Philippa Kaur

Meredith, M. et al. Polar regions. IPCC Intergov. Panel Clim. Chang. Geneva, Switz. 3, 203–320 (2019).Raftery, A. E., Zimmer, A., Frierson, D. M. W., Startz, R. & Liu, P. Less than 2 °C warming by 2100 unlikely. Nat. Clim. Chang. 7, 637–641 (2017).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Ernakovich, J. G. et al. Predicted responses of arctic and alpine ecosystems to altered seasonality under climate change. Glob. Chang. Biol. 20, 3256–3269 (2014).PubMed 
ADS 

Google Scholar 
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
PubMed 
ADS 

Google Scholar 
Myers-Smith, I. H. & Hik, D. S. Climate warming as a driver of tundra shrubline advance. J. Ecol. 106, 547–560 (2018).
Google Scholar 
Martin, A. C., Jeffers, E. S., Petrokofsky, G., Myers-Smith, I. & Macias-Fauria, M. Shrub growth and expansion in the Arctic tundra: An assessment of controlling factors using an evidence-based approach. Environ. Res. Lett. 12, (2017).Myers-Smith, I. H. et al. Climate sensitivity of shrub growth across the tundra biome. Nat. Clim. Chang. 5, 887–891 (2015).ADS 

Google Scholar 
Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Chang. 10, 106–117 (2020).ADS 

Google Scholar 
Epstein, H. E., Myers-Smith, I. & Walker, D. A. Recent dynamics of arctic and sub-arctic vegetation. Environ. Res. Lett. 8, 015040 (2013).ADS 

Google Scholar 
Ackerman, D., Griffin, D., Hobbie, S. E. & Finlay, J. C. Arctic shrub growth trajectories differ across soil moisture levels. Glob. Chang. Biol. 23, 4294–4302 (2017).PubMed 

Google Scholar 
Carrer, M., Pellizzari, E., Prendin, A. L., Pividori, M. & Brunetti, M. Winter precipitation – not summer temperature – is still the main driver for Alpine shrub growth. Sci. Total Environ. 682, 171–179 (2019).CAS 
PubMed 
ADS 

Google Scholar 
Xu, Y., Ramanathan, V. & Washington, W. M. Observed high-altitude warming and snow cover retreat over Tibet and the Himalayas enhanced by black carbon aerosols. Atmos. Chem. Phys. 16, 1303–1315 (2016).CAS 
ADS 

Google Scholar 
Francon, L. et al. Assessing the effects of earlier snow melt-out on alpine shrub growth: The sooner the better? Ecol. Indic. 115, (2020).López-Blanco, E. et al. Exchange of CO2 in Arctic tundra: impacts of meteorological variations and biological disturbance. Biogeosciences 14, 4467–4483 (2017).ADS 

Google Scholar 
Lund, M. et al. Larval outbreaks in West Greenland: Instant and subsequent effects on tundra ecosystem productivity and CO2 exchange. Ambio 46, 26–38 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Prendin, A. L. et al. Immediate and carry-over effects of insect outbreaks on vegetation growth in West Greenland assessed from cells to satellite. J. Biogeogr. 47, 87–100 (2020).
Google Scholar 
Hobbie, S. E., Nadelhoffer, K. J. & Högberg, P. A synthesis: The role of nutrients as constraints on carbon balances in boreal and arctic regions. Plant Soil 242, 163–170 (2002).CAS 

Google Scholar 
Bret-Harte, M. S., Shaver, G. R. & Chapin, F. S. Primary and secondary stem growth in arctic shrubs: Implications for community response to environmental change. J. Ecol. 90, 251–267 (2002).
Google Scholar 
Sullivan, P. F., Ellison, S. B. Z., McNown, R. W., Brownlee, A. H. & Sveinbjörnsson, B. Evidence of soil nutrient availability as the proximate constraint on growth of treeline trees in northwest Alaska. Ecology 96, 716–727 (2015).PubMed 

Google Scholar 
Craine, J. M. et al. Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability. New Phytol. 183, 980–992 (2009).CAS 
PubMed 

Google Scholar 
Shaver, G. R. & Chapin, F. S. Long-term responses to factorial, NPK fertilizer treatment by Alaskan wet and moist tundra sedge species. Ecography (Cop.) 18, 259–275 (1995).
Google Scholar 
Choudhary, S., Blaud, A., Osborn, A. M., Press, M. C. & Phoenix, G. K. Nitrogen accumulation and partitioning in a High Arctic tundra ecosystem from extreme atmospheric N deposition events. Sci. Total Environ. 554–555, 303–310 (2016).PubMed 
ADS 

Google Scholar 
Bergström, A. K. & Jansson, M. Atmospheric nitrogen deposition has caused nitrogen enrichment and eutrophication of lakes in the northern hemisphere. Glob. Chang. Biol. 12, 635–643 (2006).ADS 

Google Scholar 
Wild, B. et al. Plant-derived compounds stimulate the decomposition of organic matter in arctic permafrost soils. Sci. Rep. 6, 25607 (2016).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Pedersen, E. P., Elberling, B. & Michelsen, A. Foraging deeply: Depth-specific plant nitrogen uptake in response to climate-induced N-release and permafrost thaw in the High Arctic. Glob. Chang. Biol. 26, 6523–6536 (2020).PubMed 
ADS 

Google Scholar 
Mack, M. C., Schuur, E. A. G. & Bret-harte, M. S. Ecosystem carbon storage in arctic tundra reduced by long-term nutrient fertilization. 431, 658–661 (2004).
Google Scholar 
Zamin, T. J. & Grogan, P. Birch shrub growth in the low Arctic: the relative importance of experimental warming, enhanced nutrient availability, snow depth and caribou exclusion. Environ. Res. Lett. 7, 034027 (2012).ADS 

Google Scholar 
DeMarco, J., MacK, M. C., Bret-Harte, M. S., Burton, M. & Shaver, G. R. Long-term experimental warming and nutrient additions increase productivity in tall deciduous shrub tundra. Ecosphere 5, 1–22 (2014).
Google Scholar 
Zamin, T. J., Bret-Harte, M. S. & Grogan, P. Evergreen shrubs dominate responses to experimental summer warming and fertilization in Canadian mesic low arctic tundra. J. Ecol. 102, 749–766 (2014).
Google Scholar 
Fenger-Nielsen, R. et al. Footprints from the past: The influence of past human activities on vegetation and soil across five archaeological sites in Greenland. Sci. Total Environ. 654, 895–905 (2019).CAS 
PubMed 
ADS 

Google Scholar 
Forbes, B. C., Ebersole, J. J. & Strandberg, B. Anthropogenic disturbance and patch dynamics in Circumpolar Arctic ecosystems. Conserv. Biol. 15, 954–969 (2001).
Google Scholar 
Andersen, E. A. S. et al. Nitrogen isotopes reveal high N retention in plants and soil of old Norse and Inuit deposits along a wet-dry arctic fjord transect in Greenland. Plant Soil 455, 241–255 (2020).CAS 

Google Scholar 
Normand, S. et al. Legacies of historical human activities in Arctic woody plant dynamics. Annu. Rev. Environ. Resour. 42, 541–567 (2017).
Google Scholar 
Walker, D. A. et al. The Circumpolar Arctic vegetation map. J. Veg. Sci. 16, 267–282 (2005).
Google Scholar 
Cappelen, J., Vinther, B. M., Kern-Hansen, C., Laursen, E. V. & Jørgensen, P. V. Greenland-DMI Historical Climate Data Collection 1784–2020 (Danish Meteorological Institute, 2021).
Google Scholar 
Hollesen, J. et al. Predicting the loss of organic archaeological deposits at a regional scale in Greenland. Sci. Rep. 9, 9097 (2019).PubMed 
PubMed Central 
ADS 

Google Scholar 
Fenger-Nielsen, R. et al. Arctic archaeological sites threatened by climate change: A regional multi-threat assessment of sites in south-west Greenland. Archaeometry 62, 1280–1297 (2020).CAS 

Google Scholar 
Fettweis, X. et al. Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR model. Cryosphere 11, 1015–1033 (2017).ADS 

Google Scholar 
Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11, 1–12 (2020).ADS 

Google Scholar 
Assmann, J. J. et al. Local snow melt and temperature—but not regional sea ice—explain variation in spring phenology in coastal Arctic tundra. Glob. Chang. Biol. 25, 2258–2274 (2019).PubMed 
ADS 

Google Scholar 
Bhatt, U. S. et al. Climate drivers of Arctic tundra variability and change using an indicators framework. Environ. Res. Lett. 16, (2021).Hollesen, J., Matthiesen, H. & Elberling, B. The impact of Climate Change on an archaeological site in the Arctic. Archaeometry 59, 1175–1189 (2017).CAS 

Google Scholar 
Tolvanen, A. & Henry, G. H. R. Responses of carbon and nitrogen concentrations in high arctic plants to experimental warming. Can. J. Bot. 79, 711–718 (2001).CAS 

Google Scholar 
Oppen, J. et al. Annual air temperature variability and biotic interactions explain tundra shrub species abundance. J. Veg. Sci. 32, (2021).Hobbie, S. E. Temperature and plant species control over litter decomposition in Alaskan tundra. Ecol. Monogr. 66, 503–522 (1996).
Google Scholar 
Nadelhoffer, K. J., Giblin, A. E., Shaver, G. R. & Laundre, J. A. Effects of temperature and substrate quality on element mineralization in six Arctic soils. Ecology 72, 242–253 (1991).
Google Scholar 
Arens, S. J. T., Sullivan, P. F. & Welker, J. M. Nonlinear responses to nitrogen and strong interactions with nitrogen and phosphorus additions drastically alter the structure and function of a high Arctic ecosystem. J. Geophys. Res. Biogeosciences 113, 1–10 (2008).
Google Scholar 
Baddeley, J. A., Woodin, S. J. & Alexander, I. J. Effects of increased nitrogen and phosphorus availability on the photosynthesis and nutrient relations of three Arctic dwarf shrubs from Svalbard. Funct. Ecol. 8, 676 (1994).
Google Scholar 
Anadon-Rosell, A. et al. Xylem anatomical and growth responses of the dwarf shrub Vaccinium myrtillus to experimental CO2 enrichment and soil warming at treeline. Sci. Total Environ. 642, 1172–1183 (2018).CAS 
PubMed 
ADS 

Google Scholar 
Dawes, M. A. et al. Soil warming and CO2 enrichment induce biomass shifts in alpine tree line vegetation. Glob. Chang. Biol. 21, 2005–2021 (2015).PubMed 
ADS 

Google Scholar 
Walker, M. D. et al. Plant community responses to experimental warming across the tundra biome. Proc. Natl. Acad. Sci. 103, 1342–1346 (2006).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Matthiesen, H., Fenger-Nielsen, R. F., Harmsen, H., Madsen, C. K. & Hollesen, J. The impact of vegetation on archaeological sites in the low arctic in light of climate change. Arctic 73, 141–152 (2020).
Google Scholar 
Dahl, M. B. et al. Warming, shading and a moth outbreak reduce tundra carbon sink strength dramatically by changing plant cover and soil microbial activity. Sci. Rep. 7, 1–13 (2017).CAS 

Google Scholar 
Westergaard-Nielsen, A., Karami, M., Hansen, B. U., Westermann, S. & Elberling, B. Contrasting temperature trends across the ice-free part of Greenland. Sci. Rep. 8, 1586 (2018).PubMed 
PubMed Central 
ADS 

Google Scholar 
Schweingruber, F. H., Börner, A. & Schulze, E.-D. Atlas of Stem Anatomy in Herbs, Shrubs and Trees. (Springer, Berlin, 2013). https://doi.org/10.1007/978-3-642-20435-7Pellizzari, E., Camarero, J. J., Gazol, A., Sangüesa-Barreda, G. & Carrer, M. Wood anatomy and carbon-isotope discrimination support long-term hydraulic deterioration as a major cause of drought-induced dieback. Glob. Chang. Biol. 22, 2125–2137 (2016).PubMed 
ADS 

Google Scholar 
Myers-Smith, I. H. et al. Methods for measuring arctic and alpine shrub growth: A review. Earth-Science Rev. 140, 1–13 (2015).ADS 

