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Keywords:

  • ectomycorrhizal communities;
  • host preference;
  • Huizteco;
  • tropical cloud forest;
  • Quercus crassifolia;
  • Quercus laurina

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Interactions between host tree species and ectomycorrhizal fungi are important in structuring ectomycorrhizal communities, but there are only a few studies on host influence of congeneric trees. We investigated ectomycorrhizal community assemblages on roots of deciduous Quercus crassifolia and evergreen Quercus laurina in a tropical montane cloud forest, one of the most endangered tropical forest ecosystems. Ectomycorrhizal fungi were identified by sequencing internal transcribed spacer and partial 28S rRNA gene. We sampled 80 soil cores and documented high ectomycorrhizal diversity with a total of 154 taxa. Canonical correspondence analysis indicated that oak host was significant in explaining some of the variation in ectomycorrhizal communities, despite the fact that the two Quercus species belong to the same red oak lineage (section Lobatae). A Tuber species, found in 23% of the soil cores, was the most frequent taxon. Similar to oak-dominated ectomycorrhizal communities in temperate forests, Thelephoraceae, Russulaceae and Sebacinales were diverse and dominant.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Ectomycorrhizal fungi are an important component of many forest ecosystems and play key roles in biogeochemical cycling, plant community dynamics and maintenance of soil structure (Baxter & Dighton, 2001; Jonsson et al., 2001; Rillig & Mummey, 2006). The use of molecular methods to identify ectomycorrhizal fungi on roots has greatly increased our understanding of the diversity and composition of below-ground ectomycorrhizal communities (Horton & Bruns, 2001). Most ectomycorrhizal community studies have been carried out in the temperate zone despite the fact that many important ectomycorrhizal hosts (e.g. Pinaceae, Fagaceae, Dipterocarpaceae) occur in tropical forests (Taylor & Alexander, 2005). Few studies have used molecular methods to assess tropical ectomycorrhizal communities (Haug et al., 2005; Moyersoen, 2006; Tedersoo et al., 2007).

Tropical montane cloud forests are characterized by frequent cloud cover and abundant rainfall. These warm, mesic habitats support high levels of biodiversity and large numbers of endemic species (Gentry, 1992). These ecosystems are also hydrologically significant because they play an important role in local and regional watersheds by intercepting water from fog and clouds (Hamilton et al., 1995; Still et al., 1999). This globally threatened habitat type is under increasing pressure from deforestation, fragmentation and conversion to agriculture. In Mexico, <10% of the original tropical montane cloud forest cover still exists (Cayuela et al., 2006). A better understanding of ectomycorrhizal communities in tropical montane cloud forests is important for conservation and restoration of this unique habitat.

Traditionally, it was thought that ectomycorrhizal fungi were abundant in temperate and boreal regions but were uncommon or insignificant in tropical ecosystems (Brundrett, 2002; Alexander, 2006). However, ectomycorrhizal fungi are increasingly recognized for their ecological importance in tropical forests (Torti et al., 2001; Taylor & Alexander, 2005; McGuire, 2007; Tedersoo et al., 2008). Although species richness of many phylogenetic groups is higher in tropical latitudes compared with temperate latitudes (e.g. birds, trees, orchids, ants and lizards; Schall & Pianka, 1978; Currie & Paquin, 1987; Rahbek & Graves, 2001), little is known about variation in ectomycorrhizal diversity relative to latitude (Allen et al., 1995).

Some recent studies reported tropical ecosystems depauperate in ectomycorrhizal diversity (Andrade et al., 2000; Kottke et al., 2004; Chambers et al., 2005; Haug et al., 2005) whereas other studies reported ecosystems rife with ectomycorrhizal diversity (Henkel, 2003; Riviere et al., 2007; Tedersoo et al., 2007). Although there is not yet a consensus, it is possible that ectomycorrhizal communities are more diverse in the tropics because of higher temperatures, longer growing seasons or abundant rainfall. Mexico's ectomycorrhizal diversity is thought to be among the highest in the world but supporting data are limited (Varela & Estrada-Torres, 1997).

Host tree species and heterogeneity of soil resources are two factors that can influence ectomycorrhizal communities (Molina et al., 1992; Bruns, 1995). Host tree effects have been demonstrated for host species from different families (Ishida et al., 2007; Tedersoo et al., 2008) but can also occur in host trees within the same genus (Morris et al., 2008b). In a previous study, we detected differences in ectomycorrhizal communities on two Quercus hosts that occur in a scattered distribution with mostly nonoverlapping canopies in an arid California woodland (Morris et al., 2008b). In order to test the effect of congeneric host species on ectomycorrhizal communities in a different ecosystem, we selected a tropical montane cloud forest where Quercus species co-occur in dense stands with overlapping canopies. We compared diversity and community structure of ectomycorrhizal fungi associated with deciduous Quercus crassifolia Humb. & Bonpl. and evergreen Quercus laurina Humb. & Bonpl. by sampling at various spatial scales on two dates. Because of their contrasting phenologies, we hypothesized that we would find differences in the ectomycorrhizal communities on the two Quercus host species. Soil properties were analyzed in order to examine their influence on ectomycorrhizal composition. Furthermore, we tested the hypothesis that ectomycorrhizal diversity is higher in a tropical cloud forest than in a Mediterranean Quercus forest. To facilitate this comparison, we used similar sampling protocols, comparable sampling efforts and related host plants in this study and in our recent Mediterranean oak woodland study (Morris et al., 2008b).

