correctly attributed probability values observed for nymphs in four-caste discrimination tests is consistent with ontogenic development (Buchli 1958) since nymphs are considered as a transitional caste between workers and reproductives. Discrimination of neotenics, which form a true caste, was achieved with the ANN.

Two findings of this study are new. The first is that the total mixture is not always necessary in a discriminative process for caste diagnosis. Discrimination can also be obtained using a limited number of well correlated compounds. Soldiers and workers were successful distinguished with only 17 of the 47 hydrocarbons in R. (l.) banyulensis, with only 8 of the 21 hydrocarbons in R. santonensis, and with only 8 of the 37 hydrocarbons in R. (l.) grassei. However the total mixture was necessary for R. fla6ipes. Discrimination of four castes required 16 of the 21 hydrocarbons in R. santonensis and was poor in R. (l.) grassei even with all 37 hydrocarbons. We have observed similar findings using PCA in a lower dampwood termite species (Zootermopsis ne6adensis Hagen (Isoptera, Termopsidae) in which four castes could be discriminated with only four compounds (Bagne`res et al. unpublished). The second novel finding is that, using mathematical procedure, each species appears to have its own caste chemical signature. Each neuron network formed after training was specific and could not be used for the other species, even for sibling. This study supports the hypothesis that termites, which are blind and live in galleries, recognize the caste of congeners by antennal contact to discriminate the cuticular hydrocarbon patterns (Cle´ment 1981, 1982). Our findings show that complex patterns can be identified by a simple model of a network of neurons. Although we cannot be sure how well this mathematical method mimics the natural situation, we speculate that termites have an olfactory network able to discriminate and quantify cuticular hydrocarbons. This process could be similar to the visual process underlying perception of movement by the fly composite eyes (Pichon et al. 1990). It has been clearly demonstrated that perception of movement is less efficient using video image processing (linear discrimina-

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tion) than a system based on an artificial neural network (non linear discrimination). The finding that the neural network was unable to correctly identify the soldier caste when the sibling species R. santonensis and R. fla6ipes were used alternatively for training and query has important implications for species isolation. Interspecies discrimination was not complete even though the molecules were exactly the same, this could support their recent isolation (Bagne`res et al. 1990). This could explain why individuals from different species cannot cohabit in the same nest. As a general rule the closer species are, the more genes they have in common, and the stricter behavioral isolation mechanisms (interspecific aggression, sex pheromones) must be. Similarly recognition and agression processes are proportional to the similarity between cuticular mixtures: the more alike the mixtures are, such as in R. (l.) grassei. and R. (l.) banyulensis, the greater aggressivity is, and conversely the more dissimilar the mixture is, such as between R. santonensis and R. (l.) banyulensis or between R. santonensis and R. (l.) grassei, the lesser aggressivity is (Bagne`res 1989; Cle´ment & Bagne`res 1998). In conclusion this study corroborates the hypothesis that a chemical signature, involving minute differences in the relative proportions of cuticular hydrocarbons, allows for caste discrimination within an insect society. More work using electro-antenography will be needed to confirm perception of the cuticular mixtures and to understand the membrane receptors involved in this process. Acknowledgements Financial support for this paper was provided by the Fondation Singer Polignac and by a grant obtained in the framework of a 1987 – 1988 cooperation agreement between the University of Paris 6 (AGB & JLC) and the University of Georgia (Prof. Murray S. Blum). We are grateful to Prof. Gary J. Blomquist and Dr. Steve Seybold (Univ. of Nevada, Reno, USA) and two anonymous readers for their helpful comments. We thank Andy Corsini for his help in writing the final manuscript.

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