Discriminating the effects of local stressors from climatic factors and dispersal processes on multiple biodiversity dimensions of macroinvertebrate communities across subtropical drainage basins

Journal Pre-proofs Title: Discriminating the effects of local stressors from climatic factors and dispersal processes on multiple biodiversity dimensions of macroinvertebrate communities across subtropical drainage basins Zhengfei Li, Zhenyuan Liu, Jani Heino, Xiaoming Jiang, Jun Wang, Tao Tang, Zhicai Xie PII: DOI: Reference:

S0048-9697(19)34741-2 https://doi.org/10.1016/j.scitotenv.2019.134750 STOTEN 134750

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Science of the Total Environment

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18 June 2019 28 September 2019 29 September 2019

Please cite this article as: Z. Li, Z. Liu, J. Heino, X. Jiang, J. Wang, T. Tang, Z. Xie, Title: Discriminating the effects of local stressors from climatic factors and dispersal processes on multiple biodiversity dimensions of macroinvertebrate communities across subtropical drainage basins, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134750

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Title: Discriminating the effects of local stressors from climatic factors and dispersal processes on multiple biodiversity dimensions of macroinvertebrate communities across subtropical drainage basins Zhengfei Li1, Zhenyuan Liu1,5, Jani Heino2, Xiaoming Jiang3, Jun Wang4, Tao Tang1*, Zhicai Xie1* 1

The Key Laboratory of Aquatic Biodiversity and Conservation, Institute of Hydrobiology,

Chinese Academy of Sciences, Wuhan 430072, China 2

Freshwater Centre, Finnish Environment Institute, Paavo Havaksen Tie 3, P.O. Box 413,

FI-90014 Oulu, Finland 3

State Key Laboratory of Eco-hydraulic in Northwest Arid Region of China, Xi’an University of

Technology, Xi’an 710048, China 4 Institute

of International Rivers and Eco-security, Yunnan University, Kunming 650091, China

5 University

E-mails:

of Chinese Academy of Sciences, Beijing 100049, China [email protected];

[email protected];

[email protected]; [email protected]

Corresponding author: [email protected]; [email protected]

1

[email protected];

Title: Discriminating the effects of local stressors from climatic factors and dispersal processes on multiple biodiversity dimensions of macroinvertebrate communities across subtropical drainage basins Zhengfei Li1, Zhenyuan Liu1,5, Jani Heino2, Xiaoming Jiang3, Jun Wang4, Tao Tang1*, Zhicai Xie1* 1

The Key Laboratory of Aquatic Biodiversity and Conservation, Institute of Hydrobiology,

Chinese Academy of Sciences, Wuhan 430072, China 2

Freshwater Centre, Finnish Environment Institute, Paavo Havaksen Tie 3, P.O. Box 413,

FI-90014 Oulu, Finland 3

State Key Laboratory of Eco-hydraulic in Northwest Arid Region of China, Xi’an University of

Technology, Xi’an 710048, China 4 Institute

of International Rivers and Eco-security, Yunnan University, Kunming 650091, China

5 University

of Chinese Academy of Sciences, Beijing 100049, China

2

Abstract Metacommunity ecology emphasizes that community structure and diversity are not only determined by local environmental conditions through environmental filtering, but also by dispersal-related processes, such as mass effects, dispersal limitation and patch dynamics. However, the roles of dispersal processes are typically ignored in bioassessment approaches. Here, we simultaneously explored the potential influences of four groups of factors: local stressors, climatic factors, within-basin spatial factors and basin identity in explaining variation in diversity indices of macroinvertebrate assemblages from seven subtropical tributary rivers. A total of 12 biodiversity indices based on species identities, functional traits and taxonomic relatedness were calculated and used in the subsequent statistical analysis. Our results showed that, although differing in their relative importance, the four explanatory factor groups all played important roles in explaining variation in biodiversity indices. Of the pure fractions, index variation was best explained by local environmental stressors, whereas the other three explanatory factor groups appeared less influential. Furthermore, diversity indices from species, functional and taxonomic dimensions responded distinctly to the focal ecological factors, and differed in their abilities to portray the effects of human disturbances on macroinvertebrate communities. Taxonomic distinctness indices performed best, with the highest amount of variation associated to local stressors and hardly any variation explained by other factors, implying that these indices are robust in portraying human disturbances in streams. However, species diversity and functional diversity indices were also affected by spatial processes and climatic factors, suggesting that these indices should be used with caution in bioassessment. We hence conclude that environmental assessment of riverine ecosystems should not rely entirely on the perspective of species sorting. In 3

contrast, both roles of spatial processes and environmental variables related to human disturbances and climatic variation should be incorporated in management and conservation of riverine ecosystems. Key words: environmental filtering, dispersal processes, stream macroinvertebrates, species diversity, functional diversity, taxonomic distinctness

