Wing-beat characteristics of birds recorded withtracking radar and cine camera

BRUNO BRUDERER, DIETER PETER, ANDREAS BOLDT & FELIX LIECHTI*

Swiss Ornithological Institute, CH-6204 Sempach, Switzerland

This study presents wing-beat frequency data measured mainly by radar, complementedby video and cinematic recordings, for 153 western Palaearctic and two African species.Data on a further 45 Palaearctic species from other sources are provided in an electronicappendix. For 41 species with passerine-type flight, the duration of flapping and pausingphases is given. The graphical presentations of frequency ranges and wing-beat patternsshow within-species variation and allow easy comparison between species, taxonomicgroups and types of flight. Wing-beat frequency is described by Pennycuick (J. Exp. Biol.2001; 204: 3283–3294) as a function of body-mass, wing-span, wing-area, gravity and airdensity; for birds with passerine-type flight the power-fraction has also to be considered.We tested Pennycuick’s general allometric model and estimated the coefficients based onour data. The general model explained a high proportion of variation in wing-beatfrequency and the coefficients differed only slightly from Pennycuick’s original values.Modelling continuous-flapping flyers alone resulted in coefficients not different fromthose predicted (within 95% intervals). Doing so for passerine-type birds resulted ina model with non-significant contributions of body-mass and wing-span to the model.This was mainly due to the very high correlation between body-mass, wing-span andwing-area, revealing similar relative scaling properties within this flight type. However,wing-beat frequency increased less than expected with respect to power-fraction, indicat-ing that the drop in flight level during the non-flapping phases, compensated by thefactor (g ⁄ q)0.5 in Pennycuick’s model, is smaller than presumed. This may be due to liftproduced by the body during the bounding phase or by only partial folding of the wings.

Keywords: continuous flapping, echo-signature, flapping frequency, flight model, flight types,intermittent flapping, release effect, target identification.

Reliable data on the flight performance of free-flying birds are scarce, particularly if steady levelflight, as for example on migration, is the focus(Pennycuick 2001). More than 30 years of trackingradar studies on bird migration (Bruderer 1999,2007) allowed us to accumulate a broad set ofsuch data. In a previous paper, Bruderer and Boldt(2001) presented an annotated list of flight speedsof 139 western Palaearctic bird species, based onwind- and altitude-corrected tracking radar data,and compared these measurements with theoreti-cal predictions. We now address the correspondingdata on wing-beat pattern, complemented by

recent measurements, considering 154 westernPalaearctic and two African species.

Pennycuick (1972, 1975, 1989, 2001, 2008)provided the theoretical background for under-standing dimensional relationships in animal flight,as well as programmes to estimate characteristicflight parameters (Pennycuick 1996, 2004).Bruderer and Boldt (2001) investigated differentsettings of the first programme and concluded thatthe aerodynamic model to estimate maximumrange speed (Vmr) matched the observed data well,but should be adjusted to account for reduced pro-file drag in flap-gliding and bounding flight of smallbirds, and to account for larger birds in poweredflight being increasingly limited to minimumpower speed (Vmp). The influence of bounding

*Corresponding author.Email: felix.liechti@vogelwarte.ch

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Ibis (2010), 152, 272–291

flight on speed and wing-beat frequency wasaccounted for by Pennycuick (2001) by includingthe proportion of the actual flapping time (power-fraction) in the formula. Alerstam et al. (2007),partly using the data of Bruderer and Boldt(2001) and following their approach, concludedthat flight speed among bird species scaled signi-ficantly differently with mass and wing-loadingthan predicted from basic aerodynamic principles,phylogenetic group being the most importantsingle factor explaining variation in flight speed,and wing-loading the second. Released birds,apparently reluctant to depart at migratory speed,fly at considerably lower speeds than migratingconspecifics (Bruderer & Boldt 2001), suggestingthat small birds, particularly those using boundingflight, are capable of selecting between variousspeeds, approaching predicted Vmp when intendingto remain airborne at low cost, but flying at muchhigher speeds when attempting to make bestprogress at least cost (probably around Vmr).

We expect life-history traits within taxonomicgroups to provide restricted platforms for adaptiveradiation of flight styles, confined to ecologicaladaptations within the limits of morphological andphysiological constraints. For the calculation ofwing-beat frequencies according to Pennycuick(2008), three basic flight styles are sufficient: (1)continuous flapping, (2) flap-gliding with wingsopen during the gliding phase and (3) boundingwith wings closed during the non-flapping phases(resulting in ballistic trajectories). Considering theimportance of taxonomic group and wing-loadingfor the explanation of variation in flight speed(Alerstam et al. 2007) and taking into account theobvious differences in flight behaviour of familiarbird groups, we will adopt more than three flighttypes for our descriptive purpose and associatethem with familiar (not necessarily taxonomic)bird groups (see Methods).

Among small birds, it seems that the smaller thebird and the larger its wing-area in relation to bodymass, the more it has to reduce profile drag by par-tially or completely folding the wings (Bruderer &Boldt 2001). Very small birds such as the ZebraFinch Taeniopygia guttata may flap-bound at allspeeds (Tobalske et al. 1999), reducing the timespent flapping with increasing speed and varyingmost parameters of wing-beat kinematics continu-ously with flight speed. A major difficulty for thedetermination of wing-beat frequencies arisesbecause birds such as pigeons (Columbidae),

thrushes (Turdidae), swallows (Hirundinae) andsome waders (Scolopacidae) do not flap theirwings in a quasi-harmonic manner. Instead theystop the wing movement for a fraction of awing-beat, flying for a moment with wings foldedpartially to form an arrow shape as during par-tial bounding (Liechti & Bruderer 2002, Rosénet al. 2007, Hedenström & Spedding 2008). Theparticular flight behaviour of hirundines withbarely definable flapping and non-flapping phasesand interrupted wing-beats prompted Brudereret al. (2001) to define ‘effective wing-beat fre-quency’ as the mean number of wing-beats over 20s normalized to 1 s. In the present paper, we use ina similar manner the number of wing-beats over aperiod of time that may include periods with noflapping (wb ⁄ t, see Methods).

