Ecology

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

Abolins, S. et al. The comparative immunology of wild and laboratory mice, Mus musculus domesticus. Nat. Commun. 8, 14811. https://doi.org/10.1038/ncomms14811 (2017).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cox, F. E. G. Concomitant infections, parasites and immune responses. Parasitology 122, S23–S38. https://doi.org/10.1017/S003118200001698X (2001).Article 
PubMed 

Google Scholar 
Seder, R. A., Darrah, P. A. & Roederer, M. T-cell quality in memory and protection: Implications for vaccine design. Nat. Rev. Immunol. 8, 247–258. https://doi.org/10.1038/nri2274 (2008).CAS 
Article 
PubMed 

Google Scholar 
Demas, G. E., Zysling, D. A., Beechler, B. R., Muehlenbein, M. P. & French, S. S. Beyond phytohaemagglutinin: Assessing vertebrate immune function across ecological contexts. J. Anim. Ecol. 80, 710–730. https://doi.org/10.1111/j.1365-2656.2011.01813.x (2011).Article 
PubMed 

Google Scholar 
Pedersen, A. B. & Babayan, S. A. Wild immunology. Mol. Ecol. 20, 872–880. https://doi.org/10.1111/j.1365-294X.2010.04938.x (2011).CAS 
Article 
PubMed 

Google Scholar 
Abolins, S. et al. The ecology of immune state in a wild mammal, Mus musculus domesticus. PLoS Biol. 16, e2003538. https://doi.org/10.1371/journal.pbio.2003538 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ezenwa, V. O. Helminth–microparasite co-infection in wildlife: Lessons from ruminants, rodents and rabbits. Parasite Immunol. 38, 527–534. https://doi.org/10.1111/pim.12348 (2016).CAS 
Article 
PubMed 

Google Scholar 
Craig, B. H., Tempest, L. J., Pilkington, J. G. & Pemberton, J. M. Metazoan-protozoan parasite co-infections and host body weight in St Kilda Soay sheep. Parasitology 135, 433–441. https://doi.org/10.1017/S0031182008004137 (2008).CAS 
Article 
PubMed 

Google Scholar 
Graham, A. L. et al. Exposure to viral and bacterial pathogens among Soay sheep (Ovis aries) of the St Kilda archipelago. Epidemiol. Infect. 144, 1879–1888. https://doi.org/10.1017/S0950268816000017 (2016).CAS 
Article 
PubMed 

Google Scholar 
Murphy, K., Travers, P., Walport, M. & Janeway, C. Janeway’s Immunobiology (Garland Science, 2012).
Google Scholar 
Parkin, J. & Cohen, B. An overview of the immune system. Lancet 357, 1777–1789. https://doi.org/10.1016/S0140-6736(00)04904-7 (2001).CAS 
Article 
PubMed 

Google Scholar 
Mosmann, T. R. & Coffman, R. L. TH1 and TH2 cells: Different patterns of lymphokine secretion lead to different functional properties. Annu. Rev. Immunol. 7, 145–173. https://doi.org/10.1146/annurev.iy.07.040189.001045 (1989).CAS 
Article 
PubMed 

Google Scholar 
Nakayamada, S., Takahashi, H., Kanno, Y. & O’Shea, J. J. Helper T cell diversity and plasticity. Curr. Opin. Immunol. 24, 297–302. https://doi.org/10.1016/j.coi.2012.01.014 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gerbe, F. et al. Intestinal epithelial tuft cells initiate type 2 mucosal immunity to helminth parasites. Nature 529, 226–230. https://doi.org/10.1038/nature16527 (2016).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Jain, A. & Pasare, C. Innate control of adaptive immunity: Beyond the three-signal paradigm. J. Immunol. (Baltimore, Md.: 1950) 198, 3791–3800. https://doi.org/10.4049/jimmunol.1602000 (2017).CAS 
Article 

Google Scholar 
Schmitt, N. & Ueno, H. Regulation of human helper T cell subset differentiation by cytokines. Curr. Opin. Immunol. 34, 130–136. https://doi.org/10.1016/j.coi.2015.03.007 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Abbas, A. K., Murphy, K. M. & Sher, A. Functional diversity of helper T lymphocytes. Nature 383, 787–793. https://doi.org/10.1038/383787a0 (1996).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Seder, R. A. & Paul, W. E. Acquisition of lymphokine-producing phenotype by CD4+ T cells. Annu. Rev. Immunol. 12, 635–673. https://doi.org/10.1146/annurev.iy.12.040194.003223 (1994).CAS 
Article 
PubMed 

Google Scholar 
Grencis, R. K. Immunity to helminths: Resistance, regulation, and susceptibility to gastrointestinal nematodes. Annu. Rev. Immunol. 33, 201–225. https://doi.org/10.1146/annurev-immunol-032713-120218 (2015).CAS 
Article 
PubMed 

Google Scholar 
O’Garra, A. & Robinson, D. In Advances in Immunology vol. 83 133–162 (Academic Press, 2004).Pereira, L. M. S., Gomes, S. T. M., Ishak, R. & Vallinoto, A. C. R. Regulatory T cell and forkhead box protein 3 as modulators of immune homeostasis. Front. Immunol. https://doi.org/10.3389/fimmu.2017.00605 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Romagnani, S. T-cell subsets (Th1 versus Th2). Ann. Allergy Asthma Immunol. 85, 9–21. https://doi.org/10.1016/S1081-1206(10)62426-X (2000).CAS 
Article 
PubMed 

Google Scholar 
Sandquist, I. & Kolls, J. Update on regulation and effector functions of Th17 cells. F1000Res 7, 205–205. https://doi.org/10.12688/f1000research.13020.1 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Stockinger, B. & Omenetti, S. The dichotomous nature of T helper 17 cells. Nat. Rev. Immunol. 17, 535–544. https://doi.org/10.1038/nri.2017.50 (2017).CAS 
Article 
PubMed 

Google Scholar 
Wilson, K., Fenton, A. & Tompkins, D. Wildlife Disease Ecology: Linking Theory to Data and Application (Cambridge University Press, 2019).Book 

Google Scholar 
Graham, A. L. Ecological rules governing helminth–microparasite coinfection. PNAS 105, 566–570. https://doi.org/10.1073/pnas.0707221105 (2008).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ezenwa, V. O., Etienne, R. S., Luikart, G., Beja-Pereira, A. & Jolles, A. E. Hidden consequences of living in a wormy world: Nematode-induced immune suppression facilitates tuberculosis invasion in African Buffalo. Am. Nat. 176, 613–624. https://doi.org/10.1086/656496 (2010).Article 
PubMed 

Google Scholar 
Ezenwa, V. O. & Jolles, A. E. Opposite effects of anthelmintic treatment on microbial infection at individual versus population scales. Science 347, 175–177. https://doi.org/10.1126/science.1261714%JScience (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Arriero, E. et al. From the animal house to the field: Are there consistent individual differences in immunological profile in wild populations of field voles (Microtus agrestis)?. PLoS One 12, e0183450. https://doi.org/10.1371/journal.pone.0183450 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jackson, J. A. et al. An immunological marker of tolerance to infection in wild rodents. PLoS Biol. 12, e1001901. https://doi.org/10.1371/journal.pbio.1001901 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Beirne, C., Delahay, R. & Young, A. Sex differences in senescence: The role of intra-sexual competition in early adulthood. Proc. R. Soc. B. 282, 20151086. https://doi.org/10.1098/rspb.2015.1086 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Young, S. et al. Relationships between immune gene expression and circulating cytokine levels in wild house mice. Ecol. Evol. 10, 13860–13871. https://doi.org/10.1002/ece3.6976 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Turner, J. D. et al. Th2 cytokines are associated with reduced worm burdens in a human intestinal helminth infection. J. Infect. Dis. 188, 1768–1775. https://doi.org/10.1086/379370 (2003).CAS 
Article 
PubMed 

Google Scholar 
Craig, B. H., Pilkington, J. G., Kruuk, L. E. B. & Pemberton, J. M. Epidemiology of parasitic protozoan infections in Soay sheep (Ovis aries L.) on St Kilda. Parasitology 134, 9–21. https://doi.org/10.1017/S0031182006001144 (2006).Article 
PubMed 

