Effects of Biodiversity and Its Interactions on Ecosystem Multifunctionality

Abstract

Global change and the intensification of human activities have led to a sharp decline in global biodiversity and other ecological issues. Over the past 30 years, ecologists have increasingly focused on the question of whether and how the ongoing loss of biodiversity affects ecosystem functioning. However, historically, researchers have predominantly concentrated on individual ecosystem functions, neglecting the capacity of ecosystems to provide multiple ecosystem functions simultaneously, known as ecosystem multifunctionality (EMF). As a result, the connection between biodiversity and ecosystem multifunctionality (BEMF) has become the central theme in BEF relationship research. In recent years, the research on the BEMF relationship has developed rapidly, and new progress has been made in different ecosystems, the driving mechanism of the BEMF relationship, and the proposal and application of new quantitative methods. However, there are still shortcomings, such as the lack of uniform standards for the selection of functional indicators in EMF research, insufficient attention to belowground microbial diversity, and less research on biological interactions in addition to biodiversity. In the future, we need to enhance standard research on the selection of functional indicators, thoroughly assess the combined effects of aboveground and belowground biodiversity along with abiotic factors on EMF, and bolster the research and application of ecosystem multiserviceability (EMS) methods.

1. Introduction

The sharp decline in biodiversity caused by global change and human activities seriously impacts ecosystem function (EF) and service (ES) [1]. Since the 1990s, the relationship between biodiversity and ecosystem function (BEF) has become a hotspot in ecological research [2,3,4,5,6,7,8]. Initial studies mainly focused on the relationship between biodiversity and a single EF [5], but a single EF does not represent the overall impact of biodiversity on EFs [9,10,11]. Therefore, Sanderson et al. [12] proposed the term “ecosystem multifunctionality” (EMF). Hector and Bagchi [13] defined EMF as the ability of ecosystems to provide and maintain multiple functions and services simultaneously. Because there are trade-offs among EFs, managing ecosystems from the perspective of a single EF may weaken the provision or maintenance of other EFs [14]. Since then, EMF has been widely used. Manning et al. [4] divided multifunctionality into EF–multifunctionality and ES–multifunctionality for differentiation and calculation purposes. EMF provides a new and comprehensive perspective for ecosystem management. Researching forest EMF is of great significance for a comprehensive and systematic understanding of EF.

Human activities have significantly impacted the Earth’s biological systems, prompting investigations into how ecosystems can continue to deliver services and goods under various global change scenarios [15]. Consequently, considerable efforts have been directed at understanding how changes in biodiversity influence ecosystem processes and multifunctionality (EMF) [11,16]. In contrast to individual EFs related to resource utilization, biomass production, and decomposition, EMF reveals the ability of that ecosystem to deliver multiple functions at the same time [17,18]. This implies that focusing on a single EF may overlook the significant impact of biodiversity loss on the overall provision of multiple EFs [19]. However, biodiversity is not the sole factor affecting EF and EMF in biological systems. Studies indicate that various behavioral and physiological responses to global change drivers can significantly impact ecosystem functioning [20,21]. Losses from global change affect humans by simultaneously affecting plant diversity and soil microbial communities, thereby affecting a range of ecosystem functions and services [22].

2. Research Progress and Methods of Ecosystem Multifunctionality

Brockett et al. [23] pointed out that EF and ES are defined with an emphasis on “ecosystem” and “human”. EF refers to the capacity of ecosystems to provide services directly or indirectly to humans. It involves biological, chemical, and physical mechanisms that support and maintain ecosystem integrity but do not necessarily translate into anticipated human benefits [24]. ES represents the contributions of ecosystems to humans, including products directly contributed by ecosystems that can be valued [25]. Forest managers and policymakers can predict the impact of biodiversity management on human well-being based on EF.

Forests provide various important EFs such as climate regulation, pollination, water supply and purification, and habitat provision for species [26,27]. EFs encompass biomass production, nutrient cycling, soil organic carbon storage, and litter decomposition [5]. In recent years, researchers have attempted to characterize overall ecosystem functioning with a single value [10,11] to clarify the ability and performance of ecosystems to simultaneously provide and maintain multiple EFs [4,11,23,24], thereby transitioning from focusing on single EF driving factors [14,23,25] to understanding multiple EF driving factors [28], promoting an important new stage in this research field. Studying EMF can elucidate how human factors such as biodiversity, environmental factors, and land use changes simultaneously impact multiple EFs.

