Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2025 May 26;33(4):1675–1690. doi: 10.1111/1744-7917.70075

Early detection and tracking of wood borers using improved environmental DNA aggregation and TaqMan quantitative PCR approaches in forests

Jingyu Qi 1, Xiaomeng Gao 1, Junke Nan 2, Agbessenou Ayaovi 3,4, Mengqin Zhao 1, Jiangbin Fan 1,✉, Hong He 1,✉
PMCID: PMC13441024  PMID: 40415509

Abstract

The increased number of invasive harmful organisms has exacerbated ecosystem damage. Early monitoring and timely control measures are urgently needed. Although environmental DNA (eDNA) application for monitoring aquatic organisms is well‐established, its use in terrestrial monitoring remains limited, particularly for cryptic wood‐borer pests like Monochamus alternatus, the primary vector of pine wilt disease. We developed and validated quantitative real‐time polymerase chain reaction detection assays using TaqMan probes, targeting conserved regions of the ribosomal DNA (rDNA) internal transcribed spacer 1 and D2−D3 expansion segments of the 28S rDNA. eDNA surveys were conducted using the optimization of sample collection methods, effectively tracking M. alternatus in both laboratory and field environments. Laboratory tests revealed M. alternatus presence could be detected in emergence holes over 35 d, as well as in fresh oviposition scars from 1 to 7 d. The concentration of eDNA significantly decreased with the prolongation of sample storage time. Additionally, 3 novel eDNA aggregation approaches were developed to enhance detection sensitivity of wood‐borer pests in forests: (i) rinsing aggregation, which gathers eDNA from the vicinity of emergence holes; (ii) wet cotton ball dipping, mainly used to collect residual eDNA in the oviposition scars and emergence holes; and (iii) natural predators, where M. alternatus can be identified by collecting ants on the surface of pine trunks. The results demonstrate the effectiveness and high sensitivity of the eDNA approach for M. alternatus monitoring. These findings provide valuable insights for early detection efforts and can serve as a reference for similar eDNA surveys targeting other wood‐boring pests.

Keywords: environmental DNA, invasive pests, monitoring strategy, Monochamus alternatus, TaqMan qPCR, wood‐borer pests

Stepwise illustration of terrestrial monitoring of environmental DNA (eDNA): (I) searching for possible sources of target DNA (emergence holes, oviposition scars, within natural predators); (II) 3 techniques for eDNA aggregation; (III) target DNA enrichment (e.g., concentrate by filtration); (IV) DNA extraction, quantitative polymerase chain reaction or any other method for detecting the presence or quantity of target DNA, followed by data analysis (ee.g., occupancy modeling, spatial analysis).

graphic file with name INS-33-1675-g002.jpg

Introduction

With increasing global trade and human movement, the likelihood of nonnative pests to invade and establish in new regions has significantly risen (Siddiqui et al., 2023). These invasive pests often disrupt ecosystems, damage agricultural and forestry resources, and cause severe economic losses (Liebhold et al., 2017). For instance, insect pests such as Dendroctonus valens LeConte (Coleoptera: Curculionidae) and Bursaphelenchus xylophilus (Steiner & Buhrer, 1934) (Nematoda: Aphelenchoididae) are responsible for inflicting widespread damage to pine forests in various regions (Sun et al., 2013; Zhang et al., 2020). The pinewood nematode (B. xylophilus), a major pathogen responsible for pine wilt disease (PWD), is primarily transmitted by vector insects. Monochamus alternatus Hope (Coleoptera: Cerambycidae) adults are the primary carriers and spreaders of B. xylophilus in Asian pine forests (Chu et al., 2023). The larvae of M. alternatus feed on the phloem and xylem of trees, damaging conductive tissues and causing tree mortality (Chen et al., 2020).

Early detection and accurate identification of M. alternatus are essential steps for implementing prevention and control measures of PWD (Noureldin et al., 2022). Traditional monitoring techniques, such as pheromone trapping, are resource‐intensive, time‐consuming, and often delay early detection. Moreover, monitoring is challenging when pest populations are still low, making it difficult to control the population before it escalates (Rodriguez‐Perez et al., 2013).

Environmental DNA (eDNA) technology represents a promising approach for early detection of insect pests through the identification of DNA traces left in the environment, eliminating the need for direct species capture (Peñarrubia et al., 2016). Although eDNA was first proposed in 1987 for collecting microorganisms from sediments (Ogram et al., 1987), its application on multicellular organisms has only gained widespread recognition in recent decades (Harrison et al., 2019). DNA shed by organisms into the environment—such as hair, feathers, excreta, and other tissues—becomes free DNA that can be detected in water, soil, air, and so forth. (Hänfling et al., 2016). In nature, these DNAs are temporarily deposited in the environment before eventually degrading. With the advantages of high sensitivity, early detection, and the lack of the need to capture species, eDNA technology has been widely used in the study of aquatic ecosystems, including fish, amphibians, mammals, crustaceans, aquatic insects, microorganisms, and plankton (Renshaw et al., 2015; Cantera et al., 2019). The advancement of polymerase chain reaction (PCR)‐based methods, including quantitative PCR (qPCR), digital droplet PCR (ddPCR), and metabarcoding techniques, has made eDNA a powerful tool for biodiversity monitoring (Mauvisseau et al., 2019). Recent studies have demonstrated eDNA's effectiveness for detecting invasive species, offering a promising solution for early pest monitoring (Valentin et al., 2018; Valentin et al., 2020).

However, eDNA collection of forest wood‐borer pests like M. alternatus is more challenging since the larvae remain inside the tree until they emerge as adults. During their development, the larvae shed cells and tissues while gnawing through the wood. This trace DNA left in the environment is crucial for detecting their presence. In this study, we developed and optimized real‐time qPCR assays targeting distinct regions of the ribosomal DNA (rDNA) of M. alternatus, focusing on the internal transcribed spacer 1 (ITS1) and the D2−D3 expansion segments of the 28S rDNA. These regions exhibit low intraspecific variability and high interspecific divergence (Crabtree et al., 1995; Collins & Paskewitz, 1996; Hill et al., 2008). We also incorporated a previously designed TaqMan assay targeting the mitochondrial cytochrome oxidase I (COI) gene (Qi et al., 2024). Furthermore, we developed 3 approaches for collecting, aggregating, and detecting eDNA left by M. alternatus in forest ecosystems, aiming to improve early detection and facilitate timely pest control measures when populations are low. These methods have the potential to improve pest management strategies by providing early warnings of infestation patterns, thereby enabling more targeted interventions.

Materials and methods

Development and optimization of M. alternatus species‐specific assay

To detect M. alternatus using eDNA, we developed a species‐specific TaqMan assay (hereafter, Malt assay). A total of 14 M. alternatus adult samples were collected from Xixiang County, Ningshan County, and Huyi District in Shaanxi Province, China. These samples were used for primer and probe design and to assess the specificity of the Malt assay. Additionally, 20 other insect species, including 4 Monochamus species, were collected for comparative analysis (Table 1).

Table 1.

