Abstract
The acoustic adaptation hypothesis (AAH) states that animals communicating acoustically adapt their vocalizations to the local conditions to optimize signal transmission. We tested select predictions of the AAH by studying the relationships between avian acoustics and forest structural parameters for a community of forest birds, including native and introduced species, on the Big Island of Hawai’i, USA. In areas of dense vegetation, where sound degrades more easily, we expect animal species to reduce the frequency at which they vocalize to reduce sound distortion. Because introduced species may have had limited time to adapt to the local habitat, we also hypothesize that their vocalizations will not change with differences in vegetation. Automated sound recorders were used to obtain information on the birds’ acoustic traits. Vegetation structural characteristics were calculated using a terrestrial light detection and ranging (LiDAR) sensor, which provides highly detailed information on the structure of the vegetation, including woody and leaf density. Of the seven native species studied, only two followed the predictions of the AAH. Interestingly, these two species had the shortest vocalizations, i.e., these vocalizations have the highest chance of information loss. Likewise, for the two introduced species, we did not observe any significant correlation with LiDAR-based vegetation structure metrics. Our study indicates that the predictions of AAH only partially account for the observed acoustic patterns observed in the study system. Other factors affecting acoustic divergence may be more important than the vegetation structure for most of the studied forest birds.
Zusammenfassung
Überprüfung der Akustischen Adaptionshypothese an einheimischen und eingeführten Vogelarten in einem Wald auf Hawai’i
Die Akustische Adaptionshypothese (engl.: Acoustic Adaptation Hypothesis, AAH) besagt, dass Tiere, die sich akustisch verständigen, ihre Lautäußerungen an die örtlichen Gegebenheiten anpassen, um die Signalübermittlung zu optimieren. Wir überprüften ausgewählte Vorhersagen der AAH, indem wir die Beziehungen zwischen dem Akustikverhalten der Vögel und Parametern der Waldstruktur an einer Waldvogelgemeinschaft aus einheimischen und eingeführten Arten auf Big Island (Hawai’i, USA) untersuchten. In Bereichen mit dichter Vegetation, in denen die Geräuschstärke rasch abnimmt, wäre zu erwarten, dass Tierarten die Frequenz ihrer Lautäußerungen reduzieren, um die Klangverzerrung zu verringern. Da den eingeführten Arten möglicherweise nur begrenzt Zeit zur Verfügung stand, sich an den örtlichen Lebensraum anzupassen, stellen wir außerdem die Hypothese auf, dass sich deren Lautäußerungen bei Vegetationsunterschieden nicht verändern. Mittels automatisierter Tonaufnahmegeräte sammelten wir Informationen über die akustischen Merkmale der Vögel. Die strukturellen Eigenschaften der Vegetation bestimmten wir unter Verwendung eines Lidar-Sensors (engl.: light detection and ranging = LiDAR), der detailgenaue Informationen über die Vegetationsstruktur, unter anderem über Gehölz- und Belaubungsdichten, liefert. Von den sieben untersuchten einheimischen Arten folgten nur zwei den Vorhersagen des AAH. Interessanterweise waren dies die beiden Arten mit den kürzesten Lautäußerungen, d.h. die Lautäußerungen, bei denen die größte Gefahr von Informationsverlust besteht. Bei den beiden eingeführten Arten beobachteten wir ebenfalls keine signifikante Korrelation mit den durch LiDAR ermittelten Vegetationsstrukturdaten. Unsere Studie lässt erkennen, dass die Vorhersagen der AAH die im untersuchten System beobachteten akustischen Muster nur teilweise erklären können. Für die meisten der untersuchten Waldvögel könnten andere die akustische Divergenz beeinflussende Faktoren eine wichtigere Rolle spielen als die Vegetationsstruktur.
