The distance decay of similarity in communities of ... · The bulk of our knowledge on...

The distance decay of similarity in communities ofectomycorrhizal fungi in different ecosystems andscalesMohammad Bahram1,2*, Urmas K~oljalg1,2, Pierre-Emmanuel Courty3, Abdala G. Di�edhiou4,Rasmus Kjøller5, Sergei P~olme1,2, Martin Ryberg6†, Vilmar Veldre1 and Leho Tedersoo1,2

1Institute of Ecology and Earth Sciences, Tartu University, 14A Ravila, 50411 Tartu, Estonia; 2Natural History Museumof Tartu University, 46 Vanemuise St. 51054 Tartu, Estonia; 3Zurich-Basel Plant Science Center, Department ofEnvironmental Sciences, Botany, University of Basel, Hebelstrasse 1, 4056 Basel, Switzerland; 4Laboratoire Communde Microbiologie IRD/UCAD/ISRA, D�epartement de Biologie Végétale Université Cheikh Anta Diop (UCAD), BP 5005Dakar, Sénégal; 5Biological Institute University of Copenhagen, Øster Farimagsgade 2D, 1353 Copenhagen,Denmark; and 6Department of Plant Pathology, University of Minnesota, St. Paul, MN 55108, USA

Summary

1. Despite recent advances in understanding community ecology of ectomycorrhizal fungi, little isknown about their spatial patterning and the underlying mechanisms driving these patterns acrossdifferent ecosystems.2. This meta-study aimed to elucidate the scale, rate and causes of spatial structure of ectomycorrhi-zal fungal communities in different ecosystems by analysing 16 and 55 sites at the local and globalscales, respectively. We examined the distance decay of similarity relationship in species- and phylo-genetic lineage-based communities in relation to sampling and environmental variables.3. Tropical ectomycorrhizal fungal communities exhibited stronger distance-decay patterns comparedto non-tropical communities. Distance from the equator and sampling area were the main determi-nants of the extent of distance decay in fungal communities. The rate of distance decay was nega-tively related to host density at the local scale. At the global scale, lineage-level communitysimilarity decayed faster with latitude than with longitude.4. Synthesis. Spatial processes play a stronger role and over a greater scale in structuring local com-munities of ectomycorrhizal fungi than previously anticipated, particularly in ecosystems withgreater vegetation age and closer to the equator. Greater rate of distance decay occurs in ecosystemswith lower host density that may stem from increasing dispersal and establishment limitation. Therelatively strong latitude effect on distance decay of lineage-level community similarity suggests thatclimate affects large-scale spatial processes and may cause phylogenetic clustering of ectomycorrhi-zal fungi at the global scale.

Key-words: beta diversity, dispersal limitation, distance-decay curve, global analysis, island bioge-ography, plant–soil (below-ground) interactions, spatial autocorrelation, species-area relationship,symbiosis, variogram

Introduction

One of the primary aims of ecology and biogeography is to testhypotheses about the structure of biological diversity acrossspace and time. The distance decay of similarity – diminishingsimilarity with increasing geographical distance – is one ofthe well-known and fundamental patterns of biodiversity

(Whittaker 1975), which arises from both intrinsic processesincluding random and dispersal-related (i.e. neutral processes;Hubbell 2001) and niche-related processes (Cottenie 2005).The relative role of these processes determines the strength ofdistance decay in ecological communities and can vary betweendifferent ecosystems and organisms (Nekola & White 1999; So-ininen, McDonald & Hillebrand 2007). Fitted distance-decayrelationships are usually illustrated by variograms that includethe following key parameters: range (i.e. extent of distancedecay), still (average community dissimilarity) and nugget (ran-domness; Brownstein et al. 2012). Additionally, the slope of

*Correspondence author. E-mail: [email protected]†Current address: Department of Organismal Biology, UppsalaUniversity, Norbyv€agen 18D, 75236 Uppsala, Sweden.

© 2013 The Authors. Journal of Ecology © 2013 British Ecological Society

Journal of Ecology 2013, 101, 1335–1344 doi: 10.1111/1365-2745.12120

distance-decay relationship reflects the rate of species turnover(i.e. beta diversity) with increasing geographical distance andenables the prediction of regional species richness (gammadiversity) based on local richness (alpha diversity; Harte et al.1999; Kraft et al. 2011). Patterns of distance decay and theunderlying processes are essential for understanding the func-tion and conservation of ecosystems (Legendre, Borcard &Peres-Neto 2005) and modelling niche-neutral effects onspecies distribution (Dray et al. 2012).Ectomycorrhizal (EcM) fungi play a key role in ecosystem

nutrient cycling, tree nutrition and health (Smith & Read2008). EcM fungi are the dominant guild of soil microbes inmost boreal, temperate and many tropical forests. They formdiverse communities in many terrestrial ecosystems (Tedersooet al. 2012); hundreds of species and tens of individuals maycolonize a single tree individual (Bahram et al. 2011). Thestudy of the community ecology of this group has substan-tially benefited from advances in molecular methods over thepast decade. Several studies report that EcM fungi follow bio-geographical patterns of macroorganisms such as island bio-geography (Peay et al. 2007, 2012) and relationships withaltitude (Bahram et al. 2012). At the global scale, however,EcM fungal richness displays an unimodal relationship withlatitude (Tedersoo et al. 2012), but this effect is context-dependent (P~olme et al. 2013).The bulk of our knowledge on distance-decay relationships

is based on macroorganisms, while spatial distribution ofmicrobes has only recently received attention owing to meth-odological advances (Green et al. 2004). Although microbialcommunities have been traditionally considered to exhibitnegligible biogeographic differentiation, recent studies indi-cate that microbes may have a substantial distance-decay pat-tern (Green et al. 2004; Green & Bohannan 2006; Fierer &Jackson 2006; but see Queloz et al. 2011 for root endo-phytes). Yet, the underlying processes of spatial structure ofmicrobial communities remain poorly understood.Despite considerable progress in our understanding of alpha

