Airborne eDNA captures three decades of ecosystem biodiversity

1. Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).

   Google Scholar 

2. Ceballos, G. et al. Accelerated modern human-induced species losses: Entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).

   Google Scholar 

3. Cristescu, M. E. & Hebert, P. D. N. Uses and misuses of environmental DNA in biodiversity science and conservation. Annu. Rev. Ecol. Evol. Syst. 49, 209–230 (2018).

   Google Scholar 

4. Bálint, M. et al. Environmental DNA time series in ecology. Trends Ecol. Evol. 33, 945–957 (2018).

   Google Scholar 

5. Seeber, P. A. & Epp, L. S. Environmental DNA and metagenomics of terrestrial mammals as keystone taxa of recent and past ecosystems. Mamm. Rev. 52, 538–553 (2022).

   Google Scholar 

6. Djurhuus, A. et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. 11, 254 (2020).

   Google Scholar 

7. van der Heyde, M., Bunce, M. & Nevill, P. Key factors to consider in the use of environmental DNA metabarcoding to monitor terrestrial ecological restoration. Sci. Total Environ. 848, 157617 (2022).

   Google Scholar 

8. Clare, E. L. et al. Measuring biodiversity from DNA in the air. Curr. Biol. 32, 693–700 (2022).

   Google Scholar 

9. Lynggaard, C. et al. Airborne environmental DNA for terrestrial vertebrate community monitoring. Curr. Biol. 32, 701–707 (2022).

   Google Scholar 

10. 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).

    Google Scholar 

11. Després, V. R. et al. Primary biological aerosol particles in the atmosphere: A review. Tellus B Chem. Phys. Meteorol. 64, 15598 (2012).

    Google Scholar 

12. Šantl-Temkiv, T., Amato, P., Casamayor, E. O., Lee, P. K. H. & Pointing, S. B. Microbial ecology of the atmosphere. FEMS Microbiol. Rev. 46, fuac009 (2022).

    Google Scholar 

13. Fröhlich-Nowoisky, J. et al. Bioaerosols in the Earth system: Climate, health, and ecosystem interactions. Atmos. Res. 182, 346–376 (2016).

    Google Scholar 

14. Métris, K. L. & Métris, J. Aircraft surveys for air eDNA: probing biodiversity in the sky. PeerJ. 11, e15171 (2023).

    Google Scholar 

15. Karlsson, E. et al. Airborne microbial biodiversity and seasonality in Northern and Southern Sweden. PeerJ. 8, e8424 (2020).

    Google Scholar 

16. Bowers, R. M. et al. Seasonal variability in bacterial and fungal diversity of the near-surface atmosphere. Environ. Sci. Technol. 47, 12097–12106 (2013).

    Google Scholar 

17. Bowers, R. M., McLetchie, S., Knight, R. & Fierer, N. Spatial variability in airborne bacterial communities across land-use types and their relationship to the bacterial communities of potential source environments. ISME J. 5, 601–612 (2011).

    Google Scholar 

18. Johnson, M. D., Cox, R. D., Grisham, B. A., Lucia, D. & Barnes, M. A. Airborne eDNA reflects human activity and seasonal changes on a landscape scale. Front. Environ. Sci. 8, 563431 (2021).

    Google Scholar 

19. 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).

    Google Scholar 

20. 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).

    Google Scholar 

21. 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).

    Google Scholar 

22. Pumkaeo, P., Takahashi, J. & Iwahashi, H. Detection and monitoring of insect traces in bioaerosols. PeerJ. 9, https://doi.org/10.7717/peerj.10862 (2021).

23. 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).

    Google Scholar 

24. Helin, A. et al. Characterization of free amino acids, bacteria and fungi in size-segregated atmospheric aerosols in boreal forest: Seasonal patterns, abundances and size distributions. Atmos. Chem. Phys. 17, 13089–13101 (2017).

    Google Scholar 

25. Mamanova, L. et al. Target-enrichment strategies for next-generation sequencing. Nat. Methods 7, 111–118 (2010).

    Google Scholar 

26. Gonzalez, A. et al. Avoiding pandemic fears in the subway and conquering the platypus. mSystems 1, e00050–16 (2016).

    Google Scholar 

27. Lu, J. et al. Metagenome analysis using the Kraken software suite. Nat. Protoc. 17, 2815–2839 (2022).

    Google Scholar 

28. Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794 (2016).

29. GBIF.org GBIF Occurrence Download https://doi.org/10.15468/dl.cjxesu (2020).

30. Yates, M. C., Fraser, D. J. & Derry, A. M. Meta-analysis supports further refinement of eDNA for monitoring aquatic species-specific abundance in nature. Environ. DNA 1, 5–13 (2019).