Analogous to speeds, we expect little variabilityamong low flapping frequencies in large birds, butconsiderable adjustments of wing-beat pattern inmedium-sized and particularly in small birds withhigh flapping frequencies. In contrast to the idea ofdifferent ‘gaits’ in the flight of birds (e.g. Rayner1985), Tobalske et al. (1997) found considerablecontinuous variation in the flapping movements ofthe medium-sized Black-billed Magpie Pica pica.They suggested that the fibres commonly found inthe pectoralis muscle of small to medium-sizedbirds are capable of a wide range of efficient con-tractile velocities without requiring different gaits.This idea of continuous variation was confirmed bySpedding et al. (2003) and by Hedenström et al.(2006) for small passerines, demonstrating thatcomplex wake properties changed continuouslyfrom low to high flight speed, without any discon-tinuities that would indicate discrete ‘gaits’.

Pennycuick (1990) proposed methods to esti-mate wing-beat frequencies, assuming that massand wing morphology, together with some envi-ronmental variables such as air density, define a‘natural’ frequency, from which a bird cannot straytoo far. In sustained cruising flight, a bird wouldselect a frequency close to the natural frequency,while limited variation would be possible for par-ticular manoeuvres. The prediction of wing-beatfrequencies was improved using new measure-ments on mainly seabirds, providing a revised prac-tical formula for mainly continuous flapping(Pennycuick 2004). Pennycuick (2001) includedtwo bounding species, which led to the integrationof the power-fraction (the proportion of actualflapping time in a flap-bounding cycle) in the

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formula. For our comparison, we used the programFLIGHT (Version 1.15 from October 2004; freedownload from http://www.bio.bristol.ac.uk/people/pennycuick.htm).

Our first aim is to generate measurements ofwing-beat patterns of a large number of species toemphasize natural variation, but also potentialdistortions due to differences in the status of thebirds (e.g. migrating or non-migrating, free-flying orreleased) or due to differences in the recordingmethod (radar or cine recording). A commentedlist (see supplementary Appendix S1) provides avaluation of the data for each species and indica-tions of potential bias. Our second aim is to test thepredictions of Pennycuick’s formula (frequency f =m0.375B)0.958S)0.333(g ⁄ q)0.5q)0.375) with our data.

MATERIALS AND METHODS

Sites and periods of observation

Since 1968, the tracking radar Superfledermaus hasbeen used by the Swiss Ornithological Institute toobserve bird migration, with target identificationby wing-beat pattern being a major interest(Bruderer 1969, 1971, 1997, 2007). Additionalcinematic recordings were sporadically taken fromthe mid-1970s onwards.

Most data refer to autumn migration (August–October), fewer to spring migration (March–May),some to winter movements (November–February)and a few comprise Common Swift Apus apusflights from July. Resident birds or migrants onassumed non-migratory flights were recorded occa-sionally when flying past or when released by theobservers (see below). Various aspects of birdmigration and flight behaviour were studied at 15observation sites in different parts of Switzerland,at four sites in southern Germany, two in southernIsrael (Arava Valley and Negev Highlands) and twoin the western Mediterranean (Mallorca andMalaga). A list of sites and periods is available inBruderer (1999). Further data were added fromstudies in the Sahara (spring and autumn 2003,spring 2004) and from the Netherlands andBelgium (autumn 2007) in order to include someadditional species.

Recording and analysis of radar data

The tracking radar Superfledermaus and its applica-tion for flight path recording are described by

Bruderer and Boldt (2001) and its historical devel-opment by Bruderer (2007). Wing-beat patternsfrom radar echoes are assessed from variations inthe intensity of the radar echo due to the contrac-tion and dilation during a wing-beat cycle of thebody of birds flying through the radar beam. These‘echo signatures’ are, therefore, a means to recog-nize wing-beat frequencies as well as the sequenceof flapping and pausing phases of a bird (Bruderer1969, 1971). Recording of such echo signatureswas originally achieved by modulating a carrierfrequency with the echo signatures and recordingthis 400-Hz sound with an audiotape. The de-modulated signal was transferred to a plotter toshow the time series of the echo signature. Theduration of flapping and pausing phases as well asthe frequencies were measured manually on theplotter paper (Bruderer 1969). Later, the echosignatures were digitized and complemented byelectronic (Fast Fourier Transformation, FFT)determination of the frequencies (Bloch et al.1981). From 1975 onwards, flight-path data wererecorded by multiplex-pulse-duration-modulationon the same audiotapes as the echo signatures.From 1980 onwards the flight paths, and from1988 onwards also the raw echo signatures, weredigitized in the field, recorded on computer andlater interactively analysed.

Birds on diurnal flights were detected eithervisually by an observer at the antenna or as echoesfrom the radar screen for subsequent identification.Identification of tracked birds was by a telescope(12.4·) mounted parallel to the axis of the para-bolic antenna of the radar. To obtain additionaldata, particularly of nocturnal migrants that cannotbe tracked by radar during the day, captured birdswere released at distances of 100–150 m from theradar in the first or last daylight. The problem ofthese releases was that many birds flew low overthe ground, often below the reach of the radar andoften at lower speeds than their conspecifics onmigration (see Bruderer & Boldt 2001). Speedsdeviating from normal suggest that wing-beat pat-tern also differed from that during long-distanceflights. Most of the nocturnal migrants kept lowduring daytime. To release and track these birds atnight, a tiny gelatine capsule filled with the lumi-nescent substance Cyalume was glued to the bellyof the bird. In later years, the capsule was fixed toa thread, which was fixed with an adhesive tape onthe bird’s back and passed between the bases ofthe tail feathers. After release, these ‘glow-worms’

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could be visually tracked through the telescopeand then transferred to automatic tracking. Theseattachments only lasted for a few minutes andwere often observed to fall off shortly after releas-ing the bird. Unfortunately, the difficulties withthese nocturnal releases were not less pronouncedthan those in daytime. Most birds tended to landimmediately or after short circling flights. Long cir-cling flights provided often irregular wing-beat pat-terns, but still some individuals took off straightahead and seemed to continue their migratoryflight. Departing individuals were mainly recordedon climbing flights and only a minor fractionshowed the transition to horizontal flight. Beingaware of the problems inherent in the data fromreleased birds, all such cases are marked by a verti-cal bar in the species lists.