diversity and community composition of EcM fungi (Taylor2008), little is known about spatial structure of EcM fungalcommunities in different ecosystems and the relative roles ofneutral and niche processes in creating these patterns (Teder-soo et al. 2011; Peay et al. 2012). Much of our knowledgeabout spatial structure of EcM fungi comes from the analysisof temperate communities (Wolfe et al. 2009). In particular,Lilleskov et al. (2004) analysed spatial structure of EcMfungi in eight Northern American temperate forests dominatedby conifers and found significant distance-decay relationshipfor half of the study sites. This and some other studies havereported significant spatial autocorrelation ranging up to 3 min communities of temperate forests (Lilleskov et al. 2004;Pickles et al. 2012). The importance of several environmentalfactors such as host species (Ishida, Nara & Hogetsu 2007;Tedersoo et al. 2008), soil nutrient concentrations (Toljanderet al. 2006) and climate (Ostonen et al. 2011; Bahram et al.2012; Tedersoo et al. 2012) in structuring the EcM fungalcommunities have been documented over different geographi-cal scales. All these variables, however, exhibit a strong

spatial structure from fine (patches of host plants and micro-sites) to local (plant community) to global scales, which may,in turn, result in spatial aggregation of soil biota (Ettema &Wardle 2002).In addition, non-random distribution of genetic individuals

leads to spatial patterning of community, and thus, both thesize of genetic individuals and the agglomerative distributionof individuals of the same species may account for theobserved patchy growth habit of fungi in soil (Lilleskov et al.2004; Pickles et al. 2012). Most of the fungal spores fallwithin 0.5 m of the fruit body, which results in greater proba-bility of mycelium establishment in close proximity(Li 2005). Indeed, dispersal limitation is an important factoraffecting distribution of EcM fungi at the local scale (Peayet al. 2007) and creating patches of EcM fungal communitiesin soil (Tedersoo et al. 2010). The structure and size of extra-radical mycelium in soil varies greatly among taxa of EcMfungi (Agerer 2001). The size of EcM fungal genetic individ-uals ranges from 10�10 m2 in the germinating spores to103 m2 in old genets inhabiting undisturbed forests (Riviere,Natarajan & Dreyfus 2006; Douhan et al. 2011).The relative importance of environmental and spatial vari-

ables in structuring communities may vary between differentecosystems, geographical scales and across latitude, leading togreat differences in distance-decay relationships (Nekola &White 1999; Soininen, McDonald & Hillebrand 2007). At theregional scale, the stronger spatial structure of climate alonglatitude may lead to a stronger distance-decay pattern with lat-itude than with longitude (Qian, Ricklefs & White 2005). Inaddition to direct effects on competitive abilities, climateaffects soil processes and the distribution of EcM host trees.In particular, differences in host density may potentially affectspatial distribution of EcM fungi, because crossing uninhabit-able barriers requires spore dispersal. However, tropical EcMhost trees are mostly distributed as monodominant stands orsmall groups of tree individuals among the arbuscular mycor-rhizal vegetation, which impedes vegetative mycelium expan-sion of EcM fungi between such patches. Lower host densitymay lead to greater importance of dispersal than niche-relatedprocesses and thus more aggregation of EcM fungi in tropicalecosystems.In this study, we addressed the spatial distribution patterns

of EcM fungal communities from local to global scales. Ourmain aim was to assess distance decay of community similar-ity in different ecosystems. We also aimed to elucidate therelative contribution of biotic and abiotic factors to distancedecay at the species and phylogenetic lineage (a semi-phylogenetic measure used for macroecological analyses;Tedersoo, May & Smith 2010; Tedersoo et al. 2012) levels atthe local and global scales, respectively. We tested the follow-ing alternative hypotheses against the null hypotheses of ran-dom effect: (i) the extent of distance decay (i.e. spatialautocorrelation range) is relatively greater in tropical forests,because of lower host density compared with non-tropicalecosystems (Tedersoo & Nara 2010), leading to more pro-nounced effect of dispersal-related processes and consequentlymore aggregated distribution of species; (ii) the rate of

© 2013 The Authors. Journal of Ecology © 2013 British Ecological Society, Journal of Ecology, 101, 1335–1344

1336 M. Bahram et al.

distance decay depends on phylogenetic lineage of EcM fungi– that is, lineages comprising predominately pioneer specieswith good dispersal abilities exhibit greater extent of distancedecay; and (iii) at the global scale, the distance decay of EcMfungal lineages is greater with latitude than with longitude,following climatic patterns.

Materials and methods

DATA SOURCES

To address the relative effect of spatial variables on EcM fungal com-munities, we used community data sets including spatial metadatafrom 16 sites represented by 11 case studies that were carried out indifferent ecosystems (Table 1). In addition, we used the data fromeight studies performed in Northern American forests (Lilleskov et al.2004) (Table S1). These data were used only in some of the analysesdue to the lack of access to raw data (species by sample table withcoordinates). In all the included data sets, morphotyping in combina-tion with internal transcribed spacer (ITS) sequence analysis was usedfor species identification, but different sampling protocols were used.To address the spatial turnover of lineage-level community similarityin EcM fungal communities at the global scale, we included informa-tion on the frequency of lineages from 39 additional sites (altogether55 sites) as described in Tedersoo et al. (2012) (Table S1 and Fig. 1).We categorized the study sites into tropical (with mean annual tem-perature more than 18 °C) and non-tropical based on their meanannual temperature (MAT).

SPAT IAL ANALYSES

Individual root samples were considered as sampling units in eachstudy site. All samples included occurrence data of species andprecise geographical locations. Singletons (i.e. species occurring onlyonce) were removed prior to statistical analyses. Bray–Curtisdissimilarity measure was used to generate community distance matri-

ces. For data with non-normal distribution, the absolute and log-transformed values were compared and chosen based on normalityusing the Kolmogorov–Smirnov test (Table S2).

