17?@>75a:c7 3

Perspective and Promisea Century of Insect Acoustic Detection and Monitoring

R W Mankin D W Hagstrum M T Smith A L Roda and M T K Kairo

Abstract Acoustic devices provide nondestructive remote automated detection and monitoring of hidden insect infestationsfor pest managers regulators and researchers In recent decades acoustic devices of various kinds have been marketed for fielduse and instrumented sample containers in sound-insulated chambers have been developed for commodity inspection Theefficacy of acoustic devices in detecting cryptic insects estimating population density and mapping distributions depends onmany factors including the sensor type and frequency range the substrate structure the interface between sensor and substratethe assessment duration the size and behavior of the insect and the distance between the insects and the sensors Consider-able success has been achieved in detecting grain and wood insect pests Microphones are useful sensors for airborne signalsbut vibration sensors interface better with signals produced in solid substrates such as soil grain or fibrous plant structuresUltrasonic sensors are particularly effective for detecting wood-boring pests because background noise is negligible at gt 20 kHzfrequencies and ultrasonic signals attenuate much less rapidly in wood than in air grain or soil Problems in distinguishing soundsproduced by target species from other sounds have hindered usage of acoustic devices but new devices and signal processingmethods have greatly increased the reliability of detection One new method considers spectral and temporal pattern featuresthat prominently appear in insect sounds but not in background noise and vice versa As reliability and ease of use increase andcosts decrease acoustic devices have considerable future promise as cryptic insect detection and monitoring tools

Table 1 Publications on insect acoustic detection and monitoring perdecade

(Forrest 1988) including cicada and cricket calling songs and theytypically contain important information about the species and sexof the sender Mankin (1994) monitored wing beat sounds to detectswarms of Ochlerotatus taeniorhynchus (Wiedemann) salt marshmosquitoes and Lampson et al (2010) characterized the frequencyand temporal patterns of communication vibrations produced bydifferent species ofhemipterans to detect them in cotton fields Birch

Long ago insect predators and parasitoids began eavesdrop-ping on communication sounds (Cade 1975 Zuk and Kolluru1998) as well as feeding and movement sounds and vibrations

(Meyhiifer and Casas 1999) to find prey and hosts Humans were late-comers to eavesdropping but soon after it became possible to recordand produce sound electronically they began developing potentialapplications for detection of insect presence or bsence and for moni-toring of activity over time A journal article on acoustic detection oftermites was published in 1909 (Main 1909) Other early popularreports included those on stored-product weevils (Anon 1935) andtermites (Escherich 1911 Snyder 1935 1952) Since then at least132 additional articles or patents have been published on acousticor vibrational detection and monitoring of insects mostly within thelast 20 years (Table 1) Sound and vibration measurements have beendiscussed in numerous reviews of insect detection and monitoring(Thorne 1993 Rajendran 1999 Reynolds and Riley 2002 Hagstrumand Subramanyam 2006 Johnson etal 2007)

Adults of the many insect species that use sounds or vibrationsin communication are prominent candidates for acoustic detectionand monitoring studies Hill (2008) identified 218 such species in12 insect orders Considering the diversity of signals referenced inpapers by Rudinsky and Michael (1973) Spangler (1988) Stewart(1997) Virant-Doberlet and (ok (2004) Cocroft and Rodriguez(2005) and Wessel (2006) the total number may be much largerMany of these signals are optimized for detection over long distances

30

Decade

1901-1910

1911-1920

1921-1930

1931-1940

1941-1950

1951-1960

1961-1970

1971-1980

1981-1990

1991-2000

2001-2010

Total

No Publications

1

1

2

4

o5

4

4

224450

137

American Entomologist Spring 2011

and Menendez (1991) suggested that the female deathwatch beetleXestobium rufovillosum (DeGeer) could be detected by vibrating in-fested timbers with amplified communication taps of calling maleswhich would elicit responsive taps from the female that are loudenough to be heard by the human ear Forrest (1988) proposed theuse of communication sounds to estimate orthopteran populationsin the field Insect communication sounds also have been used tolure insects into traps (Walker 1988)

Apart from insects that produce communication signalsmany economically important coleopteran adults and larvae andlepidopteran larvae with cryptic behaviors also are candidates foracoustical and vibrational detection and monitoring especially thosethat are large and active sometimes even producing sounds audibleto humans like the red palm weevil Rhynchophorus ferrugineus(Olivier) and the coconut rhinoceros beetle Oryctes rhinoceros (L)The incidental signals that small cryptic insects produce while mov-ing and feeding can be very low in amplitude but still detectableMovement and feeding sounds of 4th instar Sitophilus oryzae (L) ingrain are only 23 dB (Mankin et al 1996) for example where dB iscalculated as 20 loglo(PjPret) P is the sound pressure and Pret = 20~Pa is the threshold of human hearing Typical noise levels in a quiet35-50 dB office are ~4 times (12-27 dB) higher Acoustic deviceswith optimized filters and sensors have detected S oryzae larvae inmany different environments however as is discussed below

As the use of acoustic technology for entomological applicationsbegan expanding in the 1980s and 1990s Walker (1996) noted thatacoustic methods seemed destined to rapidly replace many of thelabor-intensive and less effective detection and monitoring methodsthen in use However while our understanding of insect acousticaland vibrational communication has blossomed in the last decade(Cocroft and Rodriguez 2005 Drosopoulos and Claridge 2006 (okl2008 Sueur et al 2008 Barbero et al 2009) the development andadoption of inexpensive user-friendly acoustic tools for detection andmonitoring of economically important infestations of hidden insectshas lagged behind The delays have resulted partly from limitedunderstanding of acoustic signal attenuation in and across varioussubstrates from difficulties of interpreting weak insect signals inenvironments with high background noise from limited knowledgeof the behaviors of the cryptic targeted species that produce thesignals and from the small market for insect detection instrumenta-tion which limits the capability to take advantage of economies ofscale For example notably fewer insect acoustic detection devicesthan electronic stethoscopes are sold yearly Electronic stethoscopeshave been used successfully for insect detection (eg Kisternaya andKozlov 2009) but there are many insect detection applications wheremore optimal sensor-substrate interfacing or higher-gain amplifica-tion is required as is discussed in later sections

To avoid confusion about acoustics terminology we note herethat several terms used in this report including sound vibrationsignal acoustic and remote have taken on multiple meanings inthe biological acoustical and signal processing literature The termsignal will be used primarily in two contexts either physically as asound in air or a vibration in a structure and also mathematically asan amplitude-time waveform Both sound and vibration waveformsare processed by similar acoustic signal processing methods Fre-quently insect-produced signals are detectable both by microphoneas sounds and by contact sensors as vibrations in which case theymay be designated simply as acoustic signals or sounds (Webb etal 1988a) We have avoided using signal in the biological sense of

American Entomologist Volume 57 Number 1

transmitting information between or among individuals or groupsof organisms Finally acoustic detection or monitoring is remote inthe sense that it typically occurs over distances of tens of centimetersor more depending on the substrate in which the insect is hidden(Reynolds and Riley 2002) In many applications wires or wirelessdevices transmit the signals from the hidden insect to a central ob-servation station that can be hundreds of meters distant

In this report we describe the progress made during the last cen-tury in the development of acoustic tools and applications for crypticinsect detection population estimation and distribution mappingThe report is organized into nine sections the first five of which dealwith how acoustic signals are transmitted sensed and interpretedin different environments Three sections deal with applications ofacoustic methods in detection population estimation and mappingof cryptic insects in different environments In the final section weconsider the kinds of applications toward which acoustic technologymay be directed in the future

Transmission and Attenuation of Insect Acoustic Signals inand between Stored Products Wood and Other Substrates

Acoustic attenuation (the gradual loss of magnitude as an acous-tic signal passes through a substrate) is the result of absorptionwithin the substrate reflections at interfaces and dilution as thesignal enters a larger volume It can severely limit the distance overwhich sensors can detect insects reliably (the active space) and itcan strongly degrade signals that move across substrate interfacesfor example from grain to air or from a weathered palm frond to astethoscope head

In heterogeneous substrates like wood or storage bins of grainor beans reflection plays a strong role in attenuation with soundmoving a short distance in transfers or reflections from grain tograin bean to bean or across wood fibers while it can move longerdistances through air spaces (Guo et al 2005 Hickling and Wei1995 Hickling et al 1997a) or along a wood fiber The attenuationcoefficient or rate of signal decay per unit distance is ca 2-5 timesgreater across wood or plant fibers than along them (Robbins et al1991 Scheffrahn et al 1993) Termites can be detected 08 to 22m from the sensor location along the wood grain (Lemaster et al1997 Yanase et al 2000) but only ca 8 em away across the woodgrain (Scheffrahn et al 1993)

The attenuation coefficient increases lOOO-fold between 500 Hzand 120 KHz in air (Mankin et al 1996) and at even greater rates insoil and grain (Mankin et al 2000) making both substrates highlyeffective insulators against high-frequency sound Wood has a lowattenuation coefficient consequently ultrasound (i e frequenciesgt20 kHz) could be detected from termites over active spaces of up to22 m in wood (Scheffrahn et al 1993) Low-frequency sounds fromtermites and other insects can be detected over active spaces of 180em but only over 20 em in soil (Mankin etal 2002) Low-frequencysounds produced by infestations of red palm weevil larvae in thecrown of a palm tree can be detected easily by sensors positioned 2m below the crown and some of the loudest sounds are detectablefrom 4 m and further below Because of the significantly lower rateof attenuation at lower frequencies listeners often focus on low-frequency signals to increase the active space

