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Article

Root Fragment Amendments Increase Nematode Density and Mycobiome Stochasticity in Douglas-Fir Seedlings

by
Laura Super
1,*,
Monika A. Gorzelak
2 and
Robert D. Guy
1
1
Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
2
Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403 1st Avenue, Lethbridge, AB T1J 4B1, Canada
*
Author to whom correspondence should be addressed.
Forests 2023, 14(11), 2120; https://doi.org/10.3390/f14112120
Submission received: 12 September 2023 / Revised: 13 October 2023 / Accepted: 19 October 2023 / Published: 24 October 2023
(This article belongs to the Section Forest Soil)
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Abstract

:
Relatively little is known about whole-plant fungal communities (mycobiome) and associated soil nematodes, especially with respect to woody plant seedlings and disturbance caused by forest harvesting. In a growth chamber experiment, we tested simulated clear-cut soil conditions on shoot biomass, total soil nematode density, and the shoot and root mycobiome of Douglas-fir, Pseudotsuga menziesii (Mirb.) Franco, seedlings. Soil treatments included unamended bare soil and soil amended with root segments of kinnikinnick, Arctostaphylos uva-ursi (L.) Spreng., pinegrass, Calamagrostis rubescens Buckley, or P. menziesii seedlings. We used next-generation Illumina sequencing and the PIPITS pipeline to obtain fungal taxa used for mycobiome community richness and Jaccard-based taxonomic normalized stochasticity ratio to assess mycobiome community assembly stochasticity. Total nematode density, measured from Baermann funnel extractions, increased in soils supplemented with A. uva-ursi or C. rubescens root segments. Root mycobiomes were more stochastic in the A. uva-ursi than P. menziesii or the bare conditions, whereas the shoot mycobiome was more stochastic in the C. rubescens treatment than in the P. menziesii treatment. Our results suggest that refugia plants impact the phyto-biome, in this case plant-associated nematodes and the stochasticity of root and shoot mycobiome community assembly, while not showing noticeable impacts on above-ground plant growth.

