Evidence for an association between genetic and acoustic variation in the mimic poison frog Ranitomeya imitator

doi:10.21203/rs.3.rs-6607206/v1

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Evidence for an association between genetic and acoustic variation in the mimic poison frog Ranitomeya imitator

https://doi.org/10.21203/rs.3.rs-6607206/v1

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published 21 Nov, 2025

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Acoustic signals are utilized in a variety of contexts such as mating and agonistic interactions. The matched filter hypothesis proposes that hearing sensitivity and acoustic signals should be correlated in order to increase the efficiency of communication. However, the genetic variants and perceptual differences underlying the diversification in acoustic signals are unknown in many taxa. Linking genetic variants to measures of acoustic signal divergence can allow us to understand the role of perception on ecological divergence in call traits between populations. The mimic poison frog (Ranitomeya imitator) exhibits acoustic signal phenotypes that vary geographically and coincide with geographic clines of color variation. The advertisement call of this species varies between color morphs with respect to dominant frequency, pulse rate, and note length. Here we aim to determine whether genetic variants in two genes related to hearing are associated with acoustic variation in male advertisement calls. We tested for an association between these call traits and SNPs located within two candidate genes identified by previous work, Synaptojanin 1 (synj1) and Apoptosis Inducing Factor Mitochondria Associated 1 (aifm1). We identified significant call trait variation between populations in dominant frequency, pulse rate, and note length. We also identified a strong association between dominant frequency and a single, exonic aifm1 SNP that consists of a synonymous mutation coding for proline. Our results suggest that R. imitator call trait variation may be driven by both perceptual and genetic mechanisms, highlighting the importance of investigating the role of perception in trait evolution.

evolution

amphibians

Dendrobatidae

bioacoustics

aifm1

perception

anurans

Acoustic signals are important for mediating interactions between individuals and are utilized for species recognition, mate attraction, and territorial defense. Signal perception can strongly impact how individuals respond to these signals, thus shaping interactions. The genetics of acoustic signal perception has been most broadly studied in mammalian systems in the laboratory. These studies lack an emphasis on the ecological context of acoustic signal divergence, thus limiting our understanding of the role of perceptual genetics in early signal divergence. In this study, we focus on two genes implicated in the maintenance and development of mammalian hearing to determine whether they may play a role in acoustic signal divergence of poison frogs. We identified a strong association between signal dominant frequency and a SNP in the gene Apoptosis Inducing Factor Mitochondria Associated 1 (aifm1), highlighting the potential importance of signal perception in signal divergence.

Communication signals are found across taxa in various modalities (Moulton 1968; Gerhardt and Huber 2002; Schaefer 2010) and used in a variety of contexts including species recognition, mate attraction, and territorial defense (Gerhardt 1992; Seyfarth and Cheney 2003; Kaplan 2014; Rubio et al. 2024a). Due to their key roles in mate attraction and species recognition, acoustic signals are often under strong selective pressure, exhibit rapid evolutionary change, and can be used to predict patterns of species diversification (Jones and Others 1997; Seddon et al. 2008; Wilkins, M. R., Seddon, N., & Safran, R. J. 2013). Acoustic signals and any associated preferences are influenced by several sources of selection, such as ecology, morphology, genetics, and sensory perception of the species ultimately leading to signal diversification (Ryan 1986; Ryan et al. 1992; Hoy 1992; Derryberry 2009; Xu and Shaw 2021).

The perception of acoustic signals, or hearing, is associated with sensitivity to signal properties and the ability to discern various acoustic signals from background environmental noise. In the context of intraspecific communication, the evolution of sensory systems are generally expected to be accompanied by the evolution of communication signals, allowing for a potential match between acoustic signals and a receiver’s perception that is predicted to increase signal efficiency in noisy environments (Capranica and Moffat 1983; Boake 1991). The matched filter hypothesis suggests that the sensitivity of signal receivers to acoustic signals will evolve to be finely tuned to match the spectral distribution of the sender’s signal and maximize the signal-to-noise ratio (Capranica and Moffat 1983; Yang et al. 2019b; Cobo-Cuan and Narins 2020). This implies that preferences for spectral properties, such as dominant frequency, may be modulated by individual differences in perception. This hypothesis has been well supported in anurans, where female inner ear sensitivity is often tuned to the spectral characteristics of male advertisement calls, helping to distinguish conspecific calls from background noise (Narins et al. 2004; Feng et al. 2006; Moreno-Gómez et al. 2013; Zhao et al. 2017) and heterospecific calls (Luther 2009; Siegert et al. 2013; Goutte et al. 2017; Yang et al. 2019b). Additionally, sex differences in hearing sensitivity have also been identified, suggesting that male inner ear sensitivity may also be tuned to the conspecific calls to aid in the establishment of territory (Narins and Capranica 1976, 1980; Shen et al. 2011; Zhu et al. 2017).

