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Article

Exploring the Drivers Influencing Multidimensional Alpha and Beta Diversity of Macroinvertebrates in Mountain Streams

by
Shudan Li
1,2,
Xingzhong Wang
3,
Lu Tan
1 and
Qinghua Cai
1,*
1
Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
2
College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 101408, China
3
College of Life Sciences, Huzhou University, Huzhou 350108, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(20), 2915; https://doi.org/10.3390/w16202915
Submission received: 13 September 2024 / Revised: 4 October 2024 / Accepted: 12 October 2024 / Published: 14 October 2024
(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)
Browse Figures
Figure 1
<p>Location of the mountain streams and sampling sites in northwestern Hubei Province (China). SNNR denotes Shennongjia National Nature Reserve.</p> "> Figure 2
<p>Correlations of alpha diversity between multiple dimensions —taxonomic, functional, and phylogenetic—based on Pearson correlation analysis. The Pearson correlation coefficients (r) and <span class="html-italic">p</span>-values are shown.</p> "> Figure 3
<p>Pure and joint effects of local environmental, land use, climate, and spatial factors on alpha diversity: (<b>a</b>) taxonomic richness, (<b>b</b>) functional alpha diversity, and (<b>c</b>) phylogenetic alpha diversity. Negative effects are omitted. The adjusted R<sup>2</sup> values are provided. ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05. Residuals denote unexplained variations of diversity.</p> "> Figure 4
<p>Decomposition of total beta diversity for the taxonomic, functional, and phylogenetic dimensions in mountain streams of northwestern Hubei Province.</p> "> Figure 5
<p>Correlations of beta diversity (including total beta diversity, turnover, and nestedness) between multiple dimensions—taxonomic, functional, and phylogenetic—based on Mantel tests. The Pearson correlation coefficients (r) and <span class="html-italic">p</span>-values are shown.</p> "> Figure 6
<p>Pure and joint effects of local environmental, land use, climatic, and spatial factors on beta diversity: (<b>a</b>) total taxonomic beta diversity, (<b>b</b>) taxonomic turnover, (<b>c</b>) taxonomic nestedness, (<b>d</b>) total functional beta diversity, (<b>e</b>) functional turnover, (<b>f</b>) functional nestedness, (<b>g</b>) phylogenetic turnover, and (<b>h</b>) phylogenetic nestedness. Negative effects are omitted. The adjusted R<sup>2</sup> values are provided. *** <span class="html-italic">p</span> &lt; 0.001, ** <span class="html-italic">p</span> &lt; 0.01, * <span class="html-italic">p</span> &lt; 0.05. Residuals denote unexplained variations of diversity.</p> ">
Review Reports Versions Notes

Abstract

:
Understanding the driving mechanisms of diversity across multiple dimensions is a fundamental task in biodiversity conservation. Here, we examined the alpha and beta diversity of macroinvertebrates in the taxonomic, functional, and phylogenetic dimensions in mountain streams of northwestern Hubei Province, China. We also assessed how much local environmental, land use, climatic, and spatial variables affected these diversities. We found that (1) there were generally weak or no correlations of alpha and beta diversity between taxonomic, functional, and phylogenetic dimensions; (2) compared to both functional and phylogenetic beta diversity, which was mainly determined by nestedness, taxonomic beta diversity was mostly molded by turnover and was much higher; and (3) local environmental variables predominantly influenced taxonomic and functional dimensions of alpha and beta diversity, while spatial factors primarily drove phylogenetic dimension. These results suggest that regulating local habitats is crucial for lotic biodiversity conservation efforts, though spatial processes cannot be overlooked. Furthermore, our findings verify the supplemental role of functional and phylogenetic data in enriching insights provided by taxonomic data alone. This underscores the importance of a multidimensional approach for a more nuanced understanding of community assembly mechanisms, which is crucial for efficient ecosystem management and biodiversity conservation.

