The coexistence strategy during the breeding season—the acoustic niche partitioning of Pelophylax terentievi and P. nigromaculatus in Xinjiang, China

The coexistence strategy during the breeding season—the acoustic niche partitioning of Pelophylax terentievi and P. nigromaculatus in Xinjiang, China | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 February 2026 V1 Latest version Share on The coexistence strategy during the breeding season—the acoustic niche partitioning of Pelophylax terentievi and P. nigromaculatus in Xinjiang, China Authors : Mengmeng Gong , Yaming Sun , Jamila Molamat , Bayangul Mametnur , Medine Mutellip , Yan Wang , and Lu Zhou 0000-0003-2438-5198 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177218309.92589351/v1 193 views 109 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Sympatric vocal species may compete for acoustic environment resources, thereby elevating the risk of interspecific interference and mismatch in the breeding season. P. nigromaculatus , primarily distributed in East Asia, is a non-native species in Xinjiang. It was introduced to the region through aquaculture activities and now occurs sympatrically with the native species P. terentievi . This study explored the acoustic niche partitioning between the two species by analyzing the differences in the advertisement calls’ time, vocalization location and call parameters. The results showed that there were significant differences in the call parameters of the two species, and there was a clear partitioning in the choice of vocal time, which might effectively reduce interspecific interference and improve species recognition efficiency. However, there was no difference in the choice of vocalization location. In addition, temperature had an impact on the vocal rhythms and call parameters of two species. Therefore, the acoustic niche partitioning of the two was the partitioning of vocal time and call parameters. This study provided a basis for understanding the adaptive mechanisms of vocal behavior of desert amphibians and also offered scientific support for biodiversity conservation and ecological monitoring in this region. The coexistence strategy during the breeding season—the acoustic niche partitioning of Pelophylax terentievi and P. nigromaculatus in Xinjiang, China Mengmeng Gong | Yaming Sun | Jamila·Molamat | Bayangul·Mametnur | Medine·Mutellip| Yan Wang | Lu Zhou* Xinjiang Key Laboratory for Ecological Adaptation and Evolution of Extreme Environment Organisms, College of Life Sciences, Xinjiang Agricultural University, Ürümqi 830052, China Correspondence Lu Zhou, Xinjiang Key Laboratory for Ecological Adaptation and Evolution of Extreme Environment Organisms, College of Life Sciences, Xinjiang Agricultural University, Ürümqi 830052, China. Email: [email protected] Keywords: Pelophylax terentievi ; Pelophylax nigromaculatus ; call parameters; diel rhythm; sympatric distribution; acoustic niche partitioning The coexistence strategy during the breeding season—the acoustic niche partitioning of Pelophylax terentievi and P. nigromaculatus in Xinjiang, China Mengmeng Gong | Yaming Sun | Jamila·Molamat | Bayangul·Mametnur | Medine·Mutellip| Yan Wang | Lu Zhou* Xinjiang Key Laboratory for Ecological Adaptation and Evolution of Extreme Environment Organisms, College of Life Sciences, Xinjiang Agricultural University, Ürümqi 830052, China Keywords: Pelophylax terentievi ; Pelophylax nigromaculatus ; call parameters; diel rhythm; sympatric distribution; acoustic niche partitioning ABSTRACT Sympatric vocal species may compete for acoustic environment resources, thereby elevating the risk of interspecific interference and mismatch in the breeding season. P. nigromaculatus , primarily distributed in East Asia, is a non-native species in Xinjiang. It was introduced to the region through aquaculture activities and now occurs sympatrically with the native species P. terentievi . This study explored the acoustic niche partitioning between the two species by analyzing the differences in the advertisement calls’ time, vocalization location and call parameters. The results showed that there were significant differences in the call parameters of the two species, and there was a clear partitioning in the choice of vocal time, which might effectively reduce interspecific interference and improve species recognition efficiency. However, there was no difference in the choice of vocalization location. In addition, temperature had an impact on the vocal rhythms and call parameters of two species. Therefore, the acoustic niche partitioning of the two was the partitioning of vocal time and call parameters. This study provided a basis for understanding the adaptive mechanisms of vocal behavior of desert amphibians and also offered scientific support for biodiversity conservation and ecological monitoring in this region. 