Tree Functional Identity Drives Soil Enzyme Stoichiometric Ratios and Microbial Nutrient Limitation Responses to Artificial Forest Conversion

Abstract

Converting monoculture forests into mixed forests is a widely adopted strategy to enhance forest ecosystem quality. Soil enzyme activities and their stoichiometric ratios are acknowledged as critical indicators of nutrient cycling and ecosystem multifunctionality, with microbial nutrient limitation (particularly C, N, and P) being strongly influenced by forest management practices. However, the effects of this conversion on soil enzyme activities and stoichiometric ratios remain inconclusive, and the impacts of forest conversion on soil C, N, and P dynamics require further clarification. To address these uncertainties, a meta-analysis of 2113 paired observations was conducted to assess the impacts of forest conversion on soil enzyme activities, stoichiometric ratios, and microbial nutrient limitations. The activities of four key enzymes, including β-1,4-glucosidase (BG), β-1,4-N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and phosphatase (ACP) were examined. It was found that mixed forests exhibited significantly higher C-, N-, and P- enzyme activities than monocultures (increases of 36.23%, 9.85%, and 11.07%, respectively). Additionally, soil C, N, and P contents were generally enhanced following the conversion from monocultures to mixed forests. Elevated enzyme C:P and N:P ratios were observed in mixed forests, while C:N ratios were reduced. Microbial C limitation was alleviated, though C&P co-limitation remained prevalent. Notably, greater effects on enzyme activities were observed when conifer monocultures (particularly those introduced with broadleaf species) were converted, compared with conversions of broadleaf monocultures. In contrast, the introduction of additional conifer species into existing conifer stands exacerbated C limitation. These results suggest that conversion of monocultures to mixed-species forests can mitigate microbial C limitation in soils while improving soil nutrient availability. Furthermore, for conifer plantation conversion, selecting functionally complementary broadleaf species yields greater benefits than introducing additional conifer species.

1. Introduction

As a critical component of forest ecosystems, soil directly impacts plant growth, nutrient cycling, and ecological sustainability. Soil extracellular enzyme activities and their stoichiometric ratios play an essential role in soil nutrient cycling and organic matter decomposition [1,2]. Key enzymes like β-1,4-glucosidase (BG), β-1,4-N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and phosphatase (ACP) regulate nutrient cycling of carbon (C), nitrogen (N), and phosphorus (P), respectively [3,4]. Their stoichiometric ratios are widely used to assess nutrient limitations and the metabolic status of soil microbial communities [3,5]. The resource allocation theory and ecological enzyme stoichiometry frameworks indicate that microbes dynamically adjust their enzyme production in response to nutrient limitations, such as C:N:P imbalances. These allocation strategies prioritize enzyme synthesis, enabling microbes to effectively acquire scarce resources essential for their survival and growth [4,5,6,7]. This adaptive mechanism highlights the intricate relationship between nutrient availability and microbial enzyme activity [8]. On a global scale, enzyme activity ratios for C, N, and P converge at 1:1:1 (log-transformed) [3], and deviations from this indicate nutrient limitations [9]. Thus, the analysis of enzyme activity and stoichiometric ratios provide critical insights into microbial nutrient constraints and nutrient cycling in soil [10].

So far, monoculture practices in artificial forests, which cover ~131 million hectares around the world [11], often lead to nutrient depletion and microbial community decline, threatening ecosystem health [12]. In contrast, mixed forests enhance microbial activity, improve nutrient cycling, and maintain higher soil productivity and ecological services, compared with monoculture (≥80% monospecific) forests [13]. Tree functional identity, especially the contrast between coniferous and broadleaf species, is a key driver of soil microbial responses. Conifers typically produce lignin-rich, acidic litter with slow decomposition rates, leading to soil acidification [12,14], whereas broadleaf litter generally decomposes more rapidly and contains relatively higher nutrient concentrations [15,16]. These fundamental differences are recognized to profoundly influence soil microbial activity, community composition, and functional potential—such as shaping specialized decomposer communities or altering C and/or N cycling [17]. These advantages highlight the importance of including diverse tree species in forest management to improve ecosystem sustainability and resilience [18,19,20].

