Community assembly processes underlying ...

Science of the Total Environment 630 (2018) 658–667

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Community assembly processes underlying phytoplankton and bacterioplankton across a hydrologic change in a human-impacted river Alain Isabwe a,b, Jun R. Yang a,b, Yongming Wang a, Lemian Liu a, Huihuang Chen a, Jun Yang a,⁎ a b

Aquatic EcoHealth Group, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, 361021 Xiamen, PR China University of Chinese Academy of Sciences, 100049 Beijing, PR China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Riverine bacterio- and phytoplankton were studied during the dry and wet seasons. • Deterministic assembly underlies both plankton groups in the dry season. • Landscape and hydrology act together to dictate deterministic assembly. • Watershed management could consider dispersal routes for regional species pool.

a r t i c l e

i n f o

Article history: Received 10 September 2017 Received in revised form 28 January 2018 Accepted 17 February 2018 Available online xxxx Editor: Daniel Wunderlin Keywords: Phytoplankton Bacterioplankton Variation partitioning analysis Deterministic process Stochastic process Community assembly Dispersal Jiulong River

⁎ Corresponding author. E-mail address: [email protected] (J. Yang).

https://doi.org/10.1016/j.scitotenv.2018.02.210 0048-9697/© 2018 Elsevier B.V. All rights reserved.

a b s t r a c t Although the influence of microbial community assembly processes on aquatic ecosystem function and biodiversity is well known, the processes that govern planktonic communities in human-impacted rivers remain largely unstudied. Here, we used multivariate statistics and a null model approach to test the hypothesis that environmental conditions and obstructed dispersal opportunities, dictate a deterministic community assembly for phytoplankton and bacterioplankton across contrasting hydrographic conditions in a subtropical mid-sized river (Jiulong River, southeast China). Variation partitioning analysis showed that the explanatory power of local environmental variables was larger than that of the spatial variables for both plankton communities during the dry season. During the wet season, phytoplankton community variation was mainly explained by local environmental variables, whereas the variance in bacterioplankton was explained by both environmental and spatial predictors. The null model based on Raup-Crick coefficients for both planktonic groups suggested little evidences of the stochastic processes involving dispersal and random distribution. Our results showed that hydrological change and landscape structure act together to cause divergence in communities along the river channel, thereby dictating a deterministic assembly and that selection exceeds dispersal limitation during the dry season. Therefore, to protect the ecological integrity of human-impacted rivers, watershed managers should not only consider local environmental conditions but also dispersal routes to account for the effect of regional species pool on local communities. © 2018 Elsevier B.V. All rights reserved.

A. Isabwe et al. / Science of the Total Environment 630 (2018) 658–667

1. Introduction Rivers provide humans with important goods and essential ecosystem services along with acting as refuges for different ecological communities, of which prokaryotic and eukaryotic plankton are particularly diverse. These microorganisms play an important role in food webs, carbon cycling, oxygen release and biogeochemical cycles across the globe (Allan and Castillo, 2007; Aufdenkampe et al., 2011). For a long time, the understanding of local and regional processes that govern plankton communities has been a central theme in ecology (Lind and Pearsall, 1944; Vellend, 2010; Nemergut et al., 2013; Heino et al., 2014; Louca et al., 2016). However, due to the recently observed and predicted human and climate-induced biodiversity loss in the world's freshwater ecosystems (Sala et al., 2000; Vorosmarty et al., 2010; Carrara et al., 2014) there is an increasing endeavor to ascertain mechanisms governing spatial and temporal changes in those communities within and across different ecosystems. Such knowledge not only improves our understanding of plankton community ecology and biogeography, but also provides baseline information to effectively manage and conserve our planet's declining freshwater biodiversity (Dudgeon et al., 2006; Altermatt, 2013; Heino, 2013). Extensive human settlement along rivers causes substantial hydrological alterations that affect riverine community structure and function (McCluney et al., 2014; Yang et al., 2017). Change in water quality through nutrients inputs, flow modification, and altered macrosystem dynamics (i.e. watershed-scale networks of connected and interacting riverine and upland habitat patches) are among the pathways by which anthropogenic activities disrupt riverine ecosystem integrity. These can cause rapid and difficult-to-reverse ecological changes that impedes the sustainable delivery of ecosystem services (Allan and Castillo, 2007; McCluney et al., 2014; Widder et al., 2014; Schuhmacher et al., 2016). In summary, riverine biodiversity declines in response to land use, eutrophication and habitat destruction. Metacommunity (a set of local communities that are linked by dispersal of multiple potentially interacting species; Wilson, 1992; Leibold et al., 2004; Logue et al., 2011; Heino, 2013) provides information on the construction and maintenance of local communities through sequential arrival of potential colonists from an external species pool (Fukami, 2004). In turn, this information is necessary for the understanding of community assembly processes (ecological processes generating community abundances and compositional turnover through joint effects of selection, drift, speciation and dispersal; Vellend, 2010). However, a dichotomous debate upon two mechanisms by which communities are assembled is ongoing. Deterministic assembly postulates that species coexistence is determined by both environmental characteristics and species' traits, whereas stochastic assembly posits that species coexistence results from a balance between speciation and extinction of species through a random demographic drift (Hubbell, 2001; Leibold et al., 2004). In riverine environments, there is no general consensus on how phytoplankton and bacterioplankton are assembled given that studies have so far provided divergent and often equivocal evidence. For instance, phytoplankton and bacterioplankton communities studied separately were found to be mainly structured by deterministic (Heino et al., 2014; Huszar et al., 2015), stochastic (Devercelli et al., 2016), or both processes (Gibbons et al., 2014) and sometimes no evidence of either process was found (Nabout et al., 2009). Spatial scales at which ecosystems are considered often influence the results of the main process. Normally, stochastic processes dominate at large geographical scales whereas deterministic processes dominate mainly at small geographical scales thus leaving the perspective at medium geographical scales (e.g. mid-sized watersheds) questionable (Soininen et al., 2011; Heino et al., 2015). Furthermore, it has been suggested that the relative roles of the causal mechanisms structuring communities in lotic ecosystems depends largely on species traits, hydrology and local environmental dynamics as a result of the dual influence of landscape structure and water flows within the study system