Google Scholar 
Maizels, R. M., Hewitson, J. P. & Smith, K. A. Susceptibility and immunity to helminth parasites. Curr. Opin. Immunol. 24, 459–466. https://doi.org/10.1016/j.coi.2012.06.003 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ozmen, O., Adanir, R. & Haligur, M. Immunohistochemical detection of the cytokine and chemokine expression in the gut of lambs and kids with coccidiosis. Small Rumin. Res. 105, 345–350. https://doi.org/10.1016/j.smallrumres.2011.11.010 (2012).Article 

Google Scholar 
Woolhouse, M. E. J. Patterns in parasite epidemiology: The peak shift. Parasitol. Today 14, 428–434. https://doi.org/10.1016/S0169-4758(98)01318-0 (1998).CAS 
Article 
PubMed 

Google Scholar 
Gibson, T. E. & Parfitt, J. W. The effect of age on the development by sheep of resistance to Trichostrongylus colubriformis. Res. Vet. Sci. 13, 529–535 (1972).CAS 
Article 

Google Scholar 
Smith, W. D., Jackson, F., Jackson, E. & Williams, J. Age immunity to Ostertagia circumcincta: Comparison of the local immune responses of 4 1/2- and 10-month-old lambs. J. Comp. Pathol. 95, 235–245. https://doi.org/10.1016/0021-9975(85)90010-6 (1985).CAS 
Article 
PubMed 

Google Scholar 
Peters, A., Delhey, K., Nakagawa, S., Aulsebrook, A. & Verhulst, S. Immunosenescence in wild animals: Meta-analysis and outlook. Ecol. Lett. 22, 1709–1722. https://doi.org/10.1111/ele.13343 (2019).Article 
PubMed 

Google Scholar 
Sparks, A. M. et al. Natural selection on antihelminth antibodies in a wild mammal population. Am. Nat. 192, 745–760. https://doi.org/10.1086/700115 (2018).Article 
PubMed 

Google Scholar 
Froy, H. et al. Senescence in immunity against helminth parasites predicts adult mortality in a wild mammal. Science 365, 1296–1298. https://doi.org/10.1126/science.aaw5822%JScience (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Nussey, D. H., Watt, K., Pilkington, J. G., Zamoyska, R. & McNeilly, T. N. Age-related variation in immunity in a wild mammal population. Aging Cell 11, 178–180. https://doi.org/10.1111/j.1474-9726.2011.00771.x (2012).CAS 
Article 
PubMed 

Google Scholar 
Watson, R. L. et al. Cellular and humoral immunity in a wild mammal: Variation with age & sex and association with overwinter survival. Ecol. Evol. 6, 8695–8705. https://doi.org/10.1002/ece3.2584 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Pennock, N. D. et al. T cell responses: Naive to memory and everything in between. Adv. Physiol. Educ. 37, 273–283. https://doi.org/10.1152/advan.00066.2013 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Chipeta, J. et al. CD4+and CD8+Cell cytokine profiles in neonates, older children, and adults: Increasing T helper type 1 and T cytotoxic type 1 cell populations with age. Cell. Immunol. 183, 149–156. https://doi.org/10.1006/cimm.1998.1244 (1998).CAS 
Article 
PubMed 

Google Scholar 
Sakata-Kaneko, S., Wakatsuki, Y., Matsunaga, Y., Usui, T. & Kita, T. Altered Th1/Th2 commitment in human CD4+ T cells with ageing. Clin. Exp. Immunol. 120, 267–273. https://doi.org/10.1046/j.1365-2249.2000.01224.x (2000).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Duddy, M. E., Alter, A. & Bar-Or, A. Distinct profiles of human B cell effector cytokines: A role in immune regulation?. J. Immunol. (Baltimore, Md.: 1950) 172, 3422–3427. https://doi.org/10.4049/jimmunol.172.6.3422 (2004).CAS 
Article 

Google Scholar 
Varma, T. K., Lin, C. Y., Toliver-Kinsky, T. E. & Sherwood, E. R. Endotoxin-induced gamma interferon production: Contributing cell types and key regulatory factors. Clin. Diagn. Lab. Immunol. 9, 530–543. https://doi.org/10.1128/CDLI.9.3.530-543.2002 (2002).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McNeilly, T. N. et al. Suppression of ovine lymphocyte activation by Teladorsagia circumcincta larval excretory-secretory products. Vet. Res. 44, 70. https://doi.org/10.1186/1297-9716-44-70 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Restif, O. & Amos, W. The evolution of sex-specific immune defences. Proc. R. Soc. B Biol. Sci. 277, 2247–2255. https://doi.org/10.1098/rspb.2010.0188 (2010).Article 

Google Scholar 
Hayward, A. D. et al. Heritable, heterogeneous, and costly resistance of sheep against nematodes and potential feedbacks to epidemiological dynamics. Am. Nat. 184, S58–S76. https://doi.org/10.1086/676929 (2014).Article 
PubMed 

Google Scholar 
Sparks, A. M. et al. The genetic architecture of helminth-specific immune responses in a wild population of Soay sheep (Ovis aries). PLoS Genet. 15, e1008461. https://doi.org/10.1371/journal.pgen.1008461 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hayward, A. D., Wilson, A. J., Pilkington, J. G., Pemberton, J. M. & Kruuk, L. E. B. Ageing in a variable habitat: Environmental stress affects senescence in parasite resistance in St Kilda Soay sheep. Proc. R. Soc. B. 276, 3477–3485. https://doi.org/10.1098/rspb.2009.0906 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Mosmann, T. R. & Sad, S. The expanding universe of T-cell subsets: Th1, Th2 and more. Immunol. Today 17, 138–146. https://doi.org/10.1016/0167-5699(96)80606-2 (1996).CAS 
Article 
PubMed 

Google Scholar 
Hassan, M., Hanrahan, J. P., Good, B., Mulcahy, G. & Sweeney, T. A differential interplay between the expression of Th1/Th2/Treg related cytokine genes in Teladorsagia circumcincta infected DRB1*1101 carrier lambs. Vet. Res. 42, 45. https://doi.org/10.1186/1297-9716-42-45 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Noordwijk, A. J. V. & Jong, G. D. Acquisition and allocation of resources: Their influence on variation in life history tactics. Am. Nat. 128, 137–142. https://doi.org/10.1086/284547 (1986).Article 

Google Scholar 
Grainger, J. R. et al. Helminth secretions induce de novo T cell Foxp3 expression and regulatory function through the TGF-β pathway. J. Exp. Med. 207, 2331–2341. https://doi.org/10.1084/jem.20101074 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Smith, K. A. et al. Low-level regulatory T-cell activity is essential for functional type-2 effector immunity to expel gastrointestinal helminths. Mucosal Immunol. 9, 428–443. https://doi.org/10.1038/mi.2015.73 (2016).CAS 
Article 
PubMed 

Google Scholar 
Beirne, C., Waring, L., McDonald, R. A., Delahay, R. & Young, A. Age-related declines in immune response in a wild mammal are unrelated to immune cell telomere length. Proc. R. Soc. B. 283, 20152949. https://doi.org/10.1098/rspb.2015.2949 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Zaros, L. G. et al. Response of resistant and susceptible Brazilian Somalis crossbreed sheep naturally infected by Haemonchus contortus. Parasitol. Res. 113, 1155–1161. https://doi.org/10.1007/s00436-014-3753-8 (2014).CAS 
Article 
PubMed 

Google Scholar 
Gossner, A., Wilkie, H., Joshi, A. & Hopkins, J. Exploring the abomasal lymph node transcriptome for genes associated with resistance to the sheep nematode Teladorsagia circumcincta. Vet. Res. 44, 68. https://doi.org/10.1186/1297-9716-44-68 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wilkie, H., Gossner, A., Bishop, S. & Hopkins, J. Variations in T cell transcription factor sequence and expression associated with resistance to the sheep nematode Teladorsagia circumcincta. PLoS One 11, e0149644. https://doi.org/10.1371/journal.pone.0149644 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Nussey, D. H., Coulson, T., Festa-Bianchet, M. & Gaillard, J.-M. Measuring senescence in wild animal populations: Towards a longitudinal approach. Funct. Ecol. 22, 393–406. https://doi.org/10.1111/j.1365-2435.2008.01408.x (2008).Article 

