Urban bioacoustics its not just noise_Warren et al 2006.pdf

8/14/2019 Urban bioacoustics its not just noise_Warren et al 2006.pdf

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2002; Kobayasi & Okanoya 2003; Pytte et al. 2003; Brummet al. 2004 ). However, future studies should addresswhether elevated noise levels have long-term or evolu-tionary effects on animal signalling.

Frequency shifts Many noise sources have energy concentrated in par-

ticular frequency bands. For example, anthropogenic

noise sources are often concentrated at lower frequencies.Thus, frequency shifting presents an another mechanismfor avoiding masking noise ( Katti & Warren 2004 ; Fig. 1c).Slabbekoorn & Peet (2003) found that human-generatedsound energy was largely concentrated below 2000 Hzand that great tits, Parus major , in noisier portions of Lei-den, Netherlands, produced songs with higher minimumfrequencies than did those in less noisy portions of thatcity. These birds appeared to have shifted the frequencyof their songs in response to this human-altered acousticenvironment. Great tits are passerines and are capable of song learning. Thus, the authors acknowledged that thedifferences that they found might be either from learnedchanges in signal structure or evolved responses, althoughthey argued that the former is a more likely explanation.Other parids such as chickadees are known to adjustsong frequencies in winter ocks ( Mammen & Nowicki1981 ). Future studies could use technologies commonlyused in studies of song learning, such as sound isolationchambers, to test whether low-frequency noise affectsthe frequency structure of learned bird song.

The generally low-frequency character of anthropogenicnoise suggests that species using higher-frequency signalsshould be better able to tolerate this noise than thoseusing lower-frequency signals. Only one researcher to datehas tested this hypothesis and found a weak but signi-cant relation between tolerance of proximity to roads and

dominant frequency of the song ( Rheindt 2003 ). Rheindt

compared the decline in abundance in proximity to roadswith measures of dominant frequency and showed thatspecies with higher abundances near roads (indicatinggreater tolerance to elevated noise levels) had signicantlyhigher dominant frequencies than did species that wereless abundant near roads. Rheindt ruled out two potentialconfounds: body size and detectability (because birds withlower-frequency song might be masked by the road noise).However, Rheindts results rest upon low sample sizes: the

study included only 12 species and is based upon differ-ences in abundance between just two transects, which iseffectively a sample size of one. This work should be re-peated in other regions and with more robust replication.

Many species are physiologically constrained to producelow-frequency calls in the range of masking noise. Manybird and insect species either produce songs at higherfrequencies or are capable of altering their signals toescape masking noise. However, many species of frogsand birds produce signals with most of their energyoverlapping with the sound energy in anthropogenicnoise. Unless these species nd other mechanisms foradapting to noisy environments, such as shifting thetiming of calling, their signals will experience smalleffective areas, potentially impairing their ability tocommunicate ( Rabin & Greene 2002 ).

Other design features Other features of signal design allow animals to mitigate

the effects of masking noise ( Aubin 2004; Sun & Narins2005 ). For example, signals with narrower bandwidths(e.g. pure tones) are expected to be more detectableagainst background noise than are those with wider band-widths (e.g. buzzes or trills). We found few studies in gen-eral addressing the design of signals in persistently noisyhabitats. Some evidence suggests that birds living along

streamsides produce signals dominated by pure tones

Frequency shift Amplitude shiftNo noise

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Figure 1. Faced with the problem of communicating through masking noise, animals have two main options for making their calls more au-

dible: altering the frequency or altering the amplitude. Much of the noise generated by humans is concentrated at low frequencies. In thatsetting, animals may shift the frequency of calls upward to escape masking noise and/or increase the amplitude of their calls without alteringtheir frequency. Reprinted with permission from Elsevier ( Katti & Warren 2004 ).

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(e.g. American dipper, Cinclus mexicanus , in North Amer-ica: Kingery 1996 ; torrent duck, Merganetta armata , inSouth America: Niethammer 1952 ; wallcreeper, Ticho-droma muraria , and whistling thrush, Myophonus caeruleus ,in Eurasia, Lohrl 1964; Dubois & Martens 1984 ). Morecompelling is evidence that three species of frog ( Rana ros-

tandi , R. blandfordii and R. liebigii) and one species of bird( Phylloscopus magnirostris ) living alongside streams sharea number of signal features that their close relatives donot ( Dubois & Martens 1984 ). In addition, these frogand the bird species all produce short, narrow-bandsignals, in short sequences with long intervals between.Dubois & Martens speculated that the temporal featuresof the signals might also be important features for eithercontrast with the background noise or to aid in localiza-tion. Neither of these hypotheses was tested, nor did theauthors make quantitative comparisons of frequencybandwidth between the species. Quantitative, phyloge-netically controlled studies are needed to assess whethernoisy environments affect signal design, but research todate suggests that animals with narrow frequency band-width signals may be able to communicate more effec-tively in noisy urban environments.