1. Introduction

Grasping the mechanisms that drive biodiversity patterns remains a longstanding priority in ecology and conservation biology [1,2]. Conventional biodiversity studies frequently adopt a taxonomic perspective, treating all taxa equivalently and often neglecting their functional traits and phylogenetic links [3]. Functional traits, which are intrinsic characteristics of organisms influencing their growth, reproduction, and survival, are pivotal in mediating ecological functions and determining the interrelationships between organisms as well as between organisms and the environment [4]. Consequently, functional traits and diversity have emerged as a significant predictor in contemporary community ecology research [5,6]. Concurrently, there is an increasing emphasis on incorporating phylogenetic information into biodiversity studies. Phylogenetic data provide valuable insights into earlier processes, including geological events [7] and historical climate changes [8], which profoundly shape community structure. Considering that functional traits are often conserved during evolution, phylogenetic diversity can be taken as a conservative proxy for functional diversity when direct measurements of traits are unavailable [9]. Recognizing the limitations of conventional taxonomic approaches, there is a consensus among ecologists and conservation biologists on the necessity of incorporating functional and phylogenetic data into community diversity frameworks to augment the information offered by taxonomic metrics and thus enhance our comprehension of community assembly processes [10,11].
Southwood postulated that species coexist through environmental filtering, thereby underscoring its significance in community assembly [12]. A plethora of studies have shown how environmental variables drive diversity patterns. However, the nested spatial structure of environmental variables often results in overlapping effects across different scales, complicating the interpretation of their impact [13]. Furthermore, the role of dispersal-related spatial processes in regulating biodiversity has been prominently highlighted in metacommunity ecology [14]. While high level of dispersal (i.e., mass effects) due to proximity and connectivity homogenize community structure across adjacent areas, limited dispersal capabilities over larger distances can impede species from colonizing all suitable habitats, thus modulating the effects of environmental filtration [14]. Focusing on a singular perspective in community assembly studies may lead to confounding results due to the interplay of multiple factors [15,16]. Therefore, it is imperative to recognize the distinct contributions of multi-scale environmental variables and spatial processes to biodiversity patterns [17].
Alpha diversity, representing compositions within a community, and beta diversity, which captures compositional alters between communities, are both integral to understanding biodiversity [18]. Furthermore, turnover and nestedness are the two components that divide beta diversity [19]. One taxon replacing another is referred to as turnover, while nestedness describes a pattern where communities with fewer taxa represent subsets of more taxa-rich communities, resulting in changes in taxa numbers across communities [19]. This partitioning provides additional insights into the compositional variation between communities [19,20]. Within the framework of metacommunity, the simultaneous examination of alpha and beta diversity enhances our comprehension of community assembly mechanisms [21]. However, most prior studies have addressed either alpha or beta diversity, rarely addressing both. While a few have explored both, these studies have primarily focused on a single dimension of diversity [21,22], or specific categories of variables [23], or have neglected the consideration of variables altogether [24]. To date, there is a paucity of research on how multidimensional alpha and beta diversity respond to scale-hierarchical environmental and spatial factors, particularly within lotic ecosystems [25,26].
Macroinvertebrates, a complex and widely distributed assemblage, are essential components of stream ecosystems [27]. Their dispersal mechanisms (e.g., drifting, aerial dispersal), particularly in lotic ecosystems, along with their sensitivity to environmental changes, make them ideal models for studying the mechanisms driving biodiversity [16,25]. In this study, we selected 41 sampling sites from mountain streams in northwestern Hubei Province to (1) elucidate the taxonomic, functional, and phylogenetic alpha and beta diversity (along with the turnover and nestedness components) of macroinvertebrates, and (2) determine how local environmental, land use, climatic, and spatial factors driving these diversities. Building upon the existing literature, we hypothesized that correlations of alpha and beta diversity across three dimensions were weak or nonexistent (H1), suggesting that each dimension provides unique information about community composition. As functional traits are evolutionarily conserved and environmental filtering allows species with similar traits to coexist, we proposed the hypothesis that functional beta diversity, primarily composed of nestedness, would be lower than both taxonomic and phylogenetic beta diversity (H2). The habitat heterogeneity hypothesis states that community diversity is determined by the heterogeneity of habitat conditions at the local scale [28], particularly alpha diversity. In addition, both geological barriers and stream dynamics in this region may influence the dispersal processes of macroinvertebrates. We further hypothesized that multidimensional alpha diversity was predominantly influenced by local environmental variables, particularly in the functional dimension (H3), and that both local environmental and spatial variables exerted significant effects on multidimensional beta diversity (H4).

