Introduction 

Finding strategies to identify the state of biodiversity and to develop appropriate conservation and monitoring programs is one of the most important issues in the field of ecology ([19], [6], [57]). The growing impact of human activities that contribute to habitat fragmentation and decrease diversity on natural ecosystems has brought with it an urgent need for the development of simple, quick and cost-effective methodologies for quantifying and monitoring changes in biological diversity ([6], [22], [54]).

Surrogate species, whose primary purpose is to ascertain and test which groups of organisms reflect the diversity of others, can be of great help in quantifying biological diversity for less well-known groups and less easily detectable taxa ([45], [56], [41], [46]). Moreover, the possibility of high congruence between different taxa, which is extremely interesting from an ecological viewpoint, can reduce the time and costs necessary for planning conservation actions, although no single biotic group shows a perfect match with any other. The “taxon surrogacy” hypothesis ([48]) is based on the assumption of concordance among species richness or patterns of community composition across different taxonomic groups ([64], [58]). Nevertheless, the selection of surrogate taxonomic groups is not straightforward, and different methods have been applied by various authors. In fact, over the last 20 years, conservation biologists have discussed the use of surrogate species in conservation planning at great length, debating both the advantages and disadvantages of this approach ([38]). There are several different types of surrogacy ([30]), such as: (i) cross-taxon, where high species richness in one taxon is used to infer high species richness in others ([37]); (ii) within-taxon, where generic or familial richness is treated as a surrogate of species richness ([3]); and (iii) environmental, where parameters such as temperature or topographical diversity are assumed to reflect species richness ([30]). Another approach is based on “community concordance” and describes the degree to which patterns in community structure in a set of sites are similar in two different taxonomic groups ([44]). This method has been applied rarely, and mainly in aquatic ecosystems ([44], [42], [22], [27]).

According to various authors, vascular flora has a great potential to determine the diversity of other groups because it constitutes the bulk of total biomass and provides physical structure for other ecosystem components (fauna and ecological processes) through the establishment of vegetation ([48], [67], [41]). In addition, a long tradition and much experience has been gained in the sampling of vascular plants, relatively easy to perform, and plant taxonomy is sufficiently well described and standardized as well ([49], [11], [56]). Because fungi are heterotrophic organisms mainly dependent on vascular plants, the existence of a relationship between the composition of plant and fungal communities has been hypothesized ([11]). Coherently, consistent correlations have been found between macrofungi and patterns of vascular plants ([8], [43]). However, among the taxa investigated macrofungi are generally overlooked and rarely considered in reserve planning because of their small size, their ephemeral fruit bodies, their difficult identification, and the paucity of expertise concerning their taxonomy and ecology ([20], [11], [35]). Nevertheless, their inclusion in conservation planning and management is important because of their vital functional roles in ecosystems ([29], [41], [35]) and their great richness estimated worldwide ([21]). However, while at large spatial scales communities with high tree-species richness have been found to have correspondingly high macrofungal species richness ([56]), low correlations have been found at local scales (e.g., [64], [49], [57], [54]).

In this investigation we examined the concordance between vascular plants (grouped as woody plants and all plants) and macrofungi (grouped into trophic groups) at the local scale, within two nature reserves in Mediterranean forest habitats. To our knowledge, this is a new approach to specifically test the concordance between vascular plant and macrofungi communities in broadleaf deciduous forests. Our primary goal was to test how consistently plant and fungi groups classify plots in broadleaf deciduous forest ecosystems. We hypothesized that plot grouping based on plant species can be used as a surrogate for the classification of macrofungal communities. We also investigated the association between plant and fungi species for data sets showing a significant concordance, through the analysis of correlation coefficients, to ascertain whether plant community composition could be used as an “ecological indicator” for specific groups of fungi. This information will improve managers’ ability to plan effectively for the presence of these important macrofungal resources in deciduous forest ecosystems.

  Materials and methods 

Study site

The study was carried out in two nearby temperate deciduous broadleaf forests characterized by Quercus cerris, widely dominant in the canopy layer, followed by Fraxinus ornus and Q. pubescens. The number of trees with diameter at breast height (DBH) > 2 cm ranged from 7 to 33 trees per 100 m². The mean density of trees was 17 ± 7 (SD) per 100 m².

These sites are located in Tuscany (central Italy), within the State Nature Reserves of Palazzo (43° 20′ N, 11° 04′ E) and Cornocchia (43° 23′ N, 11° 10′ E). The reserves cover about 800 ha of meadows and pastures on hillsides, with a slope of about 15-25 degrees and elevation from 330 to 530 m a.s.l. The two areas are similar in terms of bedrock (limestone, sandstone and siltstone), near-neutral soils, and forest type, composition and density. No logging or harvesting have been carried out in either reserve in the last 40 years. The climate is Mediterranean and characterized by a dry summer and rain in spring and autumn; the hottest months are July-August and the coldest January-February. The mean annual precipitation is approximately 800 mm and the mean annual temperature is 13.5 °C at the nearest meteorological station (Pentolina), situated 450 m a.s.l. (ARSIA data for the period 1992-2006).

