Key factors to consider in the use of environmental DNA metabarcoding to monitor terrestrial ecological restoration

Heyde, Mieke van der; Bunce, Michael; Nevill, Paul

doi:10.1016/j.scitotenv.2022.157617

Cited by 19 publications

(16 citation statements)

References 130 publications

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“…The application of eDNA metabarcoding as a biomonitoring tool to assess biological richness in terrestrial environments is becoming more frequent in the literature (e.g., van der Heyde et al, 2022 ) and commercially (e.g., Gold et al, 2021 ). However, the level of replication required to ensure that richness is not underestimated remains largely unexplored.…”

Section: Discussionmentioning

confidence: 99%

“…Metabarcoding of eDNA has proved highly successful in freshwater and marine systems (Egeter et al, 2018 ; Furlan et al, 2020 ; Harper, Handley, et al, 2019 ; Palacios et al, 2020 ; Thomsen et al, 2012 ; Ushio et al, 2017 , Wang et al, 2021 ). However, despite rapid advancements in the technology, it is currently difficult to apply metabarcoding to terrestrial biomonitoring where eDNA may not be well preserved (van der Heyde et al, 2020 ), appropriate sampling substrates may be limited (Fahner et al, 2016 ; van der Heyde et al, 2020 , 2021 ), consistent protocols to sample substrates are not well established (Harrison et al, 2019 ; van der Heyde et al, 2022 ), and reference databases for arid habitat taxa are incomplete (Bradford et al, 2010 ; Carrasco‐Puga et al, 2021 ; Egeter et al, 2018 ; Palacios et al, 2020 ; van der Heyde et al, 2020 ). As a result, the application of eDNA for biomonitoring in arid lands is rare.…”

Section: Introductionmentioning

confidence: 99%

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Detection of vertebrates from natural and artificial inland water bodies in a semi‐arid habitat using eDNA from filtered, swept, and sediment samples

McDonald

Bateman

Cooper

et al. 2023

Ecology and Evolution

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Biomonitoring is vital for establishing baseline data that is needed to identify and quantify ecological change and to inform management and conservation activities. However, biomonitoring and biodiversity assessment in arid environments, which are predicted to cover 56% of the Earth's land surface by 2100, can be prohibitively time consuming, expensive, and logistically challenging due to their often remote and inhospitable nature. Sampling of environmental DNA (eDNA) coupled with high‐throughput sequencing is an emerging biodiversity assessment method. Here we explore the application of eDNA metabarcoding and various sampling approaches to estimate vertebrate richness and assemblage at human‐constructed and natural water sources in a semi‐arid region of Western Australia. Three sampling methods: sediment samples, filtering through a membrane with a pump, and membrane sweeping in the water body, were compared using two eDNA metabarcoding assays, 12S‐V5 and 16smam, for 120 eDNA samples collected from four gnammas (gnamma: Australian Indigenous Noongar language term–granite rock pools) and four cattle troughs in the Great Western Woodlands, Western Australia. We detected higher vertebrate richness in samples from cattle troughs and found differences between assemblages detected in gnammas (more birds and amphibians) and cattle troughs (more mammals, including feral taxa). Total vertebrate richness was not different between swept and filtered samples, but all sampling methods yielded different assemblages. Our findings indicate that eDNA surveys in arid lands will benefit from collecting multiple samples at multiple water sources to avoid underestimating vertebrate richness. The high concentration of eDNA in small, isolated water bodies permits the use of sweep sampling that simplifies sample collection, processing, and storage, particularly when assessing vertebrate biodiversity across large spatial scales.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of vertebrates from natural and artificial inland water bodies in a semi‐arid habitat using eDNA from filtered, swept, and sediment samples

McDonald

Bateman

Cooper

et al. 2023

Ecology and Evolution

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“…It is well known that a combination of traditional trapping methods significantly enhances the observed diversity in arthropod monitoring (Missa et al., 2009). Consistent with this understanding, plant‐derived eDNA can likewise function as a complementary method to saturate the taxonomic diversity of a site (Kestel et al., 2023; van der Heyde et al., 2022).…”

Section: Discussionmentioning

confidence: 93%

“…Studying plant-arthropod interactions is of particularly high relevance, as arthropods are among the most important pollinators and herbivores of plants (Crawley, 1989;Haddad et al, 2009;Knops et al, 1999;Schaffers et al, 2008;Siemann et al, 1998). For example, a single invasive herbivore or the loss of an important pollinator can have devastating effects on local plant and arthropod communities (Myers & Sarfraz, 2017;Valiente-Banuet et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Plant‐derived environmental DNA complements diversity estimates from traditional arthropod monitoring methods but outperforms them detecting plant–arthropod interactions