Most data refer to single individuals. Flocks ofbirds do not usually produce clear wing-beat pat-terns; only a few exceptional cases could beincluded. Visual inspection of each echo signature(on the plotter paper or on the computer screen)allowed choice of intervals with ‘recognizable’wing-beat patterns. For soaring birds, only flappingphases were used for the analysis. The verticalspeeds of the radar-tracked birds are usually lim-ited to a range of ± 1 m ⁄ s (89.4%). Only 8.6% arebetween ± 1 and ± 2 m ⁄ s, and 2% above ± 2 m ⁄ s(with a special remark in Appendix S1). Interac-tive analysis of the echo signatures included choos-ing good parts of at least 20 s from the recordedsignal and combining a semi-automatic electronicsignal analysis with a visual inspection and verifica-tion on the computer screen. Each individualrecord also comprised the flight altitude of the birdabove ground level and the elevation of the record-ing site above sea level.

Film and video recordings

Cinematic recording was made with a 16-mmBolex H16 EBM Electric Camera (equipped witha 400-mm telephoto lens) taking 50 images persecond. Cine recordings of released small birdscovered usually only the first 50 m after release,i.e. the flight phase with highest variation in flightbehaviour and a tendency to higher frequenciesthan in sustained flight (Stark 1996). Large birdsin free flight could be filmed up to distances of afew hundred metres usually low over the ground.Such flights may be expected to comprise morevariation in frequencies than in flights over longer

distances. Cine recordings are, therefore, denotedin Appendix S1 (names in italics). Some addi-tional wing-beat frequencies of large birds wererecorded with an electronic Canon XL S1 duringthe 2003 ⁄ 2004 Sahara project or extracted fromforeign video. As all the cine recordings weretaken low over the ground; only the elevation ofthe recording site above sea level was stored withthe birds’ records.

Input data for the prediction ofwing-beat frequencies

For the calculation of wing-beat frequency wefollowed Pennycuick (2001) and used his programFLIGHT (Version 1.15 from October 2004). Thelatest version 1.18 (free download from http://www.bio.bris.ac.uk/people/staff.cfm?key=95 inclu-ding tables with birds’ morphological data) did notchange the results presented in our tables. Hisbook Modelling the Flying Bird (Pennycuick 2008)was not available at that time, but now providesthe best background for such calculations. Penny-cuick (2008) emphasized that, instead ofcalculating a ‘natural’ wing-beat frequency basedon the physics of the beating wings, his formulaconsiders the dimension of the frequency and of allvariables believed to be involved in determining itsvalue. According to the convention in physics heselects mass (M ), length (L) and time (T ) as theprimary quantities and explains how the involvedvariables result in the formula:

f ¼ m3=8g1=2B�23=24S�1=3q�3=8 ð1Þ

where f is the frequency (with dimension T )1), mis body mass (dimension M), g is acceleration dueto gravity (dimension LT )2), B is wing-span (L), Sis wing-area (L2) and q is air density (ML)3). Inbounding flight, the downward curving of the tra-jectory during the ballistic phase is compensatedby curving the flight path upwards during the flap-ping or power phase. This is achieved by increasingthe value of gravity in the formula to (g ⁄ q)0.5,where q is the power-fraction. Wing-beat fre-quency in either flap-gliding or flap-bounding isdefined as the number of wing-beats in the powerphase, divided by the duration of the power phase(Fig. 1). We compare this theoretical approachwith the observed behaviour of birds by logarith-mic transformation of all variables and then esti-mating the five exponents by multiple regression

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analysis with all variables entered in the model,omitting an intercept. Each individual measurewas treated as an independent value.

We define the following variables:

f = median of measured wing-beat frequen-cies of one individual (if sample size n is 1)or mean of the median frequencies of severalindividuals (if sample size n > 1). fmin andfmax denote the extremes of the measuredvariation. Only the wing movements withinflapping phases are used.

fsl = median of all the measured frequencies,individually reduced to sea level. To reducethe raw frequencies to sea level pressure inthe International Standard Atmosphere, weneed an estimate of the actual air density qfor each bird.

fref = predicted wing-beat frequency = fre-quency calculated using Equation 1 with theinput data from Appendix S2 (air density setto sea level to make the values comparablewith fsl, the power-fraction set to 1 for allbirds of Tables 1 and 2 and using the indi-cated q for all birds of Table 3).

q = air density. We estimated the value of airdensity q from the recorded flight altitude ofeach bird plus the elevation of the recordingsite above sea level, deriving from this the

average value of q at the given altitude in theInternational Standard Atmosphere.

q = power-fraction = duration of the powerphase, divided by duration of the completeflap-bounding or flap-gliding cycle.

wb ⁄ t = the average number of wing-beats(wing-flaps) per unit time including the non-flapping periods of passerine-type flight(wb ⁄ t = fsl*q). This average of wing-beats overtime is (within an individual) proportional tothe power output; it changes with the rela-tive amount of flapping and pausing. Amongtwo birds with similar weight and similarwing-beat frequency the one with the lowerpower-fraction needs less energy than itscounterpart. Therefore, the number of wing-beats over time (including pauses) allows amore direct comparison of passerine-typeflight with non-passerine flight types thandoes the frequency f.

dcycle = duration of complete flap-boundingor flap-gliding cycles (median of measuredvalues).

dtot = total duration of all the intervalswith electronic frequency analysis. Cinerecordings and frequency measurementsmade by hand on recording paper have nosuch value.