Such sites provide a good location to study the relationships between fungal and plant communities since mushroom gathering and timber extraction are not permitted. In addition, they represent fairly well the type of native forest common in the Mediterranean basin and notoriously rich in fungi ([40], [51]).

Sampling design and recording of plants and fungi

Thirty 100 m² permanent plots (10×10m, marked by metal stakes in each corner) were randomly placed in the deciduous broadleaf forests (fifteen for each reserve). The plots were previously identified and mapped (scale 1:5000) by photo-interpretation, with a buffer zone of about 20 m around each polygon to reduce possible edge effects. Data were collected in each plot for all vascular plants (presence-absence), woody plants and fungal species (presence-absence and frequency). As for vascular plants, herbs, seedlings, shrubs and trees were sampled. Woody species frequency was obtained by counting the number of individuals per species per plot, including trees or shrubs with DBH > 2 cm or height > 2 m. Macrofungi were identified based on morphology with the help of general analytic keys and monographs ([52]). To quantify their abundance, their frequency was recorded as the number of carpophores (fruiting bodies) > 1 mm per species in each plot ([1]). Although above-ground fruiting bodies do not necessarily represent the abundance of fungi, they provide reliable information concerning forest diversity without excessive effort and cost ([61]). Each macrofungal taxon was attributed to the most likely trophic group, according to Arnolds et al. ([2]) and to personal field observations. Three data sets were then obtained for the plants (presence-absence of all vascular plants, presence-absence and frequency of woody plants) and ten data sets were obtained from the carpophores of fungi (presence-absence and frequency of the following trophic groups: (i) EMF, ectomycorrhizal fungi; (ii) Sh, humicolous saprotrophs; (iii) Sl, litter saprotrophs; (iv) Sw, lignicolous saprotrophs; and (v) P, parasites. Coprophilous saprotrophs were absent. The above approach was adopted because many macrofungi are related to woody plant species by their trophic requirements and trophic groups may be strongly shaped by forest composition and structure (e.g., mycorrhizal species and many saprotrophic fungi - [47], [15], [55]).

Sampling of plant species was carried out in June and July 2010, when leaves were fully extended. Sampling of macrofungi was conducted from April 2009 to November 2011, with a higher frequency (up to once a month) from September to December, when conditions were generally optimal for fungal fruiting. Nomenclature of plant species was given according to Conti et al. ([12]). Fungal species nomenclature was based on the CABI Bioscience Database of Fungal Names (⇒ http:/­/­www.­indexfungorum.­org/­Names/­names.­asp).

Statistical analysis

Data collected from the two study sites were pooled, since all plots shared similar features as for forest structure, environmental characteristics and history over the last 40 years. Only the EMF (ectomycorrhizal fungi), Sh (humicolous saprotrophs) and Sw (lignicolous saprotrophs) datasets could be used in the analysis, as Sl (litter saprotrophs) and P (parasites) were only present in a few plots. Accordingly, the analysis was carried out using three plant data sets and six fungal data sets (18 combinations), following two main steps. In the first step, a hierarchical cluster analysis using the Bray-Curtis dissimilarity index (1 - Sørensen’s index) and flexible beta (β = -0.25) was applied on the three plant species data sets following the recommendations of McCune & Grace ([32]), and three classifications were obtained based on: (1) presence/absence of all plants; (2) presence/absence of woody species; and (3) frequency of woody species.

In the second step, Multiple Response Permutation Procedures (MRPP) were used to test the performance of each classification applied to the fungi data sets. Cluster groups were subjected to a set of cross-tests on the macrofungi data sets and a cross-test was only accepted when significant (p<0.05). Moreover, MRPP for a posteriori classification (self-test) was applied to obtain the “best possible” values of such statistics, for numerical comparison with the values of the a priori classification (cross-test). MRPP is a data-dependent permutation test that compares dissimilarities within and among groups, but does not require any assumptions of multivariate normality and homogeneity of variance to test the hypothesis of no differences among groups of sampling units assessed through a Monte Carlo permutation procedure ([65], [7]). This consists of the A statistics, which estimates the within-group homogeneity (higher values indicate a high degree of homogeneity), and the T statistics, which measures the among-group separability (large negative value of T indicates a high separability of groups). When A=0, the within-group community heterogeneity equals that expected by chance, while if A<0 the heterogeneity exceeds that expected by chance. The MRPP analysis was performed using the software package PDOSE ([33]).