1. Introduction

Clear-cut harvesting has a profound impact on the landscape. Large scale removal of above-ground tree biomass also impacts below-ground biodiversity [1]. Improving the success of planted seedlings is important for forest regeneration [2], and understanding the impact of the phyto-biome and soil food web on seedling success can help to guide planting practices. Retention of understory plants that share symbionts with seedlings, or similarly the addition of root fragments from such plants as a source of inoculum to hasten colonization by beneficial mycorrhizal fungi, has been studied for planting Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) in simulated clear-cut conditions and in the field [3,4]. Hagerman and Durall [4] examined how root segments from the ectomycorrhizal plant kinnikinnick (Arctostaphylos uva-ursi (L.) Spreng.) might act as refugia in clear-cuts to promote inoculation of P. menziesii by ectomycorrhizal fungi and. alternatively, how the establishment of such symbioses might be inhibited by the addition of roots of pinegrass (Calamagrostis rubescens Buckley) which have arbuscular mycorrhizal fungi that might compete with ectomycorrhizal fungi. These authors mention that refuge plants can play an important role in maintaining ectomycorrhizal fungi following clear-cutting, but they do not explore any other parts of the complex soil food web. Adding root fragments with the intention of adding mycorrhizal inoculum may introduce other soil organisms that could impact seedling proliferation directly (i.e., pathogens) or indirectly by affecting soil food web dynamics. We build on previous work with a phyto-biome perspective to look how root segments, simulating refuge plants, impact tree seedling growth, the entire mycobiome (all fungi) of tree seedlings above- (shoot) and below-ground (root), as well as root-associated soil nematodes.
Increasingly scientists are viewing plants from a phyto-biome perspective where each individual plant is seen as an integrated ecosystem, with its environment and associated organisms [5]. Understanding plant performance in relation to these organisms, especially the plant microbiome, is crucial [6]. Microbiota associated with plant tissues and soil are involved in complex interactions that can impact plant health, influence the resilience of plants to abiotic stress, and mediate how plants respond to anthropogenic change [7]. Two important components of the phyto-biome are the fungal microbiome (or mycobiome) and nematodes. Studies of total plant mycobiome communities are relatively rare and less common than those of bacterial communities [8,9]. Previous research has predominantly focused on the mycobiome of herbaceous or crop plants; the fungal diversity and community composition for woody plants is much less commonly studied [10]. With recent advances in high-throughput sequencing, the role of the mycobiome is coming to the fore [8]. However, we still know relatively little about the complexity and diversity of mycobiomes [11,12,13] associated with conifer seedlings.
Soil nematodes occupy a variety of trophic positions in the soil food web. They interact with plants and can also feed on bacteria and fungi [14,15]. Yeates et al. [16] mention that plants greatly impact nematode communities, and in turn nematodes can impact the rhizosphere soil food web of plants, acting as root feeders, fungal feeders, bacterial feeders, and feeders on other nematodes. Nematodes can even be eaten by fungi that form nematode traps, and nematodes and fungi have evolved to interact with plants, including synergistically infecting plant hosts in a way that negatively impacts plant growth [17], and in other cases they can promote a soil food web that positively impacts plant growth [18].
Total nematode abundance or density of the nematode community is an important measure for monitoring soil ecosystems and provides useful information about soil communities [19,20]. For example, in a meta-analysis of 111 studies of forest, grassland, urban and agricultural ecosystems, Pothula et al. [21] found that nematode abundance was highest in forest ecosystems, agricultural lands and disturbed grasslands. Furthermore, currently, focus is being placed on communities of nematodes in response to anthropogenic change and management practices, such as crop-tree thinning [22], association with pests such as beetles in pine forests [23], and in relation to plant communities in forests across climate zones [24]. Clear-cutting can affect soil physical structure resulting in lower nematode abundance [15,25]. In contrast, management practices that enhance the growth of understory plants and the herb layer can promote nematodes [15]. Thus, practices that ameliorate the extreme disturbance of clear-cuts by protecting or promoting refugia plants could help nematode abundance.
It is useful to understand the relative importance of stochastic (random assembly of communities due to stochastic processes, such as random dispersal) versus deterministic assembly (assembly due to deterministic processes, such as competitive exclusion) in ecological communities [26], including mycobiome communities associated with shoots and roots [9]. Stochastic, context-dependent microbiome assembly is increasingly being recognized as important in animal microbiome research [27], and stochasticity is important for other host organisms, such as plants [9]. To better understand the mycobiome associated with conifer seedling roots and shoots, more classical uses of community composition measures, such as Jaccard’s presence/absence [28], can be supplemented with new approaches that use these measures in conjunction with null models, permutations, and high-performance computing, such as the normalized stochasticity ratio (NST) [29]. NST helps us quantify the relative importance of stochastic versus deterministic assembly processes of microbiome communities, with values below 0.5 indicating a tendency towards determinism and values above 0.5 indicating that stochastic processes predominate, and a measure of NST is the taxonomic stochasticity ratio (tNST) [29].
We conducted a phyto-biome mesocosm study in which rhizosphere root segments were added as inoculum to barren (bare) clear-cut soil to test whether there was a soil refugia effect (treatment effect) on P. menziesii seedlings: (i) shoot biomass of tree seedlings (plant performance), (ii) rhizosphere total nematode density, (iii) root mycobiome richness and stochasticity (tNST), (iv) shoot mycobiome richness and stochasticity (tNST)
We predicted that simulated bare clear-cut soil would be less enriched with soil nematodes compared to understory plant rhizosphere treatments (A. uva-ursi and C. rubescens), and that planting of P. menziesii seedlings into soil amended with similar P. menziesii seedling roots would be potentially less favourable to plant performance given the possibility of pathogen introduction. Because roots produce exudates that may impact their rhizosphere in a predictable, less stochastic fashion than shoots, we predicted that shoots would have microbiome communities assembling more stochastically than roots, as some authors have noted [30].

