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Where has the city choir gone? Loss of the temporal structure of bird dawn choruses in urban areas
Landscape and Urban Planning 194 (2020) 103665
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Research Paper
Where has the city choir gone? Loss of the temporal structure of bird dawn choruses in urban areas
T
Oscar H. Marín-Gómeza, Wesley Dáttilob, J. Roberto Sosa-Lópezc, Diego Santiago-Alarcond, ⁎ Ian MacGregor-Forsa, a
Red de Ambiente y Sustentabilidad, Instituto de Ecología A.C., Xalapa, Veracruz, Mexico Red de Ecoetología, Instituto de Ecología A.C., Xalapa, Veracruz, Mexico c CONACYT-Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional Unidad Oaxaca (CIIDIR), Instituto Politécnico Nacional, Oaxaca, Mexico d Red de Biología y Conservación de Vertebrados, Instituto de Ecología A.C., Xalapa, Veracruz, Mexico b
A R T I C LE I N FO
A B S T R A C T
Keywords: Acoustic space Ecological filtering Modularity Multi-species choruses Urban ecology
Living in the city represents a great challenge for organisms that are exposed to the novel environmental conditions inherent to urbanization. Recent studies have highlighted the ecological impact that urbanization poses on the acoustic phenotype and singing routines of birds. However, the organization and structure of avian dawn choruses in urban settings remains largely unexplored. In this study, we assessed the temporal structure of avian dawn choruses in an intra-urban area and a peri-urban forest using bipartite network analyses. We predicted a random network structuring of dawn choruses across time at the intra-urban area, while expected a non-random structure (i.e., modular or nested) at the peri-urban forest. While we detected different groups of birds vocalizing together temporarily, following a modular pattern in both studied conditions, only the one from the peri-urban forest showed a sequential temporal structure of dawn choruses. Avian dawn choruses from both intra-urban and peri-urban areas were mainly comprised by phylogenetically unrelated species (i.e., random phylogenetic structure), also exhibiting low overlap on singing frequencies. Our results are in agreement with the temporal partitioning of the acoustic space in the peri-urban forest. Our findings also suggest that the absence of temporally-structured modules of bird dawn choruses at heavily-urbanized areas could be related to the depauperization of the avian community at intra-urban areas as a sequel of ecological filtering, as well as the consequent importance of the dominance of the acoustic space by invasive species.
1. Introduction Urbanization represents a biodiversity threat across landscapes, causing the simplification of local communities in urban centers by filtering out species from regional pools (Aronson et al., 2016; Croci, Butet, & Clergeau, 2008; McKinney, 2008). Drastic habitat transformations associated with urban sprawling generate novel environmental conditions that represent an important ecological barrier, often selecting groups according to traits that make them more or less tolerant to urbanization (Aronson et al., 2014; La Sorte et al., 2018; McKinney, 2008). As a result of the transformation of habitats driven by urbanization and the non-random filtering of species into urban centers, the structure and composition of avian communities that thrive in heavilyurbanized areas differ importantly when contrasted with those from
surrounding systems, such as peri-urban and extra-urban areas (Aronson et al., 2014; MacGregor-Fors, 2010; McKinney, 2008). The plethora of urban hazards to which organisms that dwell or use cities are exposed to (e.g., vegetation replacement, urban heat island, pollution; Gaston, Davies, Nedelec, & Holt, 2017; Grimm et al., 2008; Kekkonen, 2017) have shown to trigger physiological, morphological, and behavioral adjustments (Johnson & Munshi-South, 2017; Luniak, 2004). Among the best studied behavioral modifications of urban birds, those focused on acoustic communication head the list (Brumm, 2004; Slabbekoorn & Peet, 2003; Slabbekoorn, 2013). Most avian vocal adjustments in urban areas have been related to anthropogenic noise and artificial light at night, while other urban hazards have been largely neglected (Gorissen, Snoeijs, Van Duyse, & Eens, 2005; Mendes, ColinoRabanal, & Peris, 2011). For instance, some urban birds deal with noise
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Corresponding author at: Red de Ambiente y Sustentabilidad, Instituto de Ecología, A.C., Carretera antigua a Coatepec 351 El Haya, Xalapa 91070, Veracruz, Mexico. E-mail addresses: [email protected] (O.H. Marín-Gómez), [email protected] (W. Dáttilo), [email protected] (J.R. Sosa-López), [email protected] (D. Santiago-Alarcon), [email protected] (I. MacGregor-Fors). https://doi.org/10.1016/j.landurbplan.2019.103665 Received 29 June 2018; Received in revised form 13 June 2019; Accepted 9 September 2019 0169-2046/ © 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Adjacency matrix representations of the expected patterns of avian dawn choruses for the two studied conditions (i.e., intra- and peri-urban) based on a bipartite network approach: (a) random, (b) modular, and (c) nested. Each filled square indicates an observed occupation of the acoustic space (i.e., duration of the singing activity) by each species in a given 5-min recording segment. In this illustrative example, the number of species for the peri-urban networks is nine, and five for the intra-urban condition.
of multi-species dawn choruses in urban settings remains unexplored. The use of the acoustic space in avian dawn choruses has been studied by compiling species co-occurrence lists and afterwards measuring the overlap on spectral and temporal signals, either through pairwise comparisons among multiple species or by assessing the sequence in which species join to the dawn chorus (Luther, 2009; Planqué & Slabbekoorn, 2008; Stanley et al., 2016; Tobias et al., 2014; Xia et al., 2018). However, approaches derived from network theory could help analyzing the use of the acoustic space by birds when participating in dawn choruses (Malavasi & Farina, 2013; Tobias et al., 2014; Xia et al., 2018). Specifically, studying the use of the acoustic space during dawn choruses can be tackled through bipartite networks. In this case, bipartite networks are based on co-occurrence matrices, where information is organized considering the singing activity of the species participating in the chorus in a given time (Dehling, 2018). Therefore, following this approach could bring new insights into the patterns of temporal use of the acoustic space by birds and their implications on the acoustic niche divergence of multi-species avian dawn choruses. Different types of non-random patterns could best describe avian dawn choruses, as those previously reported in other biological systems (from molecular interactions between proteins to the co-occurrence of vertebrates in metacommunities; Dormann, Fründ, & Schaefer, 2017). This is due to the fact that biological systems tend to follow the same rules of self-organization in nature (Dehling, 2018). The most frequently used descriptors of the structure of networks are modularity and nestedness (Dormann et al., 2017). Modularity describes the tendency of different subsets of species in the network to co-occur more frequently with each other than with the rest of the species in the network, generating modules or compartments (Dehling, 2018; Dormann & Strauss, 2014). Nestedness describes the hierarchical organization of co-occurring species over time, where species in less diverse sites comprise a subset of that of more diverse ones into inclusive subsets (Almeida-Neto & Ulrich, 2011; Dehling, 2018). In the context of acoustic space, a modular pattern would represent groups of co-occurring bird species singing at different times, generating compartments of choruses during the morning (i.e., temporal acoustic partitioning); while nestedness would represent groups of bird species singing at dawn as subsets of those species singing throughout morning (i.e., temporal acoustic overlap; see Fig. 1). In this study we assessed the temporal structure of avian dawn
pollution by adjusting song frequencies or changing the timing of their singing routines to avoid communication masking (Halfwerk, Lohr, & Slabbekoorn, 2018; Luther & Gentry, 2013; Slabbekoorn & Peet, 2003; Slabbekoorn, 2013; Warren, Katti, Ermann, & Brazel, 2006). Moreover, birds living in light polluted areas have shown to adjust their singing routines (Da Silva & Kempenaers, 2017; Gaston et al., 2017; Miller, 2006). Despite the wide interest on understanding shifts on the acoustic phenotype related to urbanization, such studies are focused on the behavioral responses of a handful species of temperate regions, without considering a community approach. Dawn choruses are biological phenomena where males and females of multiple species co-occur in a narrow timeframe, producing a continuous and high rate of singing activity around sunrise (Catchpole & Slater, 2008; Planqué & Slabbekoorn, 2008; Staicer, Spector, & Horn, 1996; Stanley, Walter, Venkatraman, & Wilkinson, 2016; Tobias, Planque, Cram, & Seddon, 2014; Xia, Lloyd, Shi, Wei, & Zhang, 2018). At the species level, singing at dawn can have significant advantages, such as enhancing signal transmission, improving the odds of securing a territory, and even increasing reproductive success (Brown & Handford, 2003; Catchpole & Slater, 2008; Kacelnik & Krebs, 1982; Liu & Kroodsma, 2007; Poesel, Kunc, Foerster, Johnsen, & Kempenaers, 2006; Staicer et al., 1996). However, at the community level, multi-species dawn choruses can lead to acoustic space partitioning of spectral and temporal domains, minimizing interference from sounds produced by other species or sources (Aide, Hernández-Serna, Campos-Cerqueira, Acevedo-Charry, & Deichmann, 2017; Farina et al., 2015; Hart, Hall, Ray, Beck, & Zook, 2015; Luther, 2009; Malavasi & Farina, 2013; Marler, 1960; Planqué & Slabbekoorn, 2008; Stanley et al., 2016; Tobias et al., 2014; Xia et al., 2018). The resulting pattern in the acoustic space can be random, overdispersed or clustered, and depends on factors such as regional species pools, habitat structure, environmental conditions, and phylogenetic relationships (Luther, 2009; Stanley et al., 2016; Tobias et al., 2014). Although dawn choruses have been studied mainly in non-anthropogenic ecosystems (Luther, 2009; Malavasi & Farina, 2013; Stanley et al., 2016; Tobias et al., 2014), there is a recent interest in understanding how urbanization can disrupt the onset and timing of the chorusing behavior at the species level (Da Silva & Kempenaers, 2017; Dorado-Correa, Rodríguez-Rocha, & Brumm, 2016; Gil, Honarmand, Pascual, Pérez-Mena, & Macías Garcia, 2015; Lee, MacGregor-Fors, & Yeh, 2017). Yet, the organization and structure 2
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continuous 100 min recording starting with the nautical twilight until 50 min after sunrise. Nautical twilight and sunrise times were obtained from the US Naval Observatory (http://aa.usno.navy.mil). We used a TASCAM DR-100mkII digital recorder with a Sennheiser ME66 directional microphone mounted to a tripod 1.5 m above the ground, pointing the microphone towards the locations with high birdsong activity (this microphone yields great attenuation at its sides and maximizes pickup at the front, allowing to record distant bird songs despite background noise levels; Stanley et al., 2016). We recorded all tracks in WAV format (16 bits, 44.1 kHz sampling frequency). Considering the human activity and noise levels patterns during weekends that could influence the vocal behavior of birds (Brumm, 2004), we restricted the recording sessions to weekdays. To reduce potential non-measured biases (e.g., weather, noise levels), we alternated surveys between intra-urban and peri-urban conditions on a daily basis.
