3,000 species and no end – species richness and community

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ORIGINAL ARTICLE

3,000 species and no end – species richness and community

pattern of woodland macrofungi in Mecklenburg-Western

Pomerania, Germany

Martin Unterseher & Benno Westphal &

Norbert Amelang & Florian Jansen

Received: 17 March 2011 /Revised: 18 May 2011 /Accepted: 25 May 2011# German Mycological Society and Springer 2011

Abstract In addition to newly generated and continuously

growing datasets in mycological research, existing compi-lations are of high value to assess the fungi of a wholeregion. In the present study, a private database with ca.65,000 entries of macromycetous fruit body observations inMecklenburg-Western Pomerania, Germany, was analysed.Observed species richness of tree-associated mycorrhizaland saprobic fungi exceeded 3,000 taxa. The total fungalspecies richness could not be determined with confidence

but will possibly exceed 4,000. Distinct species turnover with respect to host trees was observed. However, the rateof community overlap clearly differed between mycorrhizaland saprobic fungi and deciduous and coniferous trees. By

separating the data into abundant core species and rare

satellite taxa potential indicator species are presented,whose preservation will be beneficial to many other fungiand the entire ecosystems they live in.

Keywords Biodiversity survey. Volunteers . Indicator species . Community ecology. Species richness estimation

Introduction

Most terrestrial habitats are known to rely on fungi for key parts in the nutrient recycling process. It is mainly their

decomposing and mineralisation activities that createimportant habitats for many micro-organisms, protists,fungi, and arthropods. Their important trophic role not-withstanding, fungi remain a poorly understood kingdom of the eukaryotic tree of life with respect to species richness,species composition and ecological functionality of species(Molina et al. 2011). Their abundance in virtually allecosystems with wooden substrata has been fully realizedonly in the last few decades (Lonsdale et al. 2008;Ovaskainen et al. 2010).

Apart from biological reasons for this lack of knowledge(cryptic fungal life as subterranean mycelium), further argu-

ments are found in a comparatively low societal relevance of fungi (many fungi, such as moulds and poisonous agarics are

perceived negatively by the European public) and in the politics of science, which are partly responsible for the limitednumber of mycologists with taxonomic expertise that areemployed. Because biodiversity research is generally under-funded with significant lack of manpower, the use of volunteers is becoming increasingly important in thesescientific areas (Lovell et al. 2009). Especially in Germanythere exists a long tradition of high-quality data collection by

Electronic supplementary material The online version of this article(doi:10.1007/s11557-011-0769-7) contains supplementary material,which is available to authorized users.

M. Unterseher (*)Dept. of Systematic Botany, Insitute of Botany and LandscapeEcology, Ernst-Moritz-Arndt University,Grimmer Str. 88,17487 Greifswald, Germanye-mail: [email protected]

B. Westphal

Fachgruppe Mykologie Vorpommern,Potthäger Damm 13,17498 Weitenhagen, Germany

N. Amelang Neuhofer Weg 6,23996 Neuhof/Bobitz, Germany

F. JansenDept. of Landscape Ecology, Insitute of Botany and LandscapeEcology, Ernst-Moritz-Arndt University,Grimmer Str. 88,17487 Greifswald, Germany

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DOI 10.1007/s11557-011-0769-7

http://dx.doi.org/10.1007/s11557-011-0769-7

http://dx.doi.org/10.1007/s11557-011-0769-7



hundreds of amateur mycologists for many decades, leadingto the accumulation of millions of data of macrofungal (fruit

body) occurrence. Such information was, for instance, usedto compile distribution atlases of macrofungi in Germany(Krieglsteiner 1991, 1993), various Red Lists (e.g. Anon1992; Schwik and Westphal 1999; Schmitt 2007), or therecent Funga of Saxony-Anhalt (Täglich 2009).

The analyses of such comprehensive datasets with new andenhanced statistical tools is of particular importance for theurgently needed updates of regional, national and globalspecies richness (Hawksworth 1991; Hawksworth andRossman 1997; Mueller et al. 2007). Nearly all existingextrapolations of fungal species richness are based on a number of assumptions such as ratios of fungi to their associated hosts, anamorph – teleomorph relationships, or theconsideration of understudied fungal groups and habitats(Hawksworth 2001; Fröhlich and Hyde 1999). Consequently,species richness estimations differ strongly, depending on thetaxonomic group, the geographical area under investigation,

and the number of specialists involved in the surveys. Thishas led to sceptical considerations with respect to credibilityof species richness estimations (May 1991). In addition,undersampling might prevent meaningful estimations of anyorganism's species richness (Coddington et al. 2009). Onlyrecently, microbiologists, protistologists and mycologists

