An examination of genomic and acoustic differentiation between Eastern, Lilian’s, and Western Meadowlarks (Sturnella magna, S. m. lilianae, and S. neglecta)
By Johanna Beam Ecology and Evolutionary Biology, University of Colorado Boulder
Defense Date: March 17th, 2020
Thesis Advisor Scott Taylor, Ecology and Evolutionary Biology
Defense Committee Scott Taylor, Ecology and Evolutionary Biology Pieter Johnson, Ecology and Evolutionary Biology Nathan Pieplow, Program for Writing and Rhetoric
Table of Contents
Abstract……………………………………………………………………………….…………..2
Introduction…………………………………………………..…………………………………..3
Song Methods…………………………………………………..………………………………...7
Genomic Methods…………………………………………………..……………………………9
Song Results….……….………………………………………..……………………………..…11
Genomic Results……………………………………………..………………………………….12
Discussion……………………………………………..………………………………………...13
Taxonomic Implications………………………………………………………………………..16
Conclusions……………………………………………………………………………………...17
Acknowledgements…………………………………………….……………………………….17
Literature Cited…………………………………………….…….…………………………….19
Figures…………………………………………………………………………………………...28
ABSTRACT
Understanding species boundaries is a fundamental, but challenging, component of describing and understanding the generation and maintenance of biodiversity. Examining differences among very recently diverged populations can provide insight into the traits and evolutionary mechanisms that drive divergence. The genus Sturnella includes two recently diverged species, the Eastern (Sturnella magna) and Western (S. neglecta) Meadowlark, the former of which has a complex of subspecies distributed across the Americas. Of the Eastern
Meadowlark subspecies that occur in the U.S., S. m. lilianae is the only one with a disjunct range in the southwestern U.S. and central Mexico. It also has markedly different song patterns than all other Eastern Meadowlark subspecies. In order to assess population differentiation, we performed whole genome sequencing of 35 birds as well as analysis of various song characteristics, including maximum and minimum frequencies. Results were visualized using principal component analyses and analyzed by running linear discriminant function analyses. S. m. lilianae exhibits high levels of genetic and vocal differentiation from both the Eastern
Meadowlark and the Western Meadowlark, and likely forms a distinct evolutionary lineage.
Additionally, the subspecies S. m. auropectoralis shows no genetic or acoustic differentiation from S. m. lilianae, suggesting that the subspecies falls within the lilianae group and not the magna group.
INTRODUCTION
Understanding species boundaries is a fundamental component of describing and understanding the generation and maintenance of biodiversity (Coyne and Orr 2018). From a conservation perspective, it is important to accurately characterize existing biodiversity to inform policy (Sites and Crandall 1997). While necessary, characterizing species boundaries and delineating species is inherently challenging (Harrison and Larson 2014, Balakrishnan 2005,
Sites and Crandall 1997). This comes, in part, from the fact that species complexes can exhibit varying levels of genetic and phenotypic divergence. Recently diverged species that are reproductively isolated may share allelic variants because little time has passed for the accumulation of genomic differences due to genetic drift or selection (Campagna et al. 2017).
Alternatively, interbreeding (i.e., hybridization) between divergent species may homogenize their genomes and make understanding their evolutionary history and status as species more complicated (Toews et al. 2016c). Examining differences, both genetic and phenotypic, among recently diverged populations can provide insight into the traits and evolutionary mechanisms that drive or maintain divergence between species.
Birds exhibit considerable variation in both color and acoustic traits across species.
Variation in color and song has variously been linked to species boundaries in birds (Toews and
Irwin 2008, Irwin et al. 2018), either their generation (e.g., Toews and Irwin 2008) or their maintenance (e.g., Seddon 2005). Bird song and plumage act as vectors of communication between individuals, conveying important information such as sex, species, and fitness
(Catchpole and Slater 1995, Stein and Uy 2006). Female mate choice based on song and plumage has important implications for the maintenance or erosion of species barriers: if a
female chooses a mate from the wrong species, the offspring produced may be unviable, infertile, or have lower fitness, all of which would lead to the maintenance of species boundaries
(Collins 2004, Lanyon 1979, Baker and Boylan 1999, Price and Bouvier 2002). Alternatively, if post zygotic isolation is weak, admixed offspring may survive and reproduce leading to gene flow between species and the erosion of species boundaries. In oscine passerines (passerines that learn their song), geographical variation in song is common and often coincides with genetic variation (Price 2008, Baker and Boylan 1999, Toews and Irwin 2008).
