Ministry of Education Key Laboratory for Biodiversity and Ecological Engineering, College of Life Sciences, Beijing Normal University, Beijing 100875, China
2.
Division of Biology and Conservation Ecology, School of Science and the Environment, Manchester Metropolitan University, Chester Street, Manchester M15GD, UK
Soft song is a low-amplitude song produced by many birds. Recent studies have confirmed that soft song is an aggressive signal. For example, the Brownish-flanked Bush Warblers Cettia fortipes use soft song in male-male conflicts, particularly prior to attacks. Although stable signaling systems require that signals be honest on average, models predict that cheating is an acceptable strategy for some individuals or in some contexts.
Methods
This study aimed to test the reliability of soft song as an aggressive signal in the brownish-flanked bush warbler. We used mounted specimens accompanied by broadcast songs or soft songs to simulate a male attempting to invade an existing territory.
Results
We found the mounted specimen that coupled playback of soft songs suffered more and quicker attacks from the territory owner and that the relationship between soft song and subsequent attack in the territory owner was far from perfect. We observed territory owners that both over-signaled (i.e., produced soft song but did not attack) and under-signaled (i.e., attacked without producing soft song). Under-signaling territory owners were relatively more commonly than were over-signaling territory owners, particularly in simulated intrusion that coupled playback of soft song with a mount specimen.
Conclusions
We discuss the cost of producing soft song and the potential benefit of the unreliable use of soft song and propose a new hypothesis for under-signaling with soft song; i.e., under-signaling territory owners might benefit from taking the initiative in fights.
Understanding the origins of organismal diversity is one of the most enduring and important quests in biology. Dispersal is an important intrinsic factor to influence intraspecific divergence and speciation for organisms (Leal et al., 2019; Avaria-Llautureo et al., 2021). According to the intermediate dispersal hypothesis (hereafter, IDH) of species diversity, dispersal has both stimulatory and inhibitory effects on diversification. On one hand, dispersal maintains gene flow and keeps a high level of genetic connectivity among populations (Gavrilets and Vose, 2005), which would inhibit intraspecific genetic differentiation and speciation. On the other hand, higher dispersal ability facilitates species to surmount larger geographic barriers and colonize newly suitable habitats (Hoffmann and Sgrò, 2011; Alzate and Onstein, 2022). Lineages distributed over larger geographical range sizes are prone to subdivision by barriers and further promote intraspecific genetic differentiation and speciation. Thus IDH predicts that lineages with intermediate dispersal abilities experience a blend of range expansion and geographical subdivision that maximizes speciation rates.
Researches of dispersal on evolutionary divergence have been carried out across a variety of animal taxa, such as arachnids (Luo et al., 2020; Suárez et al., 2022), arthropods (Ortego et al., 2021), reptiles (Sheu et al., 2020), fish (Avaria-Llautureo et al., 2021), birds (Belliure et al., 2000; Claramunt et al., 2012), and mammals (Rolland et al., 2015), and birds were identified as the most frequently studied taxonomic group. The ability of flight endowed by wings, has long been regarded as a hallmark of evolutionary innovation in birds. This adaptation potentially enhances their dispersal capacity to radiate breeding and foraging activities to various areas, contributed to the expansion in genetic divergence and taxonomic diversity. Using hand-wing index (hereafter, HWI), a most commonly proxy for dispersal ability, to measure dispersal ability is widespread in the studies of birds (Lockwood et al., 1998; Claramunt et al., 2012; Sheard et al., 2020; Arango et al., 2022). Meanwhile, previous researches about the influence of dispersal (using by HWI) on evolutionary divergence in birds produced obscure results at both inter-species and intra-species levels (negative correlation: Claramunt et al., 2012; Weeks and Claramunt, 2014; no significant correlation: Kennedy et al., 2016).