choruses at two ends of an urbanization intensity gradient (i.e., a periurban forest and a heavily-urbanized area) using bipartite network analyses. Our main goal was to explore whether avian dawn choruses from heavily-urbanized areas showed a temporally structured pattern when contrasted with those from a peri-urban forest. According to the acoustic space hypothesis, populations of birds inhabiting depauperate avian communities may produce more variable vocal behaviors due to a reduction in the competition for the acoustic space, while populations of birds belonging to richer avian communities may produce less variable vocal behaviors due to an increase in the competition for the acoustic space (Marler, 1960, Miller, 1982). Thus, considering the depauperization of bird communities in heavily-urbanized areas as a result of a non-random filtering process of regional species pools (Aronson et al., 2014; La Sorte et al., 2018; MacGregor-Fors, 2010; Marzluff, 2017; McKinney, 2008), we expected a random network structuring of dawn choruses across time at the heavily-urbanized areas, while predicted a non-random structure (i.e., modular or nested) at peri-urban areas, where bird communities are more diverse and less different to regional avifaunas from well-preserved ecosystems (see Fig. 1). Given that the network structuring of dawn choruses could reflect acoustic space partitioning (Aide et al., 2017; Chek, Bogart, & Lougheed, 2003; Luther, 2009; Marler, 1960; Planqué & Slabbekoorn, 2008; Stanley et al., 2016), we tested the degree of spectral overlap (i.e., range of frequencies that coincide among two or more bird species) by comparing low, high, and peak frequencies among bird species within the assessed dawn choruses. Finally, as closely related species are prone to share similar acoustic traits (e.g., Cadotte & Davies, 2016; Tobias et al., 2014), we assessed the degree of phylogenetic relatedness within species participating in the studied dawn choruses.
2.3. Acoustic analysis We generated spectrograms using Raven Pro v.1.5 (Bioacoustics Research Program, 2014) by setting a Hanning window and FFT = 1024 (50% overlap) to create a catalog of songs and to identify vocalizations recorded during dawn choruses. For this procedure, we compared our samples with recordings available in Xenocanto (http:// xeno-canto.org/), the Macaulay Library (http://macaulaylibrary.org/), and regional audio libraries (González-García & Celis-Murillo, 2008). To ensure the accuracy and consistence of all measurements, all identifications were made by a single observer (OHM-G). Furthermore, Fernando González-García, curator of the Library of Sounds of Birds of Mexico (Biblioteca de Sonidos de Aves de México; http://www1.inecol. edu.mx/sonidos/menu.htm) and regional ornithological expert confirmed species identifications. For practical matters, we divided each single 100 min recording into 5-min segments to record the number of vocalizations emitted by each species given that previous studies have shown that processing bird vocalizations in 5-min windows effectively captures the temporal patterns of dawn chorus singing activity (Perez-Granados, Osiejuk, & Lopez-Iborra, 2018; Stanley et al., 2016). We distinguished bird songs from calls based on their acoustic structure, considering songs as long vocalizations conformed by a sequence of stereotyped syllables with gaps ≤ 1 s (Catchpole & Slater, 2008; Sosa-López & Mennill, 2014). For some species (i.e., Amazona albifrons, Cardellina pusilla, Dumetella carolinensis, Hylocichla mustelina, Psilorhinus morio), we were unable to identify the beginning and the ending of their songs. In such cases, we counted the number of syllables. Simple foraging, alarm, begging, and flight calls were not included in our analyses. Given that the number of vocalizations could lead to biases on the assessment of the temporal use of the acoustic space (Planqué & Slabbekoorn, 2008), we quantified the duration of vocalizations rather that the number of vocalization occurrences in the 5-min recording segments. We defined signal duration as the time from the beginning to the end of vocalizations. We then calculated the average duration of the vocalizations by species in each of the 5-min segments. To standardize the retrieved information, we calculated the product of the average duration by the number of vocalizations uttered within the 5-min period. As a result, we obtained one value per species for each of the 5min segments included in the analysis.
2. Methods 2.1. Study area Xalapa is a small-to-medium sized city (~64 km2) with a population of ~ 500,000 inhabitants. It is located on the easternmost mountain slopes of the Trans-Mexican Volcanic Axis, within an elevation gradient of 1120–1720 m a.s.l. (INEGI, 2009). To represent both ends of the urbanization intensity gradient of Xalapa, we selected two sampling conditions (A1. Fig. S1): (1) heavily-urbanized sites of downtown (19°31′54″ N, 96°55′28″ W; hereafter intra-urban condition) and (2) a peri-urban forest remnant (19°30′51″ N, 96°56′17″ W; hereafter periurban condition). The intra-urban condition is characterized by residential and commercial land-uses, with heavy human activity and traffic flow that result in high noise levels (38–88, SPL dB(A), mean = 63.1 ± 13.3; data from pilot survey). The peri-urban condition lies within a Natural Protected Area (i.e., Santuario del Bosque de Niebla; 30 ha of secondary cloud forest surrounded by urban settlements, forest remnants, and shade-grown coffee plantations; WilliamsLinera, 1993), which is open to the public and has low noise levels (25–42 SPL dB(A), mean = 32.7 ± 4.3; data from pilot survey). 2.2. Sampling protocol and dawn chorus recordings We set a 900 × 900 m quadrant within each study condition (i.e., intra-urban, peri-urban) and selected nine evenly distributed sampling recording sites separated by a distance of 300 m from each other (distance that meets the independence assumption among sampling sites for ornithological field studies; Ralph, Geupel, Pyle, Martin, & DeSante, 1993). The total number of independent sampling sites per condition was established based on the total number of sampling points that fit in the peri-urban condition (A1. Fig. S1). We recorded bird vocalizations over a four-week period from 27 March to 29 April 2016, during the peak of the bird breeding season (MacGregor-Fors, González-García, Hernández-Lara, & SantiagoAlarcon, 2018). At each sampling recording site, we obtained one single
2.4. Acoustic measurements In order to evaluate the temporal and spectral acoustic traits of bird vocalizations, we selected high-quality recordings (i.e., high signal to noise ratio) from two sources: (1) focal recordings (targeted to single species) performed at different locations in both intra- and peri-urban areas of Xalapa using a TASCAM DR-100mkII recorder and a Sennheiser ME66 microphone (stored in WAV format at 44.1 kHz/16 bits); and (2) natural-sound libraries (i.e., Xenocanto, Biblioteca de Sonidos de Aves 3
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species interact with high frequency with other species of a module showing fewer links with other species of other modules (see Dormann & Strauss, 2014). The degree modularity (Q) ranges from 0 (no evidence of modules) to 1 (maximum degree of modularity). Q was calculated using the function computeModules of the bipartite library (Dormann et al., 2009), adjusting the number of Markov chain Monte Carlo simulations (MCMC) to 106 steps (i.e., number of steps after which the computation of modules stops if no better division into modules than the current one can be found). We used a high number of steps since modularity maximizing algorithms tend to be stochastic (i.e., when one or more of the input parameters is subject to randomness). We ran the modularity algorithm 10 times for each sampling site, keeping the highest Q value (Dormann & Strauss, 2014). In order to calculate nestedness, we used the WNODF algorithm, a weighted measure for frequency based matrices (Almeida-Neto & Ulrich, 2011). Nestedness ranges from 0 (non-nested) to 100 (completely nested). As modularity and nestedness could both be influenced by matrix size, we generated null models to control for potential effects on our results. We first randomized all matrices using the Patefield algorithm that outputs result with fixed row and column totals. This algorithm generates null models with marginal totals identical to those of the observed network (Dormann et al., 2009). We then estimated the statistical significance of modularity and nestedness with z-score tests, contrasting the observed values (Q and WNODF) against 1000 randomized networks generated by the r2dtable function (Dormann et al., 2009). Z-score values ≥ 2 were considered significantly higher than would be expected by chance and were considered as evidence of high modularity and nestedness (Dormann & Strauss, 2014). To assess the degree of spectral overlap of peak, low, and high frequencies (kHz) of vocalizations among species within dawn choruses, we used the Czechanowski index, which was contrasted with 1000 randomly generated communities (Gotelli & Graves, 1996). The calculated Czechanowski index ranges from 0 (species share no frequency at all) and 1 (species have identical use of frequencies). We finally tested whether dawn choruses consisted of closely (clustered) or distantly (overdispersed) related species by calculating the nearest taxon distance index (MNTD) for each recording site, testing statistical differences by comparing them to 1000 random communities (Cadotte & Davies, 2016). For this, we standardized the MNTD metric by calculating the standardized effect size (SES) against the null values using a z-value. Significant positive values of SES indicate phylogenetic evenness (i.e., co-occurring species more distantly related than expected by chance), while significant negative values of SES indicate phylogenetic clustering (Cadotte & Davies, 2016). We extracted phylogenetic trees for the studied species pool from the phylogeny of extant birds (Jetz, Thomas, Joy, Hartmann, & Mooers, 2012) available in the Birdtree database (www.birdtree.org). We generated a distribution of 1000 randomly selected permutations of the global bird phylogeny (Hackett backbone), and then we used TREEANNOTATOR (Drummond, Suchard, Xie, & Rambaut, 2012) to obtain the best supported phylogenetic tree based on a maximum clade credibility approach (A1. Fig. S2). Unless specified above, we performed all analyses in R (version 3.3.1).