2. Materials and Methods

2.1. Study Area

The study was conducted in the northwestern region of Hubei Province, where China’s second and third topographic echelons transition and the north subtropical zone converges with the warm temperate zone. This area, characterized by a broad range of elevation (ranging from 100 m to 3100 m) and diverse slope aspects, exhibits varied microclimatic conditions. The unique and complex geographical environment and microclimate of this region, home to the world-renowned Shennongjia National Nature Reserve (SNNR), make it a critical transitional zone for plant species between the north and south, as well as a cross-zone for animal species to breed and thrive. This rich biodiversity provides an excellent setting to research community assembly mechanisms [29,30,31]. Furthermore, understanding and preserving biodiversity in this area is crucial for maintaining the safety of the Three Gorges Reservoirs and the Danjiangkou Reservoir, both of which receive water from the mountain streams in this area [29].

2.2. Macroinvertebrates Sampling

We designed sampling sites for all wadeable reaches according to the geographical map and then selected sampling locations according to practical feasibility. Finally, we collected 41 samples from mountain streams in northwestern Hubei Province, China, during October 2006 and 2007 (Figure 1). A 40-mesh Surber net (30 × 30 cm) was used for sampling macroinvertebrates, and samples were taken three times per site from different microhabitats. To ensure sediment flowed into the Surber net, the net was pressed up against the riverbed and the substrate was vigorously agitated. Large debris was manually removed, and the rest was kept in 70% alcohol for preservation. According to the established literature [32,33,34], macroinvertebrates were picked up and identified to the lowest taxonomic level (species or genus usually) in the laboratory.

2.3. Functional Traits and Phylogenetic Proxy

In this study, six functional traits of macroinvertebrates, classified into 21 categories, were considered based on the existing literature [33,35] (see Table S1). These traits include voltinism, adult flying strength, shape, size at maturity, habit, and trophic habit, all of which have been demonstrated to be sensitive to environmental variations in lotic ecosystems [36].
Due to the dearth of comprehensive molecular biological information for macroinvertebrates, taxonomic distances derived from Linnaean taxonomic trees were employed as proxies for actual phylogeny. This methodology was extensively employed in research on macroinvertebrates phylogenetic diversity [25,37,38,39] and accounts for evolutionary differences and relatedness between taxa [40,41].

2.4. Environmental Factors

Measurements were made of seventeen habitat environmental factors following macroinvertebrate sampling. Total dissolved solids (TDS), conductivity, salinity, and water temperature (WT) were recorded with a HORIBA W-23 multiparameter water quality analyzer. The depth of each section was measured with a ruler, while current velocity at 60% depth was measured using an LJD-type printed current meter. Additionally, two water samples were taken at each site, one of which was acid-fixed, and stored. Eleven chemical variables—total phosphorus (TP), phosphate (PO4-P), total nitrogen (TN), ammonia nitrogen (NO3-N), nitrite nitrogen (NO2-N), calcium ion (Ca), chloride ion (Cl), hardness, alkalinity, chemical oxygen demand (COD), and Silicon dioxide (SiO2)—were subsequently analyzed in the laboratory according to established standards [42].
Land use data at the catchment scale were derived using ArcGIS 10.8. We delineated catchment-scale polygons, representing the entire upstream drainage area of the sites, utilizing a digital elevation model from ASTER GDEMV2 data (30 m resolution), which was obtained from the Geospatial Data Cloud Platform (http://www.gscloud.cn) (accessed on 8 May 2024). The 2006 land cover data layer of China’s annual land cover dataset, classified at a 30 m resolution following the completion of supervised classification [43], was clipped based on these catchment-scale polygons. Land use percentages were calculated and classified into six categories: agricultural, forest, shrub, grassland, urban, and water area.
Long-term climatic information, including eleven temperature variables and eight precipitation variables, was obtained from the WorldClim database (1 km resolution), in accordance with the Bioclim framework [44].

2.5. Spatial Factors

Due to the absence of direct methods for quantifying dispersal, we utilized Moran’s Eigenvector Map (MEM) to model spatial processes as proxies for dispersal [45]. MEM, a spatial analysis model based on geographical equidistance between sites, can represent various spatial structures and is applicable to any sampling design [46]. By calculating geographic coordinates of the sites, we derived eigenvectors with positive eigenvalues. Large-scale spatial processes are represented by eigenvectors with low eigenvalues, while small-scale spatial processes are represented by eigenvectors with high eigenvalues. In total, 12 eigenvectors were generated and used as spatial factors in subsequent ranking analysis. The function dbmem from the R package “adespatial” was utilized for these calculations [47].

2.6. Data Analysis

All environmental variables that did not conform to a normal distribution were log-transformed using log (x + 1). Variables with strong correlations (|r| > 0.75) were eliminated using Pearson correlation analysis.

