Intraspecific variability in plant and soil chemical properties in a common garden plantation of the energy crop Populus

Matthew E. Craig; Anne E. Harman-Ware; Kevin R. Cope; Udaya C. Kalluri

doi:10.1371/journal.pone.0309321

Abstract

Optimizing crops for synergistic soil carbon (C) sequestration can enhance CO₂ removal in food and bioenergy production systems. Yet, in bioenergy systems, we lack an understanding of how intraspecies variation in plant traits correlates with variation in soil biogeochemistry. This knowledge gap is exacerbated by both the heterogeneity and difficulty of measuring belowground traits. Here, we provide initial observations of C and nutrients in soil and root and stem tissues from a common garden field site of diverse, natural variant, Populus trichocarpa genotypes—established for aboveground biomass-to-biofuels research. Our goal was to explore the value of such field sites for evaluating genotype-specific effects on soil C, which ultimately informs the potential for optimizing bioenergy systems for both aboveground productivity and belowground C storage. To do this, we investigated variation in chemical traits at the scale of individual trees and genotypes and we explored correlations among stem, root, and soil samples. We observed substantial variation in soil chemical properties at the scale of individual trees and specific genotypes. While correlations among elements were observed both within and among sample types (soil, stem, root), above-belowground correlations were generally poor. We did not observe genotype-specific patterns in soil C in the top 10 cm, but we did observe genotype associations with soil acid-base chemistry (soil pH and base cations) and bulk density. Finally, a specific phenotype of interest (high vs low lignin) was unrelated to soil biogeochemistry. Our pilot study supports the usefulness of decade-old, genetically-variable, Populus bioenergy field test plots for understanding plant genotype effects on soil properties. Finally, this study contributes to the advancement of sampling methods and baseline data for Populus systems in the Pacific Northwest, USA. Further species- and region-specific efforts will enhance C predictability across scales in bioenergy systems and, ultimately, accelerate the identification of genotypes that optimize yield and carbon storage.

Citation: Craig ME, Harman-Ware AE, Cope KR, Kalluri UC (2024) Intraspecific variability in plant and soil chemical properties in a common garden plantation of the energy crop Populus. PLoS ONE 19(10): e0309321. https://doi.org/10.1371/journal.pone.0309321

Editor: Benedicte Riber Albrectsen, Umeå Plant Science Centre, Umeå University, SWEDEN

Received: February 14, 2024; Accepted: August 8, 2024; Published: October 21, 2024

Copyright: © 2024 Craig et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All raw data associated with the study has been provided in a supplemental file [S2 Table_and raw data].

Funding: This work was supported by funding from the Center for Bioenergy Innovation (CBI) project to UCK. CBI is a U.S. Department of Energy Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. The funding agency played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Oak Ridge National Laboratory is managed by UT-Battelle, LLC for the US DOE under Contract Number DE-AC05-00OR22725.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Enhanced soil carbon (C) storage—i.e. removing CO₂ from the atmosphere in favor of a longer-lived terrestrial pool—represents one possible nature-based climate solution [1, 2]. Agricultural systems—including bioenergy systems—have the greatest potential for enhanced soil C because these systems have been the most depleted due to human land use [3, 4]. Frontier technologies offer the potential to enhance soil C sequestration beyond what is currently achievable with best management practices [5], especially if combined with other strategies like BECCS (bioenergy with carbon capture and storage) [6]. One promising frontier approach is to optimize crops for synergistic soil C sequestration [7]. This involves identifying candidate genotypes that associate with soil C promoting phenotypes [8] or, better, genotypes that associate with positive soil C outcomes (e.g. net accumulation). Detecting genotype-soil C associations is challenging because there are few resources suitable to answer this question, and because soil biogeochemistry changes slowly and via many mechanisms in response to plant trait variation.

Long-term (e.g. >10 years old) plant genetic diversity panels, planted in common gardens for genome wide association studies [9], offer an opportunity for unprecedented studies of associations between plant genotypes and soil C dynamics. Several such plantations exist for the bioenergy candidate, Populus trichocarpa [10]. However, in these systems, genotypes are intermixed and individual trees are planted in 1–2 m apart [11]. Studying effects of individual genotypes on soil properties, therefore, requires a focal tree approach, instead of the plot-level measurements that are typical in ecosystem-level soil biogeochemistry studies. This presents a potential methodological barrier because soil C and other biogeochemical properties are spatially heterogeneous at fine spatial scales [12], which can also drive fine-scale variability in plant traits [13]. Thus, prior to any broad-scale sampling efforts to leverage and inform plant-soil interactions in large genetic diversity plantation systems, there is a need for empirical evaluation of sampling strategies and to address whether plant-soil couplings are detectable, variable, and replicable at the scale of individual plants or genotypes.

The difficulty of measuring belowground plant traits presents an additional challenge for studying genotype associations with soil C and other properties. Belowground plant inputs, including root tissues and exudates, are now recognized as critical drivers of soil C formation [14–17], and soil microbial traits mediate both soil C formation and loss [18]. Thus, assessing belowground plant and microbial traits is critical to understanding soil C dynamics, especially in agricultural and managed forest systems where aboveground biomass is harvested. Focal tree root sampling is time consuming in these plantation systems because it requires tracing excavated roots to the genotype of interest. The challenging nature of sampling and measurement has therefore resulted in asymmetric knowledge gaps in belowground dynamics relative to heavily studied above-ground dynamics in bioenergy crop plantations. Inferring belowground traits from aboveground traits is one potential way around this issue. However, aboveground-belowground trait associations have mixed support among species [19], and not much is known about these associations among different field-cultivated genotypes for P. trichocarpa. In summary, Populus Genome-wide association study (GWAS) plantations represent a promising system for identifying important genotype-specific associations with belowground properties but given the challenges of working in such a system, an initial evaluation is critical to design informed sampling campaigns at scale.