To compare the spatial structure of EcM fungal communities, theextent of distance decay (i.e. spatial autocorrelation range) and rateand average community dissimilarity in different data sets were deter-mined by generating distance-decay curves (plots of community dis-similarity vs. geographical distance). The distance-decay curves werefitted by linear, exponential, logarithmic, polynomial and Gaussianfunctions, and the best fit was chosen based on coefficients of deter-mination using the KaleidaGraph software (Synergy Software, Read-ing, PA, USA). The significance of the relationship betweencommunity dissimilarity and geographical distance within the detecteddistance-decay extent was assessed by use of Mantel test for eachdata set. Species of EcM fungi were assigned to EcM lineagesaccording to Tedersoo, May & Smith (2010). These lineages representmonophyletic groups of EcM fungi that have evolved EcM habitindependently. To find whether EcM fungal lineages differ in theirspatial structure, the above analysis was performed based on species-level community of the most diverse lineages (n > 3 species,including the /russula–lactarius, /tomentella–thelephora, /inocybe, /cor-tinarius, /clavulina, /boletus, /genea–humaria, /tuber and /sebacinalineages), followed by Bonferroni correction for multiple tests. Thespatial autocorrelation of individual frequent species (n > 2 occur-rences) was examined by Moran’s I test. The Mantel and Moran’sI tests were performed in Ecodist (Goslee & Urban 2007) and Ape(Paradis, Claude & Strimmer 2004) packages of R, respectively.

Two-way analysis of variance (ANOVA) was used to compare thedistance-decay extent between tropical and non-tropical sites. To com-pare the rate of spatial turnover across different ecosystems, the slopeof distance-decay relationships over the distance-decay extent, smallrange (< 50 m) and maximum range (study extent) of study siteswere determined for each data set. The differences between spatialranges were tested by comparing slopes of both data sets againstslopes in the randomized data sets based on 1000 permutationsfollowing Nekola & White (1999) as implemented in the Simba pack-age of R (Jurasinski 2012).

Table 1. Original data sets that were used for the analysis of spatial turnover of species

Site name StudySamplingdesign Ecosystem type Host plant

Meanannualprecipitation

Meanannualtemperature

Benin Tedersoo & Yorouunpublished

Regular Tropical rain forests Fabaceae 1082 27.2

Cameroon-Korup Tedersoo et al. (2011) Regular Tropical rain forests Fabaceae 2900 26Denmark Kjøller (2006,

unpublished)Regular Boreal mixed forest Fagaceae/Betulaceae 820 8.2

Ecuador-Yasuni Tedersoo et al. (2010) Regular Boreal mixed forest Caryophyllales 3081 28Estonia-1 Tedersoo et al. (2003) Nested Boreal mixed forest Pinaceae 620 4.5Estonia-2 Bahram et al. (2011) Regular Boreal mixed forest Salicaceae 620 4.5France Courty et al. (2008) Regular Temperate deciduous forest Fagaceae/Betulaceae 744 9.2Gabon-Monts De Cristal Tedersoo et al. (2011) Regular Tropical rain forests Fabaceae 2100 23.6Guinea Diedhiou et al. (2010) Regular Tropical rain forests Fabaceae 3000 24Iran-Asalem Bahram et al. (2012) Nested Temperate deciduous forest Fagaceae/Betulaceae 1018 10.6Iran-Nowshahr Bahram et al. (2012) Nested Temperate deciduous forest Fagaceae/Betulaceae 940 13.8Iran-Savadkuh Bahram et al. (2012) Nested Temperate deciduous forest Fagaceae/Betulaceae 873 14.2Madagascar-Mandena Tedersoo et al. (2011) Regular Tropical savanna Dipterocarpaceae 2200 24Sweden Ryberg, Larsson & Molau

(2009)Nested Arctic-alpine Salicaceae 582 5.8

Zambia-Kashima Tedersoo et al. (2011) Regular Tropical savanna Fabaceae 1100 21.7Thailand Phosri et al. (2012) Regular Tropical rain forests Dipterocarpaceae 1250 27.1


Distance decay of similarity in ectomycorrhizal communities 1337

To identify the key variables that determine (i) the average com-munity dissimilarity, (ii) the extent of distance decay and (iii) the rateof distance-decay, model selection procedure was performed based oncorrected Akaike information criterion (AICc) values of GeneralLeast-Squares (GLS) models as implemented in the nlme package ofR (Pinheiro et al. 2011). The extent of distance decay and averagecommunity dissimilarity data of eight additional sites in NorthernAmerican temperate ecosystems (Lilleskov et al. 2004) were alsoused in the model selection procedure. The following variables wereincluded as explanatory variables in the model selection: average ageof EcM trees, total extent of sampling area (estimated based on maxi-mum distance among samples), size of individual sample, total sam-ple volume, number of samples, number of host trees, host density(determined based on relative percentage basal area contribution ofEcM to non-EcM trees in four categories (< 25%; 25–50%; 50–75%;> 75%), because precise measurements were not available in mostcases), distance from the equator, MAT, mean annual precipitation(MAP), biome (tropical vs. non-tropical), total species richness persite and average species richness per sample (Table S2). Theobserved richness was included in the model selection, because itmay be strongly correlated with measures of beta diversity (Kraftet al. 2011). Because distance from the equator and MAT werestrongly correlated (Pearson R = �0.949; n = 21; P < 0.001), modelselection was performed twice by including and excluding MAT toconsider the confounding effect of these variables. To directly esti-mate the relative contribution of spatial and environmental variablesand their shared effect, variation partitioning analysis and permuta-tional multivariate analysis of variance (PERMANOVA) were performedby including both the vectors of principal coordinates of neighbour-hood matrices (PCNM) and recorded environmental variables(Borcard, Legendre & Drapeau 1992). Variation partitioning analysiswas performed in the vegan package of R.

In addition, to better account for the varying spatial scale(i.e. study area and sampling design), the slope of the distance-decayrelationships (rate of distance decay) of data sets was calculated basedon pairwise geographical distances in the following arbitrary spatialranges that correspond to the sampling design in most of the studies:0–25, 0–50, 0–100, 0–150 and 0–300 m. Significance of the slopeswas examined using a permutation test. The intercept of the relation-

ships was interpolated as initial similarity (similarity between samplesat one metre distance from each other).

To examine the spatial turnover of EcM fungi at the global scale,we analysed the distance decay of similarity in lineage-level commu-nity by considering study sites as sampling units. We used abundanceof species in different EcM fungal lineages for creating a lineage-levelcommunity dissimilarity matrix. This semi-phylogenetic approachallows integrating information of different sites with non-alignablesequences and non-overlapping species (Tedersoo et al. 2012). Wetransformed geographical coordinates using the Haversine formula(Sinnott 1984) that reflects surface distances between any points onthe Earth. We used the Wisconsin double standardization method,which improves the gradient detection of dissimilarity measures (Ok-sanen 2011), and Bray–Curtis dissimilarity to construct the lineage-level community dissimilarity matrix. We determined the extent of dis-tance decay by plotting lineage-level community dissimilarity as afunction of geographical distance and tested the significance of dis-tance decay in the extent of distance decay by use of Mantel test. Tocompare phylogenetic turnover across different ecosystems, we testedthe distance decay of lineage-level community similarity between thestudy sites in non-tropical and tropical biomes separately. We alsocompared the lineage-level community turnover over latitudinal andlongitudinal gradients by calculating the slope of distance-decay rela-tionships against latitude and longitude in separate analyses.