However, research of this relationship in birds remains scarce, and the sample size is relatively small (Belliure et al., 2000). Birds are one of the most conspicuous and diverse groups of modern vertebrates, and flight is the most remarkable key innovation for it (Hunter, 1998; Brusatte et al., 2015; Crouch and Tobias, 2022). Improving our understanding of the effects of HWI on intraspecific divergence is therefore critical, not only because it provides an excellent opportunity to understand the relationship between life history trait and evolutionary divergence, but it also plays a fundamental role in avian diversity conservation since subspecies is widely used in conservation biology and represents a unit of biological organization (Haig et al., 2006; Braby et al., 2012).
Here, we investigate the influence of dispersal on intraspecific divergence (assessed as number of subspecies) across bird species in the world. The study system has three major advantages in addressing this issue. First, by assessing the relationship between dispersal and evolutionary divergence at a global scale (7092 species), we will obtain a broader perspective, since relatively small sample size may bias the result of study. Second, we use the HWI to quantify the dispersal ability, for which HWI has been proved to be a good proxy (Dawideit et al., 2009; Claramunt et al., 2012; Weeks and Claramunt, 2014; Pigot and Tobias, 2015; Kennedy et al., 2016; Stoddard et al., 2017; Sheard et al., 2020; Arango et al., 2022). Third, ecological factors, such as temperature and precipitation, play a key role in avian intraspecific divergence (Botero et al., 2014; Condamine et al., 2019). In this study we will control for these ecological factors that have often been overlooked in previous studies.
2.
Material and methods
2.1
Data collection
We obtained the data on subspecies richness of species and climate (annual temperature and precipitation) within a species' range from Botero et al. (2014), and data for HWI were obtained from Sheard et al. (2020). We also collected information about diet, habitat, association with islands, body mass, and range size for each species from Sheard et al. (2020). We assigned each species as being herbivorous (fruit, seeds, nectar, plants), carnivorous (invertebrates, vertebrates, scavenger), or omnivorous. Each species was classified into one of the habitat types: open (desert, grassland, open water, low shrubs, rocky habitats, seashores, and cities), semi-open (open shrub land, scattered bushes, parkland, dry or deciduous forest, thorn forest), and dense (tall evergreen forest with a closed canopy, or in the lower vegetation strata of dense thickets, shrubland or marshland), for details see Tobias et al. (2016). Association with islands is a variable that quantifies the proportion of islands in the distribution range (Sheard et al., 2020).
2.2
Statistical analysis
We employed Markov chain Monte Carlo generalized linear mixed models (MCMCglmm) to test whether intraspecific divergence was associated with HWI, and flightless birds (Sayol et al., 2020) were removed from the dataset before analysis. In any interspecific comparative analysis, a key statistical issue is that the species compared are not independent since they share a common evolutionary history. Hence, a tree describing the evolutionary relationship among the sampled species was used to solve the non-independence problem (e.g., Chen et al., 2021; Liu et al., 2023; Li et al., 2024). The phylogenetic tree used in this study was a consensus tree based on 1000 trees that were downloaded from http://birdtree.org (Jetz et al., 2012). Analyses were conducted using the MCMCglmm package (Hadfield, 2010). We ran each model with 300, 000 iterations, 2000 burn-in, and thinning of 50 to ensure satisfactory convergence. Convergence was confirmed by examining the plots of chain mixing and the degree of autocorrelation, a commonly used method for convergence diagnosis (e.g., Wang and Lu, 2018; Li et al., 2024).
For each model, we included diet, habitat, body mass, association with islands, range size, and climate to control for their potential impact on the response variables. Diet is thought to be a evolutionary force for the divergence of species (Burin et al., 2016). Habitat is related with the number of subspecies in species (Dingle et al., 2008). Body mass is an important life history trait that affects the number of subspecies (Phillimore et al., 2007; Botero et al., 2014). Island species has a higher speciation rate than the mainland species (Hastings and Gavrilets, 1999). Migration status was not included in the model given that migratory behavior is closely associated with HWI (Kennedy et al., 2016). Species with strong dispersal ability is often assumed to promote range expansion by facilitating the colonization of new areas, thereby promoting evolutionary divergence. However, the empirical evidence linking dispersal ability to range size is obscure (Alzate and Onstein, 2022). Although a recent study showed that a weak positive correlation existed between HWI and range size in birds, the study inappropriately included flightless birds (Sheard et al., 2020). Hence, we added range size as a control variable in the final model, and the variance inflation factors (VIF) of all independent variables were less than 2, indicating that no collinearity existed among variables included in the model (Montgomery et al., 2012). In addition, we also tested whether a quadratic correlation existed between HWI and number of subspecies by using MCMCglmm model.
