Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: The bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).
Pereira, H. M. et al. Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050. Science 384, 458–465 (2024).
Faria, D. et al. The breakdown of ecosystem functionality driven by deforestation in a global biodiversity hotspot. Biol. Conserv. 283, 110126 (2023).
Magurran, A. E. et al. Long-term datasets in biodiversity research and monitoring: Assessing change in ecological communities through time. Trends Ecol. Evol. 25, 574–582 (2010).
Gering, J. C., Crist, T. O. & Veech, J. A. Additive partitioning of species diversity across multiple Spatial scales: Implications for regional conservation of biodiversity. Conserv. Biol. 17, 488–499 (2003).
Olden, J. D. Biotic homogenization: A new research agenda for conservation biogeography. J. Biogeogr. 33, 2027–2039 (2006).
Dornelas, M. et al. Looking back on biodiversity change: Lessons for the road ahead. Philos. Trans. R. Soc. B Biol. Sci. 378, 20220199 (2023).
Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Conserv. 213, 280–294 (2017).
Knape, J., Coulson, S. J., van der Wal, R. & Arlt, D. Temporal trends in opportunistic citizen science reports across multiple taxa. Ambio 51, 183–198 (2022).
Geurts, E. M., Reynolds, J. D. & Starzomski, B. M. Turning observations into biodiversity data: Broadscale Spatial biases in community science. Ecosphere 14, e4582 (2023).
Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R. & Legendre, F. Taxonomic bias in biodiversity data and societal preferences. Sci. Rep. 7, 9132 (2017).
Ward, D. F. Understanding sampling and taxonomic biases recorded by citizen scientists. J. Insect Conserv. 18, 753–756 (2014).
Kosmala, M., Wiggins, A., Swanson, A. & Simmons, B. Assessing data quality in citizen science. Front. Ecol. Environ. 14, 551–560 (2016).
August, T. A., Pescott, O. L., Joly, A. & Bonnet, P. AI Naturalists might hold the key to unlocking biodiversity data in social media imagery. PATTER 1, (2020).
Selvarajah, M. Beast mode: Can technology help protect some of the world’s most endangered animals?.
Elbrecht, V., Vamos, E. E., Meissner, K., Aroviita, J. & Leese, F. Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods Ecol. Evol. 8, 1265–1275 (2017).
Buchner, D. et al. Upscaling biodiversity monitoring: Metabarcoding estimates 31,846 insect species from malaise traps across Germany. Mol. Ecol. Resour. 25, e14023 (2025).
Bunholi, I. V., Foster, N. R. & Casey, J. M. Environmental DNA and RNA in aquatic community ecology: Toward methodological standardization. Environ. DNA. 5, 1133–1147 (2023).
Thomsen, P. F., Jensen, M. R. & Sigsgaard, E. E. A vision for global eDNA-based monitoring in a changing world. Cell 187, 4444–4448 (2024).
Newton, J. P., Allentoft, M. E., Bateman, P. W., van der Heyde, M. & Nevill, P. Targeting terrestrial vertebrates with eDNA: Trends, perspectives, and considerations for sampling. Environ. DNA. 7, e70056 (2025).
Broadhurst, H. A. et al. From water to land: A review on the applications of environmental DNA and Invertebrate-Derived DNA for monitoring terrestrial and Semi-Aquatic mammals. Mammal Rev. N/a, e70006 (2025).
Nørgaard, L. et al. eDNA metabarcoding for biodiversity assessment, generalist predators as sampling assistants. Sci. Rep. 11, 6820 (2021).
Ariza, M. et al. Plant biodiversity assessment through soil eDNA reflects Temporal and local diversity. Methods Ecol. Evol. 14, 415–430 (2023).
Johnson, M. & Barnes, M. A. Macrobial airborne environmental DNA analysis: A review of progress, challenges, and recommendations for an emerging application. Mol. Ecol. Resour. n/a, e13998.
Johnson, M. D., Barnes, M. A., Garrett, N. R. & Clare, E. L. Answers blowing in the wind: Detection of birds, mammals, and amphibians with airborne environmental DNA in a natural environment over a yearlong survey. Environ. DNA. 5, 375–387 (2023).
Garrett, N. R. et al. Out of thin air: Surveying tropical Bat roosts through air sampling of eDNA. PeerJ 11, e14772 (2023).
Lynggaard, C., Frøslev, T. G., Johnson, M. S., Olsen, M. T. & Bohmann, K. Airborne environmental DNA captures terrestrial vertebrate diversity in nature. Mol. Ecol. Resour. 24, e13840 (2024).
Roger, F. et al. Airborne environmental DNA metabarcoding for the monitoring of terrestrial insects—A proof of concept from the field. Environ. DNA. 4, 790–807 (2022).
Littlefair, J. E. et al. Air-quality networks collect environmental DNA with the potential to measure biodiversity at continental scales. Curr. Biol. 33, R426–R428 (2023).