Google Scholar 
Seguel, M. et al. Immune stability predicts tuberculosis infection risk in a wild mammal. Proc. Biol. Sci. 286, 20191401. https://doi.org/10.1098/rspb.2019.1401 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pemberton, J. M. & Clutton-Brock, T. H. Soay Sheep: Dynamics and Selection in an Island Population (Cambridge University Press, 2004).
Google Scholar 
Corripio-Miyar, Y. et al. Phenotypic and functional analysis of monocyte populations in cattle peripheral blood identifies a subset with high endocytic and allogeneic T-cell stimulatory capacity. Vet. Res. 46, 112. https://doi.org/10.1186/s13567-015-0246-4 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kwong, L. S. et al. Development of an ELISA for bovine IL-10. Vet. Immunol. Immunopathol. 85, 213–223. https://doi.org/10.1016/S0165-2427(02)00007-7 (2002).CAS 
Article 
PubMed 

Google Scholar 
Wattegedera, S. R. et al. Enhancing the toolbox to study IL-17A in cattle and sheep. Vet. Res. 48, 20–20. https://doi.org/10.1186/s13567-017-0426-5 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jackson, F. New technique for obtaining nematode ova from sheep faeces. Lab. Pract. 23, 65–66 (1974).ADS 
CAS 
PubMed 

Google Scholar 
R Development Core Team. R: A language and environment for statistical computing. Accessed Feb 2020. https://www.R-project.org/ (2019).
Venables, W. N. & Ripley, B. D. Random and Mixed Effects. In Modern Applied Statistics with S. Statistics and Computing. (2002).Package “corrplot”: visualization of a correlation matrix v. (Version 0.84) (2017).Jari Oksanen, F. et al. vegan: Community Ecology Package. R package version 2.5-6. Accessed Feb 2020. https://CRAN.R-project.org/package=vegan (2019). More

Hunke, P., Mueller, E. N., Schröder, B. & Zeilhofer, P. The Brazilian Cerrado: Assessment of water and soil degradation in catchments under intensive agricultural use. Ecohydrology 8, 1154–1180 (2015).Article 

Google Scholar 
Klink, C. a. & Machado, R. B. A conservação do Cerrado brasileiro. Megadiversidade 1, 147–155 (2005).Dutra e Silva, S. Challenging the Environmental History of the Cerrado: Science, Biodiversity and Politics on the Brazilian Agricultural Frontier. LAHAC 1, (2020).Nehring, R. Yield of dreams: Marching west and the politics of scientific knowledge in the Brazilian Agricultural Research Corporation (Embrapa). Geoforum 77, 206–217 (2016).Article 

Google Scholar 
Taber, A., Navarro, G. & Arribas, M. A. A new park in the Bolivian Gran Chaco—an advance in tropical dry forest conservation and community-based management. Oryx 31, 189 (1997).Article 

Google Scholar 
Moura, de, J. B. & Cabral, J. S. R. Mycorrhiza in Central Savannahs: Cerrado and Caatinga. In Mycorrhizal Fungi in South America. vol. 1 (Springer International Publishing, 2019).de Brito Neves, B. B. & Cordani, U. G. Tectonic evolution of South America during the Late Proterozoic. Precambrian Res. 53, 23–40 (1991).ADS 
Article 

Google Scholar 
Laux, J. H., Pimentel, M. M., Dantas, E. L., Armstrong, R. & Junges, S. L. Two neoproterozoic crustal accretion events in the Brasília belt, central Brazil. J. S. Am. Earth Sci. 18, 183–198 (2005).Article 

Google Scholar 
Simon, M. F. et al. Recent assembly of the Cerrado, a neotropical plant diversity hotspot, by in situ evolution of adaptations to fire. PNAS 106, 20359–20364 (2009).ADS 
CAS 
Article 

Google Scholar 
Guimarães Andrade, R. et al. Indicativo de pastagens plantadas em processo de degradação no bioma Cerrado. In XVII Simpósio Brasileiro de Sensoriamento Remot 1585–1592 (INPE, 2015).Arruda, A. B. et al. Resistance of soil to penetration as a parameter indicator of subsolation in crop areas of sugar cane. Sci. Rep. 11, 11780 (2021).ADS 
CAS 
Article 

Google Scholar 
Bongiorno, G. et al. Sensitivity of labile carbon fractions to tillage and organic matter management and their potential as comprehensive soil quality indicators across pedoclimatic conditions in Europe. Ecol. Ind. 99, 38–50 (2019).CAS 
Article 

Google Scholar 
Dias-Filho, M. B. Desafios da produção animal em pastagens na fronteira agrícola brasileira. Embrapa Amazônia Oriental-Documentos (INFOTECA-E) (2012).Andrade Júnior, J. A. D., Ribeiro De Souza, B., Souza, R. F. & de Moura, J. B. Fixação de carbono em sistemas agroecológicos na região do vale do são patrício, goiás carbon sequestration in agroecological systems in the region of the são patrício valley, goiás. Científic@ Multidiscip. J. ISSN 5, 85–98 (2018).Andrade de Souza Moraes, J. M. et al. Arbuscular mycorrhizal fungi in integrated crop livestock systems with intercropping in the pasture phase in the Cerrado. Rhizosphere 11 (2019).Ofstehage, A. & Nehring, R. No-till agriculture and the deception of sustainability in Brazil. Int. J. Agric. Sustain. 19, 335–348 (2021).Article 

Google Scholar 
Thomazini, L. I. Mycorrhiza in plants of the ‘Cerrado’. Plant Soil 41, 707–711 (1974).Article 

Google Scholar 
Porcel, R. & Ruiz-Lozano, J. M. Arbuscular mycorrhizal influence on leaf water potential, solute accumulation, and oxidative stress in soybean plants subjected to drought stress. J. Exp. Bot. 55, 1743–1750 (2004).CAS 
Article 

Google Scholar 
Moura, de, J. B., Valentim, N. M., Ventura, M. V. A. & Junior, W. G. V. Taxa de colonização micorrízica sob diferentes sistemas de cultivo no cerrado em cana-de-açúcar. 2, 60–66 (2017).Pirozynski, K. A. Interactions between fungi and plants through the ages. Can. J. Bot. 59, 1824–1827 (1981).Article 

Google Scholar 
Muthukumar, T., Udaiyan, K. & Shanmughavel, P. Mycorrhiza in sedges—an overview. Mycorrhiza 14, 65–77 (2004).CAS 
Article 

Google Scholar 
Aliasgharzadeh, N., Rastin, S. N., Towfighi, H. & Alizadeh, A. Occurrence of arbuscular mycorrhizal fungi in saline soils of the Tabriz Plain of Iran in relation to some physical and chemical properties of soil. Mycorrhiza 11, 119–122 (2001).CAS 
Article 

Google Scholar 
Gehring, C. A. & Connell, J. H. Arbuscular mycorrhizal fungi in the tree seedlings of two Australian rain forests: Occurrence, colonization, and relationships with plant performance. Mycorrhiza 16, 89–98 (2006).Article 

Google Scholar 
Vestberg, M. Occurrence of some Glomales in Finland. Mycorrhiza 5, 329–336 (1995).Article 

Google Scholar 
Khan, A. G. Occurrence and importance of mycorrhizae in aquatic trees of New South Wales, Australia. Mycorrhiza 3, 31–38 (1993).Article 

Google Scholar 
Braz, S. P., Urquiaga, S., Alves, B. J. R. & Boddey, R. M. Degradação de Pastagens, Matéria Orgânica do Solo e a Recuperação do Potencial Produtivo em Sistemas de Baixo “Input” Tecnológico na Região dos Cerrados (2004).
Vieira Jr, W. G. et al. Seasonal variation in mycorrhizal community of different cerrado phytophysiomies. Front. Microbiol. 11 (2020).
Gerdemann, J. W. & Nicolson, T. H. Spores of mycorrhizal endogone species extracted from soil by wet sieving and decanting. Trans. Br. Mycol. Soc. 46, 235–244 (1963).Article 

Google Scholar 
INVAM. International Culture Collection of (Vesicular) Arbuscular Mycorrhizal Fungi | West Virginia University. (2018).SILVA, F. de A. ASSISTAT: Versão 7.7 beta. (DEAG-CTRN-Universidade Federal de Campina Grande, 2008).Hammer, Ø. Past 3.x—the Past of the Future. (Natural History Museum, University of Oslo, 2018).Cavalcanti, A. C. R., Cavallini, M. C. & Lima, N. R. C. de B. Estresse por Déficit Hídrico em Plantas Forrageiras. 50 https://www.infoteca.cnptia.embrapa.br/bitstream/doc/748148/1/doc89.pdf (2009).Alvares, C. A., Stape, J. L., Sentelhas, P. C., De Moraes, J. L. G. & Sparovek, G. Köppen’s climate classification map for Brazil. Metereol Z 22(6), 711–728 (2014).Article 