2.6.1. Alpha Diversity

Taxonomic richness (number of taxa) was chosen to measure taxonomic alpha diversity, whereas mean pairwise distance was calculated to quantify functional and phylogenetic alpha diversity. Gower distance, calculated using the gowdis function from the R package “FD”, was used as the functional distance between taxa [48]. The taxa2dist function in the R package “vegan” was employed to compute phylogenetic distance proxies based on the Linnaean classification tree [49]. Using the ses.mpd function in the R package “picante” [50], functional alpha diversity based on functional distance and phylogenetic alpha diversity based on proxy for phylogenetic distance was computed afterward [50].
Utilizing the rcorr function in the R package “Hmisc” [51], Pearson correlation analysis was performed to assess the complementarity of alpha diversities across different dimensions (taxonomic, functional, and phylogenetic).
To identify key factors significantly affecting alpha diversity indices, multiple linear regression (MLR) combined with forward selection was performed on four groups of variables (local environmental, land use, climatic, and spatial factors) using the function lm from the R package “stats” and the function stepAIC from the R package “MASS” [52]. The selection stopped when the adjusted R2 < 0.05. Variance partitioning, conducted using the varpart function [53], was used to determine the relative contributions of these key factors to alpha diversity variations. Additionally, we assessed the significance of the pure effects of models to identify the primary factors influencing alpha diversity using an ANOVA displacement test, performed with the anova.cca function from the R package “vegan”.

2.6.2. Beta Diversity

Beta diversity, with its turnover and nestedness components, was calculated using pairwise dissimilarity partitioning approaches [19]. First, three dissimilarity matrices representing total taxonomic beta diversity (Sorensen index), taxonomic turnover (Simpson index), and taxonomic nestedness were calculated using the function beta.pair from the R package “betapart” [54]. Subsequently, using the functional.bet.pair function in the same R package [54], total functional beta diversity, functional turnover, and functional nestedness were calculated based on Gower distance, while total phylogenetic beta diversity, phylogenetic turnover, and phylogenetic nestedness were calculated based on phylogenetic distance proxies.
To explore the association of total beta diversity as well as its components between multiple dimensions, we conducted Mantel tests with 999 permutations, a statistical method for measuring the correlation between two independent distance or dissimilarity matrices [55], using the mantel function from the R package “vegan”.
Variance partitioning was conducted to assess the relative importance of scale-hierarchical environmental factors and spatial factors in explaining total beta diversity, turnover, and nestedness in three dimensions. Firstly, distance-based redundancy analysis (dbRDA) [56], combined with forward selection, was conducted on the four groups of variables (local environment, land use, climatic, and spatial factor) using the rda and ordistep functions to identify key factors influencing each dissimilarity matrix (Sorensen index, Simpson index, and nestedness). The selection stopped when the adjusted R2 < 0.05. Variance partitioning was then performed for four sets of key factors using the varpart function. Lastly, we examined the significance of pure effects to identify the primary factors influencing total beta diversity and its components using an ANOVA displacement test with the anova.cca function. All functions used in this section can be found in the R package vegan.

3. Results

A total of 156 macroinvertebrate taxa belonging to 5 phyla, 10 classes, 20 orders, 69 families, and 129 genera were gathered. The taxonomic richness was mainly contributed to by insects (90%), among which Diptera (38 taxa), Ephemeroptera (35 taxa), Trichoptera (27 taxa), and Coleoptera (16 taxa) were dominant. These taxa varied in both functional traits and phylogenetic relationships.

3.1. Alpha Diversity

A weak negative correlation was found between taxonomic and functional alpha diversity (r = −0.38, p = 0.014) (Figure 2a). However, no significant correlation of alpha diversity was observed between taxonomic and phylogenetic dimensions or between functional and phylogenetic dimensions (Figure 2b,c). These findings are consistent with our hypothesis (H1).
The results from the MLR models, based on forward selection for taxonomic, functional, and phylogenetic alpha diversity, were not congruent (see Table S2). Functional alpha diversity was significantly influenced by local environmental, climatic, and spatial factors, with land use variables excluded from the model. In contrast, the models for taxonomic and phylogenetic alpha diversity incorporated all four types of variables. According to variation partitioning analysis, local environmental, land use, climatic, and spatial factors collectively explained 43% of the variation in alpha diversity for the taxonomic model. Local environmental factors had the most substantial pure effect (11%), followed by land use (8%), while climatic and spatial factors showed no pure effects (Figure 3a). For functional alpha diversity, local environmental, climatic, and spatial factors explained 53% of the variation, with local environmental factors having the largest impact (19%), followed by spatial factors (16%), and climate had no independent effect (Figure 3b). In the case of phylogenetic alpha diversity, 49% of the variation was determined by local environmental, land use, climatic, and spatial factors, with spatial factors accounting for the majority (10%) of the variation, while the other three sets of variables had no significant pure effect (Figure 3c). Notably, shared variations across different factor sets and unexplained variations commonly existed in all alpha diversity models (Figure 3). With the exception of the phylogenetic dimension, the results for taxonomic and functional alpha diversity support our hypothesis (H3).

