Modeling the Effects of Hydrodynamic Regimes on Microbial

Subscriber access provided by EPFL | Scientific Information and Libraries

Article

Modelling the effects of hydrodynamic regimes on microbial communities within fluvial biofilms: combining deterministic and stochastic processes Yi Li, Chao Wang, Wenlong Zhang, Peifang Wang, Lihua Niu, Jun Hou, Jing Wang, and Linqiong Wang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b03277 • Publication Date (Web): 05 Oct 2015 Downloaded from http://pubs.acs.org on October 9, 2015

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 37

Environmental Science & Technology

1

Modelling the effects of hydrodynamic regimes on microbial communities within

2

fluvial biofilms: combining deterministic and stochastic processes

3

Yi Li, Chao Wang, Wenlong Zhang*, Peifang Wang*, Lihua Niu, Jun Hou, Jing

4

Wang, Linqiong Wang

5

Key Laboratory of Integrated Regulation and Resource Development on Shallow

6

Lakes, Ministry of Education, College of Environment, Hohai University, Nanjing

7

210098, P.R. China

8 9 10

Keywords: hydrodynamics, stream biofilm, community assembly, modelling, niche, neutral

11 12

* Corresponding authors:

13

Dr. Wenlong Zhang

14

College of Environment, Hohai University, Nanjing 210098, P.R. China

15

Phone: 86-25-83786251. Fax: 86-25-83786090.

16

Email: [email protected]

17 18

Prof. Peifang Wang

19

College of Environment, Hohai University, Nanjing 210098, P.R. China

20

Phone: 86-25-83786251. Fax: 86-25-83786090.

21

Email: [email protected]

22 1

ACS Paragon Plus Environment


23

Abstract

24

To deeply understand the effects of hydrodynamics on microbial community, the

25

roles of niche-based and neutral processes must be considered in a mathematical

26

model. To this end, a two-dimensional model combining mechanisms of immigration,

27

dispersal, and niche differentiation was firstly established to describe the effects of

28

hydrodynamics on bacterial communities within fluvial biofilms. Deterministic

29

factors of the model were identified via the calculation of Spearman’s rank correlation

30

coefficients between parameters of hydrodynamics and bacterial community. It was

31

found that turbulent kinetic energy and turbulent intensity were considered as a set of

32

reasonable predictors of community composition, whereas flow velocity and turbulent

33

intensity can be combined together to predict biofilm bacterial biomass. According to

34

the modelling result, bacterial community could get its favorable assembly condition

35

with flow velocity ranging from 0.041 to 0.061 m/s. However, the driving force for

36

biofilm community assembly changed with the local hydrodynamics. Individuals

37

reproduction within biofilm was the main driving force with flow velocity less than

38

0.05 m/s, while cells migration played a much more important role with velocity

39

larger than 0.05 m/s. The developed model could be considered as a useful tool for

40

improving the technologies of water environment protection and remediation.

41

Keywords: hydrodynamics; stream biofilm; community assembly; modelling; niche;

42

neutral

2


Page 2 of 37

Page 3 of 37


43

Introduction

44

Stream biofilm is a complex aggregation of microorganisms embedded in a

45

polymer matrix and covers almost every surface in freshwater environments (1). In

46

aquatic environments, natural biofilms are able to provide a wide variety of ecosystem

47

services, involving organic matter processing and retention, energy flow and nutrients

48

cycling (2, 3). Because of their sedentary way of life, microbial communities

49

associated with biofilms are affected by past and present environmental conditions

50

and therefore constitute potential integrative indicators of stream health (4). Therefore,

51

understanding the assembly of microbial communities in biofilms could provide

52

important information for water environment protection and remediation.

53

Over the past decades, a wide range of studies have been conducted to investigate

54

the relationships between biofilm microbial communities and environmental

55

parameters using statistical analysis, such as nutrient, temperature, hydrodynamics,

56

light, and heavy metal (5-8). Among these environmental parameters, hydrodynamics

57

are considered as the major agents of physical forcing on driving dynamics of the

58

stream biofilm microbial community since streams are usually characterized by a

59

largely unidirectional downstream flow of water which can control the dispersal of

60

suspended microorganisms (9), biofilm community composition (8, 10), architecture

61

(11, 12), and metabolism (13). Besemer et al. (8) showed that the spatial variation

62

induced by hydrodynamics could result in a gradient of bacterial beta diversity. The

63

flux of microorganisms from the bulk liquid to biofilms increased with the increasing

64

of flow velocity, thereby generating higher richness in these biofilms communities 3



65

(10). Battin et al. (11) found that microstructural heterogeneity of biofilm was

66

dynamic, and biofilms that developed under slower velocities were thicker and had

67

larger surface sinuosity and higher areal densities than their counterparts exposed to

68

higher velocities. Surface sinuosity and biofilm fragmentation increased with

69

thickness, and these changes could reduce resistance to the mass transfer of solutes

70

from the water column into the biofilms. Singer et al. (13) found that the metabolic

71

rates for microbial communities within biofilms were controlled by the transfer of

72

metabolites in diffusive boundary layer. For example, the uptake of glucose was

73

largely driven by physical processes related to flow heterogeneity, whereas

74

biodiversity effects, such as complementarity, most likely contributed to the enhanced

75

uptake of putatively recalcitrant dissolved organic carbon compounds in the

76

streambeds with higher flow heterogeneity. Besides, the drag forces and skin friction

77

exerted on the stream biofilm were reported to be increased with the increasing water

78

velocity, resulting in a loss of microbial community through the chronic detachment

79

process (12). However, until now, all the effects were investigated individually in

80

each study, and there has been no research which systematically illuminates the

81

cumulative effects of hydrodynamics on biofilm microbial community.

82

In order to deeply understand the effects of hydrodynamics on the assembly of

83

biofilm microbial community, a mathematical model considering the mechanisms of

84

ecology succession is required. Recently, microbial community is thought to be

85

shaped mainly by two types of processes, i.e. deterministic process and stochastic

86

process (14-16). The former such as competition and niche differentiation came from 4


Page 4 of 37

Page 5 of 37


87

the assumption of traditional niche-based theory (17). However, such theories struggle

88

to explain very diverse environments where many rare taxa can coexist (18). The later

89

was proposed according to a neutral theory which considers birth, death, dispersal,

90

and speciation and disregards the differences between species at the same trophic

91

level (19). However, the mechanisms of neutral models are just plain “too simple” to

92

represent biological reality. Moreover, small deviations from neutrality would have

93

large repercussions for the predicted patterns (20). It is now more generally accepted

94

that deterministic and stochastic processes occur simultaneously during the assembly

95

of biofilm communities (14, 21). According to the theory, models including both

96

deterministic and stochastic elements were established. Mouquet and Loreau (22)

97

presented a model to describe community patterns in source-sink metacommunities

98

though introducing some stochastic elements to niche model. Ofiţeru et al. (14)

99

examined the microbial communities in a wastewater treatment plant by incorporating

100

environmental influences on the reproduction (or birth) rate of individual taxa.