Several questions remain regarding the effects of ele-vated noise levels on signal design. First, are therethresholds of noise above which animals cannot compen-sate through the Lombard effect? That is, the absoluteamplitude of some human-generated noise (e.g. industrialnoise, airplanes) may be just too high. What are thesethresholds? Do they differ between species? The maskingnoise levels used in several studies of the Lombard effectin birds were typically around 70 dB ( Manabe et al. 1998;Kobayasi & Okanoya 2003; Pytte et al. 2003 ), and the

maximum used in any study was 90 dB ( Cynx et al.1998 ). Zebra nches, Taeniopygia guttata , in a study usingplaybacks of masking noise at 6090 dB, showed curvilin-ear responses to increasing levels of masking noise, sug-gesting that the birds may quickly reach a thresholdabove which they no longer show the Lombard effect(Cynx et al. 1998 ). The higher-amplitude song elementsproduced by nightingales increased little in response to in-creasing levels of masking noise, suggesting that nightin-gales may already be producing some portions of theirsongs at maximum levels, around 85 dB ( Brumm & Todt2002 ). Noise levels in airport ight paths reach 74 dB,and highway noise from 500 ft (152 m) away is around70 dB, depending on the size and trafc speeds of thehighway ( Egan 1988 ). Thus, typical urban noise levelsappear to fall in the decibel range that animals studiedso far can accommodate via the Lombard effect, at leastin the short term. We encourage animal communicationresearchers to include in their experiments noise-leveltreatments that match common urban noise sources.

Second, what are the costs of signalling under persis-tently noisy conditions, such as those found in cities andalong roads? Using higher-amplitude signals should in-crease the energetic costs of signalling, although somestudies suggest that singing in birds is not as energeticallycostly as might be expected ( Brumm & Todt 2002 ). Underprolonged exposure to noise, energetic costs of signalling

might lead to lower tness for animals in noisy

environments. Studies of the Lombard effect have so farbeen conned to short-term studies, even when the focalspecies occupies persistently noisy environments such asstreamsides ( Pytte et al. 2003 ). Thus, we do not knowwhether animals can adapt to persistently noisy condi-tions by elevating the amplitude of their signals. In fact,

avian reproductive success along highways shows reduc-tions under noise levels of 4248 dB, much lower thanthe noise levels used in studies of short-term Lombard ef-fects ( Forman & Alexander 1998 ). Understanding theselong-term responses to noise has implications for commu-nication under noisy conditions more generally, such aswater noise alongside streams or the noise of animal cho-ruses and ocks.

Third, what is the effect of noise on calling effort?Decreased time spent calling is generally expected toreduce opportunities for mating; thus, suppression of calling by anthropogenic noise should have negativeeffects on breeding success. One recent study suggeststhat noise from passing airplanes and motorcycles, as wellas experimental playbacks of noise, suppresses calling insome species of amphibians, usually the dominant callingspecies ( Sun & Narins 2005 ). However, the nondominantcalling species, Rana taipehensis , increases calling duringbouts of noise, possibly because of the lull in calling byother species ( Sun & Narins 2005 ). This, to our knowledge,is the only experimental study of the effects of noise oncalling behaviour. More work is called for in this area,but Sun & Narins study suggests that noise may havecomplex direct and indirect effects on calling behaviour.

Cities and their rapid expansion provide a naturallaboratory for measuring effects of prolonged noise expo-sure. Commonly occurring species of birds that span the

urbanrural gradient include mockingbirds, Mimus poly- glottus, in many North American cities and the Europeanblackbird, Turdus merula . Some species of frogs continueto breed along roadsides or in urban areas ( Hermy & Cor-nelis 2000; Lesbarreres et al. 2003 ). Comparative studiescould measure, among other things, calling amplitudesin these species in both urban and exurban areas.Researchers should also take advantage of natural experi-ments such as the relocation of an airport or the buildingof a new road to measure effects of both increasing anddecreasing noise levels on calling amplitudes.

Noise Effects on Animal Distributionsand Reproductive Success

Chief conservation concerns about anthropogenic noiseare that it might limit the distributions of particularanimal species that are intolerant of noise or negativelyaffect reproductive success in species forced to breed innoisy environments. Most research on negative effects of noise has focused on road noise (but see Leddy et al.1999 ). Many bird species occur at lower densities closerto roads, and bird diversity is often lower in proximityto roads ( Reijnen & Foppen 1994, 1995; Forman et al.2002; Rheindt 2003; Peris & Pescador 2004 ). Both birdsand frogs appear to have lower breeding success near roads

(Reijnen & Foppen 1995; Forman & Alexander 1998;

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Spellerberg 1998; Lesbarreres et al. 2003 ). Several authorshave argued that, in addition to ecological impacts of roads, elevated noise levels along roads also impair theability of animals to effectively communicate duringbreeding, thereby impacting reproductive success ( Formanet al. 2002 ). Here, we ask what evidence there is that road

noise negatively affects animals via effects on animal com-munication. Research in this area has focused almost ex-clusively on birds.

