https://orcid.org/0000-0002-1537-0528
Figures
Abstract
Leaf litter ecosystems and their fauna are largely understudied, despite their critical ecological roles. Here, we investigate challenges associated with estimating biodiversity in terrestrial leaf litter. Current methodologies for biodiversity assessment are fraught with limitations; amongst the most significant is a decline in taxonomic expertise, complicating the process of species identification and the significant costs associated with species-level morphological identifications. DNA barcoding employs molecular markers to identify animal species, and the mitochondrial gene cytochrome c oxidase I (COI) is the agreed upon standard for animals. DNA metabarcoding facilitates the identification of multiple species without necessitating taxonomic expertise. Recent studies indicate that environmental DNA (eDNA) may exhibit greater sensitivity compared to taxonomic identifications completed on animals collected using traditional methods (e.g., pitfall traps, pan traps). To test whether eDNA methodology works in a real-world scenario, we sampled leaf litter across a temperate forest/field ecotone. Leaf litter was dried, ground and processed to extract environmental DNA. We evaluated multiple DNA extraction protocols to test their relative efficacy. We found that the Qiagen Blood and Tissue Kit was the most effective at recovering invertebrate diversity and found that there were notable differences in the biodiversity recovered between forest and field habitats. Temperature emerged as a significant factor influencing the composition of the communities observed. Our methodology is applicable across various environments for efficient biodiversity assessment and would be particularly beneficial for monitoring pests and invasive species. Our approach offers a cost-effective and timely alternative to conventional biodiversity assessment methods and underscores the significance of accurate assessment methodologies for leaf litter communities.
Citation: Castillo AH, Jacobs S, Steinke D, Smith MA (2026) Assessment of leaf-litter invertebrate biodiversity using high throughput sequencing. PLoS One 21(8): e0347811. https://doi.org/10.1371/journal.pone.0347811
Editor: Christopher Adenyo, University of Ghana, GHANA
Received: April 7, 2026; Accepted: August 4, 2026; Published: August 20, 2026
Copyright: © 2026 Castillo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The minimal data set is available at Sequence Read Archive (SRA) via https://www.ncbi.nlm.nih.gov/sra/PRJNA1448108 The accompanying code is available at Borealis via https://doi.org/10.5683/SP4/2GT4EP.
Funding: Natural Sciences and Engineering Research Council grant number RGPIN-2022-04838 was awarded to MAS (https://nserc-crsng.canada.ca/en) Canada First Research Excellence Fund to the University of Guelph’s “Food From Thought” research program (Project 000054, awarded to DS (https://foodfromthought.ca) The funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Estimating biodiversity is as challenging as it is important, especially during the Anthropocene [1,2]. A good example of a challenging and largely understudied ecosystem is found in the terrestrial leaf litter. While leaf litter communities are vital for ecosystem services and other functions [3–6], the species that comprise them are largely unknown, mainly because approaches are inconsistent, and very few methods exist to attempt more comprehensive surveys [7].
Evaluating the animal biodiversity found in the leaf litter includes collection, storage, faunal extraction, sorting and identification [8]. While each one of these stages has limitations and biases, collecting and storing leaf litter to target invertebrate biodiversity is relatively simple. For example, in one widely accepted standardized protocol, a predetermined small section, typically 25 cm x 25 cm, is collected wearing gloves and then kept cold or frozen pending further processing [9]. However, once collected, many factors influence the subsequent estimation of biodiversity, and no single sampling method can comprehensively assess all taxa encompassing the leaf litter’s terrestrial biodiversity [10]. [11] recently reviewed seven collection methods that, in their taxonomic overlap, might offer a better assessment of terrestrial biodiversity. However, the challenge of achieving a comprehensive estimate with a single approach remains largely untenable. An assessment of a sample’s biodiversity is only as good as one’s ability to identify the taxa collected. An evaluation of an area’s diversity, where specimens can only be keyed to order, will not be as good as one where we can identify them to genus, let alone species (although see [12]). Thus, the extent of available taxonomic expertise in each collected group limits the ultimate evaluation. Especially for soil invertebrates common in the leaf litter (such as nematodes, earthworms, and mites), taxonomic expertise is dwindling, and for other taxa like proturans, diplura and symphylans, it is already scarce [13]. This is exacerbated by small adult size, the prevalence of cryptic species, which can result in underestimates of morphologically-derived species richness, and changes in morphology between sexes or across life-history stages. Molecular methods of species identification, including DNA barcoding and metabarcoding, pose a potential solution to these problems and may represent a faster and more cost-effective way to assess terrestrial biodiversity, helping to mitigate its decline and manage its recovery.
