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Article

Tree Species Identity Drives Fungal, but Not Bacterial, Soil Community Shifts in Tropical Monoculture Plantations

by
Kristin Saltonstall
1,*,
Erin R. Spear
1,
Martyna A. Glodowska
2 and
Jefferson S. Hall
1,3
1
Smithsonian Tropical Research Institute, Panama Apartado 0843-03092, Panama
2
Department of Microbiology, Radboud University, 6525 AJ Nijmegen, The Netherlands
3
ForestGeo, Smithsonian Tropical Research Institute, Panama Apartado 0843-03092, Panama
*
Author to whom correspondence should be addressed.
Forests 2025, 16(9), 1366; https://doi.org/10.3390/f16091366
Submission received: 25 June 2025 / Revised: 20 August 2025 / Accepted: 20 August 2025 / Published: 23 August 2025
(This article belongs to the Section Forest Soil)
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Abstract

Tree plantations can help reverse the negative impacts of deforestation and land degradation worldwide, and soil microbial communities play key roles in tree growth and productivity. We studied microbial communities in the bulk soil of five native species monoculture plantations in the Republic of Panamá to assess how bacteria and fungi were affected by soil chemistry and plant identity after seven years of tree growth. Relative to the other species, Terminalia amazonia accumulated over three times the aboveground biomass and had lower mortality. Soil nutrients, especially phosphorus, were low, and we found no differences in soil chemistry across the five plantation types. Similarly, there was no difference in alpha diversity of the soil microbial communities across plantation types, and the bacterial communities showed no compositional variation or enrichment of any individual taxa. However, soil fungal communities differed in T. amazonia plantations as compared to the others, exhibiting enrichment or absence of specific taxa of arbuscular mycorrhizal fungi and putative phytopathogens. Our results suggest that T. amazonia may associate with certain microbial taxa that help it overcome low nutrient availability in these habitats. Consideration of plant–soil–microbe interactions in restoration efforts may facilitate tree growth and help to promote climate resilient forested areas.

1. Introduction

Forest cover in tropical regions has been significantly reduced across the globe [1], and lands cleared of forest can become rapidly degraded and less productive [2,3,4]. Today there are large areas of degraded lands across Central America, and international efforts to rehabilitate these lands and increase public awareness of the public benefits of forests are widespread [1,5,6,7,8,9]. It is well recognized that planting trees can both reverse the negative effects of deforestation by providing a variety of ecosystem services, such as carbon sequestration and restoring hydrological processes, and enhance forest connectivity [6,10]. From a socio-economic perspective, trees can also enhance local livelihoods through timber production [11,12,13], by diversifying agricultural production through non-timber forest products and improving overall productivity and sustainability [6,14]. As a result, monoculture tree plantations are now a common management tool for both timber production and the restoration of degraded lands across the tropics [1,10].
Reforestation with native timber species has gained popularity in recent years as they are typically better adapted to local environmental conditions than exotics and can offer the potential for higher yields and improved restoration [11,15,16]. In Panama, teak (Tectona grandis), which is native to south and southeast Asia, is the most common plantation species and today occupies 44,000 ha [17,18]. Nevertheless, it does not grow well on acidic, infertile soils and, as a result, can provide limited benefits for farmers and has also been associated with negative impacts, such as soil erosion and biodiversity loss [15,19]. However, survival rates and yields of native species have also been shown to be highly variable across studies (e.g., [20,21,22]), and a better understanding of their growth characteristics, survivorship, and the management conditions that maximize their growth are needed to make planting native species a viable option for many land managers.
Interactions between plants and soil microbes can have important ramifications for the growth and productivity of plants [23,24], and soil modifications by plants, including the amount of carbon (C) inputs, soil pH, or nutrient availability through litter and root exudates, can favor the growth of symbiotic or pathogenic microbes [25,26]. For example, mycorrhizal fungi and symbiotic nitrogen-fixing bacteria form beneficial relationships that involve direct exchange of nutrients acquired by microbes that are essential for plant growth for carbon fixed by the plants through photosynthesis [20,27]. Trees can facultatively adjust their association with their symbionts depending on environmental conditions, increasing or decreasing the strengths of these interactions [27], and the timing of turnover of fine root biomass can be highly dynamic [25]. Pathogenic microbes can have negative impacts on plant communities, causing disease and increased mortality, and the impacts of these microbes may vary depending on the age of the plants [28,29]. Other mechanisms, such as the release of exudates or pulses of nutrients and changes in soil moisture associated with leaf litter from deciduous trees, can attract microbes and drive indirect associations between plants and specific microbes that can have positive, neutral, or negative impacts on plant growth [26,30]. However, plant and microbial communities can also assemble at different temporal or spatial scales or in response to different abiotic variables, making it difficult to generalize patterns between plants and microbes in the soil [25].
We studied the soil microbial communities in five native species monoculture plantations in the Republic of Panamá to see if plant identity leads to changes in the diversity and composition of microbes in the bulk soil after seven years of tree growth. In 2008, experimental plantings were set up to test hypotheses of how forests maintain species diversity. Both monoculture and mixed species plantations were established in a natural setting, and treatments were randomly distributed across the landscape in two blocks. This experimental design affords the ability to isolate interactions [22,31], allowing for control of a large number of factors including land use history, soil physical and chemical properties, slope, light, and climate. For this study, we chose to work only in the monocultures as we believed that differences in microbial communities in soils that could influence the growth of each tree species would be most easily detectable in monoculture stands where no overlap between roots of neighboring trees of different species could lead to confounding results regarding interactions. At the time of soil sampling, obvious and pronounced growth differences were noted across the five focal tree species in the experiment [22]. The goal of this study was to test whether or not these growth patterns can be linked to differences in bulk soil microbial communities, in particular specific taxa that can enhance nutrient uptake or those that inhibit growth or lead to plant mortality through pathogenic effects. We hypothesized that soils under (1) each monoculture type would have different communities of soil bacteria and fungi due to directional changes associated with the different tree species after seven years of growth; (2) nitrogen-fixing tree species would have a different community of diazotrophic bacteria than other species due to increased nitrogen inputs from symbiotic nitrogen fixation; (3) tree species with high growth rates and biomass accumulation would have a higher diversity and different community of arbuscular mycorrhizal fungi (AMF; Phylum Glomeromycota) in their soils as interactions that assist with phosphorus (P) acquisition in low-P environments would benefit these plants, and (4) deciduous species would have an enriched community of saprotrophic and pathogenic microbes in their soils due to increased leaf litter accumulation and decomposition providing pulses of nutrient inputs and changes in soil moisture.