Four lines of evidence have been used to argue fornegative effects of noise on animal communicationsystems.

(1) Bird densities are depressed beyond the view of roads(Reijnen & Foppen 1994, 1995; Reijnen et al. 1995, 1996 ).A series of studies in the Netherlands makes the case thatthe effects of road noise are detectable far beyond the dis-tance at which roads are visible to birds. Noise levels inone study regularly exceeded 50 dB at 500 m from theroad, but the sight distance to the road was only 25 m(Reijnen & Foppen 1994 ). Bird densities were signicantlylower for more species at sites with higher noise loadswhen the authors controlled for trafc visibility, but therewere no signicant differences in densities when they var-ied trafc visibility and controlled for noise load ( Reijnenet al. 1995 ). These studies eliminated one confounding ef-fect of roads, the disturbance effect created by the motionof cars in trafc. The results suggest that noise impairs theeffectiveness of male songs for attracting and keepingmates ( Reijnen & Foppen 1994 ), but they cannot elimi-nate alternative explanations such as stress effects of noise(Kempf & Huppop 1996; Maschke et al. 2000 ).

(2) Bird diversity is lower in noisier sites, independent of land use type ( Stone 2000 ). Stone (2000) compared noise

levels to bird diversity over a range of surrounding landuse types (agricultural, residential, industrial, native grass-land). Observers conducted bird counts along riparianzones surrounded by the different land use types and pro-vided subjective assessments of noise level (high, medium,low). Stone compared bird diversity to estimated noiselevel for each land use type separately and found consis-tently lower diversity at noisier sites. The power of thistest comes from both good replication and coverage of a wide variety of habitats. No other habitat measureswere taken at the sites, however, so the possibility remainsthat other confounding variables accounted for the differ-ences in diversity. For example, noise level could be corre-lated with habitat features such as greater imperviousground surface and lower vegetative cover, both of whichare well known to predict avian diversity in developedareas ( DeGraaf & Wentworth 1986; Blair 1996; Germaineet al. 1998; Marzluff 2001; McKinney 2002 ).

(3) Observations of birds foraging near roads, but notbreeding there ( Forman et al. 2002 ). Breeding birds uselow-amplitude calls to communicate near their nests andto communicate with offspring. If birds are willing to for-age but not to breed near roads, it might be because of in-terference of noise in these low-amplitude signals involvedin reproduction. In other words, as Forman and colleaguessuggested, parents simply cannot hear their offspringsbegging calls in the presence of high levels of trafc noise.

There is some evidence that disrupting these signals has

effects on reproductive success. Experimentally mutingnestling birds so that they cannot produce begging callsleads to lower rates of food provisioning by parents(Glassey & Forbes 2002 ). Forman et al. (2002) argued thattheir observations refute two alternative hypotheses: (1)that birds opt not to breed near roads because there is in-

sufcient habitat to support feeding of offspring, and (2)that birds avoid breeding on roadsides to avoid exposureto the stressful effects of trafc. This hypothesis does noteliminate the possibility that birds are more tolerant of stress while foraging than while nesting.

(4) Birds with higher-frequency songs have higherabundances near roads ( Rheindt 2003 ). This is perhapsthe most compelling evidence that noise affects animaldistributions via direct effects on their ability to commu-nicate. There is, however, only a single, poorly replicatedstudy showing this pattern ( Rheindt 2003 ; also see discus-sion of Frequency shifts , above).

Taken together, these four lines of evidence are sugges-tive, but they do not present irrefutable evidence foreffects of anthropogenic noise per se on animal distribu-tions and reproductive success. Playback experiments withnoise are the most effective means of demonstrating theeffects that all of the above authors hypothesize. Thedisruption of low-amplitude communication signals couldeasily be studied using playback experiments in controlledlaboratory conditions. Field experiments could also testeffects of noise playbacks near to and further from nestingbirds. Not only could these experiments ask whether noiseper se mediates the observed effects of roads on breedingsuccess, but they would also test, more generally, theimportance of these low-amplitude signals for breedingsuccess in birds. Although these communication signals

are assumed to be important for breeding success, thishypothesis has rarely been tested ( Budden & Wright 2001;Glassey & Forbes 2002 ).

