DOI: 10.5061/dryad.tqjq2bvwf
title: Data from: Multimodal mimicry of hosts in a radiation of parasitic finches

description: Brood parasites use the parental care of others to raise their young and
 sometimes employ mimicry to dupe their hosts. The brood-parasitic finches
 of the genus Vidua are a textbook example of the role of
 imprinting in sympatric speciation. Sympatric speciation is
 thought to occur in Vidua because their mating
 traits and host preferences are strongly influenced by their early host
 environment. However, this alone may not be sufficient to
 isolate parasite lineages, and divergent ecological
 adaptations may also be required to prevent hybridisation collapsing
 incipient species. Using pattern recognition software and classification
 models, we provide quantitative evidence
 that Vidua exhibit specialist mimicry of their
 grassfinch hosts, matching the patterns, colours and sounds of their
 respective host’s nestlings. We also provide qualitative evidence of
 mimicry in postural components of Vidua begging.
 Quantitative comparisons reveal small discrepancies between parasite and
 host phenotypes, with parasites sometimes exaggerating their host’s
 traits. Our results support the hypothesis that behavioural imprinting on
 hosts has not only enabled the origin of new Vidua species, but also set
 the stage for the evolution of host-specific, ecological adaptations.
description: Materials and methods Fieldwork
 During January–April 2013, 2014, 2015, 2016 and 2017, data were
 collected on nestling morphology, begging calls and postural movements
 over an area of about 40 km<sup>2</sup> on and around
 Musumanene and Semahwa Farms (centred on 16°47′S, 26°54′E) in the Choma
 District of southern Zambia. The habitat is a mixture of miombo woodland,
 grassland and agricultural fields. Visual
 mimicry <i>Photographing Vidua and grassfinch
 nestling mouths</i> Eggs were taken from nests
 in the wild and placed in a Brinsea Octagon 20 Advance EX Incubator at
 36.7°C and 60% humidity. Nestling mouths were photographed within a few
 hours of hatching in the incubator. The chick was held below a prism until
 the mouth naturally opened, and the mouth then pressed gently over the
 apex of the prism (PEF2525 equilateral prism, UV fused silica, 25 x 25 mm
 aperture, Knight Optical, Kent, UK). This allowed the angular interior
 surfaces of the chick’s mouth to be projected onto the prism face opposite
 this edge. A wooden block secured the prism and held a 40% Spectralon grey
 standard (Labsphere, Congleton, UK) in a consistent position. Photos were
 taken with a Micro-Nikkor 105 mm lens and a Nikon D7000 camera that had
 undergone a quartz conversion (Advanced Camera Services, Norfolk, UK) to
 allow sensitivity to both human-visible and UV wavelengths, by replacing
 the UV and infrared (IR) blocking filter with a quartz sheet. The camera
 was placed on a tripod and pointed vertically down onto the flat surface
 of the prism at approximately 50 cm distance. The chick was gently held
 between thumb and forefinger as it bit on the prism. For each individual
 nestling, two photos were taken, each with a different filter. UV
 photographs were taken with a Baader UV pass filter (transmitting 320–380
 nm). Human-visible photos were taken with a Baader UV-IR blocking filter
 (transmitting 420–680 nm). For each photograph the aperture was set to
 f13, and the shutter speed varied with exposure. A flash (Metz 76 MZ-5
 digital) was attached to the camera body via a lateral bracket and had
 been modified by removal of its UV blocking filter, such that it emitted
 both visible and UV light. The flash was set to under-expose by 3 stops
 for the “visible” images, and to over-expose by 3 stops for the “UV”
 image. ISO was set at 400 and images were taken in RAW (NEF) format. All
 images were taken indoors in a dark room to minimise ambient light. The
 setup is shown in Figure S1. Once the photographs had been taken, the
 chicks were returned to their nests. <i>Pattern
 mimicry</i> Measurements of overall similarity
 between mouth marking patterns of different species were carried out using
 <i>NaturePatternMatch</i> (NPM) (Stoddard et al. 2014). NPM is
 a computer vision program that uses the Scale Invariant Feature Transform
 (SIFT) algorithm to detect local features in images and gives each
 pairwise combination of images a similarity score (Lowe 1999, 2004). These
 features are thought to correspond to those used by birds in real object
 recognition tasks (Soto and Wasserman 2012) and have been shown to be
 important in pattern recognition and egg rejection decisions in another
 host species, the tawny-flanked prinia (<i>Prinia
 subflava</i>) (Stoddard et al. 2019). Each image was scaled to the
 same size, using the width of the prism as a reference, such that the edge
 of the prism was 1500 pixels long. This value was chosen because it
 approximates the smallest image in the dataset, and thus minimizes any
 information loss or artefacts caused by scaling up. Only the green channel
 was taken from each image, as this corresponds most closely with the
 spectral sensitivity of the double cones in bird vision, thought to be
 influential in the processing of pattern information (Cronin et al. 2014).
