DOI: 10.5061/dryad.mkkwh70zj
title: Loss of vocal culture and fitness costs in a critically endangered songbird

description: Cultures in humans and other species are maintained through interactions
 among conspecifics. Declines in population density could be exacerbated by
 culture loss, thereby linking culture to conservation. We combined
 historical recordings, citizen science and breeding data to assess the
 impact of severe population decline on song culture, song complexity and
 individual fitness in critically endangered regent honeyeaters
 (Anthochaera phrygia). Song production in remaining wild males varied
 dramatically, with 27 % singing songs that differed from the regional
 cultural norm. Occurring at low population density 12% of males completely
 failed to sing any species-specific songs and instead sang other species’
 songs. Atypical song production was associated with reduced individual
 fitness, as males singing atypical songs were less likely to pair and nest
 than males that sang the regional cultural norm. Songs of captive-bred
 birds differed from those of all wild birds. The complexity of regent
 honeyeater songs has also declined over recent decades. We therefore
 provide rare evidence that a severe decline in population density is
 associated with loss of vocal culture in a wild animal, with concomitant
 fitness costs for remaining individuals. Loss of culture may be a
 precursor to extinction in declining populations that learn selected
 behaviours from conspecifics, and therefore provide a useful conservation
 indicator.
description: <em>(a) Data
 collection</em> We used data from all regent
 honeyeater sightings throughout the species’ range from July 2015 to
 December 2019 to estimate the distribution and density of the remaining
 wild population. Regent honeyeaters can be sexed in the field based on a
 combination of their size, plumage traits, behaviour, vocal attributes and
 in the hand during marking with unique combinations of coloured leg
 bands<sup>25</sup>. The database consisted of confirmed public
 sightings reported to BirdLife Australia and data from a standardized
 national monitoring program based on 1367 sites throughout the breeding
 range<sup>25</sup>. We identified males individually through a
 combination of colour bands on the focal male or partner female (n = 93),
 nest location (n = 68), unique song attributes (n = 21) or a lack of other
 males nearby (n = 42)<sup>25</sup>. We recorded males’ songs
 using a Sennheiser ME62/K6 microphone on a Telinga parabola and a Marantz
 PMD661 digital handheld recorder. We recorded captive-bred birds either
 shortly after their release into the wild in 2017 or in captivity in
 August 2019 and obtained historical song recordings (date 1986 - 2012)
 from the Atlas of Living Australia and private sound collections. All
 individuals included in this study were at least 1 year old. See
 Supplementary Methods for further details of the captive breeding program
 and the historical song recordings. <em>(b) Song
 classification</em> Wild male regent honeyeaters
 typically produce three distinct vocalisations: a soft, ‘mewing’ call; an
 alarm call consisting of a squawk and/or monosyllabic squeak; and a highly
 distinctive song, consisting of sub-chatter building to a crescendo of a
 guttural warble  produced with characteristic head-bobbing (Supplementary
 files S1 & S2). The sightings database included
 228 wild males identified since standardized contemporary monitoring
 commenced in 2015. We noted the songs of 146 of these males and were able
 to obtain quality recordings, defined as a high signal to noise ratio and
 minimal background noises, of the songs of 47 of them. These males
 occurred in two different geographical regions, the Blue Mountains south
 of latitude -31.55 and the Northern Tablelands north of latitude -30.45
 (Figure 1A). We tested whether species-specific song types produced in
 these regions were significantly different using stepwise discriminant
 function analysis (DFA) of 15 song parameters (see Table S1 and data
 analysis section below). Eighteen of the 146 males, located throughout the
 contemporary range, failed to sing any species-specific songs and instead
 produced the song of a different bird species (Figures 1A and 2). We
 classified these birds as ‘interspecific singers,’ based either on visual
 similarities between spectrograms of the songs of interspecific singers
 and of the species whose songs they had learned (n = 8) or the memory of
 an experienced observer (n = 10). We also obtained song recordings of 12
 captive-reared males (three recorded one week post-release in 2017 and
 nine recorded in captivity in 2019) and historic recordings of 14 wild
 males that were recorded prior to 2012 in the Blue Mountains. We tested
 for differences in the songs of the males of these five categories (Blue
 Mountains, Northern Tablelands, interspecific, captive-reared and
 historic) using the same DFA procedure described above. A single observer
 with 6 years’ experience of monitoring regent honeyeaters (RC) obtained
 all but seven contemporary song recordings. <em>(c) Data
 analysis</em> For all data analysis, we used R
 v3.4.3<sup>30 </sup>unless otherwise stated. We tested for
 spatial autocorrelation in the song type of contemporary wild males with
 correlograms of Moran's I, using package <em>ncf</em>