Google Scholar 
Stokes, M. A. & Smiley, T. L. Introduction to Tree-Ring Dating. (University of Chicago Press, 1968).Cook, E. R., Briffa, K., Shiyatov, S. & Mazepa, V. Methods of Dendrochronology: Applications in the Environmental Sciences. (Kluwer Academic Publisher, 1990).Gärtner, H. & Schweingruber, F. H. Microscopic preparation techniques for plant stem analysis. Kessel 95, 132–150 (2013).
Google Scholar 
von Arx, G., Crivellaro, A., Prendin, A. L., Čufar, K. & Carrer, M. Quantitative wood anatomy—practical guidelines. Front. Plant Sci. 7, 781 (2016).
Google Scholar 
Holmes, R. L. Computer-assisted quality control in tree- ring dating and measurement. Tree-ring Bulletin 43, 69–78 (1983).
Google Scholar 
Belokopytova, L. V, Babushkina, E. A., Zhirnova, D. F., Panyushkina, I. P. & Vaganov, E. A. Pine and larch tracheids capture seasonal variations of climatic signal at moisture-limited sites. Trees 33, 227–242 (2019).Büntgen, U. et al. Temperature-induced recruitment pulses of Arctic dwarf shrub communities. J. Ecol. 103, 489–501 (2015).
Google Scholar 
Fritts., H. C. Dendrochronology and Dendroclimatology. in Tree Rings and Climate 1–54 (1976). https://doi.org/10.1016/B978-0-12-268450-0.50006-9Briffa, K. & Jones, P. Basic chronology statistics and assessment. in Methods of Dendrochronology: Applications in the Environmental Sciences 137–152 (Kluwer Academic Publishers, 1990).Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. (Springer New York, 2009). https://doi.org/10.1007/978-0-387-87458-6Gazol, A. & Camarero, J. J. Mediterranean dwarf shrubs and coexisting trees present different radial-growth synchronies and responses to climate. Plant Ecol. 213, 1687–1698 (2012).
Google Scholar 
Crawley, M. J. Mixed-Effects Models. in R Book Second edition 681–714 (2007).Zar, J. H. Biostatistical analysis Fifth edition. USA Prentice Hall 4165 4159–4165, (1999).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, arXiv:1406.5823 (2015).Pinheiro, J. C. & Bates, D. M. Linear Mixed-Effects Models: Basic Concepts and Examples. in Mixed-Effects Models in S and S-PLUS 3–56 (Springer-Verlag, 2000). https://doi.org/10.1007/0-387-22747-4_1Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in Linear Mixed Effects Models. J. Stat. Softw. 82, (2017).R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. More

Lawler, J.J. Climate change adaptation strategies for resource management and conservation planning. The year in ecology and conservation biology. Ann. N.Y. Acad. Sci. 1162, 79–98. https://doi.org/10.1111/j.1749-6632.2009.04147.x (2009).Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C. & Mace, G. M. Beyond predictions: biodiversity conservation in a changing climate. Science 332(6025), 53–58. https://doi.org/10.1126/science.1200303 (2011).CAS 
Article 
PubMed 
ADS 

Google Scholar 
Walsworth, T. E. et al. Management for network diversity speeds evolutionary adaptation to climate change. Nat. Clim. Change 9(8), 632–636. https://doi.org/10.1038/s41558-019-0518-5 (2019).Article 

Google Scholar 
Morelli, T. L. et al. Climate-change refugia: Biodiversity in the slow lane. Front Ecol. Environ. 18(5), 228–234. https://doi.org/10.1002/fee.2189 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Vincent, H., Bornand, C. N., Kempel, A. & Fischer, M. Rare species perform worse than widespread species under changed climate. Biol. Conserv. 246, 108586. https://doi.org/10.1016/j.biocon.2020.108586 (2020).Article 

Google Scholar 
Corlett, R. T. & Westcott, D. A. Will plant movements keep up with climate change?. Trends Ecol. Evol. 28(8), 482–488. https://doi.org/10.1016/j.tree.2013.04.003 (2013).Article 
PubMed 

Google Scholar 
Janssen, J. & Bijlsma, R.J. Molinia meadows on calcareous, peaty or clayey-silt-laden soils (Molinion caeruleae) (6410) in the Netherlands, in: Bijlsma, R.J. et al. Defining and applying the concept of favourable reference values for species habitats under the EU Birds and Habitats Directives: examples of setting favourable reference values. Wageningen Environmental Research, Wageningen, 2929, pp. 201–203 (2019).Arnell, M., Cousins, S. A. O. & Eriksson, O. Does historical land use affect the regional distribution of fleshy-fruited woody plants?. PLoS ONE 14(12), e0225791. https://doi.org/10.1371/journal.pone.0225791 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Welk, A., Welk, E., Baudis, M., Böckelmann, J. & Bruelheide, H. Plant species range type determines local responses to biotic interactions and land use. Ecology 100(12), e02890. https://doi.org/10.1002/ecy.2890 (2019).Article 
PubMed 

Google Scholar 
Caissy, P., Klemet-N’Guessan, S., Jackiw, R., Eckert, C.G. & Hargreaves, A.L. High conservation priority of range-edge plant populations not matched by habitat protection or research effort. Biol. Conserv. 249, 108732. https://doi.org/10.1101/682823 (2020).Kreyling, J. et al. Rewetting does not return drained fen peatlands to their old selves. Nat. Commun. 12, 5693. https://doi.org/10.1038/s41467-021-25619-y (2021).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Sotek, Z. Distribution patterns, history, and dynamics of peatland vascular plants in Pomerania (NW Poland). Biodiv. Res. Conserv. 18, 1–82. https://doi.org/10.2478/v10119-010-0020-4 (2010).Article 

Google Scholar 
Hultén, E. & Fries, M. Atlas of north European vascular plants, North of the tropic of cancer, I, Introduction, taxonomic index to the maps (Koeltz Scientific Books, 1986).
Google Scholar 
Buse, J., Boch, S., Hilgers, J. & Griebeler, E. M. Conservation of threatened habitat types under future climate change—lessons from plant-distribution models and current extinction trends in southern Germany. J. Nat. Conserv. 27, 18–25. https://doi.org/10.1016/j.jnc.2015.06.001 (2015).Article 

Google Scholar 
Dítě, D., Melečková, Z. & Eliáš, P. jun. Flea sedge (Carex pulicaris)—a new species in the Great Fatra. Acta Carpathica Occidentalis 6, 23–27, (in Slovak) (2015).Sotek, Z. et al. Distribution and habitat properties of Carex pulicaris and Pedicularis sylvatica at their range margin in NW Poland. Acta Soc. Bot. Pol. 85(3), 3507. https://doi.org/10.5586/asbp.3507 (2016).Article 

Google Scholar 
Kukk, T., Kull, T., Luuk, O., Mesipuu, M. & Saar, P. Atlas of the Estonian flora 2020. Tartu, Estonia (2020).Grulich, V. Red list of vascular plants of the Czech Republic, 3rd ed. Preslia 84, 631–645 (2012).Eliáš, P. jun, Dítě, D., Kliment, J., Hrivnák, R. & Feráková, V. Red list of ferns and flowering plants of Slovakia, 5th edition (October 2014). Biologia 70(2), 218–228. https://doi.org/10.1515/biolog-2015-0018 (2015).Kaźmierczakowa, R. et al. Polish red list of pteridophytes and flowering plants. Institute of Nature Conservation of the Polish Academy of Sciences, Cracow (2016).Aronsson, M. et al. Kärlväxter—vascular plants (Tracheophyta). In The 2010 Red List of Swedish Species (ed. Gärdenfors, U.) 201–221 (ArtDatabanken, Uppsala, 2010).
Google Scholar 
Kalliovirta, M. et al. Vascular plants, in: Rassi, P., Hyvärinen, E., Juslén, A. & Mannerkoski, I. (Eds.), The 2010 red list of Finnish species. Ministry of the Environment and Finnish Environment Institute, Helsinki, pp. 183–203 (2010).Kull, T. et al. Kokkuvõte soontaimede ohustatuse hindamistulemustest 2017–2018. Liikide ohustatuse hindamine riigihanke 183098 osa nr 15 – Õistaimed (Anthophyta), okaspuutaimed (Coniferophyta), lehtsooneostaimed (Monilophyta) ja pärisraigastaimed (Lycopodiophyta) vastavalt lepingule nr 7–27/17/59 (16. juuni 2017.a.). Lõpparuanne Keskkonnaametile. Eesti Maaülikool. Lk 1–6 + lisa, (in Estonian). Available from https://infoleht.keskkonnainfo.ee/GetFile.aspx?id=1947479558 (2018).Bartoszek, W., Mirek, Z. & Koczur, A. Flea sedge – Carex pulicaris L., in: Kaźmierczakowa, R., Zarzycki, K. & Mirek, Z., (Eds), Polish red data book of plants. Pteridophytes and flowering plants, 3rd ed. Polish Academy of Sciences, Institute of Nature Conservation, Cracow, pp. 737–739, (in Polish) (2014).Matuszkiewicz, W. Guide to the identification of plant communities in Poland. Scientific Publisher Warsaw, Poland, (in Polish) (2006).Hájek, M., Horsák, M., Hájková, P. & Dítě, D. Habitat diversity of central European fens in relation to environmental gradients and an effort to standardise fen terminology in ecological studies. Perspect. Plant Ecol. Evol. Syst. 8, 97–114. https://doi.org/10.1016/j.ppees.2006.08.002 (2006).Article 

Google Scholar 
Šefferová-Stanová, V., Šeffer, J. & Janák, M. Management of Natura 2000 habitats. 7230 Alkaline fens. Technical Report 2008 20/24. European Commission. Available from http://ec.europa.eu/environment/nature/natura2000/management/habitats/pdf/7230_Alkaline_fens.pdf. Accessed 15 June 2018 (2008).O’Connell, M., Ryan, J. B. & Macgowran, B. A. Wetland communities in Ireland: a phytosociological review. In European Mires (ed. Moore, P. D.) 303–364 (Academic Press INC, LTD, 1984).Chapter 

Google Scholar 
Dítě, D., Kubandová, M. & Pukajová, D. Chorological, ecological and phytocenological notes on the occurrence of flea sedge (Carex pulicaris L.) in Slovakia. Bull. Slovak Bot. Soc. 27, 77–84, (in Slovak) (2005).Hällfors, M. H. et al. Assessing the need and potential of assisted migration using species distribution models. Biol. Conserv. 196(7), 60–68. https://doi.org/10.1016/j.biocon.2016.01.031 (2016).Article 

Google Scholar 
Emsens, W.-J., Aggenbach, C. J. S., Rydin, H., Smolders, A. J. P. & van Diggelen, R. Competition for light as a bottleneck for endangered fen species: an introduction experiment. Biol. Conserv. 220, 76–83. https://doi.org/10.1016/j.biocon.2018.02.002 (2018).Article 

Google Scholar 
Kącki, Z. & Śliwiński, M. The polish vegetation database: structure, resources and development. Acta Soc. Bot. Pol. 81(2), 75–79. https://doi.org/10.5586/asbp.2012.014 (2012).Article 

Google Scholar 
Ellenberg, H. et al. Indicator values of plants in Central Europe. 2nd ed. Scripta Geobotanica 18, 1–258 (in Germany) (1992).PN-R-04031. Chemical and agricultural analysis of soil. Sampling of soil. Polish Committee for Standardization (1997).PN-R-04024. Chemical and agricultural analysis of soil. Determination of the Content of Available P, K, Mg and Mn in organic soils. Polish Committee for Standardization (1997).PN-R-04016-21. Chemical and Agricultural Analysis of Soil. Determination of the Content of Available Zinc, Copper, Manganese, Iron. Polish Committee for Standardization. (1992).Ostrowska, A., Gawliński, S. & Szczubiałka, Z. Methods of analysis and evaluation of soil and plant properties. Institute of Environmental Protection, Warsaw, Poland, (in Polish) (1991).WRB, I.W.G. World reference base for soil resources 2014. International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Report, 106 (2014).IUNG (Institute of Soil Science and Plant Cultivation). Fertiliser Recommendations Part I. Limits for Estimating Soil Macro- and Microelement Content. Series P (44), Puławy, Poland, pp. 26–28 (1990).IUNG (Institute of Soil Science and Plant Cultivation). Evaluation of heavy metal and sulfur contamination of soils and plants. Framework guidelines for agriculture. Series P (53), Puławy, Poland, pp. 1–22 (1993).Oksanen, J. et al. Vegan: community ecology package. R package version 2.3–0. Available from https://cran.r-project.org/web/packages/vegan/vegan.pdf. Accessed date: 4 January 2021 (2019).R Core Team. R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria Accessed 30 May 2020. https://www.R-project.org (2020).Hammer, Ø., Harper, D.A.T. & Ryan, P.D. PAST: Paleontological Statistics Software Package for Education and Data Analysis. Palaeontol. Electron. 4 (1), 1–9; http://palaeo-electronica.org/2001_1/past/issue1_01.htm (2001).Zelnik, I. & Čarni, A. Wet meadows of the alliance Molinion and their environmental gradients in Slovenia. Biologia 63(2), 187–196. https://doi.org/10.2478/s11756-008-0042-y (2008).CAS 
Article 

Google Scholar 
Lindén, C. Local plant species diversity in coastal grasslands in the Stockholm archipelago. The effect of isostatic land-uplift, different management and future sea level rise. Stockholm University, Master’s thesis, Physical Geography and Quaternary Geology, 45 Credits, Stockholm, pp. 1–33 (2017).Muller, S. Diversity of management practices required to ensure conservation of rare and locally threatened plant species in grasslands: A case study at a regional scale (Lorraine, France). Biodiv. Conserv. 11(7), 1173–1184. https://doi.org/10.1023/A:1016049605021 (2002).Article 

Google Scholar 
Rodwell, J. S. (ed.) British plant communities. Grasslands and montane communities. Vol. 3 (Cambridge University Press. 1992).Rodwell, J.S., Morgan, V., Jefferson, R.G. & Moss, D. The European context of British Lowland Grasslands. JNCC Report No. 394, JNCC, Peterborough, UK (2007).Carter, S. P., Proctor, J. & Slingsby, D. R. Soil and vegetation of the Keen of Hamar serpentine. Shetland. J. Ecol. 75(1), 21–42. https://doi.org/10.2307/2260534 (1987).CAS 
Article 