Firstly, quantification methods for EMF need to be clearly defined. Current predominant quantification methods in research include the Single-Function Approach, Averaging Approach, Turnover Approach, Single-Threshold Approach, Multiple-Threshold Approach, Multivariate Model Approach, and Orthologous Approach [5,13,14,19,29,30,31]. Each method has distinct advantages, limitations, and focuses. The Single-Function Approach analyzes the performance levels of multiple functions in a general linear model simultaneously but provides only qualitative descriptions without quantitative analysis of EMF. The Turnover Approach evaluates by quantifying the redundancy level of species maintaining the number of EMFs and contributions of each species to EMFs, but does not directly measure the weight of EMF and different functions. The Averaging Approach averages standardized values of each function [23,24], which is straightforward, intuitive, and detects the relative importance of predictor variables [7,27], and is more suitable for linear model analysis of the relationship between biodiversity and EMF [8,23,24]. Threshold methods calculate the number of functions above a threshold (usually expressed as a percentage of the observed function value compared to the highest function value) or within a threshold range [17] but cannot reflect the importance of specific EFs. The orthologous approach refers to different species sharing similar functions from a common gene [32,33], particularly suitable for closely related microbial taxa. The Multivariate Model Approach evaluates the impact of various aspects of diversity (such as species composition, relative abundance, and evenness) on EMFs, losing less information in analysis, but is only applicable to studies with fewer functions (e.g., three functions). However, these methods for measuring EMFs do not consider the increased weight of EFs in certain aspects, leading to biases in multifunctionality metrics. The trade-off relationships among EFs constrain the objective evaluation of EMF; thus, Manning et al. [4] proposed a quantitative method that considers the issue of functional weighting. They first cluster EF variables involved, then quantify EMF based on a threshold approach assigning equal weights to each cluster [7,14,29].

Different species’ contributions to EFs result in trade-offs among EFs [34]. In forest management and operations, maximizing target EFs often involves diminishing other EFs [35,36]. Byrnes et al. [29] suggested addressing interactions among EFs using systematic modeling approaches such as Structural Equation Modeling (SEM) or dimensionality reduction methods like Principal Components Analysis (PCA).

Once quantification methods are established, specific measurement indicators for EFs need to be selected. To date, there is no unified system of EF indicators, typically selected based on specific needs (

Table 1. Selection of ecosystem function indicators in different studies.

Ecosystem Types	Selecting Target	References
Grassland ecosystem	Aboveground productivity, belowground productivity, aboveground vegetation carbon pool, plant biomass, root biomass, lignin and cellulose degradation rates, insect species diversity, plant nitrogen content, plant phosphorus content, soil carbon pool (soil organic carbon, total carbon), soil bulk density, soil inorganic nitrogen content, total nitrogen, hydrolyzable nitrogen, total phosphorus, available phosphorus, soil natural moisture content, capillary water holding capacity, pH, total porosity, PAR transmittance ratio, community resistance to invasion, capillary porosity, non–capillary porosity, aeration porosity, cation exchange capacity, soil clay, sand, silt, conductivity, C:P ratio, C:N ratio, N:P ratio	Hector and Bagchi, [13]; Zeller et al. [37]; Jing et al. [9]; Li et al. [38]; Resch et al. [39]; Wu et al. [40]; Cheng et al. [41]; Veldkamp et al. [42]; Guo et al. [43]
Farmland ecosystem	Soil organic matter, topsoil texture, soil profile constitution, soil bulk, pH, soil thickness, cation exchange capacity, soil total nitrogen, soil total nitrogen, soil available nitrogen, soil available phosphorus, soil available phosphorus, soil total phosphorus, electrical conductivity, soil available potassium, soil aggregate, aboveground biomass, ammonium (NH4+), nitrate (NO3−),	Wade et al. [44]; Li et al. [45]; Terres et al. [46]; Smukler et al. [47]; Andersen et al. [48]; Zhang et al. [49]
Forest ecosystem	Woody plant biomass, soil organic carbon content, soil total nitrogen, hydrolyzable nitrogen, plant nitrogen, total phosphorus, available phosphorus, plant phosphorus, nitrate (NO3−), ammonium (NH4+), amino acids, proteins, pentoses, hexoses, aromatics, phenolics, potential N transformation rate, two enzyme activities (β–1,9–glucosidase and phosphatase)	Soliveres et al. [50]; Huang et al. [29,31]; Garland et al. [51]; Li et al. [52]; Hernández-Agüero et al. [53]; Anthony et al. [54]

).