In the present study, we leveraged a genetically diverse, long-term P. trichocarpa population—with a wide range of phenotypic variation reported in aboveground traits—growing in a common garden field setting in the Pacific Northwest, USA [20]. Replication of genotypes in blocks provides the opportunity for capturing genotypic effects, but belowground soil and plant traits have not yet been studied in this population. We hypothesized that distinct P. trichocarpa genotypes determine variation in soil properties, and that this variation is observable at the scale of individual trees. We also hypothesized that plant lignin phenotype influences amount of C in soil and that belowground traits correlate positively with aboveground traits across genotypes. We evaluated these hypotheses using a woody perennial bioenergy plant in a ten-year old intraspecific GWAS site. Our objectives were to evaluate 1) the potential of focal tree soil sampling for detecting associations of poplar genotypes with soil biogeochemistry (e.g. C, nitrogen (N), phosphorus (P), and a range of soil micro- and macronutrient elements) and soil bulk density, and 2) the strength of correlations between above- and belowground traits (stem vs root and plant vs soil). To do this, we measured soil, root, and stem chemical traits for several focal trees spanning four different P. trichocarpa genotypes representative of population extremes in cell wall chemistry (Fig 1A)—i.e. phenotypes with low (BESC 24) or high (BESC 375, BESC 371, BESC 394) lignin content based on population-wide cell wall chemistry data previously reported for this population [13, 20–23]. We also explored the influence of sampling location on measured soil chemical traits, potential effects of plant tissue chemistry on soil C, relationships among nutrient concentrations among plant and soil pools.

Download:

Fig 1. Correlation analysis undertaken at the Populus common garden site at Clatskanie, OR.

(A) Graphical representation of plant-soil elemental distribution analysis undertaken in top 10 cm surface soil profile (bulk soil collected from 0–5 cm and 5–10 cm depth pools). (B) Bivariate relationship between C and N using all soil samples (r = 0.89). (C) Correlation matrix for all soil properties measured. Twelve observations for each genotype.

https://doi.org/10.1371/journal.pone.0309321.g001

Materials and methods

Field site description

The Populus trichocarpa GWAS Common Garden site field site is located in Clastkanie, Oregon, USA (coordinates: -123.26, 46.12). Sampling was conducted on this private land and research activities were permitted via Poplar Innovations Inc. through an ongoing access agreement with the owner. The site is flat with a relatively uniform soil type; Entisols [24] formed by floodplain activity adjacent to the Columbia River. Continuous land use in past decades has been for Populus plantations. For this study, we collected surface soil (top 10 cm) from planted areas and from areas that do not contain Populus individuals in the current plantation. Analysis of basic soil characteristics at the time of sampling revealed an average pH of 5.33, gravimetric soil moisture content of 31.3%, C:N ratio of 12.24, and soil texture as Silty Clay (10.0% sand, 47.5% silt and 42.5% clay). The common garden site has ~1000 genotypes of P. trichocarpa, densely planted (<2 m apart) and replicated in three blocks with randomization and surrounded by two buffer rows on all four edges to prevent edge effects. These trees had been growing for more than 11 years at the time of sampling.

Field sample collection

Bulk soil samples (0–5 cm [top] and 5–10 cm [bottom] depth) were collected 30 cm from the focal tree trunk in three randomly chosen cardinal directions, digging using graduated trowels to reference depth and collecting and storing in labeled Ziploc bags. Two replicate trees were sampled for each of the four genotypes. For bulk density analysis, soil samples were collected using AMS Soil Core Sampler Kit with Hammer Attachment (part # 77455), 2 in × 2 in Soil Core Kit and stored in 2 in × 2 in core liners Precut (plastic). Samples were shipped on ice and stored at 4°C until analysis. Prior to analyses, soils were processed using forced air drying for ~48 hours at 43°C. Stem cores (1 cm in diameter) from tree breast height (~1.3 m) were collected using increment borer and stored and shipped in zip-lock bags at −20°C. Fine roots (<2 mm diameter) were accessed by shallow digging, tracing of roots to tree stump and cut using ethanol wiped pruners and stored and shipped in ziplock bags on dry ice. We targeted the surface-most roots which usually corresponded to a depth of less than 10 cm from the surface. For elemental analysis, subsamples of roots and stems were separately aliquoted into labeled brown paper bags and dried in hot air oven 70°C for three days and shipped to soil service lab in University of Georgia.

Laboratory analyses

Basic soil analysis and elemental analysis of plant and soil samples was conducted at the soil testing service of UGA Extension, Agricultural and Environmental Services Laboratories using standard methods. Soil pH (1:1_w:v soil:0.01 M CaCl₂) and carbon and nutrient concentrations were determined using published standard methods [25, 26]. Briefly, soils were dried, ground, and analyzed for various macro and micronutrient elements; carbon and nitrogen (N) on an elemental analyzer [27, 28]; Phosphorus (P), Potassium (K), Calcium (Ca), Magnesium (Mg), Zinc (Zn), and Manganese (Mn) were extracted in a Mehlich-1 solution and analyzed via Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES). Soil texture analysis was performed using the hydrometer method [29]. Stem and root C and N were analyzed in the same manner as the soils. Additionally, concentrations of macro and micronutrients were determined via ICP-OES after microwave digestion in nitric acid.

Data analysis

To examine the extent to which soil chemical traits vary at the scale of individual trees, we fit linear models with focal tree and sample depth as predictors (R 3.6.1). Assumptions of normality and homogeneity of variance were checked by inspecting residual plots and with a Shapiro-Wilk test. Response variables were natural log-transformed where necessary to improve adherence to assumptions (specifically for soil C and N content). To determine the proportion of variance explained at the level of individual trees, partial R² were determined using the ‘asbio’ package [30]. To provide an initial look at whether soil variables are clustered with respect to genotype, we additionally calculated partial R² for models fit with genotype and sample depth as predictors. However, these analyses are considered exploratory due to inadequate replication.

Relationships, principal components and variance amongst stem, root and soil traits (elemental, chemical and physical properties) were measured by determination of Pearson Correlation Coefficients (PCC) and Principal Component Analysis (PCA). PCC values were calculated on raw data values and PCA (100 iterations of NIPALS algorithm, 20 random cross validation) was performed on mean-centered compositional data using the Unscrambler X V.10.5 (Camo Analytics, AspenTech).

Results

Analyses of field-collected plant and soil samples undertaken to assess whether genetically diverse, long-term (ten-year) stands of a natural Populus trichocarpa collection which are proven resources for aboveground studies, present a practical resource for quantifying genotype-specific plant effects on soil chemical properties, including soil C (Fig 1A). If so, studying such systems could facilitate the deeper probing and optimization of poplar plantation systems for enhancing C storage in soils. Our study system is well suited to address this question because soil biogeochemical properties change slowly, and different genotypes are intermixed within replicated blocks on the field site allowing insights into feasibility and extent thereof of studying aboveground-belowground correlations at plant, genotype, and population levels in this system.