    Google Scholar 

31. Yates, M. C. et al. The relationship between eDNA particle concentration and organism abundance in nature is strengthened by allometric scaling. Mol. Ecol. 30, 3068–3082 (2021).

    Google Scholar 

32. 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).

33. Harrison, J. B., Sunday, J. M. & Rogers, S. M. Predicting the fate of eDNA in the environment and implications for studying biodiversity. Proc. R. Soc. B Biol. Sci. 286, 20191409 (2019).

    Google Scholar 

34. Valentin, R. E. et al. Moving eDNA surveys onto land: Strategies for active eDNA aggregation to detect invasive forest insects. Mol. Ecol. Resour. 20, 746–755 (2020).

    Google Scholar 

35. Kirtane, A., Kleyer, H. & Deiner, K. Sorting states of environmental DNA: Effects of isolation method and water matrix on the recovery of membrane-bound, dissolved, and adsorbed states of eDNA. Environ. DNA 5, 582–596 (2023).

    Google Scholar 

36. Manninen, H. E. et al. Patterns in airborne pollen and other primary biological aerosol particles (PBAP), and their contribution to aerosol mass and number in a boreal forest. Boreal Environ. Res. 19, 383–405 (2014).

    Google Scholar 

37. li, S. & Georgopoulos, P. A mechanistic modeling system for estimating large-scale emissions and transport of pollen and co-allergens. Atmos. Environ. 45, 2260–2276 (2011).

    Google Scholar 

38. Nordén, J., Penttilä, R., Siitonen, J., Tomppo, E. & Ovaskainen, O. Specialist species of wood-inhabiting fungi struggle while generalists thrive in fragmented boreal forests. J. Ecol. 101, 701–712 (2013).

39. Woo, C., An, C., Xu, S., Yi, S. M. & Yamamoto, N. Taxonomic diversity of fungi deposited from the atmosphere. ISME J. 12, 2051–2060 (2018).

    Google Scholar 

40. Clauß, M. Particle size distribution of airborne micro-organisms in the environment-A review. Landbauforschung Volkenrode 65, 77–100 (2015).

    Google Scholar 

41. Ruiz-Jimenez, J. et al. Determination of free amino acids, saccharides, and selected microbes in biogenic atmospheric aerosols – Seasonal variations, particle size distribution, chemical and microbial relations. Atmos. Chem. Phys. 21, 8775–8790 (2021).

    Google Scholar 

42. Brook, J. R., Johnson, D. & Mamedov, A. Determination of the source areas contributing to regionally high warm season PM2.5 in eastern north america. J. Air Waste Manage Assoc. 54, 1162–1169 (2004).

    Google Scholar 

43. Zhou, L., Hopke, P. K. & Liu, W. Comparison of two trajectory based models for locating particle sources for two rural New York sites. Atmos. Environ. 38, 1955–1963 (2004).

44. Hopke, P. K. Review of receptor modeling methods for source apportionment. J. Air Waste Manage Assoc. 66, 237–259 (2016).

    Google Scholar 

45. Belis, C. et al. European Guide on Air Pollution Apportionment with Receptor Models. (2019).

46. Lavsund, S., Nygrén, T. & Solberg, E. J. Status of moose populations and challenges to moose management in Fennoscandia. Alces 39, 109–130 (2003).

    Google Scholar 

47. Singh, N. J., Börger, L., Dettki, H., Bunnefeld, N. & Ericsson, G. From migration to nomadism: Movement variability in a northern ungulate across its latitudinal range. Ecol. Appl. 22, 2007–2020 (2012).

    Google Scholar 

48. Watson, J. G., Chen, L. W. A., Chow, J. C., Doraiswamy, P. & Lowenthal, D. H. Source apportionment: Findings from the U.S. supersites program. J Air Waste Manage Assoc. 58, 265–288 (2008).

    Google Scholar 

49. Blackman, R. et al. Environmental DNA: The next chapter. Mol. Ecol. 33, e17355 (2024).

    Google Scholar 

50. Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 2224 (2017).

    Google Scholar 

51. Roche, K. E. & Mukherjee, S. The accuracy of absolute differential abundance analysis from relative count data. PLoS Comput. Biol. 18, e1010284 (2022).

    Google Scholar 

52. Quinn, T. P., Richardson, M. F., Lovell, D. & Crowley, T. M. Propr: An R-package for identifying proportionally abundant features using compositional data analysis. Sci. Rep. 7, 16252 (2017).

    Google Scholar 

53. Haas, J. C. et al. Microbial community response to growing season and plant nutrient optimisation in a boreal Norway spruce forest. Soil Biol. Biochem. 125, 197–209 (2018).