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(Altermatt, 2013; Widder et al., 2014; Devercelli et al., 2016; NiñoGarcía et al., 2016). Hydrological changes within connected habitats may vary across temporal and/or spatial scales (Yeh et al., 2015; Lansac-Tôha et al., 2016) and can be used to infer community assembly, thus they are important for applied perspective and crucial to determining the outcome of habitat loss and fragmentation (Brown et al., 2011). Lotic ecosystems are arguably the most appropriate systems for understanding community assembly processes and are among ecosystems undergoing habitat fragmentation (Malmqvist and Rundle, 2002; Zeglin, 2015). Given that anthropologic changes may constitute a strong environmental filter, understanding key ecological processes that govern plankton community assembly in rivers under human pressure is clearly important for sustainable watershed management. In the context of a mid-sized subtropical river, this study aimed to disentangle the main community assembly processes underlying phytoplankton and bacterioplankton communities. Subsequently, we sought to answer the question of whether both phytoplankton and bacterioplankton show similar or distinct assembly processes across contrasting hydrologic regimes (wet and dry seasons). We hypothesized that under harsh environmental conditions, the communities of the studied microbial groups could be primarily determined by deterministic processes. Conceptually, we started from the ideas that low flow regimes and longitudinal landscape structure may provoke inchannel habitat patches that could increase the importance of deterministic factors via obstructed river connectivity and enhanced site isolation which can limit species dispersal (Fig. S1). The Jiulong River, a mid-sized river in subtropical China, is an appropriate system to study community assembly processes in rivers under strong anthropogenic disturbances. The population density of N200 persons per km2 in the watershed is suspected to have detrimentally influenced biochemical processes in the river through urban runoff, sewage discharge and increasing agricultural practices (Liu et al., 2013b; Chen et al., 2014; Mo et al., 2016). Moreover, N130 hydropower dams built along the main channel and its tributaries have been found to exacerbate riverine ecosystem vulnerability and have reduced the value of ecosystem services the river could offer (Wang et al., 2010; Wang et al., 2015). 2. Materials and methods 2.1. Study area The Jiulong River (116o46′55″–118°02′17″E, 24°23′53″–25°53′38″ N) covers 14,700 km2 of the watershed area. It is formed by the confluence of two major tributaries: the North and West Rivers that drain through nine county-level administrative units of Longyan, Zhangzhou and Xiamen cities inhabited by nearly 10 million people (Huang et al., 2013). The Jiulong River watershed has a typical subtropical monsoon climate with mean annual air temperature of 19.9–21.1 °C and mean annual precipitation of 1400–1800 mm. Normally, about 75% of annual rainfall occurs between spring and summer. Its hydrology is thus characterized by two hydrographic conditions: low (October–January) and high (April–September) water level periods (referred to as dry and wet seasons in this study). With rapid economic development, urbanization and population growth within the watershed, the river suffers environmental degradation and resource disruption (Wang et al., 2010). The upstream areas of Longyan city (LY1–3; Fig. 1) have experienced an increase in poultry farming over time (Chen et al., 2015; Cui et al., 2015) while the downriver areas of both the North and West tributaries, namely Changtai (CT1–2), Longjin (LJ), Changcun (CC1–2) and Zhangzhou (ZZ), are highly populated and the land use is predominantly agricultural (Huang et al., 2013; Fig. 1). The remaining areas are primarily dominated by forested lands with relatively low population density. Previous studies have demonstrated that human-induced environmental harshness in the Jiulong River has caused high nutrient levels (Yang et al., 2012b; Chen et al., 2014; Cui et al., 2015; Mo et al.,

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Fig. 1. Location of the sampling sites in the Jiulong River watershed. The map was drawn using ArcGIS platform (http://arcg.is/1PTOfVU). Sites labels are the abbreviated names of the cities or counties in Fujian province as shown in Table S1.

