# Nutrient resorption of leaves and roots coordinates with root nutrient-acquisition strategies in a temperate forest

Dongnan Wang, Grégoire T Freschet, M Luke Mccormack, Hans Lambers, Jiacun Gu

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Dongnan Wang, Grégoire T Freschet, M Luke Mccormack, Hans Lambers, Jiacun Gu. Nutrient resorption of leaves and roots coordinates with root nutrient-acquisition strategies in a temperate forest. New Phytologist, 2025, 246 (2), pp.515-527. 10.1111/nph.70001. hal-04951412

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Submitted on 17 Feb 2025

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31 Five figures (all color figures) and one table in the main text, seven supplementary

32 figures (all color figures) and four tables in the supporting information.
## 33 Summary

34 • Nutrient acquisition, conservation and recycling are three mechanisms for plants to meet their nutritional requirements. However, how nutrient recycling relates to other mechanisms remains unknown. Here, we hypothesize that nutrient resorption processes are coordinated with plant nutrient acquisition strategies.

38 • We measured leaf and root nutrient resorption efficiencies and proficiencies and root economic traits for 34 co-existing ectomycorrhizal (ECM) and arbuscular mycorrhizal (AM) temperate woody species.

41 • Our results revealed that species with lower foraging efficiency relying on mycorrhizal fungal for nutrient absorption (e.g. larger root diameter) have high root phosphorus resorption efficiency and great phosphorus concentrations of senesced roots, while species with conservative nutrient-acquisition strategies (e.g. higher root tissue density) have lower nitrogen and phosphorus concentrations of senesced leaves and roots.

47 • Overall, our results demonstrate that plant nutrient acquisition and protection strategies are partly coordinated with plants’ ability to resorb nutrients. First, they suggest that outsourcing phosphorus acquisition to mycorrhiza may limit the value for plants to reduce phosphorus loss. Second, those species better able to protect their living leaves and roots from adversity are not necessarily the most efficient to recycle nutrients, but are nonetheless the most capable of minimizing nutrient loss during organ senescence.

54

55 **Keywords:** arbuscular mycorrhiza, ectomycorrhiza, nutrient acquisition, nutrient resorption efficiency, nutrient resorption proficiency, root economic space

56
## 57 Introduction

In terrestrial ecosystems, plants are generally limited by nutrients, especially nitrogen (N) and phosphorus (P) (Du et al., 2020). To acquire N and P from soil and protect root from nutrient loss, plants exhibit a range of ecological strategies, which can be partly described by the root economic space (Bergmann et al., 2020). In the root economic space, two orthogonal dimensions exist, i.e. the “collaboration” dimension, which ranges from a “do-it-yourself” strategy of efficient soil foraging by relatively inexpensive roots (high specific root length, SRL) to an “outsourcing” acquisition strategy involving investment in more persistent roots, often with higher root diameter (RD), as well as in mycorrhizal fungal partners in exchange for soil resources.

Another axis represents the “acquisition-conservation” dimension, from a high root N and P concentration to a high root tissue density (RTD), which represents a trade-off between fast versus slow root nutrient uptake and return on investment (Reich, 2014; Ding et al., 2020). Apart from uptake of nutrients from the soil, or their protection in long-lived, defended tissues, plants can also express nutrient recycling strategies by remobilizing nutrients from senescing organs, i.e. nutrient resorption (Wright & Westoby, 2003; Bu et al., 2019). However, the amount of nutrient resorption varies widely among species (Vergutz et al., 2012). Ultimate plant performance and adaptation depend on the coordinated functioning of nutrient uptake, protection and resorption (Ding et al., 2023). As such, it is necessary to explore the relationship between plant economic traits and the nutrient resorption of leaf and root to advance our understanding of the plant adaptive and competitive mechanisms.

The correlations between plant nutrient resorption, belowground plant traits, and ecosystem nutrient cycling have received increasing attention in recent years. For example, mycorrhizal association type, an important belowground nutrient-acquisition strategy, can influence leaf nutrient resorption (Zhang et al., 2018). At the local scale, significant differences in leaf N and P resorption efficiencies were found among ectomycorrhizal (ECM), ericoid mycorrhizal (ERM) and non-mycorrhizal (NM) plant species in boreal peatlands (Xu et al., 2020). In a global synthesis, arbuscular mycorrhizal (AM) woody plants displayed lower leaf N resorption efficiency than
ECM woody species (Guo et al., 2024), which may be due to the lower leaf nutrient concentration of ECM species than that of AM species (Averill et al., 2019), reflecting the mechanism of “nutrient concentration control” (i.e. leaves with a high nutrient concentration have a high nutrient resorption efficiency; Kobe et al., 2005; Chen et al., 2021). In addition, the above- and belowground plant economic spectrum have been shown to be related to leaf nutrient resorption. For example, along an elevation gradient, Ma et al. (2024) confirmed the link between the leaf economic spectrum (represented by specific leaf area and green leaf N concentration) and leaf N and P resorption among 101 broadleaved species. At the regional scale, Ding et al. (2023) found a relationship between leaf nutrient resorption efficiency and the root “acquisition-conservation” dimension of the root economic space (represented by RTD, root N and P concentrations), but found no relationship with the root “collaboration” dimension of the root economic space (RD, specific root length and area) among 10 ECM species. Regardless of mycorrhizal type, there was a clear trade-off between leaf N resorption efficiency and litter N mineralization across seven terrestrial biomes (Deng et al., 2018). In contrast, it remains largely unknown how belowground plant economic traits relate to leaf nutrient resorption. Moreover, those findings across large geographic scales not only reflect diverse plant ecological strategies, but also the constraints imposed by environmental gradients. Therefore, local-scale studies minimizing the effect of environmental variation are needed to reveal the correlation between leaf nutrient resorption and root functional traits among plant species, and deepen our understanding of the mechanism for multiple species coexistence and competition.

Compared with leaf nutrient resorption, much less is known about root nutrient resorption. Several studies suggest that root nutrient resorption varies largely with plant species, life form, mycorrhizal type, as well as soil conditions. For instance, Freschet et al. (2010) found in a range of subarctic plant species that mean N and P resorption efficiencies of fine roots (diameter < 2 mm) were higher among five grass species (38% and 63%, respectively) than those of four woody species (16% and 54%, respectively) and those of two fern species (22% and 44%, respectively). Huang
et al. (2016) found that N resorption efficiency of fine roots (diameter < 0.5 mm) of an ECM species (*Larix olgensis*) was more than twice that of an AM species (*Fraxinus mandshurica*). Plastic responses also influence root nutrient resorption, with a decrease in root N resorption observed with higher nutrient availability (Yan et al., 2019). Importantly, Freschet et al. (2010) suggested that N and P resorption of roots were largely independent of the root economic spectrum (one-dimension formed by a suite of traits including root dry matter content, N concentration, P concentration, lignin concentration, etc.). However, testing such relationships on larger sets of species and a wider range of ecosystem would be critical to bring further insight into this question. Recent conceptual advances made on the root economic space concept also warrant further investigation of the role of the root “collaboration” axis in root nutrient resorption, as done by Ding et al. (2023) for leaf resorption of ECM species. Based on their findings, one may expect that species outsourcing nutrient acquisition to mycorrhiza would decrease root nutrient resorption efficiency.