Species identification list of specimens collected from the field (n), and tested with the Monochamus alternatus assay to ensure specificity

Target (T)/nontarget (NT) samples Order Family Species n Accession number
T Coleoptera Cerambycidae Monochamus alternatus (Hope, 1842) (XX_1) 1 PQ285466
T Monochamus alternatus (XX_2) 1 PQ285467
T Monochamus alternatus (XX_3) 1 PQ285468
T Monochamus alternatus (XX_4) 1 PQ285469
T Monochamus alternatus (XX_5) 1 PQ285470
T Monochamus alternatus (XX_6) 1 PQ285471
T Monochamus alternatus (HY_1) 1 PQ285472
T Monochamus alternatus (HY_2) 1 PQ285473
T Monochamus alternatus (HY_3) 1 PQ285474
T Monochamus alternatus (HY_4) 1 PQ285475
T Monochamus alternatus (NS_1) 1 PQ285476
T Monochamus alternatus (NS_2) 1 PQ285477
T Monochamus alternatus (NS_3) 1 PQ285478
T Monochamus alternatus (NS_4) 1 PQ285479
NT Purpuricenus temminckii (Sinensis White, 1853) 1 PQ285446
NT Olenecamptus cretaceus (Bates, 1873) 1 PQ285447
NT Paraleprodera diophthalma (Pascoe, 1857) 1 PQ285448
NT Paraleprodera itzingeri (Breuning, 1935) 1 PQ285449
NT Spondylis buprestoides (Linnaeus, 1758) 1 PQ285450
NT Uraecha angusta (Pascoe, 1857) 1 PQ285451
NT Acanthocinus griseus (Fabricius, 1793) 1 PQ285452
NT Monochamus saltuarius (Gebler, 1830) 1 PQ285453
NT Monochamus sp. 1 PQ285454
NT Monochamus nigromaculatus (Gressitt, 1942) 2

PQ285455

PQ285456
NT Curculionidae Shirahoshizo flavonotatus (Voss, 1937) 1 PQ285457
NT Pissodes yunnanensis (Langor, 1999) 1 PQ285458
NT Aclees cribratus (Gyllenhyl, 1836) 1 PQ285459
NT Elateridae Cryptalaus larvatus (Schaufuss, 1887) 1 PQ285460
NT Cryptalaus berus (Candèze, 1874) 1 PQ285461
NT Nicrophorus nepalensis (Hope, 1831) 1 PQ285462
NT Hymenoptera Formicidae Pristomyrmex punctatus (Smith, 1860) 1 PQ285463
NT Crematogaster anthracina (Smith, 1857) 2

PQ285464

PQ285465
Open in a new tab

Prior to genomic DNA (gDNA) extraction, all insect samples were thoroughly rinsed with distilled water to remove potential external contaminants. gDNA was then extracted from 3 legs of each specimen using the HotSHOT protocol (Valentin et al., 2020). Samples were lysed in 50 µL of alkaline lysis buffer (20 mmol/L NaOH, 0.2 mmol/L Na2EDTA [disodium ethylenediaminetetraacetic acid], pH = 12.0) at 95 °C for 20 min, and then immediately at 4 °C for 20 min. The ITS1 region of rDNA and the D2−D3 regions of 28S rDNA were selected due to their high interspecies variability and low intraspecies variability at multicopy loci (Gerbi, 1986; Hillis & Dixon, 1991).

We separately amplified the ITS1 and the D2−D3 regions of 28S rDNA using region‐specific primers listed in Table 2. PCR amplification was performed in a 20 µL volume, containing 1 × PCR buffer (10 mmol/L Tris‐HCl, pH 8.3, and 50 mmol/L KCl), 2.5 mmol/L MgCl2, 150 µmol/L of each deoxynucleoside triphosphate, 200 nmol/L of each primer, 1 unit of Amplitaq Gold DNA polymerase (Applied Biosystems, Life Technologies, Carlsbad, CA, USA) and ∼20 ng gDNA. The optimized protocol was performed with an initial denaturation at 94 °C for 3 min to activate the Amplitaq Gold (Applied Biosystems), followed by 35 cycles of denaturation at 94 °C for 45 s, annealing at 53 °C and 55 °C for 60 s for ITS1 and 28S, respectively, and extension at 72 °C for 60 s. Final extension was completed at 72 °C for 2 min. Negative and positive controls were included in each PCR run. All PCRs were conducted on a Veriti 96‐well thermal cycler (Applied Biosystems, Inc., Foster City, CA, USA). Amplified products were visualized on 1% agarose gel electrophoresis (Bio‐Rad, CA, USA), of which positive ones were sent for bidirectional Sanger sequencing. The consensus sequences of ITS1 (GenBank accession nos: PQ285341–PQ285349) and 28S rDNA (D2–D3) (GenBank accession nos: PQ299027–PQ299035) were assessed and aligned with available reference sequences. Since no ITS1 sequence for M. alternatus is currently available in GenBank, we used the ITS1 sequence of Adalia bipunctata Linnaeus (GenBank accession nos: AJ272140.1 and AJ272141.1) as a reference for alignment. For the 28S rDNA D2–D3 region, we aligned M. alternatus with the available 28S sequences in GenBank. Specifically, M. alternatus was aligned with its corresponding 28S reference sequence (GenBank accession no: KF142090.1), while additional 28S sequences from related species (GenBank accession nos: KF142077.1, KF142080.1, KF142082.1, KF142096.1, KF142101.1, KF142103.1, KF142126.1, KF142128.1, KF142132.1) were included to provide broader phylogenetic context.

Table 2.

Primer pairs and probes used in quantitative real‐time polymerase chain reaction

Gene Primer Sequence (5′−3′) Length
ITS1 ITS_F ACGACGCGCGACGCACGT 154 bp
ITS_R GTCGCATCGTCCCCGTAG
ITS_P CGTTACGAAATCCG
28S 28S_F ACCTGGTGCCGGTCTCGT 251 bp
28S_R CGGAGCGACCGTACGGAAT
28S_P TTCGAGAACACCG
Open in a new tab

We designed the Malt assay by aligning the ITS1 and 28S sequences of M. alternatus with those available in the National Center for Biotechnology Information (NCBI) to identify highly variable regions (hereafter, Malt_ITS1 assay; Malt_28S assay). These regions were input into Primer Express version 3 (Applied Biosystems) for primer and probe design, following strict criteria to optimize melting temperature and avoid hairpin structures and dimers (Table 3).

Table 3.

Universal primer sets utilized throughout the project

Gene Primers Sequence (5′−3′) °C Reference
ITS1 BD1 GTCGTAACAAGGTTTCCGTA 53 von der Schulenberg et al., 2001
4S TCTAGATGCGTTCGAAGTGTCGATG
COI LCO1490 GGTCAACAAATCATAAAGATATTGG 54 Folmer et al., 1994
HCO2198 TAAACTTCAGGGTGACCAAAAAATCA
28S 28S rDNA D2_F GAGTTCAAGAGTACGTGAAACCG 55 Gillespie et al., 2003
28S rDNA D2_R CCTTGGTCCGTGTTTCAAGAC
28S rDNA D3_F GGACCCGTCTTGAAACAC 55 Raupach et al., 2010
28S rDNA D3_R GCATAGTTCACCATCTTTC
Open in a new tab

To determine the assay's limit of detection (LoD), we prepared a 1 : 10 serial dilution of M. alternatus gDNA. To assess the potential effects of nontarget DNA on qPCR performance, we prepared mixed DNA samples with M. alternatus to nontarget DNA ratios of 0 : 100, 25 : 75, 50 : 50, 75 : 25, and 100 : 0. The nontarget DNA was derived from Monochamus sparsutus. Each dilution and mixed sample were analyzed in triplicate using qPCR. The amplification curves and cycle threshold (Ct) values were recorded and compared with the pure M. alternatus DNA controls. Each reaction contained 10 ng template DNA, 10 µL 2 × T5 Fast qPCR Mix (Tsingke Biosystems, Wuhan, China), 400 nmol/L of each primer, 200 nmol/L of TaqMan probe and ultrapure water to achieve a total reaction volume of 20 µL. All reactions were performed on an ABI 7500 real‐time PCR instrument. The optimized reaction protocol was 2 steps of qPCR that consist of an initial denaturation step for 2 min at 95 °C, followed by 40 cycles of denaturing for 15 s and annealing and extension at 60 °C for 30 s.