2016). Other studies have illustrated human environmental effects due to fecal pollution, human-delivered estrogens, pharmaceuticals and personal care products, and increasing antibiotic resistant genes (Zhang et al., 2012; Lv et al., 2014b; Ouyang et al., 2015). Despite the human-induced environmental harshness shown in the previous studies on the Jiulong River, there has been no watershed-scale study that characterizes phytoplankton dynamics and ecological trends except those employing molecular techniques. These studies have collectively shown that planktonic microeukaryotic and bacterial communities are sensitive to an ongoing decline in water quality, and can serve as a good sentinel for further assessment of the environmental changes in this watershed (Liu et al., 2013b; Wang et al., 2015). 2.2. Sampling and environmental data collection Field expeditions were conducted during two contrasting hydrographic conditions: the wet (July 2012) and dry (January 2013) seasons at 16 sampling sites (Fig. 1). At each sampling site, surface water samples (0–0.5 m) were collected and transported in triplicate in order to perform phytoplankton, bacterioplankton, and chemical analyses. Samples for phytoplankton (2.5 L) were immediately fixed with 1.5% acidic Lugol's solution and stored in dark compartments at a room temperature until further processing (Lv et al., 2014a). Environmental variables were measured according to the methods used in our previous studies (Liu et al., 2011; Yang et al., 2012a; Liu et al., 2013b). Briefly, water temperature (WT), dissolved oxygen (DO), electrical conductivity (Cond), salinity (Sal), pH, oxidation reduction potential (ORP), chlorophyll-a (Chl-a) were recorded in situ using a multiparameter water quality analyzer (Hydrolab DS5, Hach Company,

Loveland, CO, USA). Current velocity (Vel) was measured with a flowtracker (SonTek, FlowTracker Handheld-ADV® YSI, San Diego, CA, USA). In the laboratory, 100 mL of water were filtered through 0.45 μm Whatman filters and employed graviometric methods to measure suspended solids (SS). Ammonium nitrogen (NH4-N), nitrite and nitrate nitrogen (NOx-N), and phosphate phosphorus (PO4-P) concentrations were determined with a flow injection analyzer (Lachat QC8500, Lachat Instruments, Hach Company, Loveland, CO, USA). Total phosphorus (TP) concentration was quantified using spectrophotometric method after digestion. The concentrations of total nitrogen (TN), total organic carbon (TOC) and total carbon (TC) were determined using TOC/TNVCPH analyzer (Shimadzu, Kyoto, Japan). Dissolved inorganic nitrogen (DIN) was considered as the sum of NH4-N and NOx-N. The upstream watercourse distance from each sampling point was calculated by measuring the polyline length from the sampling point to the watershed boundary. The obtained distance was multiplied by current velocity to estimate riverine water residence time (WRT) at each station (sensu Niño-García et al., 2016). 2.3. Plankton analysis and land use data Prior to species enumeration, the supernatant of preserved samples for phytoplankton was gently siphoned. Residuals were transferred to bottles and were left on a flat surface for 24 h, for cells to settle at the bottom. Thereafter, samples were re-siphoned, avoiding resuspending particles, diluted to a final volume of 30 mL and kept in a dark place after adding some drops of the acidic Lugol's solution. For species identification, samples were gently shaken and a 100 μL aliquot was smeared on a Sedgewick Rafter counting chamber. Units (cells,


