# Tree age affects key fine root morphological traits but does not interfere strongly with (ecto-)mycorrhizal traits

Yu Qin, Oscar Valverde-Barrantes, Guangze Jin, Zhili Liu, Grégoire Freschet

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Yu Qin, Oscar Valverde-Barrantes, Guangze Jin, Zhili Liu, Grégoire Freschet. Tree age affects key fine root morphological traits but does not interfere strongly with (ecto-)mycorrhizal traits. Annals of Botany, 2025, 10.1093/aob/mcae223. hal-04935230

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Submitted on 7 Feb 2025

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Tree age affects key fine root morphological traits but does not interfere
strongly with (ecto-)mycorrhizal traits

Yu Qin¹,²,³,⁴ Oscar J. Valverde-Barrantes⁵ Guangze Jin¹,²,³ Zhili Liu¹,²,³* Grégoire T. Freschet⁴

¹School of Ecology, Northeast Forestry University, Harbin, China; ²Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northeast Forestry University, Harbin, China; ³Northeast Asia Biodiversity Research Center, Northeast Forestry University, Harbin, China; ⁴Theoretical and Experimental Ecology Station, CNRS, Moulis, France: ⁵Department of Biological Sciences, International Center for Tropical Biodiversity, Florida International University, Miami, USA

*For correspondence: liuzl2093@126.com
**Abstract**

**Background and Aims** It is assumed that trees should adapt their above and belowground organs as they age. However, most studies to date have quantified these trait adjustments in homogeneous forest stands, confounding the effect of stand aging on soil properties and the intrinsic response of trees to aging.

**Methods** Here, we examined 11 morphological, architectural, anatomical and mycorrhizal fine root traits of each of the first five orders for 66 *Pinus koraiensis* individuals of 16 to 285 years old in northeast China, while accounting for soil characteristics (pH and total C, N and P concentrations).

**Key Results** Across all absorptive root orders, Hartig net area and mantle thickness, representative of *P. koraiensis* reliance on ectomycorrhizal association, displayed an orthogonal pattern to traits describing root economics strategy, specific root length and root tissue density. Hartig net area and mantle thickness were not significantly related to root mycorrhizal colonization intensity and root branching intensity. As tree aged, there was a trade-off between increasing specific root length and decreasing root tissue density in all root orders we measured. Trees facing soil conditions with lower N or P concentrations showed higher dependence on ectomycorrhizal fungi.

**Conclusions** This work provides evidence that tree belowground economics strategy can change significantly along the life of trees. In the ectomycorrhizal tree species studied, morphological adjustments are most pronounced, whereas mycorrhizal strategy remains largely consistent. The more conservative root strategy of younger trees than older ones contradicts previous results on the aging of homogeneous tree stands, suggesting that
individual tree responses to aging in mixed-aged forests may strongly differ from those in homogeneous forest stands experiencing multiple confounding environmental influences.

**Keywords:** Anatomical trait, Ectomycorrhizal association, Hartig net, intraspecific variation, root economics strategy, root order, root trait multidimensionality, tree age

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INTRODUCTION

Fine roots (diameter ≤ 2mm) are critical organs in plants for soil resource acquisition (McCormack *et al.*, 2015). Current understanding of fine root trait variation based on global data suggests much ecological variation can be captured along two orthogonal dimensions. One is the collaboration axis defined by a trade-off between specific root length (SRL) and root diameter (RD), representing the reliance of plants on symbiotic partners for their nutrition (Bergmann *et al.*, 2020). A higher SRL represents "do-it-yourself" acquisition strategies by fine roots with low construction costs for efficient soil exploration, while a higher RD represents "outsourcing" acquisition strategies through greater reliance on mycorrhizal fungal partners. The other axis of variation is the classical conservation axis defined by a trade-off between slow return on investment (high root tissue density, RTD) and fast return on investment (higher root nitrogen concentration) (Reich, 2014). Fine root trait syndromes define plant resource acquisition and carbon conservation strategy (Shipley *et al.*, 2016; Ding *et al.*, 2020; Yan *et al.*, 2022), but are typically assessed at the global scale and the plant species level (Iversen *et al.*, 2017). Recent syntheses have shown that there is also considerable functional variation within species and even within individuals (Pregitzer *et al.*, 2002; Hishi, 2007; Guo *et al.*, 2008). Moreover, this variation has been largely ignored in terms of its ecological importance (Siefert *et al.*, 2015; Des Roches *et al.*, 2018). For example, a growing body of research suggests that intraspecific trait variation is important to plant performance and the response of plants to environmental gradients (Kumordzi *et al.*, 2019; Weemstra *et al.*, 2021). Such intra-individual and intraspecific variation can stem from within-species genetic differences, phenotypic plasticity and ontogenetic differences among roots or among individuals (i.e. Wells and Eissenstat, 2002; Li *et al.*, 2021).
Fine roots can be classified into different orders based on their branching pattern (Fitter, 1982; Pregitzer et al., 2002). Typically, the most distal root orders (i.e. low order roots) are the younger one as each root order emerge from a parent root order, but age differences between orders on the same root branch can be small as roots of several orders can be formed in few days or weeks (Xia et al., 2010; Robin et al., 2021). The main differences between root orders may therefore not be particularly linked to root age but rather to genetically determined construction differences. As root order increases, roots undergoing secondary growth form a cork cambium with hydrophobic suberized cells that hinder the absorption of water and nutrients (Gu et al., 2014; McCormack et al., 2015), and secondary xylem growth that enhances transport capacity (Guo et al., 2008; Wang et al., 2021). The structural changes in cortex thickness (CT), stele diameter (SD) and stele-to-root diameter ratio (SDR) as roots age result in a shift in function from absorptive to transport (Hishi, 2007; Xu et al., 2011; McCormack et al., 2015). CT is negatively correlated with radial transport capacity, while SD and SDR are positively correlated with axial transport capacity (Tyree and Ewers, 1991; Zhou et al., 2022). As a result, other traits reflecting root resource foraging efficiency and stress tolerance can change (Eissenstat et al., 2000; Wahl and Ryser, 2000; Pregitzer et al., 2002). In particular, stele and root diameter generally increase with root order while SRL decreases (Pregitzer et al., 2002; Valenzuela-Estrada et al., 2008; Gu et al., 2014; Kubisch et al., 2015; Li et al., 2024).

Plants, and trees in particular, may undergo profound changes in size, vegetative and reproductive allocation over time (Meinzer et al., 2011). During their ontogeny, trees continuously adjust their traits to meet these changing needs (Meinzer et al., 2011; Martin and Thomas, 2013; Damián et al., 2018). While such changes are relatively well described and understood for aboveground parts (Kenzo et al., 2015; Liu et al., 2020), in roots such knowledge
is still scarce. Similar to what was observed aboveground, some studies on intraspecific variation
in ectomycorrhizal (EcM) tree species suggested that root resource acquisition strategies may
shift from acquisitive (fast return on investment in resources but short root lifespan) to
conservative (slow return on resource investment but long-lived and well-protected roots) with
increasing tree age or stand age (Claus and George, 2005; Tobner et al., 2013; Zhu et al., 2022;
Bi et al., 2023; Wang et al., 2024). For instance, Rosenvald et al. (2013) found for the first two
order roots of *Betula pendula* that SRL decreased with stand age while RTD increased. The same
result was found in *Pinus sylvestris* with stand development (Jagodziński and Kalucka, 2010;
Wang et al., 2024). Higher SRL represents a faster potential return on investment (longer root
length deployed) per unit cost (Ostonen et al., 2007; Tobner et al., 2013). Higher RTD represents
a higher investment cost per unit root deployed, a slower return on investment but lower
maintenance costs and potentially longer lifespan (Ryser, 1996). However, most studies to date
have quantified these trait adjustments using multiple homogeneous forest stands varying in age
of plantation, confounding the effect of stand aging on soil properties and the intrinsic response
of tree to aging. In such context, Wang et al. (2024) found that EcM colonization intensity of *P. sylvestris*, *P. densiflora*, and *P. tabuliformis* increased, decreased, and had no significant change
with stand development, respectively, and a positive effect of tree size on EcM colonization
intensity was found in yellow birch in mixed forests (Cheng et al., 2005). Moreover, we still lack
a comprehensive assessment of changes in tree root strategy, along multiple aspects of fine root
economics, including both morphological and anatomical traits related to plant reliance on EcM
association.