Google Scholar 
Nicolson, T. H. Vesicular-arbuscular mycorrhiza in the Gramineae. Nature 181, 718–719 (1958).ADS 
Article 

Google Scholar 
Soreng, R. J. et al. A worldwide phylogenetic classification of the Poaceae (Gramineae) II: An update and a comparison of two 2015 classifications. J. Syst. Evol. 55, 259–290 (2017).Article 

Google Scholar 
Teutscherova, N. et al. Differences in arbuscular mycorrhizal colonization and P acquisition between genotypes of the tropical Brachiaria grasses: Is there a relation with BNI activity?. Biol. Fertil. Soils 55, 325–337 (2019).CAS 
Article 

Google Scholar 
de Miranda, J. C. C. Cerrado: Micorriza Arbuscular, Ocorrência e Manejo. (Embrapa, 2008).Souza, B. R., Moura, J. B., Oliveira, T. C., Ramos, M. L. G. & Lopes Filho, L. C. Arbuscular Mycorrhizal fungi as indicative of soil quality in conservation systems in the region of vale do São Patrício, Goiás. Int. J. Curr. Res. 8, 43307–43311 (2016).
Google Scholar 
de Oliveira, T. C. et al. Produtividade da soja em associação ao fungo micorrízico arbuscular Rhizophagus clarus cultivada em condições de campo. Rev. Ciênc. Agrovet. 18, 530–535 (2019).Article 

Google Scholar 
Moura, J. B. et al. Arbuscular mycorrhizal fungi associated with bamboo under Cerrado Brazilian vegetation. J. Soil Sci. Plant. Nutr https://doi.org/10.1007/s42729-019-00093-0 (2019).Article 

Google Scholar 
Phillips, J. M. & Hayman, D. S. Improved procedures for clearing roots and staining parasitic and vesicular-arbuscular mycorrhizal fungi for rapid assessment of infection. Trans. Br. Mycol. Soc. 55, 158–161 (1970).Article 

Google Scholar 
Giovannetti, M. & Mosse, B. An evaluation of techniques for measuring vesicular arbuscular mycorrhizal infection in roots. New Phytol. 84, 489–500 (1980).Article 

Google Scholar 
Promita, D. & Mohan, K. Arbuscular mycorrhizal fungal diversity in sugarcane rhizosphere in relation with soil properties. Notulae Scientia Biologicae 4(1), 66–74 (2012).Aquino, S. D. S. et al. Mycorrhizal colonization and diversity and corn genotype yield in soils of the Cerrado region, Brazil. Semin. Cienc. Agrar. 36, 4107–4117 (2015).Article 

Google Scholar  More

Beger, M. et al. Conservation planning for connectivity across marine, freshwater, and terrestrial realms. Biol. Cons. 143, 565–575 (2010).Article 

Google Scholar 
Roberts, J. H., Angermeier, P. L. & Hallerman, E. M. Distance, dams and drift: What structures populations of an endangered, benthic stream fish?. Freshw. Biol. 58, 2050–2064. https://doi.org/10.1111/fwb.12190 (2013).Article 

Google Scholar 
Berejikian, B. A., Campbell, L. A., Moore, M. E. & Grant, J. Large-scale freshwater habitat features influence the degree of anadromy in eight Hood Canal Oncorhynchus mykiss populations. Can. J. Fish. Aquat. Sci. 70, 756–765. https://doi.org/10.1139/cjfas-2012-0491 (2013).Article 

Google Scholar 
Falke, J. A. & Fausch, K. D. in American Fisheries Society Symposium. 207–233.Hanski, I. & Simberloff, D. in Metapopulation Biology (eds Ilkka Hanski & Michael E. Gilpin) 5–26 (Academic Press, 1997).Cadrin, S. X., Friedland, K. D. & Waldman, J. R. in Stock Identification Methods (eds Cadrin, S. X., Friedland, K. D. & Waldman, J. R.) 3–6 (Academic Press, 2005).Hughes, J. M., Schmidt, D. J. & Finn, D. S. Genes in streams: Using DNA to understand the movement of freshwater fauna and their riverine habitat. Bioscience 59, 573–583 (2009).Article 

Google Scholar 
Gross, M. R., Coleman, R. M. & McDowall, R. M. Aquatic productivity and the evolution of diadromous fish migration. Science 239, 1291–1293 (1988).ADS 
CAS 
Article 

Google Scholar 
McDowall, R. M. The evolution of diadromy in fishes (revisited) and its place in phylogenetic analysis. Rev. Fish Biol. Fish. 7, 443–462. https://doi.org/10.1023/A:1018404331601 (1997).Article 

Google Scholar 
Myers, G. S. Usage of anadromous, catadromous and allied terms for migratory fishes. Copeia 89–97, 1949. https://doi.org/10.2307/1438482 (1949).Article 

Google Scholar 
Augspurger, J. M., Warburton, M. & Closs, G. P. Life-history plasticity in amphidromous and catadromous fishes: A continuum of strategies. Rev. Fish Biol. Fish. 27, 177–192. https://doi.org/10.1007/s11160-016-9463-9 (2017).Article 

Google Scholar 
McDowall, R. On amphidromy, a distinct form of diadromy in aquatic organisms. Fish Fish. 8, 1–13 (2007).Article 

Google Scholar 
David, B. O. et al. To sea or not to sea? Multiple lines of evidence reveal the contribution of non-diadromous recruitment for supporting endemic fish populations within New Zealand’s longest river. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 1409–1423. https://doi.org/10.1002/aqc.3022 (2019).Article 

Google Scholar 
Delgado, L. et al. Genomic basis of the loss of diadromy in Galaxias maculatus: Insights from reciprocal transplant experiments. Mol. Ecol. 29, 4857–4870. https://doi.org/10.1111/mec.15686 (2020).CAS 
Article 
PubMed 

Google Scholar 
Closs, G. P., Hicks, A. S. & Jellyman, P. G. Life histories of closely related amphidromous and non-migratory fish species: A trade-off between egg size and fecundity. Freshw. Biol. 58, 1162–1177. https://doi.org/10.1111/fwb.12116 (2013).Article 

Google Scholar 
Górski, K., Habit, E. M., Pingram, M. A. & Manosalva, A. J. Variation of the use of marine resources by Galaxias maculatus in large Chilean rivers. Hydrobiologia 814, 61–73. https://doi.org/10.1007/s10750-015-2542-4 (2018).Article 

Google Scholar 
Vega Aguayo, R. et al. Bases biológicas para el cultivo del puye Galaxias maculatus (Jenyns, 1842): Una revisión (2014).Cussac, V. E. et al. New insights into the distribution, physiology and life histories of South American galaxiid fishes, and potential threats to this unique fauna. Diversity https://doi.org/10.3390/d12050178 (2020).Article 

Google Scholar 
Hicks, A. S. et al. Lake and species specific patterns of non-diadromous recruitment in amphidromous fish: The importance of local recruitment and habitat requirements. Mar. Freshw. Res. https://doi.org/10.1071/mf16387 (2017).Article 

Google Scholar 
Manosalva, A. J. et al. Variation of stomach content and isotopic niche of puye Galaxias maculatus (Jenyns, 1842) in large river systems of southern Chile. Freshw. Biol. 66, 1110–1122. https://doi.org/10.1111/fwb.13703 (2021).CAS 
Article 

Google Scholar 
Milano, D., Aigo, J. C. & Macchi, P. J. Diel patterns in space use, food and metabolic activity of Galaxias maculatus (Pisces: Galaxiidae) in the littoral zone of a shallow Patagonian lake. Aquat. Ecol. 47, 277–290. https://doi.org/10.1007/s10452-013-9443-2 (2013).Article 

Google Scholar 
Chapman, A., Morgan, D. L., Beatty, S. J. & Gill, H. S. Variation in life history of land-locked lacustrine and riverine populations of Galaxias maculatus (Jenyns 1842) in Western Australia. Environ. Biol. Fishes 77, 21–37 (2006).Article 