Variability in soil characteristics at the Clatskanie field site

Across the entire field site soil, the correlation between soil C and soil N was strong (r = 0.89; Fig 1B), demonstrating a consistent soil C:N ratio. Correlation analysis of all measured soil parameters (pH, bulk density, and elements) showed additional strongly correlated soil properties (Fig 1C). PCA analyses conducted to explore whether there is a dependence of overall soil chemistry on either the radial direction (East, North and West) or depth (top [0–5 cm] vs bottom [5–10 cm]) of sampling showed no significant effect overall (S1 Fig). However, linear models did show an association with sample depth (p < 0.05) for specific soil variables—pH, Ca, K, Mg, and P (S1 Table)—with nutrient concentrations and pH tending to be higher in the top surface layer (e.g. Ca in Fig 2A).

Download:

Fig 2. Representative observations of elemental distribution with genotype, within tissue type and across above and belowground samples.

(A) Ca concentration in soil collected from BESC 375 from across the replicate blocks was observed to be significantly different relative to other genotypes based on ANOVA (p < 0.05). Horizontal bars indicate significant differences between 0–5 cm (green) and 5–10 cm (blue) soil samples. (B) Within the same plant tissue (root), elemental distribution (shown here Ca, K, and Mg) varied across genotypes. (C) Pearson’s correlation coefficients among stem, root, and soil concentrations for carbon and key nutrients. Bars and boxes in panels A and B represent three observations from each of two individuals. Coefficients in (C) represent correlations among average values for six individuals. A comprehensive correlation matrix generated using all parameters measured for stem, root, and soil samples obtained from the four genotypes is provided in S2 Table.

https://doi.org/10.1371/journal.pone.0309321.g002

Focal tree and genotype-specific plant-soil relationships

Data from the pilot study support that soil properties can cluster at the genotype or individual tree level (S2 Fig). Focal tree was a significant predictor of all analyzed soil properties (p < 0.001), and explained more than half of the variation ( > 0.50) in all properties except for soil C:N ( = 0.50; S1 Table). For models using data aggregated at the scale of genotype, variables related to soil acid-base chemistry clustered together for different genotypes. Specifically, genotype predicted more than half of variation in pH and Ca and predicted more than a quarter of variation in Mg and Mn (S1 Table). Visual inspection of plots reveals that one genotype, BESC 375, is a strong driver of these patterns (S2 Fig) and was associated with more acidic (low pH), calcium-depleted soil (Fig 2A).

PCA analysis comparing the genotype representing the low end of lignin distribution in the population (BESC 24) with a high lignin genotype (BESC 375) showed genotype-specific clustering of soil properties (S3 Fig), but this lignin phenotype based differential clustering was not observed when all four genotypes were included in the analysis. BESC 24 did stand out when comparing root elemental composition across genotypes (Fig 2B), having the greatest concentration of Ca, K, and Mg (important soil base cations), but also the highest root C:N ratio (62.9 versus 49.9–59.2 for the other genotypes). Lastly, we observed associations between plants and soil physical properties. BESC 24 tended to associate with higher bulk densities in the deeper measured soil layer. Moreover, Populus presence, regardless of individual, genotype, or sample depth, was associated with significantly lower soil bulk density compared with a control microsite i.e., a location containing no Populus individual within a 5 m radius and with no plant roots evident in the soil (S4 Fig).

Extent of correlation among aboveground-belowground chemical traits

We found limited evidence to suggest coordination of stem and root traits among plant genotypes (S2 Table). In general, nutrient concentrations were not correlated across tissues (stem-root) or between tissues and soil (stem-soil, root-soil; Fig 2C). One possible exception to this is stem and root K concentrations, which were found to be positively correlated (PCC = 0.72). A comprehensive correlation matrix generated using all parameters measured for stem, root and soil samples obtained from the four genotypes is provided as S2 Table. Some correlations among different elements within a pool were detected. For example, soil bivalent cations (Ca²⁺ and Mg²⁺) were positively correlated and were positively correlated with soil pH (S2 Table). Correlations were also observed across pools and elements. For example, soil N was negatively related to root K and stem K (S2 Table).

Discussion

Insights from our pilot study represent a first report on aboveground-belowground chemical trait correlations for different P. trichocarpa genotypes grown in a genetically diverse plantation. In general, our results support our first hypothesis that P. trichocarpa genotypes differentially associate with soil biogeochemical properties, and that this is observable at the scale of individual trees (Fig 2 and S2 Fig). The pronounced variability in soil biogeochemical properties across our common garden indicates that planted trees are likely driving differences in soil C and nutrient cycling, though we note that there could be other sources of soil heterogeneity (e.g. variability in microbial community or soil physical properties), even in a common garden. We found that most of the variation in soil biogeochemical properties in this system occurred among individual focal trees, exceeding micro-scale variability within each focal tree location (S1 Table). Typical common garden studies of tree effects on soil biogeochemistry employ a plot-based approach and are designed to detect changes over a larger area seeded with a common species [31]. Fine-scale heterogeneity could make it difficult to detect species effects at a smaller spatial grain. For example, working in natural forests, Fraterrigo et al. [32] measured high variability in soil chemical properties within 20 × 20 m plots, but found that previous land use generally had a homogenizing effect at this scale. It is likely that previous land use has also led to reduced fine-scale soil heterogeneity in our study site. Regardless, our finding that soil biogeochemical properties clustered at the scale of our focal tree sampling indicates that our site, and similar plantations, could be used to study genotype-specific effects on soil biogeochemistry.

