1. Introduction
Terfezia claveryi Chatin is an edible mycorrhizal hypogeous fungi belonging to the Pezizaceae family that establish mycorrhizal symbiosis with some plants of the Helianthemum genus [1]. Its natural habitats are arid and semiarid environments with a low annual rainfall inputs, mild winters, and warm summers, mainly encompassing countries of the Mediterranean geographical region [2,3]. T. claveryi was the first desert truffle to be cultivated [4], and it is known to be one of the most appreciated desert truffle species on the market [5], together with other known desert truffles (mainly Terfezia boudieri Chatin, Tirmania nivea (Desf.) Trappe and Tirmania pinoyi (Maire) Malençon [6]). In addition, desert truffles are not only an important economic resource, but contain high nutritional and antioxidant properties [7,8], including bioactive compounds with potential health benefits such as antimicrobial, anti-inflammatory, hepatoprotective, and antitumor activities [9,10,11,12].
Recently, the area cultivated with the desert truffle T. claveryi has been increased in semiarid areas of Spain [5,13], becoming an alternative agricultural crop thanks to low water requirements for cultivation [14]. Until now, some abiotic factors or agroclimatic parameters associated with plant management and the control of fungal fruiting have been studied [14,15,16]. Although this knowledge on mycorrhizal plant phenology could helped to stabilise annual fluctuations in yield ascocarps production [17], there are still high fluctuations within the same plantation, resulting in productive and non-productive areas or “patches” [18]. The analysis of ecology, phenology, and interannual fluctuations on mycelial development are also essential for the proper management of mycorrhizal plants producing truffles or mushrooms [19,20,21].
Molecular strategies such as restriction fragment length polymorphism (RLFP), denaturing gradient gel electrophoresis (DGGE) or amplified rDNA restriction analysis (ARDRA) [22] have been used for years to track or monitor edible inoculated fungal species in the field or to identify them in other applications and bioprocesses [23,24,25,26,27,28,29,30]. Among PCR-based methods, the use of species-specific primers in a quantitative real-time PCR (qPCR) approach has been widely applied to trace root and soil mycelia of different mycorrhizal fungi-producing truffles or mushrooms with high socioeconomic impact, such as Tuber melanosporum [31,32,33], Tuber magnatum [34,35,36], Tuber aestivum [37,38], Lactarius deliciosus [39,40,41,42], Tricholoma matsutake [43], and Boletus edulis [42,44,45]. In addition, the use of this technique has been used to quantify the presence of mycelium in black truffle plantations, both in the first years to control the establishment of plantations, and to subsequently evaluate its relationship with the productivity of the crop [20].
Soil properties such as pH (acid or alkaline) and the host plant species lead to the fruiting of different species of desert truffle [1,5]. In recent years, several studies on the genus Terfezia have been published to clarify and update the phylogenetic relationships among the new species and those already described within the genus [46,47,48,49,50,51,52,53,54]. These studies showed intraspecific genetic variations in the nrDNA-ITS sequence of Terfezia spp., including the identification of some cryptic species [55,56,57], in which only molecular data are required and used for species identification [20,56].
Traditionally T. claveryi and Terfezia crassiverrucosa Zitouni-Haouar, G. Moreno, Manjón, Fortas & Carlavilla have been collected and marketed together in alkaline soils, because no key differences in distribution, host plant, macroscopy, taste, and flavour characteristics can be found [49]. In fact, they are species very similar morphologically and phylogenetically [49]. Consequently, both species share their habitat in plantations and wild areas and have been called “turmas” indistinctly by gatherers. For this reason, and from now on, when the term turmas is used in this study, we refer to both marketed Terfezia species in Spanish alkaline soils (T. claveryi and T. crassiverrucosa). The internal transcribed spacer (ITS) region from ribosomal DNA (ITS1-5.8S-ITS2) has extensively been used as a universal DNA barcode marker for Fungi [58]. This region was selected to design specific primers for the detection and quantification of T. claveryi and T. crassiverrucosa DNA in soil by real-time quantitative PCR (qPCR). Thus, the objectives of this study are as follows: (a) design and check a set of specific primers for the quantification of DNA of these turmas in soil by qPCR approach; and (b) apply this strategy to determine how mycelium is distributed and spread in a desert truffle plantation.