Within species, biotic and abiotic drivers of fine root trait variation and trait covariations
remain poorly known, and most of our knowledge concerns morphological traits only (Isaac *et*
al., 2017; Wang et al., 2021; Weemstra et al., 2021). Clearly, SRL is the result of an interaction between RD and RTD (Ostonen et al., 2007). Higher root branching intensity (RBI) represents increased local soil exploitation abilities (soil mining) and higher site availability for association with EcM fungi (Kong et al., 2014; Freschet et al., 2021). Defrenne et al. (2019) studied the first three order roots of *Pseudotsuga menziesii* along a regional gradient of climate and soil factors and found RD of each order was significantly negatively correlated with both SRL and RTD, but these traits were not related to RBI. By studying the first-order roots of *Ginkgo biloba* in different climatic zones in China, Wang et al. (2021) found that RD and CT increased while SRL and RTD decreased from resource-poor to resource-rich sites. SD and RD generally increase together with root order (Guo et al., 2008; Zhou et al., 2022) and SD and RD of root tips generally increase with N fertilization (Wang et al., 2018). In EcM species, the fungal mantle (sheath) is a transition zone between the plant and the soil, whose thickness may reflect the strength of association between root and fungi (Ostonen and Lõhmus, 2003), mantle thickness (MT) also influences RD variation (RD measured in this study included mantle) (Ostonen and Lõhmus, 2003). Hyphae from the inner region of the mantle penetrate between the cells of the root cortex to form the Hartig network, which is the main interface for the exchange of water and nutrients between the host plant and its fungal partner (Bücking et al., 2012; Pandey et al., 2019), whose surface can be approximated by a measure of Hartig net area (HNA). It is also easy to conceive that mycorrhizal colonization intensity (MCI), HNA and MT decrease abruptly above certain root order as roots lose cortex and become transport roots (Guo et al., 2008; Zadworny et al., 2016; Zhou et al., 2022). However, other trait covariations, among these defining the reliance of plants on mycorrhizal fungi have been typically overlooked.
In partial contrast to the global framework found by Bergmann *et al.* (2020), recent studies further indicate that the reliance of ectomycorrhizal tree species on fungal partners may be inversely related to RD and CT (in contrast to arbuscular-mycorrhizal species) and positively related to RBI (Ding *et al.*, 2020; Yan *et al.*, 2022; Bi *et al.*, 2023). But additional traits may also be important to measure to confirm or complement such global patterns, including SD, MCI, HNA or MT. In the global context of trait coordination that defines root ecological strategies, it remains unclear how such coordination between traits is conserved across root orders and affected by internal drivers, such as plant age, and environmental drivers, such as soil nutrient content or pH.

The present study focuses on intraspecific root trait variation, including morphological, anatomical, architectural and mycorrhizal traits, driven by differences in root order and tree age, in a EcM tree species. Our overall aim was to explore shifts in tree fine root adaptation strategies in different root orders during ontogeny, while accounting for differences in soil properties. We focused on *Pinus koraiensis* (EcM species), the dominant tree species in the mountainous areas of eastern Northeast China (Xu and Jin, 2012) whose growth dynamics are crucial to forest functioning. Several studies on aboveground traits of *P. koraiensis* found that tree size plays a key role in driving intraspecific trait variations, such as a decrease in specific leaf area (SLA) (Liu *et al.*, 2020; Ji *et al.*, 2021). Specifically, within a natural mixed species forest area, we tested three hypotheses. First, we hypothesized that within a population of *P. koraiensis* trees experiencing local-scale heterogeneous environmental influences 1) separate trade-offs exist among traits related to tree mycorrhizal dependence on the one hand and among traits related to root resource conservation, respectively. More specifically, we expected both a positive intraspecific covariation in traits representative of *P. koraiensis* reliance on EcM association,
such as MCI, RBI, HNA and MT, and an orthogonal variation of traits defining root resource conservation, such as RTD. We expected such trend to hold across all absorptive root orders.

Second, we expected that 2) with increasing tree age, fine root traits should tend towards more resource conservative strategy and higher reliance on EcM fungi. Based on the current knowledge of root dependence on mycorrhizae under different soil resource gradients (such as Ding *et al.*, 2020), we further hypothesized that 3) trees facing soil conditions with low nutrient availability (i.e., low soil pH and total C, N and P) will show higher reliance on EcM fungi.

## MATERIALS AND METHODS

### *Study site*

The study area was located at Heilongjiang Liangshui National Nature Reserve in northeastern China (47°6′49″-47°16′10″N, 128°47′8″-128°57′19″E), with an area of 12 133 hectares, and an average elevation of 400 m (Geng and Jin, 2022). The typical vegetation in this area is a mixed broadleaved-Korean pine forest (Liu *et al.*, 2020). This area has a temperate continental monsoon climate, with cold, dry winters and short, warm summers. The mean annual temperature ranges from -6.6 to 7.5°C across years. The mean annual precipitation is 676 mm, with more than 60% concentrated from June to August, and 10-20% falling as snow; additionally, the area is covered by snowpack from December through April (Liu *et al.*, 2020).

Soils in the sampling area were classified using the Chinese classification as dark brown forest soils, i.e., Humaquepts or Cryboralfs based on USDA Soil Taxonomy system (Geng and Jin, 2022). The thickness of the soil layer is 30-60 cm. The surface layer of dark brown soil has a
thicker humus layer with a higher content of organic matter. The texture is mainly loam, and the
content of gravel in the lower layer gradually increases.

*Pinus koraiensis*, a long-lived evergreen conifer, is the dominant species of the broadleaved-Korean pine forests in northeastern China. As a late successional species, it is usually slow growing and forms a close symbiotic relationship with EcM fungi. It can survive in both infertile and fertile soils, but grows best on well-drained, slightly acidic fertile soils.

Sample design and tree age estimation

In mid-October 2018, we selected 66 *P. koraiensis* individuals across a wide range of tree
sizes (0.3-100 cm diameter at breast height (DBH)) in a 9-ha natural mixed broadleaved-Korean
pine forest ranging from 425 to 508 m in elevation (Xu and Jin, 2012). There were 2 individuals
with DBH < 1.0 cm (measured with a Vernier caliper, and base diameter was used instead of
DBH), 28 individuals with 1.0 ≤ DBH < 30 cm (selecting individuals gradually increasing in
DBH every 1 cm), and 36 individuals with 30 ≤ DBH ≤ 100 cm (selecting individuals gradually
increasing in DBH every 2 cm). For each tree sampled, 5.15 mm increment borer (Mora,
Sweden) was used to drill one complete sample core. Samples were taken at the base of the trees
for trees with DBH ≤ 2 cm and at DBH (tree height 1.3 m) for trees with DBH > 2 cm. After air-
drying, pasting, and fixing, all the sample cores were sequentially polished with sandpaper of
decreasing roughness until the tree ring boundaries were clearly visible for cross-ring dating of
tree age. A Velmax tree ring analysis system (accuracy of 0.001 mm) was used to measure each
sample core's annual ring width (μm). The COFECHA program was run to check the accuracy of
the visually cross-ring dating of sample cores (Holmes, 1983); the COFECHA test results were
used to eliminate the sample cores showing large deviations from other data. The tree age of *P. koraiensis* collected in this study was estimated as 16-285 years (Table 1; Fig. S1).

*Collection of fine roots and soil*

For each sampled tree, three subsamples of both fine roots and soil were collected along three directions from the base of the tree (the angle between any two sampling directions was approximately 120°). All three subsamples of each type were then pooled. Altogether this root sample included more than 15 root branches each including five root orders. After removing surface litter around the tree within about 1 m from the trunk, the base of each tree was carefully excavated with shovels and hands to expose the main roots. We use pruning shears to cut off the intact terminal branches on the thin lateral roots found along the main roots, until we collected at least five root branches in each direction. Roots were collected from the soil horizon at a depth of 0-10 cm. During root sampling, a total of about 1 kg of soil was gathered with a shovel at the same spots where roots were previously excavated. After collection, the unwashed root samples were immediately stored in cool boxes and transported to the laboratory. The root samples of each tree were then gently washed in deionized water to remove soil. Three undamaged root branches (each including five root orders) of each tree individual were frozen at -20°C for storage until morphological traits were measured within one month (Freschet *et al.*, 2021). The remaining fraction of each sample was fixed by immediately placing it into plastic tubes filled with formalin-acetic acid-alcohol (FAA) (90 ml 70% ethanol, 5 ml 100% glacial acetic acid, 5 ml 37% formaldehyde) and refrigerated at 4°C for subsequent measurement of anatomical traits (Zhou *et al.*, 2022; Liu *et al.*, 2023). The measured traits are shown in Table 2.
## Root trait measurements

According to the Strahler's stream ordering system, root branches were divided into first- to fifth-order roots (detailed description by Pregitzer *et al.*, 2002). The first-order root was the most distal and unbranched root tips, and only the finest first-order roots were included in our study, for instance excluding pioneer root tips (Guo *et al.*, 2008; Zhou *et al.*, 2022).

After scanning the fine roots of each order separately using a scanner with back-light system (Expression 10000XL1.0, Epson Electronics Inc., San Jose, USA), the diameter (µm), total length (cm), and root volume (cm³) of each root order were measured using WinRHIZO (version: Regular 2016; Regent Instruments Inc., Quebec City, Canada) to calculate specific root length and root tissue density. Although WinRHIZO provides erroneous measures of root volume (Rose, 2017), this bias was minimized since root diameters were relatively homogeneous within each root order. After drying at 65°C for over 72 h, the dry mass (accurate to 0.0001 g) of each root order was measured and used to calculate specific root length (SRL, m g⁻¹) as the root length per unit root dry mass. SRL of absorptive root was the sum of the first three order root lengths divided by the sum of their root dry mass, and SRL of transport root was the sum of the fourth- and fifth-order root lengths divided by the sum of their root dry mass. Root tissue density (RTD, g cm⁻³) was calculated as the root dry mass per unit fresh root volume. RTD of absorptive root was the sum of the first three order root dry mass divided by the sum of their fresh root volumes, and RTD of transport root was the sum of the fourth- and fifth-order root dry mass divided by the sum of their fresh root volumes. Root branching intensity (RBI, cm⁻¹) was calculated for each order of the first four order roots separately as the number of laterals on a given length of parent root (Freschet *et al.*, 2021). The number of first-order roots per centimeter
for third- or fifth-order roots was $RBI_3$ (cm⁻¹) and $RBI_5$ (cm⁻¹), respectively (Yan *et al.*, 2022).

Additionally, we calculated the acquisitive fine root length ratio (ALR; Ostonen *et al.*, 2017), conceptually very close to the ratio of absorptive to transport roots (Freschet *et al.*, 2021), which is the ratio of the length of the first three root orders (1-3) to total root length (1-5) measured on root branches featuring five orders. $RBI_3$ and ALR both belonged to the architectural traits of absorptive roots.