2. Materials and Methods

2.1. Study Conditions

Depending on the material and stage of the experiment, plants were grown in a PGR15 growth chamber (Conviron, Winnipeg, Manitoba) in unfertilized greenhouse perennial mix (Sunshine Mix, Sun Gro Horticulture, Agawam, MA, USA) or in a 50:50 mixture with field soil. Field soil was collected from the University of British Columbia (UBC) Alex Fraser Research Forest (52.52° N, 121.73° W), during clear-cutting for a landing. Rocks and large woody debris were removed, but no soil sieving was done. The field soil, the greenhouse mix and the 50:50 mixture were not sterilized in order to retain the natural soil community, as well as the community present in the greenhouse, the latter to reflect that tree seedling plugs used operationally for forest regeneration will carry organisms from their nursery. To further promote more realistic soil communities, plants were not fertilized for the entire duration of the experiment. A. uva-ursi and C. rubescens were supplied by Tipi Mountain Native Plant Nursery (Cranbrook, BC, USA). Seed of P. menziesii, seed-lot 47,826 (of similar provenance to the other species) was obtained from the Tree Seed Centre (Surrey, BC, USA). In May 2018, the A. uva-ursi and C. rubescens were planted into the 50:50 mixture while pre-stratified P. menziesii seeds were planted into perennial mix, both in Ray Leach “cone-tainer” (Stuewe and Sons, Inc., Tangent, OR, USA) tubes placed within the growth chamber. The P. menziesii treatment simulates placing a plant within its own soil from the growth chamber, which is an important treatment to understand, and expect to be less favourable and enriched in associated organisms compared to other treatments; notwithstanding, please note that this treatment has the confounding impact of not having time exposed to the bare soil at the beginning of the experiment. There were also tubes containing bare soil with no plants growing, simulating barren clear-cut soil.
After three months, plants were harvested and root segments were cut into pieces (similar to Hagerman and Durall [4]) and those pieces along with associated rhizosphere soil were mixed 50:50 with bare soil. Control bare soil from one tube was similarly also mixed 50:50 with another tube of bare soil, resulting in four treatments altogether: bare soil (Ba; n = 20), A. uva-ursi-amended soil (Au; n = 20), C. rubescens-amended soil (Cr; n = 20), and P. menziesii-amended soil (Pm; n = 20). Pseudotsuga menziesii seedlings were randomly planted into these treatments and grown in the growth chamber from late August 2018 to late January 2019 with a 14 to 21 °C daily temperature cycle, a 16-h photoperiod, and a photosynthetic photon flux density of 300 μmol m−2 s−1. Temperatures within the chamber were based on averaging the mean and maximum in June, July and August associated with the tree seedling provenance. Tubes were watered to field capacity at least weekly with tap water.

2.2. Plant Performance, Nematode Abundance and Mycobiome Sampling

Fresh shoot biomass of P. menziesii seedlings and total soil nematode density were assessed at the conclusion of the experiment, at the same time as mycobiome sampling. Biomass was measured right before sampling for mycobiome sequencing; fresh mass was used because a large portion of the sample was sent for sequencing. Biomass is a good proxy for overall plant performance differences, and biomass, survival (growth chamber reduced differences in survival) and reproductive output (the seedlings were too young to be sexually reproductive) relates to plant performance [31]. To estimate root-associated nematode density, 50 g of rhizosphere soil associated with the intact roots of each tree seedling was collected and stored at 4 °C for processing within 4–5 days of collection (N = 80). Thirty modified Baermann funnels were used for 48 h and nematodes were counted using microscopy immediately after sampling [19,32]. Nematodes were counted to obtain total nematode density; nematode taxonomy and community composition is more complex and was not included as part of this study, but Appendix A does include nematode feeding guilds (Table A1). A Kruskal–Wallis test was performed for nematode density as affected by soil treatment, given count data [33], in R. Following this test, effect sizes were calculated with the rcompanion package [34]. Following a significant Kruskal–Wallis test, Dunn’s Test was used for post hoc testing using the FSA package [35,36].
To investigate entire phyllo-sphere (shoot) and rhizosphere (root) mycobiomes, tissues were collected using a random sampling grid and sterilization of equipment between samples, and sent on dry ice within 48 h to Génome Québec (Montréal, QC, Canada) for DNA extraction and sequencing with Illumina MiSeq. Following the manufacturer’s protocols, Qiagen’s 2019 DNeasy PowerSoil Pro and DNeasy Power Plant Pro kits were used to extract root and shoot samples, respectively; roots were lightly shaken to remove the bulk of the soil. For sequencing of the mycobiome of the fresh plant samples, the fungal ITS2 region (internal transcribed region 2) was sequenced using primers fITS7 and ITS4 [37]. Fungal community DNA was amplified from all rhizosphere samples (N = 40), but not from all of the accompanying phyllo-sphere samples (N = 29 out of the original N = 40). To create an operational taxonomic unit (OTU) table at 97% similarity, the open-source PIPITS pipeline, version 2.7 (2020) [38] was run using high-performance computing for the ITS2 region with the UNITE database (https://unite.ut.ee/, accessed on 31 August 2022). No singletons were removed in the resulting dataset, which considered an OTU counted for a taxon if there was at least one read. Following the recommendation of McMurdie and Holmes [39], we did not rarefy the data. The NST package [29] was used to calculate the taxonomic normalized stochasticity ratio (tNST) using the Jaccard index.