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References
Azar JF, Bell BD (2016) Acoustic features within a forest bird community of native and introduced species in New Zealand. Emu 116:22–31
Azar JF, Bell BD, Borowiec M (2014) Temporal change of the song of a local population of the Grey Warbler (Gerygone igata): has its song changed over time? Emu 114:80–85
Baltsavias EP (1999a) Airborne laser scanning: basic relations and formulas. ISPRS J Photogramm Remote Sens 54:199–214
Baltsavias EP (1999b) Airborne laser scanning: existing systems and firms and other resources. ISPRS J Photogramm Remote Sens 54:164–198
Berger AJ (1981) Hawaiian birdlife, 2nd edn. University of Hawaii Press, Honolulu
Boncoraglio G, Saino N (2007) Habitat structure and the evolution of bird songs: a meta-analysis of the evidence for the acoustic adaptation hypothesis. Funct Ecol 21:134–142
Bradbury JW, Vehrencamp SL (1998) Principles of animal communication. Sinauer, Sunderland
Brauner N, Shacham M (1998) Role of range and precision of the independent variable in regression of data. AIChE J 44:603–611
Daniel JC, Blumstein DT (1999) A test of the acoustic adaptation hypothesis in four species of marmots. Anim Behav 56:1517–1528
Dassot M, Constant T, Fournier M (2011) The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges. Ann For Sci 68:959
Drake JB, Dubayah RO, Clark DB, Knox RG, Blair JB, Hofton MA, Chazdon RL, Weishampel JF, Prince S (2002) Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sens Environ 79:305–319
Ey E, Fischer J (2009) The ‘‘acoustic adaptation hypothesis’’—a review of the evidence from birds, anurans and mammals. Bioacoustics 19:21–48
Forrest TG (1994) From sender to receiver: propagation and environmental effects on acoustic signals. Am Zool 34:644–654
Fotheringham JR, Martin PR, Ratcliffe L (1997) Song transmission and auditory perception of distance in wood warblers (Parulinae). Anim Behav 53:1271–1285
Gill FB (2007) Cues. Ornithology, 3rd edn. Freeman, New York, pp 215–242
Gish SL, Morton ES (1981) Structural adaptations to local habitat acoustics in Carolina wren songs. Z Tiërpsychol 56:74–84
Hansen P (1979) Vocal learning: its role in adapting sound structures to long distance propagation and a hypothesis on its evolution. Anim Behav 27:1270–1271
Hardiman B, Bohrer G, Gough CM, Vogel CS, Curtis PS (2011) The role of canopy structural complexity in wood net primary production of a maturing northern deciduous forest. Ecology 92:1818–1827
Hardiman B, Bohrer G, Gough CM, Curtis PS (2013) Canopy structural changes following widespread mortality of canopy dominant trees. Forests 4:537–552
Hart PJ, Hall R, Ray W, Beck A, Zook J (2015) Cicadas impact bird communication in a noisy tropical rainforest. Behav Ecol 26:839–842
Hauglin M, Lien V, Næsset E, Gobakken T (2014) Geo-referencing forest field plots by co-registration of terrestrial and airborne laser scanning data. Int J Remote Sens 35:3135–3149
Henning JG, Radtke PJ (2006a) Detailed stem measurements of standing trees from ground-based scanning lidar. For Sci 52:67–80
Henning JG, Radtke PJ (2006b) Ground-based laser imaging for assessing three-dimensional forest canopy structure. Photogramm Eng Remote Sens 12:1349–1358
Hosoi F, Omasa K (2007) Factors contributing to accuracy in the estimation of the woody canopy leaf area density profile using 3D portable lidar imaging. J Exp Bot 58:3463–3473
Irwin DE, Bensch S, Price TD (2001) Speciation in a ring. Nature 409:333–337
Kelbe D, van Aardt J, Romanczyk P, Cawse-Nicholson K, van Leeuwen M (2015) Single-scan stem reconstruction using low-resolution terrestrial laser scanner data. IEEE J Sel Top Appl Earth Obs Remote Sens 8(7):3414–3427
Kelbe D, van Aardt J, Romanczyk P, van Leeuwen M (2016) Marker-free registration of forest terrestrial laser scanner data pairs with embedded confidence metrics. IEEE Trans Geonsci Remote Sens 54:4314–4330
Kirschel ANG, Blumstein DT, Cohen RE, Buermann W, Smith TB, Slabbekoorn H (2009) Birdsong tuned to the environment: green hylia song varies with elevation, tree cover, and noise. Behav Ecol 20:1089–1095
Knowlton JL, Flaspohler DJ, Paxton EH, Fukami T, Giardina CP, Gruner DS, Rankin Wilson EE (2017) Movements of four native Hawaiian birds across a naturally fragmented landscape. J Avian Biol 48(7):921–931
Laiolo P, Tella JL (2005) Habitat fragmentation affects culture transmission: patterns of song matching in Dupont’s lark. J Appl Ecol 42:1183–1193
Llusia D, Gómez M, Penna M, Márquez R (2013) Call transmission efficiency in native and invasive anurans: competing hypotheses of divergence in acoustic signals. PLoS ONE 8:e77312
LMS100, 111, 120, 151 (2009) Laser measurement systems operating instructions. SICK, Reute
Lovell JL, Jupp DLB, Culvenor DS, Coops NC (2003) Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests. Can J Remote Sens 29:607–622
Lynch A (1996) The population memetics of bird song. Ecology 15:181–197
