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Communication 通信

Climate Factors Influence Above- and Belowground Biomass Allocations in Alpine Meadows and Desert Steppes through Alterations in Soil Nutrient Availability
气候因素通过改变土壤养分可利用性影响高山草甸和荒漠草原的地上和地下生物量分配

Jiangfeng Wang , Xing Zhang , Ru Wang , Mengyao Yu , Xiaohong Chen , Chenghao Zhu , Jinlong Shang
王江峰 ，张星 ，王茹 ，于梦瑶 ，陈晓红 ，朱成浩 ，尚金龙 and Jie Gao  和高杰 1 College of Life Sciences, Xinjiang Normal University, Urumqi 830054, China; wjf2088683747@163.com (J.W.);
中国新疆师范大学生命科学学院，乌鲁木齐 830054，中国；wjf2088683747@163.com（J.W.）；zxyybh@163.com (X.Z.); wangruavon@163.com (R.W.); yao02292023@163.com (M.Y.);
zxyybh@163.com（X.Z.）；wangruavon@163.com（R.W.）；yao02292023@163.com（M.Y.）；cheng1352023@163.com (X.C.)
cheng1352023@163.com（X.C.）；2 East China Survey and Planning Institute, National Forestry and Grassland Administration,
中国国家林业和草原局华东勘测规划设计研究院Hangzhou 430010, China; zchmee@126.com
中国杭州 430010; zchmee@126.com3 Key Laboratory of Earth Surface Processes of Ministry of Education, College of Urban and Environmental
中国教育部地表过程重点实验室，城市与环境学院Sciences, Peking University, Beijing 100871, China
北京大学科学学院，中国北京 100871* Correspondence: shang307@126.com (J.S.); jiegao72@gmail.com (J.G.)
* 通讯作者：shang307@126.com（J.S.）；jiegao72@gmail.com（J.G.）

Citation: Wang, J.; Zhang, X.; Wang, R.; Yu, M.; Chen, X.; Zhu, C.; Shang, J.; Gao, J. Climate Factors Influence Above- and Belowground Biomass Allocations in Alpine Meadows and Desert Steppes through Alterations in Soil Nutrient Availability. Plants 2024, 13, 727. https://doi.org/10.3390/ plants13050727
引用：王，J.；张，X.；王，R.；于，M.；陈，X.；朱，C.；尚，J.；高，J. 气候因素通过改变土壤养分可利用性影响高山草甸和荒漠草原的地上和地下生物量分配。植物学，2024，13，727。https://doi.org/10.3390/plants13050727
Academic Editor: George Lazarovits
学术编辑：George Lazarovits
Received: 15 January 2024
收到日期：2024 年 1 月 15 日
Revised: 26 February 2024
修改日期：2024 年 2 月 26 日
Accepted: 29 February 2024
接受日期：2024 年 2 月 29 日
Published: 4 March 2024
发布日期：2024 年 3 月 4 日
Copyright: (C) 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ ).
版权：（C）2024 年作者。许可方 MDPI，瑞士巴塞尔。本文是根据创作共用署名（CC BY）许可证（https://creativecommons.org/licenses/by/ ）的条款和条件进行开放获取的文章。

Abstract 摘要

Biomass is a direct reflection of community productivity, and the allocation of aboveground and belowground biomass is a survival strategy formed by the long-term adaptation of plants to environmental changes. However, under global changes, the patterns of aboveground-belowground biomass allocations and their controlling factors in different types of grasslands are still unclear. Based on the biomass data of 182 grasslands, including 17 alpine meadows (AMs) and 21 desert steppes (DSs), this study investigates the spatial distribution of the belowground biomass allocation proportion (BGBP) in different types of grasslands and their main controlling factors. The research results show that the BGBP of AMs is significantly higher than that of DSs . The BGBP of AMs significantly decreases with increasing mean annual temperature (MAT) and mean annual precipitation (MAP) , while it significantly increases with increasing soil nitrogen content , soil phosphorus content , and soil . The BGBP of DSs significantly decreases with increasing MAP , while it significantly increases with increasing soil phosphorus content and soil . The random forest model indicates that soil is the most important factor affecting the BGBP of both AMs and DSs. Climate-related factors were identified as key drivers shaping the spatial distribution patterns of BGBP by exerting an influence on soil nutrient availability. Climate and soil factors exert influences not only on grassland biomass allocation directly, but also indirectly by impacting the availability of soil nutrients.
生物量是群落生产力的直接反映，地上和地下生物量的分配是植物长期适应环境变化形成的生存策略。然而，在全球变化下，不同类型草地地上-地下生物量分配的模式及其控制因素仍不清楚。本研究基于包括 17 个高山草甸（AMs）和 21 个荒漠草原（DSs）在内的 182 个草地的生物量数据，研究了不同类型草地地下生物量分配比例（BGBP）的空间分布及其主要控制因素。研究结果表明，AMs 的 BGBP 显著高于 DSs。AMs 的 BGBP 随着平均年温度（MAT）和平均年降水量（MAP）的增加而显著降低，而随着土壤氮含量、土壤磷含量和土壤... DSs 的 BGBP 随着 MAP 的增加而显著降低，而随着土壤磷含量和土壤的增加而显著增加。随机森林模型表明，土壤是影响 AMs 和 DSs 的 BGBP 最重要的因素。气候相关因素被确定为通过对土壤养分可用性施加影响来塑造 BGBP 的空间分布模式的关键驱动因素。气候和土壤因素不仅直接影响草地生物量分配，而且通过影响土壤养分的可用性间接影响。

Keywords: above-belowground biomass; alpine meadows; desert steppes; climate factors; soil nutrients
关键词：地上地下生物量；高山草甸；荒漠草原；气候因素；土壤养分