All analyses were performed with R software (R Core Team, 2022), and phylogenetic tree was drawn on https://www.chiplot.online/. All values given were mean ± standard error (S.E.). Prior to analyses, association with islands was arcsine transformed and all other continuous independent variables were standardized (scaled to a mean of 0 and a variance of 1) to meet the assumptions required for linear regressions.
3.
Results
Our data set consists of 7092 species (1787 genera, 200 families, and 28 orders; Fig. 1; Appendix Table S1). The number of subspecies ranged from 1 to 73 (3.01 ± 0.04), HWI ranged from 0.07 to 74.80 (25.60 ± 0.17), body mass ranged from 1.90 to 11, 236.10 g (234.00 ± 8.50 g), range size ranged from 1 to 6303 km2 (275.85 ± 5.67 km2), association with islands ranged from 0 to 1 (0.13 ± 0.003), annual temperature ranged from −15.36 to 28.14 ℃ (20.00 ± 0.09 ℃), and annual precipitation ranged from 12.24 to 5321.25 mm (1543.53 ± 9.78 mm).
Figure
1.
The phylogenetic distribution of 7092 bird species in the study. For aesthetic purposes and to avoid skew from extreme outlier data, the number of subspecies was set to 10 for species that contain more than 10 subspecies (n = 272, 3.8%); the score of speciation rate was set to 1 for species whose speciation rates were larger than 1 (n = 58, 0.6%); and the score of HWI was set to 60 for species whose HWI was larger than 60 (n = 516, 5.4%). HWI: hand-wing index; SR: speciation rate; NOS: number of subspecies.
The MCMCglmm analyses demonstrate that a significant negative relationship exists between the HWI and number of subspecies among 7092 bird species, after correcting for body mass, range size, climate factors, association with islands, diet and habitat type (Table 1; Fig. 2). In addition, a significant negative correlation between body mass and subspecies richness was noted (Table 1; Fig. 2). We also detected a significant positive effect of range size on variation in the number of subspecies, that is, subspecies richness was higher in species with a widespread distribution range (Table 1, Fig. 2). Our analyses revealed that annual temperature significantly predicted interspecific variation in subspecies richness, whereas annual precipitation did not (Table 1, Fig. 2). Species inhabiting an island-like habitat had lower subspecies richness (Table 1, Fig. 2). The subspecies richness of omnivorous species was higher than that of carnivorous species, while there was no significant difference in subspecies richness between herbivorous and carnivorous species (Table 1, Fig. 2). Open-habitat species appeared to have a lower subspecies number than dense-habitat species, while the species in the semi-open habitat have no significant difference from the ones in dense habitat (Table 1, Fig. 2).
Table
1.
MCMCglmm model analyses to investigate whether variation in number of subspecies depended on hand-wing index (HWI) across 7092 bird species in the world, controlling for the effect of several confounding factors. Statistically significant variables are highlighted in bold.
Figure
2.
Coefficient estimates of models predicting number of subspecies. Coefficient estimates of models considering hand-wing index (HWI), body mass, range size, annual temperature, annual precipitation, association with islands, diet, habitat as predictors, while controlling for phylogenetic effects (for details see Table 1). A negative effect means that the predictor reduces intraspecific divergence.
Moreover, we also tested whether a quadratic correlation existed between HWI and number of subspecies, and the results of repeated analyses showed that no quadratic correlation was found, and the correlations between the control variables and subspecies richness were highly consistent (Table 2).
Table
2.
MCMCglmm model analyses to investigate whether a quadratic correlation exists between hand-wing index (HWI) and number of subspecies. Statistically significant variables are highlighted in bold.