Noise and Acoustic Divergence

Some authors have argued that variation in anthropo-genic noise levels could lead to acoustic divergence of urban and nonurban populations of the same species oreven speciation ( Slabbekoorn & Peet 2003 ). Urban land-scapes have high spatial heterogeneity ( Rebele 1994 ),and this also extends to acoustic space. The most perva-sive source of noise in most cities is trafc. In general,the shape of the noise space in a city is expected to behighly variable, with bands of noise along major roadsand polygons around airports and factories ( Egan 1988 ),contrasting with quieter pockets in residential areas.Consistent spatial variation in noise over time providesthe opportunity for acoustic and even evolutionary diver-gence among populations in and around cities.

There is a small body of evidence for effects of noise onintra- and interspecic variation in signal design ( Ryan &Brenowitz 1985; Slabbekoorn & Smith 2002a ). Differencesin the minimum frequency of little green bulbul, Andropa-dus virens , song appear to be driven by differences betweenhabitats in the level of low-frequency ambient noise

(Slabbekoorn & Smith 2002b ). These song differences are

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correlated with morphological differences ( Slabbekoorn &Smith 2002b ), providing the opportunity for the habitatdifferences in song to lead to reproductive isolation(Slabbekoorn & Smith 2002a ).

The smaller scale of spatial heterogeneity in cities seemsto indicate a lower potential for reproductive isolation

among divergent populations, but Leader et al. (2000,2002) found microgeographical variation in songs of twosunbird, Nectarinia osea , populations within an Israelicity as well as acoustic discrimination between dialects,in part based on maximum frequency, despite the ex-tremely short distances between the two populations.More recently, Leader et al. (2005) examined noise levelsand acoustic transmissivity of the habitats occupied bybirds singing the two dialects as well as apportionmentof energy between low-frequency and high-frequency por-tions of the song. Noise levels were higher in the area withhigh-frequency dialects, but birds in the noisier habitatdid not apportion greater energy to the high-frequencyportions of their song. This result and the small samplesize of the study (many birds, but only two dialects intwo habitats) leave unclear whether variation in noiselevels contributes to intraspecic acoustic divergence inurban populations.

Predictable variation in noise levels For variation in noise levels to lead to acoustic di-

vergence, spatial variation in noise levels must be bothpredictable and consistent. Among urban planners andacoustic engineers, there is considerable interest inwhether this is the case. Recent European Union standardsof environmental quality ( Wolde 2003 ) have prompted

the development of noise maps for Paris ( Butler 2004 ).These maps are publicly accessible ( http://www.paris.fr/FR/Environnement/bruit ), and they depict lower noiselevels in the outer ring of Paris than in the central ring.A study in a small city in Ohio in the U.S. showed higher

noise levels in predominantly minority neighbourhoods(Forkenbrock & Schweitzer 1999 ). In Tokyo, noise attenu-ation increased (i.e. lower noise levels experienced by res-idents) with building density and average building height(Ishiyama et al. 1991 ). All three of these studies relyheavily on modelled noise levels based on trafc volume

data rather than on eld measurements of noise. Themodels used are derived from well accepted and validatedacoustic and trafc models. The Paris noise maps havebeen validated by comparing eld noise level measure-ments to the model predictions. However, it is importantto get direct empirical measures of variation in noiselevels, because there are many other sources of noise asidefrom trafc.

Preliminary data from our own eld measurements insmall parks in residential areas of Phoenix suggest thatnoise levels do vary predictably across the city. Wemeasured noise levels in 16 neighbourhood parks of similar design. Mean noise levels consistently variedbetween sites more than within sites (MANOVA includingsite, date, time interval: F 15 10.59, P < 0.0001). Further-more, noise levels were strongly inversely predicted by dis-tance from the urban centre ( Fig. 2a) and byneighbourhood income level (2000 U.S. Census data;Fig. 2b). Of the two, income level was the stronger predic-tor of mean noise level in a multiple regression( F 1,1 6.99, P 0.02). Income, distance from urban cen-tre, age of the neighbourhood and ethnic compositionof the neighbourhood were all correlated in Phoenix aswell as in many other U.S. cities ( Kinzig et al. 2005 ).Higher-income neighbourhoods tend to be further fromthe urban core, newer and predominantly Caucasian,and these same neighbourhoods appear to be subject to

lower noise levels ( Fig. 2). By far, the most commonly re-ported source of noise by observers in Phoenix was trafc.At the sites with the highest noise levels, observers alsoidentied airplanes and industrial plants as commonnoise sources. This observation suggests that the

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Figure 2. Spatial variation in noise level in Phoenix, Arizona. We measured noise levels in 16 neighbourhood parks of similar design varying insize from 1.7 to 5.6 ha (see method for site selection in Martin et al. 2004; Kinzig et al. 2005 ). Three groups of ve observers (total of 15people) visited each park once in the morning (06300830 hours) or the evening (16001900 hours) for a total of 45 measures per park during130 June 2003. At each visit the ve observers took simultaneous measures of sound pressure levels (SPL) using handheld SPL meters whilestanding at ve separate locations: the centre of the park and the edges of the park facing outward in the four cardinal directions. Distance tourban centre is measured from the intersection of Central Avenue and Jefferson Street in Phoenix ( Luck & Wu 2002 ). Income data is median

family income for the surrounding census tract (2000 U.S. Census).