For the purpose of anatomical measurements, for each sampled tree, the root segments of each order were randomly selected, and the number of root segments from first to fifth-order was 30, 30, 20, 10, 10 respectively, so a total of 100 root segments were measured for each sampled tree. One mean trait value was obtained for each root order and for each anatomical trait by averaging values measured on all root segments of that root order in each tree. Differences in the number of root segments among root orders were due to the relatively small number of higher-order roots (Guo *et al.*, 2008; Zhou *et al.*, 2022). The selected root segments were temporarily stored in 70% alcohol after dissection, dehydrated stepwise in 80, 85, 90, 95 and 100% alcohol, embedded in paraffin, cut into 8-µm-thick root sections with a paraffin microtome (cross-section only), mounted on slides and stained with safranin-fast green. The slides were observed and photographed under a compound light microscope (BX-51, Olympus Corporation, Tokyo, Japan). The root diameter (RD, mm), stele diameter (SD, mm), and cortex thickness (CT, µm) were measured for each root cross-section using electronic image analysis equipment (CellSens Standard 1.11 software). All thickness measurements were measured along two orthogonal cross-sections. The stele-to-root diameter ratio (SDR, the ratio of stele diameter to root diameter) was calculated to reflect the proportion of root diameter allocated for resource transport (Guo *et al.*, 2008).
Since *P. koraiensis* is an EcM tree species, the mantle and Hartig net are generally found on the first- to third-order roots (Guo *et al.*, 2008), EcM mantle thickness (MT, µm) and Hartig net area (HNA, mm²) of first- to third-order roots were measured. MT and HNA may even increase significantly with root order (Fig. S3). Due to the significant difference in color shades between the Hartig net and the cortical cells on the anatomical images (Fig. S2), HNA was measured after color segmentation by the "Color Threshold" option of ImageJ software (NIH Image, Bethesda, MD, USA). Since Hartig networks of EcM were formed between root cells (Bücking *et al.*, 2012; Pandey *et al.*, 2019), the measured Hartig net area refers to the area between cortical cells infected by hyphae. Cortical cell walls on the cross-section of fine roots were included in the measurement of HNA because they were too thin to be distinguished accurately (Wahl, 2000). Hartig nets formed by fungi associated with *P. koraiensis* can penetrate between cells deep in the root cortex (Kottke and Oberwinkler, 1986; Brundrett *et al.*, 1990; Brundrett and Tedersoo, 2020). Consistent with classical measurements of mycorrhizal roots including the EcM fungi mantle (Ostonen and Lõhmus, 2003, Jia *et al.*, 2013), we opted here for including the mycorrhizal mantle in our measure of RD. The presence of anatomical structures such as fungal mantle and Hartig nets was used to determine EcM colonization. The ratio of the number of roots colonized by mycorrhizal fungi to the total number of roots examined for that root order was the mycorrhizal colonization intensity (MCI, %) of each order (Guo *et al.*, 2008; Kong *et al.*, 2014). In this study, fourth- and fifth-order roots have no cortex, so only CT, MT, HNA, and MCI of the first three orders were measured. The anatomical trait values of absorptive roots and transport roots were the average values of the first three order root traits and the fourth- and fifth-order root traits, respectively.
## Soil characteristics

Each soil sample was sieved through a 2 mm mesh to eliminate roots, organic matter, and stones, and then air-dried. Soil pH was measured from each soil sample (~ 10 g) in a 1:5 soil to water solution with a pH meter (Mettler Toledo pH meter, Mississauga, ON, Canada) (Isaac *et al.*, 2017). Measurement of total carbon concentration (mg g⁻¹) in approximately 0.1 g soil sample by Multi/C3000 analyzer (Analytik Jena, Germany). Approximately 0.25 g of soil samples were digested with concentrated H₂SO₄ + catalyst (mixture of CuSO₄ and KCl 1:9 for N and HClO₄ for P), and the total N and P concentration (mg g⁻¹) were measured using an AQ400 automatic discontinuous chemical analyzer (SEAL Analytical, Mequon, USA).

## Statistical analysis

As a preliminary step, differences in fine root traits among the first five root orders were tested with the Least significant differences (LSD) test, or with the Nemenyi test in case of non-parametric data distribution. Since most fine root traits differed significantly among root orders (Fig. S3), we subsequently analyzed the effect of tree age on fine root traits on different root orders separately. To reveal the multidimensional variation in root traits of different orders, principal component analysis (PCA) was performed for the first- to fifth-order root traits. However, since traits loadings on the first and second PCA axes were similar between the first-to third-order roots, and also between the fourth- and fifth-order roots (Fig. S4, Table S1), further analyses of trait variation were conducted at the absorptive and transport root levels. Since SDR is the ratio of SD to RD, SDR was not selected to be included in the PCA analysis. In addition, Pearson correlation analysis was used to test the correlations among fine root traits.
To reveal covariation patterns of fine root traits with tree age or soil factors, multiple linear regression analysis was used to investigate the effects of tree age, soil pH, soil C, N, and P concentration on the principal component scores of the main variation axes (PC1 and PC2) of absorptive root traits and first- to third-order root trait PCAs. The independent variables in the final regression equation were screened out through the variance inflation factor (VIF) and Akaike information criterion (AIC). Because for the transport root and fourth- and fifth-order, many traits (such as RTD) still had higher loadings on both the first and second principal component axes, we performed a Varimax rotation, using the "principal" function of the R package "psych" (Revelle, 2022). The component scores of the main variation axes of varimax-rotated PCA (RC1 and RC2) of transport root, fourth- and fifth-order were also analyzed using multiple linear regression. For visualization purposes, independent variables (tree age, soil pH, soil C, N, or P concentration) that had a significant impact on the PC1, PC2, RC1, and RC2, were plotted as supplementary variables on the respective PCAs (Fig. 1; Fig. S4).

To assess the relationships between fine root traits and tree age, soil pH, soil C, N, and P concentrations, multiple linear regressions were also performed for each fine root trait × root order (first to fifth orders, but also for absorptive root and transport root) combination separately. The independent variables in the final regression equation were also screened out through VIF and AIC. We transformed the data in bivariate correlation analysis and PCA for each root trait by log10 to meet the normality assumption. All statistical analyses were conducted with R-4.2.2 (R Core Team, 2022), the R package "ggcorrplot" was used to perform the Pearson correlation analysis, the R package "FactoMineR" was used to perform the PCA, and the R package "factoextra" was used to visualize PCA.
# RESULTS

## Intraspecific covariation patterns of the fine root traits

For all root orders (absorptive root, transport root and each order of the first five orders), the PCA of root traits all identified two main axes (the first and second PCA axes) of trait variation, which together explained 52.5% - 85.4% of the respective total variance (Table S1). The first axis of the absorptive roots and the first- to third-order roots were all best described by significantly positively correlated RD and HNA (Fig. 1A; Fig. S4A-C; Fig. S5A-D; Table S1). Among the traits representing the reliance of *P. koraiensis* on ectomycorrhizal association, HNA and MT of all absorptive root orders were more related to the first axis (Table S1), and had no significant relationship with MCI and RBI (Fig. S5A-D). The first axis of the transport root, fourth- and fifth-order roots were mainly represented by RD and SD (Fig. 1B; Fig. S4D, E; Table S1), which were related to transport capacity and were positively correlated with each other (Fig. S5E-G). The second axis of the absorptive root, transport root and the first- to fifth-order roots were represented by SRL and RTD (Fig. 1; Fig. S4), which were negatively correlated with each other (Fig. S5). Across all absorptive root orders, mycorrhizal traits (HNA and MT) displayed an orthogonal pattern to SRL and RTD (Fig. 1A; Fig. S4A-C).

Our PCA displaying trait shifts among the first three root orders identified two main axes that accounted for 72.31% of the total variance (Table S2). The first PCA axis was mainly characterized by SRL, RD, SD, RBI, MT, CT and HNA (Fig. 2). There was a trade-off between decreasing SRL, RBI and increasing RD, SD, CT, MT and HNA with root order (Fig. 2; Fig. S6). The second axis was driven by RTD (Fig. 2; Table S2).

## Factors influencing intraspecific trait variations of different orders
The factors affecting intraspecific variation in third-order root traits were consistent with those for absorptive roots, and those for fourth- and fifth-order root traits were consistent with those for transport roots (Table S4). With increasing tree age, there was a trade-off between increasing SRL and decreasing RTD for all order roots (Fig. 1; Fig. S4; Table S3). SRL of absorptive or lower-order roots increased more rapidly with tree age than transport or higher-order roots (Fig. 3; Table S4). Furthermore, as tree age increased, MCI of absorptive roots and first-order roots increased while CT of first-order roots decreased; SD of second-order roots increased; SD and SDR of transport and fourth- and fifth-order roots increased (Fig. 3; Table S4).

With increasing soil N concentration, MT of the first-order decreased (Fig. 3); and with increasing soil P concentration, MCI and HNA of the second-order decreased (Fig. 3). Moreover, with increasing soil C concentration, CT of absorptive root and first-order increased, and CT and HNA of the second-order increased while SDR decreased (Fig. 3). With increasing soil pH, MT of absorptive root increased, the SDR of transport root, first-, fourth- and fifth-order and the SD of transport and fifth-order decreased (Fig. 3; Table S4), and RD and SD of fifth-order roots decreased in unison (Fig. S4E; Table S3).