101

And yet for all that, the related research on modeling the effects of

102

hydrodynamics on the assembly of biofilm microbial communities is very limited.

103

Only recently, Woodcock et al. (23) used a one-dimensional neutral model to present

104

the spatial correlation between local microbial community assembly and immigration

105

from remote community and dispersal between environmentally similar landscape

106

patches. Drummond et al. (24) developed a stochastic model to assess the transport,

107

retention, and inactivation of Escherichia coli in a small stream. Although

108

hydrodynamics were reported to play important roles in the assembly of microbial 5



109

community by controlling the transfer of metabolites in the diffusive boundary layer

110

(i.e. the hydrodynamics driving deterministic process), it has not yet been seriously

111

considered in the one-dimensional neutral/stochastic model.

112

Therefore, our hypothesis is that hydrodynamics act not only as deterministic

113

factors but also stochastic factors during the assembly of microbial communities

114

within fluvial biofilm. To test the hypothesis, this study was conducted in the

115

following three steps: 1) identifying the deterministic factors through studying the

116

correlation between local hydrodynamics and microbial community within biofilm

117

using statistical analysis, 2) studying the effect of local hydrodynamics on biofilm

118

community succession (i.e. community diversity, composition, and biomass), and 3)

119

developing a two-dimensional model considering the mechanisms of immigration,

120

dispersal, and niche differentiation to describe the effects of hydrodynamics on the

121

assembly of microbial community within fluvial biofilm. The obtained results would

122

not only be helpful for understanding the multitudinous consequences explaining the

123

relationship between microbial communities and hydrodynamics using statistical

124

analysis, but also play very important roles in water environment protection and

125

remediation.

126

2. Material and Methods

127

2.1 Experimental procedure

128

The experiment was conducted in an indoor flume located on the right bank of the

129

Qinhuai River at Nanjing, China. The sketch of the experimental system is shown in

6


Page 6 of 37

Page 7 of 37


130

Figure 1. Two pumps were used in the experimental system. One continuously

131

supplied water from the river to the downstream tank. The other ran water to the

132

upstream tank through a 80 cm main. Large particles were eliminated by the filtration

133

before river water was supplied into river tank. The upstream tank fed the downstream

134

tank by gravity with the constant flow of 10 L/s. Three flumes were operated parallel

135

during the experiment. Each flume was built with the length of 27 m, entrance width

136

of 2 m, outlet width of 0.2 m, depth of 0.2 m, and slope of 10-3. The flow conditions

137

of the flume were shaped by the diminishing width. A monolayer of clean stream

138

gravel was used to cover the flumes and mimic the benthic layer of a streambed.

139

Sterile unglazed ceramic coupons (1 by 2 cm) were placed on the gravel serving as a

140

near-natural substratum for biofilm growth (8).

141

The experiment was performed in two stages. The first stage lasted three week for

142

biofilm seeding. In this stage, the water was re-circulated in the flumes and renewed

143

weekly. After that, the flumes changed to open water circulation to allow the free

144

growth of biofilm. Two weeks later, hydrodynamic and biological measurements were

145

performed along the horizontal centerline of each flume with 1 m intervals from

146

downstream to upstream. After sampling, the samples were transferred into sealable

147

plastic bags and placed on ice until delivery to the laboratory within 1 h and then

148

stored at -80 oC in lab. The concentrations of inorganic nutrients and dissolved

149

organic carbon in the upstream tank were measured twice a week.

150

2.2 Biological measurements

151

2.2.1 Microbial biomass 7



Page 8 of 37

152

For each sampling, four coupons were selected randomly for biofilms detachment.

153

Bacterial abundance was estimated by direct microscopic counts (11, 25). The

154

detached biofilms were treated with 0.1 mM sterile tetrasodium pyrophosphate for 10

155

min, and sonicated 60 s for cells removal. Samples (1 mL) were then transferred into

156

sterile vials containing 1 mL of tetrasodium pyrophosphate, thoroughly vortexed, and

157

sonicated 30 s again. After that, samples were mixed with sterile glycerol (30 %),

158

vortexed, and spun to pellet particles that likely cause background fluorescence. After

159

staining with propidium iodide, bacterial cells in the supernatant were enumerated on

160

a black 0.2 µm pore size GTBP Millipore filter in 10 to 30 randomly selected fields to

161

account for 300 to 500 cells. Two filters were counted per ceramic coupon.

162

Chlorophyll a was extracted with acetone for 12 h at 4 °C in dark. Samples were

163

vortexed,

164

(EX435/EM675) using spinach (Sigma) as a standard (11). The detached matter was

165

weighed after combustion (500 °C, overnight) to determine ash free dry mass (AFDM)

166

(26).

167

2.2.2 Microbial community composition

and

the

supernatant

was filtered

and

assayed

fluorometrically

168

Genomic DNA of samples was extracted using an E.Z.N.A® soil DNA kit

169

(Omega Bio-Tek Inc., USA). The concentration and purity of DNA were checked by

170

Nanodrop ND-1000 Spectrophotometer (Witec-AG, Switzerland). The bacterial

171

community was detected using the T-RFLP method (23). 16S rRNA genes of bacteria

172

were amplified from DNA extract using the primer pair 27F and 1492R. Primer 27F

173

was labeled using dye 5-carboxyflurescein (FAM). PCR products were digested in 8


Page 9 of 37


174

duplicates using Hae III and Hinf I restriction endonucleases (TaKaRa, Japan) at 37

175

o

176

on an automated DNA sequencer (ABI Prism TM 3730). T-RF sizes and peak areas

177

were measured using GeneMarker.

C for 3 h. The fluorescently labeled terminal restriction fragments (T-RFs) were run

178

For the identity of bacterial phylogenetic affiliation, clone library of 16S rRNA

179

genes from the pooled samples was constructed. A total of 100 positive clones were

180

selected randomly for the subsequent sequencing of inserted DNA fragments with

181

bacterial library. The phylogenetic affiliation of these 16S rRNA gene sequences were

182

determined by Ribosomal Database Project and BALSTN online.