 The background and the edge of the prism were masked out and the images
 cropped to size. NPM calculates pairwise pattern differences between
 images. As a measure of host-parasite similarity, we calculated the mean
 distance between each <i>Vidua </i>species and each grassfinch
 species (raw distance). We additionally submitted these pairwise distances
 to classical multidimensional scaling, which embeds points in an
 n-dimensional space in which the Euclidean distances between the points
 are maintained. This allowed a centroid to be calculated for each species
 (the average of all positions of all samples from that species). We
 measured the distance between each <i>Vidua</i> species and
 each grassfinch species in this space (centroid distance). The qualitative
 results and conclusions were the same for both methods (Table S1). Sample
 sizes are summarised in Table S6. Comparison of upper
 palate spot size between parasites and hosts was carried out using the R
 package <i>patternize</i> (Van Belleghem et al. 2017), which
 quantifies variation in colour patterns from digital images. Analysis was
 carried out using R v3.4.4 (R Core Team 2018). Homologous regions of the
 mouth in each photograph were identified by placing five landmarks on
 reference points around the mouth, and the images were aligned to an
 arbitrarily chosen reference image. This allowed patterns to be compared
 among images even if there were slight differences in the distances
 between camera and chick and in the positioning of the chick within the
 image. To extract the black upper palate markings, thresholds were
 manually adjusted for red, green and blue colour channels for each image
 and their success at extracting black patterns assessed. Some manual
 adjustment of thresholds was needed between images to account for
 differences in lighting conditions and ensure that patterns were
 accurately extracted. Shaded regions that had been erroneously identified
 as pattern were manually removed from the selection.<i> </i>To
 compare spot size between hosts and parasites, the number of pixels in the
 standardised images that each of the upper palate spots contained was
 calculated for every individual. The spot size was then calculated
 relative to the overall size of the mouth. Comparisons were performed with
 Wilcoxon tests in R (R Core Team 2018). The sample sizes for the
 comparison of spot sizes were the same as for the analysis of pattern
 mimicry (see Table S6). <i>Colour
 mimicry</i> Raw pixel values from the red, green
 and blue channels for both the visual and the UV images were extracted
 from regions of interest (ROIs) in nestling mouth images using the
 Multispectral Image plugin in Image J (Schneider et al. 2012; Troscianko
 and Stevens 2015). Chosen ROIs were: 1) gape flanges, 2) outer upper
 palate (distal to medial palate spot), 3) inner upper palate (proximal to
 medial palate spot), 4) medial palate spot. ROIs 1, 2 and 3 were selected
 separately on right and left-hand sides of the chick’s mouth and a mean
 score of the two values was used. The medial palate spot lies along the
 bilateral line of symmetry for the chick’s mouth and so only a single ROI
 was required. Raw pixel values were converted into avian cone capture
 values based on the cut-throat finch (<i>Amadina fasciata</i>)
 visual system (Hart et al. 2000a) using Microsoft Excel version 15.30. The
 cut-throat finch is the most closely-related grassfinch species to the
 hosts of <i>Vidua</i> finches for which visual sensitivities
 have been calculated (Olsson and Alstrom 2020).