1. Introduction 1. 引言

Grasslands play a crucial role as carbon sinks within terrestrial ecosystems, making a significant contribution to the global carbon cycle [1]. The distribution of plant biomass between aboveground and belowground components reflects the adaptive survival strategies of plants in diverse habitats, developed over extended periods in response to environmental changes and stresses [2]. The distribution of aboveground and belowground biomass in grassland ecosystems is widely discussed, and in general, belowground biomass is higher than aboveground biomass in grassland ecosystems [3,4]. While extensive research has been conducted on aboveground plant biomass [5,6], the allocation of belowground biomass and its controlling factors remain relatively underexplored due to the challenges associated with obtaining belowground biomass data [7]. Investigating the allocation strategies of grassland plants for above- and belowground biomass, along with their governing
草地在陆地生态系统中作为碳汇起着至关重要的作用，对全球碳循环做出了重要贡献[1]。植物生物量在地上部分和地下部分之间的分布反映了植物在不同栖息地中的适应性生存策略，这些策略是在长时间内对环境变化和压力的响应中形成的[2]。草地生态系统中地上部分和地下部分生物量的分布被广泛讨论，一般来说，草地生态系统中地下部分生物量高于地上部分生物量[3,4]。虽然已经对地上植物生物量进行了广泛研究[5,6]，但由于获取地下生物量数据的挑战，地下生物量的分配及其控制因素仍然相对未被充分探索[7]。研究草地植物地上和地下生物量的分配策略以及其控制因素，可以推进我们对草地生态系统中碳分配和储存动态的理解[8]。
factors, can advance our understanding of carbon allocation and storage dynamics within grassland ecosystems [8].
因素。
Previous research has established that the allocation of plant biomass between aboveground and belowground components is primarily influenced by climatic and soil nutrient factors [9]. According to the optimal partitioning hypothesis, plants in resource-limited environments adjust their biomass allocation between above- and belowground components to adapt to environmental stresses [10]. The effects of climate change and soil nutrient availability can impact plant investment in belowground components within grassland ecosystems [11-13].
先前的研究已经确定，植物生物量在地上和地下部分之间的分配主要受气候和土壤养分因素的影响[9]。根据最佳分配假说，资源有限的环境中的植物会调整它们在地上和地下部分之间的生物量分配，以适应环境压力[10]。气候变化和土壤养分可用性的影响可能会影响草地生态系统中植物对地下部分的投资[11-13]。
Temperature and precipitation are pivotal climatic factors [14]. Existing studies demonstrate that the interaction between temperature and moisture significantly impacts plant belowground biomass allocation, particularly in arid regions [15]. In arid and semi-arid grasslands, elevated temperatures influence enzyme reaction rates and reduce the photosynthetic capacity of aboveground plant components [16]. Concurrently, higher temperatures accelerate the evaporation of surface water from the soil, diminishing soil moisture availability and, consequently, the plant's ability to access soil nutrients [17]. Consequently, in arid regions, plants allocate more resources to root biomass to secure water and adapt to drought stress [18].
温度和降水是关键的气候因素[14]。现有研究表明，温度和湿度之间的相互作用显著影响植物地下生物量分配，尤其是在干旱地区[15]。在干旱和半干旱草地中，升高的温度影响酶反应速率，降低地上植物组分的光合能力[16]。同时，较高的温度加速了土壤表面水分的蒸发，减少了土壤湿度可用性，从而降低了植物获取土壤养分的能力[17]。因此，在干旱地区，植物将更多的资源分配给根系生物量，以确保水分并适应干旱胁迫[18]。
Soil nutrients, including soil nitrogen content, soil phosphorus content, and soil , exert a significant influence on the BGBP . Research suggests that soil nutrients are the primary limiting or influential factors in shaping BGBP in alpine grasslands [21]. Elevated levels of soil nitrogen and phosphorus stimulate the allocation of a higher proportion of plant biomass to roots, enabling increased energy investment in root growth [22]. Soil serves as an indicator of soil fertility and is closely associated with soil microbial activity [23,24]. Within acidic soil, adjusting the soil within a certain range can enhance soil microbial activity, facilitating soil nutrient transformations and supply, consequently leading to an increase in BGBP [25].
土壤养分，包括土壤中的氮含量、磷含量和 ，对BGBP 产生显著影响。研究表明，土壤养分是塑造高山草地BGBP的主要限制或影响因素[21]。土壤中氮和磷的升高促进了植物生物量向根部的分配比例增加，从而增加了根系生长的能量投入[22]。土壤 是土壤肥力的指标，与土壤微生物活性密切相关[23,24]。在酸性土壤中，调整土壤 在一定范围内可以增强土壤微生物活性，促进土壤养分转化和供应，从而导致BGBP的增加[25]。
In addition to environmental factors, grassland productivity also significantly impacts the BGBP [26]. There is a strong positive correlation between aboveground biomass and net primary productivity (NPP), with biomass serving as the primary driver of NPP [27]. Grassland productivity reflects the amount of organic carbon fixed through photosynthesis in aboveground plant parts [28]. Therefore, as the NPP gradually increases in a specific grassland area, it signifies that the vegetation in that area acquires more biomass by increasing the aboveground growth, resulting in a decrease in the BGBP for the vegetation in that area [29].
除了环境因素外，草地生产力也对BGBP [26] 产生显著影响。地上生物量与净初级生产力（NPP）之间存在强烈的正相关关系，生物量是NPP的主要驱动因素 [27]。草地生产力反映了通过光合作用在地上植物部分固定的有机碳的数量 [28]。因此，随着特定草地区域的NPP逐渐增加，意味着该区域的植被通过增加地上生长获得更多的生物量，从而导致该区域植被的BGBP减少 [29]。
Temperature, precipitation, and soil nutrients have the capacity to alter plant biomass allocation strategies [9]. However, there is a scarcity of studies that specifically examine the differences in belowground biomass allocation between desert steppes (DSs) and alpine meadows (AMs) [30]. Owing to variations in geographical location, hydrothermal conditions, and soil nutrient profiles, the distribution of belowground biomass significantly differs between an AM and DS [11-13]. High-elevation grasslands are predominantly found in mountainous and plateau regions, characterized by dry and cold climates, and prolonged snow cover periods. Soil nutrient availability is relatively limited in these high-elevation areas [31], making soil nutrient availability a critical environmental factor influencing plant belowground biomass allocation in these grasslands. Conversely, desert grasslands are primarily situated in arid and semi-arid regions, characterized by low precipitation and high rates of water evaporation [5]. Therefore, climatic factors may be the primary environmental determinants influencing plant belowground biomass allocation in desert grasslands. In essence, the distribution of belowground biomass in grasslands is influenced by climate factors and soil nutrients [9]. Climate factors impact soil nutrients through processes like weathering, leaching, and biological interactions, subsequently influencing belowground biomass allocations in both AMs and DSs through their effects on the soil environment [32,33].
温度、降水和土壤养分具有改变植物生物量分配策略的能力[9]。然而，关于荒漠草原（DSs）和高山草甸（AMs）之间地下生物量分配差异的研究还很少[30]。由于地理位置、水热条件和土壤养分状况的变化，AM和DS之间的地下生物量分布显著不同[11-13]。高海拔草地主要分布在山地和高原地区，其特点是干燥寒冷的气候和长时间的积雪覆盖期。这些高海拔地区土壤养分的可利用性相对有限[31]，使得土壤养分的可利用性成为影响这些草地植物地下生物量分配的关键环境因素。相反，沙漠草地主要分布在干旱和半干旱地区，其特点是降水量较低和水分蒸发率较高[5]。因此，气候因素可能是影响沙漠草地植物地下生物量分配的主要环境决定因素。 本质上，草地地下生物量的分布受气候因素和土壤养分的影响[9]。气候因素通过风化、淋溶和生物相互作用等过程影响土壤养分，进而通过对土壤环境的影响，影响AMs和DSs的地下生物量分配[32,33]。
It remains unclear whether significant differences exist in the spatial distribution of the BGBP among various grassland types, and whether the dominant environmental factors and specific ecological processes responsible for the spatial distribution of BGBP are consistent. To address this question, we propose the following hypotheses based on biomass data from 182 plots spanning 17 AMs and 21 DSs in China: (1) substantial variations are observed in the spatial distributions of plant BGBP values across distinct grassland types; (2) climate factors are the primary environmental determinants influencing the BGBP in DSs, while soil nutrient factors play a pivotal role in shaping the BGBP in AMs; and (3) climate factors influence the spatial distribution patterns of BGBP in different grassland types by modulating the availability of soil nutrients.
尚不清楚不同草地类型之间的 BGBP 空间分布是否存在显著差异，以及导致 BGBP 空间分布的主要环境因素和特定生态过程是否一致。为了解决这个问题，我们根据中国 17 个 AM 和 21 个 DS 的 182 个样地的生物量数据，提出以下假设：（1）不同草地类型的植物 BGBP 值的空间分布存在显著变异；（2）气候因素是影响 DS 中 BGBP 的主要环境决定因素，而土壤养分因素在 AM 中塑造 BGBP 起关键作用；（3）气候因素通过调节土壤养分的可利用性，影响不同草地类型的 BGBP 的空间分布模式。

