Upscaling biodiversity monitoring: Metabarcoding estimates 31,846 insect species from Malaise traps across Germany.
DNA metabarcoding
Malaise trap
biodiversity monitoring
insect diversity
Journal
Molecular ecology resources
ISSN: 1755-0998
Titre abrégé: Mol Ecol Resour
Pays: England
ID NLM: 101465604
Informations de publication
Date de publication:
04 Oct 2024
04 Oct 2024
Historique:
revised:
05
09
2024
received:
28
12
2023
accepted:
12
09
2024
medline:
4
10
2024
pubmed:
4
10
2024
entrez:
4
10
2024
Statut:
aheadofprint
Résumé
Mitigating ongoing losses of insects and their key functions (e.g. pollination) requires tracking large-scale and long-term community changes. However, doing so has been hindered by the high diversity of insect species that requires prohibitively high investments of time, funding and taxonomic expertise when addressed with conventional tools. Here, we show that these concerns can be addressed through a comprehensive, scalable and cost-efficient DNA metabarcoding workflow. We use 1815 samples from 75 Malaise traps across Germany from 2019 and 2020 to demonstrate how metabarcoding can be incorporated into large-scale insect monitoring networks for less than 50 € per sample, including supplies, labour and maintenance. We validated the detected species using two publicly available databases (GBOL and GBIF) and the judgement of taxonomic experts. With an average of 1.4 M sequence reads per sample we uncovered 10,803 validated insect species, of which 83.9% were represented by a single Operational Taxonomic Unit (OTU). We estimated another 21,043 plausible species, which we argue either lack a reference barcode or are undescribed. The total of 31,846 species is similar to the number of insect species known for Germany (~35,500). Because Malaise traps capture only a subset of insects, our approach identified many species likely unknown from Germany or new to science. Our reproducible workflow (~80% OTU-similarity among years) provides a blueprint for large-scale biodiversity monitoring of insects and other biodiversity components in near real time.
Identifiants
pubmed: 39364584
doi: 10.1111/1755-0998.14023
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e14023Subventions
Organisme : Hessisches Landesamt für Naturschutz, Umwelt und Geologie
Organisme : EU Horizon 2020 project eLTER PLUS
ID : 871128
Organisme : Landes-Offensive zur Entwicklung Wissenschaftlich-ökonomischer Exzellenz of the German federal State of Hesse
Informations de copyright
Molecular Ecology Resources© 2024 The Author(s). Molecular Ecology Resources published by John Wiley & Sons Ltd.
Références
Aylagas, E., Borja, Á., Muxika, I., & Rodríguez‐Ezpeleta, N. (2018). Adapting metabarcoding‐based benthic biomonitoring into routine marine ecological status assessment networks. Ecological Indicators, 95, 194–202. https://doi.org/10.1016/j.ecolind.2018.07.044
Beermann, A. J., Zizka, V. M. A., Elbrecht, V., Baranov, V., & Leese, F. (2018). DNA metabarcoding reveals the complex and hidden responses of chironomids to multiple stressors. Environmental Sciences Europe, 30, 26. https://doi.org/10.1186/s12302‐018‐0157‐x
Borrell, Y. J., Miralles, L., Huu, H. D., Mohammed‐Geba, K., & Garcia‐Vazquez, E. (2017). DNA in a bottle—Rapid metabarcoding survey for early alerts of invasive species in ports. PLoS One, 12, e0183347. https://doi.org/10.1371/journal.pone.0183347