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differences between sites in noise level are not solelybecause of their proximity to roads or differences in thebehaviour of residents. Although bird species diversityvaries considerably between these parks, a core set of species, including both native species (e.g. curve-billedthrashers, Toxostoma curvirostre , and cactus wrens, Campy-

lorhynchus bruneicapillus ) and nonnative species (e.g.European starlings, Sturnus vulgaris , and house sparrows, Passer domesticus ), occurs at all 16 Phoenix parks ( Kinziget al. 2005 ). Thus, variation in noise levels in Phoenixhas the potential to contribute to acoustic divergence be-tween populations of resident urban bird species.

Despite the small body of literature on spatial variationin noise levels, our review suggests that acoustic diver-gence of bird song in cities is a fruitful area for futureresearch. Noise levels appear to vary consistently withincities and on urbanrural gradients ( Ishiyama et al. 1991;Forkenbrock & Schweitzer 1999 ; Fig. 2). Acoustic diver-gence occurs over short geographical distances withina city ( Leader et al. 2000, 2002 ) and in response to habitatdifferences in noise levels ( Slabbekoorn & Smith 2002b ).

Understanding the effects of spatial variability in noiserequires better maps of actual noise levels. This is a poten-tial area for collaboration between biologists and acousti-cal engineers and architects. For example, noise contoursaround airports are regularly developed for city planners,and architects develop and test barriers to noise alongsidehighways ( Egan 1988 ). Most of this monitoring of trafcnoise is not conducted systematically at extensive scales,but trafc ow and volume is regularly monitored ( Lomaxet al. 2003 ). Urban planners in various European citieshave begun using modelling approaches to circumventthe costs of monitoring trafc noise more directly ( Butler

2004 ). Citywide maps of noise levels could be benecialboth for city planners interested in effects of trafc noiseon humans and for biologists studying distributions of species.

OTHER ACOUSTIC FEATURES

Many other acoustic features of the urban environmentmay affect animal communication systems. We brieyhighlight two areas: effects of rush hour trafc on theavian dawn chorus and acoustic phenomena in urbancanyons. In neither case does existing research in animalbehaviour specically address urban populations. How-

ever, in each case, clear predictions arise from consideringwell-known phenomena in animal communication alongwith well-known urban phenomena.

Dawn Chorus and Trafc Noise

Many animals engaging in long-range communicationtake advantage of sound channels for maximal soundtransmission ( Wiley & Richards 1978 ). The spectral prop-erties of human-generated noise enable great tits in Leidento use a sound channel not occupied by humans (i.e. fre-quencies above the level of masking noise; Slabbekoorn &Peet 2003 ). One well-known sound channel is the tempo-

ral window around dawn ( Wiley & Richards 1978, 1982;

Brown & Handford 2003 ). In birds, a peak in singing activ-ity occurs around dawn (dawn chorus; Staicer et al. 1996;Dabelsteen & Mathevon 2002; Brown & Handford 2003 ).Although a variety of processes, including residual energyfrom overnight fat stores, may account for the peak of singing activity at dawn ( Hutchinson 2002; Thomas &

Cuthill 2002 ), acoustical experiments suggest that thedawn chorus is at least facilitated by the favourable condi-tions for sound transmission that occur around dawn.Sound transmits further and more reliably at dawn thanat midday because of lower wind noise, lower wind turbu-lence and fewer atmospheric uctuations ( Henwood &Fabrick 1979; Brenowitz 1982; Dabelsteen & Mathevon2002; Brown & Handford 2003 ).

Temporal variation in noise levels associated withhuman activity produces the potential for negativeeffects of trafc on the dawn chorus. Morning rushhour, the peak in trafc ow associated with urbancommuters, has the potential to overlap with the aviandawn chorus. In most cities in the U.S., rush hourconsistently occurs between 0600 and 0900 hours(Lomax et al. 2003 ). Solar patterns vary seasonally, sothe timing of rush hour relative to sunrise will alsovary seasonally ( Fig. 3a). As a case study, we examinedBaltimore, Maryland, U.S.A., a temperate city. Duringmuch of the breeding season in Baltimore, there is asmuch as half an hour during which the dawn choruscan occur before the onset of rush hour ( Fig. 3a). InMarch and April, however, when most bird territory es-tablishment and mate selection is occurring, sunriseoverlaps considerably with the rush-hour period(Fig. 3a). In other cities, the extent of overlap betweenrush hour and the dawn chorus is not as extreme. In

the U.S., overlap increases with decreasing latitude butalso varies with use of Daylight Savings Time ( Fig. 3b).In areas with high trafc, these trafc patterns could gen-erate selection for birds to shift the onset of the dawnchorus.

