Ecology

Anderegg, W. R. L. AGU Advances 2, e2021AV000490 (2021).Article 

Google Scholar 
Goldstein, A. et al. Nat. Clim. Chang. 10, 287–295 (2020).CAS 
Article 

Google Scholar 
Tanneberger, F. et al. Adv. Sustain. Syst. 5, 2000146 (2021).CAS 
Article 

Google Scholar 
Paustian, K. et al. Nature 532, 49–57 (2016).CAS 
Article 

Google Scholar 
Magill, B. Biden climate plan to save forests pivots on swamps, wetland (1). Bloomberg Law, https://go.nature.com/3gzaYKM (2 November 2021).Meier, J. Designating Dark Sky Areas: Actors and Interests (Routledge, 2014).Climate Action Reserve. Key Accounting Principles for Improved Forest Management Projects within the Forest Protocol (Climate Action Reserve, 2019).Verified Carbon Standard. VM0007 REDD+ Methodology Framework (REDD+ MF), v1.6. verra.org, https://go.nature.com/3GCEKbZ (8 September 2020).PCAF. The Global GHG Accounting and Reporting Standard for the Financial Industry (Partnership for Carbon Accounting Financials, 2021).Xu, J., Morris, P. J., Liu, J. & Holden, J. Catena 160, 134–140 (2018).Article 

Google Scholar 
Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W. & Hunt, S. J. Geophys. Res. Lett. 37, L13402 (2010).
Google Scholar 
Loisel, J. et al. Nat. Clim. Chang. 11, 70–77 (2021).Article 

Google Scholar 
Leifeld, J., Wüst-Galley, C. & Page, S. Nat. Clim. Chang. 9, 945–947 (2019).CAS 
Article 

Google Scholar 
Leifeld, J. & Menichetti, L. Nat. Commun. 9, 1071 (2018).CAS 
Article 

Google Scholar 
Juffe-Bignoli, D. et al. Protected Planet Report 2014 (UNEP-WCMC: 2014).Hoyos-Santillan, J., Miranda, A., Lara, A., Rojas, M. & Sepulveda-Jauregui, A. Science 366, 1207–1208 (2019).Article 

Google Scholar 
von Unger, M. & Emmer, I. Carbon Market Incentives to Conserve, Restore and Enhance Soil Carbon. (The Nature Conservancy, 2018).Bonn, A. et al. (eds). Peatland Restoration and Ecosystem Services: Science, Policy and Practice (Cambridge Univ. Press, 2016).Buotte, P. C., Law, B. E., Ripple, W. J. & Berner, L. T. Ecol. Appl. 30, e02039 (2020).Article 

Google Scholar 
Soto-Navarro, C. et al. Phil. Trans. R. Soc. Lond. B 375, 20190128 (2020).CAS 
Article 