Braukmann, T. W. A., Ivanova, N. V., Prosser, S. W. J., Elbrecht, V., Steinke, D., Ratnasingham, S., de Waard, J. R., Sones, J. E., Zakharov, E. V., & Hebert, P. D. N. (2019). Metabarcoding a diverse arthropod mock community. Molecular Ecology Resources, 19, 711–727. https://doi.org/10.1111/1755‐0998.13008
Buchner, D. (2022a). Guanidine‐based DNA extraction with silica‐coated beads or silica spin columns. https://doi.org/10.17504/protocols.io.eq2ly73mmlx9/v2
Buchner, D. (2022b). Invertebrate bulk sample metabarcoding protocol collection. https://doi.org/10.17504/protocols.io.j8nlkw4n6l5r/v4
Buchner, D. (2022c). PCR cleanup and size selection with magnetic beads. https://doi.org/10.17504/protocols.io.36wgqj45xvk5/v3
Buchner, D. (2022d). PCR normalization and size selection with magnetic beads. https://doi.org/10.17504/protocols.io.q26g7y859gwz/v3
Buchner, D. (2022e). Sample preparation and lysis of homogenized malaise trap samples. https://doi.org/10.17504/protocols.io.dm6gpjrmjgzp/v1
Buchner, D., & Leese, F. (2020). BOLDigger—A python package to identify and organise sequences with the barcode of life data systems. Metabarcoding and Metagenomics, 4, e53535. https://doi.org/10.3897/mbmg.4.53535
Buchner, D., Beermann, A. J., Leese, F., & Weiss, M. (2021). Cooking small and large portions of “biodiversity‐soup”: Miniaturized DNA metabarcoding PCRs perform as good as large‐volume PCRs. Ecology and Evolution, 11, 9092–9099. https://doi.org/10.1002/ece3.7753
Buchner, D., Haase, P., & Leese, F. (2021). Wet grinding of invertebrate bulk samples—A scalable and cost‐efficient protocol for metabarcoding and metagenomics. Metabarcoding and Metagenomics, 5, e67533. https://doi.org/10.3897/mbmg.5.67533
Buchner, D., Macher, T.‐H., & Leese, F. (2022). APSCALE: Advanced pipeline for simple yet comprehensive analyses of DNA metabarcoding data. Bioinformatics, 38, 4817–4819. https://doi.org/10.1093/bioinformatics/btac588
Buchner, D., Macher, T.‐H., Beermann, A. J., Werner, M.‐T., & Leese, F. (2021). Standardized high‐throughput biomonitoring using DNA metabarcoding: Strategies for the adoption of automated liquid handlers. Environmental Science and Ecotechnology, 8, 100122. https://doi.org/10.1016/j.ese.2021.100122
Bush, A., Sollmann, R., Wilting, A., Bohmann, K., Cole, B., Balzter, H., Martius, C., Zlinszky, A., Calvignac‐Spencer, S., Cobbold, C. A., Dawson, T. P., Emerson, B. C., Ferrier, S., Gilbert, M. T. P., Herold, M., Jones, L., Leendertz, F. H., Matthews, L., Millington, J. D. A., … Yu, D. W. (2017). Connecting Earth observation to high‐throughput biodiversity data. Nature Ecology & Evolution, 1, 1–9. https://doi.org/10.1038/s41559‐017‐0176
Cardoso, P., Barton, P. S., Birkhofer, K., Chichorro, F., Deacon, C., Fartmann, T., Fukushima, C. S., Gaigher, R., Habel, J. C., Hallmann, C. A., Hill, M. J., Hochkirch, A., Kwak, M. L., Mammola, S., Ari Noriega, J., Orfinger, A. B., Pedraza, F., Pryke, J. S., Roque, F. O., … Samways, M. J. (2020). Scientists' warning to humanity on insect extinctions. Biological Conservation, 242, 108426. https://doi.org/10.1016/j.biocon.2020.108426
Chamberlain, S., Oldoni, D., & Waller, J. (2022). rgbif: Interface to the global biodiversity information facility API. https://doi.org/10.5281/zenodo.6023735