From Table 1, it can be seen that indicators such as plant biomass, soil organic carbon, soil total nitrogen, soil total phosphorus, soil available phosphorus, soil hydrolyzable nitrogen, plant nitrogen, and plant phosphorus are commonly used EF indicators in research [55]. They are related to soil accumulation of organic carbon, nutrient cycling and decomposition, nutritional reservoir of aboveground biomass, biomass accumulation, soil water retention, and water conservation functions, involving various aspects of soil water, heat, air, fertilizer, and multiple EFs [56]. Although these indicators are not exhaustive, they are commonly used in soil and vegetation surveys, providing comprehensive reflections of soil fertility conditions, and are easy to investigate and measure [57].

3. Research Progress on the Relationship between Biodiversity and Ecosystem Multifunctionality (BEMF)

Research on Biodiversity–Ecosystem Functioning (BEF) has been ongoing for many years, with discussions on BEF increasing since the BEF conference held in Germany in 1992 [58]. Subsequent BEF experiments such as the Cedar Creek field experiment [8], European grassland BEF experiment [11], and BEF–China experiment [59] rapidly advanced BEF research and development [1,13]. Studies have found a positive correlation between EFs and biodiversity [17,34,60,61]. However, most BEF studies have focused only on the impact of biodiversity on individual EFs [11], even when multiple EFs are involved, and each EF is independently analyzed [12].

As BEF research deepened, it became apparent that individual EFs cannot represent the impact of biodiversity on overall ecosystem functioning [7,8,9]. Therefore, there is widespread interest in quantifying the impact of biodiversity on EMF, and whether biodiversity’s responses to individual EFs and EMF are consistent [24]. Hector and Bagchi [13] quantified, for the first time, the impact of biodiversity on multiple ecosystem processes and found that maintaining EMF requires a richer diversity of species. Consequently, research on biodiversity and ecosystem multifunctionality (BEMF) gradually entered the spotlight. Gamfeldt et al. [14] and Zeller et al. [37] discussed the importance of biodiversity in maintaining higher functional levels of ecosystems. Since then, an increasing number of scholars have studied BEMF. In recent years, BEMF research has grown, focusing primarily on aspects such as temporal and spatial scales, experimental design, and measurement methods [22,24,31,36,37,38]. Maestre et al. [55] first studied the relationship between plant species richness and EMF in global dryland ecosystems and experimentally explored how changes in key attributes such as species richness, community composition, evenness, and spatial patterns simultaneously affect EMF. Pasari et al. [62] studied, for the first time, the impact of biodiversity on EMF across multiple scales. Byrnes et al. [29] used BIODEPTH experimental data and the multi-fund program, which systematically addressed issues in BEMF research, and proposed the multiple-threshold approach for the first time. Wagg et al. [63] demonstrated the importance of soil microbial diversity and community composition in EMF in experiments on soil community diversity. Perkins et al. [64] first studied the relationship between changing environments and BEMF. Valencia et al. [65] first studied the relationship between functional diversity and EMF. Lefcheck et al. [17] systematically analyzed the role of biodiversity in EMF under different trophic levels, taxonomic groups, and habitat conditions. At the regional scale, forest EMF generally increases with increasing species richness [34,66,67]. Xu et al. [3] reviewed the research progress of BEMF and its future directions. In recent years, researchers have also explored whether biodiversity can promote EMF at landscape scales [26,67]. All the above studies show that biodiversity has a great impact on EMF. Despite the significant progress made in BEMF research [6,38], many questions remain unanswered. Moreno–Mateos et al. [68] found that biodiversity’s impact becomes stronger when considering multiple functions, whereas Gamfeldt and Roger [10] found that changes in the number of functions do not alter the impact of BEMF. These inconsistent conclusions underscore the necessity of fully understanding environmental changes and trade-offs between EFs to elucidate the driving mechanisms of BEMF.