Attenuation and resonances within a substrate alter the spectrumof an insect sound as it passes through a tree grain or soil (Vick1988b Mankin et al 2008a b) Consequently the mean spectrumof signals produced by a targeted species is different in different

31

substrates and it changes with distance from insect position In ad-dition many sensors are differentially sensitive to different soundfrequencies All these differences must be taken into account whensignals and spectra recorded from different sensors in differentenvironments are compared

Sound and Vibration SensorsSome of the first sensors used for acoustic insect detection (Lutz

1924 Emerson and Simpson 1929) were carbon button microphones(Dyer 1997) denoted by (m) in the tables below replaced laterby dynamic condenser or capacitive microphones (mJ after theintroduction of vacuum tube amplifiers Electret microphones (me)a special class of inexpensive capacitive microphones came intocommon use after the 1960s (Sessler and West 1962) but condensermicrophones (mJ are stilI used where high quality and good calibra-tion are needed Modern microphone systems are capable of 60-120dB gain (103-106 amplification) with nearly constant response over~0-20 kHz frequency range

Microphones are useful sensors for airborne signals but theydo not interface well with signals produced by insects in soil woodor other solid substrates Sensors that interface better with solidsubstrates include piezoelectric transducers (Gautschi 2002) thatfunction as contact microphones or pickups (p) geophones (g) accel-erometers (p) or ultrasonic sensors (pJ Magnetic cartridges wereused as inexpensive pickups before piezoelectric transducers becamepopular and are stilI used occasionally Accelerometers and somegeophones measure acceleration while other geophones measurethe velocity of substrate vibrations Geophones detect low-amplitudelow-frequency signals ~0-400 Hz and the most commonly usedaccelerometers operate up to frequencies of ~ 13 kHz Ultrasonicsensors operate at frequencies between 20 to 200 kHz and higher(Haack eta 1988) Accelerometers are more expensive but usuallybetter calibrated and more rugged than contact microphones It isimportant to note however that the choice of sensor depends sig-nificantly on the purpose of its use Farr and Chesmore (2007) foundfor example that piezoelectric sensors were preferable to electretmicrophones when the primary goal was detection of wood-boringinsects because the piezoelectric sensors have greater sensitivity butdue to their greater spectral range electret microphones were betterat distinguishing between insect sounds and background noise

Ultrasonic sensors are of particular utility for detection and moni-toring of wood-boring insects Termites (Fujii et a 1990) and otherwood -boring insects stress and snap wood fibers during movementand feeding activities which causes the wood fibers to spontaneouslyemit broad-band acoustic emissions (first characterized by Dornfeldand Kannatey-Asibu 1980) that can be detected by lead-zirconate-titanate (PZT) ceramic-disk (Gautschi 2002) and polyvinylidenefluoride (PVDF) film (Yanase et a 1998) piezoelectric transducersLemaster et a (1997) determined that the detectability of acousticemissions from termite infestations depended on the resonantfrequencies of the piezoelectric transducers with transducers thathad resonant frequencies near 60 kHz providing the best overallperformance for ultrasonic signal detection The cost and durabilityof ultrasonic and other piezoelectric transducers vary over a widerange and large differences also exist in the sensitivity and calibra-tion of their amplifiers Amplifiers with 40-100 dB gain are sufficientfor detection of most insect sounds but greater amplification canbe provided by various methods if needed eg fluidics methods(Drzewiecki and Shuman 2001)

32

The sensor-substrate interface can strongly affect the sensitivityand reliability of vibration measurements The ideal mounting foran accelerometer is to use a nail screw or other metal waveguideinserted into the substrate If the waveguide is magnetic it can beinserted into the substrate first and then connected magnetically tothe accelerometer (Mankin eta 2000) Sensors also can be attachedwith glue beeswax or solid adhesives like Ross Tac N Stick (ElmersProducts Corp Columbus OH) although loose-fitting connectionsmay have low sensitivity at high frequencies Rapid surveys can beconducted with hand-held probes or stethoscope heads but magni-tude measurements are not repeatable in such cases especially whenthe sensor is placed against a rough surface Sensitivity is reducedwhen insects strongly compromise the structural integrity of the sub-strate for example when a heavy infestation of red palm weevil larvaedestroys significant portions of the interior of a palm tree trunk

In cases where insect signals in a solid substrate have a narrowfrequency range it may be feasible to combine a microphone with aresonant acoustic coupler (Webb eta 1988a) to achieve even greatersensitivity than a contact sensor would provide Aural and electronicstethoscopes operate similarly to acoustic couplers and have beenuseful for insect detection when the base of the stethoscope canbe placed flush with the substrate surface (Kisternaya and Kozlov2009) Stethoscopes and acoustic couplers do not work as well incontact with rough surfaces however or when the insect soundscover a broad frequency range Other sensor combinations have alsobeen used in different insect acoustic detection applications includ-ing expensive but highly precise laser vibrometers (Michelsen andLarsen 1978 Zunic et a 2008) and inexpensive magneto-inductivesensors (eg Striibing 2006)

Minimizing Electrical and Background NoiseElectrical and background noise can be mistaken for insect

sounds and considerable research has been conducted to minimizeor filter out interfering signals Most of the electrical noise problemsin field environments are interconnection problems which can bereduced by placing the amplifier and the analog-to-digital converteras close to the sensor as possible When many sensors are usedhaving one amplifier for many sensors is less costly Shielding thecables connecting sensors to the amplifier eliminating ground loopsand separating power cables from inputoutput cables also reduceelectrical noise (Macatee 1995)

When feasible acoustically and vibrationally shielded anechoicchambers (Pittendrigh et a 1997 Vick et a 1988 Webb et a 1988ab) or other sound-proofing and vibration reduction methods arecommonly employed to reduce background noise (Adams eta 1954Fleurat-Lessard 1988 Vick et a 1988a Hagstrum and Flinn 1993Hickling et a 1994 2000 Mankin et a 1996 1997b) Generally abox-within-a-box construction and sound-absorbent material areused for sound-proofing Vibration is reduced by suspending thesample container and by using heavy supporting materials and shockmounts Electrical noise interference in an industrial environmentcan be reduced by enclosing the chamber within a copper Faradaycage (Adams eta 1954)

Another commonly used procedure to reduce background noise isto include reference sensors into the instrumentation to identify peri-ods of back ground sounds or electrical noise (Scheffrahn eta 1993Pittendrigh eta 1997 Hagstrum eta 1996) Reference sensors alsohave been used to subtract out signals that appear simultaneouslyin the test sensors and the external background (Mankin et a 2010


Use of Signal Features to Discriminate Target from NontargetSpecies and Other Noise

Although background noise can be reduced substantially it israrely eliminated and identifying and distinguishing low-amplitude

Os 0

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50 100

target insect signals from other sounds sometimes can seem animpossible challenge The human ear deals with such challengesroutinely however and recognition of the methods used by humanand other animal auditory systems to identify weak signals ofinteresthas led to considerable progress in development of automated insectacoustic detection and monitoring methods Recently developedmethods include recognition of spectral and temporal features thatprominently appear in target insect signals but not otherwise or viceversa and recognition of how spectral features are affected by thesubstrate as seen in Figs 1-2 The block (AC) of signals from the Achinensis larva in Fig 1 and the entire oscillogram (Os) in Fig 2 bothdisplay a series of short broadband variable-amplitude impulses(transients) that are typical oflarval sliding and scraping wood-fibersnapping and other movement and feeding activity (Mankin et ai2008c) The signals in Fig 2 were obtained from a large infestationof red palm weevil larvae found in the crown of a 5 m date palm inCuracao In both cases the sounds are a combined result of insectaction with the wood-fiber reaction

The effect of the wood fibers on the spectra of insect soundsis clearly discernable in Fig 3 where the average spectral pattern(profile) of 240 consecutive Rferrugineus impulses recorded fromthe crown is seen in line Rf of Fig 3 This is compared with the meanspectrum of 05 s of background noise (Rfb) recorded from the sameposition Also included in Fig 3 is a profile (Ac) of24 signals producedby an A chinensis larva in the tree where Fig 1 was recorded com-pared with the mean spectrum of 05 s of background noise recordedfrom the same position The Rf and Ac profiles have a similar overallshape but their peaks are shifted corresponding with shifts in thepeaks of the background noise (Rfb andAcb) The shifts in the peaksof the background noise occurred because of resonances within thetree structure that depend on the length diameter and stiffness ofthe trunk (Mankin et ai 2008c) In both cases the larvae producedsignals that had greater energy than the background at frequenciesabove the peak frequency of the background noise

The profile Rf in Fig 3 is similar to other Rferrugineus profilesin Mankin et ai (2008b) recorded from a small potted palm treeand from sugarcane The primary difference is that the resonant

Time (ms)

Fig 2 Oscillogram Os and Spectrogram Sp of a 013 s period ofimpulses recorded from a palm containing multiple R ferrugineuslarvae Darker shading in spectrogram (8 points per spectrum 90overlap) indicates higher relative spectrum level Individual impulses aremarked by arrows Recording and analysis methods were essentially asdescribed in Mankin and Moore (2010)

20

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and references therein) If enough of the background noise can beshielded it is possible to use a very simple signal processing systemthat counts sounds as the number of times the voltage rises above apredetermined threshold level (Webb et ai 1988a)

