Mycorrhizal types modulate the trade-off between leafnitrogen resorption and mineralization

Mycorrhizal types modulate the trade-off between leafnitrogen resorption and mineralization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mycorrhizal types modulate the trade-off between leafnitrogen resorption and mineralization Eryuan Zhao, Chunhua Ji, ZY SHI, Shuang Yang, Manman Jing, Yan Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8600732/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Leaf nitrogen resorption before leaf fall and mineralization after litter fall are strongly influenced by the environment, but their linkage to biotic factors remains largely unknown. Aims : This study aims to investigate the regulatory differences of arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) fungi on leaf nitrogen resorption efficiency (NRE) and nitrogen mineralization ( N min ) by utilizing a global plant-mycorrhiza and foliar traits database. Methods : This study utilized the global plant mycorrhiza database “FungalRoot” and field data on plant mycorrhizal infection characteristics in terrestrial ecosystem to establish a database of arbuscular mycorrhizal infection information for terrestrial wild plants, and conducted research based on this database. Results : The results show AM plants exhibit significantly lower NRE (39.65%) compared to ECM plants (50.37%, P < 0.0001), while demonstrating significantly higher N min (0.35) than ECM plants (0.03, P < 0.0001). When considering deciduous plants, AM plants display significantly lower leaf NRE (42.86%) compared to ECM plants (54.00%, P < 0.01), yet show significantly higher leaf N min (0.27) than ECM plants (-0.01, P < 0.01). Turning to evergreen plants, AM plants exhibit significantly higher leaf N min (0.42) compared to ECM plants (0.09, P < 0.01). Conclusions : These results indicate that mycorrhizal types significantly modulate leaf NRE and N min . In the future, introducing mycorrhizal factors into global-scale models of the dynamic interaction between nitrogen resorption and mineralization will enhance the simulation of nutrient limitations on ecosystem productivity. Arbuscular mycorrhiza Ectomycorrhiza Nitrogen trade-off Nitrogen resorption efficiency Nitrogen mineralization rate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Leaf nitrogen resorption efficiency (NRE) measures how effectively plants redistribute nitrogen from aging leaves to metabolic sinks (Aerts, 1999; Kaltenegger and Winiwarter 2020 ; Conant et al. 2013 ). This process is vital in ecology, allowing plants to reallocate nutrients from senescent leaves to storage organs or developing tissues before the leaves are shed. By doing so, plants can extend the retention time of nutrients and improve their nutrient use efficiency, significantly enhancing their adaptability to nitrogen-deficient environmental conditions (Freschet et al., 2010 ). Several factors, including mean annual precipitation, mean annual temperature, and specific foliar traits, significantly influence patterns of nitrogen resorption in plants (Du et al., 2020 ; Liu et al., 2024 ). Several empirical studies have shown that N resorption in N-fixing species is lower than in non-N-fixing species (Nongbri & Barik, 2020 ). Environmental factors have a specific influence on leaf NRE, but they have less control than it. Furthermore, leaf nitrogen mineralization ( N min ), which occurs through microbial decomposition of litter, releases bioavailable nitrogen that meets plants’ nitrogen requirements (Cleveland et al., 2013 ; Holloway and Dahlgren 2002 ). Under nitrogen-limited conditions, leaf N min can enhance soil nitrogen availability, promoting plant growth and increasing ecosystem productivity. Research shows that leaf N min gradually increases with higher mean annual temperature and precipitation (Yuan & Chen, 2009 ). However, foliar traits and soil microbial activity may significantly impact leaf N min more than climatic factors. Foliar traits, especially leaf nitrogen content, are key factors that affect plant leaf N min (Manzoni et al., 2008 ). As the key decomposers of soil organic matter, soil microbes accelerate leaf N min , thereby increasing the available nitrogen in the environment (Sinsabaugh & Shah, 2012; Tian et al., 2018 ). Plant leaf NRE and N min , the rates of these two processes largely determine the nitrogen use efficiency of ecosystems, while there exists a trade-off relationship between them (Cleveland et al., 2013 ). This is because nutrient use efficiency (NUE) at the leaf level is negatively correlated with nutrient concentration in litter. High NUE signifies that plants can more effectively absorb and utilize nutrients from the soil and within their leaves. However, it also implies that when plants efficiently recycle nutrients from their leaves, nutrient concentration in the litter decrease. Since litter decomposition and nutrient release are typically positively correlated with nutrient concentration in the litter, this affects the litter decomposition rate(Deng et al., 2018 ). High NRE may reduce the rate of N min by decreasing the nitrogen concentration in litter (Fridley, 2012 ; Parton et al., 2007), whereas slow N min may enhance NRE by limiting the availability of nitrogen in the soil (Aerts et al., 1999 ; Cleveland et al., 2013 ). As temperature and precipitation rise, the rate of nitrogen cycling accelerates, shifting from a conservative resorption pathway to a mineralization pathway (Hu et al., 2020 ; Zhang et al., 2022 ). The type of plant influences the chemical composition of litter, which in turn affects the litter decomposition rate and nutrient release (Bortolazzi et al., 2021 ). For instance, nitrogen-rich litter decomposes more quickly (Desie et al., 2020 ). Some studies have shown that the impacts of different mycorrhizal types on soil nitrogen status vary. Compared with ECM plants, AM plants generally exhibit higher rates of soil nitrogen cycling (Pellitier et al., 2021 ). Previous synthesis studies have explored leaf traits and climate as the main drivers of global nitrogen cycle changes (Brant & Chen, 2015 ; Reed et al., 2012 ; Vergutz et al., 2012 ; Yuan & Chen, 2009 ). Despite previous studies revealing global patterns of nitrogen resorption, few studies have linked nitrogen resorption dynamics to plant nutrient cycling strategies, such as mycorrhizal symbiosis. In terrestrial ecosystems, mycorrhizal symbiosis represents a mutualistic phenomenon arising between plants and soil fungi (Parton et al., 2007), which is the most extensive symbiotic relationship between plant roots and soil mycorrhizal fungi. They help plants absorb other essential mineral nutrients, thereby accelerating the cycle of inorganic nutrients and playing an important role in maintaining the nitrogen cycle of the plant-soil system (Fall et al., 2022 ). In this symbiotic relationship, AM and ECM are the most prevalent and well-studied types (Cavagnaro et al., 2014 ; Tedersoo et al., 2020 ). AM fungi hyphae grow in the apoplastic space between plant cells and they penetrate cells where they form arbuscules. They may also form vesicles. These take the form of intracellular hyphal coils, lumps and intercellular thick-walled and thin-walled fungal structures. ECM fungi creates sheath of fungal hyphae enveloping the root where the hyphae form a network between cortical cells (Strullu-Derrien et al., 2018 ). AM fungi possess elongated hyphae and highly branched hyphal networks that enhance nutrient absorption capacity (Joanne et al., 2011 ). ECM fungi form a mantle around host plant root tips and extend hyphae into the surrounding soil, with extended hyphal networks exploring larger soil volumes (Entry et al., 1991 ). For instance, most temperate forests are nitrogen-limited, and to counter this, ECM trees enhance soil nitrogen acquisition by increasing rhizosphere nitrogen transformation and root length density. The superior nitrogen acquisition ability of ECM trees may give them a competitive advantage over AM trees under nitrogen-limited conditions (LeBauer & Treseder, 2008 ; Lin et al., 2017 ) also found a similar phenomenon in a deciduous forest in Indiana, where an increase in ECM trees at the plot level was associated with increased nitrogen resorption rates (Lin et al., 2017 ). AM fungi may access nutrients locked in soil organic matter through direct enzymatic decomposition and other methods, promoting decomposition and helping plants acquire nitrogen from soil organic substrates, while ECM mycorrhizal hyphae release nitrogen from soil organic matter through secretion of organic enzymes(Joanne et al., 2011 ). ECM plants can directly decompose organic matter through extracellular enzymes, leading to more conservative nitrogen cycling in ECM-dominated soils. In contrast, N min rates are faster in AM-dominated soils, resulting in more rapid nitrogen cycling in AM-dominated ecosystems (Lin et al., 2017 ; Phillips et al., 2013 ). Overall, ECM plants dominate in high-latitude ecosystems (with slow nitrogen cycling) and adopt more conservative nitrogen resorption strategies, thus exhibiting higher nitrogen resorption efficiency. AM plants, on the other hand, dominate in low-latitude ecosystems (with rapid nitrogen cycling) (Smith & Smith, 2011 ), and the faster litter decomposition rate of AM plants compared to ECM tree species may be the reason for the faster nitrogen mineralization rate in AM-dominated ecosystems (Averill et al., 2014 ; Cornelissen et al., 2001 ). In general, the distribution of mycorrhizal types is related to nitrogen availability, with AM plants preferring nitrogen-rich environments and ECM plants thriving in nitrogen-poor areas (Cornelissen et al., 2001 ; Read & Perez-Moreno, 2003 ). However, there is a lack of global empirical research on the differences between ECM and AM plants in N resorption and mineralization. Previous research has primarily focused on the trade-off between leaf NRE and N min , and the factors influencing nitrogen cycling rates. Such research has clarified how global interactions between leaf NRE and N min influence nitrogen cycling across various biomes (Deng et al., 2018 ). The mechanism by which mycorrhizal types affect the differences and trade-offs between plant leaf NRE and N min remains unclear. We carried out a global-scale meta analysis based on prior studies. We integrated a global dataset of green and senescent leaf nutrient concentration with plant-mycorrhizal association information for species with different leaf habits (deciduous and evergreen) and from different climatic zones (temperate and tropical). We compiled a comprehensive global dataset from published literature, encompassing environmental factors (mean annual temperature and mean annual precipitation), foliar traits (litter decomposition rate, litter mean residence time, green leaf nitrogen concentration, and litter nitrogen concentration), NRE, and N min (Deng et al., 2018 ). Previous studies have demonstrated that leaf NRE and N min are influenced by environmental conditions and foliar characteristics (Liu et al., 2024 ). To further explore the global divergence in leaf NRE and N min between AM and ECM plants, and their responses to environmental variations, we integrated mycorrhizal type information into this existing database. This synthesis enabled us to propose two working hypotheses: (a) Significant differences exist in both leaf NRE and Nmin level between AM and ECM plants. (b) The trade-off of leaf NRE- N min exhibits significant divergence between AM and ECM plants. Materials and Methods Data collection This study only included data from field studies in terrestrial ecosystems, excluding data from wetlands, aquatic ecosystems, agricultural ecosystems, greenhouses, and laboratory incubation studies. The basic data used in this study were collated by Deng et al. ( 2018 ). The database covered field-measured data of leaf nitrogen resorption efficiency (NRE), nitrogen mineralization ( N min) , litter mean residence time (MRT), litter decomposition rate (K), nitrogen concentration of green leaves(leaf nitrogen concentration) and litter nitrogen concentration of the same plant species, mean annual precipitation (MAP), and mean annual temperature (MAT) from specific sites. We further expanded based on the data collected by Deng et al. ( 2018 ). The new database (Table S1 ) additionally covers relevant information on the plant types studied, their mycorrhizal types. Mycorrhizal and Vegetal Classification We searched for the mycorrhizal type of each plant species from the published literatures, especially Wang et al. (2006), Soudzilovskaia et al. ( 2020 ) and Yang et al. ( 2021 ), to determine the mycorrhizal types of plant species in the database. Plants with typical arbuscular mycorrhizal (AM) structures were classified as AM type, while those with typical ectomycorrhizal (ECM) structures were classified as ECM type, due to the insufficient data volume of AM + ECM mycorrhizal types, this thesis did not study this mycorrhizal type. Studies were excluded if they met any of the following criteria: (1) species that do not form symbiotic relationships with AM or ECM., (2) biomass data for control and treatment groups were not provided, (3) standard deviations and sample sizes for each group were not reported. Additionally, plots containing nitrogen-fixing species were excluded from the main analysis due to their potential special response to nitrogen addition. Based on the global database established by Deng et al. ( 2018 ) and the relevant literature provided, we classified mean annual precipitation and mean annual temperature as climate factors, and green leaf nitrogen concentration, litter nitrogen concentration, litter mean residence time, and litter decomposition rate as foliar traits. We also categorized 159 plant species into 69 AM plants and 90 ECM plants. Among the AM plants, 20 were deciduous and 46 were evergreen, while among the ECM plants, 58 were deciduous and 32 were evergreen, based on functional types. Ecologically, the 69 AM plants contained 55 tropical and 12 temperate species, and the 90 ECM plants included 21 tropical and 47 temperate species. Due to limited numbers, plants from boreal forests, tundras, grasslands, deserts, and ferns could not subgrouped (for specific data, Supplementary Data S1). Statistical Analysis We employed a T-test to compare leaf NRE and N min between AM and ECM plants. Similarly, leaf NRE and N min differences among functional and ecological types were analyzed using the same method. Variation partitioning analysis (VPA) under different mycorrhizal types was performed using the 'vegan' package in R. VPA decomposes the total variance into the independent and interactive effects of climate and foliar traits. Subsequently, stepwise multiple regression analysis (SMR) was conducted to explore the relationship between environmental factors and foliar traits. We compared the R 2 values of the best multiple regression models to identify the most influential environmental factors and foliar trait. All statistical analyses were conducted using IBM SPSS Statistics 26.0 (IBM Corp, 2019) and R software version 4.1.0 (R Core Team, 2021). In this study, the leaf NRE and N min of plants, as well as environmental data and foliar traits of each plant, such as mean annual precipitation(MAT), mean annual temperature(MAT), green leaf nitrogen concentration(leaf nitrogen concentration), litter nitrogen concentration, litter mean residence time (MRT), and litter decomposition rate (K), were sourced from the database established by Deng et al. ( 2018 ). The calculation formulas for NRE, N min , and MRT are following. NRE values were obtained through direct extraction from published literature or computation based on reported nitrogen concentration in both mature and senescing leaves. NRE, representing the fraction of nitrogen recovered during leaf senescence, was quantified using the following equation: $$\:\text{N}\text{R}\text{E}=\left(1-\frac{{N}_{1}}{{N}_{\text{g}}}\right)\times\:100$$ 1 Among them, N g and N l represent the nitrogen content of mature leaves and litter, respectively. The MRT values are extracted from the literature or calculated using an exponential decomposition model: L t = L 0 × e − kt (2) MRT = k − 1 (3) Where, L t denotes the time-dependent litter mass, L 0 represents the initial mass, k corresponds to the litter decomposition rate constant derived from first-order exponential decay kinetics, and MRT indicates the litter mean residence time (Kampichler and Bruckner, 2009 ). While few studies provide concurrent measurements of leaf nitrogen resorption efficiency and litter nitrogen mineralization dynamics, extensive literature documents litter decomposition rates across varied ecosystems. Parton et al. (2007) established through a decadal multisite (n = 21) decomposition experiment that N min, during decomposition, exhibits biome-independent predictability, governed dominantly by initial litter N concentration and residual mass. Parton et al. developed an empirical model to estimate N min during litter decomposition. Using the litter decomposition rate ( k ) and litter nitrogen concentration data from the dataset, we calculated N min using the formula from Parton et al.