3.2. Beta Diversity

Total taxonomic beta diversity (mean ± standard deviation: 0.57 ± 0.14) was mainly contributed by turnover (0.49 ± 0.15), with nestedness contributing only marginally (0.08 ± 0.07). In contrast, total functional beta diversity (0.10 ± 0.06) was primarily driven by nestedness (0.07 ± 0.05), with turnover contributing minimally (0.03 ± 0.04). In the phylogenetic dimension, nestedness (0.14 ± 0.29) almost entirely accounted for the total beta diversity (0.14 ± 0.29), with turnover making an insignificant contribution (0.00 ± 0.02) (Figure 4). These results support our hypothesis (H2).
The Mantel test results showed weak positive correlations of total beta diversity between taxonomic and functional dimensions (r = 0.18, p < 0.05) and between taxonomic and phylogenetic dimensions (r = 0.21, p < 0.05) (Figure 5a,d). However, no significant correlation was found between total functional beta diversity and total phylogenetic beta diversity (Figure 5g). When dividing total beta diversity into turnover and nestedness components, no correlation was found between the different dimensions, except for a moderate positive correlation between taxonomic turnover and functional turnover (r = 0.418, p < 0.01) (Figure 5b,c,e,f,h,i). Overall, these findings are consistent with our hypothesis (H1).
The dbRDA based on forward selection showed that local environmental, land use, climatic, and spatial factors significantly affected total taxonomic beta diversity and taxonomic turnover, while taxonomic nestedness was only influenced by local environmental and spatial factors (see Table S3). Variation partitioning indicated that the selected variables accounted for 78% of the total taxonomic beta diversity variation, with local environmental (19%) and spatial variables (7%) being dominant, followed by land use (3%) as pure effects. Similar trends were observed for taxonomic turnover, with 75% of the variation attributed to local environmental (15%) and spatial variables (10%), followed by land use (5%). For taxonomic nestedness, 25% of the variation was attributed to local environmental and spatial factors, with pure effects only from local environmental factors (7%) (Figure 6a–c).
For total functional beta diversity, local environmental, climatic, and spatial variables were selected in the dbRDA model, while all four groups of variables (local environmental, land use, climatic, and spatial factors) were selected in the model of functional turnover (see Table S3). Variation partitioning indicated that 30% of the total functional beta diversity variation could be attributed to the selected variables, with local environmental variables accounting for the largest portion (9%). For functional turnover, 34% of the variation was explained, though none of the variable groups had pure effects. Variation partitioning was not computed for functional nestedness, as it was determined solely by local environmental variables (17%) (Figure 6d–f).
Forward selection in the dbRDA model showed no significant influence of any variables on total phylogenetic beta diversity. However, land use variables accounted for 8% of the variation in phylogenetic turnover, and spatial factors determined 13% of phylogenetic nestedness variation (see Table S3) (Figure 6g,h).
Moreover, variations jointly explained by different factor sets and unexplained variations were commonly observed across beta diversity models (Figure 6). The results in this section partially support our hypothesis (H4).