DISCUSSION

*Multidimensional intraspecific variation in fine root traits*

Two functionally independent dimensions were found in each order of the first five root orders, but due to significant structural changes and shifts in the main function of fine root after secondary development (Hish, 2007; Guo *et al.*, 2008), the first dimension differed between absorptive root (the first- to third-order) and transport root (fourth- and fifth-order). The former represented the dependence of *P. koraiensis* on EcM association (Ostonen and Lõhmus, 2003; van
Der Heijden et al., 2015; Bergmann et al., 2020), while the latter were related to transport capacity (Tyree and Ewers, 1991; Rieger and Litvin, 1999; Valenzuela-Estrada et al., 2008). The second dimension of different root orders was mainly loaded by the negatively correlated SRL and RTD, potentially conveying a root cost trade-off between construction and maintenance (Eissenstat et al., 2000; Zhou et al., 2018; Yan et al., 2022). Both SRL and RTD are related to return per unit of investment in fine roots, and many studies have found SRL is negatively correlated with RTD (Comas and Eissenstat, 2009; Defrenne et al., 2019; Han and Zhu, 2021; Valverde-Barrantes et al., 2021).

In partial support to our first hypothesis, two of the hypothesized traits representative of *P. koraiensis* reliance on EcM association, HNA and MT, displayed an orthogonal pattern to traits describing root economics strategy, SRL and RTD. Hartig net area is a trait that potentially represents the intensity of exchange between roots and mycorrhizal fungi in EcM species (Ostonen and Lõhmus, 2003). Among *P. koraiensis* tree individuals, HNA was strongly related to RD and CT, highlighting that the area occupied by fungi was directly proportional to the cortex space provided by the roots (Zadworny et al., 2016). This suggests that, at least among EcM plant species that strongly depend on mycorrhizal association, such as *P. koraiensis*, CT and RD may be fair indicators of the intensity of plant-fungi association, thus comforting the current view of the root economics space (Bergmann et al., 2020). In contrast with results from Yan et al. (2022), who suggested that among tree species, RBI might be generally negatively related to RD and orthogonal to the SRL-RTD axis, our results showed that RBI tended to correlate positively with SRL and ALR. If RBI represents the density of root tips available to operate connection with mycorrhizal fungi (Yan et al., 2022), its variation at the intraspecific level appears largely disconnected from other traits representative of the root-fungi connection intensity, such as HNA, MCI and MT.
Mycorrhizal colonization intensity, another trait representative of tree reliance on mycorrhiza and particularly of the extent of plant-fungi exchange surface across the entire fine root system (Weemstra et al., 2023), was found very high for all tree individuals. In this study, its weak variation was quite disconnected from this of other traits (see also Ding et al., 2020, who highlighted the significance of anatomical traits in exploring root economics space). Of course, these patterns of intraspecific variation within P. koraiensis may largely differ from global patterns observed across multiple tree species (Bergmann et al., 2020). Nonetheless, our results suggest that these four traits with potential importance to describe the intensity of root-fungi association can vary largely independently of each other's among trees of the same species. As such, our results highlight the multidimensionality of intraspecific variation in fine root traits.

Although the strong axis of covariation expected between SRL and RTD appears a priori contrasting with the global root economics space (Bergmann et al., 2020), it is largely a logical consequence of the low variability of RD within each root order (Zhang et al., 2024) independent of tree age. Indeed, fine roots are approximately cylindrical structures (Holdaway et al., 2011; Kong et al., 2019) and, according to the equation SRL = 4/(π×RTD×RD²) (Ryser, 1996; Ostonen et al., 2007), SRL and RTD negative covariation is most visible when RD remains unchanged (Yan et al., 2022; Zhang et al., 2024). Possibly due to the significant differences in RD among root orders, the intraspecific covariation pattern of the first three order root traits with root order was similar to the fungal collaboration dimension found at the global species level by Bergmann et al. (2020), reflecting the change in fine root resource foraging strategies from "do-it-yourself explorative" to "mycorrhiza-dependent" with root order. This result suggests that any change in the relative abundance of first to third root orders is a major driver of plant position on the root economics space. As such, the relative length of first to third root order deployed (ALR), which
depicts the proportion of very fine root to fine root length, may be a critical determinant of plant
root system economics strategy.

In addition, significant negative correlations were found between ALR and MT and MCI.
Ostonen et al. (2017) mentioned that to maximize profits, plants may choose an extensive fine root
foraging strategy that relies on extending absorptive root length or an intensive investment on
mycorrhizal fungi (Weemstra et al., 2017; Salahuddin et al., 2018; Hogan et al., 2020). The former
facilitates the capture of poorly mobile ions by fine roots, while the latter is more conducive to the
foraging of fixed nutrients such as phosphorus, so resource limitation is a strong conditioner of the
selection for alternative acquisition strategies (Fitter et al., 2002; Comas et al., 2012; Weemstra et
al., 2016; Weemstra et al., 2023).

Intraspecific variation of different root order traits with tree age

In contrast to our second hypothesis and previous studies on homogeneous tree stands, we
found a trade-off between increasing SRL and decreasing RTD with tree age (Fig. 4). This
intraspecific shift in trait was consistent across absorptive roots, transport roots and all first- to
fifth-order roots, indicating that EcM tree species *P. koraiensis* in the early growth period tend to
be a more resource conservative fine root strategy, with higher defences and construction costs
(Ryser, 1996; Pregitzer *et al.*, 2002; Fig. 4). The main reason for these opposite results may be the
differences between our individual-based tree aging studies within a mixed-aged and mixed-
species forest and most previous studies of stand aging based on homogeneous tree stands. From
the plantation of young trees to the mature stage of forests, homogeneous tree stands typically
change in micro-climatic conditions and soil properties (Wen *et al.*, 2014; Li *et al.*, 2022; Wang *et
al.*, 2022), which should affect tree above- and below-ground strategies. In addition to being more
susceptible to herbivory (Meinzer *et al.*, 2011), small trees in the natural mixed broadleaved-Korean pine forest with more complex ecosystem structures have more limited access to light due to the shading of other large trees than small trees in younger homogeneous stands (Rosenvald *et al.*, 2013; Liu *et al.*, 2020). This long-term carbon limitation may cause small trees to allocate more carbon aboveground, slowing their growth belowground until they are able to access more light and gradually release carbon limitation. Therefore, resisting and/or avoiding disturbance to ensure survival may be more important than rapid growth for *P. koraiensis* in the early stages of growth. These results may also be interpreted as a form of insurance hypothesis where older, well-established trees may store more resources for localized regrowth after herbivore or pathogen damage (Meinzer *et al.*, 2011) and can afford more acquisitive root economics strategies, as opposed to younger trees harboring more conservative traits.

Partly consistent with our second hypothesis, MCI of absorptive roots and first-order roots increased with tree age, indicating that younger trees were slightly less dependent on EcM fungi than older trees (Fig. 4; Ren *et al.*, 2023). In dense forests, as trees are aging, the increased tree height and crown development typically allow for a higher share of light and more photosynthate production, which may stimulate EcM development (Cheng *et al.*, 2005). This study was conducted in a natural mixed broadleaved-Korean pine forest, where young trees have lower access to light and the C cost of ectomycorrhizal association may be high relative to their C gains (Smith and Read, 2010; Phillips *et al.*, 2013; Matsuo *et al.*, 2021). Another reason of this slight shift in MCI may be related to a shift in EcM fungal species composition and morphotypes, which are known to also change significantly with tree or stand age (Rosenvald *et al.*, 2013; Odriozola *et al.*, 2020). These changes can indeed affect mycorrhizal traits (such as MT) or morphological traits (such as RTD) (Ostonen and Lõhmus, 2003; Agerer, 2006; Rosenvald *et al.*, 2013). Therefore,
future studies looking into tree aging effects on plant ecological strategies would benefit from also looking at the identity of mycorrhizal fungi communities associated with tree roots. Nonetheless, overall, our results also showed that MCI was high, or very high, across trees of all ages, particularly for roots in the second and third order, suggesting a very high mycorrhizal reliance. Interestingly, the slight decrease in first-order root MCI in younger trees was concomitant with an increase in CT, which does not support the idea of a strong linkage between root investment in CT and increased fungal colonization at the intraspecific level.

According to Ohm's law and Hagen-Poiseuille equation (Steudle and Peterson, 1998), the thicker cortex impedes water diffusion to the stele (Eissenstat and Achor, 1999; Rieger and Litvi, 1999; Huang and Eissenstat, 2000), and axial transport capacity is positively correlated with SD (Tyree and Ewers, 1991). The lower CT observed for first-order and higher SD and SDR observed for transport, fourth- and fifth-order roots of older trees suggest enhanced root transport capacity with tree age (Fig. 4). Such increase may be necessary to ensure efficient transport of water and nutrients to greater heights (Wahl, 2000; Wahl and Ryser, 2000; Hietz et al., 2017). In addition, the differences in SRL between the absorptive roots (first- to third-order) and transport roots (fourth- and fifth-order) of old trees were larger than those of younger trees, indicating older trees are more specialized across root entities (lower versus higher root orders) of their root systems. Older trees have larger root systems that can be more specialized in either absorption or transport (Meinzer et al., 2011).
Intraspecific variation of different root order traits in response to soil environment

The variation in soil properties showed relatively minor and variable effects on the range of traits used to describe the root ecology of *P. koraiensis*. Such linkages appeared also variable across root orders. Overall, these linkages were generally consistent with our third hypothesis that trees facing soil conditions with lower nutrient availability show higher reliance on ectomycorrhizal fungi. Particularly, MT of first-order roots increased as soil N decreased, and HNA and MCI of second-order roots increased as soil P decreased. Similar results have been widely found (Hishi *et al.*, 2017; Ding *et al.*, 2020; Li *et al.*, 2023). The mantle that forms a thick woven structure around the root expands the absorptive area of the host plant roots (Ashford *et al.*, 1988; Bücking *et al.*, 2002; Genre *et al.*, 2020), and some hyphae from mantle extend between root cells to develop a Hartig network for resource exchange (Bücking *et al.*, 2012; Pandey *et al.*, 2019). In soil conditions with low P concentrations, roots may improve P acquisition by establishing tighter symbiotic partnerships with soil fungi (Treseder, 2004; Liu *et al.*, 2018; Yan *et al.*, 2022).