2. Materials and Methods
2.1. Environmental Sampling
A desert truffle plantation with mycorrhized H. almeriense × T. claveryi plants (located in Torre-Pacheco, Murcia, Spain, 37°46′29.7″ N, 0°57′1.2″ W) was used to investigate the mycelium spreading of the inoculated species in the soil. The plantation was established in 2016, and it was extended again twice more with new mycorrhized plants in 2018 and 2019. This plantation started to be productive one year after the first round of planting in March 2017. The plants were established in rows separated 0.75 m and 1 m between rows. A suitable management of the cultivation and watering according to the recommendations described in [5,13,14,17,59] was followed.
In total, 36 soil samples were collected in February 2020 at an equal distance from the surrounding plants and at a depth of 10–15 cm. They were maintained at 10 °C until they were transported to the laboratory and kept at −20 °C until processing. Before DNA extraction, soil samples were dried at room temperature for 24–48 h. As detailed in Figure 1, 18 samples were from the three rows planted in 2016, 12 from the two rows planted in 2018, and 6 from the one row planted in 2019. There was a separation of 2–2.5 m between the samples within each row, and a distance of 1–1.5 m from one row to the next. Moreover, a soil sample was taken as a negative control from a non-productive area outside the plantation, free of H. almeriense mycorrhizal plants. During the fruiting season in spring 2020, 3, 19, and 6 ascocarps of similar weights were collected from 2016, 2018, and 2019 planting areas, respectively.
2.2. Soil DNA Extraction
Soil samples were carefully sieved through 500 µm mesh to remove any root fragments, stones, or plant material debris. Then, genomic DNA was extracted in duplicate from 0.25 g of each sample, previously well homogenized, using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany), according to the manufacturer’s instructions. All DNA was eluted in 100 µL of elution buffer (10 mM Tris) and stored at −20 °C until processing. The concentrations of DNA extractions were measured using a NanoDrop ND-2000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and the quality was examined by 260/280 nm and 260/230 nm optical density ratios.
In the same way, DNA extracted from a mixture of 113.1 mg T. claveryi active mycelium (T7 strain), from a pure culture in MMN-O liquid medium [60], and 0.1543 g of negative control soil (twice autoclaved), was used for the generation of the standard curve.
2.3. Design of Specific Primers for Turmas
ITS-rDNA (ITS1-5.8S-ITS2) sequences of T. claveryi, T. crassiverrucosa and other desert truffle species from GenBank and RefSeq databases (Table S1) were used for primers design by two different web-based software programs: ABI PRISM Primer Express v3.0.1 (Applied Biosystems, Waltham, MA, USA) and ProbeFinder v2.50 (Universal ProbeLibrary, UPL, Assay Design Center) (Roche Molecular Systems, Pleasanton, CA, USA). Multiple sequence alignments were carried out using the MUSCLE algorithm [61] to delimit specific regions for optimal primer selection using MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms v10.0.5 [62].
Customized primer criteria were established according to the following SYBR Green qPCR assay requirements and recommendations [63,64,65]: melting temperature 55–62 °C (opt. 60 °C), GC content 40–55% (opt. 50%), primer size 15–30 nt (opt. 20 nt), amplicon size 75–150 nt (opt. 100 nt), and GC clamp 1 nt. Primer sets generated were examined for cross- and self-dimers and hairpin formations (Beacon Designer software, PREMIER Biosoft’s, Palo Alto, MA, USA). Those with ΔG values of −3.5 kcal/mol and below were avoided. Moreover, amplicon checking for secondary structures was carried out using the UNAFold web tool (IDT, Integrated DNA technologies, Coralville, IA, USA), adjusting the Mg concentration to 3 mM. All structures formed had to meet a Tm (melting temperature) less than the qPCR annealing temperature and values of ΔG above −9 kcal/mol. Furthermore, the oligonucleotides and the obtained amplicons were evaluated in silico for specificity using Megablast search at NCBI GenBank database (
Direct PCR amplifications from dried ascoma of fungal reference materials (Table 1) were performed in a FlexCycler (Analytik Jena GmbH, Jena, Germany) according to the protocol described by Bonito [67]. Each 25 µL reaction volumes was amplified with ITS1F-ITS4 primer pair [68,69] and it was composed of 0.4 mM for each primer, 0.2 mM for each dNTP, 2.0 mM MgCl2, 50 mM KCl, 20 mM Tris-HCl (pH 8.4), 0.04% BSA and 1.25 U of Taq DNA polymerase (Invitrogen). The parameters of the thermal cycler were: initial denaturation for 2 min at 94 °C, 40 cycles consisting of 30 s at 94 °C, 30 s at 55 °C, 1 min at 72 °C, and a final extension for 5 min at 72 °C. PCR products were purified using the EZNA Cycle-Pure-Kit (Omega Bio-Tek), according to the manufacturer’s instructions, and sequenced at the Molecular Biology Service of the University of Murcia. In order to check in vitro specificity, DNA extracts of different species of desert truffles were used as templates (Table 1) under qPCR conditions.