Our analysis showed that Populus genotypes could differentially affect soil biogeochemical properties, specifically, soil pH and soil base cations (except for potassium) were consistently lower for one genotype, BESC 375 (Fig 2 and S2 Fig). A number of traits could lead to differences in acid-base chemistry under different strains of poplar including association with ecto- versus arbuscular mycorrhizal fungi, N or Ca uptake mechanisms, or organic acid production [33–35]. Though we did not find evidence for genotype effects on soil C in this limited sampling effort, we note that these effects on soil acid-base chemistry could indicate or lead to effects on soil C cycling. For example, soil Ca availability could enhance mineral-associated C formation via cation bridging mechanisms [36], an effect which likely depends on soil pH [37]. Changes in soil pH have also been shown to correlate with decomposition rates [38], soil microbial community composition [39], and mineral-organic reactivity [40], among other factors. Thus, soil pH alterations may be a bellwether for soil C effects. Building upon the merit of studying impacts of intraspecies variability on associated soil properties, in the future, a more comprehensive probing that includes C forms (i.e., mineral associated or particulate organic C) and microbiome analyses across a depth and temporal profile will be useful in developing prediction frameworks. Most importantly though, our findings of differences among focal trees and trends toward genotype differences pave the way for a more comprehensive and well-replicated comparison of genotype-specific effects in this system.

Contrary to our second hypothesis, we did not find evidence of consistent aboveground-belowground correlations for tissue chemistry traits among our study individuals (Fig 2; S2 Table). Plant economic spectrum theory predicts that leaf, stem, and root traits are correlated [41], a prediction that has received support across a range of ecosystems [42–44]. However, evidence to the contrary suggests that the root economic spectrum may be multidimensional, potentially leading to a breakdown of aboveground-belowground trait correlations [45, 46]. Thus, the lack of significant couplings between root and stem traits observed under field settings—which is also supported by results obtained from independent greenhouse observations of the natural variant population (Kalluri et al. unpublished data)—indicates a need to collect both aboveground and belowground phenotypic data in future sampling efforts in this system to further evaluate this hypothesis.

Conclusions

Overall, our assessment supports the utility of long-term, genetically diverse bioenergy crop plantations to study plant-soil relationships at the scale of individual trees and genotypes. Our results show substantial variation in soil biogeochemical properties at the scale of individual trees and provide evidence of genotype-specific effects on soil acid-base chemistry. Though select correlations between plant and soil properties were detected, we generally observed poor correlations among above- and belowground chemical traits. Additionally, variability in poplar genotype showed a stronger association with soil biogeochemistry than the phenotype on which our sampling was based (i.e., high vs low lignin). Findings from the present proof-of-concept pilot study show that evenly spaced, genetic diversity plantations on relatively uniform field topology and basic soil properties can be useful for probing and identifying candidate genotypes and genes based on their effects on key soil biogeochemical properties. Such systems can contain hundreds of distinct genotypes enabling GWAS-scale investigations of soil biogeochemical properties. We conclude that these reported plant-soil relationships should be studied more comprehensively considering deeper soil profiles, more genotypes, mineral and particulate associated soil organic C fractions, and microbiome diversity, to facilitate comprehensive measurement and modeling of both the productivity and uniformity of aboveground biomass in bioenergy systems and the effects of bioenergy crop production on soil health and carbon sequestration.

Supporting information

S1 Fig. PCA analysis.

PCA analysis of all soil samples (n = 48) collected from variable depths and radial direction of sampling using compositional analysis traits. PC-1 explains 95% of variance is driven primarily by Ca content while PC-2 corresponds to variation in K content.

https://doi.org/10.1371/journal.pone.0309321.s001

(DOCX)

S2 Fig. Soil chemical property variation across individuals and genotypes.

Boxplots showing pH, nutrient concentrations, and C:N in soils sampled at each of eight focal trees belonging to four different Populus trichocarpa genotypes (delineated with shading). The two replicates of each genotype (BESC 24, BESC 371, BESC 375, BESC 394) are indicated with the suffix A and B along the x-axis. Boxes represent three observations per individual. Units for carbon (C) and nitrogen (N) are percent by mass. Soil pH (pH) and soil C-to-N ratio (CN) are unitless and the remaining nutrients are expressed in mg kg^-1.

https://doi.org/10.1371/journal.pone.0309321.s002

(DOCX)

S3 Fig. Targeted PCA analysis.

PCA analysis of chemical properties of all soil samples collected from 0–10 cm depth for BESC 24 (low lignin) and BESC 375 (high lignin). Twelve observations for each genotype.

https://doi.org/10.1371/journal.pone.0309321.s003

(DOCX)

S4 Fig. Bulk density data.

Bulk density data corresponding to soil cores obtained from (Blue) “Top” soil cores [soil cored at the 0”-2” depth from surface of the soil horizon] and (Green) “Bottom” soil cores [soil cored at the 2”-4” depth from surface of the soil horizon]. Four observations per genotype included two observations each for “top” or “bottom” cores.

https://doi.org/10.1371/journal.pone.0309321.s004

(DOCX)

S1 Table. Variance in soil biogeochemical properties.

Variance in soil biogeochemical properties explained (Partial R²) by linear models containing either individual trees (n = 3 soil samples per tree) or genotypes (2 trees per genotype, n = 6). Individual trees were significantly related to soil properties (p < 0.001) for all measured variables. No statement about statistical significance is made about genotype effects due to insufficient replication at this level of analysis. A significant effect of sample depth is denoted with an asterisk (*) for response variable.

https://doi.org/10.1371/journal.pone.0309321.s005

(DOCX)

S2 Table. Comprehensive correlation matrix.

Comprehensive correlation matrix presented as a heatmap generated using PCC of all parameters measured for stem (two observations per genotype), root (two observations per genotype) and soil (12 observations per genotype) samples obtained from the four genotypes.

https://doi.org/10.1371/journal.pone.0309321.s006

(XLSX)

Acknowledgments

We thank Drs. Eric Pierce and Gerald Tuskan for early consultations on the field sampling campaign, Sara Jawdy at Oak Ridge National Laboratory for sample aliquoting and shipment to soil service center, Kat Haiby, Brian Stanton and Rick Stonex at Poplar Innovations Inc. for facilitating field site access, and UGA soil service center for analysis of the study samples.