Results

The community variation of 7 of 16 sites showed significantlinear distance-decay relationship at the local scale based onMantel tests (Table 2). Exponential rise to maximum was thebest-fitting function according to the determination coefficientfor the distance-decay relationship in 11 of 16 data sets; in 2data sets (Madagascar and Zambia), R2 of exponential fit wascomparable to other functions and in 2 data sets (Benin andThailand) and logarithmic fit was the best-fitting function.One data set (France) showed no positive distance-decay rela-tionship; thus, it was excluded from the analyses of distance-decay extent. Based on Mantel tests, the detected extent of

Fig. 1. Global map showing location of the study sites (symbols). Triangles represent the sites that were also included in the analysis ofdistance-decay relationship at local scale (as in Table 1).



distance decay was statistically significant in 13 data sets andmarginally significant in one data set (Estonia-2). In Madagas-car, no significant distance-decay extent was detected.The best GLS model indicated that the extent of distance

decay in EcM fungal communities is significantly negatively

related to the distance from the equator (t = �4.944,P < 0.001; Fig. 2) and positively related to sampling volume(t = 3.127, P = 0.006), host density (t = 2.735, P = 0.014)and age of vegetation (t = 2.191, P = 0.043). The best modelexplained 77.8% (P < 0.001) of the variation in the extent of

Table 2.Results of Mantel tests at the scale of study and distance-decay extent, average community dissimilarity, permanova and variationpartitioning

Study site range Distance-decay extent

Averagedissimilarity

PERMANOVA

VariationpartitioningMantel r P Range (m) Mantel r P

Soil (s), climate (c),seasonality (se) andhost species (h)effect

Spatialvectors

Benin 0.021 0.235 65 0.143 < 0.001 0.901 R2h = 0.042P = 0.092

R2 = 0.45P = 0.01

a = 0.002b = 0.003c = 0.189

Cameroon-Korup 0.047 0.007 66 0.088 < 0.001 0.978 R2s = 0.036P = 0.023

R2 = 0.10P = 0.005

a = 0.006b = 0.003c = 0.120

Denmark 0.093 0.001 5.2 0.101 < 0.001 0.93 na R2 = 0.117P = 0.001

a = nab = nac = na

Ecuador-Yasuni 0.055 0.082 44 0.18 < 0.001 0.932 R2h = 0.190P = 0.001

R2 = 0.337P = 0.001

a = 0.095b = 0.044c = 0.11

Estonia-1 0.126 0.011 1.2 0.239 0.01 0.74 R2h = 0.068,P = 0.025

R2 = 0.257P = 0.001

a = �0.008b = 0.036c = 0.066

Estonia-2 0.025 0.281 3 0.034 0.05 0.854 R2 = 0.042P = 0.02

a = �0.008b = 0.003c = 0.027

France �0.043 0.904 – – R2c se = 0.31P = 0.001

R2 = 0.024P = 0.01

a = 0.317b = 0.005c = 0.002

Gabon- Monts De Cristal 0.082 0.001 73 0.092 < 0.001 0.982 R2h = 0.169P = 0.001

R2 = 17.5P = 0.01

a = 0.040b = 0.011c = 0.03

Guinea 0.066 0.001 8 0.172 < 0.001 0.981 R2 = 0.059P = 0.001

R2 = 0.137P = 0.01

a = 0.037b = 0.046c = 0.029

Iran-Asalem �0.087 0.93 140 0.104 < 0.001 0.847 R2h = 0.047P = 0.043

R2 = 0.025P = 0.048

a = 0.01b = 0c = 0

Iran-Nowshahr 0.005 0.418 28 0.041 0.03 0.813 R2h c = 0.134P = 0,01

R2 = 0.038P = 0.001

a = 0.01b = 0.02c = 0

Iran-Savadkuh �0.08 0.906 40 0.042 0.01 0.86 R2h c = 0.204P = 0.02

R2 = 0.035P = 0.02

a = 0.01b = 0.02c = 0

Madagascar- Mandena 0.044 0.151 � 0.993 � R2 = 0.057P = 0.006

a = �0.008b = 0.007c = 0.037

Sweden 0.072 0.001 6 0.112 < 0.001 0.835 R2se = 0.175P = 0.005

R2 = 0.077P = 0.01

a = 0.012b = 0.001c = 0.033

Zambia-Kashima �0.022 0.836 18 0.051 0.01 0.95 R2 = 0.053P = 0.046

R2 = 0.053P = 0.004

a = 0.002b = 0.006c = 0.013

Thailand 0.092 0.001 150 0.072 0.01 0.99 – R2 = 0.066P = 0.01

a = nab = nac = na

na: not applicable; a: variation explained by environmental factors; b: shared variation explained by spatial vectors (PCNMs) and environmentalfactors; c: variation explained by PCNMs.