Chimeno, C., Hausmann, A., Schmidt, S., Raupach, M. J., Doczkal, D., Baranov, V., Hübner, J., Höcherl, A., Albrecht, R., Jaschhof, M., Haszprunar, G., & Hebert, P. D. N. (2022). Peering into the darkness: DNA barcoding reveals surprisingly high diversity of unknown species of Diptera (Insecta) in Germany. Insects, 13, 82. https://doi.org/10.3390/insects13010082
Chiu, C.‐H., Wang, Y.‐T., Walther, B. A., & Chao, A. (2014). An improved nonparametric lower bound of species richness via a modified good–turing frequency formula. Biometrics, 70, 671–682. https://doi.org/10.1111/biom.12200
Chua, P. Y. S., Bourlat, S. J., Ferguson, C., Korlevic, P., Zhao, L., Ekrem, T., Meier, R., & Lawniczak, M. K. N. (2023). Future of DNA‐based insect monitoring. Trends in Genetics, 39, 531–544. https://doi.org/10.1016/j.tig.2023.02.012
Directorate‐General for Environment (European Commission), Hochkirch, A., Casino, A., Penev, L., Allen, D., Tilley, L., Georgiev, T., Gospodinov, K., & Barov, B. (2022). European Red List of insect taxonomists. Publications Office of the European Union, LU. https://doi.org/10.2779/364246
Elbrecht, V., Bourlat, S. J., Hörren, T., Lindner, A., Mordente, A., Noll, N. W., Schäffler, L., Sorg, M., & Zizka, V. M. A. (2021). Pooling size sorted Malaise trap fractions to maximize taxon recovery with metabarcoding. PeerJ, 9, e12177. https://doi.org/10.7717/peerj.12177
Elbrecht, V., Braukmann, T. W. A., Ivanova, N. V., Prosser, S. W. J., Hajibabaei, M., Wright, M., Zakharov, E. V., Hebert, P. D. N., & Steinke, D. (2019). Validation of COI metabarcoding primers for terrestrial arthropods. PeerJ, 7, e7745. https://doi.org/10.7717/peerj.7745
Elbrecht, V., Vamos, E. E., Meissner, K., Aroviita, J., & Leese, F. (2017). Assessing strengths and weaknesses of DNA metabarcoding‐based macroinvertebrate identification for routine stream monitoring. Methods in Ecology and Evolution, 8, 1265–1275. https://doi.org/10.1111/2041‐210X.12789
Frøslev, T. G., Kjøller, R., Bruun, H. H., Ejrnæs, R., Brunbjerg, A. K., Pietroni, C., & Hansen, A. J. (2017). Algorithm for post‐clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nature Communications, 8, 1188. https://doi.org/10.1038/s41467‐017‐01312‐x
Geiger, M. F., Moriniere, J., Hausmann, A., Haszprunar, G., Wägele, W., Hebert, P. D. N., & Rulik, B. (2016). Testing the global malaise trap program—How well does the current barcode reference library identify flying insects in Germany? Biodiversity Data Journal, 4, e10671. https://doi.org/10.3897/BDJ.4.e10671
Haase, P., Frenzel, M., Klotz, S., Musche, M., & Stoll, S. (2016). The long‐term ecological research (LTER) network: Relevance, current status, future perspective and examples from marine, freshwater and terrestrial long‐term observation. Ecological Indicators, 65, 1–3. https://doi.org/10.1016/j.ecolind.2016.01.040
Habel, J. C., Ulrich, W., Segerer, A. H., Greifenstein, T., Knubben, J., Morinière, J., Bozicevic, V., Günter, A., & Hausmann, A. (2023). Insect diversity in heterogeneous agro‐environments of Central Europe. Biodiversity and Conservation, 32, 4665–4678. https://doi.org/10.1007/s10531‐023‐02717‐5
Hajibabaei, M., Porter, T. M., Wright, M., & Rudar, J. (2019). COI metabarcoding primer choice affects richness and recovery of indicator taxa in freshwater systems. PLoS One, 14, e0220953. https://doi.org/10.1371/journal.pone.0220953