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Study site

The study site is located at Huizteco Park, c. 4.5 km north of Taxco, Guerrero state, southern Mexico (18°36′N, 99°36′W). The park is managed for recreation and conservation by the municipality of Taxco de Alarcón. Huizteco is part of the Sierra de Taxco mountain range, which forms a portion of the western extension of the Trans-Mexican Volcanic Belt. Elevation ranges between 2450 and 2550 m and the climate is humid-subtropical (Trewartha, 1954). Total annual precipitation varies between 1200 and 1500 mm, with a rainy season from June to October and a dry period from November to May. Soils are Haplic Phaeozem (Hapludolls), which are characterized by a surface layer with high humus content.

Vegetation is classified as tropical montane cloud forest or humid montane forest (Rzedowski, 1981) and is estimated to be ≥80 years old. The overstory is dominated by several oak species including Q. crassifolia, Q. laurina, Quercus magnoliifolia Née and Quercus canstanea Née (Valencia-Avalos, 1995; Martinez et al., 2004). Other overstory trees include Clethra spp. (Clethraceae) and Oreopanax sp. (Araliaceae); all are considered nonectomycorrhizal hosts (Wang & Qiu, 2006). The ectomycorrhizal shrub Arctostaphylos sp. (Ericaceae) occurs at the site, but not near our sampling areas. The understory contains scattered but diverse herbaceous angiosperms. Epiphytes (bromeliads, mosses, lichens) are abundant in the canopy.

We sampled ectomycorrhizal communities on Q. crassifolia and Q. laurina because both oak species were common at the study site and often occurred in a clumped distribution. This allowed us to sample plots where only Q. crassifolia or Q. laurina were present and decreased the probability of encountering roots from any other Quercus species. Quercus crassifolia and Q. laurina are Mexican red oaks (subgenus Quercus, section Lobatae) that have a broad distribution in Mexico (Sierra Madre Occidental, Sierra Madre del Sur and the Trans-Mexican Volcanic Belt) and extend into Central America (Romero et al., 2002). Quercus laurina is an evergreen species found in pine-oak forests and tropical montane forests at elevations of 1900–3000 m. Quercus crassifolia is a deciduous tree found in oak forests, pine-oak forests and tropical montane cloud forests at elevations of 1400–2700 m (Valencia-Avalos, 1995). Leaf fall for Q. crassifolia occurs at the beginning of the dry season in November. Average DBH was 30 and 37 cm for Q. crassifolia and Q. laurina, respectively. Oak voucher specimens were deposited at the UC Davis Herbarium (DAV).

Ectomycorrhizal root sampling

We randomly selected four plots (10 × 10 m) dominated by Q. crassifolia (no other Quercus spp. were present) and four plots dominated by Q. laurina (no other Quercus spp. were present) for ectomycorrhizal root sampling. Plots were distributed within c. 3-ha area of forest and were at least 50 m from each other. Each plot contained between three and six mature oak trees. Five soil cores (10-cm diameter × 12-cm depth) were taken in each plot at the beginning (17 June 2004) and the middle of the rainy season (21 August 2004) for a total of 40 soil cores at each sampling date. Soil cores were collected in 2-m transects at distances of 0, 0.75, 1, 1.25 and 2 m. August soil cores were collected in parallel 2-m transects c. 50 cm from soil cores taken in June. Transects were located randomly in relation to tree stems. Litter depth was measured and then the litter was removed before collection of soil cores.

Soil cores were transported to the laboratory on ice and processed within 15 days. Fine roots (<2-mm diameter) were washed over a 300-μM sieve and 100 healthy ectomycorrhizal root tips were randomly selected from each soil core following the protocols of Morris et al. (2008b). We considered root tips to be colonized by ectomycorrhizal fungi based on color, branching and the presence of a fungal mantle. Pooled root tips were cleaned with deionized water and freeze dried for DNA analysis. We used pooled root tips because a previous study (Morris et al., 2008a) demonstrated that many individual root tips are colonized by multiple fungal species making DNA sequencing of single root tips impractical for large samples. Pooling root tips is an efficient method that allows detection of a high diversity of ectomycorrhizal fungi (Morris et al., 2008b).

DNA extraction and PCR

DNA was extracted from pooled samples of ectomycorrhizal root tips with the Qiagen DNeasy Plant Mini Kit (Valencia, CA) followed by purification with UltraClean soil DNA kit (Mo Bio Laboratories, Carlsbad, CA). The internal transcribed spacer (ITS) and partial 28S rRNA gene were amplified with primers ITS1F and LR3 (Gardes & Bruns, 1993; Hopple & Vilgalys, 1994) following the protocols of Morris et al. (2008b), except 15 cycles were used instead of 20.

Cloning and RFLP screening

PCR products from four replicate reactions were pooled and cloned using the TOPO TA cloning kit (Invitrogen, Carlsbad, CA) following the manufacturer's protocols. For each sample, 72 transformed Escherichia coli colonies were randomly selected and used as template for PCR amplification with ITS1F and LR3. Plasmids were amplified according to Morris et al. (2008b) and amplicons were digested with restriction enzymes HinFI and AluI. Fragments were run simultaneously on 1.5% agarose gels stained with SYBR Green I (Applied Biosystems, Foster City, CA) and scored visually. Restriction fragment length polymorphism (RFLP) patterns were compared only within cores and not across cores.

Sequencing and identification of ectomycorrhizal root tips

From each sample, six representative clones for each unique RFLP pattern were sequenced when available, otherwise all available clones were sequenced with ITS1F using BigDye v3.1 Sequencing Kit (Applied Biosystems) on an ABI 3730 XL sequencer at the UC Davis College of Agriculture and Environmental Sciences Genomics Facility. When sequences from a single RFLP pattern resulted in multiple taxa, all clones from that RFLP type were sequenced. From the 80 clone libraries, 2270 clones were successfully sequenced. Sequences were edited using sequencher v4.2 (Gene Codes, Ann Arbor, MI) and examined by blast searches against GenBank, UNITE and to a database of sequences from locally collected fruiting bodies (Morris et al., 2008a).