183

Given the potential discrepancy between in silico-determined T-RF length and

184

actual T-RF length determined by sequencing, the origins of T-RFs were identified

185

according to the T-RFLP profiles of cloned 16S rRNA genes (27). The T-RFLP

186

analysis of cloned 16S rRNA genes was the same as above. The phylogenetic

187

affiliation of each peak was determined by the cloned 16S rRNA gene sequences with

188

the same T-RF size. The T-RFLP profiles of the same sample digested by Hae III and

189

Hinf I separately, presented a good agreement in the microbial community

190

compositions. However, the T-RFLP profiles corresponding to Hae III generated more

191

detailed T-RFLP profiles and were used for further analysis. All T-RFLP profiles

192

digested by Hae III were pooled and standardized into a T-RFLP abundance matrix

193

for the following analysis. The diversity indices (Gini-simpson coefficient and

194

evenness) based on the T-RFLP abundance matrix were calculated by PAST 4.0. Each

195

T-RF size was defined as an operational taxonomic unit (OTU) in this study. 9



196

Page 10 of 37

2.3 Hydrodynamic measurements

197

The hydrodynamic parameters over the substratum (5 mm above streambed) were

198

measured with a high-resolution acoustic Doppler velocimeter (ADV) (Nortek

199

Vectrion, Norway) (8). Data for each sections were collected at a sampling rate of 50

200

Hz (1 min), yielding a time series (n = 3000). Three-dimensional vector of flow

201

velocity (Vxyz), turbulent intensity (TI), turbulent kinetic energy (TKE), and

202

roughness Reynolds number (k+) were calculated by the equations from (1) to (4) (8,

203

26):

204

TI = 205

206 207

x2 + y 2 + z 2

Vxyz =

SD R xyz R xyz

(2)

1 n −1 ρ ( SD x2 + SD y2 + SD z2 ) 2 n

TK E = k+ =

(1)

(3)

V xy z k s

ν

(4)

208

where x, y, and z are velocity components as Cartesian coordinates, SDRxyz is the

209

standard deviation of the three-dimensional velocity, ρ is the density of water, ν is

210

water kinetic viscosity, ks is the Nikuradse’s equivalent sand roughness

211

(approximately equal to 1.03 ± 0.07 cm) (26). SDx, SDy, and SDz, are the respective

212

standard deviations (from 3000 measurements) of the velocity components.

213

2.4 Data analysis

214

After

the

measurements,

all

of

the

hydrodynamic

parameters

were

215

log-transformed for further statistical analysis. Correlation coefficients between

216

hydrodynamic parameters (i.e. flow velocity, turbulent intensity, turbulent kinetic

217

energy, and roughness Reynolds number) and biological parameters (i.e. numbers of 10


Page 11 of 37


218

OTUs, Gini-Simpson coefficient, evenness, bacterial abundance, Chlorophyll a, and

219

AFDM) were measured by the calculations of Spearman’s rank correlation

220

coefficients using SPSS 20.0 as previously described (28). To control the effect of

221

hydrodynamics, the values of bacterial diversity and biomass of the samples with the

222

same hydrodynamic parameters were averaged, log-transformed, and then used for the

223

statistical analysis.

224

2.5 Modeling the effects of hydrodynamics on microbial communities

225

The two-dimensional model was developed by incorporating the deterministic

226

factors on the reproduction (or birth) rate of individual taxa. The basic neutral model

227

is that of Hubbell (19) formulated and extended for microbial communities into a

228

continuous format that permits the inclusion of environmental effects (29). In a

229

community saturated with NT individuals, an individual must die or leave the system

230

for the assemblage to change. The dead individual would be replaced via one of three

231

different mechanisms. With probability mn, its place is taken by the offspring of one

232

of the individuals in the neighboring patches. With probability ms, an individual

233

migrates into the system from the source community. The alternative, which occurs

234

with probability ml (ml = 1-mn-ms), is that it is replaced by a duplicate of a randomly

235

selected member of the same patch. The parameters ms, ml and mn are used to

236

describe stochastic processes during the assembly of biofilm communities. The

237

parameter

238

advantage/disadvantage factors on the reproduction (or birth) rate of individual taxa.

239

For species A in patch i comprising Ni individuals, the probability that the abundance

α

is

set

as

the

deterministic

factor

11


which

describe

the


Page 12 of 37

240

increases or decreases during any given time step, are given in formulas (5) and (6),

241

where p is the relative abundance of the species in the source community (23).

242

243 244 245

I ( Ni ) =

D( Ni ) =

NT − Ni Ni m [(1+ α )ml + n ( Ni−1 + Ni+1 ) + ms p] NT NT −1 2NT

(5)

Ni N − Ni mn [(1− α )ml T + (2NT − Ni−1 − Ni+1 ) + ms (1 − p)] NT NT −1 2NT

(6)

The expected changes in the abundance and covariance of the species A in the i patch are given in formulas (7) and (8).

246

dE ( N i ) = m n [ E ( N i +1 ) + E ( N i −1 ) − 2 E ( N i )] + m s [ N T p − ( N i )] + 2α E ( N i ) m l dt (7)

247

dU k dE ( N i N i + k ) dE ( N i ) dE ( N i + k ) = − E ( N i+ k ) − E(Ni ) dt dt dt dt

248

= mn (Uk +1 +Uk−1 − 2Uk ) − 2mU s k + 2mlαUk

(8)

249

Where E(Ni) is the expected abundance of the species A in the ith patch. E(Ni+1) and

250

E(Ni-1) are the expected abundance of the species A in the (i+1)th and (i-1)th patches,

251

respectively. Uk is the covariance between two sides of the ith patch at a distance of k.

252

Uk+1 and Uk-1 are the covariances between two sides a distance (k+1) and (k-1) apart,

253

respectively. When the system reaches a stable equilibrium (i.e. dE(Ni)/dt = 0, dUk /dt

254

= 0), the solutions for E(Ni) and Uk could be expressed as formulas (9) and (10),

255

respectively.

256

E ( Ni) = µ (a − a2 − 1)i −1 + τ (a + a 2 − 1)i−1 −η

257

log(Uk ) = k log(ω1) + log(C1)

(9) (10)

258

Where E(N2) is the expected abundance of the species A in the 2th patch. The symbols

259

µ, τ, η, a, b, and ω1 are used to simplify the formulas (9) and (10). Detailed

260

expressions are shown in the formulas (11) to (16). 12


Page 13 of 37


261

µ = [(a + a 2 − 1) NT − b / (a − 1 + a 2 − 1) − E ( N 2 )] / 2 a 2 − 1

(11)

262

τ = NT −µ +η

(12)

263

η = b / (a − 1 + a2 − 1)2

(13)

ms m +α l mn mn

(14)

a = 1+ 264

265

266

b = −2

ms NT p mn

ω1 = (1 +

(15)

m s α ml m α ml 2 m α ml + )− ( s + ) + 2( s + ) mn mn mn mn mn mn

(16)

267

In the developed model, the parameters ms/mn and ml/mn are used to describe

268

stochastic processes during the assembly of biofilm communities. The parameter α is

269

set as the deterministic factor which confers advantage (α > 0) or disadvantage (α < 0)

270

in the birth rate of the ith taxon, and assumed to depend on external factors (e.g.

