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Article

Soil Prokaryotic Diversity Responds to Seasonality in Dehesas, Modulated by Tree Identity and Canopy Effect

by
José Manjón-Cabeza
1,2,*,
Mercedes Ibáñez
1,2,†,
María José Leiva
3,
Cristina Chocarro
1,
Anders Lanzén
4,
Lur Epelde
5 and
Maria Teresa Sebastià
1,2
1
Forestry and Agronomy Engineering and Science Department, University of Lleida, 25198 Lleida, Spain
2
Department of Functional Ecology, Forest Science and Technology Centre of Catalunya, 25280 Solsona, Spain
3
Department of Vegetal Biology and Ecology, University of Sevilla, 41012 Sevilla, Spain
4
AZTI (Basque Research and Technology Alliance), 20110, Pasaia, Spain
5
Department of Conservation of Natural Resources, NEIKER-Basque Institute for Agricultural Research and Development, 48160 Derio, Spain
*
Author to whom correspondence should be addressed.
†
Current address: Centre for Ecological Research and Forestry Applications (CREAF), Universitat Autónoma de Barcelona, 08193 Bellaterra, Spain.
Microbiol. Res. 2025, 16(7), 153; https://doi.org/10.3390/microbiolres16070153 (registering DOI)
Submission received: 4 April 2025 / Revised: 19 May 2025 / Accepted: 10 June 2025 / Published: 5 July 2025
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Abstract

Dehesas are mosaics of open grassland and standalone trees that are diversity reservoirs. However, they have recently faced abandonment and intensification, being replaced by plantations of fast-growing trees or subject to encroachment. Following a change in dehesa communities and structure, a change in soil microbial diversity and functionality in dehesas is expected, but dehesas’ microbial diversity is still a big unknown. In this work, we bring to light the soil prokaryotic taxonomic diversity in dehesa ecosystems and present a first approach to assessing their metabolic diversity through metabarcoding data. For this, we compared three dehesas dominated by different tree species: (i) one dehesa dominated by Quercus ilex; (ii) one dominated by Pinus pinea; and (iii) one dominated by a mixture of Q. ilex and Q. suber. At each dehesa, samples were taken under the canopy and in the open grassland, as well as through two seasons of peak vegetation productivity (autumn and spring). Our results show the following findings: (1) seasonality plays an important role in prokaryotic richness, showing higher values in autumn, and higher evenness in spring; (2) the effect of seasonality on the soil’s prokaryotic diversity is often modulated by the effect of tree species and canopy; (3) taxonomic diversity is driven mainly by the site effects, i.e., the opposite of the metabolic diversity that seemed to be driven by complex interactions among seasons, tree species, and canopies.

1. Introduction

Mediterranean wood pastures, called “dehesas” in Spain or “montados” in Portugal, are agroecosystems that involve sylvo-pastoral practices. They are formed by a mosaic of trees, traditionally Quercus species, and grassland, which normally includes annual grassland that germinates in autumn and dies out in spring as summer approaches. Due to the high plant biodiversity found in the dehesas, they are regarded as a habitat of community interest by the European Union (EU) [1]. However, land use in dehesas has changed in recent decades. Some dehesas are being abandoned and are suffering from encroachment [2], while others are experiencing changes in management, leading to intensification [3]. Among intensification practices, we can find the substitution of traditional oak species by fast-growing trees such as Eucalyptus sp. or Pinus pinea [4].
Despite being a habitat of EU community interest (protected habitat 6310; report under Article 17 of the Habitats Directive from the European Environmental Agency), little–nothing is known about the soil’s prokaryotic diversity within dehesas. In general terms, it is known that soil bacterial diversity is driven by several factors, such as macro- and microelements [5], pH [6], seasonality, or even the animals that feed on the plant aboveground [7]. Few studies have addressed soil functional diversity or functionality in dehesas [8,9], showing that higher microbial activity occurs in fall and winter, apart from nitrification, which happens at a higher rate in spring. Also, a bidirectional and complex relation between vegetation and microbiota is observed in the mentioned studies. However, to our knowledge, there are no studies to date that have used 16S rDNA to assess the prokaryotic diversity of the dehesa.
Among the many unknowns regarding the spatial and temporal distribution of soil microbial communities in dehesas, whether there is a direct dependency of the microbial communities on the vegetation structure (open grassland versus the closed canopy), as it has been observed for plant communities [10], is still unknown. Considering the recent changes in tree composition in dehesas and considering that one of the changes that threatens the dehesa is the substitution of oaks by pines, it is also important to investigate how those changes will affect the soil microbial communities. Furthermore, we ignore whether those soil microbial communities change with seasonality, as found with plants [10]. In addition, dehesas are strongly seasonal systems, with a maximum vegetation biomass peak in spring, and a secondary peak in fall, linked to the rainfall distribution and the favorable conditions [11]. How those changes affect the soil microbiome is another open question. Furthermore, since no experiment, to the best of our knowledge, has addressed prokaryotic diversity in dehesas, not much is known about the large-scale spatial distribution, although it is expected to be highly dependent on the location of the study [12].
Taking this into account, in this work, we wanted to ask the following questions: (1) Is soil prokaryotic functional and taxonomical diversity affected by the soil’s location, either under the tree canopy or in the open grasslands? (2) Are changes in prokaryotic diversity linked to the dominant tree species in an area? (3) Does seasonality affect the prokaryotic taxonomical diversity of dehesas?