Figure 1. Wing-beat patterns of different flight types based on recorded echo-signatures: (1) passerine-type flight with indication of

the duration of (A) flapping or power period (flapping or power phase), (B) non-flapping period (pausing phase, pause), (C) complete

flap-bounding or flap-gliding cycle, and (D) a wing-beat cycle. The duration of the power phase divided by the duration of the complete

flap-bounding or flap-gliding cycle corresponds to the power-fraction; (2) a flapping and gliding swift, and (3) a continuously flapping

wader. In the two latter examples the wing-beats are indicated for easiest visual counting of the frequencies.

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Table 1. List of wing-beat frequencies of birds with other than passerine-type flight.

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Table 1. (Continued ).

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To conform with the requirements of Penny-cuick’s formula we required data on the mass ofbirds, their wing-span and wing-area. The power-fraction can be derived from the recorded wing-beat pattern. Whenever possible, the structuraldata were taken from Pennycuick’s (2004) data-base or from our own measurements at the SwissOrnithological Institute (following Pennycuick’s1989 recommendations). Another importantsource was the unpublished dataset collected byMeinertzhagen and stored in the bird collection ofthe British Museum (Natural History) at Tring.Meinertzhagen’s graphs and raw data were treatedaccording to Pennycuick’s (1989) recommenda-tions (Appendix S2). Missing data were addedaccording to the best available source for the singleparameters, cross-checking various sources andchoosing the best according to expert knowledge(Appendix S2).

Different taxonomic groups may show similarflight behaviour in the sense of analogous adapta-tions (e.g. large raptors and storks or waders andwaterfowl). However, birds from the same taxo-nomic group may use different flight styles accord-ing to their different ecological niches (e.g.hirundines and corvids among the passerines). Forour descriptive purpose we therefore deviatefrom Pennycuick’s (2008) three flight types. Wemaintain Pennycuick’s flight styles 1 (continuousflapping) and 3 (bounding), but subdivide flightstyle 2. The resulting types are associated with 12recognizable bird groups (see legends to figuresand below in italics).(1) Continuous flapping occurs in birds from

different families and even orders. Most ofthem have relatively high wing-loading. Thesecomprise mainly waders and waterfowl (e.g.grebes, ducks, geese, swans, flamingos, loons

Table 1. (Continued).

Scientific name of the species in roman (not italics) indicate radar measurements, cine and video recordings are in italics. The status

specifies the type of flight: M = migrants in (assumed) migratory flight, m = migrants assumed not to be in migratory flight,

R = residents. Released birds are marked with a bold bar (|) next to the median frequency. f = median of measured frequencies;

fsl = median of individual frequencies reduced to sea level; dtot = duration of all the included time intervals with electronic frequency

analyses (s) - manually determined frequencies and cinematic recordings include no dtot, because the analysed sequences were chosen

from any part of a complete track without considering their duration; n = number of recorded individuals; fref = predicted wing-beat

frequency according to Pennycuick (2004) - for input values see Appendix S2. The last column contains a graphical presentation of the

median and range of measured frequencies. Points represent median values, and bars the observed range.

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Table 2. List of wing-beat frequencies of birds with passerine-type flight (explanations as in Table 1).

Line missing

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and auks). Gulls & terns may use this flightstyle besides style 2c, and herons are adaptedto continuous wing flapping in spite of theirrelatively large wings.

Various groups show different flight behaviourto reduce flapping time and thus power output:(2a) Soaring birds are relatively big birds with large

wing-area for soaring in thermals and gliding

between thermals or in extended updrafts(e.g. storks, pelicans and large raptors).Extended flapping phases are only occasion-ally used, mainly to reduce the descentrate or maintain flight level during glidingphases. In the present context, we deal onlywith the flapping phases of these thermalsoarers.

97

Table 2. (Continued).

Additional columns: dcycle = median of the measured values for the duration of complete flap-bounding or flap-gliding cycles;

q = power-fraction (taken into account for the calculation of fref) - in cases with very irregular pausing phases, q is given in bold (indi-

cating a rough estimate); wb ⁄ t = number of wing-flaps over time (including periods without flapping). For flap-gliding species keeping

their wings mostly stretched during non-flapping phases, we set the power-fraction to 1. In this table, fref is the wing-beat frequency

calculated according to Pennycuick (2004) taking into account the measured median power-fraction q.

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Table 3. Duration of flapping and pausing phases for 41 species with passerine-type flight (explanations as in Table 1).

Line missing

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(2b) Dynamic soaring is used by relatively largebirds with long wings and thus high aspect-ratio allowing flying with least effort overthe sea (e.g. albatrosses and shearwaters).These birds exploit either the wind shearbetween different air layers over the sea orthe updrafts along the windward side of thewaves (details in Pennycuick 2008). As wehave no own measurements on dynamicsoaring, these birds are not included in ourcalculations, but known data on wing-beatfrequencies are given in the ‘SpeciesAccount’ (Appendix S1).

Intermittent flapping comprises three differenttypes according to the wing-postures during thenon-flapping phases:(2c) Flapping and gliding (as opposed to soaring

and gliding) comprises relatively long flappingphases and comparably long gliding phaseswith fully stretched wings. This flight type isused by swifts and falcons, but also by gulls &terns (which due to their high aspect ratiomay make the transition to dynamic soaring)and by corvids (which may flap either contin-uously over relatively long periods like crows,or show irregular and rather short alternationslike magpies and jays). Near-passerines are asmall heterogeneous group that in our sample

comprises a few representatives of the Strigi-formes, Caprimulgiformes, Coraciiformes andUpupiformes.

(2d) Flap-gliding with short bursts of flapping andbrief glides with stretched wings occurs insmaller birds at low or medium speeds,as shown for starlings and budgerigars byTobalske (1995). The birds may shift to type2e at higher speeds.

(2e) Partially flexed (arrow-like) wings allowpartial bounding that is used typically byswallows & martins, flexing their wingsincreasingly with increasing speed (Brudereret al. 2001). Starlings and budgerigars showthis flight type at medium speeds, shiftingtowards complete wing-flexing (bounding) athigh speeds (Tobalske 1995). Thrushes mayalso adopt this flight type (Stark 1996).Pigeons & sandgrouse may flap their wingscontinuously over relatively long distances,but pigeons often keep their wings partiallyopen in a fixed position for a fraction of awhole wing-beat cycle, thus arriving at lowernumbers of wing-beats over time (wb ⁄ t) thanexpected with more or less sinusoidal wing-movements.

