Pervasive associations between dark septate endophytic fungi with tree root and soil microbiomes across Europe

Domain: earth_science
Published: 2024-01-02
Editorial outcome: major_revision

Abstract
Trees interact with a multitude of microbes through their roots and root symbionts such as mycorrhizal fungi and root endophytes. Here, we explore the role of fungal root symbionts as predictors of the soil and root-associated microbiomes of widespread broad-leaved trees across a European latitudinal gradient. Our results suggest that, alongside factors such as climate, soil, and vegetation properties, root colonization by ectomycorrhizal, arbuscular mycorrhizal, and dark septate endophytic fungi also shapes tree-associated microbiomes. Notably, the structure of root and soil microbiomes across our sites is more strongly and consistently associated with dark septate endophyte colonization than with mycorrhizal colonization and many abiotic factors. Root colonization by dark septate endophytes also has a consistent negative association with the relative abundance and diversity of nutrient cycling genes. Our study not only indicates that root-symbiotic interactions are an important factor structuring soil communities and functions in forest ecosystems, but also that the hitherto less studied dark septate endophytes are likely to be central players in these interactions.

Introduction
Symbioses between plants and fungi are the most widespread and integrated biotic interactions in terrestrial ecosystems, with their distributions largely driven by climate and edaphic gradients1–3. Most trees form symbioses with ectomycorrhizal (EcM) or arbuscular mycorrhizal (AM) fungi and other symbionts such as root endophytic fungi, which extensively colonize root systems4 and promote the functioning of their hosts5,6. Directly and via these symbiotic fungi, trees interact with numerous other soil and root-associated microbes, mainly bacteria and fungi, which are important for carbon (C) and nutrient cycling, as well as plant health and growth7–9. Growing evidence suggests that either directly via competition with other microbes or indirectly by influencing soil processes10, EcM and AM fungi affect soil microbial communities and their functions in different ways and contribute to the proposed conservative organic versus acquisitive inorganic nutrient economies of their hosts, respectively10–13. Therefore, the dominant tree mycorrhizal type has often been used as a proxy for soil microbiome functioning. For example, tree mycorrhizal type has been used to explain the activity of soil saprotrophs14–16, the prevalence of plant pathogens13,17, fungal diversity18,19, the bacteria/fungi biomass ratio13,20,21, and the activity of enzymes involved in nitrogen (N), phosphorus (P), and C acquisition21. Yet, an issue with the binary assignment of a mycorrhizal type (EcM or AM) to a tree or forest is that it ignores the actual presence or prevalence of the root symbioses and complex root associations such as dual-mycorrhiza10,22,23. Furthermore, little attention has been paid to the potential role of other common co-occurring and widespread tree root-associated fungi, such as dark septate endophytes (DSE)24, which may be just as important as mycorrhizal associations in shaping belowground communities and functions.

The ecological function of the facultative, host generalist, common, prolific, and root colonizing DSE, and root endophytes in general, is still under consideration, and they probably act on a spectrum between free-living saprotrophs, mycorrhizal fungi, and parasites25,26. While they are taxonomically poorly defined, DSE fungi are morphologically characterized by the presence of melanized septate (ascomycetous) hyphae and occasionally microsclerotia in living plant roots26. Increasing evidence from agricultural systems shows that these enigmatic fungi can also be associated with enhanced plant performance and stress tolerance under harsh environmental conditions by enhancing access to nutrients and protecting against pathogens27,28. While DSE have largely been overlooked in forests in favor of mycorrhizal associations, they may be just as abundant, if not more so, in forest ecosystems when considering the number of host plants they colonize and the extent to which they colonize root systems25. Thus, assessing the influence of multiple tree-fungal associations on soil and root microbiomes across large environmental gradients will help us identify the main biotic and abiotic determinants of the structure and function of microbial communities to better understand forest ecosystem processes.

We performed a large-scale field study on the potential role of root colonization by EcM, AM, and DSE fungi in shaping soil and root microbiomes. We sampled the soil and roots of 305 forest trees from 3 different deciduous broadleaf genera (Alnus: dual mycorrhizal/N-fixing Frankia bacteria, Betula: EcM, and Sorbus: AM). We considered climate, soil, and vegetation properties across 18 sites over a 3200 km European latitudinal gradient (Fig. 1a & b; Supplementary Data 1). A highly consistent sampling design and processing were used, and to analyze soil and root microbial communities, we utilized metabarcoding alongside shotgun metagenomics for the analysis of functional genes and estimated litter decomposition using tea bags. We compared these properties to the root colonization rates of EcM, AM, and DSE fungi on widespread trees. We hypothesized that both mycorrhizal and DSE fungal colonization would affect soil and root microbiome structure and potential functions in addition to and mediated by climate and soil properties. More specifically, we expected that, as they often monopolize roots, colonization by mycorrhizal and DSE fungi would suppress plant pathogens while also altering the relative abundances of taxa and genes involved in decomposition and nutrient cycling. We show that DSE colonization is more strongly and consistently associated with the structure and potential function of tree root and soil microbiomes across our sites than mycorrhizal colonization and many abiotic factors, indicating that DSE fungi are potentially important mediators of plant-soil interactions.

Results
Rootsymbiontcolonizationandtheirrelativeabundancesinsoilandroots
Rootcolonizationrates(determinedbasedonmicroscopicquantification)andtherelativeabundanceofrootsymbionts(basedoncentered-logratiotransformedmetabarcodingreads)werebestexplainedbymoistureavailabilityforEcMfungi,soilpH(colonization)andhosttreebasalarea(relativeabundance)forAMfungi,soilcarbon/nitrogen(C/N)(colonization)andpH(relativeabundance)forDSEfungi,andsoilpHforN-fixingFrankiabacteria(relativeabundance).TheaveragerootcolonizationbyEcMfungivariedbetween40and85%acrossthegradientofsites(Fig.2a).Itwasmoststronglyandpositivelycorrelatedwithsoilmoisture(R2m=0.36,p=1e-06)(Fig.2a)andwasgenerallypoorlyexplainedbyfactorsotherthanclimaticmoisturedeficit(CMD)(Fig.S1).TherelativeabundanceofEcMfungionrootswasbestexplainedbyanegativeassociationwithCMD(R2m=0.36,p=1e-05)(Fig.2b)ItwasalsopositivelycorrelatedwithsoilC/N,soilmoisture,andtheconiferousEcMtreebasalarea,whileitwasnegativelycorrelatedwithmeanannualtemperature(MAT)(Fig.S1;SupplementaryNote1).TherelativeabundanceofEcMfungionrootswaspositivelycorrelatedwithEcMcolonization(R2m=0.09,p=0.019)(Fig.2c)andtherelativeabundanceofEcMfungiinsoil,whichalsoshowedsimilarassociationswithenvironmentalfactors(Fig.S1andSupplementaryNote1).

ComparedtoEcMcolonization,AMrootcolonizationwaslow,sporadic,andabsentonmanysampledtrees,withaveragesvaryingbetween0and25%(Fig.2d).Outofenvironmentalfactors,AMcolonizationwaspositivelyassociatedwithsoilpH(R2m=0.14,p=0.011)(Fig. 2d). The relative abundance of AM fungi in roots was best explained by a negative association with the basal area of EcM/AM trees (R2m =0.24, p= 0.002) (Fig. 2e); the same relationship was observed for AM fungi in soil (Fig.S1).TherelativeabundanceofAMfungiinrootswaspositivelycorrelatedwithAMcolonization(R2m=0.18,p=0.003)(Fig.2f),aswastherelativeabundanceofAMfungiinsoilwithAMfungiinroots,AMfungiinsoil,andAMcolonization(Fig.S1;SupplementaryNote1).

RootcolonizationbyDSEwasgenerallymorevariablecomparedtoAMandEcMcolonization,withaveragesrangingbetween0and50%.ItwasbestexplainedbyapositiveassociationwithsoilC/N(R2m=0.39,p=3e-05)(Fig.2g),correlatedpositivelywiththebasalareaofconiferousEcMtreesandnegativelywithMAT(Fig.S1;SupplementaryNote1).TherelativeabundanceofpotentialDSEfungiinroots(seeSupplementaryData2foralistoffungiconsideredaspotentialDSE)wasbestexplainedbyanegativerelationshipwithsoilpH(R2m=0.32,p=3e-05)(Fig.2h)andapositivecorrelationwithDSEcolonization(R2m =0.20, p= 0.002) (Fig. 2i). It was also positively correlated with soil C/N and the coniferous EcM tree basal area (Fig.S1;SupplementaryNote1).GiventheambiguityofassigningfungiasDSEbasedonmetabarcoding,wealsoconsideredtherelationshipbetweenDSEcolonizationandtherelativeabundanceoffungiinrootswithaprimarylifestyleclassificationasrootendophytesaswellallfungalgenerawithapotentialrootendophyticcapacityaccordingtotheFungalTraitstool29andwedidnotfindasignificantrelationshipforeitheroftheseclassifications(Fig.S2).