(2007): N min =1− \(\:\frac{{L}_{r}}{100}\sqrt{\frac{{\left(\frac{2\times\:a\times\:100}{b}\right)}^{2}+{\left(1-{\left(\frac{100}{b}\right)}^{2}\right)}^{2}}{{\left(\frac{2\times\:a\times\:{L}_{r}}{b}\right)}^{2}+{\left(1-{\left(\frac{{L}_{r}}{b}\right)}^{2}\right)}^{2}}}\) (4) $$\:\:a=0.7$$ 5 b = 98 \(\:\times\:\left[1-{e}^{\left(-1.56\times\:{N}_{i}\right)}\right]\) (6) Where a functions as the regulatory parameter governing the magnitude of the curve's maximum, while b determines the position of this peak. N i represents the initial nitrogen concentration in the litter, and L r denotes the residual percentage of original litter mass after one-year decomposition, computed as L r = L 0 ×e − k . Positive N min values signify net nitrogen mineralization, whereas negative values reflect immobilization. The first-order exponential decay model tends to overestimate litter decomposition rate compared to empirical measurements (Wieder et al., 2013 ), which could lead to artificially elevated N min values in simulation outputs. Results Variation of leaf NRE and N min between AM and ECM plants AM plants show a leaf NRE of 39.65%, which is significantly lower than the 50.37% seen in ECM plants (Fig. 1 A). Concurrently, regarding leaf N min , AM plants have a value of 0.35, substantially higher than the 0.03 measured for ECM plants (Fig. 1 A). When different functional types of plants are examined, both AM and ECM plants show significant differences in leaf NRE and N min . Specifically, AM plants exhibit a leaf NRE of 42.86%, a figure that is less than the 54.00% found in ECM plants (Fig. 1 B). However, the leaf N min of AM plants is 0.27, significantly higher than the − 0.01 of ECM plants (Fig. 1 C). In the case of evergreen plants, the leaf N min of AM plants is 0.42, significantly higher than the 0.09 of ECM plants (Fig. 1 C), while there is no significant difference in leaf NRE between AM and ECM plants (Fig. 1 B). When analyzing plants from different climate types, neither AM nor ECM plants exhibit significant differences in leaf NRE and N min (Fig. 1 D, E). The relationship of leaf NRE and N min between AM and ECM species under different plant groups AM and ECM leaf N min changes significantly with the increase in leaf NRE. Overall, AM leaf N min decreases from 0.68 to 0.06, while ECM leaf N min decreases from 0.52 to -0.32, with no significant trend difference (Fig. 2 A). In deciduous plants, the leaf N min of AM decreases significantly from 0.81 to -0.10, while that of ECM also sees a marked drop from 0.48 to -0.30, both showing a significant downward trend as leaf NRE increases. For evergreen plants, a similar pattern is observed, with AM leaf N min falling from 0.63 to 0.21 and ECM leaf N min dropping from 0.55 to -0.23. However, there is no significant linear relationship between evergreen AM leaf N min and NRE, and only the trend of leaf N min change with leaf NRE is significantly different for deciduous AM and ECM plants (Fig. 2 B, C). When it comes to tropical plants, the leaf N min of AM exhibits a reduction from 0.63 to 0.28, while ECM leaf N min undergoes a drop from 0.82 to -0.02. Regarding temperate plants, the leaf N min of AM plummets from 0.10 to -0.15, and ECM's leaf N min drops from 0.11 to -0.26. Except for the absence of a significant linear relationship between tropical AM leaf N min and NRE, the leaf N min of tropical ECM plants and temperate AM and ECM plants shows a marked drop as leaf NRE rises. There is a significant difference in the response of leaf N min change to leaf NRE between temperate AM and ECM plants (Fig. 2 D, E). The impact percentage of climate and foliar traits on leaf NRE and N min between AM and ECM plants Climate and foliar traits explain 89.47% of leaf NRE variation in AM plants and 85.32% in ECM plants (Fig. 3 A), as well as 82.77% of leaf N min variation in AM plants and 80.87% in ECM plants (Fig. 3 F). Foliar traits significantly affect leaf NRE, with a greater impact on AM plants at 91.35% than on ECM plants at 65.13%, the impact on leaf N min is 68.40% in AM and 44.22% in ECM. Climate factors have a minor influence, accounting for 0.06% of NRE in AM plants, 0.03% of NRE in ECM plants, contributing 0.49% to leaf N min in AM plants, and 0.14% to leaf N min in ECM plants (Fig. 3 A, 4 A). In various functional and ecological groups, leaf NRE and N min of AM plants are highly sensitive to leaf traits. Deciduous plants have a leaf NRE of 97.07%, and temperate plants have 99.47% (Fig. 3 B, E). Leaf N min is 89.43% in deciduous plants and 99.73% in temperate plants (Fig. 4 B, E). The independent influence of leaf traits on leaf NRE and N min in AM plants is more significant than climate factors. Deciduous groups exhibited 83.22% autonomous regulation of NRE, while evergreen and tropical plants achieved superior self-regulation capacities at 90.75% and 87.64%, respectively, contrasting sharply with temperate communities' 51.47% autogenic control magnitude. Deciphering the determinants of N min revealed that leaf traits independently explained 76.48% of the variation in tropical plants, surpassing the 30.85% observed in temperate species. Similarly, leaf traits accounted for 74.58% of the variation in evergreen plants, exceeding the 61.65% in deciduous species. In contrast, climate factors exerted minimal influence on leaf NRE and N min (Fig. 3 B-E, 4 B-E). ECM plant leaf NRE and N min are also sensitive to leaf traits. For leaf NRE, deciduous communities attain peak efficiency (97.07%), tropical systems approach near-optimal level (93.74%), temperate formations show intermediate values (79.39%), while evergreen groups exhibit markedly constrained performance (54.89%) (Fig. 3 B-E). For leaf N min , evergreen species demonstrate peak nitrogen retention (83.70%), followed by deciduous communities (80.61%), with tropical and temperate systems showing attenuated capacities at 70.48% and 62.73%, respectively (Figs. 4 B-E). The independent effect of leaf traits on leaf NRE, temperate plants show the highest leaf NRE (82.19%), deciduous species following with 75.87%, whereas evergreen and tropical plants register lower values at 49.46% and 48.79%, respectively, all of which are significantly higher than the influence of climate factors. The situation for leaf N min is similar (Fig. 4 B-E). The changes of leaf NRE and N min with variation of climate and foliar traits between AM and ECM plants The leaf NRE of AM plants has no significant relationship with mean annual temperature, mean annual precipitation, leaf nitrogen concentration, and litter decomposition rate (Fig. 5 A, B, C, E), but decreases with increasing litter nitrogen concentration and litter mean residence time (Fig. 5 D, F). In contrast, the leaf NRE of ECM plants shows different characteristics: it decreases with increasing mean annual temperature, mean annual precipitation, litter decomposition rate, and litter nitrogen concentration (Fig. 5 A, B, D, E), and increases with increasing leaf nitrogen concentration (Fig. 5 C), but is unrelated to litter mean residence time (Fig. 5 F). When litter nitrogen concentration increases, the leaf NRE of both AM and ECM plants decreases, but with different trends (Fig. 5 D). The evergreen and deciduous leaf NRE of AM plants is unrelated to mean annual temperature, but the deciduous leaf NRE of ECM plants decreases with increasing mean annual temperature (Figure S1 A1-2). Both deciduous and evergreen leaf NRE of ECM plants decrease with increasing mean annual precipitation (Figure S1 B1-2). The leaf NRE of tropical, temperate, deciduous, and evergreen AM and ECM plants decreases with increasing litter nitrogen concentration, but only the trends of deciduous and tropical AM and ECM plants show significant differences (Figure S1 D1-4). The deciduous leaf NRE of ECM plants decreases with increasing litter decomposition rate (Figure S1 E1), while the deciduous and tropical leaf NRE of AM plants increases with increasing litter decomposition time (Figure S1 F1, 3). The leaf N min of AM plants is positively correlated with mean annual temperature, mean annual precipitation, leaf nitrogen concentration, and litter decomposition rate (Figs. 6 A, B, C, D, E), but decreases with increasing litter mean residence time (Fig. 6 F). However, the leaf N min of ECM plants shows different characteristics: it increases with increasing mean annual temperature, mean annual precipitation, and litter decomposition rate (Figs. 6 A, B, D, E), but is unrelated to leaf nitrogen concentration (Fig. 6 C). Additionally, AM and ECM plants exhibit different trends. For evergreen and deciduous plants, except for the evergreen AM plants whose leaf N min has no relationship with mean annual precipitation (Figure S2 B2), the leaf N min of both AM and ECM plants increases with increasing mean annual temperature, mean annual precipitation, leaf nitrogen concentration, and litter decomposition rate, and decreases with increasing litter mean residence time (Figures S2 A1-F1, A2-F2). Similarly, in tropical and temperate AM and ECM plants, leaf N min increases with increasing leaf nitrogen concentration and litter decomposition rate, with no statistically significant trend differences between them (Figures S2 C3-4, D3-4). Leaf N min significantly increases with increasing litter decomposition rate, but tropical AM and ECM plants show a logarithmic increase with significant differences in trends, while temperate AM and ECM plants show a linear increase with no differences (Figures S2 E3-4). Leaf N min significantly decreases with increasing litter mean residence time, but temperate ECM plants show no significant relationship between leaf N min and litter mean residence time (Figures S2 F3-4). The contribution of climate and foliar factors to leaf NRE and N min between AM and ECM groups among different functional plants Climate and foliar traits significantly affect the leaf NRE and N min of AM and ECM plants (Table 1 ). The R² for leaf NRE of AM plant is 94.80%, mainly influenced by litter nitrogen concentration (55.45%) and leaf nitrogen concentration (44.55%). The R² for the AM leaf N min is 91.20%, with key factors including litter nitrogen concentration (28.66%), litter decomposition rate (35.94%), and litter mean residence time (35.40%). For ECM plants, the R² for leaf NRE is 92.70%, with the main factors being litter nitrogen concentration (59.01%) and leaf nitrogen concentration (40.99%). The R² for leaf N min of the ECM plant is 90.03%, affected by litter decomposition rate (52.40%), litter nitrogen concentration (31.80%), and litter mean residence time (15.80%). Table 1 The results of the multiple regression analysis that utilized climate variables (MAT and MAP) and indicators of plant nutritional traits (Foliar N, Litter N, MRT, and K) to predict the leaf NRE and N min and their ratios in AM and ECM plants. Contribution of predictor(%) Nitrogen acquisition pathways Mycorrhizae MAT MAP Foliar N Litter N MRT K Significance R 2 NRE(%) AM - - 44.55 55.45 - - *** 0.948 ECM - - 40.99 59.01 - - *** 0.927 NRE(%)(Deciduous Plants) AM - - - - 100 - ** 0.632 ECM - - 31.49 68.15 - - *** 0.985 NRE(%)(Evergreen Plants) AM - - 45.28 54.72 - - *** 0.946 ECM 22.18 15.75 18.31 32.88 - 10.89 *** 0.882 NRE(%)(Tropical Plants) AM - - 46.33 53.67 - - *** 0.941 ECM - - 36.03 63.97 - - *** 0.972 NRE(%)(Temprate Plants) AM - 100 - - - - ** 0.753 ECM - - 37.49 62.51 - - *** 0.896 N min AM - - - 28.66 35.40 35.94 *** 0.912 ECM - - - 31.80 15.80 52.40 *** 0.903 N min (Deciduous Plants) AM 17.10 - - 36.15 46.75 *** 0.971 ECM - - - 36.22 14.59 49.19 *** 0.901 N min (Evergreen Plants) AM - - - 28.54 40.56 30.91 *** 0.915 ECM - - - 34.25 19.21 46.54 *** 0.926 N min (Tropical Plants) AM - - - 29.50 37.94 33.01 *** 0.902 ECM - - - 51.30 - 48.70 *** 0.859 N min (Temprate Plants) AM - - - 31.14 26.26 42.95 *** 0.999 ECM - - - 39.68 26.40 33.93 *** 0.805 AM: arbuscular mycorrhizae, ECM: ectomycorrhiza, MAP: mean annual precipitation, MAT: mean annual temperatur, Foliar N: nitrogen concentration of green leaf, Litter N: nitrogen concentration of litter, MRT: litter mean residence time, K: litter decomposition rate. Significance levels: ns, non-significance, * p < 0.05, ** p < 0.01, *** p < 0.001. AM deciduous plants, the R² for leaf NRE is 63.20%, primarily determined by litter mean residence time. The R² for leaf N min is 97.10%, influenced by mean annual precipitation, litter decomposition rate, and time, with the litter decomposition rate being the most significant (46.75%). For deciduous ECM plants, the R² for leaf NRE is 98.50%, affected by leaf nitrogen concentration and litter nitrogen concentration, with litter nitrogen concentration being the most pronounced (68.15%). The R² for leaf N min is 90.10%, influenced by litter nitrogen concentration, litter decomposition rate, and time, with litter decomposition rate being the most important factor (49.19%). For evergreen AM plants, the R² for leaf NRE is 94.60%, influenced by leaf nitrogen concentration and litter nitrogen concentration, with litter nitrogen concentration being the most significant (54.72%). The R² for leaf N min is 91.50%, influenced by litter nitrogen concentration, litter decomposition rate, and time, with litter mean residence time being the main factor (40.56%). For evergreen ECM plants, the R² for leaf NRE is 88.20%, affected by mean annual temperature, mean annual precipitation, leaf nitrogen concentration, litter nitrogen concentration, and litter decomposition rate, with litter nitrogen concentration having the most significant impact (32.88%). The R² for leaf N min is 92.60%, with the litter decomposition rate contributing the most to the observed changes (46.54%). For tropical AM plants, the R² for leaf NRE is 94.10%, with leaf nitrogen concentration and litter nitrogen concentration jointly influencing leaf NRE and litter nitrogen concentration being the most important factor (53.67%). The R² for leaf N min is 90.20%, influenced by litter nitrogen concentration, litter mean residence time, and rate, with litter mean residence time being the dominant factor (37.94%). In tropical ectomycorrhizal plants, leaf NRE was strongly predicted by leaf nitrogen concentration and litter nitrogen concentration (R²=97.20%), with litter nitrogen concentration emerging as the predominant contributing factor, accounting for 63.97% of the explained variance in the regression model. The R² for leaf N min is 85.90%, mainly affected by litter nitrogen concentration and litter decomposition rate, with litter