4

Introduction Anthropogenic activities and resulting environmental disturbances are recognized as the main drivers of biodiversity loss in terrestrial, marine and freshwater ecosystems across the world (Vitousek et al., 1997). Stream biological assemblages are inevitably affected by multiple human activities operating at broad spatial-temporal scales (Dudgeon et al., 2006; Malmqvist and Rundle, 2002). Therefore, developing and testing effective bioassessment indices is highly important for freshwater monitoring, conservation planning and restoration ecology (Heino et al., 2017a). In the past decades, species-based diversity indices such as taxon richness, evenness and relative abundance have been widely applied as indicators of certain human disturbances, including urbanization (Mackintosh et al., 2015), pesticide contamination (Berenzen et al., 2005) and habitat fragmentation (Krauss et al., 2010). However, these traditional species diversity indices regard all species as functionally equivalent and phylogenetically independent, which are unlikely applicable measures uncovering the mechanisms of community assembly and biodiversity maintenance (Arnan et al., 2017; Teichert et al., 2018). Community ecologists, conservation biologists and environmental managers emphasize that multiple facets of biodiversity, involving species, functional and phylogenetic knowledge should be considered in both theoretical and applied research (Bernardverdier et al., 2013; Devictor et al., 2010; Saito et al., 2015). In this context, taxonomic distinctness indices derived from different higher taxonomic categories could serve as substitutions of phylogenetic diversity (Clarke and Warwick, 1998). Therefore, taxonomic distinctness indices can represent the taxonomic relatedness or evolutionary relationships among species in a community (Clarke and Warwick, 2001). Compared to traditional species-based metrics, taxonomic distinctness indices scarcely rely 5

on sampling area and efforts, a feature allowing for direct comparisons among surveys with disparate sampling settings (Heino et al., 2007). Functional diversity is considered to be a biodiversity dimension measuring the value and range of species traits that affect their performance and ecosystem functions (Mason et al., 2005; Villéger et al., 2008), such as ecosystem dynamics, stability, nutrient availability and productivity (Goswami et al., 2017). Functional diversity is also considered to be more closely correlated with environmental variation and human disturbance than species diversity, since environmental conditions select specific traits rather than species per se (Heino and Tolonen, 2017b; Mcgill et al., 2006). Due to the fact that taxonomic and functional diversity complement the information provided by species-based metrics, bioassessment approaches may benefit from the integration of the three diversity dimensions (Saito et al., 2015; Tolonen et al., 2017). A premise underlying most bioassessment programs is that species possess different niches, and that variation in biological communities is driven by local environmental conditions, especially those resulting from human disturbance (Heino, 2013). This inference matches with the species sorting paradigm in metacommunity ecology (Leibold et al., 2004). However, in addition to local stressors, natural factors and other confounding variables at landscape or regional scales can also act as important species sorting forces, which may mask the effect of local stressors and thus bias the performance of bioassessment (Hawkins, 2006; Özkundakci et al., 2014). Consequently, before a biological index can be used to indicate human disturbances, it is essential to discriminate and eliminate the noise resulting from natural variability, such as climatic factors (Alahuhta and Aroviita, 2016; Poff et al., 2010). Metacommunity ecology also emphasizes the effects of dispersal-related processes, such as 6

mass effects, dispersal limitation and patch dynamics, on community assembly and biodiversity maintenance (Heino, 2011; Leibold et al., 2004). Of these, mass effects commonly occur between nearby sites in relatively small spatial extents or in systems characterized by high connectivity (Cottenie et al., 2003). In the mass effect paradigm, relatively high and constant flow of individuals from “source” patches to “sink” localities occurs via dispersal, which may potentially influence the effect of environmental filtering on biological communities (Amarasekare and Nisbet, 2001). However, often bioassessment and conservation programs are needed at relatively large spatial extents (e.g., across river basins, ecoregions or biogeographic regions), where dispersal limitation (preventing a large proportion of individuals from reaching favourable sites) may interfere with the influence of local environmental conditions on biological communities (Heino, 2013). It is thus necessary to develop ecologically meaningful biological indices which are widely applicable across different spatial extents as well as geographical boundaries (Poff et al., 2006; Pollard and Yuan, 2009). To our knowledge, general understanding of how biodiversity indices are jointly affected by local stressors, climatic factors and dispersal-related processes in riverine ecosystems remains limited, although several similar recent studies have been conducted in lakes (Cai et al., 2017; Cai et al., 2019; Tolonen et al., 2017). Moreover, few studies have examined the ecological factors that simultaneously affect species, functional and phylogenetic diversity in the freshwater realm (e.g., Heino and Tolonen, 2017a; Li et al., 2019a; Rocha et al., 2018), despite the recognition that multiple biodiversity dimensions can complement each other and reveal disparate information of community assembly (e.g., Devictor et al., 2010).In this study, we used macroinvertebrates, a widely-used group in bioassessment (Rosenberg and Resh, 1994), as the model organisms to 7