Google Scholar 
de Vere, N. Biological flora of the British Isles: Cirsium dissectum (L.) Hill (Cirsium tuberosum (L.) All. subsp. anglicum (Lam.) Bonnier; Cnicus pratensis (Huds.) Willd., non Lam.; Cirsium anglicum (Lam.) DC.). J. Ecol. 95, 876–894. https://doi.org/10.1111/j.1365-2745.2007.01265.x (2007).Fernández-Pascual, E. Comparative seed germination traits in bog and fen mire wetlands. Aquat. Bot. 130, 21–26. https://doi.org/10.1016/j.aquabot.2016.01.001 (2016).Article 

Google Scholar 
Otsus, M., Kukk, D., Kattai, K. & Sammul, M. Clonal ability, height and growth form explain species’ response to habitat deterioration in Fennoscandian wooded meadows. Plant Ecol. 215(9), 953–962. https://doi.org/10.1007/s11258-014-0347-6 (2014).Article 

Google Scholar 
Meusel, H., Jäger, E. & Weinert, E. Comparative chorology of the Central European flora. VEB Gustav Fischer Verlag, Jena, Germany, (in German) (1965).Hill, M.O., Preston, C.D. & Roy, D.B. PLANTATT. Attributes of British and Irish Plants: Status, Size, Life History, Geography and Habits. Centre for Ecology and Hydrology, Huntingdon, UK (2004).Dahl, E. The phytogeography of Northern Europe (British Isles, Fennoscandia and adjacent areas). Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511565182 (1998).Bartoszek, W., Koczur, A., Mirek, Z. & Oklejewicz, K. Flea sedge Carex pulicaris L., in: Mirek, Z. & Piękoś-Mirkowa, H. (Eds.), Red data book of the Polish Carpathians. Vascular plants. Polish Academy of Sciences Institute of Botany W. Szafer, Cracow, pp. 523–525, (in Polish) (2008).Hereźniak, J. Carex pulicaris L. – flea sedge, in: Olaczek R. (Ed.), Red Book of Plants of the Lodzkie Voivodship. Botanical Garden in Łódź, University of Łódź, Łódź, pp. 50–51, (in Polish) (2012).Wołejko, L., Pawlaczyk, P. & Stańko, R. (Eds.). Alkaline fens in Poland—diversity, resources, conservation. Naturalists’ Club, Świebodzin, Poland (2019).Koopman, J., Timmerman, A., Hosper, U. & Więcław, H. Distribution, ecology and morphology of three Ceratocystis hybrids in the Province of Fryslân, the Netherlands (Carex, Cyperaceae). Gorteria 41(1), 14–20 (2019).
Google Scholar 
Laughlin, D. C. & Abella, S. R. Abiotic and biotic factors explain independent gradients of plant community composition in ponderosa pine forests. Ecol. Modell. 205(1–2), 231–240. https://doi.org/10.1016/j.ecolmodel.2007.02.018 (2007).Article 

Google Scholar 
Austrheim, G., Gunilla, E., Olsson, A. & Grøntvedt, E. Land-use impact on plant communities in semi-natural sub-alpine grasslands of Budalen, central Norway. Biol. Conserv. 87(3), 369–379. https://doi.org/10.1016/S0006-3207(98)00071-8 (1999).Article 

Google Scholar 
Gough, M. W. & Marrs, R. H. A comparison of soil fertility between semi-natural and agricultural plant communities: Implications for the creations of species-rich grassland on abandoned agricultural land. Biol. Conserv. 51(2), 83–96. https://doi.org/10.1016/0006-3207(90)90104-w (1990).Article 

Google Scholar 
Bobbink, R., Hornung, M. & Roelofs, J. G. M. The effects of air-borne nitrogen pollutants on species diversity in natural and semi-natural European vegetation. J. Ecol. 86(5), 717–738 (1998).CAS 
Article 

Google Scholar 
McCrea, A. R., Trueman, I. C., Fullen, M. A., Atkinson, M. D. & Besenyei, L. Relationships between soil characteristics and species richness in two botanically heterogeneous created meadows in the urban English West Midlands. Biol. Conserv. 97(2), 171–180 (2001).Article 

Google Scholar 
Wamelink, W., van Dobben, H.F., Goedhart, P.W. & Jones-Walters, L.M. The role of abiotic soil parameters as a factor in the success of invasive plant species. Emerg. Sci. J. 2(6), 308–365. https://doi.org/10.28991/esj-2018-01155 (2018).Janssens, F. et al. Relationship between soil chemical factors and grassland diversity. Plant Soil 202(1), 69–78. https://doi.org/10.1023/A:1004389614865 (1998).CAS 
Article 

Google Scholar 
Tallowin, J. R. B. & Smith, R. E. N. Restoration of a Cirsio-Molinietum fen meadow on an agriculturally improved pasture. Restor. Ecol. 9(2), 167–178. https://doi.org/10.1046/j.1526-100x.2001.009002167.x (2001).Article 

Google Scholar 
Venterink, H. O., van der Vliet, R. E. & Wassen, M. J. Nutrient limitation along a productivity gradient in wet meadows. Plant Soil 234(2), 171–179. https://doi.org/10.1023/A:1017922715903 (2001).Article 

Google Scholar 
Linderoth, E. Management of nature reserves—with Valön nature reserve in focus. Bachelor of Science with specialization in Environmental Analysis 15 hp, VT18. Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science, pp 1–26, (in Swedish) (2018).Jansen, A. M. & Roelofs, J. G. Restoration of Cirsio-Molinietum wet meadows by sod cutting. Ecol. Eng. 7(4), 279–298. https://doi.org/10.1016/S0925-8574(96)00022-5 (1996).Article 

Google Scholar 
Jurzyk, S. & Wrobel, M. Co-occurrence of two species Molinia caerulea L. and “red-list” species Carex pulicaris L. in western Pomerania (Poland). Pol. J. Ecol. 51 (3), 363–367 (2003).Boyer, M. L. H. & Wheeler, B. D. Vegetation patterns in spring-fed calcareous fens: Calcite precipitation and constraints on fertility. J. Ecol. 77(2), 597–609. https://doi.org/10.2307/2260772 (1989).CAS 
Article 

Google Scholar  More

Daszak, P., Cunningham, A. A. & Hyatt, A. D. Emerging infectious diseases of wildlife: threats to biodiversity and human health. Science 287, 443–449 (2000).CAS 
PubMed 
ADS 

Google Scholar 
Pedersen, A. B., Jones, K. E., Nunn, C. L. & Altizer, S. Infectious diseases and extinction risk in wild mammals. Conserv. Biol. 21, 1269–1279 (2007).PubMed 
PubMed Central 

Google Scholar 
Smith, K. F., Sax, D. F. & Lafferty, K. D. Evidence for the role of infectious disease in species extinction and endangerment. Conserv. Biol. 20, 1349–1357 (2006).PubMed 

Google Scholar 
Fisher, M. C. et al. Emerging fungal threats to animal, plant and ecosystem health. Nature 484, 186–194 (2012).CAS 
PubMed 
ADS 

Google Scholar 
Paré, J. A. & Sigler, L. An overview of reptile fungal pathogens in the genera Nannizziopsis, Paranannizziopsis, and Ophidiomyces. J. Herpetol. Med. Surg. 26, 46–53 (2016).
Google Scholar 
Warnecke, L. et al. Inoculation of bats with European Geomyces destructans supports the novel pathogen hypothesis for the origin of white-nose syndrome. Proc. Natl. Acad. Sci. 109, 6999–7003 (2012).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Skerratt, L. F. et al. Spread of chytridiomycosis has caused the rapid global decline and extinction of frogs. EcoHealth 4, 125 (2007).
Google Scholar 
Franklinos, L. H. V. et al. Emerging fungal pathogen Ophidiomyces ophiodiicola in wild European snakes. Sci. Rep. 7, 3844 (2017).PubMed 
PubMed Central 
ADS 

Google Scholar 
Lorch, J. M. et al. Snake fungal disease: an emerging threat to wild snakes. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150457 (2016).
Google Scholar 
Bustos, M. L., Nicolás Sánchez, M., Peichoto, M. E. & Teibler, G. P. First report of fungal disease in a South American snake. Rev. Investig. Vet. Perú 29, 1036–1042 (2018).Sun, P.-L. et al. Infection with Nannizziopsis guarroi and Ophidiomyces ophiodiicola in reptiles in Taiwan. Transbound. Emerg. Dis. https://doi.org/10.1111/tbed.14049 (2021).Article 
PubMed 

Google Scholar 
Haynes, E. et al. First report of ophidiomycosis in a free-ranging California kingsnake (Lampropeltis californiae) in California, USA. J. Wildl. Dis. 57, 246–249 (2021).CAS 
PubMed 

Google Scholar 
Takami, Y. et al. First report of ophidiomycosis in Asia caused by Ophidiomyces ophiodiicola in captive snakes in Japan. J. Vet. Med. Sci. 83, 1234–1239 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lorch, J. M. et al. Experimental infection of snakes with Ophidiomyces ophiodiicola causes pathological changes that typify snake fungal disease. MBio 6, e01534 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Koenig, J. E. et al. Succession of microbial consortia in the developing infant gut microbiome. Proc. Natl. Acad. Sci. 108, 4578–4585 (2011).CAS 
PubMed 
ADS 

Google Scholar 
Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).CAS 
ADS 

Google Scholar 
Grice, E. A. & Segre, J. A. The skin microbiome. Nat. Rev. Microbiol. 9, 244–253 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gall, C. A. et al. The bacterial microbiome of Dermacentor andersoni ticks influences pathogen susceptibility. ISME J. 10, 1846–1855 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gould, A. L. et al. Microbiome interactions shape host fitness. Proc. Natl. Acad. Sci. 115, E11951–E11960 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hanning, I. & Diaz-Sanchez, S. The functionality of the gastrointestinal microbiome in non-human animals. Microbiome 3, 51 (2015).PubMed 
PubMed Central 

Google Scholar 
Jani, A. J. & Briggs, C. J. The pathogen Batrachochytrium dendrobatidis disturbs the frog skin microbiome during a natural epidemic and experimental infection. Proc. Natl. Acad. Sci. 111, E5049–E5058 (2014).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Walker, D. M. et al. Variability in snake skin microbial assemblages across spatial scales and disease states. ISME J. 13, 2209–2222 (2019).PubMed 
PubMed Central 

Google Scholar 
Allender, M. C., Baker, S., Britton, M. & Kent, A. D. Snake fungal disease alters skin bacterial and fungal diversity in an endangered rattlesnake. Sci. Rep. 8, 12147 (2018).PubMed 
PubMed Central 
ADS 

Google Scholar 
Rykiel, E. J. Jr. Towards a definition of ecological disturbance. Aust. J. Ecol. 10, 361–365 (1985).
Google Scholar 
Kong, H. H. et al. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 22, 850–859 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Ferrenberg, S. et al. Changes in assembly processes in soil bacterial communities following a wildfire disturbance. ISME J. 7, 1102–1111 (2013).PubMed 
PubMed Central 

Google Scholar 
Mackey, R. L. & Currie, D. J. The diversity–disturbance relationship: is it generally strong and peaked?. Ecology 82, 3479–3492 (2001).
Google Scholar 
Connell, J. H. Diversity in tropical rain forests and coral reefs: high diversity of trees and corals is maintained only in a nonequilibrium state. Science 199, 1302–1310 (1978).CAS 
PubMed 
ADS 

Google Scholar 
Guthrie, A. L., Knowles, S., Ballmann, A. E. & Lorch, J. M. Detection of snake fungal disease due to Ophidiomyces ophiodiicola in Virginia, USA. J. Wildl. Dis. 52, 143–149 (2016).PubMed 

Google Scholar 
Chandler, H. C. et al. Ophidiomycosis prevalence in Georgia’s eastern indigo snake (Drymarchon couperi) populations. PLoS ONE 14, e0218351 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tetzlaff, S. J. et al. Snake fungal disease affects behavior of free-ranging massasauga rattlesnakes (Sistrurus catenatus). Herpetol. Conserv. Biol. 12, 624–634 (2017).
Google Scholar 
Lind, C. M., McCoy, C. M. & Farrell, T. M. Tracking outcomes of snake fungal disease in free-ranging pygmy rattlesnakes (Sistrurus miliarius). J. Wildl. Dis. 54, 352–356 (2018).PubMed 

Google Scholar 
Lind, C. M., Lorch, J. M., Moore, I. T., Vernasco, B. J. & Farrell, T. M. Seasonal sex steroids indicate reproductive costs associated with snake fungal disease. J. Zool. 307, 104–110 (2019).
Google Scholar 
McKenzie, J. M. et al. Field diagnostics and seasonality of Ophidiomyces ophiodiicola in wild snake populations. EcoHealth 16, 141–150 (2019).PubMed 

Google Scholar 
McCoy, C. M., Lind, C. M. & Farrell, T. M. Environmental and physiological correlates of the severity of clinical signs of snake fungal disease in a population of pigmy rattlesnakes, Sistrurus miliarius. Conserv. Physiol. 5, cow077 (2017).Hill, A. J. et al. Common cutaneous bacteria isolated from snakes inhibit growth of Ophidiomyces ophiodiicola. EcoHealth 15, 109–120 (2018).PubMed 