WhilewedidnotquantifyN-fixingnodulesformedbybacteriafromthegenusFrankiaonAlnusroots,therelativeabundanceofFrankiaonAlnusrootswasbestexplainedbysoilpHoutofenvironmentalfactors(Fig.S2).TherelativeabundanceofFrankiaonrootswasalsopositivelyassociatedwiththerelativeabundanceofNfixinggenesinroots,thediversityofNcyclinggenesinsoilandroots,thediversityoftotalbacterialfunctionalgenediversityinroots,andtheratioofbacteria/fungiinsoil(basedonmetagenomicreadabundances;seeMethods)(Fig.S2).

RootcolonizationbyDSEhadanegativerelationshipwiththerelativeabundanceofplantpathogensandapositiverelationshipwithsoilsaprotrophs
TherewasasignificantnegativerelationshipbetweenDSEcolonizationandtherelativeabundanceofputativeplantpathogensinsoil(R2m=0.29,p=9e-04)(Fig.3a)androots(R2m=0.10,p=0.036)(Fig.S3).Structuralequationmodelling(SEM)showedthatDSEcolonizationhadastrongdirectnegativeassociation(path)withtherelativeabundanceofplantpathogensinsoil(standardcoefficient−0.44,p=0.003)(Fig.4a)afteraccountingforotherexplanatoryvariablessuchassoilpropertiesandclimateinthebestfittingmodel.RootcolonizationbyDSEcolonizationalsohadasignificant(positive)correlationwiththerelativeabundanceofsoilsaprotrophs(Fig.S3);however,wecouldnotseparatethiseffectfromsoilC/NandpHwithanonsignificantSEMpath.NeitherEcMnorAMcolonizationcorrelatedwiththerelativeabundanceofplantpathogensorsoilsaprotrophs(Figs.3b,candS3).

RootcolonizationbyDSEhadanegativeassociationwiththeratioofbacteria/fungiandwasnegativelycorrelatedwithbacterialdiversity
Darkseptateendophytecolonizationhadanegativeassociationwiththeratioofbacteria/fungiinsoil(R2m=0.18,p=0.002)(Fig.3d)(SEM:standardcoefficient−0.21,p=0.035)(Fig.4b)andthebacteria/fungiratioinroots(R2m=0.13,p=0.015)(Fig.S3).ColonizationbyDSEwasalsonegativelycorrelatedwithbacterialdiversityinbothsoil(R2m =0.14, p= 0.003) and roots (R2m =0.09, p= 0.044) (Fig. S3). Neither EcM nor AM colonization correlated with the bacteria/fungiratioortheirdiversityinsoilorroots(Fig.3e,fandS3).

RootcolonizationbyDSEinfluencedthediversityofdifferentfunctionalgenes
RootcolonizationbyDSEhadapositiveassociationwiththediversityofbacterialcarbohydrate-activeenzyme(CAZyme)genesinsoil(R2m=0.21,p=7e-04)(Fig.3g)(SEM:standardcoefficient0.44,p=0.003)(Fig.4c),whileithadanegativeassociationwithboththediversityoftotalbacterialfunctionalgenesinroots(R2m=0.36,p=2e-05)(Fig.3j)(SEM:standardcoefficient-0.58,p=1e-04)(Fig.4d)andthediversityofNcyclinggenesinroots(R2m=0.37,p=6e-05)(Fig.3k)(SEM:standardcoefficient-0.40,p=0.003)(Fig.4e).Inaddition,DSEcolonizationcorrelatedpositivelywiththediversityoftotalfungalfunctionalandCAZymegenesinsoilandnegativelywithbacterialCAZymesinroots(Fig.S3).ThenegativeassociationbetweenDSEcolonizationandthediversityofNcyclinggenesinrootsappearedtoberobustagainstthepotentialconfoundingpositiveeffectofN-fixingFrankiabacteria,whereDSEcolonizationwasamoreparsimoniouspredictor(R2m=0.37,p=6e-05,AIC=−37.17)comparedtotherelativeabundanceofFrankiaonroots(R2m=0.24,p=0.001,AIC=−35.22).Additionally,DSEcolonizationcontinuedtohaveasignificantpathandastrongerdirectassociation(standardizedcoefficient)whenbothvariableswereincludedwithinthesameSEM:DSEcolonization(standardcoefficient−0.37,p=0.002),comparedtotherelativeabundanceofFrankiaonroots(standardcoefficient0.27,p=0.006)(fordetailedSEMmodelresults,seeSupplementaryData3).OuranalysisalsorevealednosignificantrelationshipbetweenDSEcolonizationandtherelativeabundanceofFrankiaonroots(p=0.919)(seealllinearmixed-effectsmodelsummariesinSupplementaryData10–20).

Bycontrast,EcMcolonizationonlyhadapositivecorrelationwiththediversityofNcyclinggenesinsoil(Fig.S3),andAMcolonizationonlyhadapositivecorrelationwiththediversityofNcyclinggenesinroots(R2m=0.17,p=9e-04)(Fig.3n)andsoil(Fig.S3);however,theEcMandAMrelationshipsdidnothavesignificantSEMpathswhenaccountingforotherbioticandabioticfactors.

Alongsideclimate,soil,andvegetationproperties,DSEandEcMcolonizationinfluencedthecompositionofmicrobialcommunitiesandfunctionalgenes
Next,werelatedAM,EcM,andDSEcolonizationtothecompositionofbacteria,fungi,andtheirfunctionalgeneswhileconsideringtheeffectofsoil,climatic,andvegetationpropertiesusingpermutationalmultivariateanalysisofvariance(PERMANOVA).Mostvarianceinfungalandbacterialcommunitiesandgenecompositionswasgenerallyattributabletosoil,climate,vegetationproperties,andtherandomeffectofsite(Fig.5andS4),althoughbothDSEandEcMcolonizationwerealsoimportantforexplainingbacterialandfungalcommunitycompositionsandwereparticularlyimportantforexplainingthecompositionofdifferentfunctionalgenesinroots(Fig.5).

Specifically,DSEcolonizationexplainedasignificantfractionofvariationinfungalcommunitiesinsoil(1.86%explainedvariance[EV],pseudo-F=1.86, p=0.006) (Fig. 5a) and roots (2.09% EV, pseudo-F=1.91, p=1e-04) (Fig. 5b), bacterial communities in soil (1.30% EV, pseudo-F=1.72, p=0.004) (Fig. 5c) and roots (0.98% EV, pseudo-F=1.44,p=0.041)(Fig.5d),totalfungalfunctionalgenesinsoil(7.54%EV,pseudo-F=5.25,p=1e-04)(Fig.5e),totalbacterialfunctionalgenesinsoil(2.28%EV,pseudo-F=2.80,p=0.004)(Fig.5g)androots(14.26%EV,pseudo-F=9.02,p=4e-04)(Fig.5h),Ncyclinggenesinsoil(4.19%EV,pseudo-F=4.68,p=0.001)(Fig.5i)androots(15.89%EV,pseudo-F=10.54, p=1e-04) (Fig. 5j), P cycling genes in roots (15.99% EV, pseudo-F=10.69, p=2e-04) (Fig. 5k), bacterial CAZymes in roots (16.38%EV,pseudo-F=11.28,p=2e-04)(Fig.5l),andfungalCAZymesinsoil(4.22%EV,pseudo-F=3.34,p=0.011)(Fig.5m).Inaddition,EcMcolonizationexplainedasignificantfractionofvarianceinthecompositionofrootfungalcommunities(1.10% EV, pseudo-F=1.45, p=0.017)(Fig.5b),bacterialcommunitiesinsoil(1.74%EV,pseudo-F=1.89, p=8e-04) (Fig. 5c) and roots (1.25% EV, pseudo-F=1.52, p=0.024)(Fig.5d),totalfungalfunctionalgenesinroots(7.84%EV,pseudo-F=3.81,p=0.003)(Fig.5f),totalbacterialfunctionalgenesinsoil(1.74%EV,pseudo-F=2.30,p=0.018)(Fig.5g)androots(3.32%EV,pseudo-F=2.82,p=0.047)(Fig.5h),Ncyclinggenesinsoil(1.88%EV,pseudo-F=2.54, p=0.041) (Fig. 5i), and fungal CAZymes in roots (10.41%EV,pseudo-F=4.82,p=0.006)(Fig.5n).RootcolonizationbyAMfungididnotexplainanysignificantfractions(fordetailedPERMANOVAresults,seeSupplementaryData4).

