The Effects of Water Pollution on the Phylogenetic Community Structure of Aquatic Plants in the East Tiaoxi River, China

Received: 4 November 2018 | Revised: 8 October 2019 | Accepted: 6 November 2019 DOI: 10.1111/fwb.13451

ORIGINAL ARTICLE

The effects of water pollution on the phylogenetic community structure of aquatic plants in the East Tiaoxi River, China

1Center for Environmental Biology and Ecosystem Studies, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan 2Center for Asian Conservation Ecology, Kyushu University, Motooka, Fukuoka, Japan 3Department of Evolutionary Studies of Biosystems, School of Advanced Sciences, SOKENDAI (The Graduate University for Advanced Studies), Hayama, Kanagawa, Japan 4Guangxi Key Laboratory of Environmental Pollution Control and Technology, Guilin University of Technology, Guilin, Guangxi, China 5Field Science Center, Graduate School of Agricultural Science, Tohoku University, Osaki City, Miyagi, Japan 6Institute of Decision Science for a Sustainable Society, Kyushu University, Motooka, Fukuoka, Japan 7Tropical Biosphere Research Center, University of the Ryukyus, Yaeyama-gun, Okinawa, Japan 8Key Laboratory of Water Environment in the Yangtze River, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, China 9Faculty of Engineering, Kyushu University, Nishi-ku, Fukuoka, Japan

Correspondence Hironori Toyama, Center for Environmental Abstract Biology and Ecosystem Studies, National 1. Water pollution is one of the most serious aquatic environmental problems world- Institute for Environmental Studies, Tsukuba, Ibaraki, 305-8506, Japan. wide. In China, recent agricultural and industrial development has resulted in rapid Email: [email protected] changes in aquatic ecosystems. Here, we reveal the effects of water pollution on

Funding information the phylogenetic community structure of aquatic macrophytes in the Tiaoxi River, JSPS grant for Global Center of Excellence China. Program, Grant/Award Number: 2016yfe0115800; Environment Research 2. We placed a rectangular plot at 47 sites within the Tiaoxi River from the mouth and Technology Development Fund, Grant/ of the river to 88.5 km upstream, in which we recorded species abundance and Award Number: S-9 & 4-1601; Ministry of the Environment, Japan, University of measured 22 physico-chemical variables. Bayesian phylogeny using the rbcL and the Ryukyus Research Project Promotion matK gene sequences was employed to quantify phylogenetic α- and β-diversity, Grant, Grant/Award Number: 17SP01302; JSPS KAKENHI, Grant/Award Number: and test the phylogenetic signal in four growth forms: emergent, floating-leaved, 15H02640 and 17K15175 free-floating, and submerged. 3. Within communities, water contamination and phytoplankton abundance decreased species richness and phylogenetic diversity, which resulted in phylogenetic clustering; species within communities were more closely related to each other than expected. Between communities, differences in geographical distance and phytoplankton abundance resulted in phylogenetic dissimilarity among plots. Aquatic macrophytes showed phylogenetic signals in which related species re- sponded more similarly to disturbance.

4. Thus, the observed patterns could be explained by environmental filtering and suggested that water pollution by human activity has added more filters to the existing environmental filters that drive the species assembly of macrophyte communities.

KEYWORDS aquatic plant ecology, China, community assembly, community phylogenetics, water pollution

free-floating, and submerged) known to have different responses to 1 | INTRODUCTION abiotic factors (Lacoul & Freedman, 2006). Emergent, free-floating, and floating-leaved macrophytes are generally more tolerant to toxic Habitat disturbance by human activities is one of the most drastic pollutants than submerged macrophytes. Light limitation by tur- factors changing ecological communities. Freshwater systems have bidity promotes the dominance of emergent species, while deeper been influenced by the modification of water regimes, human-driven water tends to support free-floating and floating-leaved species, movement of organisms and high levels of nutrient loading in the rather than submerged species. past 50 years (Nelson, 2005). Aquatic macrophytes influence tempo- The East Tiaoxi River provides an ideal opportunity to evaluate ral, spatial, chemical, physical, and biological qualities of the aquatic the effects of water pollution on phylogenetic diversity and phylo- ecosystem, and are one of the most threatened taxa in freshwater genetic community structure. Water quality upstream of the quay ecosystems (Keddy, 2010; Lacoul & Freedman, 2006). Thus, their is relatively healthy with little anthropogenic impact (Figure 1e), but species diversity and community structure have been used for as- the lower reach of the river has been drastically changed by quar- sessing ecological status and trends of freshwater ecosystems rying and mining during the last decade (Sato, Kano, Huang, Li, & (Lacoul & Freedman, 2006). Shimatani, 2010) (Figure 1b–d). The river traffic has included mas- The influence of environmental factors on species diversity and sive cargo ships resulting in high turbidity (Kano et al., 2013), and community structure of aquatic macrophytes has been well doc- artificial embankment has been made to resistant to erosion induced umented (Bornette & Puijalon, 2011; Carpenter & Lodge, 1986; by navigation waves (Sato et al., 2010). These disturbances and envi- Lacoul & Freedman, 2006). Species richness is known to be inversely ronmental changes have threatened fishes inhabiting the river (Kano related to disturbance factors such as turbidity, nutrient concentra- et al., 2013). tion (Akasaka, Takamura, Mitsuhashi, & Kadono, 2010; Vestergaard Here, we addressed the following questions: (1) Are macrophyte & Sand-Jensen, 2000), and density of land use of surrounding areas growth forms associated with environmental factors? (2) Do the four (Akasaka et al., 2010; Hicks & Frost, 2011; Sass, Bozek, Hauxwell, growth forms show phylogenetic conservatism? (3) Does water pol- Wagner, & Knight, 2010), while community structure is altered by lution decrease phylogenetic dispersion within the community? (4) turbidity and nutrient concentration (Toivonen & Huttunen, 1995), Does phylogenetic similarity decrease with geographical distance and land use (Hicks & Frost, 2011). and disturbance gradients among communities? (5) Which species/ Previous studies used species as a unit of comparison between individuals are most important to maintain phylogenetic distinctive- communities, neglecting phylogenetic relatedness among species. ness in the East Tiaoxi River? Here, we employ a phylogenetic approach using phylogenetic diversity (PD) (Faith, 1992) and community phylogenetic indices (Graham & Fine, 2008; Webb, 2000; Webb, Ackerly, McPeek, & Donoghue, 2 | METHODS 2002) to reveal how the diversity of aquatic macrophytes changes in response to habitat disturbance in East Tiaoxi River, China. In 2.1 | Study area terrestrial habitats, the phylogenetic approach has been success- fully applied to detect phylogenetic conservatism under human The East Tiaoxi River is one of the largest rivers flowing into Lake disturbance (Zhang, Mayor, & He, 2014), but no corresponding Taihu in China (Figure 1). Lake Taihu is the third largest freshwater study has been made on freshwater ecosystems. We focus on four lake and serves as an important drinking water source for about growth forms of aquatic macrophytes (emergent, floating-leaved, 10,000,000 people. Recent agricultural and industrial development

