Study material
We established 16 F. sanguinea incipient colonies in the laboratory, which were maintained for between one and four years. The colonies were initiated with dealate queens collected in the field. Queens were spotted after mating as they were penetrated the forest litter in search of colony founding opportunities. They were collected during four consecutive seasons starting from 2017 in Solniczki and Turczyn Forests near Białystok and, in one case, near Sejny (northeastern Poland). Each queen was provided with 80 to 230 Formica fusca pupae removed from one of 18 laboratory colonies or, in the case of one queen, from a field colony. Colonies were maintained in the plastic boxes (40 × 30 × 30 cm) with the floor covered with a thin layer of mineral soil and sawdust. Test tubes wrapped in aluminium foil and partially filled with water, and closed with a cotton plug, served nesting sites for ants. The inner surface of the nest box walls was coated with Fluon to prevent ants from escaping. Colonies were fed diluted honey, fresh apple pieces, as well as crickets (Acheta domesticus), greater wax moth (Galleria mellonella) caterpillars, and male honey bee larvae and pupae all killed by freezing. Each of the developing F. sanguinea colonies was subjected to at least one census followed by the removal of 3–6 slave-maker workers (if present) and a similar number of F. fusca slaves. The timing of censuses was chosen to cover various stages of colony development spanning a range of slave-to-slave-maker ratios. Some of the samples included callow slave-maker workers, which were identified by the brighter coloration due to the incomplete cuticle pigmentation. After removal, ants were killed by freezing and stored at − 22 ºC for subsequent chemical analyses. A total of 78 mature F. fusca ants, 65 mature and 32 callow F. sanguinea ants were used for CHC profiling. The F. sanguinea colonies also served as a source of pupae for the separation experiments described below. Moreover, workers from 12 out of 19 free-living F. fusca colonies used as sources for slaves were also sampled for chemical analyses (five to eight ants per colony).
Separation of the callow workers
Since cuticular hydrocarbon (CHC) profiles of F. sanguinea callow workers from the parent colonies could have been modified by interactions with other colony members (see Ichinose & Lenoir 2009), pupae were removed from thirteen experimental colonies and incubated until they reached the adult stage. If present, cocoons were removed manually once the ants became motile, which was visible through the silk envelope after gentle pressing with an entomological pin.
After emerging from the pupae, ants were placed in plastic Petri dishes (9 cm in diameter) equipped with wet cotton to maintain humidity, left in darkness at a temperature between 21.5 and 24 ºC, and provided with diluted honey water twice a week. Ants were kept in pairs since pilot trials had shown that isolated individuals suffer high moralities. Due to the variation of pupa emergence time, there was a delay until the second ant was placed on the Petri dish (mean = 16.97 hours, maximum = 39.92 hours).
Each pair of ants was assigned to one of four treatments which differed in the period before ants were collected and killed by freezing. One of the ants was selected at random from the pair and was subsequently used for CHC extraction. For each colony, assignment of experimental units to treatments was randomized. If the number of available pupae was enough, treatment replicates per colony were performed (yielding an average of 1.82 experimental units per colony per treatment). In case an ant died precociously, the respective experimental unit was discarded.
Effect of the environmental cues on CHC production
We were interested in whether callow F. sanguinea ants respond to the presence of slaves in a colony by actively adjusting their own recognition label (chemical mimicry via biosynthesis, see Lenoir et al. 2001). We obtained callow workers by removing F. sanguinea pupae from developing colonies, and separating them in pairs for 12–15 days as described above. However, instead of using ants, we placed 3 mm glass beads in a Petri dish and stabilised them by putting them on plastic rings. Each glass bead was coated with CHC profiles equivalent to that found on 2–4 ants. As the glass beads served as “dummy ants”, this allowed us to eliminate the influence of social interactions with slaves on the CHC profile of the tested ants. Twelve F. fusca free-living colonies and the twelve F. sanguinea colonies with less than 10% slaves served as a source of ants used as CHC donors. For each colony, 24–48 ants were killed by freezing, pooled and extracted together for 10 minutes in hexane. The extract was enriched with 15 µg of docosane used as an internal standard, evaporated, and re-dissolved in 48 µl of hexane. Each glass bead was coated with 8 µl of the final extract with the use of a gas chromatography syringe. This allowed to apply small droplets on the glass beads when performed under a microscope. For the control treatment, clean glass beads were used. Twelve F. sanguinea colonies served as a source of worker pupae. Each of the three treatments (slave-makers' CHC, slaves' CHC, and control) was performed in 0–2 replicates per colony. Several experimental trials were cancelled due to precocious ant death and replaced with new trials if pupae were still available. After 12–15 days, ants were killed by freezing and stored at − 22 ºC for subsequent CHC extraction according to the protocol described below. No internal standard was added during preparation of the extract.