colonies, and filaments) were identified to the lowest possible taxonomic level and counted under 400× magnifications of an inverted microscopy (Motic AE31, Xiamen, China). More than 500 units or specimens in each sample were counted (Lv et al., 2014a; Yang et al., 2016). Species enumeration followed identification keys provided by Hu, 2006. Cell dimensions of thirty examples of each species were recorded, averaged and used for biovolume estimation as representative dimensions of that species. Biovolume calculation was based on standard geometric formulae (Hillebrand et al., 1999) and abundancebased results were converted into biomass using the Wetzel and Likens (2000) conversion factor (1 μm3 = 1 pg). For bacterioplankton, details about sampling and processing were described in our previous study (Wang et al., 2015). In this study, we directly used a binary matrix resulting from the band profiles generated by denaturing gradient gel electrophoresis (DGGE), normalized and compared using the Quantity One 4.4.0 software. Land use data were obtained from a previously classified map generated using a combination of unsupervised classification and spatial reclassification based on manual on-screen digitizing (Ren et al., 2011). ArcGIS 9.0 was used to extract land use intensities using a 5 km buffer zone around the sampling point. Agricultural (Agr), grassland (Gra), forested (For) and urban (Urb) land use classes dominated in the watershed. For simplicity, other land uses were grouped as “others” (Oth). 2.4. Data analyses Statistical calculations and plots were carried out in the R environment (R Development Core Team, 2015) with specific subroutine packages as shown below. Prior to data analyses, environmental variables were checked for homogeneity and normality distribution using the Shapiro-Wilk test. Appropriate transformations were applied with exception of the pH. Because DGGE methods can hardly detect bacterioplankton of b1% abundance in a sample, we performed statistical analyses using N1% abundant phytoplankton species for a consistent comparability (Liu et al., 2015). To explore patterns of community responses to environmental settings, we used a non-metric multidimensional scaling (NMDS) based on Bray-Curtis and Jaccard dissimilarity matrices for phytoplankton and bacterioplankton communities, respectively. Envfit function was employed to fit the scaling of community dissimilarities with environmental variables. This function performs the projections of points onto vectors that have maximum correlation with corresponding environmental variables and calculates the strength of the environmental factor association (R2). The significance level was checked using a permutation test with 999 simulations. The analysis of similarity (ANOSIM) was used to check the degree of separation of both planktonic groups across the wet and dry seasons. Complete separation and no separation among groups is suggested by R = 1 and R = 0, respectively (Clarke and Gorley, 2015). Correlation coefficients between 0.5 and 1 are considered as strong relationships when P b 0.01. Functions to perform the above analyses were from the vegan package (Oksanen et al., 2015). Spatially explicit regional analysis of both phytoplankton and bacterioplankton communities was explored by detecting change in community similarity with distance between local communities using the distance-decay of community dissimilarity. Basically, these are linear regressions of community dissimilarity matrices (i.e. Bray-Curtis and Jaccard indices for phytoplankton and bacterioplankton, respectively) and geographical distances measured using the Euclidean distance. Similar analysis was run on environmental Euclidean distance to check whether environmental heterogeneity follows the same trend along the geographical distance. Regressions were run using lm function of the stats package and plots were generated with functions available in the ggplot2 package. This analysis was supplemented with standard and partial Mantel tests through the mantel function of the ecodist package, to indicate the degree of relationships between community dissimilarities and geographical distances among the sites.

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To determine the relative importance of the environmental and spatial sets of predictors on both Hellinger-transformed phytoplankton and bacterioplankton data as the response variables, variation partitioning analysis (VPA) was performed. This technique is commonly used to discern determinants of community structure in freshwater studies (e.g. Heino et al., 2014; Huszar et al., 2015; Wang et al., 2015; Niño-García et al., 2016). It was computed using the varpart function and statistical significance of testable fractions was checked using rda and anova functions retrieved from the vegan package (Oksanen et al., 2015). The spatial variables fraction in the VPA was represented by spatial eigenvectors created using distance-based Moran's Eigenvector Maps (dbMEMs) and Asymetric Eigenvector Maps (AEMs) through the pcnm and aem functions of the PCNM (Dray et al., 2006) and AEM packages (Blanchet et al., 2011), respectively. AEMs were weighted separately using the number of dams and the distance between pairs of sites (Liu et al., 2013a). Mean of weights used to the other edges were assigned to the hypothetical sites inducing water direction. Moran's I index was calculated for the resulting eigenvectors (Borcard et al., 2011), and finally, two distance-based AEMs, one dam-based AEM and three dbMEMs eigenvectors that had significant positive Moran's I were selected as spatial predictors (Spa). Details about generating watercourse spatial eigenvectors were originally described by Blanchet and colleagues (Blanchet et al., 2011) and were followed as shown in the supplementary materials (Fig. S2). On the environmental variables; first, Pearson correlations were calculated among all variables (untransformed data in separate sampling times) and one of any two variables that had significant positive correlation N0.8 was arbitrarily removed in order to reduce the effect of confounding variables. For instance, owing to their strong significant correlations electrical conductivity and TOC were removed to be represented by salinity and TC, respectively (Fig. S3a). Correlation coefficients were calculated using rcorr function of Hmisc package and the results were visualized with corrplot function of the carrplot package. Second, to evaluate multicolinearity of multiple regression models, variance inflation factors (VIF) for each variable (standardized data) was computed. The VIF function retrieved from fmsb package was used to sequentially exclude variables with VIFs N20. Third, forward selection procedure was performed using forward.sel function of the packfor package to improve the model by adding the variables whose inclusion gives the most statistically significance of the fit, each with 999 permutations at alpha stopping criteria of 0.05. To check whether variation in the dissimilarity among both phytoplankton and bacterioplankton communities results more from changes in the underlying structure by which communities vary, or whether it is caused by difference in alpha diversity among localities, we used a null model based on Raup-Crick coefficients. For this analysis, we used R functions retrieved from Ecological Archives C002-002-S1 of Chase et al. (2011). Lastly, to account for the direct and indirect effects of land use, environmental and spatial variables on both phytoplankton and bacterioplankton communities, as a proxy for anthropologic land use effect on phytoplankton, bacterioplankton and environmental settings, partial least squares path modelling (PLS-PM) was run using functions available in the plspm package. In this analysis, environmental factors were grouped into physico-chemical and nutrient variables in order to track anthropologic land use influence (nutrients) on both communities. 3. Results 3.1. Phytoplankton community composition Across all samples, there were a total of 178 phytoplankton taxa, and most samples had low biomass (b10 mg L−1). The highest biomass (33.1 mg L−1) was observed from Changcun upstream (CC1) wet season sample and the lowest (2.9 mg L−1) in Changcun downstream (CC2) dry season sample (Fig. S3). The phytoplankton community was