2. Results 2. 结果

Distinct spatial disparities are evident in the BGBPs between AMs and DSs, with AMs exhibiting a significantly higher BGBP. As the mean annual temperature (MAT) increases, the BGBP of the AM significantly declines ( ) (Figure 1A). Similarly, with rising the mean annual precipitation (MAP), both the AM and DS show a significant decrease in the (Figure 1B). Notably, the MAP is more effective in explaining the spatial variation in the BGBP in the DS compared to the AM, as indicated by higher values ( (Figure 1B).
显然，AM 和 DS 之间的 BGBPs 存在明显的空间差异，其中 AM 表现出显著较高的 BGBP。随着年均温度（MAT）的升高，AM 的 BGBP 显著下降（ ）（图 1A）。同样，随着年均降水量（MAP）的增加，AM 和 DS 的 都显著减少（图 1B）。值得注意的是，与 AM 相比，MAP 对 DS 的 BGBP 的空间变异的解释更为有效，这表现在较高的 值上（ ）（图 1B）。
Figure 1. The linear relationship between climate factors and belowground biomass production (BGBP) of alpine meadows and desert steppes. The value represents the goodness of fit of the model, and the -values indicate the significance of the results. The climate factors considered are: (A) mean annual temperature (MAT) and (B) mean annual precipitation (MAP).
图 1. 高山草甸和沙漠草原的气候因素与地下生物量生产（BGBP）之间的线性关系。 值表示模型的拟合优度， 值表示结果的显著性。考虑的气候因素包括：（A）年均温度（MAT）和（B）年均降水量（MAP）。
The BGBPs of both the AM and DS exhibit significant increases with rising soil and (Figure 2B,C). Soil displays a significant positive correlation with the BGBP of the (Figure 2A). Among these soil factors, soil has the most pronounced impact on the AM's BGBP and emerges as the primary driver of its variation (Figure . In contrast, the net primary productivity (NPP) of the AM is significantly negatively correlated with the BGBP , while the NPP in the DS does not exhibit a significant correlation with the BGBP ( (Figure 3).
AM 和 DS 的 BGBP 随着土壤 和 的升高而显著增加（图 2B，C）。土壤 与 的 BGBP 呈显著正相关（图 2A）。在这些土壤因子中，土壤 对 AM 的 BGBP 影响最显著，并成为其变异的主要驱动因素 （图 。相反，AM 的净初级生产力（NPP）与 BGBP 呈显著负相关，而 DS 的 NPP 与 BGBP 没有显著相关性（ （图 3）。
Climatic factors, soil nutrient factors, and NPP values are crucial factors that affect the spatial distribution of BGBP values for both AMs and DSs, and they have significant correlations with each other (Figure 4). Climatic factors have a negative effect on the BGBP of AMs but a positive effect on the BGBP of DSs, while soil nutrient factors have positive effects on the BGBP values of both AMs and DSs. Additionally, NPP has a negative effect on BGBP in AMs but a positive effect on BGBP in DSs (Figure 5A,B).
气候因素、土壤养分因素和 NPP 值是影响 AM 和 DS 的 BGBP 值的关键因素，它们彼此之间存在显著相关性（图 4）。气候因素对 AM 的 BGBP 有负面影响，对 DS 的 BGBP 有正面影响，而土壤养分因素对 AM 和 DS 的 BGBP 值都有正面影响。此外，NPP 对 AM 的 BGBP 有负面影响，对 DS 的 BGBP 有正面影响（图 5A、B）。
Figure 2. Linear relationship between soil factors and BGBP (belowground biomass production) of alpine meadows and desert steppes. represents the goodness of fit of the model, and -values indicate significance. Soil factors include: (A) soil total nitrogen content (Soil N); (B) soil available phosphorus content (Soil P); (C) soil pH.
图 2.高山草甸和沙漠草原土壤因子与 BGBP（地下生物量生产）之间的线性关系。 表示模型的拟合优度， -值表示显著性。土壤因子包括：（A）土壤总氮含量（土壤 N）；（B）土壤有效磷含量（土壤 P）；（C）土壤 pH 值。
Figure 3. Linear relationship between net primary productivity (NPP) and BGBP (belowground biomass production) of alpine meadows and desert steppes. represents the goodness of fit of the model, and -values indicate significance.
图 3. 高山草甸和沙漠草原的净初级生产力（NPP）与地下生物量生产（BGBP）之间的线性关系。 表示模型的拟合优度， -values 表示显著性。
Figure 4. Multivariate correlation analysis of potential influencing factors in alpine meadows (A) and desert steppes (B), including climate factors (MAT and MAP), soil factors (Soil N, Soil P, and Soil pH), and net primary productivity (NPP). .
图 4. 高山草甸（A）和沙漠草原（B）中潜在影响因素的多元相关分析，包括气候因素（MAT 和 MAP）、土壤因素（土壤氮、土壤磷和土壤 pH）以及净初级生产力（NPP）。 。
Figure 5. The high-level effects of each influencing factor on BGBP (belowground biomass production) in alpine meadows (A) and desert steppes (B). The numerical values represent the average effect size confidence interval. The dotted line indicates an effect size of 0 . The influencing factors are MAT, MAP, NPP, Soil N, Soil P, and Soil pH. We divided each influencing factor into two levels: high and low. The high level is the experimental group, which includes data values above the mean. The low level is the control group, which includes data values below the mean.
图 5. 高山草甸（A）和沙漠草原（B）中每个影响因素对地下生物量生产（BGBP）的高级效应。数值表示平均效应大小 置信区间。虚线表示效应大小为 0。影响因素包括 MAT、MAP、NPP、土壤氮、土壤磷和土壤 pH。我们将每个影响因素分为两个水平：高水平和低水平。高水平是实验组，包括高于平均值的数据值。低水平是对照组，包括低于平均值的数据值。
Climatic factors can have a direct and significant impact on BGBP (Figure 6). However, they can also significantly affect BGBP by influencing the availability of soil nutrients (Figure 6). Overall, the indirect impact of climatic factors on BGBP is stronger than the direct impact.
气候因素可以直接且显著地影响 BGBP （图 6）。然而，它们也可以通过影响土壤养分的可利用性 （图 6）来显著影响 BGBP。总体而言，气候因素对 BGBP 的间接影响比直接影响更强。
Fisher's AIC
Fisher 的 AIC
Figure 6. The relationships between climate factors, soil factors, NPP, and BGBP (belowground biomass production) in 182 different types of grasslands (alpine meadows and desert steppes) in China. The path diagram represents the standardized results of the final structural equation model (SEM) used to study the relationships between variables. The numbers next to the paths represent the standardized SEM coefficients, and asterisks indicate significance . represents the goodness of fit of the generalized additive model (GAM). The best SEM model was selected based on the lowest AIC.
图 6. 中国 182 种不同类型的草地（高山草甸和荒漠草原）中气候因素、土壤因素、NPP 和 BGBP（地下生物量生产）之间的关系。路径图表示用于研究变量之间关系的最终结构方程模型（SEM）的标准化结果。路径旁边的数字表示标准化 SEM 系数，星号表示显著性 。 表示广义加性模型（GAM）的拟合优度。基于最低 AIC 选择最佳 SEM 模型。