2.4. Quantitative Real-Time PCR Conditions
A standard curve was generated from 1/10 dilutions of purified DNA standard (amounts of T. claveryi mycelium in soil) with nuclease-free water. Then, the efficiency of the real-time PCR was calculated for each primer pair selected from the value of the slope of the calibration curve [70] (generated as: E = (10(−1/slope) − 1) × 100), and the primer concentration was optimised in the range of 50 to 200 nM for the chosen combination of primers. In addition, the minimum amount of mycelium detected by this qPCR protocol was established.
Real-time SYBR-Green-dye-based PCR amplification was carried out for in vitro tests and experimental samples in 96-well plates using a QuantStudioTM 5 Flex (Applied Biosystems, Waltham, MA, USA) instrument. Each amplification was performed on 10 µL reaction volumes containing 5 µL of Power SYBR Green PCR Master Mix (2×) (Thermo Fisher Scientific), 0.1 µL of each primer at 10 µM, 3.8 µL of nuclease-free water, and 1 µL of 1/5 diluted DNA template. The thermal cycle protocol was 50 °C for 2 min and 95 °C for 10 min at hold stage followed by 40 cycles of 95 °C for 15 s and 60 °C for 60 s at PCR stage. After that, melting curve analysis was used to delete from the analysis those samples with non-target sequences and secondary structures. Three replicates for each standard DNA dilution, for each sample and for a no template control (NTC), were included for each run. Then, CT (cycle threshold) values were automatically converted to quantities of turmas mycelium in soil (mg mycelium/g soil) by QuantStudio Design & Analysis software v1.4.
2.5. Statistical Analysis
Statistical analyses were performed using the stats package in the R software environment (
3. Results and Discussion
3.1. In Silico Primer Screening
The ITS rDNA region is the most commonly used fragment for fungal species identification and as a target for soil fungal diversity studies; however, it shows different intraspecific variability in all groups of fungi and high length polymorphism [58,73,74]. In addition, even though many mycologists advocate LSU region as alternative, the ITS region shows greater efficiency in species discrimination [58]. The consensus sequence was generated from the independent turmas sequences (Table 1) aligned by MEGA X software. This sequence was used as a DNA template, resulting in three sets of designed primers (Table 2) based on in silico analyses. The specificity of the primers and the amplicons produced was also confirmed against the sequences of GenBank and RefSeq databases (Table S1).
ITS regions from multiple alignments of turmas and desert truffle sequences showed short and limited sections located within the ITS2 region for the optimal design of specific primers. This made it difficult to obtain primers automatically, and only the primer set TerclaF1/R1 was generated by ProbeFinder software. Moreover, some of the considerations for proper primer composition made the design even more complicated, because when SYBR Green dye is used as fluorescence marker, the presence of primer dimers, the formation of secondary structures, or non-specific amplifications may induce the detection of false signals [64,75]. All this forced the manual design of the primer set TerclaF2/R2 and primer set TerclaF3/R1, using the parameters already set as closely as possible.
3.2. Selection and Validation of qPCR-Specific Primers
In vitro specificity was also confirmed for the three set of primers designed, and non-amplifications were found for other fungal species (Table 1). However, the set TerclaF3/R1 provided lower Ct values, with the same amount of turmas DNA template as the sets TerclaF1/R1 and TerclaF2/R2. Careful focus was taken with non-specific amplifications of other desert truffle species (T. albida, T. grisea, T. eliocrocae, Picoa sp. and Geopora sp.), because they can share the habitat and the host plant with turmas [1,2,47,56,76]. Moreover, other Terfezia species from acid soils under non-Helianthemum sp. host plants were tested for cross-validation.
DNA serially diluted of the standard sample (10-fold dilutions) were performed and a calibration curve was constructed from 10−1 to 10−5 dilutions for three sets of primers designed. The results showed the highest efficiency for primer set TerclaF3/R1, 89% (Figure 2), followed by primer set TerclaF2/R2 and TerclaF1/R1 (64% and 58%, respectively). Moreover, coefficients of determination (R2) were always greater than 0.99 in all curves.