The specificity of the Malt assays was evaluated using NCBI Primer‐BLAST. The assays were tested on all collected insect samples, which were initially identified based on morphological characteristics, including key taxonomic features that distinguish M. alternatus from other Monochamus species. To ensure that the specificity of the Malt assays in qPCR was not influenced by low DNA concentrations from nontarget species, the barcode region of the mitochondrial COI gene was amplified and sequenced using the universal primers LCO1490 and HCO2198 (Folmer et al., 1994). The resulting COI sequences were blasted against the GenBank database and Barcode of Life Database (BOLD) to confirm species identity. M. alternatus was specifically identified based on sequence similarity to previously identified individuals of the species, ensuring no ambiguity in species assignment.

To further confirm the specificity of the primers and probes designed for the Malt_ITS1 and Malt_28S assays, a GenBank BLAST search was performed using the Nucleotide database. The primers and probes for M. alternatus were used as query sequences in the BLAST search. The aim was to identify any potential cross‐reactivity with similar sequences from other species, which could lead to false positives. No hits were found for either the Malt_ITS1 or Malt_28S primers and probes, indicating that these sequences are unique to M. alternatus and do not match any other available sequences in the GenBank database.

Stability and shelf time of M. alternatus eDNA in the laboratory

M. alternatus were reared at the insect breeding room at the College of Forestry, Northwest A&F University (Yangling, China) under controlled conditions (25 °C, 70% humidity, and 12 h light : 12 h dark cycle). Each larva was reared individually in a 10 mL centrifuge tube containing artificial diet, and its developmental stages were recorded. Upon adult emergence, M. alternatus were placed individually in breeding boxes (9 cm × 9 cm × 12 cm) containing pine branches (Pinus tabuliformis). The pine branches used for rearing M. alternatus adults were selected as a natural food source, providing essential nutrients for growth, maturation, and reproduction. Fresh pine branches were replaced every 2 d to ensure a continuous supply of healthy material (Nan et al., 2023). The branches were first immersed in 70% ethanol for 1–2 min to eliminate potential contaminants, followed by rinsing with distilled water to remove any residual alcohol. After drying the surface moisture on sterilized newspapers, the branches were transferred to a sterilized breeding box using sterile tweezers. Pine branches were collected from the Northwest A&F University campus (Fig. S1).

Six experimental groups and a control group (without M. alternatus) were set up. Each experimental group consisted of a pair of healthy male and female adults that emerged on the same day. Adults were placed in a 100 L bucket containing 3 sterilized pine sections for egg laying and fresh pine branches for feeding. The pine sections, each 1 m long with an average diameter of 15 cm, were cut from a region of the Qinling Mountains not infected with B. xylophilus. Pine sections with evidence of M. alternatus presence, indicated by emergence holes or oviposition scars, were discarded. Fresh pine branches were harvested every 2 d from the Northwest A&F University campus. Both pine sections and branches were sterilized (as described above for branches) prior to use to prevent external contamination. To remove potential surface contaminants and external DNA, the pine sections and branches used for eDNA sampling were treated in the same way to ensure consistency in the sterilization procedure. After sterilization, they were transferred to the bucket for further processing. The bucket was covered with an iron screen to prevent M. alternatus from escaping. Oviposition scars on the pine sections were recorded daily. The pine sections with oviposition scars were stored at room temperature, and the eDNA concentration in these areas was measured daily. The emergence time of M. alternatus was also recorded and sections with emergence holes were stored at room temperature. eDNA concentration in the emergence holes was measured on the 1st, 7th, 14th, 21st, 28th, and 35th d. eDNA samples were collected by rinsing oviposition scars and emergence holes using a 20 mL syringe filled with sterile distilled water. Specifically, 10 mL of water was slowly injected into each site to ensure thorough contact with the inner surface, and the process was repeated once. The liquid was then absorbed using sterile cotton balls, which were transferred into a 50 mL centrifuge tube containing 20 mL of deionized water for further processing.

The centrifuge tube was vigorously shaken until the eDNA adsorbed by the cotton ball was fully dissolved in deionized water. Water on the cotton ball was squeezed out using sterile tweezers and discarded. Collected water samples were then filtered through 10 µm polycarbonate (PCTE) membranes using a vacuum filter, within 24 h of collection. All equipment used during each step was disinfected or sterilized before and after filtering each sample to avoid cross‐contamination. A blank control was included in each test to ensure no external contamination. Following DNA collection, a small piece (approximately 1 mm2) from the center of the filter membrane was cut using flame‐sterilized scissors and used for DNA extraction following the HotSHOT method (Valentin et al., 2020). The samples were lysed in 50 µL of alkaline lysis buffer (20 mmol/L NaOH, 0.2 mmol/L Na2EDTA, pH = 12.0) at 95 °C for 20 min, and then immediately at 4 °C for 20 min. Subsequently, an equal volume (50 µL) of neutralization buffer (40 mmol/L tris‐HCl, pH = 5.0) was added and mixed thoroughly by vortexing. All DNA extracts were stored at −20 °C. Primers and probes targeting regions with higher sensitivity for amplification via qPCR were selected.

Designing the 3 eDNA field collection and aggregation methods

M. alternatus lives in the trunk throughout its egg, larval, and pupal stages. As adults, they leave emergence holes, feeding scars, and oviposition scars on the host tree trunk (Fig. S2). eDNA from laboratory‐reared M. alternatus was successfully detected from oviposition scars and emergence holes using qPCR, prompting the design of 3 field collection methods for eDNA from the forest. To provide a clear overview of the experimental design and procedures, the above content is illustrated in a workflow diagram (Fig. 1).

Fig. 1.

Fig. 1

Open in a new tab

Experimental workflow diagram.

Experimental sites were chosen in Xixiang County and Ningshan County, located at the southern foothills of the Qinling Mountains, and in Huyi District at the northern foothills (Fig. 2). Xixiang and Ningshan Counties are epidemic areas of PWD. In each area,3 regions in pine forests were selected based on varying population densities of M. alternatus: high population density (more than 50 individuals captured by traps in 15 d during the adult emergence period), low population density (less than 10 individuals captured in 15 d during the adult emergence period), and unknown population density (no traps set). These forest density classifications were based on the guidelines from the National Forestry and Grassland Administration and the Shaanxi Provincial Forestry Bureau.

Fig. 2.

Fig. 2

Open in a new tab

Map of the experimental sites in this study. XX denotes Xixiang County, NS denotes Ningshan County, HY denotes Huyi District, and the numbers denote distinct forest areas.

Three distinct methods for eDNA collection were designed based on varying population densities of M. alternatus consisting of: (a) rinsing aggregation—eDNA was collected from emergence holes; (b) wet cotton ball dipping—this method was primarily used to collect DNA from emergence holes and around oviposition scars; (c) natural predator—ants, which prey on M. alternatus larvae, were collected from tree trunks (Fig. 3).

Fig. 3.