better understand the determinants of variation in biodiversity indices. Our study was conducted in seven tributaries of the Han River. These tributary rivers are rather isolated from each other owing to their geographical location, mountain barriers and poor hydrologic connectivity. Furthermore, these tributary rivers also present strong environmental gradients deriving from both anthropogenic activities and natural variation (Li et al., 2019c), thus giving an ideal opportunity for examining the simultaneous effects of human disturbances and natural factors on biodiversity indices. We hypothesized that: (1) local stressors, climatic factors, and within-basin and across-basin dispersal processes should contribute importantly but differently to explain variation in biodiversity indices. Particularly, we expected a predominant effect of local stressors owing to the strong variation in the intensity of human disturbance in this region (Jiang et al., 2014; Li et al., 2019c). (2) The three dimensions of biodiversity quantified by the indices differ in their ability to distinguish effects of local stressors from other sets of explanatory factors. Specifically, species diversity indices might be associated with natural variation (i.e., climatic factors) (Clarke and Warwick, 1998), as well as dispersal-related processes (Mouquet and Loreau, 2003); trait-based functional diversity indices should be primarily shaped by both local stressors and climatic factors, because changes in environmental features act as ecological filters to select for biological traits suitable to given environmental conditions (Heino, 2005; Poff et al., 2010); and taxonomic distinctness indices were anticipated to be largely influenced by local stressors related to human disturbances (Tolonen et al., 2017). 2. Methods 2.1. Study area This study was conducted in seven subtropical mountainous rivers (i.e., JinShui, Yue, JinQian, 8

Qi, Du, Si and LaoGuan Rivers), upstream tributaries of the Han River (Fig. 1). The Han River Basin (106.15-118.78 E, 31.86-34.33 N) is a large subtropical river basin in central China, covering a drainage area of 174300 km2. Mountains and hills make up approximately 84 % of the total area, providing important environments and refuges for a wide range of plants and animals (Li et al., 2019b). However, in the past few decades, this basin has suffered from substantial human impacts, of which land use changes, sand mining and water retention facilities are the most noticeable ones (Li et al., 2019b). These multiple interferences have exerted conspicuous adverse influences on aquatic ecosystems in this basin, including physical habitat alteration, water pollution and consequent sharp decline in biodiversity (Li et al., 2009; Wang and Tan, 2017). 2.2. Macroinvertebrate collection During April and May 2017, we collected macroinvertebrate samples from 103 sites in seven tributary rivers of the Han River. These sites involved the main types of human disturbances in this basin, including agriculture and construction activities, sand mining and flood control dams (see Fig C1 and C2 for details). Using a Surber net, we took five quantitative samples at each site, involving the most typical habitats presenting in a river section of ca. 100 m. These samples were kept in a portable refrigerator which were carried back to the laboratory for further processing. Specimens were identified to the lowest feasible taxonomical level (i.e., species or genus) based on the relevant resources (Epler, 2001; Morse et al., 1994; Oscoz et al., 2011; Wang, 2002; Wiggins, 1996 ). 2.3. Local stressors and climatic factors We used riparian land use types, physical habitat and water quality variables of each site to 9

represent local environmental stressors because these variables are strongly associated with human disturbances in this basin (Li et al., 2009; Wang and Tan, 2017). Riparian land use data was extracted from Landsat Thematic Mapper imagery using supervised classification with ArcGis 10.3 (Esri, Inc.). We extracted land cover information from the 1km2 (0.5 km width and 2 km length) upstream area of each site because previous research in this area has reported that land use at this spatial scale was more influential for macroinvertebrate assemblages and water chemistry (Wang and Tan, 2017). Land use data included available remote sensing images of Landsat images, Sentinel 2 and ASTER with 30 m resolution. The images were then interpreted and expressed as percentage of seven principal land use types (i.e. forest, agriculture, grassland, urban, open water, bare land and “others”). We measured local physical habitat and water quality immediately after macroinvertebrate sampling. Physical habitat variables include channel width, water depth, current velocity, water temperature, dissolved oxygen and substratum types. Water quality variables consist of turbidity, pH, conductivity, ammonium, nitrite, nitrate, total nitrogen, soluble reactive phosphate, total phosphorus and chemical oxygen demand. The measuring methods of these variables has been described in a previous article (Li et al., 2019b), and we also detailed the methods in Supplementary A. We used elevation and bioclimatic variables (i.e., temperature and precipitation) extracted from the WorldClim database (resolution~1 km) as climate-related factors (i.e., climatic factors). These long-term bioclimatic information were subsequently processed into 19 bioclimatic variables, including 11 temperature and eight precipitation variables (Hijmans et al., 2005). 2.4 Proxies for dispersal-related processes Moran’s eigenvector maps were used to model spatial configurations among sites within river 10