4. Discussion

In most studies, taxonomic and functional alpha diversity were shown to be positively associated [37,57]. However, similar to earlier research [26], we found a weak negative correlation of alpha diversity between taxonomic and functional dimensions. This negative correlation may be attributed to high levels of functional redundancy within the community [26]. Additionally, no correlation was observed between taxonomic and phylogenetic alpha diversity, nor between functional and phylogenetic alpha diversity, which verified the findings of previous studies on macroinvertebrates [37,58]. These results support our hypothesis (H1) which suggests that taxonomic, functional, and phylogenetic diversity offer supplemental insights into community composition [37]. Previous research on stream macroinvertebrates in Southern Tibet found that the local environment predominantly influenced taxonomic and functional alpha diversity, while spatial processes were the major drivers of phylogenetic alpha diversity [58]. Our results corroborate these findings.
We observed that total beta diversity in the taxonomic dimension, which was primarily shaped by turnover, was much higher compared to that in functional and phylogenetic dimensions, which were mainly determined by nestedness. The same results were shown in another study [39]. This suggests that while most taxonomic replacements between sites do not necessarily the functional traits or phylogenetic composition, minor changes in taxonomic richness may lead to the emergence or disappearance of functional traits and phylogenetic lineages. This could be due to different taxa between sites sharing many functional traits and phylogenetic branches, indicating the presence of functional and phylogenetic redundancy [39]. In addition, the total functional beta diversity was lower than the total phylogenetic beta diversity, which could be attributed to the evolutionary conservation of functional traits [9]. The weak or nonexistent correlations of beta diversity across the three dimensions were observed in our study. This is consistent with the research by Jiang et al. [39]. and supports our hypothesis (H1). Our findings highlight the contributions of habitat conditions at the local scale in shaping taxonomic and functional beta diversity. In terms of phylogenetic beta diversity, which can be almost entirely represented by nestedness, it appeared most relevant to spatial processes. These results partially align with the findings of a previous study on the beta diversity of macroinvertebrates across three dimensions in a large montane landscape [36].
Despite some discrepancies in the key factors identified by different diversity indices, hydrological and chemical variables at the local scale generally contributed significantly to variations in community diversity, particularly in taxonomic and functional dimensions. This finding supports the habitat template theory, which posits that habitat conditions shape community structure via environmental filtering [12]. For instance, the sharp elevation changes in our study area likely lead to substantial variations in water temperature, a key factor for macroinvertebrates [59], which was identified as a key driver by several diversity indices. Depth, which may vary along with the complex geomorphological features, was also selected in multiple diversity models, consistent with previous research [25]. Nitrogen (e.g., NO3-N) and phosphorus (e.g., PO4-P) may be linked to precipitation and anthropogenic disturbances such as agricultural activities [16]. Additionally, the concentration of silica significantly affects community composition, possibly due to diatoms serving as a crucial food source for macroinvertebrates [60]. Moreover, salinity and hardness in freshwater were shown to impact macroinvertebrates [61,62].
Significant spatial responses in our study likely indicate dispersal limitations or mass effects, although current analytical methods cannot definitively distinguish between these processes [36]. The mountain streams in northwestern Hubei, which feed into the Three Gorges Reservoir and Danjiangkou Reservoir, are classified as headwaters within their river networks. Given the stronger separation of headstreams compared to downstream sections, organisms face more complex geomorphic barriers during dispersal [36,63]. We propose that dispersal limitations, rather than mass effects, play a more critical role in regulating diversity in this region. Therefore, MEMs with smaller serial numbers (e.g., MEM1-6), representing dispersal-related processes at larger spatial scales, were frequently selected. Geomorphic features may also reflect historical geological movements, closely tied to the phylogenetic distribution, such as relictual taxa or neo-evolutionary taxa [39], potentially explaining the impact of spatial factors on phylogenetic diversity. However, it is notable that dispersal processes between nearby sites, whether driven by water flow or the mobility of macroinvertebrates (e.g., crawling, aerial dispersal), cannot be entirely ruled out [64]. Therefore, MEMs with larger serial numbers (e.g., MEM8-12), representing dispersal-related processes at smaller spatial scales, were selected in some models.
The present study also revealed weak influences of land use and climatic variables on multi-dimensional diversity, consistent with previous research [39]. Temperature variables (e.g., annual mean temperature) can influence the life history of macroinvertebrates [65], while precipitation variables (e.g., annual precipitation) may alter community composition by affecting local hydrological conditions [59,66]. Land use variables, such as the percentage of urban area (% urban), may modify the nutritional conditions of streams, thereby influencing macroinvertebrate diversity [67]. Given that the study area primarily consists of the SNNR and its surrounding regions, coupled with the limited spatial scale of the study, most sites are characterized by substantial forest cover, and the large-scale climate characteristics appear to be relatively homogeneous. These factors may explain the weak influence of land use and climatic factors observed in this study. However, our results may not be generalizable to all stream ecosystems, and we anticipate that the contributions of land use and climatic variables to alpha and beta diversity variations would increase with broader spatial scales.
The combined effects of different key variable sets were widely observed in the diversity models, which may be attributable to the nested patterns of variables across multiple spatial scales [68]. For example, the effects of climatic variables at a large scale and the effects of water temperature at a local scale may overlap. Additionally, spatial autocorrelation is unavoidable for some environmental variables [69,70]. For example, from downstream to upstream, water temperature at the local scale may decrease with increasing spatial distance, which may combine with dispersal-related spatial factors. It is also noteworthy that a considerable proportion of diversity variations could not be explained, likely due to the exclusion of some significant variables, resulting in an underestimation of explanatory power. For example, an unfortunate instrument malfunction led to the loss of dissolved oxygen data, a critical variable for macroinvertebrates [71,72]. Future studies should aim to improve the collection and statistical analysis of influential variables. For instance, environmental regime variables—dynamic characteristics of the environment over time—are potent explanatory factors for the composition of aquatic assemblages [73,74]. Additionally, systematic shortcomings, such as differences in sampling effort and identification errors, introduce random noise into the research findings [39].
Our research has significant implications for biodiversity assessment and conservation. A comprehensive approach that incorporates multiple dimensions of diversity—taxonomic, functional, and phylogenetic—is essential, as relying on a single perspective is insufficient to fully capture the composition of communities [37]. While ideally, all dimensions of biodiversity should be preserved, resource constraints necessitate the development of strategic priorities [26]. The decomposition of beta diversity into turnover and nestedness components offers valuable insights for conservation planning. All sites ought to be protected while turnover is the major process, as each contributes unique information to overall biodiversity. In contrast, when nestedness predominates, conservation efforts should focus on sites with the highest richness, as other sites represent subsets of these richer communities [75]. For instance, when ecosystem service functions are highly valued, sites with greater functional diversity should be prioritized for conservation in our study area, given that functional beta diversity was primarily composed of nestedness [37]. Local environmental management plays a crucial role in the conservation of community biodiversity, especially concerning taxonomic and functional diversity. However, it is important to recognize that large-scale environmental conditions can also have an impact on community composition, either directly or indirectly. Consequently, conservation strategies must not solely focus on local habitat management. For instance, while our study found that land use was not a primary driver of biodiversity patterns, other research has demonstrated that in landscapes where farmland coverage exceeds 75%, habitat restoration may be ineffective. In such cases, the negative impacts of agricultural practices can offset or even outweigh the benefits of restoration efforts [70]. Moreover, our findings underscore the contributions of spatial processes in shaping biodiversity, especially in the phylogenetic dimension. Future research is needed to disentangle the influences of spatial factors from environmental variables, enabling a more precise evaluation and management of environmental indicators [36]. This would allow for the maximization of biodiversity protection by targeting the most critical aspects of community structure and function.