Hallmann, C. A., Sorg, M., Jongejans, E., Siepel, H., Hofland, N., Schwan, H., Stenmans, W., Müller, A., Sumser, H., Hörren, T., Goulson, D., & de Kroon, H. (2017). More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS One, 12, e0185809. https://doi.org/10.1371/journal.pone.0185809
Hardulak, L. A., Morinière, J., Hausmann, A., Hendrich, L., Schmidt, S., Doczkal, D., Müller, J., Hebert, P. D. N., & Haszprunar, G. (2020). DNA metabarcoding for biodiversity monitoring in a national park: Screening for invasive and pest species. Molecular Ecology Resources, 20, 1542–1557. https://doi.org/10.1111/1755‐0998.13212
Hartop, E., Srivathsanm, A., Ronquistm, F., … Meier, R. (2022) Towards large‐scale integrative taxonomy (LIT): Resolving the dataconundrum for dark taxa. Systematic Biology, 71, 1404–1422. https://doi.org/10.1093/sysbio/syac033
Hartop, E. (2021). A multi‐faceted approach to a “dark taxon”: The hyperdiverse and poorly known scuttle flies (Diptera: Phoridae). http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva‐192276
Hausmann, A., Segerer, A. H., Greifenstein, T., Knubben, J., Morinière, J., Bozicevic, V., Doczkal, D., Günter, A., Ulrich, W., & Habel, J. C. (2020). Toward a standardized quantitative and qualitative insect monitoring scheme. Ecology and Evolution, 10, 4009–4020. https://doi.org/10.1002/ece3.6166
Hebert, P. D. N., Braukmann, T. W. A., Prosser, S. W. J., Ratnasingham, S., deWaard, J. R., Ivanova, N. V., Janzen, D. H., Hallwachs, W., Naik, S., Sones, J. E., & Zakharov, E. V. (2018). A sequel to sanger: Amplicon sequencing that scales. BMC Genomics, 19, 219. https://doi.org/10.1186/s12864‐018‐4611‐3
Hleap, J. S., Littlefair, J. E., Steinke, D., Hebert, P. D. N., & Cristescu, M. E. (2021). Assessment of current taxonomic assignment strategies for metabarcoding eukaryotes. Molecular Ecology Resources, 21, 2190–2203. https://doi.org/10.1111/1755‐0998.13407
Hobern, D. (2021). BIOSCAN: DNA barcoding to accelerate taxonomy and biogeography for conservation and sustainability. Genome, 64, 161–164. https://doi.org/10.1139/gen‐2020‐0009
Hoppeler, F., Tachamo Shah, R. D., Shah, D. N., Jähnig, S. C., Tonkin, J. D., Sharma, S., & Pauls, S. U. (2016). Environmental and spatial characterisation of an unknown fauna using DNA sequencing—An example with Himalayan Hydropsychidae (Insecta: Trichoptera). Freshwater Biology, 61, 1905–1920. https://doi.org/10.1111/fwb.12824
Huang, J., Miao, X., Wang, Q., Menzel, F., Tang, P., Yang, D., Wu, H., & Vogler, A. P. (2022). Metabarcoding reveals massive species diversity of Diptera in a subtropical ecosystem. Ecology and Evolution, 12, e8535. https://doi.org/10.1002/ece3.8535
Høye, T. T., Ärje, J., Bjerge, K., Hansen, O. L. P., Iosifidis, A., Leese, F., Mann, H. M. R., Meissner, K., Melvad, C., & Raitoharju, J. (2021). Deep learning and computer vision will transform entomology. Proceedings of the National Academy of Sciences of the United States of America, 118, e2002545117. https://doi.org/10.1073/pnas.2002545117
IPBES. (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services. Zenodo, https://doi.org/10.5281/zenodo.3553579
Iwaszkiewicz‐Eggebrecht, E., Granqvist, E., Buczek, M., Prus, M., Kudlicka, J., Roslin, T., Tack, A. J. M., Andersson, A. F., Miraldo, A., Ronquist, F., & Łukasik, P. (2023). Optimizing insect metabarcoding using replicated mock communities. Methods in Ecology and Evolution, 14, 1130–1146. https://doi.org/10.1111/2041‐210X.14073