Male TD, Fancy SG, Ralph CJ (1998) Red-Billed Leiothrix. In: Rodewald PG (ed) The birds of North America. Cornell Lab of Ornithology, Ithaca
Martens MJM (1980) Foliage as a low pass filter: experiments with model forests in an anechoic chamber. J Acoust Soc Am 67:66–72
Medina I, Francis CD (2012) Environmental variability and acoustic signals: a multi-level approach in songbirds. Biol Lett 8:928–931
Michelsen A (1983) Biophysical basis of sound communication. In: Lewis B (ed) Bioacoustics: a comparative approach. Academic, London, pp 3–38
Morton ES (1975) Ecological sources of selection on avian sounds. Am Nat 109:17–34
Morton ES (1986) Predictions from the ranging hypothesis for the evolution of long distance signals in birds. Behaviour 99:65–86
Nemeth E, Winkler H, Dabelsteen T (2001) Differential degradation of antbird songs in a neotropical rainforest: adaptation to perch height? J Acoust Soc Am 110:3263–3274
Patten MA, Rotenberry JT, Zuk M (2004) Habitat selection, acoustic adaptation, and the evolution of reproductive isolation. Evolution 58:2144–2155
Pekin B, Jung J, Villanueva-Rivera L, Pijanowski B, Ahumada J (2012) Modeling acoustic diversity using soundscape recordings and LIDAR-derived metrics of vertical forest structure in a neotropical rainforest. Landsc Ecol 27:1513–1522
Pfaff JA, Zanette L, MacDougall-Shackleton SA, MacDougall-Shackleton EA (2007) Song repertoire size varies with HVC volume and is indicative of male quality in song sparrows (Melospiza melodia). Proc R Soc Lond B 274:2035–2040
Piza P, Sandoval L (2016) The differences in transmission properties of two bird calls show relation to their specific functions. J Acoust Soc Am 140:4271–4275
Radtke P, Bolstad PV (2001) Laser point-quadrat sampling for estimating foliage-height profiles in broad-leaved forests. Can J For Res 31:410–418
Rogers LJ, Kaplan GT (2000) Songs, roars, and rituals: communication in birds, mammals, and other animals. Harvard University Press, USA
Scott JM, Mountainspring S, Ramsey FL, Kepler CB (1986) Forest bird communities of the Hawaiian Islands: their dynamics, ecology, and conservation. Stud Avian Biol 9:1–431
Seehausen O, Terai Y, Magalhaes IS, Carleton KJ, Mrosso HDJ, Miyagi R, Sluijs I, Schneider MV, Mann ME, Tachida H, Imai H, Okada N (2008) Speciation through sensory drive in cichlidfish. Nature 455:620–626
Slabbekoorn H, den Boer-Visser A (2006) Cities change the songs of birds. Curr Biol 16:2326–2331
Slabbekoorn H, Peet M (2003) Birds sing at a higher pitch in urban noise. Nature 424:267
Slabbekoorn H, Smith TB (2002) Habitat-dependent song divergence in the little greenbul: an analysis of environmental selection pressures on acoustic signals. Evolution 56:1849–1858
Tobias JA, Aben J, Brumfield RT, Derryberry E, Halfwerk W, Slabbekoorn H, Seddon N (2010) Song divergence by sensory drive in Amazonian birds. Evolution 64:2820–2839
van Aardt JAN, Wynne RH, Oderwald RG (2006) Forest volume and biomass estimation using small-footprint lidar-distributional parameters on a per-segment basis. For Sci 52:636–649
Van der Zande D, Hoet W, Jonckheere I, van Aardt J, Coppin P (2006) Influence of measurement set-up of ground-based LiDAR for derivation of tree structure. Agric For Meteorol 141:147–160
van Dongen WFD, Mulder RA (2006) Habitat density, song structure and dialects in the Madagascar paradise flycatcher (Terpsiphone mutata). J Avian Biol 37:349–356
van Riper SG (2000) Japanese white-eye (Zosterops japonicus). In: Poole A, Gill F (eds). The birds of North America, no. 487. Academy of Natural Sciences, Philadelphia and American Ornithologists’ Union, Washington D.C
Wiley RH (1991) Associations of song properties with habitat for territorial oscine birds of Eastern North America. Am Nat 138:973–993
Wong BBM, Candolin U, Lindström K (2007) Environmental deterioration compromises socially enforced signals of male quality in three-spined sticklebacks. Am Nat 170:184–189
Acknowledgements
The authors thank A. Beck, A. Tanimoto, N. Fernandez and S. Uehana for their help with the fieldwork. We thank the Hawai’i State Department of Land and Natural Resources for permission to work at the study area. This work was supported by the National Science Foundation, USA award #1345247. The study complied with all the current laws of the US. E. Sebastián-González is currently supported by a Juan de la Cierva Grant from the Spanish Ministry of Economy, Industry and Competitivity. We are grateful to two anonymous reviewers for constructive comments on the manuscript.
Supplementary acoustic material under: https://dx.doi.org/10.7479/rxwp-77cn.
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Sebastián-González, E., van Aardt, J., Sacca, K. et al. Testing the acoustic adaptation hypothesis with native and introduced birds in Hawaiian forests. J Ornithol 159, 827–838 (2018). https://doi.org/10.1007/s10336-018-1542-3
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DOI: https://doi.org/10.1007/s10336-018-1542-3