nitrogen concentration accounting for 51.30% of the variation. For temperate AM plants, the R² for leaf NRE is 75.3%, with mean annual precipitation being the main factor. The R² for leaf N min is 99.90%, influenced by litter nitrogen concentration, litter mean residence time, and rate, with litter decomposition rate being the most important factor (42.95%). In temperate ectomycorrhizal plants, regression analysis identified leaf nitrogen concentration and litter nitrogen concentration as key predictors of leaf NRE (R 2 = 89.60%). Litter nitrogen concentration exhibited a more substantial influence on NRE variation, explaining 62.51% of the total variance accounted for by the model. The R² for leaf N min is 80.50%, with litter nitrogen concentration, litter decomposition rate, and time identified as key factors. Among these, litter nitrogen concentration is the most significant, accounting for 39.68% of the variation, emphasizing its crucial role in the nitrogen mineralization process in these ecosystems. Discussion Chuyong et al. ( 2000 ) demonstrated that, compared to AM, ECM has a stronger ability for nitric recovery. ECM plants can directly decompose organic matter within leaves through extracellular enzymes, suggesting that ECM trees' superior leaf nitrogen resorption efficiency may make them more competitive than AM trees in high-latitude regions (LeBauer & Treseder, 2008 ). In contrast, AM plants rely more on free-living microorganisms for nutrient mineralization (Lin et al., 2017 ; Phillips et al., 2013 ), with AM trees exhibiting higher leaf nitrogen mineralization rates in low-latitude regions. ECM tree leaf litter decomposes more slowly than AM tree leaf litter, which may result in slower nitrogen cycling in ECM-dominated ecosystems (Averill et al., 2014 ).In our investigation, we assessed the differences and relationships between nitrogen reuptake and litter nitrogen mineralization in plants under different mycorrhizal types and how these processes are affected by climate and plant type. Liu et al. ( 2024 ) discovered that the leaf N min of AM trees is significantly higher than ECM trees (Liu et al., 2024 ). Peay's research (2016) also revealed that ECM is more efficient at resoprtion nitrogen than AM (Peay, 2016 ). This is consistent with our findings, showing that the leaf NRE of ECM plants is significantly higher than that of AM plants, N min in leaf litter of AM plants was significantly higher than that of ECM plants. (Fig. 1 A). In subtropical and tropical evergreen forests, Chuyong et al. found that ECM trees have lower nitrogen resorption rates compared to AM trees, while in non-tropical deciduous forests, the nitrogen resorption rate of ECM plants is nearly twice that of AM plants(Chuyong et al., 2000 ). This is partially consistent with our results, indicating that the nitrogen resorption rate of deciduous AM plants is much lower than that of ECM plants. However, the lack of significant difference in nitrogen resorption rates between evergreen (primarily found in tropical and subtropical forests) AM and ECM plants contradicts previous research findings. It requires further experimental investigation (Fig. 1 B). Trees associated with AM mycorrhizae exhibit a faster rate of litter decomposition, while the nitrogen cycle and organic matter accumulation in ECM-dominated forest soils are more conservative (Seyfried et al., 2021 ). In fact, in nitrogen-limited temperate ecosystems, AM plant litter has a lower carbon-nitrogen ratio and lignin-nitrogen ratio, decomposing more rapidly than ECM plant litter (Craig et al., 2018 ; Midgley et al., 2015 ; Sun et al., 2018 ). Similarly, in tropical ecosystems, although ECM plant litter may be biochemically similar to AM plant litter, the decomposition rate of litter in ECM-dominated forests is slower than that in AM-dominated forests (McGuire et al., 2010 ; Torti et al., 2001 ), which aligns with our experimental results (Fig. 1 C). Bothwell et al. ( 2014 ) observed in subtropical and tropical forests that, regardless of litter quality, climate has a certain influence on plant nutrient resorption (Bothwell et al., 2014 ). Our findings are inconsistent with this observation, as there were no significant differences in leaf NRE and Nmin between AM and ECM plants in temperate and tropical regions (Figure. 1D, E), the specific reasons need to be further discussed. Previous studies have indicated a trade-off between leaf NRE and N min on a global scale (Deng et al., 2018 ).Experimental evidence also suggests that plant–mycorrhiza interactions may play a key role in determining the reacquisition of retained nitrogen in plant–soil systems(Suding et al., 2008 ). Especially in the short term (hours to days), when microbes are more competitive for nitrogen than plant roots, absorbing most of the available soil nitrogen, which can later be remobilized for plant uptake (Bardgett et al., 2003 ). This indicates that nitrogen resorption and leaf N min are negatively correlated under nitrogen-limited conditions, which aligns with our study results, whether AM or ECM plants, leaf NRE and leaf N min are negatively correlated (Fig. 2 A, B, E). However, subtropical evergreen AM plants do not show a significant negative correlation between leaf NRE and N min , and given the scarcity of relevant studies, this relationship warrants further investigation(Fig. 2 C, D). Leaf nutrient concentration can influence nutrient resorption (Tong R et al., 2020 ; Li et al., 2024 ). Research has found that, the concentration of nitrogen in mature leaves are positively correlated with leaf NRE, indicating that species with higher leaf nutrient concentration tend to resorb more nitrogen during leaf senescence (Wang et al., 2022 ). Inherent foliar traits significantly impact leaf NRE. In high-latitude ecosystems, ECM trees usually grow in soils rich in organic nitrogen (Corrales et al., 2016 ; Waring et al., 2016 ), and they exhibit higher nitrogen resorption rates than AM trees in these regions. Findings in the temperate deciduous forests of Indiana suggest that nitrogen resorption increases with the dominance of ECM trees at the plot level (Lin et al., 2017 ; Midgley et al., 2015 ; Phillips et al., 2013 ). Latitude and climatic conditions (mean annual precipitation or mean annual temperature) explain part of the global variation in nutrient resorption. In general, leaf NRE is mainly driven by mycorrhizal type and foliar habit, with climatic factors also playing a role(Brant & Chen, 2015 ). This is consistent with our experimental research results (Fig. 3 A, B, C, D, E).The intensity of mycorrhizal fungal colonization of plant roots is influenced by various environmental factors, including temperature and precipitation (Treseder, 2004 ). At the global scale, arbuscular mycorrhizal (AM) colonization intensity is closely related to temperature regimes. AM colonization intensity peaks at locations with warm-season high temperature but declines at colder sites. Meanwhile, both laboratory (Gavito & Azcon-Aguilar, 2012) and field (Rillig et al., 2002 ) studies have shown that low temperature can reduce the growth and function of AM fungi. Similarly, as precipitation seasonality increases, the intensity of ectomycorrhizal (ECM) fungal colonization decreases, that is, drought can suppress ECM fungal colonization intensity (Compant et al., 2010 ). Different environmental variables influence the colonization intensity of arbuscular mycorrhizal and ectomycorrhizal fungi on plant roots, indicating that specific environmental changes will differentially affect different types of mycorrhizae (Zhang et al., 2018 ). At the same time, low-quality litter substrates, characterized by high lignin content and low nutrient concentration, restrict nutrition utilization and inhibit decomposition due to nutrient limitation (Cleveland et al., 2006). Litter mycorrhizal type and litter chemical characteristics are typically interconnected, with AM-associated trees producing litter of higher chemical quality than ECM-associated trees (Cornelissen et al., 2001 ; Craig et al., 2018 ; Keller & Phillips, 2019 ; Midgley et al., 2015 ). These findings suggest that the differences in leaf N min between ECM and AM-dominated plants may primarily be attributed to variations in litter chemistry, with climate having an indirect effect. This is consistent with our study results (Fig. 4 A, B, C, D, E). Global studies have shown a trade-off relationship between leaf NRE and Nmin (Deng et al., 2018 ), originating from the negative correlation between leaf nitrogen use efficiency (NUE) and litter nitrogen concentration. Plants with high NUE efficiently utilize nutrients, leading to decreased litter nitrogen concentration, which in turn affects litter decomposition rates (Baligar et al., 2001 ). Several factors, including annual precipitation, mean annual temperature, and specific leaf traits, significantly influence plant nitrogen resorption and nitrogen mineralization (Du et al., 2020 ; Liu et al., 2024 ). Previous studies have indicated that leaf N min gradually increases with rising mean annual temperature and precipitation, while leaf NRE gradually decreases (Yuan & Chen, 2009 ). Overall, as temperature and precipitation increase, the nitrogen cycling rate accelerates, shifting from conservative resorption pathways to mineralization pathways (Hu et al., 2020 ; Zhang et al., 2022 ). The colonization intensity of plant root mycorrhizal fungi is also influenced by various environmental factors, including temperature and precipitation (Treseder, 2004 ). AM colonization intensity peaks in warm-season high-temperature regions but decreases in colder areas. Both laboratory (Gavito & Azcon-Aguilar, 2012) and field (Rillig et al., 2002 ) studies have shown that low temperatures reduce the growth and function of AM fungi. Similarly, with increasing precipitation seasonality, the colonization intensity of ectomycorrhizal (ECM) fungi decreases, indicating that drought inhibits ECM fungal colonization intensity (Compant et al., 2010 ). Leaf traits not only affect leaf NRE (Koele et al., 2012 ), but also influence litter decomposition rates and nutrient release processes by affecting the litter chemical composition determined by plant types (Bortolazzi et al., 2021 ).The key factors influencing leaf NRE are primarily foliar traits and mycorrhizal type, followed by climate. These observations indicate that mycorrhizal and plant types are crucial determinants of leaf NRE, which is consistent with our experimental findings. In our survey, the leaf NRE of AM plants was negatively correlated with litter nitrogen concentration and litter decomposition time (Fig. 5 D, F). In contrast, the leaf NRE of ECM plants was positively correlated with green leaf nitrogen concentration and negatively correlated with litter nitrogen concentration, litter decomposition rate, mean annual temperature and mean annual precipitation (Fig. 5 A, B, C, D, E). For different functional and ecological types of plants, the leaf NRE of evergreen ECM plants was negatively affected by mean annual temperature and mean annual precipitation (Figure S1 A-2, B-2). The leaf NRE of deciduous ECM plants was negatively affected by mean annual temperature (Figure S1 A-1). Our stepwise multiple regression analysis also revealed a similar phenomenon, where litter nitrogen concentration was the main factor affecting the leaf NRE of the remaining AM and ECM plants. In contrast, green leaf nitrogen concentration was the secondary influencing factor. The impact of litter nitrogen concentration on the leaf NRE of ECM plants, deciduous ECM plants, tropical ECM plants, and temperate ECM plants exceeded that of the corresponding AM plants (Table 1 ). Although the litter decomposition rate is significantly influenced by temperature and humidity (Liu et al., 2017 ; Paul et al., 2003 ) and also by the presence of microorganisms in the environment, the mineralization of nitrogen in litter is primarily controlled by the initial chemical composition of the residue (Manzoni et al., 2008 ). Similarly, mean annual temperature affects the leaf N min of deciduous AM plants, but to a lesser extent than the influence of litter decomposition time and rate. This implies that leaf N min is almost entirely related to the plant leaves, but different AM and ECM plant types exhibit varying sensitivities to different foliar trait factors.These observations suggest that climate has a relatively minor impact on the leaf N min of various AM and ECM plants (Manzoni et al., 2008 ). This is similar to our experimental results. The leaf N min of AM plants was positively correlated with green leaf nitrogen concentration, litter nitrogen concentration, litter decomposition rate, mean annual temperature and mean annual precipitation while negatively correlated with litter decomposition time. In ectomycorrhizal (ECM) plants, a positive correlation was observed between leaf N min and litter nitrogen concentration, litter decomposition rate, mean annual temperature, and mean annual precipitation, while a negative correlation was found with litter decomposition time (Fig. 6 A, B, C, D, E F). For different functional and ecological types of plants, the leaf N min was influenced by foliar traits (mainly litter nitrogen concentration, litter decomposition time, and litter decomposition rate (Figure S2 ). Except for evergreen AM plants, which were not affected by MAP, both deciduous and evergreen AM and ECM plants were affected by mean annual temperature and mean annual precipitation (Figure S2 A-1, A-2, B-1, B-2) and showed a positive correlation. This also partly explains the trade-off relationship between leaf NRE and N min , as leaf NRE decreases and leaf N min increases from high to low latitudes. Our stepwise multiple regression analysis also revealed a similar phenomenon, where the leaf N min of different AM and ECM plant types was mainly related to litter nitrogen concentration, litter decomposition time, and litter decomposition rate, but these factors had different effects on different types of AM and ECM plants (Table 1 ). AM fungi can enhance the survival ability of host plants, but more importantly, they primarily improve plant fitness through enhancing nutritional status. Although AM fungi have long been considered primarily responsible for improving plant Phosphorus nutrition, numerous studies have established that AM fungi can also transfer nitrogen to host plants from both inorganic and organic nitrogen sources. Research has shown that the amount of nitrogen delivered to host plants through hyphal networks is considerable, accounting for 20% to 74% of the total nitrogen uptake by mycorrhizal plants. AM fungi play a significant role in soil nitrogen cycling (Corrêa et al., 2015 ; Landeweert et al., 2001 ). Ectomycorrhizal (ECM) fungi improve plant nitrogen nutritional status through the dissolution, absorption, and transport of nutrients to plants. Studies have demonstrated that ECM fungi mobilize other essential plant nutrients directly from insoluble mineral sources through the excretion of organic acids. This enables ECM plants to acquire essential nutrients from insoluble mineral sources and influences nutrient cycling in plants-soil systems. The capacity of ECM fungal species to actively mobilize inorganic nutrients is particularly important for maintaining plant productivity (Corrêa et al., 2015 ; Landeweert et al., 2001 ). In conclusion, the type of mycorrhizal affects the nitrogen cycle of plants. Conclusion Globally, both foliar traits and climate influence leaf NRE and N min , but the impact of foliar traits is significantly stronger than climate factors. Litter nitrogen concentration and green leaf nitrogen concentration are the primary factors affecting leaf NRE. Similarly, leaf N min is almost entirely associated with litter nitrogen concentration, litter decomposition time, and litter decomposition rate.As climate conditions (mean annual temperature and mean annual precipitation) change, a distinct trade-off emerges between leaf NRE and N min . From high to low latitudes, leaf NRE gradually decreases while leaf Nmin increases. However, leaf NRE and Nmin are also influenced by mycorrhizal types. Significant differences exist in leaf NRE and Nmin between AM and ECM plants. Due to the different mycorrhizal associations, AM and ECM plants are affected differently by climate factors and foliar traits. As climate conditions (mean annual temperature and mean annual precipitation) change, a distinct trade-off emerges between leaf NRE and Nmin.of deciduous plants and temperate plants. Declarations Data and Software Availability Statement The global leaf nitrogen resorption efficiency data is available in Shi (2026) https://doi.org/10.6084/m9.figshare.30999964 . The expanded database containing mycorrhizal and vegetal classifications generated in this study is available in the Supplementary Material (Table S1 and Supplementary Data S1) of this article. The IBM SPSS Statistics 26.0 software is available from https://www.ibm.com/products/spss-statistics . The RStudio software is available from https://posit.co/ . The GraphPad Prism software is available from https://www.graphpad.com/ . The Microsoft Excel software is available from https://www.microsoft.com/excel . Conflict of Interest The authors declare no conflicts of interest relevant to this study. Funding This work was supported by the Science and Technology Innovation Leading Talent Program of Henan Province (254200510006), the Key Research and Development Program of Hainan Province (ZDYF2024XDNY172), Leading Talents in Scientific and Technological Innovation of Luoyang (LYSKJLJRC02), and Programs of Higher Education Institutions in Henan Province (26A210004). References Aerts R (1996) Nutrient resorption from senescing leaves of perennials: are there general patterns? J Ecol 597–608. 