Sequences from clones with <97% sequence similarity were considered to be unique taxa. Sequences from ectomycorrhizal roots that matched those from sporocarps are designated by MHM collection numbers. For root samples whose sequences did not match sporocarps, identification was based on a combination of blast searches, sequence alignments and phylogenetic analysis. When ITS sequences were insufficient for identification, we sequenced part of the 28S rRNA gene using LR3 and performed similar analysis as above.

Amplification of environmental samples containing mixed DNA templates may result in chimeric sequences. We attempted to reduce chimeras using long extension times (4 min) and a low number of PCR cycles (15). Clones occurring in only one core were checked with manual alignments and by chimera and genecov programs of the Recombination Detection Program (Martin & Rybicki, 2000).

Soil analyses

Soil analyses were performed on the soil remaining after ectomycorrhizal roots were removed. We measured gravimetric water content, pH (saturated paste in deionized water), total carbon (C) (combustion method) and total Kjeldahl nitrogen. Available phosphorus (P) was determined colorimetrically (using the molybdenum-blue method, Murphy & Riley, 1962).

Statistical analyses

Ectomycorrhizal diversity was assessed using Shannon diversity index (H′), Simpson index (1−D) and evenness (H′/ln S, where S=number of taxa) (Magurran, 2004). These indices were calculated for ectomycorrhizal communities on Q. crassifolia, Q. laurina and the community as a whole with frequency data using palaeontological statistics v1.40 (Hammer et al., 2001). First and second order jackknife species richness estimates were calculated for each oak host and for the overall community using estimates v.8.0 (Colwell, 2006).

In order to examine the variation in ectomycorrhizal community composition and relationships with measured environmental variables, we conducted correspondence analysis (CA) and canonical correspondence analysis (CCA) using canoco v4.54 (Biometris, Wageningen, the Netherlands). CA and CCA were used because detrended correspondence analysis (DCA) demonstrated that the length of the longest gradient was 4.31, indicating a unimodal distribution of species along the environmental gradient (Lepš & Šmilauer, 2003). Ordination analyses were performed using frequency of each ectomycorrhizal species/plot. We conducted CA and CCA with and without singletons because rare species may have a disproportionately large influence on the analysis. For CCA, environmental variables (oak host, soil pH, total N, total C, available P, litter depth) were tested for their contribution to the variation in the ectomycorrhizal species data using Monte Carlo permutation tests. Gravimetric water content was excluded from the analyses because soils were saturated on both sampling dates due to heavy rain. Variance partitioning was used to determine the amount of variance explained by significant environmental variables (Lepš & Šmilauer, 2003).

To test for an effect of sampling date on ectomycorrhizal community composition we conducted CCA using sampling date as an environmental variable and as a covariable. We also used cluster analysis to evaluate the similarity of ectomycorrhizal communities at different sampling dates. Cluster analysis was preformed with palaeontological statistics v1.40 using unweighted pair group method with arithmetic mean and three different distance measures (Jaccard, Bray–Curtis and CHORD).

We tested for differences in the distribution of ectomycorrhizal families on Q. laurina and Q. crassifolia at each sampling date using Fisher's exact test. Jaccard similarity indices were calculated to determine the similarity of ectomycorrhizal communities between soil cores. To test for fine-scale effects of distance (0.25, 0.5, 0.75, 1.0 and 1.25 m) on similarity between soil cores at each sampling date, we used a linear mixed model anova with oak species and distance as fixed effects and an unstructured within-plot correlation matrix. Pairwise comparisons of mean similarity coefficients for the five distance classes were made using the Tukey–Kramer adjustment. Model aptness was assessed using graphical analysis of residuals, Shapiro–Wilks test for normality and Levene's test for homogeneity of variances. Residuals showed no departures from assumptions of normality and homogeneity of variances.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Ectomycorrhizal diversity and community structure

Molecular analysis of 8000 ectomycorrhizal root tips from 80 soil cores revealed a total of 154 ectomycorrhizal taxa in this tropical montane cloud forest (Table 1). Ectomycorrhizal taxa were identified to the level of species (six taxa), genus (76 taxa), family (50 taxa), order (20 taxa) and phylum (two taxa). Eighteen sequence types from root tips were matched to sequences from sporocarps. Sequences from the 28S region were used for the taxonomic placement of 47 species (31%) and all others were determined by ITS alone or by sporocarp matches.

Table 1.   Ectomycorrhizal fungi detected on roots of Quercus crassifolia (Q.c.) and Quercus laurina (Q.l.) in a tropical montane cloud forest
TaxaNo. of coresNo. of clonesAccession no.rRNA match*Closest blast matchIdentity
Q.c.Q.l.Q.c.Q.l.
  • *

    We conducted blast searches using ITS, 28S or ITS and 28S together and report the most informative blast result. Results are from GenBank unless noted otherwise.

  • Percentage similarity, and in parentheses total number of base pairs aligned.

  • Identified according to UNITE database (Koljalg et al., 2005).