tributaries (i.e., within-rivers spatial factors) (Declerck et al., 2011). This modified spatial analysis has been commonly used to generate spatial factors when sampling schemes are characterized by separate regions (e.g., the seven tributary rivers in our study) which are not closely adjacent (Viana et al., 2016). This spatial analysis was conducted with the function “create.MEM.model” for the R environment (see Declerck et al., 2011), and we also described the details of this analysis in Supplementary A. Using this method, we obtained a total of 27 MEM vectors (spatial factors), which can be used as proxies of dispersal processes within each river. Since Moran eigenvector maps are not suitable for sampling schemes with large gaps (e.g., our tributary rivers), we produced “basin identity”, a dummy variable to describe large-scale spatial patterns among rivers. This dummy variable can be considered as a proxy of across-basin dispersal limitation or historical effects (see also Chaparro et al., 2018; Heino et al., 2017b; Li et al., 2019d). 2.5 Data analysis 2.5.1 Measurement of biodiversity Firstly, we measured taxon richness, Shannon diversity, Simpson diversity and Pielou’s evenness of each site to characterize species diversity (Hill, 1973). Secondly, we chose 10 biological traits of macroinvertebrates which were further separated into 33 categories. The 10 traits included those describing life history, resistance or resilience and basic biological characteristics (Table 1). These traits have previously been proved to be key traits responding sensitively to various environmental gradients in this river basin (Li et al., 2019c), and are thus suitable for the purposes of this study. More details on these traits can be found in Li et al. (2019c). Trait information of macroinvertebrates was mainly derived from published materials 11

(Beche et al., 2006; Morse et al., 1994; Poff et al., 2006). We further verified them by referring to some relevant Chinese literature and books (Liu et al., 1979; Wang, 2002; Zhang, 2011). Subsequently, four functional diversity indices including functional richness (FRic), functional evenness (FEve), functional divergence (FDiv) and functional dispersion (FDis) were calculated using the function “dbFD” in the R package FD. Thirdly, we used the functions “taxondive” and “taxa2dist” in the R package vegan to calculate four taxonomic distinctness indices based on the Linnean taxonomic trees (Oksanen et al., 2016). The four indices include taxonomic diversity (Delta), taxonomic distinctness (Delta*), average taxonomic distinctness (Delta+) and variation in taxonomic distinctness (Lambda+). We described the definitions and significance of each functional diversity and taxonomic distinctness index in Supplementary B. 2.5.2 Statistical analysis First, we ran canonical analysis of principal coordinates (CAP) using the abundance data to distinguish differences in community structures among the seven tributary rivers (Anderson et al., 2008).

We

also

ran

nonparametric

permutational

multivariate

analysis

of

variance

(PERMANOVA, number of permutation = 999) to test whether the differences in community composition among rivers were significant. CAP and PERMANOVA were run with PERMANOVA+ for PRIMER 6.0. Next, non-normally distributed biodiversity indices and environmental variables were properly transformed (arcsine-square root for proportional data and lg(x+1) for continuous data) to improve their normality before further statistical analysis. Pearson correlation analysis was used to test for the congruence between the biodiversity indices and between environmental variables in each set 12

(i.e., local stressors and climatic factors). Furthermore, we applied multiple linear regression analysis with forward selection to select important environmental and spatial variables (Blanchet et al., 2008). Before the analysis, we properly removed some highly correlated environmental variables (Pearson’s r > 0.80) to reduce multicollinearity. Then, we ran a global test of the regression model. If this test was significant, a forward selection was conducted on each set of explanatory factors separately to select variables with significant contribution (p < 0.05, after 999 random permutations). Forward selection was conducted with two stopping rules: either exceeding the critical p value (p = 0.05) or the adjusted R2 value of the reduced model exceeded that of the global model (Blanchet et al., 2008). Finally, to reveal the pure and shared effects of the four explanatory variable groups (i.e., local stressors, climatic factors, within-basin spatial factors and basin identity) on each diversity index, variation partitioning based on partial linear regression was performed using the varpart function in the R package vegan (Oksanen et al., 2016). The results are presented based on adjusted R2 values, which are unbiased estimates of explained variation as they are corrected for the number of explanatory variables (Miles, 2014). Pearson correlation, multiple linear regression and variation partitioning analyses were conducted in the R-language environment (R Core Team, 2016). 3. Results 3.1 Environmental features and metacommunity structure Environmental variables involving local stressors and climatic factors showed considerable variation across the 103 stream sites (Table 2). In total, we identified 257 taxa which presented considerable variation in both functional features and taxonomic relatedness (Supplementary C: Table C1 and Table C2). Aquatic insects 13

accounted for 88% of the total richness, with Diptera (101 taxa), Trichoptera (39 taxa) and Ephemeroptera (32 taxa) being the taxonomically most diverse groups. CAP and PERMERNOVA analyses indicated that community structures differed significantly among the seven tributary rivers (Fig.2, Table 3, p < 0.001). CAP analysis also revealed eight environmental variables that had significant relationships (Spearman r > 0.5, p < 0.05) with variation in macroinvertebrate communities, including both local environmental stressors (i.e., % Agriculture, % Urban, EC, NO2 and DO) and climatic factors (i.e., ELE, MCM and PDQ). 3.2 Biodiversity indices and their determinants A wide variation was also detected for biodiversity indices (Table 4). Strong congruence was detected for several indices within the species diversity dimension (r > 0.8, p < 0.01, Table C3). For indices describing different biodiversity dimensions (i.e., species, functional and taxonomic), most pairwise correlations varied from uncorrelated to weak and moderate (Table C3). According to the results of multiple linear regression, the number and identity of the selected key factors differed among diversity indices (Table C4). Variation partitioning revealed that local stressors, climatic factors, basin identity and spatial factors (MEMs) all played important roles for diversity indices, but their relative contributions depended on diversity dimensions (Fig. 3, 4, 5). The total amount of explained variations in all of the biodiversity indices ranged from 26% to 73%. For the unique fractions, variations in most of the indices (10 out of 12) were best accounted for by pure effects of local stressors, whereas other three variable groups appeared less influential. Pure effects of climatic and spatial factors were also significant for at least five biodiversity indices, while pure basin effects were significant only for Shannon diversity index (Fig. 3). In 14