Fig. 3

Open in a new tab

Stepwise illustration of terrestrial monitoring of environmental DNA (eDNA): (I) searching for possible sources of target DNA (emergence holes, oviposition scars, within natural predators); (II) 3 techniques for eDNA aggregation; (III) target DNA enrichment (e.g., concentrate by filtration); (IV) DNA extraction, quantitative real‐time polymerase chain reaction (qPCR) or any other method for detecting the presence or quantity of target DNA, followed by data analysis (e.g., occupancy modeling, spatial analysis).

Rinsing aggregation was effective in removing eDNA from emergence holes in laboratory experiments. For this method, we selected high‐density forest land for the experiments. The specific sites selected were: XX‐ Forest 1, NS‐ Forest 1, and HY‐ Forest 1. These experiments were conducted during the peak emergence period of M. alternatus.

A 30‐min visual survey was conducted within a radius of approximately 10 m at each sampling site before eDNA surveys were conducted at each confirmed site. During this survey, 3 experts identified trunks with oviposition scars or emergence holes. For each confirmed site, eDNA samples were collected by rinsing the emergence holes separately using a sterile syringe filled with 20 mL of distilled water. The water was injected at a controlled pressure to ensure thorough contact with the inner surfaces of the holes (Fig. 3; Fig. S3A). Then, the rinsed water was collected into separate sterilized containers by using the force generated by a water jet. These collected water samples were then processed according to the methods described earlier. All extraction and amplification procedures were performed in sterilized facilities, and operators wore gloves to avoid cross‐contamination.

The wet cotton ball dipping experiment was conducted in low‐density forest land, with sampling sites at XX‐ Forest 2, NS‐ Forest 2, and HY‐ Forest 2. The method draws from forensic identification methods, utilizing a sterile swab to transfer material from 1 matrix to another (Verdon et al., 2014). Absorbent cotton balls soaked in distilled water were placed into 2 mL centrifuge tubes and sterilized by high‐pressure steam (Zhang et al., 2024). All tweezers and instruments were UV sterilized and placed in sterile ziplock bags. Sterile gloves and tweezers were used to repeatedly insert cotton balls in the centrifuge tube into the emergence holes in trees identified in the forest (Fig. 3; Fig. S3B). Cotton balls were then recovered individually by placing them in 50 mL centrifuge tubes containing 20 mL of deionized water. The processing and filtration steps were performed according to the methods described earlier. A negative control was prepared by placing the cotton balls directly into a 50 mL centrifuge tube filled with 20 mL of deionized water, which was then aggregated as described to verify the absence of contamination.

The natural predator survey was conducted at unknown forest stations (no traps were hung), with sampling sites at XX‐ Forest 3, NS‐ Forest 3, and HY‐ Forest 3. Ants, which are natural predators of many wood‐borer pests (Anjos et al., 2022), were captured from pine trees to examine DNA aggregation. Clean latex gloves and tweezers were used to transfer ants of the same species from the bark into a single centrifuge tube (Fig. 3; Fig. S3C). Only Crematogaster anthracina Smith and Pristomyrmex punctatus Smith were collected. Ten ants of the same species were pooled together. All equipment was thoroughly cleaned and disinfected before and after use, and each collection was repeated 3 times at each sampling point. For the negative control, ants were collected from residential areas located 100 km away from pine forests, where M. alternatus cannot be found due to the lack of suitable habitats. Additionally, baited traps were deployed and monitored for 30 d before sampling, and no M. alternatus specimens were captured, further confirming the reliability of the negative controls. The samples were stored at −20 °C before DNA extraction using the Biospin Insect Genomic DNA Extraction Kit (Bioer Technology Co., Ltd., Hangzhou, China).

To investigate the effect of ant quantity on the detection of M. alternatus DNA, we conducted qPCR analyses on pooled samples containing varying numbers of ants. Specifically, we compared the detection efficacy using a single ant versus mixed samples with different ant quantities. Initial trials with varying ant numbers yielded inconsistent qPCR amplification, prompting refinements in the experimental design. To assess whether DNA extraction efficiency contributed to these inconsistencies, DNA was extracted from 6 individual ants (3 C. anthracina and 3 P. punctatus). This sample size was selected to ensure balanced representation of both species while maintaining methodological feasibility. A moderate number of individuals was used to obtain sufficient DNA for reliable qPCR detection while minimizing potential PCR inhibition from excessive biological material. qPCR was performed using previously designed specific primers and probes on a LightCycler 480 instrument (Roche, USA).

Data analyses

The DNA quantity data were converted to the logarithm of DNA copy number, and the mean values for each set of qPCR technical replicates were calculated. The correlation between sample copy numbers (within and between groups) and sample preservation time was evaluated using 2 iterations of general linear models (GLM). Sample preservation time was treated as a categorical variable to assess variability within and between sample groups. This analysis was repeated with sample preservation time treated as a continuous variable to evaluate the relationship between mean eDNA abundance and sample preservation time. All statistical analyses were performed using the stats package in R (version 4.4.1).

Results

Malt assay design and performance

We designed primers and probes targeting the ITS1 region (Fig. 4A) and the D2−D3 regions of 28S rDNA (Fig. 4B). The primers and probes for each region were designed separately and optimized for qPCR amplification. Both probes were labeled with a carboxyfluorescein (FAM) reporter dye at the 5′‐terminal nucleotide and a minor groove binder (MGB) quencher dye at the 3′‐terminal nucleotide. Amplification verification was performed on all samples (including 14 M. alternatus individuals and 20 common forest insect species) using COI primers, confirming the presence of amplifiable DNA in these species. This was crucial to ensure that the observed specificity of the Malt assays in qPCR was not due to low DNA concentrations in nontarget species. When the Malt_ITS1 and Malt_28S assays were applied to all samples using qPCR, only the M. alternatus samples showed positive amplification, with no amplification observed for any of the nontarget species (Fig. S4, S5). This confirmed the specificity of both assays. To evaluate sensitivity, serial dilutions of M. alternatus DNA starting from a dose of 7.232 ng was performed. The Ct values generated from the 3 replicates of the tested dilutions exhibited nearly identical results, with a variation of less than 0.05 fractions of a cycle (Fig. 5). We prepared mixed DNA samples with varying ratios of M. alternatus and nontarget samples (M. sparsutus). As expected, the Ct values increased with higher proportions of nontarget DNA (Fig. S6). In the Malt_28S assay, the Ct values remained relatively stable, ranging from 15.633 (100% M. alternatus) to 16.532 (25% M. alternatus), indicating minimal impact from nontarget DNA contamination. Even when nontarget DNA constituted 75% of the sample, the amplification remained robust. In contrast, the Malt_ITS1 assay showed a progressive increase in Ct values as the proportion of M. alternatus DNA decreased. The Ct values rose from 26.031 (100% M. alternatus) to 28.977 (25% M. alternatus), suggesting that Malt_ITS1 has lower sensitivity and is more affected by nontarget DNA presence. No amplification was observed in the M. sparsutus sample (0% M. alternatus), confirming the high specificity of both assays. These findings indicate that the Malt_28S assay is more robust in detecting M. alternatus DNA even in the presence of nontarget DNA, making it a more suitable marker for applications such as eDNA monitoring.

Fig. 4.

Fig. 4

Open in a new tab

Partial polymorphic sites within Monochamus alternatus internal transcribed spacer 1 (ITS1) (A) and the D2−D3 regions of 28S ribosomal DNA (rDNA) (B), compared with sequences obtained from GenBank. Position numbers correspond to the base pair position along the M. alternatus ITS1 sequences (accession nos: PQ285341−PQ285349) (A), and the D2−D3 regions of 28S rDNA sequences (accession nos: PQ299027−PQ299035) (B). The quantitative real‐time polymerase chain reaction (qPCR) primers and probes are indicated by light gray and dark gray shading, respectively. Colons indicate deletions and bold bases represent nucleotides that differ from the target sequence.