2.3. Statistical Analysis

Biomass data (N = 80) showed non-normality and heteroscedasticity of variance, so a Kruskal–Wallis test [35] to assess the effects of soil amendment on shoot biomass was performed utilizing R version 4.1.3 [40]. A Kruskal–Wallis test was also conducted on mycobiome richness (number of taxa) for roots and shoots given treatment. The function pairwise.wilcox.test with p.adjust.method = “BH”, Benjamini and Hochberg [41], for multiple testing p-value adjusting, was used for post hoc testing for shoots given treatment and was found to differ. Root mycobiome tNST testing was with the NST package with permutational multivariate analysis of variance (PERMANOVA) to compare among treatments (N = 40, n = 10 per treatment). Shoot mycobiome tNST testing was also with PERMANOVA to compare among treatments (N = 29, n = 6 to 7 depending on amplification). The tNST randomizations were run on the UBC’s Advanced Research Computing clusters 1000 times for mycobiome data.

3. Results

3.1. Tree Seedling Shoot Growth Did Not Change with Treatment

Soil treatment did not statistically impact fresh shoot biomass (Kruskal–Wallis chi-squared = 2.848, df = 3, N = 80, p = 0.416). See Figure 1.

3.2. Total Nematode Density Increased with the Addition of Arctostaphylos uva-ursi or Calamagrostis rubescens Root Segments

Total nematode density varied significantly with soil treatment (Kruskal–Wallis chi-squared = 9.42, df = 3, N = 80, p = 0.0243, and medium effect size of Epilson squared of 0.119), with notably higher densities of nematodes in soils supplemented with either A. uva-ursi or C. rubescens root segments than in bare soil or soil with P. menziesii root segments (Table 1). Also, see Figure 2.

3.3. Treatment Affected Mycobiome Richness of Shoots and Mycobiome Stochasticity of Roots and Shoots

After running the PIPITS pipeline on the Illumina ITS sequencing data, 2286 OTUs were retained. Further analysis of this OTU table, suggested that mycobiome samples had an average mycobiome richness (number of OTUs) of 101.8 and 7.75 for roots and shoots, respectively. Kruskall–Wallis tests showed that root mycobiomes were not impacted by treatment (Kruskal–Wallis chi-squared = 7.28, df = 3, N = 40, p = 0.063), but there was a treatment effect for shoots (Kruskal–Wallis chi-squared = 9.00, N = 29, p = 0.029). Post hoc testing showed that shoot mycobiome richness was lower for Pm than Ba (p < 0.05).
The observed tNST values for the root mycobiome varied from an average of 0.64 to 0.72 (Table 2), in the stochastic assembly range. The shoot mycobiome tNST varied from an average of 0.47 to 0.70 (Table 3), from slightly deterministic to largely stochastic, indicating that the assembly of shoot mycobiomes had a greater range of stochasticity across treatments than the assembly of root mycobiomes. For the root mycobiome, the Au tNST (0.72) differed from the tNSTs for Ba (0.66) (F = 13.02, p = 0.04) and Pm (0.64) (F = 20.67, p = 0.02) treatments. For the shoot mycobiome, the Cr tNST (0.69) differed from Pm tNST (0.47) (F = 7.013, p = 0.02). All other comparisons showed no significant difference.