distance decay. By replacing distance from the equator withMAT in the model selection procedure, the best modelincluded MAT (t = 4.495, P < 0.001; Fig. 2), samplingvolume (t = 3.612, P = 0.002), age of vegetation (t = 3.409,P = 0.003) and host presence (t = �2.06, P = 0.054). A com-parison between tropical and non-tropical ecosystems revealedthat the extent of distance decay is greater in tropical sites(mean � SD: tropical, 60.57 � 46.64 m; non-tropical,19.48 � 36.71 m; ANOVA: F1, 19 = 4.897, P = 0.039; Fig. 3).Sampling area was the only significant predictor of averagecommunity dissimilarity across sites based on the best GLSmodel (t = 5.197, P < 0.001).Based on a permutation test, the slope of distance-decay

relationship was significantly steeper in tropical than non-tropical sites on a small scale (< 50 m; average difference inslopes = 0.174, P = 0.001; difference of intercepts = 0.802;Fig. 4) and large scale (total range of study sites; differ-ence in slopes = 0.025, P = 0.001; difference of inter-

cepts = 0.753). By including the slope of distance-decayrelationship as a response variable in the model selection pro-cedure, the best GLS model showed that differences in theslope of distance-decay relationships across sites are signifi-cantly negatively related to the density of trees at the smallscale (P < 0.01; Fig. 5).Across all data sets, the proportion of significant spatially

autocorrelated species to all tested species did not show sig-nificant difference between tropical and non-tropical sitesbased on Moran’s I test (mean � SD: tropical, 42 � 27%;non-tropical, 49 � 29%; P = 0.638); the autocorrelated spe-cies mainly belonged to the lineages /russula–lactarius and /tomentella–thelephora (Table S3) that are the most species-rich groups in both non-tropical and tropical sites (Tedersoo& Nara 2010). Compared to other lineages, the species-levelcommunity of the /russula–lactarius lineage showed strongercorrelation with spatial distance in both the tropical and non-tropical sites (Table S3).At the global scale, lineage-level community dissimilarity

was significantly positively correlated with geographical dis-tance (Mantel test: r = 0.151, P = 0.001). Distance decay wassignificant up to 2800 km geographical distance (r = 0.191,P = 0.001; Fig. 6). The rate of distance decay of lineage-levelcommunity similarity was remarkably higher with latitudethan with longitude (Mantel test: latitude: r = 0.223,P = 0.001; longitude: r = 0.076, P = 0.026). The slope ofdistance-decay curve was similarly steeper across latitude thanlongitude (average difference in slopes = 0.123, P = 0.001;Fig. 7). Analysis of tropical and non-tropical sites separatelyrevealed no significant difference between the slopes of dis-tance decay of lineage-level community similarity in tropicalsites (Mantel r = 0.287, P = 0.029) and non-tropical sites(Mantel r = 0.223, P = 0.001) (difference of slopes = 0.01,P = 0.454). The initial similarity between sites was higheracross tropical sites (difference of slope = 0.280, P = 0.020).

Log

(dis

tanc

e-de

cay

exte

nt (m

))

Mean annual temperature

Distance from equator

(a)

(b)

Fig. 2. Plots of regression analysis for the main determinants ofdistance-decay extent as revealed by the best GLS model at differentscales: (a) distance from the equator (R2 = 0.395, P = 0.006); (b)mean annual temperate (R2 = 0.411, P < 0.001). Lines denote regres-sion lines.

Non-tropical

050

100

150

Tropical

Dis

tanc

e-de

cay

exte

nt (m

)

Fig. 3. Boxplot of the variation of distance-decay extent across dif-ferent sites in tropical and non-tropical forests (ANOVA: F1,19 = 4.897,P = 0.039).



Discussion

DISTANCE DECAY AT THE SAMPLE LEVEL

Our results show that the extent and rate of distance decay ofsimilarity in EcM fungal communities vary significantly acrossecosystems. Among the variables included in our study, thedistance from the equator and host density were the maindeterminants of the extents and rates of distance decay acrossdifferent ecosystems, respectively. Stronger distance-decaypatterns were found in communities closer to the equator, sug-gesting a relatively greater spatial aggregation of fungal spe-cies in tropical ecosystems. Several non-exclusive processesmay explain this pattern, which will be discussed later.First, niche differentiation by host can be typically less pro-

nounced in tropical ecosystems (Smith et al. 2011; Tedersooet al. 2011). Due to the relatively lower host density in tropicalecosystems (including the monodominant rain forests, wherethe relative basal area of EcM trees rarely exceeds 70%), dis-persal limitation is likely to play a greater role in these ecosys-tems (Tedersoo et al. 2010). Ecosystems with low host densitycan be viewed as fragmented habitats for EcM fungi, becausespore dispersal is required to cross the unsuitable landscape.Because of differential spore germination efficiency (Ishidaet al. 2008) and wind-dispersal range (Peay et al. 2012), spe-cies of EcM fungi strongly differ in their capacity to effectivelydisperse and establish in fragmented landscapes (Peay et al.2007). Host specificity (or preference) is the most importantdeterminant of EcM fungal community composition in moststudies ranging from local to global scale (Ishida, Nara &Hogetsu 2007; Tedersoo et al. 2008, 2012; Bahram et al.2012; P~olme et al. 2013). In addition, host species traitssuch as litter quality, exudation (Wardle 2002) and fine root

Log (geographical distance (m))

Log

(com

mun

ity s

imila

rity)

–7–6

–5–4

–3–2

–10

–7–6

–5–4

–3–2

–10

–2 –1 0 1 2 3 4

–2 –1 0 1 2 3 4

(a)

(b)

Fig. 4. The rate of distance decay in ectomycorrhizal communities atthe local scale (< 50 9 50 m2) for (a) non-tropical and (b) tropicalforests (difference of slopes = 0.174, P = 0.001).

Fig. 5. Variation in the rate of distance decay across different scalesand ecosystems. Only significant values based on permutation test(P < 0.05) are shown.

Geographical distance (km)

Phy

loge

netic

com

mun

ity d

issi

mila

rity

Fig 6. The rate of distance decay of lineage-level community similar-ity in ectomycorrhizal fungi with increasing geographical distance atthe global scale (linear regression: t = 5.901, R2 = 0.023, P < 0.001,blue line). Exponential rise to maximum was the best-fitting function(R2 = 0.056, red line). Dotted line, autocorrelation range.