271

environmental factors), thereby breaking the neutrality assumption. A different α can

272

be used for each taxon and hence the model allows for differential birth rates but is

273

not specific about the biological mechanisms that convey the advantage. When α = 0,

274

the model describes purely neutral dynamics, same as that reported by Woodcock et al.

275

(23). Moreover, if we allow a nonzero advantage, then the effects of environment

276

factors on the birth-death process in the community could be expressed by performing

277

the linear least-squares analysis to incorporate these independent variables (e.g.

278

environmental factors). In this study, it was hypothesized that hydrodynamics act not

279

only as deterministic factors but also stochastic factors during the assembly of

280

microbial communities within fluvial biofilm. Thus, the parameters ms/mn and αml/mn

281

could be considered as the linear functions of the measured hydrodynamic parameters 13



282

(i.e. flow velocity, turbulent intensity, turbulent kinetic energy, and roughness

283

Reynolds number).

284

3. Results and Discussion

285

3.1 Variations of community diversity, composition, and biomass with flow regime

286

The flow regime over the substratum varied significantly along the flumes (as shown

287

in Figure 1b). Flow velocity and roughness Reynolds number generally increased with

288

the diminished width. Turbulent kinetic energy increased accordingly and accelerated

289

rapidly at the end of the flumes, whereas a slight decrease was found for turbulent

290

intensity (from 6.6 % to 5.4 %). The variations of bacterial diversity, composition, and

291

biomass were shown in Figure 2. In order to minimize the influences of random errors

292

on community analyses, the data collected from all the flumes were analyzed together.

293

3.1.1 Linkage between hydrodynamics and bacterial communities.

294

Links between the measured hydrodynamic parameters and biological parameters

295

were initially explored via calculation of Spearman’s rank correlation coefficient,

296

which is a non-parametric measure of correlation and thus makes no assumption about

297

the frequency distribution of variables (28). As shown in Table S1, Gini-simpson

298

coefficient and evenness were significantly negatively correlated (P < 0.01) to

299

turbulent kinetic energy, but positively correlated (P < 0.01) to turbulent intensity.

300

AFDM and chlorophyll a were significantly positively correlated (P < 0.01) to

301

turbulent intensity, but negatively correlated (P < 0.01) flow velocity. Moreover, the

302

bacterial abundance showed strong positive correlations (P < 0.01) to turbulent

303

intensity, strong negative correlation (P < 0.01) to turbulent kinetic energy and flow 14


Page 14 of 37

Page 15 of 37


304

velocity, as well as moderate but significant (P < 0.01) correlation to roughness

305

Reynolds number. Moreover, it was also found that the correlations between bacterial

306

diversity (i.e. numbers of OTUs, Gini-Simpson coefficient and evenness) and biomass

307

(i.e. bacterial abundance, Chlorophyll a, and AFDM) were few (less than 0.1) and

308

weak (P < 0.1).

309

According to the statistical analysis, it could be deduced that the hydrodynamics

310

driving deterministic processes (i.e. niche differentiation) played an important role in

311

the assembly of bacterial community within fluvial biofilm. Niche differentiation, as a

312

model of biofilm metacommunity dynamics, could explain community composition

313

from local abiotic factors, dispersal remains restricted to colonization, and is predicted

314

to occur in the ecosystems with extended residence times (30). Therefore, the

315

hydrodynamics induced deterministic factors could be identified and the results

316

showed that turbulent kinetic energy and turbulent intensity could be considered as a

317

set of reasonable predictors of community composition, whereas flow velocity and

318

turbulent intensity could work together to predict the biomass of mature biofilm.

319

3.1.2 Bacterial diversity

320

A total of 104 OTUs were found in the biofilm located at the entrance of the

321

flume with an average of 16 ± 8 (mean ± SD). The Gini-Simpson coefficient firstly

322

increased from 0.854 to 0.915 and then decreased to 0.509 with the increasing flow

323

velocity, in accordance with the variation trend of OTUs (Figure 2a and 2b). In this

324

experimental system, hydrodynamic regime could be considered as the sole influence

325

factor during the assembly of bacterial community since the other environmental 15



326

parameters (such as temperature, inorganic nutrients and dissolved organic carbon) in

327

the flumes remain almost unchanged (data not shown). Therefore, from the

328

perspective of ecology succession, the hydrodynamics driving deterministic processes

329

played an important role in controlling the assembly of the biofilm microbial

330

community, in agreement with the results reported in previous studies (10, 31).

331

According to those reported in previous studies, bacterial diversity dynamic could

332

be speculated to be related to the transfer dynamics of biofilms, including niche

333

diversification within biofilms, cells immigration from source and neighbor

334

community, as well as biofilms detachment process (10, 26, 32). Firstly, it can be

335

found that the bacterial diversity were generally greater in flow water than those in

336

still water. For example, the numbers of OTUs and the corresponding Gini-Simpson

337

coefficients of the samples located at the entrance of the flume were significantly

338

larger in the second stage of the experiment than those in the first stage (i.e. OTUs:

339

104 > 76, Gini-Simpson coefficient: 0.854 > 0.633). This may due to the fact that, in

340

laminar flow, biofilms could grow thicker and establish internal material cycling and

341

chemical gradients through extending boundary layer, facilitating niche diversification

342

and bacterial diversity (33). Moreover, the cells immigration rate is, in fact, a function

343

of propagule abundance, which is the same in all treatments, and of advective delivery,

344

which increases with flow velocity (8). Therefore, bacterial diversity firstly increased

345

with water flow in laminar flow.

346

After that, with the increasing of rough Reynolds number (from 1256 to 5779),

347

the filamentous buildings in biofilms become detached (26). The variation of bacterial 16


Page 16 of 37

Page 17 of 37


348

diversity was decided by the trade-off between niche diversification and detachment

349

process. The increase of bacterial diversity in the flow water with rough Reynolds

350

number ranging from 1256 to 1885 indicated that niche diversification played a much

351

more important role than the detachment process, while the decreased bacterial

352

diversity with rough Reynolds number larger than 1885 suggested that the detachment

353

process was the main driving force during the assembly of biofilms. Moreover, the

354

decreased bacterial diversity at high flow velocity also indicated that the cells

355

immigration was too small to eliminate the influence of niche diversification (e.g.