Google Scholar 
Barriga, J. P. et al. Intraspecific variation in diet, growth, and morphology of landlocked Galaxias maculatus during its larval period: The role of food availability and predation risk. Hydrobiologia 679, 27–41 (2012).Article 

Google Scholar 
Campos, H. Population studies of Galaxias maculatus (Jenyns) (Osteichthys: Galaxiidae) in Chile with reference to the number of vertebrae. Stud. Neotrop. Fauna 9, 55–76. https://doi.org/10.1080/01650527409360470 (1974).Article 

Google Scholar 
Rojo, J. H., Fernandez, D. A., Figueroa, D. E. & Boy, C. C. Phenotypic and genetic differentiation between diadromous and landlocked puyen Galaxias maculatus. J. Fish Biol. 96, 956–967. https://doi.org/10.1111/jfb.14285 (2020).Article 
PubMed 

Google Scholar 
Zemlak, T. S., Habit, E. M., Walde, S. J., Carrea, C. & Ruzzante, D. E. Surviving historical Patagonian landscapes and climate: Molecular insights from Galaxias maculatus. BMC Evol. Biol. 10, 1–18 (2010).Article 

Google Scholar 
Delgado, M. L., Gorski, K., Habit, E. & Ruzzante, D. E. The effects of diadromy and its loss on genomic divergence: The case of amphidromous Galaxias maculatus populations. Mol. Ecol. 28, 5217–5231. https://doi.org/10.1111/mec.15290 (2019).Article 
PubMed 

Google Scholar 
Delgado, M. L. et al. Genomic basis of the loss of diadromy in Galaxias maculatus: Insights from reciprocal transplant experiments. Mol. Ecol. 29, 4857–4870. https://doi.org/10.1111/mec.15686 (2020).CAS 
Article 
PubMed 

Google Scholar 
Alo, D., Correa, C., Samaniego, H., Krabbenhoft, C. A. & Turner, T. F. Otolith microchemistry and diadromy in Patagonian river fishes. PeerJ 7, e6149. https://doi.org/10.7717/peerj.6149 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Campana, S. E. Chemistry and composition of fish otoliths: Pathways, mechanisms and applications. Mar. Ecol. Prog. Ser. 188, 263–297 (1999).ADS 
CAS 
Article 

Google Scholar 
Schulz-Mirbach, T., Ladich, F., Plath, M. & Heß, M. Enigmatic ear stones: What we know about the functional role and evolution of fish otoliths. Biol. Rev. 94, 457–482 (2019).Article 

Google Scholar 
Campana, S. E. Otolith science entering the 21st century. Mar. Freshw. Res. 56, 485–495 (2005).Article 

Google Scholar 
Ahn, H. et al. Effect of water temperature on embryonic development and hatching time of the Japanese eel Anguilla japonica. Aquaculture 330, 100–105 (2012).Article 

Google Scholar 
Avigliano, E., Velasco, G. & Volpedo, A. V. Use of lapillus otolith microchemistry as an indicator of the habitat of Genidens barbus from different estuarine environments in the southwestern Atlantic Ocean. Environ. Biol. Fishes 98, 1623–1632. https://doi.org/10.1007/s10641-015-0387-3 (2015).Article 

Google Scholar 
Whitledge, G. W. Otolith microchemistry and isotopic composition as potential indicators of fish movement between the Illinois River drainage and Lake Michigan. J. Great Lakes Res. 35, 101–106. https://doi.org/10.1016/j.jglr.2008.10.003 (2009).CAS 
Article 

Google Scholar 
Kraus, R. T. & Secor, D. H. Incorporation of strontium into otoliths of an estuarine fish. J. Exp. Mar. Biol. Ecol. 302, 85–106. https://doi.org/10.1016/j.jembe.2003.10.004 (2004).CAS 
Article 

Google Scholar 
Volk, E. C., Blakley, A., Schroder, S. L. & Kuehner, S. M. Otolith chemistry reflects migratory characteristics of Pacific salmonids: Using otolith core chemistry to distinguish maternal associations with sea and freshwaters. Fish. Res. 46, 251–266 (2000).Article 

Google Scholar 
Vignon, M. Extracting environmental histories from sclerochronological structures—Recursive partitioning as a mean to explore multi-elemental composition of fish otolith. Ecol. Inform. 30, 159–169. https://doi.org/10.1016/j.ecoinf.2015.10.002 (2015).Article 

Google Scholar 
Teichert, N. et al. Site fidelity and movements of an amphidromous goby revealed by otolith multi-elemental signatures along a tropical watershed. Ecol. Freshw. Fish 27, 834–846. https://doi.org/10.1111/eff.12396 (2018).Article 

Google Scholar 
Elsdon, T. S. & Gillanders, B. M. Fish otolith chemistry influenced by exposure to multiple environmental variables. J. Exp. Mar. Biol. Ecol. 313, 269–284. https://doi.org/10.1016/j.jembe.2004.08.010 (2004).CAS 
Article 

Google Scholar 
Vivancos, A. et al. Hydrological connectivity drives longitudinal movement of endangered endemic Chilean darter Percilia irwini (Eigenmann, 1927). J Fish Biol 98, 33–43. https://doi.org/10.1111/jfb.14554 (2021).Article 
PubMed 

Google Scholar 
Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLOS Biology 18, e3000411. https://doi.org/10.1371/journal.pbio.3000411 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Warburton, M. L., Reid, M. R., Stirling, C. H. & Closs, G. Validation of depth-profiling LA-ICP-MS in otolith applications. Can. J. Fish. Aquat. Sci. 74, 572–581 (2017).CAS 
Article 

Google Scholar 
Paton, C., Hellstrom, J., Paul, B., Woodhead, J. & Hergt, J. Iolite: Freeware for the visualisation and processing of mass spectrometric data. J. Anal. At. Spectrom. 26, 2508–2518. https://doi.org/10.1039/C1JA10172B (2011).CAS 
Article 

Google Scholar 
Woodhead, J. et al. A guide to depth profiling and imaging applications of LA-ICP-MS. Laser Ablation ICP-MS Earth Sci. Curr. Pract. Outst. Issues 40, 135–145 (2008).CAS 

Google Scholar 
Veinott, G., Westley, P. A. H., Purchase, C. F., Warner, L. & Gillanders, B. Experimental evidence simultaneously confirms and contests assumptions implicit to otolith microchemistry research. Can. J. Fish. Aquat. Sci. 71, 356–365. https://doi.org/10.1139/cjfas-2013-0224 (2014).Article 

Google Scholar 
Brophy, D., Jeffries, T. E. & Danilowicz, B. S. Elevated manganese concentrations at the cores of clupeid otoliths: Possible environmental, physiological, or structural origins. Mar. Biol. 144, 779–786. https://doi.org/10.1007/s00227-003-1240-3 (2004).CAS 
Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 

Google Scholar 
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x (2001).Article 

Google Scholar 
McArdle, B. H. & Anderson, M. J. Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology 82, 290–297. https://doi.org/10.1890/0012-9658(2001)082[0290:FMMTCD]2.0.CO;2 (2001).Article 

Google Scholar 
Brown, R. J., Campana, S. & Severin, K. P. Otolith chemistry analyses indicate that water Sr: Ca is the primary factor influencing otolith Sr: Ca for freshwater and diadromous fish but not for marine fish. Can. J. Fish. Aquat. Sci. 66, 1790–1808. https://doi.org/10.1139/f09-112 (2009).CAS 
Article 

Google Scholar 
Humston, R. et al. Isotope geochemistry reveals ontogeny of dispersal and exchange between main-river and tributary habitats in smallmouth bass Micropterus dolomieu. J. Fish Biol. 90, 528–548. https://doi.org/10.1111/jfb.13073 (2017).CAS 
Article 
PubMed 

Google Scholar 
Dingle, H. & Drake, V. A. What is migration?. Bioscience 57, 113–121 (2007).Article 

Google Scholar 
Hogan, J. D., Blum, M. J., Gilliam, J. F., Bickford, N. & McIntyre, P. B. Consequences of alternative dispersal strategies in a putatively amphidromous fish. Ecology 95, 2397–2408 (2014).Article 

Google Scholar 
Kelley, J. L., Grierson, P. F., Collin, S. P. & Davies, P. M. Habitat disruption and the identification and management of functional trait changes. Fish Fish. 19, 716–728. https://doi.org/10.1111/faf.12284 (2018).Article 

