Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
Ceballos, G. et al. Accelerated modern human-induced species losses: Entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).
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).
Bálint, M. et al. Environmental DNA time series in ecology. Trends Ecol. Evol. 33, 945–957 (2018).
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).
Djurhuus, A. et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. 11, 254 (2020).
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).
Clare, E. L. et al. Measuring biodiversity from DNA in the air. Curr. Biol. 32, 693–700 (2022).
Lynggaard, C. et al. Airborne environmental DNA for terrestrial vertebrate community monitoring. Curr. Biol. 32, 701–707 (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).
Després, V. R. et al. Primary biological aerosol particles in the atmosphere: A review. Tellus B Chem. Phys. Meteorol. 64, 15598 (2012).
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).
Fröhlich-Nowoisky, J. et al. Bioaerosols in the Earth system: Climate, health, and ecosystem interactions. Atmos. Res. 182, 346–376 (2016).
Métris, K. L. & Métris, J. Aircraft surveys for air eDNA: probing biodiversity in the sky. PeerJ. 11, e15171 (2023).
Karlsson, E. et al. Airborne microbial biodiversity and seasonality in Northern and Southern Sweden. PeerJ. 8, e8424 (2020).
Bowers, R. M. et al. Seasonal variability in bacterial and fungal diversity of the near-surface atmosphere. Environ. Sci. Technol. 47, 12097–12106 (2013).
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).
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).
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).
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).
Pumkaeo, P., Takahashi, J. & Iwahashi, H. Detection and monitoring of insect traces in bioaerosols. PeerJ. 9, https://doi.org/10.7717/peerj.10862 (2021).
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).
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).
Mamanova, L. et al. Target-enrichment strategies for next-generation sequencing. Nat. Methods 7, 111–118 (2010).
Gonzalez, A. et al. Avoiding pandemic fears in the subway and conquering the platypus. mSystems 1, e00050–16 (2016).
Lu, J. et al. Metagenome analysis using the Kraken software suite. Nat. Protoc. 17, 2815–2839 (2022).
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).
GBIF.org GBIF Occurrence Download https://doi.org/10.15468/dl.cjxesu (2020).
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).
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).
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).
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).
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).
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).
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).
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).
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).
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).
Clauß, M. Particle size distribution of airborne micro-organisms in the environment-A review. Landbauforschung Volkenrode 65, 77–100 (2015).
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).
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).
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).
Hopke, P. K. Review of receptor modeling methods for source apportionment. J. Air Waste Manage Assoc. 66, 237–259 (2016).
Belis, C. et al. European Guide on Air Pollution Apportionment with Receptor Models. (2019).
Lavsund, S., Nygrén, T. & Solberg, E. J. Status of moose populations and challenges to moose management in Fennoscandia. Alces 39, 109–130 (2003).
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).
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).
Blackman, R. et al. Environmental DNA: The next chapter. Mol. Ecol. 33, e17355 (2024).
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).
Roche, K. E. & Mukherjee, S. The accuracy of absolute differential abundance analysis from relative count data. PLoS Comput. Biol. 18, e1010284 (2022).
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).
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).
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).
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).
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).
Ren, F. et al. Tissue microbiome of Norway spruce affected by heterobasidion-induced wood decay. Microb. Ecol. 77, 640–650 (2019).
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).
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).
Vázquez, D. P., Gianoli, E., Morris, W. F. & Bozinovic, F. Ecological and evolutionary impacts of changing climatic variability. Biol. Rev. 92, 22–42 (2017).
Reeve, R. et al. How to partition diversity. Preprint at https://doi.org/10.48550/arXiv.1404.6520 (2016).
Leinster, T. Entropy and Diversity: The Axiomatic Approach. (Cambridge University Press, Cambridge, 2021).
Hill, M. O. Diversity and evenness: A unifying notation and its consequences. Ecology 54, 427–432 (1973).
Sax, D. F. & Gaines, S. D. Species diversity: From global decreases to local increases. Trends Ecol. Evol. 18, 561–566 (2003).
Clavel, J., Julliard, R. & Devictor, V. Worldwide decline of specialist species: Toward a global functional homogenization?. Front. Ecol. Environ. 9, 222–228 (2011).
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).
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).
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).
SLU Artdatabanken. Rödlistade Arter i Sverige 2020. (SLU, Uppsala, 2020).
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).
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).
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).
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).
Lewin, H. A. et al. Earth BioGenome project: sequencing life for the future of life. Proc. Natl. Acad. Sci. USA 115, 4325–4333 (2018).
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).
The Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO). Annu. Rep. 2022. (2023).
Söderström, C., Ban, S., Jansson, P., Lindh, K. & Tooloutalaie, N. Radionuclides in Ground Level Air in Sweden Year 2006. (2007).
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).
Slon, V. et al. Neandertal and Denisovan DNA from Pleistocene sediments. Science 356, 605–608 (2017).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).
Bushnell, B.BBMap Short Read Aligner. Joint Genome Institute, Department of Energy (2014).
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol 20, 1–13 (2019).
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).
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).
Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. InProceedings of the 9th Python in Science Conference 92–96 (2010).
van den Boogaart, K. G. & Tolosana-Delgado, R. ‘compositions’: A unified R package to analyze compositional data. Comput. Geosci. 34, 320–338 (2008).
Oksanen, J. et al. vegan: Community Ecology Package. R package at https://CRAN.R-project.org/package=vegan (2020).
GBIF.org GBIF Occurrence Download https://doi.org/10.15468/dl.xnyctg. (2020).
Danecek, P. et al. Twelve years of SAMtools and BCFtools. Gigascience 10, giab008 (2021).
Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009).
Lindqvist, J. En Stokastisk Partikelmodell i Ett Icke-Metriskt Koordinatsystem. FOI-R–99-01086-862-SE, Swedish Defence Research Agency (1999).
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).
Canty, A. & Ripley, B. boot: Bootstrap Functions (Originally by Angelo Canty for S). R package at https://cran.r-project.org/package=boot (2022).
Davison, A. C. & Hinkley, D. V. Bootstrap Methods and Their Application. Bootstrap Methods and their Application (Cambridge University Press, Cambridge, 1997).
Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077 (2015).
Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–472 (1996).
Carslaw, D. C. & Ropkins, K. Openair – An r package for air quality data analysis. Environ. Model. Softw. 27, 52–61 (2012).
Scott, S. L. & Varian, H. R. Predicting the present with Bayesian structural time series. Int. J. Math. Model. Num. Optimis. 5, 4–23 (2014).
Scott, S. L. bsts: Bayesian Structural Time Series. R package at https://CRAN.R-project.org/package=bsts (2022).
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).
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).
Durbin, J. & Koopman, S. J. Time Series Analysis by State Space Methods. Time Series Analysis by State Space Methods (Oxford University Press, Oxford, 2012).
Commandeur, J. J. F. & Koopman, S. J. An Introduction to State Space Time Series Analysis. (Oxford University Press, Incorporated, 2007).
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).
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).
Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence Diagnosis and Output Analysis for MCMC. R News 6, 7–11 (2006).
GBIF.org. GBIF Occurrence Download https://doi.org/10.15468/dl.k76kgd (2021).
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).
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).