addition, substantial shared effects of the four variable groups and unexplained variations (i.e., the residuals) were common in the models (Fig. 3, 4, 5). In the species diversity dimension, local stressors accounted for a larger proportion of variation in Evenness, Simpson and Shannon indices (17%, 12% and 23%, respectively), while spatial factors, climatic factors and basin identity were also significant in some models (Fig. 3). However, variation in species richness was equally well explained by local stressors (3%) and climatic factors (3%), followed by spatial factors (2%). For functional diversity, local stressors alone explained more of the variation (4% to 21%), followed by spatial (0 to 17%) and climatic factors (0 to 8%). Remarkably, unlike the other three functional diversity indices, variation in FEve was best explained by spatial factors (Fig 4). For taxonomic distinctness indices, only local stressors significantly explained variation in the four indices. The other three explanatory variable groups were non-significant based on variation partitioning analysis (Fig. 5). 4. Discussions 4.1 Ecological determinants of variation in biodiversity indices Findings in metacommunity ecology, biogeography and conservation biology have highlighted that community composition and diversity are shaped by factors operating at multiple scales, involving local determinants, regional effects and historical factors (Göthe et al., 2017; Heino, 2011; Leibold and Chase, 2017). In line with such perspective and also our first hypothesis, our results showed that the four explanatory factor groups (i.e., local stressors, climatic factors, within-basin spatial factors and basin identity) all played important but different roles in determining variation in biodiversity indices. 15

As expected, local stressors accounted for the highest proportion of variation in diversity indices, supporting the perspective that environmental filtering often preponderates over spatial processes in shaping metacommunities in aquatic ecosystems (Castilloescriva et al., 2017; Cottenie, 2005; Göthe et al., 2017; Nicacio and Juen, 2018). Generally, the relative variance contribution of environmental filtering (versus spatial factors) may be largely contingent on the lengths of environmental gradients, such as ranges in benthic habitat conditions and nutrient concentrations (Cottenie, 2005; Heino et al., 2015a). Anthropogenic stressors, including agricultural activities, sand dredging and dam construction have previously been recorded to be key factors generating strong environmental gradients in the studied stream system, which can provide a great power for species sorting on riverine organisms (Li et al., 2019b; Wang and Tan, 2017; Zhang et al., 2019). In addition to local stressors, community composition and diversity were also affected by large-scale factors (i.e., climatic factors) to some extent. Such findings highlight the importance of considering large-scale environmental variation at regional or landscape scales in biodiversity analysis (Burgmer et al., 2007; Li et al., 2019a). Generally, climatic factors may indirectly affect macroinvertebrate communities by exerting influences on local environmental conditions (Poff, 1997; Rocha et al., 2018). For example, variation in air temperature may regulate aquatic organisms’ growth and set limits on species distributions across landscapes indirectly by exerting influence on water temperature (Li et al., 2012). Likewise, changes in precipitation may largely affect flow regimes in rivers, which may further regulate biotic assemblages and thus ecosystem functions (Rocha et al., 2018). Although environmental filtering almost always appears to be the major determinants of 16

biological assemblages, previous studies have emphasized that aquatic metacommunities are also influenced by spatial processes (e.g., Cai et al., 2017; Castilloescriva et al., 2017; Cottenie et al., 2003). However, these studies mainly focused on community composition but rarely on biodiversity indices. In our study, spatial factors (MEMs) also explained a portion of variation in biodiversity indices, implying that

spatial effects may play an important role for community

diversity . This finding is in agreement with a few similar studies conducted at aquatic systems (Cai et al., 2019; Nicacio and Juen, 2018; Tolonen et al., 2017; Vilmi et al., 2016), which highlights the importance of considering dispersal processes when examining the variation of biodiversity indices. Surprisingly, pure effects of basin identity were almost negligible for macroinvertebrate biodiversity indices (except for Shannon diversity) in our study. This result was largely because geographical effects, like historical factors, have rather minor effects on local macroinvertebrate diversity if the spatial scale of a study area is not large enough (Rocha et al., 2017). It is also possible that the method we used to represent basin constraints was relatively simplistic, which cannot fully indicate the influence of large-scale geographical factors on community diversity. 4.2 Potential performance of the biodiversity indices in bioassessment Distinguishing the impact resulting from anthropogenic disturbances from natural variability is a pivotal step for bioassessment practice (e.g., Cai et al., 2017; Heino, 2013). Ideally, good bioassessment metrics should indicate only variation caused by local environmental stressors, whereas the effects of natural factors, dispersal processes or historical effects should be negligible (Cai et al., 2019; Vilmi et al., 2016). In our study, species, functional diversity and taxonomic distinctness indices responded differently to the focal ecological factors, and differed in their 17