Amanita flavoconia (MHM196)02028FJ196893ITSAmanita flavoconia100(654)
Amanita pantherina (MHM119)2640199FJ196894ITSAmanita pantherina99(641)
Amanita sp. MHM186 (rubescens group)0103FJ196895ITSAmanita rubescens98(660)
Amanita sp. 103012FJ196896ITSAmanita pantherina var. multisquamosa99(647)
Amanita sp. 20101FJ196897ITSAmanita lanei97(621)
Atheliaceae11040FJ196899ITS and 28SPiloderma lanatum90(1214)
Basidiomycota1 (Sistotrema clade)1080EU563507ITS and 28SSistotrema alboluteum90(1141)
Basidiomycota3 (Sistotrema clade)10240FJ196900ITS and 28SSistotrema alboluteum91(1136)
Boletellus russellii (MHM166)6321442FJ19690128SBoletellus russellii98(547)
Boletus frostii (MHM069)02018FJ19690228SBoletus coniferarum95(575)
Boletus aff. variipes (MHM163)23189FJ196903ITSBoletus aereus95(803)
Cenococcum geophilum3244FJ456977ITSCenococcum geophilum97(973)
Clavulinaceae12796187EU56350428SClavulina cinerea94(564)
Clavulinaceae2050126FJ19690428SClavulina cristata96(563)
Clavulinaceae320380FJ19690528SClavulina cristata97(563)
Clavulinaceae404039FJ19690628SClavulina cinerea92(569)
Clavulinaceae501019FJ19690728SClavulina cinerea95(565)
Clavulinaceae601013FJ19690828SClavulina cristata95(563)
Cortinarius sp. 302023FJ196913ITSCortinarius umbrinolens95(562)
Cortinarius sp. 420270FJ196914ITSCortinarius alboviolaceus96(564)
Cortinarius sp. 50208FJ196915ITSCortinarius calopus95(568)
Cortinarius sp. 60204FJ196916ITSCortinarius sertipes97(562)
Cortinarius sp. 70101FJ196917ITSCortinarius cacaocolor91(576)
Cortinarius sp. 81020FJ196918ITSCortinarius anisatus93(567)
Cortinarius sp. 901037FJ197010ITSCortinarius evernius94(567)
Cortinarius sp. 100102FJ196909ITSCortinarius alboviolaceus96(566)
Cortinarius sp. 111010FJ196910ITSCortinarius ochrophyllus96(656)
Cortinarius sp. 1203044FJ196911ITSCortinarius umbrinolens96(559)
Cortinarius sp. 1306026FJ196912ITSCortinarius fulvescens97(558)
Craterellus sp. 105080EU56347928SCraterellus lutescens93(567)
Entoloma sp. 11020FJ19692028SEntoloma alpicola98(565)
Entolomataceae11010FJ196919ITS and 28SEntoloma nitidum90(1107)
Genea sp. 154176FJ197017ITS and 28SGenea hispidula95(615)
Genea sp. 20105FJ197023ITSGenea hispidula85(667)
Genea sp. 3 (arenaria group)03036FJ19701128SGenea arenaria100(553)
Hebeloma sp. 10305FJ196921ITSHebeloma saliciphilum95(663)
Helotiales24263EU563501ITS and 28SHyphodiscus hymeniophilus93(1088)
Helotiales31010EU563495ITS and 28Sc.f. Hymenoscyphus sp.92(1078)
Helotiales40202FJ197016ITS and 28SMeliniomyces bicolor98(1399)
Humaria sp. 121257FJ19701828SHumaria hemsiphaerica96(555)
Hydnum sp. 120730FJ19692228SHydnum repandum98(563)
Hygrophorus sp. 120130FJ19692328SHygrophorus agathosmus96(556)
Hygrophorus sp. 21020FJ196924ITS and 28SHygrophorus chrysodon86(1167)
Inocybe sp. 101018EU56349928SInocybe godeyi96(589)
Inocybe sp. 320140EU56350028SInocybe godeyi98(580)
Inocybe sp. 41020EU56351028SInocybe cf. reisneri95(597)
Inocybe sp. 510310FJ196927ITS and 28SInocybe cf. flocculosa90(1263)
Inocybe sp. 61354FJ196928ITS and 28SInocybe pudica86(1283)
Inocybe sp. 70101FJ196929ITS and 28SInocybe flocculosa94(1218)
Inocybe sp. 802014FJ196930ITS and 28SInocybe pudica87(1281)
Inocybe sp. 91030FJ196931ITS and 28SInocybe whitei89(1124)
Inocybe sp. 100101FJ196925ITS and 28SInocybe aff. lanuginosa86(1187)
Inocybe sp. 1102069FJ196926ITS and 28SInocybe pudica91(1257)
Lactarius chrysorrheus (MHM143)6689160FJ196932ITSLactarius chrysorrheus95(700)
Lactarius sp. MHM3084959169FJ196936ITSLactarius yazooensis99(702)
Lactarius sp. 1355644FJ196933ITSLactarius fumosus98(661)
Lactarius sp. 222918FJ196934ITSLactarius subindigo95(694)
Lactarius sp. 31030FJ196935ITSLactarius repraesentaneus94(633)
Pachyphloeus sp. 10106FJ197022ITS and 28SPachyphloeus sp.90(1018)
Pezizaceae16110919EU563481ITS and 28SPachyphloeus sp.90(986)
Pezizaceae204043FJ197019ITS and 28SPeziza badia89(1169)
Pezizaceae30101FJ197012ITS and 28SPeziza badia95(1148)
Pezizaceae41112FJ197015ITS and 28SPachyphloeus sp.90(1010)
Pezizaceae51020FJ197014ITS and 28SHydnobolites californicus87(586)
Pezizaceae62050FJ197020ITS and 28SPachyphloeus sp.91(1014)
Pezizaceae70101FJ197013ITS and 28SHydnobolites californicus87(589)
Pseudotomentella sp. 1 (tristis group)18368EU563503ITSPseudotomentella tristis98(448)
Pseudotomentella sp. 20101EU563488ITSPseudotomentella tristis93(567)
Pseudotomentella sp. 31020FJ196937ITSPseudotomentella atrofusca97(555)
Pseudotomentella sp. 41030FJ196938ITSPseudotomentella atrofusca97(636)
Pyronemataceae13050EU56347628SPseudaleuria quinaultiana90(547)
Ramaria aff. botrytis (MHM156)0101FJ19693928SRamaria sp.99(588)
Ramaria aff. flava (MHM312)0102FJ196940ITSRamaria flava88(647)