2. Materials and Methods

2.1. Location

This study was carried out in two locations in the southwest of the Iberian Peninsula: Doñana Natural Park (DN, 37°15034″ N, 6°19055″ W, 30 m a.s.l.) and the Sierra Morena mountains (SM, 37°39050″ N, 5°56020″ W, 296 m a.s.l.) (Figure S1). Both areas have a Mediterranean climate, with warm, dry summers, and mild winters. The mean annual temperature in DN is 18.1 °C, with no temperatures below 0 °C due to proximity to the sea, and in SM, it is 16.8 °C. Mean annual precipitation in DN is 543 mm, and in SM, it is 648 mm. On average, the soils in SM have a lower proportion of sand and a greater proportion of fine elements (clay and loam) than the soil in DN (see details in Table 1). In DN, 2 sites were used for this study, a site dominated by a mix of Q. ilex and Q. suber (% Organic Carbon: 0.32; % Nitrogen: 0.15) and a site dominated by Pinus pinea (% Organic Carbon: 1.52; % Nitrogen: 0.20); meanwhile, in SM, a single site dominated by Quercus ilex was studied (% Organic Carbon: 0.8; % Nitrogen: 0.85). In total, three sites were selected.

2.2. Sampling Design

Field sampling was performed in spring (5 April 2016–10 April 2016) and late fall (13 December 2016–17 December 2016), both of which are periods when the system shows the highest productivity due to precipitation to capture seasonal variability in the microcosms of the studied sites. Half of the samples were collected in spring and half of them in fall. The former period has the highest productivity due to the most favorable temperature and conditions, while in the second one, the grassland is youngest, and the temperature is lower than in spring.
A direct sampling based on expertise was conducted to select the sites and then a systematic sampling was followed. At each site and for each dominant tree present, 12 samples were collected: 12 from the Pinus pinea site; 12 from the mixed site under the influence of Quercus ilex; 12 under the influence of Quercus suber; and 12 from the Quercus ilex site in Sierra Morena. Furthermore, we considered tree canopies and sampled soils at both microenvironments, under the canopy (UC, at 1 m maximum from the tree trunk) and in the open grassland (OG, no less than 3 m away from the treetop border). To sum up, 48 samples were taken in total, with 24 taken each season. For each possible season (spring and autumn), canopy (UC and OG), and site–tree combination (SM–ilex, DN–ilex, DN–pinea, and DN–suber), 3 samples were taken (Figure S2). Sampling points were selected following the north orientation with respect to the tree trunks. For more information, the sampling approach was the same as that used in [1].
Soil samples were extracted from the upper 10 cm soil layer, after removing the vegetation and litter layers using a probe (10 × 6 × 5 cm) in each of the sampling points. Bulk soil and rhizosphere soil were not differentiated. Then, the samples were included in a stabilization Solution C1 of MoBio’s PowerSoil DNA isolation kit until the arrival at the laboratory and immediate analysis.