However, how the conversion of different artificial monocultures (especially those with distinct functional traits) affects soil enzyme activities and stoichiometric ratios remains uncertain. Meta-analysis, a powerful quantitative synthesis approach, is particularly suited to resolve inconsistent findings across individual studies by identifying overarching patterns and contextual drivers [21]. It has been confirmed by recent meta-analyses that forest diversity globally enhances soil microbial functions and nutrient cycling, particularly in mixed-species systems [22]. Furthermore, a global review has shown that enzyme stoichiometric ratio shifts under tree diversity are linked with microbial responses [23]. Inconsistent findings have also been yielded by other research comparing different forest types [24,25]. For example, mixed forests of Eucalyptus spp. and Castanopsis hystrix have increased LAP activity but decreased BG and ACP activities, compared with their respective monoculture forests [26]. Conversely, mixed coniferous and deciduous forests in subalpine regions had reduced NAG and ACP activities without significantly affecting LAP [27], while no significant differences in soil enzyme activities have been observed between subtropical conifer–broadleaf mixed forests and their monoculture conifer counterparts [28]. These discrepancies suggest that beyond climatic zones, inherent conifer–broadleaf differences (e.g., litter quality, root exudates) likely mediate soil enzymatic responses and their associated nutrient cycling processes during the forest conversion [14,15,16,29,30]. More importantly, microbial responses, including soil enzyme activities, are largely regulated by tree functional traits [31].

In addition, factors such as elevation, forest density, and age also influence soil enzyme activity and stoichiometric ratios [22,32]. However, most studies to date have been conducted within a single region, which limits a broader understanding and systematic recognition. Recent studies revealed particularly pronounced differences in soil enzyme activities and stoichiometric ratios across various forest mixing patterns, including the conversion of conifer monoculture through broadleaf introduction, broadleaf monoculture through conifer introduction, and different conifer–conifer or broadleaf–broadleaf combinations. These variations are likely mediated by mixing-induced changes in forest structure, litter inputs, and microbial community dynamics [33,34]. Given the global prevalence of both conifer and broadleaf monocultures, a systematic comparison of their soil enzymatic responses to mixed-species conversion becomes crucial for understanding forest management impacts on soil health and ecosystem functioning.

To our knowledge, no prior meta-analysis has systematically evaluated how tree functional traits (beyond species richness) globally affect soil enzyme stoichiometric ratios and microbial nutrient limitation. Meta-analysis is a quantitative method that statistically combines independent studies. It helps reconcile conflicting evidence and identify global trends [21]. Therefore, a meta-analysis was employed here to address three key questions: (1) How does monoculture-to-mixed conversion globally alter soil enzyme activities, stoichiometric ratios, and microbial nutrient limitation? (2) Do enzymatic responses differ when conversions start from conifer vs. broadleaf monocultures? (3) How do climate and stand characteristics regulate these effects? It was hypothesized that: (i) mixed forests would increase enzyme activities (e.g., BG, NAG, LAP, and ACP) and alleviate microbial C limitation (shown by shorter vector length, explained in Section 2.1); (ii) stronger effects would occur when converting conifer monocultures, due to their high-lignin litter (slow decomposition) [14] and typically poorer initial soil conditions; and (iii) moderate annual precipitation (e.g., 800–1200 mm) and temperature (e.g., 10–20 °C) [35] would enhance these effects by altering soil properties.