In Greenish Warblers (Phylloscopus trochiloides), for example, the subspecies occurring at higher latitudes sing more complex songs, are the most genetically distinct from one another, and rarely hybridize (Irwin 2000, Alcaide 2014). This genetic and acoustic variation may be indirectly caused by ecological differences between the diverging populations across a north- south gradient (Irwin 2000). Because songbirds learn songs not only from their parents, but also from their non-related conspecific neighbors within a population, the possibility for errors to arise is high (Price, 2008). This error, called cultural mutation, can facilitate population divergence in concert with geographical variation, differential selection, and drift when populations have broad geographic distributions or are small and isolated (Price 2008, Irwin et al. 2018).
Some songbirds show preferential conspecific song learning (i.e., what songs birds learn to sing), which counteracts cultural mutation and can facilitate the maintenance of species boundaries (Price 2008, Nelson 2000). This appears to be the case in both Swamp Sparrows
(Melospiza georgiana) and White-crowned Sparrows (Zonotrichia leucophrys), which do not hybridize readily in nature. In tests of song learning, both species preferentially learned conspecific or con-subspecific song rather than the songs of a closely related heterospecific
(Nelson 2000, Marler and Peters 1977). Preferential song learning was examined in social isolation from other conspecifics, suggesting a genetic component to song learning as well as the social component of learning from neighboring birds (Nelson 2000). Presumably, a failure to learn the correct song, failure to interpret the quality of a mate using song, or lack of geographical variation in song between populations could lead to gene flow and the erosion of species boundaries between closely related species. With increasing frequency, detailed analyses of song between closely related species are providing insight into species boundaries (Baker and
Logue 2003; Mason et al. 2014). The analysis of song variation in combination with high resolution genomic data can help clarify species boundaries between taxa whose taxonomy has historically controversial (Baker and Boylan 1999, Qvarnström et al. 2010).
The genus Sturnella includes two recently diverged species, the Eastern (Sturnella magna) and Western (Sturnella neglecta) meadowlark: S. magna includes a large complex of subspecies that range from South America to the midwestern United States. Of the subspecies of
Eastern meadowlark that occur in North America, Lilian’s Eastern meadowlark (Sturnella magna lilianae) and the Cuban Meadowlark (S. m. hippocrepis) are the only subspecies with breeding ranges that are geographically disjunct from the rest of the range of the Eastern Meadowlark
(Fink et al. 2020, Arnold 2020, eBird 2020). Lilian’s meadowlark occurs in the desert lowlands of SE Arizona, New Mexico, west Texas, and occasionally into Colorado as demonstrated by
Leukering & Pieplow (2009) and may represent a divergent population (Figure 1A). The subspecies lilianae is noted to have separate breeding grounds and different habitat preferences than the Western Meadowlark (S. neglecta), despite range overlap in Arizona and New Mexico
(Lanyon W.E. 1962) (Figure 1B). Most recently, Barker et al. (2008) examined two mitochondrial genes (cytochrome b, and ND2), as well as the sex-linked intron ACO1-I9, and
presented evidence that the subspecies lilianae, including S. m. auropectoralis, represent a distinct species given their deep genetic divergence from the other S. magna subspecies in the complex.
In meadowlarks, song is used both to defend territories and in mate selection (Ordal
1974). Lanyon (1957) observed that both species of meadowlark defend their territories equally against conspecific and heterospecific meadowlarks. Rohwer (1972, 1973) also found that there is divergence in song between Lilian’s and Western taxa in areas of sympatry as well as Eastern and Western taxa in areas of sympatry, signifying that song differentiation persists when meadowlark species co-occur. The call notes of Eastern and Western meadowlarks are likely innate (i.e., genetically inherited) are have distinctive features: Eastern meadowlarks have a high frequency “dzert” call note, and Western have a lower frequency “chupp” call note (Lanyon
1962, Rohwer 1972). The differences in call note structure and frequency are a reliable way to differentiate between Eastern and Western meadowlarks and Lilian’s and Western meadowlarks in areas of sympatry (Lanyon 1962).
In a captive experiment, Lanyon (1979) found that hybrid pairings between Eastern and
Western meadowlarks produced viable offspring. However, when these hybrids were further backcrossed, nearly all eggs produced were infertile (Lanyon, 1979). Because hybrid pairings are exceedingly rare in the wild, it appears likely that meadowlark song acts as a strong signal between species and facilitates the maintenance of reproductive isolation in the absence of strong plumage differences (Fig 1. Lanyon 1979, Price 2008). Lilian’s Meadowlark has markedly different vocalization patterns than all other eastern subspecies, including Eastern (nominate magna), argutula, and hoopesi (Lanyon 1962; Cassell 2003; Leukering and Pieplow 2009, Jaster
et al. 2012, Pieplow 2009), which may play a role in species recognition and mate selection, possibly limiting gene flow between Lilian’s, Eastern, and Western meadowlarks.