Filtering out frequencies higher or lower than those typically pro-duced by the target species reduces background noise that cannot beeliminated by methods above Much background noise for exampleis low-frequency and can be reduced significantly by filtering outsignals below 200 Hz (Mankin et ai 2009b) Wind machinery andtraffic are examples of such noise Bird calls and nontarget flyinginsect sounds usually have strong harmonic components (Mankinet al 2009b Potamitis et ai 2009) that typically do not appear inmovement and feeding sounds of hidden insects Examples of bird-call noise are seen in comparison with signals produced by anAno-plophora chinensis (Forster) larva in Fig 1 The signals were digitallyrecorded (digitization rate 441 kHz) from the root system of an Acerpseudoplatanus L tree in the yard of a condominium complex nearComo Italy using a piezoelectric pickup The signals were bandpass-filtered between 02 and 10 kHz as in Mankin et ai (2008c) andindividual sounds were identified aurally by playback and by digitalsignal analyses as in Mankin et ai (2009b) The spectrogram Spin Fig 1 displays a diffuse band of background noise with a peaknear 15 kHz The blocked area AC contains numerous 3-30 msbroad -band A chinensis sound impulses that appear as brief spikesof varying amplitudes in the oscillogram and as lines that span mostor all of the frequency range in the spectrogram One of several loudmechanical impacts occurs at ~11 s marked as blocked area N Theimpact has a strong peak below 500 Hz and little energy above ~4kHz In contrast several bird calls the first one marked as block Bcontain most of their energy in a series of harmonics above 4 kHzThe dashed ovals in Nand B mark frequencies where the relativeenergy is notably different from that in larval signals

-- ----------------------------------10middot-----

Time(s)Fig 1 Oscillogram Os and spectrogram Sp of a 24 s period of signalsrecorded from a tree containing an A chinensis larva Darker shading inspectrogram (512 points per spectrum 90 overlap) indicates higher rela-tive spectrum level (higher energy) Blocked area AC marks a period withnumerous A chinensis impulses N marks a period of mechanical noiseand B marks a bird call Ovals in Nand B mark frequency ranges whererelative spectrum levels notably exceed those expected in larval signals

American Entomologist Volume 57 Number 1 33

Frequency (kHz)

Fig 3 Spectra of R ferrugineus profile recorded in crown of date palmRf background noise in same date palm Rfb A chinensis profilerecorded in A pseudoplatanus tree Ac and background noise in sametree Acb

frequency of the palm tree trunk is much lower than the resonantfrequency of the much smaller potted palm Such effects of structuresize on the resonances observed when recording larval movementand feeding activities are observed most commonly in trees but wealso have observed resonance differences between signals producedin large and small containers of ornamental plants by black vine wee-vil Otiorhynchus sulcatus (Fabricius) (Mankin and Fisher 2002)

In a given substrate stored-product insect larvae can often bedistinguished from adults through differences in movement and feed-ing activities that alter the spectral profiles of the sounds producedin a given substrate An example is the discrimination of Sitophilusgranarius (L) Tribolium confusum Jacquelin du Val and Rhyzoperthadominica (E) larvae from each other in grain (Schwab and Degoul2005) Because of their large size and distinctive acoustic profilesred palm weevil larvae have been detected on the basis of spectralfeatures alone with as high as 99 success in field trials (Potamitiset al 2009) Gutierrez et al (2010) distinguished red palm weevillarvae from other insects in date palms on the basis of spectralfeatures Adults of different stored-product insect species can bedistinguished by differences in their spectral profiles in a givenenvironment Sitophilus granarius Tconfusum and R dominica (E)in grain were distinguished from each other in a study by Schwaband Degoul (2005)

Another distinctive feature oflarval signals is their temporal pat-tern which usually contains bursts or groups of impulses separatedby intervals less than 250 ms (Mankin et al 2008c 2009b) In con-trast background noise often is continuous over periods of severalseconds or occurs as isolated impulses rather than bursts Humanears and automated detection systems are well suited to recognitionof these differences in temporal patterns which are the most reliableindicators of insect presence in environments with high levels ofbackground noise In trees Nasutitermes luzonicus Oshima and adultO rhinoceros were distinguished by a combination of spectral andtemporal pattern analyses in a study by Mankin and Moore (2010)In soil target species also have been distinguished from nontargetspecies and background noise by combinations of spectral and tem-poral pattern analyses (Mankin et al 2009b)

Communication sounds of four different orthopteran insects weredistinguished by Chesmore (2001) and Chesmore and Ohya (2004)using a feature extraction and classification procedure involving time

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domain signal coding and artificial neural networks The accuracy ofidentification ranged from ~70 to 100 depending on the callingsong and the type of background noise in analyses with Chorthip-pus albomarginatus (De Geer) Chorthippus parallelus (Zetterstedt)Myrmeleotettix maculatus (Thunberg) Omocestus viridulus (L) andseveral bird species Hussein et al (2010) considered time domainsignal coding as well as 30 other temporal and spectral pattern fea-tures of red palm weevil sounds in distinguishing them from back-ground noise They achieved a 94 rate of success in distinguishingred palm weevil sounds from background

Effects of Insect Size and Stage Disturbance Behavior andTemperature on Acoustic Signal Production

Insect size and stage strongly affect the amplitude and rate ofsound production Sensors can detectS oryzae larvae up to 10-15 cmaway in grain (Vick et al 1988a) and Tribolium castaneum (Herbst)adults up to 185 cm away (Hagstrum et al 1991) for example butthe rates of sounds detected from a small insect close to a sensor maybe similar to those detected from a larger insect further away AdultR dominica moving on the outside of the grain kernels produced 37times more sounds than larvae feeding inside the grain (Hagstrumetal1990) and Tcastaneum adults produced 80 times more soundsthan larvae (Hagstrum et al 1991) The rate of sounds produced byS oryzae larvae ingrain (Pittendrigh etal1997 Hickling etal 2000)and Callosobruchus maculatus (E) larvae in cowpeas Vigna unguicu-lata (L) Walp (Shade etal1990) increased with ins tar AdultS ory-zae and Tcastaneum are equally detectable in grain and much morereadily detected than smaller Cryptolestes ferrugineus (Stephens) orOryzaephilus surinamensis L while the size and detectability of Rdominica are intermediate (Hagstrum and Flinn 1993) However thedata collected by acoustic sensors from grain infested with a singlespecies and stage typically provides sampling statistics similar tothose estimated from grain samples for Rdominica larvae (Hagstrumet al 1988) and Tcastaneum adults (Hagstrum et al 1991) In bothof these studies the rates of insect sounds were highly correlatedwith the numbers of insects present In other studies where multiplespecies and stages of root-feeding insects were present at samplingsites the relationship between sound rate and the numbers of insectswas of relatively weak statistical significance (Mankin et al 200 IMankin and Lapointe 2003 Zhang et al 2003b)

Some insects flee and others feign death when disturbed (egMiyatake et al 2008) resulting in either positive or negative effectson sound production Disturbance of fourth-instar S oryzae by stir-ring grain may reduce sound production for periods up to 20 min(Mankin et al 1999) For termites however dropping a 6 g cointhree times to simulate disturbance during termite inspection didnot reduce detectability significantly (Scheffrahn et al 1993) andHu et al (2003) demonstrated that the response of termite coloniesto 120 and 240 Hz vibration habituates within about 250 s In theabsence of knowledge about specific effects of disturbance a listenercan perform preliminary testing to assess activity with headphonesat a location where the targeted species is known to be present anddetermine whether activity rates increase with time

Insect sound production increases as the temperature increasesfrom 10 to 25-30degC for adults or immatures of six insect species(Fig 4) probably as a result of increases in insect feeding activityT castaneum adult sound production continued to increase up to40degC Callosobruchus maculatus larval sound production (Shade etal 1990) decreased above 38degC in cowpeas Sound production of S

34 American Entomologist Spring 2011

oryzae adults decreased above 30-35degC and that of R dominica adultsbegan to level off above 30degC For all species including Zootermopsisnevadensis (Hagen) activity dropped off steeply as temperaturesincreased past optimal levels Incisitermes minor (Hagen) was moreactive at 60 or 70 relative humidity than at 80 or 90 relativehumidity (Indrayani et al 2007) For insect communication amongOrthoptera the sound pulse rate was observed to increase as thetemperature increased from 17 to 32degC (Sanborn 2006)

Several studies have investigated the effectiveness of warminginsects to increase detectability Warming grain from 11 or 17degCincreased the sound production of S oryzae larvae feeding inside byas much as 20-to 30-fold (Mankin etaI1999)A patent was issued inFrance for heating grain to increase insect sound production (Mihaly1973) Warming cotton bolls also increased sound production by pinkbollworm larvae Pectinophora gossypiella Saunders (Au 1997)

Effect of Assessment Time on DetectabilityBecause insects produce sounds of different amplitudes intermit-

tently increasing the duration of assessment can increase the likeli-hood that an insect inside the active space of a sensor will producea detectable signal Termites for example are detected with greaterreliability using 5 min compared to 1 min monitoring periods (Schef-frahn 1993) The increased duration also increases the cost of assess-ment which results in a trade-off between accuracy and cost

Under conditions oflow disturbance at temperatures that supportactivity of the target species short 30-180 s listening times are suit-able for many detection applications The stored-product insects Rdominica for example produce feeding sounds in grain 61 of thetime Sitotroga cerealella (Olivier) 71 of the time and Soryzae 90of the time (Vick et al 1988b) and sampling durations of 180 s usu-ally will include at least one period of activity Increasing the durationof monitoring of a sensor can be equivalent to increasing the numberor size of grain samples Increasing the frequency of monitoring ofa sensor increased detection of T castaneum by 60-80 as muchas adding the same number of sensors (Hagstrum et al 1991) Ingeneral however it is less costly to increase the sampling durationthan the number of sensors Stored grain beetle infestation levels