 v1.2–5<sup>31</sup>. For acoustic analysis we first used
 Audacity v2.2.2<sup>32</sup> to isolate songs in sound files
 and reduce background noise. We imported the trimmed .wav files of
 sufficient quality (n = 73 including contemporary wild, historic wild and
 contemporary captive birds) into <em>warbleR
 </em>v1.1.22<sup>33</sup>. After restricting the
 frequency range to 0.5 – 5 kHz, we manually selected the start and end
 coordinates of each song<em>, </em>used <em>sig2noise
 </em>to increase the signal to noise ratio (type = 3) and
 <em>trackfreqs </em>to identify the spectral components of
 each spectrogram. We visually inspected spectrograms to ensure track
 frequencies selections were representative of the spectral components of
 each song and used function <em>specan</em> to quantify 20
 spectral attributes of each song (Table S1). We manually calculated a
 further three attributes based on visual and audial inspection of the
 recordings<sup>11</sup>: number of syllables, number of unique
 syllables and number of notes per syllable. We checked for pairwise
 correlation across all attributes using ‘<em>GGally</em>’
 v1.4.0<sup>34</sup>, but no attributes showed consistent
 strong correlation (R &gt; 0.5 or &lt; -0.5). We log-transformed
 modulation index, kurtosis, maximum dominant frequency and notes per
 syllable to fulfil normality assumptions. We used JMP version 15.0 to
 conduct a discriminant function analysis (DFA) of songs by song type. We
 only included significant acoustic attributes (n = 15) in the final model
 via a backwards stepwise selection procedure (Table S1), and assessed the
 fit of the model by calculating the proportion of songs assigned to the
 correct song type. To determine whether
 interspecific singers more frequently occurred at lower population density
 than species-specific singers, we calculated for each wild male the number
 of other males sighted in the same breeding season (June to January)
 within distance bands of &lt;1 and &lt;50 km. We considered these
 2 spatio-temporal categories of ecological relevance to song learning,
 given the regent honeyeater’s range size and capacity to undertake
 long-distance movements<sup>23</sup>. We used Mann-Whitney U
 tests to look for a difference in the number of conspecifics located
 within both spatio-temporal windows, with interspecific singer or not as
 the binomial response. To quantify differences in song
 complexity between song types, we took 14 of the acoustic attributes that
 represented attributes of song complexity (Table S1) and fitted a general
 linear model of each attribute by song type using <em>lme4
 </em>v1.1-21<sup>35</sup>. To assess the fitness costs of
 males’ songs in the remaining wild population, we used logistic regression
 models with a binomial error structure and logit link function in
 <em>lme4</em>. For fitness analyses, we included in the
 dataset males whose songs were either not recorded or were not recorded of
 sufficient quality for acoustic analysis, but could be assigned with high
 confidence to a song type in the field (n = 105) because they were clearly
 heard singing by an experienced observer (RC). We classified the songs of
 a further 63 males, whose songs we could not assign to a song type as
 ‘unknown’ because we did not hear or record these males singing at the
 time they were detected, and not because their songs were intermediate
 between song types. The first model tested the effect of song type on
 whether a male was paired to a female or not. The second model tested
 whether song type affected the probability of paired males reaching the
 egg stage of nesting. The third model tested whether song type affected
 the probability of nesting males successfully fledging young. We then
 re-ran each model, reclassifying each male’s song type binomially as
 ‘regional cultural norm’ or ‘non regional cultural norm.’ We defined
 regional cultural norm as the typical Blue Mountains song in the Blue
 Mountains, and the Northern Tablelands song in the Northern Tablelands. We
 considered all other classified songs in each breeding area as ‘non
 regional cultural norm.’ To confirm that any fitness costs
 of male song were associated with differences from the regional cultural
 norm and were not an artefact of song type classifications, we repeated
 the ‘paired’ and ‘nested’ logistic regression analyses, replacing the song
 type of each male located in the Blue Mountains for which we had a high
 quality recording (n = 34) with the Mahalanobis distance of each males’
 song from the multivariate mean of the entire Blue Mountains population.
 We calculated the Mahalanobis distance of each male’s song using
 <em>heplots v1.3-5</em><sup>36</sup>. Larger
 Mahalanobis distances represent greater song divergence from multivariate
 mean<sup>37</sup>. For this analysis, we defined the regional
 cultural norm as the multivariate mean, rather than the most common song
 type. The small sample of quality recordings from the Northern Tablelands
 (n = 7) precluded us repeating logistic regressions with Mahalanobis
 distances on the Northern Tablelands population. To assess song repeatability, we
 used a Mantel test in <em>ade4
 </em>v1.7-15<sup>38</sup> with 9999 permutations to
 compare the song similarity distance between repeat recordings of the same
 individuals to the average distance between all other males’ songs.