356

mass transfer of solutes from the water column into the biofilms) in turbulent flow

357

(32).

358

3.1.3 Community composition

359

The biofilm OTUs were allocated to 25 classes belonging to 16 phyla. The shifts

360

of phylogeny along the flumes were shown in Figure 3. Beta-, Alpha-, Gamma-, and

361

Delta-Proteobacteria were the most abundant subphyla of Proteobacteria. The relative

362

abundance of each bacterial phylum ranged from 0.12 to 37.65 % along the flume.

363

Four of the sixteen bacterial phyla/subphyla (i.e. Beta-, Alpha-, Gamma-, and Delta-

364

Proteobacteria) presented significantly positive correlations in relative abundance

365

with flow velocity, while the remaining phyla decreased more or less. Moreover, the

366

significantly bipolarized trends in bacterial phyla should be the direct reason for the

367

declined pattern in bacterial community evenness (Figure 2c).

368

According to the ecological theory, the interplay of niche diversification and

369

neutral assembly has been suggested to drive the patterns of bacterial biodiversity in 17



370

biofilms and would result in different relative abundances of dominant taxa and in the

371

presence/absence of rare taxa (34, 35). The increase in relative abundance observed

372

for individual bacterial taxa might be due to their specialized functions in microbial

373

communities. Betaproteobacteria, for example, could attach more easily to surfaces

374

during biofilm formation than other groups of bacteria and, thus, dominate biofilm

375

assembly (36). Moreover, as the deterministic factors, local hydrodynamics may

376

select for filamentous or chain-building bacteria that can contribute to the formation

377

of streamers in high-shear microhabitats (8). However, as reported in previous study,

378

it is difficult to identify “typical” bacteria for different hydrodynamics since some

379

bacteria which colonized biofilms during the less-dynamic growth phase persisted

380

throughout the entire experiment, such as Hydrogenophaga and Herbaspirillum

381

specie (36). These genera are known for their diverse metabolic phenotypes and

382

ability to use a wide range of low-molecular-weight organic matter (36).

383

3.1.4 Biomass

384

The bacterial abundance, Chlorophyll a, and AFDM were used to determine

385

bacterial biomass, algal biomass, and bulk biofilm accumulations, respectively. The

386

variations of the biomass were shown in Figure 2d, 2e, and 2f. Flow velocity had a

387

significant influence on the values of bacterial abundance, Chlorophyll a, and AFDM.

388

The increased biomass in laminar flow water than that in still water could be

389

explained by the fact that the transfer of nutrient substance into biofilms dominated in

390

high-energy ecosystems with low residence time and continuous mixing, resulting in a

391

high-level niche diversification. The decreased biomass in the high flow velocity may 18


Page 18 of 37

Page 19 of 37


392

be due to detachment of biofilms. On one hand, biofilm detachment can result in a

393

loss of biomass. On the other hand, compared with the biofilms with filamentous

394

buildings, the biofilms without filamentous buildings exert a lower efficiency of

395

nutrient substance transfer from overlying water into biofilms, resulting in a low-level

396

niche diversification (13, 37).

397

3.2 Modeling the effects of hydrodynamics on bacterial communities

398

Our analysis began with a test of our first hypothesis that hydrodynamics act not only

399

as deterministic factors but also stochastic factors during the assembly of microbial

400

communities within fluvial biofilm. It assumed that cells invasion from the source

401

community, migration of individuals both up- and downstream, and niche

402

differentiation within the biofilm were taken as the driving forces for community

403

assembly. A purely neutral model incorporating the deterministic factors on the

404

reproduction (or birth) rate of individual taxa was used to describe the observed

405

community abundance patterns. In this study, bacterial abundance (i.e. the number of

406

individuals in all OTUs), rather than each OTU, was selected as the modeled object

407

since there is no significant difference between each OTU in the birth, death, and

408

dispersal. Moreover, all the bacteria were taken as the same trophic level during the

409

simulation. About two-thirds of the data set was used for parameters fitting and the

410

remaining third was used to test the model. The fitting and testing data points were

411

selected randomly from all the data collected in the three flumes.

412

3.2.1 Estimation of the parameters of

413

The parameter

ms / mn and αml / mn

ms / mn indicates the ratio of probabilities of the dead individual 19



Page 20 of 37

414

replaced by an individual from source community to that from neighboring patches.

415

The parameter

416

replaced by a duplicate of a member from the same patch to that from neighboring

417

patches. The parameter α is set to describe the advantage/disadvantage factors on the

418

reproduction (or birth) rate of individual taxa. The parameters

419

can be fitted at each data point by considering its nearest two neighboring

420

observations. This piecewise fitting gives estimates of the parameters at all except the

421

first and last spatial observation, since there are no adjacent neighbors for them. The

422

variations of the fitted parameters

423

roughness Reynolds number, turbulent intensity, and turbulent kinetic energy in the

424

experimental flumes are shown in Figure 4 and Figure S1. In the piecewise fitting, it

425

was found that

426

0.028 to 0.045 m/s (i.e. 1260 < rough Reynolds number < 1885), and remained more

427

or less flat after that. The result was in agreement with that reported in previous study

428

that immigration rate is a function of advective delivery, which increases with flow

429

velocity (8). The probability of an individual to migrate into the system from the

430

source community in the flume was almost constant (i.e. The parameter ms/mn was

431

approximately equal to 117 ± 5) when the roughness Reynolds number became larger

432

than 1885.

433

For

ml / mn describes the ratio of probabilities of the dead individual

ms / mn and αml / mn

ms / mn and αml / mn with flow velocity,

ms / mn slightly increased with the increasing flow velocity from

αml / mn ,

the parameter firstly increased from 48.2 to 78.9 with the

434

increasing flow velocity, and then rapidly decreased from 78.9 to 1.1 when the flow

435

velocity became larger than 0.045 m/s (i.e. rough Reynolds number = 1885). 20


Page 21 of 37


436

According to the result, the biofilms grew thicker and established internal material

437

cycling and chemical gradients in the flow water with velocity less than 0.05 m/s (i.e.

438

roughness Reynolds number = 2000), where niche differentiation played an

439

increasingly important role, resulting in an increase of

440

detachment process played a much more important role than the niche diversification

441

during the assembly of biofilms. The destroyed filamentous buildings in biofilms

442

would make against the efficiency of solutes transfer from overlying water into

443

biofilms (13, 37), and the parameter

444

Moreover, it should be noticed that the fitted parameter was exceptionally low

445

( αml / mn < 10) when the flow velocity became larger than 0.15 m/s (i.e. roughness

446

Reynolds number > 4325). It suggested that in that hydrodynamic regime individual

447

migration played much more important role than cells reproduction for biofilm

448

community assembly.

