Ethics oversight
This study was carried out within the frame of our housing and breeding permit (311.4-si) granted by the Landratsamt Starnberg, Germany. Attachment of backpacks was approved by the Regierung von Oberbayern, Germany (ROB-55-2-2532. Vet_02-17-211).
Study populations
We used four zebra finch populations that are genetically differentiated due to founder effects and selection (see Supplementary Fig. 1 & Fig. 2): two domesticated populations (D1 and D2) that have been maintained in captivity in Europe for about 150 years and two populations (W1 and W2) that have been taken from the wild about 10–30 years ago (see Supplementary Fig. 1). We ran all experiments in two independent replicates. We used individuals from populations D1 and W1 for replicate 1 and individuals from D2 and W2 for replicate 2.
Breeding experiment Generation 1
We created four groups of 36 individuals (9 males and 9 females from both a domesticated and a wild-derived population, two groups within each replicate) and put each group separately in an indoor aviary (5 m × 2.0 m × 2.5 m). All individuals had been reared normally by their genetic parents in similar breeding aviaries, were inexperienced (never mated before) and unfamiliar to all opposite-sex individuals. In replicate 1 (W1 – D1, starting December 2016), birds were 142 ± 32 days old at the start of the experiment (range: 101–191 days); in replicate 2 (W2 – D2, starting March 2017), birds were 241 ± 47 days old (range: 151–306 days). In each aviary, we provided nest material and nest boxes to stimulate breeding and observed pair-bonding behaviour for ca. 60 h spread over 14 days. Two observers recorded all instances of allopreening, sitting in bodily contact, and visiting a nest box together, which reflects pair bonding64.
In total, we observed 3166 instances of heterosexual association among the 4 × 36 individuals (Supplementary Table 3). We defined a pair-bond between two opposite-sex individuals if they were recorded in pair-bonding behaviour at least five times (mean: 22 ± 14 SD, range: 5 – 73). This cut-off was chosen (blind to the outcome of data analysis) based on the frequency distribution showing a clear deviation from a random, zero-truncated Poisson distribution (Supplementary Fig. 8). Using this definition, we identified a total of 60 pairs (30 in each replicate). Of all females, 48 and 6 had a pair-bond with one and two males, respectively (18 females remained unpaired). Conversely, 34, 10, and 2 males had a pair-bond with one, two, and three females, respectively (26 males remained unpaired).
Cross-fostering for Generation 2 experiments
After the breeding experiment of Generation 1, in 2017, we established two different cultural lineages within each genetic population by cross-fostering eggs, either within or between populations (Fig. 3). For this purpose, we used 16 aviaries (four per population), each containing 8 males and 8 females of the same population (Generation 1). Individuals were allowed to freely form pairs and breed. We reciprocally exchanged eggs shortly after laying between two aviaries per population (within-population cross-fostering) and between pairs of aviaries from different populations (between-population cross-fostering). This resulted in four cultural lineages per replicate (DD, DW, WD, and WW; Fig. 3). Each lineage was maintained in two separate breeding aviaries to ensure the availability of unfamiliar opposite-sex Generation 2 individuals from the same line. Offspring remained with their foster parents until they reached sexual maturity, when the following experiment started.
Social experiment Generation 2
Between December 2017 and March 2018, we put four groups of individuals (two groups for each replicate) in indoor aviaries (same as in Generation 1 experiment). Each group consisted of 10 males and 10 females from each of the cross-fostered groups DD, WW, DW and WD, i.e., a total of 80 birds per aviary, except that one aviary of replicate 2 only consisted of 63 individuals (7DD, 8WW, 8DW and 8WD) due to a shortage of birds. In replicate 1 (W1 – D1, starting December 2017), birds were 170 ± 25 days old at the start of the experiment (range: 105–199 days); in replicate 2 (W2 – D2, starting January 2018), birds were 200 ± 29 days old (range: 120–241 days). We recorded the position of individuals using an automated barcode-based tracking system31. We fitted each individual with a unique machine-readable barcode (Supplementary Fig. 4a) and placed eight cameras (8-megapixel Camera Module V2; RS Components Ltd and Allied Electronics Inc.), each connected to a Raspberry Pi (Raspberry Pi 3 Model Bs; Raspberry Pi Foundation) in each aviary. For 30 consecutive days, the cameras recorded individuals at six perches and at two feeders (Supplementary Fig. 4b, c). Between 05:30 and 20:00, when lights were switched on, each camera took a picture every two seconds.