Jeliazkov, A., Gavish, Y., Marsh, C. J., Geschke, J., Brummitt, N., Rocchini, D., Haase, P., Kunin, W. E., & Henle, K. (2022). Sampling and modelling rare species: Conceptual guidelines for the neglected majority. Global Change Biology, 28, 3754–3777. https://doi.org/10.1111/gcb.16114
Ji, Y., Ashton, L., Pedley, S. M., Edwards, D. P., Tang, Y., Nakamura, A., Kitching, R., Dolman, P. M., Woodcock, P., Edwards, F. A., Larsen, T. H., Hsu, W. W., Benedick, S., Hamer, K. C., Wilcove, D. S., Bruce, C., Wang, X., Levi, T., Lott, M., … Yu, D. W. (2013). Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. Ecology Letters, 16, 1245–1257. https://doi.org/10.1111/ele.12162
Karlsson, D., Hartop, E., Forshage, M., Jaschhof, M., & Ronquist, F. (2020). The Swedish malaise trap project: A 15 year retrospective on a countrywide insect inventory. Biodiversity Data Journal, 8, e47255. https://doi.org/10.3897/BDJ.8.e47255
Keck, F., & Altermatt, F. (2023). Management of DNA reference libraries for barcoding and metabarcoding studies with the R package refdb. Molecular Ecology Resources, 23, 511–518. https://doi.org/10.1111/1755‐0998.13723
Klausnitzer, B. (2005). Die Insektenfauna Deutschlands (“Entomofauna Germanica”)‐ein Gesamtüberblick. Linzer Biologische Beiträge, 37, 87–97.
Li, M., Lei, T., Wang, G., Zhang, D., Liu, H., & Zhang, Z. (2023). Monitoring insect biodiversity and comparison of sampling strategies using metabarcoding: A case study in the Yanshan Mountains, China. Ecology and Evolution, 13, e10031. https://doi.org/10.1002/ece3.10031
Marquina, D., Roslin, T., Łukasik, P., & Ronquist, F. (2022). Evaluation of non‐destructive DNA extraction protocols for insect metabarcoding: Gentler and shorter is better. Metabarcoding and Metagenomics, 6, e78871. https://doi.org/10.3897/mbmg.6.78871
Martin, M. (2011). Cutadapt removes adapter sequences from high‐throughput sequencing reads. EMBnet.Journal, 17, 10–12. https://doi.org/10.14806/ej.17.1.200
McGee, K. M., Robinson, C. V., & Hajibabaei, M. (2019). Gaps in DNA‐based biomonitoring across the globe. Frontiers in Ecology and Evolution, 7. https://doi.org/10.3389/fevo.2019.00337
Meier, R., Wong, W., Srivathsan, A., & Foo, M. (2016). $1 DNA barcodes for reconstructing complex phenomes and finding rare species in specimen‐rich samples. Cladistics, 32, 100–110. https://doi.org/10.1111/cla.12115
Meiklejohn, K. A., Damaso, N., & Robertson, J. M. (2019). Assessment of BOLD and GenBank—Their accuracy and reliability for the identification of biological materials. PLoS One, 14, e0217084. https://doi.org/10.1371/journal.pone.0217084
Mirtl, M., Borer, E. T., Djukic, I., Forsius, M., Haubold, H., Hugo, W., Jourdan, J., Lindenmayer, D., McDowell, W. H., Muraoka, H., Orenstein, D. E., Pauw, J. C., Peterseil, J., Shibata, H., Wohner, C., Yu, X., & Haase, P. (2018). Genesis, goals and achievements of long‐term ecological research at the global scale: A critical review of ILTER and future directions. Science of the Total Environment, 626, 1439–1462. https://doi.org/10.1016/j.scitotenv.2017.12.001
Montgomery, G. A., Belitz, M. W., Guralnick, R. P., & Tingley, M. W. (2021). Standards and best practices for monitoring and benchmarking insects. Frontiers in Ecology and Evolution, 8. https://doi.org/10.3389/fevo.2020.579193