(3) Flight style 3 according to Pennycuick (2008)is an extreme form of the intermittent flight

Table 3. (Continued).

Graphical presentation of wing-beat pattern (up to 1.5 s duration), range of flapping and pausing phases (s), range of vertical speed

Vz, number of individuals (n), and number of sequences (seq) considered. Released birds are marked with a bold bar (|) at the end of

the box with the name.

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style, with completely flexed wings. It is oftencalled flap-bounding (as opposed to flap-glid-ing), emphasizing the two phases of theseflight styles. Bounding is mainly used bysmall passerines at medium or high speeds.The transition from flap-gliding to partialbounding and flap-bounding seems to occurincreasingly with decreasing size of the birdsand with increasing speed. A medium-sizedgroup using pronounced bounding flight isfound in the woodpeckers. As passerines mayvary between flight types 2d, 2e and 3, wecombine these in a group called ‘passerine-type flight’ (including the Eurasian WryneckJynx torquilla, the only woodpecker in oursample).

The different flight types are indicated by foot-notes in Appendix S2.

RESULTS

Measured wing-beat frequencies

The lists of measured wing-beat frequencies(Tables 1–3) show the median and range of theavailable measurements graphically to emphasizeintra-specific variation on the horizontal axisand to allow inter-specific comparison on thevertical axis. The number of individual tracksand ⁄ or the duration of the electronically analy-sed sequences allow a judgement of the reliabil-ity of the data.

Table 1 comprises continuously flapping birds aswell as flapping phases of soaring birds, birds withflapping and gliding flight (e.g. swifts and falcons)and birds with irregular flapping phases (such ascorvids). Among those with alternating flappingand gliding, those keeping their wings stretchedduring pausing phases usually show no undulationsin their flight path. In species with very irregularflight (such as magpies and jays), variation ofpausing phases is too great to provide meaningfulmedians for Table 2.

Table 2 comprises birds with passerine-typeflight (excluding corvids, but including the onlyrecorded woodpecker). The flight of these birds ischaracterized by flapping phases and pauses. Wetherefore introduced, in addition to the frequencyduring the flapping phase, the number of wing-beats per unit time (wb ⁄ t), including the pauses.

Table 3 provides the measured duration offlapping and pausing phases for 41 species with

passerine-type flight. As passerines vary theirvertical speed mainly by varying the duration offlapping and non-flapping phases in their flight(Renevey 1981), information is provided on thevertical speed. As most of the data included inTable 3 refer to released birds, the variation in ver-tical speeds covers a much wider range than infree-flying birds maintaining more regular and levelflight. The duration of flapping phases was usuallyin the range 0.1–0.4 s; however, in larks, wagtailsand some thrushes the range typically reached0.6 s. Extreme scatter to beyond 1.5 s occurredmainly among the thrushes, in Red-backed ShrikeLanius collurio and in some species with largesample sizes. An exception is the song-flight ofCrested Lark Galerida cristata, with flappingphases of 1.3–4.1 s and non-flapping phases of0.2–1.2 s. In the other species, pausing phases arenormally between 0.1 and 0.6 s, typically beinglonger only in wagtails. In other species, such asMistle Thrush Turdus viscivorus, Eurasian ReedWarbler Acrocephalus scirpaceus, the two flycatch-ers and Chaffinch Fringilla coelebs, the wide scattermay be due to extreme flight behaviour of someindividuals.

Species accounts

In the ‘Species Accounts’ (Appendix S1) we com-ment on the measured frequencies. This annotatedlist contains the median of the observed values aswell as the median of the equivalent frequencies(fsl), comparing the latter with expected values(fref) according to Pennycuick’s (2001, 2004) for-mula. Radar and camera recordings are comparedif both are available. Biases due to release effectsand other disturbances are discussed. The data arefurther compared with published data of the samespecies, sometimes of related or morphologicallysimilar species, if the sources seemed to be reliable.The duration of flapping and non-flapping phasesis mentioned where appropriate.

Considering the difficulty in finding comparativedata during this work, we present in the ‘SpeciesAccounts’ not only an annotated list of our ownmeasurements (according to Tables 1–3), but alsoinclude data from 45 additional Palaearctic speciesfor which reliable radar or cine recordings hadbeen published (partly in ‘grey literature’ that isnot readily available, particularly Griffiths 1970,Houghton & Blackwell 1972, Houghton & Brough1974).

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284 B. Bruderer et al.

Measured and predicted wing-beatfrequencies

The benchmark wing-beat frequencies (fref at sealevel) presented in Tables 1 and 2 were estimatedaccording to Equation 1. We estimated the fiveexponents in a multiple regression analysis(Table 4). For birds with passerine-type flight, thepower-fraction was taken from Table 2; for allothers it was set to 1. In the final model, allparameters contributed significantly to the model,but air density was only marginally significant(P = 0.02). Absolute values of the exponents dif-fered only slightly from Pennycuick’s specifica-tions. For wing-span, wing-area and air densitythe predicted exponents were within the 95%confidence intervals of the empirical results,whereas the empirical exponents for body-massand power-fraction were significantly lower thanthe predictions. Estimating the exponents sepa-rately for birds with passerine-type flight (q < 1)and all other flight types (q = 1) resulted again intwo highly significant models. When the continu-ously flapping birds were modelled alone, allfive exponents were close to those predicted(within 95% intervals). Doing so for species withpasserine-type flight resulted in a model withnon-significant contributions of body-mass andwing-span to the model. This was at least partlydue to the very high correlations between body-mass, wing-span and wing-area, revealing similarrelative scaling properties within this group. How-ever, wing-beat frequency increased less thanexpected with respect to power-fraction, indicat-ing that the increased gravity during the flappingphase is smaller than presumed.