Each day, pictures stored on the Raspberry Pis were downloaded to a central server and processed using customised scripts. The customised software used the PinPoint library in Python65 to identify each barcode in each picture, allowing us to simultaneously track the position and orientation of each individual (Supplementary Fig. 4b) for the duration of the experiment. The tracking system generated 118 million observations across all four aviaries (Supplementary Fig. 4c). From these data, we extracted the average distance between the male and the female (in mm) for each male-female dyad, either daily or across the entire 30-day period (for comparison, such distance data were also extracted for all male-male and all female-female dyads). We used this dataset to identify the nearest opposite-sex individual for each of 151 males and females (55% of these 151 associations were reciprocal). Out of 151 nearest males to females, 74 (49%) paired with that female in the following breeding experiment (see below) and this proportion strongly increased as the average distance between partners decreased (Supplementary Fig. 9).
Breeding experiment Generation 2
Immediately after the social experiment, we moved each group into a separate semi-outdoor aviary (5 m × 2.5 m × 2.5 m) and provided nest material and nest boxes. During the next 2 months, three observers scored heterosexual associations to identify pair bonds as described for ‘breeding experiment Generation 1’ (ca 300 h per replicate). In total, we observed 6072 associations involving 284 individuals (Supplementary Table 3). Consistent with the previous experiment, we defined a pair-bond when a male-female dyad was observed in pair-bonding behaviour at least five times during the entire experiment (mean: 18 ± 13 SD range: 5 – 61; Supplementary Fig. 8). Using this definition, we identified 147 pairs (79 pairs in replicate 1 and 68 in replicate 2). Of all males, 97, 22 and 2 had a pair-bond with 1, 2 and 3 females, respectively (27 males remained unpaired). Conversely, 99, 21 and 2 females had a pair-bond with 1, 2 and 3 males (26 females remained unpaired).
Breeding experiment Generation 3
Between April and December 2018, we housed the four cultural lineages (DD, WW, DW and WD) separately again. We placed 8 males and 8 females in each of 16 breeding aviaries (four per lineage) and allowed them to freely form pairs and breed. The offspring belong to four lineages (Fig. 3): two lineages with individuals that were raised by parents that had not been cross-fostered between the domestic and wild-derived population (DDD and WWW) and two lineages with individuals from the same genetic background, but where their parents had been cross-fostered and raised by the other population (DDW and WWD).
Between December 2018 and February 2019, we put four groups of 36 birds (two per replicate, i.e., 2 with 18 DDD and 18 DDW individuals and 2 with 18 WWW and 18 WWD individuals; 9 males and 9 females per lineage; Supplementary Table 3) in an outdoor aviary (same as above). In replicate 1 (W1 – D1, starting December 2018), birds were 172 ± 44 days old at the start of the experiment (range: 131–195 days); in replicate 2 (W2 – D2, starting January 2019), birds were 191 ± 40 days old (range: 122–230 days). During 14 days, two observers recorded all pair-bond behaviours as described under ‘breeding experiment Generation 1’. In total, we observed 3378 instances of pair-bond behaviour involving 137 individuals (Supplementary Table 3). As above, we defined a pair-bond when a male-female dyad was observed in pair-bonding behaviour at least five times during the entire experiment (mean: 18 ± 11 SD, range: 5 – 47; Supplementary Fig. 8). We identified 82 pair bonds (37 in replicate 1 and 45 in replicate 2). Of all males, 34, 16, 4 and 1 had a pair-bond with 1, 2, 3 and 4 females, respectively (17 males remained unpaired). Conversely, 42, 16, 1 and 1 females had a pair-bond with 1, 2, 3 and 5 males, respectively (12 females remained unpaired).
Morphological measurements
After birds had reached sexual maturity (>100 days of age), we measured body mass (to the nearest 0.1 g), tarsus length (to the nearest 0.1 mm), and wing length (to the nearest 0.5 mm) of all individuals (all measured by WF). We included these three variables in a principal component analysis (PCA) and used the first principal component (PC1, 67% of variation explained) as a measure of body size.