This analysis leads to two testable predictions: (1) trafcnoise will affect birds singing in dawn choruses more atlower latitudes; (2) birds should shift their temporalpattern of singing in response to trafc noise.

Is there evidence that birds could alter the timing of thedawn chorus? Many species can alter their temporalpattern of vocal communication to avoid masking in-terference from other species ( Ficken et al. 1974;Greeneld 1988; Paez et al. 1993; Sun & Narins 2005 ). Toour knowledge, only one study addresses changes in tem-poral patterns of signalling in response to anthropogenicnoise levels. Bergen & Abs (1997) measured song rates forthree species of birds in an inner-city park in Dortmund,Germany and in a forest patch on the fringe of the city.All three species began singing signicantly earlier in theurban park than in the nonurban forest patch. Bergen &Abs suggested an alternative explanation for the differ-ences between their two study sites, that higher light levelsin the city may contribute to altering the timing of singing,if light levels act as a proximate cue for the onset of thedawn chorus. Nevertheless, the study provides an intrigu-ing demonstration that the timing of the dawn chorus can

vary signicantly over short geographical distances.

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There are additional sources of diurnal variation in noisewith the potential to affect signalling at other times of theday. Airplane noise might instead produce a relativelyeven effect, with periodic noise occurring intermittentlythroughout the day. In many cities, truck trafc isconned to nighttime, potentially affecting the timingof calling by nocturnal animals. Effects of continuoussources of noise should be compared to effects of in-termittent noise sources, such as trains.

Urban Canyons and Flutter Echo

Much information on acoustics of buildings in thearchitectural literature has not been integrated into stud-ies of animal behaviour ( Egan 1988 ). The most obviouscharacteristic of human-built structures is the presenceof multiple, often parallel, sound-reective surfaces, or ur-ban canyons. High reectance can allow sound to ricochet

and linger, potentially impeding communication through

constructive interference. Humans and many nonhumananimals have perceptual mechanisms that can suppressechoes that would otherwise degrade their ability to local-ize or perceive signals ( Snedden & Greeneld 1998;Litovsky et al. 1999; Bosch & Marquez 2002; Dent &Dooling 2004 ). Despite these perceptual accommodations,

reverberations in rooms and urban canyons often renderhuman speech unintelligible ( Egan 1988 ), and reverbera-tions in natural environments can degrade transmissionof animal signals ( Wiley & Richards 1982 ). A particularcase of reverberation from built surfaces, known as utterecho, is generated by the rapid ricocheting of sound wavesback and forth between two parallel walls (or in a room)(Fig. 4b, c). We discuss here several ways that reectionsin urban canyons, particularly the phenomenon of utterecho, might affect animal communication and signaldesign.

Flutter echo in urban canyons should have two majoreffects. First, noise levels attenuate (lose amplitude) moreslowly in canyons ( Kang 2000; Iu & Li 2002 ). Thus, mask-ing noise levels should be higher along roads surroundedby urban canyons than along more open roads ( Kang2001; Iu & Li 2002 ). Second, animal signals produced incanyons may be degraded by utter echo. Receivers willhear not just the direct sound wave, but also the many re-ected waves arriving at different times.

Suppression or detection of echoes as distinct soundsdepends on their arrival times at the receiver relative tothe original signal on its direct pathway ( D T delay inarrival time). The perception thresholds in arrival time de-lays appear to be similar across taxa ( Snedden &Greeneld 1998; Litovsky et al. 1999; Dent & Dooling2004 ). Reections may be either summed with the origi-

nal signal ( D T < 0.40.5 ms), suppressed in favour of theoriginal signal (0.51.0 ms < D T < 810 ms), or perceivedas distinct echoes ( D T > 810 ms). The earliest arriving re-ections can enhance the perceived loudness of the soundbeing created and mask or relocate the perceived source of a sound in humans and many nonhuman animals(Gardner 1968; Mills 1972; Dent & Dooling 2004 ). Forarrival time delays in the middle range (0.510 ms), the re-ections or echoes are suppressed, in favour of the signalon its direct path, which arrives rst. This phenomenonis known as the precedence effect ( Litovsky et al. 1999 ).Precedence effects and summing localization have beendemonstrated in many nonhuman animals ( Snedden &Greeneld 1998; Litovsky et al. 1999; Naguib & Wiley2001; Bosch & Marquez 2002 ), and thresholds for sum-ming in several bird species appear to be similar to thosefound in humans ( Dent & Dooling 2004 ). Yet, given theshort time intervals involved, reections in urban canyonsshould rarely arrive in such close succession to the directpath signal as to invoke either summing localization orprecedence effects. Instead, it is more likely that canyonreections, even in relatively small canyons such as walledstreets, will arrive beyond the receivers echo threshold(D T > 810 ms).