Allometric and group-related variation

Within passerine-type flight, the increase of fre-quency with decreasing mass is steeper than in theother groups (Fig. 2A) due to important numbersof released individuals with very high frequencies.Transforming frequencies to wb ⁄ t renders passer-ine-type flight to a certain extent comparable withnon-passerine flight and brings the passerines(including the released birds with excessively highfrequencies) close to the slope of the other birds,and lowers the slope of the regression line consid-erably (Fig. 2B). Near-passerines, swallows andswifts are now close to the regression line, whereasherons as well as gulls and terns remain below theline due to their long wings, and waterbirds remainabove due to high wing-loading. Although the gen-eral trend of all birds now follows the slope of theregression line, some groups (such as soaring birds,falcons, gulls and terns) show only weak or nomass dependence within the group.

The ratio of fsl to the predicted frequencies fref

plotted vs. mass (Fig. 2C) indicates no overall sys-tematic deviation between measured and predictedvalues, but wide variation and a negative trendamong birds with passerine-type flight. As inFigure 2(A) the high wing-beat frequencies ofsmall bounding flyers are probably due to releasedbirds with particularly high frequencies and lowpower-fractions (these may be considered an arte-fact), whereas larger passerines show lower thanpredicted frequencies, suggesting that Equation 1overestimates the frequencies of medium-sized,flap-gliding passerines.

Wing-beat frequency (fsl, data not shown) aswell as wb ⁄ t (Fig. 3) decrease generally with

Table 4. Multiple regression analysis of the interdependence between the mean measured wing-beat frequency (f according to

Table 1) and the five relevant parameters as proposed by Pennycuick (2001) and given in Equation 1.

Parameters

Coefficients (P-values; *£ 0.001)

Pennycuick’s coefficientsAll Non-passerines Passerines

Body mass 0.298* 0.335* 0.015(0.816) 0.375

Wing-span )0.803* )0.887* )0.258(0.289) )0.958

Wing-area )0.345* )0.312* )0.415* )0.333

Gravity ⁄ power fraction 0.423* 0.468* 0.204* 0.500

Air density )0.338(0.022) )0.320(0.082) )0.522(0.004) )0.375

n 236 150 86 236

r2 predicted-observed 0.93* 0.82* 0.84* 0.92*

Mass, wing-span and wing-area according to Appendix S2, q < 1 from Table 2, q is that used to calculate fsl. All parameters are log-

transformed. Data included correspond to those in Figures 2 and 3. *P-values < 0.001.

ª 2010 The Authors

Journal compilation ª 2010 British Ornithologists’ Union

Wing-beat patterns in birds 285

wing-span as expected from Pennycuick (2001).The correlation is even closer than with body-mass, but waterbirds are again above the regres-sion line also in this case, while the long-wingedgulls and terns are below.

DISCUSSION

Problems of recording

This second part of a study on flight characteristicsof birds (following Bruderer & Boldt 2001)presents wing-beat frequencies based mainly onanalysing the amplitude variation of radar echoesderived from tracking individual free-flying birds inlevel flight. However, as bush-dwelling and noctur-nally migrating passerines usually cannot beobserved or identified in level flight over reason-able distances, we acquired data on such birds byreleasing them. Unfortunately, released birds oftenfly at very low speeds (Bruderer & Boldt 2001).These low speeds proved to be due to very lowpower-fractions combined with high wing-beatfrequencies. The effect of this particular behaviouris even more pronounced in the case of video andcinematic recording, because these optical methodsare usually confined to the first 50 m after release,comprising the most irregular flight pattern andusually higher wing-beat frequencies than in theradar recordings, which may only start about200 m from the radar. Extremely high passerine

wing-beat frequencies recorded elsewhere (e.g.Griffiths 1970, Houghton & Blackwell 1972,Houghton & Brough 1974) also seem to involve

A

B

C

Figure 2. (A) Variation of fsl (median measured wing-beat fre-

quencies reduced to sea level from Tables 1–3) with mass

(species body-mass according to Appendix S2) classified into

12 species groups. Numbers in parentheses refer to the flight

style (see Materials and Methods). The regression line shown

is based on the exponential function fsl = 3.65*mass)0.313

(r 2 = 0.73). All data points were treated as independent values.

(B) Number of wing-beats ⁄ s (wb ⁄ t ; t including the pausing

periods) vs. body-mass. (Legend as in A). Among birds of simi-

lar weight and with similar wing-beat frequencies, the number

of wing-beats over time is proportional to their power output,

and allows a more direct comparison of passerine-type flight

with non-passerine flight types than does the frequency f. The

power-fraction q is not accounted for according to Equation 1

but by distributing the wing-beats over complete flap-bounding

or flap-gliding cycles. The regression line shown is based on

the exponential function wb/t = 3.87*mass)0.175 (r 2 = 0.48). All

data points were treated as independent values. (C) Ratio of

observed and predicted wing-beat frequencies (fsl ⁄ fref) in rela-

tion to body-mass. Legend as in A. There is no systematic

deviation between the two parameters, but deviation according

to flight types (see text).

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Journal compilation ª 2010 British Ornithologists’ Union

286 B. Bruderer et al.

released birds. Our sample comprised a highproportion of small birds, whereas birds with highwing-loading (waterbirds) and species with largeaspect ratios (seabirds) are under-represented. Tomitigate this shortcoming, we provide data fromother sources in the ‘Species Accounts’ (Appen-dix S1).

The long history of recording involved repeatedimprovements to the methods of frequency analy-sis. However, these changes did not cause system-atic differences in the output data. Although thevast majority of the data were derived from radartracking of free-flying birds in level flight, the factthat some data are based on cine recording andreleased birds, and that some samples compriseonly the variation in one individual are weaknesses.However, we accepted these limitations in order topresent the most comprehensive list of speciespossible, mitigating the problems by indicating thepotential limitations (recording method, samplesize as well as releases) in Tables 1–3, and discuss-ing their potential influence on the data inextended ‘Species Accounts’ (Appendix S1). Wealso decided in favour of a comprehensive listbecause we think that such a list fills a real gap inactual knowledge, considering that little progresshas been made since Bruderer (1971) presented hisclassification of radar targets, and Williams andWilliams (1980) envisaged A Peterson’s Guide toRadar Ornithology.