2. Materials and Methods

2.1. Study Site and Focal Species

The Agua Salud Project was established within a 15 km2 area in 2008 by the Smithsonian Tropical Research Institute (STRI), the Autoridad de Canal de Panamá (ACP), and the Ministerio Ambiente (MiAmbiente). Located in the Panama Canal Watershed and adjacent to Parque Nacional Soberanía (9°13′ N, 79°47′ W, 330 m a.s.l.), the 6.5 km2 of land includes pastures, mature and secondary forests, and experimental tree plantations [22]. The climate is humid tropical (mean daily maximum and minimum temperatures are 32 and 23 °C [31]; average annual rainfall = 2700 mm) and soils are strongly weathered, infertile, and well-drained Oxisols (Inceptic Hapludox) and Inceptisols (Oxicand Typic Dystrudepts) [22]). Prior to establishment of the experiment, the area was used as cattle pasture or was covered by very young (<5 years) secondary forest that was cleared before planting. Monoculture plantings of five native, high-value timber species, Anacardium excelsum (Anacardiaceae), Dalbergia retusa (Fabaceae), Pachira quinata (Malvaceae), Tabebuia rosea (Bignoniaceae), and Terminalia amazonia (Combretaceae), were established in 42 × 36.5 m plots [22]. These species are all long-lived pioneer species that are native to the forests of the region and were selected based on previous findings with regard to their water and resource acquisition, including crown phenology, N2-fixation, water use efficiency, and nutrient demand (Table 1; [15,32]). Trees that died in the first year were replanted in 2009, representing 12% of the plantings.
Eleven or twelve replicate plots were established for each species, and 225 seedlings (15 × 15 trees) were planted at 3 m spacing on a grid within each plot. The plantations were fertilized at time of planting (2 oz of N-P-K 12-24-12 and organic material mixed with soil and planted with seedlings, 2 oz Triple Sulfate applied several cm from roots), and the grass and other vegetation were manually cleaned four times per year up to and including the study period [22].

2.2. Soil Sampling and Soil Chemistry

We sampled in July and August 2015, during the rainy season, which is the time of year when microbial activity in the soil is high [36]. Five bulk soil samples were collected in each plot from the upper 10 cm of mineral soil for a total of 55 samples (A. excelsum (n = 11), D. retusa (n = 12), P. quinata (n = 10), T. amazonia (n = 11), and T. rosea (n = 11)). Samples were collected 5 m apart in an X pattern, within the central 81 trees at the center of each plot to avoid edge effects. Soils were transported on ice to the lab, pooled, homogenized, and stored within 24 h at either 4 °C (chemical analysis) or −20 °C (microbial community analysis) until further processing.
Soil chemical analyses were performed at the STRI Analytical Biogeochemistry Laboratory using standard protocols [33]. Nitrogen was extracted within 6 h of collection and phosphate, cations, and pH (in water) within 24 h as in Condit et al. [32]. Chemical analyses were conducted with detection by inductively coupled plasma–optical emission spectrometry (ICP-OES) on an Optima 7300 DV (Perkin Elmer, Shelton, CT, USA). Resin P (phosphate, PO43−) was measured from resin bags placed at 3 cm depth (see methods in Condit et al. [32]). The barium chloride method was used to extract cations [37]. Differences in soil chemistry were assessed using ANOVA or Kruskal–Wallis tests when normality, as assessed with Shapiro–Wilk tests, was not evident in the data.

2.3. DNA Extraction and Metabarcoding

DNA was extracted from 0.25 g of each soil subsample using the Powersoil DNA Isolation Kit (Qiagen, Germantown, MD, USA) following the manufacturer’s protocol and eluted into a final volume of 100 μl. Microbial communities were assessed using DNA metabarcoding to characterize the distribution and abundance of soil bacteria and fungi in the monoculture plantations. To amplify bacteria, we targeted the V4 hypervariable region of the 16S rRNA gene using the 515F-806R primer pair [38]. For the fungal analysis, we amplified the first internal transcribed spacer region (ITS1) of the rRNA operon, using the primers ITS1F [39] and ITS2 [40]. All primers included all necessary Illumina adapters with barcodes to distinguish samples. The bacterial 806R primer included a 10 bp barcode sequences while the ITS1F and ITS2R primers each included an 8 bp index sequence, allowing us to multiplex multiple samples. We used 5Prime Hot MasterMix (QuantaBio, Beverly, MA, USA) in PCRs with a final volume of 12.5 ul, with 25 cycles and an annealing temperature of 50 °C for both loci. PCRs were performed in triplicate and pooled, and products were cleaned and normalized using Sequelprep Normalization plates (Thermo Fisher, Waltham, MA, USA). Libraries were sequenced on a MiSeq (Illumina Inc., San Diego, CA, USA) at the STRI Naos Molecular Laboratory using 2 × 250 v.2 reagent cartridges.

2.4. Bioinformatics

All quality control and statistical analyses were completed in R v.4.0.4. We used cutadapt v. 4.4 to remove Illumina adapter and primer sequences from all sequence reads [41]. The DADA2 v 1.36.0 package was used to filter and trim reads, remove potential chimers, dereplicate quality-filtered reads, infer Amplicon Sequence Variants (ASVs) and assign taxonomy to each ASV [42]. Reads were filtered and trimmed using the standard filtering parameters and the following custom settings to relax the expected error parameter for the lower quality reverse reads and to trim low-quality ends of reads: 16S maxEE = 2,4 and trncLen = c(230,140), and ITS maxEE = 2,4 and trimRight = c(25,80). Taxonomic assignments were made using the RDP Native Bayesian Classifier algorithm [43], the SILVA 16S rRNA database v.138 (formatted for DADA2, [44]), and the UNITE nuclear ribosomal ITS region database (general dynamic FASTA release for Fungi v 19.02.2025; [45]).
Additional diversity and community analyses were performed in phyloseq v. 1.52 [46] and associated R packages. Samples were rarefied to the minimum sequencing depth of each dataset. For bacteria (16S), two of the 55 samples contained fewer than 3000 reads and were excluded from the dataset, and the remaining 53 samples were rarefied to an even depth of 17,985 sequences per sample. For the fungal dataset (ITS1), we rarefied all 55 samples to a depth of 9700 sequences per sample.
We assessed the alpha diversity of the rarefied data with the Shannon index, as it accounts for both the abundance and evenness of taxa and is the most widely used diversity metric. We first used the Shapiro–Wilk test and Bartlett’s test to assess normality and homogeneity of variances across groups. As normality could not be assumed in the dataset, we used Kruskal–Wallis tests, coupled with Wilcoxon rank sum exact tests and False Discovery Rate (FDR)-corrected p-values for pairwise comparisons, to test for significant differences in alpha diversity.
To explore whether and how microbial communities shifted across tree species, we used permutational analysis of variance (PERMANOVA) coupled with post hoc multilevel pairwise analyses, Principle Coordinate Analysis (PCoA) ordinations, and Linear Discriminant Analysis Effect Size (LEfSe) analyses [47]. The weighted unifrac and Bray–Curtis distance metrics were used for bacteria and Bray–Curtis for fungi. Specifically, we contrasted microbial community composition across the five tree species using PERMANOVAs (999 permutations) and post hoc multilevel pairwise comparisons (999 permutations), with the adonis and pairwise.adonis functions. PERMANOVA does not assume normality, but it does assume equal beta dispersion between groups. We tested for homogeneity of dispersion using the betadisper and permutest functions in the vegan v. 2.7-1 package [48].
We used LEfSe analysis [47] to identify taxa with significant differential abundances across the tree species and calculate the effect size of each of these taxa via the web-based MicrobiomeAnalyst tool [49]. For these analyses, we retained ASVs observed ≥10 times in at least 20% of samples and excluded uninformative ASVs of low abundance prevalence and/or variance across treatments (10% removed based on the interquartile range). Following this filtering, 1442 of 24,402 bacterial ASVs, 314 of 8920 fungal ASVs, and 35 of 600 Glomeromycota ASVs were retained for further analysis. Count data were normalized by total sum scaling to account for uneven sequencing depths. We used a log LDA cutoff of 2 and FDR adjusted p-values to determine significance. The web-based LefSe tool was used to plot the enrichment heatmaps [49].