There are exceptions to the matched filter hypothesis, meaning hearing sensitivity is not tuned to match the acoustic signals of that species (Zhao et al. 2017; Hoke et al. 2022). For example, perception and signaling can evolve separately under different selective regimes, such as the perceptual system evolving under high background noise and causing preferences for higher frequencies to avoid masking (Zhao et al. 2017). In addition, behavioral characteristics, such as the territoriality of a species, can lead to a mismatch between signal and perception. In one example of this, divergent lineages of Phyllobates possess a mismatch between signals and perception that allows for the flexibility to recognize both conspecific and heterospecific signals that may belong to potential competitors (González-Santoro et al. 2023).

Genetic mechanisms underlying the development and maintenance of hearing have been explored in mammals (Bowl et al. 2017; Marcovich and Holt 2020; Trigila et al. 2021), birds (Lomax et al. 2001; Sadanandan et al. 2023), insects (Wiley et al. 2012; Warren and Eberl 2024), and anurans (Serrano et al. 2001; Mangiamele and Burmeister 2008). However, few studies had addressed these mechanisms in relation to ecological divergence in acoustic signaling. Recently, Linderoth et al. (2023) identified significant divergence in poison frogs in two putatively hearing-related genes: Synaptojanin1 (synj1) and Apoptosis Inducing Factor Mitochondria Associated 1 (aifm1). In other taxa, mutations in synj1 are associated with inner hair cell dysfunction and the highly similar gene Synaptojanin2 is associated with hearing loss (McDermott et al. 2007; Trapani et al. 2009; Uthaiah and Hudspeth 2010; Manji et al. 2011; Martelletti et al. 2020). With respect to the aifm1 gene, mutations are associated with auditory neuropathy as well as hearing loss and deafness in humans (Rinaldi et al. 2012; Zong et al. 2015; Elrharchi et al. 2020; Kawarai et al. 2020; Wang et al. 2020). Experimental knock-ins of an aifm1 mutation associated with auditory neuropathy show progressive hearing loss and abnormal hair cell mitochondria morphology (Shi et al. 2024). While the functions of these genes have been characterized in mammals, their roles in anuran hearing, particularly in an ecological context, have not been investigated.

Neotropical poison frogs (family Dendrobatidae) are an interesting system for studying divergence in acoustic signaling and perception. Call diversification in poison frogs is thought to have increased due to aposematism reducing predation, which in turn allowed acoustic signals to become exaggerated through sexual selection (Santos et al. 2014). In frogs, calls are essential for species recognition, and all measured call traits show a strong phylogenetic signal within poison frogs, suggesting that they have contributed significantly to the diversification within this group (Erdtmann and Amézquita 2009). It has been suggested that advertisement calls are used over long distances for mate attraction until visual signals are able to be used over short distances, at which point multimodal signaling will occur in both mating and agonistic interactions (Ursprung et al. 2009; Dreher and Pröhl 2014; Mayer et al. 2014). For instance, the strawberry poison frog (Oophaga pumilio) mates assortatively by color in the wild (Summers et al. 1999; Gade et al. 2016; Yang et al. 2019a). However, when presented with both local and non-local male advertisement calls, female O. pumilio prefer local male calls, regardless of the color model presented (Dreher and Pröhl 2014). This suggests that acoustic signals in this species are of primary importance for mediating mating interactions when multimodal signals are present (Dreher and Pröhl 2014).