(Hagstrum and Flinn 1993) were estimated successfully using eightmicrophones in a 1 kg sample container and checking each sensorfor 10 s 72 times during a 12 min period Infestation levels of Rdominica were estimated by probing a sensor into grain and listeningfor 20 sat 36 locations (Hagstrum et al 1990) Infestation levels ofTcastaneum were estimated in small-scale studies with 16 sensorschecked 1080 times per day (Hagstrum et al 1991) The number ofoccurrences of insect sounds in wheat storage bins was correlatedwith insect density in a study using 140 sensors on 7 cables in grainbins checking each sensor for 10 s 27 times per day (Hag strum etal1996) Insects present in the wheat storage bins included R dominicaT castaneum and S oryzae Acoustic assessments of presence orabsence of sugarcane pests could be conducted in 3-5 min periodsper sampled position in a sugarcane field and this was faster than the10-12 min required to dig up and inspect a sugarcane root systemsample (Mankin et al 2009b)

When the behavior of the cryptic insect or the acoustic propertiesof the substrate are not well characterized shortassessmentperiodscan lead to high rates of false negatives if the targeted insects ceaseactivity when disturbed consequently it is safest to perform at leasta few tests over a 10-20 min listening period to determine whetherthe activity increases with time after the waveguide is inserted intothe substrate Long sampling periods can be helpful also in identifyingspecific sounds and temporal patterns that discriminate backgroundnoise from sounds produced by the targeted species Converselywhen the substrate strongly attenuates acoustic signals or the insectproduces very weak signals it may be preferable to sample multiplesites rapidly in potential areas of insect presence to determine thebest positions from which to detect an infestation In recent stud-ies of red palm weevil infestations in Curacao for example it wasadvantageous to sample at multiple locations at different positionsalong the trunks of palm trees In ~ 20 min sampling periods at eachtree it was determined that sounds of many different spectral andtemporal patterns could be detected from larvae near the centerof infestation but further away high-frequency signal attenuationreduced detectability primarily to the fiber-snapping activities thathad the highest amplitude and greatest bandwidth The red palmweevil larvae were not significantly disturbed by insertions of thesensor waveguides into the trunk

10 20 30 40

Temperature ( C)

Fig 4 Effects of temperature on relative activity levels of two termite spe-cies (adults) 1M I minor adapted from Indrayani et al (2007) and ZN Znevadensis adapted from Lemaster et al (1997) and four stored productinsect species CM (larvae) Cmaculatus adapted from Shade et a11990and adults of SO S oryzae RD R dominica and TC T castaneum alladapted from Hagstrum and Flinn (1993)

o

I

ZNI

I

1M bull

RD

Insect Species and Stages Detected with Different AcousticDevices

Adults of 16 species larvae of 40 species and pseudergatessoldiers or workers of 10 species have been detected by one ormore types of sensor in one or more substrates including 18 spe-cies of stored-product pests in grain or packaged goods (Table 2)28 wood- or stem-infesting insect species (Table 3) 13 root-feedinginsect species (Table 4) and 4 fruit-infesting insect species (Table5) Species from 21 families and 5 orders have been investigatedThree of the above species and stages were studied using geophone7 with condenser microphone 17 with electret microphone 12 withan unidentified type of microphone 31 with PZT contact pickup16 with PZT accelerometer 4 with PVDF piezoelectric film and 30with PZT ultrasonic sensors Most studies on termites were donewith ultrasonic PZTSix of the eight sensor types have been used fordetection of S oryzae

The grain pests S oryzae and R dominica were the two mostfrequently investigated species Two invasive quarantined speciesalso have been investigated frequently the termite Coptotermes


Table 2 Stored-product insect species and stages studied and detectionmonitoring devices usedSpecies (Order Family) St Seb Source

Mori etal1962

Hagstrum and Flinn 1993 Mankin et al 1997b

Andrieu and Fleurat-Lessard 1990 Mankin 2002 2006

Fleurat -Lessard 1988Kennedy and Devereau 1994Shade et al 1990

Spangler 1985

Andrieu and Fleurat-Lessard 1990

Street 1971Shade et al 1990Hanson 1993

Street 1971 Zakladnoi and Ratanova 1986 Fleurat-Lessard 1988 Hag-strum and Flinn 1993 Hagstrum et al 1996 Mankin et al 1997aVick et al 1988bHagstrum et al 1988 Hagstrum et al 1990 Fleurat-Lessard et al 19942006 Schwab and Degoul2005Bailey and McCabe 1965 Andrieu and Fleurat-Lessard 1990 Weinard 1998Zakladnoi and Ratanova 1986 Fleurat-Lessard et al 1994 Schwab andDegoul2005Welp 1994Mankin et al 2010Hagstrum and Flinn 1993 Hagstrum et al 1996 Mankin et al 1997aMankin et al 2010Adams et al 1953 1954Mankin et al 1996 Vick et al 1988abDrzewiecki and Shuman 2001 Hickling et al 2000Street 1971 Shuman et a11993 1997 Mankin et al 1997b Weaver et al1997 Weinard 1998 Mankin et al 1999 Potamitis et al 2009Shade et al 1990Pesho 1954Zakladnoi and Ratanova 1986 Fleurat-Lessard et al 1994 2006

Fleurat -Lessard 1988Andrieu and Fleurat-Lessard 1990Cross and Thomas 1978

Pittendrigh et al 1997Brain 1924Mori etal1962Fleurat -Lessard 1988Kennedy and Devereau 1994Vick et al 1988bStreet 1971 Andrieu and Fleurat-Lessard 1990 Fleurat-Lessard et al 1994Welp 1994 Schwab and Degoul2005Shade et al 1990Mankin et al 2010Mankin et al 2010Mankin et al 2010Hagstrum et al 1991 1996 1998 Hagstrum and Flinn 1993 Mankin et al1997aMankin et al 2010Zakladnoi and Ratanova 1986 Schwab and Degoul2005

Zakladnoi and Ratanova 1986 Hagstrum and Flinn 1993 Mankin et al1997a Schwab and Degoul 2005

p

p

p

p

p

p

p

p

p

p

p

p

ppp

meppp

p

p

m

m

m

p

Prm

Pum

Prp

Pumm

(A)

L

ALNRL

L

A

A

LAAA

A

AAL

ANRL

AALL

L(A)L

A

LLALL

AL

(A)AAALL

L

LLAL

AL

AL(A)

L

Sitophilus granarius (L)

(Coleoptera Curculionidae)

Tribolium confusum Jacquelin du Val(Coleoptera Tenebrionidae)Tribolium spp(Coleoptera Tenebrionidae)

Zabrotes subfasciatus (Boheman)(Coleoptera Bruchidae)

Stage A adult L larva N Nymph NR not reported P pseudergates (false worker) S soldier W workerbSensor g = geophone m = microphone (unknown type) me = capacitance (condenser) microphone m = electret microphone p = contact pickup usingPZT piezoelectric transducer P = PZT accelerometer (0-20 kHz) Pr = PVDF piezoelectric film transducer Pu = PZT ultrasonic transducer (20-200 kHz) t= tachometer

Sitophilus oryzae (L)(Coleoptera Curculionidae)

Sitophilus spp(Coleoptera Curculionidae)Sitotroga cerealella (Olivier)

(Lepidoptera Gelechiidae)

Stegobium paniceum (L)(Coleoptera Anobiidae)Tribolium castaneum (Herbst)

(Coleoptera Tenebrionidae)

Achroia grisella (F)(Lepidoptera Pyralidae)Acanthoscelides obtectus (Say)(Coleoptera Bruchidae)Alphitobius diaperinus (Panzer)(Coleoptera Tenebrionidae)Anobium puncta tum (DeGeer)(Coleoptera Anobiidae)Callosobruchus chinensis L(Coleoptera Bruchidae)Callosobruchus maculatus (E)(Coleoptera Bruchidae)Cylas formicarius elegantulus (Summers)(Coleoptera Curculionidae)Cryptolestes ferrugineus (Stephens)(Coleoptera Laemophoelidae)

Oryzaephilus surinamensis L

(Coleoptera Silvanidae)Plodia interpunctella (Hubner)(Lepidoptera Pyralidae)

Rhyzopertha dominica (F)

(Coleoptera Bostrichidae)


formosanus Shiraki that has recently been spread from Asia to Africaand the USand the red palm weevil that has recently spread fromAsia to Arabian European and Caribbean countries

Incorporation of Acoustic Methods into Insect Detection Sur-veys Population Estimation and Mapping Applications

Acoustic methods have been applied successfully in surveys forthe presence or absence of targeted insect species (Mankin et al2009b Mankin and Moore 2010) estimations of population density(Hagstrum et al 1988 1990 1991 1996) and mappings of insectdistributions (Brandhorst-Hubbard et al 200 I Mankin et al 2007)but it has been necessary to interpret the acoustic signals carefullybecause they are affected by many environmental and behavioralfactors described in sections above In determining the feasibilityof acoustic technology in a particular environment it is beneficialto have initial knowledge about the acoustic characteristics of thesubstrate the types of behavior that the target species performs toproduce sounds the effects of disturbance on sound production andthe temperature range and time of day of greatest activity

As discussed above in the section on effects of insect size andbehavior on acoustic signal production the sampling statistics foracoustic detection of single species and stage populations at constanttemperatures in the laboratory are often similar to those for grainsampling and this results in the accuracy of insect density estimatesmade with a representative sample being similar for the two meth-ods However insect density may be more difficult to estimate innatural populations and environments where temperatures varyand different species and stages are present Even in conventionalsampling considerable attention typically is given to determiningthe number of insects in a grain sample during grain inspectionbut less consideration is given to determining whether the sampleis representative of the entire lot of grain being inspected Whenestimating the number of insects in a grain sample or continuouslymonitoring for insects a representative number of locations needto be sampled to estimate overall insect density in a commercial lotof grain both for conventional and acoustic inspections Otherwiseoverestimation or underestimations of populations may result