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mainly dominated by species belonging to Bacillariophyta (Achnanthes sp., Aulacoseira granulata, Nitzschia palea and Cyclotella meneghiniana), Chlorophyta (Ankistrodesmus falcatus, Closterium gracile, Cosmarium sp., and Chlamydomonas microsphaera), Cyanophyta (Cylindrospermopsis raciborskii and Merismopedia elegans), and Cryptophyta (Komma caudata, Cryptomonas reflexa and Cryptomonas ovata). More importantly, A. granulata dominated nearly half of all samples, mainly in the middle and downstream sites of the North River (Table S1). The overall composition of the phytoplankton community was indicated by the co-dominance of Bacillariophyta, Chlorophyta and Cyanophyta in wet season which were replaced by Bacillariophyta and Cryptophyta in dry season. Euglenophyta were largely absent in dry season, whereas they were only prevalent in the lower ridges of North River in the wet season samples (Fig. S3). Cyanophyta, mainly the toxic C. raciborskii, were frequently observed in the agricultural and urban dominated sites of Changcun town (CC1–2), Longyan (LY1–3) and Zhangzhou (ZZ) cities (Table S1; Fig. 1).

3.2. Environmental conditions and community-environment relationships Environmental conditions of the Jiulong River during the studied periods varied spatially and temporally. In both wet and dry seasons, the upstream sites of the North River exhibited high nutrient concentrations (dissolved inorganic nitrogen, total nitrogen, phosphate phosphorus and total phosphate) indicating wastewater runoff from livestock and poultry farming in Longyan city. Wetter periods with storm events would expectedly cause increased water mass, runoff and more turbid conditions. However, half of the 18 parameters were not significantly different across seasons, and six variables were mainly related to water mass (i.e. suspended sediments, turbidity, velocity, water residence time, dissolved oxygen, and chlorophyll-a; Fig. S4). Both phytoplankton and bacterioplankton communities were clustered according to seasons (Fig. 2). Phytoplankton community was separated by the first NMDS axis, whereas bacterioplankton community was separated by the second NMDS axis (Fig. 2). ANOSIM R values across seasons indicated that the degree of community separation was higher for phytoplankton (R = 0.73; P b 0.01) than for bacterioplankton (R = 0.32; P b 0.01). Water temperature and total carbon were the variables that significantly fitted the ordination space of phytoplankton samples (Fig. 2a; Table S2), whereas the water temperature, total phosphate and phosphorus phosphate significantly fitted bacterioplankton community ordination (Fig. 2b; Table S2). Water temperature had the highest strength of the community-environmental association for both plankton groups (i.e. R2 = 0.81 and 0.67 for phytoplankton and bacterioplankton, respectively; Table S2).

3.3. Spatial structure of phytoplankton and bacterioplankton communities Both phytoplankton and bacterioplankton exhibited an increasing dissimilarity with increasing distance from the source (Fig. 3). However, the coefficients of determination were not significant for phytoplankton in both the wet (R2 = 0.008; P = 0.30) and the dry season (R2 = 0.007; P = 0.34) leading to the weak distance decay relationships (Fig. 3a). Nevertheless, there was a significant relationship between environmental variables and phytoplankton Bray-Curtis dissimilarity matrix using Mantel tests in both wet and dry seasons (Mantel R = 0.20 and R = 0.12; P b 0.05, in wet and dry seasons, respectively). The relationship between phytoplankton community and spatial variables was significant only in the wet season (Mantel R = 0.11; P b 0.05 and R = 0.03; P N 0.05; in wet and dry seasons, respectively Table 1). Significant positive distance decay relationship was observed for bacterioplankton only in the wet season (R2 = 0.22; P b 0.05 in the wet season and R2 = 0.06; P = 0.30 in dry season). The results of the Mantel tests suggested that there are stronger geographical distance and bacterioplankton community dissimilarity relationships (Mantel R = 0.22 and R = 0.30; P b 0.05 for wet and dry seasons, respectively) than the non-significant relationships between Euclidean distance of environmental variables and bacterioplankton community dissimilarity (Mantel R = 0.05 and R = 0.01; P N 0.05 in wet and dry seasons, respectively; Table 1). The environmental Euclidean distance followed similar positive trend with both bacterioplankton and phytoplankton but the coefficient of determination were not significant (Fig. 3c).