2.3. Library Preparation and Sequencing

Amplification was carried out using a dual indexing tag-tailed approach as described by d’Amore et al. [13] with five random nucleotides inserted between the linker and forward primer (5NDI), as described by Shcrimer et al. [14]. Briefly, adapter-linked forward and reverse primers were used in the first PCR amplification to a total volume of 20 μL using the following: 1 μL template community DNA, 1 μM each of F and R primers, and 1× Qiagen Hot Start Master Mix (Qiagen, Hilden, Germany). The following PCR parameters were used: initial denaturation at 95 °C for 15 min, 20 cycles of 95 °C for 20 s, 55 °C for 30 s, 72 °C for 30 s, and final extension at 72 °C for 7 min. Amplicon libraries were cleaned using Agencourt AMPure XP (Beckman Coulter Genomics, Pasadena, CA, USA) and eluted in 25 μL DEPC-treated water. The following adapter-linked primer pairs were used: 519F (CAGCMGCCGCGGTAA) adapted from Ovreås et al. [15] and 806R (GGACTACHVGGGTWTCTAAT) modified from Caporaso [16], targeting the prokaryotic 16S rRNA hypervariable region V4. Barcoded primers were used as template in the second amplification step using a total PCR volume of 50 μL including 5 μL template (0.5–1 ng DNA), 1 μM each of barcoded F and R adapter-linked primers, and 1× PCR mix. The same PCR parameters as in step 1 were used, except higher annealing temperature (61 °C) for 10 cycles. After again being cleaned using AMPure XP, the resulting amplicons were visualized on a 1% agarose gel next to products from the first PCR, to verify a unique product and incorporation of barcoded linkers. DNA concentrations were measured using Qubit fluorometric quantitation (Thermo-Fischer, Waltham, MA, USA); based on this, they were mixed in equimolar amounts to a final barcoded library before sequencing. Pair-ended sequencing was carried out using Illumina MiSeq v2 (approximately 2 × 250 nt length) at Tecnalia, Miñano, Spain.
Sequence data processing, taxonomic classification, and statistical analysis (bioinformatics) Amplicon sequence read-pairs were quality-filtered and overlapped using vsearch v2.4.3 [17] (default parameters except for fastq_maxdiff = 5). The overlapped sequences were trimmed to remove primers and 5NDI using cutadapt [18], discarding non-matching sequences, and thereafter quality-filtered and trimmed to a total length of 253 nt using vsearch (option fastq_maxee = 0.5), with shorter sequences discarded. Quality-filtered overlapped sequences were then clustered into OTUs (ultimately error-corrected exact sequence variants) using Swarm v2 (default parameters) [19]. Swarm OTUs were then subjected to first-reference-based filtering (method—uchime_ref using the SilvaMod Database v128 as reference; default parameters) and later de novo chimera filtering (method—uchome_denpovo; default parameters), using UCHIME as implemented in vsearch. The remaining chimera-filtered Swarm OTUs were then further clustered into OTUs based on overall sequence similarity (method cluster_size–id 0.97, i.e., with a minimum similarity of 97%) using vsearch. OTU abundances were obtained by mapping the reads back to the representative OTU sequences, again using vsearch (method–usearch_global–id 0.97) [17].
Taxonomic classification was carried out by aligning the representative OTU sequences with the SilvaMod database (v128) using blastn (v.2.6.0+ task megablast) and analysis using the CREST LCAClassifier (v3.0.3) with default parameters [20] (https://github.com/lanzen/CREST/ (last accessed on 15 May 2018). Unclassified and eukaryotic OTUs were excluded from further analyses.

2.4. Statistical Analysis

The diversity indexes of the samples were calculated using the package “phyloseq” [21]: OTU richness (S), the Shannon Wiener Diversity Index (H’), and the Pielou Evenness Index (J’). When richness represents the amount of different OTUs in each sample, the Shannon Information Index (H’) is the summatory of the proportion of species, multiplying the natural logarithm of that proportion. The Pielou Evenness Index is found by dividing the Shannon Wiener Diversity Index (H’) by the natural logarithm of the number of OTUs in the sample.
Linear models were then performed for each index using R’s built-in functions to assess the influence of different environmental conditions and their interactions on community diversity. The models were run as a function of the seasonality (either if the sample was taken in spring or fall), site as a block effect (SM–ilex, DN–mixed, or DN–pinea), tree species (Quercus ilex, Quercus suber, and Pinus pinea) and canopy (if the sample is near the area of influence of the tree in the open grassland (OG) or directly below the canopy (UC). Interactions between season, canopy, and tree species were also included. p-values between 0.05 and 0.1 were considered to be marginally significant and will be discussed. The general model was:
Y = β0 + β1 Site + β2 Season + β3 Tree species + β4 Canopy + β5 Season·Tree species + β6 Season·Canopy + β7 Tree species·Canopy + β8 Season·Tree species·Canopy
To explore soil prokaryotic functionality, we used PiCrust2. PiCrust2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2) is a software that estimates the contribution of bacteria or archaea to the metagenome, inferring the functionality of the bacteria or archaea in the sample from the phylogenetic relations [22]. These predictions can be biased due to the accuracy being linked to the availability of different genomes [23], but it is a useful tool for generating hypotheses; with this being the first study to our knowledge made in dehesas, it will be used as an approximation. Please note that this is not the result of shotgun metagenomics. A similar model as the one mentioned above was applied for the relative abundance of the metabolic pathways obtained via PiCrust2. Since the aim of this article is to unveil the interactions between the seasonality, the tree species, and the canopy effect, only the pathways affected by one or more of these variables will be commented on.
Principal coordinate analysis (PCoA) was applied using the “vegan” [24] and “ape” [25] packages for the unconstrained ordination of both microbial communities, bacteria and archaea (at Hellinger proportions). The same analysis was applied to the metabolic pathway abundances obtained with the PiCrust2 analysis. Afterward, variables representing the coordinates on the first two axes were extracted and evaluated in a linear model like the one applied above and processed similarly.
In order to assess the influence of the different treatments on the taxonomical groupings, the DESeq2 differential abundance test [26] was used to assess the statistical significance of differences between samples, and the logarithm of the fold change was used to measure the size of the difference. The taxonomic trees (heat trees) of these differences were developed using the “Metacoder” package [27].
Briefly, due to the hierarchical aspect of the data, a form in which the whole of the data can be visualized is needed. To fix this, we use heat trees proposed by the authors mentioned above in which the thickness of the branches and nodes represents the OTU count, and the color represents the logarithm of the fold change obtained after applying the DESeq2 differential abundance test. If the difference is statistically significant between two treatments, then the branch/node will appear colored; otherwise, it will appear grey. In those treatments in which we have more than two, a matrix made of several heat trees was used.

