Rapid song divergence leads to discordance between genetic distance and phenotypic characters important in reproductive isolation

Abstract

The criteria for species delimitation in birds have long been debated, and several recent studies have proposed new methods for such delimitation. On one side, there is a large consensus of investigators who believe that the only evidence that can be used to delimit species is molecular phylogenetics, and with increasing numbers of markers to gain better support, whereas on the other, there are investigators adopting alternative approaches based largely on phenotypic differences, including in morphology and communication signals. Yet, these methods have little to say about rapid differentiation in specific traits shown to be important in reproductive isolation. Here, we examine variation in phenotypic (morphology, plumage, and song) and genotypic (mitochondrial and nuclear DNA) traits among populations of yellow‐rumped tinkerbird Pogoniulus bilineatus in East Africa. Strikingly, song divergence between the P. b. fischeri subspecies from Kenya and Zanzibar and P. b. bilineatus from Tanzania is discordant with genetic distance, having occurred over a short time frame, and playback experiments show that adjacent populations of P. b. bilineatus and P. b. fischeri do not recognize one another's songs. While such rapid divergence might suggest a founder effect following invasion of Zanzibar, molecular evidence suggests otherwise, with insular P. b. fischeri nested within mainland P. b. fischeri. Populations from the Eastern Arc Mountains are genetically more distant, yet share the same song with P. b. bilineatus from Coastal Tanzania and Southern Africa, suggesting they would interbreed. We believe investigators ought to examine potentially rapid divergence in traits important in species recognition and sexual selection when delimiting species, rather than relying entirely on arbitrary quantitative characters or molecular markers.

Rampant introgressive hybridization in Pogoniulus tinkerbirds (Piciformes: Lybiidae) despite millions of years of divergence

“…Studies on passerines have found instances of song mixing both facilitating hybridization (Qvarnstrom et al, 2006) and maintaining range boundaries between ecological competitors that hybridize (McEntee et al, 2016), but song has been shown to vary along ecological gradients in Africa (Slabbekoorn & Smith, 2002;Kirschel et al, 2009aKirschel et al, , 2011Smith et al, 2013), suggesting that there is selective pressure for songs to converge where populations of different species meet. In contrast, previous work on forest tinkerbirds found that songs diverge when related species coexist, reducing costly aggressive interactions (Kirschel et al, 2009b), and that songs can diverge rapidly between populations of the same species, by an order of magnitude faster than between the pair of species studied here (Nwankwo et al, 2018). Yet, a previous study on song in yellowfronted and red-fronted tinkerbirds found that their songs were more similar in sympatry in Eswatini than in allopatry (Monadjem et al, 1994), implying that song differences may be insufficient to maintain the species boundary and that song characters may introgress among the species.…”

Range-wide spatial mapping reveals convergent character displacement of bird song

“…Evidence for spatial convergence is consistent with taxonomic patterns of vocal variation, particularly given that songs of H. s. collinsi are more similar to sympatric H. p. peruviana than they are to their own allopatric conspecifics (H. s. subflava). These findings contrast with the dominant view that song variation largely reflects phylogeny [40,54,55], and suggest that interspecific interactions have driven convergent signal evolution in both our study species. The observed patterns of song variation are opposite to the predictions of standard character displacement theory [5,27], but consistent with theoretical models of convergent ACD mediated by social competition [13,14,19].…”

“…We processed these songs using the MatLab signal processing toolbox (Mathworks, Natick, MA, USA), automatically extracting a total of 22 spectral and temporal acoustic measures for analysis (electronic supplementary material, table S3; see electronic supplementary material for a full description of song analyses). In addition to analyses performed using all 22 acoustic measures, we followed previous studies of species with similar songs [9,22,40] by performing separate analyses on two of those measures, mean note peak frequency (hereafter, peak frequency) and overall song pace (hereafter, pace). The peak frequency was generated by calculating peak frequencies for each note of the song, and then taking the average across the entire song.…”

Range-wide spatial mapping reveals convergent character displacement of bird song

“…Evidence for spatial convergence is consistent with evidence of variation among taxa given that H. s. collinsi songs are more similar to those of H. p. peruviana in sympatry than they are to the songs of allopatric conspecifics ( H. s. subflava ). This finding contrasts with the dominant view that song variation largely reflects phylogeny (Price and Lanyon 2002, Farnsworth and Lovette 2008, Nwankwo et al 2018), and suggests that interspecific interactions have driven convergent signal evolution in some Hypocnemis antbird lineages.…”

“…We processed these songs using the MatLab signal processing toolbox (Mathworks, Natick, MA), automatically extracting a total of 22 spectral and temporal acoustic measures for analysis (table S3; see electronic supplementary material for a full description of song analyses). In addition to analyses performed using all 22 acoustic measures, we followed previous studies of species with similar songs (Kirschel et al 2009, Tobias et al 2014, Nwankwo et al 2018) by performing separate analyses on two of those measures, mean note peak frequency (hereafter, peak frequency) and overall song pace (hereafter, pace). Peak frequency was generated by calculating peak frequencies for each note of the song, and then taking the average across the entire song.…”