3. Discussion 3. 讨论

Distinct geographical disparities are evident in the BGBP between the AM and DS, with the AM exhibiting a significantly higher BGBP. This observation aligns with our previous hypothesis and is consistent with the findings from other studies [34]. Different vegetation types exhibit varying responses to climate change [35]. DSs are typically found in arid plain regions, where rainfall serves as the primary limiting factor for plant growth [36]. To adapt to the impact of drought stress on plant growth and development, plants allocate a greater proportion of organic matter to their underground parts [37]. In contrast, the elevated
在 AM 和 DS 之间的 BGBP 存在明显的地理差异，其中 AM 表现出显著较高的 BGBP。这一观察结果与我们之前的假设一致，并与其他研究的发现[34]一致。不同的植被类型对气候变化表现出不同的响应[35]。DS 通常分布在干旱平原地区，降雨是植物生长的主要限制因素[36]。为了适应干旱胁迫对植物生长和发育的影响，植物将有机物的较大比例分配给地下部分[37]。相反，AM 相对于 DS 的较高 BGBP 归因于其分布在寒冷的高海拔地区，不仅气温低，降雨也较少。在这样的环境中，草本植物倾向于增加根系发育的投资，以应对双重环境压力[38]。
BGBP in an AM compared to a DS is attributed to its distribution in cold, high-altitude regions with not only cold temperatures, but also reduced rainfall. In such environments, grass plants tend to increase their investment in root development as a response to the dual environmental stresses [38].
Numerous studies have consistently shown a strong correlation between the BGBP of plants and climate factors, particularly temperature and precipitation [39,40]. In line with the Optimal Allocation Hypothesis [41,42], a temperature increase within a certain range effectively boosts enzyme activity [43]. To capture more light energy and enhance their photosynthetic capacity, plants allocate a greater proportion of organic matter to aboveground structures, consequently reducing the allocation to belowground active processes [44,45]. This results in a significant decrease in BGBP for AMs with rising temperatures. Notably, DSs show reduced sensitivity to temperature changes, indicating that temperature is not the primary limiting factor influencing DS biomass allocation.
众多研究一致表明，植物的地上地下生物量比与气候因素之间存在着强烈的相关性，尤其是温度和降水[39,40]。与最优分配假说[41,42]一致，一定范围内的温度升高能有效促进酶活性[43]。为了捕获更多的光能并增强光合能力，植物将有机物的较大比例分配给地上结构，从而减少了对地下活跃过程的分配[44,45]。这导致随着温度升高，AMs的地上地下生物量比显著降低。值得注意的是，DSs对温度变化的敏感性降低，表明温度不是影响DS生物量分配的主要限制因素。
Both AMs and DSs exhibit a significant decline in BGBP values with increased rainfall, primarily due to the correlation between root size and plant water and nutrient absorption capacity. In water-scarce grasslands, plants respond by increasing both horizontal and vertical root growth to access more water resources [46]. Consequently, they gradually reduce their investment in belowground root construction, leading to a decrease in BGBP [47]. DSs display higher sensitivity to rainfall changes compared to AMs. Plants in arid regions have adapted to limited water resources over time, relying heavily on water efficiency for survival and growth [12]. However, their biomass allocation is more responsive to fluctuations in rainfall patterns, including increases or decreases in precipitation [48]. In contrast, AM plants thrive in relatively humid environments and have a lower absolute water dependency [13].
AMs和DSs在降雨增加时都表现出BGBP值显著下降，主要是由于根系大小与植物吸水和养分吸收能力之间的相关性。在水资源匮乏的草地中，植物通过增加水平和垂直根系生长来获取更多的水资源[46]。因此，它们逐渐减少对地下根系建设的投资，导致BGBP减少[47]。与AMs相比，DSs对降雨变化更敏感。干旱地区的植物经过时间的适应，依赖水分利用效率来生存和生长[12]。然而，它们的生物量分配对降雨模式的波动更为敏感，包括降水量的增加或减少[48]。相反，AM植物在相对湿润的环境中茁壮成长，并且对水的绝对依赖较低[13]。
Soil plays a pivotal role in supplying essential nutrients for plant growth and development, thus exerting a significant impact on plant biomass allocation strategies [12]. The BGBP of AMs exhibited a notable increase with rising soil nitrogen and phosphorus contents. In alpine regions, the lower temperatures limit soil microbial activity, resulting in slower soil organic matter decomposition and organic matter accumulation. Consequently, the higher soil organic matter content in alpine regions reflects, to some extent, a reduced resource utilization capacity [49]. With increased soil nitrogen and phosphorus contents, soil nutrient availability decreases significantly, prompting plants to allocate more resources to root systems for enhanced soil resource acquisition [50]. This increased root biomass allows plants to explore a larger soil volume for nutrient uptake, leading to higher BGBP levels [51].
土壤在为植物生长和发育提供必需营养物方面起着关键作用，因此对植物生物量分配策略产生显著影响[12]。随着土壤氮磷含量的上升，AMs的BGBP显著增加。在高山地区，较低的温度限制了土壤微生物活动，导致土壤有机物分解和有机物积累速度较慢。因此，高山地区较高的土壤有机物含量在一定程度上反映了降低的资源利用能力[49]。随着土壤氮磷含量的增加，土壤养分的可利用性显著降低，促使植物将更多资源分配到根系以增强土壤资源获取[50]。这种增加的根系生物量使植物能够探索更大的土壤体积进行养分吸收，从而导致更高的BGBP水平[51]。