abilities to indicate the effects of human disturbances, basically supporting our second hypothesis. As we expected, taxonomic distinctness indices performed best with the highest part of variation associated to local stressors, whereas hardly any variation was explained by other explanatory variable groups (Fig 5). Generally, although performing well in marine ecosystems (Clarke and Warwick, 2001; Sutton and Beckley, 2017; Zhao et al., 2016), the applications of taxonomic distinctness indices in freshwater bioassessment scenarios have not yet been sufficiently validated in different geographical and environmental settings. For example, previous empirical studies showed that taxonomic distinctness indices of lake diatoms (Leira et al., 2009), aquatic beetles (Abellán et al., 2006) and stream macroinvertebrate assemblages (Heino et al., 2007) were almost insensitive to human disturbances in the respective study regions. In contrast, other case studies focused on aquatic insects (Marchant, 2007), stream chironomids (Milošević et al., 2012) and stream macroinvertebrates (Jiang et al., 2014) revealed that these indices could efficiently portray human impairment in other regions. The opposite viewpoints could result from differences in biological data sets (e.g., species composition), regional features (e.g., climatic conditions) and disturbance regimes (e.g., impairment types and intensity), which indicate context dependency of the utility of such indices in freshwater bioassessment. In addition, different findings among regions may stem from the divergent associations between traits and phylogeny, whereby the degree of trait conservatism varies among regions (see also Wiens et al., 2010). However, our results indicated that taxonomic distinctness indices were the most robust indices distinguishing human induced stressors on streams in the study area. We expected that species diversity may be related to climatic factors as well as dispersal-related processes, and this assumption was supported by our results. Previous research 18

revealed that species distribution and the consequent species diversity variations are strongly affected by natural factors, thus potentially hindering the use of diversity measure to quantify biodiversity change and its drivers (Heino et al., 2007; Larsen et al., 2018; Tolonen et al., 2017). Moreover, species composition and diversity are also strongly affected by spatial processes (Leibold and Chase, 2017; Nicacio and Juen, 2018), which can sometimes bias the results of bioassessment in freshwater ecosystems (Vilmi et al., 2016). For example, bioassessment may be severely biased because dispersal of sensitive species from neighboring reference sites could lead to their occurrence at the polluted sites (Ng et al., 2009). .Therefore, it seems that human perturbations may not be efficiently estimated based solely on species diversity indices. Our expectation that trait-based functional diversity indices should be mainly driven by local stressors and climatic factors was partially supported. Such findings supported the habitat template theory and the landscape filtering hypothesis, which propose that environmental features at various spatial scales select species possessing appropriate traits to coexist in local communities. Surprisingly, spatial processes also contributed to the variations in functional diversity indices, with contributions which were even larger than environmental filters in some cases. This result was in accordance with the findings of previous empirical research (Heino, 2005; Li et al., 2019a; Tolonen et al., 2017), and may be related to the degree of trait redundancy, whereby species with similar traits may replace each other spatially or temporally (see also Mclean et al., 2019). Trait redundancy within a community may influence the sensitivity of functional diversity indices to environmental change. This is because different degrees of redundancy across studies or study areas can result in different sensitivities of traits to natural and anthropogenic factors. It is also possible that certain unmeasured yet spatially structured environmental variables, such as food 19

resources and heavy metal salts, were portrayed by the spatial vectors (MEM) (Alahuhta and Aroviita, 2016). Therefore, our results suggest that synthesized measures of community functional structure (e.g., functional diversity indices) may not be adequate indicators

of human

disturbances (Bady et al., 2010; Poff et al., 2006). This is because variation in these synthesized measures may integrate opposite responses of single traits to various ecological factors and obfuscate variation in functional structure along certain gradients (Butterfield and Suding, 2013; Gianuca et al., 2017; Tolonen et al., 2016). 4.3 Implications for bioassessment Our results have several implications for bioassessment in riverine ecosystems. Firstly, the results revealed that involving climatic factors and spatial processes in examining responses of candidate indices to abiotic variables should be necessary, because the importance of human-induced species sorting may be misinterpreted without considering these additional factors (Alahuhta and Aroviita, 2016; Cai et al., 2019; Tolonen et al., 2017). Secondly, macroinvertebrate community structure and diversity have severely suffered from human disturbances, so that aquatic conservation and environmental assessment are urgently needed in our study area and elsewhere. In this context, taxonomic distinctness indices should be regarded as robust tools in bioassessment portraying human disturbances on community-level biodiversity variation. We do not entirely disavow the utility of species and functional diversity indices for bioassessment, but argue that these indices should be used with caution as they are potentially regulated by climatic factors and dispersal-related processes.