In this study, we found that strong dispersal ability is associated with low subspecies richness across 7092 avian species. To our knowledge, this is the first study to examine the effect of dispersal ability on subspecies richness on a global scale in birds. HWI is a phenotypic trait that exerts significant influence on avian dispersal, and subspecies is an important conception in speciation theory and conservation biology. Investigating, hence, the effect of HWI on subspecies richness would not only broaden our understanding of the association between morphological traits and evolutionary divergence in birds, but also contribute to the conservation of avian species diversity.
Our results may support the scenario that species with low HWI have low dispersal ability and less gene flow between populations, then morphological and molecular differences would be easy to form. If geographic barriers to lower dispersal ability were semipermeable, that is, if dispersal across a barrier is possible but difficult, then reduced gene flow would allow for divergence in some important traits such as sexual signals even without strong selection for local ecological adaptation (Uy et al., 2018). This result is consistent with results of previous studies carried out at both population (Crochet, 2000) and species (Belliure et al., 2000; Claramunt et al., 2012; Weeks and Claramunt, 2014) levels in birds.
Meanwhile, no quadratic correlation exists between HWI and subspecies richness in our study, which is consistent with previous studies that demonstrated that a quadratic model is not better than linear model to describe the relationship between dispersal ability and subspecies richness (Claramunt et al., 2012; Weeks and Claramunt, 2014; Kennedy et al., 2016). According to the intermediate dispersal hypothesis (hereafter, IDH), the unimodal relationship between dispersal ability and diversification is associated by the fact that dispersal could increase gene flow to inhibit differentiation and increase range expansion to promote differentiation. However, little evidence could be found in previous studies to support the latter view, despite range expansion may play an important role in diversification (Milá et al., 2007). Our results showed a significant positive correlation between range size and subspecies richness. For birds, the relative impacts of gene flow and range expansion due to dispersal may be more significant than for other animals because of their ability to fly. However, we did not observe a colinearity between range size and HWI, which means no strong correlation between them at the global scale in birds. Meanwile, existing studies also still provide limited evidence for a correlation between distributional range and HWI (Alzate and Onstein, 2022). After accounting for the potentially stimulating effects of colonization and range expansion, the inhibitory effect of increased levels of gene flow endowed by high HWI on the number of subspecies in bird is still significant. Therefore, these results suggest that stronger gene flow induced by higher HWI significantly reduced the degree of intraspecific divergence and isolation remains an important driver of intraspecific genetic divergence in birds.
However, some researchers believe that when more bird species are included, the unimodal relationship between HWI and diversification may be easier to appear (Tobias et al., 2020). We failed to verify this hypothesis by using almost all bird species in the world, which indicate that using HWI to measure dispersal ability may not effectively test IDH in birds (Alzate and Onstein, 2022). On one hand, HWI as a measurement of dispersal ability may not effectively represent the range expansion capacity of bird species. The 'transporter' hypothesis predicts that birds may experience repeated losses of dispersal in 'insular' habitats (e.g., island and alpine ecosystems), which means the variation in wing morphology may obscure this pattern. For example, reduced dispersal ability may be selected after lineages with moderate dispersal capacity colonized islands (Leisler and Winkler, 2015; Kennedy et al., 2016; Hosner et al., 2017). On the other hand, strong dispersal ability is helpful but not sufficient to drive the completion of speciation cycle of birds (Tobias et al., 2020). Hence, we should be cautious when using HWI to verify the IDH, despite HWI is a commonly proxy for flight efficiency and dispersal ability as it affects dispersal distances and decreases population geographical isolation and spatial genetic differentiation in birds (Pigot and Tobias, 2015; Sheard et al., 2020; Alzate and Onstein, 2022). Nevertheless, our study does not deny that the IDH may be verified at intra-species or other levels. IDH can still help us to understand and predict current biological patterns since it provides a conceptual link to analyze the evolutionary cost/benefit of dispersal ability and its impact on species diversity, but a better exploration of indicators for dispersal agents and diversity indices is needed.