FIGURE 1 Study sites on the East Tiaoxi River, China. (a) Map of the East Tiaoxi River. Each number indicates the site ID that is ordered by geographical distance from the river mouth. (b) Environment of Taihu Lake (photographed on 22 November 2009), (c) Environment of lower reach (photographed on 22 May 2010). (d) Environment of middle reach (photographed on 23 May 2010). (e) Environment of upper-middle reach (photographed on 26 May 2010). (f) Quay for cargo ships that transfer sediments located between plot 35 and 36 (photographed on 25 May 2010). (g) Schematic diagram of plot design. Each plot and subplot was 2 × 50 m and 30 × 30 cm, respectively. Four vertical aligned subplots were placed every 10 m 634 | TOYAMA et al.

(a) Map of the East Tiaoxi River (b) Taihu lake

E75° E95° E115° E135° Hydrocharis dubia

N45°

N35° Salvinia natans China

N65°

N31°00' Taihu lake 1 2 25 km 5 3 7 (d) 9 4 Site19 10 6 8 11 12 N30°48' 14 13 15 16 East Tiaoxi River 18 17 19 20

22 21

23 N30°36' 24 26 25 (e) Site 46 27 28 29 30 32 34 31 37 N30°24' 39 35 33 Quay 42 36 38 41 40 Qiantang River 44 43 46 45 47

E119°36' E119°48' E120°00' E120°12' (f) Quay (g) Schematic diagram of plot design 10 m

subplot 2 m 30 × 30 cm

50 m TOYAMA et al. | 635 has increased nutrient loading, resulting in eutrophication and a Team, 2012). We analysed variations along the first (PC1), second drinking water crisis (Qin et al., 2010). Even under the high level of (PC2) and third (PC3) principal components. eutrophication, some endangered species such as Salvinia natans The association between the occurrence probability of each (L.) All. (Vulnerable in Japan) and Hydrocharis dubia (Blume) Backer growth form and the abiotic factors was examined by multino- (Near Threatened in Japan) were observed in our preliminary survey mial logistic regression with the mlogit package (Croissant, 2013). in November 2009 (Figure 1b). This study was conducted from the Multinomial logistic regression is an extension of binary logistic re- lower to upper-middle reaches of the river flowing to the southern gression that allows for more than two categories of the dependent part of Lake Taihu in May of 2010 (Figure 1a, c–f). The width of the variable. river ranges from c. 70 m in the middle reaches to c. 200 m in the The dependent variable was the type of growth form (emergent, lower reaches. The altitudinal difference between the mouth and floating-leaved, free-floating and submerged) of species (modelled middle reaches of the river is c. 7 m. using occurrence data) and individuals (modelled using abundance data), and explanation variables were PC1, PC2, and PC3. The statistical significance of each factor was evaluated by the likelihood 2.2 | Floristic survey and measurements of physico- ratio test. chemical state