2. Materials and Methods

2.1. Literature Search and Inclusion Criteria

We searched for peer-reviewed articles on Web of Science, Google Scholar, and China National Knowledge Infrastructure (CNKI), using the search terms “mixed forests” OR “mixed plant” OR “specifications mixing” AND (“soil enzyme activities” OR “soil enzyme stoichiometric ratio”). To avoid publication bias, we screened articles using the following criteria: (1) data must come from field research, not reviews or simulations; (2) details of study site and study design must be provided; (3) comparisons between mixed and monoculture forests must be included; (4) mean values and standard deviations (SD) or standard errors (SE) for soil enzyme activities and stoichiometric ratios must be provided or be calculable; (5) the forests must be artificial; and (6) experimental treatment must involve only tree mixtures, excluding additional treatments like N addition or reduced rainfall. We included only surface soil data since surface soil typically has the highest enzyme activity [36]. Thus, when an article included multiple soil layers, we only used the surface soil data. The literature from 2007 to 2024 were searched, including only English and Chinese studies (no other languages). The final search was completed on 26 April 2024. Studies missing standard deviations or sample sizes were excluded. After multiple literature searches and screenings, 2113 data pairs from 37 papers were finally collected (Figure S1).

We extracted the activities and stoichiometric ratios of four key soil enzymes (i.e., β-1,4-glucosidase [BG], β-1,4-N-acetylglucosaminidase [NAG], leucine aminopeptidase [LAP], and phosphatase [ACP]). The N-acquiring enzyme activity (enzyme N) was calculated as the sum of NAG and LAP activities. We computed the vector length and angle from these enzyme activities to assess microbial C limitation and N&P limitation, respectively [37]. The longer the vector, the more C limitation the microbes faced, and vector angles of <45° and >45° indicated microbial N and P limitation, respectively. The vector length and angle were calculated as follows:

X = (BG): (BG + ACP)

(1)

Y = (BG): (BG + NAG + LAP)

(2)

Vector length = Sqrt (X² + Y²)

(3)

Vector angle = Degrees [Atan2(X, Y)]

(4)

Enzyme stoichiometric ratios were derived from the logarithmic ratios of enzyme activities. This study covers regions spanning Asia, Europe, Africa, and South America, primarily focusing on tropical and subtropical areas. The predominant soil types include acidic red soil, yellow-red soil, and mountain brown soil, with clay loam and sandy loam being the main soil textures. To facilitate research on key factors influencing the mixed effect, we conducted a comprehensive analysis of soil characteristics, including moisture, pH, bulk density, and nutrients where available. We also recorded geographical and climatic variables (longitude, latitude, elevation, mean annual precipitation [MAP], and mean annual temperature [MAT]), as well as stand characteristics (mixed forest age and density; the age of the mixed forest is between 2 and 110 years).

2.2. Meta-Analysis and Statistical Approaches

Data extraction from graphical sources was performed using GetData 2.22 to obtain mean values with corresponding SD or SE. Logarithmic response ratio (RR) was used to quantify the impact of forest mixtures on soil properties and enzyme activity and stoichiometric ratios [38,39]. The RR and its sampling variation (v) were calculated as follows:

$$RR=L\mathrm{n}({\displaystyle \frac{X_t}{X_c}})$$

(5)

$$v={\displaystyle \frac{S_t^2}{N_t{X}_t^2}}+{\displaystyle \frac{S_c^2}{N_c{X}_c^2}}$$

(6)

where Xt and Xc are the mean values of the treatment group (mixed forests) and the control group (monoculture forests), respectively. St and Sc are the standard errors for the treatment and control groups, and Nt and Nc are the corresponding sample sizes.

Meta-analysis was performed using the MetaWin 2.3 software [40]. We applied a random-effects model to calculate effect sizes and conducted 999 resampling tests to determine the effect size and the 95% confidence interval (CI), using bootstrapping. If the CI overlapped with zero, we deemed the effect size as insignificant (p > 0.05). Conversely, a CI not overlapping with zero was considered as a significant effect size (p < 0.05). When all CI values were <0, we interpreted this as a negative effect, indicating that mixed forests inhibited the parameter of interest. Conversely, when all CI values were >0, mixed forests were considered to have a promoting effect.

We applied the Rosenthal safety factor test to assess publication bias [40]. If the coefficient exceeds 5N + 10 (where N is the sample size in the meta-analysis), the results can be considered objective and free from publication bias. In our study, an additional 1.27 × 10⁶ zero-effect result had to be included in the dataset to alter the findings, confirming the absence of publication bias (Figure S2).