 Cone-capture values for each image were analysed with a
 discriminant function analysis (DFA) using the MASS package in R (Venables
 and Ripley 2002). A multinomial logistic regression (MLR) was also carried
 out on the same dataset. While both DFA and MLR can be used to address
 questions about categorisation, MLR has fewer restrictive assumptions than
 DFA. However, DFA is thought to be a better approach when sample sizes are
 small (Pohar et al. 2004). For DFA and MLR, the models were initially
 trained on cone capture values of the images from the 10 co-occurring
 grassfinch species we photographed at our study site. The results from
 both MLR and DFA were similar (Table S2) and so only the DFA results are
 reported in the main text. Sample sizes are summarised in Table S6. MLR
 was implemented using the <i>multinom </i>function from the R
 package <i>nnet</i> (Venables and Ripley 2002). DFA was
 implemented using the <i>lda</i> function from the R package
 MASS ((Venables and Ripley 2002). The observed versus expected percentages
 were compared using the binom.test function in R base
 <i>stats</i> package (R Development Core Team 2017).
 The DFA/MLR models were initially trained on cone-catch values of
 the estrildid data.  The training data consisted of 3 locust finch
 (<i>Paludipasser locustella</i>), 32 common waxbill, 10 blue
 waxbill (<i>Uraeginthus angolensis</i>), 7 green-winged
 pytilia (<i>Pytilia melba</i>), 5 orange-winged pytilia, 4
 red-billed firefinch (<i>Lagonosticta senegala</i>), 15
 Jameson’s firefinch, 5 zebra waxbill (<i>Amandava
 subflava</i>), 5 African quailfinch (<i>Ortygospiza
 atricollis</i>) and 9 bronze mannikin (<i>Spermestes
 cucullatus</i>) individuals (see Table S6). The models were then
 tested using the cone-capture values from the parasite species data. If
 the ROI colours of parasites match those of their host more closely than
 any other sympatric grassfinch, parasite data should be classified by the
 discriminant function as an instance of its specialist host species more
 frequently than would be expected if the parasite data were randomly
 assigned to any of the host species. These testing data were extracted
 from images from 17 pin-tailed whydah (<i>Vidua macroura</i>),
 5 purple indigobird (<i>V. purpurascens</i>) and 1
 broad-tailed paradise whydah (<i>V. obtusa</i>). The reason
 for the small sample size for broad-tailed paradise whydah is that it is
 an uncommon species whose host’s nest is difficult to find. To our
 knowledge our photographs and sound recordings are the first ever taken of
 this species’ nestlings in the wild. Imperfect colour
 mimicry of hosts by parasites was investigated by comparing the hues of
 corresponding mouth structures in parasites and hosts. As in the colour
 mimicry analysis, gape flange, upper palate (inner and outer) and medial
 palate spot colours were compared in hosts and parasites. To test for
 differences in hue in each host-parasite pair, multivariate analysis of
 variance (MANOVA) was carried out, using the <i>manova
 </i>function in R (R Core Team 2018), with the four cone catch
 values as the response and species identity as the explanatory variable.
 To compare luminance of these structures in each host-parasite pair, a
 t-test was carried out on the double cone channel values. The double cone
 channel (the sum of the medium and long wave cone catch values) is thought
 to be a good proxy for luminance vision in vertebrates (Pignatelli et al.