became aware of new, promising methods to extrapolatetotal species richness (Bohannan and Hughes 2003; Uglandet al. 2003; Chao et al. 2006) from physical species(Unterseher et al. 2008), molecular operational taxonomicunits (Jumpponen and Jones 2009) or from cultured isolates(Joshee et al. 2009; Unterseher and Schnittler 2010). The

next generation sequencing technologies have, more thanother methodologies, the potential to overcome undersam-

pling thus launching a new era of fungal diversity research(Öpik et al. 2009; Amend et al. 2010; Unterseher et al. 2011).

It is the large number of observed fungal species,especially the many rare and rarest ones (‘rare biosphere’)that further complicates statistical analysis and communi-cation of results (Novotny and Basset 2000; Cunninghamand Lindenmayer 2005; Reeder and Knight 2009). In the1980s, the concept of core and satellite species wasintroduced by Hanski (1982) to cope with the problematicanalysis of too strongly varying abundances of species

within a natural community.In theory, Hanski's approach distinguishes between species

with differing niche requirements: an abundant, enduringgroup of species (the core group) and a more diverse, variableand transient component of the community (the satellite/ occasional group) (Hanski 1982; Magurran and Henderson2003; Ulrich and Zalewski 2006). Recently, this theory was

picked up by Pedrós-Alió (2006), Dolan et al. (2009) andUnterseher et al. (2011) to analyse the 'rare biosphere' of marine bacteria, planctonic ciliates and fungi, respectively.

Apart from generating new and continuously growingdatasets, legacy data are of high value. Many specimensare preserved in stand-alone fungal culture collections,

public exsiccaria at Universities and botanic gardens andlinked to curated online databases and further webresources (Wilkinson and Foster 2004; Crous et al. 2004;Abarenkov et al. 2010). Moreover, there exist dozens of

private exsiccaria and even more voluminous digitaliseddatasets and printed card indices owned and maintained byhighly experienced amateur mycologists.

For the present study, we used existing data about theoccurrence of macromycetes and concentrated on specieswith mycorrhizal, saprobic, pathogenetic and otherwiseunknown modes and degrees of association to woodensubstrata in Mecklenburg-Western Pomerania (MV). Weaimed at testing the following hypotheses: (1) the present data gathered by volunteers allow analyses of fungalcommunity ecology, i.e. of species richness, speciesturnover and host preferences; (2) species richness

estimators allow serious predictions of total speciesrichness of tree-associated macrofungi in MV; and (3)the separate analysis of abundant fungal ‘core’ and rare‘satellite’ species (Magurran and Henderson 2003) helpsto define indicator taxa for species, habitat and landscapeconservation, irrespective of undersampling or an opencommunity structure with a high temporal or spatialspecies turnover.

Materials and methods

Basic geobotanical data of the collection area Mecklenburg-Western Pomerania

The German state Mecklenburg-Western Pomerania (MV)is located in the very northeast of the country (Fig. 1). It covers an area of 23,180 km2 and has the lowest populationdensity of all German states with 72 inhabitants per km2

(1.65 M inhabitants in total). Population is characterised bysmall and middle-sized cities (20,000 – 50,000 inhabitants)The climate is characterised by a transition from maritimeto continental-temperate climate.

The Pomeranian landscape was shaped by the pleisto-

cenian glacial period and has a mean elevation below50 ma.s.l. Three national parks were established in MV:the “Müritz”, the “Vorpommersche Boddenlandschaft ”and the “Jasmund”. In MV, there exist 58 habitat typesaccording to Appendix I of the EU Council Directive onthe conservation of natural habitats and of wild fauna andflora including 15 with priority (Anon 2004). Amongthese, several habitats are of particular importance for the

preservation of regional fungal diversity: (1) nutrient-poor Pinus- and Fagus-rich forests (spine fungi, coralloid

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fungi, agaricoid fungi); (2) calcareous forests of Fagus,Quercus, Pinus (Boletales, Cortinariales, Russula, Lactar-

ius); (3) dunes and dry, sandy grasslands (Geastrales); (4)rough pastures and grasslands ( Hygrocybe, Clavariaceae,Geoglossaceae, Entolomataceae); (5) fens, transitionalmires and raised bogs (bryophilous and peat-inhabitingfungi); (6) wet habitats such as marshland forests with

Alnus, Salix, Populus, Fraxinus (saprobic and mycorrhizalfungi); and (7) old-growth trees, especially of Quercus,

Fagus, Ulmus and Pinus (poroid and corticioid fungi).