The mimic poison frog, Ranitomeya imitator, has undergone a Müllerian mimetic radiation resulting in four distinct color morphs (striped, spotted, banded, Varadero red-headed) with three known transition zones between them (striped-banded, striped-spotted, and striped-varadero) in northern Peru (Fig. 1). Incipient speciation is hypothesized in R. imitator based on divergence in color morphs within the striped-varadero transition zone (Twomey et al. 2014, 2016). There is limited evidence of assortative mating by color morph, specifically within the striped-varadero transition zone as well as the allopatric banded populations (Twomey et al. 2014, 2016). A recent study found that male aggression is not associated with the visual phenotypes of color morph and body size (Bieri et al. 2024). This suggests other modalities, such as acoustic signals, may be important for mediating interactions in R. imitator. Ranitomeya imitator acoustic signals show significant variation with respect to color morph in three call traits: note length, pulse rate, and dominant frequency (Twomey et al. 2015), though the drivers and function underlying this variation are unknown. A significant amount of work has been put into unraveling the genetic mechanisms and function of visual signaling in R. imitator (Twomey et al. 2020; Linderoth et al. 2023; Rubio et al. 2024c, b; Stuckert et al. 2024), however, no studies have attempted to investigate the genetic mechanisms associated with acoustic signaling and hearing perception in this system.

Linderoth et al. (2023) found that the hearing-related genes synj1 and aifm1 show significant divergence between the striped and banded morphs of R. imitator. The matched filter hypothesis implies that hearing and perception—which are affected by genes such as synj1 and aifm1—may play a role in the species recognition and signal divergence of R. imitator. A mismatched relationship between acoustic signals and perception may arise because signal and perception may diverge at different rates depending on the strength of natural and sexual selection (Gerhardt 1994; Wilczynski et al. 2001). In this study we investigated whether R. imitator call traits vary across their range and with respect to color morph. Further, we investigated the genotypic differences between populations and whether these differences are associated with call traits. We predicted that allelic variants of synj1 and aifm1 would be associated with call traits if they are playing a role in the acoustic signal perception of R. imitator.

Field collection

We collected acoustic and genetic samples from 64 Ranitomeya imitator individuals from 10 different populations in Peru (Fig. 1); Achinamisa (Hullaga striped color morph, n = 7), Callanayacu (Intermediate color morph, n = 7), Chazuta (Intermediate color morph, n = 7), Varadero north (Varadero red-headed color morph, n = 7), Varadero south (Varadero striped color morph, n = 7), Ricardo Palma (Intermediate color morph, n = 7), San José (Spotted color morph, n = 6), Chipesa (Intermediate color morph, n = 7), Vaquero (Banded color morph, n = 7), and Santa Rosa (Banded color morph, n = 2). We recorded male advertisement calls using a Zoom H1n portable recorder with a Sennheiser ME66-K6 shotgun condenser microphone. After we recorded advertisement calls, we captured males and recorded their snout-vent length (SVL) and mass. In addition, we took toe clips from each male for genotyping and preserved them in 95% ethanol. We then released the males in the location they were found. Finally, we recorded temperature, elevation, and GPS coordinates at each study site on the day of collection.

Advertisement Call Analysis

From the call recordings of males, we analyzed 3 call traits (dominant frequency, note length, and pulse rate) using RavenPro 1.6.4 (2010). These traits are often used in studies of Dendrobatids (Brown et al., 2011). We measured note length by taking the duration (seconds) of each individual note in the call, pulse rate by counting the number of pulses per second (number of pulses/note length), and dominant frequency by using the max frequency function in RavenPro which determines the frequency where the maximum power occurs within a note in kHz. In recordings with background noise interfering with quantitative call traits, we selected the call portions to be analyzed and used the band filter in RavenPro to filter 1 kHz above and below the dominant frequency of each call. If we were able to accurately quantify each of the call traits without filtering, we did so without filtering.