dynamics (Burton, Pregitzer & Hendrick 2000) can affect thespatial structure of soil and, in turn, EcM fungal communitiesat the local scale (Toljander et al. 2006). The significant effectof host coupled with low host density could result in greaterfragmentation of EcM habitat and consequently stronger dis-tance-decay patterns (Tedersoo et al. 2010).While most of the study sites we analysed here are located

in relatively homogeneous landscapes, addressing the effectof all potentially important environmental variables is imprac-tical. We admit that stronger distance-decay patterns in tropi-cal ecosystems may be related to the unmeasured, potentiallyspatially structured environmental factors that affect EcM fun-gal community at the local scale (Ettema & Wardle 2002).The distance decay in fungal communities may correspond tothe patchiness of soil properties, which is generally more pro-nounced in tropical ecosystems than in boreal and temperateforests (Wang et al. 2002; Yavitt et al. 2009; H€akkinen,Heikkinen & M€akip€a€a 2011). For example, Wang et al.(2002) demonstrated that soil properties are spatially autocor-related even with a sampling interval of 200–1000 m in atropical ecosystem, whereas the autocorrelation range is typi-

cally several orders of magnitude lower in temperate and bor-eal ecosystems (up to 2 m, Farley & Fitter 1999; H€akkinen,Heikkinen & M€akip€a€a 2011). The spatial structure of soilnutrients is related to topography and vegetation. The rela-tively greater autocorrelation range of soil properties in tropi-cal forests is attributable to more stable climate overmillennia and low contribution of each tree species in creatingsoil patches in highly diverse ecosystems (John et al. 2007;Townsend, Asner & Cleveland 2008).Our results suggest that the dominant EcM fungal lineages

may affect distance decay of fungal communities due to theircontrasting spatial structure. The /russula–lactarius lineagewas the most frequently spatially autocorrelated group acrossdifferent sites. Lilleskov et al. (2004) also reported that mem-bers of the /russula–lactarius lineage exhibit greater than aver-age patch size and that two species of this group display thegreatest spatial autocorrelation range (> 17 m). The genet sizeof Russula brevipes may reach 18 m in an old forest (Berge-mann & Miller 2002). Riviere, Natarajan & Dreyfus (2006)also determined relatively large genets ranging from 30 to70 m for Russula sp. in an old-growth dipterocarp rain forest.There is evidence that species of the /russula–lactarius and /cortinarius lineages exhibit limited spore dispersal(Ishida et al. 2008; Tedersoo et al. 2009; Peay et al. 2012).The /russula–lactarius lineage is the most species-rich groupin tropical sites, which may also contribute to the greater spa-tial range in tropical ecosystems simply due to the greaterinformation content. The /cortinarius lineage is uncommon intropical ecosystems while it is among the dominant groups inboreal and temperate forests. The /cortinarius lineage, how-ever, showed no significant spatial autocorrelation in any ofthe sites, which could be attributable to its distribution insmall, localized patches (Genney, Anderson & Alexander2006). Among the variables included in our analysis, age oftrees significantly affected the extent of distance decay, whichcan be related to the dominance of late successional coloniz-ers, for example /russula–lactarius in older forests. In old for-ests, individuals have more stable conditions for expansion,which results in relatively large genetic individuals comparedto pioneer communities. In early successional ecosystems,species with greater spore dispersal ability, higher genet turn-over and thereby small genetic individuals dominate.Significant distance decay and spatial autocorrelation were

detected in the majority of data sets at the local scale, althoughmost individual studies were designed to avoid spatial autocor-relation by increasing the distance among samples. This indi-cates that spatial processes, particularly dispersal limitationmay play a much greater role in EcM fungal communities innon-fragmented forests than previously anticipated (Lilleskovet al. 2004). If not accounted for, the spatial structure in com-munity composition may cause autocorrelation in model resid-uals and consequently higher probability of committing type Ierror in testing the effects of environmental variables. Inaddition, neglecting the effects of dispersal-related processesmay lead to overlooking important patterns in communities(Cottenie 2005). This further highlights the importance ofincorporating spatial processes in community studies of EcM

Log

(com

mun

ity s

imila

rity)

Log (latitudinal distance (km))0

–3.0

–2.5

–2.0

–1.5

–1.0

–0.5

–3.0

–2.5

–2.0

–1.5

–1.0

–0.5

2 4 6 8

Log (longitudinal distance (km))0 2 4 6 8 10

(a)

(b)

Fig 7. The rate of distance decay in lineage-level community of ecto-mycorrhizal fungi at the global scale across (a) latitude (t = �18.6,R2 = 0.189, P < 0.001); (b) longitude (t = �1.078, R2 = 0.001,P = 0.195). Blue and red lines represent regression line.



fungi, for example by including a spatial component such asspatial eigenfunctions in models (Dray et al. 2012).All meta-studies exhibit their inherent limitations, because

individual studies differ in sampling design, sampling effortand measurement of environmental variables. Although ouranalyses accounted for the variation in sampling parameters,some important variables such as soil heterogeneity wereneglected in our study due to the lack of available informa-tion. Accumulating data from more homogeneous samplingdesigns and host-plant community can be useful in confirmingour observed patterns.

DISTANCE DECAY AT THE SITE LEVEL

At the global scale, distance decay in the distribution of EcMlineages was present only along latitude, but not along longi-tude, which resembles the continental-scale patterns in plants(Qian, Ricklefs & White 2005). This lends further support forthe role of climate in structuring EcM fungal communities atregional to global scales (e.g. Tedersoo, May & Smith 2010;Bahram et al. 2012; Tedersoo et al. 2012). Being in a symbi-otic relationship, fungi and plants may have co-evolved in dif-ferent phylogeographic regions. In addition to this, dispersallimitation and phylogeographic history together with host andenvironmental factors may regulate spatial distributions ofEcM fungal species at large scale (Mueller et al. 2001; Gemlet al. 2008; Matheny et al. 2009), consistent with other micro-organisms (Martiny et al. 2006). In particular, phylogeograph-ic history may be a strong determinant of phylogeneticcomposition of EcM fungi; for instance, some lineages arealmost lacking in tropical ecosystem (Tedersoo et al. 2010)and some others possess a lower rate of diversification in theseecosystems (Matheny et al. 2009; Kennedy et al. 2012).

Conclusions

This study demonstrates that communities of EcM fungi showa greater extent of distance decay (spatial autocorrelationrange) than previously suggested. The rate of distance decayis greater in tropical compared to non-tropical ecosystemspotentially owing to the typically lower host density. Thestrong impact of latitude, but not longitude on phylogeneticcommunity turnover, suggests that climate has an importanteffect on distance decay of EcM fungal communities at theglobal scale, directly or indirectly by influencing soil pro-cesses and host-plant distribution. Together, our results pro-vide new insights into the spatial structure of EcM fungalcommunities at various scales and raise questions regardingthe underlying mechanisms.