DNA barcoding uses a standardized fragment of the mitochondrial gene cytochrome c oxidase I (COI) to detect the presence of animal species in various environments [14]. Over the past twenty years, it has fueled a better understanding of community networks [14–16]. Its extension, DNA metabarcoding, provides a “genetic profile” that can be screened for multiple species of interest without requiring taxonomic expertise. This approach, which utilizes High-Throughput Sequencing (HTS) technology and can also be applied to soil arthropod profiling [17] In fact, several studies ([16,18] showed that environmental DNA (eDNA) as a source for metabarcoding can be more sensitive than conventional methods when detecting invertebrate species (see also [19]), opening up new sources of samples, particularly from leaf washes and drying and grinding of leaves. The critical stage of linking the produced sequences to formal names is dependent on the existence of a proper reference library [20].
Opportunity
To date, when leaf-litter diversity has been analyzed via HTS – it has been via traditional collection methods (like Winkler sifting, e.g., [21] where the arthropods are first extracted from the matrix and then the HTS protocol is applied to the emergent arthropods (i.e., after the rate-limiting sorting steps outlined earlier). However, leaf-litter animal biodiversity has yet to be directly explored using HTS when these rate-limiting steps are removed. In an intriguing parallel, [22] extracted arthropod DNA from store-bought tea using a CTAB protocol. While it may seem contradictory that they chose a protocol often used to extract plant DNA [23] the authors designed primers to minimize the amplification of plant DNA, specifically to target arthropod DNA within and upon the collected plant material. In addition, environmental assessment methods using DNA have not been extensively employed to monitor plants [24] because they may not always release detectable amounts of DNA [25]. The novel approach pioneered by Krehenwinkel et al allowed the recovery of ecologically and taxonomically diverse arthropod communities from the tea, many specific to their host plant and its geographical origin [22]. The same research group [26] applied a similar approach to herbarium leaves, enabling them to study temporal changes in arthropod communities. Atypically for eDNA, arthropod DNA from dried plants showed remarkably high temporal stability, opening the possibility for studies on the ecology of invertebrates using the plant material they inhabit, such as leaf litter. More recently, the same research group studied arthropod biodiversity on plant leaves through freeze-drying and grinding prior to HTS [16]. Here, we expand their approach to determine if HTS is feasible for studying invertebrate communities in a leaf-litter environment. We have designed an approach that involves drying and grinding leaf litter into a fine powder from which we extract environmental DNA, used to identify invertebrate taxa using conventional metabarcoding pipelines. As a proof of concept, we tested this new approach with three different extraction kits and then directly assessed the terrestrial biodiversity across a forest-to-field ecotone. Using this novel approach, we wanted to answer the questions: how does extraction protocol affect the recovery of the arthropod community, and can we use the best protocol to analyze how arthropod leaf-litter diversity varies across a temperate forest/field ecotone?
Materials and methods
Experimental design
We sampled leaf litter in both a small woodlot and an adjacent early-succession field on the University of Guelph campus (Dairy Bush, Guelph, Ontario, Canada) on October 25, 2022, following the SoilBON protocol [9]. We performed half of the sampling in the field site (top-left white cross in Fig 1) and the other half in the woodlot (bottom-right cross in Fig 1). During the growing season (i.e., the number of frost-free days between spring (~March) and fall (~September) [27], the sampled areas are characterized by significantly different temperature regimes [28]. We collected five samples in each area using a 25 cm by 25 cm quadrat, where the design follows an axis that aligns with the temperature gradient [29] (Fig 1). This step included all plant material (e.g., grass and plants) to ensure all eDNA could be recovered in the leaf-litter powder. Temperature measurements were taken every 10 minutes at ground level throughout the year using Onset HOBO MX2201 Pendant Wireless Temperature Data Loggers (Fig 2) [28].