Research suggests temperature and body size can influence both temporal and spectral traits (Ziegler et al. 2016). To account for this, we averaged the call traits for each individual over the entirety of the recorded call and determined whether they were significantly correlated with temperature. In other dendrobatid species, mass and SVL are highly correlated (e.g., r = 0.54; (Crothers and Cummings 2015). Therefore, SVL was used as a measure of body size due to the low accuracy of our scales when measuring mass in the field. All statistical analyses on call traits were completed with R version 4.2.1 (R Core Team 2022). To determine if our call traits were affected by temperature, we ran a Pearson correlation test (Hollander et al. 2013) with each call trait using the cor.test function in R. We found that all three call traits were correlated with temperature (dominant frequency: df = 62, P < 0.01, r = -0.44; pulse rate: df = 62, P < 0.01, r = 0.64 ; note length: df = 62, P < 0.01, r = -0.40). Thus, following the procedure outlined in Fitzpatrick and Gray (2001), we ran a linear regression model to obtain a slope that was used to correct the call traits to a standard temperature of 25\(\:℃\) using the equation Y = SLOPE + TRAIT * (25 - TEMPERATURE). To determine if further correction was necessary, we tested the new call trait values for a correlation with SVL. We then tested for a correlation between each temperature-corrected call trait and SVL and found that our call traits were no longer correlated with SVL (dominant frequency: df = 62, P = 0.78, r = -0.04; pulse rate: df = 62, P = 0.55, r = 0.08 ; note length: df = 62, P = 0.08, r = -0.22). Therefore, we did not subsequently correct for SVL. We followed this correction procedure in order to avoid over-correcting the call traits and inflating any observed relationships. We used these corrected call traits in the following statistical analyses.

To determine whether our call traits were normally distributed in each color morph, we conducted a Shapiro-Wilk’s Test for normality (Royston 1982) using the shapiro.test function. We also conducted a Levene’s test for equal variances (Fox 2015) using the leveneTest function. We found that the pulse rate values were normally distributed for all color morphs (Supplementary table 1). Subsequently, we used a one-way ANOVA to test for color morph differences in pulse rate values with a Tukey Honest Significant Difference method (Miller 1981) as a post-hoc test using the functions aov and TukeyHSD functions (Chambers et al. 2017). We found that dominant frequency and note length values were not normally distributed in all color morphs (Supplementary table 1). We used a Kruskal-Wallis non-parametric test for differences in means (Hollander and Wolfe 1973) and a post-hoc Dunn's test for pairwise comparisons with a Bonferroni correction (Dunn 1961, 1964; Hochberg 1988) using kruskal.test and dunnTest functions (Dinno 2017).

Finally, to determine whether our call trait variation is due to geographic distances between populations, we ran Mantel tests (Mantel 1967) for a correlation on each of the temperature-corrected call traits and geographic distance. We produced distance matrices for the call traits by calculating euclidean distances for each. We then used GPS coordinates from each of the collection sites and calculated haversine distances (Sinnott 1984) using the R package geosphere version 1.5–18 (Hijmans et al. 2019).These distance matrices were then tested for correlations using the mantel function in R package Vegan version 2.6.4 (Legendre and Legendre 2012; Gardener 2014). We ran 10,000 permutations using the Spearman rank correlation method (Spearman 1961).

Genotyping

We extracted DNA from each toe clip tissue sample using the Qiagen DNeasy Blood and Tissue Kit and associated protocol for animal tissue without the optional RNase A step. Both aifm1 and synj1 contain a short motif region with a single biallelic SNP that was used as the target region when designing primers. We selected primers for synj1 by identifying conserved regions upstream and downstream from the motif using multiple sequence alignment of the same gene region from closely related species (Supplementary table 2). For aifm1, we identified conserved regions upstream of the motif using multiple sequence alignment and then used Primer3 version 4.1.0 (Koressaar and Remm 2007; Untergasser et al. 2012; Kõressaar et al. 2018) to identify downstream regions and design a reverse primer that would pair well with the forward primer (Supplementary table 2). We followed this protocol for aifm1 because the gene was not well-conserved downstream of the motif region in our multiple sequence alignment. After extracting DNA, we used PCR to amplify the sequences (Supplementary table 3), including the target motif region and purified our PCR product using ExoSAP-It. We used Sanger sequencing in the forward and reverse directions using the SeqStudio genetic analyzer at the East Carolina University Genomics Core Facility. We then conducted sequencing analysis in Geneious Prime 2022.1.1 (https://www.geneious.com). We identified the motif containing the SNP by first aligning forward and reverse reads using the Geneious sequence alignment algorithm. We then identified the genotype of each individual at the SNP using the consensus sequence.