 2010; Cronin et al. 2014). Vocal mimicry
 <i>Recording nestling begging calls</i>
 Chicks were removed from their nest and placed in an artificial
 nest inside a box. The artificial nest consisted of a plastic bowl, used
 as a nest platform in aviculture, tightly lined with nesting material from
 abandoned grassfinch nests. Chicks were left in the artificial nest for a
 few minutes to allow acclimation. To stimulate begging, the chick was
 tapped gently with forceps on the bill. Recordings were made using an
 Audio-Technica ATR35s tie-clip microphone (or a Sennehiser ME-66 shotgun
 microphone for part of the 2014 field season) held by hand approximately 3
 cm away from the focal bird’s mouth. Vocalisations were recorded in WAV
 format on a Tascam DR-05 portable recorder. Recordings were made for
 around 2 minutes or until sufficient begging calls had been obtained (at
 least 10 seconds of continuous begging where possible). After recordings,
 the chicks were returned to their nests. Sonograms were produced and
 analysed using the default settings in Raven Pro 1.5 (Bioacoustic Research
 Program 2014). <i>Testing for mimicry in begging
 calls</i> Classification models were used to
 test the hypothesis that nestling <i>Vidua </i>mimic the
 begging calls of their hosts. To do this, 13 parameters were extracted
 from each call: frequency bandwidth, bandwidth 90% (the frequency range
 containing 90% of the total call energy), call duration, duration 90% (the
 period of time containing 90% of total call energy), peak frequency,
 centre frequency, minimum frequency, frequency 5% (the frequency above
 which 95% of the total call energy is contained), maximum frequency,
 frequency 95% (the frequency below which 95% of the total call energy is
 contained), total energy, aggregate entropy and average entropy. We used
 all these parameters to maximise the amount of information given to the
 model, and so allow it to characterise the host calls as well as possible.
 Many of these parameters have been used previously to characterise the
 vocalisations of birds, particularly to compare the begging calls of avian
 brood parasites and their hosts (Langmore et al. 2008; Anderson et al.
 2009; De Mársico et al. 2012). Calls were defined as the basic repeated
 unit within a bout of begging. For most species, this represented a single
 uninterrupted trace on the sonogram, except for common waxbill and
 pin-tailed whydah which give a two-note call (transcribed as “we-chee”)
 that is repeated rapidly. This call was described as these two units
 combined. Both a discriminant function analysis (DFA)
 and a multinomial logistic regression (MLR) model were then trained on
 begging call parameters from locally-occurring grassfinch nestlings (for
 explanation of the relative merits of DFA and MLR see “Colour mimicry”
 above). This created a function, built from the 13 parameters, which best
 separated the begging calls of each host species. The training data
 included calls from five common waxbill, one African quailfinch, four blue
 waxbill, two bronze mannikin, two Jameson’s firefinch, three green-winged
 pytilia, three orange-winged pytilia and two zebra waxbill individuals
 (see Table S6). To maximise the discriminatory ability of the DFA/MLR,
 individual call notes, rather than means for individuals, were used as
 input data points. This allowed the maximum quantity of data to be used in
 the creation of the classification function. It also means that the model
 was exposed to parameter values from actual calls rather than to abstract
 “mean calls”. Having constructed classification
 functions, we then used parasite calls as test data. We tested five
 pin-tailed whydah, two broad-tailed paradise whydah, and two purple
 indigobird individuals. Ten call notes from each parasite individual were
 entered into the MLR and DFA classification functions. To assess mimicry,
 we calculated the proportion of the ten input calls that were classified
 as belonging to the host species on which each parasitic species is
 specialised. Each parasite individual was given this “proportion correct”
 score. If the mean of these scores across individuals of a parasite
 species was significantly greater than that expected if parasites were
 randomly allocated to grassfinch species, it would suggest that parasites
 match the calls of their hosts better than the other sympatric grassfinch
 species. We quantified a “proportion correct” score for each individual
 parasitic chick. Sample sizes are summarised in Table S6.