Reections that arrive beyond the echo threshold areperceived as distinct sound images and typically do notinterfere with sound localization ( Dent & Dooling 2004 ).

In addition, echoes approaching from the contralateral

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Baltimore, Maryland, U.S.A. During the breeding season (MayJuly), dawn occurs considerably earlier than the onset of rush hour.(b) Variation among U.S. cities in April, a month in which breedingbirds are establishing territories in most of the cities depicted. Citiesat lower latitudes tend to have greater overlap between rush hour and dawn chorus. Cities in parentheses do not use Daylight SavingsTime.



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side of the receiver, relative to the signaller, should be de-tected at much lower amplitude ( Romer et al. 2002 ). Thisphenomenon of contralateral inhibition is thought bysome to be a mechanism underlying the precedence effect(see above) ( Romer et al. 2002 ). Nevertheless, the rapidityand high quantity of reections present in utter echo canstill degrade the quality of a direct path signal. First, onlysome of the echoes in a canyon will arrive at the contralat-

eral side of the receiver, particularly if a receiver is orient-ing lengthwise along the canyon. Second, reverberationscan interfere by masking or blending signal elements(Wiley & Richards 1982; Slabbekoorn et al. 2002 ).

Animal signals are commonly produced as sequences of sounds, either repeated or variable. These successivesounds are increasingly likely to overlap with reectionsof previous sounds as reverberation times increase. Re-verberations have the strongest effects on signals withrapid amplitude modulation, often called trills, and thosewith rapid and repetitive changes in frequency, or rapidfrequency modulation ( Wiley & Richards 1982 ). The effectof utter echo on animal communication signals shouldtherefore be similar to effects of reverberations in forestenvironments ( Wiley & Richards 1982 ). The chief differ-ence being that reections from the strongly reectivesurfaces of built structures will generally retain a greaterportion of the original sound energy, producing a strongerdegrading effect than reections from vegetation, whichtends to be more absorbent.

Signals with narrow frequency bandwidths (e.g. puretones) can sometimes benet from reverberations, soundwaves scattered by intervening vegetation or other struc-tures ( Slabbekoorn et al. 2002 ). For these pure tone signals,the reected sound waves effectively make the signalhigher in amplitude and longer ( Slabbekoorn et al.2002 ). Canyon birds with pure tone signals might make

similar use of the reections generated by utter echo.

Flutter echo is enhanced by the wall separation, theheight of canyon walls, and the smoothness of thereective surfaces ( Kang 2001; Iu & Li 2002 ; Fig. 4). Thatis, taller, narrower canyons produce a more pronouncedutter echo effect. Wider canyons result in longer delaytimes between the direct wave and the arrival of thereected waves. Thus, the reected waves are less likelyto interfere with the direct pathway wave. Architectural

acousticians have developed tools to plot sound wavesand their reections in canyons (rooms) of different di-mensions and of different materials ( Fig. 4) as well as tomodel signal properties after transmission through differ-ent canyon environments.

This leads to two testable predictions: (1) narrowcanyons should show longer signal decay times butgreater signal degradation due to utter echo; (2) areaswith higher densities of urban canyons will amplify trafcnoise.

Urban-dwelling species with populations in canyonenvironments provide opportunities for comparative study.The peregrines of North America are the most conspicuousexample, nesting in the large-scale high-rise apartmentand business districts. In addition, the archetypal canyonbird species, the canyon wren, Catherpes mexicanus , is alsoa common resident of Mexican towns where walledgardens and streets constitute smaller-scale urban canyons(Howell & Webb 1995 ). Similarly, row house neighbour-hoods in Baltimore, Maryland and other cities provideurban canyon environments with substantial breedingpopulations of native bird and mammal species (S. T. A.Pickett, J. M. Grove, P. M. Groffman, L. W. Band, C. G.Boone, G. S. Brush, W. R. Burch, Jr, M. L. Cadenasso, J. Hom, J. C. Jenkins, N. Law, C. H. Nilon, R. V. Pouyat,K. Szlavecz, P. S. Warren & M. A. Wilson, unpublisheddata). Geckos, lizards that use acoustic signals, also occupy

similar rocky niches in both urban and nonurban

95

85

75

65

55

45

35

(a)

(b)

A0

A0

A0

(c)

S P L

( d B

)