    Google Scholar 

54. Bowers, R. M. et al. Sources of bacteria in outdoor air across cities in the midwestern United States. Appl. Environ. Microbiol. 77, 6350–6356 (2011).

    Google Scholar 

55. van der Merwe, M., Ericson, L., Walker, J., Thrall, P. H. & Burdon, J. J. Evolutionary relationships among species of Puccinia and Uromyces (Pucciniaceae, Uredinales) inferred from partial protein coding gene phylogenies. Mycol Res. 111, 163–175 (2007).

    Google Scholar 

56. Terhonen, E., Blumenstein, K., Kovalchuk, A. & Asiegbu, F. O. Forest tree microbiomes and associated fungal endophytes: Functional roles and impact on forest health. Forests 10, 42 (2019).

    Google Scholar 

57. Ren, F. et al. Tissue microbiome of Norway spruce affected by heterobasidion-induced wood decay. Microb. Ecol. 77, 640–650 (2019).

    Google Scholar 

58. Ross, A. A., Müller, K. M., Scott Weese, J. & Neufeld, J. D. Comprehensive skin microbiome analysis reveals the uniqueness of human skin and evidence for phylosymbiosis within the class Mammalia. Proc. Natl. Acad. Sci. USA 115, E5786–E5795 (2018).

    Google Scholar 

59. Wiśniewska, K., Lewandowska, A. U. & Śliwińska-Wilczewska, S. The importance of cyanobacteria and microalgae present in aerosols to human health and the environment – Review study. Environ. Int. 131, 104964 (2019).

    Google Scholar 

60. Vázquez, D. P., Gianoli, E., Morris, W. F. & Bozinovic, F. Ecological and evolutionary impacts of changing climatic variability. Biol. Rev. 92, 22–42 (2017).

    Google Scholar 

61. Reeve, R. et al. How to partition diversity. Preprint at https://doi.org/10.48550/arXiv.1404.6520 (2016).

62. Leinster, T. Entropy and Diversity: The Axiomatic Approach. (Cambridge University Press, Cambridge, 2021).

63. Hill, M. O. Diversity and evenness: A unifying notation and its consequences. Ecology 54, 427–432 (1973).

    Google Scholar 

64. Sax, D. F. & Gaines, S. D. Species diversity: From global decreases to local increases. Trends Ecol. Evol. 18, 561–566 (2003).

    Google Scholar 

65. Clavel, J., Julliard, R. & Devictor, V. Worldwide decline of specialist species: Toward a global functional homogenization?. Front. Ecol. Environ. 9, 222–228 (2011).

    Google Scholar 

66. Ylisirniö, A. L. et al. Dead wood and polypore diversity in natural post-fire succession forests and managed stands – Lessons for biodiversity management in boreal forests. For. Ecol. Manage 286, 16–27 (2012).

    Google Scholar 

67. Uboni, A., Blochel, A., Kodnik, D. & Moen, J. Modelling occurrence and status of mat-forming lichens in boreal forests to assess the past and current quality of reindeer winter pastures. Ecol. Indic. 96, 99–106 (2019).

    Google Scholar 

68. Jonsson, B. G. et al. Rapid changes in ground vegetation of mature boreal forests—an analysis of Swedish national forest inventory data. Forests 12, 475 (2021).

    Google Scholar 

69. SLU Artdatabanken. Rödlistade Arter i Sverige 2020. (SLU, Uppsala, 2020).

70. Sandström, J. et al. Impacts of dead wood manipulation on the biodiversity of temperate and boreal forests. A systematic review. J. Appl. Ecol. 56, 1770–1781 (2019).

    Google Scholar 

71. Bergstedt, J., Hagner, M. & Milberg, P. Effects on vegetation composition of a modified forest harvesting and propagation method compared with clear-cutting, scarification and planting. Appl. Veg. Sci. 11, 159–168 (2008).

    Google Scholar 

72. Edman, M., Gustafsson, M., Stenlid, J., Jonsson, B. G. & Ericson, L. Spore deposition of wood-decaying fungi: Importance of landscape composition. Ecography 27, 103–111 (2004).

    Google Scholar 

73. Siitonen, P., Lehtinen, A. & Siitonen, M. Effects of forest edges on the distribution, abundance, and regional persistence of wood-rotting fungi. Conserv. Biol. 19, 250–260 (2005).

    Google Scholar 

74. Lewin, H. A. et al. Earth BioGenome project: sequencing life for the future of life. Proc. Natl. Acad. Sci. USA 115, 4325–4333 (2018).

    Google Scholar 

75. Masson, O. et al. Airborne concentrations and chemical considerations of radioactive ruthenium from an undeclared major nuclear release in 2017. Proc. Natl. Acad. Sci. USA 116, 16750–16759 (2019).

