Forest soils are fundamental in regulating the global carbon (C)
cycle; their capacity to accumulate large stores of C means they form a
vital role in mitigating the effects of climate change. Understanding the
processes that regulate forest soil C dynamics and stabilisation is
important to maximise the capacity and longevity of C sequestration.
Compared with surface soil layers, little is known about soil C dynamics in
subsoil layers,
Forest soils are influential in regulating the global C cycle, accounting for over 70 % of the world's soil organic C (SOC) reserves and supporting over 80 % of aboveground terrestrial C stocks (Batjes, 1996; Jobbágy and Jackson, 2000; Six et al., 2002). Developing forest management strategies that maximise soil C sequestration may serve as a powerful tool for offsetting fossil fuel emissions and mitigating the effects of climate change (Jandl et al., 2007; Mukul et al., 2020).
Compared with surface soil layers, the deeper subsoil layers of forest ecosystems are comparatively under-explored (Yost and Hartemink, 2020). Although the concentration of SOC declines with depth (Schmidt et al., 2011), studies have reported that, accumulatively, up to 50 % of total SOC may be contained within soil layers below 30 cm (Balesdent et al., 2018; Jobbágy and Jackson, 2000; Gonzalez et al., 2018; Ross et al., 2020).
Deep SOC is more resistant to decomposition by soil microorganisms due to the biological, physical, and chemical conditions of the subsoil environment (Schmidt et al., 2011). Thus, when undisturbed, deep SOC is considered to represent the “stable” fraction of the SOC pool. However, the stability of sequestered SOC is complex and is related to a range of variables including soil clay content, mineralogy, structure and texture, landscape position, association with microaggregates, climate, vegetation, and soil microorganisms (Jobbágy and Jackson, 2000; Lal, 2004; Kuzyakov, 2010; Song et al., 2020). Historical biases against sampling deeper soil layers limit our understanding of how the properties of subsoil environments influence SOC sequestration and storage. This introduces uncertainty when modelling the stability of deep SOC pools to climate change (Gross and Harrison, 2019).
Soil microorganisms are responsible for the transformation and stabilisation of SOC (Lladó et al., 2017; Spohn et al., 2016) and are influential in determining patterns of C sequestration (Jastrow et al., 2007). Studying the changes that occur to soil microbial communities with depth is essential to understand the nature and extent of their role in regulating long-term SOC dynamics. Previous studies have reported that soil microbial diversity, abundance, respiration, and C turnover decline with depth (Blume et al., 2002; Eilers et al., 2012; Fang et al., 2005; Fierer et al., 2003; Spohn et al., 2016). More recent research has explored the taxonomic and functional changes that occur in microbial communities with soil depth (Brewer et al., 2019; Frey et al., 2021; Hao et al., 2021). However, particularly for coniferous forest ecosystems, our knowledge of the response of soil microbial communities to depth and the environmental properties that drive these changes remains poorly understood. Furthermore, the inherently heterogeneous nature of soil environments, both globally and locally (Curd et al., 2018; Kuzyakov and Blagodatskaya, 2015; Štursová et al., 2016), means context-specific research is required to understand the SOC dynamics and associated microbiota of forest subsoil environments. To maximise the sink capacity of forest soils and to retain the SOC already stored at depth, we need to better understand the processes that regulate C sequestration and long-term C storage within the deep soil environment.