We sampled leaf litter in the Dairy Bush, Guelph, Ontario, Canada (lat 43.524599, lon −80.237531) using the SoilBON protocol [9]. We collected five samples in the field and five in the forest using a 25 cm by 25 cm quadrat, spacing each point 15 m apart. The sampling points follow an axis along the forest/field ecotone, which has a predictable gradient of temperature and moisture.
In this plot, each vertical line is a day, coloured by the difference between the average Guelph temperature. The growing season difference between the two habitats and their similarity in the winter are evident.
Drying and grinding of the leaf litter
We did not wash or alter the leaf litter before processing [22]. We dried the leaf litter in weighing trays inside an incubator at 56 ºC and weighed it regularly until the weight stopped decreasing (24 hours). Then, we ground the samples into a fine powder using an IKA Tube Mill control (IKA, Breisgau, Germany) at 25,000 rpm for three minutes twice [29]. Additionally, we combined an aliquot of the five samples from each site to form a sixth sample. We compared sequencing results from this mixture to those of the five original samples to determine if it was representative of the environment. If so, the cost and effort of sequencing could be reduced by 80%.
Cost estimation methodology
To estimate the costs of traditional biodiversity inventories, we used the processing effort reported by the Biological Survey of Canada [30], which estimated that a terrestrial arthropod inventory at a single site requires approximately 504 hours of processing per month under a standard sampling protocol. Labor costs were calculated using an hourly wage of US$10.95 for undergraduate biology students. Assuming sampling occurs from April through October (seven months), total processing costs were estimated by multiplying monthly labor requirements by the hourly wage and the duration of the sampling period.
For metabarcoding, laboratory costs were calculated based on the Illumina sequencing workflow used in this study. Per-sample costs were based on a batch size of 48 samples and included laboratory processing and sequencing expenses [31].
DNA extraction, PCR amplification, and primer choice
We extracted total DNA from leaf-litter powder using three different commercial kits on each sample: a DNeasy® PowerSoil® Pro Kit (Qiagen, Hilden, Germany), a DNeasy® Blood and Tissue Kit (Qiagen, Hilden, Germany), and a Quick-DNATM Plant/Seed Miniprep Kit (Zymo Research, Irvine, CA, USA) (S1 Table). Different amounts of tissue were added to each kit, which were used according to the manufacturers’ instructions.
We measured the quality and yield of the DNA extractions using a QubitTM dsDNA HS Assay Kit (Invitrogen, Life Technologies, Carlsbad, CA, USA) and on a 1% agarose gel. We performed PCR amplifications using a two-step fusion primer PCR protocol [31]. We amplified a 421 bp region of the cytochrome c oxidase subunit I (COI) during the first PCR step using the BF3 + BR2 primer set [32]. We performed PCR reactions in a 25 μL reaction volume, with 0.5 μL DNA, 0.2 μM of each primer, and 12.5 μL PCR Multiplex Plus buffer (Qiagen, Hilden, Germany). We used a Veriti thermocycler (Thermo Fisher Scientific, MA, USA) under the following cycling conditions: initial denaturation at 95 °C for five min; 25 cycles of 30 sec at 95 °C, 30 sec at 50 °C and 50 sec at 72 °C; and a final extension of five min at 72 °C. PCR success was checked on a 1% agarose gel. We used one μL of PCR product as a template for the second PCR, where we added Illumina sequencing adapters using individually tagged fusion primers [32,33]. We used the same thermocycler conditions as in the first PCR but increased the reaction volume to 35 μL by adding water, reduced the cycle number to 20, and increased the extension time to 2 minutes per cycle. We checked PCR success again on a 1% agarose gel. We purified and normalized PCR products using SequalPrep Normalization Plates (Thermo Fisher Scientific, MA, USA, Harris et al. 2010) according to manufacturer protocols. We pooled 10 μL of each normalized sample and cleaned the final library using left-sided size selection with 0.76x SPRIselect (Beckman Coulter, CA, USA). This method removes DNA fragments that are smaller than the targeted size, allowing for the selection of larger fragments. At 0.76x, SPRIselect targets fragments longer than 400 bp. The PCR fragment with the primers used is 421 bp. Sequencing was performed at the Advanced Analysis Centre at the University of Guelph using the 600-cycle Illumina MiSeq Reagent Kit v3 and 5% PhiX spike-in.