Google Scholar  More

Positive correlation between biosynthetic gene cluster (BGC) and phylogenetic distance in the genus Bacillus
BGCs are responsible for the synthesis of secondary metabolites involved in microbial interference competition. To investigate the relationship between BGC and phylogenetic distance within the genus Bacillus, we collected 4268 available Bacillus genomes covering 139 species from the NCBI database (Supplementary Data 1). Phylogenetic analysis based on the sequences of 120 ubiquitous single-copy proteins27 showed that the 139 species could be generally clustered into four clades (Fig. 1 and Supplementary Data 2; the phylogenetic tree including all the detailed species information is shown in Supplementary Fig. 1), including a subtilis clade that includes species from diverse niches and can be further divided into the subtilis and pumilus subclades, a cereus clade that contains typical pathogenic species (B. cereus, B. anthracis, B. thuringiensis, etc.), a megaterium clade, and a circulans clade.Fig. 1: Phylogram of the tested Bacillus genomes.The maximum likelihood (ML) phylogram of 4268 Bacillus genomes was based on the sequences of 120 ubiquitous single-copy proteins27. The phylogenetic tree shows that Bacillus species can be generally clustered into the subtilis (light green circle; further includes subtilis (dark green) and pumilus (blue) subclades as shown in the branches), cereus (red), megaterium (yellow), and circulans (gray) clades. For detailed information of the species, please refer to the phylogenetic tree in Supplementary Fig. 1.Full size imagePrediction using the bioinformatic tool antiSMASH15 detected 49,671 putative BGCs in the 4268 genomes, corresponding to an average of 11.6 BGCs per genome (Supplementary Data 3). The subtilis clade had the most BGCs, 13.1 BGCs per genome (Fig. 2a); the subtilis subclade especially accommodates a high abundance of BGCs as 13.6 per genome (Supplementary Fig. 2a), which corresponds to their adaptation in diverse competitive habitats such as plant rhizosphere. The cereus and megaterium clades possessed moderate number of BGCs as 11.7 and 7.4 per genome, respectively; while the circulans clade only had 4.3 BGCs/genome (Fig. 2a and Supplementary Table 1), suggesting a distinct physiological feature and niche adaptation strategy. The two most abundant BGC classes were nonribosomal peptide-synthetase (NRPS) and RiPPs, which had an abundance of 3.7 and 3.1 per genome on average, respectively (Supplementary Fig. 2b and Supplementary Table 1). Interestingly, subtilis clade accommodated significantly higher abundance of BGCs in another polyketide synthase (PKSother; 2.0 per genome vs. 0.0–1.1 per genome) and PKS-NRPS Hybrids (0.7 vs. 0.0–0.2) classes, as compared with the three other clades (Supplementary Table 1); while cereus clade had more BGCs in RiPPs than other clades on average (Supplementary Table 1). Overall, the profile of BGC products and classification was generally consistent with the phylogenetic tree (Supplementary Fig. 3).Fig. 2: Biosynthetic gene cluster (BGC) distribution is correlated with phylogeny in the genus Bacillus.a The numbers of BGCs in the 4268 Bacillus genomes from different clades as defined by antiSMASH15. In the violin plot, the centre line represents the median, violin edges show the 25th and 75th percentiles, and whiskers extend to 1.5× the interquartile range. b Hierarchal clustering among the 545 representative Bacillus genomes based on the abundance of the different biosynthesis gene cluster families (GCFs). Each column represents a GCF, which was classified through BiG-SCAPE by calculating the Jaccard index (JI), adjacency index (AI), and domain sequence similarity (DSS) of each BGC28; the color bar on the top of the heatmap represents the BGC class of each GCF, where PKS includes classes of PKSother and PKSI, PKS-NRPS means PKS-NRPS Hybrids, Others includes classes of saccharides, terpene, and others. Each row represents a Bacillus genome, and the abundance of each GCF in different genomes is shown in the heatmap. The left tree was constructed based on the distribution pattern of GCFs, which showes a similar pattern to the phylogram in Fig. 1. c The correlation between the BGC and phylogenetic distance of the 545 representative Bacillus genomes (P  More