CHC extraction and chemical analyses
Individual ants were placed in glass vials (2 ml) and extracted in 150 µl of hexane for 10 minutes. Subsequently, 15 µl of a hexane solution of docosane (12.5 µg/ml) was added to the extracts as an internal standard. Glass vials with the extracts were left open until the solvent evaporated, re-dissolved in 50 µl of hexane, transferred to 100 µl inserts, and stored at -22 ºC until analysis. Head width measurements were performed as a proxy for ant body size and used for CHC amount normalization.
We analysed the CHC extracts of all samples with an Agilent 6890 gas chromatograph coupled with an Agilent 5975 Mass Selective Detector (GC-MS, Agilent, Waldbronn, Germany): The GC (split/splitless injector in splitless mode for 1 min, injected volume 1 µl at 300°C) was equipped with a DB−5 Fused Silica capillary column (30 m x 0.25 mm ID, df = 0.25 µm; J&W Scientific, Folsom, USA). Helium served as carrier gas at a constant flow of F. The following temperature program was usedStart temperature 60°C, temperature increase by 5°C per min up to 300°C, isotherm at 300°C for 10 min.: The electron ionisation mass spectra (EI-MS) were acquired at an ionisation voltage of 70 eV (source temperature: 230°C). Chromatograms and mass spectra were recorded and quantified via integrated peak areas with the software HP Enhanced ChemStation G1701AA (version A.03.00; Hewlett Packard). CHC compounds were identified by the compound-specific retention indices and their detected diagnostic ions (Carlson et al. 1998).
Body surface area approximation
The chemical analyses yielded the amount of hydrocarbons spread over the body surface. However, to make a fair comparison across individuals, this measure needs to be normalised to account for body size differences.Therefore, for all individuals included in the CHC amount analyses, head width was measured. In addition, for a subset of samples we measured the planar projection areas of the three body parts: dorsal view of the head, the dorsal view of the thorax, and lateral view of the thorax. The body of ants was photographed under a stereomicroscope and the number of pixels within the body parts was quantified using OpenCV Python library tools. Appendages were removed either physically or digitally in the image editor. For each view, 9–12 individuals of each species were analyzed, covering a wide range of the size distribution (Online Resource Fig. S10). Body area was approximated from head as follows: second-order polynomial regression was applied to relate planar projection area to head width, separately for each species and each view. The model coefficients were then used to predict the area of individuals for which only head width was available (Online Resource Section 9). Then the estimated area (in pixels) was used as a divisor of the standard area to obtain a scaling factor for normalizing CHC amount. As the standard area, we used the sum of estimated planar projections of an F. sanguinea worker with a head width of 1.15 mm. Thus, CHC amounts were scaled as if they were extracted from the ants with similar body areas (exact normalization was not possible due to approximation error).
Statistical analyses
Marker peaks
To identify the peaks characteristic of either F. fusca or F. sanguinea ants, we used a sparse partial least squares discriminant analysis model (sPLS-DA ; Rohart et al. 2017) trained on the data collected in another study (Włodarczyk and Szczepaniak 2017), in which ants were sampled from eleven F. sanguinea dulotic colonies and 21 F. fusca free-living field colonies. The data representing peak relative abundances are compositional in nature and were therefore subjected to the centred log-ratio transformation (Aitchison 1986, Hervé et al. 2018) after adding a small constant (10− 5) to avoid log(0) operations. Since we were interested in the importance of each of the original variables (peaks) in predicting sample species, the data was subjected to PCA. Otherwise, the model might have suffered from non-identifiability due to the correlation between the peaks. Moreover, with the lasso penalization of loading vectors, the discriminant model might have disregarded some variables, relying on a few that could be sufficient to discriminate between the two species. This would remove some biologically relevant data variation, compromising the model’s robustness when making predictions about species identity of samples representing a mixed phenotype. Noise in the data was reduced by retaining only the first principal components that accounted for at least 80% of the total variance. To identify marker peaks, we calculated the weights of the input variables that are typically used for inference of observation class, i.e., species identity in this case (for details, see Online Resource Section 2.1). Since these weights were assigned to principal components, we needed to reverse the data transformation by multiplying the weight matrix by the pseudoinverse of the rotation matrix, which is used to project the original variables onto principal components. The resulting weights represented the predictive power of each peak within the context of the discriminant analysis model. The sign of each weight indicated the species towards which a peak biased the classification. We used the bottom and top 0.2 quantiles of the final scores to select peaks characteristic of F. fusca and F. sanguinea, respectively.