Finally, the primers combination chosen were TerclaF3/R1 for optimal real-time qPCR assay using SYBR green fluorescence dye, and they were used for subsequent analyses. In addition, the primer concentration was adjusted to 100 nM, and PCR inhibitors were observed when using pure soil DNA extraction as DNA template. Thus, 1/5 dilutions of each soil DNA extraction were sufficient to avoid inhibition in qPCR reactions. This was an important check point in order to prevent a drop in the efficiency of the samples analysed [70].
The minimal fungal biomass that could be reliably detected was set at 4.23 µg mycelium/g soil, because below this value, reproducibility was lost (Figure 2). Sensitivity levels were different to a greater or lesser degree for other ectomycorrhizal fungi, due to the different strategies used for standard DNA and calibration curve. The detection limit for extraradical mycelium of the edible fungi L. deliciosus, Rhizopogon roseolus and Rhizopogon luteolus was 10-fold lower (0.48 µg mycelium/g soil) from the DNA extraction of fresh mycelium in soil [40]. However, in a previous study, L. deliciosus was detected at up to 2 µg mycelium/g soil [39], and B. edulis was detected at around 39 µg mycelium/g soil [44]. Later, minimal quantities of L. deliciosus and B. edulis fungal biomass were detected: 1 and 4 µg mycelium/g soil, respectively [42]. In cases where pure in vitro culture of mycelium is difficult to achieve, such as in Tuber species [77], immature ascocarps have been used for standard DNA extraction [32,33,34,38]. Gryndler et al. [37] linked ITS rDNA copies in the PCR product with the biomass of T. aestivum mycelium for absolute quantification; however, this method has been questioned for comparison studies because there is a large variability in the number of copies of this gene between fungal species [78].
Real-time qPCR protocols could also be affected by the DNA extraction process, in which the quality of the experiment varies depending on the amount of DNA obtained and contaminants co-extracted [79,80]. However, researchers have commonly added control soil to the extraction DNA procedure in order to generate site-specific calibration curves [32,35]. Furthermore, although TaqMan-based qPCR assays that include hydrolysis probes avoid the detection of non-specific products, SYBR-Green-dye-based techniques have shown the same high-performance results when appropriate qPCR protocols are followed [63,81].
3.3. Spatial Dynamic of Turmas Mycelium in a Desert Truffle Orchard
A four-year-old desert truffle orchard was sampled for environmental validation of the primer pair selected, TerclaF3/R1. Mycorrhized plants, inoculated with T. claveryi spores, were planted in three different years (2016, 2018, and 2019) (Figure 1); therefore, mycelia from three different ages could be cohabiting. Soil samples were collected in winter, before the fruiting season (spring) of desert truffles in the Mediterranean area [1]. Moreover, winter is the plant’s physiology stage of maximum activity over the year [15]. H. almeriense shows a high photosynthetic rate and gas exchange together with a vigorous vegetative growth and flower bud production [15].
Mycelial distribution in the plantation is shown in Figure 3, in which a high variability in fungal biomass in soil between the samples can be appreciated. The range of fungal biomass detected and quantified was from 0.079 to 4.798 mg mycelium/g soil, and only 2 of the 36 samples were undetected. The specificity was also confirmed through checking melting curves after PCR cycles.
No differences in soil fungal biomass were found between years of the planting area (Kruskal–Wallis chi-squared = 0.7417, df = 2, p-value = 0.6901) (Table 3). However, significant differences were found between the sampling points (Kruskal–Wallis chi-squared = 12.188, df = 5, p-value = 0.0323) (Table 3).
In contrast to the idea of finding a pattern over the years, we detected a heterogeneous mycelial spreading in that plantation, which does not seem to respond to the year of planting. In winter, turmas mycelium may concentrate in those areas where the plant requires nutritional support, either due to sub-optimal soil conditions or due to increased plant needs. Moreover, agroclimatic parameters may also have an effect on mycelial development.