Furthermore, researchers have deepened their understanding of biodiversity attributes, proposing multiple attributes such as species richness reflecting species abundance and richness, functional diversity reflecting resource use strategies and life forms, and phylogenetic diversity reflecting different lineage evolution [9]. Research on how these multiple biodiversity attributes affect EFs and EMF is gaining increasing attention.

4. The Impact of Aboveground Plant Diversity on Ecosystem Functioning

Early research on BEF relationships primarily focused on the species level [35]. Species richness is the most widely applied biodiversity metric in BEMF research [11,23,38]. Reduction in species is a primary cause of biodiversity loss. Different species contribute differently to EFs, and maintaining multiple functions in ecosystems requires higher levels of species diversity [34,63]. Gamfeldt and Roger [10] noted that species richness is widely used because it is the simplest and most operationally feasible metric. Increasingly, studies focus on the impact of species richness on EMF, with Gottschall et al. [69] finding a positive effect of species richness on EFs. Based on species abundance, the Shannon–Wiener index and Simpson’s diversity index can concurrently consider rare and common species with different weights, providing a more realistic measure of species diversity [7]. Recent studies have found that although communities with high species diversity exhibit higher productivity, stronger nutrient cycling capabilities, and greater stability [70], not all forms of species diversity are positively correlated with EFs [5]. Thompson et al. [71] found that species diversity and EFs may exhibit negative correlations or even unimodal relationships.

In recent years, functional diversity and phylogenetic diversity have gained attention in BEMF research. Functional diversity extends from functional traits, mainly reflecting the composition and variation in physiological, morphological, or phenological traits of plants [72]. Functional traits are biological characteristics that influence species growth, survival, reproduction rates, and ultimate fitness [73]. They are closely related to individual growth, dispersal, and ecological strategies, playing a crucial role in BEMF research [74]. Functional traits relate to how species utilize resources, making them effective predictors of ecosystem functioning [75]. Phylogenetic diversity reflects the total sum of lineage distances among species in a community, relating to species richness and average phylogenetic relationships within the community [76]. It comprehensively reflects the assembly processes of communities. Venail et al. [77] found that when phylogenetic diversity effectively includes unmeasured biological traits related to EFs, it becomes a key factor influencing EFs.

5. The Impact of Belowground Microbial Diversity on Ecosystem Functioning

Soil microbial communities are highly diverse, comprising a quarter of Earth’s total biodiversity [63], and are among the most abundant and diverse organisms on the planet [78]. Soil plays a crucial role in biogeochemical cycles [79]. Soil microbial diversity also plays a key role in maintaining EMF, facilitating material cycling and energy flow between aboveground and belowground communities through processes such as litter decomposition and organic matter mineralization [80]. In agricultural ecosystems, soil microbial diversity shows a significant positive correlation with EMF [81]. Based on early experiments, Bradford et al. [82] found that the functional complexity of soil communities enhances EMF indices derived from multiple methods. Wagg et al. [63] demonstrated that soil microbial community composition regulates and maintains EMF, with higher soil microbial diversity contributing to EMF enhancement. Research confirms that soil microbes are directly involved in complex physicochemical processes related to soil nutrients, thus influencing soil EF and EMF [15,27].

While aboveground biodiversity has received more attention in BEMF research, studies on the impact of belowground biodiversity on overall EMF have lagged [79,83]. Soil microbial diversity profoundly affects plant nutrient uptake and nutrient cycling between aboveground and belowground biological communities [84]. Therefore, understanding the impact of belowground biodiversity on EF and EMF is of paramount importance [79,82].

6. Effects of the Complexity of Ecological Network between Aboveground Plants and Belowground Soil Microorganisms on Ecosystem Functioning

In ecosystems, different species interact through material flow and exchange of energy and information, resulting in complex interactions such as competition, mutualism, predation, symbiosis, and parasitism, which are influenced by various biotic and abiotic factors [85]. Soil microorganisms are not isolated; they create complex interspecies networks that significantly influence the structure of an ecological community and consequently, the functions it provides to the ecosystem [86]. Ecological network analysis is a useful tool for depicting and analyzing microbial relationships [81,82]. Ecological networks are the basis for describing species interactions and ecosystem dynamics [87]. Recently, ecological network analysis has been widely concerned, ranging from the human gut [88] to plants [89], the soil [90], and the interaction or coexistence patterns among microorganisms in various environments, such as Ma et al. [91] and marine ecosystems [92]. Existing research mainly focuses on single biodiversity [93], ignoring the fact that EF also depends on complex interactions between aboveground and belowground organisms [18,50].