de México) and regional audio libraries (González-García & CelisMurillo, 2008). For species with repertoires (i.e., more than one version of their song), we selected the most common song type used during the dawn chorus, as suggested by Tobias et al. (2014). To account for inter-specific variability, we selected a minimum of three independent recordings per species containing multiple vocalizations (4.6 ± SD 1.5 recordings on average) and extracted at least five vocalizations per recording. Our final dataset for assessing temporal and spectral acoustic traits comprised 1377 vocalizations of 64 species (19.7 ± SD 15.4 songs on average per species; A1. Table S1). We conducted acoustic measurements using the threshold method, thereby minimizing human subjectivity in collecting acoustic parameters (RiosChelen et al., 2017). We generated a power spectrum for each vocalization to extract frequency variables using a threshold of −25 dB in relation to the maximum amplitude of the vocalizations, while duration was measured in the waveform. Temporal and spectral variables were measured in spectrograms generated in Raven Pro v.1.5 with a 1024 FFT (Hanning window, 50% overlap), resulting in a spectral resolution of 46.9 Hz and temporal resolution of 0.021 s. For each vocalization, we measured three spectral traits: maximum frequency (kHz), minimum frequency (kHz), peak frequency (kHz, the frequency in the signal with the greatest amplitude); and one temporal trait: signal duration (s), determined as the beginning of the first syllable to the end of the last one (Sosa-López & Mennill, 2014). 2.5. Data analysis To evaluate differences in species richness between the studied intra-urban and peri-urban conditions, we considered an incidencebased matrix of bird species from recordings at each one of the 18 sampling sites. To contrast such richness, we compared sample-based rarefaction estimation of species (Sest; Colwell et al., 2012) for both conditions. Given that statistical inferences should not be drawn with overlapping 95% confidence intervals due the high probability of leading to Type I errors (Payton, Greenstone, & Schenker, 2003), we determined statistical differences for species richness between intraand peri-urban areas by comparing their 84% confidence intervals, which represents a statistical test with an α ≈ 0.05 (MacGregor-Fors & Payton, 2013). However, we also report 95% confidence intervals to allow comparisons with previous studies. We also assessed changes in species composition between the studied intra- and peri-urban conditions using βsim (Lennon, Koleff, Greenwood, & Gaston, 2001). This index measures the relative magnitude of shared species in relation to the community with less unique species, which is ideal for contrasts with differing species richness. For both studied intra-urban and peri-urban conditions, we generated weighted matrices of the vocalization duration of each species (columns) and the time period (5-min intervals) in which vocalizations were emitted (rows). In this study, time of day and species represent nodes, while their links correspond to bird vocalizations. We further evaluated the patterns of dawn choruses using bipartite network analysis (Dormann, Fründ, Blüthgen, & Gruber, 2009). Although these metrics are widely used in interaction studies (Borrett, Sheble, Moody, & Anway, 2018), and recently in behavioral ecology, (e.g., Pasquaretta & Jeanson, 2018; Sebastián-González & Hart, 2017), we employed them to explore the temporal patterns of vocal activity of birds during dawn choruses. For this study, bipartite networks are represented by co-occurrence matrices B, where Bij represents the singing activity of i bird species in j 5-min periods (Dehling, 2018). We tested whether the surveyed dawn choruses showed modular (QuanBiMo algorithm: Dormann & Strauss, 2014) or nested patterns (WNODF index: AlmeidaNeto & Ulrich, 2011), considering two scales of analysis: sampling recording site and condition (i.e., intra-urban, peri-urban). We used the QuanBiMo algorithm to calculate weighted modularity, as this metric measures the relative magnitude of species interaction by allocating each species in an unspecified number of modules, where
3. Results Estimated species richness was significantly higher in the peri-urban condition (Sest = 59 ± 5.7 CI 84%; 9.0 CI 95%) when contrasted to the intra-urban one (Sest = 17 ± 4.8 CI 84%; 6.7 CI 95%). Moreover, the species composition analysis revealed that the avian community from the intra-urban condition basically represented a subgroup of the one recorded at the peri-urban condition (βsim = 0.29). We recorded a total of 45,290 vocalizations of 59 species at the periurban condition, and 6,713 vocalizations of 17 species at the intraurban condition (Fig. 2). Dawn choruses from both peri-urban and intra-urban conditions had a modular structure (peri-urban: Q = 0.29, 4
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Fig. 2. Accumulated use of the acoustic space during dawn choruses: (a) peri-urban and (b) intra-urban conditions of the city of Xalapa. We ranked species according to the total duration of recorded avian vocal activity. N = 900 min per condition in the nine sampling sites at each studied condition. All values depicted in the graph are > 0.05 min (i.e., > 3 s).