Weber,

Stothut,

Mahla

et al. 2023

Molecular Ecology Resources

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Our limited knowledge about the ecological drivers of global arthropod decline highlights the urgent need for more effective biodiversity monitoring approaches. Monitoring of arthropods is commonly performed using passive trapping devices, which reliably recover diverse communities, but provide little ecological information on the sampled taxa. Especially the manifold interactions of arthropods with plants are barely understood. A promising strategy to overcome this shortfall is environmental DNA (eDNA) metabarcoding from plant material on which arthropods leave DNA traces through direct or indirect interactions. However, the accuracy of this approach has not been sufficiently tested. In four experiments, we exhaustively test the comparative performance of plant‐derived eDNA from surface washes of plants and homogenized plant material against traditional monitoring approaches. We show that the recovered communities of plant‐derived eDNA and traditional approaches only partly overlap, with eDNA recovering various additional taxa. This suggests eDNA as a useful complementary tool to traditional monitoring. Despite the differences in recovered taxa, estimates of community α‐ and β‐diversity between both approaches are well correlated, highlighting the utility of eDNA as a broad scale tool for community monitoring. Last, eDNA outperforms traditional approaches in the recovery of plant‐specific arthropod communities. Unlike traditional monitoring, eDNA revealed fine‐scale community differentiation between individual plants and even within plant compartments. Especially specialized herbivores are better recovered with eDNA. Our results highlight the value of plant‐derived eDNA analysis for large‐scale biodiversity assessments that include information about community‐level interactions.

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“…This is due to the complexity of eDNA data (Miya, 2022; Xiong et al, 2022). Indeed, despite its potential in biodiversity monitoring (Mathon et al, 2022; Pawlowski et al, 2022; van der Heyde et al, 2022), eDNA metabarcoding can be limited by false reads due to contamination, errors that can occur during the extraction, PCR or sequencing process (Bohmann et al, 2014; Calderón‐Sanou et al, 2020; Creer et al, 2016; Ficetola et al, 2016; Hering et al, 2018). Although field and laboratory practices can mitigate some of this, the risk of error cannot be eliminated and must be considered (Burian et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

New deep learning‐based methods for visualizing ecosystem properties using environmental DNA metabarcoding data

Lamperti,

Sanchez,

Si Moussi

et al. 2023

Molecular Ecology Resources

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Environmental DNA (eDNA) metabarcoding provides an efficient approach for documenting biodiversity patterns in marine and terrestrial ecosystems. The complexity of these data prevents current methods from extracting and analyzing all the relevant ecological information they contain, and new methods may provide better dimensionality reduction and clustering. Here we present two new deep learning‐based methods that combine different types of neural networks (NNs) to ordinate eDNA samples and visualize ecosystem properties in a two‐dimensional space: the first is based on variational autoencoders and the second on deep metric learning. The strength of our new methods lies in the combination of two inputs: the number of sequences found for each molecular operational taxonomic unit (MOTU) detected and their corresponding nucleotide sequence. Using three different datasets, we show that our methods accurately represent several biodiversity indicators in a two‐dimensional latent space: MOTU richness per sample, sequence α‐diversity per sample, Jaccard's and sequence β‐diversity between samples. We show that our nonlinear methods are better at extracting features from eDNA datasets while avoiding the major biases associated with eDNA. Our methods outperform traditional dimension reduction methods such as Principal Component Analysis, t‐distributed Stochastic Neighbour Embedding, Nonmetric Multidimensional Scaling and Uniform Manifold Approximation and Projection for dimension reduction. Our results suggest that NNs provide a more efficient way of extracting structure from eDNA metabarcoding data, thereby improving their ecological interpretation and thus biodiversity monitoring.

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Key factors to consider in the use of environmental DNA metabarcoding to monitor terrestrial ecological restoration

Cited by 19 publications

References 130 publications

Detection of vertebrates from natural and artificial inland water bodies in a semi‐arid habitat using eDNA from filtered, swept, and sediment samples

Detection of vertebrates from natural and artificial inland water bodies in a semi‐arid habitat using eDNA from filtered, swept, and sediment samples

Plant‐derived environmental DNA complements diversity estimates from traditional arthropod monitoring methods but outperforms them detecting plant–arthropod interactions

New deep learning‐based methods for visualizing ecosystem properties using environmental DNA metabarcoding data

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