Using Puruki Experimental Forest (central North Island, New Zealand) as a
case study, this research aimed to improve our understanding of subsoil C
dynamics within a production forest. Using a wide range of analytical
methods, we examined the variability in subsoil C in 10 cm increments down a
1 m soil profile. Changes in the diversity, composition, and abundance
of bacterial and fungal communities with depth were quantified using 16S
rRNA/ITS amplicon sequencing and quantitative real-time PCR (qPCR). To
investigate changes in C cycling dynamics down each soil core, measurements
of soil isotopic composition (
Soils were sampled from Puruki Experimental Forest (38
Puruki Forest sits at an elevation range of 500–700 m a.s.l. and varies from
gently (less than 12
Soil parent materials at Puruki originated from the Taupo volcanic centre
(1850
A permanent sample plot (PSP) within the Rua sub-catchment of Puruki Forest
was targeted for sampling. As Puruki Forest has a varied topology, our
sampling plot was located along a 12
To prepare soils for DNA analysis, moist soil samples were sieved to less
than 2 mm and stored at
Total C and N were determined on fine (
Fine-ground soil samples were sent to GNS Stable Isotope Laboratory
(Wellington, New Zealand) for
Fine ground soil samples were graphitised at the Houghton Carbon, Water, and
Soils Lab (USDA Forest Service Northern Research Station, Michigan, US), and
radiocarbon measurements were conducted at the DirectAMS facility (Bothell,
Washington, US). Soil samples were combusted at 900
Following the Earth Microbiome Project (EMP) protocols, barcoded forward and reverse primers 515F-806R were used to amplify the 16S rRNA V4 region (Caporaso et al., 2012). Nested PCRs were performed using the non-barcoded primers 27F-1492R on DNA samples that initially failed to amplify. The EMP's standard ITS amplicon protocol was followed to amplify the fungal ITS gene region using the barcoded primers ITS1f-ITS2 (Bokulich and Mills, 2013; Hoggard et al., 2018). DNA samples that initially failed to amplify were targeted for nested PCR using primers NSA3-ITS4. Full details of the PCR reaction protocols are provided in the Supplement Methods. Successful PCR amplicons were sent for Illumina 500 MiSeq 250 bp (16S rRNA) and Illumina 600 MiSeq 300 bp (ITS) paired-end sequencing at the Australian Genome Research Facility (AGRF; Melbourne, Australia). For the 16S rRNA amplicon libraries, eight no-template control (NTC) samples, which showed no visible bands during agarose gel electrophoresis, were sent for sequencing to check for contaminants.
The absolute concentration of bacterial and fungal DNA in each DNA sample was determined from standard curves using AriaMx SYBR Green qPCR (Agilent Technologies). Broad-range forward and reverse primers 338F-518R and ITS1F-5.8s were selected to target the 16S rRNA (V3–V4) and ITS gene region for quantification (Shahsavari et al., 2016). Full details of the qPCR reaction protocol and standard curve preparation are provided in the Supplement Methods. Following quantification, 16S rRNA and ITS copy numbers were adjusted to copy numbers per nanogram of DNA.
Before analysis, slope-corrected C stocks (Mg ha
To calculate differences in the allocation of C stocks down each soil
profile, the proportion of C (Mg ha
Base calling and demultiplexing of sequencing libraries to per-sample fastQ
files was performed using Illumina's bcl2fastq software (version
2.20.0.422). The DADA2 version 1.18 workflow (Callahan et al., 2016) was
used to process paired-end fastQ files into amplicon sequence variants
(ASVs). Briefly, forward and reverse reads were quality filtered, trimmed,
and de-noised before being merged into ASVs. Chimeric ASVs were removed, and
taxonomies were assigned to ASVs using the Ribosomal Database Project (RDP)
Classifier (Wang et al., 2007) and the UNITE (Abarenkov et al., 2021)
databases. After DADA2 processing, 74.92
Microbial alpha and beta diversity analyses were calculated on rarefied ASV tables using the R packages phyloseq (McMurdie and Holmes, 2013) and vegan (Oksanen et al., 2020). Changes in alpha diversity with depth were tested for statistical significance using Kruskal–Wallis chi-squared tests as described previously. Pairwise Spearman's correlation tests with Bonferroni adjustment were used to correlate alpha diversity values with soil chemical parameters using psych R (Revelle, 2021). Bray–Curtis distance matrices were calculated to measure community dissimilarity and ordinated using non-metric multidimensional scaling (NMDS). Differences in community dissimilarity by depth were tested for significance using PERMANOVA, and pairwise differences were tested using pairwiseAdonis R with Bonferroni adjustment (Arbizu, 2017). For both alpha and beta diversity analyses, the impacts of transect sampling position and PCR type (nested or single) were tested for significance. Mantel tests were used to correlate Euclidean distance matrices of soil chemical properties to Bray–Curtis matrices of microbial community composition. Venn diagrams were produced to show the proportion (%) shared versus unique ASVs between depth increments using MicEco R (Russel, 2021).
Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC; Lin and Peddada, 2020) was used to identify microbial taxa which had a significant log-fold change between topsoil (0–30 cm) and subsoil layers (30–100 cm). Hierarchical clustering was performed on Euclidean distances of log-adjusted abundances, using the average linkage method (hclust R). Dendrograms were constructed using ggtree (Yu et al., 2017) to visualise the hierarchical clustering patterns of microbial orders based on their log-adjusted abundances. The log-adjusted abundances of microbial taxa obtained from ANCOM-BC analysis were correlated to soil chemical properties using pairwise Spearman's correlation tests with Bonferroni adjustment using psych R (Revelle, 2021). To assess changes in the diversity of high-level 16S rRNA functional genes with soil depth, PICRUSt2 was used to predict MetaCyc pathway abundances (Douglas et al., 2020; Caspi et al., 2018). MetaCyc pathway abundance predictions were analysed in phyloseq R using the same alpha and beta diversity methods as already outlined.
ASV tables were split into samples from topsoil (0–30 cm) and subsoil (30 –100 cm) layers to obtain microbial communities specific to each soil layer. Topsoil and subsoil ASV tables were then filtered to retain only “core” communities (i.e. ASVs with a total relative abundance of at least 0.05 % in a minimum of three samples) and then summarised to genus level.
SPIEC-EASI networks were constructed using the Meinshausen–Buhlmann (MB)
neighbourhood algorithm in SpiecEasi R (Kurtz et al., 2015). Networks were
visualised using igraph R (Csárdi and Nepusz, 2006) and network
statistics were calculated using Cytoscape 3.8.2 (Shannon et al., 2003).
Significant differences (
Total C (Mg ha
The average soil C stock of the 1 m soil cores extracted from Puruki Forest
was 99.71
On average, 34.58
The relative proportion (%) of
Overall,
The mean
The richness, diversity, and evenness of fungal and bacterial communities
significantly declined with depth (
The mean
A pairwise Spearman's correlation matrix showing the correlation
between microbial richness, diversity, DNA abundance, and the associated
soil chemical properties. Blank cells indicate non-significant (
Bacterial (PERMANOVA:
NMDS ordination plots that were calculated using Bray–Curtis
dissimilarity matrices showing the differences in
Mantel tests identified that
Only 9 % of bacterial ASVs and 2 % of fungal ASVs were present in all depth increments down the entire soil profile (Fig. S11). Upper depth increments (0–20 cm) had the highest number of unique bacterial (20 %) and fungal (32 %) ASVs, suggesting that these upper layers supported a greater range of unique bacterial and fungal taxa than the lower depths. Less than 3 % of bacterial ASVs and 1 % of fungal ASVs were found to be unique within depth increments from the 40 to 100 cm soil layer, suggesting that subsoils did not harbour vastly unique or distinctive microbial communities from those of topsoil layers.
Archaeal taxa belonging to Thermoplasmata, Desulfurococcales, Thermoprotei,
Woesearchaeota incertae sedis (AR15, AR18, AR16, AR20), Euryarchaeota,
Candidatus Aenigmarchaeum, and Thermoproteales exhibited significant
positive log-fold changes with depth (Fig. 6). Furthermore, bacterial taxa
belonging to Acidobacteria (e.g.
Cluster dendrograms of bacterial and fungal orders based on their
bias-adjusted abundances (ANCOM-BC analysis). Hierarchical clustering was
performed on Euclidean distances of bias-adjusted abundances using the
average linkage method. The log-fold change (LFC) value of each taxon
between topsoil and subsoil is displayed. Taxa with positive LFC values were
higher in subsoils and taxa with negative LFC values were lower in subsoils.