In our system, increased soil C concentration typically indicates a slow dynamics of organic matter decomposition due to litter recalcitrance and unfavorable decomposition conditions, and the lock-up of soil nutrients (Gao *et al.*, 2020; Hussain *et al.*, 2022). In these conditions, HNA and CT increased with soil C concentration, while SDR decreased, indicating that the resource exchange interface between the fungus and the host increased (Bücking *et al.*, 2012; Pandey *et al.*, 2019), while the proportion of RD allocated for resource transport decreased (Guo *et al.*, 2008). This pattern is consistent with increased reliance of trees on EcM fungi that can stimulate the degradation of soil organic matter for their nutrition and this of the plant. In such conditions, highly mycorrhized plants have a strong competitive advantage over less intensively mycorrhized plants
(Read and Perez-Moreno, 2003; Phillips *et al.*, 2013).

CONCLUSION

First, this work provides evidence that traits describing complementary aspects of the intensity of root-ectomycorrhizal fungi association, such as mycorrhizal colonization intensity, Hartig net area, mantle thickness and root branching intensity, can vary largely independently of each other's among trees of the same species. Such finding suggests that plant reliance on ectomycorrhizal fungi does not necessarily imply a coordinated response among all traits related to the intensity of this association, highlighting the multidimensionality of intraspecific variation in fine root traits. Second, this work brings strong and novel evidence for change in tree belowground economics strategy over time. Morphological changes linked to both the conservation and the collaboration axes of the root economics space (defining plant belowground economics) are more important than changes linked to several aspects of the mycorrhizal association, which appear largely consistent along the entire life of the trees. Third, the more conservative root strategy of younger trees contradicts previous results on the aging of homogeneous tree stands, suggesting that the intrinsic response of tree to aging in mixed-aged forests may strongly differ from that of homogeneous forest stands. The latter typically experience overarching changes in key environmental variables (micro-climatic and soil properties) along the maturation of the stands, confounding the effect of tree aging and stand aging. Overall, our results suggest that older, well-established trees may afford more acquisitive root economics strategies, as opposed to younger trees harboring more conservative root traits improving their survival.
## FUNDING

This work was supported by the National Key R&D Program of China (2022YFD2201100), the National Natural Science Foundation of China (32071533), and the Fundamental Research Funds for the Central Universities (2572022DS13). Yu Qin was further supported by a Chinese Scholarship Council (CSC) grant to work at the Theoretical and Experimental Ecology Station (France). Grégoire T. Freschet was supported by the ‘Laboratoire d'Excellence’ (LabEx) TULIP (ANR-10-LABX-41).

## ACKNOWLEDGEMENTS

Zhili Liu conceived the ideas and designed methodology. Yu Qin performed experiments and collected the data. Yu Qin analysed the data and completed the writing of the manuscript with the help of Grégoire T. Freschet. Oscar J. Valverde-Barrantes, Zhili Liu and Guangze Jin provided helpful comments on the draft. All authors contributed critically to the drafts and gave final approval for publication. We thank Heilongjiang Liangshui National Nature Reserve for providing the experimental platform.

## DATA AVAILABILITY

The data that support the findings of this study are available from the corresponding author upon reasonable request.

## CONFLICT OF INTEREST

The authors have no relevant financial or non-financial interests to disclose.
LITERATURE CITED

Agerer R. 2006. Fungal relationships and structural identity of their ectomycorrhizae.
*Mycological progress* 5: 67-107. DOI: 10.1007/s11557-006-0505-x.

Ashford AE, Peterson CA, Carpenter JL, Cairney JWG, Allaway WG. 1988. Structure and permeability of the fungal sheath in the *Pisonia mycorrhiza*. *Protoplasma* 147: 149-161.
DOI: 10.1007/BF01403343.

Bergmann J, Weigelt A, van Der Plas F, et al. 2020. The fungal collaboration gradient dominates the root economics space in plants. *Science Advances* 6: eaba3756. DOI: 10.1126/sciadv.aba3756.

Bi B, Yin Q, Hao Z. 2023. Root phosphatase activity is a competitive trait affiliated with the conservation gradient in root economic space. *Forest Ecosystems* 10: 100111. DOI: 10.1016/j.fecs.2023.100111.

Brundrett MC, Murase G, Kendrick B. 1990. Comparative anatomy of roots and mycorrhizae of common Ontario trees. *Canadian Journal of Botany* 68: 551-578. DOI: 10.1139/b90-076.

Brundrett MC, Tedersoo L. 2020. Resolving the mycorrhizal status of important northern hemisphere trees. *Plant and Soil* 454: 3-34. DOI: 10.1007/s11104-020-04627-9.

Bücking H, Kuhn AJ, Schroëder WH, Heyser W. 2002. The fungal sheath of ectomycorrhizal pine roots: an apoplastic barrier for the entry of calcium, magnesium, and potassium into the root cortex? *Journal of Experimental Botany* 53: 1659-1669. DOI: 10.1093/jxb/erf011.
Bücking H, Liepold E, Ambilwade P. 2012. The role of the mycorrhizal symbiosis in nutrient uptake of plants and the regulatory mechanisms underlying these transport processes. *Plant Science* 4: 108-132. DOI: 10.5772/52570.

Cheng S, Widden P, Messier C. 2005. Light and tree size influence belowground development in yellow birch and sugar maple. *Plant and Soil* 270: 321-330. DOI: 10.1007/s11104-004-1726-x.

Claus A, George E. 2005. Effect of stand age on fine-root biomass and biomass distribution in three European forest chronosequences. *Canadian Journal of Forest Research* 35: 1617-1625. DOI: 10.1139/x05-079.

Comas LH, Eissenstat DM. 2009. Patterns in root trait variation among 25 co-existing North American forest species. *New Phytologist* 182: 919-928. DOI: 10.1111/j.1469-8137.2009.02799.x.

Comas LH, Mueller KE, Taylor LL, Midford PE, Callahan HS, Beerling DJ. 2012. Evolutionary patterns and biogeochemical significance of angiosperm root traits. *International Journal of Plant Sciences* 173: 584-595. DOI:10.1086/665823.

Damián X, Fornoni J, Domínguez CA, Boege K. 2018. Ontogenetic changes in the phenotypic integration and modularity of leaf functional traits. *Functional Ecology* 32: 234-246. DOI: 10.1111/1365-2435.12971.

Defrenne CE, McCormack ML, Roach WJ, Addo-Danso SD, Simard SW. 2019. Intraspecific fine-root trait-environment relationships across interior Douglas-fir forests of Western Canada. *Plants* 8: 199. DOI: 10.3390/plants8070199.
Des Roches S, Post DM, Turley NE, et al. 2018. The ecological importance of intraspecific variation. *Nature ecology & evolution* 2: 57-64. DOI: 10.1038/s41559-017-0402-5.

Ding J, Kong D, Zhang Z, et al. 2020. Climate and soil nutrients differentially drive multidimensional fine root traits in ectomycorrhizal-dominated alpine coniferous forests. *Journal of Ecology* 108: 2544-2556. DOI: 10.1111/1365-2745.13407.

Eissenstat DM, Achor DS. 1999. Anatomical characteristics of roots of citrus rootstocks that vary in specific root length. *The New Phytologist* 141: 309-321. DOI: 10.1046/j.1469-8137.1999.00342.x.

Eissenstat DM, Wells CE, Yanai RD, Whitbeck JL. 2000. Building roots in a changing environment: implications for root longevity. *New Phytologist* 147: 33-42. DOI: 10.1046/j.1469-8137.2000.00686.x.

Fitter A, Williamson L, Linkohr B, Leyser O. 2002. Root system architecture determines fitness in an *Arabidopsis mutant* in competition for immobile phosphate ions but not for nitrate ions. *Proceedings of the Royal Society of London. Series B: Biological Sciences* 269: 2017-2022. DOI: 10.1098/rspb.2002.2120.

Fitter AH. 1982. Morphometric analysis of root systems: application of the technique and influence of soil fertility on root system development in two herbaceous species. *Plant, Cell & Environment* 5: 313-322. DOI: 10.1111/1365-3040.ep11573079.

Fort F, Freschet GT. 2020. Plant ecological indicator values as predictors of fine-root trait variations. *Journal of Ecology* 108: 1565-77. DOI: 10.1111/1365-2745.13368.
Freschet GT, Pagès L, Iversen CM, et al. 2021. A starting guide to root ecology: strengthening ecological concepts and standardizing root classification, sampling, processing and trait measurements. *New Phytologist* 232: 973-1122. DOI: 10.1111/nph.17572.

Gao F, Cui X, Sang Y, Song J. 2020. Changes in soil organic carbon and total nitrogen as affected by primary forest conversion. *Forest Ecology and Management* 463: 118013. DOI: 10.1016/j.foreco.2020.118013.