We surveyed the macrophytic flora of East Tiaoxi River and meas- 2.3 | DNA sequencing and phylogenetic analysis ured the physical chemistry at 47 sites from the river mouth to 88.5 km upstream (Figure 1a, c–f). For the floristic survey, we placed Leaf pieces were dried in silica in the field, from which DNA was iso- a rectangular plot of 50 × 2 m in each site that was divided into 24 lated by the CTAB method (Doyle & Doyle, 1987), with minor modi- subplots of 30 × 30 cm, in which we collected and recorded macro- fications; before the DNA extraction, dry leaf material was milled phytic species (Figure 1g). The distance of the plot was measured using a QUIAGEN TissueLyser to obtain fine powder, which was using a laser range finder (TruPulse 200; Laser Technology Inc.). washed up to five times with 1 ml buffer (0.1 M HEPES, pH 8.0; 2% The size of the subplot was measured using folding ruler. Sampling mercaptoethanol; 1% PVP; 0.05 M ascorbic acid). We sequenced the was performed by hands and a mesh net. The abundance of each partial genes for the large subunit ribulose-1,5-bisphosphate car- species was calculated as the number of subplots with the species boxylase oxygenase (rbcL) and maturase K (matK) according to pub- present at each site. Species identification was conducted accord- lished protocols (Dunning & Savolainen, 2010; Kress et al., 2009). ing to the flora of China (eFloras, 2014). We determined the relative MEGA v 6.06 (Tamura, Stecher, Peterson, Filipski, & Kumar, 2013) abundance of species of aquatic macrophytes by rank abundance was used to check the electropherograms, align the sequences, and curve using R ver. 2.15.1 (R Core Team, 2012) with the BiodiversityR select the best nucleotide substitution models by BIC scores. We package (Kindt & Coe, 2005). used the Bayesian methods implemented in the program BEAST v The measurement of the water depth was performed at the 1.6.1 (Drummond & Rambaut, 2007). We set the GTR + Γ + I model flora surveyed plots as described by Kano et al. (2013). Water of molecular evolution for each region and used an uncorrelated depth was measured every 5 m along the 50-m line using a mea- lognormal relaxed-clock model to infer divergence times. A ran- suring pole, and the averaged values were used for the analysis. dom starting tree was used with a Yule process of speciation for The other 21 physico-chemical variables were measured once at the tree prior. As topological constraints, we assumed the mono- each site (Table S1) as described by Li et al. (2012), as well as the phyly of orders in APG III (Angiosperm Phylogeny Group, 2009) and concentration of heavy metals. We collected water samples into a the minimum ages of 11 clades as fossil calibrations following Bell, sterilised bottle at a depth of 0.5 m and kept the collected samples Soltis, and Soltis (2010). We ran five independent chains of 100 mil- cool with ice packs. All samples were frozen and stored at −18°C lion generations and sampled every 10,000 generations. The first immediately upon returning from the fields following the monitor- 1,000 trees were discarded as burn-in from each run, after which ing method of the State Environmental Protection Administration 9,000 trees were resampled from all runs using LogCombiner v. of China (2002). Suspended solids were measured according to the 1.6.1 (Drummond & Rambaut, 2007). The maximum clade credibil- water quality-determination of suspended substance-gravimetric ity tree was identified using TreeAnnotator v 1.6.1 (Drummond & method in China (GB11901-89). Nitrate nitrogen, ammonium nitro- Rambaut, 2007) with a posterior probability limit of 0.5 and median gen, and orthophosphate were measured using a continuous flow node heights. automatic chemical analyser (QuAAtrO, Bran + LuebbeGmbH) To evaluate the phylogenetic trait conservatism of the four (Zou, Li, Huang, & Jiang, 2010). The concentration of heavy metals growth forms, we used the phylo.signal.disc procedure (Paleo- was analysed at 43 sites by inductively coupled plasma mass spec- Lopez et al., 2016), which estimates the minimum number of char- trometry (Agilent Technologies 7,700 Series ICP-MS) (He, Wang, acter-state transitions at each node that account for the observed & Tang, 1998). distribution. The obtained value was then compared with the me- Principal component (PC) analysis was performed to identify un- dian of a randomised distribution (1,000 randomisations were used). correlated components in 22 variables using R ver. 2.15.1 (R Core Statistical analyses were performed with R ver. 2.15.1 (R Core Team, 636 | TOYAMA et al.

2012) using the script of the supplement (Paleo-Lopez et al., 2016). in which 1,000 values were sampled with 100 iteration intervals Ancestral states and character-state transitions were visualised after a burn-in of 30,000 iterations. The convergence of the Markov under parsimony method using the software package Mesquite 3.6 chains was checked by visualising the sampling paths for each pa- (Maddison & Maddison, 2018). rameter. The mean, standard deviation and 95% Bayesian credible interval (CI) for the parameters of fixed effects were obtained from the posterior distributions. The statistical significance of a fixed ef- 2.4 | Species richness and phylogenetic community fect was evaluated whether the 95% CI included zero or not, and analysis within plot (phylogenetic α-diversity) p-values were evaluated by the two-tailed test. Statistical analyses were performed with R ver. 2.15.1 (R Core Team, 2012) using Species richness (SR) and PD (Faith, 1992) were calculated for the ecoPD (Cadotte et al., 2010), Picante (Kembel et al., 2010), ape each plot and four functional groups of macrophytes (emergent, (Paradis, Claude, & Strimmer, 2004) and R2WinBUGS (Sturtz, Ligges, floating-leaved, free-floating, and submerged) in each plot. The & Gelman, 2005) packages. mean pairwise distance (αMPD) was calculated to evaluate phylogenetic relatedness of species in a plot (Webb, 2000). Abundance- weighted PD (PDab) and αMPD (αMPDab) were calculated 2.5 | Phylogenetic community analysis between assuming polytomy (Cadotte et al., 2010). We tested the effects plots (phylogenetic β-diversity) of water quality (PC1, PC2 and PC3) on diversity indices using hierarchical Bayesian models with logit link and Poisson error (for The phylogenetic similarity between communities was measured SR) or Gamma error (for PD, PDab, MPD, MPDab). Because a spa- in a way analogous to the measurements of αMPD. We computed tial autocorrelation test based on Moran's I using a distance matrix the mean pairwise phylogenetic distance between each of the obtained from GPS coordinates showed that SR, PD, PDab, and plots using occurrence (βMPD) and abundance data (βMPDab). To αMPDab were spatially autocorrelated (SR: Moran's I = 0.27, p < examine the relationships between phylogenetic β-diversity meas- 0.001, PD: I = 0.36, p < 0.001, PDab: I = 0.22, p < 0.01, αMPD: ures (βMPD, βMPDab) and environmental distances (geographical I = 0.0016, p = 0.67, αMPDab: I = 0.35, p < 0.001), we set plot distance along the river, PC1, PC2, PC3), we used the Mantel test ID as a random effect to remove the effects of autocorrelation (Mantel, 1967) with 9,999 permutations. The geographical distance in the hierarchical Bayesian models. The model structure for the along the river between each pair of plots was measured using the analysis of SR was: Google Earth GIS software. The aquatic environmental distance between each pair of plots was calculated as the absolute value of the SR[i] ∼ Poisson ( [i]), pairwise difference of PC1, PC2, and PC3. Statistical analyses were performed with R ver. 2.15.1 (R Core Team, 2012) using the package log [i] = + × PC1[i] + × PC2[i] + × PC3[i] + r[i], 0 1 2 3 ade4 (Chessel, Dufour, & Thioulouse, 2004). and that of the other diversity indices, phylogenetic diversity (PD, PDab), and mean pairwise phylogenetic distance (MPD, MPDab) was: 2.6 | The relative importance of each species in the