Pawlowski, J., Bruce, K., Panksep, K., Aguirre, F. I., Amalfitano, S., Apothéloz‐Perret‐Gentil, L., Baussant, T., Bouchez, A., Carugati, L., Cermakova, K., Cordier, T., Corinaldesi, C., Costa, F. O., Danovaro, R., Dell'Anno, A., Duarte, S., Eisendle, U., Ferrari, B. J. D., Frontalini, F., … Fazi, S. (2022). Environmental DNA metabarcoding for benthic monitoring: A review of sediment sampling and DNA extraction methods. Science of the Total Environment, 818, 151783. https://doi.org/10.1016/j.scitotenv.2021.151783
Pereira, C. L., Gilbert, M. T. P., Araújo, M. B., & Matias, M. G. (2021). Fine‐tuning biodiversity assessments: A framework to pair eDNA metabarcoding and morphological approaches. Methods in Ecology and Evolution, 12, 2397–2409. https://doi.org/10.1111/2041‐210X.13718
Ratnasingham, S., & Hebert, P. D. N. (2007). bold: The barcode of life data system (http://www.barcodinglife.org). Molecular Ecology Notes, 7, 355–364. https://doi.org/10.1111/j.1471‐8286.2007.01678.x
Ratnasingham, S., & Hebert, P. D. N. (2013). A DNA‐based registry for all animal species: The barcode index number (BIN) system. PLoS One, 8, e66213. https://doi.org/10.1371/journal.pone.0066213
Resh, V., & Jackson, J. (1993). Rapid assessment approaches to biomonitoring using benthic macroinvertebrates. In D. M. Rosenberg & V. H. Resh (Eds.), Freshwater biomonitoring and benthic macroinvertebrates (pp. 195–233). Chapman & Hall.
Rognes, T., Flouri, T., Nichols, B., Quince, C., & Mahé, F. (2016). VSEARCH: A versatile open source tool for metagenomics. PeerJ, 4, e2584. https://doi.org/10.7717/peerj.2584
Ronquist, F., Forshage, M., Häggqvist, S., Karlsson, D., Hovmöller, R., Bergsten, J., Holston, K., Britton, T., Abenius, J., Andersson, B., Buhl, P. N., Coulianos, C.‐C., Fjellberg, A., Gertsson, C.‐A., Hellqvist, S., Jaschhof, M., Kjærandsen, J., Klopfstein, S., Kobro, S., … Gärdenfors, U. (2020). Completing Linnaeus's inventory of the Swedish insect fauna: Only 5,000 species left? PLoS One, 15, e0228561. https://doi.org/10.1371/journal.pone.0228561
Sickel, W., Zizka, V., Scherges, A., Bourlat, S. J., & Dieker, P. (2023). Abundance estimation with DNA metabarcoding—Recent advancements for terrestrial arthropods. Metabarcoding and Metagenomics, 7, e112290. https://doi.org/10.3897/mbmg.7.112290
Srivathsan, A., Ang, Y., Heraty, J. M., Hwang, W. S., Jusoh, W. F. A., Kutty, S. N., Puniamoorthy, J., Yeo, D., Roslin, T., & Meier, R. (2023). Convergence of dominance and neglect in flying insect diversity. Nature Ecology & Evolution, 7, 1012–1021. https://doi.org/10.1038/s41559‐023‐02066‐0
Steinke, D., Braukmann, T. W., Manerus, L., Woodhouse, A., & Elbrecht, V. (2021). Effects of Malaise trap spacing on species richness and composition of terrestrial arthropod bulk samples. Metabarcoding and Metagenomics, 5, e59201. https://doi.org/10.3897/mbmg.5.59201
Stork, N. E. (2018). How many species of insects and other terrestrial arthropods are there on Earth? Annual Review of Entomology, 63, 31–45. https://doi.org/10.1146/annurev‐ento‐020117‐043348
Sturmbauer, C., Opadiya, G. B., Niederstätter, H., Riedmann, A., & Dallinger, R. (1999). Mitochondrial DNA reveals cryptic oligochaete species differing in cadmium resistance. Molecular Biology and Evolution, 16, 967–974. https://doi.org/10.1093/oxfordjournals.molbev.a026186
Telenius, A. (2011). Biodiversity information goes public: GBIF at your service. Nordic Journal of Botany, 29, 378–381. https://doi.org/10.1111/j.1756‐1051.2011.01167.x