3.4. Relative roles of environmental and spatial processes Variation partitioning analysis showed that the explanatory power of local environmental variables was significantly higher than that of spatial variables for phytoplankton during the wet and dry seasons and for bacterioplankton only during the dry season (Fig. 4). The variance explained uniquely by environmental variables (adjusted R2) was 0.24 compared to 0.19 explained by spatial variables for phytoplankton in the wet season. In the dry season, the variance explained by environmental variables slightly increased to 0.29 whereas the variance explained by spatial variable shrunk to 0.15. In contrast, bacterioplankton had similar explanatory power explained by environmental and spatial predictors during the wet season, that is, 0.19 whereas the importance of spatial variables for bacterioplankton was negligible and not significant (R2 = 0.03) compared to that of environmental variables (R2 = 0.18) in the dry season (Fig. 4). Further, except for Cyanophyta, other phytoplankton groups showed mean value closer to zero when comparing wet and dry seasons (Fig. 5). Bacterioplankton

Fig. 2. Non-metric multidimensional scaling (NMDS) of phytoplankton (a) and bacterioplankton (b) communities. Analysis of similarity (ANOSIM) statistic R and its significance level are indicated. The direction of the strongly significant factors (P b 0.001) obtained by fitting environmental factors in the ordination space of samples is shown by the arrows. The values of water temperature (WT), the factor that the best fitted the ordination space of both communities, are displayed in the plots and referred to the palette (dark to sky blue in the online version of the figure). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)


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Fig. 3. Distance decay of phytoplankton (a) and bacterioplankton (b) communities based on Bray-Curtis and Jaccard dissimilarity matrices during the wet (filled dots) and dry (empty dots) seasons, and the trend of the Euclidean distance of environmental variables along the geographical distance (c). Significant (P b 0.05) coefficient of determination (R2) for the regression is indicated by an asterisk.

had mean Raup-Crick coefficients that tend to be closer to −1 and thus considered as less dissimilar than expected by random chance. 3.5. Direct and indirect effects of land use variables The direct and indirect effects of land use variables on both phytoplankton and bacterioplankton communities showed a stronger influence during the wet season than the dry season (Fig. 6). Land use variables had stronger positive direct effects on physico-chemical and nutrients in the wet season and a negative influence on bacterioplankton community. Although land use strongly influenced nutrients, the later showed little positive or negative influence on phytoplankton and bacterioplankton, respectively (Fig. 6a). However, strong positive effect of land use on bacterioplankton could be observed in the dry season (Fig. 6b). Despite the observed positive influence of physico-chemical variables on phytoplankton community, land use showed a negative direct influence on the physico-chemical variables, nutrients and phytoplankton community in the dry season. 4. Discussion 4.1. Determinants of community assembly processes Under human-impacted environmental conditions, we anticipated that phytoplankton and bacterioplankton communities in the Jiulong River could be primarily determined by deterministic processes. Indeed, the explanatory power of environmental variables surpassed that of spatial variables for both phytoplankton and bacterioplankton during the dry season when watercourse and overland spatial variables were taken into account (Fig. 4). This observation was further supported by a null model approach that indicated a less dissimilar community than expected by null model (Fig. 5). These results suggested little evidence of the stochastic processes that involve dispersal and random distribution. They were in agreement with some recent studies that Table 1 Results of Mantel tests between community similarity matrices (Bray-Curtis and Jaccard indices for phytoplankton and bacterioplankton, respectively) with local environmental factors (Environmental) and regional spatial factors (Spatial) measured with Euclidean distance index across the wet and dry seasons using the Spearman correlation coefficients. Factors

Environmental Spatial ⁎ P b 0.05.

Phytoplankton

Bacterioplankton

Wet

Dry

Wet

Dry

0.12⁎ 0.11⁎

0.20⁎ 0.03

0.05 0.22⁎

0.01 0.30⁎

distinctively focused on either phytoplankton (Huszar et al., 2015; Ding et al., 2017) or bacterioplankton (Dai et al., 2017; Liao et al., 2017). More recently, Hu and colleagues found similar patterns for archaeal communities in the Jiulong River (Hu et al., 2016). Owing to seasonal variation in the mechanisms underlying selection (Dini-Andreote et al., 2015; Yeh et al., 2015), seasonal samplings were used to discern the main community assembly across high and low water-level river stages (wet and dry seasons). These periods appear to be a good proxy for riverine connectivity due to the fact that low water-level periods are characterized by slower and less flushing water regimes and could be intensified when rivers are dammed (Poff et al., 2007; Campbell et al., 2015). Indeed, the ordination space of both planktonic communities separated samples according to their corresponding sampled seasons (Fig. 2). Similar assembly processes were associated with hydrologic change confirming that hydrology is a major driver of community assembly for freshwater microbial communities as was proposed by previous studies (Dini-Andreote et al., 2015; Niño-García et al., 2016). In the same sense, Widder et al. (2014) showed that dispersal limitation of biofilm microbial communities in small region compared to large catchment streams was caused by distinctiveness of hydrological regimes, whereas Besemer et al. (2012) showed that deterministic factors governed the assembly of stream biofilm. Deterministic assembly was more pronounced during the dry season, a period during which droughts limits the overall habitat availability and quality and may thus act as “natural” environmental filter (Dudgeon et al., 2006; Chase, 2007). Two scenarios may explain a relatively pronounced influence of deterministic assembly on the studied plankton communities during the dry season: first, this period is often characterized by a weak site-to-site connectivity and longer water residence times. Second, both phytoplankton and bacterioplankton species are able to cross most dispersal barriers in geographical scales with higher migratory rate (Finlay, 2002) and can therefore be better dispersed during the wet season. Strong distance decay relationships typically indicate dispersaldriven dynamics (Brown et al., 2011). Low dissimilarity coefficient observed during the dry season (b0.50; Fig. 3a) could indicate a less spatially structured phytoplankton community during this season. The increase in community dissimilarity with increasing distance was previously observed but it was unclear whether this effect was solely caused by dispersal limitation and/or species sorting (Huszar et al., 2015). The responses of phytoplankton to spatial and environmental predictors across contrasting water-flow regimes in the Jiulong River suggests that both dispersal limitation and species sorting contributed to the deterministic assembly governing phytoplankton community. Dispersal limitation is of particular interest in a river system, where one might assume that down river dispersal was relatively easy. Because dispersal