Association between genetic and acoustic variation

We conducted a linear quantitative trait association analysis using PLINK whole genome analysis toolset version 1.9 (Purcell and Chang; Purcell et al. 2007). This analysis constructs regression models based on a likelihood-ratio test as well as the Wald test. We used the temperature-corrected call trait values in the regression. We adjusted all p-values for multiple tests using a Bonferroni single-step correction.

After determining whether a SNP was associated with a call trait, we used a newly assembled R. imitator genome (Dye et al., unpublished data) to determine whether the SNP was located in an exonic region and whether the substitution was synonymous or nonsynonymous. We used the getfasta function of Bedtools version 2.31.1 (Quinlan and Hall 2010) to extract exonic regions from the gene annotation file. To determine if the SNP is located in an exonic region, we used MAFFT version 7.525 (Katoh and Standley 2013, 2016) allowing for gappy regions to prevent over-alignment to align the full gene with predicted isoforms and the consensus sequence determined from our sequencing. To confirm whether the SNP is located in an exon or intron, we aligned the R. imitator sequences with the Xenopus laevis gene and isoforms downloaded from NCBI (Xenopus_laevis_v10.1 (GCF_017654675.1)). Additionally, we determined which exon the SNP is located in for both R. imitator and X. laevis. Finally, if the SNP was located in an exon, we translated the exon it is located in to determine if the substitution is synonymous or nonsynonymous.

Acoustic variation

Dominant frequency

Using a Pearson correlation test, we identified a negative relationship between dominant frequency and temperature (r₆₂ = -0.44, t = -3.81, p-value < 0.001). After correcting dominant frequency to the standard temperature of 25\(\:℃\), the temperature-corrected pulse rate was not correlated with SVL (r₆₂ = -0.04, t = -0.28, p-value = 0.78). Using the Levene’s test for equal variance, we did not find a significant difference in the variance of dominant frequency between groups when grouping by color morph (F_5,58 = 2.02, p-value = 0.09). We tested for differences in temperature-corrected dominant frequency values between color morphs using a Kruskal-Wallis non-parametric test for differences in means and a post-hoc Dunn's test with a Bonferroni correction. Our Kruskal-Wallis test identified significant variation in dominant frequency (χ²₅ = 20.47, p-value < 0.01). Our pairwise Dunn’s test results show that the Varadero striped color morph has a significantly higher dominant frequency than the banded (z-score = -3.97, p-value < 0.01), Huallaga striped (z-score = -3.46, p-value < 0.01), intermediate (z-score = -3.43, p-value < 0.01), and spotted (z-score = -3.33, p-value = 0.01) color morphs (Table 1; Fig. 2A). Using Mantel tests, we also identified a weak positive correlation between geographic distance and the identified variation in dominant frequency (r = 0.09, p-value = 0.01).

Pulse rate

Using a Pearson correlation test, we identified a strong positive relationship between pulse rate and temperature (r₆₂ = 0.64, t = 6.50, p-value < 0.001). After correcting pulse rate to the standard temperature of 25\(\:℃\), the temperature-corrected pulse rate was not correlated with SVL (r₆₂ = 0.08, t = 0.60, p-value = 0.55). Using the Levene’s test for equal variance, we did not find a significant difference in the variance of pulse rate between groups when grouping by color morph (F_5,58 = 0.96, p-value = 0.45). We tested for differences in temperature-corrected pulse rate values between color morphs using a one-way ANOVA and a post-hoc Tukey Honest Significant Difference test. Our ANOVA identified significant variation in pulse rate (F_5,58 = 5.78, p-value < 0.01). Our pairwise Tukey test results show that the spotted color morph has a lower pulse rate compared to the Huallaga striped, (T = 4.42, p-value = 0.01) and intermediate, (T = -4.08, p-value < 0.01) color morphs (Table 1; Fig. 2B). We also found the banded color morph has a lower pulse rate compared to the intermediate (T = 2.82, p-value = 0.02) color morph (Table 1; Fig. 2B). Additionally, the banded color morph has a marginally lower pulse rate compared to the Huallaga striped color morph (T = 3.15, p-value = 0.06) (Table 1; Fig. 2B). Using Mantel tests, we also identified a weak positive correlation between geographic distance and the identified variation in pulse rate (r = 0.11, p-value < 0.01).