3. Results

3.1. Tree Communities and Soil Chemistry

At the time of soil sampling, the monoculture plantations were seven years old and showed varying levels of mortality and growth. Both T. amazonia and D. retusa had experienced lower mortality than the other species. Terminalia amazonia was by far the best performer in terms of overall biomass, and T. rosea the worst as it suffered higher mortality than the other species and also exhibited poor growth. (Table 1; [22]).
The study site is characterized by infertile soils, with very low levels of phosphorus (mean 0.48 ± 0.27 mg kg−1 Resin P; also see Mayoral et al. [50] for an analysis of soil fertility and its impact on tree growth in these plantations). Average soil pH across the plots was 5.49 ± 0.23, and no significant differences in soil pH were found across the monoculture plantings of the five tree species (ANOVA: R2 = 0.14, F4,50 = 2.07, p = 0.099; Tukey’s HSD: p ≥ 0.1 for all pairwise comparisons). Similarly, no significant differences were found in any of the soil chemical measurements across the plantations (ANOVA or Kruskal–Wallis, p ≥ 0.05 for all measurements; see also Mayoral et al. [50]), although Resin p levels in the T. amazonia plots tended to be lower than other plots (Table 2; range T. amazonia = 0.10–0.77 mg/kg vs. 0.17–1.43 mg/kg in other species).

3.2. Microbial Diversity and Community Composition

Our metabarcoding analysis recovered a total of 24,402 16S rRNA bacterial amplicon sequence variants (ASVs) and 8920 fungal ASVs from the 55 samples. Sequencing depth per sample averaged 108,242 reads for 16S and 51,933 reads for ITS (range: 17,985–362,105 for 16S and 9731–83,472 for ITS). Soil microbial communities were dominated by microbes that are typical of the acidic soils in the Agua Salud region [51], including members of the Acidobacteriota (relative abundance 31.0%–32.7%), Proteobacteria (18.6%–19.7%), and Verrucomicrobiota (12.3%–15.1%), as well as the fungal groups Ascomycota (68.2%–78.4%), Basidiomycota (16.6%–23.7%), and Glomeromycota (3.0%–5.8%) (Figure S1). No significant differences in Shannon diversity were observed across tree species for either bacteria (Kruskal–Wallis: n = 53, Χ2(4) = 2.054, p = 0.726; Padj > 0.1 for all pairwise comparisons) or fungi (Kruskal–Wallis: n = 55, Χ2(4) = 2.416, p = 0.66; Padj > 0.1 for all pairwise comparisons; Figure S2).
The bacterial community showed little compositional variation using either the phylogenetically informative weighted unifrac distance (PERMANOVA: F4,51 = 0.9277; R2 = 0.0732, p = 0.66) or Bray–Curtis distance (PERMANOVA: F4,51 = 0.9452; R2 = 0.0745, p = 0.62; Figure 1a). We subset the communities of diazotrophic bacteria in the soil from the total 16S rRNA community and found them to be dominated by Bradyrhizobium and Bacillus. These communities also showed no differences between plantations (PERMANOVA: F4,51 = 0.9452; R2 = 0.0720, p = 0.65; Figure 1b).
However, there were differences in the fungal communities (PERMANOVA: F4,51 = 1.315; R2 = 0.095, p = 0.001; Figure 1c). Pairwise tests indicated that the soil fungal communities of T. amazonia monocultures were significantly different from all other plantation types (Padj < 0.05; Figure 1c, Table S1). As all of these tree species are known to associate with AMF, and these fungi play an important role in P acquisition, we looked further at the Glomeromycota communities. No differences were seen in the Shannon diversity of Glomeromycota between the five tree species (Padj = 1.0 for all pairwise comparisons), but, as seen in the total fungal community, T. amazonia AMF communities were significantly different from the other four tree species (PERMANOVA: F4,51 = 1.315; R2 = 0.095, p = 0.001; Figure 1d).

3.3. Differential Abundance of Microbial Taxa

To explore the taxa driving the observed differences in the soilborne microbial communities across tree species, we used linear discriminant analysis effect size (LEfSe) to identify differentially abundant and discriminative ASVs. No bacterial ASVs showed significant differential abundance across the plantations. However, thirteen fungal ASVs showed differentiation, with LDA scores > 3.0 (Figure 2). These included representatives of the three dominant fungal phyla, Ascomycota, Basidiomycota, and Glomeromycota, with the majority being potential plant pathogens, saprotrophs, or arbuscular mycorrhizal fungi (AMF; Table S2). Most of the enriched fungal ASVs were associated with three tree species, A. excelsum, P. quinata and T. amazonia, and only 2–3 ASVs were enriched for D. retusa and T. rosea. The soils in the A. excelsum plantation were enriched for eight of the thirteen ASVs, including several potential pathogens and two AMF. Pachira quinata soils were enriched for several potential pathogens, including a Pseudopestalotiopsis, a Neopestalotiopsis, a Pyrenochaetopsis, and a Clonostachys, as well as a Rhizophagus (AMF), which was also enriched in D. retusa. Terminalia amazonia soils were enriched for four ASVs, including the potential pathogen Pseudocercospora and two AMF, and had low to zero relative abundance of a Rhizophagus (AMF), the phytopathogens Pseudocercospora, Neopestalotiopsis, and Clonostachys, as well as two Ceratobasidiaceae, a Simplicidiella, and a Pyrenochaetopsis.
When considering only AMF (Glomeromycota), seven ASVs were differentially abundant among the tree species: one was significant at α = 0.05 (ASV170, Rhizophagus) and six at α = 0.1 (Figure 3). Most of the tree species showed higher abundance of one or two of these ASVs and low–moderate abundance of others. However, A. excelsum soils were enriched for six of the seven ASVs, two ASVs were enriched, and five had low abundance in T. amazonia soils (LDA score > 4.0 and p < 0.1; Figure 3), suggesting that these species might associate with different AMF communities than the other three tree species.