Dataset

This study relied on wood-inhabiting, mycorrhizal andsaprobic macrofungi associated with abundant and botheconomical and ecological important deciduous and conif-erous tree species. The data are entirely based on fruitbodyobservations and belonged to the private database of Mr. Benno Westphal. Data were gathered by numeroushonorary mycologists and nature conservationists through-out decades (see Acknowledgements). Data were regularlyverified by B.W. They are currently made available throughthe internet portal “Biological survey databases and

herbaria in Mecklenburg-Vorpommern”1 of the University

of Greifswald, Germany.Fungi associated with herbs, shrubs and further non-tree

plants, parasitic rusts, smuts, mildews as well as myxomycota,asexual taxa (e.g. Coelomycetes) or taxa with tiny fruit bodies(e.g. cyphelloid Agaricales) were removed. The remaining data were then sorted for nutrition strategy (mycorrhiza, sapro-

phyte) at the generic level according to ecological annotations,authors' knowledge and literature (e.g. Breitenbach andKränzlin 1984 – 2005; Horak 2005; Kirk et al. 2008).Additionally, data were filtered with respect to eight treegenera: Abies (minus Picea abies), Acer , Fagus, Fraxinus,

Picea (minus Abies alba), Pinus, Quercus and Tilia.

Analysis of community ecology

Species abundance distribution

To avoid bumbling through dozens of different speciesabundance distribution (SAD) models (McGill et al. 2007),

1 http://geobot.botanik.uni-greifswald.de/portal; last accessed March2011

Fig. 1 The German state Mecklenburg-Western Pomerania ( shaded

area, left ). The right part displays an ordinance survey map(“Messtischblatt ”) of Mecklenburg-Western Pomerania and the loca-tions of the database entries (black dots) as used for the present paper.

Size of the dots correspond to the number of database entries in that grid. (source: map of Germany from http://de.wikipedia.org/wiki/ Mecklenburg-Vorpommern)

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http://de.wikipedia.org/wiki/Mecklenburg-Vorpommern




http://geobot.botanik.uni-greifswald.de/portal



a simple hypothesis was presumed according to current discussions in community ecology (Coddington et al. 2009;Ulrich et al. 2010; Unterseher et al. 2011): the communityof macrofungi in MV follows a log-normal distribution.

In case the fungal data did not follow a log-normal SAD,the core-satellite analysis (Hanski 1982) was applied to test for the possibility of overlapping SADs, e.g. a log-series

pattern overlapping a log-normal SAD (Magurran andHenderson 2003). For this analysis, 20 years (1988 – 2007)were extracted from the dataset and analysed with respect to persistence (number of years in which a species wasrecorded) and abundance (the number of occurrences in a

particular sample or year) of that species according toMagurran and Henderson (2003).

Different methods of separating core from satellitespecies exist (Magurran and Henderson 2003; Ulrich andZalewski 2006; Galand et al. 2009; Dolan et al. 2009)

because it is the organism's ecology and the nature of thedata that deserve particular attention.The following assump-

tions should therefore account for the nature of fungi in core-satellite analysis: fungal species may be well established andabundant in their habitat but grow most of the time asinvisible vegetative mycelium. Fruit body formation mayoccur rarely, e.g. every 5 – 10 years. If, for example, a 50%

persistence threshold would be applied separating satellitetaxa with a persistence of less than 10 years from corespecies rarely fruiting species would then be classified assatellite species by mistake and conclusions might depart from reality. To account for such phenomena, core specieswere defined as those species that were recorded with ≥75%of maximal persistence (more than 15 years) but down to

25% (15 quadrants of ordinance survey maps: MTB-Q) of maximal abundance for any year in MV. The remainingspecies were treated as satellite group and analysedaccordingly. In order to assign possible surrogate species(Favreau et al. 2006), the core group was examined in detail.

Observed and estimated species richness

Randomised (rarefied) species accumulation curves (SAC)were calculated in the ‘vegan’ package (Oksanen et al. 2010)of the R environment (R Development Core Team 2011) todisplay the accumulation of species when the number of sites

or individuals increases (Gotelli and Colwell 2001). By theanalysis of the curves' shape (e.g. initial slope, approachingan asymptote or not), it was possible to evaluate basic

patterns of species richness as well as sampling efforts.The species richness estimators Chao1, Jackknife1 and

Bootstrap (e.g. Colwell and Coddington 1994) werecalculated in R. By analysing the estimator curves' shape,only those values were considered as serious extrapolations,that remained stable, i.e. whose curve showed a stableasymptote for a considerable part at the right end of the

diagram. A fourth estimator was used for extrapolationsover the whole of Germany. It was introduced by Ugland et al. (2003) and since then has also been used for fungal data (Unterseher et al. 2008).

