Abstract
Playbacks are one of the most useful experimental tools in animal communication research. Playbacks of substrate vibrations present special challenges, but conducting high-fidelity vibrational playbacks is not difficult and depends less on the specific equipment used than on avoiding some common pitfalls. We review the major issues, describing both the problems and a range of solutions. Our focus is on playback through living plants, but most of the issues apply to playback through other substrates as well. The major challenge for playback through any substrate is that the vibrational signal is almost always changed by the playback equipment and the substrate, so that the signal received by the focal animal is different from the one intended by the experimenter. The general solution to this problem is to measure the changes imposed by the playback system and to pre-filter the playback signal to compensate for them. A second challenge is to ensure that the focal animal receives a signal at the appropriate amplitude. Achieving the proper amplitude is a straightforward process. However, amplitude is substrate dependent (e.g., on a plant, amplitude is inversely proportional to stem diameter), and the experimenter should choose a realistic amplitude for the substrate. Other issues include choices of playback device, natural versus artificial substrates, single versus multiple substrate exemplars, and playback in laboratory versus field. Our goal in this chapter is to give experimenters, especially those just starting out, the knowledge and confidence needed to conduct high-quality vibrational playbacks.
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References
Barth FG (1982) Spiders and vibratory signals: sensory reception and behavioral significance. In: Witt PW, Rovner JS (eds) Spider communication: mechanisms and ecological significance. Princeton University Press, Princeton, pp 67–120
Barth FG (2002) A spider’s world: senses and behavior. Springer, Heidelberg
Brumm H, Slabbekoorn H (2005) Acoustic communication in noise. Adv Stud Behav 35:151–209
Caldwell MS, Johnston GR, McDaniel JG, Warkentin KM (2010) Vibrational signaling in the agonistic interactions of red-eyed treefrogs. Curr Biol 20:1012–1017
Cocroft RB (2005) Vibrational communication facilitates cooperative foraging in a phloem-feeding insect. Proc R Soc B Biol 272:1023–1029
Cocroft RB (2010) Vibrational communication. In: Breed MD, Moore J (eds) Encyclopedia of animal behavior, vol 3. Academic Press, Oxford, pp 498–505
Cocroft RB, RodrÃguez RL (2005) The behavioral ecology of insect vibrational communication. Bioscience 55:323–334
Cocroft RB, Tieu T, Hoy RR, Miles R (2000) Mechanical directionality in the response to substrate vibration in a treehopper. J Comp Physiol 186:695–705
Cocroft RB, Shugart HJ, Konrad KT, Tibbs K (2006) Variation in plant substrates and its consequences for insect vibrational communication. Ethology 112:779–789
Cokl A, Virant-Doberlet M (2003) Communication with substrate-borne signals in small plant-dwelling insects. Annu Rev Entomol 48:29–50
Cokl A, Zorovic M, Millar JG (2007) Vibrational communication along plants by the stink bugs Nezara viridula and Murgantia histrionica. Behav Process 75:40–54
Cuthill IC, Hart NS, Partridge JC, Bennett ATD, Hunt S, Church SC (2000) Avian colour vision and avian video playback experiments. Acta Ethol 3:29–37
Fleishman LJ, McClintock WJ, D’Eath RB, Brainard DH, Endler JA (1998) Colour perception and the use of video playback experiments in animal behavior. Anim Behav 56:1035–1040
Gerhardt HC (1995) Phonotaxis in female frogs and toads: execution and design of experiments. In: Klump GM, Dooling RR, Fay RR, Stebbins WC (eds) Animal psychophysics: design and conduct of sensory experiments. Birkhäuser Verlag, Basel, pp 209–220
Gordon SD, Uetz GW (2011) Multimodal communication of wolf spiders on different substrates: evidence for behavioural plasticity. Anim Behav 81:367–375
Gordon SD, Uetz GW (2012) Environmental interference: impact of acoustic noise on seismic communication and mating success. Behav Ecol 23:700–714
Hill PSM (2008) Vibrational communication in animals. Harvard University Press, Cambridge
Hill PSM, Shadley JR (2001) Talking back: sending soil vibration signals to lekking prairie mole cricket males. Am Zool 41:1200–1214
Kroodsma DE, Byers BE, Goodale E, Johnson S, Liu W-C (2001) Pseudoreplication in playback experiments, revisited a decade later. Anim Behav 61:1029–1033
Kroodsma DE (1986) Design of playback experiments. Auk 103:640–642
Kroodsma DE (1989) Suggested experimental designs for song playbacks. Anim Behav 37:600–609