Google Scholar 
Baker, S. et al. Case definition and diagnostic testing for Snake Fungal Disease. Herpetol. Rev. 50, 279–285 (2019).
Google Scholar 
Chase, J. M., Kraft, N. J. B., Smith, K. G., Vellend, M. & Inouye, B. D. Using null models to disentangle variation in community dissimilarity from variation in α-diversity. Ecosphere 2, art24 (2011).Agugliaro, J., Lind, C. M., Lorch, J. M. & Farrell, T. M. An emerging fungal pathogen is associated with increased resting metabolic rate and total evaporative water loss rate in a winter-active snake. Funct. Ecol. 34, 486–496 (2020).
Google Scholar 
Frick, W. F. et al. Pathogen dynamics during invasion and establishment of white-nose syndrome explain mechanisms of host persistence. Ecology 98, 624–631 (2017).PubMed 

Google Scholar 
Gervasi, S. S., Hunt, E. G., Lowry, M. & Blaustein, A. R. Temporal patterns in immunity, infection load and disease susceptibility: understanding the drivers of host responses in the amphibian-chytrid fungus system. Funct. Ecol. 28, 569–578 (2014).
Google Scholar 
Allender, M. C. et al. Development of snake fungal disease after experimental challenge with Ophidiomyces ophiodiicola in cottonmouths (Agkistrodon piscivorous). PLoS ONE 10, e0140193 (2015).PubMed 
PubMed Central 

Google Scholar 
Briggs, C. J., Knapp, R. A. & Vredenburg, V. T. Enzootic and epizootic dynamics of the chytrid fungal pathogen of amphibians. Proc. Natl. Acad. Sci. 107, 9695–9700 (2010).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Neuman-Lee, L. A. et al. Assessing multiple endpoints of atrazine ingestion on gravid Northern Watersnakes (Nerodia sipedon) and their offspring. Environ. Toxicol. 29, 1072–1082 (2014).CAS 
PubMed 
ADS 

Google Scholar 
Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).CAS 
PubMed 
ADS 

Google Scholar 
Kueneman, J. G. et al. Community richness of amphibian skin bacteria correlates with bioclimate at the global scale. Nat. Ecol. Evol. 3, 381–389 (2019).PubMed 

Google Scholar 
Manichanh, C. et al. Reduced diversity of faecal microbiota in Crohn’s disease revealed by a metagenomic approach. Gut 55, 205–211 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Murphy, G. E. P. & Romanuk, T. N. A meta-analysis of declines in local species richness from human disturbances. Ecol. Evol. 4, 91–103 (2014).PubMed 

Google Scholar 
Jani, A. J. et al. The amphibian microbiome exhibits poor resilience following pathogen-induced disturbance. ISME J. 15, 1628–1640 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Zaneveld, J. R., McMinds, R. & Vega Thurber, R. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat. Microbiol. 2, 1–8 (2017).
Google Scholar 
Anderson, M. J. et al. Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. Ecol. Lett. 14, 19–28 (2011).PubMed 
ADS 

Google Scholar 
Lankau, E. W., Hong, P.-Y. & Mackie, R. I. Ecological drift and local exposures drive enteric bacterial community differences within species of Galápagos iguanas. Mol. Ecol. 21, 1779–1788 (2012).PubMed 

Google Scholar 
Mebert, K. Good species despite massive hybridization: genetic research on the contact zone between the watersnakes Nerodia sipedon and N. fasciata in the Carolinas, USA. Mol. Ecol. 17, 1918–1929 (2008).CAS 
PubMed 

Google Scholar 
Bohuski, E., Lorch, J. M., Griffin, K. M. & Blehert, D. S. TaqMan real-time polymerase chain reaction for detection of Ophidiomyces ophiodiicola, the fungus associated with snake fungal disease. BMC Vet. Res. 11, 95 (2015).PubMed 
PubMed Central 

Google Scholar 
Wiens, J. A. Spatial scaling in ecology. Funct. Ecol. 3, 385–397 (1989).
Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522 (2011).CAS 
PubMed 
ADS 

Google Scholar 
Fadrosh, D. W. et al. An improved dual-indexing approach for multiplexed 16S rRNA gene sequencing on the Illumina MiSeq platform. Microbiome 2, 6 (2014).PubMed 
PubMed Central 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the Miseq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).CAS 
PubMed 
PubMed Central 
ADS 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucl. Acids Res. 41, D590–D596 (2013).CAS 
PubMed 

Google Scholar 
Yilmaz, P. et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucl. Acids Res. 42, D643–D648 (2014).CAS 
PubMed 

Google Scholar 
Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27 (2017).PubMed 
PubMed Central 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2021).Bozdogan, H. Model selection and akaike’s information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52, 345–370 (1987).MathSciNet 
MATH 

Google Scholar 
Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE Publications, Thousand Oaks, 2011).
Google Scholar 
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).MATH 

Google Scholar 
Heip, C. A new index measuring evenness. J. Mar. Biol. Assoc. UK 54, 555–557 (1974).
Google Scholar  More

Leibniz Institute DSMZ German Collection of Microorganisms and Cell Cultures, Braunschweig, GermanyAmber Hartman Scholz, Rodrigo Sara, Scarlett Sett, Andrew Lee Hufton & Jörg OvermannLeibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, GermanyJens FreitagNatural History Museum, London, UKChristopher H. C. LyalOne Planet Solutions, Montpellier, FranceRodrigo SaraUniversidad de los Andes, Bogotá, ColombiaMartha Lucia CepedaPlentzia Marine Station (PiE-UPV/EHU), European Marine Biological Resource Centre – Spain (EMBRC-Spain), Plentzia, SpainIbon CancioEthiopian Biotechnology Institute, Addis Ababa, EthiopiaYemisrach Abebaw & Kassahun TesfayeNational Academy of Agricultural Science and Global Plant Council, New Delhi, IndiaKailash BansalNational Council of Scientific Research and Technologies (NCSRT), Algiers, AlgeriaHalima BenbouzaMinistry of Agriculture, Livestock, Fisheries and Cooperatives, Nairobi, KenyaHamadi Iddi BogaInstitut Pasteur, Paris, FranceSylvain Brisse, Anne-Caroline Deletoille & Raquel Hurtado-OrtizSchool of Biosciences, Cardiff University, Cardiff, UKMichael W. BrufordWellcome Sanger Institute, Hinxton, UKHayley Clissold & David NicholsonEuropean Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI), Hinxton, UKGuy CochraneGlobal Genome Initiative, Smithsonian National Museum of Natural History, Washington, DC, USAJonathan A. CoddingtonAlexander von Humboldt Biological Resources Research Institute, Bogota, ColombiaFelipe García-CardonaSouth African National Biodiversity Institute, Cape Town, South AfricaMichelle Hamer, Jessica da Silva & Krystal A. TolleyUniversity of Nairobi, Nairobi, KenyaDouglas W. MianoInstituto Tecnologico Vale (ITV), Belem, BrazilGuilherme OliveiraMinistry of Environment and Sustainable Development, Bogota, ColombiaCarlos Ospina BravoUniversity of Lethbridge, Lethbridge, CanadaFabian RohdenNatural History Museum of Denmark, Copenhagen, DenmarkOle SebergUniversity of Freiburg, Freiburg, GermanyGernot SegelbacherNational Centre for Cell Science, Pune, IndiaYogesh ShoucheMariano Galvez University, Guatemala City, GuatemalaAlejandra Sierra National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USAIlene Karsch-MizrachiCentre for Ecological Genomics and Wildlife Conservation, University of Johannesburg, Johannesburg, South AfricaJessica da Silva & Krystal A. TolleyUniversity of the Philippines Los Banos, Laguna, PhilippinesDesiree M. HauteaFundação Oswaldo Cruz (FIOCRUZ), Rio de Janeiro, BrazilManuela da SilvaNational Institute of Genetics, Mishima, JapanMutsuaki SuzukiInstitute of Biotechnology, Addis Ababa University, Addis Ababa, EthiopiaKassahun TesfayeCentre for Tropical Livestock Genetics and Health (CTLGH) – International Livestock Research Institute (ILRI), Nairobi, KenyaChristian Keambou TiamboMurdoch University, Murdoch, AustraliaRajeev VarshneyCorporación CorpoGen, Bogotá, ColombiaMaría Mercedes ZambranoTechnical University of Braunschweig, Braunschweig, GermanyJörg OvermannConceptualization: A.H.S., J.F., C.H.C.L., R.S., M.L.C., I.C., S.S., Y.A., K.B., H.B., H.I.B., S.Y., M.W.B., H.C., G.C., J.A.C., A.D., F.G.C., M.H., R.H.O., D.W.M., G.O., C.O.B., F.B., O.S., G.S., Y.S., A.S., J.d.S., M.d.S., M.S., K.T., K.A.T., M.M.Z., and J.O. Visualization: J.O., I.C., S.S., R.S., C.H.C.L., G.C., and A.H.S. Funding acquisition: A.H.S., J.F., and J.O. Writing—original draft: A.H.S., R.S., M.L.C., C.H.C.L., I.C., and S.S. Writing—review & editing: A.H.S., J.F., C.H.C.L., R.S., M.L.C., I.B., S.S., A.L.H., D.N., M.d.S., S.B., M.M.Z., O.S., K.T., K.A.T., R.H.O., J.d.S., C.K.T., R.V., J.O., D.H., and I.K.M. More

Rykiel EJ. Towards a definition of ecological disturbance. Austral Ecology. 1985;10:361–5.
Google Scholar 
Glasby TM, Underwood AJ. Sampling to differentiate between pulse and press perturbations. Environ Monit. Assess. 1996;42:241–52.CAS 
PubMed 

Google Scholar 
Sullivan TP, Sullivan DS. Vegetation management and ecosystem disturbance: impact of glyphosate herbicide on plant and animal diversity in terrestrial systems. Environ Rev. 2003;11:37–59.CAS 

Google Scholar 
Landers TF, Cohen B, Wittum TE, Larson EL. A review of antibiotic use in food animals: perspective, policy, and potential. Public Health Rep. 2012;127:4–22.PubMed 
PubMed Central 

Google Scholar 
Shade A, Peter H, Allison SD, Baho DL, Berga M, Bürgmann H, et al. Fundamentals of microbial community resistance and resilience. Front Microbiol. 2012;3:417.PubMed 
PubMed Central 

Google Scholar 
Hahn M. The rising threat of fungicide resistance in plant pathogenic fungi: Botrytis as a case study. J Chem Biol. 2014;7:133–41.PubMed 
PubMed Central 

Google Scholar 
Schaeffer RN, Vannette RL, Brittain C, Williams NM, Fukami T. Non-target effects of fungicides on nectar-inhabiting fungi of almond flowers. Environ. Microbiol. Rep. 2017;9:79–84.PubMed 

Google Scholar 
Zubrod JP, Bundschuh M, Arts G, Brühl CA, Imfeld G, Knäbel A, et al. Fungicides: An Overlooked Pesticide Class? Environ Sci Technol. 2019;53:3347–65.CAS 
PubMed 
PubMed Central 

Google Scholar 
Delmas CEL, Dussert Y, Delière L, Couture C, Mazet ID, Richart Cervera S, et al. Soft selective sweeps in fungicide resistance evolution: recurrent mutations without fitness costs in grapevine downy mildew. Mol. Ecol. 2017;26:1936–51.CAS 
PubMed 

Google Scholar 
McDonald MC, Renkin M, Spackman M, Orchard B, Croll D, Solomon PS, et al. Rapid parallel evolution of azole fungicide resistance in Australian populations of the wheat pathogen Zymoseptoria tritici. Appl Environ Microbiol. 2019;85:e01908–e01918.CAS 
PubMed 
PubMed Central 

Google Scholar 
Riat A, Plojoux J, Gindro K, Schrenzel J, Sanglard D. Azole Resistance of environmental and clinical Aspergillus fumigatus isolates from Switzerland. Antimicrob Agents Chemother. 2018;62:e02088–e02017.CAS 
PubMed 
PubMed Central 

Google Scholar 
Verweij PE, Snelders E, Kema GHJ, Mellado E, Melchers WJG. Azole resistance in Aspergillus fumigatus: a side-effect of environmental fungicide use? Lancet Infect Dis. 2009;9:789–95.CAS 
PubMed 

Google Scholar 
Wise K, Mueller D Are fungicides no longer just for fungi? An analysis of foliar fungicide use in corn. APSnet Features doi 2011; 10.Kandel YR, Hunt C, Ames K, Arneson N, Bradley CA, Byamukama E, et al. Meta-Analysis of Soybean Yield Response to Foliar Fungicides Evaluated from 2005 to 2018 in the United States and Canada. Plant Dis. 2021;105:1382–9.PubMed 

Google Scholar 
Imfeld G, Vuilleumier S. Measuring the effects of pesticides on bacterial communities in soil: A critical review. Eur J Soil Biol. 2012;49:22–30.CAS 

Google Scholar 
Fournier B, Dos Santos SP, Gustavsen JA, Imfeld G, Lamy F, Mitchell EAD, et al. Impact of a synthetic fungicide (fosetyl-Al and propamocarb-hydrochloride) and a biopesticide (Clonostachys rosea) on soil bacterial, fungal, and protist communities. Sci Total Environ. 2020;738:139635.CAS 
PubMed 

Google Scholar 
Morton V, Staub T A Short History of Fungicides. APSnet Feature Articles. 2008.Brent KJ, Hollomon DW Fungicide resistance in crop pathogens: how can it be managed? 2007. FRAC Monogr. No. 1, Global Prot. Fed.Perazzolli M, Antonielli L, Storari M, Puopolo G, Pancher M, Giovannini O, et al. Resilience of the natural phyllosphere microbiota of the grapevine to chemical and biological pesticides. Appl Environ Microbiol. 2014;80:3585–96.PubMed 
PubMed Central 