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Acknowledgement This work was supported by Biodiversity Survey, Monitoring and Assessment (2019HB2096001006), the key deployment project of the Chinese Academy of Sciences (ZDRW-ZS-2016-7-1), the National Natural Science Foundation of China 31720103905, the National Science and Technology Basic Research Program (No. 2015FY110400-4), the National Science Foundation for Young Scientists of China (51609205), and the National Natural Science Foundation of China (No. 31770460, 31400469, 41571495, 31720103905). We appreciate Dr. Wu CX, Jia YT and Peng CR for their valuable assistance in field sampling. We also thank Dr Xiong X and Chen XC for helping measuring environmental variables.

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Figure captions: Fig. 1

Location of the upstream Han River Basin in China and the distribution of 103 sampling sites in the seven tributaries across the study area. Tributaries are marked by the following abbreviations: JSR-JinShui River, YR-Yue River, JQR-JinQian River, QR-Qi River, DR-Du River, SR-Si River, LGR-LaoGuan River.

Fig. 2

Canonical analysis of principal coordinates (CAP) ordination plots based on macroinvertebrate abundance dataset using Bray-Curtis similarity matrices. ELE: Elevation, MCM: Min Temperature of Coldest Month, PDQ: Precipitation of Driest Quarter, DO: Dissolved oxygen, EC: Conductivity.

Fig. 3

Results of variation partitioning based on Venn diagrams, showing the relative contribution of local stressors, climatic factors, within-basin spatial factors and basin identity on species diversity indices.

Fig. 4

Results of variation partitioning based on Venn diagrams, showing the relative contributions of local stressors, climatic factors, within-basin spatial factors and basin identity on functional diversity indices.

Fig. 5

Results of variation partitioning based on Venn diagrams, showing the relative contributions of local stressors, climatic factors, within-basin spatial factors and basin identity on taxonomic distinctness indices.

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29

30

31

32

33

34

35

Table 1 Functional trait classifications of benthic macroinvertebrate in the tributaries of the Han River Basin. Trait groups

Trait

Trait state

Code

Life history

Voltinism

Semivoltine (< 1 generation/y) Univoltine (1 generation/y) Bi- or multivoltine (> 1 generation/y) Fast seasonal Slow seasonal Non seasonal < 1weak < 1month > 1month Small (< 9 mm) Medium (9–16 mm) Large (>16 mm) Weak (e.g., cannot fly into light breeze) Strong None (soft-bodied forms) Poor (heavily sclerotized) Good (e.g., some cased caddisflies) Streamlined (flat, fusiform) Not streamlined (cylindrical, round, or bluff) Burrowers Climbers Sprawlers Clingers Swimmers Skaters Respiration tegument

Vol1 Vol2 Vol3 Dev1 Dev2 Dev3 Life1 Life2 Life3 Size1 Size2 Size3 Fly1 Fly2 Arm1 Arm2 Arm3 Shp1 Shp2 Hab1 Hab2 Hab3 Hab4 Hab5 Hab6 Res1

Development

Adult life duration

Body size

Resistance and resilience

Adult flying strength Armoring

Shape Habit

Biological characteristics

Respiration

36

Trophic groups

Gills Valve, trachea, gas film Collector-gatherer Collector-filterer Herbivore (scraper, piercer, and shedder) Predator (piercer and engulfer) Shredder (detritivore)

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Res2 Res3 Tro1 Tro2 Tro3 Tro4 Tro5

Table 2 Descriptive statistics of local stressors and climatic factors across the 103 stream sites in the Han River Basin.

Local stressors Percentage of Agriculture Percentage of Forest Percentage of Grassland Percentage of Open water Percentage of Urban Percentage of Bare land Percentage of Others Conductivity (μs/cm) Dissolved oxygen (mg/L) Water temperature (℃) pH Width (m) Mean depth (m) Current velocity (m/s) Percentage of Boulder Percentage of Cobble Percentage of Pebble Percentage of Gravel Percentage of Sand Chemical oxygen demand (mg/L) Total phosphorus (mg/L) Phosphate (mg/L) Nitrate (mg/L) Nitrite (mg/L) Ammonium nitrogen (mg/L) Total nitrogen (mg/L)

Abbreviation

Min

Max

Median

Mean

SD

CV (%)

% Agriculture % Forest % Grass % Water % Urban % Bare land % Others EC DO WT pH WD MD Velocity % Boulder % Cobble % Pebble % Gravel % Sand COD TP SRP NO3 NO2 NH4 TN

0 0.45 0 0 0 0 0 48.5 4.11 5.8 7.54 0.45 0.10 0 0 2 3 5 0 1.12 0 0 0.02 0 0 0.45

39.58 95.01 50.8 1.36 7.76 3.47 3.29 760 13.44 29.2 9.12 60 1.20 1.98 55 50 45 45 65 8.25 2.14 0.58 4.36 0.38 1.3 4.61

5.56 54.72 2.94 0.02 0.03 0.06 0.38 232.2 10.07 13.95 8.37 18 0.25 0.56 10 30 20 20 10 2.09 0.05 0.02 1.29 0 0.49 2.16