In this study, we measured 13 leaf and root nutrient resorption characteristics and root economic traits (Table 1) for 34 coexisting ECM and AM species from a temperate forest in northeastern China. Our overall aim was to determine the correlation between nutrient resorption efficiencies and proficiencies of leaves and roots and root economic traits. We tested the following three hypotheses: 1) leaf nutrient resorption of ECM species is higher than that of AM species (Zhang et al., 2018), and similar patterns will occur in root nutrient resorption; 2) leaf and root nutrient resorption increase with species position on the “acquisition-conservation” dimension of the root economic space (i.e. with increasing RTD and decreasing root N and P concentrations) and decrease for species with more outsourcing strategy on the “collaboration” axis (Ding et al., 2023); and finally 3) leaf nutrient resorption is positively correlated with root nutrient resorption across species, representing a synergy of nutrient recycling strategies above and belowground. We also explored how plant phylogeny affects these nutrient resorption and economic traits of leaves and roots (Killingbeck, 1996; Guo et al., 2024).
147 **Materials and Methods**

148 **Study site**

149 We conducted the study in the Liangshui National Nature Reserve of Northeast
150 Forestry University, located in Heilongjiang Province in northeastern China
151 (128°53′20″E, 47°10′50″N). This natural reserve comprises mainly mixed
152 broadleaved-Korean pine forests without management. This region has a temperate
153 continental monsoon climate with a mean annual temperature of -0.3 °C. The mean
154 annual precipitation is 676 mm, mainly occurring in summer. The soil type belongs to
155 dark brown forest soil, having high organic matter content and well-developed
156 horizons, with an average soil depth of 40 cm. The soil total N and P concentrations
157 are 7.6 mg g⁻¹ and 0.47 mg g⁻¹, respectively.

158

159 **Experimental design**

160 In this study, we established a 1 ha plot along a shallow West slope in May of 2021.
161 The plot represented the typical vegetation type of the mixed broadleaved-Korean
162 pine forest. The vegetation was dominated by *Pinus koraiensis* Siebold & Zucc.,
163 *Picea koraiensis* Nakai, *Picea jezoensis* (Siebold & Zucc.) Carrière, *Abies
164 nephrolepis* (Trautv. ex Maxim.) Maxim., and *Fraxinus mandshurica* Rupr.. We
165 sampled the most dominant species in the mixed broadleaved-Korean pine forest
166 stand in August and October 2021. In total, 34 species including 13 ECM and 21 AM
167 species were sampled for the measurement of leaf and root traits (Fig. S1). These
168 species accounted for 71% of all woody plants in typical broadleaved-Korean pine
169 forest in the study region (Xu & Jin, 2012). Details of each sampled species are
170 shown in Table S1.

171

172 **Field sampling of leaves and roots**

173 At least three mature and healthy individuals of each target tree species and at least 10
174 individuals of target shrub species were randomly selected within the plot for leaf and
175 root collections. Leaves and roots were sampled from the same plant individuals.

176 In August 2021, mature leaf samples were collected following the method of
“cohorts” (Aerts, 1989): 30-150 leaves for broadleaved tree and shrub species and 80-100 needles for coniferous tree species. For each tree species, the representative twigs from sun-exposed branches were collected from the middle and upper canopy, and then the current-year leaves, which were fully expanded and not damaged by insects were immediately detached from the twigs, and kept separated for each tree individual. For the shrub species, the current-year leaves with the same sample selection standard were immediately collected by hand from all plant individuals, and pooled into one composite sample per species. In mid-September to mid-October, freshly fallen leaves were collected by three litter traps (1 × 1 m, 2-mm mesh) under each individual of targeted tree species in a similar position to that of the green leaves. Specifically, current-year leaf litter of four coniferous tree species were recognized by their color. During the sampling period, the senesced leaves were collected once a day. For the shrub species, recently senesced leaf litter was collected by shaking each individual gently. The collected mature and senesced leaves were placed in a closed paper bag, transported to the laboratory and processed as described below.

In October 2021, fine roots were collected because of a peak of fine root death in this month (Geng & Jin, 2016). For root sampling, at least five root branches of each individual were collected from the top 20 cm mineral soil layer following the procedure of Guo *et al.* (2008). Root branches were traced to the stem to confirm their identity and cut from the main lateral woody roots. Once collected, the soil particles attached to each root sample were carefully removed, and samples were transported to the laboratory within 4 h.

## Morphological and chemical traits of leaves and roots

In the laboratory, all fresh and senesced leaf samples were cleaned with deionized water. For trees with compound leaves with small leaflets, we randomly selected 30 leaflets for each tree individual. Then, the numbers of green and senesced leaf samples for each shrub species and each tree individual were counted. Root samples were similarly cleaned with deionized water to remove soil particles. Large mycorrhizal rhizomorphs, if present, were brushed off the roots. Based on the root
branch ordering described by Pregitzer *et al.* (2002) and McCormack *et al.* (2015),
root samples were dissected with forceps into the distal two order roots, and these
roots were retained and used for all further analyses. This is because a study on
*Quercus robur* had shown a strong increase in live root N in root orders 4th, 5th, and
6th in October (compared to April and June) and this increase was not seen in root
orders 1st, 2nd, and 3rd, suggesting that there is some resorption from lower order roots
and translocation to higher order roots (Zadworny *et al.*, 2015). Live vs. senesced
roots were determined according to visual and textural criteria (Kunkle *et al.*, 2009;
Freschet *et al.*, 2010). Roots that appeared healthy and fully alive were separated from
all other roots and referred to as live roots. All selected species presented a
progressive darkening for the root surface, loss of root cortex and a darkening of the
stele in the root tissues during senescence. Roots displaying various stages of
senescence were further sorted into roots having retained all cortex tissue, referred to
as senesced fine roots, versus those having lost part of it and referred to as
decomposing roots. Among senesced fine roots, both roots in the early stages of
senescence (browning cortex) and completely dead roots (dark and broken but
entirely present cortex) were collected to best represent the compromise between
capturing the majority of the senescence processes and avoiding major nutrient losses
during the first stages of root decomposition. As lower order roots have very slow
decomposition rates (Fan & Guo, 2010; Sun *et al.*, 2018), losses of nutrients are
assumed to be minimal on recently senesced roots still adhering to the root branches.
For some species with little root material, such as shrubs, pooling root samples that
contained the two stages of senescence was also instrumental to ensure that there was
enough mass for P analysis. We note that a study comparing the fine root N
concentration of *Populus trichocarpa* between the first stage of senescence and the
later stage of completely dead tissues observed no significant difference
(Wojciechowska *et al.*, 2020).