Google Scholar 
Vivancos, A. et al. Hydrological connectivity drives longitudinal movement of endangered endemic Chilean darter Percilia irwini (Eigenmann, 1927). J. Fish Biol. 98, 33–43 (2020).Article 

Google Scholar 
Hicks, A. S., Closs, G. P. & Swearer, S. E. Otolith microchemistry of two amphidromous galaxiids across an experimental salinity gradient: A multi-element approach for tracking diadromous migrations. J. Exp. Mar. Biol. Ecol. 394, 86–97 (2010).Article 

Google Scholar 
Miller, J. A. Effects of water temperature and barium concentration on otolith composition along a salinity gradient: Implications for migratory reconstructions. J. Exp. Mar. Biol. Ecol. 405, 42–52. https://doi.org/10.1016/j.jembe.2011.05.017 (2011).CAS 
Article 

Google Scholar 
Walsh, C. T. & Gillanders, B. M. Extrinsic factors affecting otolith chemistry—Implications for interpreting migration patterns in a diadromous fish. Environ. Biol. Fishes 101, 905–916. https://doi.org/10.1007/s10641-018-0746-y (2018).Article 

Google Scholar 
Walther, B. D. & Limburg, K. E. The use of otolith chemistry to characterize diadromous migrations. J. Fish Biol. 81, 796–825. https://doi.org/10.1111/j.1095-8649.2012.03371.x (2012).CAS 
Article 
PubMed 

Google Scholar 
Hicks, A. S. et al. Lake and species specific patterns of non-diadromous recruitment in amphidromous fish: The importance of local recruitment and habitat requirements. Mar. Freshw. Res. 68, 2315–2323 (2017).Article 

Google Scholar 
Hickford, M. J. & Schiel, D. R. Population sinks resulting from degraded habitats of an obligate life-history pathway. Oecologia 166, 131–140 (2011).ADS 
Article 

Google Scholar 
Barriga, J., Battini, M. & Cussac, V. Annual dynamics variation of a landlocked Galaxias maculatus (Jenyns 1842) population in a Northern Patagonian river: Occurrence of juvenile upstream migration. J. Appl. Ichthyol. 23, 128–135 (2007).Article 

Google Scholar 
Huey, J. A. et al. Is variable connectivity among populations of a continental gobiid fish driven by local adaptation or passive dispersal?. Freshw. Biol. 59, 1672–1686 (2014).CAS 
Article 

Google Scholar 
Catlin, A. K., Collier, K. J. & Duggan, I. C. Zooplankton generation following inundation of floodplain soils: Effects of vegetation type and riverine connectivity. Mar. Freshw. Res. https://doi.org/10.1071/mf15273 (2017).Article 

Google Scholar 
Górski, K., Collier, K. J., Duggan, I. C., Taylor, C. M. & Hamilton, D. P. Connectivity and complexity of floodplain habitats govern zooplankton dynamics in a large temperate river system. Freshw. Biol. 58, 1458–1470. https://doi.org/10.1111/fwb.12144 (2013).Article 

Google Scholar 
Sturrock, A. M. et al. Quantifying physiological influences on otolith microchemistry. Methods Ecol. Evol. 6, 806–816. https://doi.org/10.1111/2041-210x.12381 (2015).Article 

Google Scholar 
Doubleday, Z. A., Izzo, C., Woodcock, S. H. & Gillanders, B. M. Relative contribution of water and diet to otolith chemistry in freshwater fish. Aquat. Biol. 18, 271–280. https://doi.org/10.3354/ab00511 (2013).Article 

Google Scholar 
Elsdon, T. S. et al. Oceanography and Marine Biology 303–336 (CRC Press, 2008).
Google Scholar 
Izzo, C., Doubleday, Z. A., Schultz, A. G., Woodcock, S. H. & Gillanders, B. M. Contribution of water chemistry and fish condition to otolith chemistry: Comparisons across salinity environments. J Fish Biol 86, 1680–1698. https://doi.org/10.1111/jfb.12672 (2015).CAS 
Article 
PubMed 

Google Scholar 
Walther, B. D. The art of otolith chemistry: interpreting patterns by integrating perspectives. Mar. Freshw. Res. 70, 1643–1658 (2019).CAS 
Article 

Google Scholar 
Hüssy, K. et al. Trace element patterns in otoliths: The role of biomineralization. Rev. Fish. Sci. Aquacult. 29, 1–33 (2020).
Google Scholar 
Nazir, A. & Khan, M. A. Spatial and temporal variation in otolith chemistry and its relationship with water chemistry: Stock discrimination of Sperata aor. Ecol. Freshw. Fish 28, 499–511. https://doi.org/10.1111/eff.12471 (2019).Article 

Google Scholar 
Vera-Escalona, I., Habit, E. & Ruzzante, D. E. Invasive species and postglacial colonization: Their effects on the genetic diversity of a Patagonian fish. Proc. Biol. Sci. 286, 20182567. https://doi.org/10.1098/rspb.2018.2567 (2019).Article 
PubMed 
PubMed Central 

Google Scholar  More

Brugiroux S, Beutler M, Pfann C, Garzetti D, Ruscheweyh HJ, Ring D, et al. Genome-guided design of a defined mouse microbiota that confers colonization resistance against Salmonella enterica serovar Typhimurium. Nat Microbiol. 2016;2:16215.CAS 
PubMed 

Google Scholar 
Buffie CG, Bucci V, Stein RR, McKenney PT, Ling L, Gobourne A, et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 2015;517:205–8.CAS 
PubMed 

Google Scholar 
He M, Shi B. Gut microbiota as a potential target of metabolic syndrome: the role of probiotics and prebiotics. Cell Biosci. 2017;7:54.PubMed 
PubMed Central 

Google Scholar 
Ma W, Mao Q, Xia W, Dong G, Yu C, Jiang F. Gut microbiota shapes the efficiency of cancer therapy. Front Microbiol. 2019;10:1050.PubMed 
PubMed Central 

Google Scholar 
Rodriguez J, Hiel S, Neyrinck AM, Le Roy T, Potgens SA, Leyrolle Q, et al. Discovery of the gut microbial signature driving the efficacy of prebiotic intervention in obese patients. Gut 2020;69:1975–87.CAS 
PubMed 

Google Scholar 
Schubert AM, Sinani H, Schloss PD. Antibiotic-induced alterations of the murine gut microbiota and subsequent effects on colonization resistance against Clostridium difficile. mBio 2015;6:e00974.CAS 
PubMed 
PubMed Central 

Google Scholar 
Vivarelli S, Salemi R, Candido S, Falzone L, Santagati M, Stefani S, et al. Gut microbiota and cancer: From pathogenesis to therapy. Cancers (Basel). 2019;11:38.Pasolli E, Truong DT, Malik F, Waldron L, Segata N. Machine learning meta-analysis of large metagenomic datasets: Tools and biological insights. PLoS Comput Biol. 2016;12:e1004977.PubMed 
PubMed Central 

Google Scholar 
Walters WA, Xu Z, Knight R. Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Lett. 2014;588:4223–33.CAS 
PubMed 
PubMed Central 

Google Scholar 
Rastelli M, Knauf C, Cani PD. Gut microbes and health: A focus on the mechanisms linking microbes, obesity, and related disorders. Obes (Silver Spring) 2018;26:792–800.
Google Scholar 
Sonnenburg JL, Backhed F. Diet-microbiota interactions as moderators of human metabolism. Nature 2016;535:56–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
Costello EK, Stagaman K, Dethlefsen L, Bohannan BJ, Relman DA. The application of ecological theory toward an understanding of the human microbiome. Science 2012;336:1255–62.CAS 
PubMed 
PubMed Central 

Google Scholar 
Koskella B, Hall LJ, Metcalf CJE. The microbiome beyond the horizon of ecological and evolutionary theory. Nat Ecol Evol. 2017;1:1606–15.PubMed 

Google Scholar 
Walter J, Ley R. The human gut microbiome: Ecology and recent evolutionary changes. Annu Rev Microbiol. 2011;65:411–29.CAS 
PubMed 

Google Scholar 
Walter J, Maldonado-Gomez MX, Martinez I. To engraft or not to engraft: An ecological framework for gut microbiome modulation with live microbes. Curr Opin Biotechnol. 2018;49:129–39.CAS 
PubMed 

