The major histocompatibility complex (MHC) plays a key role in vertebrate immunity, and pathogen-mediated selection often favours certain allelic combinations. Assessing potential mates' MHC profiles may provide receivers with genetic benefits (identifying MHC-compatible mates and producing optimally diverse offspring) and/or material benefits (identifying optimally diverse mates capable of high parental investment). Oscine songbirds learn songs during early life, such that song repertoire content can reflect population of origin while song complexity can reflect early life condition. Thus birdsong may advertise the singer's genetic dissimilarity to others in the population (and, presumably, compatibility with potential mates), or individual genetic diversity (and thus condition-dependent material benefits). We tested whether song repertoire content and/or complexity signal MHC class IIβ dissimilarity and/or diversity in male song sparrows (Melospiza melodia). Pairwise dissimilarity in repertoire content did not predict MHC dissimilarity between males, suggesting that locally rare songs do not signal rare MHC profiles. Thus, geographical variation in song may not facilitate MHC-mediated inbreeding or outbreeding. Larger repertoires were associated with intermediate MHC diversity, suggesting intermediate rather than maximal MHC diversity is optimal. This could reflect trade-offs between resisting infection and autoimmune disorders. Song complexity may advertise optimal MHC diversity, a trait affecting disease resistance and capacity for parental care.
The subjects were 32 male song sparrows at a single breeding site (less than 1 km diameter, and not physically isolated from other suitable habitat) near Newboro, Ontario, Canada (44.633° N, 76.330° W). Between 13 April and 3 May 2015, we captured sparrows in seed-baited traps, collected blood for genetic analysis, applied individually unique colour band combinations for field identification, then released birds.
We recorded song onto Marantz Professional PMD 671 recorders using Telinga Twin Science Pro parabolic microphones. Recording 200 songs per individual, not necessarily consecutive, is sufficient in most cases to characterize complete repertoires in this population . To be conservative, we recorded 300 songs per individual and confirmed by accumulation curves that a plateau had occurred. We digitized recordings in Raven Pro 1.5 (Cornell Lab of Ornithology), inspected spectrograms to identify song types, and noted song repertoire size as the number of different song types each individual produced. We identified a total of 235 syllables (i.e. one or more traces on a spectrogram that always occurred together ) across all song types. As detailed elsewhere , we screened each individual's repertoire for each syllable, constructed a presence–absence syllable matrix and calculated pairwise Jaccard dissimilarity coefficients adjusted for differences in syllable repertoire size.
We used primers SospMHCint1f  and Int2r.1  to amplify MHC class II, exon 2 (β subunit). Details of polymerase chain reaction (PCR) and sequencing conditions are available in Dryad, and hereafter referred to as ‘electronic supplementary material’ . We sorted sequences into stacks of identical reads using a pipeline  and removed chimeras using UCHIME . As detailed elsewhere [20,25], we used a 1% threshold frequency to remove rare reads that could represent PCR or sequencing errors, and compared a subset of reads to complementary DNA (cDNA)-derived sequences to confirm transcription of at least some alleles.
We trimmed sequences to remove introns, translated them into amino acid sequences of 70–74 codons, and removed apparent pseudogenes based on premature termination codons. Based on a maximum-likelihood allele phylogeny with WAG substitution and five discrete gamma categories, we used the unweighted UniFrac algorithm in the R package GUniFrac  to calculate pairwise genetic distances between individuals. Alleles in the same clade are presumably similar functionally, so to be conservative in estimating genetic diversity, we clustered them into ‘superalleles’ based on well-defined clade membership . We scored each individual's MHC diversity as the number of different superalleles.