All selected leaf samples were oven dried at 65 °C to determine their dry weight
(nearest 0.0001 g). Then, N of leaves were measured with a Macro Elemental
Analyzer (vario MACRO, Elementar Co., Hanau, Germany). P concentrations of
237 leaves were measured with a flow-injection autoanalyzer (AA3, Seal Co., Germany)
238 after digestion of samples in H₂SO₄-HClO₄. Green leaf mass per leaf (LMgr; g leaf⁻¹)
239 and senesced leaf mass per leaf (LMse, respectively; g leaf⁻¹) were calculated as dry
240 weight divided by green or senesced leaf numbers, respectively, for the purpose of
241 leaf nutrient resorption calibration.

242 For root morphological and chemical measurements, more than 500 root
243 fragments of intact 1st and 2nd order branches were randomly selected and scanned at
244 800 dpi with a flatbed scanner with backlighting (Epson Expression 10000XL, Epson
245 Telford Ltd, Suwa Nagano, Japan). Then, all root samples were oven dried at 65 °C to
246 determine their dry weight (nearest 0.0001 g), and N and P concentration of roots (RN
247 and RP, respectively; mg g⁻¹) were measured with the same method as used for leaves.
248 Average root diameter (RD; mm), total root length and the volume of each root
249 sample were determined using root system analysis software (WinRHIZO Pro 2016,
250 Regent Instruments Inc., Québec, QC, Canada). Specific root length (SRL; m g⁻¹) was
251 calculated as total root length divided by dry weight. Root tissue density (RTD; g cm⁻³) was calculated as dry weight divided by root volume. Root mass per unit length of
252 living and senesced roots (RMLli and RMLse, respectively; g m⁻¹) were calculated for
253 the purpose of nutrient resorption calibration.
254

255
256 **Nutrient resorption efficiency (NuRE) and nutrient resorption proficiency**
257 (NuRP) of leaves and roots

258 NuRE is defined as the proportional withdrawal of a nutrient during senescence
259 (Vergutz et al., 2012) and NuRP is the terminal nutrient concentration in senesced
260 organs (Killingbeck, 1996):

$$
261 \quad \mathrm{NURE}(\%) = \left(1 - \frac{\text{mass of nutrient in senesced leaves (senesced roots)}}{\text{mass of nutrient in green leaves (living roots)}}\right) \times 100 \quad (1)
$$

262 Using nutrient concentrations and mass in green and senesced leaves (living and
263 senesced roots), Eq. 1 can be written as follows:

$$
264 \quad \mathrm{NURE\_L}(\%) = \frac{\mathrm{Nu}_{\mathrm{gr}} - \mathrm{Nu}_{\mathrm{se\_L}} \times \mathrm{MLCF\_L}}{\mathrm{Nu}_{\mathrm{gr}}} \times 100 \qquad (2)
$$

$$
265 \quad \mathrm{NURE\_R}(\%) = \frac{\mathrm{Nu}_{\mathrm{li}} - \mathrm{Nu}_{\mathrm{se\_R}} \times \mathrm{MLCF\_R}}{\mathrm{Nu}_{\mathrm{li}}} \times 100 \quad (3)
$$
where $Nu_{gr}$ and $Nu_{se\_L}$ are the nutrient concentration of green and senesced leaves, respectively. $Nu_{li}$ and $Nu_{se\_R}$ are the nutrient concentration of living and senesced roots, respectively. $MLCF\_L$ and $MLCF\_R$ are the mass loss correction factors of leaves and roots, respectively (Table S1).

$$MLCF\_L = 1 - \frac{ML\_L}{100} \qquad (4)$$

$$MLCF\_R = 1 - \frac{ML\_R}{100} \qquad (5)$$

where $ML\_L$ is the percentage mass loss of senesced leaves to green leaves (Wang et al., 2020), $ML\_R$ is the percentage mass loss of senesced roots to living roots (Kunkle et al., 2009). The accuracy of $MLCF\_L$ relied on the method of cohorts, i.e. on the use of similarly large numbers of green and senesced leaves (Aerts, 1989; Lü et al., 2015). Specifically, the probability to obtain comparable pools of green versus senesced leaves increases with the size of leaf cohort sampled, providing thereby robust method for avoiding biases in the calculation of resorption efficiency resulting from mass loss and area shrinkage occurring during leaf senescence (van Heerwaarden et al., 2003). Since this method cannot be applied to root fragments, the accuracy of $MLCF\_R$ was increased by standardizing root mass of living and senesced roots per unit root length, assuming a negligible root length shrinkage during senescence (Kunkle et al., 2009).

$$ML\_L (\%) = \frac{LM_{gr} - LM_{se}}{LM_{gr}} \times 100 \qquad (6)$$

$$ML\_R (\%) = \frac{RML_{li} - RML_{se}}{RML_{li}} \times 100 \qquad (7)$$

Moreover, nutrient resorption proficiency (NuRP) was defined as nutrient concentration of senesced leaf and root, expressed as $N\_L_s$ and $P\_L_s$, $N\_R_s$ and $P\_R_s$, for N and P of senesced leaf and root (Table 1), respectively.

## Data analysis

For each species, the mean and standard error of leaf and root nutrient resorption metrics were calculated using measurements from three individuals for tree species and from three to five subsamples of the composite sample of shrub leaves for shrub species. For each root trait, the mean, standard error, and coefficient of variation (CV)
for the combined first two order roots were calculated across all 34 species. A T-test was used to test for differences between ECM and AM species for the five root traits (RN, RP, RD, SRL and RTD) and for all leaf and root nutrient resorption metrics. T-tests were also used to test for differences between leaves and roots for all nutrient resorption metrics. Moreover, after grouping species by life forms and mycorrhizal types, T-test was used to test the differences in nutrient resorption metrics among the groups. Since there were only two species of ECM shrubs, no T-test was performed in comparison with other groups. Considering that the probability of obtaining false positive results (i.e. finding significant differences by chance) increases as the number of comparisons increases, the Holm-Bonferroni method was employed to correct the P-values in the context of multiple comparisons.