1 | Introduction Most amphibians primarily transmit social information through acoustic signals (Zhu et al., 2025). Studying these vocalization characteristics provides an avenue to understand their individual behaviors and the evolutionary functions of their calls (Sugai et al., 2021). During the breeding season, acoustic signals produced by anurans hold multitudinous information (Pekny et al., 2026), including species identity (Blair, 1955; Littlejohn, 1965), territory (Roithmair, 1994; Chen et al., 2017), individual body size and condition (Davies and Halliday, 1978; Bee et al., 1999), and mating attractiveness (Ryan et al., 1981). When two species with close genetic relationships occur sympatrically, they often compete for resources, including acoustic niches (Orci et al., 2023). Facing limited resources, different amphibian species may be compelled to share the same vocalization location, which may weaken the pre-mating isolation barriers maintained by species-specific vocalizations (Gerhardt et al., 2003; Chen et al., 2017), increase the risk of mismatch, and lead to reproductive failure (Ryan, 1981; Gerhardt et al., 2003). For this situation, sympatric species usually achieve coexistence through niche differentiation: partitioning and preferences in the dimensions of space, time and acoustics (Márquez and Bosch, 1997; Garcia-Rutledge et al., 2001; Wells, 2007; Gröning and Hochkirch, 2008; Villanueva-Rivera, 2014; Herrick et al., 2018). When sympatric vocal species are under concurrent reproductive pressure, differences in their advertisement calls enable species-specific recognition of acoustic signals (Ryan, 1988; Boullhesen et al., 2023). Thus, species inhabiting similar breeding water often employ differentiation in acoustic characteristics (frequency and duration) or vocal behaviors (vocal time and vocalization location) to mitigate interspecific interference (Bhat et al., 2022). For example, Eleutherodactylus species who are sympatric in Puerto Rico shared similar vocal time but exhibited frequency partitioning (Villanueva-Rivera, 2014). Similarly, Lithobates catesbeiana and L. clamitans in the United States occurred in similar ponds, breeding seasons, and diel period, however, the former produced longer calls with a higher frequency than the latter. To avoid acoustic interference, L. clamitans adjusted its calling to the time intervals between the calls of L. catesbeiana (Herrick et al., 2018). The partitioning of the calling times is sometimes affected by temperature and humidity. The vocal behavior of P. ridibunda in the breeding season is highly temperature-dependent, with calling ceasing when water temperatures fall below 15°C (Obert, 1975). P. nigromaculatus is a widely distributed anuran in East Asia and ranks among the most extensively distributed amphibians in China. The species is not native to northwestern China but was introduced to Xinjiang through aquaculture activities and now holds significant ecological and economic value (Wang et al., 2020). Its vocalization typically comprises four pulse trains, each consisting of a series of waveforms with varying amplitudes, and lasts for 370 to 510 ms per call (Jiang et al., 1995). P. terentievi is a native species in Tajikistan and Xinjiang, China. It mainly inhabits still water environments such as marshes, river beaches, farmlands, and grasslands at an altitude of 500 to 700 m (Fei et al., 2012). It has a similar appearance to P. nigromaculatus but with different vocalization characteristics. The advertising calls of these two species sound somewhat similar, but in fact they are completely different (ready for publication). Our preliminary analysis indicated that P. terentievi produced complex advertisement calls, comprising abundant harmonic signals and a small number of pulsed signals, with substantial variation in syllable duration (ready for