Google Scholar 
Knorr K, Jørgensen LN, Nicolaisen M. Fungicides have complex effects on the wheat phyllosphere mycobiome. PLoS One. 2019;14:e0213176.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sapkota R, Knorr K, Jørgensen LN, O’Hanlon KA, Nicolaisen M. Host genotype is an important determinant of the cereal phyllosphere mycobiome. New Phytol. 2015;207:1134–44.CAS 
PubMed 

Google Scholar 
Southwell RJ, Brown JF, Welsby SM. Microbial interactions on the phylloplane of wheat and barley after applications of mancozeb and triadimefon. Australasian Plant Pathol. 1999;28:139.
Google Scholar 
Dickinson CH, Wallace B. Effects of late applications of foliar fungicides on activity of micro-organisms on winter wheat flag leaves. Trans Br Mycological Soc. 1976;67:103–12.
Google Scholar 
Freimoser FM, Rueda-Mejia MP, Tilocca B, Migheli Q. Biocontrol yeasts: mechanisms and applications. World J Microbiol Biotechnol. 2019;35:154.PubMed 
PubMed Central 

Google Scholar 
Fonseca Á, Inácio J Phylloplane Yeasts. In: Péter G, Rosa C (eds). Biodiversity and Ecophysiology of Yeasts. 2006. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 263–301.Cobban A, Edgcomb VP, Burgaud G, Repeta D, Leadbetter ER. Revisiting the pink-red pigmented basidiomycete mirror yeast of the phyllosphere. Microbiologyopen. 2016;5:846–55.CAS 
PubMed 
PubMed Central 

Google Scholar 
Cadez N, Zupan J, Raspor P. The effect of fungicides on yeast communities associated with grape berries. FEMS Yeast Res. 2010;10:619–30.CAS 
PubMed 

Google Scholar 
Schaeffer RN, Mei YZ, Andicoechea J, Manson JS, Irwin RE. Consequences of a nectar yeast for pollinator preference and performance. Funct Ecol. 2017;31:613–21.
Google Scholar 
Agler MT, Ruhe J, Kroll S, Morhenn C, Kim S-T, Weigel D, et al. Microbial Hub Taxa Link Host and Abiotic Factors to Plant Microbiome Variation. PLoS Biol. 2016;14:e1002352.PubMed 
PubMed Central 

Google Scholar 
Tilman D. The ecological consequences of changes in biodiversity: a search for general principles. Ecology. 1999;80:1455.
Google Scholar 
Ripple WJ, Beschta RL. Linking wolves and plants: aldo leopold on trophic cascades. BioScience. 2005;55:613.
Google Scholar 
Sahasrabudhe S, Motter AE. Rescuing ecosystems from extinction cascades through compensatory perturbations. Nat Commun. 2011;2:170.PubMed 

Google Scholar 
Zhou J, Deng Y, Luo F, He Z, Tu Q, Zhi X. Functional molecular ecological networks. mBio. 2010;1:e00169–e00210.PubMed 
PubMed Central 

Google Scholar 
Wagg C, Schlaeppi K, Banerjee S, Kuramae EE, van der Heijden MGA. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat Commun. 2019;10:4841.PubMed 
PubMed Central 

Google Scholar 
Claassen R, Bowman M, McFadden J, Smith D, Wallander S. Tillage intensity and conservation cropping in the United States. US Dep Agric Bull Econ Rese Ser. 2018;197:1–21.
Google Scholar 
Gdanetz K, Trail F. The wheat microbiome under four management strategies, and potential for endophytes in disease protection. Phytobiomes J. 2017;1:158–68.
Google Scholar 
Longley R, Noel ZA, Benucci GMN, Chilvers MI, Trail F, Bonito G. Crop management impacts the soybean microbiome. Front Microbiol. 2020;11:1116.PubMed 
PubMed Central 

Google Scholar 
Sułowicz S, Cycoń M, Piotrowska-Seget Z. Non-target impact of fungicide tetraconazole on microbial communities in soils with different agricultural management. Ecotoxicology. 2016;25:1047–60.PubMed 
PubMed Central 

Google Scholar 
Karlsson I, Friberg H, Steinberg C, Persson P. Fungicide effects on fungal community composition in the wheat phyllosphere. PLoS One. 2014;9:e111786.PubMed 
PubMed Central 

Google Scholar 
Robertson GP, Hamilton SK Long-term ecological research at the Kellogg Biological Station LTER site. The ecology of agricultural landscapes: Long-term research on the path to sustainability 2015; 1–32.Fehr WR, Caviness CE, Burmood DT, Pennington JS. Stage of development descriptions for soybeans, Glycine max (L.) Merrill 1. Crop Sci. 1971;11:929–31.
Google Scholar 
Gdanetz K, Noel Z, Trail F. Influence of plant host and organ, management strategy, and spore traits on microbiome composition. Phytobiomes J. 2021;5:202–19.
Google Scholar 
Lundberg DS, Yourstone S, Mieczkowski P, Jones CD, Dangl JL. Practical innovations for high-throughput amplicon sequencing. Nat Methods. 2013;10:999–1002.CAS 
PubMed 

Google Scholar 
Bowsher AW, Benucci GMN, Bonito G, Shade A. Seasonal Dynamics of Core Fungi in the Switchgrass Phyllosphere, and Co-Occurrence with Leaf Bacteria. Phytobiomes J. 2021;5:60–68.
Google Scholar 
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package v. 2.5-7. 2020Anderson MJ, Willis TJ. Canonical analysis of principle coordinates: a useful method of constrained ordination for ecology. Ecology. 2003;84:511–25.
Google Scholar 
Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015;26:27663.PubMed 

Google Scholar 
Shade A, Stopnisek N. Abundance-occupancy distributions to prioritize plant core microbiome membership. Curr Opin Microbiol. 2019;49:50–58.PubMed 

Google Scholar 
Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2:18–22.
Google Scholar 
Kursa MB, Rudnicki WR. Feature selection with the Boruta package. J Stat Softw. 2010;36:1–13.
Google Scholar 
Kurtz ZD, Müller CL, Miraldi ER, Littman DR, Blaser MJ, Bonneau RA. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput Biol. 2015;11:e1004226.PubMed 
PubMed Central 

Google Scholar 
Lindow SE, Brandl MT. Microbiology of the phyllosphere. Appl Environ Microbiol. 2003;69:1875–83.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang K, Sipilä TP, Overmyer K. The isolation and characterization of resident yeasts from the phylloplane of Arabidopsis thaliana. Sci Rep. 2016;6:39403.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sun PF, Fang WT, Shin LY, Wei JY, Fu SF, Chou JY. Indole-3-acetic acid-producing yeasts in the phyllosphere of the carnivorous plant Drosera indica L. PLoS One. 2014;9:e114196.PubMed 
PubMed Central 

Google Scholar 
Yurkov AM, Kurtzman CP. Three new species of Tremellomycetes isolated from maize and northern wild rice. FEMS Yeast Res. 2019;19:foz004.CAS 
PubMed 

Google Scholar 
Sommermann L, Geistlinger J, Wibberg D, Deubel A, Zwanzig J, Babin D, et al. Fungal community profiles in agricultural soils of a long-term field trial under different tillage, fertilization and crop rotation conditions analyzed by high-throughput ITS-amplicon sequencing. PLOS ONE. 2018;13:e0195345.PubMed 
PubMed Central 

Google Scholar 
Li A-H, Yuan F-X, Groenewald M, Bensch K, Yurkov AM, Li K, et al. Diversity and phylogeny of basidiomycetous yeasts from plant leaves and soil: proposal of two new orders, three new families, eight new genera and one hundred and seven new species. Stud Mycol. 2020;96:17–140.PubMed 
PubMed Central 

Google Scholar 
Gilbert DG. Dispersal of yeasts and bacteria by Drosophila in a temperate forest. Oecologia. 1980;46:135–7.PubMed 

Google Scholar 
Starmer WT, Peris F, Fontdevila A. The transmission of yeasts by Drosophila buzzatii during courtship and mating. Animal Behaviour. 1988;36:1691–5.
Google Scholar 
Murrell EG. Can agricultural practices that mitigate or improve crop resilience to climate change also manage crop pests? Curr Opin Insect Sci. 2017;23:81–88.PubMed 

Google Scholar 
Latin R A Practical Guide to Turfgrass Fungicides. 2017.Banerjee S, Walder F, Büchi L, Meyer M, Held AY, Gattinger A, et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. ISME J. 2019;13:1722–36.PubMed 
PubMed Central 

Google Scholar 
Schmidt JE, Kent AD, Brisson VL, Gaudin ACM. Agricultural management and plant selection interactively affect rhizosphere microbial community structure and nitrogen cycling. Microbiome. 2019;7:146.PubMed 
PubMed Central 

Google Scholar 
Brockhurst MA, Buckling A, Gardner A. Cooperation peaks at intermediate disturbance. Curr Biol. 2007;17:761–5.CAS 
PubMed 

Google Scholar 
Brockhurst MA, Habets MGJL, Libberton B, Buckling A, Gardner A. Ecological drivers of the evolution of public-goods cooperation in bacteria. Ecology. 2010;91:334–40.PubMed 

Google Scholar 
Kwak M-J, Jeong H, Madhaiyan M, Lee Y, Sa T-M, Oh TK, et al. Genome information of Methylobacterium oryzae, a plant-probiotic methylotroph in the phyllosphere. PLoS One. 2014;9:e106704.PubMed 
PubMed Central 

Google Scholar 
Yoshida S, Hiradate S, Koitabashi M, Kamo T, Tsushima S. Phyllosphere Methylobacterium bacteria contain UVA-absorbing compounds. J Photochem Photobiol B. 2017;167:168–75.CAS 
PubMed 

Google Scholar 
Grady KL, Sorensen JW, Stopnisek N, Guittar J, Shade A. Assembly and seasonality of core phyllosphere microbiota on perennial biofuel crops. Nat Commun. 2019;10:4135.PubMed 
PubMed Central 

Google Scholar 
Delmotte N, Knief C, Chaffron S, Innerebner G, Roschitzki B, Schlapbach R, et al. Community proteogenomics reveals insights into the physiology of phyllosphere bacteria. Proc Natl Acad Sci USA. 2009;106:16428–33.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Bauer, J. E. et al. The changing carbon cycle of the coastal ocean. Nature 504, 61–70 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Murphy, R. R., Kemp, W. M. & Ball, W. P. Long-term trends in Chesapeake Bay seasonal hypoxia, stratification, and nutrient loading. Estuar. Coast. 34, 1293–1309 (2011).CAS 

Google Scholar 
Nixon, S. W. et al. The impact of changing climate on phenology, productivity, and benthic–pelagic coupling in Narragansett Bay. Estuar. Coast. Shelf S. 82, 1–18 (2009).ADS 
CAS 

Google Scholar 
Testa, J. M., Murphy, R. R., Brady, D. C. & Kemp, W. M. Nutrient-and climate-induced shifts in the phenology of linked biogeochemical cycles in a temperate estuary. Front. Mar. Sci. 5, 114 (2018).
Google Scholar 
Scanes, E., Scanes, P. R. & Ross, P. M. Climate change rapidly warms and acidifies Australian estuaries. Nat. Commun. 11, 1–11 (2020).
Google Scholar 
Statham, P. J. Nutrients in estuaries—An overview and the potential impacts of climate change. Sci. Total Environ. 434, 213–227 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Kolber, Z. S. et al. Contribution of aerobic photoheterotrophic bacteria to the carbon cycle in the ocean. Science 292, 2492–2495 (2001).CAS 
PubMed 

Google Scholar 
Giovannoni, S. J. & Vergin, K. L. Seasonality in ocean microbial communities. Science 335, 671–676 (2012).ADS 
CAS 
PubMed 

Google Scholar 
Sul, W. J., Oliver, T. A., Ducklow, H. W., Amaral-Zettler, L. A. & Sogin, M. L. Marine bacteria exhibit a bipolar distribution. Proc. Natl. Acad. Sci. 110, 2342–2347 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Agogué, H., Lamy, D., Neal, P. R., Sogin, M. L. & Herndl, G. J. Water mass-specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing. Mol. Ecol. 20, 258–274 (2011).PubMed 

Google Scholar 
Ghiglione, J.-F. et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc. Natl. Acad. Sci. 109, 17633–17638 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Techtmann, S. M. et al. The unique chemistry of eastern Mediterranean water masses selects for distinct microbial communities by depth. PLoS ONE 10, e0120605 (2015).PubMed 
PubMed Central 

Google Scholar 
Han, D. et al. Bacterial communities along stratified water columns at the Chukchi Borderland in the western Arctic Ocean. Deep-Sea Res. Pt. II 120, 52–60 (2015).
Google Scholar 
Han, D. et al. Bacterial communities of surface mixed layer in the Pacific sector of the western Arctic ocean during sea-ice melting. PLoS ONE 9, e86887 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Hernando-Morales, V., Ameneiro, J. & Teira, E. Water mass mixing shapes bacterial biogeography in a highly hydrodynamic region of the Southern Ocean. Environ. Microbiol. 19, 1017–1029 (2017).CAS 
PubMed 

Google Scholar 
Han, D., Kang, H. Y., Kang, C.-K., Unno, T. & Hur, H.-G. Seasonal mixing-driven system in Estuarine-Coastal Zone triggers an ecological shift in bacterial assemblages involved in phytoplankton-derived DMSP degradation. Microb. Ecol. 79, 12–20 (2020).CAS 
PubMed 