Bothrootsymbiontcolonizationandsoilpropertiesexplainedmorevarianceinbacterialandfungalcommunitiesatmid-latitudes
Toexaminethemediatingeffectsofclimateandgeographyontherelationshipsbetweenrootsymbioses,soilproperties,andmicrobiomes,weconductedwithin-sitepartitioningofvarianceforbacterialandfungalcommunitycompositionsinsoilandrootsacrosslatitude.Thecombinedexplainedvariancebyrootsymbioses(AM,EcM,andDSEcolonization)consistentlyshowedunimodalrelationshipswithlatitude,indicatingenhancedeffectsinthemiddleofourlatitudinalgradientonsoilfungi(Adj R2=0.18, p=0.047) (Fig. 6a),rootfungi(AdjR2=0.18,p=0.045)(Fig.6b),soilbacteria(AdjR2=0.25,p=0.020)(Fig.6c),androotbacteria(AdjR2=0.18,p=0.023)(Fig.6d).Incomparison,thecombinedexplainedvariancebysoilproperties(soilpH,C/N,andmoisture)hadamarginallysignificantnegativerelationshipwithlatitude forsoilfungalcommunities(AdjR2=0.16,p=0.056)(Fig.6a).Similarly,torootsymbioses,unimodalrelationshipswithlatitudewerefoundforthevarianceexplainedbysoilpropertiesinthecompositionofrootfungalcommunities(AdjR2=0.25,p=0.020)(Fig.6b)androotbacterialcommunities(AdjR2=0.36,p=0.005)(Fig.6d).

RootcolonizationbyDSEhadaconsistentassociationwiththerelativeabundanceofCAZymesandnutrientcyclinggenes
TherelativeabundanceofspecificclassesofbacterialCAZymesshowedassociationswithallthreetypesofsymbiontcolonizationinsoilbutonlywithDSEcolonizationinroots(Fig.S5andSupplementaryNote2).ThestrongerassociationsinsoilwereanegativerelationshipbetweenEcMcolonizationandbacterialCAZymestargetingcellulose(R2m=0.22,p=0.001),anegativerelationshipbetweenAMcolonization and bacterial CAZymes targeting fungal glucans (R2m =0.25, p=0.001) in contrast to a positive relationship with DSE colonization (R2m =0.30, p=2e-05), and a negative relationship between DSE colonization and bacterial CAZymes targeting lignin (R2m =0.18, p=0.004).Inroots,DSEcolonizationwasnegativelycorrelatedwiththerelativeabundanceofallbacterialCAZymegroups(Fig.S5).

FortherelativeabundanceofgroupsofNcyclinggenesinvolvedindifferentprocesses,DSEcolonizationwasconsistentlynegativelyassociatedwiththoseinsoilandroots(Fig.S6;SupplementaryNote3).Mostnotablyforassimilatory(soil: R2m =0.27, p=8e-05, roots: R2m=0.20,p=2e-04)anddissimilatorynitratereductiongenes(soil: R2m =0.32, p=1e-04, roots: R2m=0.43,p=0.003),denitrificationgenes(soil:R2m=0.40,p=9e-06,roots:R2m=0.64,p=1e-09),nitrificationgenes(soil:R2m=0.22,p=0.002,roots:R2m=0.51,p=1e-04),andhydroxylaminereductiongenes(soil:R2m=0.11,p=0.031,roots:R2m=0.40,p=1e-05).

Both DSE and EcM colonization showed significant relationships with the relative abundance of P cycling genes involved in specific processes in soil (Fig.S7;SupplementaryNote4),includingnegativerelationshipsbetweenEcMcolonizationandinorganicPmobilizationgenes(R2m=0.12,p=0.022)andDSEcolonizationwithgenesinvolvedinPstarvationresponseregulation(R2m=0.11,p=0.005).OnlyDSEcolonizationwasconsistentlynegativelyassociatedwithPcyclinggenegroupsinroots(organicPmineralization: R2m =0.36, p=4e-05, inorganic P mobilization: R2m =0.21, p=0.001,andPstarvationresponseregulation:R2m=0.31,p=3e-05)(Fig.S7).

BothEcMandDSEcolonizationinfluencedteabagdecomposition
Intermsofrelationshipsbetweensymbiontrootcolonizationandrooibosteabagdecomposition,wefoundapositivecorrelationwithDSEcolonization(R2m=0.12,p=0.026)andanegativecorrelationwithEcMcolonization(R2m=0.21,p=0.010).Nevertheless,abioticfactorsrelatedtoclimateandsoilandbioticfactorssuchasspecificCAZymegenesgenerallyhadstrongercorrelationswithbothrooibosandgreenteabagdecompositioncomparedtorootsymbiontcolonization(Fig.S8andSupplementaryNote5).

PotentialDSEtaxa,specificfungalCAZymes,andgeneralfunctionalgenesassociatedwithDSEcolonization
AsDSEcolonizationhadaconsistentassociationwithvariousmicrobiomeproperties,ourfinalstepofanalysiswastoidentifyspecificfungaltaxainrootsfromourlistofpotentialDSEfungi(SupplementaryData2)thatwerecorrelatedwithDSEcolonization,aswellasspecificfungalCAZymesandgeneralfunctionalgenes.TherelativeabundanceofthegenusCladophialophora(R2m=0.28,p=2e-04)andspecificOTUsfromthegeneraCladophialophora(R2m=0.41,p=2e-06)andPhialocephala(R2m=0.19,p=0.004)inrootsweremoststronglyandpositivelycorrelatedwithDSEcolonization(Fig.S9).ForfungalCAZymesinsoilandroots,DSEcolonizationwasmoststronglyandpositivelycorrelatedwiththerelativeabundanceofspecificfamiliesofglycosidetransferases(GT)insoilandglycosidehydrolases(GH)inroots(Fig.S9).Inaddition,forgeneralfunctionalgenes(orthologousgenes[OG])inbothsoilandroots,DSEcolonizationwaspositivelycorrelatedwiththerelativeabundanceofvariousgenes,includingthoseencodingtransposableelementssuchasretrotransposonproteins(SupplementaryData5).

Methods
Samplingsitesandhosttreeselection
Thestudywasconductedacrossa3220kmlatitudinalgradient,whichincluded18locationsthatwereeitherestablishedresearchstationsorvegetationreservesfromnorthernNorway(69.46°N, 30.02°E) to central Italy (42.08°N, 9.82°E) (Fig. 1a). Mean annual temperature (MAT) ranged between 0.8 and 15.1°C, mean annual precipitation (MAP) between 486 and 1213 mm, and altitude between 7 and 1211 m above sea level (Supplementary Data 1). Selected sites contained widespread deciduous broadleaf tree species from three genera (Alnus, Betula, and Sorbus), differing in their preference for different mycorrhizal associations and N-fixing status (Alnus: EcM/AM/N-fixing, Betula: EcM, and Sorbus: AM). Alnus incana was present at 13 sites from northern Norway to northern Italy; A. glutinosa at 12 sites from central Sweden to central Italy; Betula pendula was present at all sites; and Sorbus aucuparia was present at all but the two southernmost sites in Italy, where it was replaced by S. torminalis or S. domestica (Fig. 1a). At each site, 5 trees of each target species were selected with a diameter at breast height (DBH) of 10 and 20 cm, and a distance of at least 10 m within a 50 m radius of one another. If different target species were growing separately (>50 m) or in clearly different habitats, we considered these habitats as separate plots, resulting in 11 sites with 1 plot, 3 sites with 2 plots, 3 sites with 3 plots, and 1 site with 4 plots, leading to a total of 30 plots across the 18 locations. In each plot (2500 m2), all tree species (target and non-target) >10 cm DBH were recorded; their relative basal areas were estimated based on DBH measurements and assigned a mycorrhizal type according to the Fungal Root database88. Sites generally were EcM-dominated mixed forests (60 and 95% relative basal area).