publication). Understanding the mechanisms of acoustic niche partitioning in amphibians is helpful for developing more effective biodiversity conservation strategies, especially in desertification areas. It is vital for protecting endangered species and maintaining ecological balance. Currently, there is no research on how the two species coexist in the desert wetland habitats of Xinjiang through acoustic signals. Therefore, this study aims to collect and analyze the advertisement calls and environmental data during the breeding season of the two species to explore their acoustic behavior adaptation strategies under sympatric distribution. This will provide basic data for the behavioral ecology and evolutionary biology of anurans. It also offers a foundation for understanding how alien and native species coexist and for formulating effective biodiversity conservation strategies in desert wetlands. 2 | Materials and Methods 2.1 | Data collection The study was conducted at Qinggeda Lake Wetland (Wujiaqu City, Xinjiang), a 9-km² wetland bordered by the Tianshan Mountains to the south and the Gurbantunggut Desert to the north. P. nigromaculatus and P. terentievi often coexist in the same water around the wetland. We collected their advertisement calls in May (breeding season) from three selected ponds: one pond with only P. nigromaculatus (named Pond 1, individuals of P. nigromaculatus in Pond 1 are denoted as population A), another pond with only P. terentievi (named Pond 2, individuals of P. terentievi in Pond 2 are denoted as population B), and the last pond with both species (named Pond 3, individuals of P. nigromaculatus in Pond 3 are denoted as population a, individuals of P. terentievi in Pond 3 are denoted as population b). Recordings of advertisement calls from both species in these ponds were obtained using a recorder (Songmeter SM4 mini, Wildlife Acoustics, US) with the sampling rate set at 44.1 kHz and the data saved in 16-bit format. The device was placed by the pond for 24-hour continuous recording. In the meantime, we used a hygrothermograph (LogEt 5 TE, Jingchuang Co., Ltd., China) to collect the air temperature (accurate to 0.1 °C), with a recording interval of one minute. We also measured off-shore distance of each calling individual with a laser rangefinder (SW-G, Shenweida, China). Additionally, we obtained the sunrise and sunset times of the day when the advertisement call samples were collected through a sunrise and sunset query website (https://richurimo.bmcx.com/wujiaqushi__richurimo/). This study employed passive acoustic monitoring, which neither captured nor touched the animals. Therefore, it caused no disturbance to the animals’ survival or the environment and did not involve any animal experiments. 2.2 | Data analysis 2.2.1 | Acoustic analysis We used the Raven Pro 1.6 software to create waveforms and spectra of the audio files we collected. Due to the obvious differences in their calls, we used visual and auditory methods to separately extract the advertisement calls of the two species from the audio files. Then the number of calls made by each species in the three ponds every ten minutes was counted. The high frequency (Hz), low frequency (Hz), peak frequency (Hz), bandwidth (Hz), and duration (s) of advertisement calls were also calculated by this software. 2.2.2 | Statistical analysis To verify whether the data met the normality assumption, we assessed the number of calls per ten minutes, the off-shore distance and call parameters using the Shapiro-Wilk test. For data that were normally distributed, analysis of variance (ANOVA) was used to compare data differences between populations; otherwise, the non-parametric test (Mann-Whitney U test or Kruskal-Wallis test) was applied. To explore the influence of environmental factors on the vocal behavior of the two species, we conducted a correlation analysis between air temperature and the number of calls per ten minutes as well as the call parameters. If the data were normally distributed, the Pearson correlation coefficient was used, otherwise, the Spearman coefficient was applied. To assess whether acoustic niche partitioning occurred in vocalization location, we first examined the normality of the off-shore distance data for each species. If the data for both species followed a normal distribution, an ANOVA was used to test for significant differences in their off-shore distances. Otherwise, the non-parametric test was applied. Due to the different numbers of individuals in the two species, the absolute quantity of their calls varies greatly. To eliminate the influence of the difference in species numbers on the comparison of their vocal rhythms, we conducted ”normalization” processing on the data before comparing the vocal rhythm differences. 