Problems of the input data forcalculations

The body measurements used (Appendix S2) werenot taken from the individuals that provided theirwing-beat pattern. The data taken from Pennycu-ick’s (2004) tables, from our own measurementsand from the Meinertzhagen database in the Natu-ral History Museum at Tring (treated by ourselvesto conform with Pennycuick’s 1989 recommenda-tions) correspond to the requirements of Pennycu-ick’s formula. With respect to the data from othersources it was not always clear how well theyfulfilled Pennycuick’s requirements. The closeagreement between our data and Pennycuick’smodel (see below) suggests that the input data arenot too far from those required, although some ofthe scatter may be due to inaccuracies of the inputdata in Appendix S2.

The air density q included in our data is not adirectly measured value, but derived from theInternational Standard Atmosphere, the measuredflight altitude of the bird and the elevation of therecording equipment above sea level. This is a rea-sonable approximation, but the real value maydeviate according to the actual temperature andthe pressure pattern of the weather systems.

The input data for the multiple regression inTable 4 are based on the values indicated inTables 1–3 (f and q); q is the same as used to calcu-late fsl. Data derived from different methods areequally weighed. By this, we accept that the morequestionable data from cine recordings and releasesreceive the same weight as the other methods.This may increase the scatter, but avoids an arbi-trary prioritizing of the data and has the advantagethat the data points included are the same as in thefigures.

Potential application

In the ‘Species Accounts’ we discuss and evaluateour own measurements (f, fsl) and compare themwith expected values (fref) according to Pennycu-ick’s (2001, 2004) formula and with publisheddata of the same or morphologically similarspecies. The aim of this valuation is the potentialapplication of the reported wing-beat pattern inthe identification of radar targets. For this purpose,we need to exclude released birds if theyshow exaggerated frequencies. However, it is alsonecessary to consider an opposite effect, which

Figure 3. Variation of wb ⁄ t (number of wing-beats over time)

with wing-span. Legend as in Figure 2A. The regression line

shown is based on the exponential function wb/t = 4.14*wing-

span)0.491 (r 2 = 0.59). All data points were treated as indepen-

dent values.

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Wing-beat patterns in birds 287

seems to occur in passerines on long-distanceflights (probably with relatively high fat loads).There were indications that passerines crossingthe Sahara or the Mediterranean may show parti-cularly low frequencies combined with highpower-fractions (even when released). Avoidingthese extremes, we aimed to estimate frequenciesthat are thought to be typical for migratory flightswith normal landing possibilities, e.g. over main-land Europe. However, these frequencies may notbe valid for samples of birds on extreme long-distance flights.

Extreme variation in flapping behaviour mayresult in unrealistic median values of frequency.For species with broad variation or extreme valuesof wing-beat frequencies we therefore suggest‘reasonable’ frequencies for migratory flights inEurope. In spite of inherent uncertainties, theseestimates in combination with data on the migra-tion phenology of the species in question mayprovide means for the interpretation of largesamples of radar-recorded echo signatures, as firstattempted by Stark (1996). An encouragingfeature of the available data is that the general dis-tribution of wing-beat frequencies in nocturnallymigrating passerines is similar above the Alps(Bloch et al. 1981) and in northern Europe (Bäck-man & Alerstam 2003), although the Scandinaviansample comprises a higher proportion of largethrushes with low wing-beat frequencies.

Predictions and flight types

For continuously flapping birds, Pennycuick’s(2004) model provides excellent predictions ofwing-beat frequencies, with all five estimated coef-ficients being close to those predicted, in spite ofthe fact that different taxonomic groups wereincluded in this flight style and that the mean val-ues used for morphological data were derived fromindividuals different to those tracked. The inputdata compiled in Appendix S2 appear to conformto Pennycuick’s requirements, in spite of heteroge-neous sources.

In the model with only passerine-type flyers, thewing-beat frequency increased less than expectedwith respect to the power-fraction. This suggeststhat the birds lost less height during the non-flapping phase than assumed according to a purelyballistic trajectory. As released birds might be areason for the lower levels of consistency betweenthe data and predictions, we tested the model

without the released birds. With much smallersample size (n = 27 instead of 86), the wholemodel remained significant (r2 = 0.77), in spite oflower significance levels for the single factors. Thissuggests that the (g ⁄ q)0.5 compensation of the dropin flight level undergone during the non-flappingphases may be too great. This is probably due tolift produced by the body and in particular by onlypartially flexed wings during the non-flappingperiods of flap-gliding birds, thus supporting to acertain extent the body-lift hypothesis of Rayner(1985).

The idea of residual lift being produced by onlypartially flexed wings gains support from Figure 2Band 2C. In the plot of fsl ⁄ fref against mass (Fig. 2c)the measured wing-beat frequencies of largepasserines were lower than expected. These lowfrequencies seem to be a result of the increased useof flap-gliding with increasing size, which reducesthe influence of q (assumed by the model for ballis-tic trajectories). This corroborates the idea that theinfluence of (g ⁄ q)0.5 in Equation 1 is too strong (atleast for the larger birds with passerine-type flight),because it assumes ballistic trajectories during thenon-flapping phase. It seems that birds with passer-ine-type flight get some residual lift by partiallyflexed wings, increasingly with increasing size.