The statistical significance of each taxa's differential abundance between
topsoil and subsoil is displayed, with TRUE
In contrast, bacterial taxa which had a significantly negative log-fold
change in subsoils (Fig. 6) included Chlamydiae (Chlamydiales),
Bacteroidetes (Sphingobacteriales, Cytophagales), Acidobacteria
(
When correlating the abundance of microbial taxa to soil chemical
properties, the microbial phyla Bacteriodetes, Chlamydiae, Chloroflexi,
Gemmatimonadetes, Mortierellomycota, and Mucoromycota were strongly
correlated (Spearman
The average node degree and neighbourhood connectivity of the topsoil
interkingdom network was significantly higher than that of the subsoil
network. In contrast, the average shortest path length and clustering
coefficient were significantly higher in the subsoil interkingdom network
(Fig. S12; Table S15). Compared with subsoils, the topsoil interkingdom
network had a more connected structure with a greater degree of
co-occurrences between genera. For both topsoil and subsoil interkingdom
networks, bacterial genera were the dominant community members compared with
fungi and archaea (Fig. 7). Networks of fungal-only topsoil communities
had a significantly (
SPIEC-EASI networks that were constructed on core microbial genera associated with topsoil (0–30 cm depth) and subsoil (30–100 cm depth) layers. Node size represents the normalised (centred log ratio) mean abundance of each genus. Blue nodes represent bacterial genera, red nodes represent archaeal genera, and yellow nodes represent fungal genera. Positive edge weights are shown by black lines connecting nodes and negative edge weights are shown by red lines connecting nodes.
Previous research has identified large stores of unaccounted C stocks
located in subsoil layers below 30 cm (Balesdent et al., 2018; Gonzalez et
al., 2018; Ross et al., 2020). Our research identified that within Puruki
Forest, 35 % of soil C stocks were allocated to the subsoil layers below
30 cm. Within our sampling site, subsoil C storage patterns were highly
variable and ranged from 19 % to 46 % of the total soil C stocks.
Previously, Oliver et al. (2004) remarked upon the large variability in soil
C storage patterns across Puruki Forest, reporting average C stocks of 143 Mg ha
We observed an overall enrichment of soil
Although microbial DNA abundance declined with depth, accumulatively subsoils held 49 % of bacterial and 33 % of fungal DNA abundance which corresponded to values for subsoil C stocks. Soil microorganisms are responsible for the mineralisation of SOC and are influential in determining SOC formation, composition, and persistence (Domeignoz-Horta et al., 2021; Jansson and Hofmockel, 2020). Although subsoil C is considered more resistant to microbial decomposition (Schmidt et al., 2011), an accumulation of microbial “stocks” in proximity to subsoil C stores presents concerns, particularly given our uncertainty on the effects of climate change on soil microbial activity and C substrate use (Yang et al., 2021). However, the contribution of microbial necromass C to total soil C is known to increase with soil depth (Ni et al., 2020). As our research does not provide information on the viability or activity of subsoil microbial DNA, much of the accumulated bacterial and fungal DNA in the subsoil may be non-viable microbial cells. Much like incorporating measures of soil C age and stability when quantifying subsoil C stocks, further research should measure the abundance of subsoil microbial cells which are viable and active, as well as those which are dormant but have the potential to become active under global change. Doing so will help us better investigate the vulnerability of subsoil C stocks to microbial decomposition under climate change.
Soil microbial diversity and abundance declined sharply with depth which fits in agreement with previous research findings (Blume et al., 2002; Eilers et al., 2012; Mundra et al., 2021; Rosling et al., 2003). These declines occurred alongside clear shifts in microbial community composition. The magnitude of the depth-driven changes slowed past 30 cm, which marked a clear transitional change between the topsoil–subsoil boundary. Although fungal diversity and DNA abundance were generally much lower than that of bacteria, patterns in response to depth were consistent across both microbial kingdoms. The changes in microbial diversity and abundance were highly correlated with the concentration (total C %) and radiocarbon age of soil C. These findings support research that has proposed declines in soil C quantity, quality, and availability to be strong drivers of declines in microbial diversity and abundance (Eilers et al., 2012). Due to their proximity to aboveground vegetation, topsoil layers typically receive a wider load and range of fresh C inputs from surface litter and plant roots (Fierer et al., 2003; Spohn et al., 2016). This may explain why soil depths 0–20 cm supported the greatest number of unique ASVs (i.e. those which occurred only in that soil layer), with the number of unique ASVs declining sharply with depth.