Linear regression analysis was employed to investigate the relationships between environmental factors (latitude, elevation, MAP, and MAT) and stand characteristics (forest density and forest age) with the RR value of soil enzyme activity and stoichiometric ratios. Structural equation modeling (SEM) was conducted using the “lavaan” R package to identify direct and indirect factors influencing enzyme stoichiometric ratios. In the SEM, exogenous variables (e.g., MAP, MAT) and endogenous variables (e.g., soil C:N, enzyme ratios) were included. The maximum likelihood estimation method was used to fit the models. The goodness-of-fit of the models was evaluated by Chi-squared tests (p > 0.05), comparative fit index (CFI), and root mean square error of approximation (RMSEA).

The data labeled with RR in the figures represent response ratios, while unmarked data indicate raw measurements. All statistical analyses were performed with MetaWin 2.3 and R 4.3.1.

3. Results

3.1. Effects of Conversion from Monoculture to Mixed Forests on Soil Properties

The conversion from monoculture to mixed forests had no significant effect on soil bulk density or the C:N ratio (p > 0.05, Figure 1). However, mixed forests had higher soil moisture, pH, total C, total N, total P, available N (dissolved organic N, nitrate N, ammonia N), available phosphorus, and C:P (p < 0.05), while dissolved organic C and N:P were decreased (p < 0.05).

Compared with monoculture plantations, mixed forests significantly enhanced soil enzyme activities (C-, N-, and P- enzymes increased by 36.23%, 9.85%, and 11.07%, respectively) and altered enzyme stoichiometry. Specifically, mixed forests increased enzyme C:P (eC:P, +2.94%) and N:P (eN:P, +0.71%) ratios but significantly decreased enzyme C:N (eC:N, −2.91%) and vector length (−0.61%) (p < 0.05).

3.2. Differences Between Mixed Forest Patterns

Forest mixing patterns included mixed coniferous (CPF + CF) and broadleaf species (CPF + BF) in coniferous forests (CPF), and mixed coniferous (BPF + CF) and broadleaf species (BPF + BF) in broadleaf forests (BPF). Compared with the corresponding monoculture forests, CPF + CF increased soil moisture, pH, and total N, while reducing total C, dissolved organic C, and available P (p < 0.05, Figure 2a). CPF + BF increased total C, total P, available N, and available P, while decreasing bulk density, total N, and soil C:P and N:P (p < 0.05). Regarding soil enzymes, CPF + CF enhanced the activities of enzyme C and P, and eC:P, while reducing enzyme N; whereas CPF + BF increased the activities of enzyme C, N and P but decreased eC:N and eN:P (p < 0.05).

Correspondingly, compared with BPF, the BPF + CF led to increased ammonia N and available P levels but decreased soil pH, total N, C:P, and N:P (p < 0.05, Figure 2b). In contrast, BPF + BF increased total N, nitrate N, ammonia N, available P, and C:P (p < 0.05). Additionally, only enzyme C increased under BPF + CF, while BPF + BF significantly affected enzyme N (positive) and enzyme P (negative). However, there were no significant differences in enzyme stoichiometry, vector length, and the angle between BPF + CF or BPF + BF compared with BPF (p < 0.05).

We observed microbial C and P co-limitation in nearly all forest patterns (Figure 3). Compared with CPF, in CPF + CF, the eC:N and eN:P deviated further from 1:1, which indicates an exacerbation of C&P limitation. Conversely, in CPF + BF these ratios trended towards 1:1, indicating alleviated C&P limitation. Similarly, the enzyme stoichiometric ratios in BPF + CF and BPF + BF were closer to 1:1 compared with BPF, with BPF + BF showing a more pronounced effect.

3.3. Factors Influencing the Responses of Soil Enzyme to Forest Mixing

No significant linear relationships were found between the mixed-forest effects on soil C-, N-, and P- enzyme activities and environmental/stand factors (p > 0.05, Figure 4). However, eC:N responded positively to MAP and MAT but negatively to latitude and forest density (p < 0.05). Neither eC:P nor eN:P correlated with any tested factors (p > 0.05, Figure 5).