In addition, our comparative analyses revealed that body mass is significantly linked to subspecies richness, which is consistent with previous researches (Belliure et al., 2000; Botero et al., 2014). The results also showed that the larger geographical range is associated with higher subspecies richness across bird species, which is probably because a widespread distribution range can lead to population differentiation due to different local selective pressures (Belliure et al., 2000; Phillimore et al., 2007). The annual temperature is positively associated with subspecies richness, which is consistent with the argument that higher temperatures cause shorter generation times, faster mutation rates and hence higher rates of diversification (Rohde, 1992). The subspecies richness of omnivores is higher than that of carnivores, possibly due to that the former occupies larger range size (Galiana et al., 2023). Species in dense habitats possess higher subspecies richness than those in open habitats, presumably because dispersal limitation drives allopatric divergence by restricting gene flow between populations.
Dispersal plays an important role in the conservation and recovery of wild populations under habitat fragmentation and loss (Smith et al., 2014; Kennedy et al., 2016). Meanwhile, dispersal traits can help us move towards an in-depth understanding of how species respond to climate and land-use changes (Travis et al., 2013; Bregman et al., 2014). In addition, subspecies is widely used in conservation biology since it represents a unit of biological organization (Haig et al., 2006; Braby et al., 2012). Especially in the absence of molecular data, the conservation utility of subspecies becomes more important because subspecies provides an effective way to estimate intraspecific genetic diversity (Phillimore and Owens, 2006). Therefore, in the context of biodiversity loss caused by human activities, the negative correlation between dispersal ability and subspecies richness will provide a theoretical basis for the formulation of bird species diversity conservation strategies.
CRediT authorship contribution statement
Haiying Fan: Writing – original draft, Formal analysis, Data curation. Weibin Guo: Writing – original draft, Formal analysis, Data curation. Buge Lin: Investigation, Data curation. Zhiqing Hu: Investigation, Data curation. Changcao Wang: Writing – review & editing, Writing – original draft, Investigation, Conceptualization. Shaobin Li: Writing – review & editing, Investigation, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We thank Hongtao Xiao for helpful suggestions on statistical analyses.
Akcay C, Beecher MD (2012) Signalling while fighting: further comments on soft song. Anim Behav 83:e1-e3
Akcay C, Reed VA, Campbell SE, Templeton CN, Beecher MD (2010) Indirect reciprocity: song sparrows distrust aggressive neighbours based on eavesdropping. Anim Behav 80:1041-1047
Akcay C, Tom ME, Holmes D, Campbell SE, Beecher MD (2011) Sing softly and carry a big stick: signals of aggressive intent in the song sparrow. Anim Behav 82:377-382
Akcay C, Tom ME, Campbell SE, Beecher MD (2013) Song type matching is an honest early threat signal in a hierarchical animal communication system. P Roy Soc Lond B Bio 280:20122517
Anderson RC, Searcy WA, Hughes M, Nowicki S (2012) The receiver-dependent cost of soft song: a signal of aggressive intent in songbirds. Anim Behav 83:1443-1448
Ballentine B, Searcy WA, Nowicki S (2008) Reliable aggressive signalling in swamp sparrows. Anim Behav 75:693-703
Dabelsteen T, McGregor PK, Lampe H, Langmore N, Holland J (1998) Quiet song in song birds: an overlooked phenomenon. Bioacoustics 9:89-105
del Hoyo A, Elliott A, Sargatal J (2006) Handbook of the birds of the world, vol. 11: old world flycatchers to old world warblers. Lynx Edicions, Barcelona
Grafen A (1990) Biological signals as handicaps. J Theor Biol 144:517-546
Hauser MD (1996) The evolution of communication. MIT Press, Cambridge
Hof D, Hazlett H (2010) Low-amplitude song predicts attack in a North American wood warbler. Anim Behav 80:821-828