To evaluate the effect of phylogeny on the variation of root economic traits and leaf and root nutrient resorption metrics, we used the backbone phylogeny including angiosperms and gymnosperms from the website of The Plant List to construct a phylogenetic tree for 34 tree species (Fig. S1) by using the “phylo.maker” function in the R package V.PHYLOMAKER (Jin & Qian, 2019). The phylogenetic signal of each trait was tested using Blomberg’s *K* statistic with the “phyloSignal” function in the R package PHYLOSIGNAL (Keck *et al.*, 2016). A value of *K* close to 0 indicates that trait evolution is independent of phylogeny; a value of *K* close to 1 indicates greater phylogenetic conservatism for a given trait; and *K* > 1 indicates a stronger similarity between close relatives than expected under a random model (Blomberg *et al.*, 2003).

We found that there were no significant differences in nutrient resorption metrics of leaf and root between mycorrhizal types (Fig. S2); therefore, AM and ECM data were pooled in the subsequent analyses. Then, we used Pearson’s correlation analyses to test the relationships between eight nutrient resorption metrics of leaf and root, and five root economic traits, by analyzing the original trait data as their lower phylogenetic signals. To explore whether the root economic space can predict the leaf and root nutrient resorption, we first performed a phylogenetically-informed principal component analysis (pPCA) for the four common root economic traits (RD, SRL, RTD, RN) and adding RP across all species by using the “phyl.pca” function in the R
package PHYTOOLS (Revell, 2012). We then tested the predictive value of the
resulting principal component axes (PC1 and PC2), in relation to their leaf and root
nutrient resorption efficiencies and proficiencies in ECM and AM species separated
and pooled using linear regressions. Finally, we performed an additional pPCA to
visualize PC1 and PC2 relationships with the eight leaf and root nutrient resorption
metrics across all species. All analyses were carried out in R software 4.2.3 (R
Development Core Team, 2023).

331

332 **Results**

333 **Leaf and root nutrient resorption**

334 Across all species, N and P resorption efficiencies of leaf were significantly higher
335 than those of root (Fig. 1a-b). On average, N and P resorption efficiencies in leaf were
336 58% and 56%, respectively, and notably lower in root N (20%) and P (25%) (Table
337 1). Compared with senesced root, N concentration of senesced leaf was significantly
338 lower but P concentration was somewhat higher (Fig. 1c-d). On average, N and P
339 concentrations of senesced leaf were 13.4 mg g⁻¹ and 0.8 mg g⁻¹, and they were 19.8
340 mg g⁻¹ and 0.6 mg g⁻¹ for senesced root, respectively (Table 1). Across all species,
341 there were no significant phylogenetic signals detected in the nutrient resorption
342 metrics of leaf and root, as tested by Blomberg's *K* statistic (Table 1).

343

344 **Root economic traits**

345 Across all species, the CVs of most root traits were less than 50%, with the maximum
346 for SRL (79.9%). There was a significant phylogenetic signal for two of five root
347 traits (RN and SRL, Table 1). The pPCA results showed a two-dimensional root
348 economic space across 34 woody species; the first axis represented the root
349 “collaboration” dimension, defined as a negative relationship between SRL and RD.
350 And the second axis represented the root “acquisition-conservation” dimension,
351 defined as a negative relationship between RN and RTD. The first two axes explained
352 67.6% of the total variation of all traits (Fig. 2; Table S2). Across all species,
353 Pearson's correlation with original data showed a positive correlation between RN
and RP, and a negative correlation between RN and RTD (P<0.05, Table S3). In addition, there was a significant negative correlation between RD and SRL.

356

## Trait differences between ECM and AM species and among life forms

For nutrient resorption metrics of leaf and root, there were no significant differences between AM and ECM species across all life forms (Fig. S2). Within broadleaved trees, no significant difference of nutrient resorption metrics of leaf and root was found between AM and ECM species (Figs. S3 and S4). Meanwhile, with the exception of N concentration of senesced leaf, the majority of nutrient resorption metrics showed no significant differences between broadleaved trees and shrubs within AM species, as well as between coniferous trees and broadleaved trees within ECM species (Figs. S3 and S4). For root economic traits, RP of ECM species was significantly lower than that of AM species (p=0.038), while RD was significantly higher in ECM species (p=0.037; Fig. S5). However, there were no significant differences between mycorrhizal types in RN, SRL and RTD.

369

## Correlations between leaf and root nutrient resorption and root economic traits

Pearson's correlation with original data showed that there were significantly positive correlations between N and P concentrations of senesced leaf, N concentration of senesced root, and RN among all species (Table S3). In comparison, the N and P concentrations of senesced root were positively correlated with RP. However, there were weak correlations between most nutrient resorption metrics and root morphological traits (i.e. RD, SRL and RTD), except a positive correlation between P resorption efficiency of root and RD, and a negative correlation between N concentration of senesced root and RTD (Table S3).

The dimensions of root "collaboration" and root "acquisition-conservation" had different associations with leaf and root nutrient resorption metrics, depending on mycorrhizal type. For AM species only, N and P concentrations of senesced root and P resorption efficiency of root were positively correlated with the root "collaboration" dimension, respectively. However, when AM and ECM species were pooled, the

383
positive correlations were found between the root "collaboration" dimension and P resorption efficiency of root and P concentration of senesced root, but not for N concentration of senesced root (Fig. 3). In comparison, irrespective of mycorrhizal type, N and P concentrations in both senesced leaf and root were negatively correlated with the root "acquisition-conservation" dimension, respectively (Fig. 4). However, for ECM species, a negative correlation was found between the N concentration of senesced leaf and the root "acquisition-conservation" dimension, while for AM species, the N concentration of senesced root correlated negatively with the root "acquisition-conservation" dimension (Fig. 4). Notably, whether AM and ECM species were pooled or not, there were no significant correlations between the root "collaboration" dimension and leaf N or P resorption efficiency and proficiency (Fig. S6), and between the root "acquisition-conservation" dimension and N or P resorption efficiency of both leaf and root (Fig. S7).