Similarly, we used the sPLS-DA method to identify markers distinguishing callow and mature F. sanguinea individuals. In this case, a multilevel data structure was imposed on the model with colony-sample date combination as a grouping factor. Each sample was classified into one of the three groups: F. fusca slaves, callow F. sanguinea, and mature F. sanguinea. By incorporating F. fusca samples into the discriminant analysis we corrected for the potential effect of F. fusca slaves on the differences between CHC profiles of callow and mature F. sanguinea ants. The trained discriminant analysis model was used, as before, to retrieve weights representing the contribution of each variable to the classification score. Peaks within the top 0.2 quantile were classified as markers.
Since the discriminant analysis is designed to identify differences between pre-defined groups of observations, we needed to ensure that our model's performance exceeded that of randomly selected samples. Otherwise, the peak markers could merely be statistical artefacts. Consequently, we randomly shuffled the age status of F. sanguinea samples within each sample date/colony combination, and we ran the perf function from mixOmics package. This function performed a cross-validation using 75% of samples to train the model which was then used to make predictions on all F. sanguinea samples and to assess its accuracy we calculated AUROC sliding the score indicating prediction of callow ant. The same was done for the data set with true age status labels and the difference in AUROC was computed. The procedure was repeated 103 times to produce the sampling distribution of the differences in AUROC. The p-value was calculated as a proportion of differences equal to or less than zero.
Species Identity Score
The model trained for the identification of species-characteristic peaks was also used to generate predictions for new samples in the form of a continuous numerical value (henceforth Species Identity Score). Higher values indicated a stronger match to F. sanguinea and a weaker resemblance to F. fusca. This approach provided insights into the development of chemical identity in slave-maker ants as their proportion in a colony increased. We also compared nestmates of different species and ages by calculating the difference in Species Identity Score and regressing it against the proportion of F. sanguinea ants in the colony. When multiple samples per individual category (species/age) were available, we computed the average CHC profile and used it to calculate the difference in Species Identity Score.
Quantitative changes in the CHC profile
We fitted linear mixed models to see how the total CHC amount changed as the proportion of slave-making workers in a colony was becoming greater. We used all compounds or subsets composed of the markers of F. fusca, callow F. sanguinea, or mature F. sanguinea ants. The full model comprised following random effects: intercept nested within colony, intercept nested within interaction of colony and sampling date, and slope nested within colony. When necessary, random terms were dropped one by one to ensure model convergence, avoid singularity issues, and pass diagnostic tests. This process was repeated until a model that met these criteria was obtained.
We also fitted linear mixed models to track changes in CHC amounts over time in separated callow ants. As before, we conducted separate analyses on the data subsets with marker peaks only. Since ants in Petri dishes were not individually marked and there was a time lag between the introduction of the first individual and its pairing, we could not determine the exact separation time of the individual that was selected for CHC extraction. Therefore, we used the mean separation time of both ants from the same experimental unit as an approximation. If necessary, predictor or response variable were transformed by power or log function.
We used the lme4 package in R to fit the models and the lmerTest package to obtain p-values for the coefficients. Model diagnostics were performed using the DHARMa R package (Hartig 2021), which evaluates scaled residuals obtained through simulations from the fitted model and tests their distribution using the Kolmogorov-Smirnov test (for quantile-quantile distribution), an outlier test, a dispersion test, and a uniformity test. Additionally, we assessed response residuals of the fitted models using the Shapiro-Wilk normality test.
Permutation tests
We investigated whether the newly eclosed slave-maker workers adopt the chemical camouflage strategy. To examine this, we compared their recognition labels to those of the free-living relatives of the slaves. This approach eliminated the confounding effect of F. sanguinea ants on their slaves, which can lead to similarity in recognition odours (Włodarczyk and Szczepaniak 2017). We used the Bray-Curtis method (implemented in the R package vegan; Oksanen et al. 2020) on sum-normalized data to compute chemical distances between CHC profiles. Accordingly, we calculated the chemical distance of separated F. sanguinea ants to free-living F. fusca ants from slaves' parent colonies (Fig. 1). Using Wilcoxon matched pairs test, we compared these distances against those to a randomly selected unrelated F. fusca colony. The distance for samples collected from the same colony were averaged, to account for non-independence of observations. Since the unrelated colony was assigned randomly we repeated the procedure 103 times for each treatment and report the mean p-value along with its range across all tests.
Non-parametric tests of the difference of means
We used the Wilcoxon signed-rank test to check the effect of species and maturity on the total amount of CHC or proportions of marker compounds. The samples were paired according to the colony of origin, and samples from the same category and colony were averaged to avoid pseudoreplication. In all our analyses, when the absolute amounts of CHC were taken into consideration, the corresponding values were corrected by dividing by the square of head width, which was used as a proxy for the body surface area. A similar procedure was used to determine if the chemical distance to the CHC mixture applied to the glass dummy ants was different between treatment and control ants. The dissimilarity between profiles was calculated as explained in the section on permutation tests.