Slabbekoorn, 2013). In this study we found a modular pattern of avian dawn choruses at both intra- and peri-urban conditions, but only a temporal ordering of bird vocalization modules at the peri-urban condition. Thus, despite the modular structure of avian dawn choruses from intra-urban sites (which is in disagreement with our prediction of a random pattern), our results indicate the loss of a temporal ordering of the multi-species avian dawn choruses. Furthermore, the identified modules at both intra- and peri-urban conditions were comprised of phylogenetically unrelated species that showed low overlap in their spectral acoustic traits. In agreement with the generalized global pattern of regional species pool filtering in urban areas (Croci et al., 2008; MacGregor-Fors, 2010; McKinney, 2008), our results reveal a depauperated intra-urban community basically comprised by a subgroup of the species recorded at the peri-urban condition. Contrary to our prediction (i.e., random structure), bird communities from the intra-urban condition showed a modular pattern of their dawn choruses; yet, such modules were not ordered temporally. Thus, the loss of temporal organization of dawn choruses may be a consequence of the ecological filtering via the simplification of bird communities in urban areas, which are dominated by range-expanding and invasive species (Aronson et al., 2014, 2016; La Sorte et al., 2018; MacGregor-Fors, 2010). Given the exclusion of an important proportion of species from the regional pool at intra-urban areas, fewer species are using the acoustic space, which could result in a higher amount of acoustic gaps. The lack of temporally structured dawn choruses at the studied intra-urban condition could also be related with the behavioral adjustments of species (e.g., dawn chorus onset and/or peek of activity shifts) in response to the anthropogenic noise and artificial light levels (Da Silva & Kempenaers, 2017; Halfwerk et al., 2018; Marín-Gómez & MacGregor-Fors, 2019; Slabbekoorn, 2013; Warren et al., 2006). Moreover, invasive species that dominated the intra-urban acoustic space could also play an important role in interfering with the acoustic niche of other urban species. In fact, a recent study has shown evidence of the avoidance of competition of native passerine birds in the French Polynesia with non-natives by calling before sunrise when non-natives are less vocally active (ZoBell & Furnas, 2017). Interestingly, any of the aforementioned non-mutually exclusive explanations to our results are predicted by the acoustic space hypothesis (Marler, 1960; Miller, 1982). As expected, we found a modular pattern of the dawn choruses recorded at the peri-urban condition, which had a temporal ordering throughout the morning. Such a temporally ordered sequence of singing modules suggests a temporal partitioning of the acoustic space in the studied peri-urban condition (Aide et al., 2017; Luther, 2009; Marler,
z-score = 313.99; intra-urban: Q = 0.23, z-score = 57.98, Fig. 3, A1. Table S2) but no nestedness was found (peri-urban: WNODF = 45.48, zscore = −7.67; intra-urban: WNODF = 38.35, z-score = −5.53; see A1. Table S2 for results at a survey site level, which agree with the condition level). However, only the peri-urban condition showed a temporal structuring of dawn choruses (Fig. 3a). Although avian dawn choruses from intra-urban areas were also modular, they were basically comprised of two main modules, each of which occurred throughout the morning simultaneously (Fig. 3b). These results held at a survey-site scale on choruses at the peri-urban condition, while those from intraurban sites showed no temporal-structured modules (Fig. 4, A1 Fig. S3, A1 Fig. S4). Among the three recorded dawn chorus modules identified at the intra-urban condition, three species dominated the acoustic space (Fig. 3b): an abundant non-native invasive species (House Sparrow–Passer domesticus), an abundant native range-expanding species (Greattailed Grackle–Quiscalus mexicanus), and a native urban utilizer (Claycolored Thrush–Turdus grayi). Unlike the Clay-colored Thrush, who mainly sang before sunrise, the House Sparrow and Great-tailed Grackle co-occurred vocally throughout the morning. At the peri-urban condition, we identified four modules that occurred in a sequential way at differing times throughout the morning (Fig. 3a). The acoustic space of these modules was dominated by native species, such as the Clay-colored Thrush, Plain Chachalaca (Ortalis vetula), Brown Jay (Psilorhinus morio), Chestnut-capped Brush-finch (Arremon brunneinucha), Spotbreasted Wren (Pheugopedius maculipectus), Grey-breasted Wood-wren (Henicorhina leucophrys), and Stripe-crowned Warbler (Basileuterus culicivorus). Regarding our spectral analysis, we found low overlap of high, low, and peak singing frequencies (< 0.45 overlap) in both studied intraand peri-urban conditions at the survey site and within modules, being always lower in the intra-urban condition at both studied scales (i.e., sites, modules; Fig. 5). Finally, our results show a random phylogenetic structure of the assemblages comprising the recorded dawn choruses at the survey site level (A1. Table S3). Although the general pattern was also random at the module level, we detected one clustered and two evenly aggregated peri-urban choruses (A1. Table S3).
4. Discussion Besides the intrinsic novelty of heterogeneously built environments, urban systems represent an adaptive challenge for organisms in terms of the use of different signaling strategies to increase their acoustic active space and improve their communication (Halfwerk et al., 2018; 5
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Fig. 3. Adjacency matrix representations of the use of acoustic space by each species (columns) at each 5-min time period (rows) in: (a) peri-urban and b) intra-urban conditions of the city of Xalapa, Mexico. Note the modular pattern of dawn choruses at both peri- and intra-urban conditions, and the temporal order of the acoustic activity across time of avian dawn choruses in the peri-urban condition only. The intensity of shading represents the use of acoustic space (min) by each species at each 5-min period. Negative and positive values on the time axis are relative to sunrise.
Fig. 4. Comparison of (a) modularity results, (b) number of modules, and (c) number of time ordered modules of dawn bird choruses from intra- and peri-urban conditions. Circles represent average values and bars standard errors.
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Fig. 5. Niche overlap: (a) high, (b) low, and (c) peak frequencies (mean ± SE) among bird species vocalizing in the recorded dawn choruses at the module and site levels.
and temporal scale, and that seasonality can influence the singing activity of dawn choruses (e.g., chorus onset; Catchpole & Slater, 2008; Staicer et al., 1996), further research ought to consider biologically and ecologically relevant factors related to the phenology of dawn chorus structuring (e.g., lower avian vocal activity in the non-breeding season, species composition shifts driven by the arrival of migratory species). Moreover, by including a broader spatial scale, a better understanding of avian dawn chorus structure across urbanization intensity gradients could arise. This particular issue could be explored with long-term acoustic monitoring using autonomous recording units to test the influence of the overall acoustic space on the structure of avian dawn choruses (Shonfield & Bayne, 2017).
1960; Planqué & Slabbekoorn, 2008; Stanley et al., 2016). Finding low song frequency overlap among bird species conforming each module supports the ordered temporal occurrence of the acoustic niche space in the peri-urban condition. This result is in agreement with the hypothesis of acoustic space partitioning at the spectral and temporal scales, as has been previously recorded in tropical natural ecosystems (Berg, Brumfield, & Apanius, 2006; Hart et al., 2015; Luther, 2008, 2009; Planqué & Slabbekoorn, 2008; Stanley et al., 2016; Xia et al., 2018). Additionally, the low spectral overlap we found in this study could be related to the non-saturation of the studied acoustic spaces, where inter-specific competition would be expected to decline (Luther, 2008, 2009; Tobias et al., 2014). The lack of phylogenetic structure within the identified modules at both the intra- and peri-urban conditions indicates a low probability of vocalization co-occurrence among closely related species during the studied dawn choruses. This result contrasts with the acoustic clustering of avian choruses reported in tropical rainforest birds, where acoustic communication occurs among closely related species with similar biological and ecological requirements, suggesting niche conservatism (Berg et al., 2006; Chen et al., 2015; Stanley et al., 2016; Tobias et al., 2014). In this sense, our study suggests that heavily-urbanized sites are related with a decrease of the phylogenetic diversity of avian communities (La Sorte et al., 2018). The non-significant phylogenetic structuring of the identified dawn chorus modules could be related with: (1) the size of the species pool, the evolutionary model, and tree topology, as recently evidenced by Cadotte, Davies, and PeresNeto (2017); and (2) the representation of dawn chorus modules by subgroups of closely and distantly related species, which in turn could result in random patterns. Thus, the random phylogenetic structuring in our results needs to be interpreted cautiously. The use of the acoustic space in dawn choruses has been assessed through spectral and temporal traits of bird species in a multi-variate fashion (Luther, 2009; Stanley et al., 2016; Tobias et al., 2014). Although this approach has brought important advances in our understanding of how acoustic communication is influenced by multi-species signals (Luther, 2008; Xia et al., 2018), current knowledge is biased toward single bird species studies and does not include the acoustic community (e.g., Aide et al., 2017). Given that background noise (i.e., both natural and anthropogenic) can hinder species communication by attenuating or degrading signals and changing the acoustic routines (Brumm, 2004; Halfwerk et al., 2018; Luther & Gentry, 2013), further studies need to consider the influence of different sources of noise in the partitioning of the acoustic niche, as well as the effect of such acoustic masking on the temporal ordering of dawn choruses. Finally, considering that our study is focused on a limited spatial
5. Conclusion To our knowledge, this study is the first to analyze the structure of multi-species bird dawn choruses at two ends of an urbanization intensity gradient. Altogether, our findings show a modular pattern of avian dawn choruses at both intra- and peri-urban conditions; yet, a temporal ordering of bird vocalizations was only recorded at peri-urban areas. The loss of the temporal order of dawn choruses at intra-urban areas may be related to the depauperization of its avian community as a consequence of the urban ecological filtering process, in addition to the dominance of the acoustic space by invasive species. Our results clearly show that urbanization can drive the structure of avian dawn choruses, which are a dominant component of soundscapes. Thus, studying the diversity and structure of urban soundscapes could widen our view of the ecological consequences of urbanization. The importance of soundscapes as providers of multiple important ecosystem services has been shown to be directly associate with human health and well being, sense of place, people-nature interactions, and ecological integrity (Aletta, Kang, & Axelsson, 2016; Dumyahn & Pijanowski, 2011).