Comparing acoustic estimates with actual counts acousticmethods overestimated the number of S oryzae larvae in 6 ofgrain samples (false positives) and underestimated their numbersin 34 of grain samples (false negatives) in a study by Shuman et al(1993) False positives and false negatives both can be reduced usingacoustic signatures spectral profiles or temporal pattern analyses(see Signal Features section above) In tests with a continuous moni-toring device adult R dominica were identified successfully by theiracoustic signatures 73 of the time with success rates of72 for Tconfusum 63 for Sgranarius and 61 for Osurinamensis (Schwaband Degoul 2005) The percentages for successful identification oflarvae were 73 for S granarius 58 for S cerealella 57 for Rdominica and 52 for Tconfusum

Typically prevalence of false positive and negative detectionsboth decrease as insect size increases False positives are reducedbecause larger insects tend to produce large numbers of energeticsignals with broadband frequency components that are relativelyeasy to distinguish from low-frequency background noise (Mankinet al 2010) False negatives are reduced because larger insects canproduce louder signals that carryover longer distances to a sensorSmall insects are more likely to be detected if they are present at highdensities because the sensors are more likely to be placed within the


active space of an insect when the insect is present at high densitiesin the substrate

Despite the factors above that may increase the need for numbersor durations of samples acoustic monitoring has important advan-tages that enable earlier detection than conventional grain samplingincluding the detection of internal feeding larvae and the capabilityof remote continuous surveillance Because adults sieved from grainsamples represent only 2 R dominica and 5 of Cferrugineuspopulations infestations are more likely to be identified iflarvae aswell as adults are detected (Perez-Mendoza et al 2004) Automaticcontinuous monitoring detected insects 3 to 28 d earlier than takinggrain samples (Hagstrum et al 1996)

For Caribbean fruitfliesAnastrepha suspensa (Loew) in grapefruitat room temperature acoustic monitoring for 05-10 min intervalsdaily has been found to be a more reliable detection method thancutting fruit open for visual inspection (Calkins and Webb 1988)Larvae were detected soon after hatch and were detected most read-ily in mature fruit because they fed more continuously Continuousacoustic monitoring also was more effective than cutting open cottonbolls for detection of Pgossypiella (Hickling et al 2000) Eighty-sixpercent oflarvae were detected by listening while only 53 weredetected by the conventional method of cutting open and visuallyinspecting bolls There were 12 false positives and 4 false nega-tives using the acoustic method

The distributions of soil insects have been mapped successfullyusing acoustic sensors (Brandhorst-Hubbard et al 2001 Mankinet al 2001 2007) Statistically significant associations have beenidentified between acoustic indicators of infestation likelihood andthe measured counts of sound-producing soil invertebrates andacoustic indicator-based mapping has been used to successfullyidentify locations needing treatment against white grubs (Mankinet al 2007)

Development and Marketing of Insect Acoustic Detectionand Monitoring Devices

During the last few decades various types of acoustic deviceshave been marketed for detection and monitoring of insect popula-tions (Table 6) including a sample container in a sound-insulatedchamber for laboratory use (Sito Detect Pest-bin detector EWDLab)and probe sensors for field use Probes may be pushed directly intoa commodity (Larva sound detector and EWD Portable) or may beattached to a waveguide that is inserted into the substrate or com-modity (Pest probe detector Termite tracker AED-2000 AED-20 10and WD60) Marketing has focused on stored grain insects andtermites but recently has been expanded to pink bollworm and redpalm weevil Pallaske (1990) patented the monitoring of the vibrationpattern associated with the behavior of wood-boring insects andGobernado et al (2005) received a similar patent for stored graininsects Hickling etal (1994 1997b 2000) developed and patented amultisensor box for monitoring pink bollworm and the Laar WD-60and other instruments (Siriwardena etal 2010) have been marketedfor detection of red palm weevil in date palm orchards

Some equipment has been developed but not marketed Webband Landolt (1984) and Webb et al (1988a b) developed equipmentfor detecting tephritid larva within fruit Shuman etal (1993 1997)Mankin et al (1997a b) and Weaver et al (1997) developed andVick et al (1995) patented equipment and software for detectingstored-product insect larvae feeding inside kernels of grain and Shadeet al (1989 1990) developed and patented equipment for detect-

37

Table 3 Wood- or plant-stem infesting insect species and stages studied and detectionmonitoring devices used

Species (Order Family) St Seb Source

Agrilus dozieri Fisher(Coleoptera Buprestidae)Agrilus planipennis Fairmaire(Coleoptera Buprestidae)Anoplophora glabripennis (Motschulsky)(Coleoptera Cerambycidae)Anoplophora chinensis (Forster)(Coleoptera Cerambycidae)Cephus cinctus Norton(Hymenoptera Cephidae)Coptotermes domesticus Haviland(Isoptera Rhinotermitidae)Coptotermes formosanus Shiraki(Isoptera Rhinotermitidae)

Coryphodema tristis (Frury)(Lepidoptera Cossidae)Cryptotermes brevis (Walker)(Isoptera Rhinotermitidae)Hylotrupes bajulus (L)(Coleoptera Cerambycidae)lncisitermes minor (Hagen)(Isoptera Kalotermitidae)

lncisitermes snyderi (Light)(Isoptera Kalotermitidae)Lucanus cervus L(Coleoptera Lucanidae)Monochamus titillator (E)(Coleoptera Cerambycidae)Nasutitermes luzonicus Oshima(Isoptera Termitidae)Oryctes rhinoceros (L)(Coleoptera Scarabaeidae)Neotermes jouteli (Banks)(Isoptera Kalotermitidae)Prionus coriarius (L)Coleoptera CerambycidaeReticulitermes flavipes (Kollar)(Isoptera Rhinotermitidae)

Reticulitermes grassei Clement(Isoptera Rhinotermitidae)Reticulitermes hesperus (Banks)(Isoptera Rhinotermitidae)Reticulitermes lucifugus Rossi(Isoptera Rhinotermitidae)Reticulitermes virginicus Banks(Isoptera Rhinotermitidae)Reticulitermes speratus (Kolbe)(Isoptera Rhinotermitidae)Rhynchophorus ferrugineus (Olivier)(Coleoptera Curculionidae)

Trichoferus griseus (Fabricius)Coleoptera CerambycidaeXestobium rufovillosum (DeGeer)(Coleoptera Anobiidae)Zootermopsis nevadensis (Hagen)[lsoptera Termopsidae) see Table 2

38

L P

L P

L P

L P

L mL PpW Pu

NR PuP PupW PuSW PSW PuW PrW Pu

mL m

NR Pu

L mL pASW PuP PuW PuP PuNR PuAL P

L P

W P

AL P

P Pu

L P

P PuS mW PS Pu

W Pu

NR PuS PuW P

pW Pu

L mL mL P

L PuL P

L P

W Pu

Mankin et al 2008b

Chesmore and Schofield 2010

Mankin et al 2008c Chesmore and Schofield 2010

Chesmore and Schofield 2010 This paper

Mankin et al 2000Mankin et al 2000 2004Matsuoka et al 1996 Indrayani et al 2003

Noguchi et al 1991 Robbins et al 1991 Weissling and Thoms 2000Scheffrahn et al 1993Matsuoka et al 1996Mankin et al 2002Fujii et al 1990Yanaseetal1998Yanase et al 2000Mori et al 1962Brain 1924

Thoms 2000 Woodrow et al 2006

Schwarz etal1935Farr and Chesmore 2007 Chesmore and Schofield 2010Pence et al 1954 Lewis et al 2004Indrayani et al 2007Lemaster et al 1997Scheffrahn et al 1993Thoms 2000Farr and Chesmore 2007

Mankin et al 2008b

Mankin and Moore 2010

Mankin et al 2009a Mankin and Moore 2010

Scheffrahn et al 1993

Farr and Chesmore 2007

Scheffrahn et al 1993Emerson 1929Mankin et al 2002De la Rosa et al 2005

Lemaster et al 1997

De la Rosa et al 2008a bDe la Rosa et al 2006Mankin 2002

Matsuoka et al 1996

Hetzroni et al 2004Gutierrez et al 2010Abraham et al 1966 Soroker et al 2004 AI-Manie and Alkanhal2005 Mankinet al 2008b Hussein et al 2010 Siriwardena et al2010Pinhas et al 2008 Potamitis et al 2009 Sivaraman et al1989Chesmore and Schofield 2010

Colebrook 1937 Birch and Menendez 1991

Lemaster et al 1997


ing several internally feeding stored-product insects Litzkow et al(1990) patented the use of a piezoelectric sensor on a probe used byHagstrum et al (1988 1990) to detect insects in stored wheat Thesame sensor was used in a flow-through eight-sensor grain samplecontainer in the laboratory (Hag strum and Flinn 1993) in grain tomonitor insect response to a temperature gradient (Hagstrum et al1998) and on cables ingrain stored in bins (Hagstrum etal 1996) Anadvantage of automatic continuous monitoring with sensors in grainis that insects are very mobile and many will eventually crawl past asensor A microphone system was constructed and used to monitoradult population levels in bag storage in Zimbabwe (Kennedy andDevereau 1994) A phonograph pickup system was developed by AT Davis (Abraham et al 1966) to detect red palm weevil

Many of the marketing efforts have been short-lived but in somecases instruments remain available for use with insects because theyhave other uses as well For termites there have been a number ofcompeting devices and some are currently available because thetermite industry is very large Locator and Scout It Out (Potter 2004)are no longer commercially available but the marketer of TermiteTracker also sells acoustic emission detectors for leaks The AED-