3. Results

3.1. Sequencing

Sequencing resulted in a total of 3,375,714 read pairs, of which 3,158,431 (94%) were retained after read-overlapping and quality filtering (Samples_and_diversity.xlsx). An additional 2.1% of read pairs were removed as putative chimeras and 0.3% as eukaryotic or unclassified taxonomically, leaving 3,081,341 HQ reads. Singletons (retained) accounted for 0.3% of HQ reads or 25% of OTUs, indicating a relatively good sequence quality and coverage of total diversity (a Good’s coverage of 99.7%).

3.2. Alpha-Diversity

Prokaryotic species richness was affected by the site (p-value < 0.05) (Table 2), with the site in SM showing a higher number of OTUs than the DN sites. There was a canopy effect dependent on seasonality (p-value = 0.062). While species richness under the canopy (UC) was not affected by the change of season, species richness in the open grassland (OG) seems to be lower in spring than in fall. The Shannon Information Index did not respond to any of the factors. On the other hand, the Pielou Evenness Index was influenced by seasonality (p-value = 0.072) (Table 3), with the evenness in spring being higher than in fall.

3.3. Community Structure and Inferred Functionality

Regarding the prokaryotic composition in dehesas (Figure 1A), samples were grouped by site (p-value = 0.061) and season (p-value = 0.078) on the first axis. A pattern in which samples are separated by site (p-value < 0.05) and canopy (p-value = 0.087) emerged, clustering Q. ilex opposite to P. pinea and Q. suber and OG opposed to the under-canopy area (Figure 1A).
Regarding sample ordination according to functionality, the samples appeared to be ordinated by a triple interaction of the seasonality, tree species, and the canopy effect (p-value = 0.14); the samples were separated into open grassland and under-canopy (higher values), with the clear exception of the mixed-suber samples in fall under the canopy, which were distributed the furthest away from the rest. The second axis is driven by the seasonality as well as the tree species (p-value < 0.05) (Table 4), separating the spring and the fall samples and distributing samples linked to Q. ilex on the opposite side from those of the other species (Figure 2).
From all the pathways analyzed, 32 were influenced either by the seasonality, the tree species, the canopy effect, or the interactions between these factors. The most common of these interactions was the interaction between seasonality and the canopy effect, appearing in 13 out of the total 32 (Table 5).

3.4. Differences in Taxonomical Groupings

The SM site was highly differentiated from the other sites in DN in terms of the taxonomical grouping; the phylum Chloroflexi and the class Deltaproteobacteria especially, and some other smaller groups in the Actinobacteria phylum, appeared abundantly in the SM site. Samples in the Pinus pinea site presented differences from Q. ilex in the mixed site by including some small groups like the order Ktedonobacterales and the families Bogoriellaceae and Burkholderiaceae. Order Nitrosphaerales from the Archaea were found there as well. P. pinea and Q. suber did not show taxonomical differences in this analysis. Q. suber showed a higher abundance of the order Clostridiales and the class Ktedonobacteria compared to Q. ilex, while Q. ilex showed a higher number of the families Blastocatellaceae and the genus Georgenia (Figure 3).
The spring samples showed a higher relative abundance in groups belonging to the clade Terrabacteria and the phyla Planctomycetes and Acidobacteria compared to the fall samples, which showed a higher relative abundance in smaller groups from phylum Proteobacteria (Sphingobacteriaceae and Acinetobacter) (Figure 4).
Soil in the open grassland had a higher relative abundance of families Caulobacteraceae and Pseudonocardiaceae and genus Reyranella than those under the canopy. The latter had a higher relative abundance of order Frankiales (Figure 5).