Song recording and analysis approach
We recorded the songs of the parental males from Generation 1 (16 aviaries x 8 males = 128 males, of which 122 were successfully recorded between November and December in 2017) and of their offspring (Generation 2; 146 out of 152 males were successfully recorded between March and May 2018). To elicit courtship song, each male was placed together with an unfamiliar female in a metal wire cage (50 cm × 30 cm × 40 cm) equipped with three perches and containing food and water. The cage was placed within one of two identical sound-attenuated chambers. We mounted a Behringer condenser microphone (TC20, Earthworks, USA) at a 45° angle between the ceiling and the side wall of the chamber, such that the distance to each perch was approximately 35 cm. The microphone was connected to a PR8E amplifier (SM Pro Audio, Melbourne, Australia) from which we recorded directly through a M-Audio Delta 44 sound card (AVID Technology GmbH, Hallbergmoos, Germany) onto the hard drive of a computer.
Previous studies that quantified differentiation of songs between zebra finch populations using specific song parameters (e.g., duration and frequency measures) largely failed to detect prominent differences12,49,50. We, therefore, used the following two approaches (Sound Analysis Pro and Machine Learning) to quantify the extent to which a given male’s song resembled the songs of other males.
Song similarity analysis with SAP
Using Sound Analysis Pro (SAP) version 2011.10427, we quantified song similarity (ranging from 0 to 100) by direct pairwise comparison of song motifs (the main part of a male’s song that is stereotypically repeated and about 0.8 s long, excluding introductory syllables). Pair-wise comparisons of two males (based on one representative motif recording per male) revealed higher within-population similarity than between-population similarity (Supplementary Table 2, data from Generation 1). Further, for offspring that were cross-fostered between populations (N = 73 males from Generation 2) song similarity to their foster father was higher than song similarity to their genetic father (80 versus 68, paired t-test: p < 0.0001). For each of the 146 recorded males of Generation 2, we calculated three measures of song similarity with regard to each of the females encountered in the social experiment with automated tracking of birds. (1) ‘SAP song similarity to foster father’: the pairwise similarity between the motif of the focal male and the motif of the foster father of the focal female. (2) ‘SAP song similarity to parents’: we first combined the song motifs of all eight parental males that were present in the female’s rearing aviary (Generation 1) into a single ‘super-motif’ (simply placing all recordings into a single sound file) and then calculated the similarity of the motif of the focal male to this super-motif from the female’s rearing aviary. (3) ‘SAP song similarity to peers’: we combined the song motifs of all 7-10 recorded peer males present in the female’s rearing aviary (Generation 2) into a single ‘super-motif’ and calculated the similarity of the motif of a focal male to this super-motif.
Song categorization based on machine learning
We used the Sound Classifier tool in Apple Create ML (https://developer.apple.com/machine-learning/create-ml/; Version 1.0; 16019; Apple Inc. 2019) in an Xcode environment (Version 11.7; 11E801a) to (1) assess the proportion of individual song recordings that can be correctly assigned to their population (Table 1), and (2) to quantify the confidence with which songs of individual males are assigned to a given population (Fig. 7). We interpret the former as a measure of overall divergence between two populations and the latter as a measure of song similarity of an individual to a population. As input, we used two recordings for each individual male (mean ± SD duration per recording: 6.8 ± 1.6 s, range 4.5–10.2 s; n = 536).
To quantify the overall classification success, we first trained the sound classifier on two categories of songs (e.g., songs of population W1 versus D1) using all available recordings from individuals from Generation 1 (i.e., 30-32 males per population, represented by 60–64 song recordings). After the training phase, the software reports a validation statistic, which is the proportion of training songs that are classified correctly with the algorithms derived from the training set (this value has to be interpreted cautiously, see below). For independent validation, we then tested the classification success (proportion of tested songs that are classified correctly) on recordings from individuals from Generation 2 (i.e., 17-20 males per population, using 34–40 songs). We did this separately for the males that had been cross-fostered within and between populations. All steps (training, validation, and testing) were carried out for all six pairwise combinations of the four captive populations used in this study.