Acknowledgments We are most grateful to Christopher A. Lepczyk and the anonymous reviewers for improving our manuscript. Fernando González García confirmed all bird song identifications. Carlos Trujillo and Julian Avila assisted in the field. OHM-G acknowledges the scholarship and support provided by the National Council of Science and Technology (CONACYT 417094), and the Doctoral Program of the Instituto de Ecología, A.C. (INECOL). JRSL acknowledges the support of CONACYT (chair fellowship at CIIDIR, researcher number 1640, project number 1781). 7
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Appendix A. Supplementary data
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Research Paper
Where has the city choir gone? Loss of the temporal structure of bird dawn choruses in urban areas
T
Oscar H. Marín-Gómeza, Wesley Dáttilob, J. Roberto Sosa-Lópezc, Diego Santiago-Alarcond, ⁎ Ian MacGregor-Forsa, a
Red de Ambiente y Sustentabilidad, Instituto de Ecología A.C., Xalapa, Veracruz, Mexico Red de Ecoetología, Instituto de Ecología A.C., Xalapa, Veracruz, Mexico c CONACYT-Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional Unidad Oaxaca (CIIDIR), Instituto Politécnico Nacional, Oaxaca, Mexico d Red de Biología y Conservación de Vertebrados, Instituto de Ecología A.C., Xalapa, Veracruz, Mexico b
A R T I C LE I N FO
A B S T R A C T
Keywords: Acoustic space Ecological filtering Modularity Multi-species choruses Urban ecology
Living in the city represents a great challenge for organisms that are exposed to the novel environmental conditions inherent to urbanization. Recent studies have highlighted the ecological impact that urbanization poses on the acoustic phenotype and singing routines of birds. However, the organization and structure of avian dawn choruses in urban settings remains largely unexplored. In this study, we assessed the temporal structure of avian dawn choruses in an intra-urban area and a peri-urban forest using bipartite network analyses. We predicted a random network structuring of dawn choruses across time at the intra-urban area, while expected a non-random structure (i.e., modular or nested) at the peri-urban forest. While we detected different groups of birds vocalizing together temporarily, following a modular pattern in both studied conditions, only the one from the peri-urban forest showed a sequential temporal structure of dawn choruses. Avian dawn choruses from both intra-urban and peri-urban areas were mainly comprised by phylogenetically unrelated species (i.e., random phylogenetic structure), also exhibiting low overlap on singing frequencies. Our results are in agreement with the temporal partitioning of the acoustic space in the peri-urban forest. Our findings also suggest that the absence of temporally-structured modules of bird dawn choruses at heavily-urbanized areas could be related to the depauperization of the avian community at intra-urban areas as a sequel of ecological filtering, as well as the consequent importance of the dominance of the acoustic space by invasive species.
1. Introduction Urbanization represents a biodiversity threat across landscapes, causing the simplification of local communities in urban centers by filtering out species from regional pools (Aronson et al., 2016; Croci, Butet, & Clergeau, 2008; McKinney, 2008). Drastic habitat transformations associated with urban sprawling generate novel environmental conditions that represent an important ecological barrier, often selecting groups according to traits that make them more or less tolerant to urbanization (Aronson et al., 2014; La Sorte et al., 2018; McKinney, 2008). As a result of the transformation of habitats driven by urbanization and the non-random filtering of species into urban centers, the structure and composition of avian communities that thrive in heavilyurbanized areas differ importantly when contrasted with those from
surrounding systems, such as peri-urban and extra-urban areas (Aronson et al., 2014; MacGregor-Fors, 2010; McKinney, 2008). The plethora of urban hazards to which organisms that dwell or use cities are exposed to (e.g., vegetation replacement, urban heat island, pollution; Gaston, Davies, Nedelec, & Holt, 2017; Grimm et al., 2008; Kekkonen, 2017) have shown to trigger physiological, morphological, and behavioral adjustments (Johnson & Munshi-South, 2017; Luniak, 2004). Among the best studied behavioral modifications of urban birds, those focused on acoustic communication head the list (Brumm, 2004; Slabbekoorn & Peet, 2003; Slabbekoorn, 2013). Most avian vocal adjustments in urban areas have been related to anthropogenic noise and artificial light at night, while other urban hazards have been largely neglected (Gorissen, Snoeijs, Van Duyse, & Eens, 2005; Mendes, ColinoRabanal, & Peris, 2011). For instance, some urban birds deal with noise
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Corresponding author at: Red de Ambiente y Sustentabilidad, Instituto de Ecología, A.C., Carretera antigua a Coatepec 351 El Haya, Xalapa 91070, Veracruz, Mexico. E-mail addresses: [email protected] (O.H. Marín-Gómez), [email protected] (W. Dáttilo), [email protected] (J.R. Sosa-López), [email protected] (D. Santiago-Alarcon), [email protected] (I. MacGregor-Fors). https://doi.org/10.1016/j.landurbplan.2019.103665 Received 29 June 2018; Received in revised form 13 June 2019; Accepted 9 September 2019 0169-2046/ © 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Adjacency matrix representations of the expected patterns of avian dawn choruses for the two studied conditions (i.e., intra- and peri-urban) based on a bipartite network approach: (a) random, (b) modular, and (c) nested. Each filled square indicates an observed occupation of the acoustic space (i.e., duration of the singing activity) by each species in a given 5-min recording segment. In this illustrative example, the number of species for the peri-urban networks is nine, and five for the intra-urban condition.
of multi-species dawn choruses in urban settings remains unexplored. The use of the acoustic space in avian dawn choruses has been studied by compiling species co-occurrence lists and afterwards measuring the overlap on spectral and temporal signals, either through pairwise comparisons among multiple species or by assessing the sequence in which species join to the dawn chorus (Luther, 2009; Planqué & Slabbekoorn, 2008; Stanley et al., 2016; Tobias et al., 2014; Xia et al., 2018). However, approaches derived from network theory could help analyzing the use of the acoustic space by birds when participating in dawn choruses (Malavasi & Farina, 2013; Tobias et al., 2014; Xia et al., 2018). Specifically, studying the use of the acoustic space during dawn choruses can be tackled through bipartite networks. In this case, bipartite networks are based on co-occurrence matrices, where information is organized considering the singing activity of the species participating in the chorus in a given time (Dehling, 2018). Therefore, following this approach could bring new insights into the patterns of temporal use of the acoustic space by birds and their implications on the acoustic niche divergence of multi-species avian dawn choruses. Different types of non-random patterns could best describe avian dawn choruses, as those previously reported in other biological systems (from molecular interactions between proteins to the co-occurrence of vertebrates in metacommunities; Dormann, Fründ, & Schaefer, 2017). This is due to the fact that biological systems tend to follow the same rules of self-organization in nature (Dehling, 2018). The most frequently used descriptors of the structure of networks are modularity and nestedness (Dormann et al., 2017). Modularity describes the tendency of different subsets of species in the network to co-occur more frequently with each other than with the rest of the species in the network, generating modules or compartments (Dehling, 2018; Dormann & Strauss, 2014). Nestedness describes the hierarchical organization of co-occurring species over time, where species in less diverse sites comprise a subset of that of more diverse ones into inclusive subsets (Almeida-Neto & Ulrich, 2011; Dehling, 2018). In the context of acoustic space, a modular pattern would represent groups of co-occurring bird species singing at different times, generating compartments of choruses during the morning (i.e., temporal acoustic partitioning); while nestedness would represent groups of bird species singing at dawn as subsets of those species singing throughout morning (i.e., temporal acoustic overlap; see Fig. 1). In this study we assessed the temporal structure of avian dawn
pollution by adjusting song frequencies or changing the timing of their singing routines to avoid communication masking (Halfwerk, Lohr, & Slabbekoorn, 2018; Luther & Gentry, 2013; Slabbekoorn & Peet, 2003; Slabbekoorn, 2013; Warren, Katti, Ermann, & Brazel, 2006). Moreover, birds living in light polluted areas have shown to adjust their singing routines (Da Silva & Kempenaers, 2017; Gaston et al., 2017; Miller, 2006). Despite the wide interest on understanding shifts on the acoustic phenotype related to urbanization, such studies are focused on the behavioral responses of a handful species of temperate regions, without considering a community approach. Dawn choruses are biological phenomena where males and females of multiple species co-occur in a narrow timeframe, producing a continuous and high rate of singing activity around sunrise (Catchpole & Slater, 2008; Planqué & Slabbekoorn, 2008; Staicer, Spector, & Horn, 1996; Stanley, Walter, Venkatraman, & Wilkinson, 2016; Tobias, Planque, Cram, & Seddon, 2014; Xia, Lloyd, Shi, Wei, & Zhang, 2018). At the species level, singing at dawn can have significant advantages, such as enhancing signal transmission, improving the odds of securing a territory, and even increasing reproductive success (Brown & Handford, 2003; Catchpole & Slater, 2008; Kacelnik & Krebs, 1982; Liu & Kroodsma, 2007; Poesel, Kunc, Foerster, Johnsen, & Kempenaers, 2006; Staicer et al., 1996). However, at the community level, multi-species dawn choruses can lead to acoustic space partitioning of spectral and temporal domains, minimizing interference from sounds produced by other species or sources (Aide, Hernández-Serna, Campos-Cerqueira, Acevedo-Charry, & Deichmann, 2017; Farina et al., 2015; Hart, Hall, Ray, Beck, & Zook, 2015; Luther, 2009; Malavasi & Farina, 2013; Marler, 1960; Planqué & Slabbekoorn, 2008; Stanley et al., 2016; Tobias et al., 2014; Xia et al., 2018). The resulting pattern in the acoustic space can be random, overdispersed or clustered, and depends on factors such as regional species pools, habitat structure, environmental conditions, and phylogenetic relationships (Luther, 2009; Stanley et al., 2016; Tobias et al., 2014). Although dawn choruses have been studied mainly in non-anthropogenic ecosystems (Luther, 2009; Malavasi & Farina, 2013; Stanley et al., 2016; Tobias et al., 2014), there is a recent interest in understanding how urbanization can disrupt the onset and timing of the chorusing behavior at the species level (Da Silva & Kempenaers, 2017; Dorado-Correa, Rodríguez-Rocha, & Brumm, 2016; Gil, Honarmand, Pascual, Pérez-Mena, & Macías Garcia, 2015; Lee, MacGregor-Fors, & Yeh, 2017). Yet, the organization and structure 2
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continuous 100 min recording starting with the nautical twilight until 50 min after sunrise. Nautical twilight and sunrise times were obtained from the US Naval Observatory (http://aa.usno.navy.mil). We used a TASCAM DR-100mkII digital recorder with a Sennheiser ME66 directional microphone mounted to a tripod 1.5 m above the ground, pointing the microphone towards the locations with high birdsong activity (this microphone yields great attenuation at its sides and maximizes pickup at the front, allowing to record distant bird songs despite background noise levels; Stanley et al., 2016). We recorded all tracks in WAV format (16 bits, 44.1 kHz sampling frequency). Considering the human activity and noise levels patterns during weekends that could influence the vocal behavior of birds (Brumm, 2004), we restricted the recording sessions to weekdays. To reduce potential non-measured biases (e.g., weather, noise levels), we alternated surveys between intra-urban and peri-urban conditions on a daily basis.