4. Discussion

In our results, the effect from the canopy of the trees is, in almost every result, linked to seasonality (Table 1, Table 2 and Table 3). It seems that, in the warm seasons, the soil outside the canopy could reach very high temperatures; thus, the canopy of trees could allow for less extreme environments [28]. In autumn, on the other hand, in Doñana, the temperature does not drop very much; however, in Sierra Morena, it could be a limiting factor. Moreover, authors have pointed out that the canopy effect in dehesas produces a mosaic of nutrients [29] in the soil; some others have also assessed the effect of the canopy on plant diversity [30,31]. In this work, we explain the effect that the canopy effect in dehesas produces on soil prokaryotic diversity. Whether this diversity is related to the mosaic of nutrients or the mosaic of vegetation remains to be researched. It is interesting how the different tree species can shape the bacterial community. Other studies [32,33] have shown that Pinus pinea modifies the soil in Q. ilex climax zones in almost every possible variable and also modifies the microbial community. These studies have shown that one of the main factors that help Pinus pinea modify its community is through pH, which has a great effect on microbial diversity. Nonetheless, it seems that the type and amount of volatile compounds may vary depending on the tree species and their sites, which could act as another differentiator in the direct recruitment of bacteria in soil [34]. The similarities that occur in the Q. suber and P. pinea are yet to be tested, as the cork oak seems to have very different litter and soil dynamics from those of P. pinea.
As stated before, although the diversity indexes in the dehesa are somewhat stable, there are differences in the specific taxa of the bacteria under each of the treatments (Figure 3, Figure 4 and Figure 5). Despite having differences that depend on the site, it is shown in the results that some trees would recruit specific bacteria: this is the case for Georgenia with Quercus ilex. It is impossible to unravel the role of specific trees with Q. ilex here; normally, it is bacteria that work in extreme environments and range greatly in functionality, from halotolerant to thermophilic [35,36,37]. After site, seasonality was found to be one of the main sources of variability in this study. The spring samples show a greater proportion of Planctomycetes, especially Gemmata and Thepidisphaerales (Figure 4). This indicates that, in spring, the community adapts to a more thermophilic community [38]. Moreover, the bacteria in the Planctomycetes group are responsible for the anammox cycle, liberating N2 to the atmosphere [39]. This group has been shown to increase under warming conditions in meadows [40]; it is probable that increasing temperatures could enhance the conditions in which this microorganism develops, increasing the N2 released into the atmosphere. In a scenario of climate change, this could lead to the depletion of NO3− in soils that have great proportions of these microorganisms. On the other hand, in autumn, there is an increase in the Acinetobacter and Sphingobacteraceae that degrade complex materials [41]. Overall, the results indicate a moderately thermophilic community with an abundance of PGPRs and a degrading community in the warmer season. This last result aligns with another study carried out in the dehesa, related to C and N cycling [1].
Although a few significant differences were seen to be attributable only to the canopy effect (Figure 5), it was shown that, outside of the canopy, higher proportions of Nitrobacteraceae and Reyranella (N-oxidizing and -reducing bacteria, respectively) [42,43,44] were present in higher proportions in the grassland. This could a result of their being coupled to the higher proportion of legumes in the open grassland and their N fixation [10]. Also, PGPRs such as the Pseudonocardiales have been observed in the open grassland in a higher proportion in the open grasslands, which would improve the productivity of such plants [45,46].
Lastly, it is interesting to note that, in our data and from our experience working with the PiCrust2 pipeline data (although sequence and/or identity seems to be driven by the site), functionality seems to be driven by the interaction of seasonality, tree species, and the canopy effect. In fact, in her work in 2020, Ibáñez et al. determined that the methane cycle in the dehesas was due to seasonality; however, in this work, we can see how the pathways related to methanotrophs or methanogens do not only depend on seasonality but also on tree species and the canopy effect. In the same work, she described how the N2O occurred in autumn under the canopy. In this study, we could identify the nitrifier denitrification pathway, and it was influenced both by canopy and seasonality. The fact that the identity of the microorganisms is driven by the site while the functionality is driven by the environmental conditions tested (seasonality, tree species, and canopy) implies that these ecosystems present some kind of functional redundancy [47,48], providing these systems with a “shield” when they face disturbances. Since these systems are usually affected by periodic or isolated disturbances, it is likely that this ecosystem has adapted to fulfill several functions when faced with disturbances.

5. Conclusions

After analyzing the differences in the soil bacterial community in dehesas, we determined that the differences in the spatio-temporal aspects of the dehesa influence the diversity and composition of soil prokaryotic communities. Seasonality was found to be a relevant factor in driving the evenness and composition of the community but was modulated by the tree species and the presence/absence of tree canopies. As this is one of the first studies that has been conducted on the bacterial taxonomic and functional diversity in dehesas, it is important to discuss the way in which the functional and taxonomical diversities in these ecosystems are dependent on seasonality and canopies. It would be interesting in further studies to repeat the data collection over a series of years to observe clear patterns of seasonality; moreover, we might be able to determine how the variability between years affects the prokaryotic diversity. Another branch of possible studies is to assess the possible influences of the management, livestock pressure [49], and large animals [50] on the dynamics of microbial diversity and functionality in the dehesa. Furthermore, since PiCrust serves as a first approach to the functionality of the system, further advances towards understanding the different microbial-level processes in dehesas could help us in maintaining these ecosystems. As research into this type of ecosystem is scarce, research accounting for soil microbial diversity as well as functionality in dehesas should be developed to increase our knowledge of this rare, semi-natural type of ecosystem.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microbiolres16070153/s1, Figure S1: Location of the study; Figure S2: Scheme of the sampling design carried out; Figure S3: Legend of the differential diversity of trees shown in the article.