Google Scholar 
Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 

Google Scholar 
Higashi, K., Suzuki, S., Kurosawa, S., Mori, H. & Kurokawa, K. Latent environment allocation of microbial community data. PLoS Comput. Biol. 14, e1006143 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Kim, D. et al. Water quality assessment at Jinhae Bay and Gwangyang Bay, South Korea. Ocean Sci. J. 49, 251–264 (2014).CAS 

Google Scholar 
Chen, M., Kim, D., Liu, H. & Kang, C.-K. Variability in copepod trophic levels and feeding selectivity based on stable isotope analysis in Gwangyang Bay of the southern coast of the Korean Peninsula. Biogeosciences 15, 2055–2073 (2018).ADS 
CAS 

Google Scholar 
Lee, J. H. et al. The effects of different environmental factors on the biochemical composition of particulate organic matter in Gwangyang Bay, South Korea. Biogeosciences 14, 1903–1917 (2017).ADS 
CAS 

Google Scholar 
Fine, P. V. & Kembel, S. W. Phylogenetic community structure and phylogenetic turnover across space and edaphic gradients in western Amazonian tree communities. Ecography 34, 552–565 (2011).
Google Scholar 
Stegen, J. C., Lin, X., Konopka, A. E. & Fredrickson, J. K. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 6, 1653–1664 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tripathi, B. M. et al. Soil pH mediates the balance between stochastic and deterministic assembly of bacteria. ISME J. 12, 1072–1083 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Feng, Y. et al. Two key features influencing community assembly processes at regional scale: Initial state and degree of change in environmental conditions. Mol. Ecol. 27, 5238–5251 (2018).PubMed 

Google Scholar 
Stegen, J. C., Lin, X., Fredrickson, J. K. & Konopka, A. E. Estimating and mapping ecological processes influencing microbial community assembly. Front. Micorbiol. 6, 370 (2015).
Google Scholar 
Dini-Andreote, F., Stegen, J. C., van Elsas, J. D. & Salles, J. F. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc. Natl. Acad. Sci. 112, E1326–E1332 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, J. et al. Phylogenetic beta diversity in bacterial assemblages across ecosystems: Deterministic versus stochastic processes. ISME J. 7, 1310–1321 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lindemann, S. R. et al. The epsomitic phototrophic microbial mat of Hot Lake, Washington: Community structural responses to seasonal cycling. Front. Micorbiol. 4, 323 (2013).
Google Scholar 
Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).
Google Scholar 
Amaral-Zettler, L. A. et al. Microbial community structure across the tree of life in the extreme Rio Tinto. ISME J. 5, 42–50 (2011).PubMed 

Google Scholar 
Han, D. et al. Survey of bacterial phylogenetic diversity during the glacier melting season in an Arctic Fjord. Microb. Ecol. 81, 579–591 (2021).CAS 
PubMed 

Google Scholar 
Brand, L. E., Campbell, L. & Bresnan, E. Karenia: The biology and ecology of a toxic genus. Harmful Algae 14, 156–178 (2012).
Google Scholar 
Escalera, L., Pazos, Y., Moroño, Á. & Reguera, B. Noctiluca scintillans may act as a vector of toxigenic microalgae. Harmful Algae 6, 317–320 (2007).
Google Scholar 
Tada, K., Pithakpol, S., Yano, R. & Montani, S. Carbon and nitrogen content of Noctiluca scintillans in the Seto Inland Sea, Japan. J. Plankton. Res. 22, 1203–1211 (2000).
Google Scholar 
Hyun, B. et al. Effects of increased CO2 and temperature on the growth of four diatom species (Chaetoceros debilis, Chaetoceros didymus, Skeletonema costatum and Thalassiosira nordenskioeldii) in laboratory experiments. J. Environ. Sci. Int. 23, 1003–1012 (2014).
Google Scholar 
Park, J. S., Lee, S. D. & Lee, J. H. Taxonomic study on the euryhaline Cyclotella (Bacillariophyta) species in Korea. J. Ecol. Environ. 36, 407–409 (2013).
Google Scholar 
Yun, S. M. & Lee, J. H. Morphology and distribution of some marine diatoms, Family Rhizosoleniaceae, in Korean coastal waters: A genus Rhizosolenia. Algae 25, 173–182 (2010).
Google Scholar 
Park, J. S., Jung, S. W., Lee, S. D., Yun, S. M. & Lee, J. H. Species diversity of the genus Thalassiosira (Thalassiosirales, Bacillariophyta) in South Korea and its biogeographical distribution in the world. Phycologia 55, 403–423 (2016).
Google Scholar 
Dupont, C. L. et al. Genomic insights to SAR86, an abundant and uncultivated marine bacterial lineage. ISME J. 6, 1186–1199 (2012).CAS 
PubMed 

Google Scholar 
Landa, M. et al. Sulfur metabolites that facilitate oceanic phytoplankton–bacteria carbon flux. ISME J. 13, 2536–2550 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Müller, A. L. et al. Bacterial interactions during sequential degradation of cyanobacterial necromass in a sulfidic arctic marine sediment. Environ. Microbiol. 20, 2927–2940 (2018).PubMed 
PubMed Central 

Google Scholar 
Morris, R. M. et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420, 806–810 (2002).ADS 
CAS 
PubMed 

Google Scholar 
Giovannoni, S. J. SAR11 bacteria: The most abundant plankton in the oceans. Annu. Rev. Mar. Sci. 9, 231–255 (2017).ADS 

Google Scholar 
Mühlenbruch, M., Grossart, H. P., Eigemann, F. & Voss, M. Mini-review: Phytoplankton-derived polysaccharides in the marine environment and their interactions with heterotrophic bacteria. Environ. Microbiol. 20, 2671–2685 (2018).PubMed 

Google Scholar 
Stock, W. et al. Host specificity in diatom–bacteria interactions alleviates antagonistic effects. FEMS Microbiol. Ecol. 95, 171 (2019).
Google Scholar 
Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Illumina. 16S Metagenomic sequencing library preparation. Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System (2013).Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: Software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics 24, 2098–2100 (2008).CAS 
PubMed 

Google Scholar 
R Core Team. The R Stats Package (R Core Team, 2002).
Google Scholar 
Oksanen, J. & Blanchet, F. G. Package ‘vegan’ (2017).Martinez Arbizu, P. pairwiseAdonis: Pairwise multilevel comparison using adonis. 1 (2017).Roberts, D. W. & Roberts, M. D. W. Package ‘labdsv’. (2016).Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).CAS 
PubMed 

Google Scholar 
Gustavsen, J. A., Pai, S., Isserlin, R., Demchak, B. & Pico, A. R. RCy3: Network biology using cytoscape from within R. F1000Research 8, 1774 (2019).PubMed 
PubMed Central 

Google Scholar 
Kahle, D., Wickham, H. & Kahle, M. D. Package ‘ggmap’ (2019). More

DataSpatial gridWe created a grid whose units measure 250 m by 250 m based on the census tract layer for the city of Rio de Janeiro from the Instituto Brasileiro de Geografia e Estatística [Brazilian Institute of Geography and Statistics] (IBGE) website https://www.ibge.gov.br/geociencias/organizacao-do-territorio/malhas-territoriais. Uninhabited locations were excluded.Dengue cases on the gridDengue is a disease of compulsory notification in Brazil, and cases are notified at the Sistema de Informação de Agravos de Notificação [Information System on Diseases of Compulsory Declaration] (SINAN). Dengue cases notified in Rio de Janeiro between January 2010 and March 2015 were geocoded according to address of residency, and then counted for each grid unit by the Secretariat of Health of the city. We obtained the monthly dengue cases data aggregated at the grid level.Population on the gridThe population data is obtained from the Census 2010 (IBGE) (https://www.ibge.gov.br/estatisticas/downloads-estatisticas.html) and it is available at the census tract level. The census tract areas vary in size and can be bigger than the unit of the grid, primarily in the least densely populated zones of the city. To overcome this issue, we cropped from the census tract layer the areas classified as non-urbanized (such as water bodies, swamps, agricultural areas, green areas, beaches, rocky outcrops) in 2010 by the City Hall of Rio de Janeiro (layer available at http://www.data.rio/datasets/uso-do-solo-2010). The population of each census tract is distributed randomly (uniformly) in the areas obtained after deleting the non-urban areas. The population within the units is computed by adding the grid layer. To create the grid and edit the census tract layer we used QGIS (version 3.6.3)45, and to obtain the population in the grid we used the R software46 with the packages tidyverse47 and sf48. We verify the accuracy of our estimated population by comparison with the WordPop dataset49 (see detailed description and Supplementary Fig. 12 and Supplementary Note 2). We chose the WorldPop dataset because: (i) the estimates are also calculated based on census data and are available for 2010, (ii) the pixel size is 100 m, smaller than the size of our grid unit, and (iii) it is open access.Since the units are in fact small and most of them conserve their area of 250 m by 250 m (Supplementary Fig. 1A), we consider population density as the population of each unit. For consistency, we do not consider units with small effective areas and/or populations sizes less than, or equal to, 10 in our analysis. In total, 8954/20212 units were so excluded. This choice circumvents the problem of high sensitivity to random population distribution, and urban vs. non-urban classification, in very small and/or sparsely populated areas. It also facilitates model simulation and does not affect the peak ratio pattern (Supplementary Fig. 1B).Peak ratio and spatial aggregationSince units are small, we binned them into G groups and aggregated their times series of reported cases. The groups were generated according to two aspects: (1) the geographical location of the units as determined by the administrative divisions of the city (10 areas, 33 regions, and 160 neighborhoods); and (2) the population of the units based on quantiles in order to obtain equal size groups. We considered specifically four different partition levels, resulting in 12, 25, 50, and 100 groups with about 900, 450, 225, and 100 units, respectively (from a total number of 11,247 units for the whole city). Groups of unequal size can introduce different statistical effects (it is not the same, for example, to calculate a mean value using 1000 or 10 elements). To compare quantities across groups it is therefore prudent to define groups with the same number of elements. In particular, this consideration becomes important for a large number of groups. Since the population density distribution (number of individuals per unit) is not uniform, groups defined with “equidistant” boundaries would exhibit very different numbers of elements.Given a unit u, we define its time series ({{{{{{bf{v}}}}}}}_{{{{{{bf{u}}}}}}}={{c}_{u}({t}_{1}),{c}_{u}({t}_{2}),…,{c}_{u}({t}_{f})}), where ({c}_{u}({t}_{i})) is the number of reported cases of dengue at time ({t}_{i}) (i = 1, 2, …f) (and the bold symbol is used to indicate a vector). Thus, the aggregated time series is given by$${{{{{{bf{V}}}}}}}_{{{{{{bf{g}}}}}}}=mathop{sum}limits_{uin g}{{{{{{bf{v}}}}}}}_{{{{{{bf{u}}}}}}}={{C}_{g}({t}_{1})=mathop{sum}limits_{uin g}{c}_{u}({t}_{1}),{C}_{g}({t}_{2})=mathop{sum}limits_{uin g}{c}_{u}({t}_{2}),…,{C}_{g}({t}_{f})=mathop{sum}limits_{uin g}{c}_{u}({t}_{f})},$$with (g=1,2,…,G). Then, for each ({{{{{{bf{V}}}}}}}_{{{{{{bf{g}}}}}}}) we computed the ratio between the sizes of the second and first DENV4 peaks, that is$${{{{{rm{peakrati}}}}}}{{{{{{rm{o}}}}}}}_{{{{{{rm{g}}}}}}}=frac{{ma}{x}_{tin {season}2}{{C}_{g}({t}_{1}),{C}_{g}({t}_{2}),…,{C}_{g}({t}_{f})}}{{ma}{x}_{tin {season}1}{{C}_{g}({t}_{1}),{C}_{g}({t}_{2}),…,{C}_{g}({t}_{f})}}$$
(1)
(Supplementary Fig. 2).The deterministic SIR modelAlthough dengue is a vector-borne disease, for simplicity we omitted the explicit representation of the dynamics of the mosquito population, and treated vector transmission via the seasonality of the transmission rate26. Thus, for each unit u, the deterministic SIR model is based on the following traditional differential equations:$$frac{d{S}_{u}}{{dt}}=mu {N}_{u}-beta {S}_{u}frac{{I}_{u}}{{N}_{u}}-mu {S}_{u}$$$$frac{d{I}_{u}}{{dt}}=beta {S}_{u}frac{{I}_{u}}{{N}_{u}}-gamma {I}_{u}-mu {I}_{u}$$
(2)
$$frac{d{R}_{u}}{{dt}}={gamma I}_{u}-mu {R}_{u},$$where ({S}_{u},{I}_{u},{R}_{u}), are, respectively, the number of susceptible, infected, and recovered individuals, and ({N}_{u}) the number of inhabitants, of the spatial unit u. Parameter (mu) is the mortality rate (equal to the birth rate), and (gamma) is the recovery rate. The seasonal transmission rate is specified as (beta (t)={beta }_{0}(1+delta {{sin }},(omega t+phi ))). The units are considered independent of each other, and the initial conditions establish that the whole population of each unit is susceptible to the virus (({S}_{u}(t=0)={N}_{u}) and ({I}_{u}left(t=0right)={R}_{u}left(t=0right)=0forall u)). Transmission begins with one infected individual at a time ({t}_{0u}ge t=0) where ({t}_{0u}) is obtained from the data.Since the goal of this model is to examine the representative dynamics of different population densities, we binned the units according to their population into 12 groups, and computed the mean value of their number of inhabitants ({N}_{g}=langle {N}_{uin g}rangle) and of their arrival times of the infection ({t}_{0g}sim langle {t}_{0uin g}rangle) (where g = 1, …, 12). We then simulated the system considering the 12 sets ({{N}_{g},{t}_{0g}}) as given.The stochastic modelSince units will suffer local extinction of transmission, a major component of a stochastic implementation is the description of the local reintroduction of the virus, namely the arrival of a ‘spark’ or imported infection, in analogy to fire spread. Because space is described by a highly-resolved lattice, we considered that well-mixed transmission applies within each unit. Moreover, in lieu of  explicit spatial coupling between units, we postulated  the importation of infection through the specification of a spark rate.For this purpose, we constructed a binary representation of the time series of cases per month by defining the spatial units either as positive or negative according to whether they reported cases or not (Supplementary Fig. 3). Then, to derive a spark rate we explored the dynamics of the number of positive units as follows,$${U}^{{{mbox{+}}}}(t+{dt})={U}^{{{mbox{+}}}}(t)+{U}_{{{{{{{mathrm{new}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})-{U}_{{{{{{{mathrm{extinct}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})$$
(3)
The number of positive units at time ({t+dt}) is equal to the number of positive units at time t, plus the number of units that have been infected ({{U}_{{{{{{{mathrm{new}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})) between t and t + dt, minus the number of units that were infected at t but are no longer infected at t + dt (i.e., the number of ‘extinctions’ between t and t + dt, ({{U}_{{{{{{{mathrm{extinct}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt}))).Since uninfected units (i.e., negative units) require the arrival of a spark to become positive, the following equation specifies the mean of ({{U}_{{{{{{{mathrm{new}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})) under the assumption that a small unit is unlikely to receive more than a single spark in a period of time dt$${{langle }}{U}_{{{{{{{mathrm{new}}}}}}}}^{{+}}(t,t+{dt}){{rangle }}simeq {N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})frac{{U}^{{-}}(t)}{U},$$
(4)
where ({N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})) is the number of sparks produced between t and t + dt, ({U}^{{{{-}}}}(t)) is the number of negative units at a time t, and (U) is the total number of units in the city ((U={U}^{{{mbox{+}}}}+{U}^{{{{-}}}})).By introducing Eq. (4) into Eq. (3) we obtain,$${U}^{{{mbox{+}}}}(t+{dt})simeq {U}^{{{mbox{+}}}}(t)+{N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})frac{{U}^{{{{-}}}}(t)}{U}-{{U}_{{{{{{{mathrm{extinct}}}}}}}}}^{{{mbox{+}}}}(t,t+{dt})$$
(5)
From Eq. (5) we can now compute the spark rate per unit ({{sigma }_{u}}^{{emp}}(t,t+{dt})) from the high-resolution incidence data as$${{sigma }_{u}}^{{emp}}(t,t+{dt})=frac{{N}_{{{{{{{mathrm{sparks}}}}}}}}(t,t+{dt})}{U}simeq frac{{U}^{{{mbox{+}}}}(t+{dt})-{U}^{{{mbox{+}}}}(t)+{U}_{{{{{{{mathrm{extinct}}}}}}}}(t,t+{dt})}{{U}^{{{{-}}}}(t)}$$
(6)
In order to address the effects of human density on the spark rate, we binned the spatial units according to their population into G groups. To avoid statistical effects due to group size, we considered population quantiles. Then, by applying Eq. (6) to each of these groups, we obtained an empirical spark rate per unit that depends on human density,$${sigma }_{uin g}^{{emp}}(t,t+{dt})={sigma }_{u}^{{emp}}(t,t+{dt}{{{{{rm{;}}}}}}{N}_{g}),$$
(7)
where ({N}_{g}={{langle }}{N}_{uin g}{{rangle }}) with g = 1, 2, …, G.SimulationsThe associated differential equations of the stochastic model are those shown on Eq. (2) but the transmission component has now an additional term ({sigma }_{u}) to describe the importation of infections.$$frac{d{S}_{u}}{{dt}}=mu {N}_{u}-left(beta {S}_{u}frac{{I}_{u}}{{N}_{u}}+{sigma }_{u}right)-mu {S}_{u}$$$$frac{d{I}_{u}}{{dt}}=left(beta {S}_{u}frac{{I}_{u}}{{N}_{u}}+{sigma }_{u}right)-gamma {I}_{u}-mu {I}_{u}$$
(8)
$$frac{d{R}_{u}}{{dt}}={gamma I}_{u}-mu {R}_{u}$$Since the inferred spark rate from the data (Eq. (7)) is obtained from observed infections, we computed the spark rate ({sigma }_{u}) as:$${sigma }_{uin g}={{{{{{mathrm{Poisson}}}}}}}({{sigma }_{uin g}}^{{emp}}/rho )$$
(9)
where (rho) is the reporting rate.The model shown on Eq. (8) was formulated as stochastic by incorporating demographic noise (with the different events represented as Poisson processes). It was implemented in R with the package pomp50. We also considered measurement error by assuming that the observed number of cases ({{C}_{u}}^{{obs}}) during a period of time T is,$${{C}_{u}}^{{obs}}left(Tright)={{{{{{mathrm{binomial}}}}}}}left(rho ,{C}_{u}left(Tright)right),$$
(10)
where ({C}_{u}(T)) is the number of cases computed in the unit u. We simulated the 11,247 units that compose the city of Rio de Janeiro, and aggregated the resulting time series as for the empirical data (see Peak ratio section).The parameters of the model are given in Supplementary Table 1. We relied on parameters estimated for dengue transmission in Rio de Janeiro by ref. 26. Those estimates were obtained for the aggregated city and for the emergence of DENV1. We use these parameters here as a sufficiently realistic set for illustrating and exploring the behavior of the stochastic model with population density. Moreover, with the exception of the spark rate, the model parameters were considered the same for all units. In particular, we applied a uniform reporting rate because access to the nearest public healthcare clinic does not show a dependency on population density (see Supplementary Note 1).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