Note length

Using a Pearson correlation test, we identified a negative relationship between note length and temperature (r₆₂ = -0.40, t = -3.48, p-value < 0.001). After correcting note length to the standard temperature of 25\(\:℃\), the temperature-corrected note length was not correlated with SVL (r₆₂ = -0.22, t = -1.76, p-value = 0.08). Using the Levene’s test for equal variance, we did not find a significant difference in the variance of note length between groups when grouping by morph (F_5,58 = 1.97, p-value = 0.10). We tested for differences in temperature-corrected note length values between color morphs using a Kruskal-Wallis non-parametric test for differences in means and a post-hoc Dunn's test with a Bonferroni correction. Our Kruskal-Wallis test identified significant variation in note length ((χ²₅ = 12.66, p-value = 0.03). Our pairwise Dunn’s test results show that the Huallaga striped color morph has a shorter note length compared to the spotted (z-score = 2.97, p-value = 0.04) and Varadero striped (z-score = -3.01, p-value = 0.04) color morphs (Table 1; Fig. 2C). Using Mantel tests, we identified that geographic distance was not correlated with variation in note length (r = 0.04, p-value = 0.14).

Association between genetic and acoustic variation

We tested for associations between allelic variation in our candidate genes and acoustic variation using a linear quantitative trait association analysis with temperature-corrected trait values. When testing synj1, we did not find a significant association between the genotypes and any of the call traits (dominant frequency: β = -48.22, T = -1.10, p-value = 0.27 ; pulse rate: β = 0.18, T = 0.40, p-value = 1.0; note length: β = -0.04, T = -1.20, p-value = 0.47). Additionally, we did not find a significant association between aifm1 genotypes and pulse rate (β = 0.08, T = 0.17, p-value = 1.0) or note length (β < 0.01, T = 0.20, p-value = 1). However, when testing for an association between aifm1 and dominant frequency, we did find a significant association (β = -153.90, T = -3.55, p-value < 0.01). To investigate potential outlier effects, we tested the influence of a single banded morph individual with a much lower dominant frequency than most frogs in our association results and identified that there was still a significant association between aifm1 and dominant frequency when that individual was removed (β = -118.5 T = -2.75, p-value = 0.02). This shows that our association between dominant frequency and allelic variation in aifm1 is not driven by a single outlier and remains robust with or without this individual.

After identifying an association between R. imitator dominant frequency and the SNP located in the gene aifm1, we determined that the SNP is located within the exonic region of the gene. The Xenopus laevis sequence contains 5 isoforms and our motif region is located within exons 16 or 17, depending on the isoform. The R. imitator sequence contains three predicted isoforms with exon numbers varying from 16 to 18. Our motif region and SNP are located in exons 14, 15, or 16 depending on the isoform. Finally, when translating the exonic region we determined that the SNP is a synonymous substitution coding for the amino acid Proline. This interpretation does not vary between exons of different isoforms.

Acoustic signals are influenced by signaler aims and tactics, receiver preferences, ecology, morphology, genetics, and perception (Derryberry, 2009; Hoy, 1992; Ryan, 1986; Ryan et al., 1992; Xu & Shaw, 2021). In poison frogs (family Dendrobatidae), acoustic signals may have played a role in species diversification, with high levels of signal diversity showing a strong phylogenetic signal (Erdtmann and Amézquita 2009; Santos et al. 2014). In this study we sought to broaden our understanding of acoustic variation and the factors driving divergence in poison frog acoustic signaling. We focused on variation in acoustic traits between color morphs of the mimic poison frog Ranitomeya imitator and allelic variation present in a possible hearing-related gene. Our results highlight the importance of considering ecology, genetics, and perception in R. imitator acoustic divergence.

We identified distinct variation in all call traits analyzed between color morphs of R. imitator. We did not find a significant difference in dominant frequency between the Varadero striped and Varadero red-headed color morphs, however, we found that call dominant frequency was significantly higher in the Varadero striped color morph compared to the other color morphs. Additionally, pulse rate was higher in the Huallaga striped and intermediate color morphs compared to the spotted and banded color morphs. Finally, note length was shorter in the Huallaga striped color morph compared to the Varadero red-headed and spotted color morphs. Finally, we identified evidence of acoustic signal divergence due to both geographic distance and a hearing-related gene, suggesting both isolation by distance and selection are playing a role in R. imitator acoustic signal divergence.