397

### 398 Correlations between leaf and root nutrient resorption

Leaf and root nutrient resorption metrics were weakly correlated across all species, except N concentration of senesced root was positively correlated with N concentration of senesced leaf ($r$=0.55) and P concentration of senesced leaf ($r$=0.35), respectively (Table S3). The pPCA of leaf and root nutrient resorption metrics presented two main dimensions of variation (Fig. 5, Table S4). The first two axes explained 52.3% of the total variation of all metrics. All four leaf-associated resorption metrics (N and P resorption efficiencies, and N and P concentrations in senesced leaf) formed one dimension that strongly loaded on the first PC axis (Table S4). By contrast, most root nutrient resorption metrics such as root N and P resorption efficiencies, and P concentration of senesced root loaded most heavily on the second axis, whereas N concentration of senesced roots contributed strongly to the variation of both axes (Fig. 5, Table S4).

411

### 412 Discussion

#### 413 No differences in nutrient resorption patterns of ECM and AM species
The resorption of N and P from senescing organs is an important process limiting the loss of these key nutrients during litter fall and root senescence and supplies substantial amounts of resources for future growth of plant biomass (Freschet et al., 2010; Vergutz et al., 2012). In this study, we observed wide variation in both leaf and root nutrient resorption efficiencies and proficiencies across 34 woody species, confirming the critical importance of nutrient recycling processes for defining plant ecological strategies with respect to nutrient cycling. We found that the leaf N and P resorption efficiencies were not particularly high in this study, and fall within the range of the results of previous studies (Yuan & Chen, 2009; Vergutz et al., 2012).

Compared with leaf, in this study, the root N resorption efficiency of all 34 woody plants (19.5%) was within the range of previous studies (3.0%-35.5%; Freschet et al., 2010), but the root P resorption efficiency (24.7%) was lower than that of previous studies (25.0%-64.5%; Freschet et al., 2010; Huang et al., 2016). Nevertheless, considering the small number of species cases in root nutrient resorption research, such comparison results need to be treated with caution. It is worth noting that, according to Killingbeck's criterion, when the N and P concentrations of senesced leaf are above 10 mg g⁻¹ and 0.8 mg g⁻¹ respectively, they are considered as incomplete resorption (Killingbeck, 1996). Therefore, the P resorption in two organs of most species was more complete than that of N in this study (Fig. 1), assuming that the criterion for roots was the same as that for leaves. This may be attributed to a P-limitation at our study site, as the high N:P in both green leaves (18.8) and living roots (34.6) exceed the typical threshold of 16 (Koerselman & Meuleman, 1996). In addition, we also noticed that the vast majority of leaf and root nutrient resorption metrics did not differ among life forms (Figs. S3 and S4), which was consistent with results of previous studies on leaf nutrient resorption of different life forms in forest ecosystems (Aerts, 1996; Vergutz et al., 2012). Overall, our results suggest that nutrient status plays a stronger role in regulating leaf and root nutrient resorption than life form at the local scale.

Nonetheless, we observed no significant differences in N and P resorption efficiencies of leaf and root between AM and ECM species (Fig. S2), leading us to
reject our first hypothesis. Consistent with our findings, other studies also found no
difference in resorption patterns between mycorrhizal types, for either N (Liu *et al.*,
2024) or for P (Zhang *et al.*, 2018). Therefore, the lack of significant differences in
resorption between AM and ECM species suggests a limited direct role of the type of
mycorrhizal association in determining resorption in temperate forests, either in leaf
or root. This could be due to the strong phylogenetic conservatism of mycorrhizal
type (Brundrett, 2002; Kong *et al.*, 2014) which limits variation in mycorrhizal
association within groups while the weak phylogenetic signal of resorption allows
more flexibility, even among closely related species. However, our findings contrast
with a global synthesis suggesting that among woody plants AM species display lower
N resorption efficiencies than ECM species (Guo *et al.*, 2024). In mycorrhizal tree
species, symbiotic fungi are responsible for a large part of the tree nutrition (Read,
1991). Indeed, AM and ECM fungi have been demonstrated to display contrasting
strategies of nutrient uptake and contrasting abilities to mine for organic nutrient
sources (Read, 1991; Phillips *et al.*, 2013). Moreover, mycorrhizal structures may
represent a large part of root biomass and the presence of contrasting mycorrhizal
structures (e.g. intraradical hyphae, arbuscules and vesicles for AM species; mantle
and hartig net for ECM species) among AM and ECM species might logically lead to
differences in senescence and resorption processes in root. Therefore, it is necessary
to carry out research on root nutrient resorption among mycorrhizal types in different
forest types in the future. However, fully understanding nutrient resorption in root
may be complicated by potential concomitant senescence processes in root-associated
fungal mycelium and the unknown extent to which nutrients in roots may be
solubilized and leached throughout the senescence process.

Species with most conservative economic strategies also minimize nutrient losses
during leaf and root senescence

In this study, a two-dimensional root economic space was evident in the pPCA across
34 woody species, in which the first axis represented the root "collaboration"
dimension and the second axis represented the root "acquisition-conservation"
dimension. This is consistent with the two-dimensional root economic space that has been identified at global (Bergmann et al., 2020; Laughlin et al., 2021), regional (Pierick et al., 2024), and community scales (Han et al., 2021; Bi et al., 2023). In contrast to our hypothesis, and recent findings by Ding et al. (2023), we did not observe higher leaf N and P resorption efficiency of species displaying “slow” root economic strategies. However, we found that species from the ‘slow’ end of the root “acquisition-conservation” dimension displayed lower nutrient concentrations of senesced leaf and root (Fig. 4), exhibiting a more “complete resorption” strategy for both N and P (Killingbeck, 1996). This result is partly consistent with that of Freschet et al. (2010), who observed the same pattern for leaf N resorption proficiency. Overall, our results suggest that among woody species with low nutrient concentrations and high structural C concentrations in leaf and root of conservative species may generally translate into lower loss of nutrients during senescence. In other words, those who have little can only lose little, independent of their ability to withdraw nutrients from their senescing organs. Nonetheless, only a full accounting of annual litter inputs among conservative and acquisitive species would allow us to assess the success of this strategy in avoiding nutrient loss during senescence.