In accordance with the desert truffle life cycle [82,83], the first rainfalls of late summer and early autumn promote primordia formation, which is associated with the high production of ascocarps in the next spring [14,15]. As well as in the genus Tuber [84,85], T. claveryi exhibited a heterothallic lifestyle, which requires the combination of mycelia with different mating type genes in order to form fruiting bodies [82]. Thus, another reason for the heterogeneity of soil mycelium detected in our case study could be related to the mating type mycelia frequency across plantation, but there are still no studies in desert truffles and, thus far, no clear evidence has been found in the genus Tuber either. The detection of both mating types was correlated with productive trees in black truffle plantations [86,87], but in other studies, mating type frequency was extended randomly across plantation, and it was not significantly related to black truffle ascocarps harvested [88,89].
The amount of fungal biomass in winter showed no significant relationship with the amount of ascocarps harvested by year (3, 19, and 11 ascocarps collected in 2016, 2019, and 2018, respectively; Poisson regression p-value = 0.573). It seems that the middle age section accumulated higher desert fruiting bodies. Although the plantation was very successful in coming into production in the first year after planting, it did not reach its maximum productivity, which would occur around the eighth year [14]. In natural and cultivated experiments of black truffle, significant differences were found between mycelial abundance and productive areas [31,32,33,87]. T. magnatum mycelium was significantly higher in the surrounding fruiting areas [35]. Other ectomycorrhizal fungi, such as L. deliciosus and B. edulis, showed a non-correlation between the productivity of the different plots with the soil fungal biomass [42], but the soil mycelium was strongly related to climatic parameters. The results derived from this assay should be analysed with prudence, because long-term annual studies across season are necessary to explain the mycelial behaviour of desert truffles in soil, as discussed above for other mycorrhizal fungi.
Although the plantation of our study was irrigated and the competing vegetation had been eliminated, other strategies, such as mechanical tilling practices [90,91,92], should be investigated in order to maintain a youthful plantation and avoid the loss of crops after a few years. Therefore, it is necessary to explore the mycelial dynamics over time, because the plantation may be at risk of ageing, with mycelium and mycorrhizae displaced and no ascocarp production.
4. Conclusions
In conclusion, the selected primers designed within ITS regions are sufficiently accurate to develop a real-time qPCR protocol for the quantification of fungal biomass of T. claveryi and T. crassiverrucosa in soil samples. The TerclaF3/R1 primer set was tested and validated for the SYBR-Green-based qPCR assay. Moreover, the preliminary study on soil samples from a desert truffle plantation showed no correlation between winter soil fungal biomass and truffle productivity in spring. However, the amount of fungal soil mycelium seemed to trend to decrease with the years, indicating that, after a certain time, the plantation could become unproductive. In-depth knowledge of mycelial dynamics over the years would help us to develop proposals for plantation management to extend the useful life of plantations.
Conceptualization, F.A. and A.M.; methodology, F.A. and A.N.-R.; formal analysis, F.A. and A.N.-R.; investigation, all authors; writing—original draft preparation, F.A.; writing—review and editing, all authors; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.
All sequences used during this experimental study are available from GenBank and accession numbers are given in
The authors are grateful to Tomás López for allowing the sampling in his desert truffle plantation and providing the desert truffle production data and to Angel Guarnizo Serrudo for his support in statistical analyses.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. Diagram of sampling points (pyramid marks) in different years of plantation establishment.
Enlarge this image.Figure 2. Real-time qPCR standard curve for T. claveryi DNA quantification in soil. The curve was generated by plotting the Ct values obtained from 10-fold serial dilutions of DNA standard sample against the logarithm of the quantity of mycelium in soil (μg/g). Efficiency for the primer set TerclaF3/R1 was 89%.
Enlarge this image.Figure 3. Distribution of turmas mycelium in the different planting years (2019 in black, 2018 in grey, and 2016 in white raster) across sampling points (1–6).
Fungal reference materials used in this study.
