Author(s)

Qiang Xu, ChenXi Shao, HaiJun Liu^*, Yu Xing, Shuang Wei

Affiliation(s)

Guangzhou Customs District Technology Center, Guangzhou 510623, Guangdong, China.

Corresponding Author

HaiJun Liu

ABSTRACT

This study aims to investigate the feasibility of acoustic monitoring for distinguishing species and behavioral activities of wood-boring pests, specifically the bark beetle Hylurgus ligniperda Fabricius and jewel beetles (Buprestidae), to provide technical support for early non-destructive monitoring of cryptic pests. Under controlled conditions (temperature 25 ± 1°C, humidity 45 ± 5%), acoustic signals generated during feeding and crawling activities of the two pests on the bark of Japanese black pine (Pinus thunbergii Parl.) were collected using self-made soundproof equipment and an acoustic acquisition system. Parameters including pulse count, peak amplitude (dB), and peak frequency (Hz) were extracted through acoustic processing software and statistically compared via one-way ANOVA. Results showed significant differences in acoustic parameters between H. ligniperda and Buprestidae during feeding (p < 0.001). H. ligniperda exhibited higher pulse count (27.00 ± 22.31), peak amplitude (-37.34 ± 3.40 dB), and peak frequency (3208.25 ± 783.62 Hz) compared to Buprestidae (pulse count 2.25 ± 2.20; peak amplitude -25.11 ± 4.73 dB; peak frequency 1291.40 ± 1154.58 Hz). Significant differences were also observed between feeding and crawling behaviors of H. ligniperda (p < 0.01): feeding yielded fewer pulses (27.00 ± 22.31) but higher peak frequency (3208.25 ± 783.62 Hz), while crawling produced more pulses (91.75 ± 29.66) and higher amplitude (-31.59 ± 5.58 dB). Acoustic parameters effectively distinguished both species and their behavioral patterns, confirming the potential of acoustic monitoring for early detection of wood-boring pests. Further field validation is required to enhance practical applicability.

KEYWORDS

Acoustic monitoring; Wood-boring pests; Bark beetle; Buprestidae; Acoustic parameters

CITE THIS PAPER

Qiang Xu, ChenXi Shao, HaiJun Liu, Yu Xing, Shuang Wei. Acoustic monitoring of forest pests Hylurgus ligniperda Fabricius and Buprestidae in Pinus thunbergii Parl. World Journal of Agriculture and Forestry Sciences. 2025, 3(1): 36-42. DOI: https://doi.org/10.61784/wjafs3020.

REFERENCES

[1] Lin W, Park S, Jiang Z R, et al. Native or invasive? The red-haired pine bark beetle Hylurgus ligniperda (Fabricius) (Curculionidae: Scolytinae) in East Asia. Forests, 2021, 12(7): 950.

[2] Wu Z, Gao T, Luo Y, et al. Prediction of the global potential geographical distribution of Hylurgus ligniperda using a maximum entropy model. Forest Ecosystems, 2022, 9: 100042.

[3] Choi W I, Park Y S. Monitoring, assessment and management of forest insect pests and diseases. Forests, 2019, 10(10): 865.

[4] Brockerhoff E G, Corley J C, Jactel H, et al. Monitoring and surveillance of forest insects. Forest Entomology and Pathology, 2023, 1: 669-705.

[5] Adedeji A A, Ekramirad N, Rady A, et al. Non-destructive technologies for detecting insect infestation in fruits and vegetables under postharvest conditions: A critical review. Foods, 2020, 9(7): 927.

[6] Anukiruthika T, Jian F, Jayas D S. Movement and behavioral response of stored product insects under stored grain environments—a review. Journal of Stored Products Research, 2021, 90: 101752.

[7] Preti M, Verheggen F, Angeli S. Insect pest monitoring with camera-equipped traps: strengths and limitations. Journal of Pest Science, 2021, 94(2): 203-217.

[8] Sachse S, Krieger J. Olfaction in insects: the primary processes of odor recognition and coding. e-Neuroforum, 2011, 2(3): 49-60.

[9] Mankin R W, Hagstrum D W, Smith M T, et al. Perspective and promise: A century of insect acoustic detection and monitoring. American Entomologist, 2011, 57(1): 30-44.

[10] Fleurat-Lessard F, Tomasini B, Kostine L, et al. Acoustic detection and automatic identification of insect stages activity in grain bulks by noise spectra processing through classification algorithms. 9th International Working Conference on Stored Product Protection, 2006: 476-486.

[11] Eliopoulos P A, Potamitis I, Kontodimas D C, et al. Detection of adult beetles inside the stored wheat mass based on their acoustic emissions. Journal of Economic Entomology, 2015, 108(6): 2808-2814.

[12] Kim T K. Understanding one-way ANOVA using conceptual figures. Korean Journal of Anesthesiology, 2017, 70(1): 22.

[13] Njoroge A W, Affognon H, Mutungi C, et al. Frequency and time pattern differences in acoustic signals produced by Prostephanus truncates (Horn) (Coleoptera: Bostrichidae) and Sitophilus zeamais (Motschulsky) (Coleoptera: Curculionidae) in stored maize. Journal of Stored Products Research, 2016, 69: 31-40.

[14] Banga K S, Kotwaliwale N, Mohapatra D, et al. Bioacoustic detection of Callosobruchus chinensis and Callosobruchus maculatus in bulk stored chickpea (Cicer arietinum) and green gram (Vigna radiata). Food Control, 2019, 104: 278-287.

[15] Banlawe I A P, Cruz J C D. Acoustic sensors for mango pulp weevil (Stretochenus frigidus sp.) detection. 2020 IEEE 10th International Conference on System Engineering and Technology (ICSET), Malaysia, 2020: 191-195.