References

1. Lal R. Soil Carbon Sequestration Impacts on Global climate change and food security. Science. 2004;304: 1623–1627. pmid:15192216
- View Article
- PubMed/NCBI
- Google Scholar
2. Minasny B, Malone BP, McBratney AB, Angers DA, Arrouays D, Chambers A, et al. Soil carbon 4 per mille. Geoderma. 2017;292: 59–86.
- View Article
- Google Scholar
3. Davidson EA, Ackerman IL. Changes in soil carbon inventories following cultivation of previously untilled soils. Biogeochemistry. 1993;20: 161–193.
- View Article
- Google Scholar
4. Sanderman J, Hengl T, Fiske GJ. Soil carbon debt of 12,000 years of human land use. Proceedings of the National Academy of Sciences. 2017;114: 9575–9580. pmid:28827323
- View Article
- PubMed/NCBI
- Google Scholar
5. Paustian K, Larson E, Kent J, Marx E, Swan A. Soil C sequestration as a biological negative emission strategy. Front Clim. 2019;1.
- View Article
- Google Scholar
6. Field JL, Richard TL, Smithwick EAH, Cai H, Laser MS, LeBauer DS, et al. Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels. Proceedings of the National Academy of Sciences. 2020;117: 21968–21977. pmid:32839342
- View Article
- PubMed/NCBI
- Google Scholar
7. Paustian K, Lehmann J, Ogle S, Reay D, Robertson GP, Smith P. Climate-smart soils. Nature. 2016;532: 49–57. pmid:27078564
- View Article
- PubMed/NCBI
- Google Scholar
8. Yang X, Liu D, Lu H, Weston DJ, Chen J-G, Muchero W, et al. Biological parts for plant biodesign to enhance land-based carbon dioxide removal. BioDesign Research. 2021;2021. pmid:37849951
- View Article
- PubMed/NCBI
- Google Scholar
9. Muchero W, Sondreli KL, Chen J-G, Urbanowicz BR, Zhang J, Singan V, et al. Association mapping, transcriptomics, and transient expression identify candidate genes mediating plant–pathogen interactions in a tree. Proceedings of the National Academy of Sciences. 2018;115: 11573–11578. pmid:30337484
- View Article
- PubMed/NCBI
- Google Scholar
10. Evans LM, Slavov GT, Rodgers-Melnick E, Martin J, Ranjan P, Muchero W, et al. Population genomics of Populus trichocarpa identifies signatures of selection and adaptive trait associations. Nat Genet. 2014;46: 1089–1096. pmid:25151358
- View Article
- PubMed/NCBI
- Google Scholar
11. Chhetri HB, Furches A, Macaya-Sanz D, Walker AR, Kainer D, Jones P, et al. Genome-wide association study of wood anatomical and morphological traits in Populus trichocarpa. Front Plant Sci. 2020;11: 545748. pmid:33013968
- View Article
- PubMed/NCBI
- Google Scholar
12. Garten Jr. CT, Wullschleger SD. Soil carbon inventories under a bioenergy crop (Switchgrass): Measurement limitations. Journal of Environmental Quality. 1999;28: 1359–1365.
- View Article
- Google Scholar
13. Harman-Ware AE, Macaya-Sanz D, Abeyratne CR, Doeppke C, Haiby K, Tuskan GA, et al. Accurate determination of genotypic variance of cell wall characteristics of a Populus trichocarpa pedigree using high-throughput pyrolysis-molecular beam mass spectrometry. Biotechnology for Biofuels. 2021;14: 59. pmid:33676543
- View Article
- PubMed/NCBI
- Google Scholar
14. Keller AB, Brzostek ER, Craig ME, Fisher JB, Phillips RP. Root-derived inputs are major contributors to soil carbon in temperate forests, but vary by mycorrhizal type. Ecology Letters. 2021;24: 626–635. pmid:33492775
- View Article
- PubMed/NCBI
- Google Scholar
15. Rasse DP, Rumpel C, Dignac M-F. Is soil carbon mostly root carbon? Mechanisms for a specific stabilisation. Plant Soil. 2005;269: 341–356.
- View Article
- Google Scholar
16. Sokol NW, Bradford MA. Microbial formation of stable soil carbon is more efficient from belowground than aboveground input. Nature Geosci. 2019;12: 46–53.
- View Article
- Google Scholar
17. Villarino SH, Pinto P, Jackson RB, Piñeiro G. Plant rhizodeposition: A key factor for soil organic matter formation in stable fractions. Sci Adv. 2021;7: eabd3176. pmid:33853771
- View Article
- PubMed/NCBI
- Google Scholar
18. Schimel J, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol. 2012;3. pmid:23055998
- View Article
- PubMed/NCBI
- Google Scholar
19. Sun T, Hobbie SE, Berg B, Zhang H, Wang Q, Wang Z, et al. Contrasting dynamics and trait controls in first-order root compared with leaf litter decomposition. Proceedings of the National Academy of Sciences. 2018;115: 10392–10397. pmid:30254167
- View Article
- PubMed/NCBI
- Google Scholar
20. Happs RM, Bartling AW, Doeppke C, Harman-Ware AE, Clark R, Webb EG, et al. Economic impact of yield and composition variation in bioenergy crops: Populus trichocarpa. Biofuels, Bioproducts and Biorefining. 2021;15: 176–188.
- View Article
- Google Scholar
21. Harman-Ware AE, Happs RM, Macaya-Sanz D, Doeppke C, Muchero W, DiFazio SP. Abundance of major cell wall components in natural variants and pedigrees of Populus trichocarpa. Frontiers in Plant Science. 2022;13. Available: https://www.frontiersin.org/articles/10.3389/fpls.2022.757810
- View Article
- Google Scholar
22. Muchero W, Guo J, DiFazio SP, Chen J-G, Ranjan P, Slavov GT, et al. High-resolution genetic mapping of allelic variants associated with cell wall chemistry in Populus. BMC Genomics. 2015;16: 24. pmid:25613058
- View Article
- PubMed/NCBI
- Google Scholar
23. Studer MH, DeMartini JD, Davis MF, Sykes RW, Davison B, Keller M, et al. Lignin content in natural Populus variants affects sugar release. Proceedings of the National Academy of Sciences. 2011;108: 6300–6305. pmid:21444820