In a study of morphological characteristics, Rohwer (1972) found that, even in areas of sympatry, Western meadowlarks and S. m.lilianae form separate clusters using PCA analysis.
Lilian’s meadowlarks have paler plumage than Eastern meadowlarks (Leukering and Pieplow
2009), which falls in accordance with Gloger’s rule, which states that animals that occur in drier environments and higher latitudes have lighter colored plumage than lower latitudes and more humid environments (Delhey 2017). In comparison with Western meadowlarks, Lilian’s have consistently more white in the fifth and sixth rectrices (tail feathers), and lack any yellow in the region separating the throat from the cheek (i.e., malar region) (Lanyon 1962). Western meadowlarks also have slightly lighter plumage on the back and crown than Lilian’s, and when combined with rectrix and malar analysis, this measure can be used to reliably distinguish the groups.
Hereafter, Lilian’s refers to the group S. m. lilianae and S. m. auropectoralis, and Eastern refers to the group of S. m. magna, S. m. argutula, and S. m. hoopesi. When referencing a subspecific population, the subspecific epithet (e.g. auropectoralis) is used.
METHODS
Song Analysis
I obtained song recordings of Eastern, Western, and Lilian's Meadowlarks from the
Macaulay Library at the Cornell Lab of Ornithology and Xeno-Canto databases. I selected recordings based on location, quality, and confidence of bird identification. I discarded recordings that were rated less than three stars on Macaulay or as ‘C’ grade or below on Xeno-
Canto due to excess noise in the recording or faintness of song. I also discarded duplicate recordings (i.e., recordings taken minutes/hours apart at the same location) due to the high likelihood of pseudoreplication. Recordings taken from range edges of Lilian’s, Eastern, and/or
S. m. auropectoralis without subspecies metadata were also discarded due to the uncertain nature of the bird’s identity. Songs from vagrant (out of normal range) birds were included only if the identity of the bird was confirmed visually. In order to obtain an accurate representation of both the individual and geographic variation in song across each species’ range, recordings from diverse locations across the United States and Mexico were prioritized (Figure 1B and Table 1).
To supplement song recordings available online, I collected additional field recordings of
Lilian’s Meadowlark songs from their breeding range in Arizona and New Mexico during the summer of 2019. These filtering steps resulted in a total of 85 high-quality recordings (Eastern n=27, S. m. lilianae n=26, S. m. auropectoralis n=5, Western n=27).
I characterized song variation by measuring song length, minimum and peak frequencies, starting and ending frequencies, and median frequency using the bioacoustic software Raven Pro
1.6.1. I gathered longitude and latitude metadata for each recording to evaluate geographic variation within song type or any of the measured song characteristics. I also preliminarily gathered measurement the of frequency at peak amplitude but discarded this metric because the placement of peak amplitude varied greatly in the song due to external and confounding factors such as wind, car noise, other birds calling, and distance of the recorder and microphone from the bird itself. Frequency of peak amplitude was also found to have non-equal variance and non- significance.
I analyzed all song data in R version 3.6.1 (R Core Team, 2019). Total song variation was visualized using principal component analyses using the command “prcomp” from the package
“ggbiplot” v. 0.55 (Vu, 2011). In order to assess measured song characteristics and their ability to predict species, I ran both a generalized linear model (GLM) and a linear discriminant analysis
(LDA) using R packages MASS and stats (Venables and Ripley 2002, R Core Team 2019). I used PC axes to collapse all song variables into two axes. For the binomial GLM (equation 1), I used type (Lilian’s and Eastern) as the response variables and PC1 and PC2 as the predictor variables.
Equation 1: Type = � + �!��1 + �"��2
In order to evaluate if song characteristics could accurately and reliably predict species designations, I ran a linear discriminant function analysis. The LDA was trained using 70 of the total 85 songs and iterated 100 times in succession to create a distribution of prediction data. For each iteration of the LDA run, any bird that was being misidentified as a Lilian’s when it was an
Eastern (or vice versa) was then counted and compiled. These data were then sorted by proportion correct to incorrect to evaluate the accuracy of the LDA at correctly identifying species by song attribute.
Genomic Methods
I obtained 32 tissue samples for genomic analysis from museum specimens that covered multiple localities across the North American range of Eastern, Western, and Lillian’s meadowlarks. The requested 32 tissue samples included 10 Eastern Meadowlark (Sturnella magna, n=5; S. m. argutula, n=3; and S. m. hoopesi n=2), 13 Lilian’s Eastern Meadowlark (S. m. lilianae, n=10; and S. m. auropectoralis, n=3), 9 Western Meadowlark (S. neglecta)(Table 1).