    Google Scholar 

76. The Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). Annu. Rep. 2022. (2023).

77. Söderström, C., Ban, S., Jansson, P., Lindh, K. & Tooloutalaie, N. Radionuclides in Ground Level Air in Sweden Year 2006. (2007).

78. Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl. Acad. Sci. USA 110, 15758–15763 (2013).

    Google Scholar 

79. Slon, V. et al. Neandertal and Denisovan DNA from Pleistocene sediments. Science 356, 605–608 (2017).

    Google Scholar 

80. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

81. Bushnell, B.BBMap Short Read Aligner. Joint Genome Institute, Department of Energy (2014).

82. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 1–13 (2019).

    Google Scholar 

83. Martín-Fernández, J. A., Hron, K., Templ, M., Filzmoser, P. & Palarea-Albaladejo, J. Bayesian-multiplicative treatment of count zeros in compositional data sets. Stat. Modelling 15, 134–158 (2015).

    Google Scholar 

84. Palarea-Albaladejo, J. & Martín-Fernández, J. A. zCompositions — R package for multivariate imputation of left-censored data under a compositional approach. Chemometr. Intell. Lab. Syst. 143, 85–96 (2015).

    Google Scholar 

85. Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. InProceedings of the 9th Python in Science Conference 92–96 (2010).

86. van den Boogaart, K. G. & Tolosana-Delgado, R. ‘compositions’: A unified R package to analyze compositional data. Comput. Geosci. 34, 320–338 (2008).

    Google Scholar 

87. Oksanen, J. et al. vegan: Community Ecology Package. R package at https://CRAN.R-project.org/package=vegan (2020).

88. GBIF.org GBIF Occurrence Download https://doi.org/10.15468/dl.xnyctg. (2020).

89. Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).

    Google Scholar 

90. Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009).

    Google Scholar 

91. Lindqvist, J. En Stokastisk Partikelmodell i Ett Icke-Metriskt Koordinatsystem. FOI-R–99-01086-862-SE, Swedish Defence Research Agency (1999).

92. Muñoz-Sabater, J. ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.e2161bac (Accessed September 2019) (2019).

93. Canty, A. & Ripley, B. boot: Bootstrap Functions (Originally by Angelo Canty for S). R package at https://cran.r-project.org/package=boot (2022).

94. Davison, A. C. & Hinkley, D. V. Bootstrap Methods and Their Application. Bootstrap Methods and their Application (Cambridge University Press, Cambridge, 1997).

95. Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077 (2015).

    Google Scholar 

96. Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–472 (1996).

    Google Scholar 

97. Carslaw, D. C. & Ropkins, K. Openair – An r package for air quality data analysis. Environ. Model. Softw. 27, 52–61 (2012).

    Google Scholar 

98. Scott, S. L. & Varian, H. R. Predicting the present with Bayesian structural time series. Int. J. Math. Model. Num. Optimis. 5, 4–23 (2014).

    Google Scholar 

99. Scott, S. L. bsts: Bayesian Structural Time Series. R package at https://CRAN.R-project.org/package=bsts (2022).

100. Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).

     Google Scholar 

101. Bürkner, P. C., Gabry, J. & Vehtari, A. Approximate leave-future-out cross-validation for Bayesian time series models. J. Stat. Comput. Simul. 90, 2499–2523 (2020).

     Google Scholar 

102. Durbin, J. & Koopman, S. J. Time Series Analysis by State Space Methods. Time Series Analysis by State Space Methods (Oxford University Press, Oxford, 2012).

103. Commandeur, J. J. F. & Koopman, S. J. An Introduction to State Space Time Series Analysis. (Oxford University Press, Incorporated, 2007).

104. Geweke, J. Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments. in Bayesian Statistics (eds. Bernardo, J. M., Berger, O., Dawid, A. P. & Smith, A. F. M.) vol. 4 169–193 (Clarendon Press, Oxford, 1992).

105. Raftery, A. E. & Lewis, S. M. Comment: One long run with diagnostics: Implementation strategies for markov chain monte carlo. Stat. Sci. 7, 493–497 (1992).

     Google Scholar 

106. Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence Diagnosis and Output Analysis for MCMC. R News 6, 7–11 (2006).

     Google Scholar 

107. GBIF.org. GBIF Occurrence Download https://doi.org/10.15468/dl.k76kgd (2021).

108. Holmes, E. E., Ward, E. J. & Wills, K. MARSS: Multivariate autoregressive state-space models for analyzing time-series data. R J 4, 11–19 (2012).

     Google Scholar 

109. Holmes, E. E., Scheuerell, M. D. & Ward, E. J. Detecting a signal from noisy sensors. in Applied Time Series Analysis for Fisheries and Environmental Data. (2021).

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