Our results with regard to signal variation are mostly consistent with those of Twomey et al. (2015) with a few exceptions. We identified additional variation in note length and pulse rate between the spotted and Huallaga striped morphs. In dominant frequency, we identified similar trends in divergence between morphs in both spotted-striped and striped-banded transition zones. Additionally, we identified less divergence in dominant frequency between morphs in the striped-varadero transition zone. Twomey et al. (2015) identified significant divergence and a sigmoidal cline in dominant frequency between the Huallaga striped and Varadero morphs, where the Huallaga striped morph dominant frequency was higher. Our results showed limited variation between the Huallaga striped morph and the Varadero red-headed morph mimetic morph, but the Varadero striped morph was higher than both. The higher dominant frequency in the Varadero striped morph is in line with the results from Twomey et al. (2015), which showed the Varadero striped morph had a significantly higher dominant frequency compared to the Varadero red-headed morph, though the difference between these morphs was non-significant in our dataset. These discrepancies are likely due to differences in sampling protocol and sample size.

As noted, the Varadero striped morph showed a higher dominant frequency than the other populations and morphs, and the differences, compared to other morphs and populations, were significant with the exception of the Varadero red-headed morph. While the difference between the Varadero striped morph and the Varadero red-headed morph was not significant in our analysis, it did match the difference identified by Twomey et al. (2015), which was statistically significant. In combination, these results suggest the possibility of reproductive character displacement at the interface between the Varadero striped morph and the Varadero red-headed morph. This would be consistent with the genetic and morphological results from previous analyses (Twomey et al. 2014, 2016) that revealed steep clines in microsatellite allele frequencies and coloration between these two morphs in the same region, and suggest the possibility of incipient speciation between these morphs, as described by Twomey et al. (2014, 2016).

We found that geographic distance was weakly correlated with variation in dominant frequency and pulse rate. In several taxa, geographic distance is considered a large driver of acoustic divergence (González et al. 2011; Jang et al. 2011; Velásquez et al. 2013; Pato et al. 2019). For instance, in poison frogs, Prohl et al. (2007), found that geographic distance between populations of Oophaga pumilio was highly correlated with variation in acoustic signals. Similarly, Prohl et al. (2007) and Amezquita et al. (2009) found that acoustic divergence in Allobates femoralis was associated with geographic distance between populations. In other cases, no relationship between geography and acoustic variation has been found, suggesting that geography is not the only factor affecting variation (Castellano et al. 2000; Deng et al. 2021; Kaefer and Lima 2012). In R. imitator, geographic distance has contributed to increasing genetic divergence between populations, suggesting a role of isolation by distance (Twomey et al. 2014, 2016). Our study identified a weak positive correlation between geographic distance and acoustic variation in both pulse rate and dominant frequency. This result suggests that distance separating populations very weakly contributes to acoustic divergence. However, we also identified strong signatures of divergence in call elements between color morphs, which is more consistent with the hypothesis that ecological divergence in color morph is driving concomitant divergence in acoustic signaling and perception.

We identified a strong association between variation in the hearing related gene aifm1 (apoptosis inducing factor mitochondria associated 1) and variation in dominant frequency. This sequence variant appears to be a synonymous SNP that codes for the amino acid proline. While it is possible this synonymous mutation may not affect protein function, synonymous mutations have been demonstrated to alter expression levels, protein folding, and translation speed (Hunt et al. 2009). Variation in aifm1 coincides with ecological divergence in coloration of the banded color morph. The allelic variation driving an association between dominant frequency and aifm1 occurred in the striped-banded transition zone. Aifm1 was fixed for a single allele in the spotted, Huallaga striped, Varadero striped, and Varadero red-headed color morphs (Supplementary Fig. 1). This suggests there may be selection for differences in hearing between the striped and banded color morphs that drives higher acoustic signal divergence in this region. In addition, it is possible that the mutation identified here in aifm1 may be linked to causal mutation in a nearby gene, which may offer an explanation for why we did not identify allelic variation in the varadero-striped transition zone where we see more variation in dominant frequency.