- View Article
- PubMed/NCBI
- Google Scholar
24. Veach AM, Morris R, Yip DZ, Yang ZK, Engle NL, Cregger MA, et al. Rhizosphere microbiomes diverge among Populus trichocarpa plant-host genotypes and chemotypes, but it depends on soil origin. Microbiome. 2019;7: 76. pmid:31103040
- View Article
- PubMed/NCBI
- Google Scholar
25. Kissel DE, Sonon LS, Cabrera ML. Rapid measurement of soil pH buffering capacity. Soil Science Society of America Journal. 2012;76: 694–699.
- View Article
- Google Scholar
26. Mehlich A. Determination of P, Ca, Mg, K, Na, NH4. North Carolina Soil Testing Division, Department of Agriculture, Raleigh, North Carolina; 1953.
27. Bremner JM. Nitrogen-total. Methods of soil analysis. John Wiley & Sons, Ltd; 1996. pp. 1085–1121. https://doi.org/10.2136/sssabookser5.3.c37
28. Nelson DW, Sommers LE. Total carbon, organic carbon, and organic matter. Methods of Soil Analysis. John Wiley & Sons, Ltd; 1996. pp. 961–1010. https://doi.org/10.2136/sssabookser5.3.c34
29. Gee GW, Bauder JW. Particle Size Analysis by Hydrometer: A simplified method for routine textural analysis and a sensitivity test of measurement parameters. Soil Science Society of America Journal. 1979;43: 1004–1007.
- View Article
- Google Scholar
30. Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied linear statistical models. 5th Edition. Irwin, New York: McGraw-Hill; 2005.
31. Vesterdal L, Schmidt IK, Callesen I, Nilsson LO, Gundersen P. Carbon and nitrogen in forest floor and mineral soil under six common European tree species. Forest Ecology and Management. 2008;255: 35–48.
- View Article
- Google Scholar
32. Fraterrigo JM, Turner MG, Pearson SM, Dixon P. Effects of past land use on spatial heterogeneity of soil nutrients in Southern Appalachian forests. Ecological Monographs. 2005;75: 215–230.
- View Article
- Google Scholar
33. Ehrenfeld JG, Kourtev P, Huang W. Changes in soil functions following invasions of exotic understory plants in deciduous forests. Ecological Applications. 2001;11: 1287–1300.
- View Article
- Google Scholar
34. Finzi AC, Canham CD, Van Breemen N. Canopy tree-soil interactions within temperate forests: Species effects on pH and cations. Ecological Applications. 1998;8: 447–454.
- View Article
- Google Scholar
35. Lin G, Craig ME, Jo I, Wang X, Zeng D-H, Phillips RP. Mycorrhizal associations of tree species influence soil nitrogen dynamics via effects on soil acid–base chemistry. Global Ecology and Biogeography. 2022;31: 168–182.
- View Article
- Google Scholar
36. von Lützow M, Kögel-Knabner I, Ekschmitt K, Flessa H, Guggenberger G, Matzner E, et al. SOM fractionation methods: Relevance to functional pools and to stabilization mechanisms. Soil Biology and Biochemistry. 2007;39: 2183–2207.
- View Article
- Google Scholar
37. Rasmussen C, Heckman K, Wieder WR, Keiluweit M, Lawrence CR, Berhe AA, et al. Beyond clay: Towards an improved set of variables for predicting soil organic matter content. Biogeochemistry. 2018;137: 297–306.
- View Article
- Google Scholar
38. Leifeld J, Bassin S, Conen F, Hajdas I, Egli M, Fuhrer J. Control of soil pH on turnover of belowground organic matter in subalpine grassland. Biogeochemistry. 2013;112: 59–69.
- View Article
- Google Scholar
39. Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75: 5111–5120. pmid:19502440
- View Article
- PubMed/NCBI
- Google Scholar
40. Kleber M, Eusterhues K, Mikutta C, Mikutta R, Nico PS. Chapter One—Mineral–organic associations: Formation, properties, and relevance in soil environments. In: Sparks DL, editor. Advances in Agronomy. Academic Press; 2015. pp. 1–140. https://doi.org/10.1016/bs.agron.2014.10.005
41. Reich PB. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. Journal of Ecology. 2014;102: 275–301.
- View Article
- Google Scholar
42. Freschet GT, Cornelissen JHC, Van Logtestijn RSP, Aerts R. Evidence of the ‘plant economics spectrum’ in a subarctic flora. Journal of Ecology. 2010;98: 362–373.
- View Article
- Google Scholar
43. Pérez-Ramos IM, Roumet C, Cruz P, Blanchard A, Autran P, Garnier E. Evidence for a ‘plant community economics spectrum’ driven by nutrient and water limitations in a Mediterranean rangeland of southern France. Journal of Ecology. 2012;100: 1315–1327.
- View Article
- Google Scholar
44. Shen Y, Gilbert GS, Li W, Fang M, Lu H, Yu S. Linking aboveground traits to root traits and local environment: Implications of the plant economics spectrum. Front Plant Sci. 2019;10: 1412. pmid:31737024
- View Article
- PubMed/NCBI
- Google Scholar
45. Bergmann J, Ryo M, Prati D, Hempel S, Rillig MC. Root traits are more than analogues of leaf traits: the case for diaspore mass. New Phytologist. 2017;216: 1130–1139. pmid:28895147
- View Article
- PubMed/NCBI
- Google Scholar
46. Weemstra M, Mommer L, Visser EJW, van Ruijven J, Kuyper TW, Mohren GMJ, et al. Towards a multidimensional root trait framework: a tree root review. New Phytologist. 2016;211: 1159–1169. pmid:27174359
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Lal R. Soil Carbon Sequestration Impacts on Global climate change and food security. Science. 2004;304: 1623–1627. pmid:15192216
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Minasny B, Malone BP, McBratney AB, Angers DA, Arrouays D, Chambers A, et al. Soil carbon 4 per mille. Geoderma. 2017;292: 59–86.
View Article
Google Scholar