Author Contributions

Material preparation, data collection, and analysis were performed by M.I., L.E., A.L., and J.M.-C. The first draft of the manuscript was written by J.M.-C., and all authors commented on previous versions of the manuscript. The design of the study was developed by M.T.S., M.J.L., and C.C. Funding for the study was obtained by M.T.S. All authors read and approved of the final manuscript.

Funding

This work was funded by the BIOGEI (GL2013-49142-C2-1-R) and IMAGINE (CGL2017-85490-R) projects from the Spanish Science Foundation FECYT-MINECO and supported by a FI fellowship to José Manjón-Cabeza (PRE2018-086312).

Data Availability Statement

All the data and code used in this work are available under reasonable request.

Acknowledgments

The authors would like to acknowledge the people involved in fieldwork and laboratory tasks. Special thanks to Doñana Research Coordination Office and Dehesa de Gato S.L. for their facilities and support.

Conflicts of Interest

All the authors declare that there are no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
SMSierra Morena
DNDoñana National Park
OGopen grassland
UCunder canopy
OTUOperational Taxonomic Unit

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Figure 1. PCoA of the taxonomical data. (A) Complete PCoA separated by the tree species and the location in which the sample was taken for clarity. The seasonality is represented by the color, while the canopy treatment is represented by full/open dots. (B) Distribution of the second axis represents the values of the samples based on the canopy and tree species treatment.
Figure 1. PCoA of the taxonomical data. (A) Complete PCoA separated by the tree species and the location in which the sample was taken for clarity. The seasonality is represented by the color, while the canopy treatment is represented by full/open dots. (B) Distribution of the second axis represents the values of the samples based on the canopy and tree species treatment.
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Figure 2. PCoA on metabolic pathways. (A) Complete PCoA of the metabolic pathways obtained via PiCrust2. The seasonality is represented by the color, while the canopy treatment is represented by full/open dots. A separation is made by site and tree species for clearer lecture. (B) Distribution of values of the samples in the second axis according to the seasonality and the tree species.
Figure 2. PCoA on metabolic pathways. (A) Complete PCoA of the metabolic pathways obtained via PiCrust2. The seasonality is represented by the color, while the canopy treatment is represented by full/open dots. A separation is made by site and tree species for clearer lecture. (B) Distribution of values of the samples in the second axis according to the seasonality and the tree species.
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Figure 3. Differences in taxonomical groupings between sites and tree species. Each color corresponds to one of the sites and tree species compared to another one (blue vs. brown). The thickness of the branch and nodes represents the OTU count, while the color represents the log 2 median ratio of the counts. This is a value used to represent differential genetic expression, but in this work, it is used to represent the differential representations of the taxa in our samples. Nodes and branches in grey mean that no statistical differences were found between groupings for that taxon. Colored nodes represent statistically significant changes between taxa. Branches or nodes colored in blue correspond to the environment indicated in the rows, while those colored in brown correspond to taxa that are in higher proportions in the environment indicated in the columns. For a legend of this figure, see Figure S3.
Figure 3. Differences in taxonomical groupings between sites and tree species. Each color corresponds to one of the sites and tree species compared to another one (blue vs. brown). The thickness of the branch and nodes represents the OTU count, while the color represents the log 2 median ratio of the counts. This is a value used to represent differential genetic expression, but in this work, it is used to represent the differential representations of the taxa in our samples. Nodes and branches in grey mean that no statistical differences were found between groupings for that taxon. Colored nodes represent statistically significant changes between taxa. Branches or nodes colored in blue correspond to the environment indicated in the rows, while those colored in brown correspond to taxa that are in higher proportions in the environment indicated in the columns. For a legend of this figure, see Figure S3.
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Figure 4. Differences between seasons in the taxonomical groupings (spring in blue, fall in fuchsia). Thickness of the branch and nodes represents the OTU count, while the color represents the log 2 median ratio of counts. This is a value used to represent differential genetic expression, but in this work, it is used to represent differential representation of the taxa in our samples. Nodes and branches in grey mean that no statistical differences were found between groupings for that taxon. Colored nodes represent statistically significant changes between taxa. Branches or nodes colored in blue correspond to spring samples, while those colored in fuchsia correspond to taxa that are in higher proportions in the environment indicated in the columns.