2000 andAED-2010 are marketed for leak detection as well as woodpests The Laar WD-60 is marketed for medical use as well as forred palm weevil detection The multi sensor box for pink bollwormdetection remains commercially available because its marketer alsodoes a broad range of acoustic consulting This device also has beenshown to be effective in detecting tephritid fruit flies and rice wee-vils Systelia Technologies plans to sell their Early Warning Device(EWD) to the grain industry in Europe to help meet new regulationsrequiring a 50 reduction in pesticide use

The Promise of Future Insect Acoustic Detection andMonitoring Methods and Studies

The need for nondestructive rapid and inexpensive means ofdetecting hidden insect infestations is not likely to diminish in thenear future and there are several areas where acoustic methods arelikely to expand to help meet the needs of entomologists businessesand regulators First the costs of presently available instruments(see above) that provide rapid aural assessment of insect presenceor absence will decrease as technology improves Likely these willbe combined with playback devices such as iPods or iPhones which

Mankin et a12000 Zhang 2003a b

Mankin et al2000

Mankin et al2000

Brandhorst et al 2001 Mankin et al 2007

Mankin et al 2001

Brandhorst et a12001 Mankin et al 2007



Mankin et al2000

Mankin et al2000

Mankin and Benshemesh 2006

Brandhorst et al2001

Mankin et al 2007

Mankin et al 2009a

Mankin et al 2009a

Mankin et al2000

Mankin et al2000

Mankin et a12000 2001 Mankin and Lapointe 2003

Mankin and Lapointe 2003



Mankin et al 2009b

Mankin et al 2009b

Zhang 2003a

Mankin et al2000

Mankin et al2000

Mankin et a12000 Mankin and Fisher 2002

Zhang 2003a

p

p

p

pum

p

m

p

p

m

p

m

g

g

p

pu

p

pu

p

g

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

W

L

L

L

W

L

W

Drepanotermes sp

(lsoptera Termitidae)

Otiorhynchus sulcatus (E)


Phyllophaga congrua (LeConte)

(Coleoptera Scarabaeidae)

Phyllophaga crassissima (Blanchard)


Phyllophaga crinita (Burmeister)


Cyclocephala spp


Dermolepida albohirtum (Waterhouse)


Diaprepes abbreviatus (L)


Phyllophaga spp


Polyphylla spp


Rhytidoponera taurus (Forel)(Hymenoptera Formicidae) see Table 2

Table 4 Root-infesting insect species and stages studied and detectionmonitoring devices usedSpecies (Order Family)StSe bSource

An titrog us parvulus Britton


Camponotus denticulatus Kirby

(Hymenoptera Formicidae)

Cotinis nitida (L)


Cyclocephala lurida (Bland)



Table 5 Fruit-infesting insect species and stages studied and detectionmonitoring devices used


Anastrepha suspensa (Loew) L mc

Calkins and Webb 1988 Sharp et al 1988 Webb 1988a(Diptera Tephritidae) L m Hickling et al 2000

L p Webb and Landolt 1984Ceratitis capitata (Wiedemann) L m Mankin et al 2006

c

(Diptera Tephritidae)Dacus dorsalis Hendel L m Hansen et al 1988

c

(Diptera Tephritidae)Pectinophora gossypiella Saunders A P Schouest and Miller 1994

(Lepidoptera Gelechiidae) L m Hickling et al 1994 2000 Schneider 1995

a b see Table 2

can be used by nontechnical personnel to compare what they arehearing with sounds produced by different pest insects This tech-nology already enables networking among different businesses orregulatory agencies that monitor insects which permits the trackingof insect infestations in grain and other commodities as they movethrough the marketing system

Second the technology for discriminating insect sounds fromnoise and for distinguishing among different insect species is likelyto improve significantly Entomologists have begun to make use ofstandard speech recognition tools like Gaussian mixture models(Pinhas eta 2008 Potamitis eta 2009) and hidden Markov models(Mankin et a 2009b) only recently This technology should enhancethe capability of remote sensing nondestructive and automateddetection and monitoring tools and trapping systems (Mankin eta 2006 2010 Tobin et a 2009 Potamitis et a 2009 Siriwardenaet a 2010 Gutierrez et a 2010)

Third acoustic methods are likely to gain broad acceptance fordetection oflarge active insects hidden in high-value substratesThe use of acoustic sensors may reduce costs by avoiding the need-less destruction of the commodity and allowing for more materialto be inspected upon importation into the US or other countriesFor example acoustic sensors may provide a more effective meansto detect internal woodborers in the buprestid and cerambycidfamilies that are pests ofimported Chinese bonsai trees Currentlyinspectors conduct destructive sampling on a random numberof this high-value commodity Acoustic sensors may also aid in

surveys for invasive pests that are not known or have recentlyestablished in an area The visual signs of important invasivepests such as emerald ash borer (Agrilus planipennis Fairmaire)red palm weevil (Potamitis et a 2009) and coconut rhinocerosbeetle (Mankin and Moore 2010) are very difficult to detect andnormally appear in the tree only following severe infestationswhen it is too late to prevent their spread We note however thatthere are some insects for which acoustic detection methods arenot likely to be useful such as quiescent pupae sedentary insectsthat feed on plant juices small larvae like wireworms or rootwormsthat weigh less than 3-8 mg (Mankin et a 2004) or insects infruit that are kept in cold storage primarily because of low ratesof sound production

Acoustic detection methods may greatly enhance the ability ofa surveyor to find and quickly dispose of infested material therebyincreasing the likelihood of a successful eradication program Acous-tic detection systems may be useful in commodity preclearanceprograms where an importer would use a system to cull infestedmaterial before the product is shipped to another country In ad-dition acoustic devices would greatly aid nursery and landscapemanagers dealing with pests such as red palm weevil (Potamitis eta 2009) or black vine weevil (Mankin and Fisher 2002) by helpingthem locate infested nursery stock and by determining if a chemicaltreatment was effective

Acoustic methods may be even more important in several low-value commodities like grain fresh fruit and cotton because large

Busnel and Andrieu 1966 Mihaly 1973Betts 1991

Hickling et al 1997b

Betts 1990Robbins and Mueller 1994

Patent

Dunegan 2005

Gobernado et al 2005

France

Sanametrics Huntington Woods MILaarTech Agric Germany

Sound Technologies Kilgore TXDow AgroSciences IndianapolisAcoustic Technology Group Sacramento CAAcoustic Emission Consulting Fair Oaks CADunegan Engineering Midland TX

Pestcon Systems IAPestcon Systems IANIR - Service GermanySystelia Technologies FranceSystelia Technologies France

EWDPortableEWDLab

TermitesInsecta-scopeLocatorScout It OutAED-2000Termite Tracker

Other insectsmultisensor boxLaarWD60

Table 6 Marketing of acoustic monitoring devices for insectsInsects Trade name CompanyStored grain insectsSito DetectPest-probe detectorPest-bin detectorLarven Lausher


volumes need to be sampled for insects economically Acousticmethods for these commodities were developed initially for regula-tory or food safety agencies but these methods could be even moreimportant to the industries being regulated Acoustic methods fordetecting and monitoring insect pests also may be useful for turf andpackaged processed commodities

A full assessment of costs and benefits of acoustic methods isbeyond the scope of this report partly because the costs and benefitsare changing rapidly and partly because no citable studies on thesubject have been published Although they are ultimately importantconcerns cost and benefit ratios are not yet the primary issue drivingacoustic methods into commercial use Arguably the unique capabili-ties of acoustic methods for automatic continuous non-destructiveremote monitoring and detection of hidden infestations are currentlythe main drivers for acoustic insect detection development Nicheapplications already exist where benefits of sensitive rapid detectionjustify the costs of instrumentation and signal analysis and the costswill continue to decline

Finally apart from the economic benefits there are many researchquestions that can best be answered by using acoustic technologyThe feeding and movement activities of internal tree-feeding orsubterranean larvae for example cannot be studied easily in anondestructive manner without the use of acoustic methods X-raytomography or similar technology (Johnson et al 2007) bullbullbullbullbull

AcknowledgmentsWe thank Everett Foreman and Betty Weaver (ARS) Clinton Jo-

hanns and Kenneth Heidweiller (CuraltaoDepartment ofAgriculture)and Facundo Franken and Teophilo Damien (Aruba Department ofAgriculture) for research assistance Funding for studies in Curaltaoand Aruba was provided in part by Section 10201 of the 2008 FarmBill awarded in 2010

The use of trade firm or corporation names in this publica-tion does not constitute an official endorsement or approval bythe United States Department of Agriculture Agricultural ResearchService of any product or service to the exclusion of others that maybe suitable

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Richard w Mankin is a Research Entomologist with USDA-ARSatthe Centerfor Medical Agricultural and Veterinary Entomology Gainesville FL Hefocuses on development and application of technology for detection andcontrol of insect infestations and considers also how insects use olfactionand mechanoreception in communication David W Hagstrum served as aResearch Entomologist atthe USDA-ARS Centerfor Grain and Animal HealthResearch Manhattan KS for 30 years As an adjunct emeritus professor atKansas State University he continues with studies of stored product insectbehavior and ecology Michael T Smith is a Research Entomologist at theUSDA-ARS Beneficial Insects Introduction Unit Newark DE He conductsresearch on the behavior and ecology of invasive insect species with anemphasis on early detection Amy 1 Roda is an entomologist with USDA-APHIS-CPHST at the ARS Subtropical Horticulture Research Station MiamiFL She develops and evaluates biological control technology for earlydetection of invasive pests in the Caribbean region Moses T K Kairo isDirector of the Center for Biological Control Florida AampM University Tal-lahassee FL He conducts research on early detection and management ofinvasive insect species


and Menendez (1991) suggested that the female deathwatch beetleXestobium rufovillosum (DeGeer) could be detected by vibrating in-fested timbers with amplified communication taps of calling maleswhich would elicit responsive taps from the female that are loudenough to be heard by the human ear Forrest (1988) proposed theuse of communication sounds to estimate orthopteran populationsin the field Insect communication sounds also have been used tolure insects into traps (Walker 1988)