Community analyses

Multivariate statistics, such as ordination, and an appropri-ate graphical display of results are of great importance toevaluate and explain any community structure in the light of natural fluctuations (i.e. species turnover betweendifferent habitats, host plants or ecosystems). As for theissue of species richness estimations, there exist different algorithms to analyse such multivariate (multidimensional)community data. All have their strengths and weaknesses(Gauch 1982; McCune and Grace 2002) whose description,however, is beyond the scope of this communiciation. Inany case, it is recommended to apply different methods in

parallel. If the results then show similar patterns, one can

demonstrate and discuss the findings with more confidencethan if only one calculation was done.

In the present study, community structures of macrofungiin MV were traced with the three ordination techniques:canonical correspondence analysis (CCA), detrended cor-respondence analysis (DCA) and non-metric multidimen-sional scaling (NMDS or NMS). Analyses were performedfor those species that were recorded at least at ten different MTB-Q samples. Deleting the rarest species prior toordination is a common procedure to strengthen theapparent differences among habitats by reducing noisefrom very infrequent species (McCune and Grace 2002).

The raw data were not transformed or relativised, thusallowing differences in sample totals to be expressed in theanalyses. Multivariate analysis included (1) detrendedcorrespondence analysis (DCA) with default settings of the 'decorana' command of the R-package 'vegan'; (2) non-metric multidimensional scaling (NMDS) with the 'auttrans-form' option of command 'metaMDS' (also R-package'vegan') set to FALSE; and (3) canonical correspondenceanalysis (CCA) with default settings of the 'cca' commandand the four environmental variables: mycorrhiza, sapro-

phytism, coniferous host and deciduous host.All data used here (Online Resources 1 and 2) as well as

analyses in the R environment (Online Resource 3) are provided as electronic supplementary material.

Results

Observed and predicted species richness

The total dataset used for this study comprised 65,535 geo-referenced observations (counts) with host tree and sub-

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Table 1 Basic data of macrofungal species richness in Mecklenburg-Western Pomerania

Host tree Area (%)a Ecology Counts Samples Species observed b Species estimatedc Validation of estimatorsd

All 76.5% (325,000 ha) 65,535 1,019 3,307/0.1/3.2 3,710 – 4,682 PreliminaryConiferous 51.7 All 11,274 672 1,089/0.1/1.6 1,283 – 1,827 Preliminary

Mycorrhizal 5,272 487 370/0.1/0.8 430 – 602 Preliminary

Saprobic 5,826 606 690/0.1/1.1 820 – 1,178 Preliminary

Abies 0.2 All 526 126 165/0.3/1.3 209 – 464 Preliminary

Mycorrhizal 42 20 22/0.5/1.1 29 – 202 Preliminary

Saprobic 329 85 72/0.2/0.8 89 – 188 Preliminary

Picea 8.6 All 3,344 419 604/0.2/1.4 730 – 1,111 Preliminary

Mycorrhizal 1,219 249 205/0.2/0.8 247 – 358 ±Stablee

Saprobic 2,138 366 393/0.2/1.1 475 – 740 Preliminary

Pinus 42.9 All 7,374 548 802/0.1/1.5 950 – 1,394 Preliminary


Saprobic 3,359 469 511/0.2/1.1 612 – 913 Preliminary

Deciduous ca. 24.8 All 27,345 1,947 789/0.1/2.5 2,215 – 2,935 Preliminary


Saprobic 13,297 629 1,066/0.1/1.7 1,224 – 1,655 Preliminary

Acer 1 Saprobic 575 182 218/0.4/1.2 270 – 424 Preliminary

Fagus 11.8 All 15,247 526 1,229/0.1/2.3 1,403 – 1,739 Preliminary


Saprobic 8,067 464 788/0.1/1.7 907 – 1,229 Preliminary

Fraxinus 3.7 Saprobic 1583 292 384/0.2/1.3 457 – 651 Preliminary

Quercus 8.1 All 4,881 537 775/0.2/1.4 920 – 1,311 Preliminary


Saprobic 2,780 482 480/0.2/1 580 –

857 PreliminaryTilia 0.2 All 489 145 230/0.5/1.6 292 – 601 Preliminary

Mycorrhizal 215 67 87/0.4/1.3 108 – 169 Preliminary

Saprobic 292 120 151/0.5/1.3 194 – 481 Preliminary

a The percentage of total forest area in MV b Total species number/species per count/species per sample (MTB-Q); values less than 0.1 species per count were rounded up to 0.1c Range of estimators Chao1, Jack1, Bootstrapd Based on the estimators curve's shape (Online Resource 3); if none of the curves displayed a stable value (i.e. levelling off) but continued to rise,

the result of estimators was considered preliminarye According to the Chao estimator