I completed DNA extractions in the Taylor lab using a salt extraction protocol for whole genomic DNA. Samples were first lysed using a homogenizing solution consisting of 0.4 M
NaCl, 10 mM Tris–HCl pH 8.0, and 2 mM EDTA pH 8.0, Protenase K, and a 20% SDS solution.
Samples were then incubated at 56C overnight. 150ul of a 6M NaCl solution was then added and samples were centrifuged at 13300rpm for 30 minutes. The resulting supernatant was then decanted into a fresh tube and the waste discarded. 2ul of glycoblue dye was added to aid in the identification of the DNA pellet. 1000ul of ice cold 100% EtOH was added to the samples and samples were placed in the freezer for 15 minutes to incubate. After incubation, samples were centrifuged for 30min at 13300rpm. With the DNA in pellet form, the resulting supernatant waste was decanted and discarded. DNA was washed using a room temperature 70% EtOH solution and centrifuged at 13300rpm for 10 minutes. DNA pellets were resuspended in 100ul of
TE buffer consisting of 10 mM Tris, 1mM EDTA at pH 8-9, incubated at 37C for 15 minutes, and placed in a 4C fridge overnight to fully dissolve the pellet. Samples were then sent to the
University of Colorado Boulder Biofrontiers Facility for library preparation using a Nextera XT
DNA Library Preparation Kit following standard protocol, except using half reaction volumes.
All samples were sequenced on a single S4 flow cell of an Illumina NovaSeq (Illumina, San
Diego, CA). Sequences were then trimmed using TrimmomaticPE (Bolger et al., 2014) and aligned to a Brown-headed Cowbird (Molothrus ater) genome. Variants were called using
Samtools’ SNP calling pipeline with the ‘mpileup’ command (Li et al., 2009). Resulting vcf files were then filtered using VCFtools (Turner et al., 2011), filtering out insertions and deletions, keeping only bi-allelic sites, and creating a minimum mean depth of 2 and maximum mean depth of 14 per variant. I allowed for a minor allele frequency of 10% and no missing data and removed variants from all scaffolds shorter than 5000bp. Resulting variant call files were used
in all downstream analyses. I used principal component analyses to visualize total genomic variation using the R package SNPRelate 1.12.2 (Zheng et al. 2012). In order to calculate genetic diversity between populations, I calculated mean pairwise FST values using 25kb non- overlapping windows and constructed genome-wide plots of FST using the package “qqman” v0.1.6 (Turner, 2014) in R 3.6.1.
RESULTS
Song variation in meadowlarks
Principal component analysis strongly separates Lilian’s and Eastern meadowlark song along both axes of the PCA (PC1 = 61.0%, PC2 = 23.7%; Figure 2A), while Western meadowlarks differ from both Lilian’s and Eastern most strongly along PC1. PC2 explains starting frequency while PC1 explains the remaining measurements. Importantly, meadowlark species and subspecies cluster by named group, with Lilian’s as vocally distinct from Western as from Eastern. In addition, the Eastern Meadowlark subspecies S. m. auropectoralis included in my analyses group consistently with Lilian’s meadowlarks and do not exhibit distinct or unique vocalization patterning (Figure 2A).
The GLM showed that songs with higher PC1 and PC2 scores were strongly correlated with the Lilian’s group (P-values <0.001, SE = 0.0004155 & 0.0004150, z-values = 4.090 &
3.625). The LDA showed that Eastern songs are more likely to be misidentified as Lilian’s songs, but not vice versa. However, the rate at which the LDA was able to correctly assign a song to its species was 90.33% meaning that there was a high ability for the LDA to have successful assignment.
This is the first quantitative analysis of song that includes all three taxa. Lilian’s song nearly always starts with a high pitch and descends in a series of three to four whistles throughout the song. Another variant of the song starts with a single low-pitched note and continues into the same pattern as above. The ending frequency is almost always the lowest frequency in the song. Eastern songs generally have an upside down ‘v’ shape, with the maximum frequency occurring in the middle of the song versus the beginning of the song with
Lilian’s. Eastern songs start out with two consecutive descending notes, then jump to the maximum frequency and end on average at slightly lower frequency than they started. Western songs are much more complex. Anecdotally, Western are known to have a ‘gurgle’ towards the end of their song that sets them apart from their other meadowlark counterparts. Visualization of a spectrogram actually shows a phrase of complex and quickly dropping notes that make up this
‘gurgle’ that we hear (Figure. 2C) (Pieplow 2019).