Samplingmethod
Ateachsite,soilandrootsampleswerecollectedfromeachoftheselectedtrees(5treespertreespeciespersite)bythesamepersonwithhelpfromothersbetweenthe5thofAugust(Norway)andthe11thofSeptember2019(centralItaly)fromsitesinanorth-southdirectiontoroughlyapproximateasimilarlaterstageinthegrowingseason.Aroundeachtree,foursoilcores(5cmindiameterand10cmindepth,withnodistinctionmadebetweenmineralandorganiclayers)weretakenfromeachcardinaldirection 50cmfromthebaseofthetreeafterremovinglooselitterandpooledintoonecompositesamplepertree.Subsamplesofeachsoilsamplewereairdriedatroomtemperatureformolecularanalysisorfurtherdriedat70°Cfor48hforphysicochemicalanalysis.Fineroots(<2mm)werecollectedfromthetop10cmofsoilbytracingthemtothemajorlateralrootsofeachtargettree,whichwerecleanedwithwaterandstoredin70%ethanoluntilfurtheranalysis.Additionally,at10sitesfromNorwaytoGermany,oneLiptonteabagwithrooibos(C/N=43)andonewithgreentea(C/N=12)wereburiedto10cmdeepatonesoilsamplingpointpertreeandcollectedafter12monthsaccordingtoamodifiedversionoftheTeabagIndexprotocol89;atotalof284outof330teabagswererecovered(leavingatleastthreereplicatesperspeciespersite)anddriedat70°Cuntilconstantweighttomeasuremassloss.Allsampleswerecollectedfromlocationsinthepublicdomainorinpreviousagreementwiththelocalcommunityand/orpropertyowner.

Rootcolonizationanalysis
Therootcolonizationrate(%ofroottipscolonized)ofEcMfungiwasassessedonBetulaandAlnusfinerootsunderadissectingmicroscopeat×20magnification.Cleanedrootswerecutinto1cmpieces(totalaround20cm),spreadrandomlyinapetridishwithwater,andatotalof100tipswerescoredEcMifamantlewaspresent(swollenshort-roottipscoveredinhyphaewithroothairsabsent)oruncolonizedifnotpresent.Eachsampleofrootswasanalyzedtwiceaftertherandomrearrangementofrootsections.Therootcolonizationrate(%ofrootlengthcolonized)ofAMfungiandDSEwasassessedonAlnusandSorbusfinerootsbythegridlineintersectionmethod90.First,1cmpiecesofcleanedroots(around10cmtotal)fromeachtreesamplewereclearedina2.5%KOHsolutionandstainedwithtrypanblueinanacidicglycerolsolution91.Stainedrootswerethenmountedonslides,andcolonizationwasestimatedunderaminimumof100randomfieldsofintersectionbyusingaZEISSAxioscope5DigitalMicroscope(CarlZeissMicroscopy,Oberkochen,Germany)at×400magnification.ForAMfungi,thepresenceofstainedarbuscles,hyphae,andvesicles(ifpresent)werecountedseparately,andDSEstructureswerecountedwhendarklypigmentedseptatehyphaeandclustersofinflated,rounded,andthick-walledcells(microsclerotia)werepresentwithinthecorticalcellsofroots92(forphotographicexamplesofDSEstructuresinourrootsamples,seeFig.S10).

Soilchemicalanalysis
Gravimetricsoilmoisturewasobtainedfrom25goffieldmoistsoilpersample,followedbyovendrying(70°C)untilaconstantweightwasreachedandre-weighedtocalculatethemoisturecontent(%)offieldmoistsoil.SoilpH,availablephosphorus(P),potassium(K),calcium(Ca),magnesium(Mg),aluminum(Al),andiron(Fe)wereanalyzedafterextractioninammoniumlactate(AL)solutionatAgrilab(Uppsala,Sweden).Soil13Cand15NnaturalabundancesandtotalsoilCandNcontentsweredeterminedwithanIsotopeRatioMassSpectrometer/IRMS(deltaVAdvantage,Thermo-FisherScientific,Dreieich,Germany)coupledtoanElementalAnalyzer(EuroEA,Eurovector,Milano,Italy).

Climatedata
ClimatedatawasobtainedforgeographiccoordinatesfromCHELSA V2.193. We also obtained the annual climatic moisture deficit (CMD) from INDECIS (https://indecis.csic.es/) as a proxy of water availability, which is the difference between atmospheric evaporative demand and precipitation94.

MetabarcodingofDNAfromsoilandrootsamples
TotalsoilDNAwasextractedfrom250mgofair-driedandpulverizedsoilfromeachsampleusingtheDNeasyPowerSoilProKit(QiagenGmbH,Hilden,Germany)followingthemanufacturer’sinstructions.TotalrootDNAwasextractedfrom15mgofair-driedandpulverizedrootsfromeachsampleusingtheMag-Bind®PlantDNADS96kit(OmegaBio-Tek,Norcross,GA,USA)accordingtothemanufacturers'instructionsandaKingfisher™Flexextractionrobot(Thermo-FisherScientific),carriedoutbytheCentreforGeneticIdentification(CGI),SwedishMuseumofNaturalHistory,Stockholm,Sweden.ThequalityofDNAwascheckedbasedon260/280and260/230nmwavelengthratiosusingaNanoDrop1000spectrophotometer(Thermo-FisherScientific).ThequantityofDNAwasmeasuredusingaQubitdsDNABRAssayKitandtheQubit2.0Fluorometer(Thermo-FisherScientific).

RootDNAsamplesweresubjectedtometabarcodingbytheSLUMetabarcodingLaboratory(UMBLA,SLU,Uppsala,Sweden)andsoilDNAmetabarcodingwasperformedbytheauthors.Metabarcodingofbacteriainbothrootsandsoilwasperformedusingtheprimers515FBand926RtotargettheribosomalrRNA16SgeneV4-V5regions95.TheITS2subregionwastargetedforsoilfungiusingtheprimersgITS7ngsandITS4ngsUni96, and for root fungi using the primers gITS797 and ITS498/ITS4arc(adaptedforArchaeorhizomycetes59.

ForsoilITS,soil16S,androotITSsamples,allprimershadunique8or10bpidentificationtagsattachedforbioinformaticidentificationofindividualsamplesafterpooling.BothITS2andsoil16Ssampleswereamplifiedinduplicate50μlPCRreactionsconsistingof:8.25μlofddH2O,5μlof10XDreamTaqBuffer(Thermo-FisherScientific),5μlof dNTPs(2mM),1.5μlofMgCl2(25mM),0.25μlDreamTaqGreenDNAPolymerase(5units/µl)(Thermo-FisherScientific),2.5μlofbothforwardandreverseprimers(0.4μM;forroots3/4ITS4at0.3μMand1/4ITS4arcat0.1μM),and25μloftemplateDNA(1ng/μl)accordingto99.Thermalcyclingconditionswereasfollows:95°Cfor15min,30cyclesof95°Cfor30s,56°Cfor45s,and72°Cfor1min,withafinalextensionat72°Cfor10min.

Root16ssampleswereamplifiedusingatwo-stepPCRprocedureinduplicates;thefirstconsistedof10ngextractedDNA,1×PhusionPCRMastermix(Thermo-FisherScientific),1mg/mlBSA,and0.25μMoftheprimers515FBand926RtotargettheribosomalrRNA16SgeneV4-V5regions95in15μlreactionsamplifiedunderthefollowingconditions:3minat98°C,followedby25cyclesof98°Cfor15s,50°Cfor30s,and72°Cfor40s,andafinalextensionstepof10minat72°C.ThePCRproductswerethenpooledandcheckedbyagarosegelelectrophoresis,followedbycleaningwithSera-Magbeadpurification(CytivaLifeSciences,Marlborough,MA,USA).Asingle30μlreactionwasperformedforthesecondPCR,using0.2μMofprimerswithIlluminaNexteraadaptorandindexsequencesand3μlofthepooledPCRproductfromthefirstPCR.Conditionswerethesameasthefirststep,exceptforanannealingtemperatureof55°Candanextensiontimeof45swith8cycles.