In addition, we found that the root “collaboration” dimension of the root economic space was correlated with root P resorption efficiency (Fig. 3c). That is, species with thicker roots, which are expected to rely on fungal hyphae for soil nutrient uptake were also more likely to resorb a larger proportion of P from senescing roots than species with thinner roots. However, the root “collaboration” dimension was negatively related to the root P resorption proficiency (Fig. 3d); that is, species with an “outsourcing” strategy had higher P concentration in senesced roots than those possessing a “do-it-yourself” strategy. Such relationships were mainly driven by AM species (Fig. 3). The seemingly opposite patterns between root acquisition strategies and nutrient resorption metrics in terms of both P resorption efficiency and P resorption proficiency in the “outsourcing” species, may reflect the complex impact of mycorrhizal fungi on the P resorption of root. On the one hand, since subcontracting the P acquisition to fungi incurs high C costs to the plants (Raven et
al., 2018), recycling P may be particularly important for plants that rely strongly on mycorrhizal fungi for their nutrition, showing higher P resorption efficiency. However, on the other hand, the low P resorption proficiency associated to the “outsourcing” strategy, that is the higher P concentrations in senesced root, which may be caused by the higher P concentration in living root, indicating that these species lose larger amounts of P during senescence. Overall, “outsourcing” strategy via mycorrhizal fungi are particularly efficient in acquiring P in ecosystems where P is moderately limiting, but which might therefore limit the value for plants to reduce P loss. Finally, it remains unknown whether mycorrhizal fungi can also contribute to the resorption process in the root during its senescence, and be responsible either for improved or reduced P resorption efficiency by retrieving or leaving behind P during root senescence.

## 517 Leaf and root nutrient resorption proficiencies are correlated

We found that mean leaf N and P resorption efficiencies were significantly higher than those of root across 34 woody species in this temperate forest (Fig. 1a-b). This is consistent with the results of Freschet *et al.* (2010) and Yu *et al.* (2022). Moreover, whereas such studies provided evidence for a decrease in nutrient content of root during senescence, the degree to which resorption, but also leaching, immobilization or other factors contributed to these changes remains unknown (Kunkle *et al.*, 2009), suggesting a potential overestimation of the role of root nutrient resorption. While leaching losses during leaf senescence is limited (Freschet *et al.*, 2010), leaching from senescing roots has never been tested but is likely substantial. Fine roots die in close contact with soil and the soil solution, and their anatomy and physiology reflect their role in allowing exchanges of solutes with the soil environment (Kochsiek *et al.*, 2013; Gao *et al.*, 2024), in contrast with the often thick cuticle of leaves that protect them against desiccation and pathogen attacks (Riederer & Schreiber, 2001). Future measurements of root resorption may therefore need to move beyond the quantification of differences in nutrient content across living and senesced roots to explore the fate of root nutrients into other plant organs (e.g. coarse roots) and the soil
(Zhao *et al.*, 2024).

Regarding N and P resorption efficiencies, N concentration of senesced leaf was significantly lower, but P concentration of senesced leaf was significantly higher than that of senesced root (Fig. 1c-d). In a context where only a few studies have measured root nutrient resorption, our results provide several new insights. First, N and P resorption efficiencies of root are substantial, despite being much lower than the average resorption efficiencies for leaf. Leaves contain far more N and P than roots owing to specificities of the photosynthetic machinery, with no physiological analog in roots. As such, this trend possibly reflects the feasibility to deconstruct large parts of the leaf photosynthetic machinery without causing non-reversible damages to the senescing leaf. Second, on average, senesced roots have a higher N concentration and similar P concentration as those of senesced leaves, which may play a crucial role in the biogeochemical cycles of N and P. This result implies that, although lower amounts of root litter are produced annually than leaf litter (Xia *et al.*, 2015), roots may contribute disproportionately to the soil N inputs and nutrient cycling overall (Freschet *et al.*, 2013). It would be critical to determine whether such findings can be generalized among forests, climate, as well as soil fertility conditions (Vergutz *et al.*, 2012; Huang *et al.*, 2023).

In this study, leaf and root nutrient resorption efficiencies were largely independent and sometimes negatively correlated (Table S3, Fig. 5), in contrast to our expectations of coordinated nutrient recycling strategies above and belowground. However, species with higher N concentration in senesced leaf also had higher N concentration in senesced root (Table S3, Fig. 5). Previous studies seldom explored the associations between leaf and root resorption. In particular, since live root litter is typically used in litter decomposition studies (Freschet *et al.*, 2021), senesced root litter nutrient concentrations data is rare. As far as we are aware, the only study which considered leaf and root resorption coordination observed independent leaf and root resorption efficiencies and proficiency among 11 subarctic species (Freschet *et al.*, 2010), in partial contrast to our findings. While live leaf and root nutrient concentrations (for both N and P) are coordinated at the global scale (Valverde-
Barrantes *et al.*, 2017; Weigelt *et al.*, 2021) and assuming a substantial control of organ nutrient concentrations on nutrient resorption efficiency (Kobe *et al.*, 2005; Vergutz *et al.*, 2012), the lack of coordination in leaf and root nutrient resorption efficiency is surprising. There are several potential reasons for that. First of all, while nutrient resorption processes are strongly influenced by external climatic conditions (e.g. frost occurrence, soil humidity), the synchronicity of leaf and root senescence and resorption processes differ among species (Blume-Werry *et al.*, 2016). Soil and aboveground conditions also strongly differ implying contrasting external influences on the leaf and root senescence processes. Second, the potential influence of mycorrhizal symbionts in roots, whose intensity of association with roots differ among species, may interfere with root nutrient resorption processes.

In contrast, the coordination between nutrient concentrations in senesced leaf and root indicates that species with more protected organs (e.g. higher structural C compounds such as lignins and hemicellulose, or with secondary compounds such as tannins) and lower nutrient concentrations (e.g. lower enzyme concentrations and metabolic activities), which have lower amount of nutrient to resorb, will generally achieve lower level of nutrient loss. On the contrary, species with high amount of nutrient, and therefore high potential for nutrient resorption, typically leave behind higher amount of nutrient per leaf and root biomass lost. In other words, while the ability of species to recycle nutrients is not coordinated among organs, the ability to minimize nutrient loss may be coordinated. These findings highlight the importance of both of the complementary concepts of resorption efficiency, indicative of the efficiency of nutrient recycling, and resorption proficiency, indicative of senescence-related nutrient conservation, in understanding plant economic strategies.
588 Acknowledgments

589 We thank Dr Siyuan Wang, Zhi Liu, Hao Ren, Yaoyuan Ma, Yan Wang, Guoqiang

590 Gao and other graduates for their assistance in field and laboratory works. This study

591 was supported by the National Natural Science Foundation of China (No. 32071749).

592

593 Author contributions

594 JG and DW conceived the ideas, DW and JG designed methodology; DW collected

595 and analyzed the data; DW, JG, GTF, MLM, and HL led the writing of the

596 manuscript. All authors contributed critically to the drafts and gave final approval for

597 publication.

598

599 Data availability statement

600 The data and code that support the main findings of this study are publicly available

601 at https://doi.org/10.5281/zenodo.14505627, doi: 10.5281/zenodo.14505627.