Fig. 5.

Fig. 5

Open in a new tab

Quantitative real‐time polymerase chain reaction (qPCR) standard curves and sensitivity of Malt_ITS1 (internal transcribed spacer 1) and Malt_28S assays. (A) The standard curve for the Malt_28S assay was y = −3.592 x + 44.182 (R 2 = 1.000). (B) The standard curve for the Malt_ITS1 assay was y = −4.304 x + 54.149 (R 2 = 0.999). The detection limits of the Malt_ITS1 assay and Malt_28S assay were 0.72 ng and 0.72 fg, respectively.

Relationship between eDNA concentration and sample preservation time

The analysis of emergence hole samples obtained under laboratory conditions revealed that the highest eDNA copy number was detected in samples collected immediately after emergence (mean: 2.072 × 106). However, the average copy number of the sample dropped to 9.921 × 105 after 7 d of emergence, 2.811 × 105 after 14 d, 1.198 × 105 after 21 d, 3.880 × 104 after 28 d, and the lowest copy number (1.163 × 104) was detected in samples after 35 d of emergence. Statistical analysis (F‐test, F = 46.54, df = 1, P < 0.01) revealed a significant variation in eDNA copy numbers between the 1st and 7th d, as well as between the initial and subsequent time points (Fig. 6A). Moreover, a continuous variable analysis showed a significant decline in eDNA concentration over time, with a decrease of 5.95 × 105 copies per 7‐d period (slope = −0.38, standard error [SE] = 0.06; P < 0.01).

Fig. 6.

Fig. 6

Open in a new tab

Sample deviation from global mean DNA copy number, categorized by sample preservation duration. (A) Emergence holes; (B) oviposition scars. The red dots indicate the average environmental DNA (eDNA) abundance in each group of samples, while the black dots depict the mean (± 1 standard error) deviation from the global mean. Statistically significant differences (Tukey's Honestly Significant Difference test, P < 0.01) are indicated by different letters (a, b).

Similarly, the examination of oviposition scar samples showed a high initial eDNA copy number (mean: 3.02 × 105), which decreased progressively over time. After 7 d, the lowest copy number was detected (1.49 × 103). Statistically significant differences in eDNA concentrations were observed between fresh oviposition scars and samples collected at later time points (F‐test, F = 34.29, df = 1, P < 0.01) (Fig. 6B). The preservation time of oviposition scar samples also showed a significant negative relationship with eDNA copy number, with a decrease of 4.97 × 104 copies/d (slope = −0.43, SE = 0.07; P < 0.01). The Ct values greater than 32 were considered undetectable.

eDNA field collection, aggregation, and detection

We conducted eDNA surveys across 9 forest sites in 3 regions, successfully detecting M. alternatus in 8 of these sites (Table 4). Visual observations of XX‐ Forest 1, XX‐ Forest 2, and HY‐ Forest 2 revealed the presence of M. alternatus emergence holes. However, 1 sampling site in each forest yielded negative eDNA results. The visual survey results at the remaining sampling locations were consistent with the eDNA survey results.

Table 4.

Survey sites for Monochamus alternatus at different population densities designated by China Forestry and Grassland Administration, Shaanxi Provincial Forestry Bureau, and Ningshan County Forestry Bureau

Forest lands Method Density No. of locations Real‐time polymerase chain reaction
HY‐ Forest 1 Rinsing aggregation High 3 Y/Y/Y
NS‐ Forest 1 High 5 Y/Y/Y/Y/Y
XX‐ Forest 1 High 5 Y/Y/Y/Y/N
HY‐ Forest 2 Wet cotton ball dipping Low 2 Y/N
NS‐ Forest 2 Low 2 Y/Y
XX‐ Forest 2 Low 3 Y/N/N
HY‐ Forest 3 Natural predator Unknown 2 N/N
XX‐ Forest 3 Unknown 2 Y/N
NS‐ Forest 3 Unknown 2 Y/N
Open in a new tab

Note: XX denotes Xixiang County, NS denotes Ningshan County, and HY denotes Huyi District. Density was determined by the number of M. alternatus individuals captured in traps, with high > 50 individuals, low < 10 individuals, and unknown means no trap. The presence of M. alternatus at the sampling site was recorded as Y; otherwise, it was recorded as N.

To further investigate the presence of M. alternatus in areas with unknown population densities, we conducted natural predator experiments at these sites. Notably, eDNA samples yielded positive results in forested areas where M. alternatus had not been trapped. One year prior to our eDNA survey, both XX‐ Forest 3 and NS‐ Forest 3 were healthy pine forests without any traps installed, and visual surveys at these sites did not detect M. alternatus. However, one sample from each forest yielded a positive qPCR result in natural predator trials. M. alternatus was not detected in the extraction results of any of the 6 individually processed ant samples. All experimental controls (i.e., field test controls, extracted DNA controls, and qPCR controls) yielded negative results, indicating that no cross‐contamination occurred during the experiments. The combination of multiple detection methods reinforces the reliability of our findings and supports the validity of the positive eDNA detections.

Discussion

We designed 2 qPCR assays, the Malt_28S assay and Malt_ITS1 assay, targeting the ITS1 region and the D2−D3 region of 28S rDNA, respectively. Both assays exhibited high specificity for M. alternatus (Figs. S3, S4). However, the Malt_28S assay exhibited higher sensitivity with a detection limit of 0.72 fg, compared to 0.72 ng of the Malt_ITS1 assay and 0.64 pg of the COI‐based TaqMan assay reported by Qi et al. (2024). Additionally, in mixed DNA samples with varying proportions of M. alternatus and nontarget DNA (M. sparsutus), the Malt_28S assay maintained reliable amplification even with up to 75% of nontarget DNA (Ct = 16.532), further supporting its high sensitivity and robustness. In contrast, the Malt_ITS1 assay exhibited a progressive increase in Ct values as the proportion of M. alternatus DNA decreased, suggesting lower sensitivity and more susceptibility to nontarget DNA contamination. This aligns with the findings of Shuvo et al. (2025), who emphasized that primer selection is a key factor for high taxonomic resolution and successful eDNA detection. As a result, the Malt_28S assay was selected for subsequent eDNA detection experiments in the field.

eDNA preservation and degradation

Laboratory tests revealed a progressive decline in eDNA over time, with distinct degradation patterns observed in different sample types. In samples collected from emergence holes, eDNA copy number significantly declined within 35 d postemergence (P < 0.01) (Fig. 6A). Similarly, eDNA from oviposition scars exhibited a more rapid decline, with copy numbers significantly decreasing within just 7 d (Fig. 6B). These findings align with previous studies showing that eDNA degradation is influenced by environmental factors such as temperature, humidity, and UV radiation (Barnes & Turner, 2016; Davis et al., 2018).

