Philippa Kaur

Chemical and pathogenic hazards in aquaculture supply chains threaten the provision of safe aquatic food. The Seafood Risk Tool is an integrated, semi-quantitative system that develops bespoke supply chain and risk management strategies.Although wild fish catches have plateaued globally, the aquaculture sector continues to expand in response to increasing demand for fish and other aquatic foods. Economic progress and the growing consumer awareness of aquatic foods in sustainable, healthy and nutritious diets are contributing to sector expansion1. However, rapid expansion must be achieved in a socially responsible and environmentally sustainable manner. Aquaculture produces over 400 different species across marine and freshwater fish, crustaceans, molluscs, plants and algae2 — all presenting complex and unique risk profiles to the environment, the industry, investors and consumers. Measures to mitigate these risks, including aquaculture certification and legislation, are inconsistent across nations and regions, and cohesive risk management has been difficult to manage and implement. More

Barcoding of Pseudomonas isolates and experimental designTo test possible host–commensal–pathogen dynamics in a local population, we spray inoculated six A. thaliana accessions with synthetic bacterial communities composed of pathogenic and commensal Pseudomonas candidates. Because we wanted to study interactions that are likely to occur in nature, we used A. thaliana genotypes that originated from the same plant populations near Tübingen, Germany28, from which the Pseudomonas strains had been isolated (Fig. 1a). Classification of Pseudomonas lineages as pathogenic or commensal was based on observed effects in axenic infections11. Only one lineage, previously named OTU5, which dominated local plant populations, was associated with pathogenicity, both based on negative impact on rosette weight and visible disease symptoms11. We henceforth call this lineage ATUE5 (isolates sampled from ‘Around TUEbingen, group 5’) and all other Pseudomonas lineages from the Karasov collection non-ATUE5. We interchangeably use the terms ‘pathogens’ or ‘ATUE5’, and ‘commensals’ or ‘non-ATUE5’.Fig. 1: Study system.a, Location of original a. thaliana and Pseudomonas sampling sites around Tübingen. b, Taxonomic representation of the 14 Pseudomonas isolates used and the prevalence of closely related strains (divergence 0.99 and P value |± 0.2| shown. Node colours indicate the bacterial isolate classification, ATUE5 or non-ATUE5. e, In planta abundance change of the seven ATUE5 isolates in non-ATUE5 inclusive treatments in comparison with PathoCom. Abundance mean difference was estimated with the model [log10(isolate load) ~ treatment × experiment + treatment + experiment + error] for each individual strain. Thus, the treatment coefficient was estimated per isolate. Dots indicate the median estimates, and vertical lines represent 95% Bayesian credible intervals of the fitted parameter. ‘Combi’ indicates combination of the isolates C3,C4,C5 and C7, and n = 23.Full size imageEach of the 14 isolates was examined for growth inhibition against all other isolates, covering all possible combinations of binary interactions. In total, three strains out of the 14 had inhibitory activity; all were non-ATUE5 (Fig. 4c). Specifically, C4 and C5 showed the same pattern: both inhibited all pathogenic isolates but P1, and both inhibited the same two commensals, C6 and weakly C3. C3 inhibited three ATUE5 isolates: P5, P6 and P7. In summary, the in vitro assay provides evidence that among the tested Pseudomonas isolates, direct inhibition was a trait unique to commensals, and susceptible bacteria were primarily pathogens. This supports the notion that ATUE5 and non-ATUE5 isolates employ divergent competition strategies, or that if they use the same mechanism, they differ in the effectiveness of such a mechanism.The in vitro results recapitulated the general trend of pathogen inhibition found among treatments in planta. Nevertheless, we observed major discrepancies between the two assays. First, P1 was not inhibited by any isolate in the host-free assay (Fig. 4c), though it was the most inhibited member in planta among the communities (Fig. 4b). Second, no commensal isolate was inhibited in planta among communities (Fig. 4b), while two commensals, C3 and C6, were inhibited in vitro (Fig. 4c). Both observations are compatible with an effect of the host on microbe–microbe interactions. To explore such effects, we analysed all pairwise microbe–microbe abundance correlations within MixedCom-infected hosts. When we used absolute abundances, all pairwise correlations were positive, also in CommenCom and PathoCom (Extended Data Fig. 8a), consistent with there being a positive correlation between absolute abundance of individual isolates and total abundance of the entire community (Supplementary Fig. 7), that is, no isolate was less abundant in highly colonized plants than in sparsely colonized plants. This indicates that there does not seem to be active killing of competitors in planta in the CommenCom, which is probably not surprising. With relative abundances, however, a clear pattern emerged with a cluster of commensals that were positively correlated, possibly reflecting mutual growth promotion, and several commensal strains being negatively correlated with both P6 and C7, possibly