Chemoecology 8:201 –209 (1998)
0937 – 7409/98/040201–09 $1.50 +0.20
© Birkhäuser Verlag, Basel, 1998
Artificial neural network modeling of caste odor discrimination based on
cuticular hydrocarbons in termites
A.-G. Bagnères, G. Rivière1 and J.-L. Clément
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. Bagnères
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 (Clément 1982; Howard et al. 1982;
Bonavita-Cougourdan et al. 1987; Nowbahari et al.
1990; Bagnères 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; Bagnères et al. 1996; Vauchot et al.
1997, 1998), particularly when necessary to preserve
colonial cohesion (Bonavita-Cougourdan et al. 1989,
1996, 1997; Bagnères 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 (Clément, 1981). This instantaeous identification of the chemical signature has favored social
behavior by allowing nestmate recognition and caste
discrimination (Clément & Bagnères 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-
202
A.-G. Bagnères, G. Rivière and J.-L. Clément
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 ‘‘Forêt 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 Pyrénéesorientales 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) (Bagnères 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
(Bagnères 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; Bagnères 1989; Bagnères 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 (Bagnères 1989; Bagnères 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
(Bagnères 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.
Vol. 8, 1998
Artificial neural network modeling of caste odor discrimination based on cuticular hydrocarbons in termites
203
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.
+
++
+++
++
++++
+
++++
+
+
+
+++
+
++++
++++
+++
+++
++
++
+
++
+
++
++++
+++
+
++++
+
+
+
+
+
+
+
+
++++
+
+
+
+
+
+
+++
++
+
++
++++
++++
+
+
+++
++++
++++
+++
+
++++
++++
++
++
+
+
+++
+
++
+
++++
++
++++
+
++
+
+
++
+
+
+
+
+
++++
+
+
+
+
+
+
204
A.-G. Bagnères, G. Rivière and J.-L. Clément
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 (Bagnères 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
(Bagnères 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
Vol. 8, 1998
Artificial neural network modeling of caste odor discrimination based on cuticular hydrocarbons in termites
205
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
Probability
value (S.D.)
100%
100%
100%
0%
0.97
0.97
0.67
0.72
(0.01 )
(0.04 )
(1.18 )
(0.13 )
Soldiers
Success
rate
Probability
value (S.D.)
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 (Bagnères 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.
206
A.-G. Bagnères, G. Rivière and J.-L. Clément
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.
Vol. 8, 1998
Artificial neural network modeling of caste odor discrimination based on cuticular hydrocarbons in termites
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 (Bagnères 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 (Clément 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-
208
A.-G. Bagnères, G. Rivière and J.-L. Clément
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 (Bagnères 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 (Bagnères 1989;
Clément & Bagnères 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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