| Taxon | Specimen ID 1 | GenBank |
|---|---|---|
| Terfezia albida Ant. Rodr., Muñoz-Mohedano & Bordallo | j574 | OP458226 |
| Terfezia eliocrocae Bordallo, Morte & Honrubia | j579 | OP458228 |
| Terfezia olbiensis (Tul. & C. Tul.) Sacc. | j588 | OP458229 |
| Terfezia claveryi Chatin | j592 | OP458224 |
| Terfezia claveryi Chatin | j596 | OP458223 |
| Terfezia claveryi Chatin | j597 | OP458222 |
| Terfezia claveryi Chatin | j216 | OP458220 |
| Terfezia claveryi Chatin | j73 | OP458219 |
| Terfezia crassiverrucosa Zitouni-Haouar, G. Moreno, Manjón, Fortas, & Carlavilla | j53 | OP458218 |
| Terfezia crassiverrucosa Zitouni-Haouar, G. Moreno, Manjón, Fortas, & Carlavilla | j235 | OP458221 |
| Tirmania pinoyi (Maire) Malençon | j601 | MG920185.1 |
| Tirmania nivea (Desf.) Trappe | j590 | OP458225 |
| Terfezia grisea Bordallo, V. Kaounas & Ant. Rodr. | j485 | KP189333 |
| Terfezia fanfani Mattir. | j484 | OP458230 |
| Terfezia pseudoleptoderma Bordallo, Ant. Rodr. & Muñoz-Mohedano | j478 | OP458231 |
| Terfezia arenaria (Moris) Trappe | j466 | OP458227 |
| Terfezia boudieri Chatin | j371 | OP458234 |
| Tirmania honrubiae Morte, Bordallo & Ant. Rodr. | j366 | OP458233 |
| Terfezia fanfani Mattir. | L14 | HM056219 |
| Terfezia extremadurensis Muñoz-Mohedano, Ant. Rodr. & Bordallo | j96 | OP458232 |
| Terfezia pini Bordallo, Ant. Rodr. & Muñoz-Mohedano | j151 | OP458235 |
| Picoa sp. Vittad. | j442 | OP458217 |
| Picoa sp. Vittad. | j17 | OP458215 |
| Picoa sp. Vittad. | j59 | OP458214 |
| Picoa sp. Vittad. | j41 | OP458213 |
| Picoa sp. Vittad. | j45 | OP458212 |
| Picoa sp. Vittad. | j20 | OP458216 |
| Geopora sp. Harkn. | R21b | OP458210 |
| Geopora sp. Harkn. | R23 | OP458209 |
| Geopora sp. Harkn. | j121 | OP458211 |
1 Herbarium of University of Murcia (MUB-FUNGI).
Set of primers designed and tested.
| Primer Set | Sequence (5′ → 3′) | Length (nt) | Tm (°C) | GC (%) | Amplicon (nt) |
|---|---|---|---|---|---|
| TerclaF1 |
ATAGGGCATGCCTGTCTGAG | 20 | 60.0 | 55 | 106 |
| TGGAGGGCAACTTAATACACAGT | 23 | 59.2 | 43 | ||
| TerclaF2 |
TAACTGTGTATTAAGTTGCCCTCCAG | 26 | 59.0 | 42 | 120 |
| GAGTTGAGGCAAGTACAATCAATCATAC | 28 | 59.2 | 39 | ||
| TerclaF3 |
GCTCCCCCTCACTCAAGTAT | 20 | 59.1 | 55 | 79 |
| TGGAGGGCAACTTAATACACAGT | 23 | 59.2 | 43 |
Tm = melting temperature; GC = guanine–cytosine.
Mycelia detected in soil for each variable in desert truffle orchard.
| Variable | Samples (N) | Mean Fungal Biomass |
SD | Significance Level (p-Value < 0.05) |
|---|---|---|---|---|
| Year 2016 | 18 | 0.386 | 0.350 | a |
| Year 2018 | 12 | 0.574 | 0.684 | a |
| Year 2019 | 6 | 0.577 | 0.451 | a |
| SP-1 | 5 | 0.394 | 0.474 | ab |
| SP-2 | 6 | 0.142 | 0.215 | a |
| SP-3 | 6 | 0.588 | 0.792 | ab |
| SP-4 | 6 | 0.358 | 0.214 | ab |
| SP-5 | 6 | 0.700 | 0.592 | b |
| SP-6 | 6 | 0.695 | 0.383 | b |
SP: sampling point; SD: standard deviation.
Supplementary Materials
The following supporting information can be downloaded at:
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; Morte, Asunción 2
; Navarro-Ródenas, Alfonso 2
1 Departamento de Biología Vegetal (Botánica), Facultad de Biología, Universidad de Murcia, CEIR Campus Mare Nostrum (CMN), Campus de Espinardo, 30100 Murcia, Spain; Forest Science and Technology Centre of Catalonia (CTFC), Carretera de Sant Llorenç de Morunys, Km 2, 25280 Solsona, Spain
2 Departamento de Biología Vegetal (Botánica), Facultad de Biología, Universidad de Murcia, CEIR Campus Mare Nostrum (CMN), Campus de Espinardo, 30100 Murcia, Spain