Besides reporting a classification result for each tested recording, the sound classifier also reports a confidence statistic (complementary likelihoods of belonging to each of the two classes) for each 1 s interval of the recording in a sliding window with 50% overlap. As the classification success and overall confidence may increase with the length of recording, we trimmed all recordings to 4.5 s and averaged for each recording the confidence scores for a given class from the first (0 to 1 s) to the last (3.5 to 4.5 s) time interval. We interpret this mean confidence value in belonging to a certain class as a measure of similarity to that class. In analogy to the similarity values from SAP (see above), we retrieved ‘ML similarity values’ from the perspective of each female from Generation 2 with regard to the males from her rearing aviary. Hence, we trained the sound classifier to distinguish the songs of the eight parental males (Generation 1) of a female’s rearing aviary from those of the other population type which the female would later encounter (e.g., W1 vs D1, 16 parental recordings each). The classifier was then tested with each of the songs of the (usually 40) males that the female would later encounter, to obtain values of their song similarity to the parents in her rearing aviary (‘ML song similarity to parents’). The similarity values from each of the two recordings of a male were averaged (repeatability: r = 0.88, n = 584 pairs of values from 146 males, each combined with four female rearing aviaries). Similarly, we trained the sound classifier using the respective peer males of Generation 2 (males with whom females grew up in their rearing aviary) in contrast to peers from the other population type, to obtain values of similarity of males to those peer members (‘ML song similarity to peers’, repeatability r = 0.91, n = 584).
To further validate the classification procedure, we ran a negative control by training on two sets of 25 songs (mean duration 16.4 s per recording) from a single population. Classification success was 49.5% in the testing phase, which is close to the 50% chance level. Note that validation after training indicated a 80% classification ability within the training set, indicating that the utility of a trained classifier should be judged by independent testing and not from the validation percentages. We recorded all birds in one of two identical sound-proof chambers (see above), which ensured that classification success during testing stemmed from properties of the recorded songs rather than from idiosyncratic background noises. For example, such background noises might differ when wild populations would be recorded in their respective natural habitats. To avoid this problem, both sound-proof chambers were used about equally often in each of the four populations within each of the two recorded generations. As the two generations were recorded about 1 year apart, confounding effects of background noise that would closely match the offspring to their population of foster parents (depending on whether they had been cross-fostered between or within populations) can be excluded. No pre-processing of sound files (e.g., noise-reduction) was carried out prior to analysis.
Data analysis
To investigate whether pair-bonding and heterosexual social associations depended on culture (population of rearing) or on genetic background (population of origin), we used two statistical approaches. First, for the data set of identified pairs, we tested whether the observed degree of mating assortment by either population of rearing or by population of origin differed from expectations under random mating (50:50), using an exact binomial test. We tested each replicate separately for each of the three generations.
Second, for the data set on heterosexual interactions (also including individuals that were defined as unpaired, see above), we constructed a social network, where nodes represented individuals and edges represented pair-bonding interactions between individuals. We did this separately for each aviary and for each breeding experiment (Generations 1–3). We then quantified the extent to which social interactions were clustered by culture by calculating the assortativity coefficient for each social network66. The assortativity coefficient is a network version of the Pearson’s correlation coefficient, where the value from −1 to 1 reflects the tendency for individuals with similar attributes (here: population of rearing) to be associated in the network (r = 1), randomly associated (r = 0), or disassociated (r = −1). We used permutation tests to assess whether the association by culture was significantly non-random44. To obtain a p-value, we randomly re-allocated the phenotype value (population of rearing) across the nodes in the network (10,000 times) and calculated the assortativity coefficient for each permutated network. The p-value then equals the proportion of assortativity coefficients that were larger than the observed coefficient.
For the ‘social experiment generation 2’, we derived a daily social network using the pair-wise distance data and compiled this into a dynamic network video across the 30 days to visualise the association pattern. We also calculated the corresponding assortativity coefficients by culture for each day. Further, we analysed these daily social networks across 30 days within and between sexes to reveal the temporal patterns of assortment by song or by population of origin (genetic background) of each sex. This was done to investigate the differences of social patterns between heterosexual relationships and same-sex relationships.
We tested whether the daily pair-wise distance (from the social experiment Generation 2) can be explained by cultural (song) similarity and by genetic (size) similarity between females and males that participated in this social experiment. We used generalised mixed-effect models67 with distance of each male-female combination as the response variable and with female identity (151 levels), male identity (151 levels), the combination of male and female identity (pair ID: 5752 levels), and the combination of the male’s and the female’s rearing aviaries (64 levels) as random effects. As fixed effects of interest, we fitted several categorical predictors that distinguish different types of male-female combinations (for details, see Supplementary Table 5) and several continuous predictors (measures of body size and song similarity, see above) that reflect individual-specific traits in a male-female combination.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Source: Ecology - nature.com