4. Discussion

Contrary to our hypotheses, soil bacteria and fungi from the bulk soil showed few responses to tree identity in these monoculture plantations. Our selection of tree species included several deciduous species (Dr, Pq, Tr), two evergreen species (Ae, Ta), one nitrogen-fixing species (Dr), and one species that has low phosphorus affinity (Ta). Soil chemistry showed no significant variation, neither bacteria nor fungi showed differences in alpha diversity, and bacteria showed no significant community changes across the plantations. However, soil fungi in the plantations did show differences in overall community composition, particularly in T. amazonia, and there were fungal amplicon sequence variants (ASVs) that showed significant differential levels of abundance in all plantation types. Physiological and ecological differences between bacteria and fungi may drive these patterns. Soil bacterial communities tend to be heavily influenced by soil chemistry, pH in particular [52], and bacterial communities may have faster turnover than fungi due to shorter life cycles. While the availability of soil nutrients can also impact fungi [24], they are key decomposers of plant necromass and are thus dependent on leaf litter components as well as root exudates for growth and survival, strengthening their interactions with the plant community. The homogeneity across our study site of many abiotic conditions that have been shown to strongly influence microbial communities (e.g., pH, soil chemistry, temperature, precipitation, land use history) may be driving the community composition of all microbes in these soils, but the interactions between plants and fungi may be more tightly linked.
Terminalia amazonia is by far the best performer in the Agua Salud experimental plantations, when grown in both monoculture and species mixtures. It showed the highest productivity, was the first plantation type to achieve crown closure, and had very low mortality rates [22]. It also is the only species to show differentiation in its soil fungal community, suggesting that it may be interacting with the soil community, for example, by producing root exudates [26], in ways that enhance microbes that help to facilitate its growth. Not only did the soils in the T. amazonia plantations have the highest relative abundance of the phylum Glomeromycota (5.8%; hereafter AMF), but they were also enriched for two AMF ASVs and lacked several other AMF ASVs that were common in the soils of the other monocultures. As soil chemistry showed no variation across our plantations, at least for the macro- and micro-nutrients that we measured, we can eliminate soil fertility as being a strong driver of AMF community composition. AMF are obligate symbionts of plant roots that cannot reproduce independently of their host. They are typically thought to be generalists, but there is accumulating evidence that their distribution can vary according to the plant community at a given site [20,53]. Terminalia amazonia may have a specific relationship with certain AMF, which could facilitate its phosphorus uptake under limiting conditions [54] and promote its growth in low-P soils [33]. While it has significantly higher nutrient use efficiency than the other tree species in these plantations, it also has over twice as much P stored in its biomass [55], suggesting a need for fungal partners that would enhance P uptake. In contrast, A. excelsum soils were enriched for six AMF ASVs, yet the trees showed only moderate growth and had over twice the mortality of T. amazonia. This observed pattern could be, in part, the result of A. excelsum associating with less efficient fungal partners [54]. Further work looking at root colonization and sequencing with a better marker for AMF, such as the 18S locus, is needed to confirm these potential mutualistic associations and provide finer scale detail on the role that AMF could play in enhancing the growth of T. amazonia.
It is possible that physical differences in the roots of these five plant species have influenced our ability to detect differences in the bulk soil microbial communities in the plantations. While we did not assess root biomass in this study, root excavation and mapping data for these species suggest that roots of all monocultures should be interacting, as the average root diameter distance from tree boles is 1.5 times that of the average crown width [56]. Further, these five tree species have different root architectures, with T. amazonia being characterized by higher lateral root production, A. excelsum and P. quinata allocating more biomass to a central tap root, and D. retusa and T. rosea having intermediate morphologies [56]. It is possible that, while roots of all adjacent trees could be interacting, the greater lateral spread of T. amazonia roots increases the zone of influence of the plants in the upper 10 cm of soil, making microbial community shifts more detectable from bulk soil collected under this species.
Plant phytopathogens can also play a key role in the productivity and mortality in monoculture plantations, as well as natural forests. We identified several potential pathogens that showed different patterns of enrichment across the plantations. In these plantations, P. quinata soils showed the highest enrichment of pathogen ASVs (four total), and the species had moderate levels of mortality over time. Anacardium excelsum also experienced moderate levels of mortality, but its soil was enriched for only one potential pathogen (a Pseudocercospora), while D. retusa, which had lower mortality, was enriched for a Clonostachys. Tabebuia rosea had the highest mortality rates in these plantations, which has been attributed to pathogen attack [22], yet showed enrichment of only one potential pathogen, a Ceratobasidium, which is also often described as a saprotroph. Previous work looking at susceptibility of seedlings to pathogens looked at two of our five tree species and found that A. excelsum had high resistance to the pathogens that were tested while D. retusa showed significant mortality in response to some pathogens and no effect from others [29]. While we are unable to say if any of the potential phytopathogen ASVs that we have detected are host-specific or generalist or if they played a role in the growth and survivorship of the trees, their differential abundance across the plantations points to the role that different plant species may play in maintaining the diversity of pathogens across the landscape.
Saprotrophic fungi can influence pathogenicity and plant growth by influencing soil nutrient availability and soil moisture. Fungi, in particular, can exhibit remarkable ecological versatility, which can make functional classification challenging, as phylogenetic similarity does not necessarily predict ecological behavior, and their ecological roles can shift rapidly given new environmental conditions. We hypothesized that we would find community shifts between deciduous and evergreen trees, due to pulses of nutrients associated with decomposition of accumulated leaf litter. However, we did not see this pattern, possibly because of management activities in the plantations, which included regular cleanings of understory vegetation to facilitate growth of the planted trees. As cut materials were left in place, all plantations had an abundance of detritus available for decomposition throughout the year, and shifts in microbial communities due to nutrient inputs from deciduous trees may not have been detectable.
The lack of change in diversity for the functional groups of microbes that we have assessed could be a consequence of the molecular markers that we used. While the 16S rRNA and ITS1 regions are commonly used to characterize soil microbial communities [57], they, like any PCR-based assay, have some amplification biases and may be less efficient at detecting diversity in specific functional groups, such as nitrogen-fixing bacteria and AMF, which are relatively rare in the total microbial communities. Studies that focus on nitrogen-fixing bacteria often use primer sets amplifying the nitrogen reductase gene (nifH) [58], and the 18S ribosomal gene is commonly used for studies on AMF diversity and community composition [59]. Working in the same plantations to evaluate hypotheses related to N2 fixation, Batterman et al. [30] collected fine roots and soil in the rhizosphere in 2011 and found that D. retusa fixed copious amounts of N2, an order of magnitude more than the next highest N2 fixer among the companion species in the experiment, which were not part of this study as they were not planted in monocultures. Given this, we expected to find a different community and enrichment of nitrogen-fixing bacteria in the D. retusa plantations, but we did not observe this. While soil chemistry, notably pH, may play a role in controlling the distribution of these bacteria [52], the use of multiple primer sets that target specific functional groups or metagenomic sequencing to look for variation in functional genes may provide better resolution for microbial community differences between the five plantation types.
We also did not measure the soil microbial communities prior to establishment of the plantations, and we only sampled once during the rainy season. Although we found different fungal communities in the T. amazonia plantations, we do not know if the communities changed as a result of planting. The age of our experiment may have also prevented us from measuring a stronger relationship between soil microbes and tree identity. Microbial community assembly is a dynamic process that is affected by a variety of environmental factors, such as soil chemistry, dispersal ability, soil moisture, and nutrient availability [52,60,61,62]. The trees in our plantations were still in early growth stages, having survived the seedling phase, but were not yet reproductive. Although specific groups of organisms, such as AMF in this study, can clearly respond to different tree species within a seven-year period, it may take more time for the entire soil communities to differentiate following plantation establishment. A similar study at the Agua Salud experimental site found that while there were no differences in alpha diversity, soil community structure in 20-year-old naturally developing secondary forests were more similar to much older forests than to pastures [51], suggesting that recovery of typical forest soil communities was already well under way after two decades of growth. However, tree diversity is much higher and plant spacing is more heterogeneous in natural forests, which may also have an effect on microbial diversity [63]. More recent data on growth of the trees in these plantations show that the differences in both growth and mortality that were seen in 2015 persisted as recently as 2021, with T. amazonia continuing to grow better than the other four species. Moreover, T. amazonia and D. retusa had significantly lower mortality than the other three species in 2021, at around 11% versus greater than 40% mortality [55]. It is possible that the accumulation of beneficial AMF in the soils of T. amazonia and the high nitrogen-fixing capacity of D. retusa [30] have provided these two species with a higher level of resistance to pathogens by affording them the ability to acquire nutrients and resist environmental perturbations such as drought.