10.2307/2261481 Aerts R, Verhoeven JTA, Whigham DF (1999) Plant-mediated controls on nutrient cycling in temperate fens and bogs. Ecology 80(7):2170–2181 Averill C, Turner BL, Finzi AC (2014) Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505(7484):543–545 Baligar V, Fageria N, He Z (2001) Nutrient use efficiency in plants. 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European Journal of Soil Science Zhang K, Li M, Yan Z, Li M, Kang E, Yan L, Zhang X, Li Y, Wang J, Yang A (2022) Changes in precipitation regime lead to acceleration of the N cycle and dramatic N2O emission. Sci Total Environ 808:152140 Supplementary Files S1data.xlsx S1.docx S2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8600732","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588628270,"identity":"72588e82-d42e-482d-9089-7f8592d67cca","order_by":0,"name":"Eryuan Zhao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Eryuan","middleName":"","lastName":"Zhao","suffix":""},{"id":588628271,"identity":"5fdec150-6c24-46c9-9d73-6a4815a895f4","order_by":1,"name":"Chunhua Ji","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chunhua","middleName":"","lastName":"Ji","suffix":""},{"id":588628272,"identity":"2db87214-f5ba-4e4c-9e3a-014bd23b738e","order_by":2,"name":"ZY SHI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDACCcYGBgYDCR5+9sbGBx+I11JgISPZc7jZcAZxWkDEhwobgxnpbdIcxOiQn93c/JkH6DADyYcN0gwMdnK6DQS0GNw52GAM0mIundhgXMCQbGx2gJAWicSGZJAWy9lAxgyGA4nbCGmRn5HYcBjssJsHgQxitDDcSGxsBmu5wQhkEKPF4EZiM+McoBbJHiBjhgERfpGfkf74w5s/dfb87Mef//hQYSdHUAsIMPEgLCVCOQgw/iBS4SgYBaNgFIxQAADSM0GHeOXVIgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5754-1698","institution":"Henan Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"ZY","middleName":"","lastName":"SHI","suffix":""},{"id":588628273,"identity":"34a57d25-f864-467c-9bcf-91f931dbc289","order_by":3,"name":"Shuang Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Yang","suffix":""},{"id":588628274,"identity":"7355391c-348b-49d1-9430-62a122f1ec42","order_by":4,"name":"Manman Jing","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Manman","middleName":"","lastName":"Jing","suffix":""},{"id":588628275,"identity":"ebc2427a-14ee-4b7d-8e3f-c1abeba4f12e","order_by":5,"name":"Yan Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""},{"id":588628276,"identity":"ef192abf-90f2-49c0-b3c2-a4695dfe0232","order_by":6,"name":"Zhen Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Wang","suffix":""},{"id":588628277,"identity":"2876547d-bfb3-4298-a0f0-f36ee30e216e","order_by":7,"name":"Jiakai Gao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jiakai","middleName":"","lastName":"Gao","suffix":""},{"id":588628278,"identity":"87389039-3743-4803-91d8-9c219082ec31","order_by":8,"name":"Shanwei Wu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shanwei","middleName":"","lastName":"Wu","suffix":""},{"id":588628279,"identity":"379769ad-0696-4eed-a311-98fb3c6acbd8","order_by":9,"name":"Xin Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-01-14 10:37:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8600732/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8600732/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102546379,"identity":"9bdaa57d-cb31-4600-a900-c1dc2eb2c83f","added_by":"auto","created_at":"2026-02-12 21:02:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":341126,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e (A) associated with AM and ECM. Differences in leaf NRE of deciduous plants and evergreen plants (B) associated with AM and ECM. Differences in leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of deciduous plants and evergreen plants leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e (C) associated with AM and ECM. Differences in leaf NRE of tropical plants and temperate plants (D) associated with AM and ECM. Differences in leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e (E) of tropical plants, temperate plants associated with AM and ECM.\u003c/p\u003e\n\u003cp\u003eThe red line denotes the median and the two white lines denote the 95% confidence interval. The number of samples is blue figure at the bottom, and the average is black figure at the bottom. AM: arbuscular mycorrhizae, ECM: ectomycorrhiza. “*”, “**”, “***”, respectively, indicate a significant difference when \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, while “ns” indicates no significance. Leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e represents the fraction of litter nitrogen that is mineralized during the first year of decomposition. Positive values of leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e indicate net mineralization, while negative values indicate net immobilization of nitrogen after one year.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8600732/v1/1c9f65852a11dd80fd9b66d4.jpg"},{"id":102546381,"identity":"fc78e2fc-e68e-4170-90e6-b3d838fe728f","added_by":"auto","created_at":"2026-02-12 21:02:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":454617,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationships between leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e under different conditions, total (A), deciduous plants (B) and evergreen plants (C), tropical plants (D) and temperate plants (E).\u003c/p\u003e\n\u003cp\u003eThe blue lines represent AM plants and the red lines represent ECM plants. AM: arbuscular mycorrhizae, ECM: ectomycorrhizal. The \u003cem\u003ep-\u003c/em\u003evalues are indicated in the each chart.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8600732/v1/8e437eabd99af1a6598fee47.jpg"},{"id":102746546,"identity":"56e9ed95-aaa5-43e9-9b02-368fd3297184","added_by":"auto","created_at":"2026-02-16 08:58:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":268927,"visible":true,"origin":"","legend":"\u003cp\u003eResults of variation partitioning analysis for the effects (R\u003csup\u003e2\u003c/sup\u003e, %) of climate and foliar traits on leaf NRE of total plants (A), leaf NRE of deciduous plants (B), leaf NRE of evergreen plants( C), leaf NRE of tropical plants (D) and leaf NRE of temperate plants (E).\u003c/p\u003e\n\u003cp\u003ewhere red font represents AM plants and black font represents ECM plants . The red, and blue circles represent the independent effects of climate, and foliar traits, respectively, and their interactions.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8600732/v1/80f0675abaffbc69cf19f707.jpg"},{"id":102546380,"identity":"1123a7bc-659a-45aa-b1a8-d67b0b53684f","added_by":"auto","created_at":"2026-02-12 21:02:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":258291,"visible":true,"origin":"","legend":"\u003cp\u003eResults of variation partitioning analysis for the effects (R\u003csup\u003e2\u003c/sup\u003e, %) of climate and foliar traits on leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of total plants (A), leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of deciduous plants( B), leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of evergreen plants (C), leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of tropical plants (D) and leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of temperate plants (E).\u003c/p\u003e\n\u003cp\u003ewhere red font represents AM plants and black font represents ECM plants . The red, and blue circles represent the independent effects of climate, and foliar traits, respectively, and their interactions.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8600732/v1/fd1e2903d935a04aa3e0397c.jpg"},{"id":102747240,"identity":"4097a564-d1dc-4ba3-82c0-efd4726fa179","added_by":"auto","created_at":"2026-02-16 09:04:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":451705,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in leaf NRE of AM and ECM plants along MAT (A), MAP (B), Green leaf nitrogen concentration (C), Litter nitrogen concentration(D), K(E) and MRT(F) gradients.\u003c/p\u003e\n\u003cp\u003eThe blue lines represent AM plants and the red lines represent ECM plants. AM: arbuscular mycorrhizae, ECM: ectomycorrhiza, MAP: Deng, Foliar N: nitrogen concentration of green leaf, Litter N: nitrogen concentration of litter, MRT: litter mean residence time, K: litter decomposition rate. The \u003cem\u003ep\u003c/em\u003e-values are indicated in the each chart.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8600732/v1/4f3471ea1b44b4d19380a60d.jpg"},{"id":102546384,"identity":"3e2f50f0-ab08-42f7-8ce7-0a7cbe119eb6","added_by":"auto","created_at":"2026-02-12 21:02:55","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":408687,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of AM and ECM plants along MAT (G), MAP (H), Green leaf nitrogen concentration (I), litter nitrogen concentration (J), K and MRT(L) gradients.\u003c/p\u003e\n\u003cp\u003eThe blue lines represent AM plants and the red lines represent ECM plants. AM: arbuscular mycorrhizae, ECM: ectomycorrhiza, MAP: Deng, Foliar N: nitrogen concentration of green leaf, Litter N: nitrogen concentration of litter, MRT: litter mean residence time, K: litter decomposition rate. The \u003cem\u003ep\u003c/em\u003e-values are indicated in the each chart.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8600732/v1/72e4bf4d8ea905187aae8da9.jpg"},{"id":109397372,"identity":"ad005d68-b68e-4e25-a610-fc79b88ff9e2","added_by":"auto","created_at":"2026-05-17 08:51:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2642583,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8600732/v1/70dc51f4-0794-4e5b-a74c-3248f8682503.pdf"},{"id":102546385,"identity":"0d2a3b04-62e7-431a-9008-2cb4ffdad55a","added_by":"auto","created_at":"2026-02-12 21:02:55","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":33679,"visible":true,"origin":"","legend":"","description":"","filename":"S1data.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8600732/v1/f6c940b69b016a660637c11b.xlsx"},{"id":102546387,"identity":"8712db2d-6aa3-4a55-b495-b590b328ca79","added_by":"auto","created_at":"2026-02-12 21:02:55","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2117463,"visible":true,"origin":"","legend":"","description":"","filename":"S1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8600732/v1/15018f6e61e44699b1976c2d.docx"},{"id":102546386,"identity":"d880cc39-8882-4cc0-924f-df046914de43","added_by":"auto","created_at":"2026-02-12 21:02:55","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":2219014,"visible":true,"origin":"","legend":"","description":"","filename":"S2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8600732/v1/9ef053ab18117100acc9dc08.docx"}],"financialInterests":"","formattedTitle":"Mycorrhizal types modulate the trade-off between leafnitrogen resorption and mineralization","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLeaf nitrogen resorption efficiency (NRE) measures how effectively plants redistribute nitrogen from aging leaves to metabolic sinks (Aerts, 1999; Kaltenegger and Winiwarter \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Conant et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This process is vital in ecology, allowing plants to reallocate nutrients from senescent leaves to storage organs or developing tissues before the leaves are shed. By doing so, plants can extend the retention time of nutrients and improve their nutrient use efficiency, significantly enhancing their adaptability to nitrogen-deficient environmental conditions (Freschet et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Several factors, including mean annual precipitation, mean annual temperature, and specific foliar traits, significantly influence patterns of nitrogen resorption in plants (Du et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Several empirical studies have shown that N resorption in N-fixing species is lower than in non-N-fixing species (Nongbri \u0026amp; Barik, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Environmental factors have a specific influence on leaf NRE, but they have less control than it. Furthermore, leaf nitrogen mineralization (\u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e), which occurs through microbial decomposition of litter, releases bioavailable nitrogen that meets plants\u0026rsquo; nitrogen requirements (Cleveland et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Holloway and Dahlgren \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Under nitrogen-limited conditions, leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e can enhance soil nitrogen availability, promoting plant growth and increasing ecosystem productivity. Research shows that leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e gradually increases with higher mean annual temperature and precipitation (Yuan \u0026amp; Chen, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, foliar traits and soil microbial activity may significantly impact leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e more than climatic factors. Foliar traits, especially leaf nitrogen content, are key factors that affect plant leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e (Manzoni et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). As the key decomposers of soil organic matter, soil microbes accelerate leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e, thereby increasing the available nitrogen in the environment (Sinsabaugh \u0026amp; Shah, 2012; Tian et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePlant leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e, the rates of these two processes largely determine the nitrogen use efficiency of ecosystems, while there exists a trade-off relationship between them (Cleveland et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This is because nutrient use efficiency (NUE) at the leaf level is negatively correlated with nutrient concentration in litter. High NUE signifies that plants can more effectively absorb and utilize nutrients from the