Ramaria sp. 10101FJ196941ITS and 28SRamaria cf. formosa86(1231)
Ramaria sp. 20101FJ196942ITS and 28SRamaria cystidiophora86(1027)
Ramaria sp. 30101FJ196943ITS and 28SRamaria cystidiophora88(1030)
Russula sp. 155107111EU563497ITSRussula betularum96(668)
Russula sp. 202070EU563492ITSRussula integriformis93(670)
Russula sp. 302012FJ196944ITSRussula risigallina94(666)
Russula sp. 4354120FJ196945ITSRussula risigallina97(662)
Russula sp. 501010FJ196946ITSRussula romellii95(643)
Russula sp. 630390FJ196947ITSRussula romellii93(646)
Russula sp. 710430FJ196948ITSRussula aff. delica94(674)
Russula sp. 81030FJ196949ITSRussula aff. delica91(599)
Russula sp. 90101FJ196950ITSRussula crustosa93(673)
Russula sp. MHM0150202FJ196951ITSRussula raoultii96(656)
Russula sp. MHM07140310FJ196952ITSRussula flavida97(677)
Russula sp. MHM0790105FJ196953ITSRussula integriformis94(668)
Russula sp. MHM08720910FJ196954ITSRussula basifurcata96(630)
Russula sp. MHM09627814FJ196955ITSRussula risigallina95(668)
Russula sp. MHM097917835FJ196956ITSRussula persicina97(640)
Russula sp. MHM10801019FJ196957ITSRussula cf. maculata90(689)
Scleroderma sp. 102014FJ196958ITS and 28SScleroderma areolatum99(724)
Sebacinales11011366EU563487ITSSebacina incrustans99(573)
Sebacinales215153EU563483ITSSebacina sp.92(589)
Sebacinales4142182FJ196968ITSSebacina sp.91(592)
Sebacinales5461171FJ196969ITSSebacina sp.91(593)
Sebacinales6335426FJ196970ITSSebacina sp.89(600)
Sebacinales7233961FJ196971ITSSebacina aff. epigaea97(570)
Sebacinales8421811FJ196972ITSSebacina sp.97(578)
Sebacinales902068FJ196973ITSSebacina aff. epigaea94(580)
Sebacinales100106FJ196959ITSSebacina epigaea93(528)
Sebacinales1110300FJ196960ITSSebacina sp.90(590)
Sebacinales121070FJ196961ITSSebacina epigaea92(574)
Sebacinales1310160FJ196962ITSSebacina sp.87(600)
Sebacinales140102FJ196963ITSSebacina sp.94(535)
Sebacinales1510110FJ196964ITSSebacina sp.98(533)
Sebacinales160101FJ196965ITSSebacina sp.94(586)
Sebacinales170106FJ196966ITSSebacina sp.93(578)
Sebacinales1811516FJ196967ITSSebacina sp.91(589)
Tarzetta sp. 130480FJ19702128STarzetta catinus98(558)
Thelephoraceae12040EU563502ITSTomentella lilacinogrisea90(582)
Thelephoraceae750210EU563485ITSTomentella ellisii96(628)
Thelephoraceae80407EU563490ITSTomentella badia98(578)
Thelephoraceae912109FJ197004ITSTomentella stuposa96(581)
Thelephoraceae1030560FJ196974ITSTomentella sp.92(633)
Thelephoraceae111328FJ196975ITSThelephora penicillata95(632)
Thelephoraceae1230410FJ196976ITSTomentella ferruginea93(631)
Thelephoraceae1332223FJ196977ITSTomentella sp.91(634)
Thelephoraceae1420320FJ196978ITSTomentella sp.97(623)
Thelephoraceae15543637FJ196979ITSTomentella botryoides94(584)
Thelephoraceae162020FJ196980ITSTomentella pilosa96(562)
Thelephoraceae1705047FJ196981ITSTomentella bryophila92(617)
Thelephoraceae181144FJ196982ITSTomentella lilacinogrisea98(556)
Thelephoraceae1905014FJ196983ITSTomentella bryophila94(629)
Thelephoraceae2080190FJ196984ITSTomentella stuposa97(624)
Thelephoraceae210307FJ196985ITSTomentella ferruginea94(565)
Thelephoraceae2220100FJ196986ITSThelephora anthocephala95(586)
Thelephoraceae230102FJ196987ITSTomentella ferruginea94(637)
Thelephoraceae2471641FJ196988ITSTomentella ramosissima97(628)
Thelephoraceae2501012FJ196989ITSTomentella fuscocinerea90(587)
Thelephoraceae260101FJ196990ITSTomentella ferruginea94(631)
Thelephoraceae271020FJ196991ITSTomentella stuposa94(622)
Thelephoraceae280101FJ196992ITSTomentella cf. sublilacina88(634)
Thelephoraceae290106FJ196993ITSTomentella punicea98(549)
Thelephoraceae3010450FJ196994ITSTomentella subtestacea97(627)
Thelephoraceae310102FJ196995ITSTomentella cinerascens97(583)
Thelephoraceae3251967FJ196996ITSTomentella atroarenicolor96(556)
Thelephoraceae3340480FJ196997ITSTomentella ferruginea92(633)
Thelephoraceae3426424FJ196998ITSTomentella botryoides93(583)
Thelephoraceae3510150FJ196999ITSTomentella coerulea97(583)
Thelephoraceae3610180FJ197000ITSTomentella sp.88(638)
Thelephoraceae371020FJ197001ITSTomentella lateritia94(584)
Thelephoraceae381010FJ197002ITSTomentella sp.96(633)
Thelephoraceae3930100FJ197003ITSTomentella atramentaria95(631)
Tomentellopsis sp. 10102FJ196898ITSTomentellopsis submollis95(653)
Tricholoma sp. 10103EU563477ITSTricholoma fulvum95(637)
Tricholoma sp. 221145113EU563482ITSTricholoma fulvum98(659)
Tricholoma sp. 44115835FJ197005ITSTricholoma pessundatum99(654)
Tricholoma sp. 51030FJ197006ITSTricholoma sejunctum96(663)
Tricholoma sp. 61010FJ197007ITS and 28STricholoma orirubens92(1089)
Tricholoma sp. 720600FJ197008ITSTricholoma japonicum99(647)
Tuber sp. 11177657EU563484ITSTuber pacificum89(630)
Xerocomus sp. MHM129343140FJ197009ITS and 28SXerocomus chrysenteron93(1235)