5. Conclusions

Our study underscores the important benefits that soil microbial communities, particularly fungi such as AMF, may contribute to tree performance in tropical monoculture plantations. The strong association between both T. amazonia and A. excelsum with certain AMF ASVs, along with the high nitrogen-fixing ability of D. retusa, suggests that future tropical reforestation efforts should go beyond tree selection and incorporate microbial considerations into restoration strategies. As tropical restoration shifts toward native species plantations, actively managing and enhancing soil microbiomes through targeted inoculation or by fostering plant–microbe compatibility could improve tree survival, growth, and resilience. These insights point to the need for integrating microbial monitoring, rhizosphere-focused studies, and functional sequencing tools in reforestation planning to create more productive and climate-resilient forest ecosystems.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16091366/s1, Figure S1: Relative abundances at the phylum or class levels of bacteria and fungi in the five plantation types; Figure S2: Shannon diversity of bacterial and fungal communities; Table S1: Pairwise comparisons of the fungal community composition based on Bray–Curtis distance of the soil of monoculture plantings of five tree species; Table S2: Functional guilds of fungal ASVs.

Author Contributions

Conceptualization, K.S. and J.S.H.; Data curation, K.S.; Formal analysis, K.S. and E.R.S.; Funding acquisition, J.S.H.; Investigation, K.S. and M.A.G.; Methodology, K.S. and J.S.H.; Project administration, K.S. and J.S.H.; Resources, J.S.H.; Supervision, K.S. and J.S.H.; Writing—original draft, K.S.; Writing—review and editing, K.S., E.R.S., M.A.G. and J.S.H. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by Stanley Motta, Frank and Kristin Levinson, the Hoch family, the U Trust and a grant from the Simons Foundation (No. 429440, J.T.T & E.R.S.).

Data Availability Statement

Raw sequence files and sample metadata can be found in NCBI Bioproject PRJNA1284877 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1284877, accessed on 10 July 2025). ASV tables, R scripts, and additional metadata can be found in Smithsonian Figshare at doi: 10.25573/data.28939769.