Klepke, M. J., Sigsgaard, E. E., Jensen, M. R., Olsen, K. & Thomsen, P. F. Accumulation and diversity of airborne, eukaryotic environmental DNA. Environ. DNA. 4, 1323–1339 (2022).
Di Marco, M. et al. Changing trends and persisting biases in three decades of conservation science. Global Ecol. Conserv. 10, 32–42 (2017).
Everts, T. et al. Phenological mismatches mitigate the ecological impact of a biological invader on amphibian communities. Ecol. Appl. e3017 https://doi.org/10.1002/eap.3017 (2024).
Tsuji, S., Doi, H., Hibino, Y., Shibata, N. & Watanabe, K. Rapid assessment of invasion front and biological impact of the invasive fish Coreoperca herzi using quantitative eDNA metabarcoding. Biol. Invasions. https://doi.org/10.1007/s10530-024-03364-9 (2024).
Després, V. R. et al. Primary biological aerosol particles in the atmosphere: A review | tellus B: Chemical and physical meteorology. Tellus B Chem. Phys. Meteorol. 64, 15598 (2012).
Johnson, M. D., Cox, R. D. & Barnes, M. A. The detection of a non-anemophilous plant species using airborne eDNA. PLoS ONE. 14, e0225262 (2019).
Wittmaack, K., Wehnes, H., Heinzmann, U. & Agerer, R. An overview on bioaerosols viewed by scanning electron microscopy. Sci. Total Environ. 346, 244–255 (2005).
Pumkaeo, P., Takahashi, J. & Iwahashi, H. Detection and monitoring of insect traces in bioaerosols. PeerJ 9, e10862 (2021).
Turner, C. R. et al. Particle size distribution and optimal capture of aqueous macrobial eDNA. Methods Ecol. Evol. 5, 676–684 (2014).
Barnes, M. A. et al. Environmental conditions influence eDNA particle size distribution in aquatic systems. Environ. DNA. 3, 643–653 (2021).
Moushomi, R., Wilgar, G., Carvalho, G., Creer, S. & Seymour, M. Environmental DNA size sorting and degradation experiment indicates the state of Daphnia magna mitochondrial and nuclear eDNA is subcellular. Sci. Rep. 9, 12500 (2019).
Barberán, A. et al. Continental-scale distributions of dust-associated bacteria and fungi. Proc. Natl. Acad. Sci. 112, 5756–5761 (2015).
Williams, K. R. et al. Annual Report for 2023 on the UK Heavy Metals Monitoring Network. (2024). https://doi.org/10.47120/npl.ENV55
Schwendemann, A. B. et al. Aerodynamics of saccate pollen and its implications for wind pollination. Am. J. Bot. 94, 1371–1381 (2007).
Abrego, N. et al. Give me a sample of air and I will tell which species are found from your region: Molecular identification of fungi from airborne spore samples. Mol. Ecol. Resour. 18, 511–524 (2018).
Bowen, A. J. & Lindley, D. A wind-tunnel investigation of the wind speed and turbulence characteristics close to the ground over various escarpment shapes. Boundary-Layer Meteorol. 12, 259–271 (1977).
Hesp, P. A., Davidson-Arnott, R., Walker, I. J. & Ollerhead, J. Flow dynamics over a foredune at Prince Edward Island, Canada. Geomorphology 65, 71–84 (2005).
Lynggaard, C. et al. Airborne environmental DNA for terrestrial vertebrate community monitoring. Curr. Biol. 32, 701–707e5 (2022).
Polling, M., Buij, R., Laros, I. & de Groot, G. A. Continuous daily sampling of airborne eDNA detects all vertebrate species identified by camera traps. Environ. DNA. 6, e591 (2024).
Cáceres, C. E. & Soluk, D. A. Blowing in the wind: A field test of overland dispersal and colonization by aquatic invertebrates. Oecologia 131, 402–408 (2002).
Brendonck, L. & Riddoch, B. J. Wind-borne short-range egg dispersal in anostracans (Crustacea: Branchiopoda). Biol. J. Linn. Soc. 67, 87–95 (1999).
Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J. C. & Altermatt, F. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat. Commun. 7, 12544 (2016).
Brehm, G. et al. Turning up the heat on a hotspot: DNA barcodes reveal 80% more species of geometrid moths along an Andean elevational gradient. PLOS ONE. 11, e0150327 (2016).
Fediajevaite, J., Priestley, V., Arnold, R. & Savolainen, V. Meta-analysis shows that environmental DNA outperforms traditional surveys, but warrants better reporting standards. Ecol. Evol. 11, 4803–4815 (2021).
Maracle, S. R. et al. Nearshore fish diversity changes with sampling method and human disturbance: Comparing eDNA metabarcoding and Seine netting along the upper St. Lawrence river. J. Great Lakes Res. 50, 102317 (2024).