 Metadata detailing how we identified individuals and which individuals we
 included in each component of the statistical analysis is provided in the
 Supplementary Data. REFERENCES: 11. Paxton K L,
 Sebastián-González E, Hite J M, Crampton L H, Kuhn D, Hart P J. 2019 Loss
 of cultural song diversity and the convergence of songs in a declining
 Hawaiian forest bird community. <em>R. Soc. Open Sci.</em> 6,
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 Franklin D C, Menkhorst P W, Robinson J L. 1989 Ecology of the regent
 honeyeater <em>Xanthomyza phrygia</em>.
 <em>Emu</em> 89, 140-154. 25. Crates R A, Rayner L,
 Stojanovic D, Webb M H, Terauds A, Heinsohn R. 2019 Contemporary breeding
 biology of critically endangered regent honeyeaters: implications for
 conservation. <em>Ibis </em>161, 521-532. 31. Bjornstad O N. 2018 Package
 ‘ncf’ v1.2-5: spatial covariance functions. https://cran.r-
 project.org/web/packages/ncf/ncf.pdf. 32. Team Audacity. 2014 Audacity
 (r): Free audio editor and recorder [computer program]. 33. Araya-Salas M, Smith-Vidaurre
 G. 2017 warbleR: an r package to streamline analysis of animal acoustic
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 34. Schloerke B. 2018
 Package <em>GGally. </em>Extension to ‘<em>ggplot2.’
 </em>https://cran.r-project.org/web/packages/GGally/GGally.pdf. 35. Bates D. 2019 Package ‘lme4’ v1.1-21: Linear mixed-effects models using ‘Eigen’ and S4. https://cran.r-project.org/web/packages/lme4/lme4.pdf. 36. Fox J, Friendly M, Monette G. 2018 Package ‘heplots’: visualising hypothesis tests in multivariate linear models. https://cran.r-project.org/web/packages/heplots/heplots.pdf. 37. De Maesschalck R, Jouan-Rimbaud D, Massart D. 2000 The Mahalanobis distance. <em>Chemomet. Intel. Lab. Syst.</em> 50, 1-18. 38. Dray S, Dufour A-B, Thiolouse J. 2020 Package ade4: Analysis of Ecological Data: Exploratory and Euclidean Methods in Environmental Sciences. https://cran.r-project.org/web/packages/ade4/ade4.pdf.
description: The dataset contains X files: 
 "Recordings_summary.xlsx" = full details of each male
 regent honeyeater, where, when and how it was recorded, whether that male
 was observed and or recorded in multiple years, which analysis each male
 could be included in and which sound file(s) correspond to which male. We
 have withheld sensitive location data (lat,long) because (i) of the
 critically endangered population status of the species and (ii) it is not
 required to replicate the data analysis with the files available here.
 Specific location data is available from the corresponding author upon
 reasonable written request. "Recordings for
 manuscript analysis.zip"  = .wav regent honeyeater song sound files
 used in the manuscript. "Repeat
 recordings.zip" = .wav regent honeyeater song sound files, including
 repeat files of some males where available, for use in modelling song
 repeatability. "Regent honeyeater song
 script.R"  "paired1.csv" = for
 re-running GLM of pairing status of males by song type and cultural
 norm. "nested2.csv" = for re-running GLM of
 nesting status of paired males by song type and cultural norm.
 "fledged1.csv" = for re-running GLM of nest success of
 nesting males by song type and cultural norm.
 "complexity_conspecs_mahal.csv" = for calculating
 Mahalanobis distances for the Blue Mountains population.
 "mahala.paired.bmtn.csv" = for re-running logistic
 regression of pairing status of Blue Mountains males by Mahalanobis
 distance. "mahala.nested.bmtn.csv" = for
 re-running logistic regression of nesting status of paired Blue Mountains
 males by Mahalanobis distance.
 "conspecifics.csv" = for re-running Mann-Whitney U
 tests on conspecific density versus species-specific or interspecific
 songs in the wild population.
 "manualoc_output1.csv" = for specifying start and end
 timings of songs in sound files for analysis in warbleR.
 "regent_PCA_params3.csv" = for multivariate analysis
 of spectral components of regent honeyeater songs- checking
 cross-correlations, distributions, log-transformations etc.
 "complexitymetrics1.csv" = for GLMs of song complexity
 by song type. "manualoc_duplicates.csv" =
 for specifying start and end timings of repeat recordings for analysis in
 warbleR.