Acknowledgments

We acknowledge Ben Turner for his contributions to soil nutrient analyses. We further thank Estrella Yanguas, Dayana Agudo, Guillermo Fernandez, and Marta Vargas for assistance with field and lab work. Soil samples were collected under MiAmbiente permits SE/AP-23-14 and SE/APO-2-15.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. IPBES. The IPBES Assessment Report on Land Degradation and Restoration; Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services: Bonn, Germany, 2018; p. 744. [Google Scholar] [CrossRef]
  2. DeFries, R.S.; Rudel, T.; Uriarte, M.; Hansen, M. Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nat. Geosci. 2010, 3, 178–181. [Google Scholar] [CrossRef]
  3. Maranguit, D.; Guillaume, T.; Kuzyakov, Y. Land-use change affects phosphorus fractions in highly weathered tropical soils. CATENA 2017, 149, 385–393. [Google Scholar] [CrossRef]
  4. Qu, X.; Li, X.; Bardgett, R.D.; Kuzyakov, Y.; Revillini, D.; Sonne, C.; Xia, C.; Ruan, H.; Liu, Y.; Cao, F.; et al. Deforestation impacts soil biodiversity and ecosystem services worldwide. Proc. Natl. Acad. Sci. USA 2024, 121, e2318475121. [Google Scholar] [CrossRef]
  5. Bastin, J.-F.; Finegold, Y.; Garcia, C.; Mollicone, D.; Rezende, M.; Routh, D.; Zohner, C.M.; Crowther, T.W. The global tree restoration potential. Science 2019, 365, 76–79. [Google Scholar] [CrossRef]
  6. Griscom, B.W.; Adams, J.; Ellis, P.W.; Houghton, R.A.; Lomax, G.; Miteva, D.A.; Schlesinger, W.H.; Shoch, D.; Siikamäki, J.V.; Smith, P.; et al. Natural climate solutions. Proc. Natl. Acad. Sci. USA 2017, 114, 11645–11650. [Google Scholar] [CrossRef] [PubMed]
  7. Ministerio de Ambiente. Estrategia Nacional Forestal, 2050; Ministerio de Ambiente: Panama City, Panama, 2019. Available online: https://dcc.miambiente.gob.pa/wp-content/uploads/2021/07/Estrategia_nacional_de_cambio_climatico.pdf (accessed on 10 July 2025).
  8. Pawson, S.M.; Brin, A.; Brockerhoff, E.G.; Lamb, D.; Payn, T.W.; Paquette, A.; Parrotta, J.A. Plantation forests, climate change and biodiversity. Biodivers. Conserv. 2013, 22, 1203–1227. [Google Scholar] [CrossRef]
  9. Cook-Patton, S.C.; Leavitt, S.M.; Gibbs, D.; Harris, N.L.; Lister, K.; Anderson-Teixeira, K.J.; Briggs, R.D.; Chazdon, R.L.; Crowther, T.W.; Ellis, P.W.; et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 2020, 585, 545–550. [Google Scholar] [CrossRef] [PubMed]
  10. Evans, J.; Turnbull, J.W. Plantation Forestry in the Tropics: The Role, Silviculture, and Use of Planted Forests for Industrial, Social, Environmental, and Agroforestry Purposes; Oxford University Press: Oxford, UK, 2004. [Google Scholar]
  11. Hall, J.S.; Ashton, M.S.; Garen, E.J.; Jose, S. The ecology and ecosystem services of native trees: Implications for reforestation and land restoration in Mesoamerica. For. Ecol. Manag. 2011, 261, 1553–1557. [Google Scholar] [CrossRef]
  12. Hauff, R.D. A Case Study Assessment of Agroforestry. J. Sustain. For. 1998, 8, 39–51. [Google Scholar] [CrossRef]
  13. Sinacore, K.; García, E.H.; Howard, T.; van Breugel, M.; Lopez, O.R.; Finkral, A.J.; Hall, J.S. Towards effective reforestation: Growth and commercial value of four commonly planted tropical timber species on infertile soils in Panama. New For. 2023, 54, 125–142. [Google Scholar] [CrossRef]
  14. Shackleton, C.M.; Garekae, H.; Sardeshpande, M.; Sinasson Sanni, G.; Twine, W.C. Non-timber forest products as poverty traps: Fact or fiction? For. Policy Econ. 2024, 158, 103114. [Google Scholar] [CrossRef]
  15. Wishnie, M.H.; Dent, D.H.; Mariscal, E.; Deago, J.; Cedeño, N.; Ibarra, D.; Condit, R.; Ashton, P.M.S. Initial performance and reforestation potential of 24 tropical tree species planted across a precipitation gradient in the Republic of Panama. For. Ecol. Manag. 2007, 243, 39–49. [Google Scholar] [CrossRef]
  16. Wheeler, C.E.; Omeja, P.A.; Chapman, C.A.; Glipin, M.; Tumwesigye, C.; Lewis, S.L. Carbon sequestration and biodiversity following 18years of active tropical forest restoration. For. Ecol. Manag. 2016, 373, 44–55. [Google Scholar] [CrossRef]
  17. Ugalde, L.; Gómez, M. Perspectivas Economicas y Ambientales de las Plantaciones de Teca Bajo Manejo Sostenible, en Panamá; CATIE: Cartago, Costa Rica, 2006. [Google Scholar]
  18. Ministerio de Ambiente & GIZ. Programa Nacional de Restauración Forestal; Ministerio de Ambiente: Panama City, Panama, 2020. Available online: https://miambiente.gob.pa/conozca-mas-del-plan-nacional-de-restauracion-forestal-en-nuestra-web/ (accessed on 10 July 2025).
  19. Lamb, D.; Erskine, P.D.; Parrotta, J.A. Restoration of Degraded Tropical Forest Landscapes. Science 2005, 310, 1628–1632. [Google Scholar] [CrossRef] [PubMed]
  20. Holste, E.K.; Holl, K.D.; Zahawi, R.A.; Kobe, R.K. Reduced aboveground tree growth associated with higher arbuscular mycorrhizal fungal diversity in tropical forest restoration. Ecol. Evol. 2016, 6, 7253–7262. [Google Scholar] [CrossRef]
  21. Hutchison, C.; Gravel, D.; Guichard, F.; Potvin, C. Effect of diversity on growth, mortality, and loss of resilience to extreme climate events in a tropical planted forest experiment. Sci. Rep. 2018, 8, 15443. [Google Scholar] [CrossRef]
  22. Mayoral, C.; van Breugel, M.; Cerezo, A.; Hall, J.S. Survival and growth of five Neotropical timber species in monocultures and mixtures. For. Ecol. Manag. 2017, 403, 1–11. [Google Scholar] [CrossRef]
  23. Prescott, C.E.; Grayston, S.J. Tree species influence on microbial communities in litter and soil: Current knowledge and research needs. For. Ecol. Manag. 2013, 309, 19–27. [Google Scholar] [CrossRef]
  24. Tedersoo, L.; Bahram, M.; Põlme, S.; Kõljalg, U.; Yorou, N.S.; Wijesundera, R.; Ruiz, L.V.; Vasco-Palacios, A.M.; Thu, P.Q.; Suija, A.; et al. Global diversity and geography of soil fungi. Science 2014, 346, 1256688. [Google Scholar] [CrossRef]
  25. Barberán, A.; McGuire, K.L.; Wolf, J.A.; Jones, F.A.; Wright, S.J.; Turner, B.L.; Essene, A.; Hubbell, S.P.; Faircloth, B.C.; Fierer, N. Relating belowground microbial composition to the taxonomic, phylogenetic, and functional trait distributions of trees in a tropical forest. Ecol. Lett. 2015, 18, 1397–1405. [Google Scholar] [CrossRef]
  26. Wardle, D.A.; Bardgett, R.D.; Klironomos, J.N.; Setälä, H.; van der Putten, W.H.; Wall, D.H. Ecological linkages between aboveground and belowground biota. Science 2004, 304, 1629–1633. [Google Scholar] [CrossRef]
  27. Barron, A.R.; Purves, D.W.; Hedin, L.O. Facultative nitrogen fixation by canopy legumes in a lowland tropical forest. Oecologia 2011, 165, 511–520. [Google Scholar] [CrossRef]
  28. Augspurger, C.K.; Kelly, C.K. Pathogen mortality of tropical tree seedlings: Experimental studies of the effects of dispersal distance, seedling density, and light conditions. Oecologia 1984, 61, 211–217. [Google Scholar] [CrossRef]
  29. Spear, E.R.; Broders, K.D. Host-generalist fungal pathogens of seedlings may maintain forest diversity via host-specific impacts and differential susceptibility among tree species. New Phytol. 2021, 231, 460–474. [Google Scholar] [CrossRef] [PubMed]
  30. Batterman, S.A.; Hall, J.S.; Turner, B.L.; Hedin, L.O.; LaHaela Walter, J.K.; Sheldon, P.; van Breugel, M. Phosphatase activity and nitrogen fixation reflect species differences, not nutrient trading or nutrient balance, across tropical rainforest trees. Ecol. Lett. 2018, 21, 1486–1495. [Google Scholar] [CrossRef]