 Begging call recordings were taken from chicks in mid to late
 development, the stage at which their begging calls become most
 crystallised and stereotyped. Chicks from several grassfinch species in
 our study gave various call types earlier in development but settled to
 consistent calls in mid to late development. Mid-development stage was
 characterised as being the point at which the primaries had erupted from
 their pins. This has been used as an indicator of developmental stage in
 other studies of brood parasite begging (Briskie et al. 1999; Ranjard et
 al. 2010). The nest composition at the time the chick was recorded varied
 from one to five host chicks. One species, the
 pin-tailed whydah, showed four call types throughout development (Jamie et
 al. submitted). However, one call is made only by nestlings in mid to late
 development: a distinctive, two note “we-chee” call, whereas the other
 three are made earlier in the nestling period. Common waxbill nestlings
 also make a two-note call in mid to late development (Jamie 2017a). To
 simplify the analysis, only two-note call types of pin-tailed whydahs and
 common waxbills were included in the analysis. Three of the five
 pin-tailed whydah chicks used in the analysis of begging call mimicry
 (individuals 3, 4 and 5 in Table S3) had been raised in the nest of a blue
 waxbill and not the natural common waxbill nest. These chicks had been
 transferred to blue waxbill nests as part of transfer experiments for
 another study (Jamie et al. submitted). If the calls of pin-tailed whydahs
 raised in a blue waxbill nest are still assigned as most similar to common
 waxbill calls by the model, this would suggest that the pin-tailed whydah
 begging call mimicry is largely innate and not dependent on interactions
 with its specific host. <i>Testing for
 imperfections in vocal mimicry</i> Differences
 in the structures of parasite and host begging calls were analysed using
 linear mixed models. We constructed models using the “lmer” function in
 the R package lme4 (Bates et al. 2015). As explanatory variables, species
 identity was a fixed factor and individual identity a random factor, thus
 avoiding pseudoreplication. To assess whether species identity had a
 significant effect on call structure, we compared the fit of a model which
 included species identity and individual identity as explanatory variables
 with that of a model which included only individual identity. To test for
 differences in the rate of calling between parasites and hosts, we counted
 the number of begging calls made over a 6s period of consistent begging.
 This was done at three points of consistent begging across each recording
 and the mean call rate taken for that individual. Call rates between
 pin-tailed whydah and common waxbill were compared using a Wilcoxon
 test. Postural mimicry Chicks were
 filmed on a Canon Powershot SX50 HS Digital Camera while audio recordings
 were being made of their begging calls, to record the chicks’ head
 movements during begging. Examples of begging displays of each species are
 included in the supplementary materials. Mimicry was
 quantified by showing human participants (n = 12) a series of silent,
 unlabelled videos of nestling grassfinch and <i>Vidua</i>
 chicks begging. There are currently no avian models of movement
 perception, and it is difficult to accurately extract quantitative data on
 chick movement given the inconsistent angle and distance between camera
 and bird. Therefore, we instead made use of humans, naïve to the
 hypothesis being tested, as natural movement and pattern
 recognisers. Participants were asked to categorise
 three aspects of movement during the begging display: 1) head rotation, 2)
 tongue movement, 3) wing movement. Head rotation could be classified as
 being in the pitch, roll or yaw axes, or absent. Tongue movement could be
 classified as extended, rapid buzzing, or absent. Wing movement could be
 classified as waving or absent. For each video, participants described the
 postural aspects of the begging display according to these characters. The
 videos were unlabelled so participants did not know what species they were
 being shown. The order of presentation of videos was randomised. Sample
 sizes of videos presented to participants are summarised in Table S6.
 Videos of the begging movements of each species are uploaded with the
 online supplementary material. We presented videos in a
 random sequence and asked participants to characterise the head, tongue
 and wing movements. This approach, rather than asking participants to
 match a video to a range of possible reference videos, was chosen to
 prevent participants from using morphological similarity between chicks
 (which would be apparent in the videos in addition to the movement) to
 help make the decision rather than focussing only on movement. By
 presenting them with videos in sequence and asking them to describe the
 footage, the descriptions of host and parasite movements could be compared
 without the confounding effect of morphological similarity. The modal
 description of each movement for each species by the participants is
 reported in Table S4.