Google Scholar 
Le Roy T, Debedat J, Marquet F, Da-Cunha C, Ichou F, Guerre-Millo M, et al. Comparative evaluation of microbiota engraftment following fecal microbiota transfer in mice models: Age, kinetic and microbial status matter. Front Microbiol. 2018;9:3289.PubMed 

Google Scholar 
Maldonado-Gomez MX, Martinez I, Bottacini F, O’Callaghan A, Ventura M, van Sinderen D, et al. Stable engraftment of Bifidobacterium longum AH1206 in the human gut depends on individualized features of the resident microbiome. Cell Host Microbe. 2016;20:515–26.CAS 
PubMed 

Google Scholar 
Martinez I, Maldonado-Gomez MX, Gomes-Neto JC, Kittana H, Ding H, Schmaltz R, et al. Experimental evaluation of the importance of colonization history in early-life gut microbiota assembly. Elife. 2018;7:e36521.Podlesny D, Durdevic M, Paramsothy S, Kaakoush NO, Högenauer C, Gorkiewicz G, et al. Intraspecies strain exclusion, antibiotic pretreatment, and donor selection control microbiota engraftment after fecal transplantation. medRxiv. 2021;08.18.21262200.Li SS, Zhu A, Benes V, Costea PI, Hercog R, Hildebrand F, et al. Durable coexistence of donor and recipient strains after fecal microbiota transplantation. Science 2016;352:586–89.CAS 
PubMed 

Google Scholar 
Seekatz AM, Aas J, Gessert CE, Rubin TA, Saman DM, Bakken JS, et al. Recovery of the gut microbiome following fecal microbiota transplantation. mBio 2014;5:e00893–00814.PubMed 
PubMed Central 

Google Scholar 
Shahinas D, Silverman M, Sittler T, Chiu C, Kim P, Allen-Vercoe E, et al. Toward an understanding of changes in diversity associated with fecal microbiome transplantation based on 16S rRNA gene deep sequencing. mBio. 2012;3:e00338–12.Hardin G. The competitive exclusion principle. Science 1960;131:1292–7.CAS 
PubMed 

Google Scholar 
Stecher B, Chaffron S, Kappeli R, Hapfelmeier S, Freedrich S, Weber TC, et al. Like will to like: Abundances of closely related species can predict susceptibility to intestinal colonization by pathogenic and commensal bacteria. PLoS Pathog. 2010;6:e1000711.PubMed 
PubMed Central 

Google Scholar 
Chesson P. Mechanisms of maintenance of species diversity. Annu Rev Ecol Syst. 2000;31:343–66.
Google Scholar 
Grainger TN, Letten AD, Gilbert B, Fukami T. Applying modern coexistence theory to priority effects. Proc Natl Acad Sci USA. 2019;116:6205–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee SM, Donaldson GP, Mikulski Z, Boyajian S, Ley K, Mazmanian SK. Bacterial colonization factors control specificity and stability of the gut microbiota. Nature 2013;501:426–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Onderdonk A, Marshall B, Cisneros R, Levy SB. Competition between congenic Escherichia coli K-12 strains in vivo. Infect Immun. 1981;32:74–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Everard A, Belzer C, Geurts L, Ouwerkerk JP, Druart C, Bindels LB, et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc Natl Acad Sci USA. 2013;110:9066–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
Shin NR, Lee JC, Lee HY, Kim MS, Whon TW, Lee MS, et al. An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut 2014;63:727–35.CAS 

Google Scholar 
Dingemanse C, Belzer C, van Hijum SA, Gunthel M, Salvatori D, den Dunnen JT, et al. Akkermansia muciniphila and Helicobacter typhlonius modulate intestinal tumor development in mice. Carcinogenesis 2015;36:1388–96.CAS 
PubMed 

Google Scholar 
Png CW, Linden SK, Gilshenan KS, Zoetendal EG, McSweeney CS, Sly LI, et al. Mucolytic bacteria with increased prevalence in IBD mucosa augment in vitro utilization of mucin by other bacteria. Am J Gastroenterol. 2010;105:2420–8.CAS 
PubMed 

Google Scholar 
Zhai R, Xue X, Zhang L, Yang X, Zhao L, Zhang C. Strain-specific anti-inflammatory properties of two Akkermansia muciniphila strains on chronic colitis in mice. Front Cell Infect Microbiol. 2019;9:239.CAS 
PubMed 
PubMed Central 

Google Scholar 
Martens EC, Lowe EC, Chiang H, Pudlo NA, Wu M, McNulty NP, et al. Recognition and degradation of plant cell wall polysaccharides by two human gut symbionts. PLoS Biol. 2011;9:e1001221.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pudlo NA, Urs K, Crawford R, Pirani A, Atherly T, Jimenez R, et al. Phenotypic and genomic diversification in complex carbohydrate-degrading human gut bacteria. mSystems. 2022;7:e0094721.Lagkouvardos I, Pukall R, Abt B, Foesel BU, Meier-Kolthoff JP, Kumar N, et al. The mouse intestinal bacterial collection (miBC) provides host-specific insight into cultured diversity and functional potential of the gut microbiota. Nat Microbiol. 2016;1:16131.CAS 
PubMed 

Google Scholar 
Weldon L, Abolins S, Lenzi L, Bourne C, Riley EM, Viney M. The gut microbiota of wild mice. PLoS One. 2015;10:e0134643.PubMed 
PubMed Central 

Google Scholar 
Segura Munoz RR, Quach T, Gomes-Neto JC, Xian Y, Pena PA, Weier S, et al. Stearidonic-enriched soybean oil modulates obesity, glucose metabolism, and fatty acid profiles independently of Akkermansia muciniphila. Mol Nutr Food Res. 2020;64:e2000162.PubMed 
PubMed Central 

Google Scholar 
Bindels LB, Segura Munoz RR, Gomes-Neto JC, Mutemberezi V, Martinez I, Salazar N, et al. Resistant starch can improve insulin sensitivity independently of the gut microbiota. Microbiome 2017;5:12.PubMed 
PubMed Central 

Google Scholar 
Chen IA, Chu K, Palaniappan K, Pillay M, Ratner A, Huang J, et al. IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Res. 2019;47:D666–D677.CAS 
PubMed 

Google Scholar 
Rozen S, Skaletsky H. Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol. 2000;132:365–86.CAS 
PubMed 

Google Scholar 
Mukherjee S, Stamatis D, Bertsch J, Ovchinnikova G, Katta HY, Mojica A, et al. Genomes OnLine database (GOLD) v.7: Updates and new features. Nucleic Acids Res.2019;47:D649–D659.CAS 
PubMed 

Google Scholar 
Schneeberger M, Everard A, Gomez-Valades AG, Matamoros S, Ramirez S, Delzenne NM, et al. Akkermansia muciniphila inversely correlates with the onset of inflammation, altered adipose tissue metabolism and metabolic disorders during obesity in mice. Sci Rep. 2015;5:16643.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gomes-Neto JC, Mantz S, Held K, Sinha R, Segura Munoz RR, Schmaltz R, et al. A real-time PCR assay for accurate quantification of the individual members of the Altered Schaedler Flora microbiota in gnotobiotic mice. J Microbiol Methods. 2017;135:52–62.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gomes-Neto JC, Kittana H, Mantz S, Segura Munoz RR, Schmaltz RJ, Bindels LB, et al. A gut pathobiont synergizes with the microbiota to instigate inflammatory disease marked by immunoreactivity against other symbionts but not itself. Sci Rep. 2017;7:17707.PubMed 
PubMed Central 

Google Scholar 
Wingett SW, Andrews S. FastQ Screen: A tool for multi-genome mapping and quality control. F1000Res. 2018;7:1338.PubMed 
PubMed Central 

Google Scholar 
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754–60.CAS 
PubMed 
PubMed Central 

Google Scholar 
Garcia-Alcalde F, Okonechnikov K, Carbonell J, Cruz LM, Gotz S, Tarazona S, et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics 2012;28:2678–79.CAS 
PubMed 

Google Scholar 
Thomsen MCF, Hasman H, Westh H, Kaya H, Lund O. RUCS: rapid identification of PCR primers for unique core sequences. Bioinformatics 2017;33:3917–21.PubMed 
PubMed Central 

Google Scholar 
Darling AC, Mau B, Blattner FR, Perna NT. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 2004;14:1394–1403.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.PubMed 
PubMed Central 

