**1. Introduction**

Nutrient resorption is a process by which plants transfer nutrients from senescent organs to fresh tissues [1–3]. It is an important strategy for trees to conserve and use nutrients efficiently and profoundly affects several key processes in ecosystems, such as plant nutrient uptake, ecochemical balance, and carbon cycling [4–6]. The efficiency of this nutrient transfer (nutrient resorption efficiency) can be quantified by the percentage reduction in nutrient content between mature and senescent leaves [7–9]. However, nutrients that are not transferred from senescent leaves fall to the ground with withered leaves and are recycled in the ecosystem after the withered leaves are decomposed. Therefore, the final nutrient content in senescent leaves can also be used as an indicator of nutrient resorption (nutrient resorption proficiency) [7], which reflects the extent of nutrient transfer from

**Citation:** Zhang, Y.; Yang, J.; Wei, X.; Ni, X.; Wu, F. Monthly Dynamical Patterns of Nitrogen and Phosphorus Resorption Efficiencies and C:N:P Stoichiometric Ratios in *Castanopsis carlesii* (Hemsl.) Hayata and *Cunninghamia lanceolata* (Lamb.) Hook. Plantations. *Forests* **2022**, *13*, 1458. https://doi.org/10.3390/ f13091458

Academic Editor: Heinz Rennenberg

Received: 30 August 2022 Accepted: 8 September 2022 Published: 10 September 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

leaves [7,8]. However, it is well known that trees often have various nutrient requirements to satisfy the growing need with monthly climate changes [10–12], but the dynamical patterns of nutrient resorption are often neglected.

Temperature and precipitation are important drivers for regulating the nutrient utilization of trees [13–15]. Previous studies have shown that P resorption efficiency increases with increasing average annual temperature and average annual precipitation, while N resorption efficiency decreases [16]. However, other studies have suggested that N and P resorption efficiencies decrease with increasing average annual temperature or average annual precipitation [17,18]. Most of these studies were limited to large regional scales, and differences in the species selected as well as the sampling months may contribute to the uncertainty of the results. Currently, even less is known about the dynamics of leaf resorption of forest trees in response to seasonal climate changes. Given the importance of resorption for plants and ecosystems, more data are needed to clarify this issue so that we can incorporate nutrient resorption into models of plant responses to climate changes [19].

The trees with different functional types also develop various nutrient resorption strategies [8,20,21]. A meta-analysis proposed that the P resorption efficiency was higher in coniferous plantations than in broadleaf plantations, while the N resorption efficiency has no significant difference [22]. Consistent results were also observed in many natural forests [21,23]. However, a study in the karst ecosystems of Southwestern China documented that coniferous trees have a lower P resorption efficiency and a higher N resorption efficiency than broadleaf trees [24]. The differences in nutrient resorption among functional types of forests remain uncertain.

In addition, element stoichiometry focuses on the interactions and balances among elements [25–27]. Element stoichiometric balance exerts an essential role in species development, community structure, and adaptation to environmental stresses [28,29]. Specifically, C:N:P stoichiometric ratios of plants can reveal the nutrient utilization and relative nutrient limitation [30,31]. At present, C:N:P stoichiometric ratios have been evaluated at both global and regional scales, and vary with several factors such as plant functional types [2,8], soil nutrients [5,6,32], and climate changes [33,34], which can provide theoretical support for the managemen<sup>t</sup> and conservation of forest.

Compared to other regions at the same latitude, the subtropical region of China nurtures vast humid subtropical forests [35]. In recent years, amounts of natural broadleaf evergreen forests in the region have been replaced by large areas of mono-structured plantations to meet the needs of economic development [36]. More than 60% of China's plantations are located in warm and humid subtropical regions [37]. However, N and P in subtropical forest soils are highly susceptible to leaching out of the ecosystem by rainfall [18], thus limiting plant growth. Therefore, the N and P resorption strategies of subtropical plants may be more important compared to other regions. The *Cunninghamia lanceolate* (Lamb.) Hook. is an important fast-growing tree for afforestation in subtropical regions, accounting for 17.3% of total plantations in China [38]. *Castanopsis carlesii* (Hemsl.) Hayata is one of the most typical trees of evergreen broadleaf forests in subtropical regions of China [39]. The results in studying the nutrient resorption and C:N:P stoichiometric characteristics in *Castanopsis carlesii* and *Cunninghamia lanceolata* plantations can help to understand the nutrient use strategies of subtropical forests.

Therefore, to understand the dynamics of nutrient use strategies in response to changes in temperature and precipitation in different plantations, we investigated the C, N, and P contents and stoichiometric ratios in mature and senescent leaves and nutrient resorption of *Cunninghamia lanceolata* plantation and *Castanopsis carlesii* plantation in a typical subtropical region, where both plantations have similar climates and land-use histories. We hypothesized that N and P resorption and C:N:P stoichiometric ratios in the leaves may vary significantly with forest types and are regulated by seasonal climate changes. Our objectives were to determine (i) what are the differences of nutrient resorption and C:N:P stoichiometric ratios between the two plantations, and (ii) whether and how nutrient resorption strategies were influenced by monthly temperature and precipitation dynamics.

These results can provide further insights into a better understanding of nutrient cycling and ecological processes in different forest plantations in the subtropics and provide a theoretical basis for the managemen<sup>t</sup> of plantations.