Geng P, Jin G. 2022. Fine root morphology and chemical responses to N addition depend on root function and soil depth in a Korean pine plantation in Northeast China. *Forest Ecology and Management* 520: 120407. DOI: 10.1016/j.foreco.2022.120407.

Genre A, Lanfranco L, Perotto S, Bonfante P. 2020. Unique and common traits in mycorrhizal symbioses. *Nature Reviews Microbiology* 18: 649-660. DOI: 10.1038/s41579-020-0402-3.

Gu J, Xu Y, Dong X, Wang H, Wang Z. 2014. Root diameter variations explained by anatomy and phylogeny of 50 tropical and temperate tree species. *Tree physiology* 34: 415-425. DOI: 10.1093/treephys/tpu019.

Guo D, Xia M, Wei X, Chang W, Liu Y, Wang Z. 2008. Anatomical traits associated with absorption and mycorrhizal colonization are linked to root branch order in twenty-three Chinese temperate tree species. *New Phytologist* 180: 673-683. DOI: 10.1111/j.1469-8137.2008.02573.x.

Han M, Zhu B. 2021. Linking root respiration to chemistry and morphology across species. *Global Change Biology* 27: 190-201. DOI: 10.1111/gcb.15391.
Hietz P, Rosner S, Hietz- Seifert U, Wright SJ. 2017. Wood traits related to size and life history of trees in a Panamanian rainforest. *New Phytologist* 213: 170-180. DOI: 10.1111/nph.14123.

Hishi T. 2007. Heterogeneity of individual roots within the fine root architecture: causal links between physiological and ecosystem functions. *Journal of forest research* 12: 126-133. DOI: 10.1007/s10310-006-0260-5.

Hishi T, Tateno R, Fukushima K, Fujimaki R, Itoh M, Tokuchi N. 2017. Changes in the anatomy, morphology and mycorrhizal infection of fine root systems of *Cryptomeria japonica* in relation to stand ageing. *Tree physiology* 37: 61-70. DOI: 10.1093/treephys/tpw076.

Hogan JA, Valverde-Barrantes OJ, Ding Q, Xu H, Baraloto C. 2020. Morphological variation of fine root systems and leaves in primary and secondary tropical forests of Hainan Island, China. *Annals of Forest Science* 77: 1-21. DOI: 10.1007/s13595-020-00977-7.

Holdaway RJ, Richardson SJ, Dickie IA, Peltzer DA, Coomes DA. 2011. Species-and community-level patterns in fine root traits along a 120 000-year soil chronosequence in temperate rain forest. *Journal of Ecology* 99: 954-963. DOI: 10.1111/j.1365-2745.2011.01821.x.

Holmes RL. 1983. Computer-assisted quality control in tree-ring dating and measurement. *Tree-Ring Bulletin* 43: 69-75. DOI: 10150/261223.

Huang BR, Eissenstat DM. 2000. Linking hydraulic conductivity to anatomy in plants that vary in specific root length. *Journal of the American Society for Horticultural Science* 125: 260-264. DOI: 10.21273/JASHS.125.2.260.
Hussain A, Jamil MA, Abid K, Duan W, Chen L, Li C. 2022. Effects of fertilizers and litter treatment on soil nutrients in Korean Pine plantation and its natural forest of Northeast China. *Forests* 13: 1560. DOI: 10.3390/f13101560.

Isaac ME, Martin AR, de Melo Virginio Filho E, Rapidel B, Roupsard O, Van den Meersche K. 2017. Intraspecific trait variation and coordination: Root and leaf economics spectra in coffee across environmental gradients. *Frontiers in plant science* 8: 1196. DOI: 10.3389/fpls.2017.01196.

Iversen CM, McCormack ML, Powell AS, et al. 2017. A global Fine- Root Ecology Database to address below- ground challenges in plant ecology. *New Phytologist* 215: 15-26. DOI: 10.1111/nph.14486.

Jagodziński AM, Kalucka I. 2010. Fine roots biomass and morphology in a chronosequence of young *Pinus sylvestris* stands growing on a reclaimed lignite mine spoil heap. *Dendrobiology* 64: 19-30.

Ji M, Jin G, Liu Z. 2021. Effects of ontogenetic stage and leaf age on leaf functional traits and the relationships between traits in *Pinus koraiensis*. *Journal of Forestry Research* 32: 2459-2471. DOI: 10.1007/s11676-021-01308-w.

Jia S, McLaughlin NB, Gu J, Li X, Wang Z. 2013. Relationships between root respiration rate and root morphology, chemistry and anatomy in *Larix gmelinii* and *Fraxinus mandshurica*. *Tree physiology* 33: 579-589. DOI: 10.1093/treephys/tpt040.

Kenzo T, Inoue Y, Yoshimura M, Yamashita M, Tanaka-Oda A, Ichie T. 2015. Height-related changes in leaf photosynthetic traits in diverse Bornean tropical rain forest trees. *Oecologia* 177: 191-202. DOI: 10.1007/s00442-014-3126-0.
Kong D, Ma C, Zhang Q, et al. 2014. Leading dimensions in absorptive root trait variation across 96 subtropical forest species. *New Phytologist* 203: 863-872. DOI: 10.1111/nph.12842.

Kong D, Wang J, Wu H, et al. 2019. Nonlinearity of root trait relationships and the root economics spectrum. *Nature communications* 10: 2203. DOI: 10.1038/s41467-019-10245-6.

Kottke I, Oberwinkler F. 1986. Mycorrhiza of forest trees - structure and function. *Trees* 1: 1-24. DOI: 10.1007/BF00197021.

Kubisch P, Hertel D, Leuschner C. 2015. Do ectomycorrhizal and arbuscular mycorrhizal temperate tree species systematically differ in root order-related fine root morphology and biomass? *Frontiers in Plant Science* 6: 64. DOI: 10.3389/fpls.2015.00064.

Kumordzi BB, Aubin I, Cardou F, et al. 2019. Geographic scale and disturbance influence intraspecific trait variability in leaves and roots of North American understorey plants. *Functional Ecology* 33: 1771-1784. DOI: 10.1111/1365-2435.13402.

Li Z, Liu Z, Gao G, Yang X, Gu J. 2021. Shift from acquisitive to conservative root resource acquisition strategy associated with increasing tree age: a case study of *Fraxinus mandshurica*. *Forests* 12: 1797. DOI: 10.3390/f12121797.

Li J, Sun X, Li M, Zou J, Bian H. 2022. Effects of stand age and soil organic matter quality on soil bacterial and fungal community composition in *Larix gmelinii* plantations, Northeast China. *Land Degradation & Development* 33: 1249-1259. DOI: 10.1002/ldr.4219.
Li Z, Wang S, Wang W, Gu J, Ding Y, Wang Y. 2023. Contrasting responses of new pioneer and fibrous roots exposed to nitrogen deposition: a field study using three woody species. *Plant and Soil* 493: 459-474. DOI: 10.1007/s11104-023-06241-x.

Li J, Le X, Chen X, et al. 2024. Divergent intra-and interspecific root order variability identifies a two-dimensional root economics spectrum. *Plant and Soil* 1-18. DOI: 10.1007/s11104-023-06473-x.

Liu X, Burslem DF, Taylor JD, et al. 2018. Partitioning of soil phosphorus among arbuscular and ectomycorrhizal trees in tropical and subtropical forests. *Ecology Letters* 21: 713-23. DOI: 10.1111/ele.12939.

Liu Z, Hikosaka K, Li F, Jin G. 2020. Variations in leaf economics spectrum traits for an evergreen coniferous species: Tree size dominates over environment factors. *Functional Ecology* 34: 458-467. DOI: 10.1111/1365-2435.13498.

Liu X, Du Y, Ren Y, et al. 2023. New insights into the cortex-to-stele ratio show it to effectively indicate inter-and intraspecific function in the absorptive roots of temperate trees. *Frontiers in Plant Science* 14: 1061503. DOI: 10.3389/fpls.2023.1061503.

Martin AR, Thomas SC. 2013. Size-dependent changes in leaf and wood chemical traits in two Caribbean rainforest trees. *Tree Physiology* 33: 1338-1353. DOI: 10.1093/treephys/tpt085.

Matsuo T, Martínez-Ramos M, Bongers F, van der Sande MT, Poorter L. 2021. Forest structure drives changes in light heterogeneity during tropical secondary forest succession. *Journal of Ecology* 109: 2871-2884. DOI: 10.1111/1365-2745.13680.
McCormack ML, Dickie IA, Eissenstat DM, et al. 2015. Redefining fine roots improves understanding of below-ground contributions to terrestrial biosphere processes. *New Phytologist* 207: 505-518. DOI: 10.1111/nph.13363.

Meinzer FC, Lachenbruch B, Dawson TE. 2011. Size-and age-related changes in tree structure and function (Vol. 4). Springer Science and Business Media.

Odriozola I, Martinovic T, Bahnmann BD, et al. 2020. Stand age affects fungal community composition in a Central European temperate forest. *Fungal Ecology* 48: 100985. DOI: 10.1016/j.funeco.2020.100985.

Ostonen I, Lõhmus K. 2003. Proportion of fungal mantle, cortex and stele of ectomycorrhizas in *Picea abies* (L.) Karst. in different soils and site conditions. *Plant and Soil* 257: 435-442. DOI: 10.1023/A:1027305906159.

Ostonen I, Püttsepp Ü, Biel C, et al. 2007. Specific root length as an indicator of environmental change. *Plant Biosystems* 141: 426-442. DOI: 10.1080/11263500701626069.