5. Conclusions

A comprehensive perspective is essential for understanding the mechanisms driving community biodiversity [2]. Our findings suggest that for macroinvertebrates in mountain streams, local environmental factors predominantly influence taxonomic and functional diversity, while phylogenetic diversity is primarily driven by spatial factors, although the detailed key factors and their specific effects may vary in alpha and beta diversity. On the contrary, the influence of land use information as well as climatic factors on biodiversity appears relatively weak. Our results indicate minimal correlation among taxonomic, functional, and phylogenetic diversity, which highlights the necessity of integrating multiple dimensions of information into the community biodiversity framework. Biodiversity conservation efforts for mountain stream ecosystems should consider regional scales for taxonomic diversity and smaller, high-diversity areas for functional and phylogenetic diversity. In conclusion, we recommend employing an all-encompassing approach that includes multiple dimensions (e.g., taxonomic, functional, and phylogenetic), different diversity components (e.g., alpha and beta), and hierarchical-scale variables at various spatial scales. This comprehensive strategy will enhance the efficiency of biodiversity assessments and conservation works, thereby promoting the sustainability of ecosystems and their biodiversity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16202915/s1, Table S1: Macroinvertebrate functional traits and states (coded) (modified from Poff et al., 2006 [35]); Table S2: Key factors selection of alpha diversity indices using multiple linear regression (MLR) and forward selection; Table S3: Key factors selection of beta diversity indices using db-RDA and forward selection.

Author Contributions

Conceptualization, S.L.; methodology, S.L.; software, S.L.; validation, S.L.; formal analysis, S.L.; investigation, X.W.; resources, L.T. and Q.C.; data curation, L.T.; writing—original draft preparation, S.L.; writing—review and editing, S.L. and Q.C.; visualization, S.L.; supervision, Q.C.; project administration, Q.C.; funding acquisition, Q.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA, grant number 30330140; and the NATIONAL KEY R & D PROGRAM OF CHINA, grant number 2017YFC0506406.