soil and within their leaves. However, it also implies that when plants efficiently recycle nutrients from their leaves, nutrient concentration in the litter decrease. Since litter decomposition and nutrient release are typically positively correlated with nutrient concentration in the litter, this affects the litter decomposition rate(Deng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). High NRE may reduce the rate of \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e by decreasing the nitrogen concentration in litter (Fridley, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Parton et al., 2007), whereas slow \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e may enhance NRE by limiting the availability of nitrogen in the soil (Aerts et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Cleveland et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As temperature and precipitation rise, the rate of nitrogen cycling accelerates, shifting from a conservative resorption pathway to a mineralization pathway (Hu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The type of plant influences the chemical composition of litter, which in turn affects the litter decomposition rate and nutrient release (Bortolazzi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For instance, nitrogen-rich litter decomposes more quickly (Desie et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Some studies have shown that the impacts of different mycorrhizal types on soil nitrogen status vary. Compared with ECM plants, AM plants generally exhibit higher rates of soil nitrogen cycling (Pellitier et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious synthesis studies have explored leaf traits and climate as the main drivers of global nitrogen cycle changes (Brant \u0026amp; Chen, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Reed et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Vergutz et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yuan \u0026amp; Chen, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Despite previous studies revealing global patterns of nitrogen resorption, few studies have linked nitrogen resorption dynamics to plant nutrient cycling strategies, such as mycorrhizal symbiosis. In terrestrial ecosystems, mycorrhizal symbiosis represents a mutualistic phenomenon arising between plants and soil fungi (Parton et al., 2007), which is the most extensive symbiotic relationship between plant roots and soil mycorrhizal fungi. They help plants absorb other essential mineral nutrients, thereby accelerating the cycle of inorganic nutrients and playing an important role in maintaining the nitrogen cycle of the plant-soil system (Fall et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this symbiotic relationship, AM and ECM are the most prevalent and well-studied types (Cavagnaro et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tedersoo et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AM fungi hyphae grow in the apoplastic space between plant cells and they penetrate cells where they form arbuscules. They may also form vesicles. These take the form of intracellular hyphal coils, lumps and intercellular thick-walled and thin-walled fungal structures. ECM fungi creates sheath of fungal hyphae enveloping the root where the hyphae form a network between cortical cells (Strullu-Derrien et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). AM fungi possess elongated hyphae and highly branched hyphal networks that enhance nutrient absorption capacity (Joanne et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). ECM fungi form a mantle around host plant root tips and extend hyphae into the surrounding soil, with extended hyphal networks exploring larger soil volumes (Entry et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). For instance, most temperate forests are nitrogen-limited, and to counter this, ECM trees enhance soil nitrogen acquisition by increasing rhizosphere nitrogen transformation and root length density. The superior nitrogen acquisition ability of ECM trees may give them a competitive advantage over AM trees under nitrogen-limited conditions (LeBauer \u0026amp; Treseder, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) also found a similar phenomenon in a deciduous forest in Indiana, where an increase in ECM trees at the plot level was associated with increased nitrogen resorption rates (Lin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). AM fungi may access nutrients locked in soil organic matter through direct enzymatic decomposition and other methods, promoting decomposition and helping plants acquire nitrogen from soil organic substrates, while ECM mycorrhizal hyphae release nitrogen from soil organic matter through secretion of organic enzymes(Joanne et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). ECM plants can directly decompose organic matter through extracellular enzymes, leading to more conservative nitrogen cycling in ECM-dominated soils. In contrast, \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e rates are faster in AM-dominated soils, resulting in more rapid nitrogen cycling in AM-dominated ecosystems (Lin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Overall, ECM plants dominate in high-latitude ecosystems (with slow nitrogen cycling) and adopt more conservative nitrogen resorption strategies, thus exhibiting higher nitrogen resorption efficiency. AM plants, on the other hand, dominate in low-latitude ecosystems (with rapid nitrogen cycling) (Smith \u0026amp; Smith, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and the faster litter decomposition rate of AM plants compared to ECM tree species may be the reason for the faster nitrogen mineralization rate in AM-dominated ecosystems (Averill et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cornelissen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In general, the distribution of mycorrhizal types is related to nitrogen availability, with AM plants preferring nitrogen-rich environments and ECM plants thriving in nitrogen-poor areas (Cornelissen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Read \u0026amp; Perez-Moreno, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, there is a lack of global empirical research on the differences between ECM and AM plants in N resorption and mineralization.\u003c/p\u003e \u003cp\u003ePrevious research has primarily focused on the trade-off between leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e, and the factors influencing nitrogen cycling rates. Such research has clarified how global interactions between leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e influence nitrogen cycling across various biomes (Deng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The mechanism by which mycorrhizal types affect the differences and trade-offs between plant leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e remains unclear. We carried out a global-scale meta analysis based on prior studies. We integrated a global dataset of green and senescent leaf nutrient concentration with plant-mycorrhizal association information for species with different leaf habits (deciduous and evergreen) and from different climatic zones (temperate and tropical). We compiled a comprehensive global dataset from published literature, encompassing environmental factors (mean annual temperature and mean annual precipitation), foliar traits (litter decomposition rate, litter mean residence time, green leaf nitrogen concentration, and litter nitrogen concentration), NRE, and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e (Deng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Previous studies have demonstrated that leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e are influenced by environmental conditions and foliar characteristics (Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To further explore the global divergence in leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e between AM and ECM plants, and their responses to environmental variations, we integrated mycorrhizal type information into this existing database. This synthesis enabled us to propose two working hypotheses: (a) Significant differences exist in both leaf NRE and Nmin level between AM and ECM plants. (b) The trade-off of leaf NRE-\u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e exhibits significant divergence between AM and ECM plants.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThis study only included data from field studies in terrestrial ecosystems, excluding data from wetlands, aquatic ecosystems, agricultural ecosystems, greenhouses, and laboratory incubation studies. The basic data used in this study were collated by Deng et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The database covered field-measured data of leaf nitrogen resorption efficiency (NRE), nitrogen mineralization (\u003cem\u003eN\u003c/em\u003e\u003csub\u003emin)\u003c/sub\u003e, litter mean residence time (MRT), litter decomposition rate (K), nitrogen concentration of green leaves(leaf nitrogen concentration) and litter nitrogen concentration of the same plant species, mean annual precipitation (MAP), and mean annual temperature (MAT) from specific sites. We further expanded based on the data collected by Deng et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The new database (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) additionally covers relevant information on the plant types studied, their mycorrhizal types.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMycorrhizal and Vegetal Classification\u003c/h3\u003e\n\u003cp\u003eWe searched for the mycorrhizal type of each plant species from the published literatures, especially Wang et al. (2006), Soudzilovskaia et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Yang et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), to determine the mycorrhizal types of plant species in the database. Plants with typical arbuscular mycorrhizal (AM) structures were classified as AM type, while those with typical ectomycorrhizal (ECM) structures were classified as ECM type, due to the insufficient data volume of AM\u0026thinsp;+\u0026thinsp;ECM mycorrhizal types, this thesis did not study this mycorrhizal type. Studies were excluded if they met any of the following criteria: (1) species that do not form symbiotic relationships with AM or ECM., (2) biomass data for control and treatment groups were not provided, (3) standard deviations and sample sizes for each group were not reported. Additionally, plots containing nitrogen-fixing species were excluded from the main analysis due to their potential special response to nitrogen addition.\u003c/p\u003e \u003cp\u003eBased on the global database established by Deng et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and the relevant literature provided, we classified mean annual precipitation and mean annual temperature as climate factors, and green leaf nitrogen concentration, litter nitrogen concentration, litter mean residence time, and litter decomposition rate as foliar traits. We also categorized 159 plant species into 69 AM plants and 90 ECM plants. Among the AM plants, 20 were deciduous and 46 were evergreen, while among the ECM plants, 58 were deciduous and 32 were evergreen, based on functional types. Ecologically, the 69 AM plants contained 55 tropical and 12 temperate species, and the 90 ECM plants included 21 tropical and 47 temperate species. Due to limited numbers, plants from boreal forests, tundras, grasslands, deserts, and ferns could not subgrouped (for specific data, Supplementary Data S1).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe employed a T-test to compare leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e between AM and ECM plants. Similarly, leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e differences among functional and ecological types were analyzed using the same method. Variation partitioning analysis (VPA) under different mycorrhizal types was performed using the 'vegan' package in R. VPA decomposes the total variance into the independent and interactive effects of climate and foliar traits. Subsequently, stepwise multiple regression analysis (SMR) was conducted to explore the relationship between environmental factors and foliar traits. We compared the R\u003csup\u003e2\u003c/sup\u003e values of the best multiple regression models to identify the most influential environmental factors and foliar trait. All statistical analyses were conducted using IBM SPSS Statistics 26.0 (IBM Corp, 2019) and R software version 4.1.0 (R Core Team, 2021).\u003c/p\u003e \u003cp\u003eIn this study, the leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of plants, as well as environmental data and foliar traits of each plant, such as mean annual precipitation(MAT), mean annual temperature(MAT), green leaf nitrogen concentration(leaf nitrogen concentration), litter nitrogen concentration, litter mean residence time (MRT), and litter decomposition rate (K), were sourced from the database established by Deng et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The calculation formulas for NRE, \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e, and MRT are following.\u003c/p\u003e \u003cp\u003eNRE values were obtained through direct extraction from published literature or computation based on reported nitrogen concentration in both mature and senescing leaves. NRE, representing the fraction of nitrogen recovered during leaf senescence, was quantified using the following equation:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{N}\\text{R}\\text{E}=\\left(1-\\frac{{N}_{1}}{{N}_{\\text{g}}}\\right)\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAmong them, N\u003csub\u003eg\u003c/sub\u003e and N\u003csub\u003el\u003c/sub\u003e represent the nitrogen content of mature leaves and litter, respectively.\u003c/p\u003e \u003cp\u003eThe MRT values are extracted from the literature or calculated using an exponential decomposition model:\u003c/p\u003e \u003cp\u003e \u003cem\u003eL\u003c/em\u003e \u003csub\u003e \u003cem\u003et\u003c/em\u003e \u003c/sub\u003e=\u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026times;\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u0026minus;\u0026thinsp;\u003cem\u003ekt\u003c/em\u003e\u003c/sup\u003e(2)\u003c/p\u003e \u003cp\u003eMRT\u0026thinsp;=\u0026thinsp;\u003cem\u003ek\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u003c/em\u003e1\u003c/sup\u003e(3)\u003c/p\u003e \u003cp\u003eWhere, \u003cem\u003eL\u003c/em\u003e\u003csub\u003et\u003c/sub\u003e denotes the time-dependent litter mass, \u003cem\u003eL\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e represents the initial mass, \u003cem\u003ek\u003c/em\u003e corresponds to the litter decomposition rate constant derived from first-order exponential decay kinetics, and MRT indicates the litter mean residence time (Kampichler and Bruckner, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile few studies provide concurrent measurements of leaf nitrogen resorption efficiency and litter nitrogen mineralization dynamics, extensive literature documents litter decomposition rates across varied ecosystems. Parton et al. (2007) established through a decadal multisite (n\u0026thinsp;=\u0026thinsp;21) decomposition experiment that \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin,\u003c/sub\u003e during decomposition, exhibits biome-independent predictability, governed dominantly by initial litter N concentration and residual mass. Parton et al. developed an empirical model to estimate \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e during litter decomposition. Using the litter decomposition rate (\u003cem\u003ek\u003c/em\u003e) and litter nitrogen concentration data from the dataset, we calculated \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e using the formula from Parton et al.