Bioinformatic analyses and taxonomic classification
We performed Bioinformatic analysis as outlined in [34]: after demultiplexing, we used the APSCALE pipeline [34], and taxonomy was assigned to Operational Taxonomic Units (OTUs) using a curated Canadian Reference Library [35]
Statistical analyses
All statistical analyses were executed in R (R version 4.3.3 (2024-02-29), [35,36]) using RStudio (Version 2025.05.0 + 496). We conducted sample-based rarefaction analyses to confirm that our sequencing depth was adequate. To visualize the differences in community structure and composition among sites [37], we employed non-metric multidimensional scaling (nMDS) ordination with k = 2, utilizing the ‘vegan’ package [38]. The nMDS plot is based on Bray-Curtis distances, using species presence/absence data. All the necessary code and datasets for reproducing these results, including data visualization, are available online at https://doi.org/10.5683/SP4/2GT4EP.
Results
Cost estimates
Based on the Biological Survey of Canada’s reported labor requirements [30], processing a terrestrial arthropod inventory would cost approximately US$5,518.65 per site per month. Over a seven-month sampling season, this totals approximately US$38,000 per site. Additional expenditures, including sampling supplies and travel, are relatively minor compared with personnel costs. However, these estimates do not include the costs of specialist taxonomic identification, which become increasingly difficult to quantify once specimens have been sorted and require expert determination [30].
Using the Illumina sequencing technology employed in this study, the estimated cost averaged US$54.50 per sample for a batch of 48 samples [39] (Table 1). These costs can be further reduced by increasing sample throughput.
A total of 20,361,353 paired-end raw COI reads were sequenced (an average of 535,825 reads per sample). After merging, removal of the primer sequences, length selection, quality filtering, and denoising, 16,289,082 reads were used for dereplication and subsequent species identification. Dereplicated raw reads are available at the NCBI Short Read Archive (SRA), BioProject PRJNA1448108 at https://www.ncbi.nlm.nih.gov/sra/PRJNA1448108.
All three DNA extraction protocols successfully recovered DNA from the leaf-litter powder (S1 Table). We found different levels of success in recovering the leaf-litter invertebrate communities according to the protocol used (Fig 2). Because all extractions came from the same sample, we considered the most successful protocol to be the one exhibiting the highest alpha diversity (number of different taxa).
There was a significant interaction between habitat and DNA extraction method (i.e., not all methods performed equally in all habitats). PowerSoil Pro and Blood and Tissue methods exhibited greater diversity in the field than in the forest, whereas Zymo showed no difference between the habitats. The Blood and Tissue Kit appeared to capture a greater diversity compared to PowerSoil Pro and Zymo (Fig 3) (PERMANOVA, pseudo-F2,24 = 4.09, p = 0.023). When analyzed according to different habitats, PowerSoil Pro performed well in the field but less effectively in the forest. Blood and Tissue performed exceptionally well in the field and reasonably well in the forest. In contrast, Zymo performed average in both environments (see Fig 2). For all three protocols, the field samples resulted in higher DNA yields than the forest samples (S1 Table), likely because the field samples were collected while the plants were alive. In contrast, the forest samples consisted primarily of dead leaves. An nMDS plot illustrating the beta diversity by extraction type revealed that the Blood and Tissue Kit captured a greater breadth of diversity (Fig 4).