8.51 49.41 9.68 0.15 0.51 0.33 0.68 253.9 9.94 14.93 8.49 21.11 0.26 0.61 15.23 29.28 20.6 19.4 15.49 2.51 0.11 0.05 1.52 0.01 0.46 2.28

9.52 36.46 13.26 0.29 1.42 0.63 0.77 135.58 1.75 5.43 0.49 20.76 0.15 0.27 13.26 11.75 8.47 9.29 15.33 1.24 0.25 0.09 0.99 0.04 0.34 0.89

111.83 73.79 136.97 194.79 275.83 191.14 113.36 53. 40 17.60 36.41 5.71 98.36 57.76 44.77 87.03 40.11 41.11 47.87 99.01 49.32 229.17 171.7 65.1 314.88 73.31 39.23

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climatic factors Elevation Annual Mean Temperature Mean Monthly Temperature Range Isothermality Temperature Seasonality Max Temperature of Warmest Month Min Temperature of Coldest Month Temperature Annual Range Mean Temperature of Wettest Quarter Mean Temperature of Driest Quarter Mean Temperature of Warmest Quarter Mean Temperature of Coldest Quarter Annual Precipitation Precipitation of Wettest Month Precipitation of Driest Month Precipitation Seasonality Precipitation of Wettest Quarter Precipitation of Driest Quarter Precipitation of Warmest Quarter Precipitation of Coldest Quarter

ELE AMT MMTR ISO TS MWM MCM TAR MWQ MDQ MWQ MCQ AP PWM PDM PS PWQ PDQ PWA PCQ

165 9.3 7.8 26 781.9 24.8 -7.5 29.3 18.4 -1.3 19.6 -1.3 770 130 5 64.5 364 22 335 22

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1310 15.1 11.2 31.3 930.7 31.7 -1.3 35.9 24.7 4.2 25.9 4.2 1164 190 17 78.1 516 57 487 57

494.5 12.5 9.4 28.6 871 29 -4.4 32.9 22 1.5 23.25 1.5 845 156.5 10 71.05 404 36 376 36

580.73 12.56 9.58 28.75 872.64 28.82 -4.45 33.26 22.07 1.47 23.26 1.47 855.37 154.39 10.1 71.53 415.88 36.7 378.68 36.7

304.48 1.75 1.05 1.74 36.69 2.1 1.67 1.76 1.89 1.66 1.9 1.66 73.36 11.5 3.07 4.21 33.15 9.25 25.15 9.25

52.43 13.96 10.97 6.05 4.2 7.28 -37.44 5.28 8.58 112.87 8.16 112.85 8.58 7.45 30.38 5.88 7.97 25.2 6.64 25.2

Table 3 PERMANOVA results for testing differences between the macroinvertebrate communities in the seven studied rivers. Source

df

SS

MS

Pseudo-F

p (perm)

Rivers

6

79932

13322

5.8431

0.001

Residual

96

218870

2279.9

Total

102

298810 df degrees of freedom, SS square sum, MS mean sum, P (perm) P values using

permutation of residuals under a reduced model.

40

Table 4 Descriptive statistics of 12 biodiversity indices of macroinvertebrate assemblages across the 103 stream sites in the Han River Basin. Also showed the abbreviations and the related references. Diversity indices Species diversity Species richness Pielou's evenness Shannon-wiener diversity Simpson diversity Taxonomic distinctness Taxonomic diversity Taxonomic distinctness Average taxonomic distinctness Variation in taxonomic distinctness Functional diversity Functional richness Functional evenness Functional divergence Functional dispersion

Abbreviation

References

Max

Min

Richness Evenness Shannon Simpson

(Hill 1973) (Hill 1973) (Hill 1973) (Hill 1973)

53 0.98 3.41 0.95

7 0.1 0.49 0.09

Delta Delta* Delta+ Lambda+

(Warwick and Clarke 1995) (Warwick and Clarke 1995) (Clarke and Warwick 1998) Clarke and Warwick (2001)

75.32 669.51 63.68 72.43

5.46 45.09 6.75 5.51

FRic FEve FDiv FDis

(Mason et al., 2005) (Mason et al., 2005) (Mason et al., 2005) (Laliberté and Legendre, 2010)

67.36 0.79 0.96 3.41

0.91 0.09 0.09 0.41

41

Graphical abstract

42

Highlights 1. We tested ecological drivers of biodiversity indices across subtropical rivers. 2. Local environmental stressors were more important than the other factor groups. 3. Taxonomic distinctness indices performed best in portraying human disturbances. 4. Species and functional diversity indices were also related to climatic and spatial factors. 5. Bioassessment should consider various factors causing variation in bio-indicators.

43

Conflict of interest statement The authors declare that they have no conflicts of interest to this work (STOTEN-D-19-08688: Discriminating the effects of local stressors from climatic factors and dispersal processes on multiple

biodiversity

dimensions

of

macroinvertebrate

communities across subtropical drainage basins). We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Zhengfei Li Zhenyuan Liu Jani Heino Xiaoming Jiang Jun Wang Tao Tang* Zhicai Xie* 2019-9-28

44