3 | Result 3.1 | Vocal diel rhythm of P. nigromaculatus and P. terentievi The peak calling periods differed among populations: for population A, 21:50–06:30 (next day); for population B, 18:00–06:40 (next day); for population a, 22:30–06:30 (next day); and for population b, 08:40–10:10. Corresponding low calling periods were 06:40–21:40 for population A; 06:50–17:50 for population B; 06:40–22:20 for population a; and 10:20–08:30 (next day) for population b (Figure 1). Thus, the peak calling period of population A began 3.8 hours later than that of population B and ended 10 minutes earlier. Relative to population A, the peak calling period of population a began 40 minutes later but ended at the same time. In contrast, the peak calling period of population b was markedly delayed and shortened: it began 14.7 hours later and ended 3.5 hours later than that of population B, lasting only 1.5 hours (12.8 hours shorter than the peak calling period of population B). Vocal rhythms data across all populations (A, B, a, b) were non-normally distributed (Shapiro–Wilk test, p < 0.05; Table 1). Non-parametric test (Mann–Whitney U tests) revealed significant differences in vocal rhythm between population A and B, population A and a, population B and b, and population a and b ( p < 0.05; Table 2 and Figure 2). Under allopatric conditions, population A began calling immediately after sunset (21:50) and ceased calling at sunrise (06:30), with call numbers declining sharply thereafter. Population B initiated its peak calling period 4 hours before sunset (18:00) and exhibited a more gradual decline after sunrise (06:30), the number of calls gradually decreased after sunrise. Under sympatry conditions, the vocal rhythms of the two species differed significantly ( p < 0.05). Population a called from midnight (00:00) until sunrise (06:30), whereas population b called between 08:40 and 10:10 (after sunrise). Table 1 | Descriptive statistics of the vocal rhythms after normalization processing Kolmogorov-Smirnova Populations Mean ± SD Median ± IQR Maximum Minimum D Df p A 0.047±0.080 0.002 ± 0.068 0.348 0.000 0.295 144 0.000 B 0.325±0.325 0.189 ± 0.606 1.000 0.000 0.22 144 0.000 a 0.128±0.203 0.012 ± 0.203 0.647 0.000 0.349 144 0.000 b 0.005±0.015 0.000 ± 0.001 0.098 0.000 0.374 144 0.000 Note: SD stands for standard deviation, and IQR stands for interquartile range. Figure 1 | Vocal diel Rhythm of different populations. The sun symbol represents the time of sunrise, and the moon symbol represents the time of sunset. Table 2 | Comparison of vocal rhythms between populations (Mann-Whitney U Test) Populations z p A VS B 7.431 0.000 A VS a 4.150 0.000 B VS b -10.071 0.000 a VS b -9.269 0.000 Figure 2 | Differences in vocal rhythms between populations in different ponds. ” ***” ”a” indicates p < 0.001. 3.2 | Vocalization location In Pond 3, the off-shore distances of the two vocal species (population a and b) were non-normally distributed. ( p < 0.001). Consequently, we used a non-parametric test (Kruskal–Wallis test) to compare off-shore distances between the two species, which revealed no significant interspecific difference in their vocalization location (H = 0.806, Df = 24, p = 0.824). 3.3 | Call parameters As the low frequency, high frequency, peak frequency, bandwidth, and duration of advertisement calls for both species across the three ponds were non-normally distributed ( p < 0.05; Table 3), we conducted a non-parametric test (Mann–Whitney U test). The results showed significant differences in these five parameters between population A and B, as well as between population a and b ( p 0.05). Whereas, significant differences were observed in high frequency, peak frequency, bandwidth, and call duration among all populations ( p < 0.05; Table 4; Figure 3). Table 3 | Descriptive statistics and normality test of call parameters Kolmogorov-Smirnova Populations Call parameters Mean ± SD Median ± IQR Maximum Minimum Dn Df