Species accumulation curves indicate that new species continued to be detected even after sampling 80 soil cores (Fig. 1). Mean ectomycorrhizal species richness was 6.2±2.6 (mean±SD) per soil core and 31.1±7.7 (mean±SD) per plot (five soil cores). Similar species richness was found on Q. crassifolia (92 taxa) and Q. laurina (102 taxa). Species richness was not significantly different between oak hosts based on overlapping confidence intervals of species accumulation curves (data not shown). Diversity indices, evenness and mean number of taxa per core were similar for both oaks (Supporting Information, Table S1). The first- and second-order jackknife estimates of species richness were 130 and 147 for Q. crassifolia and 146 and 172 for Q. laurina. The overall species richness of the ectomycorrhizal community was estimated to be 219 and 256 using the first- and second-order jackknife, respectively.

image

Figure 1.  Species accumulation curves for ectomycorrhizal species on the roots of Quercus crassifolia (black circles), Quercus laurina (light grey squares) and data pooled from both oak species (dark grey triangles). One hundred ectomycorrhizal root tips were sampled from each soil core. Mean cumulative number of ectomycorrhizal species for each soil core was computed using 50 randomizations without replacement with the program estimates version 8.0.

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Many taxa occurred only in a single soil core (Q. crassifolia=39; Q. laurina=46) and almost two-thirds (61%) of the taxa were restricted to a single plot. Even the most abundant species were not particularly dominant. For example, Tuber sp. 1 was the most frequent species on Q. crassifolia, but this taxon accounted for only 11 of 233 total occurrences (5%). Tricholoma sp. 2 was the most frequent species on Q. laurina and accounted for 11 out of 264 total occurrences (4%). Twelve nonectomycorrhizal taxa from Ascomycota, Basidiomycota, Zygomycota, Chytridiomycota and Plasmodiophorida were detected in 12 soil cores (15%) but represented only one to four clones within a core (Table S2).

Influence of host and soil properties on ectomycorrhizal taxa distributions

Of the 154 ectomycorrhizal species detected, 52 (34%) were found only on Q. crassifolia, 62 (40%) were found only on Q. laurina and 40 (26%) were found on both oak hosts. CA resulted in distinct groupings of ectomycorrhizal samples from Q. crassifolia and Q. laurina although there was some overlap between samples from the two oak hosts (Fig. S1). The first and second CA axes explained 11.4% and 10.3% of the total variation in species data. Ordination of ectomycorrhizal communities using CCA demonstrated that the conditional effects of oak species (P=0.01) and available P (P=0.03) were significant in explaining the distribution of ectomycorrhizal species (Fig. 2). The first and second CCA axes explained 9.8% and 9.1% of the species variance, respectively. Variance partitioning revealed that oak species explained 16.8% of the variation in the species data and available P explained 16.3%. Together oak species and available P explained 32.6% of the variance, indicating that they had a shared variance of 0.5%. The conditional effect of total C was almost significant (P=0.052) and this variable explained an additional 13.4% of the variance. Results were consistent when ordination was conducted with and without singletons.

image

Figure 2.  Canonical correspondence analysis of the relationship between ectomycorrhizal species and environmental variables. Plot labels are in three parts: host, Quercus crassifolia (C) or Quercus laurina (L); plot number; and sampling date, August (Aug) or June. Squares represent samples from Q. crassifolia plots. Triangles represent samples from Q. laurina plots. Circles are centroids for Q. crassifolia and Q. laurina. Arrows indicate directions of increasing values of particular environmental factors.

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Ectomycorrhizal taxa in the Ascomycota were slightly more frequent on Q. crassifolia (relative frequency=17%) than on Q. laurina (relative frequency=11%). We detected 19 and 17 ectomycorrhizal families/orders on Q. crassifolia and Q. laurina, respectively. The families Atheliaceae, Corticiaceae, Entolomataceae, Hydnaceae and Hygrophoraceae were found only on Q. crassifolia whereas the families Cantharellaceae, Gomphaceae and Sclerodermataceae were detected only on Q. laurina. Cortinariaceae demonstrated significantly biased distribution on Q. laurina in June (Fisher's exact test: n=8, P=0.03) but not in August (n=8, P=0.49). The frequency of other families was not statistically different between the two oak host species. There were no significant differences in the frequency of individual ectomycorrhizal taxa, although several common species tended to be more frequent on a particular host (Fig. 3).

image

Figure 3.  Relative frequency of ectomycorrhizal taxa found in six or more soil cores on roots of Quercus crassifolia (black bars) or on Quercus laurina (grey bars). Relative frequency was calculated as the number of occurrences of each species divided by the total number of occurrences of all taxa.