Fig. 2 Species accumulationcurves of macrofungi inMecklenburg-WesternPomerania. Line numbes:1 Fagus; 2 Quercus; 3 Pinus;4 Picea; 5 Fraxinus; 6 Tilia;7 Acer ; 8 Abies

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stratum characteristics. In total, 3,307 species wererecorded from 1,019 MTB-Q. Database entries weredominated by saprobic fungi associated with deciduoustrees (Fig. 2a ). The overall number of saprobic fungi clearlyoutnumbered mycorrhizal fungi (Fig. 2a ). Fagus, Quercus

and Pinus trees harboured the highest absolute numbers of macrofungi, Tilia, Acer and Abies the lowest (Fig. 2b). With

respect to the mean number of species per MTB-Q, Faguswas most species-rich with 2.3, followed by Tilia and Pinus

(Table 1). A third value of observed species richness wascalculated, the number of species per count. The higher those values are (Table 1), the faster fungal speciesaccumulated with increasing number of counts. Here, Tilia

is taking the lead with one new species every two counts,followed by Acer (0.4 species per count).

Table 1 further displays values of three different speciesrichness estimators and an assessment of the estimatorsconfidence (rightmost two columns). With exception of the Chao1 estimator for mycorrhizal fungi on Picea, none

of the estimators' curves displayed a stable levelling-off (Online Resource 3). The resulting equation of Ugland'sestimator was y=821.8 × log (x) – 2,401.31 with y=thenumber of predicted species and x=the number of MTB-Q(calculations not shown). For all MTB-Q, Ugland'sformula estimated 3,291 species. Extrapolation to theentire forest area in MV resulted in 3,511 predictedwoodland species.

Fungal communities

Canonical correspondence analysis (CCA) displayed

species scores with respect to the parameters host group (deciduous-coniferous) and ecology (saprophyte-mycorrhiza) in a two-dimensional ordination space(Fig. 3). Table 2 lists taxonomic information, number of occurrences and preferred habitat for the five most distinct species of each community: deciduous-mycorrhiza (upper right of Fig. 3), deciduous-saprophyte (lower right),coniferous-saprophyte (lower left), coniferous-mycorrhiza (upper left). The five species closest to the centroid couldnot be assigned clearly to one of the four groups and arealso considered in Table 2.

Using NMDS and DCA and focusing on host trees

instead of fungal species, the fungal assemblage appearedtripartite (Fig. 4a, b): saprophytes of the five deciduoustrees clearly separated from saprophytes of the threeconiferous hosts. A third group consisted of mycorrhizalfungi of both coniferous and deciduous host trees (right hand side of Fig. 4a, b). Separate analysis of mycorrhizalcommunities (Fig. 4c, d) and saprobes (Fig. 4e, f ) providedfurther details: For mycorrhizae, the eight host trees,representing their respective fungal assemblages, wereclearly separated from each other (Fig. 4c, d). Deciduous

trees and their associated saprobic fungi were overlappingfor DCA on both axes (Fig. 4f ), and separated clearly for

NMDS (Fig. 4e). Coniferous host trees and their fungalcommunities separated clearly from each other and fromthose of deciduous trees. Saprobic fungi of Abies were themost distinct assemblage for both NMDS and DCA(Fig. 4e, f ).

Core-satellite analysis

The 20-year dataset comprised 2,697 macrofungi. Thecore group comprised 100 taxa (4% of total speciesnumber and 25% of total records; Online Resource 1).The remaining 2,597 species (35,055 records) were

treated as occasional species, i.e. satellite group (OnlineResource 1). Visual inspection and statistical testing(Chi-square, Kolmogorov – Smirnov, Shapiro – Wilk andAnderson – Darling) for log-normal SAD of all data werenegative, i.e. not significant (Fig. 5a, b; statistical tests not shown). Analyses of the core group revealed a significant approach to log-normality (Fig. 5c, d; statistical tests not shown). Analysis of satellite taxa resulted in rejection of log-normality (Fig. 5e) and confirmation of a log-linear relationship (Fig. 5f ).