Uhler, J., Redlich, S., Zhang, J., Hothorn, T., Tobisch, C., Ewald, J., Thorn, S., Seibold, S., Mitesser, O., Morinière, J., Bozicevic, V., Benjamin, C. S., Englmeier, J., Fricke, U., Ganuza, C., Haensel, M., Riebl, R., Rojas‐Botero, S., Rummler, T., … Müller, J. (2021). Relationship of insect biomass and richness with land use along a climate gradient. Nature Communications, 12, 5946. https://doi.org/10.1038/s41467‐021‐26181‐3
Vamos, E., Elbrecht, V., & Leese, F. (2017). Short COI markers for freshwater macroinvertebrate metabarcoding. Metabarcoding and Metagenomics, 1, e14625. https://doi.org/10.3897/mbmg.1.14625
van Klink, R., August, T., Bas, Y., Bodesheim, P., Bonn, A., Fossøy, F., Høye, T. T., Jongejans, E., Menz, M. H. M., Miraldo, A., Roslin, T., Roy, H. E., Ruczyński, I., Schigel, D., Schäffler, L., Sheard, J. K., Svenningsen, C., Tschan, G. F., Wäldchen, J., … Bowler, D. E. (2022). Emerging technologies revolutionise insect ecology and monitoring. Trends in Ecology & Evolution, 37, 872–885. https://doi.org/10.1016/j.tree.2022.06.001
Wagner, D. L. (2020). Insect declines in the Anthropocene. Annual Review of Entomology, 65, 457–480. https://doi.org/10.1146/annurev‐ento‐011019‐025151
Welti, E. A. R., Zajicek, P., Frenzel, M., Ayasse, M., Bornholdt, T., Buse, J., Classen, A., Dziock, F., Engelmann, R. A., Englmeier, J., Fellendorf, M., Förschler, M. I., Fricke, U., Ganuza, C., Hippke, M., Hoenselaar, G., Kaus‐Thiel, A., Kerner, J., Kilian, D., … Haase, P. (2022). Temperature drives variation in flying insect biomass across a German malaise trap network. Insect Conservation and Diversity, 15, 168–180. https://doi.org/10.1111/icad.12555
Wetzel, F. T., Saarenmaa, H., Regan, E., Martin, C. S., Mergen, P., Smirnova, L., Tuama, É. Ó., García Camacho, F. A., Hoffmann, A., Vohland, K., & Häuser, C. L. (2015). The roles and contributions of biodiversity observation networks (BONs) in better tracking progress to 2020 biodiversity targets: A European case study. Biodiversity, 16, 137–149. https://doi.org/10.1080/14888386.2015.1075902
Wilkinson, M. D., Dumontier, M., IjJ, A., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.‐W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18
Wührl, L., Pylatiuk, C., Giersch, M., Lapp, F., von Rintelen, T., Balke, M., Schmidt, S., Cerretti, P., & Meier, R. (2022). DiversityScanner: Robotic handling of small invertebrates with machine learning methods. Molecular Ecology Resources, 22, 1626–1638. https://doi.org/10.1111/1755‐0998.13567
Zizka, V. M. A., Elbrecht, V., Macher, J.‐N., & Leese, F. (2019). Assessing the influence of sample tagging and library preparation on DNA metabarcoding. Molecular Ecology Resources, 19, 893–899. https://doi.org/10.1111/1755‐0998.13018
Zizka, V. M. A., Geiger, M. F., Hörren, T., Kirse, A., Noll, N. W., Schäffler, L., Scherges, A. M., & Sorg, M. (2022). Repeated subsamples during DNA extraction reveal increased diversity estimates in DNA metabarcoding of Malaise traps. Ecology and Evolution, 12, e9502. https://doi.org/10.1002/ece3.9502
Zizka, V. M. A., Koschorreck, J., Khan, C. C., & Astrin, J. J. (2022). Long‐term archival of environmental samples empowers biodiversity monitoring and ecological research. Environmental Sciences Europe, 34, 40. https://doi.org/10.1186/s12302‐022‐00618‐y