choruses at two ends of an urbanization intensity gradient (i.e., a periurban forest and a heavily-urbanized area) using bipartite network analyses. Our main goal was to explore whether avian dawn choruses from heavily-urbanized areas showed a temporally structured pattern when contrasted with those from a peri-urban forest. According to the acoustic space hypothesis, populations of birds inhabiting depauperate avian communities may produce more variable vocal behaviors due to a reduction in the competition for the acoustic space, while populations of birds belonging to richer avian communities may produce less variable vocal behaviors due to an increase in the competition for the acoustic space (Marler, 1960, Miller, 1982). Thus, considering the depauperization of bird communities in heavily-urbanized areas as a result of a non-random filtering process of regional species pools (Aronson et al., 2014; La Sorte et al., 2018; MacGregor-Fors, 2010; Marzluff, 2017; McKinney, 2008), we expected a random network structuring of dawn choruses across time at the heavily-urbanized areas, while predicted a non-random structure (i.e., modular or nested) at peri-urban areas, where bird communities are more diverse and less different to regional avifaunas from well-preserved ecosystems (see Fig. 1). Given that the network structuring of dawn choruses could reflect acoustic space partitioning (Aide et al., 2017; Chek, Bogart, & Lougheed, 2003; Luther, 2009; Marler, 1960; Planqué & Slabbekoorn, 2008; Stanley et al., 2016), we tested the degree of spectral overlap (i.e., range of frequencies that coincide among two or more bird species) by comparing low, high, and peak frequencies among bird species within the assessed dawn choruses. Finally, as closely related species are prone to share similar acoustic traits (e.g., Cadotte & Davies, 2016; Tobias et al., 2014), we assessed the degree of phylogenetic relatedness within species participating in the studied dawn choruses.
2.3. Acoustic analysis We generated spectrograms using Raven Pro v.1.5 (Bioacoustics Research Program, 2014) by setting a Hanning window and FFT = 1024 (50% overlap) to create a catalog of songs and to identify vocalizations recorded during dawn choruses. For this procedure, we compared our samples with recordings available in Xenocanto (http:// xeno-canto.org/), the Macaulay Library (http://macaulaylibrary.org/), and regional audio libraries (González-García & Celis-Murillo, 2008). To ensure the accuracy and consistence of all measurements, all identifications were made by a single observer (OHM-G). Furthermore, Fernando González-García, curator of the Library of Sounds of Birds of Mexico (Biblioteca de Sonidos de Aves de México; http://www1.inecol. edu.mx/sonidos/menu.htm) and regional ornithological expert confirmed species identifications. For practical matters, we divided each single 100 min recording into 5-min segments to record the number of vocalizations emitted by each species given that previous studies have shown that processing bird vocalizations in 5-min windows effectively captures the temporal patterns of dawn chorus singing activity (Perez-Granados, Osiejuk, & Lopez-Iborra, 2018; Stanley et al., 2016). We distinguished bird songs from calls based on their acoustic structure, considering songs as long vocalizations conformed by a sequence of stereotyped syllables with gaps ≤ 1 s (Catchpole & Slater, 2008; Sosa-López & Mennill, 2014). For some species (i.e., Amazona albifrons, Cardellina pusilla, Dumetella carolinensis, Hylocichla mustelina, Psilorhinus morio), we were unable to identify the beginning and the ending of their songs. In such cases, we counted the number of syllables. Simple foraging, alarm, begging, and flight calls were not included in our analyses. Given that the number of vocalizations could lead to biases on the assessment of the temporal use of the acoustic space (Planqué & Slabbekoorn, 2008), we quantified the duration of vocalizations rather that the number of vocalization occurrences in the 5-min recording segments. We defined signal duration as the time from the beginning to the end of vocalizations. We then calculated the average duration of the vocalizations by species in each of the 5-min segments. To standardize the retrieved information, we calculated the product of the average duration by the number of vocalizations uttered within the 5-min period. As a result, we obtained one value per species for each of the 5min segments included in the analysis.
2. Methods 2.1. Study area Xalapa is a small-to-medium sized city (~64 km2) with a population of ~ 500,000 inhabitants. It is located on the easternmost mountain slopes of the Trans-Mexican Volcanic Axis, within an elevation gradient of 1120–1720 m a.s.l. (INEGI, 2009). To represent both ends of the urbanization intensity gradient of Xalapa, we selected two sampling conditions (A1. Fig. S1): (1) heavily-urbanized sites of downtown (19°31′54″ N, 96°55′28″ W; hereafter intra-urban condition) and (2) a peri-urban forest remnant (19°30′51″ N, 96°56′17″ W; hereafter periurban condition). The intra-urban condition is characterized by residential and commercial land-uses, with heavy human activity and traffic flow that result in high noise levels (38–88, SPL dB(A), mean = 63.1 ± 13.3; data from pilot survey). The peri-urban condition lies within a Natural Protected Area (i.e., Santuario del Bosque de Niebla; 30 ha of secondary cloud forest surrounded by urban settlements, forest remnants, and shade-grown coffee plantations; WilliamsLinera, 1993), which is open to the public and has low noise levels (25–42 SPL dB(A), mean = 32.7 ± 4.3; data from pilot survey). 2.2. Sampling protocol and dawn chorus recordings We set a 900 × 900 m quadrant within each study condition (i.e., intra-urban, peri-urban) and selected nine evenly distributed sampling recording sites separated by a distance of 300 m from each other (distance that meets the independence assumption among sampling sites for ornithological field studies; Ralph, Geupel, Pyle, Martin, & DeSante, 1993). The total number of independent sampling sites per condition was established based on the total number of sampling points that fit in the peri-urban condition (A1. Fig. S1). We recorded bird vocalizations over a four-week period from 27 March to 29 April 2016, during the peak of the bird breeding season (MacGregor-Fors, González-García, Hernández-Lara, & SantiagoAlarcon, 2018). At each sampling recording site, we obtained one single
2.4. Acoustic measurements In order to evaluate the temporal and spectral acoustic traits of bird vocalizations, we selected high-quality recordings (i.e., high signal to noise ratio) from two sources: (1) focal recordings (targeted to single species) performed at different locations in both intra- and peri-urban areas of Xalapa using a TASCAM DR-100mkII recorder and a Sennheiser ME66 microphone (stored in WAV format at 44.1 kHz/16 bits); and (2) natural-sound libraries (i.e., Xenocanto, Biblioteca de Sonidos de Aves 3
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species interact with high frequency with other species of a module showing fewer links with other species of other modules (see Dormann & Strauss, 2014). The degree modularity (Q) ranges from 0 (no evidence of modules) to 1 (maximum degree of modularity). Q was calculated using the function computeModules of the bipartite library (Dormann et al., 2009), adjusting the number of Markov chain Monte Carlo simulations (MCMC) to 106 steps (i.e., number of steps after which the computation of modules stops if no better division into modules than the current one can be found). We used a high number of steps since modularity maximizing algorithms tend to be stochastic (i.e., when one or more of the input parameters is subject to randomness). We ran the modularity algorithm 10 times for each sampling site, keeping the highest Q value (Dormann & Strauss, 2014). In order to calculate nestedness, we used the WNODF algorithm, a weighted measure for frequency based matrices (Almeida-Neto & Ulrich, 2011). Nestedness ranges from 0 (non-nested) to 100 (completely nested). As modularity and nestedness could both be influenced by matrix size, we generated null models to control for potential effects on our results. We first randomized all matrices using the Patefield algorithm that outputs result with fixed row and column totals. This algorithm generates null models with marginal totals identical to those of the observed network (Dormann et al., 2009). We then estimated the statistical significance of modularity and nestedness with z-score tests, contrasting the observed values (Q and WNODF) against 1000 randomized networks generated by the r2dtable function (Dormann et al., 2009). Z-score values ≥ 2 were considered significantly higher than would be expected by chance and were considered as evidence of high modularity and nestedness (Dormann & Strauss, 2014). To assess the degree of spectral overlap of peak, low, and high frequencies (kHz) of vocalizations among species within dawn choruses, we used the Czechanowski index, which was contrasted with 1000 randomly generated communities (Gotelli & Graves, 1996). The calculated Czechanowski index ranges from 0 (species share no frequency at all) and 1 (species have identical use of frequencies). We finally tested whether dawn choruses consisted of closely (clustered) or distantly (overdispersed) related species by calculating the nearest taxon distance index (MNTD) for each recording site, testing statistical differences by comparing them to 1000 random communities (Cadotte & Davies, 2016). For this, we standardized the MNTD metric by calculating the standardized effect size (SES) against the null values using a z-value. Significant positive values of SES indicate phylogenetic evenness (i.e., co-occurring species more distantly related than expected by chance), while significant negative values of SES indicate phylogenetic clustering (Cadotte & Davies, 2016). We extracted phylogenetic trees for the studied species pool from the phylogeny of extant birds (Jetz, Thomas, Joy, Hartmann, & Mooers, 2012) available in the Birdtree database (www.birdtree.org). We generated a distribution of 1000 randomly selected permutations of the global bird phylogeny (Hackett backbone), and then we used TREEANNOTATOR (Drummond, Suchard, Xie, & Rambaut, 2012) to obtain the best supported phylogenetic tree based on a maximum clade credibility approach (A1. Fig. S2). Unless specified above, we performed all analyses in R (version 3.3.1).