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

Plant materials, growth conditions and salt treatmentsSoil samples were performed as in previous experiments with S. europaea25, seeds were collected at two maternal sites, the first of which represents natural salinity related to inland salt springs at the health resort of Ciechocinek (Cie) (52°53′N, 18°47′E) characterised by a high soil salinity of ca 100 dS m−1 (~ 1000 mM NaCl), and the second of which is associated with soda factory waste that affects the local environment in Inowrocław-Mątwy (Inw) (52°48′N, 18°15′E) and with a lower salinity of ca 55 dS m−1 (~ 550 mM NaCl). The complete soil description is reported in Piernik et al.51 and Szymanska et al.52,53. Populations are isolated by a distance of ca 40 km without any saline environment between them, however, they were somehow connected due to the presence of salt springs in the nineteenth century. The seeds came from one generation and were collected in early November 2018. The seeds were germinated and grown according to the same steps reported in Cárdenas-Pérez et al.25 with a slight modification in the number of salt treatments at 0, 200, 400, 600, 800 and 1000 mM NaCl. In total, 144 plants were cultivated, and, therefore, a complete randomised factorial design 26 was used, which included (12 plants × 6 treatments × 2 populations) with 14 response variables. After 2 months of development, anatomical analysis such as cell area (A), roundness (R) and maximum cell diameter (Cdiam) were estimated in 12 samples, whereas high and low methyl esterified pectins (HM-HGs and LM-HGs), proline (P), hydrogen peroxide (HP), total soluble protein (Prot), catalase activity (CAT), peroxidase activity (POD), chlorophyll a, b and total (Cha, Chb and TC), carotenoid (Carot) contents, as well as SeNHX1 and SeSOS1 gene expression, were all determined per triplicate (plants were randomly selected). The collection of plant material, comply with relevant institutional, national, and international guidelines and legislation, IUCN Policy Statement on Research Involving Species at Risk of Extinction and Convention on the Trade in Endangered Species of Wild Fauna and Flora. The voucher specimen of the plant material has been deposited in a publicly available herbarium of the Nicolaus Copernicus University in Toruń (Index Herbarium code TRN), deposition number not available (dr. hab. Agnieszka Piernik, prof. NCU undertook the formal identification of plant species, and permission to work with the seeds was provided by the Regional Director of Environmental Protection in Bydgoszcz, WOP.6400.12.2020.JC).Anatomical image analysisFrom the middle primary branch (fleshy segment shoot) of S. europaea plant treatments (0, 200, 400, 600, 800 and 1000 mM NaCl), slices of fresh tissue were obtained by cutting them with a sharp bi-shave blade. The thinner slices of approximately 0.5 mm were selected and used in the microstructure analysis. The size and shape of the stem-cortex cells from the fresh water-storing tissue were characterised by a light microscope (Olympus BX51, USA) connected to a digital camera (DP72 digital microscope camera) and digital acquisition software (DP2-BSW). The microscope images were captured at a magnification of 10 ×/0.30 in RGB scale and stored in TIFF format at 1280 × 1024 pixels. A total of 300 ± 50 cells from five individuals per treatment were analysed. Finally, the shape and size of the cells were obtained from the captured images. Cell image analysis (IA) was performed in ImageJ v. 1.47 (National Institutes of Health, Bethesda, MD, USA). The following anatomical parameters were obtained. Firstly, the cell area (A) was estimated as the number of pixels within the boundary. Secondly, the maximum cell’s diameter (Cdiam) was determined by the distance between the two points separated by the largest coordinates in different orientations, and the cell roundness (R) was obtained through the equation R = (4 A)/(π (Cdiam)2)—where a perfectly round cell has R = 1.0, while elongated cells will show an R → 0. Finally, the degree of succulence (S) in stem was calculated according to24 with slight change S = (Fresh Weight-Dry Weight)/stem Area, where the Area of the stem (As) was calculated as: As = π × r2, the diameter of the stems was obtained according to Cárdenas-Pérez et al.25.Immunolocalisation experimentsThe samples dissected from the middle segment of the shoot (3 individuals per treatment) were prepared for embedding in BMM resin (butyl methacrylate, methyl methacrylate, 0.5% benzoyl ethyl ether (Sigma) with 10 mM DDT (Thermo Fisher Scientific) according to Niedojadło et al.54. Next, specimens were cut on a Leica UCT ultramicrotome into serial semi-thin cross sections (1.5 µm) that were collected on Thermo Scientific Polysine adhesion microscope slides. Before immunocytochemical reaction, the resin was removed with two changes of acetone and washed in distilled water and PBS pH 7.2. After incubation with blocking solution containing 2% BSA (bovine serum albumin, Sigma) in PBS pH 7.2 for 30 min at room temperature, the sections were incubated with anti-pectin rat monoclonal primary antibody JIM7 (recognises partially methylesterified epitopes of homogalacturonan [HG] but does not bind to fully de-esterified HGs) or antibody LM19 (recognises partially methylesterified epitopes of HG and binds strongly to de-esterified HGs) (Plant Probes) diluted 1:50 in 0.2% BSA in PBS pH 7.2 overnight at 4 °C. After washing with PBS pH 7.2, the material was incubated with AlexaFluor 488-conjugated goat anti-rat secondary antibody (Thermo Fisher Scientific) diluted 1:1000 in 0.2% BSA in PBS pH 7.2 for 1 h at 37 °C. Finally, the sections were washed in PBS pH 7.2, dried at room temperature and covered with ProLongTMGold antifade reagent (Thermo Fisher Scientific). The control reactions were performed with the omission of incubation with primary antibodies. Semithin sections were analysed with an Olympus BX50 fluorescence microscope, with an UPlanFI 1009 (N.A. 1.3) oil immersion lens and narrow band filters (U-MNU, U-MNG). The results were recorded with an Olympus XC50 digital colour camera and CellB software (Olympus Soft Imaging Solutions GmbH, Germany).Fluorescence quantitative evaluationFor the quantitative measurement, each experiment was performed using consistent temperatures, incubation times and concentrations of antibodies. The aforementioned ImageJ (1.47v) software was used for image processing and analysis. The fluorescence intensity was measured for five semi-thin sections for each experimental population (Inowrocław and Ciechocinek) at the same magnification (100 ×) and the constant exposure time to ensure comparable results. The threshold fluorescence in the sample was established based on the autofluorescence of the control reaction. The level of signal intensity was expressed in arbitrary units (a.u.) as the mean intensity per μm2 according to Niedojadło et al.54.Biochemical analysisProline content (P) was measured according to Ábrahám et al.55. Five hundred milligrams of fresh stem material was minced on ice and homogenised with 3% aqueous sulfosalicylic acid solution (5 μl mg−1 fresh plant material), centrifuged at 18,000×g, 10 min at 4 °C, and the supernatant was collected. The reaction mixture: 100 μl of 3% sulphosalicylic acid, 200 μl of glacial acetic acid, 200 μl of acidic ninhydrin reagent and 100 μl of supernatant. Acidic ninhydrin reagent was prepared according to Bates et al.56. The standard curve for proline in the concentration range of 0 to 40 μg ml−1. The standard curve equation was y = 0.0467x − 0.0734, R2 = 0.963. P was expressed in mg of proline per gram of fresh weight. Hydrogen peroxide (HP) levels were determined according to the methods described by Velikova et al.57, and 500 mg of stem tissues were homogenised with 5 ml trichloroacetic acid 0.1% (w:v) in an ice bath. The homogenate was centrifuged (12,000×g, 4 °C, 15 min) and 0.5 ml of the supernatant was added to potassium phosphate buffer (0.5 ml) (10 mM, pH 7.0) and 2 ml of 1 M KI. The absorbance was read at 390 nm, and the HP content was given on a standard curve from 0 to 40 mM. The standard curve equation was y = 0.0188x + 0.046, R2 = 0.987. HP concentrations were expressed in nM per gram of fresh weight. Chlorophylls (Cha and Chb) and carotenoids were extracted from fresh plant stems (100 mg) using 80% acetone for 6 h in darkness, and then centrifuged at 10,000 rpm, 10 min. Supernatants were quantified spectrophotometrically. Absorbance was determined at 646, 663 and 470 nm and calculations were performed according to Lichtenthaler and Wellburn58, when 80% of acetone is used as dissolvent. Total chlorophyll content was calculated as the sum of chlorophyll a and b contents.Total CAT activity was determined spectrophotometrically by following the decline in A240 as H2O2 (ε = 39.9 M−1 cm−1) was catabolised, according to the method of Beers and Sizer59. Decrease in absorbance of the reaction at 240 nm was recorded after every 20 s. One unit CAT was defined as an absorbance change of 0.01 units min−1. Total POD activity was determined spectrophotometrically by monitoring the formation of tetraguaiacol (ε = 26.6 mM−1 cm−1) from guaiacol at A470 in the presence of H2O2 by the method of Chance and Maehly60. Increase in absorbance of the reaction solution at 470 nm was recorded after every 20 s. One unit of POD activity was defined as an absorbance change of 0.01 units min−1. Total soluble protein (Prot) content was measured according to Bradford61 using bovine serum albumin (BSA) as a protein standard. Fresh leaf samples (1 g) were homogenised with 4 ml Na-phosphate buffer (pH 7.2) and then centrifuged at 4 °C. Supernatant and dye were pipetted in spectrophotometer cuvettes and absorbances were measured using a UV–vis spectrophotometer (PG instruments T80) at 595 nm62. Prot was determined based on the standard curve y = 1.6565x + 0.0837, R2 = 0.982, for total soluble protein in the concentration range of 0 to 1.2 mg ml−1 BSA. Triplicates per treatment were used for each analysis.Total RNA isolationAfter 2 months of salt treatment, shoots of S. europaea plants (3 individuals per treatment) were washed several times with tap water and then three times with miliQ water. After drying, plant material was frozen in liquid nitrogen, and stored at − 80 °C. Total RNA isolation was performed using RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. The