In general, acidic soil tends to exhibit greater soil nutrient availability compared to alkaline soil. Therefore, as soil pH increases (shifting from acidic to alkaline), the stress related to soil resource availability intensifies, resulting in higher BGBP levels [52]. In contrast, DSs display lower sensitivity to soil nutrient changes. Desert plants tend to adopt conservative growth strategies that they maintain even when nutrient availability increases, as water often serves as the more critical limiting factor. This strategy reduces their dependence on and sensitivity to nutrient changes [47]. In contrast, plants in AMs may rely more on soil nutrients due to the need for rapid growth and reproduction within a short growing season, rendering them more sensitive to shifts in the soil nutrient status [21].
一般来说，酸性土壤相对于碱性土壤更容易提供更多的土壤养分。因此，随着土壤pH的增加（从酸性转变为碱性），与土壤资源可用性相关的压力加剧，导致更高的BGBP水平[52]。相反，沙漠植物对土壤养分变化的敏感性较低。沙漠植物倾向于采取保守的生长策略，即使在养分可用性增加时也能保持，因为水通常是更关键的限制因素。这种策略减少了它们对养分变化的依赖和敏感性[47]。相反，AM中的植物可能更依赖土壤养分，因为它们需要在短暂的生长季节内快速生长和繁殖，使它们对土壤养分状况的变化更敏感[21]。
In our research findings, net primary productivity (NPP) plays a crucial role in structural equation models (SEMs), revealing a negative correlation between BGBP and grassland NPP (Figure 6). As the NPP increases, plant photosynthesis generally intensifies, necessitating a larger leaf area to capture sunlight . Consequently, plants allocate a greater portion of their biomass to aboveground structures to support increased leaf areas and faster growth rates [54]. Moreover, high-productivity environments often signify lower environmental stress, encouraging plants to allocate more resources to belowground biomass as a strategy to withstand stress [26]. In less stressful conditions, plants may not heavily rely on these defense strategies. As productivity rises, grassland species composition may undergo changes [55]. Certain plant species favoring fertile soils may become
在我们的研究结果中，净初级生产力（NPP）在结构方程模型（SEMs）中起着关键作用，揭示了 BGBP 和草地 NPP 之间的负相关关系（图 6）。随着 NPP 的增加，植物光合作用通常会加强，需要更大的叶面积来捕捉阳光 。因此，植物将更多的生物量分配给地上结构，以支持增加的叶面积和更快的生长速率[54]。此外，高生产力环境通常意味着较低的环境压力，鼓励植物将更多资源分配给地下生物量，作为应对压力的策略[26]。在较少压力的条件下，植物可能不会过分依赖这些防御策略。随着生产力的提高，草地物种组成可能发生变化[55]。某些偏爱肥沃土壤的植物物种可能变得更为普遍。这些植物通常在地上生长上投入更多资源，而不是地下生物量[56]。因此，草地的 BGBP 随着 NPP 的增加而显著下降。
more prevalent. These plants typically invest more resources in aboveground growth than in belowground biomass [56]. Consequently, the BGBP of grasslands exhibits a significant decline with the increasing NPP.
The interplay between climate and soil factors can significantly impact plant biomass allocation strategies . Our structural equation model (SEM) results demonstrate that climate factors and soil nutrient factors exert both direct and indirect effects on grassland biomass allocation. Climate factors, such as temperature and precipitation, play a direct role in determining water and temperature stress levels, which subsequently influence the allocation of plant resources, favoring root system investment under growth and survival pressures [39]. Additionally, climate change impacts the physical and chemical properties of the soil, including nutrient availability and microbial activity, integral components of nutrient cycling. These changes further affect plant biomass allocation strategies, with resource-rich environments promoting aboveground growth for reproduction and competitiveness, while resource-limited environments encourage increased subsurface biomass for improved access to water and nutrients [51].
气候和土壤因素之间的相互作用可以显著影响植物的生物量分配策略。我们的结构方程模型（SEM）结果表明，气候因素和土壤养分因素对草地生物量分配产生直接和间接影响。气候因素，如温度和降水，直接影响水分和温度应激水平，进而影响植物资源的分配，在生长和生存压力下倾向于根系投资[39]。此外，气候变化影响土壤的物理和化学性质，包括养分可利用性和微生物活性，这些是养分循环的重要组成部分。这些变化进一步影响植物的生物量分配策略，资源丰富的环境促进地上生长以进行繁殖和竞争，而资源有限的环境则鼓励增加地下生物量以改善水分和养分的获取[51]。
Consequently, climate change not only directly influences plant physiological responses, but also indirectly shapes grassland BGBP through soil nutrient dynamics regulation . As global climate conditions become hotter and water scarcity increases, plants allocate more biomass to the belowground portion. Our study not only elucidates the regulatory impacts of climate change and soil nutrients on grassland BGBP, but also unveils the response mechanisms of grassland plant biomass allocation to global warming.
结果，气候变化不仅直接影响植物的生理反应，还通过土壤养分动态调节间接塑造了草地 BGBP。随着全球气候条件变得更热和水资源短缺，植物将更多的生物量分配到地下部分。我们的研究不仅阐明了气候变化和土壤养分对草地 BGBP 的调节影响，还揭示了草地植物生物量分配对全球变暖的响应机制。