Legendre F, Marting PR, Cocroft RB (2012) Competitive masking of vibrational signals during mate searching in a treehopper. Anim Behav 83:361–368
Lohrey AK, Clark DL, Gordon SD, Uetz GW (2009) Antipredator responses of wolf spiders (Araneae: Lycosidae) to sensory cues representing an avian predator. Anim Behav 77:813–821
Magal C, Scholler M, Tautz J, Casas J (2000) The role of leaf structure in vibration propagation. J Acoust Soc Am 108:2412–2418
McGregor PK, Catchpole CK, Dabelsteen T, Falls JB, Fusani L, Gerhardt HC, Gilbert F, Horn AG, Klump GM, Kroodsma DE, Lambrechts MM, McComb KE, Nelson DA, Pepperberg IM, Ratcliffe L, Searcy WA, Weary DM (1992) Design and interpretation of playback: the Thornbridge Hall NATO ARW consensus. In: McGregor PK (ed) Playback and studies of animal communication. Plenum Press, New York, pp 1–9
McGregor PK (2000) Playback experiments: design and analysis. Acta Ethol 3:3–8
McNett GD, Miles RN, Homentcovschi D, Cocroft RB (2006) A method for two-dimensional characterization of animal vibrational signals transmitted along plant stems. J Comp Physiol A 192:1245–1251
McNett GD, Cocroft RB (2008) Host shifts favor vibrational signal divergence in Enchenopa binotata treehoppers. Behav Ecol 19:650–656
McNett GD, Luan L, Cocroft RB (2010) Wind-induced noise alters signaler and receiver behavior in vibrational communication. Behav Ecol Sociobiol 64:2043–2051
Michelsen A, Fink F, Gogala M, Traue D (1982) Plants as transmission channels for insect vibrational songs. Behav Ecol Sociobiol 11:269–281
Morales MA, Barone JL, Henry CS (2008) Acoustic alarm signalling facilitates predator protection of treehoppers by mutualist ant bodyguards. Proc R Soc B Biol 275:1935–1941
O’Connell-Rodwell CE, Wood JD, Rodwell TC, Puria S, Partan SR, Keefe R, Shriver D, Arnason BT, Hart LA (2006) Wild elephant (Loxodonta africana) breeding herds respond to artificially transmitted seismic stimuli. Behav Ecol Sociobiol 59:842–850
O’Connell-Rodwell CE, Wood JD, Kinzley C, Rodwell RC, Poole JH, Puria S (2007) Wild African elephants (Loxodonta africana) discriminate between familiar and unfamiliar conspecific seismic alarm calls. J Acoust Soc Am 122:823–830
Polajnar J, Svensek D, Cokl A (2012) Resonance in herbaceous plant stems as a factor in vibrational communication of pentatomid bugs (Heteroptera: Pentatomidae). J R Soc Interface 9:1898–1907
Rebar D, Höbel G, RodrÃguez RL (2012) Vibrational playback by means of airborne stimuli. J Exp Biol 215:3513–3518
Rohrseitz K, Kilpinen O (1997) Vibration transmission characteristics of the legs of freely standing honeybees. Zoology 100:80–84
Sattman DA, Cocroft RB (2003) Phenotypic plasticity and repeatability in the mating signals of Enchenopa treehoppers, with implications for reduced gene flow among host-shifted populations. Ethology 109:981–994
Shaw S (1994) Detection of airborne sound by a cockroach ‘vibration detector’: a possible missing link in insect auditory evolution. J Exp Biol 193:13–47
Tishechkin DY (2007) Background noises in vibratory communication channels of Homoptera (Cicadinea and Psyllinea). Russ Entomol J 16:39–46
Uetz GW, Roberts JA (2002) Multisensory cues and multimodal communication in spiders: insights from video/audio playback studies. Brain Behav Evol 59:222–230
Wiley RH (2003) Is there an ideal behavioural experiment? Anim Behav 66:585–588
Wood JD, O’Connell-Rodwell CE (2010) Studying vibrational communication: equipment options, recording, playback and analysis techniques. In: O’Connell-Rodwell CE (ed) The use of vibrations in communication: properties, mechanisms and function across taxa. Transworld, Kerala, pp 163–182
Zunic A, Virant Doberlet M, Cokl A (2008) Preference of the southern green stink bug (Nezara viridula) males for female calling song parameters. B Insectol 61:183–184
Acknowledgments
RBC acknowledges support from NSF IOB 0820533. The manuscript benefitted from conversations with Ron Miles, Carol Miles, Peggy Hill, Matija Gogala, and Andreas Wessel.
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Appendix
Appendix
The following MATLAB programs use the Signal Processing Toolbox and the Control System Toolbox and have been confirmed to work in MATLAB 6.5 (R13) through to MATLAB 8.1 (R2013a).
1.1 Digital Equalization Filter
This program obtains the system equalization filter from stored measurements. A typical system would include the digital-to-analog converter, amplifier and vibration exciter, vibration medium, measurement transducer, anti-aliasing filter, and analog-to-digital converter. Prior to running this code, a continuous random signal (stored in WAVE file ‘Playback1.wav’) is played through the system, and the response is measured and saved (WAVE file ‘Recording1.wav’).