Figure 4. Differences between seasons in the taxonomical groupings (spring in blue, fall in fuchsia). Thickness of the branch and nodes represents the OTU count, while the color represents the log 2 median ratio of counts. This is a value used to represent differential genetic expression, but in this work, it is used to represent differential representation of the taxa in our samples. Nodes and branches in grey mean that no statistical differences were found between groupings for that taxon. Colored nodes represent statistically significant changes between taxa. Branches or nodes colored in blue correspond to spring samples, while those colored in fuchsia correspond to taxa that are in higher proportions in the environment indicated in the columns.
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Microbiolres 16 00153 g005
Figure 5. Differences in taxonomical groupings by canopy (OG in blue, UC in fuchsia). Thickness of the branch and nodes represents the OTU count, while the color represents the log 2 median ratio of counts. This is a value used to represent differential genetic expression, but in this work, it is used to represent differential representation of the taxa in our samples. Nodes and branches in grey mean that no statistical differences were found between groupings for that taxon. Colored nodes represent statistically significant changes between taxa. Branches or nodes colored in blue correspond to samples taken in the open grasslands while those colored in fuchsia correspond to taxa collected under the canopy.
Figure 5. Differences in taxonomical groupings by canopy (OG in blue, UC in fuchsia). Thickness of the branch and nodes represents the OTU count, while the color represents the log 2 median ratio of counts. This is a value used to represent differential genetic expression, but in this work, it is used to represent differential representation of the taxa in our samples. Nodes and branches in grey mean that no statistical differences were found between groupings for that taxon. Colored nodes represent statistically significant changes between taxa. Branches or nodes colored in blue correspond to samples taken in the open grasslands while those colored in fuchsia correspond to taxa collected under the canopy.
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Table 1. Soil characteristics of every site. Adapted from [1].
Table 1. Soil characteristics of every site. Adapted from [1].
SiteSM–ilexDN-MixedDN–pinea
pH6.77.97.0
Organic C (%)0.80.320.51
Total N (%)0.850.150.11
Clay (%)181016
Silt (%)292521
Sand (%)546563
Table 2. Analysis of variance: Species richness as a function of the season, the tree species, and the canopy effect from the trees. F statistic as the mean of the squares of the factor divided by the mean of the squares of the residuals. Asterisks (*) means p-value < 0.05.
Table 2. Analysis of variance: Species richness as a function of the season, the tree species, and the canopy effect from the trees. F statistic as the mean of the squares of the factor divided by the mean of the squares of the residuals. Asterisks (*) means p-value < 0.05.
Degrees of FreedomSum of SquaresMean of SquaresF Statistic p-Value
Site257,819,71028,909,8559.1451410.00066 *
Season16,631,6836,631,6832.097820.15666
Tree sp.12,451,2042,451,2040.775390.38473
Canopy17,753,6397,753,6392.452730.12658
Season: Tree sp.29,026,2124,513,1061.427640.25388
Season: Canopy111,788,96811,788,9683.729240.06183
Tree sp.:Canopy21,143,875571,9380.180920.83529
Season: Tree sp.:Canopy28,784,1814,392,0911.389360.26302
Residuals34107,481,6313,161,224
Table 3. Analysis of the variance Pielou Evenness Index as a function of the season, the tree species, and the canopy effect from the trees. F statistics are presented as the means of the squares of the factor divided by the means of the squares of the residuals.
Table 3. Analysis of the variance Pielou Evenness Index as a function of the season, the tree species, and the canopy effect from the trees. F statistics are presented as the means of the squares of the factor divided by the means of the squares of the residuals.
Degrees of FreedomSum of SquaresMean of SquaresF Statistic p-Value
Site20.0101240.00506192.44210.10216
Season10.0071270.00770393.73730.0724
Tree sp.10.0000000.00000020.00010.9331
Canopy10.0043470.00434682.09710.1567
Season: Tree sp.20.0004530.00022650.10930.8968
Season: Canopy10.0004700.00047010.22680.6369
Tree sp.:Canopy20.0044160.00220781.06510.3559
Season: Tree sp.:Canopy20.0099490.00497462.40000.1059
Residuals340.0704750.0020728
Table 4. Analysis of variance table from PiCrust2 Axis 2 PCoA model as a function of the season, the tree species, and the canopy effect from the trees. F statistics are the means of the squares of the factors divided by the means of the squares of the residuals. Asterisks (*) means p-value < 0.05.