Figures 1 – 3 indicate opportunities to discrimi-nate bird groups with different flight behaviourand thus suggest potential reasons behind residualdeviations from the model. The importance ofsuch groups has already been emphasized by Bru-derer and Boldt (2001) and confirmed by Alerstamet al. (2007). Air speed was shown to depend lesson body-mass than suggested by the model, partic-ularly in soaring birds, falcons, and gulls and terns.Particularly low speeds were found in the poweredflight of soaring birds, whereas deviations from themodel towards high speeds occurred mainly inwaterbirds, small waders, falcons, pigeons andsandgrouse. Small passerines were faster thanexpected, because at that time, the power-fractionwas not yet included in the model. Similar group-specific deviations are now observed in the wing-beat pattern (Fig. 2A,B). Soaring birds, falcons,and gulls and terns show little mass dependence ofwing-beat frequencies. Waterfowl and most of thewaders fall above the regression line due to rela-tively high wing-loading, whereas the long-wingedgulls and terns fall below. These differencesbetween flight types highlight the problem thatthe correlation depends strongly on the taxonomic

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288 B. Bruderer et al.

groups included. In the dependence of frequencyon mass as well as on wing-span there are impor-tant group-specific deviations from the generalcorrelation. The weak dependence of frequency onmass- and wing-span in soaring birds and in gullsand terns induces a levelling-out of the wing-beatfrequencies in large and long-winged birds.

Variation of wing-beat pattern

The wide variation of wing-beat frequencies in thedata suggests that small, intermittently flappingbirds can vary their wing-beat frequency withinastonishingly wide ranges: variation of ± 10%seems quite normal for cruising flight, and varia-tion of ± 30% is possible if the extremes of differ-ent flight behaviours are included. Whereasvertical speed is mainly varied by the duration offlapping and ⁄ or pausing phases, flapping frequencymay shift slightly up- or downwards during suchmanoeuvres. Flap-gliding swallows and martinsvaried mechanical power output by controlling thenumber of single, pulse-like wing-beats per unittime (Bruderer et al. 2001). The ‘effective wing-beat frequency’, as termed by those authors,decreased with decreasing flight angle and accord-ing to a U-shaped curve with air speed.

Within species or individuals, wing-beat fre-quency was shown to increase with the 0.36 powerof body mass in a continuously flapping EurasianTeal Anas crecca and with the 0.43 power in anearly continuously flapping Thrush NightingaleLuscinia luscinia (Pennycuick et al. 1996). Actually,the bird held its wings in horizontal position for afraction of a wing-beat period and never closed itswings completely. Among 51 flap-bounding Eur-asian Pied Flycatchers Ficedula hypoleuca, Stark(1996) found an inter-individual increase of wing-beat frequency with the 0.55 power of body mass.

The normal durations of flapping and pausingphases in level flight of passerine-type birds seemto cover the range 0.1–0.4 s and 0.1–0.6 s, respec-tively. Longer cycles occur mainly among wagtails,thrushes and flycatchers. Other scatter may be dueto wide variation of vertical speeds and extremeflight behaviour of some individuals in large sam-ples. The fact that the flapping phases are usuallyshorter in the cine recording than in radar-trackedbirds confirms the earlier statement that cinerecording of released birds or birds flying close byis prone to document disturbed flight. Bloch et al.(1981) and Renevey (1981) showed that small

passerines vary their rate of climb and descent byvarying the relative duration of flapping and paus-ing phases in their wing-beat pattern. Large passe-rines (mainly large thrushes) vary flapping andpausing phases to change vertical speed, whereasmedium-sized passerines (e.g. small thrushes)mainly vary the flapping phases (Bloch et al.1981). Stark (1996) confirmed such a relationshipfor the Song Thrush Turdus philomelos (flappingphases increasing and pauses decreasing withincreasing vertical speed, frequency slightlydecreasing) and for the European Robin Erithacusrubecula (duration of flapping phases positivelycorrelated with vertical speed, whereas frequencyand pauses remained constant). For a released Eur-asian Pied Flycatcher he found continuous flappingcombined with a wing-beat frequency of 13.5 Hzduring a first interval of a 20-s climb (1 m ⁄ s). Dur-ing the following four intervals of 20 s the flappingphase decreased from 0.75 s to 0.45 s, while inter-mittent pauses were maintained at 0.1 s, verticalspeed decreased to 0.2 m ⁄ s and wing-beatfrequency fell to 12 Hz. After about 3 min, thebird flew horizontally, with the typical wing-beatfrequency of 13 Hz and flapping phases of 0.25 scombined with pauses of 0.2 s.

Cochran et al. (2008) showed continuous flap-ping combined with high wing-beat frequencies forthe evening climb-out of medium-sized Americanthrushes, about 10% lower frequencies and about12% time spent for pauses during cruising flight;birds descending after a nocturnal flight increasedpauses to 15–45% and increased wing-beatfrequencies to nearly the values of evening ascent.The American thrushes varied wing-beat patternnot only between climb and cruise, but also duringcruising flights. Particularly high frequencies andvariable patterns were recorded during short diur-nal flights. It seems that wing-beat frequency doesnot change in all species in the same manner withvarying vertical speed.

We thank all the numerous voluntary collaborators whohelped to collect the data over nearly 40 years. We aregrateful to Oerlikon-Contraves AG and to the SwissArmy for the loan and final donation of the radars. TheSwiss National Science Foundation supported most ofthe scientific projects; the most recent grant was number31-65349, the Sahara Project. The development of therecording equipment started with Alfred Bertschi (Con-traves) and Jürg Joss (Osservatorio Ticinese della Cent-rale Meteorologica Svizzera), continued with RaymondBloch, and was led to perfection by Thomas Steuri from

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Wing-beat patterns in birds 289

1980 onwards. Reto Spaar and Herbert Stark contrib-uted considerably to the collection and analysis of echosignatures while working for their PhD theses. Specialthanks go to Colin J. Pennycuick, William W. Cochranand Jeremy Wilson who considerably helped to improvethe manuscript. We thank the editors for improving thetext.

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Received 19 May 2009;revision accepted 28 January 2010.

SUPPORTING INFORMATION

Additional Supporting information may be foundin the online version of this article:

Appendix S1. Species accounts.Appendix S2. Body measurements of investi-

gated bird species.Please note: Wiley-Blackwell are not responsible

for the content or functionality of any supportingmaterials supplied by the authors. Any queries(other than missing material) should be directed tothe corresponding author for the article.

ª 2010 The Authors

Journal compilation ª 2010 British Ornithologists’ Union

Wing-beat patterns in birds 291