Other indices[i] ∼ Gamma(a,b[i]), East Tiaoxi River

To evaluate the contribution of each species/individual to the evo- b[i] = a∕ [i], lutionary history of plant community in the East Tiaoxi River, we computed evolutionary distinctiveness (ED) (Isaac, Turvey, Collen, log μ[i] = + × PC1[i] + × PC2[i] + × PC3[i] + r[i], 0 1 2 3 Waterman, & Baillie, 2007) and abundance-weighted ED (AED) (Cadotte et al., 2010). The abundance of each species was pooled where i represents a plot ID, β0 represents the intercept, β1, β2, and β3 among plots. To detect outliers, we used the Mahalanobis distances at represent the linear coefficients of explanatory variables, and r rep- a 0.05 level of significance. Statistical analyses were performed with resents the random effects of plot ID. A non-informative prior distri- R ver. 2.15.1 (R Core Team, 2012) using the ecoPD package (Cadotte bution given for the fixed effect parameters of β0, β1, β2, and β3 was a et al., 2010) and mvoutlier (Filzmoser & Gschwandtner, 2013). Gaussian distribution with a mean of 0 and standard deviation of 102. The hierarchical prior was given for the random effects parameter of r as the Gaussian distribution, with a mean of 0 and standard deviation 2.7 | Sensitivity analyses of phylogenetic σ, which was a hyperparameter given as a non-informative uniform dis- uncertainty 4 tribution, 0 < σ < 10 . The posterior distribution was obtained using the Markov chain Topological uncertainty could raise the bias of measures of the com- Monte Carlo method with WinBUGS ver. 1.4.3 (Spiegelhalter, munity phylogenetic structure (e.g. Toyama et al., 2015). To evaluate Thomas, Best, & Lunn, 2003) on R ver. 2.15.1 (R Core Team, 2012), the effects of topological uncertainty on the tests, we conducted TOYAMA et al. | 637 simulations according to Donoghue and Ackerly (1996). In hierarchi- 2009), produce oxygen by photosynthesis, and are known to play cal Bayesian models, we computed β1, β2, and β3, and their standard an important role in As cycling that is related to the changes in nu- deviations and 95% CI, and a p-value for each of the 9,000 phylog- trients such as P and N in the cells (Wang et al., 2015). The third enies used to identify the maximum clade credibility tree. In the axis (PC3), explaining 10.4% of the total variation, had positive load- Mantel test, we computed the observed correlation and a p-value ing with Cr and depth, and negative loading with nitrate (Figure 2b). for each of the 9,000 phylogenies. While detecting outliers of ED PC3 represents the amount of water inflow from surface sources and AED, we computed the Mahalanobis distances for each of the because environmental contamination by chromium (Cr) would be 9,000 phylogenies. The degree of uncertainty was evaluated based primarily from wastewater of the leather tanning facilities surround- on the proportion of the number of phylogenetic trees that showed ing the area (Qu, Dickman, & Wang, 2001), and its inflow to the river different results (whether rejected or not) from the test to the maxi- would increase the depth. Conversely, nitrate would originate from mum clade credibility tree among the total 9,000. Simulations were the groundwater, where it is known to be a major agricultural pollut- performed by R ver. 2.15.1 (R Core Team, 2012) using ade4 (Chessel ant (Liu, Wu, & Zhang, 2005). et al., 2004), ape (Paradis et al., 2004), ecoPD (Cadotte et al., 2010), The relative occurrence probability (ROP) of each growth form mvoutlier (Filzmoser & Gschwandtner, 2013), Picante (Kembel et al., was significantly influenced by PC1 (df = 3, LR stat. = 117.7, p < 2010), and R2WinBUGS (Sturtz et al., 2005). 0.001), PC2 (df = 3, LR stat. = 102.5, p < 0.001) and PC3 (df = 3, LR stat. = 22.6, p < 0.001). The response to PC1, PC2 and PC3 differed among growth forms (Figure 3a,b, Table S4). Expected ROP of 3 | RESULTS emergent macrophytes increased with PC1 and decreased with PC3. Expected ROP of submerged macrophytes decreased with PC1 and 3.1 | Floristic survey PC2. Expected ROP of floating-leaved macrophytes decreased with PC1 and increased with PC2 and PC3. Expected ROP of free-float- We collected 353 specimens and identified 44 species of 20 families ing-leaved plants decreased slightly with PC2. consisting of 14 submerged, four floating-leaved, four free-floating and 22 emergent macrophytes (Tables S2 and S3). The most abundant species was Phragmites australis (Cav.) Trin. ex Steud., an emer- 3.3 | Phylogenetic analysis gent macrophyte of Poaceae with 177 records, followed by four submerged macrophytes, Vallisneria sp. 1 (139 records), Potamogeton We determined sequences of rbcL and matK for 310 (accession No. crispus L. (122 records), Myriophyllum spicatum L. (109 records) and LC218821–LC219130) and 208 individuals (accession No. LC219131– Potamogeton wrightii Morong (88 records; Figure S1). The most abun- LC219338), respectively (Table S3). The aligned sequence length of dant floating-leaved species was Nymphoides peltata (S.G.Gmel.) rbcL was 552, in which 239 sites were parsimony-informative. The Kuntze (59 records) and the most abundant free-floating species was aligned sequence length of matK was 930, in which 519 sites were Ceratophyllum demersum L. (53 records). parsimony-informative and 20 indels were observed. The topology of the Bayesian phylogeny of rbcL and matK sequences (Figure 4) was consistent with APG classification III (Angiosperm Phylogeny Grp, 3.2 | Physico-chemical state and its effect on each 2009). All families were monophyletic and supported by high poste- functional group rior probabilities (≥0.98) under no monophyletic constraints. Significant phylogenetic conservatism was observed in the Principal component analysis indicated that 74.1% of the total vari- growth forms (Figure 4). There were 11 observed transitions, and ance in the physico-chemical state is explained by three principal the randomised median was 21 (p < 0.001). Topological uncertainty components. The first axis (PC1), explaining 51.5% of the total varia- did not affect the results, with all 9,000 trees showing significant tion, had positive loading with turbidity, heavy metals (Al, Cd, Co, Cu, phylogenetic signals (p < 0.001). Cr, Mn, Ni, Pb, Se, V, Zn) and suspended solids, and negative loading with temperature (Figure 2a). Although water turbidity increases the temperature of the near-surface water (Paaijmans, Takken, Githeko, 3.4 | Species richness and phylogenetic α-diversity & Jacobs, 2008), our deeper water collection (0.5 m) and more turbid water would result in weaker sunlight, and give the consequence of The spatial change of SR, PD, and PDab from the river mouth showed lower temperature. For simplification, here we assume that PC1 rep- trends that were similar to one another (Figure S2). Specifically, resents the degree of water contamination. The second axis (PC2), all were lower in the middle reach, where P. australis dominated explaining 12.2% of the total variation, had positive loading with (Figure 1d), and higher in the lower and upper reach, where multiple chlorophyll a, pH, arsenic (As), and dissolved oxygen, and negative vegetation types were observed (Figure 1c,e). loading with ammonium and orthophosphate (Figure 2a). PC2 prob- The relationship between each chemical variable of water and ably associates with the amount of phytoplankton because phyto- diversity indices is described in Figures S3 to S7. The effects of water plankton increase their biomass using N and P (Smith & Schindler, quality on diversity indices were tested by hierarchical Bayesian 638 | TOYAMA et al.