[6] View Article

[7] Google Scholar

[ref3] 3. Davidson EA, Ackerman IL. Changes in soil carbon inventories following cultivation of previously untilled soils. Biogeochemistry. 1993;20: 161–193.
View Article
Google Scholar

[9] View Article

[10] Google Scholar

[ref4] 4. Sanderman J, Hengl T, Fiske GJ. Soil carbon debt of 12,000 years of human land use. Proceedings of the National Academy of Sciences. 2017;114: 9575–9580. pmid:28827323
View Article
PubMed/NCBI
Google Scholar

[12] View Article

[13] PubMed/NCBI

[14] Google Scholar

[ref5] 5. Paustian K, Larson E, Kent J, Marx E, Swan A. Soil C sequestration as a biological negative emission strategy. Front Clim. 2019;1.
View Article
Google Scholar

[16] View Article

[17] Google Scholar

[ref6] 6. Field JL, Richard TL, Smithwick EAH, Cai H, Laser MS, LeBauer DS, et al. Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels. Proceedings of the National Academy of Sciences. 2020;117: 21968–21977. pmid:32839342
View Article
PubMed/NCBI
Google Scholar

[19] View Article

[20] PubMed/NCBI

[21] Google Scholar

[ref7] 7. Paustian K, Lehmann J, Ogle S, Reay D, Robertson GP, Smith P. Climate-smart soils. Nature. 2016;532: 49–57. pmid:27078564
View Article
PubMed/NCBI
Google Scholar

[23] View Article

[24] PubMed/NCBI

[25] Google Scholar

[ref8] 8. Yang X, Liu D, Lu H, Weston DJ, Chen J-G, Muchero W, et al. Biological parts for plant biodesign to enhance land-based carbon dioxide removal. BioDesign Research. 2021;2021. pmid:37849951
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref9] 9. Muchero W, Sondreli KL, Chen J-G, Urbanowicz BR, Zhang J, Singan V, et al. Association mapping, transcriptomics, and transient expression identify candidate genes mediating plant–pathogen interactions in a tree. Proceedings of the National Academy of Sciences. 2018;115: 11573–11578. pmid:30337484
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref10] 10. Evans LM, Slavov GT, Rodgers-Melnick E, Martin J, Ranjan P, Muchero W, et al. Population genomics of Populus trichocarpa identifies signatures of selection and adaptive trait associations. Nat Genet. 2014;46: 1089–1096. pmid:25151358
View Article
PubMed/NCBI
Google Scholar

[35] View Article

[36] PubMed/NCBI

[37] Google Scholar

[ref11] 11. Chhetri HB, Furches A, Macaya-Sanz D, Walker AR, Kainer D, Jones P, et al. Genome-wide association study of wood anatomical and morphological traits in Populus trichocarpa. Front Plant Sci. 2020;11: 545748. pmid:33013968
View Article
PubMed/NCBI
Google Scholar

[39] View Article

[40] PubMed/NCBI

[41] Google Scholar

[ref12] 12. Garten Jr. CT, Wullschleger SD. Soil carbon inventories under a bioenergy crop (Switchgrass): Measurement limitations. Journal of Environmental Quality. 1999;28: 1359–1365.
View Article
Google Scholar

[43] View Article

[44] Google Scholar

[ref13] 13. Harman-Ware AE, Macaya-Sanz D, Abeyratne CR, Doeppke C, Haiby K, Tuskan GA, et al. Accurate determination of genotypic variance of cell wall characteristics of a Populus trichocarpa pedigree using high-throughput pyrolysis-molecular beam mass spectrometry. Biotechnology for Biofuels. 2021;14: 59. pmid:33676543
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref14] 14. Keller AB, Brzostek ER, Craig ME, Fisher JB, Phillips RP. Root-derived inputs are major contributors to soil carbon in temperate forests, but vary by mycorrhizal type. Ecology Letters. 2021;24: 626–635. pmid:33492775
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref15] 15. Rasse DP, Rumpel C, Dignac M-F. Is soil carbon mostly root carbon? Mechanisms for a specific stabilisation. Plant Soil. 2005;269: 341–356.
View Article
Google Scholar

[54] View Article

[55] Google Scholar

[ref16] 16. Sokol NW, Bradford MA. Microbial formation of stable soil carbon is more efficient from belowground than aboveground input. Nature Geosci. 2019;12: 46–53.
View Article
Google Scholar

[57] View Article

[58] Google Scholar

[ref17] 17. Villarino SH, Pinto P, Jackson RB, Piñeiro G. Plant rhizodeposition: A key factor for soil organic matter formation in stable fractions. Sci Adv. 2021;7: eabd3176. pmid:33853771
View Article
PubMed/NCBI
Google Scholar

[60] View Article

[61] PubMed/NCBI

[62] Google Scholar

[ref18] 18. Schimel J, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol. 2012;3. pmid:23055998
View Article
PubMed/NCBI
Google Scholar

[64] View Article

[65] PubMed/NCBI

[66] Google Scholar

[ref19] 19. Sun T, Hobbie SE, Berg B, Zhang H, Wang Q, Wang Z, et al. Contrasting dynamics and trait controls in first-order root compared with leaf litter decomposition. Proceedings of the National Academy of Sciences. 2018;115: 10392–10397. pmid:30254167
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref20] 20. Happs RM, Bartling AW, Doeppke C, Harman-Ware AE, Clark R, Webb EG, et al. Economic impact of yield and composition variation in bioenergy crops: Populus trichocarpa. Biofuels, Bioproducts and Biorefining. 2021;15: 176–188.
View Article
Google Scholar

[72] View Article

[73] Google Scholar

[ref21] 21. Harman-Ware AE, Happs RM, Macaya-Sanz D, Doeppke C, Muchero W, DiFazio SP. Abundance of major cell wall components in natural variants and pedigrees of Populus trichocarpa. Frontiers in Plant Science. 2022;13. Available: https://www.frontiersin.org/articles/10.3389/fpls.2022.757810
View Article
Google Scholar

[75] View Article

[76] Google Scholar

[ref22] 22. Muchero W, Guo J, DiFazio SP, Chen J-G, Ranjan P, Slavov GT, et al. High-resolution genetic mapping of allelic variants associated with cell wall chemistry in Populus. BMC Genomics. 2015;16: 24. pmid:25613058
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref23] 23. Studer MH, DeMartini JD, Davis MF, Sykes RW, Davison B, Keller M, et al. Lignin content in natural Populus variants affects sugar release. Proceedings of the National Academy of Sciences. 2011;108: 6300–6305. pmid:21444820
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref24] 24. Veach AM, Morris R, Yip DZ, Yang ZK, Engle NL, Cregger MA, et al. Rhizosphere microbiomes diverge among Populus trichocarpa plant-host genotypes and chemotypes, but it depends on soil origin. Microbiome. 2019;7: 76. pmid:31103040
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref25] 25. Kissel DE, Sonon LS, Cabrera ML. Rapid measurement of soil pH buffering capacity. Soil Science Society of America Journal. 2012;76: 694–699.
View Article
Google Scholar

[90] View Article

[91] Google Scholar

[ref26] 26. Mehlich A. Determination of P, Ca, Mg, K, Na, NH4. North Carolina Soil Testing Division, Department of Agriculture, Raleigh, North Carolina; 1953.