Hof D, Podos J (2013) Escalation of aggressive vocal signals: a sequential playback study. P Roy Soc Lond B Bio 280:20131553
Kennerley P, Pearson D (2010) Reed and bush warblers. Christopher Helm, London
Martin WF (1971) Mechanics of sound production in toads of the genus Bufo: passive elements. J Exp Zool 176:273-276
Martin WF (1972) Evolution of vocalizations in the genus Bufo. In: Blair WF (ed) Evolution in the genus Bufo. University Texas Press, Austin, pp 279-309
Maynard SJ, Harper D (2003) Animal signals. Oxford University Press, Oxford
Maynard SJ, Parker GA (1976) The logic of asymmetric contests. Anim Behav 24:159-175
Maynard SJ, Price GR (1973) The logic of animal conflict. Nature 246:15-18
Molles LE, Vehrencamp SL (2001) Songbird cheaters pay a retaliation cost: evidence for auditory conventional signals. P Roy Soc Lond B Bio 268:2013-2019
Morton ES (2000) An evolutionary view of the origins and functions of avian vocal communication. Jpn J Ornithol 49:69-78
Moseley DL, Lahti DC, Podos J (2013) Responses to song playback vary with the vocal performance of both signal senders and receivers. P Roy Soc Lond B Bio 280:20131401
Podos J (1996) Motor constraints on vocal development in a songbird. Anim Behav 51:1061-1070
Podos J (1997) A performance constraint on the evolution of trilled vocalizations in a songbird family (Passeriformes: Emberizidae). Evolution 51:537-551
Rek P, Osiejuk TS (2011) Nonpasserine bird produces soft calls and pays retaliation cost. Behav Ecol 22:657-662
Searcy WA, Beecher MD (2009) Song as an aggressive signal in songbirds. Anim Behav 78:1281-1292
Searcy WA, Nowicki S (2005) The evolution of animal communication: reliability and deception in signaling systems. Princeton University Press, Princeton
Searcy WA, Nowicki S (2006) Signal interception and the use of soft song in aggressive interactions. Ethology 112:865-872
Searcy WA, Anderson RC, Nowicki S (2006) Bird song as a signal of aggressive intent. Behav Ecol Sociobiol 60:234-241
Searcy WA, Anderson RC, Ballentine B, Nowicki S (2013) Limits to reliability in avian aggressive signals. Behaviour 150:1129-1145
Searcy WA, Akcay C, Nowicki S, Beecher MD (2014) Aggressive signaling in song sparrows and other songbirds. Adv Stud Behav 46:89-125
Sprau P, Roth T, Amrhein V, Naguib M (2012) Distance-dependent responses by eavesdroppers on neighbour-stranger interactions in nightingales. Anim Behav 83:961-968
Stoddard PK, Beecher MD, Campbell SE, Horning CL (1992) Song-type matching in the song sparrow. Can J Zool 70:1440-1444
Szalai F, Szamado S (2009) Honest and cheating strategies in a simple model of aggressive communication. Anim Behav 78:949-959
Szamado S (2000) Cheating as a mixed strategy in a simple model of aggressive communication. Anim Behav 59:221-230
Templeton CN, Akcay C, Campbell SE, Beecher MD (2012) Soft song is a reliable signal of aggressive intent in song sparrows. Behav Ecol Sociobiol 66:1503-1509
van Staaden MJ, Searcy WA, Hanlon RT (2011) Signaling aggression. In: Huber R, Bannasch DL, Brennan P (eds) Advances in genetics, vol 75. Academic, Burlington, pp 23-49
Vehrencamp SL (2000) Handicap, index, and conventional signal elements of bird song. In: Espmark Y, Amundsen T, Rosenqvist G (eds) Signals: signalling and signal design in animal communication. Tapir Academic Press, Trondheim, pp 277-300
Xia CW, Liu JY, Zhang YY (2013a) Are different song types used in different contexts in brownish-flanked bush warblers? Zool Sci 30:699-703
Xia CW, Liu JY, Alström P, Wu Q, Zhang YY (2013b) Is the soft song of the brownish-flanked bush warbler an aggressive signal? Ethology 119:653-661
Table
1.
MCMCglmm model analyses to investigate whether variation in number of subspecies depended on hand-wing index (HWI) across 7092 bird species in the world, controlling for the effect of several confounding factors. Statistically significant variables are highlighted in bold.
Table
2.
MCMCglmm model analyses to investigate whether a quadratic correlation exists between hand-wing index (HWI) and number of subspecies. Statistically significant variables are highlighted in bold.
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