(a) PC1 and PC2 (b) PC2 and PC3

−6 −4 −2 0246 −4 −2 0246

25 0. 4

.3 pH 7 2 9 6 As Cr

46 Dep 3 24 DO 0. 20 4 Chl_a .2 31 1 8 NH4_N 13 118 3 7 As 11 246 41 2 23 12 10 .1 27 42 10 19 6 46 32 Se 30 Ni 4 43 Dep 22 Al 1 Cr21 18 SS Pb 2928 13 Ni 36 2 20 Turb Cd 16CoV 0 16 15 V 0 Mn 15 Chl_a 0. 00 0. 00 17 Cu 26 Cu PC 2 12N03_N Co PC 3 47 33 35 CdPb Se 9 37 37 46 Zn 34 Turb Al 35 31 SS 32 34 PO4_P Temp 3033 Mn 44 4 −0.1

NH _N −2 39 Temp 17 21 19 pH 28 25 Zn 23 −2 29 44 20 43 3647 27 PO4_P 38 1822

26 −0.2

−0.2 39

−4 42 24 DO −4 N03_N

−0.3 38 41 −6

−0.3 −0.2 −0.1 0.0 0.1 0.2 0.3 −0.2 0.00.2 0.4 PC1 PC2

FIGURE 2 Principal component analysis (PCA) applied to the data set describing the physicochemical state of water in the East Tiaoxi River: (a) PCA biplot with 47 study sites in two dimensions of PC1 and PC2; (b) PCA biplot in two dimensions of PC2 and PC3. The number represents the site ID. The vector of each factor indicates the contribution to principal components

models (Table 1, Figure S8). Species richness, PD, PDab, and αMP- (Table 2). Topological uncertainty influenced the results of βMPDab, Dab decreased with PC1 and PC2. αMPD was not significantly af- in which the correlation between PC2 and βMPDab were not signifi- fected by water quality. Topological uncertainty affected the results cant in 61% of the phylogenies (4,890/9,000). of PDab, in which the effects of PC2 were not significant in 27% of the phylogenies (2,457/9,000). The effects of water quality on diversity indices in each functional 3.6 | Evolutionary distinctiveness group were tested by hierarchical Bayesian models (Table S5, Figures S9– S12). Species richness, PD, and PDab of emergent macrophytes decreased Fifteen species were detected as outliers, among which Azolla sp., with PC1 (SR: β1 = −0.092, p = 0.014, PD: β1 = −0.047, p < 0.001, PDab: Nitella sp. 1 and Nitella sp. 2 showed relatively higher ED and AED,

β1 = −0.064, p = 0.01) and PC2 (SR: β2 = −0.14, p = 0.030, PD: β2 = −0.060, and Pterocarya stenoptera C.DC., Rosa multiflora Almq., Ludwigia p = 0.038), except for the effects of PC2 on PDab (β2 = −0.068, p = 0.15). ovalis Miq., Lemna minor L., Cynanchum stauntonii (Decne.) Hand.- Topological uncertainty influenced the results of PD, in which the effects Mazz., Salix nipponica Franch. & Sav., Rumex sp. Cabomba caroliniana of PC2 were not significant in 34% of phylogenies (3,097/9,000). The SR, A.Gray (type 1), Sagittaria trifolia L., C. caroliniana (type 2), Lobelia PD and PDab of floating-leaved and free-floating macrophytes were not chinensis Lour., and Acorus calamus L. showed relatively higher AED significantly affected by water quality, but those of submerged macro- (Figure 5). Topological uncertainty had some effects on the detec- phytes decreased with PC1 (SR: β1 = −0.13, p = 0.002, PD: β1 = −0.053, tion of outliers, A. calamus, C. caroliniana (type 1), C. caroliniana (type p = 0.004, PDab: β1 = −0.11, p = 0.004) and PC2 (SR: β2 = −0.15, p = 0.014, 2), L. minor, L. chinensis, and S. trifolia were not significant in 2.1%