Our structural equation modeling revealed that elevation, soil pH, and soil C:N directly affected eC:N, while forest age and MAT exerted indirect effects through soil C:N mediation (Figure 6a). For eC:P, MAP, MAT, and soil C:P showed direct effects, with MAP additionally operating indirectly via soil C:P (Figure 6b). In contrast, eN:P was primarily driven by direct effects of soil N:P and available phosphorus, with MAP influencing eN:P exclusively through indirect pathways (Figure 6c).

4. Discussion

4.1. Global Pattern Effects of Mixed Artificial Forests

Our meta-analysis demonstrates that converting monocultures to mixed artificial forests significantly enhances the activities of C-, N-, and P- enzymes (Figure 1), which directly supports our hypothesis 1. This finding holds particular importance for artificial forest ecosystems, proving that mixed forests can improve microbial functionality compared with traditional monocultures. The higher enzyme activities in mixed artificial forests are associated with improved soil conditions, including increased moisture, enhanced nutrient availability, and optimized pH levels (Figure 1) [41]. These changes create more favorable conditions for microbial metabolism in these human-managed systems [42,43].

The shifts in enzymatic stoichiometric ratios provide crucial insights into microbial nutrient limitations in artificial forest ecosystems. The 1:1:1 ratio of enzyme activities (BG:NAG + LAP:ACP) represents an ecological equilibrium point where microbial communities are not constrained by any specific nutrient limitation [3]. Deviations from this ratio indicate that nutrient limitations may constrain ecosystem functioning in artificial forests. Our analysis clearly reveals widespread C&P co-limitation in artificial forests (Figure 3), as evidenced by enzymatic ratios deviating from 1:1:1. This co-limitation carries significant ecological implications, reflecting both the stressed and imbalanced nutrient cycling in monocultures and the tight coupling of C and P cycles in artificial forests.

The reduced enzyme vector length in mixed artificial forests (Figure 1) indicates an alleviated microbial C limitation compared with monocultures, as longer vector lengths signify more severe C limitation [3,37]. This finding supports the concept that afforestation with diverse tree species can alleviate key limitations on microbial activity in artificial forest ecosystems [44]. Maintaining balanced enzymatic stoichiometric ratios (approaching 1:1:1) is particularly critical in artificial forests, as it ensures efficient nutrient cycling and supports long-term productivity. However, despite the alleviation of C limitation, the aforementioned C&P co-limitation remains prevalent in artificial forests. This suggests a relative lag in the alleviation of P limitation, which may be attributed to inherently low P availability or persistent output pressures in artificial forest soils [25,45], especially in rapidly decomposing ecosystems like subtropical regions [45,46].

Regarding soil physicochemical properties, an interesting observation is the significant enhancement in soil moisture, pH, and total C following mixed-species conversion, yet no significant change in soil bulk density (Figure 1). This may relate to the history of disturbance associated with forest management (e.g., site preparation, planting) affecting the topsoil, the focus of this study. These activities may have partially homogenized the soil profile and disrupted natural soil structure formation processes. Bulk density, reflecting soil compaction and porosity, may require longer time scales to recover or change than the chemical and biological properties observed to respond in this study [36]. In contrast, moisture, pH, and C content respond more sensitively and rapidly to changes in vegetation inputs (litter, root exudates) and microbial activity. In addition, the similar mechanical disturbance regimes in mixed and monoculture forests maintain comparable soil structures. These findings highlight that mixed artificial forests can improve soil biological conditions without necessarily changing physical structure.

4.2. Differentiated Responses Based on Artificial Forest Origin

One of our most significant findings is that the magnitude of response varies substantially on whether the conversion originates from coniferous or broadleaf monocultures. Notably, the conversion initiated from coniferous monocultures (CPF + CF, CPF + BF) demonstrates significantly stronger responses in both enzyme activity and stoichiometric ratios compared with those originating from broadleaf monocultures (BF + BF, BF + CF) (Figure 2). This pronounced divergence can be attributed to fundamental differences in the initial ecological conditions between these artificial forest types.