4. Conclusions 4. 结论

Utilizing the biomass data from 182 plots across 17 alpine meadows (AMs) and 21 desert steppes (DSs) in China, this study examined the influence of climate factors, soil nutrients, and net primary productivity (NPP) on the BGBP across various grassland types. The findings reveal significant spatial distribution differences in BGBP values among the grassland types. Climate-related factors, by modulating soil nutrient availability, emerged as the primary determinants of the BGBP spatial distribution patterns. Consequently, emphasizing the impact of climate change on the allocation of biomass above- and belowground in grasslands is essential for forecasting terrestrial ecosystems' reactions to global climate change. Additionally, investigating the allocation strategies and influential factors on grassland plants' above- and belowground biomass contributes to a deeper understanding of carbon allocation and storage dynamics within grassland ecosystems. This knowledge is crucial for accurately predicting carbon feedback on a regional scale in the future.
利用中国 17 个高山草甸（AMs）和 21 个沙漠草原（DSs）中 182 个样地的生物量数据，本研究考察了气候因子、土壤养分和净初级生产力（NPP）对不同草地类型 BGBP 的影响。研究结果显示，不同草地类型的 BGBP 值在空间分布上存在显著差异。气候相关因素通过调节土壤养分的可利用性，成为 BGBP 空间分布模式的主要决定因素。因此，强调气候变化对草地地上和地下生物量分配的影响对于预测陆地生态系统对全球气候变化的反应至关重要。此外，研究草地植物地上和地下生物量的分配策略和影响因素有助于更深入地了解草地生态系统中的碳分配和储存动态。这些知识对于准确预测未来区域尺度上的碳反馈至关重要。