reflecting unidirectional growth inhibition (Fig. 4d). We did not observe the same correlations within CommenCom among commensals and within PathoCom among pathogens as we did for either subgroup in MixedCom, reflecting higher-order interactions. Thus, interactions among pathogens were constrained by the presence of commensals and vice versa (Extended Data Fig. 8b).The in planta patterns measured in complex communities did not fully recapitulate what we had observed in vitro with pairwise interactions. We therefore investigated individual commensal isolates for their ability to suppress pathogens in planta and also tested the entourage effect. We focused on the three commensals C3, C4 and C5, which had directly inhibited pathogens in vitro, and as a control C7, which had not shown any inhibition activity in vitro. We infected plants with mixtures of PathoCom and each of the four individual commensals and also with PathoCom mixed with all four commensals. Because pathogen inhibition seemed to be independent of the host genotype, we arbitrarily chose HE-1. Regardless of the commensal isolate, only P1 was suppressed with high probability in all commensal-including treatments (Fig. 4e), with P2, P3 and P4 being substantially inhibited only by the mixture of all four commensals. Together with the lack of meaningful differences between individual commensals, this indicates that pathogen inhibition is either a function of commensal dose or a result of interaction among commensals.An important finding was that four commensal strains had much more similar inhibitory activity in planta than in vitro and that the combined action was greater than the individual effects. Together, this suggested that the host contributes to the observed interactions between commensal and pathogenic Pseudomonas isolates. To begin to investigate this possibility, we next studied potential host immune responses with RNA sequencing.Defensive response elicited by non-ATUE5For the RNA-sequencing experiment, we treated plants of the genotype Lu3-30 with the three synthetic communities and also used a bacteria-free control treatment. We sampled the treated plants 3 DPI and 4 DPI, thus increasing the ability to pinpoint differentially expressed genes (DEGs) between treatments that are not highly time specific. Exploratory analysis indicated that the two time points behaved similarly, and they were combined for further in-depth analysis.We first looked at DEGs in a comparison between infected plants and control (Supplementary Table 5); with PathoCom, there were only 14 DEGs; with CommenCom, there were 1,112 DEGs; and with MixedCom, there were 1,949 DEGs, suggesting that the CommenCom isolates, which are also present in the MixedCom, elicited a stronger host response than the PathoCom members. Furthermore, the high number of DEGs in MixedCom, higher than both PathoCom and CommenCom together, suggested a synergistic response derived from inclusion of both PathoCom and CommenCom members. Alternatively, this could also be a consequence of the higher initial inoculum in the 14-member MixedCom than either the 7-member PathoCom or 7-member CommenCom, or a combination of the two effects (Fig. 5a,b and Extended Data Fig. 9). The genes induced by the MixedCom fell into two classes: Group 5 (Fig. 5a,b) was also induced, albeit more weakly, by the CommenCom but not by the PathoCom. This group was overrepresented for non-redundant gene ontology (GO) categories linked to defence (Fig. 5c) and most likely explains the protective effects of commensals in the MixedCom. Specifically, among the top ten enriched GO categories in the shared MixedCom and CommenCom set, eight relate to immune response or response to another organism (‘defence response’, ‘multi-organism process’, ‘immune response’, ‘response to stimulus’, ‘response to biotic stimulus’, ‘response to other organism’, ‘immune system process’, ‘response to stress’; Fig. 5c).Fig. 5: Only commensal members elicit a strong host-defensive response.a, Relative expression (RE) pattern of 2,727 DEGs found in at least one of the comparisons of CommenCom, PathoCom and MixedCom with Control. DEGs were hierarchically clustered. b, Euler diagram of DEGs in PathoCom-, CommenCom- and MixedCom-treated plants compared with Control (log2[fold change]  >|± 1|; false discovery rate (FDR) 0.05); n = 4.Full size imageGroup 4 was only induced in MixedCom, either indicating synergism between commensals and pathogens or reflecting a consequence of the higher initial inoculum. This group included a small number of redundant GO categories indicative of defence, such as ‘salicylic acid mediated signalling pathway’, ‘multi-organism process’, ‘response to other organism’ and ‘response to biotic stimulus’ (Supplementary Table 6). Moreover, the MixedCom response cannot simply be explained by synergistic effects or commensals suppressing pathogen effects because there was a prominent class, Group 2, which included genes that were induced in the CommenCom but to a much lesser extent in the PathoCom or MixedCom. From their annotation, it was unclear how they can be linked to infection (Fig. 5c). About 500 genes (Group 1) that were downregulated by all bacterial communities are unlikely to contain candidates for commensal protection (Fig. 5a).Cumulatively, these results imply that the CommenCom members elicited a defensive response in the host regardless of PathoCom