Google Scholar 
Genome [Internet] (2004). National Library of Medicine (US), National Center for Biotechnology Information: Bethesda (MD). https://www.ncbi.nlm.nih.gov/genome/browse/#!/prokaryotes/1218/Genome [Internet] (2004). National Library of Medicine (US), National Center for Biotechnology Information: Bethesda (MD). https://www.ncbi.nlm.nih.gov/genome/browse/#!/prokaryotes/1598/Beghini F, McIver LJ, Blanco-Miguez A, Dubois L, Asnicar F, Maharjan S, et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife. 2021;10:e65088.Asnicar F, Weingart G, Tickle TL, Huttenhower C, Segata N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ 2015;3:e1029.PubMed 
PubMed Central 

Google Scholar 
Varghese NJ, Mukherjee S, Ivanova N, Konstantinidis KT, Mavrommatis K, Kyrpides NC, et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res 2015;43:6761–71.CAS 
PubMed 
PubMed Central 

Google Scholar 
Mavromatis K, Chu K, Ivanova N, Hooper SD, Markowitz VM, Kyrpides NC. Gene context analysis in the Integrated Microbial Genomes (IMG) data management system. PLoS One 2009;4:e7979.PubMed 
PubMed Central 

Google Scholar 
El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res 2019;47:D427–D432.CAS 
PubMed 

Google Scholar 
The UniProt Consortium. The universal protein resource (UniProt). Nucleic Acids Res 2008;36:D190–195.
Google Scholar 
Obadia B, Guvener ZT, Zhang V, Ceja-Navarro JA, Brodie EL, Ja WW, et al. Probabilistic invasion underlies natural gut microbiome stability. Curr Biol 2017;27:1999–2006 e1998.CAS 
PubMed 
PubMed Central 

Google Scholar 
Meszena G, Gyllenberg M, Pasztor L, Metz JA. Competitive exclusion and limiting similarity: A unified theory. Theor Popul Biol. 2006;69:68–87.PubMed 

Google Scholar 
Cavender-Bares J, Kozak KH, Fine PV, Kembel SW. The merging of community ecology and phylogenetic biology. Ecol Lett. 2009;12:693–715.PubMed 

Google Scholar 
Tramontano M, Andrejev S, Pruteanu M, Klunemann M, Kuhn M, Galardini M, et al. Nutritional preferences of human gut bacteria reveal their metabolic idiosyncrasies. Nat Microbiol 2018;3:514–22.CAS 
PubMed 

Google Scholar 
Derrien M, Vaughan EE, Plugge CM, de Vos WM. Akkermansia muciniphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int J Syst Evol Microbiol 2004;54:1469–1476.CAS 
PubMed 

Google Scholar 
Walker AW, Lawley TD. Therapeutic modulation of intestinal dysbiosis. Pharm Res 2013;69:75–86.CAS 

Google Scholar 
Livanos AE, Greiner TU, Vangay P, Pathmasiri W, Stewart D, McRitchie S, et al. Antibiotic-mediated gut microbiome perturbation accelerates development of type 1 diabetes in mice. Nat Microbiol 2016;1:16140.CAS 
PubMed 
PubMed Central 

Google Scholar 
Perez-Cobas AE, Gosalbes MJ, Friedrichs A, Knecht H, Artacho A, Eismann K, et al. Gut microbiota disturbance during antibiotic therapy: A multi-omic approach. Gut 2013;62:1591–1601.CAS 
PubMed 

Google Scholar 
Adler PB, Hillerislambers J, Levine JM. A niche for neutrality. Ecol Lett. 2007;10:95–104.PubMed 

Google Scholar 
Levine JM, HilleRisLambers J. The importance of niches for the maintenance of species diversity. Nature 2009;461:254–57.CAS 
PubMed 

Google Scholar 
Forstner G. Signal transduction, packaging and secretion of mucins. Annu Rev Physiol. 1995;57:585–605.CAS 
PubMed 

Google Scholar 
Ottman N, Davids M, Suarez-Diez M, Boeren S, Schaap PJ, Martins Dos Santos VAP, et al. Genome-scale model and omics analysis of metabolic capacities of Akkermansia muciniphila reveal a preferential mucin-degrading lifestyle. Appl Environ Microbiol. 2017;83:e01014-17.Duar RM, Frese SA, Lin XB, Fernando SC, Burkey TE, Tasseva G et al. Experimental evaluation of host adaptation of Lactobacillus reuteri to different vertebrate species. Appl Environ Microbiol. 2017;83:e00132–17.Frese SA, Benson AK, Tannock GW, Loach DM, Kim J, Zhang M, et al. The evolution of host specialization in the vertebrate gut symbiont Lactobacillus reuteri. PLoS Genet. 2011;7:e1001314.CAS 
PubMed 
PubMed Central 

Google Scholar 
Rosshart SP, Vassallo BG, Angeletti D, Hutchinson DS, Morgan AP, Takeda K, et al. Wild mouse gut microbiota promotes host fitness and improves disease resistance. Cell 2017;171:1015–1028 e1013.CAS 
PubMed 
PubMed Central 

Google Scholar 
Karcher N, Nigro E, Puncochar M, Blanco-Miguez A, Ciciani M, Manghi P, et al. Genomic diversity and ecology of human-associated Akkermansia species in the gut microbiome revealed by extensive metagenomic assembly. Genome Biol. 2021;22:209.CAS 
PubMed 
PubMed Central 

Google Scholar 
Rosshart SP, Herz J, Vassallo BG, Hunter A, Wall MK, Badger JH, et al. Laboratory mice born to wild mice have natural microbiota and model human immune responses. Science. 2019;365.Mark Welch JL, Hasegawa Y, McNulty NP, Gordon JI, Borisy GG. Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. Proc Natl Acad Sci USA. 2017;114:E9105–E9114.CAS 
PubMed 
PubMed Central 

Google Scholar 
Whitaker WR, Shepherd ES, Sonnenburg JL. Tunable expression tools enable single-cell strain distinction in the gut microbiome. Cell 2017;169:538–546. e512.CAS 
PubMed 
PubMed Central 

Google Scholar 
Becken B, Davey L, Middleton DR, Mueller KD, Sharma A, Holmes ZC, et al. Genotypic and phenotypic diversity among human isolates of Akkermansia muciniphila. mBio. 2021;12:e00478–21.Truong DT, Tett A, Pasolli E, Huttenhower C, Segata N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 2017;27:626–38.
Google Scholar 
Faith JJ, Guruge JL, Charbonneau M, Subramanian S, Seedorf H, Goodman AL, et al. The long-term stability of the human gut microbiota. Science 2013;341:1237439.PubMed 
PubMed Central 

Google Scholar 
Mehta RS, Abu-Ali GS, Drew DA, Lloyd-Price J, Subramanian A, Lochhead P, et al. Stability of the human faecal microbiome in a cohort of adult men. Nat Microbiol. 2018;3:347–355.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ferretti P, Pasolli E, Tett A, Asnicar F, Gorfer V, Fedi S, et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe. 2018;24:133–45 e135.CAS 
PubMed 
PubMed Central 

Google Scholar 
Korpela K, Costea P, Coelho LP, Kandels-Lewis S, Willemsen G, Boomsma DI, et al. Selective maternal seeding and environment shape the human gut microbiome. Genome Res. 2018;28:561–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Freitag TL, Hartikainen A, Jouhten H, Sahl C, Meri S, Anttila VJ, et al. Minor effect of antibiotic pre-treatment on the engraftment of donor microbiota in fecal transplantation in mice. Front Microbiol. 2019;10:2685.PubMed 
PubMed Central 

Google Scholar 
Ji SK, Yan H, Jiang T, Guo CY, Liu JJ, Dong SZ, et al. Preparing the gut with antibiotics enhances gut microbiota reprogramming efficiency by promoting xenomicrobiota colonization. Front Microbiol. 2017;8:1208.PubMed 
PubMed Central 

Google Scholar 
Divya Ganeshan S, Hosseinidoust Z. Phage therapy with a focus on the human microbiota. Antibiotics (Basel). 2019;8:131.Ramachandran G, Bikard D. Editing the microbiome the CRISPR way. Philos Trans R Soc Lond B Biol Sci. 2019;374:20180103.CAS 
PubMed 
PubMed Central 

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