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Temporal dynamics and spatial diversity of ectomycorrhizal fungi

Ectomycorrhizal species composition was similar across sampling dates and the majority of frequent ectomycorrhizal taxa were detected in both June and August (data not shown). Cluster analysis using several distance measures demonstrated that samples taken from the same plot in June and August grouped together for six out of eight plots (Fig. S2). CCA of species composition also showed that the composition of ectomycorrhizal taxa in any given plot was similar in June and August (Fig. 2). Furthermore, sampling date was not significant when it was included as an environmental variable in CCA. When sampling date was included in CCA as a covariable, only oak host species was significant (P=0.04) and no soil variables were significant. Although we did not detect statistically significant temporal differences, two ectomycorrhizal species appeared more frequently at a single sampling date. Sebacinales2 was detected in 10 cores in five plots, but only in August, whereas Boletellus russellii occurred in eight cores in June but only one core in August.

There was a significant effect of distance on Jaccard similarity of soil cores within plots in both June (P=0.0006) and August (P=0.0001) and no significant interaction between oak species and distance. Soil cores that were 25 cm apart tended to be more similar than soil cores separated by greater distances (50–125 cm).

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Ectomycorrhizal diversity and community structure

The paradigm that ectomycorrhizal associations are primarily restricted to temperate ecosystems has recently changed as many studies have documented ectomycorrhizal tree species in a wide variety of tropical habitats (Alexander & Högberg, 1986; Henkel et al., 2002; Alexander, 2006; Tedersoo et al., 2007). The diversity of ectomycorrhizal communities in tropical ecosystems, however, remains largely unknown. This study documents highly diverse ectomycorrhizal fungal communities in a tropical montane Quercus forest. We found similar species richness and diversity in this tropical ecosystem and in a Mediterranean woodland (Table 2). Surprisingly, the sampling curves in this Quercus-dominated tropical forest were almost identical to the sampling curves in a Quercus-dominated woodland studied with similar methods (Fig. 4). At the level of family/order, diversity was also similar with 20 ectomycorrhizal families/orders found in the Mediterranean oak-woodland study and 22 families/orders found in this cloud forest. This suggests that ectomycorrhizal diversity in oak forests in temperate and tropical ecosystems may be similar.

Table 2.   Comparison of site characteristics, sampling methods and ectomycorrhizal diversity from a tropical cloud forest and a Mediterranean oak woodland
 This studyMorris et al. (2008b)
  • *

    Mean annual temperature at both study sites was 18°C.

  • Evergreen and deciduous oaks with a mean DBH of 34 (this study) and 45 cm (Morris et al., 2008b) were sampled in both studies.

  • Sampling was conducted on 17 June 2004 and 21 August 2004 (this study) and on 20 March 2004 and 3 May 2004 (Morris et al., 2008b).

Latitude/longitude18°36′N/99°36′W39°14′N/121°18′W
Elevation (m)2500500
Mean annual precipitation (mm)1350750
Climate*Humid subtropicalMediterranean
Forest structureClosed canopy cloud forestOpen woodland-savanna
Soil typeHapludollsMollic Haploxeralfs
Host speciesQ. crassifolia and Q. laurinaQ. douglasii and Q. wislizeni
Oak lineagesSection LobataeSection Quercus and Lobatae
Area sampled (m2)8001216
Soil core volume (cm3)942900
No. of cores sampled8064
No. of root tips processed/core100100
Total no. of ectomycorrhizal species154140
No. of singletons6659
Mean no. of ectomycorrhizal species/core6.26.5
No. of Basidiomycota/Ascomycota taxa136/18100/40
Shannon (H′)4.74.5
Simpson (1–D)0.990.98
Evenness0.690.66
image

Figure 4.  Species accumulation curves for ectomycorrhizal species from a tropical cloud forest (this study) and a Mediterranean oak woodland (Morris et al., 2008b).

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The major taxonomic groups found in this tropical montane forest are similar to those found in temperate climates. Thelephoroid (38 taxa), Russuloid (21 taxa), and Sebacinoid (17 taxa) fungi were frequent and speciose groups in this study. These groups are well documented on roots in various temperate oak ecosystems (Avis et al., 2003; Dickie & Reich, 2005; de Roman & de Miguel, 2005; Richard et al., 2005; Walker et al., 2005). The ectomycorrhizal community in this tropical oak ecosystem is qualitatively similar to temperate oak ectomycorrhizal communities but different in some ways from ectomycorrhizal communities on Mediterranean oaks. Ectomycorrhizal communities on Fagaceae from Mediterranean climates appear to support a higher diversity and greater abundance of Ascomycota (Bergemann & Garbelotto, 2006; Smith et al., 2007; Morris et al., 2008b) whereas studies from mesic oak forests in the United States are dominated by Basidiomycota with less ascomycete diversity (Avis et al., 2003, 2008; Walker et al., 2005).

In this study, several ectomycorrhizal species detected on roots are also prevalent in oak-dominated ecosystems of the eastern United States and Central America. Two distinctive species, Boletus frostii and Lactarius chrysorrheus, are reported in Quercus forests from New England to Costa Rica (Halling & Mueller, 2005; Mueller et al., 2006) and L. chrysorrheus was a common belowground symbiont of Quercus rubra and Quercus prinus in North Carolina (Walker et al., 2005). The similarity of the ectomycorrhizal cloud forest community to the mycoflora of the eastern United States is further evidenced by Amanita flavoconia, Amanita pantherina, Amanita rubescens and Boletellus russellii; sporocarps of these taxa are all found in association with oaks in the eastern United States (Walker & Miller, 2002; Binion et al., 2008). This pattern has also been documented for the oak-associated Boletus rubropunctus in Massachusetts and Central Mexico; collections from these distant sites were almost identical based on five different DNA loci (Smith & Pfister, 2009). It is striking that some of the species found in this study are also found in different habitats thousands of miles away with a completely different climate. This suggests that ectomycorrhizal fungi may have migrated with their oak host trees and that oak host might be as important or more important in influencing the distribution of ectomycorrhizal fungi than either climate or habitat type (Halling et al., 2008).

