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Timing is everything: Acoustic niche partitioning in two tropical wet forest bird communities

Cite this dataset

Hart, Patrick; Ibanez, Thomas; Paxton, Kristina; Sebastián-González, Esthér (2021). Timing is everything: Acoustic niche partitioning in two tropical wet forest bird communities [Dataset]. Dryad.


When acoustic signals sent from individuals overlap in frequency and time, acoustic interference and signal masking may occur. Under the acoustic niche hypothesis (ANH), signaling behavior has evolved to partition acoustic space and minimize overlap with other calling individuals through selection on signal structure and/or the sender’s ability to adjust the timing of signals. Alternately, under the acoustic clustering hypothesis, there is potential benefit to convergence and synchronization of the structural or temporal characteristics of signals in the avian community, and organisms produce signals that overlap more than would be expected by chance. Interactive communication networks may also occur, where species living together are more likely to have songs with convergent spectral and or temporal characteristics. In this study, we examine the fine-scale use of acoustic space in montane tropical wet forest bird communities in Costa Rica and Hawai‘i. At multiple recording stations in each community, we identified the species associated with each recorded signal, measured observed signal overlap, and used null models to generate random distributions of expected signal overlap. We then compared observed vs. expected signal overlap to test predictions of the acoustic niche and acoustic clustering hypotheses. We found a high degree of overlap in the signal characteristics (frequency range) of species in both Costa Rica and Hawai‘i, however, as predicted under ANH, species significantly reduced observed overlap relative to the random distribution through temporal partitioning. There was little support for acoustic clustering or the prediction of the network hypothesis that species segregate across the landscape based on the frequency range of their vocalizations. These findings constitute strong support that there is competition for acoustic space in these signaling communities, and this has resulted primarily in temporal partitioning of the soundscape.


We used a novel null model approach to test the acoustic niche hypothesis. For each of the six recording locations in Costa Rica and Hawai‘i (10-15-min recording period), we computed the observed number of overlapping pairs of vocalizations as the number of times any two vocalizations overlapped simultaneously on the temporal and spectral axes by at least 1-Hz. We then generated an expected level of overlap by randomizing the beginning of each vocalization (within each 10-15-min recording period) but keeping the duration and frequency range unchanged. This was repeated 500 times to generate an expected distribution of vocalization overlaps. We then used the pnorm R function to calculate P values as the probabilities of getting the observed numbers of overlapping pairs of vocalizations, or more extreme numbers, if the null hypothesis is true. Significantly fewer (P value < 0.025) observed overlaps than expected based on the null distribution supports the ANH, while significantly more observed overlaps than expected (P value > 0.975) supports the acoustic clustering hypothesis. 

To test an additional prediction of the acoustic clustering hypothesis that species with similar vocalization characteristics are more likely to co-occur across the landscape, we calculated the observed number of species pairs with vocalizations that overlapped in mean frequency range at each recording location (six in Costa Rica and six in Hawai‘i). We then calculated an expected number of species pairs with overlapping vocalizations by randomizing the occurrence of species at each recording location (from the pool of all species recorded in either Costa Rica or Hawai‘i) while keeping species richness at that location unchanged. This was repeated 500 times to generate an expected distribution of number of species pairs that overlap in frequency and used the pnorm function in R to calculate P values as described above. This allowed us to examine if the species detected at a recording location converge in their song characteristics more than those collected randomly from the pool of species that were detected from the study site.


National Science Foundation, Award: 1345247