4. Discussion

In this experiment, we added root fragments of common understory plants to simulated clear-cut soils in a growth chamber to determine their impact on the developing mycobiome and soil nematodes associated with P. menziesii seedlings. While we did not observe differences in plant growth (Figure 1), the application of treatments did impact the nematode density (Table 2 and Figure 2) and the stochasticity (tNST) of the mycobiomes of roots (Table 2) and shoots (Table 3). The richness of the shoot mycobiome was found to be the lower for plants in P. menziesii-amended soil than for bare soil.
To our knowledge, similar studies have not been conducted on other plant species. Although surprising, plant growth was unaffected by changes to nematode density and the mycobiome. This may be explained by the slower growth rate of conifer seedlings as compared to faster growing plants, such as the commonly used model laboratory plant Arabidopsis thaliana [42].
Our research emphasizes that root fragments add more than just mycorrhizal inoculum, with the addition of other soil denizens impacting a complex phyto-biome food web. This growth chamber work on P. menziesii seedlings points to a need for a more holistic soil food web analysis of tree seedling phyto-biomes. Indeed, while mycorrhizal research is important for tree seedlings, treatments often studied in the light of mycorrhizal fungi, such as plant root segments by Hagerman and Durall [4], can result in a more complex ecosystem, including other parts of the mycobiome and soil nematodes. In line with this, we show that soil nematode densities were higher in A. uva-ursi and C. rubescens plant rhizosphere treatments than the bare soil (Table 2). We did not identify the nematodes, but this could be done in future follow-up studies with DNA sequencing of nematode communities, which is becoming more common as nematode databases [43] and sequencing methods [44] develop. We did, however, quantify trophic groups (Appendix A, Table A1). Our results suggest that soils with added roots have more fungal-feeding nematodes than bare soil. Many nematodes feed on plant matter and fungi [45], and many fungi prey on nematodes [46]; the nematodes are likely to exert selective forces on the fungal community over time.
The tNST metric is a proxy estimate of stochasticity, with values greater than 0.5 (50%) indicating a more stochastic assembly [29]. This metric captures a continuum of stochastic to deterministic assembly of communities by including both possible scenarios wherein similarity of communities is either increased or decreased in response to selection (a tNST less than 0.5 indicates more selection) and drift (a tNST greater than 0.5 indicates more drift), respectively. We found that the tNST for the root mycobiome was overwhelmingly stochastic regardless of soil treatment (Table 2). Selection of a detectable mycorrhizal community in P. menziesii occurs after 8 months [47]; our seedlings grew for only 4 months, and our results showing stochasticity of fungal community assembly at this seedling age are consistent with this finding (and see large numbers of saprotrophic fungi in Appendix A, Table A2). Root exudates select for and influence the microbial community in the soil [48] and this changes over the development of the plant [49]. Nonetheless, we did see differences in tNST values in the shoots, with the addition of P. menziesii root fragments resulting in a value (0.47) slightly below the cut-off for determinism with this metric, indicating a selective force associated with the addition of root fragments into soils. The presence of conspecific root fragments impacting above-ground leaf associated mycobiomes, as suggested by this outcome, could reflect the influence of organisms other than fungi in the soil food web.
Future studies can explore NST and related metrics for the mycobiome and other above- and below-ground plant and soil communities. Since the publication of the Ning et al. [29] paper explaining NST, there have been many related papers. For example, Yan et al. [50] used MST, the modified stochasticity ratio, a metric similar to NST, when studying secondary succession of soil communities in four broad-leaved mixed forest stands 20, 32, 47 and 61 years after clear-cutting. With their Illumina next-generation sequencing data, they found that bacterial community assembly was stochastic and related to the abiotic environment in all stands, while fungal community assembly increased in stochasticity with older age of stands and seemed likely to be due to plant traits shaping fungal community composition. While we only used Illumina sequencing for fungal communities, the microscopy work to assess nematodes suggested that there might have been a shift in bacterial communities depending on treatment (Appendix A, Table A1) because soils with plant roots seemed to have more bacterial-feeding nematodes compared to bare soil.
Future research can also relate how various forest management practices shift the nematode food web. Nematode distributions are patchy in the soil, and local conditions (e.g., soil horizon, organic matter content, etc.) can shift feeding types of nematodes [51]. In our work, the soil was well mixed with organic and mineral components and highly disturbed, but different forest harvesting approaches will have variation in disturbance of the soil. Disturbed soils are more difficult to restore and management practices that foster particular nematode trophic groups can better improve soils for ecosystem functions [51]. Linking nematode communities to soil health is particularly relevant to the restoration and management of soils given climate change [52].

5. Conclusions

While lab-based studies, such a as ours, help identify complex soil food webs and treatment impacts, it is useful to understand in situ phyto-biomes of P. menziesii seedlings given the complexity of site-specific interactions in harvesting plots depending on the type of harvesting practice and impacts of harvesting on below-ground ecosystems [1]. Thus, we suggest that transplant experiments could be conducted in the field by planting trees into different soil refugia rooting media to determine their performance and that of their associated organisms. Our results suggest that, until trees form clear relationships with symbionts, their root mycobiome may be quite stochastic. Recent research in a common garden on poplar trees (Populus spp.) shows that in the initial growing season the microbial community assembly is moderated by stochastic and deterministic factors [53]. As with that study, we suggest that our work is applicable to understanding tree health and could help with the design of ecologically-informed inoculations. Similar studies of P. menziesii trees within common garden environments would be useful for comparative purposes. Furthermore, stochasticity, including from stochastic events, such as weather influencing biotic and abiotic conditions, would be expected to be important for tree phyto-biomes in situ in clear-cuts or in other forest harvest scenarios, with the removal and retention of large timber trees potentially having a noticeable signature on the above- and below-ground biota of tree seedlings. Forest soil microbiomes shift with disturbance, but how is still poorly understood and worthy of more research using soil community sequencing approaches [54]. Site preparation, including stump retention or removal, greatly impacts above- and below-ground organisms in harvested forests [1]. This has implications for the performance of planted tree seedlings and above- and below-ground associated organism components of seedling phyto-biomes in reforesting efforts.