AllPCRproductsweresizecheckedbyrunning2μlofDNAona1%agarosegelfor15to20min.TheduplicatePCRampliconswerethenpooledandcleanedusingSera-Magbeadpurification(CytivaLifeSciences)accordingtothemanufacturer’sinstructions,thenallsampleswerepooledintoanequimolarmixof100to105samplesandcleanedwiththeE.Z.N.A.CyclePureKit(OmegaBio-Tek),followedbyaqualitycheckonanAgilentBioanalyzer(Agilent,SantaClara,CA).Poolswerefinallyshippedforlibrarypreparationand250PEsequencingontheIlluminaNovaSeq6000SequencingSystem(Illumina,SanDiego,CA,USA)attheserviceprovider’s(NovogeneEurope)laboratory.

Bioinformaticsofampliconsequences
TheLotuS2 pipeline100 was used to quality filter, demultiplex, and process filtered reads into operational taxonomic units (OTUs). The ITS2 region was extracted, and non-ITS sequences were eliminated using ITSx101. Uchimewasusedforchimeradetectionandremoval102, and all singletons and sequences shorter than 100 bp were discarded. Clustering of sequences was done using a de-novo clustering algorithm in UPARSE103 with a 97% similarity threshold, and taxonomy was assigned against the SILVA and UNITE databases for prokaryotic and fungal sequences, respectively. All OTUs representing archaea, chloroplasts, eukaryotes, and mitochondria were omitted from the bacterial dataset, and OTUs not assigned to fungi were omitted from the ITS dataset after OTU clustering. This resulted in a total of 5,326,931 and 3,272,525 reads, respectively covering 9772 fungal and 8142 bacterial OTUs for soil samples, and 10,511,905 and 23,407,319 reads, respectively covering 4322 fungal and 19,355 bacterial OTUs for root samples. Two soil 16S and ITS samples, four root 16S samples, and 24 root ITS samples yielded insufficient reads or failed to amplify and were excluded from further analysis. Fungal OTUs were subsequently assigned to a primary lifestyle at the genus level using the Fungal Traits tool29. In addition to the primary lifestyle alignments, we assigned a secondary lifestyle to potential DSE fungi (genus level) based on genera that contain species or strains with potential DSE capacity based on the category ‘root endophytic capacity’ in the Fungal Traits tool (for a list of the potential DSE taxa used in this study see Supplementary Data 2).

Shotgunmetagenomicsandbioinformatics
Shotgun metagenomic sequencing was performed on pooled equimolar amounts of DNA from five replicate soil and root samples per tree species per site. All 61 composite soil DNA pools (Alnus glutinosa n=12, A. incana n=13, Betula pendula n=18, Sorbus aucuparia n=16, S. domestica n=1, and S. torminalis n=1) passed initial quality control checks and proceeded to library preparation. Eight out of 61 composite root DNA pools failed quality control checks and were excluded, leaving 53 pools for library preparation (Alnus glutinosa n=11, A. incana n=11, Betula pendula n=14, Sorbus aucuparia n=15, S. domestica n=1, S. torminalis n=1). Library preparation and sequencing were performed at the service provider facilities (Novogene Europe) using the Novogene NGS DNA Library Prep Set kit and sequenced on Illumina NovaSeq with 2×150 bp paired end reads, with an average of 34,625,862 (±6,134,210) reads per sample for soil and 33,456,982 reads for roots (±5,292,126) (Supplementary Data 6).

AnalysisofmetagenomicreadswasdoneusingtheMATAFILER pipeline104, following a bioinformatic strategy developed in 71. Briefly, reads obtained from the shotgun metagenomic sequencing of soil and root samples were quality-filtered by removing reads shorter than 70% of the maximum expected read length (150 bp), with an observed accumulated error >2 or an estimated accumulated error >2.5 with a probability of ≥0.01, or >1 ambiguous position. Using sdm software (version 1.46)105, reads were trimmed if base quality dropped below 20 in a window of 15 bases at the 3′ end or if the accumulated error exceeded 2. Altogether, 61 soil samples (between 27,950,077 and 56,986,895 total reads per sample) and 53 root samples (between 22,600,117 and 51,501,361 total reads per sample) produced a sufficient quantity of reads and were retained for statistical analyses. To estimate the functional composition of each sample, we implemented a similarity search approach using DIAMOND (version 2.0.5; options -k 5 -e 1e-4–sensitive) in blastx mode106. Prior to that, the quality-filtered read pairs were merged using sdm. The mapping scores of two unmerged query reads that mapped to the same target were combined to avoid double counting. In these cases, the hit scores were combined by averaging the percent identity of both hits. The best hit for a given query was based on the highest bit score and highest percent identity to the subject sequence. Using this method, we calculated the relative abundance of (clusters of) orthologous gene groups (OG) by mapping quality-filtered reads against the eggnog database (version 4)107, reads were also mapped against the KEGG (Kyoto Encyclopedia of Genes and Genomes) database108, and for functional specific carbohydrate-active enzyme (CAZyme) annotations, we mapped reads against the latest version of the CAZy database109. We further grouped selected CAZymes (Supplementary Data 7) based on their active substrates, including plant biomass (cellulose, hemicellulose, and lignin), fungal biomass (chitin and glucans), and bacterial biomass (peptidoglycan), according to the literature110,111 for analysis of relative abundances. For all databases that included taxonomic information (eggNOG, KEGG, and CAZyme), reads were mapped competitively against all kingdoms and assigned into prokaryotic and eukaryotic groups on the basis of the best bit score in the alignment and the taxonomic annotation provided within the database at the kingdom level.

For diversity of N cycling genes, we focused on those (sub)families listed in the NCycDB database112 (Supplementary Data 8). For relative abundances, we grouped genes into the following N cycling gene pathways, also according to the NCycDB: nitrogen fixation, nitrification, denitrification, assimilatory nitrate reduction, dissimilatory nitrate reduction, organic N degradation, organic N synthesis, and hydroxylamine reduction. For diversity of P cycling genes, we focused on those we could match with KEGG IDs in the PCycDB database113 (Supplementary Data 9). For relative abundances, we focused on genes involved in the following P cycling pathways: organic P mineralization, inorganic P solubilization, and P-starvation response regulation, grouped according to the literature114.

We also calculated metagenomic relative abundances (i.e., miTag115) of different taxonomic groups, including the metagenomic bacteria/fungi ratio, based on small subunit (SSU) rRNA genes. For this, SortMeRNA (version 2.0)115 was used to extract and blast search rRNA genes against the SILVA SSU database (v128). Reads approximately matching this database with e< 10−4 were further filtered with custom Perl and C++ scripts116 and merged using sdm. In cases where read pairs could not be merged, the reads were interleaved such that the second read pair was reverse complemented and then sequentially added to the first read. Of these preselected reads, 50,000 reads were fine-matched in the Silva SSU database using Lambda and the lowest common ancestor (LCA) algorithm adapted from LotuS100.

Dataanalysis
Data management, statistical analyses and data visualizations were primarily done using the R platform117 v.4.0.3 and primarily the following packages: phyloseq118 v.1.22.3, microbiome119 v.1.20.0, vegan120 v.2.6.2121, v.1.0.9, ggplot2122 v.3.3.6, Hmisc123 v.4.7.0, corrplot124 v.0.92, adespatial125 v.0.3.16, randomForest126 v.4.7.1, piecewiseSEM127 v.2.1.2, lme4128 v.1.1.32, lmerTest129 v.3.1.3, ggeffects130 v.1.2.1, and MuMln131 v.1.46.0. Additional analysis was performed in the Primer v7 software (PRIMER-e Quest Research Limited, Auckland, New Zealand) using the PERMANOVA+ routine132.

The Shannon diversity (H) index was calculated using the vegan package on rarefied OTU and gene counts to account for differences in sequencing depth. Both ITS and 16S samples were rarefied to 2000 reads for soil and 5000 reads for roots. For the functional gene categories, total functional genes (OG), CAZymes, P cycling, and N cycling gene counts were rarefied to a sampling depth of 27,950,077 reads for soil or 22,600,117 reads for roots. For the analysis of the relative abundance of different taxa or functional guilds, raw OTU counts were centered-log ratio (clr) transformed with an added pseudocount using the microbiome and phyloseq packages. By comparing read abundances to the geometric mean, this method is less sensitive to sequencing depth and accounts for the compositional nature of microbial high-throughput sequencing data while retaining relative and absolute abundance information133. The relative abundance of potential DSE taxa was calculated relative to all other fungi separately to the primary fungal lifestyle assignments, meaning that these taxa were included in other lifestyles within the relative abundance calculations for primary lifestyles. For the analysis of functional gene relative abundances from shotgun metagenomic data, all abundance matrices were normalized as a percentage of the total number of reads used for mapping; this takes into account differences in library size and has the advantage of including the fraction of unmapped (functionally unclassified) reads71. All statistical analyses were performed on either the mean from at least 3 replicate tree individuals (metabarcoding data) or composite (shotgun metagenomic data) value per tree species per site, unless otherwise specified. We also calculated and reported the standard error for all mean values, where applicable.