PD: β2 = −0.077, p = 0.012, PDab: β2 = −0.12, p = 0.038). Topological un- (187/9,000), 1.4% (129/9,000), 4.9% (440/9,000), 0.03% (3/9,000), certainty affected the results of PDab, with the effects of PC2 not being 4.0% (364/9,000), and 6.8% (608/9,000) of the phylogenies, significant in 33% of phylogenies (2,952/9,000) and those of PC3 being respectively. significant in 20% (1,825/9,000).

4 | DISCUSSION 3.5 | Phylogenetic β-diversity Our study demonstrated that anthropogenic disturbance resulted Phylogenetic β-diversity (βMPD and βMPDab) was positively cor- in a loss of diversity and phylogenetically clustered community related with pairwise spatial distance and pairwise PC2 difference structure in aquatic macrophytes. In α-diversity, SR, PD, PDab, TOYAMA et al. | 639

(a) Incidence data .6 .6 .6 EM 40 40 40 0. 0. 0. SM .2 .2 .2 Probability FF FL 00 00 00 0. 0. 0. –4 –2 02468 –3 –2 –1 0123 –4 –2 024 PC1 PC2 PC3

(b) Abundance data .0 .0 .0 5 5 5 0. 0. 0. Probability 01 01 01 0. 0. 0. –4 –2 02468 –3 –2 –1 0123 –4 –2 024 PC1 PC2 PC3

FIGURE 3 Expected relative occurrence probability of each growth type of aquatic macrophytes along three pollution gradients estimated using (a) occurrence and (b) abundance data. Green, blue, red, and black lines indicate emergent (EM), submerged (SM), floating- leaved (FL) and free-floating (FF) macrophytes, respectively and αMPDab decreased with PC1 (high water contamination) anthropogenic disturbance on phylogenetic diversity/community and PC2 (high phytoplankton abundance). This non-random pat- structure of aquatic macrophytes and suggest the effects of en- tern could be explained by the environmental filtering that select vironmental filtering for its community assembly. Of course, com- species from a regional pool because they possess specified community assembly is affected by the effects of biotic factors in the binations of traits for a given habitat (Keddy, 1992). In most previ- field (Cavender-Bares, Kozak, Fine, & Kembel, 2009; Mayfield & ous studies, phylogenetic clustering has been used as a proxy for Levine, 2010), but Keddy (2010) suggested that the relative impor- environmental filtering under insufficient evaluation of phyloge- tance of biotic factors in structuring natural wetland communities netic niche conservatism (Cadotte & Tucker, 2017; Gerhold, Cahill, is typically <5% (competition <5%, grazing <5%), while abiotic fac- Winter, Bartish, & Prinzing, 2015), while our study demonstrated tors account for about 95% (hydrology 50%, fertility 15%, salinity it in two steps. First, the presence/absence of four functional 15%, disturbance 15%, and burial <5%). groups of macrophytes (emergent, floating-leaved, free-floating, The effects of anthropogenic disturbances on the diversity indi- and submerged) in each plot depended on environmental factors ces differed among the four functional groups. The SR/PD of emer- (niche conservatism). Second, a strong phylogenetic signal was de- gent and submerged macrophytes decreased with PC1 and PC2, tected for growth forms (phylogenetic conservatism). To the best but was not significant in free-floating and floating-leaved macro- of our knowledge, this is the first study to reveal the effects of phytes. These results suggest that the diversity of emergent and

FIGURE 4 Phylogeny of 47 plant taxa in the East Tiaoxi River, China. Blanches are labelled with posterior probabilities. Asterisks show 11 nodes where prior information on the minimum age for calibration of rbcL and matK divergence (see Methods). Ancestral states and character-state transitions were reconstructed under parsimony method. Green, blue, red, and black branches indicate emergent (EM), submerged (SM), floating-leaved (FL), and free-floating (FF) macrophytes, respectively 640 | TOYAMA et al.

1 Nitella sp. 1 Charales Nitella sp. 2 Azolla sp. Salviniales

1 Cabomba caroliniana 1 Nymphaeales Cabomba caroliniana 2 Acorus calamus Acorales 1 ∗ Lemna minor 1 Spirodela polyrhiza Sagittaria trifolia

1 1 Hydrocharis dubia 1 Elodea nuttallii 1 0.98 Vallisneria sp. 1 0.98 1 1 Vallisneria sp. 2 1 1 Hydrilla verticillata Alismatales Hydrilla verticillata var. roxburghii

1 Potamogeton cristatus 1 Potamogeton octandrus 1 ∗ Potamogeton wrightii

0.9 Potamogeton maackianus 0.87 Potamogeton crispus 1 0.99 Potamogeton crispus 2 1 ∗ Schoenoplectus triqueter 11∗ Phalaris arundinacea Polypogon sp. 1 ∗ Zizania latifolia 0.58 Cynodon dactylon Poales 0.8 Phragmites australis 1 1 Miscanthus sp. 1 1 Miscanthus sp. 2 Paspalum sp. Ceratophyllum demersum 1 1 Ceratophyllales Ceratophyllum demersum 2 Myriophyllum spicatum Saxifragales Salix nipponica 1 ∗ 1 1 Salix eriocarpa Malpighiales 0.92 Emergent 1 Salix chaenomeloides Submerged Rosa multiflora Rosales 1 Floating-leaved Pterocarya stenoptera 0.99 Fagales Free-floationg Ludwigia ovalis 1 1 ∗ 1 Trapa natans Myrtales Trapa incisa Alternanthera philoxeroides 1 ∗ 1 ∗ Polygonum pubescens Caryophyllales Rumex sp.