602

603 Conflict of interest

604 None declared
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793 **Supporting Information**

794 **Fig. S1** Phylogenetic tree of 34 woody species in the mixed broadleaved-Korean pine
795 forest in this study, including 13 ectomycorrhizal (ECM) plants (orange font) and 21
796 arbuscular mycorrhizal (AM) plants (blue font). Mycorrhizal status was determined
797 by direct visual observation of root cross-sections.

798

799 **Fig. S2** The nitrogen and phosphorus resorption of leaves and roots in
800 ectomycorrhizal (ECM) (n=13) and arbuscular mycorrhizal (AM) species (n=21).
801 Boxplots include the median (horizontal line within boxes), the first and third
802 quartiles (bottom and top parts of the box, respectively), and the two whiskers (10th
803 and 90th percentiles). The value (P-value) above the boxplot < 0.05 means significant
804 difference between ECM and AM species based on an independent sample T-test.
805 NRE_L, nitrogen resorption efficiency of leaf; N_Ls, nitrogen concentration of
806 senesced leaf; PRE_L, phosphorus resorption efficiency of leaf; P_Ls, phosphorus
807 concentration of senesced leaf; NRE_R, nitrogen resorption efficiency of root; N_Rs,
808 nitrogen concentration of senesced root; PRE_R, phosphorus resorption efficiency of
809 root; P_Rs, phosphorus concentration of senesced root.

810

811 **Fig. S3** The nitrogen and phosphorus resorption efficiencies of leaves and roots in
812 arbuscular mycorrhizal (AM) shrub species (n=13), and AM broadleaved tree species
813 (n=8), ectomycorrhizal (ECM) broadleaved tree species (n=7), ECM coniferous tree
814 species (n=4), and ECM shrub species (n=2). Boxplots include the median (horizontal
815 line within boxes), the first and third quartiles (bottom and top parts of the box,
816 respectively), and the two whiskers (10th and 90th percentiles). The value (P-value)
817 above the boxplot < 0.05 means significant difference based on an independent
818 sample T-test with Holm-Bonferroni corrections. NRE_L, nitrogen resorption
819 efficiency of leaf; PRE_L, phosphorus resorption efficiency of leaf; NRE_R, nitrogen
820 resorption efficiency of root; PRE_R, phosphorus resorption efficiency of root.

821

822 **Fig. S4** The nitrogen and phosphorus concentration of senesced leaves and roots in
arbuscular mycorrhizal (AM) shrub species (n=13), AM broadleaved tree species (n=8), ectomycorrhizal (ECM) broadleaved tree species (n=7), ECM coniferous tree species (n=4), and ECM shrub species (n=2). Boxplots include the median (horizontal line within boxes), the first and third quartiles (bottom and top parts of the box, respectively), and the two whiskers (10th and 90th percentiles). The value (P-value) above the boxplot < 0.05 means significant difference based on an independent sample T-test with Holm-Bonferroni corrections. N_Ls, nitrogen concentration of senesced leaf; P_Ls, phosphorus concentration of senesced leaf; N_Rs, nitrogen concentration of senesced root; P_Rs, phosphorus concentration of senesced root.

**Fig. S5** Root economic traits in ectomycorrhizal (ECM) species (n=13) and arbuscular mycorrhizal (AM) species (n=21). Boxplots include the median (horizontal line within boxes), the first and third quartiles (bottom and top parts of the box, respectively), and the two whiskers (10th and 90th percentiles). The value (P-value) above the boxplot < 0.05 means significant difference between ECM and AM species based on an independent sample T-test. RN, root nitrogen concentration; RP, root phosphorus concentration; RD, root diameter; SRL, specific root length; RTD, root tissue density.

**Fig. S6** Relationships between root "collaboration" dimension (the first axis) of the phylogenetically-informed principal component analyses (pPCA) on root economic traits and **a.** nitrogen resorption efficiency of leaf (NRE_L), **b.** nitrogen concentration of senesced leaf (N_Ls), **c.** phosphorus resorption efficiency of leaf (PRE_L), and **d.** phosphorus concentration of senesced leaf (P_Ls) of ectomycorrhizal (ECM) and arbuscular mycorrhizal (AM) species separated and pooled. Goodness-of-fit (r²) and significance (P-value) of the correlations are displayed.

**Fig. S7** Relationships between root "acquisition-conservation" dimension (the second axis) of phylogenetically-informed principal component analyses (pPCA) on root economic traits and **a.** nitrogen resorption efficiency of leaf (NRE_L), **b.** phosphorus resorption efficiency of leaf (PRE_L), **c.** nitrogen resorption efficiency of root
853 (NRE_R), and d. phosphorus resorption efficiency of root (PRE_R) of
854 ectomycorrhizal (ECM) and arbuscular mycorrhizal (AM) species separated and
855 pooled. Goodness-of-fit (r²) and significance (P-value) of the correlations are
displayed.
856

857

858 **Table S1** List of 34 woody species sampled from a mixed broadleaved-Korean pine
859 forest in this study. AM: arbuscular mycorrhiza; ECM: ectomycorrhiza. Mean ± SE
860 for the data. GD, ground diameter. MLCF_L, the mass loss correction factors of
861 leaves. MLCF_R, the mass loss correction factors of roots.
862

862

863 **Table S2** Loading scores of five root economic traits in the phylogenetically-informed
864 principal component analyses (pPCA) for 34 temperate woody species. RN, root
865 nitrogen concentration; RP, root phosphorus concentration; RD, root diameter; SRL,
866 specific root length; RTD, root tissue density.
867

867

868 **Table S3** Pearson's correlation coefficients among eight leaf and root nutrient
869 resorption metrics and five root economic traits for 34 temperate woody species.
870 NRE_L, nitrogen resorption efficiency of leaf; N_Ls, nitrogen concentration of
871 senesced leaf; PRE_L, phosphorus resorption efficiency of leaf; P_Ls, phosphorus
872 concentration of senesced leaf; NRE_R, nitrogen resorption efficiency of root; N_Rs,
873 nitrogen concentration of senesced root; PRE_R, phosphorus resorption efficiency of
874 root; P_Rs, phosphorus concentration of senesced root; RN, root nitrogen
875 concentration; RP, root phosphorus concentration; RD, root diameter; SRL, specific
876 root length; RTD, root tissue density. *, P<0.05; **, P<0.01.
877

877

878 **Table S4** Loading scores of nutrient resorption metrics of leaves and roots in the
879 phylogenetically-informed principal component analyses (pPCA) for all 34 species.
880 NRE_L, nitrogen resorption efficiency of leaf; N_Ls, nitrogen concentration of
881 senesced leaf; PRE_L, phosphorus resorption efficiency of leaf; P_Ls, phosphorus
882 concentration of senesced leaf; NRE_R, nitrogen resorption efficiency of root; N_Rs,
883 nitrogen concentration of senesced root; PRE_R, phosphorus resorption efficiency of

884 root; P_Rs, phosphorus concentration of senesced root.
885 **Figure legends**

886 **Fig. 1** The nitrogen (N) and phosphorus (P) resorption of leaves and roots for all 34 woody species. NRE (a), N resorption efficiency; PRE (b), P resorption efficiency; N_s (c), N concentration of senesced organ; P_s (d), P concentration of senesced organ. The value (P-value) above the boxplot < 0.05 means a significant difference between leaves and roots based on an independent sample T-test.