de México) and regional audio libraries (González-García & CelisMurillo, 2008). For species with repertoires (i.e., more than one version of their song), we selected the most common song type used during the dawn chorus, as suggested by Tobias et al. (2014). To account for inter-specific variability, we selected a minimum of three independent recordings per species containing multiple vocalizations (4.6 ± SD 1.5 recordings on average) and extracted at least five vocalizations per recording. Our final dataset for assessing temporal and spectral acoustic traits comprised 1377 vocalizations of 64 species (19.7 ± SD 15.4 songs on average per species; A1. Table S1). We conducted acoustic measurements using the threshold method, thereby minimizing human subjectivity in collecting acoustic parameters (RiosChelen et al., 2017). We generated a power spectrum for each vocalization to extract frequency variables using a threshold of −25 dB in relation to the maximum amplitude of the vocalizations, while duration was measured in the waveform. Temporal and spectral variables were measured in spectrograms generated in Raven Pro v.1.5 with a 1024 FFT (Hanning window, 50% overlap), resulting in a spectral resolution of 46.9 Hz and temporal resolution of 0.021 s. For each vocalization, we measured three spectral traits: maximum frequency (kHz), minimum frequency (kHz), peak frequency (kHz, the frequency in the signal with the greatest amplitude); and one temporal trait: signal duration (s), determined as the beginning of the first syllable to the end of the last one (Sosa-López & Mennill, 2014). 2.5. Data analysis To evaluate differences in species richness between the studied intra-urban and peri-urban conditions, we considered an incidencebased matrix of bird species from recordings at each one of the 18 sampling sites. To contrast such richness, we compared sample-based rarefaction estimation of species (Sest; Colwell et al., 2012) for both conditions. Given that statistical inferences should not be drawn with overlapping 95% confidence intervals due the high probability of leading to Type I errors (Payton, Greenstone, & Schenker, 2003), we determined statistical differences for species richness between intraand peri-urban areas by comparing their 84% confidence intervals, which represents a statistical test with an α ≈ 0.05 (MacGregor-Fors & Payton, 2013). However, we also report 95% confidence intervals to allow comparisons with previous studies. We also assessed changes in species composition between the studied intra- and peri-urban conditions using βsim (Lennon, Koleff, Greenwood, & Gaston, 2001). This index measures the relative magnitude of shared species in relation to the community with less unique species, which is ideal for contrasts with differing species richness. For both studied intra-urban and peri-urban conditions, we generated weighted matrices of the vocalization duration of each species (columns) and the time period (5-min intervals) in which vocalizations were emitted (rows). In this study, time of day and species represent nodes, while their links correspond to bird vocalizations. We further evaluated the patterns of dawn choruses using bipartite network analysis (Dormann, Fründ, Blüthgen, & Gruber, 2009). Although these metrics are widely used in interaction studies (Borrett, Sheble, Moody, & Anway, 2018), and recently in behavioral ecology, (e.g., Pasquaretta & Jeanson, 2018; Sebastián-González & Hart, 2017), we employed them to explore the temporal patterns of vocal activity of birds during dawn choruses. For this study, bipartite networks are represented by co-occurrence matrices B, where Bij represents the singing activity of i bird species in j 5-min periods (Dehling, 2018). We tested whether the surveyed dawn choruses showed modular (QuanBiMo algorithm: Dormann & Strauss, 2014) or nested patterns (WNODF index: AlmeidaNeto & Ulrich, 2011), considering two scales of analysis: sampling recording site and condition (i.e., intra-urban, peri-urban). We used the QuanBiMo algorithm to calculate weighted modularity, as this metric measures the relative magnitude of species interaction by allocating each species in an unspecified number of modules, where
3. Results Estimated species richness was significantly higher in the peri-urban condition (Sest = 59 ± 5.7 CI 84%; 9.0 CI 95%) when contrasted to the intra-urban one (Sest = 17 ± 4.8 CI 84%; 6.7 CI 95%). Moreover, the species composition analysis revealed that the avian community from the intra-urban condition basically represented a subgroup of the one recorded at the peri-urban condition (βsim = 0.29). We recorded a total of 45,290 vocalizations of 59 species at the periurban condition, and 6,713 vocalizations of 17 species at the intraurban condition (Fig. 2). Dawn choruses from both peri-urban and intra-urban conditions had a modular structure (peri-urban: Q = 0.29, 4
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Fig. 2. Accumulated use of the acoustic space during dawn choruses: (a) peri-urban and (b) intra-urban conditions of the city of Xalapa. We ranked species according to the total duration of recorded avian vocal activity. N = 900 min per condition in the nine sampling sites at each studied condition. All values depicted in the graph are > 0.05 min (i.e., > 3 s).