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Fig. 4. The explanatory power of environmental (Env) and spatial factors (Spa) and their shared fractions for phytoplankton (left-side panels) and bacterioplankton (right-side panels) communities during the wet (top panels) and the dry (bottom panels) seasons.

Fig. 5. Variation of the Raup-Crick metric within major phytoplankton groups (Bacillariophyta, Chlorophyta, Cryptophyta, Cyanophyta and other taxa) and bacterioplankton during the wet and dry seasons. Raup-Crick probability metric indicates whether local communities are more dissimilar (values closer to 1), dissimilar (near 0), or less dissimilar (values closer to −1), than expected by random chance.

rates vary with organism type and size, and the length of the ecosystem in consideration, actual ecological distance takes into account landscape structure and species dispersal mode (Logue et al., 2011; Sutherland et al., 2015). Moreover, stronger direct and indirect effects of land use variables on phytoplankton and bacterioplankton communities were observed during the wet and dry season, respectively, collaborating with the previous multivariate results. Thus, conclusions on the relative importance of stochastic vs deterministic processes in a given community may vary considerably if dispersal is taken into account, as was observed in this study. To this end, despite their high abilities for passive dispersal, we argue that plankton communities in lotic environments are, to some extent, dispersal limited. Although both phytoplankton and bacterioplankton undergo seasonal succession, they often exhibit distinct responses to environmental changes at temporal scales (Liu et al., 2015). A stronger role of dispersal for bacterioplankton than phytoplankton was previously observed in a microcosm study (Verreydt et al., 2012) and in subtropical reservoirs (Liu et al., 2015). Similarly, the importance of spatial variables for bacterioplankton tended to overcome that of environmental variables in the wet season (Fig. 4) owing to passive dispersal of bacterioplankton. Dispersal ability varies among species; e.g. individual mobility, propensity to drift, and aerial flight ability are important traits that may permit the persistence of weak competitors and vulnerable prey in environments where they might otherwise lose out (Allan and Castillo, 2007). One can therefore argue that relative role of deterministic versus stochastic processes in shaping phytoplankton and bacterioplankton is not simply a function of organism size but other traits. Although we tackled ecological effects of habitat deterioration and hydrological alteration in the Jiulong River, trends in single community ecology may not fully explain environmentally degraded freshwaters (Heino, 2013). Indeed, biodiversity loss is affected by the cumulative impact of multiple stressors (Schuhmacher et al., 2016; Yang et al., 2017). Thus, sampling multiple communities with distinct dispersal abilities at distinct temporal scales can provide an in-deep approach to community assembly in human-impacted rivers. The divergent opinions upon the main processes underlying community assembly may


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Fig. 6. The final path of partial least squares path model (PLS-PM) showing direct and indirect effects of land use (Land), physico-chemical (Phys) and nutrients (Nutr) factors on phytoplankton (Phyt) and bacterioplankton (Bact) communities during the wet (a) and dry (b) seasons in the Jiulong River. Solid (blues) and dashed (red) lines represent positive and negative effects, respectively. The wider the line the higher the absolute value of the coefficient obtained with PLS-PM. The initial path is shown in supplementary materials (Fig. S5). The goodness-of-fit index was 0.393 and 0.378 for wet and dry seasons, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