p A Low Frequency (Hz) 397.7±294.4 541.0 ± 451.2 2141.1 149.7 0.352 4473 0.000 High Frequency (Hz) 2392.8±976.4 2262.7 ± 799.2 7161.3 950.4 0.150 4473 0.000 Peak Frequency (Hz) 1265.9±3843.6 559.9 ± 775.2 38088.0 301.5 0.375 4473 0.000 Bandwidth (Hz) 1344.6±489.1 1465.3 ± 818.3 3057.7 258.4 0.135 4473 0.000 Duration (s) 0.234±0.197 0.270 ± 0.300 2.550 0.010 0.519 4473 0.000 B Low Frequency (Hz) 382.7±322.6 281.8 ± 167.7 5633.650 15.5 0.246 103051 0.000 High Frequency (Hz) 2668.7±841.3 2583.0 ± 763.4 10836.0 215.7 0.117 103051 0.000 Peak Frequency (Hz) 1534.9±519.5 1636.5 ± 602.9 8354.9 86.1 0.125 103051 0.000 Bandwidth (Hz) 1521.3±429.6 1636.5 ± 516.8 6115.4 86.1 0.119 103051 0.000 Duration (s) 0.294±0.143 0.279 ± 0.191 2.084 0.01 0.061 103051 0.000 a Low Frequency (Hz) 330.2±187.6 295.3 ± 126.7 2983.3 18.0 0.196 39528 0.000 High Frequency (Hz) 3371.3±1400.4 2804.8 ± 580.6 8361.8 689.1 0.307 39528 0.000 Peak Frequency (Hz) 1722.4±625.1 1981.1 ± 516.8 3531.5 86.1 0.236 39528 0.000 Bandwidth (Hz) 1830.4±299.8 1894.9 ± 258.4 4478.9 86.1 0.201 39528 0.000 Duration (s) 0.358±0.181 0.325 ± 0.157 3.059 0.005 0.141 39528 0.000 b Low Frequency (Hz) 353.2±222.1 294.5 ± 198.9 1522.4 18.1 0.156 1445 0.000 High Frequency (Hz) 2799.8±749.5 2803.6 ± 198.9 7094.9 864.3 0.134 1445 0.000 Peak Frequency (Hz) 1589.4±650.8 1808.8 ± 861.3 3703.7 86.1 0.179 1445 0.000 Bandwidth (Hz) 1637.4±533.1 1722.7 ± 602.9 3273.0 86.1 0.147 1445 0.000 Duration (s) 0.258±0.171 0.238 ± 0.151 2.310 0.029 0.168 1445 0.000 Note: SD stands for standard deviation, and IQR stands for interquartile range. Table 4 | Mann-Whitney U Test of call parameters Populations Low Frequency High Frequency Peak Frequency Bandwidth Duration A VS B z -32.18 31.316 54.725 22.215 18.364 p 0.000 0.000 0.000 0.000 0.000 A VS a z 5.329 64.869 62.226 70.422 40.513 p 0.000 0.000 0.000 0.000 0.000 B VS b z 1.65 10.798 13.095 -12.667 9.989 p 0.094 0.000 0.000 0.000 0.000 a VS b z -0.882 9.533 12.586 29.280 10.615 p 0.378 0.000 0.000 0.000 0.000 Figure 3 | The difference of call parameters between populations. ” ***” ”a” indicates p < 0.001. In addition, we conducted a principal component analysis (PCA) on the call parameters of the two species in Pond 3. The analysis yielded two principal components (PCs), with eigenvalues of 1.924 and 1.093, cumulatively explaining 60.35% of the total variance (Table 5). PC1 accounted for 38.48% of the total variation and was primarily loaded by low frequency, high frequency, bandwidth, and call duration. PC2 accounted for 21.87% of the variation and was primarily loaded by low frequency and peak frequency. Non-parametric test (Mann–Whitney U test) revealed significant interspecific differences along both PC1 (z = 6.692, p < 0.05) and PC2 (z = 6.150, p < 0.05); degrees of freedom are not applicable for this rank-based test. The scatter plots for each PC showed partial, but not complete, separation (Figure 4). Table 5 | PCA of call parameters between the two species Principal components PC1 PC2 Low frequency -0.707 0.551 High frequency 0.625 Peak frequency 0.566 Bandwidth 0.772 Duration 0.542 Percentage of Variance 38.481 21.869 Eigenvalues 1.924 1.093 Mann-Whitney U Test p 0.000 0.000 Df 40973 40973 z 6.692 6.150 Note: Since the data were non-normally distributed, the non-parametric test (Mann-Whitney U test) was employed. Figure 4 | PCA plot of five call parameters for species in Pond 3 . Green indicated the call parameters of P. nigromaculatus , and blue indicates the call parameters of P. terentievi . 3.4 | Correlation between the number of calls and air temperature The number of calls per 10 minutes across the three ponds were non-normally distributed ( p < 0.05). Therefore, Spearman’s rank coefficient was used to assess the correlation between air temperature and the number of calls. Significant negative correlations were found between temperature and the number of calls in all three ponds ( p < 0.05; Table 6 and Figure 5). Table 6 | Spearman’s rank correlation analysis of the number of calls and air temperature Populations Correlation coefficient p N A -0.438 0.000 144 B -0.184 0.048 144 a -0.408 0.000 144 b -0.296 0.000 144 Figure 5 | Correlation between call rhythms and air temperature 3.5 | Correlation between call parameters and air temperature The call parameters of the two species in Pond 3 were non-normally distributed (p < 