5. Material and Methods
5. 材料和方法

5.1. Sample Plots and BGBP Data
5.1. 样地和 BGBP 数据

Most grasslands in China are situated in the arid and semi-arid regions of Northern China and Tibetan Plateau [59]. In this study, we investigated the spatial distribution patterns and influencing factors of BGBP in different grassland types in China (Figure 7A).
中国大部分的草地位于中国北方和青藏高原的干旱和半干旱地区[59]。在本研究中，我们调查了中国不同草地类型中BGBP的空间分布模式和影响因素（图7A）。
In order to reveal the relationship between climatic factors and soil factors on the belowground biomass of grasslands, the grassland communities far away from human disturbance were selected. Consider comprehensively the distribution characteristics of grassland types and grassland vegetation at each research point. In order to avoid the non-independence of sample plot data, at least four sample plots with typical regional vegetation were randomly selected at each point, and the precise geographical information of each sample plot, such as longitude and latitude, was recorded. Set up 3 sample quadrats with a size of . The root system was collected by the root excavation method in each sample plot, and the digging depth was . When digging roots, care should be taken not to damage the roots, and the entire root system should be dug out as much as possible, including taproots, lateral roots, and fine roots. The leaves and stems of the aboveground part of the plant were harvested by the harvest method.
为了揭示气候因素和土壤因素对草地地下生物量的关系，选择了远离人为干扰的草地群落。综合考虑每个研究点的草地类型和草地植被的分布特征。为了避免样地数据的非独立性，每个点随机选择至少四个具有典型区域植被的样地，记录每个样地的精确地理信息，如经度和纬度。设置3个尺寸为的样方。每个样地采用根系挖掘法收集根系，挖掘深度为。挖掘根系时，要注意不损坏根系，并尽可能挖出整个根系，包括主根、侧根和细根。采用收获法收获植物地上部分的叶片和茎。
All plant samples above- and belowground were cured at for two hours and then dried at until they reached a constant weight; the samples were then weighed using a balance.
所有地上和地下的植物样本在 下进行了两小时的固定处理，然后在 下干燥，直到达到恒定重量；然后使用 天平称重样本。
Aboveground biomass stem dry matter weight + leaf dry matter weight .
地上生物量 茎干物质重量+叶干物质重量。
Belowground biomass (BGB) Root dry weight
地下生物量（BGB） 根干重量。
Belowground biomass proportion
地下生物量比例
Figure 7. The spatial distribution and sample locations of alpine meadows (AMs) and desert steppes (DSs) in China (A). The comparison of belowground biomass production (BGBP) between alpine meadows and desert steppes is statistically significant at the 0.05 level (B). The -value is less than .
图 7. 中国高山草甸（AMs）和荒漠草原（DSs）的空间分布和样点位置（A）。高山草甸和荒漠草原的地下生物量生产（BGBP）比较在 0.05 水平上具有统计学意义（B）。 -值小于 。

5.2. Environmental Data 5.2. 环境数据

The mean annual temperature (MAT) and mean annual precipitation (MAP) for each site were extracted from the WorldClim (version 2.0) database (https: / / worldclim.org, last accessed on 10 October 2022) at a spatial resolution of .
每个站点的年均温度（MAT）和年均降水量（MAP）从 WorldClim（版本 2.0）数据库（https://worldclim.org，最后访问于 2022 年 10 月 10 日）中提取，空间分辨率为 。
Soil pH, N, and P within the uppermost of soil were obtained from https://www. csdn.store (accessed on 10 October 2022) and https:/ /www.osgeo.cn/data/wc137 (accessed on 10 October 2022). In the abovementioned data acquisition website, soil concentrations of all samples were measured by a C-N analyzer (PE-2400 II; Perkin-Elmer, Boston, MA, USA), while soil concentrations were measured using the molybdate-ascorbic acid method after digestion [60]. The soil was determined in a 1:2.5 soil/water solution using a meter [61].
土壤 pH 值、氮和磷在土壤的最上层 中获取自 https://www.csdn.store（于 2022 年 10 月 10 日访问）和 https://www.osgeo.cn/data/wc137（于 2022 年 10 月 10 日访问）。在上述数据获取网站中，所有样品的土壤 浓度是通过 C-N 分析仪（PE-2400 II; Perkin-Elmer, Boston, MA, USA）测量的，而土壤 浓度是在 消化后使用钼酸-抗坏血酸法测量的[60]。土壤 是在 1:2.5 的土壤/水溶液中使用 计测得的[61]。

5.3. NPP Data 5.3. NPP 数据

The grassland NPP data were obtained from NASA with a resolution of (https://search.earthdata.nasa.gov/search, last accessed 10 October 2022). The Carnegie Ames Stanford Approach (CASA) model was used to estimate the NPP, following the method described by Du et al. (2022) [62]:
草原 NPP 数据是从 NASA 获取的，分辨率为 （https://search.earthdata.nasa.gov/search，最后访问于 2022 年 10 月 10 日）。使用 Carnegie Ames Stanford Approach（CASA）模型估计 NPP，按照 Du 等人（2022 年）[62]所描述的方法进行。
where represents the photosynthetically active radiation (PAR, in units of ) absorbed at pixel in month , and represents the actual light-energy utilization at pixel in month .
其中 表示在第 个月的像素 处吸收的光合有效辐射（PAR，单位为 ），而 表示在第 个月的像素 处实际利用的光能。