(2007):\u003c/p\u003e \u003cp\u003e \u003cem\u003eN\u003c/em\u003e \u003csub\u003emin\u003c/sub\u003e=1\u0026minus;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{{L}_{r}}{100}\\sqrt{\\frac{{\\left(\\frac{2\\times\\:a\\times\\:100}{b}\\right)}^{2}+{\\left(1-{\\left(\\frac{100}{b}\\right)}^{2}\\right)}^{2}}{{\\left(\\frac{2\\times\\:a\\times\\:{L}_{r}}{b}\\right)}^{2}+{\\left(1-{\\left(\\frac{{L}_{r}}{b}\\right)}^{2}\\right)}^{2}}}\\)\u003c/span\u003e\u003c/span\u003e(4)\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\:a=0.7$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eb\u0026thinsp;=\u0026thinsp;98\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\times\\:\\left[1-{e}^{\\left(-1.56\\times\\:{N}_{i}\\right)}\\right]\\)\u003c/span\u003e\u003c/span\u003e(6)\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003ea\u003c/em\u003e functions as the regulatory parameter governing the magnitude of the curve's maximum, while \u003cem\u003eb\u003c/em\u003e determines the position of this peak. \u003cem\u003eN\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e represents the initial nitrogen concentration in the litter, and \u003cem\u003eL\u003c/em\u003e\u003csub\u003er\u003c/sub\u003e denotes the residual percentage of original litter mass after one-year decomposition, computed as \u003cem\u003eL\u003c/em\u003e\u003csub\u003er\u003c/sub\u003e\u003cem\u003e= L\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u003cem\u003e\u0026times;e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;\u0026thinsp;k\u003c/em\u003e\u003c/sup\u003e. Positive \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e values signify net nitrogen mineralization, whereas negative values reflect immobilization. The first-order exponential decay model tends to overestimate litter decomposition rate compared to empirical measurements (Wieder et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which could lead to artificially elevated \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e values in simulation outputs.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003eVariation of leaf NRE and N\u003c/em\u003e \u003csub\u003emin\u003c/sub\u003e \u003cem\u003ebetween AM and ECM plants\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAM plants show a leaf NRE of 39.65%, which is significantly lower than the 50.37% seen in ECM plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Concurrently, regarding leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e, AM plants have a value of 0.35, substantially higher than the 0.03 measured for ECM plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). When different functional types of plants are examined, both AM and ECM plants show significant differences in leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e. Specifically, AM plants exhibit a leaf NRE of 42.86%, a figure that is less than the 54.00% found in ECM plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). However, the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of AM plants is 0.27, significantly higher than the \u0026minus;\u0026thinsp;0.01 of ECM plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). In the case of evergreen plants, the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of AM plants is 0.42, significantly higher than the 0.09 of ECM plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), while there is no significant difference in leaf NRE between AM and ECM plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). When analyzing plants from different climate types, neither AM nor ECM plants exhibit significant differences in leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe relationship of leaf NRE and N\u003c/em\u003e \u003csub\u003emin\u003c/sub\u003e \u003cem\u003ebetween AM and ECM species under different plant groups\u003c/em\u003e\u003c/p\u003e \u003cp\u003eAM and ECM leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e changes significantly with the increase in leaf NRE. Overall, AM leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e decreases from 0.68 to 0.06, while ECM leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e decreases from 0.52 to -0.32, with no significant trend difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn deciduous plants, the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of AM decreases significantly from 0.81 to -0.10, while that of ECM also sees a marked drop from 0.48 to -0.30, both showing a significant downward trend as leaf NRE increases. For evergreen plants, a similar pattern is observed, with AM leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e falling from 0.63 to 0.21 and ECM leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e dropping from 0.55 to -0.23. However, there is no significant linear relationship between evergreen AM leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e and NRE, and only the trend of leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e change with leaf NRE is significantly different for deciduous AM and ECM plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, C). When it comes to tropical plants, the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of AM exhibits a reduction from 0.63 to 0.28, while ECM leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e undergoes a drop from 0.82 to -0.02. Regarding temperate plants, the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of AM plummets from 0.10 to -0.15, and ECM's leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e drops from 0.11 to -0.26. Except for the absence of a significant linear relationship between tropical AM leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e and NRE, the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of tropical ECM plants and temperate AM and ECM plants shows a marked drop as leaf NRE rises. There is a significant difference in the response of leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e change to leaf NRE between temperate AM and ECM plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E).\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe impact percentage of climate and foliar traits on leaf NRE and N\u003c/em\u003e \u003csub\u003emin\u003c/sub\u003e \u003cem\u003ebetween AM and ECM plants\u003c/em\u003e\u003c/p\u003e \u003cp\u003eClimate and foliar traits explain 89.47% of leaf NRE variation in AM plants and 85.32% in ECM plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), as well as 82.77% of leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e variation in AM plants and 80.87% in ECM plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Foliar traits significantly affect leaf NRE, with a greater impact on AM plants at 91.35% than on ECM plants at 65.13%, the impact on leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is 68.40% in AM and 44.22% in ECM. Climate factors have a minor influence, accounting for 0.06% of NRE in AM plants, 0.03% of NRE in ECM plants, contributing 0.49% to leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e in AM plants, and 0.14% to leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e in ECM plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In various functional and ecological groups, leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of AM plants are highly sensitive to leaf traits. Deciduous plants have a leaf NRE of 97.07%, and temperate plants have 99.47% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, E). Leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is 89.43% in deciduous plants and 99.73% in temperate plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, E). The independent influence of leaf traits on leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e in AM plants is more significant than climate factors. Deciduous groups exhibited 83.22% autonomous regulation of NRE, while evergreen and tropical plants achieved superior self-regulation capacities at 90.75% and 87.64%, respectively, contrasting sharply with temperate communities' 51.47% autogenic control magnitude. Deciphering the determinants of \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e revealed that leaf traits independently explained 76.48% of the variation in tropical plants, surpassing the 30.85% observed in temperate species. Similarly, leaf traits accounted for 74.58% of the variation in evergreen plants, exceeding the 61.65% in deciduous species. In contrast, climate factors exerted minimal influence on leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-E, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eECM plant leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e are also sensitive to leaf traits. For leaf NRE, deciduous communities attain peak efficiency (97.07%), tropical systems approach near-optimal level (93.74%), temperate formations show intermediate values (79.39%), while evergreen groups exhibit markedly constrained performance (54.89%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-E). For leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e, evergreen species demonstrate peak nitrogen retention (83.70%), followed by deciduous communities (80.61%), with tropical and temperate systems showing attenuated capacities at 70.48% and 62.73%, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E). The independent effect of leaf traits on leaf NRE, temperate plants show the highest leaf NRE (82.19%), deciduous species following with 75.87%, whereas evergreen and tropical plants register lower values at 49.46% and 48.79%, respectively, all of which are significantly higher than the influence of climate factors. The situation for leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is similar (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E).\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe changes of leaf NRE and N\u003c/em\u003e \u003csub\u003emin\u003c/sub\u003e \u003cem\u003ewith variation of climate and foliar traits between AM and ECM plants\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe leaf NRE of AM plants has no significant relationship with mean annual temperature, mean annual precipitation, leaf nitrogen concentration, and litter decomposition rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, C, E), but decreases with increasing litter nitrogen concentration and litter mean residence time (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, F). In contrast, the leaf NRE of ECM plants shows different characteristics: it decreases with increasing mean annual temperature, mean annual precipitation, litter decomposition rate, and litter nitrogen concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, D, E), and increases with increasing leaf nitrogen concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), but is unrelated to litter mean residence time (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). When litter nitrogen concentration increases, the leaf NRE of both AM and ECM plants decreases, but with different trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The evergreen and deciduous leaf NRE of AM plants is unrelated to mean annual temperature, but the deciduous leaf NRE of ECM plants decreases with increasing mean annual temperature (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA1-2). Both deciduous and evergreen leaf NRE of ECM plants decrease with increasing mean annual precipitation (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB1-2). The leaf NRE of tropical, temperate, deciduous, and evergreen AM and ECM plants decreases with increasing litter nitrogen concentration, but only the trends of deciduous and tropical AM and ECM plants show significant differences (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD1-4). The deciduous leaf NRE of ECM plants decreases with increasing litter decomposition rate (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE1), while the deciduous and tropical leaf NRE of AM plants increases with increasing litter decomposition time (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eF1, 3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of AM plants is positively correlated with mean annual temperature, mean annual precipitation, leaf nitrogen concentration, and litter decomposition rate (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B, C, D, E), but decreases with increasing litter mean residence time (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). However, the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of ECM plants shows different characteristics: it increases with increasing mean annual temperature, mean annual precipitation, and litter decomposition rate (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B, D, E), but is unrelated to leaf nitrogen concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Additionally, AM and ECM plants exhibit different trends. For evergreen and deciduous plants, except for the evergreen AM plants whose leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e has no relationship with mean annual precipitation (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB2), the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of both AM and ECM plants increases with increasing mean annual temperature, mean annual precipitation, leaf nitrogen concentration, and litter decomposition rate, and decreases with increasing litter mean residence time (Figures \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA1-F1, A2-F2). Similarly, in tropical and temperate AM and ECM plants, leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e increases with increasing leaf nitrogen concentration and litter decomposition rate, with no statistically significant trend differences between them (Figures \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC3-4, D3-4). Leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e significantly increases with increasing litter decomposition rate, but tropical AM and ECM plants show a logarithmic increase with significant differences in trends, while temperate AM and ECM plants show a linear increase with no differences (Figures \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eE3-4). Leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e significantly decreases with increasing litter mean residence time, but temperate ECM plants show no significant relationship between leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e and litter mean residence time (Figures \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eF3-4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe contribution of climate and foliar factors to leaf NRE and N\u003c/em\u003e \u003csub\u003emin\u003c/sub\u003e \u003cem\u003ebetween AM and ECM groups among different functional plants\u003c/em\u003e\u003c/p\u003e \u003cp\u003eClimate and foliar traits significantly affect the leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of AM and ECM plants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The R\u0026sup2; for leaf NRE of AM plant is 94.80%, mainly influenced by litter nitrogen concentration (55.45%) and leaf nitrogen concentration (44.55%). The R\u0026sup2; for the AM leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is 91.20%, with key factors including litter nitrogen concentration (28.66%), litter decomposition rate (35.94%), and litter mean residence time (35.40%). For ECM plants, the R\u0026sup2; for leaf NRE is 92.70%, with the main factors being litter nitrogen concentration (59.01%) and leaf nitrogen concentration (40.99%). The R\u0026sup2; for leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of the ECM plant is 90.03%, affected by litter decomposition rate (52.40%), litter nitrogen concentration (31.80%), and litter mean residence time (15.80%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of