Acknowledgements

We thank Jaak Truu for useful comments on the early draft and Andy Taylorand Petr Kohout for discussions. We also thank Nina Wurzburger (the editor)and two anonymous referees for their constructive comments on the manuscript.The bulk of this project was funded by grants ESF-7434, 9286, PUT171 andFIBIR. PEC was supported by Swiss National Science Foundation (Ambizionegrant N� PZ00P3_136651).

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Received 21 February 2013; accepted 20 May 2013Handling Editor: Nina Wurzburger

Supporting Information

Additional Supporting Information may be found in the onlineversion of this article:

Table S1. Study sites and data sets used for spatial analyses.

Table S2. Variables included in the model selection procedures.

Table S3. Species and lineages with significant spatial autocorrelationin different data sets.



Table S2. Variables included in the model selection procedures. Transformation

Autocorrelation range analysis

Average community dissimilarity analysis

Species turnover analysis

A. Geographical variables

Latitude n.a. n.a.* n.a.* n.a.* Longitude n.a. n.a.* n.a.* n.a.* Altitude (m) Log10(x) yes yes yes Distance to equator (km) - yes yes yes

B. Climatic variables

Mean annual temperature (°C)

- yes yes yes

Mean annual precipitation (mm)

Log10(x) yes yes yes

C. Site variables

The number of hosts sampled Log10(x) yes yes yes The number of potential hosts present

Log10(x) yes yes yes

Age of vegetation (years) Log10(x) yes yes yes D. Sampling variables

Volume of all soil cores (dm3) - yes yes yes Sample volume (cm3) - yes yes yes Number of samples, n Log10(x) yes yes yes Site area (ha) Log10(x) yes yes yes

*used to create a geographical distance matrix and calculate distance to the equator.

Table S1. Study sites and datasets used for spatial analyses.

Site reference Country Autocorrelation range analysis

Average community dissimilarity analysis

Species turnover analysis

Phylogenetic community turnover analysis

Aponte et al. 2011 Spain no no no yes

Bahram et al. 2011 Estonia yes yes yes yes

Bahram et al. 2012 (Asalem) Iran yes yes yes yes

Bahram et al. 2012 (Nowshahr) Iran yes yes yes yes

Bahram et al. 2012 (Savadkuh) Iran yes yes yes yes

Bergemann & Garbelotto 2006 USA no no no yes

Bidartondo & Read 2008 (Betzenstein) Germany no no no yes

Bidartondo & Read 2008 (Chappett’s Copse I) Great Britain no no no yes

Courty et al. 2008 France yes yes yes yes

Diedhiou et al. 2010, unpublished Guinea yes yes yes yes

Gao & Yang 2010 China no no no yes

Ishida et al. 2007 (Irikawa) Japan no no no yes

Ishida et al. 2007 (Iriyama) Japan no no no yes

Jairus et al. 2011 Zambia no no no yes

Jones et al. 2008 Canada no no no yes

Kennedy et al. 2003 USA no no no yes

Kjøller & Clemmensen 2009 (Bäckefors) Sweden no no no yes

Kjøller & Clemmensen 2009 (Mullsjö) Sweden no no no yes

Kjøller & Clemmensen 2009 (Munkedal) Sweden no no no yes

Kjoller et al. 2006; unpublished Denmark yes yes yes yes

Krpata et al. 2008 Austria no no no yes

Lang et al. 2011 Germany no no no yes

Lian et al. 2006 Japan no no no yes

Morris et al. 2009 Mexico no no no yes Mühlmann & Peintner 2008; Mühlmann et al. 2008 Austria no no no yes

Nara 2006 Japan no no no yes

Nara unpublished Indonesia no no no yes

Nara unpublished (Omata) Japan no no no yes

Nara unpublished (Sannokou) Japan no no no yes

Nara unpublished (Senbonyama) Japan no no no yes

Nara unpublished (Yasudagawa) Japan no no no yes

Nouhra & Tedersoo, unpublished Argentina no no no yes

Palmer et al. 2008 USA no no no yes

Parrent & Vilgalys 2007 USA no no no yes

Peay et al. 2010 Malaysia no no no yes

Pena et al. 2010 Germany no no no yes

Phosri et al. 2012 Thailand yes yes yes yes

Ryberg et al. 2009 Sweden yes yes yes yes

Ryberg et al. 2010 Sweden no no no yes

Smith et al. 2007, 2009; Morris et al. 2008 USA no no no yes

Smith et al. 2011 Guyana no no no yes

Tedersoo & Yorou, unpublished Benin yes yes yes yes

Tedersoo et al. 2003 Estonia yes yes yes no

Tedersoo et al. 2006 Estonia no no no yes

Tedersoo et al. 2008a Australia no no no yes

Tedersoo et al. 2008a Australia no no no yes

Tedersoo et al. 2008b (Järvselja) Estonia no no no yes

Tedersoo et al. 2008b (Sookuninga) Estonia no no no yes

Tedersoo et al. 2008b (Välgi) Estonia no no no yes

Tedersoo et al. 2010 Ecuador yes yes yes no

Tedersoo et al. 2011 Zambia yes yes yes yes

Tedersoo et al. 2011 Cameroon yes yes yes yes

Tedersoo et al. 2011 Madagascar yes yes yes yes

Tedersoo et al. 2011 Gabon yes yes yes yes

Toljander et al. 2006 Sweden no no no yes

Twieg et al. 2007 Canada no no no yes

Walker et al. 2005 (LM site) USA no no no yes

Stendell et al. 1999 USA yes yes no no

Horton, unpub. USA yes yes no no

Grogan et al. 2000 USA yes yes no no

Horton & Bruns 1998 USA yes yes no no

Horton et al. 1999 USA yes yes no no

Taylor and Bruns, 1999 USA yes yes no no

Lilleskov and Bruns, unpub. USA yes yes no no

Lilleskov and Bruns, unpub. USA yes no no no

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Table S3. Species and lineages with significant spatial autocorrelation in different datasets. Study Autocorrelated species based

on Moran’s I correlograms1 Frequency of autocorrelated species2

Autocorrelated lineages

lineages Mantel test Autocorrelation range

Range (m)

Mantel test statistics

Tedersoo et al. 2003

Inocybe_sp4 Elaphomyces_sp1 Elaphomyces _sp2 Cortinarius_sp3 Laccaria_sp1 Lactarius_sp1 Genea_sp1 Hydnotrya Piloderma fallax Sebacina_sp3 Tomentella lilacinogrisea Tomentella _sp1 Tomentella _sp2 Tomentella subclavigera Tuber_sp1