Genomic Results
After filtering, my final dataset contained 484,816 single nucleotide polymorphisms
(SNPs). Principal component analysis of the genomic data shows strong clustering among the three taxonomic groups present (Figure 3A) with easily identifiable population structure present between Lilian’s and Eastern, as well as between Western and Eastern meadowlarks. Both PC axes explain nearly equal genomic variance, with PC1 explaining 13.91% and PC2 explaining
11.37% of all variance in the dataset. The species groups in this PCA do not correlate with current taxonomy regarding meadowlark distribution in the U.S. and Mexico. The mean pairwise weighted FST value between Lilian’s and Eastern was 0.13, 0.16 between Eastern and Western, and 0.14 between Western and Lilian’s: the genomic differentiation between named meadowlark
species is equivalent to differentiation between the Eastern Meadowlark and Lilian’s
Meadowlark, which is currently considered a subspecies of the Eastern Meadowlark (Figure 3A).
Although genome-wide average FST shows moderate differentiation between all three taxa, the windowed FST scans demonstrate that the degree of divergence varies considerably across the genome (Figures 3B-D).
DISCUSSION
Eastern Meadowlark genomes and songs are as divergent from Lilian’s Meadowlark as they are from Western Meadowlark. Based on high resolution genomic data and acoustic analysis, Eastern and Lilian’s Meadowlarks appear to form distinct evolutionary lineages.
Results from the LDA showed that differences in song traits (e.g., max and min frequencies, beginning and ending frequencies, and song length) are consistent between Eastern, Western, and Lilian’s meadowlarks and can be used to accurately distinguish between different species
(Figure 2B-D). The LDA was able to correctly classify both Lilian’s and Eastern songs ~91% of the time, providing evidence that these songs are distinct. Song differentiation between meadowlark populations may indicate that it is a trait important in reproductive isolation and population differentiation (Rohwer 1973) and may be an important trait in species recognition and mate choice.
Broadly, the results from this study agree with and expand upon previous research on meadowlarks (Barker et al. 2008, Lanyon 1962, Rohwer 1972). By using both whole genome and song analysis, we provide support for the hypothesis that Lilian’s Meadowlark has substantially diverged from both Eastern and Western meadowlarks. The approximately equal variation explained by each of the first two genomic principal component axes, as well as the
consistently high FST estimates, suggest that the amount of differentiation between Lilian’s
Meadowlark and the Eastern Meadowlark is equal to that between Eastern and Western meadowlarks, which are currently recognized as distinct species. My results show no signs of divergence between S. m. auropectoralis and Lilian’s Meadowlarks, congruent with Barker et al.’s (2008) findings using three genetic markers. The subspecies S. m. auropectoralis, which occurs in central Mexico, was previously shown to be very similar both genetically and morphologically to the current subspecies S. m. lilianae (Barker et al. 2008). Current taxonomy, however, recognizes both Lilian’s Meadowlark and S. m. auropectoralis as subspecies of Eastern
Meadowlark (Clements et al. 2019). Based on genetic data and morphology, Barker et al. (2008) proposed that S. m. auropectoralis be reclassified as a subspecies of Lilian’s Meadowlark, which they also proposed should be given species status. The results from both the song and genomic analyses agree with these results: S. m. auropectoralis group with Lilian’s Meadowlark both acoustically and from a whole genome variation perspective. This however disagrees with previous taxonomic groupings (see Saunders 1934 and Dickerman and Phillips 1970) that used only morphological characteristics to define subspecies.
The use of whole-genome and reduced representation DNA sequencing approaches to study genomic divergence and speciation is still relatively new, but both approaches have been used to resolve species boundaries in birds (Toews et al. 2016a), and the number of studies using high resolution genomic data to better understand species boundaries in birds has rapidly increased over the past decade (Taylor et al. 2014, Toews et al. 2016, Campagna et al. 2017,
Alcaide et al. 2014, Linck et al. 2019). The degree of genomic differentiation between Eastern,
Western, and Lilian’s meadowlarks is similar in magnitude to genomic differentiation between a number of closely related bird species including Black-capped and Carolina chickadees (FST =
0.1, Taylor et al 2014), Collared and Pied flycatchers (FST = 0.2, Nadachowska-Brzyska et al.
2013), and Audubon’s and Myrtle warblers compared to Goldman’s Warbler (FST = ~0.18 –
0.26, Toews et al. 2016b). In each case, there is no uncertainty that the species being compared are independent lineages.
Allopatry, or divergence in isolation, is the dominant form of speciation in avian systems
(Chesser and Zink 1994). Allopatric populations can diverge because of prezygotic and postzygotic isolation mechanisms that often work in tandem to form reproductive isolation between taxa (see Taylor et al. 2014, Alcaide et al. 2014, Sætre et al. 2003, Baker and Boylan
1999). Hybrid inviability and infertility is perhaps the most well-known example of postzygotic isolation and is present in many systems of closely related taxa, such as Black-capped and
Carolina chickadees, Lazuli and Indigo Buntings, and Collared and Pied Flycatchers (Taylor et al. 2014, Baker and Boylan 1999, Sætre et al. 2003). This post-mating reproductive isolation occurs last during the process of speciation as mates select against low hybrid fitness (Price
2008). Lilian’s meadowlarks form an allopatric population distinct from Eastern meadowlarks and few instances of hybridization exist between Eastern and Western meadowlarks, with any hybrids having markedly low infertility and inviability (Lanyon 1979) Although we do not have data on hybrid viability between Eastern and Lilian’s meadowlarks, similar measure of genetic divergence between Western, Eastern, and Lilian’s meadowlarks suggest there may be equally strong reproductive isolation between these taxa as well.