[ref27] 27. Bremner JM. Nitrogen-total. Methods of soil analysis. John Wiley & Sons, Ltd; 1996. pp. 1085–1121. https://doi.org/10.2136/sssabookser5.3.c37

[ref28] 28. Nelson DW, Sommers LE. Total carbon, organic carbon, and organic matter. Methods of Soil Analysis. John Wiley & Sons, Ltd; 1996. pp. 961–1010. https://doi.org/10.2136/sssabookser5.3.c34

[ref29] 29. Gee GW, Bauder JW. Particle Size Analysis by Hydrometer: A simplified method for routine textural analysis and a sensitivity test of measurement parameters. Soil Science Society of America Journal. 1979;43: 1004–1007.
View Article
Google Scholar

[96] View Article

[97] Google Scholar

[ref30] 30. Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied linear statistical models. 5th Edition. Irwin, New York: McGraw-Hill; 2005.

[ref31] 31. Vesterdal L, Schmidt IK, Callesen I, Nilsson LO, Gundersen P. Carbon and nitrogen in forest floor and mineral soil under six common European tree species. Forest Ecology and Management. 2008;255: 35–48.
View Article
Google Scholar

[100] View Article

[101] Google Scholar

[ref32] 32. Fraterrigo JM, Turner MG, Pearson SM, Dixon P. Effects of past land use on spatial heterogeneity of soil nutrients in Southern Appalachian forests. Ecological Monographs. 2005;75: 215–230.
View Article
Google Scholar

[103] View Article

[104] Google Scholar

[ref33] 33. Ehrenfeld JG, Kourtev P, Huang W. Changes in soil functions following invasions of exotic understory plants in deciduous forests. Ecological Applications. 2001;11: 1287–1300.
View Article
Google Scholar

[106] View Article

[107] Google Scholar

[ref34] 34. Finzi AC, Canham CD, Van Breemen N. Canopy tree-soil interactions within temperate forests: Species effects on pH and cations. Ecological Applications. 1998;8: 447–454.
View Article
Google Scholar

[109] View Article

[110] Google Scholar

[ref35] 35. Lin G, Craig ME, Jo I, Wang X, Zeng D-H, Phillips RP. Mycorrhizal associations of tree species influence soil nitrogen dynamics via effects on soil acid–base chemistry. Global Ecology and Biogeography. 2022;31: 168–182.
View Article
Google Scholar

[112] View Article

[113] Google Scholar

[ref36] 36. von Lützow M, Kögel-Knabner I, Ekschmitt K, Flessa H, Guggenberger G, Matzner E, et al. SOM fractionation methods: Relevance to functional pools and to stabilization mechanisms. Soil Biology and Biochemistry. 2007;39: 2183–2207.
View Article
Google Scholar

[115] View Article

[116] Google Scholar

[ref37] 37. Rasmussen C, Heckman K, Wieder WR, Keiluweit M, Lawrence CR, Berhe AA, et al. Beyond clay: Towards an improved set of variables for predicting soil organic matter content. Biogeochemistry. 2018;137: 297–306.
View Article
Google Scholar

[118] View Article

[119] Google Scholar

[ref38] 38. Leifeld J, Bassin S, Conen F, Hajdas I, Egli M, Fuhrer J. Control of soil pH on turnover of belowground organic matter in subalpine grassland. Biogeochemistry. 2013;112: 59–69.
View Article
Google Scholar

[121] View Article

[122] Google Scholar

[ref39] 39. Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75: 5111–5120. pmid:19502440
View Article
PubMed/NCBI
Google Scholar

[124] View Article

[125] PubMed/NCBI

[126] Google Scholar

[ref40] 40. Kleber M, Eusterhues K, Mikutta C, Mikutta R, Nico PS. Chapter One—Mineral–organic associations: Formation, properties, and relevance in soil environments. In: Sparks DL, editor. Advances in Agronomy. Academic Press; 2015. pp. 1–140. https://doi.org/10.1016/bs.agron.2014.10.005

[ref41] 41. Reich PB. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. Journal of Ecology. 2014;102: 275–301.
View Article
Google Scholar

[129] View Article

[130] Google Scholar

[ref42] 42. Freschet GT, Cornelissen JHC, Van Logtestijn RSP, Aerts R. Evidence of the ‘plant economics spectrum’ in a subarctic flora. Journal of Ecology. 2010;98: 362–373.
View Article
Google Scholar

[132] View Article

[133] Google Scholar

[ref43] 43. Pérez-Ramos IM, Roumet C, Cruz P, Blanchard A, Autran P, Garnier E. Evidence for a ‘plant community economics spectrum’ driven by nutrient and water limitations in a Mediterranean rangeland of southern France. Journal of Ecology. 2012;100: 1315–1327.
View Article
Google Scholar

[135] View Article

[136] Google Scholar

[ref44] 44. Shen Y, Gilbert GS, Li W, Fang M, Lu H, Yu S. Linking aboveground traits to root traits and local environment: Implications of the plant economics spectrum. Front Plant Sci. 2019;10: 1412. pmid:31737024
View Article
PubMed/NCBI
Google Scholar

[138] View Article

[139] PubMed/NCBI

[140] Google Scholar

[ref45] 45. Bergmann J, Ryo M, Prati D, Hempel S, Rillig MC. Root traits are more than analogues of leaf traits: the case for diaspore mass. New Phytologist. 2017;216: 1130–1139. pmid:28895147
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref46] 46. Weemstra M, Mommer L, Visser EJW, van Ruijven J, Kuyper TW, Mohren GMJ, et al. Towards a multidimensional root trait framework: a tree root review. New Phytologist. 2016;211: 1159–1169. pmid:27174359
View Article
PubMed/NCBI
Google Scholar

[146] View Article

[147] PubMed/NCBI

[148] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Field site description

Field sample collection

Laboratory analyses

Data analysis

Results

Variability in soil characteristics at the Clatskanie field site

Focal tree and genotype-specific plant-soil relationships

Extent of correlation among aboveground-belowground chemical traits

Discussion

Conclusions

Supporting information

S1 Fig. PCA analysis.

S2 Fig. Soil chemical property variation across individuals and genotypes.

S3 Fig. Targeted PCA analysis.

S4 Fig. Bulk density data.

S1 Table. Variance in soil biogeochemical properties.

S2 Table. Comprehensive correlation matrix.

Acknowledgments

References