Ostonen I, Truu M, Helmisaari HS, et al. 2017. Adaptive root foraging strategies along a boreal-temperate forest gradient. *New Phytologist* 215: 977-991. DOI: 10.1111/nph.14643.

Pandey D, Kehri HK, Zoomi I, Akhtar O, Singh AK. 2019. Mycorrhizal Fungi: Biodiversity, Ecological Significance, and Industrial Applications. In: Yadav A, Mishra S, Singh S, Gupta A, eds. *Recent Advancement in White Biotechnology Through Fungi*. Springer: Cham, 181-199.
Phillips RP, Brzostek E, Midgley MG. 2013. The mycorrhizal- associated nutrient economy: a new framework for predicting carbon-nutrient couplings in temperate forests. *New Phytologist* 199: 41-51. DOI: 10.1111/nph.12221.

Pregitzer KS, DeForest JL, Burton AJ, Allen MF, Ruess RW, Hendrick RL. 2002. Fine root architecture of nine North American trees. *Ecological monographs* 72: 293-309. DOI: 10.1890/0012-9615(2002)072[0293:FRAONN]2.0.CO;2.

R Core Team 2022. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Read DJ, Perez-Moreno J. 2003. Mycorrhizas and nutrient cycling in ecosystems - a journey towards relevance? *New Phytologist* 157: 475-492. DOI: 10.1046/j.1469-8137.2003.00704.x.

Reich PB. 2014. The world- wide ‘fast-slow’ plant economics spectrum: a traits manifesto. *Journal of Ecology* 102: 275-301. DOI: 10.1111/1365-2745.12211.

Ren H, Gao G, Ma Y, Li Z, Wang S, Gu J. 2023. Shift of root nitrogen-acquisition strategy with tree age is mediated by root functional traits along the collaboration gradient of the root economics space. *Tree Physiology* 43: 1341-1353. DOI: 10.1093/treephys/tpad047.

Revelle W. 2022. psych: procedures for psychological, psychometric, and personality research. [WWW document] URL https://CRAN.R-project.org/package=psych [accessed 5 May 2023].

Rieger M, Litvin P. 1999. Root system hydraulic conductivity in species with contrasting root anatomy. *Journal of experimental botany* 50: 201-209. DOI: 10.1093/jxb/50.331.201.
Robin AHK, Irving LJ, Crush J, Schnyder H, Lattanzi FA, Matthew C. 2021. Time course of root axis elongation and lateral root formation in perennial ryegrass (Lolium perenne L.). *Plants* 10: 1677. DOI: 10.3390/plants10081677.

Rose L. 2017. Pitfalls in root trait calculations: how ignoring diameter heterogeneity can lead to overestimation of functional traits. *Frontiers in Plant Science* 8: 898. DOI: 10.3389/fpls.2017.00898.

Rosenvald K, Ostonen I, Uri V, Varik M, Tedersoo L, Lõhmus K. 2013. Tree age effect on fine-root and leaf morphology in a silver birch forest chronosequence. *European Journal of Forest Research* 132: 219-230. DOI: 10.1007/s10342-012-0669-7.

Ryser P. 1996. The importance of tissue density for growth and life span of leaves and roots: a comparison of five ecologically contrasting grasses. *Functional Ecology* 717-723. DOI: 10.2307/2390506.

Salahuddin, Rewald B, Razaq M, et al. 2018. Root order-based traits of Manchurian walnut & larch and their plasticity under interspecific competition. *Scientific Reports* 8: 9815. DOI: 10.1038/s41598-018-27832-0.

Shipley B, De Bello F, Cornelissen JHC, Laliberté E, Laughlin DC, Reich PB. 2016. Reinforcing loose foundation stones in trait-based plant ecology. *Oecologia* 180: 923-931. DOI: 10.1007/s00442-016-3549-x.

Siefert A, Violle C, Chalmandrier L, et al. 2015. A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. *Ecology letters* 18: 1406-1419. DOI: 10.1111/ele.12508.
Smith SE, Read DJ. 2010. *Mycorrhizal Symbiosis*, third edn. Academic Press, London.

Steudle E, Peterson CA. 1998. How does water get through roots? *Journal of experimental Botany* 49: 775-788. DOI: 10.1093/jxb/49.322.775.

Tobner CM, Paquette A, Messier C. 2013. Interspecific coordination and intraspecific plasticity of fine root traits in North American temperate tree species. *Frontiers in plant science* 4: 242. DOI: 10.3389/fpls.2013.00242.

Treseder KK. 2004. A meta-analysis of mycorrhizal responses to nitrogen, phosphorus, and atmospheric CO₂ in field studies. *New Phytologist* 164: 347-355. DOI: 10.1111/j.1469-8137.2004.01159.x.

Tyree MT, Ewers FW. 1991. The hydraulic architecture of trees and other woody plants. *New Phytologist* 119: 345-360. DOI: 10.1111/j.1469-8137.1991.tb00035.x.

Valenzuela-Estrada LR, Vera- Caraballo V, Ruth LE, Eissenstat DM. 2008. Root anatomy, morphology, and longevity among root orders in *Vaccinium corymbosum* (Ericaceae). *American journal of botany* 95: 1506-1514. DOI: 10.3732/ajb.0800092.

Valverde-Barrantes OJ, Authier L, Schimann H, Baraloto C. 2021. Root anatomy helps to reconcile observed root trait syndromes in tropical tree species. *American Journal of Botany* 108: 744-755. DOI: 10.1002/ajb2.1659.

van Der Heijden MG, Martin FM, Selosse MA, Sanders IR. 2015. Mycorrhizal ecology and evolution: the past, the present, and the future. *New Phytologist* 205: 1406-1423. DOI: 10.1111/nph.13288.
Wahl S, Ryser P. 2000. Root tissue structure is linked to ecological strategies of grasses. *New Phytologist* 148: 459-471. DOI: 10.1046/j.1469-8137.2000.00775.x.

Wahl S. 2000. *The ecological significance of interspecific variation and phenotypic plasticity in root tissue structure of grasses*. PhD Thesis, ETH Zurich.

Wang W, Wang Y, Hoch G, Wang Z, Gu J. 2018. Linkage of root morphology to anatomy with increasing nitrogen availability in six temperate tree species. *Plant and Soil* 425: 189-200. DOI: 10.1007/s11104-018-3563-3.

Wang S, Wang W, Wang S, Yang L, Gu J. 2021. Intraspecific variations of anatomical, morphological and chemical traits in leaves and absorptive roots along climate and soil gradients: a case study with *Ginkgo biloba* and *Eucommia ulmoides*. *Plant and Soil* 469: 73-88. DOI: 10.1007/s11104-021-05149-8.

Wang S, Zhou P, Luo B, Ye S. 2022. Stoichiometric characteristics of medium-and micro-elements (Ca, Mg, Fe, and Mn) in soil aggregates as affected by stand age in Chinese fir plantations. *Land Degradation & Development* 33: 3991-4003. DOI: 10.1002/ldr.4439.

Wang G, Lin G, Zhang Y, Zheng L, Zeng DH, Lambers H. 2024. Shifts from an extensive to an intensive root nutrient- acquisition mode with stand development of three *Pinus* species. *Journal of Ecology* 112: 886-900. DOI: 10.1111/1365-2745.14277.

Weemstra M, Mommer L, Visser EJ, et al. 2016. Towards a multidimensional root trait framework: a tree root review. *New Phytologist* 211: 1159-1169. DOI: 10.1111/nph.14003.
Weemstra M, Sterck FJ, Visser EJ, Kuyper TW, Goudzwaard L, Mommer L. 2017. Fine-root trait plasticity of beech (*Fagus sylvatica*) and spruce (*Picea abies*) forests on two contrasting soils. *Plant and Soil* 415: 175-188. DOI: 10.1007/s11104-016-3148-y.

Weemstra M, Freschet GT, Stokes A, Roumet C. 2021. Patterns in intraspecific variation in root traits are species-specific along an elevation gradient. *Functional Ecology* 35: 342-356. DOI: 10.1111/1365-2435.13723.

Weemstra M, Kuyper TW, Sterck FJ, Umaña MN. 2023. Incorporating belowground traits: avenues towards a whole-tree perspective on performance. *Oikos* 2023: e08827. DOI: 10.1111/oik.08827.

Wells CE, Eissenstat DM. 2002. Beyond the roots of young seedlings: the influence of age and order on fine root physiology. *Journal of Plant Growth Regulation* 21: 324-334. DOI: 10.1007/s00344-003-0011-1.

Wen L, Lei P, Xiang W, Yan W, Liu S. 2014. Soil microbial biomass carbon and nitrogen in pure and mixed stands of *Pinus massoniana* and *Cinnamomum camphora* differing in stand age. *Forest Ecology and Management* 328: 150-158. DOI: 10.1016/j.foreco.2014.05.037.

Xia M, Guo D, Pregitzer KS. 2010. Ephemeral root modules in *Fraxinus mandshurica*. *New Phytologist* 188: 1065-1074. DOI: 10.1111/j.1469-8137.2010.03423.x.

Xu LN, Jin GZ. 2012. Species composition and community structure of a typical mixed broadleaved-Korean pine (*Pinus koraiensis*) forest plot in Liangshui Nature Reserve, northeast China. *Biodiversity Science* 20: 470-481. DOI: 10.3724/SP.J.1003.2012.12233.
Xu Y, Gu JC, Dong XY, Liu Y, Wang ZQ. 2011. Fine root morphology, anatomy and tissue
nitrogen and carbon contents of the first five orders in four tropical hardwood species in
Hainan Island, China. *Chinese Journal of Plant Ecology* 35: 955-964. DOI:
10.3724/SP.J.1258.2011.00955.