0.99 Cynanchum stauntonii Gentianales 1 Lobelia chinensis 1 Asterales Nymphoides peltata

400 350 300 250 200 150 100 50 0 TOYAMA et al. | 641

TABLE 1 Summary of the hierarchical Bayesian models for species richness (SR), phylogenetic diversity (PD, PDab), and mean pairwise distance (αMPD, αMPDab)

Model for factors Mean SD 2.50% 97.50% p

SR PC1 −0.13 0.027 −0.19 −0.081 <0.001 PC2 −0.14 0.040 −0.22 −0.059 <0.001 PC3 0.024 0.047 −0.071 0.12 0.59 PD PC1 −0.099 (−0.11 to 0.016 −0.13 (−0.15 to −0.069 (−0.079 to <0.001 (<0.001) −0.089) (0.014–0.018) −0.11) −0.061) PC2 −0.10 (−0.11 to 0.033 −0.17 (−0.19 to −0.040 (−0.053 0.004 (<0.001–0.032) −0.096) (0.026–0.037) −0.15) to −0.023) PC3 0.011 (0.0043–0.021) 0.034 −0.060 (−0.071 to 0.075 (0.060–0.099) 0.77 (0.50–0.90) (0.030–0.040) −0.043) PDab PC1 −0.13 (−0.13 to −0.12) 0.022 −0.17 (−0.18 to −0.085 (−0.088 <0.001 (<0.001–0.002) (0.021–0.026) −0.16) to −0.070) PC2 −0.097 (−0.10 to 0.045 −0.18 (−0.20 to −0.0080 0.032 (0.006–0.088) −0.087) (0.023–0.051) −0.17) (−0.019–0.017) PC3 −0.043 (−0.052 to 0.051 −0.14 (−0.16 to 0.065 (0.039–0.078) 0.40 (0.27–0.47) −0.038) (0.047–0.059) −0.13)

αMPD PC1 −0.014 (−0.021 to 0.014 −0.040 (−0.053 to 0.013 (0.0069–0.022) 0.29 (0.17–0.52) −0.0094) (0.012–0.017) −0.034) PC2 −0.028 (−0.039 to 0.026 −0.076 (−0.099 to 0.026 (0.012–0.040) 0.27 (0.12–0.54) −0.017) (0.022–0.033) −0.065) PC3 0.0087 (0.0027–0.016) 0.031 −0.055 (−0.066 to 0.070 (0.057–0.087) 0.75 (0.61–0.93) (0.026–0.037) −0.039)

αMPDab PC1 −0.085 (−0.091 to 0.018 −0.12 (−0.13 to −0.051 (−0.056 to <0.001 (<0.001) −0.073) (0.016–0.021) −0.10) −0.040) PC2 −0.083 (−0.094 to 0.031 −0.14 (−0.16 to −0.023 (−0.035 to 0.010 (<0.001–0.004) −0.075) (0.029–0.037) −0.13) −0.0034) PC3 0.036 (0.027–0.043) 0.037 −0.040 (−0.054 to 0.11 (0.092–0.13) 0.33 (0.21–0.48) (0.032–0.041) −0.022)

Note: Explanation variables are shown in the second column (PC1, PC2, PC3). The mean, standard deviation (SD) and 95% confidence intervals of the posterior distribution and p-values are shown for each variable. The possible range of each value by topological uncertainty is shown in parentheses. Statistical significance is indicated by bold.

TABLE 2 Results of the Mantel test to compare phylogenetic β diversity (βMPD and βMPDab) and spatial and environmental distance matrices

βMPD βMPDab

r p r p

Distance 0.22 (0.18 –0.24) <0.001 (0.0001– 0.001) 0.36 (0.33–0.37) <0.001 (<0.001) PC1 −0.017 (−0.066–0.042) 0.56 (0.28–0.75) −0.022 (−0.061–0.022) 0.58 (0.35–0.75) PC2 0.23 (0.21–0.25) <0.001 (0.0001–0.004) 0.11 (0.099–0.12) 0.05 (0.043–0.077) PC3 0.034 (0.010–0.056) 0.33 (0.25–0.43) 0.087 (0.066–0.11) 0.14 (0.099–0.20)

Note: The possible range of each value by topological uncertainty is shown in parentheses. Statistical significance is indicated by bold.

submerged macrophytes were more sensitive to disturbances than traffic (c. 40–50 m length) that causes 1-m waves near the ship and free-floating and floating-leaved macrophytes. One plausible com- c. 0.2-m waves near shore (Sato et al., 2010). However, the interac- mon factor that reduces the diversity of two growth forms would tions among toxic pollutants, eutrophic conditions, and wave action be frequent water waves that directly and indirectly damage to the would be complex. Further studies are needed to reveal the relative plant growth, typically stimulating both abundance and diversity of importance of them. macrophytes (Bornette & Puijalon, 2011; Lacoul & Freedman, 2006; The effects of anthropogenic disturbances on the relative Madsen, Chambers, James, Koch, & Westlake, 2001). In the sites of occurrence probability also differed among the four functional Tiaoxi river showing higher PC1 and PC2, there is a lot of cargo ship groups. Emergent/floating-leaved macrophytes showed opposite 642 | TOYAMA et al.