0.05; Table 3). Consequently, Spearman’s rank correlation coefficient was used to assess associations between each call parameters and air temperature. For population a, low frequency was negatively correlated with temperature, whereas high frequency, peak frequency, bandwidth, and duration were positively correlated ( p <0.05). Conversely, for population b, low frequency was positively correlated with air temperature, whereas high frequency, peak frequency, bandwidth, and duration were negatively correlated ( p < 0.05) (Table 7 and Figure 6). Table 7 | Spearman’s rank correlation analysis between call parameters of populations and air temperature in Pond 3 Populations Call parameters Correlation coefficient p N a Low Frequency -0.291 0.000 38290 High Frequency 0.464 0.000 38290 Peak Frequency 0.037 0.000 38290 Bandwidth 0.192 0.000 38290 Duration 0.117 0.000 38290 b Low Frequency 0.104 0.000 1428 High Frequency -0.122 0.000 1428 Peak Frequency -0.069 0.009 1428 Bandwidth -0.157 0.000 1428 Duration -0.073 0.006 1428 Figure 6 | Correlation analysis between call parameters and air temperature. 4 | Discussion 4.1 | The impact of sympatric distribution on vocal rhythms The vocal rhythm varied between P. nigromaculatus and P. terentievi under allopatric ponds, thereby establishing the foundational basis for temporal partitioning of their advertisement calls. However, the two species exhibited a complete temporal partitioning of their vocal rhythms when they stayed in a pond. Compared with the populations in allopatric ponds, P. nigromaculatus in sympatry exhibited a delayed onset and a shortened peak calling period, and P. terentievi shifted its peak calling period from night (18:00–06:40) to early morning (08:40–10:10) in sympatry and reduced the period significantly. These indicated that the two species influenced each other under sympatric conditions and adjusted their vocal timing accordingly. The phenomenon of temporal partitioning in calling is common among species with overlapping call frequencies and similar body structures (Schwartz and Wells, 1983), and they can coexist by calling at times when other species are not calling (Kraus and Schultz, 2017). Male frogs’ calls are usually inhibited by the calls of other species (Wells, 2007), so they choose to call alternately with other species. For instance, male Heteroplexis microcephala will change their call types and vocal rhythms based on the characteristics of the calls of their own and other species (Schwartz and Wells, 1985). Additionally, the calls of different cicada species coexisting in the same area (or in mixed populations) will form subtle temporal differences to achieve interspecific recognition and avoid interference (Shieh et al., 2015). On the other hand, the number of calls per ten minutes of P. nigromaculatus varied more sharply with temperature compared to that of P. terentievi , indicating that the vocal behavior of P. nigromaculatus is more sensitive to temperature. This indicates that the vocal rhythm of these two species responds differently to temperature, which could also lead to the difference in their rhythms. Compared with the sympatrically distributed Rana dybowskii , the P. nigromaculatus call at a higher relative humidity, resulting in a temporal partition of their calling times (Yoo and Jang, 2012). In addition, the vocal rhythms of P. nigromaculatus and P. terentievi might be related to their respective ecological characteristics. P. nigromaculatus is a plain species (Fei et al., 2009), showing dual sensitivity to temperature and humidity (Park et al., 2021). The temperature and humidity in the plain change rapidly around sunrise and sunset. After sunrise, the temperature on the plain rises rapidly and the moisture evaporates quickly, which is not suitable for the calling of P. nigromaculatus . After sunset, the temperature on the plain drops rapidly and the humidity increases rapidly, and soon reaches the conditions conducive to the calling. This leads to a significant difference in the calling behavior of P. nigromaculatus before and after sunset. P. terentievi is a typical mountain species (Fei et al., 2009). The mountains prevent sunlight from reaching the valleys quickly after sunrise, and the sunlight has a significant impact on the temperature and humidity in the valleys until noon. Therefore, P. terentievi calls continuously until noon. After noon, the temperature rises rapidly and they stop calling. In the late afternoon, the light in the valley gradually weakens due to the obstruction of the mountains. Before the sun sets, the temperature begins to drop and the humidity increase, and P. terentievi begins its peak calling period. 