Callaghan, C. T., Poore, A. G. B., Hofmann, M., Roberts, C. J. & Pereira, H. M. Large-bodied birds are over-represented in unstructured citizen science data. Sci. Rep. 11, 19073 (2021).
Koch, W., Hogeweg, L., Nilsen, E. B., O’Hara, R. B. & Finstad, A. G. Recognizability bias in citizen science photographs. R. Soc. Open. Sci. 10, 221063 (2023).
Goldstein, B. R. et al. Logistical and preference bias in participatory science butterfly data. Front. Ecol. Environ. e2783 https://doi.org/10.1002/fee.2783 (2024).
Wong, M. K. L. & Didham, R. K. Global meta-analysis reveals overall higher nocturnal than diurnal activity in insect communities. Nat. Commun. 15, 3236 (2024).
Lamb, P. D. et al. How quantitative is metabarcoding: A meta-analytical approach. Mol. Ecol. 28, 420–430 (2019).
Garrett, N. R. et al. Airborne eDNA documents a diverse and ecologically complex tropical Bat and other mammal community. Environ. DNA. 5, 350–362 (2023).
Zinger, L. et al. DNA metabarcoding—Need for robust experimental designs to draw sound ecological conclusions. Mol. Ecol. 28, 1857–1862 (2019).
Mathon, L. et al. Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification. Mol. Ecol. Resour. 21, 2565–2579 (2021).
Gold, Z. et al. Signal and noise in metabarcoding data. PLoS ONE. 18, e0285674 (2023).
Sullivan, A. R. et al. Airborne eDNA captures three decades of ecosystem biodiversity. BioRxiv (2023).
Lanzén, A., Lekang, K., Jonassen, I., Thompson, E. M. & Troedsson, C. DNA extraction replicates improve diversity and compositional dissimilarity in metabarcoding of eukaryotes in marine sediments. PLoS ONE. 12, e0179443 (2017).
Adams, C. I. M. et al. Beyond biodiversity: Can environmental DNA (eDNA) cut it as a population genetics tool?? Genes 10, 192 (2019).
Taylor, P. Reproducibility of ancient DNA sequences from extinct pleistocene fauna. Mol. Biol. Evol. 13, 283–285 (1996).
Calvignac-Spencer, S. et al. Carrion fly-derived DNA as a tool for comprehensive and cost-effective assessment of mammalian biodiversity. Mol. Ecol. 22, 915–924 (2013).
Ushio, M. et al. Environmental DNA enables detection of terrestrial mammals from forest pond water. Mol. Ecol. Resour. 17, e63–e75 (2017).
Thalinger, B., Empey, R., Cowperthwaite, M. & Coveny, K. & Steinke, D. BirT: A novel primer pair for avian environmental DNA metabarcoding. bioRxiv 2023–08 (2023).
Zeale, M. R. K., Butlin, R. K., Barker, G. L. A., Lees, D. C. & Jones, G. Taxon-specific PCR for DNA barcoding arthropod prey in Bat faeces. Mol. Ecol. Resour. 11, 236–244 (2011).
Cheng, T. et al. Barcoding the Kingdom plantae: New PCR primers for regions of plants with improved universality and specificity. Mol. Ecol. Resour. 16, 138–149 (2016).
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: A fast and accurate illumina Paired-End read merger. Bioinformatics 30, 614–620 (2014).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Callahan, B. J. et al. DADA2: High-resolution sample inference from illumina amplicon data. Nat. Methods. 13, 581–583 (2016).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2024).
Edgar, R. SINTAX: A simple non-Bayesian taxonomy classifier for 16S and ITS sequences. biorxiv 074161 (2016).
Banchi, E. et al. PLANiTS: A curated sequence reference dataset for plant ITS DNA metabarcoding. Database baz155 (2020). (2020).
Abarenkov, K. et al. The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: Sequences, taxa and classifications reconsidered. Nucleic Acids Res. 52, D791–D797 (2024).
Tournayre, O. et al. Enhancing metabarcoding of freshwater biotic communities: A new online tool for primer selection and exploring data from 14 primer pairs. Environ. DNA. 6, e590 (2024).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Wickham, H., Vaughan, D. & Girlich, M. Tidy Messy Data. (2024).
Wickham, H., François, R., Henry, L. & Müller, K. & Vaughan, D. dplyr: A grammar of data manipulation. (2023).
Wickham, H. Simple, Consistent Wrappers for Common String Operations. (2023).
NBN Trust. The National Biodiversity Network (NBN) Atlas. (2024). https://ror.org/00mcxye41
Pebesma, E. Simple features for R: standardized support for Spatial vector data. R J. 10, 439–446 (2018).
Massicotte, P. & South, A. rnaturalearth: World Map Data from Natural Earth. (2023).