Our findings are directly applicable to other meadowlark populations, such as S. m. hippocrepis, as its allopatric population occurs solely on the island of Cuba and is distinct from the Eastern meadowlarks of the U.S. when using mitochondrial DNA (Barker et al. 2008). Other members of the family Icteridae with members across North and South America have speciated
rapidly in the Caribbean, suggesting the same might be true of S. m. hippocrepis in Cuba (Sturge et al. 2009, Powell et al. 2014). Future studies of S. m. hippocrepis using genetic and song analysis would aid in resolving this taxonomic uncertainty.
Taxonomic Implications
It appears highly likely that Lilian’s forms its own species group, separate from Eastern
Meadowlarks. The subspecies S. m. auropectoralis shows no differentiation from Lilian’s using genetic data or song data, implicating that they fall within the Lilian’s group and not the Eastern group as previously thought. Therefore, a new species, Sturnella lilianae, should be formed with two subspecies, S. l. lilianae and S. l. auropectoralis, based on differentiation of whole genome data and complete song analysis. Previous papers on meadowlark taxonomy in Mexico list several subspecies in southern Mexico: S. m. saundersi just south of the range of auropectoralis,
S. m. mexicana, which primarily resides in Veracruz, and S. m. griscomi, which appears isolated on the Yucatan peninsula (Dickerman and Phillips, 1970). However, the type specimens used to define all the subspecies mentioned above were collected during the nonbreeding season, thereby introducing large margins of error as meadowlarks in the area may be wintering birds and are undoubtedly in basic (nonbreeding) plumage. Dickerman and Phillips (1970), as well as
Saunders (1934), note that there is a gap between the ranges of S. m. auropectoralis and S. m. lilianae. These desert subspecies (S. m. lilianae and auropectoralis) appear to be geographically isolated from any of the Eastern subspecies by the Sierra Madres de Oaxaca in the south, and the
Sierra Madres Oriental in the east (eBird, 2020). Barker et al. (2008) included a specimen from
Veracruz and the Yucatán in their analysis and concluded that the southern Mexico populations group with Eastern and not Lilian’s. However, until more samples from the breeding season are
taken from Veracruz, Oaxaca, and the Yucatan peninsula, the status of these southern Mexico populations remains unknown.
Conclusions
In the case of North American meadowlarks, genomic and acoustic differentiation is moderate and equal between Eastern, Western and Lilian’s. Song is, perhaps, acting as a strong barrier to reproduction between these taxa and likely plays an important role in mate choice in this system. If hybridization is happening, the strong postzygotic isolation between Eastern and
Western meadowlarks documented by Lanyon (1979) likely also limit gene flow between
Lilian’s and Eastern meadowlarks given their similar levels of genetic variation. The dominant geographic mode of speciation in birds is allopatric speciation, wherein divergence occurs in the absence of gene flow (Chesser and Zink 1994). It seems likely that Eastern, Western, and
Lilian’s meadowlarks experienced a period of isolation at some point during their evolutionary history during which time they accumulated the differences I documented. Importantly, despite broad range overlap between Eastern, Western, and Lilian’s meadowlarks, there is no evidence of intermediate individuals in my dataset and the taxa possess distinct genomes. Furthermore, it appears that song in oscine passerines plays a heavy role in the speciation process and provides additional resolution to taxonomy when combined with genetic studies.
ACKNOWLEDGEMENTS
S. Taylor and E. Funk helped establish and assist with the project. N. Pieplow gave advice and provided additional vocalization recordings. M. K. Pants provided substantial support throughout the writing and editing process. Many thanks to the Denver Museum of Nature and
Science, the Bell Museum, the Royal Ontario Museum, the Field Museum of Natural History, and the Museum of Southwestern Biology for providing tissue samples. J. Beam was supported by a grant from the Undergraduate Research Opportunities Program at University of Colorado
Boulder as well as by the Taylor Lab.