PlantProbs.net. Nickel in plants and soil https://plantprobs.net/plant/nutrientImbalances/sodium.html (accessed Apr 28, 2021).Guodong Liu, E. H. Simonne, and Y. L. Nickel Nutrition in Plants | EDIS. EDis 2011.Liu, G. D. “A New Essential Mineral Element–Nickel.” Plants Nutr. Fertil. Sci. 2001.Kabata-Pendias, A.; Mukherjee, A. Trace Elements from Soil to Human; 2007.Kasprzak, K. S. Nickel advances in modern environmental toxicology. Environ. Toxicol. 11, 145–183 (1987).CAS 

Google Scholar 
Cempel, M. & Nikel, G. Nickel: A review of its sources and environmental toxicology. Polish J. Environ. Stud. 15, 375–382 (2006).CAS 

Google Scholar 
Bradl, H. B. Chapter Sources and origins of heavy metals. Interface Sci. Technol. 6, 1–27 (2005).CAS 
Article 

Google Scholar 
Von Burg, R. Nickel and some nickel compounds. J. Appl. Toxicol. 17, 425–431 (1997).Article 

Google Scholar 
Freedman, B. & Hutchinson, T. C. Pollutant inputs from the atmosphere and accumulations in soils and vegetation near a nickel–copper smelter at Sudbury, Ontario, Canada. Can. J. Bot. 58(1), 108–132. https://doi.org/10.1139/b80-014 (1980).CAS 
Article 

Google Scholar 
Manyiwa, T. et al. Heavy metals in soil, plants, and associated risk on grazing ruminants in the vicinity of Cu–Ni mine in Selebi-Phikwe, Botswana. Environ. Geochem. Health https://doi.org/10.1007/s10653-021-00918-x (2021).Article 
PubMed 

Google Scholar 
Kabata-Pendias. Kabata-Pendias A. 2011. Trace elements in soils and… – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Kabata-Pendias+A.+2011.+Trace+elements+in+soils+and+plants.+4th+ed.+New+York+%28NY%29%3A+CRC+Press&btnG= (accessed Nov 24, 2020).Almås, A., Singh, B., Agricultural, T. S.-N. J. of & 1995, undefined. The impact of nickel industry in Russia on concentrations of heavy metals in agricultural soils and grass in Soer-Varanger, Norway. agris.fao.org.Nielsen, G. D. et al. Absorption and retention of nickel from drinking water in relation to food intake and nickel sensitivity. Toxicol. Appl. Pharmacol. 154, 67–75 (1999).CAS 
Article 

Google Scholar 
Costa, M. & Klein, C. B. Nickel carcinogenesis, mutation, epigenetics, or selection. Environ. Health Perspect. 107, 2 (1999).Article 

Google Scholar 
Agyeman, P. C.; Ahado, S. K.; Borůvka, L.; Biney, J. K. M.; Sarkodie, V. Y. O.; Kebonye, N. M.; Kingsley, J. Trend Analysis of Global Usage of Digital Soil Mapping Models in the Prediction of Potentially Toxic Elements in Soil/Sediments: A Bibliometric Review. Environmental Geochemistry and Health. Springer Science and Business Media B.V. 2020. https://doi.org/10.1007/s10653-020-00742-9.Minasny, B. & McBratney, A. B. Digital soil mapping: A brief history and some lessons. Geoderma 264, 301–311. https://doi.org/10.1016/j.geoderma.2015.07.017 (2016).ADS 
Article 

Google Scholar 
McBratney, A. B., Mendonça Santos, M. L. & Minasny, B. On digital soil mapping. Geoderma 117(1–2), 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4 (2003).ADS 
Article 

Google Scholar 
Deutsch.C.V. Geostatistical Reservoir Modeling,… – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=C.V.+Deutsch%2C+2002%2C+Geostatistical+Reservoir+Modeling%2C+Oxford+University+Press%2C+376+pages.+&btnG= (accessed Apr 28, 2021).Olea, R. A. Geostatistics for engineers & earth scientists. Stoch. Environ. Res. Risk Assess. 14(3), 207–209. https://doi.org/10.1007/pl00009782 (2000).Article 

Google Scholar 
Gumiaux, C., Gapais, D. & Brun, J. P. Geostatistics applied to best-fit interpolation of orientation data. Tectonophysics 376(3–4), 241–259. https://doi.org/10.1016/j.tecto.2003.08.008 (2003).ADS 
Article 

Google Scholar 
Wadoux, A. M. J. C., Minasny, B. & McBratney, A. B. Machine learning for digital soil mapping: applications, challenges and suggested solutions. Earth-Sci Rev. https://doi.org/10.1016/j.earscirev.2020.103359 (2020).Article 

Google Scholar 
Tan, K. et al. Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest. J. Hazard. Mater. 382, 120987. https://doi.org/10.1016/j.jhazmat.2019.120987 (2020).CAS 
Article 
PubMed 

Google Scholar 
Sakizadeh, M., Mirzaei, R. & Ghorbani, H. Support vector machine and artificial neural network to model soil pollution: a case study in Semnan Province, Iran. Neural Comput. Appl. 28(11), 3229–3238. https://doi.org/10.1007/s00521-016-2231-x (2017).Article 

Google Scholar 
Vega, F. A., Matías, J. M., Andrade, M. L., Reigosa, M. J. & Covelo, E. F. Classification and regression trees (CARTs) for modelling the sorption and retention of heavy metals by soil. J. Hazard. Mater. 167(1–3), 615–624. https://doi.org/10.1016/j.jhazmat.2009.01.016 (2009).CAS 
Article 
PubMed 

Google Scholar 
Sun, H. et al. Prediction of distribution of soil cd concentrations in Guangdong Province, China. Huanjing Kexue/Environmental Sci. 38(5), 2111–2124. https://doi.org/10.13227/j.hjkx.201611006 (2017).Article 

Google Scholar 
Woodcock, C. E. & Gopal, S. Fuzzy set theory and thematic maps: accuracy assessment and area estimation. Int. J. Geogr. Inf. Sci. 14(2), 153–172. https://doi.org/10.1080/136588100240895 (2000).Article 

Google Scholar 
Finke, P. A. Chapter 39 Quality assessment of digital soil maps: producers and users perspectives. Dev. Soil Sci. https://doi.org/10.1016/S0166-2481(06)31039-2 (2006).Article 

Google Scholar 
Pontius, R. G. & Cheuk, M. L. A generalized cross-tabulation matrix to compare soft-classified maps at multiple resolutions. Int. J. Geogr. Inf. Sci. 20(1), 1–30. https://doi.org/10.1080/13658810500391024 (2006).Article 

Google Scholar 
Grunwald, S. Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma 152(3–4), 195–207. https://doi.org/10.1016/j.geoderma.2009.06.003 (2009).ADS 
Article 

Google Scholar 
Nelson, M. A., Bishop, T. F. A., Triantafilis, J. & Odeh, I. O. A. An error budget for different sources of error in digital soil mapping. Eur. J. Soil Sci. 62, 417–430 (2011).Article 

Google Scholar 
McBratney, A. B., Minasny, B. & ViscarraRossel, R. Spectral soil analysis and inference systems: A powerful combination for solving the soil data crisis. Geoderma 136, 272–278 (2006).ADS 
CAS 
Article 

Google Scholar 
Stumpf, F. et al. Uncertainty-guided sampling to improve digital soil maps. CATENA 153, 30–38 (2017).Article 