Author Contributions

L.S. conceptualized and conducted the growth chamber experiment, with feedback from M.A.G. on the mycobiome sampling, primers and sequencing. L.S. analyzed the data with assistance and feedback from M.A.G. and R.D.G. L.S., M.A.G. and R.D.G. contributed to writing the manuscript. M.A.G. assisted L.S. with running PIPITS and mycobiome-specific ideas for approaches and analyses. In other words, conceptualization, L.S., M.A.G. and R.D.G.; methodology; all; formal analysis, L.S.; writing—original draft preparation, all; writing—review and editing, all; visualization, L.S.; funding acquisition, M.A.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Strategic Project Grant STPGP 478832-15 and Agriculture and Agri-Food Canada’s Research Accelerating Innovation: Rhizosphere Processes J-00179. L.S. was supported by an NSERC CGS-D3 (Doctoral) scholarship.

Data Availability Statement

Data are available in the Federated Research Data Repository (FRDR). (https://www.frdr-dfdr.ca/repo/) at https://doi.org/10.20383/103.0828 (accessed on 23 October 2023).

Acknowledgments

We thank Santokh Singh, Amy Angert, Richard Hamelin, Sean Smukler, Allen Larocque, Jiarui Li, and Patrick Neuberger for feedback, ideas and assistance. Lab assistance from Melody Fu, Arsalan Mohammadi, Kristine Lin, Farhad Rahimi, Victoria Wu, Chis Wu and Eully Ao is greatly appreciated. L.S. thanks Biljana Jonoska Stojkova for statistical suggestions. Thank you to the Federated Research Data Repository, part of Digital Research Alliance of Canada, for helping with depositing the data and providing associated metadata relevant to this publication.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Average nematode guild classifications per treatment. Codes are Au, Ba, Cr and Pm (same codes as Table A1). Below gives counts in 50 g for bacterivore (BF), fungivore (FF), plant-parasite (PF), omnivore (OM) and predator (PD). Please note these classifications were from looking under the microscope at mouth parts to classify nematode feeding guilds, which has its caveats compared to exact taxa identification [19]. With each mean, standard error is shown in parentheses.
Table A1. Average nematode guild classifications per treatment. Codes are Au, Ba, Cr and Pm (same codes as Table A1). Below gives counts in 50 g for bacterivore (BF), fungivore (FF), plant-parasite (PF), omnivore (OM) and predator (PD). Please note these classifications were from looking under the microscope at mouth parts to classify nematode feeding guilds, which has its caveats compared to exact taxa identification [19]. With each mean, standard error is shown in parentheses.
Nematode ClassificationAuBaCrPm
BF20.5 (2.8)10.3 (3.3)17.1 (2.5)10.8 (2.6)
FF41.3 (5.7)14.1 (3.5)31.2 (3.7)29.7 (5.5)
PF9.9 (1.5)6.0 (1.4)11.3 (1.9)13.6 (1.9)
OM9.8 (1.1)5.3 (0.9)10.1 (1.5)6.6 (0.9)
PD2.2 (0.3)2.9 (0.5)2.6 (0.5)2.5 (0.5)
Table A2. Average FUNGuild classifications per treatment for root (R) and shoot (S) mycobiomes. Codes are Au, Ba, Cr and Pm for Arctostaphylos uva-ursi, bare soil, Calamagrostis rubescens and Pseudotsuga menziesii root-amended soil treatments, respectively. Below gives numbers of operational taxonomic units (OTUs), which from PIPITS are at the 97% clustering [37]. After the creation of the OTU table, data were run through FUNGuild, which is an open-source annotation tool for grouping fungi into guilds [55]. This table shows most taxa as undefined, a caveat for whole mycobiome work.
Table A2. Average FUNGuild classifications per treatment for root (R) and shoot (S) mycobiomes. Codes are Au, Ba, Cr and Pm for Arctostaphylos uva-ursi, bare soil, Calamagrostis rubescens and Pseudotsuga menziesii root-amended soil treatments, respectively. Below gives numbers of operational taxonomic units (OTUs), which from PIPITS are at the 97% clustering [37]. After the creation of the OTU table, data were run through FUNGuild, which is an open-source annotation tool for grouping fungi into guilds [55]. This table shows most taxa as undefined, a caveat for whole mycobiome work.
FUNGuild ClassificationR_AuR_BaR_CrR_PmS_AuS_BaS_CrS_Pm
Undefined224.0267.4296.4230.434.445.045.829.9
Pathotroph4.24.15.14.10.51.30.70.1
Pathotroph-Saprotroph8.48.19.14.81.10.81.02.3
Pathotroph-Saprotroph-Symbiotroph20.318.318.415.22.93.14.83.1
Pathotroph-Symbiotroph1.92.12.62.00.40.30.30.1
Saprotroph57.770.575.161.49.616.813.88.0
Saprotroph-Symbiotroph48.645.345.742.110.517.112.58.0