Yan H, Freschet GT, Wang H, et al. 2022. Mycorrhizal symbiosis pathway and edaphic fertility frame root economics space among tree species. *New phytologist* 234: 1639-1653. DOI: 10.1111/nph.18066.

Zadworny M, McCormack ML, Mucha J, Reich PB, Oleksyn J. 2016. Scots pine fine roots adjust along a 2000- km latitudinal climatic gradient. *New Phytologist* 212: 389-399. DOI: 10.1111/nph.14048.

Zhang Y, Cao J, Lu M, et al. 2024. The origin of bi-dimensionality in plant root traits. *Trends in Ecology & Evolution* 39: 78-88. DOI: 10.1016/j.tree.2023.09.002.

Zhou M, Bai W, Zhang Y, Zhang WH. 2018. Multi- dimensional patterns of variation in root traits among coexisting herbaceous species in temperate steppes. *Journal of Ecology* 106: 2320-2331. DOI: 10.1111/1365-2745.12977.

Zhou M, Guo Y, Sheng J, Yuan Y, Zhang WH, Bai W. 2022. Using anatomical traits to understand root functions across root orders of herbaceous species in a temperate steppe. *New Phytologist* 234: 422-434. DOI: 10.1111/nph.17978.

Zhu L, Sun J, Yao X, et al. 2022. Fine root nutrient foraging ability in relation to carbon availability along a chronosequence of Chinese fir plantations. *Forest Ecology and Management* 507: 120003. DOI: 10.1016/j.foreco.2021.120003.
Zu ZH. 1987. A research of reasonably differentiating the age groups of natural korean pine forest. *Scientia Silvae Sinicae* **23**: 68-75.
**Figure legends**

**Fig. 1.** Fine root trait coordination and relation to tree age among 66 individuals of *Pinus koraiensis* visualized from principal component analyses (PCA) of the absorptive root and transport root traits, respectively. Bivariate correlation matrix between traits is available as supplementary Fig. S5. Upper panels represent trait loading and lower panels represent the individual projection plot. Independent variables that had a significant impact on the first and second component axes of the PCAs were added to the trait loading plot as supplementary variables (dark blue labels; based on supplementary Table S3). According to the characteristics of different ontogenetic stages, tree age was divided into four groups (Zu, 1987; Ji *et al.*, 2021). Because transport roots have no cortex and the RBI of fifth-order roots was not measured, the PCA of transport root traits only included morphological traits and SD. PC1 and PC2 are the first and second principal component axes of a PCA; RC1 and RC2 represent the first and second component axes of varimax-rotated PCA. For trait abbreviation, see Table 2.

**Fig. 2** Trait variation among the first three fine root orders among 66 individuals of the ectomycorrhizal tree *P. koraiensis*, as visualized by principal component analysis (PCA). For trait abbreviation, see Table 2. Dots represent individual trees and ellipses represent the distribution range of different root orders (95% confidence interval). PC1 and PC2 are the principal component scores of the first and second variation axes, respectively.

**Fig. 3** Heatmaps of the estimated effects of tree age and soil variables on the root traits of absorptive, transport, and first and second orders, as obtained through multiple linear regression analyses (See Table S4 for details). Colored squares indicate variables retained in the final best model selected by AIC. Larger squares and darker colors correspond to the larger absolute values of estimated coefficients. Squares that appear to be white inside indicate estimated coefficients
close to zero. Green and red correspond to positive and negative effects, respectively. Symbols
(*P* < 0.05, **P* < 0.01, ***P* < 0.001) indicate the significance of the effects. For trait
abbreviation, see Table 2. Note that estimated coefficients for the effect of soil pH on absorptive
and second-order root MT, which were 1.15 and 0.96 respectively, were reduced to the highest
displayed value and the corresponding darkest green color of this figure. RBI₃ or RBI₅: the
number of first-order roots per centimeter of third-order or fifth-order roots. Age: tree age; pH:
soil pH; SC: soil C concentration; SP: soil P concentration; SN: soil N concentration.

**Fig. 4** Conceptual diagram of the changes in absorptive, transport and first-to fifth-order root traits and corresponding strategies of *P. koraiensis* with tree age. Different colors represent different aspects of fine root strategies, and arrows indicate the change from younger to older trees.
<table><caption>**Table 1** Statistical information on tree age and soil factors.</caption><thead><tr><th>Factor</th><th>Maximum</th><th>Minimum</th><th><i>M</i> (SD)</th><th>CV (%)</th></tr></thead><tbody><tr><td>Tree age (year)</td><td>285</td><td>16</td><td>143.2 (72.8)</td><td>51</td></tr><tr><td>Soil pH</td><td>5.91</td><td>3.15</td><td>4.7 (0.6)</td><td>13</td></tr><tr><td>Soil N concentration (mg g<sup>-1</sup>)</td><td>15.61</td><td>3.18</td><td>7.2 (2.7)</td><td>37</td></tr><tr><td>Soil P concentration (mg g<sup>-1</sup>)</td><td>2.64</td><td>0.25</td><td>0.95 (0.44)</td><td>46</td></tr><tr><td>Soil C concentration (mg g<sup>-1</sup>)</td><td>274.5</td><td>42.78</td><td>119.6 (56.8)</td><td>47</td></tr></tbody></table>

Abbreviations: *M*, mean values; CV, the coefficient of variation; *SD*, standard deviation.
<table><caption>**Table 2 Background information on measured fine root traits.**</caption><thead><tr><th>Trait category</th><th>Functional trait</th><th>Unit</th><th>Abbreviation</th><th>Ecological significance</th><th>Strategy</th><th>References (examples of)</th></tr></thead><tbody><tr><td rowspan="3">Morphological</td><td>Specific root length</td><td>m g<sup>-1</sup></td><td>SRL</td><td>Higher SRL represents a higher length of root exploring the soil per unit root mass invested and is related to the resource uptake strategy of "do-it-yourself" soil exploration.</td><td>Resource capture</td><td>Ostonen et al. (2007); Bergmann et al. (2020)</td></tr><tr><td>Root tissue density</td><td>g cm<sup>-3</sup></td><td>RTD</td><td>Higher RTD represents a higher investment cost per unit root deployed and is associated with a conservative strategy with slow return on investment but longer lifespan.</td><td>Root construction and defence</td><td>Ryser (1996); Freschet et al. (2021); Yan et al. (2022)</td></tr><tr><td>Root average diameter</td><td>mm</td><td>RD</td><td>Reflects root cortex and stele thickness, and thick root may be associated with an "outsourcing" resource uptake strategy that relies more on mycorrhizal partners.</td><td>Resource capture, transport, and root construction</td><td>Kong et al. (2014); Bergmann et al. (2020); Fort and Freschet (2020)</td></tr><tr><td rowspan="2">Architectural</td><td>Root branching intensity</td><td>cm<sup>-1</sup></td><td>RBI</td><td>Higher branching density represents increased local soil exploitation abilities (soil mining) and higher site availability for association with EcM fungi.</td><td>Resource capture</td><td>Kong et al. (2014); Freschet et al. (2021)</td></tr><tr><td>Acquisitive fine root length ratio</td><td>/</td><td>ALR</td><td>Higher ALR is associated with increased soil resource uptake capacities.</td><td>Resource capture</td><td>Zadorny et al. (2016); Ostonen et al. (2017)</td></tr><tr><td rowspan="3">Anatomical</td><td>Stele diameter</td><td>mm</td><td>SD</td><td>Positively correlated with axial transport capacity.</td><td>Transport</td><td>Freschet et al. (2021); Steudle and Peterson (1998)</td></tr><tr><td>The stele-to-root diameter ratio</td><td>/</td><td>SDR</td><td>Positively correlated with the proportion of root diameter devoted to resource transport.</td><td>Transport</td><td>Guo et al. (2008)</td></tr><tr><td>Cortex thickness</td><td>µm</td><td>CT</td><td>Thinner cortex results in less impedance to water movement toward the stele, while thicker cortex provides larger space for mycorrhizal colonization.</td><td>Resource capture and transport</td><td>Zadorny et al. (2016); Freschet et al. (2021); Zhou et al. (2022)</td></tr><tr><td rowspan="3">Mycorrhizal</td><td>Ectomycorrhizal mantle thickness</td><td>µm</td><td>MT</td><td>The mantle is the transition zone between plants and soil, and its thickness is related to the intensity of the association with the fungi.</td><td>Resource capture</td><td>Ostonen and Löhmus (2003); Bücking et al. (2012); Zadorny et al. (2016)</td></tr><tr><td>Hartig net area</td><td>mm<sup>2</sup></td><td>HNA</td><td>The area where nutrients and water are exchanged between the host and the fungus.</td><td>Resource capture</td><td>Ostonen and Löhmus (2003); Bücking et al. (2012)</td></tr><tr><td>Mycorrhizal colonization intensity</td><td>%</td><td>MCI</td><td>Related to the degree of plant reliance on mycorrhiza and intensity of exchange between partners.</td><td>Resource capture</td><td>Guo et al. (2008); Ostonen et al. (2017); Freschet et al. (2021)</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Figure 1</figcaption></figure>

<figure><img src="image_4.png" /></figure>
<figure><img src="image_5.png" /><figcaption>Figure 2</figcaption></figure>

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /><figcaption>Figure 3</figcaption></figure>

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /><figcaption>Figure 4</figcaption></figure>

<figure><img src="image_10.png" /></figure>