Coniferous monocultures are characterized by slow litter decomposition due to high lignin and cellulose content [12,47], soil acidification, and potential initial nutrient scarcity [12], leading to long-term, stronger nutrient limitation for soil microbes. Conversion to mixed forests (especially by introducing broadleaf species) significantly optimizes the microbial habitat by improving the understory microclimate [48], providing more easily decomposable litter [15,49], and enabling potential complementary decomposition pathways [49], thus resulting in a more pronounced positive effect on enzyme activities. In contrast, broadleaf monocultures inherently have faster decomposition rates for their N-rich litter [15,16,29], meaning microbes may face relatively weaker initial limitations. These differences extend to the rhizosphere effect, where broadleaf trees often release more easily decomposable C compounds to stimulate microbial activity [50]. Thus, the response of enzymes to the mixing conversion is less pronounced in broadleaf than coniferous monocultures.

Notably, different mixed planting regimes implemented on coniferous monocultures (CPF) yield distinct effects. The conifer–broadleaf mixture (CPF + BF) alleviates microbial C&P co-limitation, as evidenced by its enzymatic stoichiometric ratio approaching a more balanced 1:1 state (Figure 3). This benefit stems from increased input of labile litter [34,51], which enhances the availability of both C and P [28]. Furthermore, the introduction of broadleaf species modifies the rhizosphere environment through distinct root exudation patterns and potentially shifts the fungi–bacteria ratio toward a decomposer-favoring community [30]. These modifications are reflected in the more balanced enzymatic stoichiometric ratios and alleviated microbial nutrient limitations.

In contrast, the conifer–conifer mixture (CPF + CF) produces results contradicting our hypothesis 1. The increased vector length indicates aggravated microbial C limitation (Figure 2a), and its stoichiometric ratio deviates from the 1:1:1 equilibrium compared with CPF (Figure 3). This unexpected outcome may involve multiple reasons: (1) the introduction of functionally similar (i.e., coniferous) species intensifies soil nutrient competition [52], leading to reduced total soil C (Figure 2a); (2) elevated soil pH in CPF + CF (Figure 2a) might accelerate organic matter mineralization while altering microbial community structure (e.g., reducing acidophilic fungal dominance [53]), thereby decreasing microbial C use efficiency and available C sources [53,54]; and (3) the conifer–conifer mixture might maintain or reinforce an ectomycorrhizal fungal-dominated community that relies on recalcitrant organic matter [30], whose C acquisition strategy could exacerbate C limitation. Collectively, these shifts in microbial community composition reduce overall C use efficiency in conifer–conifer mixtures [55]. Consequently, interspecific interactions among conifers create unique belowground dynamics distinct from conifer–broadleaf mixtures, particularly in C allocation patterns [56,57]. These findings underscore that during coniferous forest conversion, introducing functionally complementary broadleaf species offers greater ecological advantages than introducing conifers for improving microbial nutrient limitations [30,56].

4.3. Regulatory Roles of Environmental, Stand Characteristics, and the Artificial Forest Context

Our analysis further reveals the significant regulatory roles of environmental and stand characteristics on the mixing effect. The SEM and regression analyses indicate (Figure 4, Figure 5 and Figure 6) that the effect of mixing on enzyme stoichiometric ratios, rather than on enzyme activity, exhibits a significant environmental dependency. This is likely because enzyme activity is also influenced by factors such as soil texture and aboveground species composition. We find that among the enzyme stoichiometric ratios, only eC:N shows a linear correlation with latitude, MAP, MAT, and forest density (Figure 5), yet the SEM shows that all enzyme stoichiometric ratios can be influenced by MAP and/or MAT. This suggests that the climate’s influence on the mixing effect regarding stoichiometric ratios (particularly for eC:P and eN:P) is non-linear. Moderate hydrothermal conditions may be most conducive to the positive effects of forest mixing. However, some studies show that higher MAT and MAP can enhance C enzyme secretion by intensifying plant–microbe competition [58], thereby increasing eC:N. Concurrently, the synergistic effect of heat and moisture enhances P enzyme activity, leading to a decrease in eC:P [59].