Compared to field conditions, laboratory settings limit eDNA exposure to UV radiation and precipitation, slowing degradation. Collins et al. (2018) also highlighted how eDNA degradation rates vary with season and location. Our findings show that eDNA degradation differs between microhabitats, with emergence holes retaining detectable eDNA longer than oviposition scars. This highlights the necessity of considering both microhabitat conditions and sampling timing in eDNA‐based pest monitoring strategies. To enhance detection accuracy, field sampling should be conducted during the peak emergence and oviposition periods of M. alternatus and other wood‐borer pests. Targeting microhabitats that extend eDNA persistence can further enhance detection efficiency. Integrating these considerations into eDNA sampling protocols can optimize pest monitoring efficiency in forest ecosystems.

eDNA in field surveys

Our field‐based eDNA surveys further validated the practical application of the Malt_28S assay for detecting M. alternatus in forest environments. The positive eDNA results from emergence holes, combined with the consistent detection of M. alternatus DNA in samples collected from multiple forest sites, demonstrated the effectiveness of this method (Table 4). These results provide preliminary evidence that eDNA can detect M. alternatus in the locations that cannot be visually examined. For example, the eDNA detection at XX‐ Forest 3 and NS‐ Forest 3, where no traps were installed and M. alternatus was not detected by visual inspection, highlights the ability of eDNA to detect the pest. This aligns with Valentin et al. (2021) who demonstrated that terrestrial eDNA surveys could detect the invasive spotted lanternfly (Lycorma delicatula) in forested environments, even before visual surveys confirmed its presence. Their study highlights the increased sensitivity of eDNA for forest pest detection, supporting our findings that eDNA can reveal M. alternatus presence in locations where traditional survey methods fail.

The natural predator experiment showed positive qPCR results for samples from XX‐ Forest 3 and NS‐ Forest 3. Also, retesting confirmed that our findings were not false positives. These data demonstrated the effectiveness of eDNA surveys when compared with the labor and material costs of visual and trap surveys. At the same time, early detection through eDNA offers significant financial savings for forest resource managers by enabling more targeted preventative and control measures.

However, the results also reveal some limitations of the method. Although eDNA successfully detected M. alternatus DNA in most locations, negative results were obtained at locations with emergence holes (XX‐ Forest 1, XX‐ Forest 2, and HY‐ Forest 2). Further evaluation revealed that these sampling sites were all located in areas with direct sunlight exposure, which may accelerate eDNA degradation due to elevated temperatures and solar radiation (Qu et al., 2020). This aligns with McCartin et al. (2022), who emphasize the importance of considering environmental conditions when designing eDNA surveys. Similarly, Valentin et al. (2021) found that solar radiation accelerates the degradation of extracellular eDNA on leaf surfaces, suggesting that eDNA degradation rates may vary depending on environmental exposure. In addition to environmental factors, biological processes such as microbial decomposition and tree wound healing may also affect eDNA persistence in the field. As emphasized by Winiger et al. (2022), eDNA metabarcoding could not reliably detect saproxylic beetles in wood samples due to rapid DNA degradation, highlighting the potential influence of biological degradation in terrestrial environments. These findings suggest that both abiotic and biotic factors may contribute to eDNA degradation, necessitating further investigation into their relative effects and interactions. Given these challenges, optimizing eDNA sampling strategies is crucial. Factors such as sampling time, frequency, and site selection should be carefully considered to maximize eDNA integrity and detection reliability in field surveys.

Ecological basis of eDNA monitoring for wood‐borer pests

Our technique was inspired by the inherent characteristics of wood‐borer pests and holds potential as an effective tool for their early detection. Wood‐borer larvae primarily damage the xylem and phloem of trees, feeding, excreting, and molting within the borer tunnels, where they remain until adulthood (Wang et al., 2022). These tunnels provide some protection to the DNA debris of the pests, thereby increasing the chances of successful detection. Intriguingly, we discovered that C. anthracina and P. punctatus frequently forage in the emergence holes of other insects like bark beetles and weevils on pine trees. These omnivorous ants also feed on M. alternatus larvae within the tunnels (Zhang & Yang, 2006), potentially spreading pest DNA. In addition to ants, the natural predators of M. alternatus include Dastarcus helophoroides Faimaire (Coleoptera: Bothrideridae) (Zhang et al., 2016), Sclerodermus harmandi Bursson (Hymenoptera: Bethylidae) (Li & Sun, 2011), and various parasitoid wasps (Zhou et al., 2015). However, further research is needed to determine the extent to which ant colonies prey on the eggs, larvae, and pupae of M. alternatus. In the future, the guano of natural predator birds of M. alternatus such as woodpeckers, could also serve as a valuable source of eDNA for monitoring wood‐borer pests. Additionally, M. alternatus has a high dispersal capacity, with adults flying up to 3.2 km and females laying 100s of eggs throughout their lifetime (Togashi, 1981). During unfavorable environmental conditions, the species may spread further. This prolific dispersal and oviposition capacity increase the likelihood of detecting M. alternatus DNA over a wide area. Although eDNA technique provides a powerful tool for detecting M. alternatus in forest environments, it should be considered a complementary approach rather than a complete substitute for traditional methods such as visual surveys and trapping.

Implications for forest pest management

Our study demonstrates the potential of eDNA for the early detection of forest pests like M. alternatus. By providing early detection capabilities, eDNA can assist forest managers in identifying pest populations before they cause significant damage, potentially reducing management costs (Table 5). Similarly, Kirtane et al. (2022) demonstrated the efficacy of eDNA analysis for detecting invasive forest pests, such as Adelges tsugae Annand, and their biological control predators. They found that eDNA methods had significantly higher positive detection rates compared to conventional methods, highlighting eDNA's potential as a more sensitive tool for early pest detection.

Table 5.

Cost comparison of different monitoring methods for Monochamus alternatus

Method Cost details Estimated time for results
Trap Trap cost (approx.)/per trap $20 5−7 d
Scope of application/per trap 30 acres
Bait cost (approx.)/month $10
Bait purchase frequency Monthly
Labor cost/per trap $5
Overall cost for a year (April−September)/per trap $210
Environmental DNA Reagent cost/100 samples $12 4−5 h
Testing costs/100 samples $5
Overall cost/100 samples $17
Open in a new tab

Additionally, the flexibility of the eDNA collection methods—rinsing, wet cotton ball dipping, and natural predator methods—allows for targeted sampling based on specific indicators of pest presence, further increasing the efficiency of monitoring efforts. Future research should continue to refine eDNA collection techniques, including optimizing sample preservation and exploring the impact of different environmental conditions on eDNA degradation. Moreover, additional studies should investigate the dynamics of eDNA release from M. alternatus and its interaction with other forest organisms, such as ants and natural predators, to further enhance our understanding of eDNA in forest ecosystems.

Disclosure

The authors declare they have no conflicts of interest with respect to the contents of this article.

Supporting information

Fig. S1 Monochamus alternatus rearing in the laboratory.

Fig. S2 Oviposition scars (A) and emergence holes (B) of Monochamus alternatus in the forest.

Fig. S3 The 3 new methods of environmental DNA (eDNA) aggregation introduced in this study: (A) Rinsing aggregation; (B) wet cotton ball dipping; (C) natural predators.

Fig. S4 Specificity of Malt_28S assay.

Fig. S5 Specificity of Malt_ITS1 assay.

Fig. S6 Cycle threshold (Ct) values of the Malt_28S (A) and Malt_ITS1 (B) assays for mixed DNA samples with different proportions of Monochamus alternatus and Monochamus sparsutus.

INS-33-1675-s001.docx (2.7MB, docx)

Acknowledgments

This work was supported by the Forestry Science and Technology Innovation Project of Shaanxi Province (No. SXLK2021‐0101), the Special Fund of Shaanxi Provincial Forest Pest Control and Quarantine Station (No. 20191217000008, 20191217000009), and China Postdoctoral Science Foundation Funded Project (No. 2022M712602).

Contributor Information

Jiangbin Fan, Email: fanjiangbin1898@nwafu.edu.cn.