To account for multicollinearity of biotic and abiotic predictor variables, we removed variables with a Spearman’s rank correlation > 0.7134, while also considering the most important variables for answering our hypotheses. In our final analysis, we included the climatic variables of MAT, MAP, and CMD, the soil variables of pH, C/N, gravimetric soil moisture content, δ15N, δ13C, and P, and the vegetation variables of tree diversity (Shannon diversity index), EcM/AM tree basal area, and coniferous EcM tree basal area. We were interested in EcM, AM, and DSE root colonization as both response and predictor variables.

For soil and root microbiome response variables, we focused on the diversity of bacteria and fungi (metabarcoding), the ratio of bacteria/fungi (metagenomics), the diversity of total bacterial and fungal functional genes, CAZymes, P cycling genes, and N cycling genes (metagenomics). For the relative abundance of different fungal guilds, we focused on soil saprotrophs, plant pathogens, and the symbiotic guilds of EcM, AM, and potential DSE fungi (for a list of potential DSE taxa, see Supplementary Data 2). We also considered the relative abundance of the bacterial genus Frankia (metabarcoding) and the relative abundance of specific bacterial and fungal CAZyme, P cycling, and N cycling gene groups based on process or substrate (metagenomics). And finally, we analyzed the mass loss of rooibos and green tea bags.

To get a general idea of the strength and direction of relationships between the abiotic and biotic predictors of root colonization, tea bag decomposition, and our soil and root microbiome properties of interest, we used a combination of the random forest algorithm, a machine learning approach126, using the randomForest package, together with Spearman rank correlation analysis using the corrplot package, and we adjusted p values for multiple comparisons using the Benjamini & Hochberg method135, followed by forward selection using the adespatial package. To test individual relationships and to account for the effect of site, plot, and tree species, we fitted linear mixed-effects models using the lme4 package with plot embedded in site crossed with tree species as a random effects structure, unless a random effect explained zero variance, in which case they were removed from a given model. Marginal (fixed effects only) and conditional (random and fixed effects) R2 values for each model were calculated using the MuMln package, and p values were calculated in the lmerTest package using the Satterthwaite approximation. To assess model fit, compare models, and select the best fitting models, we evaluated standardized model residuals, calculated and compared the Akaike information criterion (AIC), and considered p values and R2 values. Fitted models and their standard error were plotted using the ggeffects package.

To examine the robustness of the significant relationships observed between root colonization by different root symbionts and microbiome properties while accounting for the effect of abiotic and other biotic factors together with direct and indirect effects, we performed structural equation modeling (SEM) using the piecewiseSEM package incorporating linear mixed-effects models using the lme4 package with plot embedded in site crossed with tree species as a random effects structure, and the Kenward-Roger approximation was used to calculate the significance of individual paths. We determined the best fitting models based on AIC values using a backwards selection approach and achieved optimal model fit through subsequent iterative revisions based on modification indices.

For the multivariate analysis of factors explaining variation in bacterial and fungal communities and functional gene compositions (composite values per tree species per site), we performed permutational multivariate analysis of variance (PERMANOVA) using PERMANOVA+ in Primer v7. For this analysis, we created distance matrices for bacterial and fungal communities using Euclidean distances on clr transformed data and the Bray-Curtis dissimilarity on normalized gene count matrices. We specified site and tree species as random effects and the climatic factors MAT, MAP, and CMD, the soil factors pH, C/N, soil moisture, and the root symbiotic factors DSE, EcM, and AM colonization as fixed effects. The analysis was performed using 9999 permutations with a sequential sum of squares for the construction of the pseudo-F test statistic and its statistical significance. The same variable order was used for each response matrix (Supplementary Data 4). In the case that an explanatory variable had a negative estimated component of variation, we removed them from the model and reperformed the analysis, and the proportion of variance explained by each component (both fixed and random effects) was calculated by dividing each estimated component of variation by the total variance explained in the model, including residual variance.