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Forests 14 02120 g001
Figure 1. Violin plot of tree seedling biomass with soil refugia treatment. Codes are Ba, Au, Cr, and Pm for the bare soil, Arctostaphylos uva-ursi, Calamagrostis rubescens and Pseudotsuga menziesii root-amended soil treatments, respectively. For each treatment there are 20 replicates.
Figure 1. Violin plot of tree seedling biomass with soil refugia treatment. Codes are Ba, Au, Cr, and Pm for the bare soil, Arctostaphylos uva-ursi, Calamagrostis rubescens and Pseudotsuga menziesii root-amended soil treatments, respectively. For each treatment there are 20 replicates.
Forests 14 02120 g001
Forests 14 02120 g002
Figure 2. Violin plot of nematode density with soil refugia treatment. Codes are Ba, Au, Cr, and Pm for the bare soil, Arctostaphylos uva-ursi, Calamagrostis rubescens and Pseudotsuga menziesii root-amended soil treatments, respectively. For each treatment, there are 20 replicates.
Figure 2. Violin plot of nematode density with soil refugia treatment. Codes are Ba, Au, Cr, and Pm for the bare soil, Arctostaphylos uva-ursi, Calamagrostis rubescens and Pseudotsuga menziesii root-amended soil treatments, respectively. For each treatment, there are 20 replicates.
Forests 14 02120 g002
Table 1. Dunn’s Test of multiple comparisons using rank sums for total nematode density given soil treatment (N = 80). The adjusted p-value was calculated in R with the Benjamini–Hochberg method to correct for multiple pairwise testing [41]. Codes are Ba, Au, Cr, and Pm for the bare soil, Arctostaphylos uva-ursi, Calamagrostis rubescens and Pseudotsuga menziesii root-amended soil treatments, respectively. Significance at the 0.05 cut-off level is indicated with ‘*’.
Table 1. Dunn’s Test of multiple comparisons using rank sums for total nematode density given soil treatment (N = 80). The adjusted p-value was calculated in R with the Benjamini–Hochberg method to correct for multiple pairwise testing [41]. Codes are Ba, Au, Cr, and Pm for the bare soil, Arctostaphylos uva-ursi, Calamagrostis rubescens and Pseudotsuga menziesii root-amended soil treatments, respectively. Significance at the 0.05 cut-off level is indicated with ‘*’.
ComparisonZp (Adjusted)
Au–Ba2.730.038 *
Au–Cr0.170.862
Ba–Cr−2.56 0.032 *
Au–Pm0.78 0.651
Ba–Pm−1.950.102
Cr–Pm0.610.651
Table 2. Pairwise comparisons of the stochasticity of root mycobiome communities by treatment. The Root-1 and Root-2 are the first and second root mycobiome communities given soil refugia treatment respectively. The tNST-Root-1 and tNST-Root-2 give tNST values for these communities. The difference (tNST-Root-1−tNST-Root-2) and F statistic (F) are given as well as the p value (p) of the permutational test of the difference. The permutational multivariate analysis of variance (PERMANOVA) values are with p < 0.05 as the cut-off for statistical significance. Note numbers below are rounded to the nearest two decimal places, so the differences might slightly vary from the unrounded difference. Codes below are root mycobiome communities given: R_Ba = bare soil; R_Pm = Pseudotsuga menziesii-amended soil; R_Cr = Calamagrostis rubescens-amended soil; and R_Au = Arctostaphylos uva-ursi-amended soil. Significance at the 0.05 cut-off level is indicated with ‘*’.