Apart from insects that produce communication signalsmany economically important coleopteran adults and larvae andlepidopteran larvae with cryptic behaviors also are candidates foracoustical and vibrational detection and monitoring especially thosethat are large and active sometimes even producing sounds audibleto humans like the red palm weevil Rhynchophorus ferrugineus(Olivier) and the coconut rhinoceros beetle Oryctes rhinoceros (L)The incidental signals that small cryptic insects produce while mov-ing and feeding can be very low in amplitude but still detectableMovement and feeding sounds of 4th instar Sitophilus oryzae (L) ingrain are only 23 dB (Mankin et al 1996) for example where dB iscalculated as 20 loglo(PjPret) P is the sound pressure and Pret = 20~Pa is the threshold of human hearing Typical noise levels in a quiet35-50 dB office are ~4 times (12-27 dB) higher Acoustic deviceswith optimized filters and sensors have detected S oryzae larvae inmany different environments however as is discussed below

As the use of acoustic technology for entomological applicationsbegan expanding in the 1980s and 1990s Walker (1996) noted thatacoustic methods seemed destined to rapidly replace many of thelabor-intensive and less effective detection and monitoring methodsthen in use However while our understanding of insect acousticaland vibrational communication has blossomed in the last decade(Cocroft and Rodriguez 2005 Drosopoulos and Claridge 2006 (okl2008 Sueur et al 2008 Barbero et al 2009) the development andadoption of inexpensive user-friendly acoustic tools for detection andmonitoring of economically important infestations of hidden insectshas lagged behind The delays have resulted partly from limitedunderstanding of acoustic signal attenuation in and across varioussubstrates from difficulties of interpreting weak insect signals inenvironments with high background noise from limited knowledgeof the behaviors of the cryptic targeted species that produce thesignals and from the small market for insect detection instrumenta-tion which limits the capability to take advantage of economies ofscale For example notably fewer insect acoustic detection devicesthan electronic stethoscopes are sold yearly Electronic stethoscopeshave been used successfully for insect detection (eg Kisternaya andKozlov 2009) but there are many insect detection applications wheremore optimal sensor-substrate interfacing or higher-gain amplifica-tion is required as is discussed in later sections

To avoid confusion about acoustics terminology we note herethat several terms used in this report including sound vibrationsignal acoustic and remote have taken on multiple meanings inthe biological acoustical and signal processing literature The termsignal will be used primarily in two contexts either physically as asound in air or a vibration in a structure and also mathematically asan amplitude-time waveform Both sound and vibration waveformsare processed by similar acoustic signal processing methods Fre-quently insect-produced signals are detectable both by microphoneas sounds and by contact sensors as vibrations in which case theymay be designated simply as acoustic signals or sounds (Webb etal 1988a) We have avoided using signal in the biological sense of


transmitting information between or among individuals or groupsof organisms Finally acoustic detection or monitoring is remote inthe sense that it typically occurs over distances of tens of centimetersor more depending on the substrate in which the insect is hidden(Reynolds and Riley 2002) In many applications wires or wirelessdevices transmit the signals from the hidden insect to a central ob-servation station that can be hundreds of meters distant

In this report we describe the progress made during the last cen-tury in the development of acoustic tools and applications for crypticinsect detection population estimation and distribution mappingThe report is organized into nine sections the first five of which dealwith how acoustic signals are transmitted sensed and interpretedin different environments Three sections deal with applications ofacoustic methods in detection population estimation and mappingof cryptic insects in different environments In the final section weconsider the kinds of applications toward which acoustic technologymay be directed in the future

Transmission and Attenuation of Insect Acoustic Signals inand between Stored Products Wood and Other Substrates

Acoustic attenuation (the gradual loss of magnitude as an acous-tic signal passes through a substrate) is the result of absorptionwithin the substrate reflections at interfaces and dilution as thesignal enters a larger volume It can severely limit the distance overwhich sensors can detect insects reliably (the active space) and itcan strongly degrade signals that move across substrate interfacesfor example from grain to air or from a weathered palm frond to astethoscope head

In heterogeneous substrates like wood or storage bins of grainor beans reflection plays a strong role in attenuation with soundmoving a short distance in transfers or reflections from grain tograin bean to bean or across wood fibers while it can move longerdistances through air spaces (Guo et al 2005 Hickling and Wei1995 Hickling et al 1997a) or along a wood fiber The attenuationcoefficient or rate of signal decay per unit distance is ca 2-5 timesgreater across wood or plant fibers than along them (Robbins et al1991 Scheffrahn et al 1993) Termites can be detected 08 to 22m from the sensor location along the wood grain (Lemaster et al1997 Yanase et al 2000) but only ca 8 em away across the woodgrain (Scheffrahn et al 1993)

The attenuation coefficient increases lOOO-fold between 500 Hzand 120 KHz in air (Mankin et al 1996) and at even greater rates insoil and grain (Mankin et al 2000) making both substrates highlyeffective insulators against high-frequency sound Wood has a lowattenuation coefficient consequently ultrasound (i e frequenciesgt20 kHz) could be detected from termites over active spaces of up to22 m in wood (Scheffrahn et al 1993) Low-frequency sounds fromtermites and other insects can be detected over active spaces of 180em but only over 20 em in soil (Mankin etal 2002) Low-frequencysounds produced by infestations of red palm weevil larvae in thecrown of a palm tree can be detected easily by sensors positioned 2m below the crown and some of the loudest sounds are detectablefrom 4 m and further below Because of the significantly lower rateof attenuation at lower frequencies listeners often focus on low-frequency signals to increase the active space

Attenuation and resonances within a substrate alter the spectrumof an insect sound as it passes through a tree grain or soil (Vick1988b Mankin et al 2008a b) Consequently the mean spectrumof signals produced by a targeted species is different in different

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pu

p

g

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

W

L

L

L

W

L

W

Drepanotermes sp










Cyclocephala spp






Phyllophaga spp


Polyphylla spp








Cotinis nitida (L)










c


c



a b see Table 2










Patent

Dunegan 2005


France




EWDPortableEWDLab



















































41
























42







































































43


























44

























32










Os 0

~CiElaquo 0gt~a

a

-1Sp


50 100




Time (ms)


20

IAC--------------------------Os

0

~CiE 0laquogt~a

a-1

Sp10

NI6raquo

5-c=gt0-U



-- ----------------------------------10middot-----



Frequency (kHz)






iii~ -10QigtQ)

J

E~ -20Q)Q(f)Q)

gt~Qi -300

2 4



6














10 20 30 40

Temperature ( C)


o

I

ZNI

I

1M bull

RD






Mori etal1962




Spangler 1985







p

p

p

p

p

p

p

p

p

p

p

p

ppp

meppp

p

p

m

m

m

p

Prm

Pum

Prp

Pumm

(A)

L

ALNRL

L

A

A

LAAA

A

AAL

ANRL

AALL

L(A)L

A

LLALL

AL

(A)AAALL

L

LLAL

AL

AL(A)

L































37








38

L P

L P

L P

L P

L mL PpW Pu


mL m

NR Pu


L P

W P

AL P

P Pu

L P

P PuS mW PS Pu

W Pu

NR PuS PuW P

pW Pu

L mL mL P

L PuL P

L P

W Pu

Mankin et al 2008b








Mankin et al 2008b






Lemaster et al 1997


Matsuoka et al 1996



Lemaster et al 1997








Mankin et al2000

Mankin et al2000


Mankin et al 2001




Mankin et al2000

Mankin et al2000



Mankin et al 2007

Mankin et al 2009a

Mankin et al 2009a

Mankin et al2000

Mankin et al2000





Mankin et al 2009b

Mankin et al 2009b

Zhang 2003a

Mankin et al2000

Mankin et al2000


Zhang 2003a

p

p

p

pum

p

m

p

p

m

p

m

g

g

p

pu

p

pu

p

g

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

W

L

L

L

W

L

W

Drepanotermes sp










Cyclocephala spp






Phyllophaga spp


Polyphylla spp








Cotinis nitida (L)










c


c



a b see Table 2










Patent

Dunegan 2005


France




EWDPortableEWDLab



















































41
























42







































































43


























44






















Os 0

~CiElaquo 0gt~a

a

-1Sp


50 100




Time (ms)


20

IAC--------------------------Os

0

~CiE 0laquogt~a

a-1

Sp10

NI6raquo

5-c=gt0-U



-- ----------------------------------10middot-----



Frequency (kHz)






iii~ -10QigtQ)

J

E~ -20Q)Q(f)Q)

gt~Qi -300

2 4



6














10 20 30 40

Temperature ( C)


o

I

ZNI

I

1M bull

RD






Mori etal1962




Spangler 1985







p

p

p

p

p

p

p

p

p

p

p

p

ppp

meppp

p

p

m

m

m

p

Prm

Pum

Prp

Pumm

(A)

L

ALNRL

L

A

A

LAAA

A

AAL

ANRL

AALL

L(A)L

A

LLALL

AL

(A)AAALL

L

LLAL

AL

AL(A)