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FIGURES
Figure 1: Sturnella. (A) The plumage differences of magna and Lilian’s meadowlarks. Note the paler mantle, brown versus black head coloration in Lilian’s, smaller black chest stripe in Lilian’s, lesser extent of yellow in the belly in Lilian’s, and thicker and darker side streaking in Eastern. (B) Distribution of Lilian’s, Eastern, and Western. Note the allopatric distribution of Lilian’s in the SW United States and Mexico. Range gaps between Eastern and Lilian’s in south Mexico are likely due to geographical barriers (Sierra Madre Oriental y Sierra Madre de Oaxaca). Points (interior contrasting color points in legend) are song sample locations.
Figure 2: Song classification. (A) Song PCA plot showing consistent separate clustering of Lilian’s, Western, and Eastern. The subspecies S. m. auropectoralis clusters within Lilian’s. (B) The song spectrogram of Eastern showing the maximum frequency occurring in the middle of the song. (C) The song spectrogram of Western. Note the lower overall frequency of the song as well as the quickly descending “gargle” that occurs just prior to the 19s mark. (D)
The song spectrogram of Lilian’s Meadowlark. Note the highest frequency occurring at the beginning of the song and the lowest frequency occurring at the end of the song.
Figure 3: Genomic differentiation between Eastern, Western, and Lilian’s meadowlarks. (A) Genetic PCA plot indicating strong population structure between Lilian’s, Eastern, and Western groups. The subspecies S. m. auropectoralis is shown in dark olive and clustering consistently with Lilian’s. The grouping of the lone Lilian’s in the Western group is of unknown cause as the bird represents a morphologically sound Lilian’s specimen skin.
Pairwise FST values are shown next to the lines between groups. (B-D) A non-overlapping window FST scan of pairwise FST between Lilian’s and Eastern, Western and Eastern, and Lilian’s and Western, showing strong differences between the two taxa as represented by high levels of background variation and frequent SNP peaks reaching towards 1.0.
Table 1: Songs used in the study with their corresponding taxonomy, database identifier, locality, and latitude and longitude. Identifier acronyms are located below the table.
Identifier Taxon Locality Latitude Longitude
XC130571 Sturnella magna argutula Okeechobee, FL 27.5597 -81.0127
XC139962 Osceola, FL 27.9639 -81.0339
XC163993 Brevard, FL 28.229 -80.813
XC311866 Jefferson Davis, LA 30.29 -92.898
XC360824 Okeechobee, FL 27.5597 -81.0127
XC403845 Dare, NC 35.8195 -75.5628
XC416546 Chambers, TX 29.592 -94.5562
XC468589 Brevard, FL 27.8256 -80.708
NP0311 S. m. auropectoralis Jalisco, MX 20.305728 -103.18065
NP0342 Jalisco, MX 20.305728 -103.18065
XC230117 Mexico, MX 19.166666 -99.333333
XC379528 Mexico, MX 19.314 -99.5098
XC230110 S. m. hoopesi Tamaulipas, MX 22.26666667 -97.916667
XC371791 Nuevo Leon, MX 25.7581 -98.7098
XC453031 Brownsville, TX 26.0241 -97.4098
XC320443 S. m. magna Washington, MN 44.8591 -92.7919
XC320477 Washington, MN 44.8591 -92.7919
XC320486 Washington, MN 44.8591 -92.7919
XC13800 Loveland, CO 40.37554 -105.11498
XC368636 Denver, CO 39.8224 -104.8577
XC370243 Hoisington, KS 38.4716 -98.6727
XC370382 Cottonwood Falls, KS 38.4314 -96.5608
XC370493 Manhattan, KS 39.107 -96.6085
XC144089 Blount, TN 35.5984 -83.8098
XC320443 Washington, MN 44.8591 -92.7919
XC320477 Washington, MN 44.8591 -92.7919
XC320486 Washington, MN 44.8591 -92.7919
XC465730 Burlington, IA 40.8918 -91.0756
XC466335 Burlington, IA 40.8918 -91.0756
XC179181 Union, GA 34.7947 -83.8823
XC4899163 Norfolk, CO 40.8922 -104.9243
XC52428 Knox, TN 35.954 -83.849
JB1017 S. m. lilianae Portal, AZ 31.864799 -109.04908
JB1018 Portal, AZ 31.864799 -109.04908