Fig. 3 Position of macrofungi within a two-dimensional ordinationspace after canonical correspondence analysis (CCA). The first two

axes account for 56 and 31% of the constrained variability. Theordination shows species (circles) in sample space according tosubstratum requirement (coniferous-deciduous) and nutrition strategy(mycorrhiza-saprophyte). The five most distinct species for eachgroup (those nearest to the four corners of the ordination) arehighlighted as black dots. Additionally the five species nearest to thecentroid (without clear ecology) are highlighted. All 25 species aredescribed in Table 2. Abbreviations are composed of the first four letters each of genus (upper case) and epithet (normal case), e.g.BOLEreti= Boletus reticulatus

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Table 2 Full species names, abundance and habitat requirements of those fungi highlighted in Fig. 3

Species Occurrence inMV

Member of thecore group

Habitat and ecologya

Deciduous-mycorrhiza in Fig. 3

Boletus reticulatus Schaeff. Common Yes Quercus- and Fagus-rich deciduous forests;ectomycorrhiza with Fagus sylvatica andQuercus robur

Inocybe asterospora Quél. Common No Quercus- and Fagus-rich deciduous forests;ectomycorrhiza with Fagus sylvatica andQuercus robur

Russula velutipes Velen. Scattered Yes Deciduous forests with heavy, loamy soils (not basophil), ectomycorrhiza with Fagus sylvatica

and Quercus robur (and Carpinus)

Russula violeipes Quél. Scattered Yes Deciduous forests with heavy, loamy soils (not basophil), ectomycorrhiza with Fagus

sylvatica, Quercus robur and Carpinus betulus,rarely with Pinus

Russula olivacea (Schaeff.) Fr. Scattered Yes Deciduous forests with basophil, heavy morainesoils, ectomycorrhiza with Fagus sylvatica andCarpinus betulus

Deciduous-saprophyte in Fig. 3 Peniophora limitata (Chaillet ex Fr.)

CookeCommon Yes Saprobe on dead corticated twigs and branches of

Fraxinus excelsior

Diatrype disciformis (Hoffm.) Fr. Common Yes Saprobe on dying and dead twigs and branches of Fagus sylvatica

Hymenochaete rubiginosa (Dicks.)Lév.

Common Yes Saprobe on old decorticated branches and stumpsof Quercus spp.

Xylaria carpophila (Pers.) Fr. Common Yes Saprobe on lying cupulae of Fagus sylvatica

Lachnum virgineum (Batsch) P. Karst. Common Yes Saprobe on lying Fagus cupulae, also on lyingcones of Larix decidua, Pinus and Pseudotsuga

Coniferous-saprophyte in Fig. 3

Gyromitra esculenta (Pers.) Fr. Scattered tocommon

Yes Saprobe of acidophilic pine ( Pinus) forests,especially on disturbed grounds

Ciboria rufofusca (O. Weberb.) Sacc. Scattered Yes Saprobe on lying cone scales of Abies spp., rarelyon Pseudotsuga cones

Strobilurus tenacellus (Pers.) Singer Common Yes Saprobe on lying pine cones, especially Pinus

sylvestris

Phellinus pini (Brot.) Bondartsev &Singer (current name: Porodaedalea

pini (Brot.) Murrill)

Scattered No Secondary parasite of old pine trees, mainly Pinus sylvestris, seldom Pinus strobus

Auriscalpium vulgare Gray Common No Saprobe of lying cones of Pinus nigra, Pinus

sylvestris, rarely on other coniferes such as Pseudotsuga

Coniferous-mycorrhiza in Fig. 3

Suillus variegatus (Sw.) Kuntze Scattered Yes Poor, old-growth pine forests, quaking bogs,ectomycorrhiza with Pinus sylvestris and

Betula pubescens

Tricholoma imbricatum (Fr.) P.Kumm.

Scattered Yes Acidophile pine forests and special stands, suchas gravel pits, ectomycorrhiza with Pinus

sylvestris

Russula decolorans (Fr.) Fr. Common Yes Old-growth pine forests, on sandy grounds,acidophil, ectomycorrhiza with Pinus sylvestris

Russula drimeia Cooke (current name: Russula sardonia Fr.)

Common Yes Old-growth pine forests, on sandy grounds,acidophil, ectomycorrhiza with Pinus sylvestris

Inocybe lacera (Fr.) P. Kumm. Scattered Yes Pine- and spruce forests, willow coppices, pioneer woods, ectomycorrhiza with Picea

abies, Pinus sylvestris, also Salix and Alnus

spp.