αml / mn

αml / mn .

After that, the

rapidly decreased accordingly.

449

It has been revealed that turbulent kinetic energy and turbulent intensity could be

450

considered as a set of reasonable predictors of community composition, whereas flow

451

velocity and turbulent intensity could work together to predict the biomass of mature

452

biofilm. Therefore, the linkage between hydrodynamics and biofilm community

453

assembly could be further explained by matching the linear models between

454

hydrodynamic parameters and

455

estimators, respectively (14). A series of linear models that included each of

456

hydrodynamic parameters individually and combinations of these parameters were

457

tested and only the models that explained the most variance over and-above the purely

ms / mn and αml / mn using statistically significant

21



Page 22 of 37

458

neutral model were included (as shown in Figure S2). For all the bacteria, the models

459

which best met the criteria were shown in equations (17) and (18).

460

ms / mn = f (TKE, TI ) = −0.75TKE + 3543.36TI + 327.38(R2 = 0.538)

(17)

461

αml / mn = f (Vxyz , TI ) = 9120.28TI +145.21Vxyz − 525.73(R2 = 0.755)

(18)

462

3.2.2 Modeling the effects of hydrodynamics on bacterial abundance

463

The simulation of the effects of hydrodynamics on bacterial abundance was

464

shown in Figure 5. As shown in Figure 5a, it could be found that the model performed

465

well for modeling the effects of hydrodynamics on bacterial abundance with the

466

correlation coefficient (CC) and root mean square error (RMSE) 0.61 and 7.79,

467

respectively. The model was able to clearly differentiate the driving force for

468

community assembly, where two clusters of data were formed along the ideal

469

prediction line. The outer loose cluster was present to represent the section where

470

cells migration is the main driving force during community assembly. The inner tight

471

cluster implied that cells reproduction within the biofilm played more important role

472

during community assembly, since the niche differentiation driving cells reproduction

473

is easier to be simulated compared to the migration. Therefore, it could be concluded

474

that cells reproduction within the biofilm is the main driving force during community

475

assembly in the flow water with the velocity less than 0.05 m/s (i.e. roughness

476

Reynolds number = 2000), while cells migration from the source community plays

477

much more important role in the flow water with the velocity larger than 0.05 m/s.

478

As shown in Figure 5b, the effects of hydrodynamics on community assembly

479

could clearly be divided into three scenarios. In scenario a (flow velocity < 0.041 m/s, 22


Page 23 of 37


480

roughness Reynolds number < 1750), bacterial abundance began to increase with the

481

increasing flow velocity due to the increased probabilities of cells migration and

482

reproduction. In scenario b (0.041 m/s < flow velocity < 0.061 m/s, 1750 < roughness

483

Reynolds number < 2400), the hydrodynamic regimes provided a favorable condition

484

for biofilm community assembly, where the bacterial abundance was no less than 90 %

485

of its maximum. In scenario c (flow velocity > 0.061 m/s, roughness Reynolds

486

number > 2400), the bacterial abundance started to decrease with the increasing flow

487

velocity due to the detachment of biofilms.

488

3.2.3 Implications for water environment protection and remediation

489

Streams are characterized by a largely unidirectional downstream flow of water

490

which could control the assembly of biofilm community. Biofilm bacterial abundance,

491

which is significantly affected by the local hydrodynamics, is closed related to the

492

biodegradability of organic maters in stream. However, the variation of bacterial

493

abundance is usually not considered during the prediction of fates of organic matters

494

in stream due to lack of supportable model. Therefore, from an engineer perspective,

495

quantifying the effects of hydrodynamics on biofilm community assembly may

496

change the way for us to make planning of water environment protection.

497

Moreover, biofilm community, which is an important indicator of river health, is

498

significantly affected by various environmental factors, especially by the pollutants

499

discharged into rivers. Reducing the discharged pollutants and protecting integrity of

500

the ecological system are the core contents of water ecological restoration. Therefore,

501

defining pollutants rational control level is one of the core technologies for water 23



502

ecological restoration. The parameter α, which was set to describe the

503

advantage/disadvantage factors on the reproduction (or birth) rate individual taxa, was

504

known as the real parameter characterizing the relationship between the growth

505

bacteria in biofilm and the environmental factors. According to the model, the critical

506

thresholds of indexes (including all kinds of environmental factors) which affect the

507

stream biofilm bacterial abundance could be deduced by fitting parameter α.

508

Moreover, the behaviors of specific degradation bacteria and indicative bacteria of

509

stream health could also be predicted by modelling the number of individuals in

510

specific OTUs. The obtained results could be provided as useful information for

511

improving the theories and technologies of water ecological restoration.

512

Acknowledgements

513

The study was financially supported by the National Natural Science Foundation of

514

China (51322901 and 51479066), the Foundation for Innovative Research Groups of

515

the National Natural Science Foundation of China (51421006), the Fundamental

516

Research Funds for the Central Universities (2015B02114 and 2014B07614), and the

517

Priority Academic Program Development of Jiangsu Higher Education Institutions.

518

Literature Cited

519

(1)

Araya, R.; Tani, K.; Takagi, T.; Yamaguchi, N.; Nasu, M., Bacterial activity and

520

community composition in stream water and biofilm from an urban river

521

determined by fluorescent in situ hybridization and DGGE analysis. FEMS

522

Microbiol. Ecol. 2003, 43, 111-119. 24


Page 24 of 37

Page 25 of 37


523

(2)

Emtiazi, F.; Schwartz, T.; Marten, S. M.; Krolla-Sidenstein, P.; Obst, U.,

524

Investigation of natural biofilms formed during the production of drinking water

525

from surface water embankment filtration. Water Res. 2004, 38, 1197-1206.

526

(3)

Schwartz, T.; Hoffmann, S.; Obst, U., Formation and bacterial composition of

527

young, natural biofilms obtained from public bank-filtered drinking water

528

systems. Water Res. 1998, 32, 2787-2797.

529

(4)

Wang, Q.; Wang, R.; Tian, C.; Yu, Y.; Zhang, Y.; Dai, J., Using microbial

530

community functioning as the complementary environmental condition

531

indicator: A case study of an iron deposit tailing area. Eur. J. Soil Biol. 2012, 51,

532

22-29.

533

(5)

Larkin, R. P.; Honeycutt, C. W.; Griffin, T. S., Effect of swine and dairy

534

manure amendments on microbial communities in three soils as influenced by

535

environmental conditions. Biol. Fert. Soils 2006, 43, 51-61.