JB1022 Portal, AZ 31.864799 -109.04908
JB1023 Portal, AZ 31.864799 -109.04908
ML146804 Kent, TX 30.9767 -104.18
ML172451 Portal, AZ 31.8935 -109.1669
ML56852 Sonoita, AZ 31.760913 -110.84533
NP1330 Willcox, AZ 32.139231 -109.84669
NP1331 Willcox, AZ 32.139231 -109.84669
NP1334 Cochise, AZ 32.064644 -109.76262
NP1335 Cochise, AZ 32.064644 -109.76262
NP1513 Willcox, AZ 32.139231 -109.84669
NP1518 Willcox, AZ 32.139231 -109.84669
XC163988 Elgin, AZ 31.6929 -110.5204
XC163989 Harshaw, AZ 31.4554 -110.6481
XC163997 Fort Davis, TX 30.6271 -106.8681
XC230111 McNeal, AZ 31.5668 -109.7217
XC321339 Fort Sumner, NM 34.3234 -104.1584
XC321834 Rodeo, NM 31.864 -109.047
XC321838 Todeo, NM 31.864 -109.047
XC375065 San Antonito, NM 33.8062 -106.8787
XC415789 Alpine, TX 30.323 -103.7428
XC451433 Sonorita, AZ 31.7141 -110.6055
XC490755 Sinaloa, MX 25.7324 -109.0271
XC492292 Empire Ranch, AZ 31.7721 -110.6565
XC234211 Sturnella neglecta Erie, CO 40.0215 -105.0941
XC254455 Custer, SD 43.6651 -103.3862
XC306749 Tunica, MS 34.6562 -90.2743
XC337363 Ciudad Madera, MX 29.1834 -108.1226
XC357087 Morganza, LA 30.7795 -91.6116
XC361852 Cedaredge, CO 38.8351 -107.9574
XC362531 Mesa Co., CO 39.309 -108.998
XC368591 Briggsdale, CO 40.6595 -104.3516
XC368644 Hale, CO 39.6428 -102.1585
XC370746 San Antonio, NM 33.9053 -106.8681
XC371788 Nuevo Leon, MX 25.0202 -100.5896
XC398813 Jahuey, Coahuila, MX 25.1137 -101.093
XC407866 Sante Fe, NM 35.5914 -106.0253
XC416307 Kicking Horse, MT 47.3815 -114.0322
XC420263 Oceola, NV 39.1778 -114.389
XC442212 Canby Cross, CA 41.6927 -121.548
XC453337 Ramona, CA 33.0461 -116.9362
XC453905 Dell City, TX 31.9387 -105.2013
XC465605 Albuquerque, NM 34.9808 -106.677
XC466361 Burlington, IA 40.9006 -91.0928
XC467104 Davis, CA 38.5441 -121.8544
XC473804 Boise City, OK 36.72 -102.5354
XC477781 Dyke, NV 41.6372 -118.6945
XC482210 Henry, NV 41.7539 -115.1074
XC482953 Henry, NV 41.7797 -115.1471
XC500166 St. Marie, MT 48.4182 -106.4554
XC509569 Ramona, CA 33.0461 -116.9362
NP = Nathan Pieplow’s personal recordings JB = Johanna Beam’s personal recordings XC = Songs sourced from Xeno-Canto ML = Songs sourced from Macaulay Library
Table 2: Genetic samples used in the study with their corresponding taxonomy, museum accession number, and county or state of collection. Museum acronyms are labeled below the table. Taxon Locality Museum Accession Number
Sturnella neglecta Montezuma County, CO DMNS43126
Kit Carson County, CO DMNS34868
Denver County, CO DMNS45298
Riverside County, CA FMNH330040
Brewster County, TX ROM152591
Bernalillo County, NM MSB22754
Jefferson County, OR MMNH43739
Jefferson County, OR MMNH43737
Liberty County, MT MMNH43736
S. magna magna Cook County, IL FMNH: 477600
Vermilion County, IL FMNH: 477723
Brown County, WI FMNH: 494969
Ramsey County, MN MMNH: 46009
Cottle County, TX ROM: 155012
S. m. hoopesi Tom Green County, TX ROM: 154994
Tom Green County, TX ROM: 154993
S. m. argutula Collier County, FL FMNH: 393588
Highlands County, FL FMNH: 432674
Highlands County, FL FMNH: 393590
S. m. lilianae Sonora State, Mexico FMNH: 393903
Brewster County, TX MSB: 24469 NK: 130542
Santa Cruz County, TX MSB: 23221 NK: 103437
Cochise County, AZ MSB: 19902 NK: 8864
Lincoln County, NM MSB: 21253 NK: 37156
Lea County, NM MSB: 21246 NK: 37149
Roosevelt County, NM MSB: 45880 NK: 276674
Bernalillo County, NM MSB: 28704 NK:169990
Jeff Davis County, TX ROM: 152733
Jeff Davis County, TX ROM: 152734
S. m. auropectoralis Nayarit State, Mexico ROM: 157835
Nayarit State, Mexico ROM: 157836
Nayarit State, Mexico ROM: 157837 DMNS = Denver Museum of Nature & Science, Denver, CO FMNH = Field Museum of Natural History, Chicago, IL MMNH = Bell Museum of Natural History, St. Paul, MN MSB = Museum of Southwestern Biology, Albuquerque, NM ROM = Royal Ontario Museum, Toronto, ON, Canada