#### **2. Materials and Methods**

## *2.1. Study Site*

The study site was located in Fujian Sanming Forest Ecosystem National Observation and Research Station, China (26◦19 N, 117◦36 E), in the southeast of the Wuyishan National Nature Reserve and northwest of the Daiyun Mountains. The area is dominated by low hills, with an average elevation of 300 m and a slope of 25–45◦. The climate is maritime subtropical monsoonal, with an average annual temperature of 19.3 ◦C and an annual precipitation of 1610 mm from 1960 to 2019, with about 80% of the rainfall occurring between March and August [40]. The soils were developed from granite and can be classified as Hapludults under the Ultisols order according to the United States Department of Agriculture Soil Taxonomy. The soil pH is 4.38 [37,38]. Before 1958, the area was covered by natural broadleaf forests dominated by *Castanopsis carlesii* [41]. However, as the demand for timber for construction increased, the natural forests were gradually logged and replaced by plantations (mainly *Castanopsis carlesii* and *Cunninghamia lanceolata*).

In 2011, six 20 m × 20 m plots were set up in a randomized group design in a natural forest of *Castanopsis carlesii* formed in 1976, including two treatments, a *Castanopsis carlesii* plantation and a *Cunninghamia lanceolata* plantation (Table 1), with three replicates in each plantation [40]. We investigated in 2021 when both plantations were 10 years old and in a rapid growth stage with high nutrient demand.


**Table 1.** The characteristics of the two plantations.

Values are meant ± standard errors (*n* = 3).

#### *2.2. Sampling and Chemical Analysis*

At the end of each month from April to October 2021, five trees with similar height and diameter at breast height were selected in each replicate sample plot to collect the mature leaves. A total of 15 trees from each plantation were collected. The green mature leaves of *Castanopsis carlesii* were collected in the *Castanopsis carlesii* plantation, while the green needles on the second or third node branches were collected in the *Cunninghamia lanceolata* plantation. The collected mature leaves were placed in moist self-sealing bags and brought back to the laboratory through an insulated box (internal temperature < 4 ◦C) [42]. In addition, senescent leaves were also collected at the end of each month. Three separate 0.7 m × 0.7 m nylon mesh frames were set up in each replicate sample plot in the *Castanopsis carlesii* plantation. The fresh leaf litter of *Castanopsis carlesii* in nylon mesh frames was selected as senescent leaf samples. Because of the persistence effect, the needle litter of branches of *Cunninghamia lanceolata* can retain on the trunk for many years [43], and so the newly senescent brownish-yellow needles on the trees were collected as senescent needle samples in the *Cunninghamia lanceolata* plantation. In addition, soil samples were collected at a depth of 0–20 cm in the *Castanopsis carlesii* plantation and the *Cunninghamia lanceolata* plantation to assess the chemical composition in August 2021. During the sampling period,

three tipping bucket rainfall barrels were placed in a non-forested area near the sample plots for automatic recording of precipitation. A temperature logger was also placed in the non-forested area for recording air temperature (Figure 1).

**Figure 1.** Monthly average air temperature and precipitation during the experiment. Each column represents the average value of three replicates (*n* = 3). Error bars represent standard errors (SE).

The collected mature and senescent leaves were dried in an oven at 65 ◦C for more than 72 h to a constant weight. Afterward, the dried samples were ground with a grinder (Tube Mill 100 control) and sieved through 100 mesh. The soil samples were air dried and also sieved through 100 mesh. The C and N contents of leaves and soil were determined using an elemental analyzer (Elemental Analyzer Vario EL III, Langenselbold, Germany) [37]. The P content of leaves and soil was determined using a continuous flow analyzer (SAN++, SKALAR, Holland) after the preparation of the solution, to be measured by H2SO4-HClO4decoction [44].

#### *2.3. Calculation and Analysis*

To eliminate the error caused by the loss of leaf mass during senescence in the calculation of the nutrient resorption efficiency (*NuRE*) during leaf senescence, we used the following formula [9,43]:

$$NuRE = \left(1 - \frac{Nu\_{s\text{senscent}}}{Nu\_{\text{muture}}}MLCF\right) \times 100\tag{1}$$

where *Numature* and *Nusenescent* are the nutrient contents on a mass basis in mature and senescent leaves (mg·g<sup>−</sup>1), respectively; and *MLCF* is the mass loss correction factor used to compensate for the loss of leaf mass during senescence (specifically the ratio of the dry mass of senescent leaves to the dry mass of mature leaves).

The nutrient resorption proficiency (*NuRP*) is expressed directly as the nutrient contents in senescent leaves [7]. Furthermore, the amount of nutrient changes during leaf senescence was calculated by subtracting the nutrient contents of mature leaves from the nutrient contents of senescent leaves.

The C:N, C:P, and N:P ratios were determined using the C, N, and P contents in each leaf sample. The C:N:P stoichiometric ratios were calculated as mass ratios. The normality and chi-squareness of the data were checked and transformed if necessary. A two-way analysis of variance (two-way ANOVA) was used to test the effects of forest type and sampling time on leaf element contents, resorption efficiencies, and stoichiometric ratios. A one-way analysis of variance (one-way ANOVA) and Tukey's multiple comparison test were used to analyze the differences between plantations at the *P* < 0.05 level. Pearson correlation was used to determine the correlations among leaf nutrient contents, stoichiometric ratios, resorption and temperature, and precipitation. The above analyses were performed using SPSS 23.0 (IBM SPSS, Chicago, IL, USA).