be partially due to the fact that the proxies used to infer the processes vary from a study to another, or perhaps because an important explanatory variable has been left unmeasured. Therefore, further studies could simultaneously analyze all plankton categories (including phytoplankton, zooplankton and bacterioplankton) and incorporate more predictors (light availability, grazing pressure etc.) to fully uncover the whole ecological breadth of the plankton. Such holistic appraisals could be facilitated by the current next generation sequencing technologies that could be used instead of time consuming morphological species counts, to accurately tackle all plankton sizes, but see Liu et al. (2017). The appropriateness of the DGGE methods for detecting the full breadth of bacterioplankton is particularly limited because the rare bacterial taxa are ecologically important as the abundant ones. 4.2. Community change within landscape structure The phytoplankton community structure reflected the plankton seasonal succession, although diatoms remained noticeable in both wet and dry seasons. Diatoms are often used to assess ecological status of rivers and are the main phytoplankton phylum found therein (Abonyi et al., 2012; Stanković et al., 2012; Townsend and Douglas, 2017) as they are more favored by turbidity and mixing than other taxa (Huisman et al., 2004). Regardless of seasonality, Aulacoseira granulata, a species observed in most potamoplankton studies (e.g. Abonyi et al., 2012; Devercelli et al., 2016), dominated half of all sites in the Jiulong River. Bhattacharya et al. (2016) recently showed that A. granulata and Cyclotella meneghiniana, the latter being a co-dominant in some of our samples (Table S1), prevailed in conditions of reduced hydrological connectivity and increased agricultural practices. The co-dominance of Cyanophyta with Bacillariophyta in the wet season was arguably due to the high water temperature in the wet season and they were replaced by Cryptophyta in the dry and cold season. Cryptomonas was previously associated to disconnected river during dry and cold season (Townsend and Douglas, 2017). High chlorophyll-a concentration in ZZ wet season sample could have been the result of existing broader chlorophyll bands in Cosmarium spp. that dominated in the sample. (Table S1; Fig. S4). 4.3. Implications for the watershed management Watershed management and riverine ecosystem conservation require holistic approaches that assess collectively climatic, biotic and abiotic data to better predict human and climate induced biodiversity loss (Dudgeon et al., 2006). Chase (2010) has shown that higher productivity results from a stronger role for stochastic relative to deterministic assembly processes and that stochastic assembly causes higher biodiversity. Our findings suggested that riverine longitudinal

connectivity along the Jiulong River is likely obstructed during the dry season. Consequently, passive dispersal of migratory phytoplankton and bacterioplankton between the sites was less intense. Since dispersal links local communities to the regional species pool via connectivity (Altermatt, 2013; Powell et al., 2015; Heino et al., 2017), and because the relationship between diversity and ecosystem stability depends upon the relative strength of local environmental and dispersal processes (Howeth and Leibold, 2010), traditional management practices which focus on improving local abiotic variables to increase local biodiversity, but ignoring dispersal across sites and biotic interactions, should be revisited. Dai and colleagues have shown that eutrophication linearly increased the importance of deterministic processes underlying bacterioplankton in human-impacted Hangzhou Bay (Dai et al., 2017) similarly, deterministic processes govern bacterioplankton in the eutrophic Lake Donghu (Yan et al., 2017). Furthermore, it has been suggested that disturbance can cause convergence in community composition by increasing niche selection of disturbance-tolerant species (Wang et al., 2013). Indeed, because communities within even the most pristine areas are also at risk when isolated within heavily impacted regions (Merriam and Petty, 2016), therefore whole river watershed management strategies are crucial to care about the sustainability of the riverine ecosystems under human pressure.

5. Conclusion Our study described patterns of riverine phytoplankton and bacterioplankton assembly processes during two contrasting seasonal hydrologic regimes (wet and dry seasons). Deterministic assembly underlies the communities of both planktonic groups during the dry season, suggesting that obstructed longitudinal connectivity might have hindered species dispersal. During the wet season, phytoplankton community appeared to be driven by deterministic processes whereas an equal role of both deterministic and stochastic processes for bacterioplankton community was identified. Taken together, our results suggested that anthropologic activities act as an important ecological filter of species composition from the regional pool in the Jiulong River and this could be intensified during the period of low waterflow. In this Anthropocene era, rigorous ecosystem management practices that take into account dispersal opportunities across sites are urgently needed for a smooth continuity of the ecological integrity in human-impacted rivers and biodiversity-ecosystem function. For the Jiulong River such strategies and practices could focus not only on local species diversity but also regional dispersal routes in order to balance deterministic and stochastic assembly for local biodiversity and productivity.

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Acknowledgments We acknowledge Dr. Xian Zhang for her assistance in field sampling and Dr. Shenghui Cui group for providing the LandSat image. We thank Dr. Jani Heino and Prof. David M. Wilkinson for their constructive comments on an early version of the manuscript. We also greatly appreciate the two anonymous reviewers and the editors for giving important comments that significantly improved the quality of this paper. The first author acknowledges the CAS-TWAS President's Fellowship for international students in China. This research was supported by Xiamen Municipal Bureau of Science and Technology (3502Z20172024 and 3502Z20171003), the National Natural Science Foundation of China (31370471) and the Natural Science Foundation for Distinguished Young Scholars of Fujian Province (2012J06009). Appendix A. Supplementary data The following are the supplementary data related to this article: supplementary figures (Figs. 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