Tuber is one taxonomic group that appears to be dominant on Quercus roots regardless of the ecosystem type. Tuber sp. 1 was the most frequently detected taxon in this tropical forest. Tuber species are also dominant root-inhabiting taxa in Mediterranean and mesic Quercus forests and woodlands (Murat et al., 2005; Walker et al., 2005; Smith et al., 2007; Morris et al., 2008b). Tuber species have a hypogeous fruiting habit and are often overlooked in routine sporocarp surveys, but are commonly detected during studies of hypogeous fungi with Quercus (Smith et al., 2007; M. Smith, pers. obs.).

The ectomycorrhizal community remained relatively stable between June and August, possibly because the sampling dates did not correspond to a significant change in environmental conditions or because of limited turnover of ectomycorrhizal roots tips during this time period. Of the 44 ectomycorrhizal species found on Q. crassifolia in October/November 2003 (Morris et al., 2008a), 30(68%) were detected again in this study (June/August 2004), further suggesting that ectomycorrhizal composition is not highly influenced by short-term temporal dynamics. Our results are similar to findings from a Quercus douglasii woodland where winter and spring ectomycorrhizal communities were not significantly different over a 2-year period (Smith et al., 2007), but are in contrast to other studies that found rapid ectomycorrhizal turnover at relatively short time scales (Izzo et al., 2005; Courty et al., 2008).

Influence of host and soil characteristics on ectomycorrhizal taxa distributions

Abiotic soil properties can influence sporocarp production, ectomycorrhizal formation on roots and ectomycorrhizal community structure (Peter et al., 2001; Erland & Taylor, 2002). Soil properties can also have indirect effects on ectomycorrhizal communities by influencing plant community composition. Thus, the interacting factors of host plant species and direct and indirect effects of soil properties may influence ectomycorrhizal community composition (Claridge et al., 2000; Wardle, 2002). Oak host species and available P were significant in explaining some variation in the distribution of ectomycorrhizal fungi in this study. However, available P was not significant when sampling date was used as a covariable, indicating that the effect of available P was strongly influenced by high phosphorus levels in a single plot in August. Furthermore, correlation between soil variables makes it difficult to separate effects of individual soil factors. Similarly, unmeasured soil factors could also influence ectomycorrhizal communities. Leaf litter chemistry varies between plant species (Hättenschwiler et al., 2008), which may have important consequences for ectomycorrhizal communities (Conn & Dighton, 2000). Litter quality may affect soil chemistry and fungi may respond differently to secondary metabolites in leaf litter (Jonsson et al., 2006). Differences in litter quality between the deciduous Q. crassifolia and evergreen Q. laurina could play a role in host preference of ectomycorrhizal fungi. In addition, tree phenology can influence ectomycorrhizal community composition and metabolic activity (Buee et al., 2005; Courty et al., 2006, 2007) and Talbot et al. (2008) hypothesize that ectomycorrhizal fungi may decompose soil C when photosynthate supplies are reduced, such as during plant dormancy.

Several common species (Sebacinales1, Russula sp. MHM097, Tricholoma sp. 2) were differentially distributed on the two oak hosts although this was not statistically significant. However, small sample size and the high diversity of ectomycorrhizal fungi limited our ability to detect significant differences of individual species on the two oaks. Cortinariaceae did exhibit significant preference for Q. laurina in June and Gomphaceae (five Ramaria species) was found exclusively on Q. laurina.

Tedersoo et al. (2008) found strong host preference of ectomycorrhizal fungi for three co-occurring trees from different families. Host preference was also demonstrated for trees from three families in a mixed conifer-broadleaf forest (Ishida et al., 2007). Although ectomycorrhizal host preference has not been extensively documented for different species within the same genus, two studies have reported distinct differences in ectomycorrhizal communities on co-occurring oak species (Morris et al., 2008b; Cavender-Bares et al., 2009). This study provides additional evidence that host preference may play an important role in structuring ectomycorrhizal communities on host trees that are phylogenetically similar. Conspicuous differences in ectomycorrhizal communities were found on two oak hosts that were more distantly related (e.g. one species was section Lobatae whereas the other was section Quercus) and were more scattered in distribution (Morris et al., 2008b). Further research is needed to better understand the role of taxonomic relatedness, spatial distribution and ecological differences (e.g. leaf phenology) in ectomycorrhizal host specificity.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

We thank Ken Oyama for his support and valuable contributions to this research. We gratefully acknowledge Mauricio Quesada and Kathy Stoner for use of laboratory space and equipment in Mexico, Maribel Nava-Mendoza and Dolores Lugo Aquino for conducting soil analyses, Susana Valencia Avalos and John Tucker for identification of oak specimens, Jenny Moreno Miranda for laboratory assistance and Marcel Rejmánek for statistical advice. This research was supported by a UC MEXUS Dissertation Research Grant, UC Davis Ecology Block Grant fellowships and a National Science Foundation Grant (#DEB-99-81711) to C.S.B. Participation by M.E.S. was made possible by the Harvard University Herbaria (HUH).

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  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Fig. S1. CA of the ectomycorrhizal communities on Quercus crassifolia and Quercus laurina.

Fig. S2. Cluster analysis using unweighted pair group method with arithmetic mean and Bray–Curtis distances showing similarity of ectomycorrhizal fungi in plots across sampling dates.

Table S1. Species richness and diversity indices of ectomycorrhizal fungi on the roots of Quercus crassifolia and Quercus laurina in a tropical montane cloud forest.

Table S2. Nonectomycorrhizal taxa on Quercus crassifolia (Q.c.) and Quercus laurina (Q.l.) in a tropical montane cloud forest.

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