L































37








38

L P

L P

L P

L P

L mL PpW Pu


mL m

NR Pu


L P

W P

AL P

P Pu

L P

P PuS mW PS Pu

W Pu

NR PuS PuW P

pW Pu

L mL mL P

L PuL P

L P

W Pu

Mankin et al 2008b








Mankin et al 2008b






Lemaster et al 1997


Matsuoka et al 1996



Lemaster et al 1997








Mankin et al2000

Mankin et al2000


Mankin et al 2001




Mankin et al2000

Mankin et al2000



Mankin et al 2007

Mankin et al 2009a

Mankin et al 2009a

Mankin et al2000

Mankin et al2000





Mankin et al 2009b

Mankin et al 2009b

Zhang 2003a

Mankin et al2000

Mankin et al2000


Zhang 2003a

p

p

p

pum

p

m

p

p

m

p

m

g

g

p

pu

p

pu

p

g

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

W

L

L

L

W

L

W

Drepanotermes sp










Cyclocephala spp






Phyllophaga spp


Polyphylla spp








Cotinis nitida (L)










c


c



a b see Table 2










Patent

Dunegan 2005


France




EWDPortableEWDLab



















































41
























42







































































43


























44




















Frequency (kHz)






iii~ -10QigtQ)

J

E~ -20Q)Q(f)Q)

gt~Qi -300

2 4



6














10 20 30 40

Temperature ( C)


o

I

ZNI

I

1M bull

RD






Mori etal1962




Spangler 1985







p

p

p

p

p

p

p

p

p

p

p

p

ppp

meppp

p

p

m

m

m

p

Prm

Pum

Prp

Pumm

(A)

L

ALNRL

L

A

A

LAAA

A

AAL

ANRL

AALL

L(A)L

A

LLALL

AL

(A)AAALL

L

LLAL

AL

AL(A)

L































37








38

L P

L P

L P

L P

L mL PpW Pu


mL m

NR Pu


L P

W P

AL P

P Pu

L P

P PuS mW PS Pu

W Pu

NR PuS PuW P

pW Pu

L mL mL P

L PuL P

L P

W Pu

Mankin et al 2008b








Mankin et al 2008b






Lemaster et al 1997


Matsuoka et al 1996



Lemaster et al 1997








Mankin et al2000

Mankin et al2000


Mankin et al 2001




Mankin et al2000

Mankin et al2000



Mankin et al 2007

Mankin et al 2009a

Mankin et al 2009a

Mankin et al2000

Mankin et al2000





Mankin et al 2009b

Mankin et al 2009b

Zhang 2003a

Mankin et al2000

Mankin et al2000


Zhang 2003a

p

p

p

pum

p

m

p

p

m

p

m

g

g

p

pu

p

pu

p

g

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

W

L

L

L

W

L

W

Drepanotermes sp










Cyclocephala spp






Phyllophaga spp


Polyphylla spp








Cotinis nitida (L)










c


c



a b see Table 2










Patent

Dunegan 2005


France




EWDPortableEWDLab



















































41
























42







































































43


























44



























10 20 30 40

Temperature ( C)


o

I

ZNI

I

1M bull

RD






Mori etal1962




Spangler 1985







p

p

p

p

p

p

p

p

p

p

p

p

ppp

meppp

p

p

m

m

m

p

Prm

Pum

Prp

Pumm

(A)

L

ALNRL

L

A

A

LAAA

A

AAL

ANRL

AALL

L(A)L

A

LLALL

AL

(A)AAALL

L

LLAL

AL

AL(A)

L































37








38

L P

L P

L P

L P

L mL PpW Pu


mL m

NR Pu


L P

W P

AL P

P Pu

L P

P PuS mW PS Pu

W Pu

NR PuS PuW P

pW Pu

L mL mL P

L PuL P

L P

W Pu

Mankin et al 2008b








Mankin et al 2008b






Lemaster et al 1997


Matsuoka et al 1996



Lemaster et al 1997








Mankin et al2000

Mankin et al2000


Mankin et al 2001




Mankin et al2000

Mankin et al2000



Mankin et al 2007

Mankin et al 2009a

Mankin et al 2009a

Mankin et al2000

Mankin et al2000





Mankin et al 2009b

Mankin et al 2009b

Zhang 2003a

Mankin et al2000

Mankin et al2000


Zhang 2003a

p

p

p

pum

p

m

p

p

m

p

m

g

g

p

pu

p

pu

p

g

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

W

L

L

L

W

L

W

Drepanotermes sp










Cyclocephala spp






Phyllophaga spp


Polyphylla spp








Cotinis nitida (L)










c


c



a b see Table 2










Patent

Dunegan 2005


France




EWDPortableEWDLab



















































41
























42







































































43


























44





















Mori etal1962




Spangler 1985







p

p

p

p

p

p

p

p

p

p

p

p

ppp

meppp

p

p

m

m

m

p

Prm

Pum

Prp

Pumm

(A)

L

ALNRL

L

A

A

LAAA

A

AAL

ANRL

AALL

L(A)L

A

LLALL

AL

(A)AAALL

L

LLAL

AL

AL(A)

L































37








38

L P

L P

L P

L P

L mL PpW Pu


mL m

NR Pu


L P

W P

AL P

P Pu

L P

P PuS mW PS Pu

W Pu

NR PuS PuW P

pW Pu

L mL mL P

L PuL P

L P

W Pu

Mankin et al 2008b








Mankin et al 2008b






Lemaster et al 1997


Matsuoka et al 1996



Lemaster et al 1997








Mankin et al2000

Mankin et al2000


Mankin et al 2001




Mankin et al2000

Mankin et al2000



Mankin et al 2007

Mankin et al 2009a

Mankin et al 2009a

Mankin et al2000

Mankin et al2000





Mankin et al 2009b

Mankin et al 2009b

Zhang 2003a

Mankin et al2000

Mankin et al2000


Zhang 2003a

p

p

p

pum

p

m

p

p

m

p

m

g

g

p

pu

p

pu

p

g

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

W

L

L

L

W

L

W

Drepanotermes sp










Cyclocephala spp






Phyllophaga spp


Polyphylla spp








Cotinis nitida (L)










c


c



a b see Table 2










Patent

Dunegan 2005


France




EWDPortableEWDLab



















































41
























42







































































43


























44


































37








38

L P

L P

L P

L P

L mL PpW Pu


mL m

NR Pu


L P

W P

AL P

P Pu

L P

P PuS mW PS Pu

W Pu

NR PuS PuW P

pW Pu

L mL mL P

L PuL P

L P

W Pu

Mankin et al 2008b








Mankin et al 2008b






Lemaster et al 1997


Matsuoka et al 1996



Lemaster et al 1997








Mankin et al2000

Mankin et al2000


Mankin et al 2001




Mankin et al2000

Mankin et al2000



Mankin et al 2007

Mankin et al 2009a

Mankin et al 2009a

Mankin et al2000

Mankin et al2000





Mankin et al 2009b

Mankin et al 2009b

Zhang 2003a

Mankin et al2000

Mankin et al2000


Zhang 2003a

p

p

p

pum

p

m

p

p

m

p

m

g

g

p

pu

p

pu

p

g

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

W

L

L

L

W

L

W

Drepanotermes sp










Cyclocephala spp






Phyllophaga spp


Polyphylla spp








Cotinis nitida (L)










c


c



a b see Table 2










Patent

Dunegan 2005


France




EWDPortableEWDLab



















































41
























42







































































43


























44



























38

L P

L P

L P

L P

L mL PpW Pu


mL m

NR Pu


L P

W P

AL P

P Pu

L P

P PuS mW PS Pu

W Pu

NR PuS PuW P

pW Pu

L mL mL P

L PuL P

L P

W Pu

Mankin et al 2008b








Mankin et al 2008b






Lemaster et al 1997


Matsuoka et al 1996



Lemaster et al 1997








Mankin et al2000

Mankin et al2000


Mankin et al 2001




Mankin et al2000

Mankin et al2000



Mankin et al 2007

Mankin et al 2009a

Mankin et al 2009a

Mankin et al2000

Mankin et al2000





Mankin et al 2009b

Mankin et al 2009b

Zhang 2003a

Mankin et al2000

Mankin et al2000


Zhang 2003a

p

p

p

pum

p

m

p

p

m

p

m

g

g

p

pu

p

pu

p

g

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

W

L

L

L

W

L

W

Drepanotermes sp










Cyclocephala spp






Phyllophaga spp


Polyphylla spp








Cotinis nitida (L)










c


c



a b see Table 2










Patent

Dunegan 2005


France




EWDPortableEWDLab



















































41
























42







































































43


























44


























Mankin et al2000

Mankin et al2000


Mankin et al 2001




Mankin et al2000

Mankin et al2000



Mankin et al 2007

Mankin et al 2009a

Mankin et al 2009a

Mankin et al2000

Mankin et al2000





Mankin et al 2009b

Mankin et al 2009b

Zhang 2003a

Mankin et al2000

Mankin et al2000


Zhang 2003a

p

p

p

pum

p

m

p

p

m

p

m

g

g

p

pu

p

pu

p

g

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

L

W

L

L

L

W

L

W

Drepanotermes sp










Cyclocephala spp






Phyllophaga spp


Polyphylla spp








Cotinis nitida (L)










c


c



a b see Table 2










Patent

Dunegan 2005


France




EWDPortableEWDLab



















































41
























42







































































43


























44

























c


c



a b see Table 2










Patent

Dunegan 2005


France




EWDPortableEWDLab



















































41
























42







































































43


























44


































































41
























42







































































43


























44











































42







































































43


























44


































































43


























44













































44




















17?@>75a:c7 3

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