5.4. Statistical Analyses
5.4. 统计分析

Significance tests were conducted at a significance level of 0.05 to evaluate the differences in BGBP values between the AM and DS. The R package "agricolae" (version 4.1.0, R Core Team, 2020) was used for these significance tests [57]. In order to study the impact of biotic and abiotic factors on the spatial variation in BGBP and consider the impact of random effects on biomass allocation, we constructed linear regression models and nonlinear regression models. Finally, we selected the linear mixed-effects model with the lowest Akaike information criterion (AIC) value. The goodness of fit of the model was evaluated using . A linear mixed-effects analysis was performed using the package "Ime4". To analyze the effects of climate factors, soil factors, and NPP on BGBP, the natural logarithm of the response ratio (LnRR) was used [63]. Environmental factors were divided into experimental and control groups, with the experimental group consisting of samples above the mean and the control group consisting of samples below the mean. The response ratio (RR) was the ratio of BGBP between the experimental group (Xt) and the control group (Xc). A logarithmic transformation was used to facilitate the statistical analysis.
在显著性水平为0.05的情况下，进行了显著性检验，以评估AM和DS之间BGBP值的差异。使用R软件包"agricolae"（版本4.1.0，R Core Team，2020）进行这些显著性检验[57]。为了研究生物因素和非生物因素对BGBP空间变异的影响，并考虑随机效应对生物量分配的影响，我们构建了线性回归模型和非线性回归模型。最后，我们选择了具有最低Akaike信息准则（AIC）值的线性混合效应模型。使用 评估模型的拟合优度。使用 软件包"Ime4"进行线性混合效应分析。为了分析气候因素、土壤因素和NPP对BGBP的影响，使用响应比的自然对数（LnRR）[63]。将环境因素分为实验组和对照组，实验组包括高于平均值的样本，对照组包括低于平均值的样本。响应比（RR）是实验组（Xt）和对照组（Xc）之间BGBP的比值。 使用对数转换以便于进行统计分析。
We utilized a random-effects model in our analysis to compute the effect sizes (LnRRs) for both high and low levels of environmental factors, soil factors, NPP, and BGBP. All statistical analyses were carried out in R, and the meta-analysis was performed using the metafor package [64]. The results were then presented in a forest plot. Furthermore, we conducted a correlation analysis between different environmental factors, soil factors, NPP, and BGBP using the Mantel test method in the "vegan" package of language [65]. We also plotted the correlation heatmap between each factor and BGBP.
我们在分析中使用了随机效应模型来计算环境因素、土壤因素、NPP 和 BGBP 的效应大小（LnRRs）。所有的统计分析都是在 R 中进行的，并且使用 metafor 包[64]进行了元分析。结果以森林图的形式呈现。此外，我们还使用 语言的"vegan"包中的 Mantel 检验方法进行了不同环境因素、土壤因素、NPP 和 BGBP 之间的相关性分析[65]。我们还绘制了每个因素与 BGBP 之间的相关热图。
To determine whether climate and soil nutrient factors had a direct or indirect impact on BGBP through community characteristics, we created two structural equation models (SEMs) [66]. These SEMs were built using the R package "piecewiseSEM". The models assumed that: (1) climate factors affected soil nutrient factors, and both climate factors and soil nutrient factors together affected BGBP, and (2) climate factors had a direct effect on BGBP.
为了确定气候和土壤营养因子是否通过群落特征对 BGBP 产生直接或间接影响，我们建立了两个结构方程模型（SEMs）[66]。这些 SEMs 是使用 R 包"piecewiseSEM"构建的。模型假设：（1）气候因素影响土壤营养因子，气候因素和土壤营养因子共同影响 BGBP；（2）气候因素对 BGBP 有直接影响。
Author Contributions: J.G.: conceptualization, methodology, and investigation. J.W., X.Z., R.W., M.Y., X.C., C.Z., J.S. and J.G.: formal analysis. J.W.: writing-original draft. J.W., X.Z. and R.W. contribute equally to this work. All authors have read and agreed to the published version of the manuscript.
作者贡献：J.G.：概念化、方法论和调查。J.W.、X.Z.、R.W.、M.Y.、X.C.、C.Z.、J.S.和 J.G.：正式分析。J.W.：撰写原稿。J.W.、X.Z.和 R.W.对本工作的贡献相同。所有作者已阅读并同意发表版本的手稿。
Funding: This work was supported by the Xinjiang Normal University Young Top Talent Project (No. XJNUQB2023-14), Natural Science Foundation of Xinjiang Uygur Autonomous Region (No. 2022D01A213), Fundamental Research Funds for Universities in Xinjiang (No. XJEDU2023P071), National Natural Science Foundation of China (Grant No. 32201543), Innovation and Entrepreneurship Training Program for College Students in 2023 (No. S202310762004), Xinjiang Normal University Landmark Achievements Cultivation Project (No. XJNUBS2301), Xinjiang Graduate Innovation and Entrepreneurship Project and Tianchi Talent Program.
资助：本研究得到新疆师范大学青年顶尖人才项目（编号：XJNUQB2023-14）、新疆维吾尔自治区自然科学基金（编号：2022D01A213）、新疆高校基本科研业务费项目（编号：XJEDU2023P071）、国家自然科学基金（编号：32201543）、2023 年大学生创新创业训练计划（编号：S202310762004）、新疆师范大学标志性成果培育项目（编号：XJNUBS2301）、新疆研究生创新创业项目和天池人才计划的支持。
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.
数据可用性声明：本研究未创建或分析新数据。本文不适用于数据共享。
Conflicts of Interest: The authors declare no conflict of interest.
利益冲突：作者声明无利益冲突。

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