Ordination analysis, formerly applied to investigate the congruence among taxonomic groups, including fungal species ([49], [57], [54]), was used to evaluate the congruence of species composition between the plant and the macrofungal data sets considered. To investigate the main gradients in the species data for the two taxonomic groups, Detrended Correspondence Analysis (DCA) was applied for each group ([23]), including down-weighting of rare species. Principal Component Analysis (PCA), was then used to analyse the congruence of the data sets because of: (i) the relatively short length of the compositional gradients; and (ii) their potential use with empty samples, contrary to unimodal methods ([28]). Ordination analysis was performed using the CANOCO v. 4.5 software package ([60]). The potential use of the compositional patterns of vascular plant data sets as surrogates for those of different macrofungal data sets was tested by Spearman’s rank correlation of the sample scores along the first PCA axis (a total of nine PCAs were extracted). Significant (positive or negative) correlation indicates a concurrent variation in the species composition among taxonomic groups. Furthermore, Spearman’s correlation coefficient was used to assess the association between plant and fungi species usng the data sets for which significant concordance was found.

  Results 

Plant community composition

A total of 108 plant species were found, including 18 species of trees and shrubs taller than 2 m (woody plants). The mean number of species per plot was 27 ± 8 (SD) and that of woody plants was 4.3 ± 1.7. Concerning trees, Quercus cerris was dominant in all plots, with a higher mean number of individuals (11.2 /100 m²) than other tree species (such as Fraxinus ornus, Quercus pubescens and Ulmus minor). Tall shrubs (such as Cornus mas, Crataegus monogyna, Juniperus communis and Prunus spinosa) and vines (Hedera helix and Tamus communis) were also frequent. The most common herbaceous plants were perennials with underground tissues (rhizomes and bulbs), such as Brachypodium sylvaticum, B. rupestre, Viola alba and Melica uniflora.

Fungal community composition

A total of 333 macrofungal species were found in the study plots. The three most representative trophic groups were: ectomycorrhizal fungi (EMF) with 157 species and a mean number per plot of 20.6 ± 7.7 (SD); humicolous saprotrophs (Sh) with 81 species and a mean number per plot of 8.3 ± 3.4; and lignicolous saprotrophs (Sw) with 78 species, whose mean number per plot was 11.0 ± 4.4. Mycena vitilis (Sw) was the most common species (present in 93% of plots), followed by Cortinarius rigens (EMF), Entoloma rhodopolium and Rhodocollybia butyracea (Sh). Litter saprotrophs (Sl), with 10 species, and parasites (P), with 7 species, had the lowest mean number of carpophores (1.8 and 0.9, respectively) and were not detected in many plots.

Community concordance between plants and fungi

The three classifications identified by clusters analysis (see “Materials and Methods”) were cut to hierarchical levels (nodes) corresponding to three distinct groups, each containing at least 2 plots (from 5 to 19 plots for each group). Among the cut levels of classifications the percentage of information left had quite similar values (from 10 to 20%).

The cross-test concordance analysis carried out revealed five significant results out of eighteen combinations (Tab. 1), and all the three classifications gave the best results when applied to the fungal data set based on frequency (number of fruiting bodies per species - see MRPP statistics and significance). All the three classifications showed significant concordance when applied to mycorrhizal fungi. Considering each classifications individually, that of woody plants based on frequency data also gave significant results when applied to the frequency of humicolous fungi. On the other hand, the classifications based on fungal presence-absence data gave poor results (woody plant presence/absence data applied to mycorrhizal fungi). Lignicolous fungi gave no significant results.

The correlations between the sample scores on the first PCA axis for the different groups were weak and mostly not statistically significant (Tab. 2). The two groups, plants and fungi, did not follow comparable compositional gradients (presence-absence data) as revealed by rather different positions of the plots in the PCA scatter-plots (not shown). The correlations between woody plants (frequency data) and EMF (presence/absence data) and Sh (presence/absence and frequency data) fungi were significant.

Associations between plants and mycorrhizal fungi

Results of the correlation analysis between woody and fungi species (EMF and Sh) based on frequency data are reported in Tab. 3 and Tab. 4. Overall, a significant positive association was detected between 46 EMF and 17 woody plants, including tree and shrub species (Tab. 3). Sorbus domestica and Prunus spinosa were correlated with a greater number of EMF (11 and 8 correlations, respectively) than any other plants. The genus Russula includes the largest number of EMF species correlated with woody plants; all species of the genus Russula found in this study were included in Tab. 3. Concerning Sh fungi, significant positive association were found between 19 Sh fungi and 13 woody plants (Tab. 4). Tree species as Fraxinus oxycarpa and Quercus petraea were associated exclusively to the same assembly of EMF (Cortinarius casimiri and Hygrophorus roseodiscoideus) and Sh fungi (Clavariadelphus pistillaris and Mycena epipterygia).