536

(6)

Ramsey, P. W.; Gibbons, S. M.; Rice, P.; Mummey, D. L.; Feris, K. P.; Moore,

537

J. N.; Rillig, M. C.; Gannon, J. E., Relative strengths of relationships between

538

plant, microbial, and environmental parameters in heavy-metal contaminated

539

floodplain soil. Pedobiologia 2012, 55, 15-23.

540

(7)

Tsiknia, M.; Paranychianakis, N. V.; Varouchakis, E. A.; Moraetis, D.;

541

Nikolaidis, N. P., Environmental drivers of soil microbial community

542

distribution at the Koiliaris Critical Zone Observatory. FEMS Microbiol. Ecol.

543

2014, 90, 139-152.

544

(8)

Besemer, K.; Singer, G.; Hoedl, I.; Battin, T. J., Bacterial community 25



545

composition of stream biofilms in spatially variable-flow environments. Appl.

546

Environ. Microbiol. 2009, 75, 7189-7195.

547

(9)

Ben-Dan, T. B.; Shteinman, B.; Kamenir, Y.; Itzhak, O.; Hochman, A.,

548

Hydrodynamical effects on spatial distribution of enteric bacteria in the Jordan

549

River - Lake Kinneret contact zone. Water Res. 2001, 35, 311-314.

550

(10) Besemer, K.; Peter, H.; Logue, J. B.; Langenheder, S.; Lindstrom, E. S.;

551

Tranvik, L. J.; Battin, T. J., Unraveling assembly of stream biofilm communities.

552

The ISME Journal 2012, 6, 1459-1468.

553

(11) Battin, T. J.; Kaplan, L. A.; Newbold, J. D.; Cheng, X. H.; Hansen, C., Effects

554

of current velocity on the nascent architecture of stream microbial biofilms.

555

Appl. Environ. Microbiol. 2003, 69, 5443-5452.

556

(12) Battin, T. J.; Sloan, W. T.; Kjelleberg, S.; Daims, H.; Head, I. M.; Curtis, T. P.;

557

Eberl, L., Microbial landscapes: new paths to biofilm research. Nat. Rev.

558

Microbiol. 2007, 5, 76-81.

559

(13) Singer, G.; Besemer, K.; Schmitt-Kopplin, P.; Hödl, I.; Battin, T. J., Physical

560

heterogeneity increases biofilm resource use and its molecular diversity in

561

stream mesocosms. PLoS ONE 2010, 5, e9988-e9988.

562

(14) Ofiteru, I. D.; Lunn, M.; Curtis, T. P.; Wells, G. F.; Criddle, C. S.; Francis, C.

563

A.; Sloan, W. T., Combined niche and neutral effects in a microbial wastewater

564

treatment community. Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 15345-15350.

565

(15) Scheffer, M.; van Nes, E. H., Self-organized similarity, the evolutionary

566

emergence of groups of similar species. Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 26


Page 26 of 37

Page 27 of 37


567

6230-6235.

568

(16) Tilman, D., Niche tradeoffs, neutrality, and community structure: a stochastic

569

theory of resource competition, invasion, and community assembly. Proc. Natl.

570

Acad. Sci. U.S.A. 2004, 101, 10854-10861.

571

(17) Ramette, A.; Tiedje, J. M., Multiscale responses of microbial life to spatial

572

distance and environmental heterogeneity in a patchy ecosystem. Proc. Natl.

573

Acad. Sci. U.S.A. 2007, 104, 2761-2766.

574 575 576 577 578 579

(18) Herault, B., Reconciling niche and neutrality through the Emergent Group approach. Perspect. Plant Ecol. Evol. Syst. 2007, 9, 71-78. (19) Hubbell, S. P., The unified neutral theory of biodiversity and biogeography (MPB-32). Princeton University Press: 2001; Vol. 32. (20) McGill, B. J.; Maurer, B. A.; Weiser, M. D., Empirical evaluation of neutral theory. Ecol. 2006, 87, 1411-1423.

580

(21) Zhou, J.; Deng, Y.; Zhang, P.; Xue, K.; Liang, Y.; Van Nostrand, J. D.; Yang,

581

Y.; He, Z.; Wu, L.; Stahl, D. A.; Hazen, T. C.; Tiedje, J. M.; Arkin, A. P.,

582

Stochasticity, succession, and environmental perturbations in a fluidic

583

ecosystem. Proc. Natl. Acad. Sci. U.S.A. 2014, 111, E836-E845.

584 585

(22) Mouquet, N.; Loreau, M., Community patterns in source-sink metacommunities. Am. Nat. 2003, 162, 544-557.

586

(23) Woodcock, S.; Besemer, K.; Battin, T. J.; Curtis, T. P.; Sloan, W. T., Modelling

587

the effects of dispersal mechanisms and hydrodynamic regimes upon the

588

structure of microbial communities within fluvial biofilms. Environ. Microbiol. 27



589

2013, 15, 1216-1225.

590

(24) Drummond, J. D.; Davies-Colley, R. J.; Stott, R.; Sukias, J. P.; Nagels, J. W.;

591

Sharp, A.; Packman, A. I., Microbial transport, retention, and inactivation in

592

streams–a combined experimental and stochastic modeling approach. Environ.

593

Sci. Technol. 2015, 49, 7825-7833.

594

(25) Griffitt, K. J.; Noriea, N. F.; Johnson, C. N.; Grimes, D. J., Enumeration of

595

Vibrio parahaemolyticus in the viable but nonculturable state using direct plate

596

counts and recognition of individual gene fluorescence in situ hybridization. J.

597

Microbiol. Meth. 2011, 85, 114-118.

598

(26) Graba, M.; Sauvage, S.; Moulin, F. Y.; Urrea, G.; Sabater, S.; Sanchez-Perez, J.

599

M., Interaction between local hydrodynamics and algal community in epilithic

600

biofilm. Water Res. 2013, 47, 2153-2163.

601

(27) Pervin, H. M.; Dennis, P. G.; Lim, H. J.; Tyson, G. W.; Batstone, D. J.; Bond, P.

602

L., Drivers of microbial community composition in mesophilic and thermophilic

603

temperature-phased anaerobic digestion pre-treatment reactors. Water Res. 2013,

604

47, 7098-7108.

605

(28) Wells, G. F.; Park, H. D.; Yeung, C. H.; Eggleston, B.; Francis, C. A.; Criddle,

606

C. S., Ammonia ‐ oxidizing communities in a highly aerated full ‐scale

607

activated sludge bioreactor: betaproteobacterial dynamics and low relative

608

abundance of Crenarchaea. Environ. Microbiol. 2009, 11, 2310-2328.