the multiple regression analysis that utilized climate variables (MAT and MAP) and indicators of plant nutritional traits (Foliar N, Litter N, MRT, and K) to predict the leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e and their ratios in AM and ECM plants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eContribution of predictor(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen acquisition pathways\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMycorrhizae\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMAP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFoliar N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLitter N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNRE(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eECM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNRE(%)(Deciduous Plants)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eECM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNRE(%)(Evergreen Plants)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eECM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.882\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNRE(%)(Tropical Plants)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eECM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eNRE(%)(Temprate Plants)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eECM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003csub\u003e\u003cb\u003emin\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eECM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003csub\u003e\u003cb\u003emin\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(Deciduous Plants)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eECM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003csub\u003e\u003cb\u003emin\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(Evergreen Plants)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eECM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003csub\u003e\u003cb\u003emin\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(Tropical Plants)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eECM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003csub\u003e\u003cb\u003emin\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e(Temprate Plants)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eECM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAM: arbuscular mycorrhizae, ECM: ectomycorrhiza, MAP: mean annual precipitation, MAT: mean annual temperatur, Foliar N: nitrogen concentration of green leaf, Litter N: nitrogen concentration of litter, MRT: litter mean residence time, K: litter decomposition rate. Significance levels: ns, non-significance, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAM deciduous plants, the R\u0026sup2; for leaf NRE is 63.20%, primarily determined by litter mean residence time. The R\u0026sup2; for leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is 97.10%, influenced by mean annual precipitation, litter decomposition rate, and time, with the litter decomposition rate being the most significant (46.75%). For deciduous ECM plants, the R\u0026sup2; for leaf NRE is 98.50%, affected by leaf nitrogen concentration and litter nitrogen concentration, with litter nitrogen concentration being the most pronounced (68.15%). The R\u0026sup2; for leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is 90.10%, influenced by litter nitrogen concentration, litter decomposition rate, and time, with litter decomposition rate being the most important factor (49.19%).\u003c/p\u003e \u003cp\u003eFor evergreen AM plants, the R\u0026sup2; for leaf NRE is 94.60%, influenced by leaf nitrogen concentration and litter nitrogen concentration, with litter nitrogen concentration being the most significant (54.72%). The R\u0026sup2; for leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is 91.50%, influenced by litter nitrogen concentration, litter decomposition rate, and time, with litter mean residence time being the main factor (40.56%). For evergreen ECM plants, the R\u0026sup2; for leaf NRE is 88.20%, affected by mean annual temperature, mean annual precipitation, leaf nitrogen concentration, litter nitrogen concentration, and litter decomposition rate, with litter nitrogen concentration having the most significant impact (32.88%). The R\u0026sup2; for leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is 92.60%, with the litter decomposition rate contributing the most to the observed changes (46.54%).\u003c/p\u003e \u003cp\u003eFor tropical AM plants, the R\u0026sup2; for leaf NRE is 94.10%, with leaf nitrogen concentration and litter nitrogen concentration jointly influencing leaf NRE and litter nitrogen concentration being the most important factor (53.67%). The R\u0026sup2; for leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is 90.20%, influenced by litter nitrogen concentration, litter mean residence time, and rate, with litter mean residence time being the dominant factor (37.94%). In tropical ectomycorrhizal plants, leaf NRE was strongly predicted by leaf nitrogen concentration and litter nitrogen concentration (R\u0026sup2;=97.20%), with litter nitrogen concentration emerging as the predominant contributing factor, accounting for 63.97% of the explained variance in the regression model. The R\u0026sup2; for leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is 85.90%, mainly affected by litter nitrogen concentration and litter decomposition rate, with litter nitrogen concentration accounting for 51.30% of the variation.\u003c/p\u003e \u003cp\u003eFor temperate AM plants, the R\u0026sup2; for leaf NRE is 75.3%, with mean annual precipitation being the main factor. The R\u0026sup2; for leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is 99.90%, influenced by litter nitrogen concentration, litter mean residence time, and rate, with litter decomposition rate being the most important factor (42.95%). In temperate ectomycorrhizal plants, regression analysis identified leaf nitrogen concentration and litter nitrogen concentration as key predictors of leaf NRE (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;89.60%). Litter nitrogen concentration exhibited a more substantial influence on NRE variation, explaining 62.51% of the total variance accounted for by the model. The R\u0026sup2; for leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is 80.50%, with litter nitrogen concentration, litter decomposition rate, and time identified as key factors. Among these, litter nitrogen concentration is the most significant, accounting for 39.68% of the variation, emphasizing its crucial role in the nitrogen mineralization process in these ecosystems.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eChuyong et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) demonstrated that, compared to AM, ECM has a stronger ability for nitric recovery. ECM plants can directly decompose organic matter within leaves through extracellular enzymes, suggesting that ECM trees' superior leaf nitrogen resorption efficiency may make them more competitive than AM trees in high-latitude regions (LeBauer \u0026amp; Treseder, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In contrast, AM plants rely more on free-living microorganisms for nutrient mineralization (Lin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), with AM trees exhibiting higher leaf nitrogen mineralization rates in low-latitude regions. ECM tree leaf litter decomposes more slowly than AM tree leaf litter, which may result in slower nitrogen cycling in ECM-dominated ecosystems (Averill et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).In our investigation, we assessed the differences and relationships between nitrogen reuptake and litter nitrogen mineralization in plants under different mycorrhizal types and how these processes are affected by climate and plant type.\u003c/p\u003e \u003cp\u003eLiu et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) discovered that the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of AM trees is significantly higher than ECM trees (Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Peay's research (2016) also revealed that ECM is more efficient at resoprtion nitrogen than AM (Peay, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This is consistent with our findings, showing that the leaf NRE of ECM plants is significantly higher than that of AM plants, \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e in leaf litter of AM plants was significantly higher than that of ECM plants. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In subtropical and tropical evergreen forests, Chuyong et al. found that ECM trees have lower nitrogen resorption rates compared to AM trees, while in non-tropical deciduous forests, the nitrogen resorption rate of ECM plants is nearly twice that of AM plants(Chuyong et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This is partially consistent with our results, indicating that the nitrogen resorption rate of deciduous AM plants is much lower than that of ECM plants. However, the lack of significant difference in nitrogen resorption rates between evergreen (primarily found in tropical and subtropical forests) AM and ECM plants contradicts previous research findings. It requires further experimental investigation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Trees associated with AM mycorrhizae exhibit a faster rate of litter decomposition, while the nitrogen cycle and organic matter accumulation in ECM-dominated forest soils are more conservative (Seyfried et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In fact, in nitrogen-limited temperate ecosystems, AM plant litter has a lower carbon-nitrogen ratio and lignin-nitrogen ratio, decomposing more rapidly than ECM plant litter (Craig et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Midgley et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, in tropical ecosystems, although ECM plant litter may be biochemically similar to AM plant litter, the decomposition rate of litter in ECM-dominated forests is slower than that in AM-dominated forests (McGuire et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Torti et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), which aligns with our experimental results (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Bothwell et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) observed in subtropical and tropical forests that, regardless of litter quality, climate has a certain influence on plant nutrient resorption (Bothwell et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Our findings are inconsistent with this observation, as there were no significant differences in leaf NRE and Nmin between AM and ECM plants in temperate and tropical regions (Figure. 1D, E), the specific reasons need to be further discussed.\u003c/p\u003e \u003cp\u003ePrevious studies have indicated a trade-off between leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e on a global scale (Deng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).Experimental evidence also suggests that plant\u0026ndash;mycorrhiza interactions may play a key role in determining the reacquisition of retained nitrogen in plant\u0026ndash;soil systems(Suding et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Especially in the short term (hours to days), when microbes are more competitive for nitrogen than plant roots, absorbing most of the available soil nitrogen, which can later be remobilized for plant uptake (Bardgett et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This indicates that nitrogen resorption and leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e are negatively correlated under nitrogen-limited conditions, which aligns with our study results, whether AM or ECM plants, leaf NRE and leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e are negatively correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B, E). However, subtropical evergreen AM plants do not show a significant negative correlation between leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e, and given the scarcity of relevant studies, this relationship warrants further investigation(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003eLeaf nutrient concentration can influence nutrient resorption (Tong R et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Research has found that, the concentration of nitrogen in mature leaves are positively correlated with leaf NRE, indicating that species with higher leaf nutrient concentration tend to resorb more nitrogen during leaf senescence (Wang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Inherent foliar traits significantly impact leaf NRE. In high-latitude ecosystems, ECM trees usually grow in soils rich in organic nitrogen (Corrales et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Waring et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and they exhibit higher nitrogen resorption rates than AM trees in these regions. Findings in the temperate deciduous forests of Indiana suggest that nitrogen resorption increases with the dominance of ECM trees at the plot level (Lin et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Midgley et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Latitude and climatic conditions (mean annual precipitation or mean annual temperature) explain part of the global variation in nutrient resorption. In general, leaf NRE is mainly driven by mycorrhizal type and foliar habit, with climatic factors also playing a role(Brant \u0026amp; Chen, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This is consistent with our experimental research results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B, C, D, E).The intensity of mycorrhizal fungal colonization of plant roots is influenced by various environmental factors, including temperature and precipitation (Treseder, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). At the global scale, arbuscular mycorrhizal (AM) colonization intensity is closely related to temperature regimes. AM colonization intensity peaks at locations with warm-season high temperature but declines at colder sites. Meanwhile, both laboratory (Gavito \u0026amp; Azcon-Aguilar, 2012) and field (Rillig et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) studies have shown that low temperature can reduce the growth and function of AM fungi. Similarly, as precipitation seasonality increases, the intensity of ectomycorrhizal (ECM) fungal colonization decreases, that is, drought can suppress ECM fungal colonization intensity (Compant et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Different environmental variables influence the colonization intensity of arbuscular mycorrhizal and ectomycorrhizal fungi on plant roots, indicating that specific environmental changes will differentially affect different types of mycorrhizae (Zhang et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At the same time, low-quality litter substrates, characterized by high lignin content and low nutrient concentration, restrict nutrition utilization and inhibit decomposition due to nutrient limitation (Cleveland et al., 2006). Litter mycorrhizal type and litter chemical characteristics are typically interconnected, with AM-associated trees producing litter of higher chemical quality than ECM-associated trees (Cornelissen et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Craig et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Keller \u0026amp; Phillips, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Midgley et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These findings suggest that the differences in leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e between ECM and AM-dominated plants may primarily be attributed to variations in litter chemistry, with climate having an indirect effect. This is consistent with our study results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B, C, D, E).