Several studies have found that mutations of the aifm1 gene are associated with a loss in function of signaling between the inner ear and brain, as well as progressive hearing loss and deafness (Rinaldi et al. 2012; Zong et al. 2015; Elrharchi et al. 2020; Kawarai et al. 2020; Wang et al. 2020). In the mammalian inner ear, aifm1 is localized to inner hair cells, outer hair cells, and spiral ganglion neurons, suggesting a role in normal auditory function (Zong et al. 2015). However, these mutations have only been identified in mammals and the mechanisms involved in frequency discrimination differ between vertebrate taxa (Fettiplace 2020). The amphibian inner ear contains two main organs for perceiving sound, the amphibian papilla and basilar papilla (Feng et al. 1975). Similar to other vertebrates, these organs contain hair cells that transduce auditory stimuli into neural signals. Unlike in mammals, anurans rely on hair cells for frequency selectivity (Schoffelen et al. 2008). The electrical tuning of hair cells is often responsible for frequency selectivity that may contribute to signal preferences in anurans (Van Dijk et al. 2011; Ryan and Cummings, 2013). It is possible that variation in aifm1 may contribute to the electrical tuning of hair cells in the amphibian inner ear and ultimately impact frequency selectivity. We have identified aifm1 in a novel, ecological context, with potential implications for the production and maintenance of intraspecific signaling.

Acoustic signal divergence in Ranitomeya imitator supports the hypothesis that ecological divergence of color morphs is driving concomitant divergence in acoustic signaling and perception. This is the first study to explore how the genetics of hearing may play a role in R. imitator acoustic divergence. Our results highlight the importance of understanding the role of perception in early phenotypic divergence. Here, we identify an association between variation in the hearing gene aifm1 and dominant frequency within the striped-banded transition zone. Our results support the potential role of hearing in the divergence of dominant frequency between the mimetic morphs of R. imitator. While we did not find significant differences in dominant frequency between color morphs in this transition zone, there is variation in dominant frequency that could allow for selection to act. In particular, it appears as though the intermediate populations in the striped-banded populations are trending towards a higher dominant frequency compared to the mimetic morphs in the transition zone. Future research should focus on further quantifying acoustic signal differences throughout the striped-banded transition zone and the selective pressures on acoustic signals in this region. Additionally, while we did not identify allelic variation in the striped-varadero transition zone, dominant frequency is demonstrated to be significantly diverged between color morphs. There should be a focus on further quantifying this divergence and assessing the role of acoustic signaling and hearing throughout this region, with an emphasis on understanding perceptual differences between populations.

Ethics approval

Animal care and use for this research was approved by East Carolina University's Institutional Animal Care and Use Committee at East Carolina University (AUP D376). All fieldwork was conducted under Servicio Nacional Forestal y de Fauna Silvestre (SERFOR) permit (#D000005-2022-MIDAGRI-SERFOR-DGGSPFFS-DGSPFS).

Data availability

All code is available on Github for review (https://github.com/AshleyDye/Ranitomeya_imitator_calls_genes) and will be available on Zenodo upon acceptance. Raw acoustic and genetic data will be available on Dryad upon acceptance.

Acknowledgements

We are grateful to Dr. Mysia Dye for feedback on the analytical methods and manuscript draft.

Funding

Funding for this project was provided by the National Science Foundation DEB grant to KS NSF-DEB1655336, NSF 2319712 to AMMS, and support from the University of Houston to AMMS.

Author contributions

AOR and KS conceptualized and designed the project. AMD, AOR, and EB collected the data. AMD analyzed the data. AMD, AOR, AMMS, and KS contributed to the interpretation of the results. AMD wrote the initial draft of the manuscript, and all others contributed to the editing and revision process.

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Charif, RA, LM Strickman, and AM Waack

Table 1 is available in the Supplementary Files section.

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Evidence for an association between genetic and acoustic variation in the mimic poison frog Ranitomeya imitator

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Advertisement Call Analysis

Genotyping

Association between genetic and acoustic variation

Results

Acoustic variation

Dominant frequency

Pulse rate

Note length

Association between genetic and acoustic variation

Discussion

Conclusion

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References

Table 1

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