Vibrational long-distance communication in the termites<i>Macrotermes natalensis</i>and<i>Odontotermes</i>sp.

Hager, Felix A.; Kirchner, Wolfgang H.

doi:10.1242/jeb.086991

Cited by 52 publications

(47 citation statements)

References 33 publications

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“…However, it is necessary to know that we can not accurately identify the termite acoustic signal delivered by its activity. Interesting facts about the termite behavior is alarm signal for intraspecific communication; Hager and Kirchner [48] reported that the velocity of propagation of individual vibrational signals in the nest substrate was found to be about 130 m s −1 .…”

Section: Discussionmentioning

confidence: 99%

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

et al. 2018

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Abstract:Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood damage is occurring. Based on the phenomenon of acoustic signals generated by termites when attacking wood, we proposed a practical framework to detect termites nondestructively, i.e., by using the acoustic signals extraction. This method has the pros to maintain the quality of wood products and prevent higher termite attacks. In this work, we inserted 220 subterranean termites into a pine wood for feeding activity and monitored its acoustic signal. The two acoustic features (i.e., energy and entropy) derived from the time domain were used for this study's analysis. Furthermore, the support vector machine (SVM) algorithm with different kernel functions (i.e., linear, radial basis function, sigmoid and polynomial) were employed to recognize the termites' acoustic signal. In addition, the area under a receiver operating characteristic curve (AUC) was also adopted to analyze and improve the performance results. Based on the numerical analysis, the SVM with polynomial kernel function achieves the best classification accuracy of 0.9188.

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Section: Discussionmentioning

confidence: 99%

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

et al. 2018

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“…The galleries have physical features that promote vibrational communication and are used as channels for long-distance communication (Hager and Kirchner, 2013). A variety of predators are of potential danger to foraging termites (Longhurst et al, 1978;Huey and Pianka, 1981;Redford, 1984;Kok and Hewitt, 1990).…”

Section: Introductionmentioning

confidence: 99%

“…Each pulse arises from a single tap of the termite's head against the ground. The number of pulses per signal varies between five and 22 (Hager and Kirchner, 2013). Nestmates are extremely sensitive to these vibrations.…”

Section: Introductionmentioning

confidence: 99%

“…In response to drumming nestmates, soldiers also start to drum themselves, thereby amplifying the intensity of the signal, leading to a social long-distance communication through chains of signalreamplifying termites (Röhrig et al, 1999;Hager and Kirchner, 2013). If a termite somewhere in the vast gallery system detects alarm signals, it has to make a decision to walk either in one direction or the other.…”

Section: Introductionmentioning

confidence: 99%

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Directional vibration sensing in the termite Macrotermes natalensis

Hager

Kirchner

2014

Journal of Experimental Biology

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Although several behavioural studies demonstrate the ability of insects to localise the source of vibrations, it is still unclear how insects are able to perceive directional information from vibratory signals on solid substrates, because time-of-arrival and amplitude difference between receptory structures are thought to be too small to be processed by insect nervous systems. The termite Macrotermes natalensis communicates using vibrational drumming signals transmitted along subterranean galleries. When soldiers are attacked by predators, they tend to drum with their heads against the substrate and create a pulsed vibration. Workers respond by a fast retreat into the nest. Soldiers in the vicinity start to drum themselves, leading to an amplification and propagation of the signal. Here we show that M. natalensis makes use of a directional vibration sensing in the context of colony defence. In the field, soldiers are recruited towards the source of the signal. In arena experiments on natural nest material, soldiers are able to localise the source of vibration. Using two movable platforms allowing us to vibrate the legs of the left and right sides of the body with a time delay, we show that the difference in time-of-arrival is the directional cue used for orientation. Delays as short as 0.2 ms are sufficient to be detected. Soldiers show a significant positive tropotaxis to the platform stimulated earlier, demonstrating for the first time perception of time-of-arrival delays and vibrotropotaxis on solid substrates in insects.

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“…Acoustic signals can be produced by simple termite activities; for instance, when termites are feeding and moving in the wood, they generate acoustic signals. Acoustic signals can also be produced by termites as alarm signals through head banging to the wood (Hager & Kirchner, 2013). Farkhanda (2013) proposed that biosensor devices could be a combination of several factors, such as temperature, moisture, movement, feeding and behavior of the termites, in the sensing technology.…”

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confidence: 99%

Discriminant Analysis as a Tool for Detecting the Acoustic Signals of Termites Coptotermes Curvignathus (Isoptera: Rhinotermitidae)

Nanda¹,

Seminar²,

Nandika³

et al. 2018

IJTech

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Various methods for termite detection have been developed, one of which is purely based on their acoustic signals. However, this method has a weakness, as it is difficult to separate the signals generated by the termites from noise in the environment. A combination of the feature extraction of the acoustic signals and a classification model is expected to overcome this weakness. In this investigation, we inserted 220 subterranean termites Coptotermes curvignathus into pine wood for feeding activity and observed their acoustic signals. In addition, three acoustic features (shortterm energy, entropy and zero moment power) were proposed to recognize the termites' acoustic signals. Subsequently, these features were analyzed and combined with discriminant analysis to produce a robust classification model. According to the numerical results, the integrated discriminant analysis and the acoustic feature in our termite detection system has an accuracy of 83.75%.

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Vibrational long-distance communication in the termitesMacrotermes natalensisandOdontotermessp.

Abstract: ) of the signal without decrement over distances of several metres.

Cited by 52 publications

References 33 publications

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

Directional vibration sensing in the termite Macrotermes natalensis

Discriminant Analysis as a Tool for Detecting the Acoustic Signals of Termites Coptotermes Curvignathus (Isoptera: Rhinotermitidae)

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