Google Scholar 
Legates, D. R. & McCabe, G. J. Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35, 233–241 (1999).ADS 
Article 

Google Scholar 
Sergeev, A. P. et al. High variation subarctic topsoil pollutant concentration prediction using neural network residual kriging. AIP Conf. Proc. 2017, 1836. https://doi.org/10.1063/1.4981963 (2017).CAS 
Article 

Google Scholar 
Subbotina, I. E. et al. Multilayer perceptron, generalized regression neural network, and hybrid model in predicting the spatial distribution of impurity in the topsoil of urbanized area. AIP Conf. Proc. https://doi.org/10.1063/1.5045410 (2018).Article 

Google Scholar 
Tarasov, D. A., Buevich, A. G., Sergeev, A. P. & Shichkin, A. V. High variation topsoil pollution forecasting in the Russian subarctic: using artificial neural networks combined with residual kriging. Appl. Geochemistry 88, 188–197. https://doi.org/10.1016/j.apgeochem.2017.07.007 (2018).CAS 
Article 

Google Scholar 
Tarasov, D.; Buevich, A.; Shichkin, A.; Subbotina, I.; Tyagunov, A.; Baglaeva, E. Chromium Distribution Forecasting Using Multilayer Perceptron Neural Network and Multilayer Perceptron Residual Kriging. In AIP Conference Proceedings; American Institute of Physics Inc., 2018; Vol. 1978, p 440019. https://doi.org/10.1063/1.5044048.John, K. et al. Hybridization of cokriging and gaussian process regression modelling techniques in mapping soil sulphur. CATENA 206, 2 (2021).Article 

Google Scholar 
Gribov, A. & Krivoruchko, K. Empirical Bayesian Kriging Implementation and Usage. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.137290 (2020).Article 
PubMed 

Google Scholar 
Samsonova, V. P., Blagoveshchenskii, Y. N. & Meshalkina, Y. L. Use of empirical Bayesian kriging for revealing heterogeneities in the distribution of organic carbon on agricultural lands. Eurasian Soil Sci. 50(3), 305–311. https://doi.org/10.1134/S1064229317030103 (2017).ADS 
CAS 
Article 

Google Scholar 
Fabijańczyk, P., Zawadzki, J. & Magiera, T. Magnetometric assessment of soil contamination in problematic area using empirical bayesian and indicator kriging: a case study in upper Silesia, Poland. Geoderma 308, 69–77. https://doi.org/10.1016/j.geoderma.2017.08.029 (2017).ADS 
CAS 
Article 

Google Scholar 
John, K. et al. Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. Int. J. Environ. Sci. Technol. 2, 1–16. https://doi.org/10.1007/s13762-020-03089-x (2021).CAS 
Article 

Google Scholar 
Li, T. et al. Using self-organizing map for coastal water quality classification: Towards a better understanding of patterns and processes. Sci. Total Environ. 628–629, 1446–1459. https://doi.org/10.1016/j.scitotenv.2018.02.163 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Wang, Z. et al. Elucidating the differentiation of soil heavy metals under different land uses with geographically weighted regression and self-organizing map. Environ. Pollut. https://doi.org/10.1016/j.envpol.2020.114065 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Hossain Bhuiyan, M. A., Chandra Karmaker, S., Bodrud-Doza, M., Rakib, M. A. & Saha, B. B. Enrichment, sources and ecological risk mapping of heavy metals in agricultural soils of dhaka district employing SOM PMF and GIS Methods. Chemosphere https://doi.org/10.1016/j.chemosphere.2020.128339 (2021).Article 
PubMed 

Google Scholar 
Kebonye, N. M. et al. Self-organizing map artificial neural networks and sequential gaussian simulation technique for mapping potentially toxic element hotspots in polluted mining soils. J. Geochemical Explor. 222, 106680. https://doi.org/10.1016/j.gexplo.2020.106680 (2021).CAS 
Article 

Google Scholar 
Weather Spark. Average Weather in Frýdek-Místek, Czechia, Year Round – Weather Spark https://weatherspark.com/y/83671/Average-Weather-in-Frýdek-Místek-Czechia-Year-Round (accessed Sep 14, 2020).Kozák, J. Soil Atlas of the Czech Republic. 2010, 150.Vacek, O., Vašát, R. & Borůvka, L. Quantifying the pedodiversity-elevation relations. Geoderma 373, 114441. https://doi.org/10.1016/j.geoderma.2020.114441 (2020).ADS 
Article 

Google Scholar 
Krivoruchko, K. Empirical Bayesian Kriging; 2012; Vol. Fall 2012.Vapnik, V. The nature of statistical learning theory. Technometrics 38(4), 409. https://doi.org/10.2307/1271324 (1995).Article 
MATH 

Google Scholar 
Li, Z., Zhou, M., Xu, L. J., Lin, H. & Pu, H. Training sparse SVM on the core sets of fitting-planes. Neurocomputing 130, 20–27. https://doi.org/10.1016/j.neucom.2013.04.046 (2014).Article 

Google Scholar 
Cherkassky, V.; Mulier, F. Learning from Data: Concepts, Theory, and Methods: Second Edition; 2006. https://doi.org/10.1002/9780470140529.John, K. et al. Using machine learning algorithms to estimate soil organic carbon variability with environmental variables and soil nutrient indicators in an alluvial soil. Land 9(12), 1–20. https://doi.org/10.3390/land9120487 (2020).CAS 
Article 

Google Scholar 
Vohland, M., Besold, J., Hill, J. & Fründ, H. C. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy. Geoderma 166(1), 198–205. https://doi.org/10.1016/j.geoderma.2011.08.001 (2011).ADS 
CAS 
Article 

Google Scholar 
Fraser, S. J.; Dickson, B. L. A New Method for Data Integration and Integrated Data Interpretation: Self-Organising Maps; 2007.Melssen, W. J.; Smits, J. R. M.; Buydens, L. M. C.; Kateman, G. Using Artificial Neural Networks for Solving Chemical Problems Part II. Kohonen Self-Organising Feature Maps and Hopfield Networks. Chemometrics and Intelligent Laboratory Systems. Elsevier, Amsterdam, 1, 1994, pp 267–291. https://doi.org/10.1016/0169-7439(93)E0036-4.Kooistra, L. et al. The potential of field spectroscopy for the assessment of sediment properties in river floodplains. Anal. Chim. Acta 484(2), 189–200. https://doi.org/10.1016/S0003-2670(03)00331-3 (2003).CAS 
Article 

Google Scholar 
Li, L. et al. Methods for estimating leaf nitrogen concentration of winter oilseed rape (Brassica Napus L.) using in situ leaf spectroscopy. Ind. Crops Prod. 91, 194–204. https://doi.org/10.1016/j.indcrop.2016.07.008 (2016).CAS 
Article 

Google Scholar 
Różański, S. Ł, Kwasowski, W., Castejón, J. M. P. & Hardy, A. Heavy metal content and mobility in urban soils of public playgrounds and sport facility areas, Poland. Chemosphere 212, 456–466. https://doi.org/10.1016/j.chemosphere.2018.08.109 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Bretzel, F. & Calderisi, M. Metal contamination in urban soils of coastal Tuscany (Italy). Environ. Monit. Assess. 118(1–3), 319–335. https://doi.org/10.1007/s10661-006-1495-5 (2006).CAS 
Article 
PubMed 

Google Scholar 
Jim, C. Y. Urban soil characteristics and limitations for landscape planting in hong kong. Landsc. Urban Plan. 40(4), 235–249. https://doi.org/10.1016/S0169-2046(97)00117-5 (1998).Article 

Google Scholar 
Birke, M.; Rauch, U.; Chmieleski, J. Environmental Geochemical Survey of the City of Stassfurt: An Old Mining and Industrial Urban Area in Sachsen-Anhalt, Germany. In Mapping the Chemical Environment of Urban Areas; John Wiley and Sons, 2011; pp 269–306. https://doi.org/10.1002/9780470670071.ch18.Khodadoust, A. P., Reddy, K. R. & Maturi, K. Removal of nickel and phenanthrene from kaolin soil using different extractants. Environ. Eng. Sci. 21(6), 691–704. https://doi.org/10.1089/ees.2004.21.691 (2004).CAS 
Article 

Google Scholar 
Jakovljevic, M.; Kostic, N.; Antic-Mladenovic, S. The Availability of Base Elements (Ca, Mg, Na, K) in Some Important Soil Types in Serbia; 2003. https://doi.org/10.2298/zmspn0304011j.Orzechowski, M.; Smolczynski, S. IN SOILS DEVELOPED FROM THE HOLOCENE DEPOSITS IN NORTH-EASTERN POLAND*; -, 2007; Vol. 15.Pongrac, P. et al. Mineral element composition of cabbage as affected by soil type and phosphorus and zinc fertilisation. Plant Soil 434(1–2), 151–165. https://doi.org/10.1007/s11104-018-3628-3 (2019).CAS 
Article 

Google Scholar 
Kingston, G.; Anink, M. C.; Clift, B. M.; Beattie, R. N. Potassium Management for Sugarcane on Base Saturated Soils in Northern New South Wales; 2009; Vol. 31.Santo, L. T., Nakahata, M. H., & Schell, V. P. Santo LT, Nakahata MH, Ito GP and Schell VP (2000)…. – Google Scholar https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Santo+LT%2C+Nakahata+MH%2C+Ito+GP+and+Schell+VP+%282000%29.+Calcium+and+liming+trials+from+1994+to+1998+at+HC%26S.+Technical+supplement+to+Agronomy+Report+83%2C+Hawaiian+Agricultural+Research+Centre. (accessed May 16, 2021).Burgos, P., Madejón, E., Pérez-de-Mora, A. & Cabrera, F. Horizontal and vertical variability of soil properties in a trace element contaminated area. Int. J. Appl. Earth Obs. Geoinf. 10(1), 11–25. https://doi.org/10.1016/j.jag.2007.04.001 (2008).ADS 
Article 

Google Scholar 
Olinic, T. & Olinic, E. The effect of quicklime stabilization on soil properties. Agric. Agric. Sci. Procedia 10, 444–451. https://doi.org/10.1016/j.aaspro.2016.09.013 (2016).Article 

Google Scholar 
Madaras, M.; Lipavský, J. Interannual Dynamics of Available Potassium in a Long-Term Fertilization Experiment; 2009; Vol. 55. https://doi.org/10.17221/34/2009-pse.Madaras, M., Koubova, M. & Lipavský, J. Stabilization of available potassium across soil and climatic conditions of the Czech Republic. Arch. Agron. Soil Sci. 56(4), 433–449. https://doi.org/10.1080/03650341003605750 (2010).CAS 
Article 

Google Scholar 
Pulkrabová, J. et al. Is the long-term application of sewage sludge turning soil into a sink for organic pollutants?: Evidence from field studies in the Czech Republic. J. Soils Sedim. 19(5), 2445–2458. https://doi.org/10.1007/s11368-019-02265-y (2019).CAS 
Article 

Google Scholar 
Asare, M. O., Horák, J., Šmejda, L., Janovský, M. & Hejcman, M. A medieval hillfort as an island of extraordinary fertile archaeological dark earth soil in the Czech Republic. Eur. J. Soil Sci. 72(1), 98–113. https://doi.org/10.1111/ejss.12965 (2021).CAS 
Article 

Google Scholar 
Zádorová, T. et al. Identification of Neolithic to Modern Erosion-Sedimentation Phases Using Geochemical Approach in a Loess Covered Sub-Catchment of South Moravia Czech Republic. Geoderma 195–196, 56–69. https://doi.org/10.1016/j.geoderma.2012.11.012 (2013).ADS 
CAS 
Article 

Google Scholar 
Tlustoš, P. et al. Nutrient status of soil and winter wheat (Triticum Aestivum L.) in response to long-term farmyard manure application under different climatic and soil physicochemical conditions in the Czech Republic. Arch. Agron. Soil Sci. 64(1), 70–83. https://doi.org/10.1080/03650340.2017.1331297 (2018).Article 

Google Scholar 
Wang, Z. et al. Elucidating the differentiation of soil heavy metals under different land uses with geographically weighted regression and self-organizing map. Environ. Pollut. 260, 2 (2020).
Google Scholar 
Yan, P., Peng, H., Yan, L. & Lin, K. Spatial variability of soil physical properties based on GIS and geo-statistical methods in the red beds of the Nanxiong Basin, China. Polish J. Environ. Stud. 28, 2961–2972 (2019).Article 

Google Scholar 
Beguin, J., Fuglstad, G. A., Mansuy, N. & Paré, D. Predicting soil properties in the Canadian boreal forest with limited data: Comparison of spatial and non-spatial statistical approaches. Geoderma 306, 195–205 (2017).ADS 
CAS 
Article 

Google Scholar 
Adhikary, P. P., Dash, C. J., Bej, R. & Chandrasekharan, H. Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India. Environ. Monit. Assess. 176, 663–676 (2011).CAS 
Article 

Google Scholar 
John, K. et al. Mapping soil properties with soil-environmental covariates using geostatistics and multivariate statistics. Int. J. Environ. Sci. Technol. 18, 3327–3342 (2021).CAS 
Article 

Google Scholar 
Eldeiry, A. A. & Garcia, L. A. Detecting soil salinity in alfalfa fields using spatial modeling and remote sensing. Soil Sci. Soc. Am. J. 72, 201–211 (2008).ADS 
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