Hong He, Email: hehong@nwsuaf.edu.cn.

Data Availability Statement

The sequencing data were uploaded to GenBank (accession numbers: COI: PQ285446−PQ285479; ITS1: PQ285341−PQ285349; 28S_D2−D3: PQ299027−PQ299035). Additional data files were submitted to Open Science Framework (https://osf.io/dashboard) under https://doi.org/10.17605/OSF.IO/BE47F

References

  1. Anjos, D.V.V. , Tena, A. , Viana‐Junior, A.B. , Carvalho, R.L.L. , Torezan‐Silingardi, H. , Del‐Claro, K. et al. (2022) The effects of ants on pest control: a meta‐analysis. Proceedings of the Royal Society B‐Biological Sciences, 289, 20221316. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Barnes, M.A. and Turner, C.R. (2016) The ecology of environmental DNA and implications for conservation genetics. Conservation Genetics, 17, 1–17. [Google Scholar]
  3. Cantera, I. , Cilleros, K. , Valentini, A. , Cerdan, A. , Dejean, T. , Iribar, A. et al. (2019) Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers. Scientific Reports, 9, 3085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Chen, H.J. , Hao, D.J. , Wei, Z.Q. , Wang, L.J. and Lin, T. (2020) Bacterial communities associated with the pine wilt disease insect vector Monochamus alternatus (Coleoptera: Cerambycidae) during the larvae and pupae stages. Insects, 11, 376. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chu, X. , Jiang, D. , Yu, L. , Li, M. , Wu, S. , Zhang, F. et al. (2023) Heterologous expression and bioactivity determination of Monochamus alternatus antibacterial peptide gene in Komagataella phaffii (Pichia pastoris). International Journal of Molecular Sciences, 24, 5421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Collins, F.H. and Paskewitz, S.M. (1996) A review of the use of ribosomal DNA (rDNA) to differentiate among cryptic Anopheles species. Insect Molecular Biology, 5, 1–9. [DOI] [PubMed] [Google Scholar]
  7. Collins, R.A. , Wangensteen, O.S. , O'Gorman, E.J. , Mariani, S. , Sims, D.W. and Genner, M.J. (2018) Persistence of environmental DNA in marine systems. Communications Biology, 1, 185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Crabtree, M.B. , Savage, H.M. and Miller, B.R. (1995) Development of a species‐diagnostic polymerase chain reaction assay for the identification of Culex vectors of St. Louis encephalitis virus based on interspecies sequence variation in ribosomal DNA spacers. American Journal of Tropical Medicine and Hygiene, 53, 105–109. [PubMed] [Google Scholar]
  9. Davis, A.J. , Williams, K.E. , Snow, N.P. , Pepin, K.M. and Piaggio, A.J. (2018) Accounting for observation processes across multiple levels of uncertainty improves inference of species distributions and guides adaptive sampling of environmental DNA. Ecology and Evolution, 8, 10879–10892. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Folmer, O. , Black, M. , Hoeh, W. , Lutz, R. and Vrijenhoek, R. (1994) DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology, 3, 294–299. [PubMed] [Google Scholar]
  11. Gerbi, S.A. (1986) The evolution of eukaryotic ribosomal DNA. Bio Systems, 19, 247–258. [DOI] [PubMed] [Google Scholar]
  12. Gillespie, J.J. , Kjer, K.M. , Duckett, C.N. and Tallamy, D.W. (2003) Convergent evolution of cucurbitacin feeding in spatially isolated rootworm taxa (Coleoptera: Chrysomelidae; Galerucinae, Luperini). Molecular Phylogenetics and Evolution, 29, 161–175. [DOI] [PubMed] [Google Scholar]
  13. Hänfling, B. , Handley, L.L. , Read, D.S. , Hahn, C. , Li, J.L. , Nichols, P. et al. (2016) Environmental DNA metabarcoding of lake fish communities reflects long‐term data from established survey methods. Molecular Ecology, 25, 3101–3119. [DOI] [PubMed] [Google Scholar]
  14. Harrison, J.B. , Sunday, J.M. and Rogers, S.M. (2019) Predicting the fate of eDNA in the environment and implications for studying biodiversity. Proceedings of the Royal Society B‐Biological Sciences, 286, 20191409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hillis, D.M. and Dixon, M.T. (1991) Ribosomal DNA: molecular evolution and phylogenetic inference. Quarterly Review of Biology, 66, 411–453. [DOI] [PubMed] [Google Scholar]
  16. Hill, L.A. , Davis, J.B. , Hapgood, G. , Whelan, P.I. , Smith, G.A. , Ritchie, S.A. et al. (2008) Rapid identification of Aedes albopictus, Aedes scutellaris, and Aedes aegypti life stages using real‐time polymerase chain reaction assays. American Journal of Tropical Medicine and Hygiene, 79, 866–875. [PubMed] [Google Scholar]
  17. Kirtane, A. , Dietschler, N.J. , Bittner, T.D. , Lefebvre, M.B. , Celis, S. , O'Connor, K. et al. (2022) Sensitive environmental DNA (eDNA) methods to detect hemlock woolly adelgid and its biological control predators Leucotaraxis silver flies and a Laricobius beetle. Environmental DNA, 4, 1136–1149. [Google Scholar]
  18. Li, L. and Sun, J.H. (2011) Host suitability of a gregarious parasitoid on beetle hosts: Flexibility between fitness of adult and offspring. PLoS ONE, 6, e18563. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Liebhold, A. , Brockerhoff, E.G. , Kalisz, S. , Nuñez, M.A. , Wardle, D.A. and Wingfield, M.J. (2017) Biological invasions in forest ecosystems. Biological Invasions, 19, 3437–3458. [Google Scholar]
  20. Mauvisseau, Q. , Burian, A. , Gibson, C. , Brys, R. , Ramsey, A. and Sweet, M. (2019) Influence of accuracy, repeatability and detection probability in the reliability of species‐specific eDNA based approaches. Scientific Reports, 9, 580. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. McCartin, L.J. , Vohsen, S.A. , Ambrose, S.W. , Layden, M. , McFadden, C.S. , Cordes, E.E. et al. (2022) Temperature controls eDNA persistence across physicochemical conditions in seawater. Environmental Science and Technology, 56, 8629–8639. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Nan, J.K. , Qi, J.Y. , Yang, Y.X. , Zhao, M.Q. , Liang, C.Q. , He, H. et al. (2023) Population density and host preference of the Japanese pine sawyer (Monochamus alternatus) in the Qinling‐Daba Mountains of China. Insects, 14, 181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Noureldin, E. , Tan, D. , Daffalla, O. , Almutairi, H. , Ghzwani, J. , Torno, M. et al. (2022) DNA barcoding of potential mosquito disease vectors (Diptera, Culicidae) in Jazan region, Saudi Arabia. Pathogens, 11, 486. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Ogram, A. , Sayler, G.S. and Barkay, T. (1987) The extraction and purification of microbial DNA from sediments. Journal of Microbiological Methods, 7, 57–66. [Google Scholar]