Table 2. Pairwise comparisons of the stochasticity of root mycobiome communities by treatment. The Root-1 and Root-2 are the first and second root mycobiome communities given soil refugia treatment respectively. The tNST-Root-1 and tNST-Root-2 give tNST values for these communities. The difference (tNST-Root-1−tNST-Root-2) and F statistic (F) are given as well as the p value (p) of the permutational test of the difference. The permutational multivariate analysis of variance (PERMANOVA) values are with p < 0.05 as the cut-off for statistical significance. Note numbers below are rounded to the nearest two decimal places, so the differences might slightly vary from the unrounded difference. Codes below are root mycobiome communities given: R_Ba = bare soil; R_Pm = Pseudotsuga menziesii-amended soil; R_Cr = Calamagrostis rubescens-amended soil; and R_Au = Arctostaphylos uva-ursi-amended soil. Significance at the 0.05 cut-off level is indicated with ‘*’.
Root-1Root-2NST-Root-1NST-Root-2DifferenceFp
R_BaR_Pm0.660.640.020.890.33
R_BaR_Cr0.660.69−0.021.640.16
R_BaR_Au0.660.72−0.0613.020.04 *
R_PmR_Cr0.640.69−0.044.730.10
R_PmR_Au0.640.72−0.0820.670.02 *
R_CrR_Au0.690.72−0.044.520.11
Table 3. Pairwise comparisons of the stochasticity of shoot mycobiome communities by treatment. The Shoot-1 and Shoot-2 are the first and second shoot mycobiome communities given soil refugia treatment, respectively. The tNST-Shoot-1 and tNST-Shoot-2 give tNST values for these root communities. The difference (tNST-Shoot-1−tNST-Shoot-2) and F statistic (F) are given as well as the p value (p) of the permutational test of the difference. The permutational multivariate analysis of variance (PERMANOVA) values are with p < 0.05 as the cut-off for statistical significance. Note numbers below are rounded to the nearest two decimal places, so the differences might slightly vary from the unrounded difference. Codes below are shoot mycobiome communities given: S_Ba = bare soil; S_Pm = Pseudotsuga menziesii-amended soil; S_Cr = Calamagrostis rubescens-amended soil; and S_Au = Arctostaphylos uva-ursi-amended soil. Significance at the 0.05 cut-off level is indicated with ‘*’.
Table 3. Pairwise comparisons of the stochasticity of shoot mycobiome communities by treatment. The Shoot-1 and Shoot-2 are the first and second shoot mycobiome communities given soil refugia treatment, respectively. The tNST-Shoot-1 and tNST-Shoot-2 give tNST values for these root communities. The difference (tNST-Shoot-1−tNST-Shoot-2) and F statistic (F) are given as well as the p value (p) of the permutational test of the difference. The permutational multivariate analysis of variance (PERMANOVA) values are with p < 0.05 as the cut-off for statistical significance. Note numbers below are rounded to the nearest two decimal places, so the differences might slightly vary from the unrounded difference. Codes below are shoot mycobiome communities given: S_Ba = bare soil; S_Pm = Pseudotsuga menziesii-amended soil; S_Cr = Calamagrostis rubescens-amended soil; and S_Au = Arctostaphylos uva-ursi-amended soil. Significance at the 0.05 cut-off level is indicated with ‘*’.
Shoot-1Shoot-2NST-Shoot-1NST-Shoot-2DifferenceFp
S_BaS_Pm0.700.470.2316.560.48
S_BaS_Cr0.700.690.010.020.28
S_BaS_Au0.700.590.113.770.43
S_PmS_Cr0.470.69−0.227.010.02 *
S_PmS_Au0.470.59−0.123.240.09
S_CrS_Au0.690.590.101.480.38
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Super, L.; Gorzelak, M.A.; Guy, R.D. Root Fragment Amendments Increase Nematode Density and Mycobiome Stochasticity in Douglas-Fir Seedlings. Forests 2023, 14, 2120. https://doi.org/10.3390/f14112120

AMA Style

Super L, Gorzelak MA, Guy RD. Root Fragment Amendments Increase Nematode Density and Mycobiome Stochasticity in Douglas-Fir Seedlings. Forests. 2023; 14(11):2120. https://doi.org/10.3390/f14112120

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Super, Laura, Monika A. Gorzelak, and Robert D. Guy. 2023. "Root Fragment Amendments Increase Nematode Density and Mycobiome Stochasticity in Douglas-Fir Seedlings" Forests 14, no. 11: 2120. https://doi.org/10.3390/f14112120

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