Soil biodiversity encompasses not only the abundance and number of species but also numerous complex interconnections. Ecological networks are based on their relationships as links and plant/microbial taxa as nodes [94,95]. In most cases, microbial communities (fungi and bacteria) are important mediators of biogeochemical processes and play an important role in the establishment of plant communities. The plant microbiome is the most effective group in the higher bio-microbiome that actively participates in balancing EF [89]. The diverse microbiota associated with plants help to increase or restore plant ecosystem productivity through carbon cycling, nutrient cycling, water cycling, and crop production, improve plant stress responses to various environments, and affect soil carbon storage by regulating ecosystems to mitigate the effects of climate change on them [96]. In forest ecosystems, various soil microorganisms and plant species interact to form complex ecological networks [97,98]. Ecological network analysis can reveal the ecological processes of ecosystems and provide information for revealing the complex interactions between key species and species [99]. In natural ecosystems, macroscopic organisms (such as plants and animals) and microscopic species (such as archaea, fungi, and bacteria) coexist to form a collective community. Networks are considered to compose the backbone of the effective flow of energy, material, and information within soil microbial systems and are thus imperative for the key functions of ecosystems [63]. While coexisting soil microbial taxa provide raw materials for ecological interactions, changes in microbial community structure and their relationship to EFs are not solely driven by univariate diversity or specific indicators [100,101,102]. Moreover, the interaction among taxa may vary across time, space, or environments. These findings suggest that interaction between two species can supersede the effects of species number and identity, and the coexisting interactions between coexisting taxa may be the major driver of ecosystem processes [103].

In recent years, ecological network analysis has gained widespread acceptance among microbial ecologists, and network complexity is important for ecosystem stability and EMF [25,68]. The complexity of ecological networks is closely related to the stability of EF [104], and whether and how the complexity of ecological networks affects EF is a key issue [87,105]. Chen et al. [106] used link density as a microbial network complexity index to characterize the complex interactions between microorganisms, confirming that the complexity of soil microbial networks plays an active role in maintaining EFs. Jiao et al. [107] found that the complexity of the food web based on ecological networks would significantly affect EMF. Chen et al. [106] also found that the complexity of microbial networks can directly affect EF, so the study of combining network complexity with EMF is very important for predicting the response of ecosystems to environmental changes and maintaining important EF.

7. Conclusions

The drivers of EMF are also affected by various abiotic and biotic factors [55], which suggests that distinguishing the effects of biodiversity from these other factors will be a significant challenge [6]. Forest management directly impacts aboveground biomass, which, in turn, can significantly affect the diversity and composition of soil microbial communities [108]. In addition, soil conditions are also the major drivers of EMF including soil pH [9], soil water content [109], and several other edaphic factors [110]. Climatic conditions, including temperature and rainfall, are crucial in influencing the relationship between biodiversity and EMF [9,59,80]. However, an increase in precipitation and soil temperature from the dry to rainy season may cause shifts in soil microbial community composition [111]. Environmental changes could significantly and distinctly impact EF and EMF (e.g., simultaneous performance of multiple EFs) by altering the biomass and diversity of ecological communities [50]. Importantly, while research has shown that climate at the regional scale could regulate the relationship between plant diversity or soil microbial and EMF [9], the extent of change in these relationships and whether their relative strength varies with plant or microbial diversity remain largely untested. Therefore, while considering the direct and indirect impacts of environmental conditions on EF and EMF, further research is needed on the interactions between aboveground and belowground biota, especially the effects of ecological networks on forest EMF.

In conclusion, we recommend that future research on BEMF relationships should progress by concurrently distinguishing between functions and services. Guidelines should be established for selecting indicators of functions or services for reference purposes. Future studies could focus on how typical forest BEMF relationships at different scales respond to global changes. This includes enhancing research on the comprehensive impacts of different dimensions of biodiversity, microbial diversity, and abiotic factors on EMFs, as well as studying the mechanisms of biological interactions’ effects on EMFs. It is advisable to promptly apply newly proposed concepts (such as EMF) and developed methods (such as standardized methods based on variable numerical ranges) in relevant research.