Whilst bacterial diversity and abundance declined sharply with depth, bacterial subsoil communities exhibited relatively minor differences in network structure compared with topsoil communities. Furthermore, subsoil fungal networks exhibited greater structural differences between topsoil and subsoil layers compared with bacterial communities. Subsoil fungal networks were almost entirely disconnected and there was no suite of co-occurring fungal genera that characterised the subsoil microbiome. The breakdown in subsoil fungal network structure may be explained simplistically by reduced community size limiting the ability for fungal co-occurrences to establish. Although still in low abundance, the relatively larger bacterial community abundance may have contributed to the lesser effects of soil depth on their co-occurrence patterns compared with fungi. Furthermore, as the properties of soil fungal communities are strongly driven by characteristics of aboveground vegetation (Likulunga et al., 2021; Urbanová et al., 2015), the breakdown in subsoil fungal co-occurrences may be due to their increasing physical distance from the surface litter and plant rhizosphere.
Consistent with previous research, we observed an increased abundance of archaeal taxa in subsoils, mainly the phyla Euryarchaeota, Crenarchaeota, and Woesearchaeota (Brewer et al., 2019; Eilers et al., 2012; Frey et al., 2021; Hartmann et al., 2009). Proposed reasons for the increased abundance of archaea with depth include their adaptation to chronic energy stress (Valentine, 2007), involvement as ammonia oxidizers in driving autotrophic nitrification in deep soil layers (Eilers et al., 2012), preference as methanogens for anaerobic environments (Feng et al., 2019), and adaptation to nutrient-poor environments as slow growing oligotrophs (Turner et al., 2017).
Several representatives of the bacterial phyla Acidobacteria and Firmicutes
increased in abundance with depth which may be explained by their tolerance
for nutrient-limited environments (Bai et al., 2017; Brewer et al., 2019;
Frey et al., 2021; Feng et al., 2019; Lladó et al., 2017). However,
there were several representatives of Acidobacteria and Firmicutes that also
declined with depth, such as
Strong declines in the abundance of Bacteroidetes with depth have been
previously attributed to their copiotrophic behaviour (Eilers et al., 2012;
Feng et al., 2019; Mundra et al., 2021). In our study, the abundance of
Bacteroidetes was positively correlated with
The findings of our research address knowledge gaps related to subsoil C dynamics for production forests such Puruki Forest. A considerable proportion (at least 35 %) of soil C stocks within Puruki Forest were allocated to the subsoil layers below 30 cm, with this value reaching up to 49 % in some soil cores. In the deepest soil layers, the mean C age was 1571 yr BP, which corresponded closely to the estimated age of the soil parent materials. Although there were large declines in bacterial and fungal DNA abundance and diversity with soil depth, the magnitude of these changes slowed past 30 cm depth. This marked a topsoil–subsoil transitional boundary in the soil profile. In total, 49 % of bacterial and 33 % of fungal DNA abundance was allocated to the subsoil. Quantifying the viable activity of this microbial “biomass” is essential for predicting the vulnerability of subsoil C stocks to microbial decomposition under climate change. Numerous archaeal taxa exhibited an increased abundance in subsoils, indicating their potential ecological importance in deep soil environments. Furthermore, numerous fungal and bacterial taxa exhibited significant differential abundances between topsoil and subsoil layers. This supports the notion that subsoil environments may act as an environmental filter, with the vertical distribution of soil microorganisms reflecting the sharp changes in soil physical and chemical properties with depth.
The R code and datasets generated during this current study are available from the corresponding author upon reasonable request.
The supplement related to this article is available online at:
AKB led the data processing, statistical analysis, original writing of the manuscript, and the preparation the manuscript for publication. LGG and SAW contributed to developing the methodology, research supervision, and project administration. CA and FD contributed to developing the methodology. All authors provided critical feedback and helped shape the research, analysis and manuscript.
The contact author has declared that none of the authors has any competing interests.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Thanks to Angela Wakelin, Stephen Pearce, and Scion analytical laboratory services (Scion, NZ). Further thanks to Jeff Hatten (Oregon State University, US) and Katherine Heckman (U.S. Department of Agriculture, US) for their assistance in the soil radiocarbon analysis.
This research has been supported by the New Zealand Ministry of Business, Innovation and Employment (MBIE) Strategic Science Investment Fund held by Scion (grant no. C04X1703).
This paper was edited by Ashish Malik and reviewed by Qiufang Zhang and one anonymous referee.