By habitat, the results indicate that PowerSoil performs well in the field but lags behind in the forest. Conversely, Blood and Tissue performs exceptionally well in the field and reasonably well in the forest. In contrast, Zymo performs average in both environments. Overall, Blood and Tissue is the most effective at reliably recovering the diversity from both environments.
(stress value: 0.118).
An nMDS plot displaying beta diversity by habitat, with vectors corresponding to average air temperature, average maximum temperature, average minimum temperature, soil conductivity, water content, and soil temperature (Fig 5), revealed that temperature was the most significant factor explaining the majority of the variance (p = 0.001). Interestingly, an nMDS plot of the samples that includes the taxa illustrated the retrieval of different taxa within three of the most abundant classes we observed (e.g., Arachnida, Insecta and Collembola) across the field and forest environments (see Fig 5).
The points represent taxa, and the vectors correspond to average air temperature (avg_airtemp), average maximum temperature (maxtemp) and average minimum temperature (mintemp), soil conductivity (SC), soil water content (SWC), and soil temperature (SoilTemp). Only max_temp and min_temp were retained in the ordination for visual clarity (R2 = 0.8497, p = 0.001). However, all vectors were significant (S2 Table).The first NMDS dimension is largely an expression of the abiotic environment across the ecotone. The communities in both environments (colored 95% ellipsoids) are significantly different (ANOSIM statistic R: 0.877, significance: 0.001; PERMANOVA R2 = 0.344, p = 0.001).
Discussion
To our knowledge, this is the first study to apply eDNA metabarcoding to leaf-litter biodiversity assessment. Based on earlier work [16,22,26,40], we assumed that drying and grinding leaf litter with subsequent DNA extraction would be an effective way to evaluate the resident terrestrial arthropod communities. Here, we successfully applied our approach to distinguish between markedly different invertebrate communities in adjacent field and forest environments.
Protocols
To find the optimal method for extracting DNA from leaf litter, we tested three commercial DNA extraction protocols. Based on our results, the Qiagen Blood and Tissue Kit was most successful, as it returned the highest quantities of DNA and recovered the greatest alpha diversity (measured as BINs recovered). This kit represents the most versatile of all three options, as Qiagen PowerSoil Pro is designed for microbial DNA extraction, while Zymo is more specialized for plant DNA extraction.
We predicted that combining replicates before processing would retrieve similar alpha diversity and thereby reduce labor and costs. However, combining aliquots from each replicate did not result in taxonomic coverage representative of the sum of the replicates. This confirms earlier findings that technical replicates are required for eDNA metabarcoding to accurately recover alpha diversity [41].
Environmental comparison
We anticipated that warmer areas in the field would exhibit greater alpha diversity, and that field and forest communities would be different. Our approach has successfully differentiated between invertebrate communities found in adjacent sites across a forest/field ecotone. Notably, we found that none of the species were shared across the ecotone for the three most common groups we sequenced (Arachnida, Insecta and Collembola, Fig 4). The section of the field from which we collected samples has not been cut since 2018, and as such, it has been left to develop into a forest through a process of secondary succession. Currently, there are many young tree saplings from numerous species common in the adjacent forest that are emerging and (at writing) are upwards of meters tall [28]. What will happen to the distinct community of arthropod species we found in the field if it becomes a forest? Will they be replaced by the nearby forest species, become locally extinct, or move to another location? Alternatively, what will happen if the University landowners decide to cut the emergent forest? Our findings in this small methodological case study not only underscore the need for further research but also reflect how subtle abiotic gradients can produce significant biological effects – even in a developed urban ecosystem and hopefully inspire future studies in invertebrate community ecology.