609

(29) Sloan, W. T.; Lunn, M.; Woodcock, S.; Head, I. M.; Nee, S.; Curtis, T. P.,

610

Quantifying the roles of immigration and chance in shaping prokaryote 28


Page 28 of 37

Page 29 of 37


611

community structure. Environ. Microbiol. 2006, 8, 732-740.

612

(30) Besemer, K.; Singer, G.; Limberger, R.; Chlup, A.-K.; Hochedlinger, G.; Hoedl,

613

I.; Baranyi, C.; Battin, T. J., Biophysical controls on community succession in

614

stream biofilms. Appl. Environ. Microbiol. 2007, 73, 4966-4974

615

(31) Tyson, G. W.; Chapman, J.; Hugenholtz, P.; Allen, E. E.; Ram, R. J.;

616

Richardson, P. M.; Solovyev, V. V.; Rubin, E. M.; Rokhsar, D. S.; Banfield, J.

617

F., Community structure and metabolism through reconstruction of microbial

618

genomes from the environment. Nature 2004, 428, 37-43.

619

(32) Beecroft, N. J.; Zhao, F.; Varcoe, J. R.; Slade, R. C.; Thumser, A. E.;

620

Avignone-Rossa, C., Dynamic changes in the microbial community

621

composition in microbial fuel cells fed with sucrose. Appl. Microbial. Biot.

622

2012, 93, 423-437.

623 624

(33) Brendan Logue, J.; Lindström, E. S., Biogeography of bacterioplankton in inland waters. Freshwater Rev. 2008, 1, 99-114.

625

(34) Reveillaud, J.; Maignien, L.; Eren, A. M.; Huber, J. A.; Apprill, A.; Sogin, M.

626

L.; Vanreusel, A., Host-specificity among abundant and rare taxa in the sponge

627

microbiome. The ISME Journal 2014, 8, 1198-1209.

628

(35) Hödl, I.; Mari, L.; Bertuzzo, E.; Suweis, S.; Besemer, K.; Rinaldo, A.; Battin, T.

629

J., Biophysical controls on cluster dynamics and architectural differentiation of

630

microbial biofilms in contrasting flow environments. Environ. Microbiol. 2014,

631

16, 802-812.

632

(36) Besemer, K.; Singer, G.; Limberger, R.; Chlup, A.-K.; Hochedlinger, G.; Hoedl, 29



633

I.; Baranyi, C.; Battin, T. J., Biophysical controls on community succession in

634

stream biofilms. Appl. Environ. Microbiol. 2007, 73, 4966-4974.

635

(37) Jackson, A. C.; Underwood, A. J.; Murphy, R. J.; Skilleter, G. A., Latitudinal

636

and environmental patterns in abundance and composition of epilithic

637

microphytobenthos. Mar. Ecol. Prog. Ser. 2010, 417, 27-38.

638

30


Page 30 of 37

Page 31 of 37


639

Figure captions

640

Figure 1 (a) Sketch of the principal laboratory flume and (b) the mean values of

641

measured hydrodynamic parameters along the horizontal centerline of each flume

642

with 1 m intervals. V: Three-dimensional vector of flow velocity, TI: turbulent

643

intensity, TKE: turbulent kinetic energy, k+: roughness Reynolds number.

644 645

Figure 2 Variations of (a) numbers of OTUs, (b) Gini-simpson coefficient, (c)

646

evenness, (d) bacterial abundance, (e) chlorophyll a, and (f) ash free dry mass (AFDM)

647

with flow velocity in the experimental flumes.

648 649

Figure 3 Variations of the relative abundance of each bacterial phylum/subphyla with

650

flow velocity in the experimental flumes.

651

ms / mn and (b) αml / mn with flow

652

Figure 4 Variations of the fitted parameters (a)

653

velocity. The parameters ms, mn and ml describe the probabilities of dead individual

654

replaced by an individual from source community, an individuals from neighboring

655

patches, and a duplicate of a randomly selected member of the same patch. The

656

parameter α was used to describe the advantage/disadvantage factors on the

657

reproduction (or birth) rate individual taxa.

658 659

Figure 5 Modelling the effects of flow velocity on bacterial abundance within fluvial

660

biofilms. (a) The x-axis includes the measured value and the y-axis exhibits the

661

corresponding values predicted by the model. The dashed line through the plot shows

662

the ideal “y = x” line along which the points would lie for a model that perfectly fit

663

the data set. (b) Simulation of the variation of bacterial abundance with flow velocity.

664

Scenario a: flow velocity < 0.041; Scenario b: 0.041 < flow velocity < 0.061; Scenario

665

c: flow velocity > 0.061.

666 667 668 669

31



a

670

b

671 672

Figure 1 (a) Sketch of the principal laboratory flume and (b) the mean values of

673

measured hydrodynamic parameters along the horizontal centerline of each flume

674

with 1 m intervals. Vxyz: Three-dimensional vector of flow velocity, TI: turbulent

675

intensity, TKE: turbulent kinetic energy, k+: roughness Reynolds number.

676

32


Page 32 of 37

Page 33 of 37


677 678

Figure 2 Variations of (a) numbers of OTUs, (b) Gini-simpson coefficient, (c)

679

evenness, (d) bacterial abundance, (e) chlorophyll a, and (f) ash free dry mass (AFDM)

680

with flow velocity in the experimental flumes.

681

33



682 683

Figure 3 Variations of the relative abundance of each bacterial phylum/subphyla with

684

flow velocity in the experimental flumes.

685 686

34


Page 34 of 37

Page 35 of 37


687

688

ms / mn and (b) αml / mn with flow

689

Figure 4 Variations of the fitted parameters (a)

690

velocity. The parameters ms, mn and ml describe the probabilities of dead individual

691

replaced by an individual from source community, an individuals from neighboring

692

patches, and a duplicate of a randomly selected member of the same patch. The

693

parameter α is used to describe the advantage/disadvantage factors on the

694

reproduction (or birth) rate individual taxa.

695 35



696

697 698

Figure 5 Modelling the effects of flow velocity on bacterial abundance within fluvial

699

biofilms. (a) The x-axis includes the measured value and the y-axis exhibits the

700

corresponding values predicted by the model. The dashed line through the plot shows

701

the ideal “y = x” line along which the points would lie for a model that perfectly fit

702

the data set. (b) Simulation of the variation of bacterial abundance with flow velocity.

703

Scenario a: flow velocity < 0.041; Scenario b: 0.041 < flow velocity < 0.061; Scenario

704

c: flow velocity > 0.061. 36


Page 36 of 37

Page 37 of 37


705

Table of Contents graphic

706

37


Modeling the Effects of Hydrodynamic Regimes on Microbial

Recommend Documents