4.2 | The effect of sympatric distribution on call parameters Numerous studies have demonstrated that anuran mating is non-random, with female mate selection typically based on specific call parameters or combinations (Gerhardt, 1994; Pröhl, 2003). Closely related sympatric species typically adjust their call parameters to accentuate differences in their advertisement calls, thereby facilitating females to identify the species that are vocalizing. A study revealed that Chiromantis doriae , when sympatric with Feihyla vittata , exhibits a reduced call frequency and an increased call duration to amplify vocal divergence between the two species and mitigate interspecific reproductive interference (Yang et al., 2019). The calls of P. nigromaculatus and P. terentievi have distinct differences under allopatric conditions. Thus, the calls of the two species were innately different, providing the basis for the partitioning of call parameters. Under sympatric conditions, P. nigromaculatus exhibited a higher high frequency, higher peak frequency, wider bandwidth, and longer call duration than P. terentievi . This indicated the presence of call parameter partitioning between the two species under sympatric conditions. In addition, the call parameters of the two species under sympatric changed significantly compared to those under allopatric conditions. The results suggested that the two species mutually influenced each other under sympatric conditions, which led to a more significant partition of call parameters. Temperature affects the calling characteristics of anurans (Steelman and Dorcas, 2010). The call parameters of most species that convey information through calls change with temperature, among which the time-domain characteristic parameters change most significantly (Chen, 2012). The time-domain characteristics of calls (call duration, inter-call interval, syllable duration, etc.) of many anuran show a negative correlation with temperature (Sullivan, 1992). Meanwhile, some studies have shown that temperature also affects frequency-domain parameters. For instance, the peak frequency of Quasipaa spinosa during the breeding season is negatively correlated with environmental temperature (Yu and Zheng, 2009). Calling in the same pond, the correlations between the call parameters of the two species and air temperature were diametrically opposed. For P. nigromaculatus , low frequency exhibited a negative correlation with air temperature, whereas high frequency, peak frequency, bandwidth, and duration showed positive correlations with air temperature. In contrast, P. terentievi displayed a positive correlation between low frequency and air temperature, while high frequency, peak frequency, bandwidth, and duration demonstrated negative correlations with air temperature. This revealed that the call parameters of the two sympatric species responded differently to air temperature. This further facilitated the partition of the calling parameters of the two species. It is worth noting that when sympatric with P. nigromaculatus , the advertisement calls of P. terentievi exhibit traits of higher high frequency, higher peak frequency, broader bandwidth, and longer duration. This implied that the energy expenditure for its vocal had increased, thereby raising the survival risk. In addition, increasing signal duration could enhance transmission fidelity (Picheny et al., 1986). For frogs, shortening the duration of specific notes in the advertisement call might reduce signal fidelity, resulting in incomplete information transmission that males desire, impairing females’ ability to accurately assess male characteristics and thereby reducing the attractiveness of the call (Deng et al., 2026). Therefore, the presence of P. nigromaculatus might intensified the survival pressure on P. terentievi and may further jeopardize its reproductive success. Accordingly, we recommend conducting regular surveys on the local distribution range and population abundance of P. nigromaculatus , which is of great significance to assess its negative impacts on P. terentievi and other local amphibian species. 