  31. Sinacore, K.; Asbjornsen, H.; Hernandez-Santana, V.; Hall, J.S. Drought differentially affects growth, transpiration, and water use efficiency of mixed and monospecific planted forests. Forests 2019, 10, 153. [Google Scholar] [CrossRef]
  32. Condit, R.; Engelbrecht, B.M.J.; Pino, D.; Pérez, R.; Turner, B.L. Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proc. Natl. Acad. Sci. USA 2013, 110, 5064–5068. [Google Scholar] [CrossRef] [PubMed]
  33. Turner, B.L.; Brenes-Arguedas, T.; Condit, R. Pervasive phosphorus limitation of tree species but not communities in tropical forests. Nature 2018, 555, 367–370. [Google Scholar] [CrossRef] [PubMed]
  34. Cernusak, L.A.; Aranda, J.; Marshall, J.D.; Winter, K. Large variation in whole-plant water-use efficiency among tropical tree species. New Phytol. 2007, 173, 294–305. [Google Scholar] [CrossRef] [PubMed]
  35. Potvin, C.; Mancilla, L.; Buchmann, N.; Monteza, J.; Moore, T.; Murphy, M.; Oelmann, Y.; Scherer-Lorenzen, M.; Turner, B.L.; Wilcke, W.; et al. An ecosystem approach to biodiversity effects: Carbon pools in a tropical tree plantation. For. Ecol. Manag. 2011, 261, 1614–1624. [Google Scholar] [CrossRef]
  36. Turner, B.L.; Joseph Wright, S. The response of microbial biomass and hydrolytic enzymes to a decade of nitrogen, phosphorus, and potassium addition in a lowland tropical rain forest. Biogeochemistry 2014, 117, 115–130. [Google Scholar] [CrossRef]
  37. Hendershot, W.H.; Duquette, M. A simple barium chloride method for determining Cation Exchange Capacity and exchangeable cations. Soil Sci. Soc. Am. J. 1986, 50, 605–608. [Google Scholar] [CrossRef]
  38. Caporaso, J.G.; Lauber, C.L.; Walters, W.A.; Berg-Lyons, D.; Lozupone, C.A.; Turnbaugh, P.J.; Fierer, N.; Knight, R. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. USA 2011, 108, 4516–4522. [Google Scholar] [CrossRef] [PubMed]
  39. Gardes, M.; Bruns, T.D. ITS primers with enhanced specificity for basidiomycetes—Application to the identification of mycorrhizae and rusts. Mol. Ecol. 1993, 2, 113–118. [Google Scholar] [CrossRef] [PubMed]
  40. White, T.J.; Bruns, T.D.; Lee, S.; Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications; Innis, M.A., Gelfand, D.H., Sninsky, J.J., White, T.J., Eds.; Academic Press: Cambridge, MA, USA, 1990; pp. 315–322. [Google Scholar]
  41. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. Next Gener. Seq. Data Anal. 2011, 17, 10–12. [Google Scholar] [CrossRef]
  42. Callahan, B.J.; McMurdie, P.J.; Rosen, M.J.; Han, A.W.; Johnson, A.J.A.; Holmes, S.P. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 2016, 13, 581–583. [Google Scholar] [CrossRef]
  43. Wang, Q.; Garrity, G.M.; Tiedje, J.M.; Cole, J.R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 2007, 73, 5261–5267. [Google Scholar] [CrossRef]
  44. Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2012, 41, D590–D596. [Google Scholar] [CrossRef] [PubMed]
  45. Abarenkov, K.; Nilsson, R.H.; Larsson, K.-H.; Taylor, A.F.S.; May, T.W.; Frøslev, T.G.; Pawlowska, J.; Lindahl, B.; Põldmaa, K.; Truong, C.; et al. The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: Sequences, taxa and classifications reconsidered. Nucleic Acids Res. 2023, 52, D791–D797. [Google Scholar] [CrossRef]
  46. McMurdie, P.J.; Holmes, S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 2013, 8, e61217. [Google Scholar] [CrossRef]
  47. Segata, N.; Izard, J.; Waldron, L.; Gevers, D.; Miropolsky, L.; Garrett, W.S.; Huttenhower, C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011, 12, R60. [Google Scholar] [CrossRef] [PubMed]
  48. Oksanen, J.; Simpson, G.; Blanchet, F.; Kindt, R.; Legendre, P.; Minchin, P.R.; O’Hara, R.B.; Solymos, P.; Stevens, M.H.H.; Szoecs, E.; et al. vegan: Community Ecol. Package 2.6—6.1; 2024. Available online: https://cran.r-project.org/web/packages/vegan/index.html (accessed on 19 August 2025). [CrossRef]
  49. Dhariwal, A.; Chong, J.; Habib, S.; King, I.L.; Agellon, L.B.; Xia, J. MicrobiomeAnalyst: A web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 2017, 45, W180–W188. [Google Scholar] [CrossRef]
  50. Mayoral, C.; van Breugel, M.; Turner, B.L.; Asner, G.P.; Vaughn, N.R.; Hall, J.S. Effect of microsite quality and species composition on tree growth: A semi-empirical modeling approach. For. Ecol. Manag. 2019, 432, 534–545. [Google Scholar] [CrossRef]
  51. Saltonstall, K.; van Breugel, M.; Navia, W.; Castillo, H.; Hall, J.S. Soil microbial communities in dry and moist tropical forests exhibit distinct shifts in community composition but not diversity with succession. Microbiol. Spectr. 2025, 13, e01931-24. [Google Scholar] [CrossRef]
  52. Lauber, C.L.; Strickland, M.S.; Bradford, M.A.; Fierer, N. The influence of soil properties on the structure of bacterial and fungal communities across land-use types. Soil Biol. Biochem. 2008, 40, 2407–2415. [Google Scholar] [CrossRef]
  53. Kiers, E.T.; Lovelock, C.E.; Krueger, E.L.; Herre, E.A. Differential effects of tropical arbuscular mycorrhizal fungal inocula on root colonization and tree seedling growth: Implications for tropical forest diversity. Ecol. Lett. 2000, 3, 106–113. [Google Scholar] [CrossRef]
  54. Sheldrake, M.; Rosenstock, N.P.; Mangan, S.; Revillini, D.; Sayer, E.J.; Olsson, P.A.; Verbruggen, E.; Tanner, E.V.J.; Turner, B.L.; Wright, S.J. Responses of arbuscular mycorrhizal fungi to long-term inorganic and organic nutrient addition in a lowland tropical forest. ISME J. 2018, 12, 2433–2445. [Google Scholar] [CrossRef] [PubMed]
  55. Gallegos, M.; Hall, J.S.; Sinacore, K. Nutrient Strategies Used by Native Tree Species in Low-Fertility Soils [Poster]; BCI100 Symposium: Smithsonian Tropical Research Institute: Gamboa, Panamá, 2024. [Google Scholar]
  56. Sinacore, K.; Hall, J.S.; Potvin, C.; Royo, A.A.; Ducey, M.J.; Ashton, M.S. Unearthing the hidden world of roots: Root biomass and architecture differ among species within the same guild. PLoS ONE 2017, 12, e0185934. [Google Scholar] [CrossRef]
  57. Thompson, L.R.; Sanders, J.G.; McDonald, D.; Amir, A.; Ladau, J.; Locey, K.J.; Prill, R.J.; Tripathi, A.; Gibbons, S.M.; Ackermann, G.; et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 2017, 551, 457–463. [Google Scholar] [CrossRef]
  58. Gaby, J.C.; Buckley, D.H. A Comprehensive Evaluation of PCR Primers to Amplify the nifH Gene of Nitrogenase. PLoS ONE 2012, 7, e42149. [Google Scholar] [CrossRef]
  59. Stefani, F.; Bencherif, K.; Sabourin, S.; Hadj-Sahraoui, A.L.; Banchini, C.; Séguin, S.; Dalpé, Y. Taxonomic assignment of arbuscular mycorrhizal fungi in an 18S metagenomic dataset: A case study with saltcedar (Tamarix aphylla). Mycorrhiza 2020, 30, 243–255. [Google Scholar] [CrossRef] [PubMed]
  60. Fierer, N. Embracing the unknown: Disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 2017, 15, 579–590. [Google Scholar] [CrossRef]
  61. Delgado-Baquerizo, M.; Reich, P.B.; Trivedi, C.; Eldridge, D.J.; Abades, S.; Alfaro, F.D.; Bastida, F.; Berhe, A.A.; Cutler, N.A.; Gallardo, A.; et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 2020, 4, 210–220. [Google Scholar] [CrossRef]