15/16 (94%) /tomentella-thelephora

R=0.326 P=0.040

0-3 R=0.512 P=0.005

Courty et al. 2008

Xerocomus sp Tomentella punicea Tomentella botryoides Cortinarius sp4

4/55 (7%) -

Bahram et al. 2011

Cadophora finlandica Cenococcom geophilum Clavulina crist Cor tinarius sub2 Entoloma1 Genea hispidula Humaria hemisphaerica Inocybe sub Piloderma oli Russula nigra(acrifolia) Scleroderma areola Tomentella 25 Tomentella 12 Tomentella 5 Tomentella 7 Tomentella 13 Tomentella 11 Tuber2

18/46 (39%) /russula-lactarius

R=0.151 P=0.048

0-3 R=0.131 P<001

Ryberg et al. 2009

Cenoccum geophilum Clavulinaceae sp. 1 Hebeloma aff. cavipes/vaccinum Hebeloma aff. polare/monticola Hebeloma aff. vinosophyllum (hiemale) Inocybe cf. rufuloides 2 Russula sp. Sebacina epigaea Sebacina sp. 1 Sebacina sp. 3 Thelephora sp. 1 Tomentella aff. ramossisima Tomentella aff. stuposa Tomentella sp. 12 Tomentella sp. 13 Tomentella sp. 15


R=0.201 P=0.006

0-6 R=0.308 P=0.006

Tomentella sp. 7 Tomentella sp. 9


/amanita Gab01 /russula-lactarius Gab04 /russula-lactarius Gab11 /russula-lactarius Gab12 /russula-lactarius Gab18

5/9 (56%) /boletus R=0.292 P=0.03

0-73 R=0.273 P=0.318

/russula-lactarius

R=0.115 P=0.006

0-73 R=0.277 P=0.006


/boletus Cam03 /boletus Cam05 /russula-lactarius Cam02 /russula-lactarius Cam21 /russula-lactarius Cam23 /russula-lactarius Cam25 /russula-lactarius Cam27 /tomentella-thelephora Cam02 /tomentella-thelephora Cam05 /tomentella-thelephora Cam10 /tomentella-thelephora Cam11 /tomentella-thelephora Cam12


R=0.058 P=0.126

0-68 R=0.088 P=0.007


/russula-lactarius Zam02 /russula-lactarius Zam15 /russula-lactarius Zam16 /russula-lactarius Zam17 /tomentella-thelephora Zam18

7/29 (24%) -


/boletus MAD08 /russula-lactarius MAD20 /russula-lactarius MAD22

3/12 (25%) -


/tomentella-thelephora Y3 /tomentella-thelephora Y8 /russula-lactarius Y1 /russula-lactarius Y5 /russula-lactarius Y6 /russula-lactarius Y9 /inocybe Y1 /inocybe Y2

8/8 (100%)

/russula-lactarius

R=0.281 P<0.001

0-44 R=0.536 P=0.005

Phosri et al., 2012

/russula-lactarius Thai05 /russula-lactarius Thai06 /russula-lactarius Thai09 /russula-lactarius Thai15


R=0.197 P=0.003

0-150 R=0.198 P=0.003

/russula-lactarius Thai07 Cenococcum geophilum

/sordariales Thai02 /sordariales Thai03 /sordariales Thai04

/laccaria Thai01 Scleroderma columnare

Thai01 /elaphomyces Thai01

Tedersoo & Yorou, unpublished

/boletus Ben04 /inocybe Ben01 /inocybe Ben03 /inocybe Ben10 /inocybe Ben13 /russula-lactarius Ben07 /tomentella-thelephora Ben22

7/30 (23%) - -

Diédhiou et al. 2010

Boletaceae #1 Boletaceae #2 Boletaceae #4 Clavulinaceae #1 Russulaceae #1 Russulaceae #11 Russulaceae #13 Russulaceae #17 Russulaceae #19 Russulaceae #3 Russulaceae #4 Russulaceae #4 Russulaceae #5 Russulaceae #8 Sclerodermataceae #1 Thelephoraceae/Tomentellaceae #1 Thelephoraceae/Tomentellaceae #10 Thelephoraceae/Tomentellaceae #3 Thelephoraceae/Tomentellaceae #6 Thelephoraceae/Tomentellaceae #8 Tricholomataceae #3

21/26 (81%) /boletus R=0.310 P<0.001

0-8 R=0.501 P=0.004

/russula-lactarius /tomentella-thelephora

R=0.174 P=0.001 R=0.034 P=0.36

0-8 0-8

R=0.221 P=0.004 R=0.258 P=0.004

Kjoller et al., 2006; unpublished

Amanita spissa boletoid sp. 2 cantharelloid sp. 4 Cortinarius casimiri Cortinarius cf. decipiens Cortinarius diasemospermus Inocybe asterospora Inocybe petiginosa Inocybe sp. 2 Lactarius camphoratus Lactarius subdulcis Pezizales sp. 4


R=0.105 P<0.001

0-5.2 R=0.119 P=0.008

Piloderma sp. 2 Russula mairei Russula vesca Sebacinoid sp. 1 Sebacinoid sp. 2 Tomentella bryophila Tomentella sp. 6 Tomentella substestacea Tomentella terrestris

Bahram et al. 2012 (Asalem)

Leccinum Piloderma Inocybe7 Tricholoma5 Laccaria1 Tomentella3 Cenococcum


R=0.152 P=0.04

0-140 R=0.054 P=0.204

Bahram et al. 2012 (Nowshahr)

Pachyphloeus 3 Tomentella9 Cortinarius1 Russula4 Sebacina1 Lactarius1

6/30 (20%)

/russula-lactarius

R=0.125 P=0.17

0-26 R=0.154 P=0.015

Bahram et al. 2012 (Savadkuh)

Dermocybe Lactarius20 Tomentella17 Tricholoma4


R=0.096 P=0.005

0-40 R=0.080 P=0.060

/russula-lactarius

R=0.141 P=0.05

0-40 R=0.298 P=0.005

1 Statistical significance was based on P<0.05.

2 Number of autocorrelated species divided by number of all species examined

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