The power spectral density functions are estimated and used to obtain the magnitude of the input-to-output transfer function. The useful data range is taken between the specified lower and upper frequencies in hertz (variables ‘f_lo’ and ‘f_hi’), and the digital filter coefficients are estimated and saved (MATLAB data file ‘FilterCoefs.mat’). For evaluation purposes, the equalization filter is applied to the original playback signal and stored (WAVE file ‘Playback2.wav’). Arbitrary signals of different duration can be filtered this way using the identified filter coefficients, as long as the sample rates are the same.
MATLAB script for acquiring and implementing digital equalization filter:
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close all, clear all
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[out,fs,NBITS]=wavread(‘Playback1.wav’); %WAVE file with original playback
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[in,fs,NBITS]=wavread(‘Recording1.wav’); %WAVE file with recorded signal dt=1/fs;
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t_out = [0:dt:(length(out)-1)*dt];
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t_in = [0:dt:(length(in)-1)*dt];
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fftLength=4096;
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[PSDout,Freq]=pwelch(out,ones(fftLength,1),[],fftLength,fs);
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[PSDin,Freq]=pwelch(in,ones(fftLength,1),[],fftLength,fs);
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Hcmp=sqrt(PSDout./PSDin); %Amplitude compensation filter
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f_lo=40; f_hi=10000; %lower and upper cutoff frequencies in Hz.
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lo=round(f_lo/(fs/fftLength))+1;
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hi=round(f_hi/(fs/fftLength))+1;
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Hcmp(1:lo)=0; Hcmp(hi:length(Hcmp))=0;
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wn=Freq/max(Freq);
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B=fir2(fftLength,wn,Hcmp); %this calculates the digital filter coefficients
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A=1;
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save FilterCoefs.mat B A
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outcmp=filter(B,A,out); %this applies the digital filter to the signal
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outcmp=outcmp*.9/max(abs(outcmp));
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wavwrite(outcmp,fs,16,’Playback2.wav’);
1.2 Differentiation and Integration of Playback Signal
This MATLAB script numerically differentiates and integrates the time signal stored in a WAVE file (‘ArbPlayback.wav’). Differentiation of the signal can be approximated using the finite difference method (with ‘diff.m’), while integration of the signal can be approximated using trapezoidal integration (with ‘cumtrapz.m’). These methods work well if the time step is sufficiently small and if there is no noise in the signal.
When the signal has additional noise, the higher-frequency noise is increased by the differentiation process, while the lower-frequency noise is increased by integration. This noise can be reduced by using a first-order band-pass filter to perform the differentiation or integration. The band-pass center frequency is set to a high frequency for differentiation (variable ‘f_hi’), so the frequencies below the center frequency approximate a differentiation filter, while frequencies above are attenuated. For integration, the center frequency is set to a low frequency (variable ‘f_lo’), so frequencies below the center frequency are attenuated, while frequencies above approximate an integration filter. The appropriate center frequency also depends on the frequency content of the signal.
MATLAB script for differentiation and integration of playback signal:
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wavfile=‘ArbPlayback.wav’; %WAVE file name with playback signal
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[out,fs,NBITS]=wavread(wavfile);
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dt=1/fs;
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nt=length(out);
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t_out =[0:dt:(nt-1)*dt];
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%numerical differentiation by finite difference:
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outdiff=diff(out)/dt;
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outdiff(nt)=outdiff(nt-1);
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%numerical integration by trapezoidal rule:
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outint=cumtrapz(t_out,out);
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% Differentiation filter: Band pass filter with high corner frequency
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f_hi=10000; %upper cutoff frequency in Hz.
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SYSc=tf((2*pi*f_hi)^2*[1 0],conv([1 f_hi*2*pi],[1 f_hi*2*pi])); SYSd=c2d(SYSc,1/fs,’foh’);
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[Bcmp,Acmp]=tfdata(SYSd);
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outfiltdiff=filter(Bcmp{1},Acmp{1},out);
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% Integration filter: Band pass filter with low corner frequency
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f_lo=10; %lower cutoff frequency in Hz.
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SYSc=tf([1 0],conv([1 f_lo*2*pi],[1 f_lo*2*pi]));
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SYSd=c2d(SYSc,1/fs,’foh’);
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[Bcmp,Acmp]=tfdata(SYSd);
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outfiltint=filter(Bcmp{1},Acmp{1},out);
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Cocroft, R.B., Hamel, J., Su, Q., Gibson, J. (2014). Vibrational Playback Experiments: Challenges and Solutions. In: Cocroft, R., Gogala, M., Hill, P., Wessel, A. (eds) Studying Vibrational Communication. Animal Signals and Communication, vol 3. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43607-3_13
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DOI: https://doi.org/10.1007/978-3-662-43607-3_13
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