For the multivariate analysis of factors explaining variation in bacterial and fungal community compositions (roots and soil) within sites (using all site-level tree sample replicates for each tree species), and to explore the effect of latitude on the extent of variation explained by soil properties and root symbioses, we performed variation partitioning analysis using the vegan package. For this analysis, we partitioned variance in bacterial and fungal community distance matrices (Euclidean distances from clr transformed abundance matrices) between soil properties (composite of soil pH, C/N, and moisture), root symbioses (composite of AM, EcM, and DSE colonization), and tree species at each site. We then plotted the adjusted R2 for the total explained variance by the components of soil properties and root symbioses against latitude. Then for each component, we fitted simple linear or first-order polynomial models in the case of unimodal relationships (selected based on AIC values).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Overall, this paper presents the results a large scale field sampling campaign. They leverage\nmetabarcoding studies of bacteria and fungi to understand correlations between different\nmycorrhizal types, the soil microbiome and various climatatological variables. This study is well\nconducted, but fails to meet the mark of novelty and significant insight that I have come to expect\nfrom Nature Communications.\nSpecific elements where this manuscript can be improved include delineation of the hypotheses.\nMy understanding is that there is already decent evidence for Hypothesis 1, from global topsoil\nsurveys. In particular hypotheses 2 and 3 are not aligned with the evidence provided by the\nintroduction. For example, what does temperate mean in this case? It is poorly operationalized in\nthis hypothesis and throughout the paper.\nPositive elements of this paper include the work done to study the percent colonization of various\nsymbionts and the overall methods, including the bioinformatics are state of the art. Besides the\nteabags decay experiment, I am missing a functional characterization of the soil communities that\nwould aid in the interpretation and significance of the work.\nFinally, the title of the paper is misleading, as it is unclear why warming would lead to any changes\nbased on the evidence at hand. The title needs to be rewritten to encapsulate the main findings of\nthe study, which are limited in their capacity to predict future microbiome activity and function in\nforest landscapes", "reviewer_2": "Netherway and colleagues examined how mycorrhizal associations affect surrounding soil\nmicrobiomes and ecosystem function across a latitudinal gradient in Europe. They found strong\neffects of EM and DSE fungi but smaller effects of AM fungi on the relative abundance of soil\nsaprotrophs, pathogens and decomposition. In general, effect sizes were larger in temperate\ncompared to boreal forests. This work is a nice overview of fungal effects belowground across\nclimatic gradients and one of the first to included DSEs as well as AM and EM mycorrhizal fungi.\nHowever, I have several comments on the novelty, hypotheses, analysis, and conclusions of this\nwork. Detailed comments are below.\nNovelty: Several studies have targeted similar questions. Here it would be helpful to distinguish\nhow the current work is different from previous studies on microbiomes associated with different\nmycorrhizal types (e.g., Bahram et al. 2018) or those focused on biogeochemical differences\namong mycorrhizal types.\nHypotheses: Overall the hypotheses are difficult to follow in the results and discussion sections. It\nwould be helpful to include language in both sections about testing certain hypotheses and if they\nwere rejected.\nIn addition, the hypotheses could be clearer by pointing out the contrasting conditions you’ve\nstudied. For example, why would temperate forests have the highest impact? Compared to what\nother ecosystem(s)?\nIs colonization reflective of the strength of symbiosis? What if the colonizing fungi are more\nparasitic than mutualistic?\nStatistics. It is unclear how the linear models accounted for the hierarchical design of the plots and\nsites. Each model should have plot within site as a random effect. Do the spearman rank\ncorrelations include this information?\nColonization v. molecular relative abundance: It’s unclear why both methods were performed. In\naddition, how was the relative abundance of the different fungal groups calculated in the soil? Are\nthe databases good enough to define DSEs accurately? How many taxa were assigned to each\ngroup? If most of the sequences were assigned to a fungal functional group, then relationships in\neach focal group could be caused by the change in another group, not necessarily the focal group.\nReferences\nBahram et al. 2018. Structure and fuction of the global topsoil microbiome. Nature 560:233-237.", "reviewer_3": "This study collected root soil and root samples from 308 individual trees across a 3 220 km\nlatitudinal gradient incorporating 18 locations between northern Norway and central Italy. Tree\nspecies were collected that host different plant symbionts (e.g. (Alnus: EcM/AM/N-fixing, Betula:\nEcM, and Sorbus:AM). These tree species co-occurred at most locations. It was tested whether\nsymbiotic interactions contribute to shaping microbial communities and functions. Focus was on\nthe soil microbiome. The manuscript is well written, presents an extensive data-set and contains\nsome interesting observations, especially those related to dark septate endophytes (DSE).\nComments (in order of appearance).\nTitle: Enhanced potential of tree symbioses in shaping the soil microbiome under warmer climates.\nIs the title strongly supported by the presented data?\nL28 across a European gradient. Perhaps add the size (3000 km) and mention the number of sites\nor tree individuals investigated to demonstrate the size of the study to the readers.\nL38: probably also protists, nematodes and other soil biota.\nL90: this is interesting. The correlation between root colonization and proportion of EcM fungal\nsequences is relatively weak (explained variance R2 = 0.15). Is this often observed (e.g. for\nsimilar studies)? Or do they explain more (or less)?\nL147: plant pathogens were significant less abundant with DSE. Would it be possible to show this\nas a scatterplot (without the different DSE abundance categories). This sounds very interesting.\nGenerally, it would be worthwhile to further explain which symbiosis types explain the occurrence\nof plant pathogens best (e.g. SEM including all three symbionts assessed as explaining variables).\nThere is a lot of literature demonstrating EcM can suppress plant pathogens while this is less well\nobserved for AMF. However, links between plant pathogens and DSE has rarely been assessed.\nThis last aspect is interesting and should get some more attention.\nThe DSE results are most novel and interesting and it would be worthwhile to give this more\nattention (e.g. in the abstract).\nL185: This study demonstrates that the effect of tree symbioses on soil microbial community\nproperties and decomposition depends strongly on climate (Fig. 3a-c). However, Figure 3a-c only\npresents data for dual trees? Are the conclusions valid for all trees species (so including EcM and\nAM). Is there a way to integrate the results (including dual trees, EcM and AM trees into a\ncoherent picture demonstrating the influence of climate on symbiosis and soil microbes in general\n(as the title suggests)?\nL263: In this study we found little evidence to support the negative association of the EcM\nsymbiosis with plant pathogens apart from the negative association of EcM conifer trees with the\nproportion of plant pathogens (Fig. 3b & Fig. S1), and we found that compared to AM roots,\nroots with low levels of EcM colonization were associated with higher proportions of plant\npathogens in the soil (Fig. 3f). Reviewer: how does this look like when one puts the data together (e.g. without\nthe three abundance levels).\nL310 Did you assess root colonization of S. torminalis or S. domestica\nL414: Fungal OTUs were subsequently assigned to a primary lifestyle at the genus level using the\nFungalTraits tool [69]. How good is this data base to characterise DSE? How many DSE are in the\nfungal traits tool (would it be possible to add a table with the DSE taxa – based on sequences\nincluding those listed in the FungalTraits tool)? Please provide some more data. Where the roots\nthat were heavily colonized by DSE sequenced (to identify the identity of the DSE)?\nAccording to the methods, the root microbiome was not assessed. This is a pity as this would\nhave provided further appealing information (e.g. links between root colonization and symbiont\ncolonization, or symbiont diversity; links between the soil and root microbiome).\nWhy is DSE so strongly correlated with C/N ratio? This is interesting? Is EcM correlated with the\nC/N ratio? Any biological reason for the differences.\nAre there any technical issues linked to this study (e.g. effects of primer choice on detected fungi,\nsampling intensity curves (number of OTUs versus sequencing depth) that are worth discussing?\nAre there further data linked to this data-set (meta-genomics or the occurrence of functional\ngenes, functional gene diversity). If so, are these data linked to the occurrence of symbiosis types\n(e.g. link to DSE)? If available, it would be worthwhile to include such data.\nFig 1: it would be possible to indicate the tree species found at each site to see where there is\noverlap and where not.\nFig 3: Would it be possible to show the actual strengths and significance levels of the different\narrows (in addition to arrow thickness). Are the different levels of colonization defined in the\nmethods – please describe carefully if not done-.\nFigures 3 demonstrates Structural equation modelling showing the prosed direct and indirect\ndrivers for specific groups focusing on dual-mycorrhizal trees. It would be worthwhile to also\npresent such SEM models for other trees species (e.g. in the supplement).\nFig S4 demonstrates nicely that particular bacterial classes are linked to symbiosis types. Does\nthis confirm other studies. Please provide a simple table (or an additional column with references if this\nhas been found). For instance negative correlations with AMF abundance but also positive\ncorrelations with DSE (DA052) or EcM (e.g. Clostridia)"}, "author_response": "We thank the reviewer for their comments and time taken for reviewing our manuscript. We\nhave now substantially expanded our dataset to include both shotgun metagenomic data of\nfunctional genes for both soil and roots, as well as metabarcoding data of fungal and bacterial\ncommunities from the root microbiome. We believe these changes have greatly increased the\nsignificance and novelty of our insights, especially regarding the importance of dark septate\nendophytes.\nSpecific elements where this manuscript can be improved include delineation of the hypotheses.\nMy understanding is that there is already decent evidence for Hypothesis 1, from global topsoil\nsurveys. In particular hypotheses 2 and 3 are not aligned with the evidence provided by the\nintroduction. For example, what does temperate mean in this case? It is poorly operationalized\nin this hypothesis and throughout the paper.\nWe have re-written the introduction and re-formulated our hypotheses to better match the\ninsights gained from our study (L87-92). Specifically, we have focused on the effect of root\ncolonization and removed our hypothesis about climate.\nPositive elements of this paper include the work done to study the percent colonization of\nvarious symbionts and the overall methods, including the bioinformatics are state of the art.\nBesides the teabags decay experiment, I am missing a functional characterization of the soil\ncommunities that would aid in the interpretation and significance of the work.\nWe thank the reviewer for this comment. We have now expanded our dataset to include\nshotgun metagenomic data for functional gene characterization of soil and root microbiomes.