  25. Peñarrubia, L. , Alcaraz, C. , de Vaate, A.B. , Sanz, N. , Pla, C. , Vidal, O. et al. (2016) Validated methodology for quantifying infestation levels of dreissenid mussels in environmental DNA (eDNA) samples. Scientific Reports, 6, 39067. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Qi, J.Y. , Nan, J.K. , Zhao, X.G. , Zhao, M.Q. , Zhang, X. , Gao, X.M. et al. (2024) Non‐destructive molecular methods to identify Monochamus alternatus (Coleoptera: Cerambycidae), a major vector of Bursaphelenchus xylophilus (Nematoda: Aphelenchoididae). Journal of Applied Entomology, 148, 1037–1048. [Google Scholar]
  27. Qu, C.J. , Stewart, K.A. , Clemente‐Carvalho, R. , Zheng, J.S. , Wang, Y.X. , Gong, C. et al. (2020) Comparing fish prey diversity for a critically endangered aquatic mammal in a reserve and the wild using eDNA metabarcoding. Scientific Reports, 10, 16715. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Raupach, M.J. , Astrin, J.J. , Hannig, K. , Peters, M.K. , Stoeckle, M.Y. and Wägele, J.W. (2010) Molecular species identification of Central European ground beetles (Coleoptera: Carabidae) using nuclear rDNA expansion segments and DNA barcodes. Frontiers in Zoology, 7, 26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Renshaw, M.A. , Olds, B.P. , Jerde, C.L. , McVeigh, M.M. and Lodge, D.M. (2015) The room temperature preservation of filtered environmental DNA samples and assimilation into a phenol‐chloroform‐isoamyl alcohol DNA extraction. Molecular Ecology Resources, 15, 168–176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Rodriguez‐Perez, M.A. , Adeleke, M.A. , Burkett‐Cadena, N.D. , Garza‐Hernandez, J.A. , Reyes‐Villanueva, F. , Cupp, E.W. et al. (2013) Development of a novel trap for the collection of black flies of the Simulium ochraceum complex. PLoS ONE, 8, e76814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Shuvo, M.J. , Tabassum, T. and Segelbacher, G. (2025) Optimizing eDNA metabarcoding techniques for assessing arthropod communities in tree‐related microhabitats. Environmental DNA, 7, e70064. [Google Scholar]
  32. Siddiqui, J.A. , Fan, R.D. , Naz, H. , Bamisile, B.S. , Hafeez, M. , Ghani, M.I. et al. (2023) Insights into insecticide‐resistance mechanisms in invasive species: challenges and control strategies. Frontiers in Physiology, 13, 1112278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Sun, J.H. , Lu, M. , Gillette, N.E. and Wingfield, M.J. (2013) Red turpentine beetle: innocuous native becomes invasive tree killer in China. Annual Review of Entomology, 58, 293–311. [DOI] [PubMed] [Google Scholar]
  34. Togashi, K. (1981) Lifetime fecundity and body size of Monochamus alternatus (Coleoptera: Cerambycidae) at a constant temperature. Japanese Journal of Entomology, 65, 458–470. [Google Scholar]
  35. Valentin, R.E. , Fonseca, D.M. , Gable, S. , Kyle, K.E. , Hamilton, G.C. , Nielsen, A.L. et al. (2020) Moving eDNA surveys onto land: strategies for active eDNA aggregation to detect invasive forest insects. Molecular Ecology Resources, 20, 10. [DOI] [PubMed] [Google Scholar]
  36. Valentin, R.E. , Fonseca, D.M. , Nielsen, A.L. , Leskey, T.C. and Lockwood, J.L. (2018) Early detection of invasive exotic insect infestations using eDNA from crop surfaces. Frontiers in Ecology and the Environment, 16, 265–270. [Google Scholar]
  37. Valentin, R.E. , Kyle, K.E. , Allen, M.C. , Welbourne, D.J. and Lockwood, J.L. (2021) The state, transport, and fate of aboveground terrestrial arthropod eDNA. Environmental DNA, 3, 1081–1092. [Google Scholar]
  38. Verdon, T.J. , Mitchell, R.J. and van Oorschot, R.A.H. (2014) Swabs as DNA collection devices for sampling different biological materials from different substrates. Journal of Forensic Sciences, 59, 1080–1089. [DOI] [PubMed] [Google Scholar]
  39. von der Schulenburg, J.H.G. , Hancock, J.M. , Pagnamenta, A. , Sloggett, J.J. , Majerus, M.E. and Hurst, G.D. (2001) Extreme length and length variation in the first ribosomal internal transcribed spacer of ladybird beetles (Coleoptera: Coccinellidae). Molecular Biology and Evolution, 18, 648–660. [DOI] [PubMed] [Google Scholar]
  40. Wang, L.X. , Li, C.C. , Wang, X. , Wang, G.J. , Shang, S.Q. , Dou, Z.P. et al. (2022) Gut lignocellulose activity and microbiota in Asian longhorned beetle and their predicted contribution to larval nutrition. Frontiers in Microbiology, 13, 899865. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Winiger, N. , Seibold, S. , Lucek, K. , Müller, J. and Segelbacher, G. (2022) Lost in dead wood? Environmental DNA sequencing from dead wood shows little signs of saproxylic beetles. Environmental DNA, 4, 654–660. [Google Scholar]
  42. Zhang, B. , Zhao, L.L. , Ning, J. , Wickham, J.D. , Tian, H.K. , Zhang, X.M. et al. (2020) MiR‐31‐5p regulates cold acclimation of the wood‐boring beetle Monochamus alternatus via ascaroside signaling. BMC Biology, 18, 184–184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Zhang, C.P. , Qu, G.W. , Wang, Q.T. , Zhao, Y.P. , Xie, Y.L. , Xu, S.M. et al. (2024) Comparative effects of in‐package high pressure steam sterilization and high temperature boiling on the quality changes and shelf life of ready‐to‐eat sea cucumber. Journal of Food Composition and Analysis, 129, 106138. [Google Scholar]
  44. Zhang, Y.N. and Yang, Z.Q. (2006) Studies on the natural enemies and biocontrol of Monochamus alternatus Hope (Coleoptera: Cerambycidae). Plant Protection, 32, 9–14. [Google Scholar]
  45. Zhang, Z.Q. , Wang, H.P. , Hao, C.F. , Zhang, W. , Yang, M.M. , Chang, Y. et al. (2016) Identification, characterization and expression of Methuselah‐like genes in Dastarcus helophoroides (Coleoptera: Bothrideridae). Genes, 7, 91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Zhou, C.X. , Sun, X. , Mi, F. , Chen, J.Y. and Wang, M.Q. (2015) Antennal sensilla in the parasitoid Sclerodermus sp. (Hymenoptera: Bethylidae). Journal of Insect Science, 15, 36. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Fig. S1 Monochamus alternatus rearing in the laboratory.

Fig. S2 Oviposition scars (A) and emergence holes (B) of Monochamus alternatus in the forest.

Fig. S3 The 3 new methods of environmental DNA (eDNA) aggregation introduced in this study: (A) Rinsing aggregation; (B) wet cotton ball dipping; (C) natural predators.

Fig. S4 Specificity of Malt_28S assay.

Fig. S5 Specificity of Malt_ITS1 assay.

Fig. S6 Cycle threshold (Ct) values of the Malt_28S (A) and Malt_ITS1 (B) assays for mixed DNA samples with different proportions of Monochamus alternatus and Monochamus sparsutus.

INS-33-1675-s001.docx (2.7MB, docx)

Data Availability Statement

The sequencing data were uploaded to GenBank (accession numbers: COI: PQ285446−PQ285479; ITS1: PQ285341−PQ285349; 28S_D2−D3: PQ299027−PQ299035). Additional data files were submitted to Open Science Framework (https://osf.io/dashboard) under https://doi.org/10.17605/OSF.IO/BE47F

Articles from Insect Science are provided here courtesy of Wiley

RESOURCES