891

892 **Fig. 2** Phylogenetically-informed principal component analyses (pPCA) of root economic traits of ectomycorrhizal (ECM) (n=13) and arbuscular mycorrhizal (AM) species (n=21). The first pPCA axis (34.8%) represents the root "collaboration" dimension, describing from large root diameter (RD) that are characterized by low foraging efficiency to high specific root length (SRL) that are related to high foraging efficiency. The second pPCA axis (32.8%) represents the root "acquisition-conservation" dimension, describing low absorptive activity roots with high root tissue density (RTD) to high absorptive activity roots with high root N and P concentrations (RN and RP). Points indicate the position of species along the first two axes. Loading scores for the first three PC axes are reported in Table S2. RN, root nitrogen concentration; RP, root phosphorus concentration; RD, root diameter; SRL, specific root length; RTD, root tissue density.

904

905 **Fig. 3** Relationships between root "collaboration" dimension (the first axis) of phylogenetically-informed principal component analyses (pPCA) on root economic traits and **a.** nitrogen resorption efficiency of root (NRE_R), **b.** nitrogen concentration of senesced root (N_Rs), **c.** phosphorus resorption efficiency of root (PRE_R), and **d.** phosphorus concentration of senesced root (P_Rs) of ectomycorrhizal (ECM) and arbuscular mycorrhizal (AM) species separated and pooled. Goodness-of-fit (r²) and significance (P-value) of the correlations are displayed.

912

913 **Fig. 4** Relationships between root "acquisition-conservation" dimension (the second axis) of phylogenetically-informed principal component analyses (pPCA) on root economic traits and **a.** nitrogen concentration of senesced leaf (N_Ls), **b.** phosphorus concentration of senesced leaf (P_Ls), **c.** nitrogen concentration of senesced root (N_Rs), and **d.** phosphorus concentration of senesced root (P_Rs) of ectomycorrhizal (ECM) and arbuscular mycorrhizal (AM) species separated and pooled. Goodness-of-
919 fit ($r^2$) and significance ($P$-value) of the correlations are displayed.

920

921 **Fig. 5** Phylogenetically-informed principal component analyses (pPCA) of leaf and
922 root nitrogen and phosphorus resorption metrics of ectomycorrhizal (ECM) ($n=13$)

923 and arbuscular mycorrhizal (AM) species ($n=21$). Points indicate the position of
924 species along the first two axes. Loading scores for the first three PC axes are reported
925 in Table S4. NRE_L, nitrogen resorption efficiency of leaf; N_Ls, nitrogen
926 concentration of senesced leaf; PRE_L, phosphorus resorption efficiency of leaf;
927 P_Ls, phosphorus concentration of senesced leaf; NRE_R, nitrogen resorption
928 efficiency of root; N_Rs, nitrogen concentration of senesced root; PRE_R, phosphorus
929 resorption efficiency of root; P_Rs, phosphorus concentration of senesced root.
<table><thead><tr><th>Traits</th><th>Abbreviation</th><th>Unit</th><th>Mean</th><th>SE</th><th>CV/%</th><th>Blomberg's K</th></tr></thead><tbody><tr><td>Nitrogen resorption efficiency of leaf</td><td>NRE_L</td><td>%</td><td>58.01</td><td>1.88</td><td>18.9</td><td>0.051</td></tr><tr><td>Nitrogen concentration of senesced leaf</td><td>N_Ls</td><td>mg g<sup>-1</sup></td><td>13.40</td><td>0.84</td><td>36.8</td><td>0.166</td></tr><tr><td>Phosphorus resorption efficiency of leaf</td><td>PRE_L</td><td>%</td><td>56.16</td><td>2.13</td><td>22.1</td><td>0.062</td></tr><tr><td>Phosphorus concentration of senesced leaf</td><td>P_Ls</td><td>mg g<sup>-1</sup></td><td>0.79</td><td>0.06</td><td>41.6</td><td>0.114</td></tr><tr><td>Nitrogen resorption efficiency of root</td><td>NRE_R</td><td>%</td><td>19.45</td><td>1.46</td><td>43.8</td><td>0.038</td></tr><tr><td>Nitrogen concentration of senesced root</td><td>N_Rs</td><td>mg g<sup>-1</sup></td><td>19.76</td><td>0.79</td><td>23.2</td><td>0.207</td></tr><tr><td>Phosphorus resorption efficiency of root</td><td>PRE_R</td><td>%</td><td>24.74</td><td>2.10</td><td>49.5</td><td>0.020</td></tr><tr><td>Phosphorus concentration of senesced root</td><td>P_Rs</td><td>mg g<sup>-1</sup></td><td>0.58</td><td>0.04</td><td>40.7</td><td>0.182</td></tr><tr><td>Root nitrogen concentration</td><td>RN</td><td>mg g<sup>-1</sup></td><td>21.09</td><td>0.82</td><td>22.6</td><td><strong>0.409</strong></td></tr><tr><td>Root phosphorus concentration</td><td>RP</td><td>mg g<sup>-1</sup></td><td>0.67</td><td>0.05</td><td>42.2</td><td>0.170</td></tr><tr><td>Root diameter</td><td>RD</td><td>mm</td><td>0.27</td><td>0.02</td><td>39.3</td><td>0.069</td></tr><tr><td>Specific root length</td><td>SRL</td><td>m g<sup>-1</sup></td><td>123.06</td><td>16.86</td><td>79.9</td><td><strong>0.330</strong></td></tr><tr><td>Root tissue density</td><td>RTD</td><td>g cm<sup>-3</sup></td><td>0.22</td><td>0.01</td><td>26.4</td><td>0.052</td></tr></tbody></table>
<figure><img src="image_2.png" /><figcaption>Fig. 1</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>936 Fig. 2</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>937 Fig. 3</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 4</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>939 Fig. 5</figcaption></figure>