  Discussion 

The high number of fungal species found in this investigation confirmed the evidence previously reported by Salerni et al. ([50]) that broadleaved deciduous forests dominated by Quercus cerris support a high fungal richness.

According to observations from previous studies ([42], [27]), each plant data set gave a better value of MRPP statistics under a posteriori classification (self-test) than under a priori classification (cross-test).

Considering the a priori classification, better results were obtained in the MRPP analysis when the classification based only on woody species was used, in comparison with the classification obtained including both herbaceous and woody species. Such result may be interpreted as due to the fact that herbaceous species are not functionally relevant to EMF species, therefore their inclusion in the analysis provides a lower variance accounted for in the data sets analyzed. The concordance between the woody species community with the EMF community found in this investigation agrees with previous studies demonstrating that the EMF community composition is mainly related to tree and shrub species ([25], [13], [29], [15], [26]). A similar concordance was also found between the woody species and the Sh fungi communities, but only when both data sets were based on frequency. Analogously, this may be interpreted as an effect of the chemical composition of the litter that varies among different plant communities ([5]), thus affecting the composition of the Sh fungal community. Moreover, it is possible that the species classified as Sh have expanded their trophism ([66]).

The ordination analysis applied in this study revealed that the scores on the ordination axes for woody species were significantly correlated with those obtained for EMF and Sh species, clearly indicating a spatial covariation of EMF and Sh fungal groups and woody species along the same environmental gradients.

Concerning the association between communities of woody plants and fungi, it is well known that many EMF show a degree of host specificity ([36], [62], [66]). Moreover, multivariate statistics have shown that macrofungal communities can be clearly defined and delineated from the abundance patterns of their host tree species in temperate forests ([24], [18], [9], [39]). In this study, EMF were associated with woody plants, including not only trees but also aged shrubs (taller than 2m).

In Italy, the intensive exploitation occurred in the past has deeply modified the forest composition and structure, affecting in particular the understorey layer that was removed to ensure optimal growing conditions to trees. On the other hand, the results of this study suggest that the presence of old shrubs in the understorey have an overriding influence on EMF communities in broadleaf deciduous forests dominated by Quercus cerris. Indeed, it may be hypothesized that the presence of a shrub understorey can be used as an “ecological indicator” for EMF, which seem to prefer mature forests (e.g., genus Russula - [31], [17], [14]).

Our data indicates that many EMF exhibit preferences for one or two hosts. However, some woody plants, such as Sorbus domestica and Prunus spinosa, appear to be associated with many EMF. To our knowledge, these species are not thought to host symbiotic fungi, though it has been hypothesized that they play an important role during the fruiting process of some fungal species ([10], [4]). McDonald et al. ([34]) identified ectomycorrhizal species of the genera Cortinarius, Inocybe and Tricholoma that form epigeous fruiting bodies with a species of Rosaceae. On the other hand, compared to other higher taxa of the northern hemisphere (e.g., Pinaceae and Fagales), only a few studies have investigated the ectomycorrhizal fungi on Rosaceae ([16]).

Our results also showed that the co-occurrence of Fraxinus oxycarpa and Quercus petraea, both associated with peculiar ecological conditions ([59], [53]) seems to promote distinct assemblages of EMF and Sh fungi, as compared with other woody species. As a consequence, strategies for the conservation of fungi should aim at retaining diverse assemblages of host species and different structures across forests.

In this study, few Sh fungal species were significantly associated with woody plants. This may be due to the fact that Sh species are more dependent on the whole community (and its soil niches) than to individual trees. In any case, abiotic factors (e.g., soil nutrients and microclimate - [63], [55]) may also play an important role in the distribution of such fungal trophic groups, and then the host specificity of macrofungi observed on a local scale can be different at a regional scale.

  Conclusion 

The results of our investigation support the evidence of woody plant communities as a useful indicator of the community of EMF. As a consequence of fungal host preferences, characteristic assemblages of EMF can be found in association with different tree and shrub species combinations.

Intensive silvicultural practices may dramatically change the composition and structure of woody species, affecting therefore their potential for colonization by host-specific symbionts. Consequently, strategies for the conservation of fungi should aim at increasing the biodiversity of host species and retaining different structures in broadleaf deciduous forests of the Mediterranean area.

To test the general applicability of the relationships found in this study, and to predict the fungal communities based on the woody species communities in Mediterranean deciduous forests, further investigations are needed including more replications over a broader range of sites.

  Acknowledgements 

This work was partly supported by a Management Project of the Italian Forest Service (Corpo Forestale dello Stato). We thank all our colleagues who participated in the sampling efforts, particularly Pamela Leonardi, Flavio Frignani, Martino Danielli and Lorenzo Pecoraro, also for their precious help with plant and fungal determination, and Emma Thorley for language editing.

  References