Slabbekoorn, 2013). In this study we found a modular pattern of avian dawn choruses at both intra- and peri-urban conditions, but only a temporal ordering of bird vocalization modules at the peri-urban condition. Thus, despite the modular structure of avian dawn choruses from intra-urban sites (which is in disagreement with our prediction of a random pattern), our results indicate the loss of a temporal ordering of the multi-species avian dawn choruses. Furthermore, the identified modules at both intra- and peri-urban conditions were comprised of phylogenetically unrelated species that showed low overlap in their spectral acoustic traits. In agreement with the generalized global pattern of regional species pool filtering in urban areas (Croci et al., 2008; MacGregor-Fors, 2010; McKinney, 2008), our results reveal a depauperated intra-urban community basically comprised by a subgroup of the species recorded at the peri-urban condition. Contrary to our prediction (i.e., random structure), bird communities from the intra-urban condition showed a modular pattern of their dawn choruses; yet, such modules were not ordered temporally. Thus, the loss of temporal organization of dawn choruses may be a consequence of the ecological filtering via the simplification of bird communities in urban areas, which are dominated by range-expanding and invasive species (Aronson et al., 2014, 2016; La Sorte et al., 2018; MacGregor-Fors, 2010). Given the exclusion of an important proportion of species from the regional pool at intra-urban areas, fewer species are using the acoustic space, which could result in a higher amount of acoustic gaps. The lack of temporally structured dawn choruses at the studied intra-urban condition could also be related with the behavioral adjustments of species (e.g., dawn chorus onset and/or peek of activity shifts) in response to the anthropogenic noise and artificial light levels (Da Silva & Kempenaers, 2017; Halfwerk et al., 2018; Marín-Gómez & MacGregor-Fors, 2019; Slabbekoorn, 2013; Warren et al., 2006). Moreover, invasive species that dominated the intra-urban acoustic space could also play an important role in interfering with the acoustic niche of other urban species. In fact, a recent study has shown evidence of the avoidance of competition of native passerine birds in the French Polynesia with non-natives by calling before sunrise when non-natives are less vocally active (ZoBell & Furnas, 2017). Interestingly, any of the aforementioned non-mutually exclusive explanations to our results are predicted by the acoustic space hypothesis (Marler, 1960; Miller, 1982). As expected, we found a modular pattern of the dawn choruses recorded at the peri-urban condition, which had a temporal ordering throughout the morning. Such a temporally ordered sequence of singing modules suggests a temporal partitioning of the acoustic space in the studied peri-urban condition (Aide et al., 2017; Luther, 2009; Marler,
z-score = 313.99; intra-urban: Q = 0.23, z-score = 57.98, Fig. 3, A1. Table S2) but no nestedness was found (peri-urban: WNODF = 45.48, zscore = −7.67; intra-urban: WNODF = 38.35, z-score = −5.53; see A1. Table S2 for results at a survey site level, which agree with the condition level). However, only the peri-urban condition showed a temporal structuring of dawn choruses (Fig. 3a). Although avian dawn choruses from intra-urban areas were also modular, they were basically comprised of two main modules, each of which occurred throughout the morning simultaneously (Fig. 3b). These results held at a survey-site scale on choruses at the peri-urban condition, while those from intraurban sites showed no temporal-structured modules (Fig. 4, A1 Fig. S3, A1 Fig. S4). Among the three recorded dawn chorus modules identified at the intra-urban condition, three species dominated the acoustic space (Fig. 3b): an abundant non-native invasive species (House Sparrow–Passer domesticus), an abundant native range-expanding species (Greattailed Grackle–Quiscalus mexicanus), and a native urban utilizer (Claycolored Thrush–Turdus grayi). Unlike the Clay-colored Thrush, who mainly sang before sunrise, the House Sparrow and Great-tailed Grackle co-occurred vocally throughout the morning. At the peri-urban condition, we identified four modules that occurred in a sequential way at differing times throughout the morning (Fig. 3a). The acoustic space of these modules was dominated by native species, such as the Clay-colored Thrush, Plain Chachalaca (Ortalis vetula), Brown Jay (Psilorhinus morio), Chestnut-capped Brush-finch (Arremon brunneinucha), Spotbreasted Wren (Pheugopedius maculipectus), Grey-breasted Wood-wren (Henicorhina leucophrys), and Stripe-crowned Warbler (Basileuterus culicivorus). Regarding our spectral analysis, we found low overlap of high, low, and peak singing frequencies (< 0.45 overlap) in both studied intraand peri-urban conditions at the survey site and within modules, being always lower in the intra-urban condition at both studied scales (i.e., sites, modules; Fig. 5). Finally, our results show a random phylogenetic structure of the assemblages comprising the recorded dawn choruses at the survey site level (A1. Table S3). Although the general pattern was also random at the module level, we detected one clustered and two evenly aggregated peri-urban choruses (A1. Table S3).
4. Discussion Besides the intrinsic novelty of heterogeneously built environments, urban systems represent an adaptive challenge for organisms in terms of the use of different signaling strategies to increase their acoustic active space and improve their communication (Halfwerk et al., 2018; 5
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Fig. 3. Adjacency matrix representations of the use of acoustic space by each species (columns) at each 5-min time period (rows) in: (a) peri-urban and b) intra-urban conditions of the city of Xalapa, Mexico. Note the modular pattern of dawn choruses at both peri- and intra-urban conditions, and the temporal order of the acoustic activity across time of avian dawn choruses in the peri-urban condition only. The intensity of shading represents the use of acoustic space (min) by each species at each 5-min period. Negative and positive values on the time axis are relative to sunrise.
Fig. 4. Comparison of (a) modularity results, (b) number of modules, and (c) number of time ordered modules of dawn bird choruses from intra- and peri-urban conditions. Circles represent average values and bars standard errors.
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Fig. 5. Niche overlap: (a) high, (b) low, and (c) peak frequencies (mean ± SE) among bird species vocalizing in the recorded dawn choruses at the module and site levels.
and temporal scale, and that seasonality can influence the singing activity of dawn choruses (e.g., chorus onset; Catchpole & Slater, 2008; Staicer et al., 1996), further research ought to consider biologically and ecologically relevant factors related to the phenology of dawn chorus structuring (e.g., lower avian vocal activity in the non-breeding season, species composition shifts driven by the arrival of migratory species). Moreover, by including a broader spatial scale, a better understanding of avian dawn chorus structure across urbanization intensity gradients could arise. This particular issue could be explored with long-term acoustic monitoring using autonomous recording units to test the influence of the overall acoustic space on the structure of avian dawn choruses (Shonfield & Bayne, 2017).
1960; Planqué & Slabbekoorn, 2008; Stanley et al., 2016). Finding low song frequency overlap among bird species conforming each module supports the ordered temporal occurrence of the acoustic niche space in the peri-urban condition. This result is in agreement with the hypothesis of acoustic space partitioning at the spectral and temporal scales, as has been previously recorded in tropical natural ecosystems (Berg, Brumfield, & Apanius, 2006; Hart et al., 2015; Luther, 2008, 2009; Planqué & Slabbekoorn, 2008; Stanley et al., 2016; Xia et al., 2018). Additionally, the low spectral overlap we found in this study could be related to the non-saturation of the studied acoustic spaces, where inter-specific competition would be expected to decline (Luther, 2008, 2009; Tobias et al., 2014). The lack of phylogenetic structure within the identified modules at both the intra- and peri-urban conditions indicates a low probability of vocalization co-occurrence among closely related species during the studied dawn choruses. This result contrasts with the acoustic clustering of avian choruses reported in tropical rainforest birds, where acoustic communication occurs among closely related species with similar biological and ecological requirements, suggesting niche conservatism (Berg et al., 2006; Chen et al., 2015; Stanley et al., 2016; Tobias et al., 2014). In this sense, our study suggests that heavily-urbanized sites are related with a decrease of the phylogenetic diversity of avian communities (La Sorte et al., 2018). The non-significant phylogenetic structuring of the identified dawn chorus modules could be related with: (1) the size of the species pool, the evolutionary model, and tree topology, as recently evidenced by Cadotte, Davies, and PeresNeto (2017); and (2) the representation of dawn chorus modules by subgroups of closely and distantly related species, which in turn could result in random patterns. Thus, the random phylogenetic structuring in our results needs to be interpreted cautiously. The use of the acoustic space in dawn choruses has been assessed through spectral and temporal traits of bird species in a multi-variate fashion (Luther, 2009; Stanley et al., 2016; Tobias et al., 2014). Although this approach has brought important advances in our understanding of how acoustic communication is influenced by multi-species signals (Luther, 2008; Xia et al., 2018), current knowledge is biased toward single bird species studies and does not include the acoustic community (e.g., Aide et al., 2017). Given that background noise (i.e., both natural and anthropogenic) can hinder species communication by attenuating or degrading signals and changing the acoustic routines (Brumm, 2004; Halfwerk et al., 2018; Luther & Gentry, 2013), further studies need to consider the influence of different sources of noise in the partitioning of the acoustic niche, as well as the effect of such acoustic masking on the temporal ordering of dawn choruses. Finally, considering that our study is focused on a limited spatial
5. Conclusion To our knowledge, this study is the first to analyze the structure of multi-species bird dawn choruses at two ends of an urbanization intensity gradient. Altogether, our findings show a modular pattern of avian dawn choruses at both intra- and peri-urban conditions; yet, a temporal ordering of bird vocalizations was only recorded at peri-urban areas. The loss of the temporal order of dawn choruses at intra-urban areas may be related to the depauperization of its avian community as a consequence of the urban ecological filtering process, in addition to the dominance of the acoustic space by invasive species. Our results clearly show that urbanization can drive the structure of avian dawn choruses, which are a dominant component of soundscapes. Thus, studying the diversity and structure of urban soundscapes could widen our view of the ecological consequences of urbanization. The importance of soundscapes as providers of multiple important ecosystem services has been shown to be directly associate with human health and well being, sense of place, people-nature interactions, and ecological integrity (Aletta, Kang, & Axelsson, 2016; Dumyahn & Pijanowski, 2011).
Acknowledgments We are most grateful to Christopher A. Lepczyk and the anonymous reviewers for improving our manuscript. Fernando González García confirmed all bird song identifications. Carlos Trujillo and Julian Avila assisted in the field. OHM-G acknowledges the scholarship and support provided by the National Council of Science and Technology (CONACYT 417094), and the Doctoral Program of the Instituto de Ecología, A.C. (INECOL). JRSL acknowledges the support of CONACYT (chair fellowship at CIIDIR, researcher number 1640, project number 1781). 7
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Appendix A. Supplementary data
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