\u003c/p\u003e \u003cp\u003eGlobal studies have shown a trade-off relationship between leaf NRE and Nmin (Deng et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), originating from the negative correlation between leaf nitrogen use efficiency (NUE) and litter nitrogen concentration. Plants with high NUE efficiently utilize nutrients, leading to decreased litter nitrogen concentration, which in turn affects litter decomposition rates (Baligar et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Several factors, including annual precipitation, mean annual temperature, and specific leaf traits, significantly influence plant nitrogen resorption and nitrogen mineralization (Du et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Previous studies have indicated that leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e gradually increases with rising mean annual temperature and precipitation, while leaf NRE gradually decreases (Yuan \u0026amp; Chen, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Overall, as temperature and precipitation increase, the nitrogen cycling rate accelerates, shifting from conservative resorption pathways to mineralization pathways (Hu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The colonization intensity of plant root mycorrhizal fungi is also influenced by various environmental factors, including temperature and precipitation (Treseder, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). AM colonization intensity peaks in warm-season high-temperature regions but decreases in colder areas. Both laboratory (Gavito \u0026amp; Azcon-Aguilar, 2012) and field (Rillig et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) studies have shown that low temperatures reduce the growth and function of AM fungi. Similarly, with increasing precipitation seasonality, the colonization intensity of ectomycorrhizal (ECM) fungi decreases, indicating that drought inhibits ECM fungal colonization intensity (Compant et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Leaf traits not only affect leaf NRE (Koele et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), but also influence litter decomposition rates and nutrient release processes by affecting the litter chemical composition determined by plant types (Bortolazzi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).The key factors influencing leaf NRE are primarily foliar traits and mycorrhizal type, followed by climate. These observations indicate that mycorrhizal and plant types are crucial determinants of leaf NRE, which is consistent with our experimental findings. In our survey, the leaf NRE of AM plants was negatively correlated with litter nitrogen concentration and litter decomposition time (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, F). In contrast, the leaf NRE of ECM plants was positively correlated with green leaf nitrogen concentration and negatively correlated with litter nitrogen concentration, litter decomposition rate, mean annual temperature and mean annual precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, C, D, E). For different functional and ecological types of plants, the leaf NRE of evergreen ECM plants was negatively affected by mean annual temperature and mean annual precipitation (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-2, B-2). The leaf NRE of deciduous ECM plants was negatively affected by mean annual temperature (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-1). Our stepwise multiple regression analysis also revealed a similar phenomenon, where litter nitrogen concentration was the main factor affecting the leaf NRE of the remaining AM and ECM plants. In contrast, green leaf nitrogen concentration was the secondary influencing factor. The impact of litter nitrogen concentration on the leaf NRE of ECM plants, deciduous ECM plants, tropical ECM plants, and temperate ECM plants exceeded that of the corresponding AM plants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although the litter decomposition rate is significantly influenced by temperature and humidity (Liu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Paul et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and also by the presence of microorganisms in the environment, the mineralization of nitrogen in litter is primarily controlled by the initial chemical composition of the residue (Manzoni et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Similarly, mean annual temperature affects the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of deciduous AM plants, but to a lesser extent than the influence of litter decomposition time and rate. This implies that leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is almost entirely related to the plant leaves, but different AM and ECM plant types exhibit varying sensitivities to different foliar trait factors.These observations suggest that climate has a relatively minor impact on the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of various AM and ECM plants (Manzoni et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This is similar to our experimental results. The leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of AM plants was positively correlated with green leaf nitrogen concentration, litter nitrogen concentration, litter decomposition rate, mean annual temperature and mean annual precipitation while negatively correlated with litter decomposition time. In ectomycorrhizal (ECM) plants, a positive correlation was observed between leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e and litter nitrogen concentration, litter decomposition rate, mean annual temperature, and mean annual precipitation, while a negative correlation was found with litter decomposition time (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B, C, D, E F). For different functional and ecological types of plants, the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e was influenced by foliar traits (mainly litter nitrogen concentration, litter decomposition time, and litter decomposition rate (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Except for evergreen AM plants, which were not affected by MAP, both deciduous and evergreen AM and ECM plants were affected by mean annual temperature and mean annual precipitation (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA-1, A-2, B-1, B-2) and showed a positive correlation. This also partly explains the trade-off relationship between leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e, as leaf NRE decreases and leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e increases from high to low latitudes. Our stepwise multiple regression analysis also revealed a similar phenomenon, where the leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e of different AM and ECM plant types was mainly related to litter nitrogen concentration, litter decomposition time, and litter decomposition rate, but these factors had different effects on different types of AM and ECM plants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). AM fungi can enhance the survival ability of host plants, but more importantly, they primarily improve plant fitness through enhancing nutritional status. Although AM fungi have long been considered primarily responsible for improving plant Phosphorus nutrition, numerous studies have established that AM fungi can also transfer nitrogen to host plants from both inorganic and organic nitrogen sources. Research has shown that the amount of nitrogen delivered to host plants through hyphal networks is considerable, accounting for 20% to 74% of the total nitrogen uptake by mycorrhizal plants. AM fungi play a significant role in soil nitrogen cycling (Corr\u0026ecirc;a et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Landeweert et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Ectomycorrhizal (ECM) fungi improve plant nitrogen nutritional status through the dissolution, absorption, and transport of nutrients to plants. Studies have demonstrated that ECM fungi mobilize other essential plant nutrients directly from insoluble mineral sources through the excretion of organic acids. This enables ECM plants to acquire essential nutrients from insoluble mineral sources and influences nutrient cycling in plants-soil systems. The capacity of ECM fungal species to actively mobilize inorganic nutrients is particularly important for maintaining plant productivity (Corr\u0026ecirc;a et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Landeweert et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In conclusion, the type of mycorrhizal affects the nitrogen cycle of plants.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGlobally, both foliar traits and climate influence leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e, but the impact of foliar traits is significantly stronger than climate factors. Litter nitrogen concentration and green leaf nitrogen concentration are the primary factors affecting leaf NRE. Similarly, leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e is almost entirely associated with litter nitrogen concentration, litter decomposition time, and litter decomposition rate.As climate conditions (mean annual temperature and mean annual precipitation) change, a distinct trade-off emerges between leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e. From high to low latitudes, leaf NRE gradually decreases while leaf Nmin increases. However, leaf NRE and Nmin are also influenced by mycorrhizal types. Significant differences exist in leaf NRE and Nmin between AM and ECM plants. Due to the different mycorrhizal associations, AM and ECM plants are affected differently by climate factors and foliar traits. As climate conditions (mean annual temperature and mean annual precipitation) change, a distinct trade-off emerges between leaf NRE and Nmin.of deciduous plants and temperate plants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eData and Software Availability Statement\u003c/h2\u003e \u003cp\u003eThe global leaf nitrogen resorption efficiency data is available in Shi (2026) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6084/m9.figshare.30999964\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.30999964\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The expanded database containing mycorrhizal and vegetal classifications generated in this study is available in the Supplementary Material (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Supplementary Data S1) of this article. The IBM SPSS Statistics 26.0 software is available from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ibm.com/products/spss-statistics\u003c/span\u003e\u003cspan address=\"https://www.ibm.com/products/spss-statistics\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The RStudio software is available from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://posit.co/\u003c/span\u003e\u003cspan address=\"https://posit.co/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The GraphPad Prism software is available from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.graphpad.com/\u003c/span\u003e\u003cspan address=\"https://www.graphpad.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The Microsoft Excel software is available from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.microsoft.com/excel\u003c/span\u003e\u003cspan address=\"https://www.microsoft.com/excel\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest relevant to this study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Science and Technology Innovation Leading Talent Program of Henan Province (254200510006), the Key Research and Development Program of Hainan Province (ZDYF2024XDNY172), Leading Talents in Scientific and Technological Innovation of Luoyang (LYSKJLJRC02), and Programs of Higher Education Institutions in Henan Province (26A210004).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAerts R (1996) Nutrient resorption from senescing leaves of perennials: are there general patterns? 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European Journal of Soil Science\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang K, Li M, Yan Z, Li M, Kang E, Yan L, Zhang X, Li Y, Wang J, Yang A (2022) Changes in precipitation regime lead to acceleration of the N cycle and dramatic N2O emission. Sci Total Environ 808:152140\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Arbuscular mycorrhiza, Ectomycorrhiza, Nitrogen trade-off, Nitrogen resorption efficiency, Nitrogen mineralization rate","lastPublishedDoi":"10.21203/rs.3.rs-8600732/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8600732/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLeaf nitrogen resorption before leaf fall and mineralization after litter fall are strongly influenced by the environment, but their linkage to biotic factors remains largely unknown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims\u003c/strong\u003e: This study aims to investigate the regulatory differences of arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) fungi on leaf nitrogen resorption efficiency (NRE) and nitrogen mineralization (\u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e) by utilizing a global plant-mycorrhiza and foliar traits database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: This study utilized the global plant mycorrhiza database “FungalRoot” and field data on plant mycorrhizal infection characteristics in terrestrial ecosystem to establish a database of arbuscular mycorrhizal infection information for terrestrial wild plants, and conducted research based on this database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The results show AM plants exhibit significantly lower NRE (39.65%) compared to ECM plants (50.37%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001), while demonstrating significantly higher \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e (0.35) than ECM plants (0.03, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001). When considering deciduous plants, AM plants display significantly lower leaf NRE (42.86%) compared to ECM plants (54.00%, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01), yet show significantly higher leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e (0.27) than ECM plants (-0.01, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). Turning to evergreen plants, AM plants exhibit significantly higher leaf \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e (0.42) compared to ECM plants (0.09, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: These results indicate that mycorrhizal types significantly modulate leaf NRE and \u003cem\u003eN\u003c/em\u003e\u003csub\u003emin\u003c/sub\u003e. In the future, introducing mycorrhizal factors into global-scale models of the dynamic interaction between nitrogen resorption and mineralization will enhance the simulation of nutrient limitations on ecosystem productivity.\u003c/p\u003e","manuscriptTitle":"Mycorrhizal types modulate the trade-off between leafnitrogen resorption and mineralization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-12 21:02:44","doi":"10.21203/rs.3.rs-8600732/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3e99cdcf-e3ff-48db-ad78-1998cb6f1f43","owner":[],"postedDate":"February 12th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Reject after review","date":"2026-05-17T04:50:43+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T08:51:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-12 21:02:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8600732","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8600732","identity":"rs-8600732","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}