members, while the mixture of both led to additional responses. To better understand if selective suppression of ATUE5 in MixedCom infections may have resulted from the recognition of both non-ATUE5 and ATUE5 (reflected by a unique MixedCom set of DEGs) or solely non-ATUE5 (a set of DEGs shared by MixedCom and CommenCom), we examined the expression of key genes related to the salicylic acid pathway and downstream immune responses. Activation of the salicylic acid pathway was previously related to increased fitness of A. thaliana in the presence of wild bacterial pathogens, a phenomenon which was attributed to an increased systemic acquired resistance32.We observed a general trend of higher expression in MixedCom- and CommenCom-infected hosts for several such genes (Fig. 5d). Examples are PR1 and PR5, marker genes for systemic acquired resistance and resistance execution. Therefore, according to the marker genes we tested, non-ATUE5 elicited a defensive response in the host, regardless of ATUE5 presence.We conclude that the expression profiles of non-ATUE5-infected Lu3-30 plants point to an increased defensive status, supporting our hypothesis regarding host-mediated ATUE5 suppression. We note that ATUE5 suppression was not associated with full plant protection and thus control-like weight levels in all plant genotypes. One accession, Ey15-2, was only partially protected in the MixedCom (Fig. 2), despite levels of pathogen inhibition being not very different from other host genotypes (Extended Data Fig. 7).Lack of protection explained by a single pathogenic isolateThe fact that Ey15-2 was only partially protected by MixedCom (Fig. 2) underlines the importance of the host genotype in plant–microbe–microbe interactions, apparently reflecting the dynamics between microbes and plants in wild populations. We therefore wanted to reveal the cause for this differential interaction.Our first aim was to rank compositional variables in MixedCom according to their impact on plant weight, regardless of host genotype. Next, we asked whether any of the top-ranked variables could explain the lack of protection in Ey15-2. With Random Forest analysis, we estimated the weight-predictive power of all individual isolates in MixedCom and three cumulative variables: total bacterial abundance, total ATUE5 abundance and total non-ATUE5 abundance. We found that the best weight-predictive variable was the abundance of pathogenic isolate P6, followed by total bacterial load and total ATUE5 load, which were probably confounded by the abundance of P6 (Fig. 6a). In agreement, P6 was the dominant ATUE5 in MixedCom (Fig. 6b and Extended Data Fig. 10a). We thus hypothesized that the residual pathogenicity in MixedCom-infected Ey15-2 was caused by P6. Although P6 grew best in Ey15-2, the difference to most other genotypes was unlikely to be important (Extended Data Fig. 10b). However, P6 was particularly dominant in Ey15-2 (Fig. 6b).Fig. 6: The effect of isolate P6 on weight in MixedCom-infected hosts and particularly on accession Ey15-2.a, Relative importance (mean decrease accuracy; ‘MSE’) of 20 examined variables in weight prediction of MixedCom-infected hosts as determined by Random Forest analysis. The best predictor was the abundance of isolate P6. ‘Total bacterial’, ‘Total ATUE5’ and ‘Total non-ATUE5’ indicate the cumulative abundances of the 14 isolates, seven ATUE5 isolates and seven non-ATUE5 isolates, respectively. b, Abundance of P6 compared with the other 13 barcoded isolates in MixedCom-infected hosts across the six A. thaliana genotypes used in this study. Dots indicate the median estimates, and vertical lines represent 95% Bayesian credible intervals of the fitted parameter, following the model [log10(isolate load) ~ isolate × experiment + isolate + experiment + error]. Each genotype was analysed individually, thus the model was utilized for each genotype separately. The shaded area denotes the 95% Bayesian credible intervals for the isolate P6. c, Fresh rosette weight of Ey15-2 plants treated with Control, MixedCom and MixedCom without P6 (MixedCom ΔP6). Fresh rosette weight was measured 12 DPI. The top panel presents the raw data, with the breaks in the vertical black lines denoting the mean value of each group, and the vertical lines indicating standard deviation. The lower panel presents the mean difference to control, plotted as bootstrap sampling55,56, indicating the distribution of effect size that is compatible with the data. The 95% confidence intervals are indicated by the black vertical bars, and n = 19.Full size imageGiven that pathogen load in Ey15-2 was driven to a substantial extent by P6, we assumed that this isolate had a stronger impact on the weight of Ey15-2 than on other accessions. We experimentally validated that removal of P6 restored protection when Ey15-2 was infected with the MixedCom (Fig. 6c). To confirm that restored protection was due to the interaction of commensals with the five other pathogenic isolates (P1–P5), rather than simply removal of P6, we also treated Ey15-2 with PathoCom only, but not P6. The removal of P6 did not diminish the negative weight impact of PathoCom (P1–P5, Supplementary Fig. 8), implying that it was indeed the interaction between commensals with five out of six pathogenic isolates that mitigated the harmful effect of pathogens in Ey15-2 plants. 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