1. Azolla sp. 2. Nitella sp. 2 00 3. Nitella sp. 1 1 4. Pterocarya stenoptera 5. Rosa multiflora

15 02 6. Ludwigia ovalis

) 2 7. Lemna minor 4 5 8. Cynanchum stauntonii

00 3 (Mys 9. Salix nipponica 6 10. Rumex sp. AE D 9 7 8 11. Cabomba caroliniana Type 1 01 10 12. Sagittaria trifolia 11 14 15 13 13. Cabomba caroliniana Type 2 12 14. Lobelia chinensis 05 15. Acorus calamus 0 100 200300 400 ED (Mys)

FIGURE 5 The relationship between evolutionary distinctiveness (ED) and abundance-weighted ED (AED). The ED is the sum of branch lengths divided by the number of species subtending the branch for all branches from which the species is descended. The AED is the modification of ED that has been proposed to incorporate abundance information at the individual level. The ED and AED can respectively identify a species and an individual whose loss results in the greatest loss of evolutionary information (Cadotte et al., 2010; Isaac et al., 2007). The filled circles represent outliers at the 0.05 level of significance. The numbers represent the species listed on the right side. The bars represent the possible range of each value by topological uncertainty

response to all three environmental factors. Emergent/submerged this is the first study to reveal the phylogenetic turnover depending macrophytes, floating-leaved/free-floating macrophytes, and float- on the spatial distance and anthropogenic disturbance in aquatic ing-leaved/submerged macrophytes showed opposite response macrophytes. The phylogenetic turnover accounts for the evolu- to PC1, PC2, and PC2, respectively. These would be explained by tionally dissimilarity between communities that usually allows us to the niche differentiation among growth form, but further labora- interpret a signature of abiotic filtering. If species traits are phylo- tory-based measurements are warranted to reveal the effects of genetically conserved, environmental filtering favours phenotypic environmental factors. Another possibility is that competitive ex- and phylogenetic clustering within communities, leading to phylo- clusion might play a role in community assembly. Particularly, float- genetic turnover among communities subject to different environ- ing-leaved and free-floating macrophytes produce their leaves on mental conditions (Graham & Fine, 2008). Interestingly, PC1 did not water surface, so the abundant floating-leave macrophytes would show significant correlation, suggesting that PC2 (phytoplankton leave little water surface available for free-floating macrophytes. As abundance) was more important for phylogenetic turnover among noted before, the relative importance of competition in structuring communities. However, the effect of PC2 on βMPDab depends on natural wetland communities is typically <5% (Keddy, 2010), but fur- the tree topology; 61% of the phylogenies produced showed no re- ther studies are needed to reveal the interaction between environ- lationship. Further studies are needed to reveal the effect of phyto- mental factors and competition among growth forms. plankton abundance on phylogenetic turnover. Interestingly, in emergent macrophytes, PC1 and PC2 decreased The ED and AED analyses indicate that Azolla sp. and Nitella sp. the diversity, but increased the relative occurrence probability. made a higher contribution to phylogenetic diversity in the East In Tiaoxi river, P. australis and Zizania latifolia (Griseb.) Turcz. ex Tiaoxi River at both the species and individual levels as compared Stapf were dominated lower reach of the quay, while A. calamus, with those of other species. These species are phylogenetically C. stauntonii, Cynodon dactylon (L.) Pers., L. chinensis, Miscanthus sp. more distinct from other species, and usually associated with stand- 1 & 2, Polypogon sp., P. stenoptera, R. multiflora, Salix chaenomeloi- ing water regions, such as lakes and ponds, resulting in higher ED des Kimura, and S. nipponica were only found in upper reach of the and AED values. Additionally, 12 species were detected as outli- quay. This would be associated with the different response to the ers showing relatively higher AED. The individuals of these species wave action that decreases the diversity of emergent macrophytes, are important for conservation of the phylogenetic diversity in this but increases the relative abundance of wave-tolerant species in the river. Conversely, Alternanthera philoxeroides (Mart.) Griseb., which community such as P. australis and Z. latifolia. is known as one of the world's worst tropical aquatic weeds (Wang, Our study also demonstrated that the mean pairwise phylo- Wang, Bowler, & Xiong, 2016), showed relatively lower AED (2.3) genetic distance between each of the plots (βMPD) and abun- and higher ED (127.5) values. Thus, the AED is useful to identify in- dance-weighted βMPD (βMPDab) were negatively correlated with dividuals that make a lower contribution to the overall evolutionary pairwise geographical distance and pairwise difference of PC2. history and serves as a basis to evaluate species conservation. There are several studies to reveal the species turnover along the en- In most cases, abundance data showed a much stronger signal vironmental gradients (e.g. Heegaard, 2004), but to our knowledge, than occurrence data, suggesting that shifts in species abundance TOYAMA et al. | 643 due to water pollution change the pattern of the phylogenetic Grant) (No. 17SP01302) and JSPS KAKENHI (15H02640 and community structure before the loss and gain of species. This 17K15175). finding is important because many community phylogenetic studies have drawn conclusions based on species occurrence data. By CONFLICT OF INTEREST using abundance data, we could implement conservation planning The authors report no conflicts of interest. before extinction of species has taken place. Additionally, phylogenetic uncertainty affected the results in some cases. This is also DATA AVAILABILITY STATEMENT important to confirm the robustness of statistical analysis. Future DNA sequences are available at DDBJ (https://www.ddbj.nig. studies integrating well-resolved phylogenies including sensitiv- ac.jp/index-e.html) under accession numbers shown in Table S3. ity analysis, abundance data and long-term monitoring will pro- Physicochemical data for water and plant community data are shown vide increased understanding of anthropogenic disturbances that in Table S1 and S2. determine phylogenetic diversity and phylogenetic community structure. 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