4.3 | No spatial partitioning was observed in the vocalization location of the two species. Previous studies have reported the phenomenon of vocalization location partitioning among sympatric species. For instance, in the case of Uperodon systoma and U. globulosus shared the same habitat. U. globulosus called near the water’s edge while U. systoma called in the water far from the shore (Prasad et al., 2022). This phenomenon also existed among tree frogs, with Dryophytes japonicus calling at the edge of rice fields and D. suweonensis calling inside the rice fields (Borzée et al., 2016). In the same pond, there was no significant difference in their off-shore distance (p > 0.05). Through field observation, we had not yet found any preference for vocalization location between P. nigromaculatus and P. terentievi . They merely required support from aquatic plants, pebbles, and other objects to perch and call. This might suggest that competition for spatial resources had not yet became a major limiting factor for their coexistence. 5 | Conclusion This study conducted a comparative analysis of the advertisement calls of P. nigromaculatus and P. terentievi distributed in the Qinggeda Lake Wetland. There were fundamental differences in the vocal rhythm and call parameters of these two species, which met the conditions for ecological partition of sound producers. Calling in the same pond, the vocal rhythms and call parameters of these two species had significantly partitioned, but the vocalization location had not. Therefore, the differentiation of call time and call parameters was the main behavioral mechanism for their coexistence during the breeding season and alleviation of sound ecological competition. The introduced species ( P. nigromaculatus ) might affect the calls of the local species ( P. terentievi ), causing an increase in the energy consumption of P. terentievi ’s calls and thereby raising their survival risks. Therefore, we believe that regular assessment of the distribution range and population size of the introduced species is obviously necessary. This research can provide a scientific basis for sound ecological monitoring and the protection of amphibians in arid areas. Author Contributions Mengmeng Gong: conceptualization (equal); data curation (lead); formal analysis (lead); investigation (equal); methodology (equal); project administration (supporting); writing – original draft (equal); writing – review and editing (equal). Yaming Sun: investigation (equal); methodology (equal). Jamila·Molamat: data curation (supporting). Bayangul·Mametnur: data curation (supporting). Medine·Mutellip: data curation (supporting). Yan Wang: data curation (supporting). Lu Zhou: conceptualization (equal); funding acquisition (lead); investigation (equal); methodology (equal); project administration (lead); resources (lead); supervision (lead); writing – original draft (equal); writing – review and editing (equal). Acknowledgments The authors are grateful to Lei Shi, Jing An, and Peng Ding for assistance with field recordings. 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Keywords behavioral ecology comparative ecological experiment freshwater vertebrate Authors Affiliations Mengmeng Gong Xinjiang Agricultural University View all articles by this author Yaming Sun Xinjiang Agricultural University View all articles by this author Jamila Molamat Xinjiang Agricultural University View all articles by this author Bayangul Mametnur Xinjiang Agricultural University View all articles by this author Medine Mutellip Xinjiang Agricultural University View all articles by this author Yan Wang Xinjiang Agricultural University View all articles by this author Lu Zhou 0000-0003-2438-5198 [email protected] Xinjiang Agricultural University View all articles by this author Metrics & Citations Metrics Article Usage 193 views 109 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mengmeng Gong, Yaming Sun, Jamila Molamat, et al. The coexistence strategy during the breeding season—the acoustic niche partitioning of Pelophylax terentievi and P. nigromaculatus in Xinjiang, China. Authorea . 27 February 2026. DOI: https://doi.org/10.22541/au.177218309.92589351/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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