Table 4. Analysis of variance table from PiCrust2 Axis 2 PCoA model as a function of the season, the tree species, and the canopy effect from the trees. F statistics are the means of the squares of the factors divided by the means of the squares of the residuals. Asterisks (*) means p-value < 0.05.
Degrees of FreedomSum of SquaresMean of SquaresF Statisticp-Value
site20.00025930.000129631.28360.2887751
season10.00131570.0013157513.02820.0008824 *
tree_sp10.00040330.000403263.99300.0528829
canopy10.00007430.000037170.36800.6945274
season: tree_sp20.00006130.000030640.30340.7400813
season: canopy10.00045640.000228182.25930.1182688
tree_sp: canopy20.00017700.000088510.87640.4245050
season: tree_sp:canopy20.00005160.000025820.25570.7757173
Residuals340.000383770.00010099
Table 5. Significant factors or interactions of factors for the different metabolic pathways analyzed. After performing linear regressions, an Anova of each model was developed. T = tree species; S = seasonality; C = canopy effect. The interaction between these factors is represented by the symbol “*”. “X” marks the statistical significance of the treatment.
Table 5. Significant factors or interactions of factors for the different metabolic pathways analyzed. After performing linear regressions, an Anova of each model was developed. T = tree species; S = seasonality; C = canopy effect. The interaction between these factors is represented by the symbol “*”. “X” marks the statistical significance of the treatment.
CodePathwayTSCT*ST*CC*ST*S*CComments
Synthesis
PWY.6350archaetidylinositol biosynthesis X Archaea
PWY.6167flavin biosynthesis II (archaea) X Archaea
PWY.6654phosphopantothenate biosynthesis III (archaea) X Archaea
PWY.6349C20,20 CDP-archaeol biosynthesis X Archaea
P381.PWYadenosylcobalamin biosynthesis II (aerobic) XB12 Vitamin
PWY.3081adenosylcobalamin biosynthesis II (aerobic) X B12 Vitamin
PWY.3941β-alanine biosynthesis IIX X Bacteria (General)
PWY.622starch biosynthesis X Bacteria (General)
PWY.7316dTDP-N-acetylviosamine biosynthesis XXX Gram-Bacteria
KDO.NAGLIPASYN.PWYsuperpathway of (Kdo)2-lipid A biosynthesis X Gram-Bacteria
PWY.7198pyrimidine deoxyribonucleotides de novo biosynthesis IV X Methane involved organisms
PWY.5198factor 420 biosynthesis II (mycobacteria)X Methane involved organisms
PWY.1622formaldehyde assimilation I (serine pathway) X Methane involved organisms
PWY.7347sucrose biosynthesis III X Methane involved organisms
SUCSYN.PWYsucrose biosynthesis I (Photos) X Photosynthetic organisms
PWY.1422vitamin E biosynthesis (tocopherols) X Photosynthetic organisms
PWY.72867-(3-amino-3-carboxypropyl)-wyosine biosynthesis X X RNA modification
AEROBACTINSYN.PWYaerobactin biosynthesis XSiderophore
PWY.6749CMP-legionaminate biosynthesis I X Signaling molecules
Degradation
PWY.3801Sucrose degradation II X Anaerobic
PWY.6572chondroitin sulfate degradation I (bacterial)X Animal matter degradation
PWY.5532nucleoside and nucleotide degradation (archaea) X X Archaea
PWY.5519D-arabinose degradation III X Archaea
PWY.6713L-rhamnose degradation II X Bacteria (General)
PWY.5499vitamin B6 degradation I XBacteria (General)
PWY5F9.12biphenyl degradation XBacteria (General)
PWY.7013(S)-propane-1,2-diol degradation X Bacteria (General)
LACTOSECAT.PWYlactose degradation I X X Gram+ Bacteria
P441.PWYsuperpathway of N-acetylneuraminate degradation XGram+ Bacteria
PWY.5677succinate fermentation to butanoate X Gram+ Bacteria
GLCMANNANAUT.PWYsuperpathway of N-acetylglucosamine, N-acetylmannosamine and N-acetylneuraminate degradation XN related metabolisms
PWY.3661glycine betaine degradation I XOsmoregulation
VALDEG.PWYL-valine degradation I X Bacteria (General)
Nitrogen
PWY.7084nitrifier denitrification X N related metabolisms
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Manjón-Cabeza, J.; Ibáñez, M.; Leiva, M.J.; Chocarro, C.; Lanzén, A.; Epelde, L.; Sebastià, M.T. Soil Prokaryotic Diversity Responds to Seasonality in Dehesas, Modulated by Tree Identity and Canopy Effect. Microbiol. Res. 2025, 16, 153. https://doi.org/10.3390/microbiolres16070153

AMA Style

Manjón-Cabeza J, Ibáñez M, Leiva MJ, Chocarro C, Lanzén A, Epelde L, Sebastià MT. Soil Prokaryotic Diversity Responds to Seasonality in Dehesas, Modulated by Tree Identity and Canopy Effect. Microbiology Research. 2025; 16(7):153. https://doi.org/10.3390/microbiolres16070153

Chicago/Turabian Style

Manjón-Cabeza, José, Mercedes Ibáñez, María José Leiva, Cristina Chocarro, Anders Lanzén, Lur Epelde, and Maria Teresa Sebastià. 2025. "Soil Prokaryotic Diversity Responds to Seasonality in Dehesas, Modulated by Tree Identity and Canopy Effect" Microbiology Research 16, no. 7: 153. https://doi.org/10.3390/microbiolres16070153

APA Style

Manjón-Cabeza, J., Ibáñez, M., Leiva, M. J., Chocarro, C., Lanzén, A., Epelde, L., & Sebastià, M. T. (2025). Soil Prokaryotic Diversity Responds to Seasonality in Dehesas, Modulated by Tree Identity and Canopy Effect. Microbiology Research, 16(7), 153. https://doi.org/10.3390/microbiolres16070153

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