Applications
Our approach complements comprehensive environmental assessments and can be applied to various types of plant residue. This could include applications such as screening crop samples for pests or invasive species in shipping materials [42–44]. Leaf litter assessments can be conducted in a variety of environments, including grasslands, forests, and agricultural and horticultural fields. However, this approach can only be used when leaves, either as leaf litter or still on the live plant, are available. Additionally, it is conceivable that if the leaf litter has recently fallen, its inhabiting fauna might not have crawled over it and would not have left behind recoverable DNA. The only DNA available would be that of organisms that left it on the leaves while they were part of the canopy.
Compared to other methods of monitoring arthropods in the applied agricultural world, our method is efficient. Residual plant material can be collected quickly, and the entire process takes only a few weeks, primarily consisting of waiting for sequencing results, which can be expedited to a few days. Furthermore, it is cost-effective, as sampling efforts and laboratory work can be less expensive than those required by traditional methods. Additionally, it offers an out-of-season opportunity for sampling, as leaf litter collected in non-active seasons could provide insight into the biodiversity present in the environment during the previous active season.
Implications for biodiversity monitoring
Ji et al. [39] estimated that the active workload associated with metabarcoding, including laboratory and bioinformatic processing, requires approximately one-quarter of the effort required for the visual sorting of arthropod specimens into morphospecies. The advantage is likely even greater for microscopic taxa that are abundant in ground-level samples and particularly challenging to identify with traditional methods.
An important distinction between the two approaches is that metabarcoding costs scale primarily on a per-sample basis, whereas traditional biodiversity assessments scale on a per-specimen basis. This difference helps explain why conventional monitoring programs often focus on a limited set of indicator taxa rather than attempting comprehensive inventories [39]. As a result, a modestly staffed molecular laboratory can process hundreds of samples annually from geographically dispersed locations, generating data at a scale and rate difficult to achieve with traditional taxonomic workflows alone.
Nevertheless, metabarcoding should be viewed as complementary to, rather than a replacement for, traditional taxonomy. Molecular identifications depend on comprehensive and well-curated reference libraries, whose development and maintenance remain reliant on taxonomic expertise.
Conclusion
We have demonstrated the feasibility of capturing invertebrate communities in leaf litter by drying and grinding leaves. This illustrates that HTS-derived data can be used to assess interactions between these communities. Such a method will be extremely useful in theoretical and applied conditions.
Supporting information
S1 Table. Total DNA extraction yield for each of the three extraction protocols.
(ps) DNeasy PowerSoil Pro; (bt) DNeasy Blood and Tissue; (zm) Quick-DNATM Plant/Seed Mini.
https://doi.org/10.1371/journal.pone.0347811.s001
(DOCX)
S2 Table. Fit of Environmental Vectors onto NMDS.
Vectors included are average temperature (avg), maximum average temperature (max), minimum average temperature (min), soil conductivity (SC), water volume (WV), and soil temperature (SoilTemp).
https://doi.org/10.1371/journal.pone.0347811.s002
(XLSX)
Acknowledgments
We thank two anonymous reviewers for their help in improving earlier versions of this manuscript.
References
- 1. Anthony MA, Bender SF, van der Heijden MGA. Enumerating soil biodiversity. Proc Natl Acad Sci U S A. 2023;120(33):e2304663120. pmid:37549278
- 2.
Kolbert E. The Sixth Extinction: An Unnatural History. New York: Macmillan Publishers; 2014.
- 3. Hättenschwiler S, Tiunov AV, Scheu S. Biodiversity and litter decomposition in terrestrial ecosystems. Annu Rev Ecol Evol Syst. 2005;36:191–218.
- 4. Liu S, Plaza C, Ochoa-Hueso R, Trivedi C, Wang J, Trivedi P, et al. Litter and soil biodiversity jointly drive ecosystem functions. Glob Chang Biol. 2023;29(22):6276–85. pmid:37578170
- 5. Hartshorn J. A review of forest management effects on terrestrial leaf litter inhabiting arthropods. Forests. 2020;12(1):23.