  62. McGuire, K.L.; Fierer, N.; Bateman, C.; Treseder, K.K.; Turner, B.L. Fungal community composition in Neotropical rain forests: The influence of tree diversity and precipitation. Microb. Ecol. 2012, 63, 804–812. [Google Scholar] [CrossRef] [PubMed]
  63. Rivest, M.; Whalen, J.K.; Rivest, D. Tree diversity is not always a strong driver of soil microbial diversity: A 7-yr-old diversity experiment with trees. Ecosphere 2019, 10, e02685. [Google Scholar] [CrossRef]
Forests 16 01366 g001
Figure 1. Principle Coordinates Analysis (PCoA) ordination plots displaying compositional variation in soil fungal communities based on Bray–Curtis distance: (a) all bacteria, (b) nitrogen-fixing bacteria, (c) all fungi, and (d) Glomeromycota. Within-group dispersion was homogeneous for all comparisons (betadisper: p = >0.05). Percent dissimilarity values that are explained by each axis are shown in brackets. Ae = Anacardium excelsum; Dr = Dalbergia retusa; Pq = Pachira quinata; Ta = Terminalia amazonia; Tr = Tabebuia rosea.
Figure 1. Principle Coordinates Analysis (PCoA) ordination plots displaying compositional variation in soil fungal communities based on Bray–Curtis distance: (a) all bacteria, (b) nitrogen-fixing bacteria, (c) all fungi, and (d) Glomeromycota. Within-group dispersion was homogeneous for all comparisons (betadisper: p = >0.05). Percent dissimilarity values that are explained by each axis are shown in brackets. Ae = Anacardium excelsum; Dr = Dalbergia retusa; Pq = Pachira quinata; Ta = Terminalia amazonia; Tr = Tabebuia rosea.
Forests 16 01366 g001
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Figure 2. Heatmap of soilborne fungi ASVs that vary in their relative abundance across monoculture plantings of five tree species. Thirteen ASVs were differentially abundant among the tree species (α = 0.1), using FDR-adjusted p-values (p = 0.093 for all). The ASVs are listed in decreasing order according to their LDA scores (effect size), and the heat map indicates whether a given ASV is more (red) or less (blue) abundant across tree species based on normalized counts. Taxonomic identifications are provided to the lowest taxonomic level possible.
Figure 2. Heatmap of soilborne fungi ASVs that vary in their relative abundance across monoculture plantings of five tree species. Thirteen ASVs were differentially abundant among the tree species (α = 0.1), using FDR-adjusted p-values (p = 0.093 for all). The ASVs are listed in decreasing order according to their LDA scores (effect size), and the heat map indicates whether a given ASV is more (red) or less (blue) abundant across tree species based on normalized counts. Taxonomic identifications are provided to the lowest taxonomic level possible.
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Figure 3. Heatmap of arbuscular mycorrhizal fungi (AMF; Phylum Glomeromycota) ASVs that have differential abundance in the five monocultures, using FDR-adjusted p-values. The ASVs are listed in decreasing order according to their LDA scores (effect sizes), and the heat map indicates whether a given ASV is more (red) or less (blue) abundant across tree species based on normalized counts.
Figure 3. Heatmap of arbuscular mycorrhizal fungi (AMF; Phylum Glomeromycota) ASVs that have differential abundance in the five monocultures, using FDR-adjusted p-values. The ASVs are listed in decreasing order according to their LDA scores (effect sizes), and the heat map indicates whether a given ASV is more (red) or less (blue) abundant across tree species based on normalized counts.
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Table 1. Functional traits of the tree species planted in the Agua Salud native species monoculture plantations.
Table 1. Functional traits of the tree species planted in the Agua Salud native species monoculture plantations.
Crown
Phenology a		Nutrient
Cycling b		P
Affinity c		Water Use
Efficiency d		Aboveground
Biomass (kg/tree) e		Mortality
(%) f
Anacardium excelsumE-0.90Low9.63.6 ± 2.4
Dalbergia retusaD (briefly)N-High9.41.2 ± 1.1
Pachira quinataD-1.01Low3.15.9 ± 6.6
Tabebuia roseaD-1.62-2.84.0 ± 2.4
Terminalia amazoniaE-−1.19Low29.11.6 ± 2.8
a E = evergreen, D = deciduous; b N = nitrogen-fixing; c a positive effect size for P (phosphorus) indicates that a species occurs predominantly on high P soils, while a negative effect size indicates that the species occurs on low P soils [33]; d [31,34,35]; e average aboveground biomass of trees planted in the Agua Salud plantations in 2015, 7 years post planting. Calculations take mortality into account [22]; f average mortality between 2010 and 2015. Presented as mean ± SD [22].
Table 2. Bulk soil chemical properties under five native tree species in 2015, after seven years of growth in monoculture plantations. Data are shown as mean ± standard deviation. No significant differences were found between tree species for any measurements (ANOVA or Kruskal–Wallis (depending on normality of the data), p > 0.05). N = number of plots; P = phosphorus; K = potassium; NO3 = nitrate; NH4 = ammonium; TOC = total organic carbon; TN = total nitrogen.
Table 2. Bulk soil chemical properties under five native tree species in 2015, after seven years of growth in monoculture plantations. Data are shown as mean ± standard deviation. No significant differences were found between tree species for any measurements (ANOVA or Kruskal–Wallis (depending on normality of the data), p > 0.05). N = number of plots; P = phosphorus; K = potassium; NO3 = nitrate; NH4 = ammonium; TOC = total organic carbon; TN = total nitrogen.
Tree
Species		NpH
(in H2O)		Resin P (mg/kg)K
(mg/L)		NO3
(mg/g)		NH4
(mg/g)		TOC
(mg/L)		TN
(mg/L)
A. excelsum115.6 ± 0.10.5 ±0.11.3 ± 0.210.0 ± 5.729.6 ± 2.511.9 ± 1.81.4 ± 0.2
D. retusa125.6 ± 0.10.5 ±0.11.1 ± 0.210.8 ± 2.729.1 ± 2.911.5 ± 0.91.4 ± 0.1
P. quinata105.4 ± 0.10.5 ± 0.10.9 ± 0.111.6 ± 2.030.6 ± 2.214.3 ± 1.11.7 ± 0.1
T. amazonia115.4 ± 0.10.4 ± 0.10.8 ± 0.114.7 ± 8.126.3 ± 2.113.0 ± 1.11.4 ± 0.1
T. rosea115.5 ± 0.10.5 ± 0.11.4 ± 0.26.8 ± 1.423.6 ± 1.214.5 ± 1.01.6 ± 0.1
Overall Means555.5 ± 0.20.5 ± 0.31.1 ± 0.510.8 ± 15.427.8 ± 7.713.0 ± 4.101.5 ± 0.4
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Saltonstall, K.; Spear, E.R.; Glodowska, M.A.; Hall, J.S. Tree Species Identity Drives Fungal, but Not Bacterial, Soil Community Shifts in Tropical Monoculture Plantations. Forests 2025, 16, 1366. https://doi.org/10.3390/f16091366

AMA Style

Saltonstall K, Spear ER, Glodowska MA, Hall JS. Tree Species Identity Drives Fungal, but Not Bacterial, Soil Community Shifts in Tropical Monoculture Plantations. Forests. 2025; 16(9):1366. https://doi.org/10.3390/f16091366

Chicago/Turabian Style

Saltonstall, Kristin, Erin R. Spear, Martyna A. Glodowska, and Jefferson S. Hall. 2025. "Tree Species Identity Drives Fungal, but Not Bacterial, Soil Community Shifts in Tropical Monoculture Plantations" Forests 16, no. 9: 1366. https://doi.org/10.3390/f16091366

APA Style

Saltonstall, K., Spear, E. R., Glodowska, M. A., & Hall, J. S. (2025). Tree Species Identity Drives Fungal, but Not Bacterial, Soil Community Shifts in Tropical Monoculture Plantations. Forests, 16(9), 1366. https://doi.org/10.3390/f16091366

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