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Fig. 4 Ordination plots of nonmetrical multidimensional scaling(NMDS; upper row) and detrended correspondence analysis (DCA;lower row) display host trees ( grey dots) in fungal species (black

crosses) space. a, b Three distinct groupings of host trees and their associated fungal communities are visible. c, d Patterns from themycorrhizal dataset. The trees' coordinates in ordination space are

separated clearly. This means that each tree houses a uniquecommunity of mycorrhizal fungi. Several fungal species (black

crosses) are located in between thus occurring on several hosts. e, f

Coniferous trees and their fungal saprophytes separate from each other and from deciduous trees. Fungi without clear host preferences arelocated inbetween

Table 2 (continued)

Species Occurrence inMV

Member of thecore group

Habitat and ecologya

Without clear ecology in Fig. 3

Clitocybe nebularis (Batsch) P.Kumm.

Common Yes Saprobe on dead leaf litter and needles, variousdeciduous and coniferous trees

Gymnopus dryophilus (Bull.) Murrill Common Yes Saprobe on dead leaf litter and needles, variousdeciduous and coniferous trees, also in swamps

Hypholoma fasciculare sensu Massee(current name: Hypholoma acutum

(Cooke) E. Horak)

Common No Saprobe on various deciduous and coniferoustrees, dead wood and stumps of all kinds

Laccaria amethystina (Huds.) Cooke Common No Ectomycorrhiza with Fagus sylvatica, Quercus

robur , Carpinus betulus and Abies spp.

Lepista flaccida (Sowerby) Pat. Common Yes Saprobe on dead leaf litter and needles, variousdeciduous and coniferous trees

a The following references were used to validate the authors' knowledge: Horak (2005), Breitenbach and Kränzlin (1984 – 2005), Hansen andKnudsen (1992 – 2000)

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Discussion

Observed and predicted species richness

Despite the long-term voluminous dataset used here,several aspects of fungal diversity could not be resolved.On the basis of the present data, it is hard to tell which host tree supports the richest fungal community (Fig. 1).Without doubt, beech ( Fagus sylvatica) and oak (Quercus

spp.) accommodate very high numbers of fungi (Heilmann-Clausen 2001; Heilmann-Clausen and Christensen 2005;Binion et al. 2008). However, the observed species richnesson the different trees (Fig. 2a ; Table 1) seemed to be rather a function of host tree abundance (column “area ” in Table 1)than of the trees' ability to support high fungal diversity. Byconsidering the number of species per count, other host trees became more important for maintenance of fungaldiversity, such as Tilia (Unterseher et al. 2005) or Acer

(Hein et al. 2009).

Estimations of total species richness ranged widelyfrom 3,710 to 4,682 fungal woodland species (Table 1)and should therefore be communicated with caution.However, somewhat more than 4,000 macrofungal species(not only woodland species) in MV may be a realisticfigure when compared with the recently published Funga of Saxony-Anhalt (Täglich 2009; 3,612 asco- and basi-diomycota), or the Funga of Saxony that currentlycomprise 3,378 Basidiomycota and 1,593 Ascomycota

from about 190,000 observations (Dämmrich, personalcommunication).

Ugland et al. (2003) provided an extrapolation methodthat could also be used with fungal communities(Unterseher et al. 2008), because their species richnessestimator takes into account very diverse communitiesfrom large and heterogeneous environments. Whereas thechosen host trees represent dominant genera and 76.5% of total forest area in MV, extrapolation to 100% increasedspecies numbers to ca. 3,500.

Fig. 5 Species abundance distributions (SADs) of all fungi (left

column), core species (middle column) and satellite group (right

column) of a 20-year dataset. a, c, e ‘Binned’ species abundancesoverlaid with a fitted truncated log-normal SAD. Inserts showWhittaker plots (rank-log-abundance plots) which are better suited

for identification of SAD other than log-normal. b, d The probability

plots additionally test for log-normality of the data. The better the grey

line in a probability plot fits to the plotted abundances ( open circles)the better is the fit to a log-normal species abundance distribution. f

Satellite taxa follow a log-linear SAD: only a few species had highnumbers of counts whereas most fungi were rarely recorded

Mycol Progress



Fungal communities and the benefit of surrogate speciesfor biodiversity and conservation

Multivariate statistics revealed distinct mycorrhizal com-munities of both coniferous and deciduous trees. Sapro-

phytes seemed to behave in a more complex manner thanmycorrhizal fungi. They showed clearly overlapping spe-

cies composition on deciduous wood with DCA (Fig. 4f ), but well-separated groups with NMDS (Fig. 4e). In bothcases, deciduous hosts clearly separated from coniferoustrees with respect to their fungal species composition(Fig. 4e, f ).