Data Availability Statement

Data will be available from the authors upon reasonable request.

Acknowledgments

We thank Fengzhi He for his insightful comments during the editing of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of the mountain streams and sampling sites in northwestern Hubei Province (China). SNNR denotes Shennongjia National Nature Reserve.
Figure 1. Location of the mountain streams and sampling sites in northwestern Hubei Province (China). SNNR denotes Shennongjia National Nature Reserve.
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Figure 2. Correlations of alpha diversity between multiple dimensions —taxonomic, functional, and phylogenetic—based on Pearson correlation analysis. The Pearson correlation coefficients (r) and p-values are shown.
Figure 2. Correlations of alpha diversity between multiple dimensions —taxonomic, functional, and phylogenetic—based on Pearson correlation analysis. The Pearson correlation coefficients (r) and p-values are shown.
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Figure 3. Pure and joint effects of local environmental, land use, climate, and spatial factors on alpha diversity: (a) taxonomic richness, (b) functional alpha diversity, and (c) phylogenetic alpha diversity. Negative effects are omitted. The adjusted R2 values are provided. ** p < 0.01, * p < 0.05. Residuals denote unexplained variations of diversity.
Figure 3. Pure and joint effects of local environmental, land use, climate, and spatial factors on alpha diversity: (a) taxonomic richness, (b) functional alpha diversity, and (c) phylogenetic alpha diversity. Negative effects are omitted. The adjusted R2 values are provided. ** p < 0.01, * p < 0.05. Residuals denote unexplained variations of diversity.
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Figure 4. Decomposition of total beta diversity for the taxonomic, functional, and phylogenetic dimensions in mountain streams of northwestern Hubei Province.
Figure 4. Decomposition of total beta diversity for the taxonomic, functional, and phylogenetic dimensions in mountain streams of northwestern Hubei Province.
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Figure 5. Correlations of beta diversity (including total beta diversity, turnover, and nestedness) between multiple dimensions—taxonomic, functional, and phylogenetic—based on Mantel tests. The Pearson correlation coefficients (r) and p-values are shown.
Figure 5. Correlations of beta diversity (including total beta diversity, turnover, and nestedness) between multiple dimensions—taxonomic, functional, and phylogenetic—based on Mantel tests. The Pearson correlation coefficients (r) and p-values are shown.
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Figure 6. Pure and joint effects of local environmental, land use, climatic, and spatial factors on beta diversity: (a) total taxonomic beta diversity, (b) taxonomic turnover, (c) taxonomic nestedness, (d) total functional beta diversity, (e) functional turnover, (f) functional nestedness, (g) phylogenetic turnover, and (h) phylogenetic nestedness. Negative effects are omitted. The adjusted R2 values are provided. *** p < 0.001, ** p < 0.01, * p < 0.05. Residuals denote unexplained variations of diversity.
Figure 6. Pure and joint effects of local environmental, land use, climatic, and spatial factors on beta diversity: (a) total taxonomic beta diversity, (b) taxonomic turnover, (c) taxonomic nestedness, (d) total functional beta diversity, (e) functional turnover, (f) functional nestedness, (g) phylogenetic turnover, and (h) phylogenetic nestedness. Negative effects are omitted. The adjusted R2 values are provided. *** p < 0.001, ** p < 0.01, * p < 0.05. Residuals denote unexplained variations of diversity.
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Li, S.; Wang, X.; Tan, L.; Cai, Q. Exploring the Drivers Influencing Multidimensional Alpha and Beta Diversity of Macroinvertebrates in Mountain Streams. Water 2024, 16, 2915. https://doi.org/10.3390/w16202915

AMA Style

Li S, Wang X, Tan L, Cai Q. Exploring the Drivers Influencing Multidimensional Alpha and Beta Diversity of Macroinvertebrates in Mountain Streams. Water. 2024; 16(20):2915. https://doi.org/10.3390/w16202915

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Li, Shudan, Xingzhong Wang, Lu Tan, and Qinghua Cai. 2024. "Exploring the Drivers Influencing Multidimensional Alpha and Beta Diversity of Macroinvertebrates in Mountain Streams" Water 16, no. 20: 2915. https://doi.org/10.3390/w16202915

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