- 6. Tedersoo L, Drenkhan R, Anslan S, Morales-Rodriguez C, Cleary M. High-throughput identification and diagnostics of pathogens and pests: overview and practical recommendations. Mol Ecol Resour. 2019;19(1):47–76. pmid:30358140
- 7. Geisen S, Briones MJI, Gan H, Behan-Pelletier VM, Friman V-P, de Groot GA, et al. A methodological framework to embrace soil biodiversity. Soil Biol Biochem. 2019;136:107536.
- 8. González G, Barberena-Arias MF, Huang W, Ospina-Sánchez CM. Sampling methods for soil and litter fauna. Measuring arthropod biodiversity. Cham: Springer International Publishing; 2021. 495–522.
- 9. Potapov AM, Sun X, Briones MJI, Brown GG, Cameron EK, Cortet J, et al. Global monitoring of soil animal communities using a common methodology. bioRxiv. 2022.
- 10. Santos JC, Fernandes GW. Measuring arthropod biodiversity. Cham: Springer International Publishing; 2021.
- 11. Montgomery GA, Belitz MW, Guralnick RP, Tingley MW. Standards and best practices for monitoring and benchmarking insects. Front Ecol Evolut. 2021;8:513.
- 12. Timms LL, Bowden JJ, Summerville KS, Buddle CM. Does species‐level resolution matter? Taxonomic sufficiency in terrestrial arthropod biodiversity studies. Insect Conserv Diversity. 2012;6(4):453–62.
- 13. Drew LW. Are we losing the science of taxonomy?. Bioscience. 2011;61:942–6.
- 14. Cristescu ME. From barcoding single individuals to metabarcoding biological communities: towards an integrative approach to the study of global biodiversity. Trends Ecol Evol. 2014;29(10):566–71. pmid:25175416
- 15. Orgiazzi A, Dunbar MB, Panagos P, de Groot GA, Lemanceau P. Soil biodiversity and DNA barcodes: opportunities and challenges. Soil Biol Biochem. 2015;80:244–50.
- 16. Weber S, Stothut M, Mahla L, Kripp A, Hirschler L, Lenz N, et al. Plant-derived environmental DNA complements diversity estimates from traditional arthropod monitoring methods but outperforms them detecting plant-arthropod interactions. Mol Ecol Resour. 2024;24(2):e13900. pmid:38010630
- 17. Oliverio AM, Gan H, Wickings K, Fierer N. A DNA metabarcoding approach to characterize soil arthropod communities. Soil Biol Biochem. 2018;125:37–43.
- 18. Allen MC, Nielsen AL, Peterson DL, Lockwood JL. Terrestrial eDNA survey outperforms conventional approach for detecting an invasive pest insect within an agricultural ecosystem. Environ DNA. 2021;3(6):1102–12.
- 19. Robinson CV, Porter TM, McGee KM, McCusker M, Wright MTG, Hajibabaei M. Multi-marker DNA metabarcoding detects suites of environmental gradients from an urban harbour. Sci Rep. 2022;12(1):10556. pmid:35732669
- 20. Taberlet P, Coissac E, Pompanon F, Brochmann C, Willerslev E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol Ecol. 2012;21(8):2045–50. pmid:22486824
- 21. Yang C, Wang X, Miller JA, de Blécourt M, Ji Y, Yang C. Using metabarcoding to ask if easily collected soil and leaf-litter samples can be used as a general biodiversity indicator. Ecol Indic. 2014;46:379–89.
- 22. Krehenwinkel H, Weber S, Künzel S, Kennedy SR. The bug in a teacup-monitoring arthropod-plant associations with environmental DNA from dried plant material. Biol Lett. 2022;18(6):20220091. pmid:35702982
- 23. Aboul-Maaty NA-F, Oraby HA-S. Extraction of high-quality genomic DNA from different plant orders applying a modified CTAB-based method. Bull Natl Res Cent. 2019;43:25.