Throughout various studies applying the core – satellitetheory, core taxa were discussed as species that are

biologically related to the habitat they live in (Magurranand Henderson 2003) or having the largest impact on their ecosystem (Ulrich and Zalewski 2006). Furthermore,changes in core species composition may be translatableto changes in environmental conditions (e.g. climate

change, habitat disturbance), when new core species, drawnfrom the pool of satellite species, will replace existing ones(Magurran and Henderson 2003). Such interpretation of core species is close to the concept of surrogate species(Favreau et al. 2006) that comprises flagship (Dietz et al.1994), focal (Lambeck 1997), indicator (Landres et al.1988; Noss 1999) or keystone (Power et al. 1996) species.According to Rolstad et al. (2002), the biology of indicator species should be, among others, fairly well known. Thiswould be the case for those four species groups in Fig. 3,which are located close to the four corners of theordination. Except for 3 species, all those 20 fungi

belonged to the core group (Online Resource 1).Among the actual core group, Amanita muscaria, A.

phalloides, Boletus edulis or Fomes fomentarius canwithout doubt be considered flagship species, given their charismatic appearance and their high level of publicawareness that might support the protection of that species' entire habitat. Focal species are, e.g., thoseectomycorrhizal taxa that are especially threatened byoverfertilisation of forest soils (Gryndler and Lipavsky1995) and deforestation (Zhang et al. 2004; Tedersoo et al.2007). In order to protect focal species, such threatening

processes should be eliminated so that further species will

also benefit. The indicator species approach itself iscomplex and applicable in a hierarchical manner (Noss1990). Fungal indicators can be assigned to plant communities and ecotypes (e.g. fungi associated withconiferous trees), ecosystem processes (e.g. litter andwood decay by the core group species Mycena galericu-

lata, Auriscalpium vulgare, Fomitopsis pinicola, Hypoxy-

lon fragiforme or Oudemansiella mucida) or to healthassessment of major plant groups (e.g. Armillaria spp.,

Heterobasidion annosum). Additional to their significant

role in forest ecosystems, all core species are common inMV, fruit regularly, are well known to most mycologists,and many of them are well represented in scientificliterature (Kirk et al. 2008). They further represent allhost trees and all ecologies considered in the present study.

Whereas more than 75% of total forest area inMecklenburg-Western Pomerania were considered in the

present study, numerous, from a fungal perspective,important host trees were neglected, such as Populus, Salix,Corylus, Larix, Pseudotsuga or the wooden rosids Malus,Sorbus and Prunus. These substrata surely provide richsources for fungal species, and studies incorporating thosetrees will unravel further, so far unknown and unpredict-able, patterns of fungal diversity.

Acknowledgements We are grateful to Hanns Kreisel for many veryhelpful comments on the manuscript and for providing access to hisunpublished Funga of Mecklenburg-Western Pomerania. Many thanksgo to numerous honorary mycologists, especially Ria Bütow, Joe Duty,Hanns Kreisel, Siegmund Olm, Torsten Richter, Katrin Richter,

Gerhard Rüdiger, Ingeborg Schmidt, Manfred Schubert, BrigitteSchurig and Jürgen Schwik as well as the Arbeitsgruppe MykologieHamburg for professional mapping of fungal occurrences in MV. The“Landesforstanstalt Mecklenburg-Vorpommern” is thanked for provid-ing areal data of host trees. Hans-Jürgen Hardtke and Frank Dämmrichare thanked for providing basic fungal data from Saxony. Thereprocessing of fungal data for public access within the “Biologicalsurvey databases and herbaria in Mecklenburg-Vorpommern”

(programming by Florian Jansen and Falco Glöckler) is currentlyfunded by the “Landesamt für Umwelt, Naturschutz und Geologie(LUNG)”, the “ Norddeutsche Stiftung für Umwelt und Entwicklung”,the Institute of Botany and Landscape Ecology, the “Institut für Dauerhaft Umweltgerechte Entwicklung von Naturräumen der Erde(DUENE)”, and coordinated by the working group “MykologieMecklenburg-Vorpommern” (responsibility Norbert Amelang) andthe Institute of Botany and Landscape Ecology (responsibility FlorianJansen). Two anonymous reviewers are thanked for valuable com-ments on a previous manuscript.

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3,000 species and no end – species richness and community