\nSpecifically, we have analyzed the effect of root symbiont colonization on the diversity,\ncomposition and proportions of different C, N, and P cycling genes, as well as general functional\ngenes. The results are presented in lines 151-214, and Figs. 4 and 5. We revised the discussion\naccordingly in lines 288-340. This additional data facilitated additional novel insights on how\ndark septate endophytes may impact the functioning of belowground microbial communities.\nFinally, the title of the paper is misleading, as it is unclear why warming would lead to any\nchanges based on the evidence at hand. The title needs to be rewritten to encapsulate the main\nfindings of our (revised) study. The new title is: ‘Pervasive effect of dark septate fungi on tree root and soil\nmicrobiomes ‘\n[... Author response continues for Reviewer 2 and 3 ...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Overall, I have significant concerns primarily about the designation of DSE as a group for analysis,\nas well as the justification of the hypotheses. Both points can potentially be fixed in revisions,\nbut they are substantial, requiring rewriting and reanalyzing substantial portions of the\ndatasets and may not be of interest for the authors. Overall, the methodology used is cutting-edge\nand reflects a substantial improvement over the previous iteration of the ms. The gradient\nstyle approach utilizing multiple tree hosts is of interest, and allows for some level of generality\nabout the abundance of root-endophytes and their relative impact on root/soil microbiomes\nOverall:\nDark septate endophytes are an ambiguous group of fungi, with poorly defined taxonomy.\nMeaning that whether an Ascomycete fungi detected in a root, is or is not, a DSE is poorly\nunderstood. The typical definition of DSE reflects this ambiguity : “Fungi that colonize living\nplant roots by melanized septate hyphae” Ruotsalainen et al., 2022 TREE. Endophytic root-fungi\nare highly diverse, many of which form close associations with EM roots (Toju & Sato 2018 Fronteirs\nMicrobio; Pellitier et al., 2021 Fungal Ecology). The functioning of these highly cryptic\nascomycetous fungi is poorly known with many having dual roles in databases such as\nFungalTraits as pathogens/saprotrophs/endophytes. I think that exploring the role of root\nendophytes is very important, but am concerned that not enough information on DSE is known to\nmake the sweeping claims presented in this ms. As it stands, our ability to characterize DSE from\nsequence data is poor. Calling them Potential DSE is helpful, but care must be taken to explain the\nchallenges inherent with characterizing DSE to start with, given that it is purely a\nmorphologically defined definition. The inclusion of the Table S1, is also helpful, however, only in\nfew cases are the morphological analyses attached to species names or even genera. My\nsuggestion is to reanalyze the data using similar analyses but with all endophytic root fungi,\nand make a special discussion section for the potential role of DSE in driving the broader\nendophyte patterns observed here.\nFocusing on all root endophytes will have broader appeal, and likely broader impact.\nMethods:\nHow were DSE fungi identified? Presumably they were identified using the taxonomic identify at\nthe genus level. My concern however is about the fact that many fungi in FungalTraits are not\nknown to be exclusively DSE, many are identified as endophytes, pathogens and or DSE. In the\nintroduction, and here, much more stringent paramaters needed to be provided to first describe\nthe biology and phylogenetic breadth of DSE, as well as how they were identified in FungalTraits\nwhen multiple entries for lifestyle were given.\nAlnus roots host N fixing bacteria, please clarify if nodules were accounted for or measured and\nhow they were incorporated into the analyses. The measurement solely of N fixing genes seems\ninsufficient to account for confounding effects of these nodules on overall community structure.\nWithout measuring the proportion of these important symbionts, I am not clear on what soil\nchemical analyses can be effectively utilized as predictors.\nPlease clarify how AM vs DSE were independently measured using the grid-intersection\nmethod. They both stain nearly identically with Trypan, so it is not clear what morphological\ncharacter was used to distinguish them.\nHow was host identity utilized in the LME? Was it included an effect? If so how? The challenge\nbeing is that most of the tree hosts used are not distributed across the breadth of the gradients\npresented.\nFigures:\n2c in particular is not clear what is being measured on the y axis. How can the proportion be\nlarger than 100? Please relabel or replot the data to make this more clear, both here and\nelsewhere where the clr is utilized. The above comment applies to figure 3 as well.\nFigure 4, the potential for N-fixing bacteria in alnus to be impacting the results presented in\npanel j,k,l, as well as p,q,r, needs to be considered.\nResults/Discussion:\nI am concerned about the overall justification of the hypotheses. While I agree that endophytic\nor mycorrhizal fungi could influence the soil or root microbiome, I would argue that climate plays\na significant role in shaping the distribution of AM or EM symbiose (sensu Steidinger et al., 2019\nNature, or van der Linde 2018 Nature). Accordingly the presentation of the results as being able\nto derive variance partitioned between climate and EM/AM/DSE feels misleading. I feel a key\nanalysis missing is exploring climate/soil variable explaining the occurrence and abundance of\nDSE/EM/or AM fungi. No presentation of the latent impacts of climate in driving the distribution\nof these major AM/EM or DSE is presented, leading to misleading claims about the general role\nof these fungi in forest systems. I understand that such perspectives run somewhat counter to\nthose presented by Phillips et al., 2013, and the MANE hypothesis, but the role of climate in\nmediating the distribution of key symbioses cannot be ignored.\nSimilar to the above point, the key drivers of soil fungi,root fungi, and most other measured\nvariables are primarily attributable to soil and climatic variables, not the presence or AM/EM or\nDSE. i.e. Figure 5, to this point, Section starting at line 267 needs to present a much more\nnuanced picture of the relative importance of climate, alongside DSE (or endophytes more\nbroadly). As written this section is misleading when compared against Figure 5a,b.\nLine 222, why do you think this is the case here, when it has been widely found to be the\nopposite in many studies.", "reviewer_3": "The manuscript has much improved and I really like it now. Earlier I was not so convinced that it\nwas strong enough for Nature Communications (in that form), but now it includes many appealing\ndata and the role of dark sepatate endophytes is much clearer shown now. I have a few minor\ncomments:\nLine 2: title suggestion: dark septate fungi drive tree root and soil microbiome composition and\nfunction in European forests.\n- if you would choose this title (and the editor agrees), obvioulsy it needs to be made clear in the\ndiscussion that further field and experimental studies are needed to verify the observations made\nhere (especially the important role of dark septate endophytes).-\nAbstract: it would be worthwhile to mention the effects of dark septate endophytes (DSE) on\npathogens (e..g. linked to the results presented in figure 3). Generally, the effects of DSE could\neven be more stronger emphasized in the abstract, also specifically mentioning their effects on a\nrange of parameters including effects on pathogens, on fungal diversity, on functional gene\ncomposition etc. (to “visualize” the strong effects to the reader).\nL97-101: very long sentence. Consider to have two sentences. In general, the manuscript could\nbenefit a little (minor issue, no major issue) if a native speaker goes over it (this reviewer is also\nnot a native speaker).\nThe discussion on the importance of DSE and their role in the soil is very useful (including\npotential mechanistic insights of how DSE influence pathogens and microbiome composition.\nL446: the description of DSE detection (microscopy) is very short. Please add a few pictures\n(supplement) to show how DSE look like and explain the morphological characteristics used to\ncount a DSE hyphae in more detail.", "reviewer_4": "The authors present a thorough contribution detailing the potential effects of EcM, AM, and DSE on\nsoil and root microbiomes and functional potentials. While I was charged by the editor to address\none specific aspect of this work, namely the efficacy of previous revisions in responding to\nprevious reviewers, I do also include a few other concerns here.\nOverall, the authors present interesting results.\nLines 48-50: I do not think that this is a fair statement. There have been many previous studies\nthat have explored how root symbionts impacts below ground studies, even with differential\nmycorrhizal status. This statement suggests a poor understanding of literature from the last 20\nyears.\nLines 105-106: Why do you think that EcM does not correlated with EcM here? And do you think\nthat this lack of correlation where there should be one is concerning? How does this reflect of\nthe rest of the data analyses?\nLines 168-178: DSE can be hard to find and can be prone to false negatives. That is, if you have a\nzero for colonization, how confident are you that it is a true zero? But these zeros are included in\nall of these analyses. It would be nice to omit these zeros and re-run these statistics to confirm\nthat these general patterns still hold without a plethora of zero counts potentially artificially pulling\nthe slope down,\n180-185: I would suggest omitting this (and in methods) as it adds little to the story.\nMy biggest concern is that plant species was not controlled for. You have the categories of AM,\nEcM and DSE across a few species within genera. But individual species are not controlled from\nin analyses. With the exception of a recent paper in Fagaceae, almost all literature suggests that\nspecies (really, host genetics) play the predominant role in structuring mycorrhizal and soil\ncommunities. It is possible that all patterns seen here are species effects, rather than symbiont\neffects. This must be accounted for and discussed."}, "author_response": "We thank the reviewer for their in-depth and constructive review of our manuscript. [... Author response addresses all points ...]"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have satisfied my concerns.", "reviewer_4": "This MS is greatly improved and I commend the authors' for taking previous reviews to heart. This\nis a very extensive study and an interesting one.\nOverall, this is a great study and I just have a few points. 1) All of the major results are\ncorrelation-based, which is to be expected for this type of work. But, the authors' overstate the\nlevel of inference possible based on these correlations. With correlational analyses, once cannot\nreally understand mechanisms so all discussions need to take this into account and not over-\npromise what these data can and cannot tell us. 2) Lines 211-224 should also include some\nmeasure of effect (R-squared or similar) rather than just P-values. 3) Be very careful with\ndiscussing DSE colonization and genes, specifically TEs. The authors did not sequences\nmetagenomes particularity deep, thus I can presume that there was not very many high-length\nMAGs, which would be needed to tie these TEs to taxa, they make a big leap trying to connect\nthese to DSE."}, "author_response": "We thank the reviewer for their time and for going through the manuscript again and for helping\nto improve the overall quality of the manuscript. In regards to point #1 we have tempered our\ndiscussion and the manuscript overall by mentioning ‘potential’ effects and associations due to\nthe correlation-based nature of our analysis. In regards to point #2 we have now listed the\nexplained variance (%) and pseudo-F values to accompany the p-values (now on Lines\n215-235). In regards to point #3 the reviewer is correct that we did not sequence very deep and\ndid not construct MAGs, so we cannot tie TEs to taxa other than broadly to kingdoms. To\naddress this concern we have now removed our statement from the discussion linking DSE to\nTEs in our study."}]

Decision letter