- 24. Banerjee P, Stewart KA, Dey G, Antognazza CM, Sharma RK, Maity JP, et al. Environmental DNA analysis as an emerging non-destructive method for plant biodiversity monitoring: a review. AoB Plants. 2022;14(4):plac031. pmid:35990516
- 25. Matsuhashi S, Doi H, Fujiwara A, Watanabe S, Minamoto T. Evaluation of the environmental DNA method for estimating distribution and biomass of submerged aquatic plants. PLoS One. 2016;11(6):e0156217. pmid:27304876
- 26. Krehenwinkel H, Weber S, Broekmann R, Melcher A, Hans J, Wolf R, et al. Environmental DNA from archived leaves reveals widespread temporal turnover and biotic homogenization in forest arthropod communities. Elife. 2022;11:e78521. pmid:36354219
- 27. Qian B, Zhang X, Chen K, Feng Y, O’Brien T. Observed long-term trends for agroclimatic conditions in Canada. J Appl Meteorol Climatol. 2010;49:604–18.
- 28. Smith A. Temperature along an field-forest ecotone in Guelph Ontario: the Dairy Bush. Borealis. 2024.
- 29. Steinke D, Braukmann TW, Manerus L, Woodhouse A, Elbrecht V. Effects of Malaise trap spacing on species richness and composition of terrestrial arthropod bulk samples. MBMG. 2021;5.
- 30.
Biological Survey of Canada. Terrestrial arthropod biodiversity: planning a study and recommended sampling techniques. 1994. https://esc-sec.ca/wp/wp-content/uploads/2017/03/Bulletin-volume26-number1-Mar1994-supplement-Terrestrial-arthropod-biodiversity.pdf
- 31. Milián-García Y, Young R, Madden M, Bullas-Appleton E, Hanner RH. Optimization and validation of a cost-effective protocol for biosurveillance of invasive alien species. Ecol Evol. 2021;11(5):1999–2014. pmid:33717437
- 32. Elbrecht V, Steinke D. Scaling up DNA metabarcoding for freshwater macrozoobenthos monitoring. Freshwater Biology. 2018;64:fwb.13220.
- 33. Elbrecht V, Braukmann TWA, Ivanova NV, Prosser SWJ, Hajibabaei M, Wright M, et al. Validation of COI metabarcoding primers for terrestrial arthropods. PeerJ. 2019;7:e7745. pmid:31608170
- 34. Buchner D, Macher TH, Leese F. Bioinformatics. 2022;38:4817–9.
- 35.
Pentinsaari M. Canadian Reference Library (CANREF22). Centre for Biodiversity Genomics; 2022. https://www.boldsystems.org
- 36.
R Core Team. R: A language and environment for statistical computing. Austria: R Foundation for Statistical Computing; 2024.
- 37. Gotelli NJ, Colwell RK. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett. 2001;4:379–91.
- 38. Oksanen J, Kindt R, Legendre P, Hara BO’, Simpson GL, Solymos P. The vegan package title community ecology package. 2008. http://cran.r-project.org/
- 39. Ji Y, Ashton L, Pedley SM, Edwards DP, Tang Y, Nakamura A, et al. Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. Ecol Lett. 2013;16(10):1245–57. pmid:23910579
- 40. Thomsen PF, Sigsgaard EE. Environmental DNA metabarcoding of wild flowers reveals diverse communities of terrestrial arthropods. Ecol Evol. 2019;9(4):1665–79. pmid:30847063
- 41. Mata VA, Rebelo H, Amorim F, McCracken GF, Jarman S, Beja P. How much is enough? Effects of technical and biological replication on metabarcoding dietary analysis. Mol Ecol. 2019;28(2):165–75. pmid:29940083
- 42. Stanaway MA, Zalucki MP, Gillespie PS, Rodriguez CM, Maynard GV. Pest risk assessment of insects in sea cargo containers. Aust J Entomol. 2001;40:180–92.
- 43. Paini DR, Yemshanov D. Modelling the arrival of invasive organisms via the international marine shipping network: a Khapra beetle study. PLoS One. 2012;7(9):e44589. pmid:22970258
- 44. Ormsby MD. Elucidating the efficacy of phytosanitary measures for invasive alien species moving in wood packaging material. J Plant Dis Prot. 2022;129(2):339–48.