C a n y o n w

i d t h

Canyon length

Figure 4. Wave reections in: (a) an open eld; (b) a small canyon 16 300 16 m (w l h); and (c) a large canyon 48 300 48 m. In(b) and (c), the source was 0.25 m from one wall, one-fourth of the way up the wall (4 and 16 m off the ground, respectively) and 40 m fromone end of the canyon. Models were conceived in Catt-Acoustics (Gothenburg, Sweden) room acoustics ray tracing software, assuming thateach canyon had brick walls and a concrete oor. The height of the receiver mapping plane was 1 m below the source. The source wasomnidirectional at 90 db at each octave. For this study, 10 000 rays/octave were propagated for 1 s. All sound pressure level (SPL) measurementswere taken at 2000 Hz.

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environments. The role of canyon acoustics in the com-munication systems has not been studied even in naturalcanyon systems. Since urban canyons are in many re-spects more accessible than natural ones, this providesan opportunity for urban research to model animal com-munication systems more generally.

Flutter echo in urban canyons is only one of thepotential implications of architectural acoustics for urbananimal communication systems. We note, however, thateven the extensive body of work on urban acoustics doesnot yet provide explicit models or even rules-of-thumbfor many of the situations encountered in built environ-ments. We strongly encourage future research to focus onsound transmission through areas with varying densitiesand congurations of built structures.

CONCLUSIONS

Our review found a small body of literature on the effectsof urban acoustic environments on animal signallingbehaviour. Animals may behaviourally alter signal char-acteristics such as minimum frequency ( Slabbekoorn &Peet 2003 ) or amplitude ( Brumm & Todt 2002 ); theymight also shift the timing of signal to avoid maskingnoise ( Bergen & Abs 1997 ). Animals lacking learning orother forms of phenotypic plasticity and that are forcedto respond via natural selection to altered environmentsmay be unable to adapt to the rapidly changing condi-tions generated by urbanization ( Rabin & Greene 2002 ).However, there is a growing body of literaturedemonstrating rapid evolutionary change in animals oc-cupying human-altered landscapes ( St Louis & Barlow

1988; St Louis & Barlow 1991; Badyaev et al. 2000 ). Ani-mal behaviourists have much to contribute towards un-derstanding which species will be able to adapt to thesenovel environments and which will be forced to abandonthem.

Many questions remain unanswered. For example,which species can compensate for the extremely elevatednoise levels found in cities? What are the effects of prolonged signalling at high amplitudes on animal t-ness? It is becoming well accepted that roads with highertrafc loads have negative impacts on reproductive suc-cess. It is still not clear, however, whether this effect is dueto effects of noise per se or whether noise affects animaltness indirectly by impairing the ability of animals tocommunicate effectively.

We highlight three relatively new areas for futureresearch: spatial distribution of noise and acoustic di-vergence in animal signals, temporal variability in noiselevels and effects on timing of animal signalling (e.g.dawn chorus), and the acoustics of canyons. For all of these topics, there are parallel phenomena found innatural systems, providing opportunities to make directcomparisons between signalling behaviour of the samespecies in both their native habitat and nearby urbanhabitat. Some phenomena, such as canyon acoustics, haveyet to be fully explored in natural systems. Future work inthis area can, therefore, contribute powerfully to the study

of animal communication generally as well as to

conservation and management of urban wildlife. Manyof the tools to address these questions, such as playbacksof anthropogenic noise, are already in use by biologicalresearchers, but others will require or will benet frominterdisciplinary collaboration with acoustical engineersand architects. Figure 4 provides an example of the contri-

bution of architectural acousticians to modelling acoustictransmission in built environments. Other opportunitiesfor collaboration include quantifying spectral propertiesof anthropogenic noise, spatial mapping of noise contoursin eld settings and temporal mapping of noise.

Finally, animals that live in urban environments mustcommunicate in all the usual ways to obtain mates,defend territories, or maintain ock dynamics, so un-derstanding how they adapt their communication systems(and the constraints upon adaptation) to the acousticfeatures of these environments is a promising area of research. Increasing our understanding of these adapta-tions and constraints has the potential to generate newinsights into the biology of communicating in general. Wehope that this review will serve as a call to action, bringingmore animal behaviour researchers into our ever-expand-ing urban areas.

Acknowledgments

We thank S. Bertram, B. Dawson, G. Uetz, M. Ryan, theRyan laboratory reading group, and four anonymousreferees for their assistance with the development of thismanuscript. The students in Anthony Brazels urbanclimate eld course at Arizona State University providedassistance with collecting noise level measurements. TheCentral Arizona-Phoenix Long-Term Ecological Researchsite (National Science Foundation, Division of Environ-mental Biology grant number 9714833) provided fundingfor P. Warren and M. Katti.

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