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. 2022 Nov 17:10:e14143.
doi: 10.7717/peerj.14143. eCollection 2022.

Extending the vibroscape to agroecosystems: investigating the influence of abiotic factors and monitoring insect vibrational signaling

Imane Akassou  1   2 , Livia Zapponi  2 , Vincenzo Verrastro  3 , Marco Ciolli  1   4 , Valerio Mazzoni  2
Affiliations

Extending the vibroscape to agroecosystems: investigating the influence of abiotic factors and monitoring insect vibrational signaling

Imane Akassou et al. PeerJ. .

Abstract

Environmental conditions are crucial factors that influence communication systems and affect animal behavior. Research in the field of biotremology has improved our understanding of insect behavior, ecology, and evolution. However, the interactions between vibrational signaling and environmental factors are less studied, mainly because of technical issues faced in field trials. We therefore developed and tested an approach to investigate the effect of abiotic factors on insect vibrational signaling and explored its implementation as a monitoring tool for insect vibrational signals, using a vineyard as an agroecosystem model. Our results showed a significant decrease in insect signaling activity during unsuitable conditions of high temperature and wind velocity. We determined for the first time, the daily signaling pattern of the two insect pests, Scaphoideus titanus and Halyomorpha halys, in natural conditions. Biotremology techniques could be profitably used to monitor not only the presence of target pest species but also the biodiversity associated with vibrational signaling insects. In particular, the method implemented in this study could be used as a tool to compare the quality of cultivated areas under different management systems.

Keywords: Abiotic factors; Vibrational signals; Vibroscape; Vineyard.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Spectrogram of vibrational signals emitted by: (A) Scaphoideus titanus, (B and C) unidentified insects.
Selections labelled with the same letters were classified as the same VST. Spectrogram was generated in Raven Pro 1.6 with Hanning window of 1,024 samples with 75% overlap.
Figure 2
Figure 2. Variation of the covariates during the day: (A) number of VSTs; (B) temperature and (C) wind velocity. n = 173.
Figure 3
Figure 3. Scatterplots showing the effect of time of the day (A), the number of VSTs (B), temperature (C), and wind velocity (D) on the signaling activity.
Boxplots on the right (A) or above (B, C, D) each scatterplot indicate the variation of the covariates between sessions, lowercase letters indicate significant differences (p < 0.05) after Wilcoxon rank sum test. n = 173.
Figure 4
Figure 4. Signaling activity of H. halys, S. titanus and other, not identified, insects in the vineyard during the day, letters indicate significant differences (p < 0.05) between period of the day after Wilcoxon rank sum test.
Figure 5
Figure 5. Daily pattern of the signaling activity of S. titanus in the vineyard (in green) and in semi-field conditions (in blue) presented as the average time insect spent signaling per hour.
Standard error of the mean is shown with error bars.
See this image and copyright information in PMC

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