quality and quantity of RNA was checked on 1.5% agarose gels in TAE (Tris–HCl, acetic acid, EDTA, pH 8.3) buffer stained with ethidium bromide, and by spectrophotometric measurement (NanoDrop Lite, Thermo Fisher Scientific, Waltham, MA, USA).Cloning of SOS1 gene from S. europaea (SeSOS1)One microgram (1 µg) of total RNA isolated from shoots of S. europaea was primed with 0.5 µg of oligo (dT)20 primer for 5 min at 70 °C. Then 4 µl of ImProm-II 5 × reaction buffer, 2 mM MgCl2, 0.5 mM each dNTP, 20 U of recombinant RNasin ribonuclease inhibitor, and 1 µl of ImProm-II reverse transcriptase (Promega, Madison, WI, USA) were added to a final volume of 20 µl. The reaction was performed at 42 °C for 60 min. To design degenerate primers for SOS1, cDNA sequences from Arabidopsis thaliana (NM_126259.4), Lycopersicon esculentum (AJ717346.1), Mesembryanthemum crystallinum (EF207776.1), Oryza sativa (AY785147.1), Triticum aestivum (AY326952.3), Salicornia brachiata (EU879059.1) were obtained from NCBI GeneBank. The sequences were aligned using the Clustal Omega tool (https://www.ebi.ac.uk/Tools/msa/clustalo/) and three pairs of degenerate primes were designed (listed in Table 4). The PCR reaction mixture includes cDNA, 0.2 µM each primer, 0.2 mM each dNTP, 4 µl of 5 × HF buffer, and 0.5 U of Phusion High-Fidelity DNA polymerase (Thermo Fisher Scientific, Waltham, MA, USA) in a total volume of 20 µl. The thermal conditions were as follows: 98 °C for 30 s, 98 °C for 10 s, gradient between 48 °C and 56 °C for 20 s, 72 °C for 60 s, 32 cycle, final extension for 10 min at 72 °C. A pair of primers deg2_F and deg2_R yielded a PCR product with expected size. The PCR product was purified from agarose gel, cloned into pJET1.2 vector (Thermo Fisher Scientific, Waltham, MA, USA) according to manufacturer’s protocol and sequenced (Genomed, Warsaw, Poland). The obtained partial cDNA sequence was named SeSOS1 and deposited in NCBI GeneBank (acc. no. MZ707082).Table 4 Sequences of the primers used for cloning of SeSOS1 and quantitative real-time PCR.Full size tableReverse transcription reaction and quantitative real-time PCR (qPCR) SeNHX1 and SeSOS1 gene expression analysisPrior to reverse transcription reaction, RNA was treated with DNaseI (Thermo Fisher Scientific, Waltham, MA, USA). The cDNA was synthesised from 1.5 µg of total RNA using a mixture of 2.5 µM oligo(dT)20 primer and 0.2 µg of random hexamers with NG dART RT Kit (Eurx, Gdańsk, Poland) according to the manufacturer’s protocol. The reaction was performed at 25 °C for 10 min, followed by 50 min at 50 °C. The cDNA was stored at − 20 °C.The PCR reaction mixture includes 4 µl of 1/20 diluted cDNA, 0.5 µM gene-specific primers (Table 4) and 5 µl of LightCycler 480 SYBR Green I Master (Roche, Penzberg, Germany) in a total volume of 10 µl. Clathrin adaptor complexes (CAC) was used as a reference gene63. The reaction was performed in triplicate (technical replicates) in LightCycler 480 Instrument II (Roche, Penzberg, Germany). The thermal cycling conditions were as follows: 95 °C for 5 min, 95 °C for 10 s, 60 °C for 20 s, 72 °C for 20 s, 40 cycles. The SYBR Green I fluorescence signal was recorded at the end of the extension step in each cycle. The specificity of the assay was confirmed by the melt curve analysis i.e., increasing the temperature from 55 to 95 °C at a ramp rate 0.11 °C/s. The fold-change in gene expression was calculated using LightCycler 480 Software release 1.5.1.62 (Roche, Penzberg, Germany).Statistical and multivariate analysisIn order to determine the projection of the effect of salt treatment in plants we followed Cárdenas-Pérez et al.25 methodology. A principal component analysis (PCA) was developed using XLSTAT software version 2019.4.165. For this analysis, 14 variables were used, (A, Cdiam, R, Prot, CAT, POD, HM-HGs, LM-HGs, P, HP, Cha, Chb, TC, Carot), arranged in a matrix with the average values obtained from replicates of each treatment and population. A two-way ANOVA comparing treatments within populations and populations within treatments was conducted for all the results with the Holm–Sidak method. The data was fit with a modified three parameter exponential decay using SigmaPlot version 11.066. The relationships between variables were performed using a Pearson analysis, while a significance test (Kaisere Meyere Olkin) was performed in order to determine which variables had a significant correlation with each other (α = 0.05). Then, a 3D plot was developed using the three principal component factors according to the Kaiser criterion which stated that the factors below the unit are irrelevant. The three main factorial scores of the PCA from each sample were used to calculate the distance (D) between the two points (populations) under the same treatment P1 = (x1, y1, z1) and P2 = (x2, y2, z2) in 3D space of the PCA (Eq. 1).$$D ( {P_{1} ,, P_{2} } ) = sqrt {( {x_{2} – x_{1} } )^{2} + ( {y_{2} – y_{1} } )^{2} + ( {z_{2} – z_{1} } )^{2} }$$
(1)
where x, y, and z are the three main factorial scores in the PCA corresponding to the evaluated treatment in Inw and in Cie. Distances were used to evaluate and determine in which salt treatment the greatest differences between the populations were recorded. More