Furthermore, forest density (Figure 5) and age (Figure 6) also influence eC:N. Excessively high forest density may lead to N depletion [32], thereby decreasing eC:N. The effect of forest age on eC:N varies across different age stages. Within a certain range, an increase in forest age leads to accumulated C input, increasing eC:N [60]. This is attributed to the direct link between vegetation dynamics (e.g., litter input, root exudates) and microbial resource allocation strategies [61]. Future studies should investigate how soil biological quality (e.g., microbial biomass C/N/P, community composition, fungi–bacteria ratio) changes with stand age to better link stoichiometry with microbial community structure.

Our study advances beyond previous meta-analyses that focus primarily on biomass or biodiversity responses [62] by providing a stoichiometric perspective on microbial functioning. However, we acknowledge the limitations of this study. Unconsidered factors such as soil texture and key microbial groups (e.g., symbiotic mycorrhizal fungi) could also influence the mixing effect. The low explanatory power observed for eC:P (Figure 6b) strongly suggests the existence of other significant but unmeasured environmental drivers. Future research would benefit from adopting a more integrated approach that systematically examines the complex relationships between soil microbial community structure and function, litter decomposition, and enzyme activity responses across different mixed-forest patterns. In-depth research will help explain why certain mixtures (particularly CPF + BF) show stronger positive effects than others.

It is critical to emphasize that this study is centered on artificial forest ecosystems, with a high proportion of studies from China, which may introduce regional bias and limit generalizability. Enzyme responses likely vary with biogeographic regions (e.g., temperate vs. tropical) and forest management history [63]. Given the focus on surface soils, sampling locations have likely experienced cultivation or site preparation, leading to the mixing of natural organic and organo-mineral horizons. This disturbance partially homogenizes the soil profile and disrupts pre-existing microbial networks. Consequently, microbial communities in these artificial systems must rebuild functional relationships over time, primarily through interactions with the roots of tree species intentionally selected and planted by humans. Compared with the more complex dynamics expected in pristine (or low-impact) natural mixed or pure forests, shaped by long-term co-evolution (e.g., richer mycorrhizal networks, more mature decomposer communities), the microbial response patterns within this context of “human-imposed neo-evolution” may exhibit distinct characteristics. Future research should compare enzyme stoichiometric ratios and microbial limitations in artificial mixed stands versus natural mixed forests and investigate how soil biological quality changes with stand age to gain deeper insights into the long-term trajectory of ecosystem restoration in artificial forests.

5. Conclusions

To our knowledge, this is the first meta-analysis to systematically evaluate how tree functional traits globally alter soil enzyme stoichiometry and microbial nutrient limitation. It is demonstrated that conversion from monoculture to mixed forests enhances soil C-, N-, and P- enzyme activities through micro-environmental improvements, including increased moisture, enhanced N/P availability, and optimized pH conditions, while simultaneously alleviating microbial C limitation as evidenced by reduced enzyme vector length. The key scientific advancement lies in demonstrating the effects: (1) conversion originating from conifer monocultures (particularly the CPF + BF model incorporating broadleaf species) generate significantly stronger enzymatic responses than broadleaf-based conversion, while (2) conifer–conifer mixtures (CPF + CF) intensify C limitation. MAP and MAT primarily modulate enzyme stoichiometric ratios rather than absolute activity levels. Regarding forest characteristics, both forest density and age exert measurable influences on eC:N. These findings suggest that coniferous forest conversion prioritize functionally complementary broadleaf species to maximize ecological benefits. Future studies could focus on deciphering mycorrhizal-mediated C partitioning and age-dependent shifts in microbial dynamics to refine forest management across biogeographic regions.